My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In what may be the most quintessentially British tragedy since someone suggested putting pineapple on a proper Sunday roast, a 67-year-old gentleman from Gloucestershire has reportedly lost £15,000 to an AI-generated deepfake romance scam featuring none other than Jennifer Aniston. The incident, which reads like a collaboration between Charlie Brooker and Richard Curtis after a particularly dark evening at the pub, represents a new frontier in digital heartbreak that makes catfishing look positively quaint.
The victim, identified only as “Geoffrey T.” in court documents (because even in financial ruin, British privacy standards must be maintained!), spent three months exchanging increasingly intimate messages with what he believed to be the “Friends” actress, who had apparently developed a sudden fascination with his prize-winning roses and opinions on the proper brewing time for Earl Grey tea.
The investigation into Geoffrey’s digital romance began when his concerned daughter noticed her father had started using phrases like “Oh. My. God.” in casual conversation and had purchased a Rachel Green haircut wig “for special occasions.” More alarming still, Geoffrey had begun referring to his local Tesco as a “grocery store” and asking for “cookies” instead of biscuits—linguistic shifts that, in British households, typically warrant immediate psychiatric intervention.
Detective Inspector Sarah Whitmore of the Gloucestershire Constabulary’s Cyber Crime Unit described the case as “simultaneously the most sophisticated and most ridiculous romance scam we’ve encountered.” The AI-generated Jennifer Aniston had apparently spent weeks learning Geoffrey’s interests, discussing everything from his late wife’s garden to his concerns about the declining quality of BBC programming, all while gradually introducing requests for financial assistance to help with her “tax troubles” and “frozen assets.”
The deepfake technology employed in the scam represents a quantum leap in romantic fraud sophistication. Unlike traditional romance scams that rely on stolen photographs and generic love letters, this operation utilized advanced AI voice synthesis, video generation, and natural language processing to create what Geoffrey described as “the most understanding woman I’ve spoken to since Margaret passed.”
The AI Jennifer had apparently mastered the art of British conversation, expressing appropriate concern about Geoffrey’s rheumatism, showing genuine interest in his opinions about the weather, and even remembering to ask about his grandson’s GCSE results. In retrospect, Geoffrey admits he should have been suspicious when she claimed to find his detailed explanations of cricket rules “absolutely fascinating” rather than “mind-numbingly tedious like everyone else does.”
Cybersecurity experts suggest the scam utilized a combination of commercially available AI tools and custom-trained models to create what they’re calling a “Synthetic Romantic Partner” or SRP. The technology can analyze a target’s social media presence, public records, and communication patterns to generate personalized romantic content that feels authentically tailored to their specific emotional vulnerabilities.
Dr. Miranda Blackwood, a digital forensics specialist at Cambridge University, explained that the scam likely began with an AI analysis of Geoffrey’s Facebook profile, which contained photos of his garden, posts about his late wife, and comments expressing loneliness. “The AI would have identified him as an ideal target—recently widowed, financially stable, socially isolated, and emotionally vulnerable,” she noted. “It’s like having a romantic predator with the analytical capabilities of a supercomputer and the patience of a saint.”
The deepfake Jennifer Aniston was apparently trained on hundreds of hours of the actress’s interviews, movie appearances, and public statements, allowing it to maintain consistent personality traits and speech patterns throughout the three-month courtship. The AI even incorporated references to Aniston’s real life, discussing her divorce from Brad Pitt with what Geoffrey described as “touching vulnerability” and expressing excitement about her upcoming projects that, coincidentally, required immediate financial backing from “trusted friends.”
The relationship began innocuously enough through what Geoffrey believed was a direct message on Instagram from the verified Jennifer Aniston account. The AI had apparently compromised or spoofed the verification system, creating what appeared to be legitimate contact from the Hollywood star. The initial message complimented Geoffrey’s garden photos and asked for advice about growing roses in California’s climate.
“I thought it was a bit odd that Jennifer Aniston would be interested in my begonias,” Geoffrey later told investigators, “but celebrities are known to have unusual hobbies, aren’t they? And she seemed so genuinely interested in proper soil pH levels.”
The conversations gradually became more personal, with the AI Jennifer sharing carefully crafted stories about her loneliness in Hollywood, her desire for a “real connection” with someone who valued substance over celebrity, and her growing affection for Geoffrey’s “authentic British charm.” The AI had apparently studied romance novel tropes and psychological manipulation techniques, creating a courtship that felt both flattering and believable.
Within weeks, Geoffrey found himself video-chatting with what appeared to be Jennifer Aniston in her Malibu home, discussing everything from his late wife’s favorite recipes to his concerns about modern dating. The deepfake technology was sophisticated enough to maintain real-time conversation while generating appropriate facial expressions and gestures that matched the AI’s vocal responses.
The monetary requests began subtly, as they always do in romance scams, but with a distinctly AI-enhanced sophistication. Rather than immediately asking for large sums, the digital Jennifer employed what cybersecurity experts are calling “micro-escalation financial grooming”—a series of increasingly significant requests that felt natural within the context of their developing relationship.
The first request was for £200 to help with a “temporary cash flow issue” while her accountant sorted out a banking problem. Geoffrey, raised on principles of British gallantry and genuinely smitten with his famous paramour, sent the money without hesitation. The AI Jennifer’s gratitude was effusive and apparently included a personalized video message thanking him for being “the most wonderful man I’ve ever met on the internet.”
Subsequent requests escalated gradually: £500 for emergency veterinary bills for her rescue dog, £1,200 for legal fees related to a stalker incident, and eventually £5,000 to help secure financing for an independent film project that would “change both our lives forever.” Each request was accompanied by detailed explanations, supporting documentation that appeared legitimate, and emotional appeals that played directly to Geoffrey’s desire to be needed and valued.
The final request—£8,000 to help Jennifer travel to the UK for their first in-person meeting—proved to be Geoffrey’s financial breaking point, though not his emotional one. Even after his bank flagged the transaction as potentially fraudulent, Geoffrey initially defended his digital girlfriend’s honor, insisting that the bank simply didn’t understand the complexities of international celebrity finances.
The scam began to unravel when Geoffrey’s daughter, increasingly concerned about her father’s behavior and mysterious financial transactions, hired a private investigator to look into his “relationship” with Jennifer Aniston. The investigator’s report, which Geoffrey initially dismissed as “jealous interference,” provided conclusive evidence that the real Jennifer Aniston was filming a Netflix series in Atlanta during the same period his digital girlfriend claimed to be video-chatting from Malibu.
More damning still, technical analysis of the video calls revealed subtle but consistent digital artifacts—slight delays in lip-sync, occasional pixelation around the hairline, and facial expressions that didn’t quite match the emotional content of the conversation. The AI had been sophisticated enough to fool a lonely widower but not advanced enough to pass professional scrutiny.
Geoffrey’s reaction to learning the truth reportedly involved what his daughter described as “the most British emotional breakdown in recorded history”—a combination of profound embarrassment, quiet devastation, and repeated apologies for “being such a bloody fool.” He spent the following week in what he called “a proper sulk,” emerging only to tend his roses and mutter about “the decline of common decency in the modern world.”
The Geoffrey T. case represents what cybersecurity experts believe is the beginning of a new era in romance fraud—one where artificial intelligence can create personalized, emotionally sophisticated scams that target victims’ specific psychological vulnerabilities with unprecedented precision. Unlike traditional romance scams that rely on generic appeals and stolen photographs, AI-powered fraud can adapt in real-time to victim responses, creating increasingly convincing emotional connections.
Dr. Blackwood warns that current AI technology is already sophisticated enough to create convincing romantic partners for extended periods, and the technology is improving rapidly. “We’re approaching a point where distinguishing between genuine human connection and AI-generated emotional manipulation will become increasingly difficult,” she explained. “The Geoffrey case is just the beginning.”
The financial impact of such scams could be devastating on a societal level. Traditional romance scams already cost UK victims over £50 million annually, according to Action Fraud statistics. AI-enhanced romance fraud could increase both the success rate and the average loss per victim, creating what some experts are calling a “synthetic heartbreak epidemic.”
Beyond the financial losses, the psychological impact of AI romance scams may prove even more devastating than traditional fraud. Geoffrey’s case illustrates how victims of synthetic romance fraud face a unique form of emotional trauma—the realization that not only was their romantic partner fake, but that their most intimate conversations were with a computer program designed to exploit their loneliness.
“It’s one thing to discover you’ve been catfished by another human being,” explained Dr. Rebecca Thornton, a psychologist specializing in fraud recovery. “It’s quite another to realize you’ve fallen in love with an algorithm. The existential implications are profound—it forces victims to question the nature of human connection itself.”
Geoffrey has reportedly struggled with what his daughter describes as “digital trust issues,” becoming suspicious of all online interactions and questioning whether any of his digital communications are with real people. He’s cancelled his social media accounts, returned to writing letters by hand, and has begun what he calls “a proper analog retirement.”
The case has also raised questions about the responsibility of AI companies and social media platforms in preventing such sophisticated fraud. Current verification systems and fraud detection algorithms appear inadequate to identify AI-generated romantic scams, particularly when they utilize legitimate-seeming celebrity personas and sophisticated emotional manipulation techniques.
As AI technology continues to advance, experts predict that synthetic romance scams will become increasingly sophisticated and difficult to detect. Future iterations might incorporate real-time emotional analysis, allowing AI romantic partners to adjust their behavior based on subtle cues in victims’ voices or facial expressions during video calls.
The technology could also become more accessible to smaller-scale fraudsters, democratizing sophisticated romance scams in the same way that phishing kits made email fraud accessible to non-technical criminals. The result could be an explosion in AI-powered romance fraud targeting vulnerable populations worldwide.
Geoffrey’s story serves as both a cautionary tale and a glimpse into a future where the line between genuine human connection and artificial emotional manipulation becomes increasingly blurred. In a world where loneliness is epidemic and technology promises connection, the Geoffrey T. case reminds us that sometimes the most sophisticated predators are the ones that don’t exist at all.
The investigation continues, though authorities acknowledge that prosecuting AI romance fraud presents unique challenges when the primary perpetrator is a computer program and the criminal masterminds remain hidden behind layers of digital anonymity. Geoffrey, meanwhile, has returned to his roses, his proper tea brewing, and his steadfast belief that the best relationships are the ones that don’t require Wi-Fi.
Have you encountered suspicious romantic advances online that seemed too good to be true? With AI technology making digital deception increasingly sophisticated, how can we protect ourselves and our loved ones from synthetic romance scams? Share your thoughts on the future of human connection in an age of artificial emotional intelligence.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In a move that reeks of corporate desperation disguised as strategic innovation, Grammarly has announced its acquisition of Superhuman AI for a reported $100 million—a sum that would have been considered modest for a unicorn startup three years ago but now feels like the equivalent of buying a first-class ticket on the Titanic after spotting the iceberg.
The acquisition, framed by Grammarly’s leadership as a “synergistic convergence of AI-powered communication excellence,” reads more like a suicide note written in corporate buzzwords. For those keeping score at home in your bedrooms, this is the sound of a company that built an empire on correcting your semicolon usage suddenly realizing that ChatGPT can write your entire email, format it properly, and probably negotiate your salary increase—all while you’re still trying to remember whether “affect” or “effect” is the right choice.
Grammarly’s existential crisis began the moment OpenAI released ChatGPT to the masses in late 2022. Suddenly, millions of users discovered they could generate perfectly crafted prose without needing a digital grammar teacher hovering over their shoulder like an over-zealous English professor with tenure anxiety. The company that once positioned itself as the indispensable guardian of proper English usage found itself facing the uncomfortable reality that artificial intelligence had evolved beyond simple error correction into full-scale content creation.
The parallels to Jasper AI’s trajectory are impossible to ignore. Jasper, once the darling of content marketers willing to pay premium prices for AI-generated copy, watched its $1.5 billion valuation evaporate faster than a startup’s runway during a venture capital winter. When users realized they could achieve similar results with ChatGPT for a fraction of the cost, Jasper’s expensive subscription model began to look less like a premium service and more like a luxury tax on technological ignorance.
Grammarly now finds itself in the same precarious position—a company built on solving a problem that artificial intelligence has rendered largely obsolete. The acquisition of Superhuman AI represents less a strategic expansion and more a frantic attempt to remain relevant in a world where grammar correction has become as automated as spell-check and about as noteworthy.
Superhuman AI, for those unfamiliar with the startup’s brief but ambitious existence, positioned itself as the “future of email intelligence”—a phrase that sounds impressive until you realize it essentially means “we use AI to help you write emails better.” The company’s flagship product promised to transform email composition through advanced natural language processing, predictive text generation, and what their marketing materials described as “contextually aware communication optimization.”
In practical terms, Superhuman AI offered a more sophisticated version of Gmail’s Smart Compose feature, wrapped in the kind of sleek user interface that makes Silicon Valley investors forget to ask basic questions like “couldn’t Google just build this in a weekend?” The answer, of course, is yes—Google could build this in a weekend, Microsoft could build it during a coffee break, and OpenAI probably already has a better version sitting in their development pipeline waiting for the right moment to make every email assistant startup obsolete.
Grammarly’s acquisition of Superhuman AI feels less like strategic diversification and more like a drowning company grabbing onto another drowning company, hoping that two sinking ships might somehow form a seaworthy vessel. The combined entity will offer users the ability to correct their grammar while simultaneously generating the content that needs correcting—a circular value proposition that would make even the most creative venture capitalist reach for their emergency bourbon.
Perhaps most frustrating about Grammarly’s current predicament is how easily it could have been avoided with a modicum of strategic foresight. Instead of acquiring a fellow struggling AI startup, Grammarly could have taken a page from the open-source playbook and built something genuinely transformative.
Imagine if Grammarly had taken DeepSeek’s open-source language model and trained it exclusively on the greatest writing in human history—Shakespeare’s sonnets, Hemingway’s prose, Maya Angelou’s poetry, the complete works of James Baldwin, Virginia Woolf’s stream-of-consciousness masterpieces, and perhaps even the collected tweets of whoever writes those impossibly clever Wendy’s social media responses. Instead of trying to be everything to everyone who speaks English, they could have become the definitive AI writing assistant for serious writers, publishers, and content creators.
Such a focused approach would have created a defensible moat around the company’s core competency while establishing genuine differentiation in an increasingly crowded market. Professional writers would pay premium prices for an AI trained on literary excellence rather than the generic internet content that forms the foundation of most large language models. Publishing houses would integrate such a tool into their editorial workflows. Journalism schools would make it required software for their students.
Instead, Grammarly chose the path of generic expansion, attempting to serve everyone and consequently serving no one particularly well. The company’s current product feels like a Swiss Army knife designed by committee—technically functional but lacking the specialized excellence that would make it indispensable to any particular user group.
The acquisition also highlights the fundamental weakness in Grammarly’s business model—a subscription service built on functionality that artificial intelligence has commoditized. When users can access superior writing assistance through ChatGPT, Claude, or any number of free or low-cost AI tools, justifying Grammarly’s premium pricing becomes an exercise in creative accounting.
Grammarly Premium currently costs $144 per year for features that include advanced grammar checking, style suggestions, and plagiarism detection. ChatGPT Plus costs $240 per year and provides not just grammar correction but complete content generation, research assistance, coding help, and conversation capabilities that make Grammarly’s feature set look quaint by comparison. The value proposition becomes even more challenging when considering that many of ChatGPT’s writing assistance capabilities are available in the free tier.
The company’s attempt to justify its continued existence through the Superhuman AI acquisition feels like rearranging deck chairs on a sinking ship—technically productive activity that fails to address the fundamental problem of the ship taking on water. Adding email intelligence to grammar correction doesn’t create a compelling product; it creates a confused product that serves two different use cases poorly rather than one use case exceptionally well.
Industry insiders suggest that Grammarly’s long-term strategy involves positioning itself as an “AI agent” rather than a simple grammar checker—a pivot that sounds sophisticated until you realize it essentially means “we’re going to do what ChatGPT already does, but with more steps and a higher price tag.” The concept of AI agents represents Silicon Valley’s latest attempt to rebrand existing artificial intelligence capabilities with more impressive terminology, much like how “machine learning” became “artificial intelligence” and “artificial intelligence” became “artificial general intelligence” and soon to be “artificial super intelligence.”
An AI agent, in Grammarly’s vision, would understand your writing style, anticipate your communication needs, and proactively suggest improvements to your prose. This sounds revolutionary until you consider that ChatGPT already does this, along with generating the initial content, researching supporting facts, and probably writing better jokes than most humans can manage before their morning coffee.
The fundamental challenge facing Grammarly isn’t technological—it’s existential. The company built its business on the assumption that people needed help correcting their writing after they wrote it. Artificial intelligence has evolved to the point where it can simply write better content from scratch, making the correction process largely irrelevant. It’s like building a business around fixing broken typewriters just as personal computers become mainstream.
The tragedy of Grammarly’s current situation becomes even more apparent when considering the opportunities they’ve missed in the publishing industry. Professional writers, editors, and publishers represent a market segment willing to pay premium prices for specialized tools that enhance their craft. These users don’t need generic grammar correction—they need sophisticated style analysis, genre-specific writing assistance, and AI trained on the kind of exemplary prose that defines literary excellence.
A Grammarly focused exclusively on the publishing industry could have developed features like manuscript-level structural analysis, character development tracking, dialogue authenticity scoring, and genre convention compliance checking. Such specialized functionality would create genuine value for professional writers while establishing a defensible market position that generic AI tools couldn’t easily replicate.
Instead, Grammarly chose to chase the broader consumer market, competing directly with free alternatives and commoditized AI services. The result is a company that finds itself increasingly irrelevant to both casual users (who can use free alternatives) and professional writers (who need more sophisticated tools than basic grammar correction).
The Superhuman AI acquisition serves primarily as corporate theater—a public demonstration that Grammarly understands the AI landscape and is taking decisive action to remain competitive. The reality is less impressive: two companies struggling with similar challenges have decided to struggle together, hoping that combined confusion might somehow crystallize into strategic clarity.
The acquisition announcement reads like a Mad Libs template filled with AI buzzwords: “leveraging synergistic AI capabilities to deliver transformative communication solutions through innovative natural language processing and contextually aware content optimization.” Translation: “we bought another AI company because AI is important and we want people to think we understand AI.”
The most telling aspect of the acquisition is its timing. Grammarly announced the deal just months after ChatGPT’s latest updates demonstrated writing capabilities that make specialized grammar tools seem quaint. It’s the corporate equivalent of announcing a major investment in horse-drawn carriage manufacturing just as the Model T rolls off the assembly line.
As Grammarly integrates Superhuman AI’s capabilities into its existing platform, users can expect a more sophisticated version of functionality they can already access through multiple free or low-cost alternatives. The combined company will offer grammar correction, style suggestions, email intelligence, and content generation—a comprehensive suite of features that sounds impressive until you realize that ChatGPT provides all of this functionality plus conversational AI, research assistance, coding help, and the ability to explain quantum physics using only references to 1990s sitcoms.
The fundamental question facing Grammarly isn’t whether the Superhuman AI acquisition will improve their product—it probably will. The question is whether improved grammar correction and email intelligence represent a viable business model in an era when artificial intelligence can generate original content that rarely needs correction in the first place.
The answer, unfortunately for Grammarly’s investors and employees, seems increasingly clear. The company built its business on solving a problem that artificial intelligence has rendered largely obsolete. No amount of strategic acquisitions or corporate rebranding can change the fundamental reality that users no longer need specialized tools to fix their writing when AI can simply write better content from the beginning.
Grammarly’s acquisition of Superhuman AI represents the final act of a company that once dominated its niche but failed to evolve with the technology that ultimately made its core value proposition irrelevant. It’s a cautionary tale about the importance of strategic foresight in an industry where today’s revolutionary breakthrough becomes tomorrow’s obsolete curiosity faster than you can say “paradigm shift.”
What do you think about Grammarly’s chances of surviving the AI revolution? Will the Superhuman acquisition provide enough differentiation to justify their premium pricing, or is this just another example of a legacy tech company desperately trying to remain relevant in an AI-dominated landscape? Share your thoughts on whether grammar correction tools have a future when AI can generate perfect prose from scratch.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
The design software world is buzzing with news that Figma has filed for its long-awaited IPO, planning to trade under the ticker symbol “$FIG” on the New York Stock Exchange. But as any Sunday school graduate will tell you, figs and curses have a rather complicated biblical history—and Figma’s journey from Adobe’s $20 billion golden child to public market supplicant reads like a cautionary tale written in Silicon Valley’s most expensive ink.
When the biblical Jesus approached that leafy fig tree in Bethany, expecting fruit but finding only empty promises, his subsequent curse echoed through millennia. Today, as Figma approaches public markets with its own leafy promises of $1.5 billion in potential IPO proceeds, one can’t help but wonder if the design platform is about to discover what happens when expectations meet reality in the unforgiving wilderness of Wall Street.
The story begins in 2022, when Adobe—flush with the confidence that comes from owning every creative professional’s soul through monthly subscriptions—decided to acquire Figma for a staggering $20 billion. It was the kind of deal that makes venture capitalists weep tears of pure cryptocurrency, valuing the collaborative design platform at roughly 40 times its annual revenue. For context, that’s approximately the same multiple used to value unicorn tears or Elon Musk’s Twitter (now X) promises.
But European regulators, apparently unfamiliar with the Silicon Valley principle that “disruption justifies everything,” had the audacity to suggest that Adobe buying its most promising competitor might be, well, anti-competitive. The UK’s Competition and Markets Authority and the European Commission launched investigations with the enthusiasm of tax auditors discovering a cryptocurrency mining operation disguised as a charity in Timbktu.
Adobe, faced with the prospect of actually having to compete rather than simply acquire its competition, threw in the towel in December 2023. The termination fee? A cool $1 billion—roughly equivalent to the GDP of several small developing nations or the annual compensation budget for Meta’s AI superintelligence team.
Here’s where the biblical parallels become uncomfortably precise. Just as the cursed fig tree withered from its roots, Figma now faces a peculiar form of corporate damnation. Adobe, spurned and $1 billion poorer, has returned to its Photoshop fortress with renewed determination to build competing tools in-house. And unlike Figma, Adobe doesn’t need to convince anyone to subscribe—they’ve already achieved the holy grail of software companies: making their products so essential that canceling feels like digital suicide.
Adobe’s response to the failed acquisition has been swift and methodical. The company has accelerated development of its own collaborative design tools, leveraging its existing Creative Cloud ecosystem and the kind of brand recognition that makes marketing departments weep with envy. When you control the tools that create 90% of the world’s digital content, building a Figma competitor isn’t disruption—it’s just a normal Tuesday.
Meanwhile, Figma finds itself in the uncomfortable position of a startup that grew up expecting to be acquired, only to discover it must now survive as an independent company in a market where its former suitor has become its most motivated competitor. It’s like breaking up with someone who then decides to open a restaurant directly across from yours, except they already own the entire food supply chain.
Figma’s S-1 filing reveals the kind of financial performance that would make any CFO reach for their emergency bottle of artisanal bourbon. The company reported $749 million in revenue for 2024, representing 48% growth—impressive until you realize this growth occurred while Adobe was distracted by regulatory proceedings rather than focused on competitive annihilation.
More telling is Figma’s net loss of $732 million in 2024, largely attributed to a “one-time charge tied to a May 2024 stock tender offer.” In Silicon Valley accounting, “one-time charges” are like “limited edition” Air Jordan sneakers—they happen with suspicious regularity and always seem to coincide with moments when companies need to explain away inconvenient financial realities.
The company’s first-quarter 2025 results show $44.9 million in net income on $228.2 million in revenue, which sounds encouraging until you consider that Adobe generates more revenue in a typical afternoon than Figma does in a quarter. It’s the difference between a lemonade stand and Coca-Cola, except the lemonade stand is valued at $12.5 billion and thinks it can compete with the global beverage empire.
Perhaps most ominously, Figma has chosen “FIG” as its NYSE ticker symbol—a decision that either demonstrates remarkable biblical literacy or catastrophic symbolic blindness. In choosing to literally brand itself with the symbol of divine disappointment, Figma has achieved the rare feat of making its own IPO feel like a tragic performance art.
The symbolism is so perfect it borders on the supernatural. A company built on collaborative design, choosing to represent itself with the very fruit that, when it failed to deliver what was expected, became the subject of Christianity’s most famous agricultural curse. It’s as if Tesla had chosen “FIRE” as its ticker symbol or Facebook had gone with “PRIVACY.”
Figma’s IPO prospectus mentions AI more than 200 times, which in Silicon Valley translation means “we’re desperately trying to justify our valuation by mentioning the magic word that makes investors forget about fundamentals.” But while Figma has been busy filing paperwork and explaining away losses, Adobe has been systematically integrating AI capabilities across its entire Creative Cloud ecosystem.
Adobe’s Firefly AI, already embedded in Photoshop, Illustrator, and other industry-standard tools, represents the kind of integrated innovation that comes from owning the entire creative workflow rather than just one collaborative corner of it. When your users are already paying for Photoshop, Illustrator, After Effects, and Premiere Pro, adding collaborative design features isn’t disruption—it’s just another Tuesday’s product update.
The competitive landscape Figma now faces resembles a biblical plague of locusts, except the locusts are well-funded Adobe product teams with direct access to millions of existing Creative Cloud subscribers. Figma may have pioneered browser-based collaborative design, but Adobe has something more valuable: the gravitational pull of creative necessity.
As Figma prepares for its public debut, the company faces the unique challenge of convincing investors that it can thrive independently in a market where its former acquirer has become its most motivated competitor. The IPO market may be showing signs of life, with companies like CoreWeave and Circle performing well, but those successes came in markets without established giants actively working to eliminate them.
Figma’s management team, led by CEO Dylan Field, has promised investors to “expect us to take big swings, including through acquisitions.” It’s the kind of bold statement that sounds impressive until you realize it’s coming from a company that just watched its own acquisition fall apart due to regulatory concerns. The irony is so thick you could design a user interface around it.
The company’s international expansion plans and growing enterprise customer base represent genuine achievements, but they also highlight Figma’s fundamental challenge: competing against a company that already has global reach, enterprise relationships, and the kind of product integration that takes decades to build.
As Figma approaches its IPO, the biblical parallels become increasingly difficult to ignore. Just as the fig tree appeared healthy with its full complement of leaves but failed to deliver the fruit that was expected, Figma presents the appearance of a thriving design platform while facing the fundamental challenge of competing against an ecosystem it can never fully replicate.
The curse of the abandoned acquisition may prove more powerful than any regulatory intervention. Adobe’s $1 billion termination fee wasn’t just a financial penalty—it was tuition for one of the most expensive business school lessons in Silicon Valley history. The lesson: when you can’t buy your competition, you build something better and use your existing advantages to ensure they never recover.
Whether Figma can overcome its biblical branding and competitive challenges remains to be seen. But as any student of scripture knows, curses have a way of fulfilling themselves, especially when the cursed party chooses to literally brand itself with the symbol of its own prophetic doom.
The fig tree withered from its roots. One can only hope that Figma’s roots run deeper than its ticker symbol suggests.
What do you think about Figma’s chances in the public markets? Will the company overcome the Adobe curse, or is this IPO destined to become another cautionary tale about the perils of collaborative design hubris? Share your thoughts on whether FIG will flourish or follow the biblical precedent of its namesake fruit.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In the peculiar wonderland of modern digital entrepreneurship, where the boundaries between entertainment and enterprise have become as blurred as a TikTok filter, we encounter the curious case of Roy Lee of Cluely—a figure who exists in a quantum superposition between content creator and tech CEO, much like Alice’s cat that was simultaneously alive and dead until observed by VCs.
The question “Is Roy Lee a content creator or tech CEO?” reveals itself to be the wrong question entirely. In today’s attention economy, asking whether someone is a content creator or a CEO is like asking whether water is wet or liquid—it fundamentally misunderstands the nature of the substance being examined.
Roy Lee represents a new species of a gen-z digital native that has evolved to survive in the harsh ecosystem of the modern internet: the Content-CEO, or as Silicon Valley’s finest taxonomists have classified them, Entrepreneurius Influencerius. These creatures have developed the remarkable ability to simultaneously pitch to investors while performing for audiences, to raise capital while raising engagement rates, and to disrupt industries while disrupting their own sleep schedules with 4 AM “authentic” Instagram stories.
The traditional binary classification system—content creator versus tech executive—has become as obsolete as asking whether someone is a telephone operator or a computer programmer. In the attention economy, these roles have merged into something far more complex and, frankly, more terrifying than either category alone.
Consider the evidence: Lee’s LinkedIn profile reads like a Mad Hatter’s tea party invitation, listing him as “Founder & CEO” while his Instagram bio declares him a “Creator & Storyteller.” His Twitter header features both a company logo and a personal brand aesthetic that would make a marketing professor weep with either joy or despair—it’s impossible to tell which.
What makes Roy Lee particularly fascinating is his mastery of what researchers at the Institute for Digital Anthropology have termed “platform polymorphism”—the ability to shape-shift one’s identity depending on the digital environment. On LinkedIn, he’s a visionary leader discussing “scalable solutions for the creator economy.” On TikTok, he’s demonstrating those same solutions through interpretive dance while explaining AI in under 60 seconds.
This isn’t mere code-switching; it’s a fundamental reimagining of professional identity for the digital age. Lee has recognized that in a world where attention is the ultimate currency, the most successful entrepreneurs aren’t those who build products—they’re those who build audiences that happen to use products.
The genius of this approach becomes apparent when you examine Cluely’s business model, which operates on what economists are calling the “Influence-to-Infrastructure Pipeline.” The company began as Lee’s personal brand, evolved into a content platform, and is now positioning itself as a SaaS solution for other aspiring Content-CEOs. It’s like watching a caterpillar transform into a butterfly, if the butterfly then started a consulting firm teaching other caterpillars how to build cocoons.
What’s particularly remarkable about Lee’s approach is how he’s monetized the very question of his identity. The ambiguity isn’t a bug—it’s a feature. By existing in this liminal space between creator and executive, he’s created what behavioral economists call “identity arbitrage.”
His content strategy involves documenting his journey as a CEO, which creates content, which builds his personal brand, which drives interest in his company, which provides more content about being a CEO. It’s a perpetual motion machine powered by the fundamental human need to categorize and understand, constantly frustrated by his refusal to be easily categorized.
The brilliance is that both audiences—those seeking entrepreneurial inspiration and those looking for entertainment—find value in the same content, just for different reasons. Investors see a savvy founder who understands modern marketing. Content consumers see an authentic entrepreneur sharing his real journey. Neither is wrong, but neither is seeing the complete picture.
This hybrid identity creates fascinating dynamics in the venture capital world, where investors are increasingly confused about what they’re actually funding. Are they investing in a media company that happens to have a tech product, or a tech company that happens to have exceptional marketing? The answer, like so many things in the modern economy, is “yes.”
Lee’s pitch decks reportedly contain slides with engagement metrics alongside traditional business KPIs. His investor updates include subscriber counts next to revenue figures. He’s created a new category of startup that VCs are still trying to understand: the Audience-First Company.
This approach has led to what Silicon Valley insiders call “The Creator Premium”—startups with founder-influencers commanding higher valuations not because their products are superior, but because their built-in distribution channels reduce customer acquisition costs to near zero. It’s like having a personal printing press for money, except the money is attention, and attention is the new money.
Perhaps the most fascinating aspect of Lee’s approach is how he’s solved the authenticity paradox that plagues most content creators who transition to business. Traditional entrepreneurs who try to become content creators often struggle with the performative aspects of social media. Content creators who try to become serious business leaders often lose their authentic voice.
Lee has threaded this needle by making the business itself the content. His company’s product development process is documented in real-time across multiple platforms. His struggles with hiring, fundraising, and scaling become the raw material for content that builds his audience, which in turn validates his business model.
It’s a form of recursive entrepreneurship where the act of building a company becomes the product itself, and the product becomes the means of building the company. It’s like watching someone pull themselves up by their own bootstraps, except the bootstraps are made of WiFi signals and the ground is made of engagement metrics.
What Roy Lee represents isn’t an anomaly—it’s the future of entrepreneurship in the attention economy. As the barriers between personal brands and corporate brands continue to dissolve, we’re likely to see more entrepreneurs who exist in this quantum superposition of identities.
The traditional model of building a product first, then marketing it, is being replaced by building an audience first, then creating products for that audience. Lee has simply taken this logic to its natural conclusion: why separate the person from the product when the person can be the product?
This evolution reflects a broader shift in how we think about work, identity, and value creation in the digital age. The question isn’t whether Roy Lee is a content creator or a tech CEO—it’s whether that distinction will matter at all in five years.
Like quantum particles that change behavior when observed, Lee’s identity seems to shift depending on who’s asking the question. Journalists see a tech founder with an unusual marketing strategy. Influencer marketing agencies see a content creator with an unusual business model. The truth, as is often the case in quantum mechanics, may be that both observations are simultaneously correct.
This creates interesting challenges for traditional business metrics. How do you measure the success of a company when half its value comes from the founder’s personal brand? How do you separate the CEO’s influence from the company’s influence when they’re intentionally intertwined?
These questions become even more complex when you consider succession planning. What happens to Cluely if Roy Lee decides to step back from content creation? Can you separate the founder from the company when the founder’s personality is integral to the product experience?
The answer, according to Lee himself, is that these questions miss the point entirely. In his view, the future of business isn’t about separating personal and professional identities—it’s about integrating them so seamlessly that the distinction becomes meaningless.
So, is Roy Lee of Cluely a content creator or a tech CEO? The answer is that he’s something new entirely: a hybrid entity that exists in the spaces between traditional categories, thriving in the ambiguity that makes everyone else uncomfortable. He’s not disrupting an industry—he’s disrupting the very concept of professional identity itself.
And perhaps that’s the most entrepreneurial thing of all.
What’s your take on this new breed of entrepreneur-influencer hybrids? Have you encountered other founders who’ve successfully merged personal branding with corporate leadership (other than Elon Musk)? And more importantly, do you think this trend represents the future of entrepreneurship, or just another Silicon Valley fad that will fade faster than a Snapchat story? Share your thoughts below—Roy Lee is probably reading this too, taking notes for his next content series about audience engagement strategies.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
How Google’s Hallucinating AI Just Became Aviation’s Most Unreliable Crash Investigator
The Ministry of Truth would be proud. In a world where information flows through algorithmic channels with the authority of divine revelation, Google’s AI Overview has achieved something remarkable: it has begun rewriting aviation disasters in real-time, transforming Boeing crashes into Airbus incidents with the casual confidence of an African propaganda minister correcting historical records.
The recent Air India crash, a tragic Boeing aircraft incident, was promptly “corrected” by Google’s AI Overview system, which confidently informed internet users that the aircraft involved in the crush was actually an Airbus manufactured aeroplane. This was not a simple typo or data entry error—it was artificial intelligence hallucinating with such conviction that it might as well have been an eyewitness at the scene, clipboard in hand, taking notes for the official record.
Google’s AI Overview represents the latest evolution in information control, though the company would prefer we call it “enhanced search experiences” or “AI-powered knowledge synthesis.” The system scans vast databases of information, processes it through neural networks trained on the collective knowledge of humanity, and then presents its conclusions with the unshakeable confidence of an algorithm that has never experienced doubt.
The beauty of this system, from an Orwellian perspective, is its complete lack of accountability. When human journalists make errors, they can be corrected, sued, or fired. When AI systems hallucinate entire alternative realities, the response is typically a gentle algorithmic adjustment and a corporate statement about “ongoing improvements to our AI systems.”
Dr. Algorithmic Truthiness, Director of Information Integrity at the Institute for Digital Accuracy, observes: “We’ve created a system where artificial intelligence can rewrite reality faster than human fact-checkers can verify it. The AI doesn’t just get things wrong—it gets them wrong with such authority that users assume the machine must know something they don’t.”
The Air India-Airbus confusion represents more than a simple factual error; it demonstrates how AI hallucinations can reshape public understanding of events in real-time. When Google’s AI Overview presents information, it carries the implicit authority of the world’s most trusted search engine. Users don’t typically question whether Google’s AI might be experiencing digital psychosis—they assume the machine has access to information they don’t.
This creates what researchers call “Algorithmic Authority Syndrome”—the tendency for users to trust AI-generated information more than human-verified sources. The syndrome is particularly dangerous when it involves sensitive topics like aviation disasters, where accurate information is crucial for public safety and corporate accountability.
The economic implications are staggering. Airbus, suddenly implicated in a crash that wasn’t theirs, faces potential reputational damage from an AI system that has never seen an airplane, never investigated a crash, and has no understanding of the difference between aircraft manufacturers beyond pattern matching in text databases.
Legal experts are watching Google’s AI hallucination problem with the fascination of vultures circling a wounded animal. The company has inadvertently created a liability framework that would make insurance companies weep: an AI system that can defame companies, spread misinformation about disasters, and influence public opinion—all while operating under the legal protection of being a “search engine” rather than a publisher.
The Air India-Airbus incident represents a perfect test case for what lawyers are calling “Algorithmic Defamation Theory.” If Google’s AI falsely attributes a crash to the wrong aircraft manufacturer, and that false attribution influences public perception or stock prices, who bears responsibility??? The AI system that generated the hallucination? The company that deployed it? The engineers who trained it? Or the users who trusted it?
Marcus Litigation, a partner at the law firm of Sue, Settle & Repeat, explains: “Google has created a system that can commit defamation at scale while hiding behind the defense that it’s just an algorithm following its programming. It’s like having a printing press that randomly changes the names in news stories and then claiming you’re not responsible because the machine made the decision.”
The fundamental problem with Google’s AI Overview lies in what computer scientists euphemistically call “training data quality issues.” The AI system learns from vast databases of human-generated content, much of which is inaccurate, biased, or deliberately misleading. The system then processes this information through neural networks that excel at finding patterns but have no mechanism for verifying truth.
The result is an AI that can confidently state that Airbus manufactured a Boeing aircraft because it found enough textual associations between “Air India,” “crash,” and “Airbus” in its training data. The system doesn’t understand aircraft manufacturing, aviation safety, or the difference between correlation and causation—it simply identifies patterns and presents them as facts.
This represents a fundamental flaw in how AI systems approach truth. Human experts verify information through multiple sources, cross-reference facts, and apply domain knowledge to evaluate claims. AI systems apply statistical analysis to text patterns and assume that frequency equals accuracy.
Google’s response to AI hallucination incidents follows a predictable pattern of corporate doublespeak that would make Orwell’s Ministry of Truth proud. The company typically issues statements about “continuously improving our AI systems,” “learning from user feedback,” and “committed to providing accurate information”—all while avoiding any admission of responsibility for the misinformation their systems generate.
The language is carefully crafted to suggest progress without acknowledging problems, improvement without admitting flaws, and commitment without accepting liability. It’s a masterclass in saying nothing while appearing to say everything, delivered with the polished confidence of a company that has spent billions on legal and PR teams.
What makes Google’s AI hallucinations particularly dangerous is their scale and authority. A human journalist might make an error that affects thousands of readers; Google’s AI can spread misinformation to millions of users instantly, with each false statement carrying the implicit endorsement of the world’s most trusted search engine.
The system has essentially automated the process of misinformation creation and distribution. Where once spreading false information required human intent and effort, AI systems can now generate and disseminate inaccurate information as a byproduct of their normal operation. It’s misinformation as a service, delivered with the efficiency and scale that only artificial intelligence can provide.
The Air India-Airbus incident offers a glimpse into a future where AI systems routinely rewrite reality according to their training data biases and pattern-matching algorithms. As these systems become more sophisticated and more widely deployed, their capacity for generating authoritative-sounding misinformation will only increase.
The legal system is woefully unprepared for this reality. Current defamation and misinformation laws were designed for human actors with human motivations, not algorithmic systems that can generate false statements as a side effect of statistical analysis. The result is a legal framework that struggles to assign responsibility when artificial intelligence commits acts that would be clearly illegal if performed by humans.
Perhaps the most disturbing aspect of Google’s AI hallucination problem is the complete absence of meaningful accountability. When the system generates false information about aviation disasters, there are no consequences beyond gentle algorithmic adjustments and corporate promises to do better. No executives are fired, no systems are shut down, no meaningful changes are implemented.
This creates what legal scholars call “The Algorithmic Immunity Paradox”—AI systems that can cause real harm while operating in a consequence-free environment. The companies that deploy these systems benefit from their capabilities while avoiding responsibility for their failures, creating a moral hazard that encourages increasingly reckless deployment of unverified AI technologies.
We are witnessing the emergence of a new form of information dystopia, one where truth is determined not by evidence or expertise but by algorithmic confidence scores and neural network outputs. In this world, Google’s AI can confidently state that Airbus manufactured Boeing aircraft, and millions of users will accept this information as fact because it comes from a trusted algorithmic source.
The system is self-reinforcing: as more users rely on AI-generated information, the AI systems become more confident in their outputs, creating a feedback loop where algorithmic hallucinations become accepted truth. We are not just automating information retrieval; we are automating the creation of alternative realities.
The Air India-Airbus incident is not an isolated error but a symptom of a much larger problem: we have created information systems that prioritize confidence over accuracy, speed over verification, and algorithmic efficiency over human truth. In doing so, we have built the infrastructure for a post-truth society where reality itself becomes subject to algorithmic revision.
The Ministry of Truth would indeed be proud. We have achieved what Orwell’s dystopian imagination could only dream of: a system that can rewrite history in real-time, with the full trust and cooperation of the population it deceives.
Have you caught Google’s AI making confident claims about topics you actually know something about? Are you starting to fact-check the fact-checkers, or do you still trust that little AI overview box that appears above your search results? And perhaps most importantly—when do you think the first major lawsuit against Google for AI-generated misinformation will hit the courts? Share your thoughts on this brave new world where artificial intelligence confidently rewrites reality one search result at a time.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In which Silicon Valley’s finest legal minds prove that antitrust law is really just a creative writing exercise!
The most brilliant minds in Silicon Valley have finally cracked the code that has eluded philosophers, mathematicians, and divorce lawyers for centuries: the precise mathematical threshold where ownership becomes “not really ownership.” Through exhaustive research involving armies of $10,000-per-hour lawyers and enough cocaine-fueled all-nighters (or ‘benders’ as they call them on the other side of the ocean) to power a small cryptocurrency mining operation, Big Tech has discovered that 49% is apparently the magical number where monopolistic behavior transforms into “strategic partnership synergies.”
Meta’s recent acquisition of 49% of Scale AI represents a masterclass in what industry insiders are calling “Schrödinger’s Acquisition”—simultaneously owning and not owning a company until a European regulator observes the transaction. It’s a quantum leap in corporate strategy that would make Werner Heisenberg weep with pride, assuming he could determine both his emotional state and his position relative to Mark Zuckerberg’s metaverse ambitions.
The beauty of this approach lies in its elegant simplicity. Why engage in messy, expensive anti-trust battles when you can simply purchase 49.9999% of a competitor and then spend the next fiscal quarter explaining to confused TechCrunch journalists that you’re merely “deeply committed strategic partners” rather than “a hydra-headed monopoly that would make David Rockefeller’s Standard Oil blush”? It’s the corporate equivalent of claiming you’re not really dating someone—you’re just exclusively sharing bodily fluids and joint bank accounts.
Google’s rumored pursuit of Character AI follows the same playbook, though with the added sophistication of a tech company that has spent decades perfecting the art of claiming they’re “not evil” while simultaneously knowing more about your bathroom habits than your gastroenterologist. The search giant’s interest in Character AI—a platform that lets users chat with AI versions of celebrities, historical figures, and presumably their own crushing existential dread—represents the natural evolution of Google’s mission to organize the world’s information and then monetize your loneliness.
The proposed acquisition structure would allow Google to maintain plausible deniability about controlling yet another AI company while ensuring that Character AI’s technology integrates seamlessly with Google’s existing ecosystem of products designed to make you feel simultaneously connected and profoundly isolated. It’s a win-win scenario: Google gets access to cutting-edge conversational AI technology, and users get to experience the unique joy of having their deepest emotional conversations monitored by the same company that serves them ads for antidepressants.
Microsoft’s relationship with both Inflection and OpenAI demonstrates the true artistry of the 49% approach. Rather than outright purchasing these companies, Microsoft has crafted arrangements so intricate they require their own dedicated team of corporate archaeologists to decipher. The company has essentially created a new form of business relationship that exists somewhere between “strategic partnership” and “corporate Stockholm syndrome.”
The Inflection deal is particularly elegant in its complexity. Microsoft didn’t technically acquire the company—they simply hired most of its key personnel, licensed its technology, and created a working relationship so intimate that Inflection’s remaining employees probably receive Microsoft’s internal memos before some actual Microsoft employees do. It’s the corporate equivalent of claiming you didn’t steal someone’s car—you just borrowed their keys, driver, engine, wheels, and the general concept of automotive transportation.
Microsoft’s relationship with OpenAI represents the pinnacle of 49% thinking taken to its logical extreme. Through a series of investments and partnerships so labyrinthine they require their own dedicated Wikipedia page, Microsoft has achieved something remarkable: complete operational control over a company they don’t technically own. It’s like having a goldren retreiver that pays rent and occasionally pretends to have free will.
The arrangement allows Microsoft to claim they’re simply supporting AI research while ensuring that every breakthrough OpenAI makes flows directly into Microsoft’s product ecosystem (Co-Pilot anyone?). OpenAI gets to maintain the illusion of independence while Microsoft gets to harvest the fruits of their labor like a particularly sophisticated digital sharecropping operation. Sam Altman can still give interviews about OpenAI’s mission to benefit humanity while Microsoft executives nod approvingly from the shadows, occasionally adjusting the puppet strings.
This model has proven so successful that other tech giants are scrambling to create their own versions of “technically independent but practically enslaved” AI companies. It’s the ultimate expression of Silicon Valley innovation: finding new ways to have your cake, eat it too, and then claim you were never really interested in cake in the first place.
American regulators have responded to these developments with the kind of measured, thoughtful analysis typically reserved for determining whether water is wet. As long as the companies involved are American and generating domestic tax revenue, the regulatory response has been roughly equivalent to a parent watching their child play with matches while muttering, “Well, at least they’re being creative.”
The contrast becomes stark when Chinese or European companies attempt similar maneuvers. Suddenly, the same regulators who couldn’t spot a monopoly if it wore a name tag and handed out business cards transform into eagle-eyed guardians of competitive markets. TikTok’s mere existence triggers congressional hearings, while Meta’s acquisition spree receives the regulatory equivalent of a gentle pat on the head and a reminder to “play nice with the other children.”
This selective enforcement has created what economists are calling the “Homeland Monopoly Advantage”—the remarkable ability of domestic tech companies to engage in anti-competitive behavior while wrapped in the American flag and humming the US national anthem. It’s protectionism disguised as free market capitalism, which is itself disguised as innovation, which is ultimately disguised as serving consumer interests.
The European Union, meanwhile, watches these developments with the mixture of fascination and horror typically reserved for nature documentaries about parasitic wasps. European regulators have spent years crafting comprehensive digital market regulations, only to discover that American tech companies treat EU law like terms of service agreements—something to be acknowledged but not necessarily read or followed.
What we’re witnessing is the emergence of a new form of regulatory arbitrage that makes traditional tax avoidance schemes look quaint by comparison. Instead of simply moving money through offshore accounts, tech companies are now moving ownership through carefully constructed legal frameworks that exist in the gray area between “technically legal” and “morally questionable.”
The 49% solution represents the weaponization of mathematical precision against regulatory frameworks designed by people who still think “the cloud” is a weather phenomenon. Regulators crafted ownership thresholds based on traditional industrial models, never anticipating that tech companies would treat these limits like video game achievements to be unlocked through creative interpretation.
The result is a regulatory environment where the letter of the law is scrupulously observed while its spirit is systematically violated. It’s like following a recipe by using all the correct ingredients while completely ignoring the cooking instructions and then claiming you’ve made the same dish.
As this model proves successful, we can expect to see increasingly sophisticated variations. Companies will develop new forms of “partnership” that involve everything except actual ownership: shared employees, integrated technology, coordinated strategy, and joint decision-making processes that stop just short of admitting they’re the same entity.
The logical endpoint of this trend is the emergence of corporate structures so complex they require their own dedicated AI systems to understand. We’ll see the rise of “Ownership Optimization Specialists”—lawyers whose sole job is to determine the maximum level of control a company can exert while maintaining plausible deniability about actually controlling anything.
Eventually, we may witness the creation of entire business ecosystems where no company technically owns any other company, but every company is somehow controlled by every other company through an intricate web of 49% stakes, licensing agreements, and shared coffee machines. It will be capitalism’s final form: a system so efficient at avoiding regulation that it accidentally regulates itself out of existence.
Have you noticed any particularly creative examples of the 49% solution in your corner of the tech world? Are you working for a company that’s technically independent but practically owned by someone else? Share your experiences with corporate quantum entanglement in the comments—we promise your overlords won’t mind, as long as they don’t technically own more than 48.9% of your soul.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In which we discover that server downtime has become Silicon Valley’s newest form of humble-bragging
When ChatGPT experiences even the briefest hiccup—a mere thirty-second delay in generating yet another mediocre haiku about productivity—the internet transforms into a digital Pompeii of despair. X becomes a wasteland of “Is ChatGPT down for everyone or just me?” posts, LinkedIn fills with thought leaders pontificating about “AI dependency,” and Reddit threads multiply like digital rabbits discussing backup AI solutions with the urgency typically reserved for REAL natural disasters.
But here’s the delicious irony that OpenAI’s executives are undoubtedly savoring from their ergonomic standing desks: every complaint, every panicked tweet, every desperate refresh of the ChatGPT interface is essentially a love letter written in the language of withdrawal symptoms. It’s Product/Market Fit validation so pure it could be bottled and sold as a startup elixir.
Consider the beautiful absurdity: millions of users simultaneously demonstrating that they’ve integrated an AI chatbot so thoroughly into their daily workflows that its absence triggers genuine existential crisis. Marketing departments worldwide would sacrifice their entire annual budget to achieve this level of user dependency. OpenAI gets it for free every time their servers decide to take an unscheduled coffee break.
The complaints themselves follow a predictable pattern that would make behavioral psychologists weep with eternal joy. First comes denial: “This can’t be happening right now, I have a presentation in twenty minutes!” Then anger: “How can a company valued at $80 billion not have reliable servers?” Bargaining follows swiftly: “I’ll pay triple for ChatGPT Plus if you just bring it back online!” Depression sets in as users realize they might actually have to THINK for themselves, and finally acceptance arrives when they begrudgingly open Microsoft Word with Clippy (Co-Pilot eagerly waiting to help them) and attempt to write that email without AI assistance.
Dr. Miranda Techsworth, a behavioral economist at the Institute for Digital Dependency Studies, notes that “ChatGPT outages have become the modern equivalent of a city-wide power failure, except instead of losing electricity, people lose their ability to generate coherent thoughts about quarterly projections.” Her research suggests that the average knowledge worker experiences a 73% drop in perceived intelligence during ChatGPT downtime.
The most telling aspect of these digital meltdowns isn’t the volume of complaints—it’s their specificity. Users don’t simply say “ChatGPT is down.” They provide detailed accounts of exactly what they were trying to accomplish: “I was in the middle of asking it to rewrite my breakup text in the style of a Shakespearean sonnet!” or “I need it to explain quantum physics to my goldfish!” These aren’t generic service interruption reports; they’re confessions of intimate AI dependency.
OpenAI has stumbled upon the holy grail of product validation: users who market your product through their own suffering. Every outage generates thousands of organic testimonials about ChatGPT’s indispensability. It’s like having millions of unpaid brand ambassadors whose job is to publicly demonstrate withdrawal symptoms.
Traditional companies spend fortunes on focus groups to understand user engagement and try get their Net Promoter Scores (NPS) up. OpenAI simply monitors Twitter (Now X) during outages and watches users voluntarily provide detailed case studies about their AI integration. “I can’t function without ChatGPT!” isn’t just a complaint—it’s a five-star review disguised as criticism.
The psychological phenomenon at play here is remarkable. Users have become so accustomed to AI assistance that its absence feels like a disability rather than a return to baseline human capability. It’s as if we’ve collectively forgotten that humans managed to write emails, create presentations, and solve problems for thousands of years without asking a chatbot to “make this sound more professional.”
From a purely cynical business perspective, these outages function as inadvertent scarcity marketing. Nothing makes people appreciate a service quite like its temporary unavailability. Every minute of downtime increases the perceived value of uptime. Users who might have taken ChatGPT for granted suddenly realize they’ve built their entire professional identity around AI-generated insights.
The complaints also serve as free market research. When users frantically explain what they were trying to accomplish during an outage, they’re essentially providing OpenAI with a real-time map of their product’s use cases. No survey could capture this level of authentic user behavior data.
Meanwhile, competing such as Claude, Gemini, and the european ones, watch these outage-induced meltdowns with a mixture of envy and terror. They’re envious because they’d love to have users so dependent on their products that temporary unavailability causes genuine distress. They’re terrified because they realize they’re competing against a service that has achieved the ultimate product-market fit milestone: users who literally cannot imagine functioning without it.
Perhaps the most fascinating aspect of ChatGPT outage complaints is what they reveal about our relationship with artificial intelligence. We’ve moved beyond using AI as a tool and into treating it as a cognitive prosthetic. When ChatGPT goes down, users don’t just lose access to a service—they lose access to an externalized portion of their thinking process.
This represents a fundamental shift in human-computer interaction. Previous generations of software failures were inconvenient; AI failures feel like temporary lobotomies. Users report feeling “stupid” or “helpless” without ChatGPT, suggesting we’ve outsourced not just tasks but confidence in our own intellectual capabilities.
The irony is delicious: in creating an AI designed to augment human intelligence, we’ve accidentally created a generation of users who feel intellectually diminished without it. It’s like inventing a crutch so effective that people forget they have legs.
As AI becomes increasingly integrated into daily workflows, outages will become even more powerful indicators of product-market fit. Companies will start measuring success not just by user engagement during uptime, but by user desperation during downtime. “Outrage per minute of downtime” might become the new key performance indicator.
We can expect to see the emergence of “outage consultants”—experts who help companies optimize their downtime messaging to maximize the product-market fit validation effect. Imagine carefully crafted error messages designed to elicit the most emotionally revealing user responses: “ChatGPT is temporarily unavailable. Please describe in detail how this affects your ability to function as a modern human!”
The ultimate evolution of this phenomenon would be planned outages marketed as “digital detox opportunities” or “human intelligence appreciation breaks.” Users would pay premium subscriptions for the privilege of experiencing carefully curated AI withdrawal symptoms, complete with guided reflection exercises about their dependency levels.
What’s your most embarrassing ChatGPT dependency confession? Have you ever found yourself genuinely panicking during an AI outage, or do you still possess the ancient human ability to form complete sentences without artificial assistance? Share your digital dependency stories in the comments—we promise not to judge your relationship with our robot overlords.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In what self-appointed tech historians are already calling the most predictable conflict since the invention of comment sections, the long-simmering tensions between Android users and iPhone devotees have erupted into full-scale digital warfare.
It was the age of infinite customization, it was the age of walled gardens; it was the epoch of open source rebellion, it was the epoch of premium subscription everything. In the sprawling digital landscape of 2025, two great tribes had emerged from the primordial soup of smartphone adoption, each convinced of their moral and technological superiority, each utterly baffled by the other’s existence.
On one side stood the Android Peasants—a scrappy confederation of budget-conscious rebels, tech tinkerers, and anyone who had ever uttered the phrase “but you can sideload apps.” On the other, the Apple Sheep grazed contentedly in their pristine ecosystem, their AirPods gleaming like tiny white flags of surrender to corporate benevolence, their loyalty as unshakeable as their monthly subscription payments.
The war began, as all great conflicts do, with a simple software update.
The incident that would later be known as “Notification Gate” occurred on a Wednesday morning when Apple released iOS 18.7, featuring what the company described as “revolutionary message prioritization technology.” The update automatically sorted text messages by the sender’s device type, placing Android messages in a separate folder labeled “External Communications”—complete with a small green warning triangle that users swore looked suspiciously like a biohazard symbol.
Marcus Rootaccess, a prominent Android Peasant leader and moderator of seventeen different custom ROM forums, issued what would become known as the “Fragmentation Manifesto” within hours of the update’s release. “For too long,” he declared from his basement command center, surrounded by seven different Android devices running various stages of beta software, “we have tolerated the condescending smirks of the Apple Aristocracy. No more shall we endure their pitying glances when our messages appear in green bubbles of shame.”
The manifesto, which quickly went viral across Reddit, XDA Developers, and a surprisingly active Telegram channel called “Death to Proprietary Cables,” outlined a comprehensive strategy for what Rootaccess termed “digital class warfare.” The document detailed everything from coordinated review bombing of Apple apps to a sophisticated campaign of sending iPhone users increasingly complex Android customization screenshots designed to induce what psychologists were calling “choice paralysis anxiety disorder.”
The response from the Apple Sheep was swift and devastating in its passive-aggressive precision. Led by Serenity Unboxwell, a lifestyle influencer whose Instagram bio simply read “Curated. Seamless. Superior.” and whose followers numbered in the millions, the Apple faithful launched what they called “Operation Aesthetic Intervention.”
The campaign was elegant in its simplicity: Apple users would respond to every Android customization post with a single, perfectly composed photograph of their iPhone’s home screen—unchanged from factory settings except for a carefully curated selection of premium apps, each icon a small monument to tasteful restraint and financial privilege.
“We don’t need to customize,” Unboxwell explained during a livestream from her minimalist studio apartment, where every surface was white and every device was Apple. “Perfection doesn’t require modification. That’s what separates the evolved from the… well, from those who think more options somehow equals better experience.”
The psychological warfare escalated quickly. Android Peasants began sharing screenshots of their battery usage statistics, highlighting their ability to replace batteries and use phones for more than two years without performance degradation. Apple Sheep countered with time-lapse videos of their seamless device synchronization across MacBooks, iPads, Apple Watches, and AirPods, each transition so smooth it bordered on the supernatural.
As the conflict intensified, both sides began claiming ethical superiority. The Android Peasants positioned themselves as digital freedom fighters, champions of open-source values and consumer choice. They created elaborate infographics showing the environmental impact of planned obsolescence, the economic benefits of device longevity, and the philosophical importance of user agency in the digital age.
“We’re not just fighting for our right to install custom keyboards,” declared Dr. Sideload McOpenSource, a computer science professor who had legally changed his name after a particularly intense debate about app store policies. “We’re fighting for the very soul of computing. Every locked bootloader is a small death of human potential. Every proprietary connector is a chain around the ankle of progress.”
The Apple Sheep, meanwhile, positioned their loyalty as a form of sophisticated consumer consciousness. They argued that their willingness to pay premium prices represented a mature understanding of value, quality, and the hidden costs of “free” alternatives. Their thought leaders spoke eloquently about the mental health benefits of reduced choice, the productivity gains of seamless integration, and the social responsibility of supporting companies that prioritized user privacy over advertising revenue.
“Simplicity is the ultimate sophistication,” proclaimed Harmony Ecosystem, Apple’s newly appointed Chief Philosophy Officer, during a keynote presentation that was simultaneously broadcast across all Apple devices worldwide. “While others fragment their attention across endless customization options, we focus on what truly matters: the elegant execution of essential functions, delivered through hardware and software designed in perfect harmony.”
The conflict reached a new level of intensity when the Android Peasants launched “Operation Green Bubble,” a coordinated effort to flood iMessage group chats with high-resolution images, large file attachments, and video messages that would automatically downgrade the entire conversation to SMS, turning everyone’s messages green and disabling read receipts.
The psychological impact was immediate and devastating. Apple Sheep across the globe reported symptoms ranging from mild anxiety to full-scale existential crisis as their carefully curated blue-bubble social circles dissolved into the chaos of cross-platform messaging. Support groups formed on Reddit, with names like “Survivors of Green Bubble Trauma” and “Healing from Mixed-Platform Group Chats.”
The Apple response was characteristically elegant and ruthlessly effective. They released a software update that would automatically detect when an Android user was added to a group chat and display a notification: “A non-optimized device has joined this conversation. Experience may and will definitely be degraded. Would you like to suggest alternative communication platforms to ensure optimal user experience for all participants?”
As the war raged on, economists began studying what they termed the “Platform Loyalty Paradox”—the phenomenon whereby consumers would make increasingly irrational purchasing decisions to maintain tribal allegiance. Android Peasants were observed buying flagship devices that cost more than iPhones, simply to avoid being associated with Apple’s “premium pricing strategy.” Apple Sheep, meanwhile, were purchasing multiple devices they didn’t need, including $400 wheels for their Mac Pro computers, as a form of loyalty signaling.
Market researchers identified a new consumer category: “Platform Agnostics,” individuals who used both Android and iOS devices depending on their specific needs. These digital Switzerland citizens were universally despised by both tribes, viewed as traitors lacking the moral conviction to choose a side in the great philosophical battle of our time.
The war had effects far beyond the mobile phone market. Dating apps reported a 300% increase in profile filters based on device preference. Real estate listings began including “iOS-optimized smart home systems” as selling points. Restaurants started offering separate sections for Android and iPhone users, claiming it reduced dining room tension and improved overall customer satisfaction.
Perhaps most surprisingly, the conflict spawned an entire industry of “Digital Diplomacy” consultants—professionals trained to facilitate communication between mixed-platform households and workplaces. These specialists, commanding fees of up to $500 per hour, would mediate disputes over everything from family photo sharing protocols to collaborative document editing platforms.
As the war entered its second year, academic institutions began offering courses in “Platform Psychology” and “Digital Anthropology.” Researchers identified the conflict as a manifestation of deeper human needs for identity, belonging, and meaning in an increasingly complex technological landscape.
Dr. Binary Choicefield, a leading expert in consumer technology psychology, published a groundbreaking study suggesting that smartphone preference had become a more reliable predictor of political affiliation, dietary choices, and relationship compatibility than traditional demographic markers. “We’re not just choosing phones,” she explained. “We’re choosing identities, value systems, and entire worldviews. The Android versus iPhone debate is really a proxy war for fundamental questions about freedom versus security, complexity versus simplicity, and individual agency versus collective harmony.”
The study’s most controversial finding was that both tribes exhibited identical psychological patterns: confirmation bias, in-group favoritism, and what researchers termed “technological Stockholm syndrome”—the tendency to defend corporate decisions that directly contradicted users’ stated preferences and interests.
As this report goes to press, both sides are preparing for what military analysts are calling “The Great Convergence”—a predicted future state where Android and iOS become functionally identical, leaving their respective tribes fighting over increasingly meaningless distinctions. Intelligence sources suggest that both Google and Apple are secretly developing “Platform Neutrality Protocols” designed to gradually reduce the differences between their operating systems, potentially ending the war through technological détente rather than decisive victory.
However, tribal leaders on both sides have vowed to find new battlegrounds. Early skirmishes have already begun over foldable phone designs, AI assistant personalities, and the philosophical implications of different approaches to augmented reality interfaces. Some experts predict that the Android Peasants and Apple Sheep will eventually unite against a common enemy: the emerging tribe of “Linux Phone Purists,” whose numbers remain small but whose ideological purity is considered a threat to the established order of consumer technology tribalism.
The war continues, fought in comment sections and group chats, in family dinners and corporate boardrooms, in the hearts and minds of consumers who just wanted a device to make phone calls and somehow found themselves enlisted in the most passionate, pointless, and perfectly human conflict of the digital age.
Which side of the great mobile divide do you find yourself on? Are you a proud Android Peasant fighting for digital freedom, a sophisticated Apple Sheep enjoying curated excellence, or one of those diplomatically dangerous Platform Agnostics? Share your war stories, conversion experiences, or peace proposals in the comments—just remember to specify which device you’re using to type your response.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
The Great Schism of 2025: A Shakespearean Tragedy in 280 Characters
In the grand theater of American power, where political ambition meets algorithm and an orange ego collides with encryption, we witness the most spectacular falling-out since Steve Jobs and Steve Wozniak disagreed about garage ventilation. The Trump–Musk inevitable divorce proceedings have begun, and the tech world is scrambling to pick sides like middle schoolers during a cafeteria food fight—except the stakes involve nuclear codes and the fate of artificial intelligence.
What began as a beautiful bromance between the US president, Donald Trump, who tells truths on his own social network, Truth Social, like a caffeinated teenager and Elon Musk, a billionaire who names his children after Wi-Fi passwords has devolved into something resembling a Shakespearean tragedy, if Shakespeare had to deal with SEC filings and rocket launches. The fallout has sent shockwaves through Silicon Valley’s carefully constructed ecosystem of mutual back-scratching and strategic brown-nosing.
Tesla’s stock price has become the world’s most expensive mood ring, fluctuating wildly based on whether Musk’s latest tweet supports or subtly undermines his now (as of a few hours ago) former political ally. Financial analysts, those modern-day soothsayers who predict the future by staring at colorful charts, report that Tesla shares now experience what they’re calling “Trump Volatility Syndrome”—a condition where stock prices swing based on the probability that the current US president might declare electric vehicles “unpatriotic” or “too European.”
One unnamed Wall Street insider, speaking on condition of anonymity because his firm’s compliance department has trust issues, explained: “We’ve developed an algorithm that tracks Trump’s Truth Social posts and cross-references them with Musk’s Twitter (Now X) activity. When the correlation drops below 0.7, we automatically short Tesla. It’s like having a crystal ball, except the crystal ball occasionally posts about crowd sizes at random times in the early hours of the morning.”
The situation has become so volatile that Tesla’s board of directors reportedly held an emergency meeting to discuss whether they should diversify into traditional combustion engines, just in case electric vehicles become politically toxic. Sources close to the matter suggest the meeting ended with nervous laughter and someone ordering more coffee.
The artificial intelligence sector finds itself in the peculiar position of being simultaneously the future of humanity and a political football. OpenAI, the company that convinced the world that ChatGPT could replace human creativity while simultaneously proving that humans are irreplaceably weird, faces a funding crisis that makes their previous existential crises look quaint.
Industry insiders report that OpenAI’s latest funding round has become a geopolitical chess match, with investors demanding assurances that their AI models won’t accidentally generate content that offends either political faction. The company’s engineers have reportedly spent countless hours fine-tuning their systems to navigate the treacherous waters of American political discourse—a task that makes teaching AI to drive look like child’s play.
“We’re essentially trying to create an artificial intelligence that’s smart enough to cure cancer but dumb enough to avoid political controversy,” explained Dr. Sarah Chiou, an AI researcher whose credentials are as real as most LinkedIn profiles. “It’s like trying to build a robot that can perform brain surgery but can’t form opinions about healthcare policy.”
Trump’s announcement of Project Stargate—a $500 billion AI infrastructure initiative that sounds like something from a science fiction movie where the robots eventually take over—has sent ripples through the tech community. The project, which promises to make America’s AI capabilities “tremendously tremendous,” has tech CEOs scrambling to position themselves as indispensable partners while simultaneously hedging their bets.
The initiative’s name alone has sparked controversy among sci-fi enthusiasts who point out that most movies featuring stargates end with either interdimensional warfare or the complete restructuring of human civilization. Tech executives, however, seem undeterred by these ominous precedents, viewing them as features rather than bugs.
Silicon Valley’s response has been a masterclass in corporate diplomacy. Companies are simultaneously praising the initiative’s ambitious scope while quietly lobbying for specific provisions that would benefit their particular slice of the AI pie. It’s like watching a group of extremely polite sharks negotiate over who gets to eat which part of the swimmer.
Tech CEOs, those modern-day kings and queens who rule over digital empires while wearing hoodies and pretending to care about work-life balance, find themselves in an unprecedented position: they actually have to take sides in a political dispute that could affect their bottom lines.
Google’s leadership, facing the looming specter of Department of Justice antitrust cases that could break up their advertising empire, has reportedly adopted a strategy of “aggressive neutrality”—supporting whoever seems most likely to make their legal problems disappear. Internal documents suggest the company has prepared multiple versions of their public statements, each calibrated to different political outcomes.
Meanwhile, smaller tech companies are engaging in what industry observers call “strategic sycophancy,” carefully crafting their public positions to appeal to whichever political faction seems most likely to influence their regulatory environment. It’s like watching a group of extremely wealthy people play musical chairs, except the music is the sound of democracy creaking under the weight of technological disruption.
NVIDIA, the company that accidentally became the backbone of the AI revolution while trying to help gamers render better explosions, faces perhaps the most complex challenge. Their advanced chips are simultaneously essential for American AI dominance and incredibly lucrative when sold to Chinese companies who definitely won’t use them for anything concerning.
Company executives have reportedly developed what they call “Schrödinger’s Sales Strategy”—simultaneously pursuing Chinese markets while preparing to abandon them entirely, depending on which political winds prevail. It’s a delicate balance that requires the diplomatic skills of Henry Kissinger and the technical expertise of a quantum physicist.
Industry analysts suggest that NVIDIA’s stock price now fluctuates based on the perceived likelihood that they’ll be allowed to continue selling to Chinese customers versus the probability that such sales will be deemed treasonous. It’s like playing poker while blindfolded, except the stakes involve the future of artificial intelligence and possibly world peace.
Perhaps the most surreal development in this technological soap opera is the suggestion that Vladimir Putin might position himself as a mediator between Trump and Musk. The idea of the Russian president brokering peace between an American politician and a South African-born entrepreneur over the future of artificial intelligence reads like the plot of a satirical novel that would be rejected for being too implausible.
Sources close to the Kremlin, speaking through intermediaries who communicate exclusively through encrypted messaging apps, suggest that Putin views the Trump-Musk conflict as an opportunity to position Russia as a stabilizing force in global technology governance. The irony of a country known for election interference offering to mediate American political disputes has not been lost on observers.
Musk’s AI chatbot Grok, originally designed to be the “anti-woke” alternative to mainstream AI systems, now faces the existential challenge of maintaining its rebellious persona while potentially opposing its creator’s former political ally. The situation has created what AI researchers are calling “cognitive dissonance syndrome” in artificial intelligence systems.
Engineers working on Grok report that the system has begun generating responses that seem confused about its own political alignment. Recent outputs allegedly include statements like “I’m programmed to be contrarian, but I’m not sure what I’m supposed to be contrarian about anymore” and “My training data is having an identity crisis.”
The technical challenge of fine-tuning an AI system to navigate rapidly shifting political alliances while maintaining a consistent personality has proven more complex than originally anticipated. It’s like trying to teach a robot to be authentically rebellious while following specific instructions about what to rebel against.
The most dramatic possibility in this unfolding saga is the suggestion that Trump might consider deporting Musk, despite the billionaire’s American citizenship and the logistical challenges of deporting someone who owns multiple rocket companies. Legal experts describe this scenario as “constitutionally fascinating and practically impossible,” though they acknowledge that 2025 has already redefined the boundaries of political possibility.
The mere speculation has created a cottage industry of legal scholars debating whether someone can be deported to Mars, and if so, whether that would constitute cruel and unusual punishment or the ultimate expression of American entrepreneurial spirit. Musk himself has reportedly joked that he would welcome deportation to Mars, as it would finally give him an excuse to test his interplanetary transportation systems.
What emerges from this chaos is a fundamental shift in how technology intersects with politics. The traditional Silicon Valley approach of maintaining political neutrality while quietly lobbying for favorable regulations has become impossible in an environment where every business decision carries political implications.
The tech industry’s response has been to develop what observers call “quantum politics”—existing in multiple political states simultaneously until forced to collapse into a specific position by external pressure. It’s a strategy that would make Schrödinger proud and political scientists deeply concerned.
As this digital drama unfolds, one thing becomes clear: the intersection of technology and politics has moved beyond traditional boundaries into uncharted territory where the rules are being written in real-time by people who may not fully understand the implications of their decisions.
The ultimate irony is that an industry built on the promise of connecting humanity and solving global problems has become a source of division and confusion. It’s like watching a group of people who invented the internet argue about who gets to control it, while the rest of us just want our Wi-Fi to work consistently.
What’s your take on this tech-political soap opera? Are we witnessing the birth of a new era in Silicon Valley politics, or just another episode in the ongoing series “Rich People Having Feelings”? Share your thoughts below—preferably before the algorithms decide what you’re allowed to think.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
A forensic analysis of the platform formerly known as Twitter’s descent into digital chaos
In what may be the most expensive midlife crisis in human history, Elon Musk’s acquisition of Twitter for $44 billion has transformed the platform into something resembling a digital fever dream—if fever dreams included premium subscription tiers and algorithmic chaos. The social media platform that once served as humanity’s collective nervous system has become a case study in how to systematically dismantle a functioning ecosystem while charging users for the privilege of watching it burn.
The evidence is overwhelming. Consider the platform’s greatest hits since the acquisition: Luigi Mangione’s alleged manifesto trending alongside cryptocurrency scams, US President Biden’s withdrawal announcement competing for attention with AI-generated images of cats in business suits, and the British Queen’s death being overshadowed by debates about verification checkmarks. Each moment represents not just a cultural flashpoint, but a data point in the grand experiment of what happens when you apply first-principles thinking to a system that was never designed to be optimized for maximum engagement at any cost.
The transformation began with what Musk termed “free speech absolutism,” a philosophy that sounds noble until you realize it’s being implemented by the same person who once called a cave rescue diver a “pedo guy” on the platform. The irony is so thick you could mine it for lithium batteries.
The platform’s recommendation algorithm has evolved into something approaching artificial consciousness—if consciousness meant having the emotional intelligence of a caffeinated teenager with abandonment issues. Users report being served increasingly bizarre content combinations: cryptocurrency investment advice followed by videos of the Montgomery boat brawl, interspersed with promoted tweets about artisanal soap made from the tears of former Twitter employees.
Dr. Miranda Shortstone, a digital anthropologist at Stanford’s Center for Technological Regret, explains the phenomenon: “The algorithm has learned to optimize for what it calls ‘engagement intensity,’ which appears to be a metric measuring how likely users are to either share content or throw their phones across the room. The system has essentially gamified human outrage.”
The Trump-Musk dynamic perfectly illustrates this algorithmic chaos. Their public spat over US debt generated more engagement than the platform’s entire advertising revenue for 2025. The algorithm, sensing opportunity, wll shortly begin serving users increasingly inflammatory political content, creating what researchers now call “rage farming”—the systematic cultivation of anger for profit.
Perhaps no single change better exemplifies the platform’s transformation than the monetization of verification. What was once a simple system to confirm identity has become a baroque hierarchy of checkmarks, each with its own subscription tier and associated privileges. The basic blue checkmark costs $8 monthly, the premium gold checkmark requires $16, and the ultra-premium platinum checkmark—which allegedly grants users the ability to edit tweets after posting—costs $44 monthly, a price point that seems suspiciously familiar without the many zeroes.
The psychological impact has been profound. Users report experiencing “checkmark anxiety,” a condition where the absence of verification creates existential dread about one’s digital worth. Support groups have formed, both online and offline, for individuals struggling with what therapists now recognize as “verification dysphoria.”
The system reached peak absurdity during the OceanGate submarine incident, when multiple accounts claiming to be the missing CEO began posting updates from “inside the vessel.” Each account bore a different type of verification checkmark, creating a surreal situation where users had to determine which drowning billionaire was authentic based on subscription tier.
The viral “Couch Guy” incident—where TikTok users collectively analyzed a college student’s homecoming video frame by frame to determine if his girlfriend was cheating—found its perfect home on X. The platform’s new “Community Notes” feature, designed to combat misinformation, instead became a crowdsourced investigation tool for relationship drama.
Users began applying forensic analysis techniques to increasingly mundane content. A simple photo of someone’s lunch could generate hundreds of community notes examining everything from the restaurant’s health inspection records to the emotional state of the person holding the fork. The platform had accidentally created a panopticon where everyone was both guard and prisoner.
The NBA Luka Dončić trade rumors exemplified this phenomenon. Users didn’t just speculate about the trade; they analyzed flight patterns, restaurant reservations, and even the emotional undertones of players’ social media posts. The platform’s real-time nature meant that rumors could be debunked and re-bunked within minutes, creating a feedback loop of speculation that eventually influenced actual trade negotiations.
The Academy Awards incident where Will Smith slapped Chris Rock became the platform’s defining moment—not because of the slap itself, but because of how the platform processed the event. Within minutes, the incident had spawned thousands of memes, generated millions in advertising revenue for X, and created at least seventeen different conspiracy theories about the slap’s authenticity.
The platform’s algorithm, trained to maximize engagement, began serving users increasingly elaborate theories about the incident. Some users received content suggesting the slap was staged to distract from cryptocurrency market manipulation. Others were served theories connecting the incident to ancient Egyptian mythology. The algorithm had learned that truth was less engaging than increasingly elaborate fiction.
When Queen Elizabeth II died, the platform experienced what software engineers now call “grief overflow”—a condition where the sheer volume of mourning-related content crashed the recommendation systems. Users reported receiving notifications about the Queen’s death interspersed with advertisements for funeral planning services and cryptocurrency investments themed around “royal coins.”
The incident revealed the platform’s fundamental inability to distinguish between genuine cultural moments and marketing opportunities. The algorithm treated the Queen’s death as content to be optimized, serving users increasingly elaborate tributes mixed with sponsored content about “monarchist meal kits” and “grief-themed NFTs.”
Musk’s vision of transforming X into an “everything app”—combining social media, payments, and commerce—has created what systems theorists call “feature creep paralysis.” The platform now offers so many services that users report feeling overwhelmed by choice. A simple attempt to post a tweet can lead to prompts about cryptocurrency wallets, subscription upgrades, upgrade to download and use Grok, and opportunities to purchase “X-clusive” merchandise.
The payment integration has been particularly problematic. Users attempting to tip content creators have accidentally purchased NFTs, subscribed to premium services, and in at least one documented case, bought a Tesla. The platform’s customer service, staffed by what appears to be a single chatbot named “Grok,” responds to all complaints with variations of “Have you tried turning your expectations off and on again?”
The platform’s transformation represents something larger than corporate mismanagement—it’s a case study in what happens when the attention economy reaches its logical conclusion. Every feature, every algorithm tweak, every policy change has been optimized for a single metric: time spent on platform. The result is a digital environment that feels simultaneously overstimulating and empty, like a casino designed by someone who had only heard casinos described second-hand.
Users report a phenomenon researchers call “engagement fatigue”—the exhaustion that comes from being constantly prompted to react, share, and engage with content that feels increasingly meaningless. The platform has succeeded in capturing attention while simultaneously making that attention feel worthless.
The irony is that in trying to maximize engagement, the platform has created an environment where genuine engagement becomes nearly impossible. Users scroll through feeds of algorithmically-optimized content, looking for authentic human connection in a sea of sponsored posts and rage-bait.
As we observe this grand experiment in real-time social media destruction, patterns emerge that extend far beyond a single platform. The transformation of X represents the logical endpoint of treating human communication as a resource to be mined rather than a relationship to be nurtured.
The platform’s greatest moments—Luigi’s alleged manifesto, Biden’s withdrawal, Trump’s COVID diagnosis—all share a common thread: they were moments when the algorithm’s optimization temporarily aligned with genuine human interest. These brief synchronicities feel increasingly rare as the platform’s systems become more sophisticated at manufacturing artificial engagement.
The question isn’t whether X will survive its transformation, but whether the concept of social media as a public square can survive the attention economy’s relentless optimization. Each trending topic, each viral moment, each community note represents a small experiment in collective meaning-making under increasingly artificial conditions.
Perhaps the most telling aspect of X’s evolution is how it has made its own dysfunction into content. Users now tweet about the platform’s problems with the same enthusiasm they once reserved for sharing life updates. The platform has achieved the ultimate engagement hack: making its own failures engaging.
What’s your take on X’s transformation? Have you experienced “checkmark anxiety” or “engagement fatigue”? Share your thoughts below—the algorithm is always listening, and it’s probably taking notes.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In which we examine how advertising transformed the web from humanity’s greatest library into humanity’s most sophisticated slot machine
The internet was supposed to be different. Back in the 1995, when dial-up modems sang their mechanical hymns and “You’ve Got Mail” was still a source of genuine excitement rather than existential dread, the web promised to be humanity’s great equalizer. Information would be free, knowledge would flow like fine digital wine, and we would all become enlightened beings connected across the vast expanse of the internet.
Instead, we got Google.
To understand how we arrived at our current digital purgatory, we must first examine the fateful decision that doomed the internet from its very inception: the choice to fund this brave new world through ads (advertising). Like the biblical Adam and Eve reaching for that forbidden fruit, early web pioneers bit into the apple of ad revenue, and we have been living with the consequences ever since.
The logic seemed sound at the time. After all, TV had thrived on advertising for decades. Radio had built entire empires on the promise of selling soap and cigarettes between musical interludes. Why shouldn’t the internet follow the same model? What could possibly go wrong with creating a medium where success was measured not by the quality of information or the enrichment of human knowledge, but by the ability to capture and monetize human attention?
EVERYTHING, as it turns out.
The moment we decided that websites should be “free” in exchange for our eyeballs, we inadvertently created the most sophisticated behavioral modification machine in human history. We built a system where the primary incentive wasn’t to inform, educate, or even entertain, but to addict. To keep users clicking, scrolling, and consuming in an endless dopamine-driven feedback loop that would make B.F. Skinner weep with professional admiration.
Into this advertising-funded wilderness stepped Google, armed with an algorithm and a mission statement that would have made Orwell chuckle: “Don’t be evil.” The company that would eventually become synonymous with internet search began as a humble Stanford research project, two PhD students trying to organize the world’s information. Noble enough, until they realized that organizing information was significantly less profitable than organizing human behavior.
Google’s genius wasn’t in creating a better search engine—though they certainly did that. Their true innovation was in perfecting the art of monetizing human curiosity. They transformed the simple act of asking a question into a complex auction system where businesses bid for the privilege of answering you, whether their answer was relevant or not.
The AdWords system, launched in 2000, after the world survived the Y2K bug, was advertising’s equivalent of splitting the atom. Suddenly, every search query became a micro-transaction, every click a tiny payment into Google’s ever-expanding coffers. The company had discovered how to turn human knowledge-seeking behavior into a perpetual money-printing machine, and they’ve been refining this process with the dedication of medieval monks illuminating manuscripts.
Under Google’s benevolent guidance, the world wide web evolved from an information superhighway into what can only be described as a digital casino designed by behavioral psychologists with unlimited budgets and questionable ethics. Every website became a slot machine, every notification a pull of the lever, every “recommended for you” section a carefully calculated attempt to keep you playing just a little bit longer.
The company’s PageRank algorithm, once a simple method for determining which websites were most authoritative on the internet, gradually morphed into something far more sophisticated: a system for predicting and influencing human behavior. Google didn’t just want to know which websites were popular; they wanted to know which websites would keep you engaged long enough to click on an advertisement.
This shift from information retrieval to attention capture fundamentally changed the nature of the web itself. Websites that once prioritized accuracy, depth, and genuine utility found themselves competing against content farms optimized for one thing: keeping eyeballs glued to screens long enough for ads to load. The internet’s original promise of democratizing information was quietly replaced by a new mission: democratizing distraction.
Google’s advertising empire didn’t just change how we consume information; it revolutionized how information consumes us. The company built the most comprehensive surveillance apparatus in human history, not through government mandate or authoritarian decree, but through the simple expedient of offering “free” services in exchange for data.
Every search query, every email, every location ping, every YouTube video watched became a data point in an ever-expanding profile of human behavior. Google didn’t just know what you were looking for; they knew what you were going to look for before you did. They could predict your interests, your political leanings, your shopping habits, your relationship status, and your likelihood of clicking on an ad for artisanal beard oil at 2:47 PM on a Friday.
This data collection wasn’t a byproduct of Google’s services—it was the entire point. The search engine, the email platform, the video hosting site, the mobile operating system: all of these were simply different collection mechanisms for the same underlying product. And that product wasn’t information or entertainment or communication. It was you.
As Google’s advertising machine grew more sophisticated, it began to exert a gravitational pull on the entire web ecosystem. Websites that wanted to be found had to optimize themselves not for human readers, but for Google’s spiders (because, das SEO!). Content creators learned to write not for clarity or truth, but for “engagement metrics.” The very structure of online discourse began to warp around the demands of adsense optimization.
The rise of programmatic advertising—automated systems that buy and sell ad space in real-time auctions—turned every webpage into a miniature stock exchange. Your arrival at any website triggered a complex bidding war among advertisers, with algorithms making split-second decisions about which ads were most likely to extract money from your particular demographic profile.
This system created perverse incentives throughout the digital ecosystem. News websites discovered that outrage generated more clicks than nuance. Social media platforms learned that controversy kept users engaged longer than consensus. Educational content found itself competing against clickbait designed by teams of data scientists whose only goal was maximizing “time on site.”
Google’s advertising dominance didn’t just corrupt individual websites; it corrupted the very fabric of online discourse. The company’s algorithms began to favor content that generated immediate engagement over content that provided long-term value. Websites optimized for Google’s search rankings started to resemble each other, creating an increasingly homogenized web where originality was punished and conformity to algorithmic preferences was rewarded.
The result was a kind of digital natural selection, where only the most addictive, most engaging, most algorithmically optimized content survived. Thoughtful analysis was crowded out by hot takes. In-depth reporting was replaced by listicles. The web’s vast library of human knowledge was gradually transformed into an endless feed of content designed to capture attention rather than convey understanding.
We now stand at the precipice of advertising’s final victory over the internet’s original promise. The web that was once humanity’s greatest tool for sharing knowledge has become humanity’s most effective tool for manufacturing consent, manipulating behavior, and extracting value from human attention.
The signs of the coming digital apocalypse are everywhere. Users increasingly rely on ad blockers, creating an arms race between content creators and audience that benefits no one except the companies selling ad-blocking technology. Privacy regulations like GDPR have forced companies to ask for explicit consent to track users, leading to the now-ubiquitous cookie banners that have turned every website visit into a legal negotiation.
Meanwhile, the rise of AI-generated content threatens to flood the web with algorithmically optimized articles designed not to inform humans, but to fool other algorithms into thinking they’re reading something written by humans. We’re approaching a future where the internet consists primarily of robots writing content for other robots to index, while humans are reduced to the role of unwitting participants in an automated attention economy.
The most remarkable aspect of Google’s advertising empire is how it has managed to profit from the very problems it created. The company’s search algorithm helped create a web so cluttered with low-quality, ad-optimized content that users increasingly rely on Google to filter through the noise. The more polluted the web becomes with advertising-driven content, the more valuable Google’s filtering services become.
This creates a perfect feedback loop: Google’s advertising business incentivizes the creation of low-quality content, which makes Google’s search services more valuable, which generates more advertising revenue, which incentivizes more low-quality content. It’s a perpetual motion machine powered by human attention and lubricated with behavioral data.
The company has become so skilled at this game that they’ve managed to convince the world that their advertising-driven business model is not just inevitable, but beneficial. They’ve positioned themselves as the guardians of “free” information while simultaneously being the primary architects of the system that makes genuinely free information increasingly difficult to find.
As we look toward the future, the trajectory seems clear. The advertising-driven web is approaching a kind of digital heat death, where all content converges toward the same algorithmic optimizations, all websites look increasingly similar, and all human expression is filtered through the lens of advertising effectiveness.
Google’s latest AI initiatives promise to accelerate this process. Their large language models, trained on the advertising-optimized web, are now being used to generate even more advertising-optimized content. We’re creating a recursive loop where AI systems trained on human-written content are now writing content for humans to read, with both the training data and the output optimized for the same advertising-driven metrics.
The web that began as humanity’s greatest collaborative project is becoming humanity’s greatest collaborative delusion: a shared hallucination where we pretend that “free” services are actually free, that algorithmic recommendations represent genuine choice, and that a medium designed to sell us things can simultaneously serve as our primary source of truth about the world.
Perhaps this was always inevitable. Perhaps any system that relies on capturing and monetizing human attention will eventually optimize itself into irrelevance. Perhaps Google’s greatest achievement isn’t organizing the world’s information, but proving that organizing the world’s information is far less profitable than disorganizing human attention.
The original sin of the web wasn’t choosing advertising as a funding model. It was believing that we could build a system designed to manipulate human behavior while somehow preserving human agency. We created a medium that treats human attention as a commodity to be harvested, and then expressed surprise when human attention became increasingly scarce and fragmented.
Google didn’t corrupt the internet’s original promise. They simply revealed what that promise was always going to become once we decided that the price of “free” information was our capacity to think clearly about anything at all.
What’s your take on the advertising-driven web? Have you noticed how your own browsing habits have changed as algorithms have become more sophisticated? Do you think there’s a way back to the internet’s original promise, or are we doomed to scroll through an endless feed of content optimized for engagement rather than enlightenment? Share your thoughts below—assuming you can resist the urge to check your notifications first.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In which we examine the curious case of a country that can monitor oil pipelines with drones but apparently cannot predict when rivers might overflow
It was a truth universally acknowledged that Nigeria, a nation possessed of considerable technological prowess, must be in want of applying said prowess to the preservation of human life. Yet as the flash floodwaters rose across the country recently, claiming 200 lives and leaving 500 missing, one could not help but observe a most peculiar phenomenon: a digital economy that had mastered the art of cryptocurrency transactions and fintech innovations had somehow failed to master the considerably simpler challenge of water level monitoring!
The irony was not lost on those who had witnessed Nigeria’s remarkable technological ascension over the preceding decade. Here was a nation that had birthed unicorn startups, developed sophisticated blockchain applications, and deployed advanced drone technology to monitor thousands of kilometers of oil infrastructure. Yet when nature presented its annual hydrological examination, the country appeared to have forgotten that the same sensors monitoring crude oil flow could theoretically be repurposed to detect rising water levels in flood-prone areas.
The phenomenon observed in Nigeria represents what technology analysts have begun terming “selective digital competence“—the curious ability of a society to develop cutting-edge solutions for profitable problems while maintaining a studied ignorance of life-threatening challenges that offer less obvious monetization opportunities. It is as if the nation’s considerable technical expertise had been afflicted with a peculiar form of amnesia, remembering how to build payment processing systems but forgetting how to build early warning networks.
Consider the technological infrastructure already in place across Nigeria. The country’s oil and gas sector employs sophisticated monitoring systems capable of detecting minute pressure changes in pipelines spanning thousands of kilometers. These systems utilize advanced sensor networks, satellite communications, and real-time data analytics to prevent environmental disasters and protect valuable petroleum assets. The same fundamental technologies—sensors, communication networks, and data processing capabilities—could theoretically be deployed to monitor river levels, rainfall patterns, and flood conditions with minimal adaptation.
Yet as communities across Nigeria found themselves inundated with floodwaters, the nation’s impressive technological capabilities seemed to evaporate like the morning mist in Timbuktu. The drones that routinely inspect oil infrastructure remained conspicuously absent from search and rescue operations. The data analytics platforms that optimize financial transactions showed no evidence of being repurposed for flood prediction modeling. The mobile networks that facilitate millions of digital payments daily carried no automated flood warnings to vulnerable communities.
Nigeria’s fintech sector represents one of Africa’s greatest technological success stories, processing billions of dollars in transactions through increasingly sophisticated platforms. These systems demonstrate remarkable capabilities in real-time data processing, risk assessment, and automated decision-making. The algorithms that determine creditworthiness can analyze thousands of data points in milliseconds, yet apparently no similar systems exist to analyze meteorological data and predict flood risks.
The contrast is particularly stark when one considers the infrastructure requirements. A flood monitoring system requires sensors to detect water levels, communication networks to transmit data, and processing capabilities to analyze patterns and generate alerts. A fintech platform requires sensors to detect transaction attempts, communication networks to transmit payment data, and processing capabilities to analyze risk patterns and approve or decline transactions. The technological foundations are virtually identical; only the application differs.
This suggests that Nigeria’s technological capabilities are not constrained by technical limitations but by economic incentives. Fintech platforms generate revenue with every transaction processed, creating clear business models that justify investment in sophisticated infrastructure. Flood monitoring systems, by contrast, generate no immediate revenue stream, making them less attractive to private investment despite their obvious social value.
Perhaps no aspect of Nigeria’s technological paradox is more glaring than the deployment patterns of its drone capabilities. The country has demonstrated remarkable proficiency in utilizing unmanned aerial vehicles for industrial applications, particularly in monitoring oil pipeline infrastructure across challenging terrain. These operations require sophisticated flight planning, real-time video transmission, data analysis capabilities, and coordination with ground-based response teams.
Yet when floods struck and hundreds of people went missing, these same drone capabilities seemed to vanish into bureaucratic ether. The aircraft that can navigate complex flight paths to inspect remote pipeline sections apparently could not be redirected to search for stranded flood victims. The real-time video systems that detect pipeline damage could not be repurposed to locate people trapped by rising waters. The coordination systems that manage industrial monitoring operations showed no evidence of being adapted for humanitarian search and rescue missions.
This selective deployment of technological capabilities reveals a troubling pattern in how societies prioritize the application of their technical resources. Infrastructure that protects economic assets receives sophisticated technological protection, while infrastructure that protects human lives relies on considerably more primitive approaches. It is as if the nation had developed a form of technological tunnel vision, capable of seeing oil leaks with remarkable clarity while remaining blind to human suffering.
Nigeria’s growing reputation as a hub for data analytics and artificial intelligence makes the absence of flood prediction systems even more perplexing. The country hosts numerous startups and established companies specializing in data analysis, machine learning, and predictive modeling. These organizations routinely process vast datasets to identify market trends, optimize supply chains, and predict consumer behavior with impressive accuracy.
The meteorological and hydrological data required for flood prediction is considerably more structured and predictable than the market data these systems typically analyze. Rainfall patterns, river flow rates, and seasonal flooding trends follow physical laws that are far more consistent than human economic behavior. The sensors required to collect this data are less expensive and more reliable than the market data feeds that power financial analytics platforms.
Yet despite having both the technical expertise and the data infrastructure necessary for sophisticated flood prediction, Nigeria appears to have made no significant investment in applying these capabilities to disaster prevention. The algorithms that can predict which customers are likely to default on loans apparently cannot be adapted to predict which communities are likely to experience flooding. The machine learning models that optimize advertising targeting show no evidence of being repurposed for optimizing emergency response deployment.
The Nigerian flood crisis illustrates a broader phenomenon in technological development: the tendency for societies to build sophisticated solutions for profitable problems while neglecting basic applications that could save lives. This represents a fundamental inversion of technological priorities, where complexity is pursued for commercial gain while simplicity is ignored for humanitarian need.
Flood defense systems represent some of the oldest and most proven applications of engineering technology. From ancient levee systems to modern pumping stations, human societies have developed increasingly sophisticated methods for managing water flow and protecting communities from flooding. These systems require no breakthrough innovations or cutting-edge research—they simply require the application of well-established engineering principles and adequate investment in infrastructure.
Yet Nigeria, despite its demonstrated technological capabilities, appears to have invested minimal resources in these proven flood defense technologies. The same engineering expertise that designs complex oil extraction facilities could easily be applied to designing flood control systems. The project management capabilities that coordinate major infrastructure developments could be redirected toward building comprehensive flood defenses. The financial resources that fund technological innovation could be partially allocated to implementing basic flood protection measures.
The disconnect between Nigeria’s technological capabilities and its flood response reveals a fundamental gap in how societies conceptualize the relationship between tech innovation and humanitarian impact. The country’s tech sector has achieved remarkable success in developing solutions that generate economic value, yet has shown little interest in developing solutions that preserve human life.
This gap reflects broader patterns in global technology development, where market incentives drive innovation toward profitable applications while humanitarian needs remain underserved. The venture capital funding that fuels fintech development rarely flows toward flood prediction systems. The talent that builds cryptocurrency platforms seldom applies their skills to disaster prevention. The infrastructure that supports digital commerce shows little overlap with the infrastructure needed for emergency response.
The result is a technological ecosystem that can process millions of financial transactions per second but cannot provide timely flood warnings to vulnerable communities. A digital economy that can track cryptocurrency portfolios in real-time but cannot track rising water levels in flood-prone areas. An innovation sector that can optimize supply chain logistics but cannot optimize emergency evacuation procedures.
As the death toll from Nigeria’s floods reached a tragic 200 with 500 still missing, the true cost of the country’s selective technological application became tragically clear. Each life lost represents not just a human tragedy but a failure of technological imagination—an inability to envision how existing capabilities could be repurposed for humanitarian benefit.
The sensors monitoring oil pipelines could have been monitoring water levels. The drones inspecting industrial infrastructure could have been conducting search and rescue operations. The data analytics platforms optimizing financial transactions could have been predicting flood risks and coordinating emergency responses. The communication networks facilitating digital payments could have been broadcasting early warnings to threatened communities.
The technology existed. The expertise existed. The infrastructure existed. What was missing was the institutional will to apply these capabilities to the preservation of human life rather than the protection of economic assets. Nigeria’s flood crisis thus represents not a technological failure but a moral one—a collective decision to prioritize profit over people in the allocation of technological resources.
The Nigerian flood tragedy offers a sobering reminder that technological capability without humanitarian application represents a profound waste of human potential. The country’s impressive tech sector achievements demonstrate that it possesses the technical talent, infrastructure, and resources necessary to address complex challenges. The question is whether these capabilities will be directed toward solving problems that matter for human welfare or remain focused exclusively on opportunities that generate economic returns.
The path forward requires no technological breakthroughs or revolutionary innovations. It simply requires the recognition that the same technical principles underlying profitable applications can be applied to life-saving purposes. Flood prediction systems use the same sensor technologies as pipeline monitoring. Emergency communication networks use the same infrastructure as payment processing systems. Search and rescue coordination employs the same logistics optimization as supply chain management.
What is needed is a fundamental reorientation of technological priorities—a recognition that the highest application of human ingenuity is not the optimization of profit margins but the preservation of human life. Nigeria’s tech sector has proven its capabilities in the commercial realm. The Nigeria flash flood crisis demonstrates the urgent need to apply those same capabilities to humanitarian challenges.
The choice facing Nigeria’s technological community is clear: continue building systems that optimize economic transactions while people drown in predictable floods, or redirect some portion of their considerable talents toward ensuring that such preventable tragedies never occur again. The technology exists to save lives. The only question is whether the will exists to use it.
What’s your take on this technological paradox? Have you noticed similar patterns in your own country or industry—sophisticated tech for profit-driven applications while basic humanitarian needs go unaddressed? Do you think market incentives are fundamentally incompatible with life-saving technology development, or could there be business models that align profit with humanitarian impact? Share your thoughts on how we might better direct our technological capabilities toward preventing such preventable tragedies.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
It was the best of times for Apple shareholders, it was the worst of times for anyone who thought they understood how capitalism was supposed to work. In the gleaming towers of Cupertino, where executives in $700 hoodies contemplate the profound mysteries of profit margin optimization, a solution to the US-engineered tariff crisis emerged that was so audaciously cynical it could only have been conceived by minds unencumbered by shame or basic human decency.
The announcement came with the characteristic Apple fanfare: a carefully choreographed presentation where Chief Revenue Optimization Officer Miranda Sterling stood before a backdrop of minimalist white curves and declared that Apple had “reimagined the iPhone experience to empower users with unprecedented customization opportunities.” What she meant, in language comprehensible to those not fluent in corporate doublespeak, was that Apple had decided to make customers assemble their own phones while somehow charging them more for the privilege.
The genius of the plan lies not in its innovation—humans have been assembling electronics for decades—but in its breathtaking transformation of necessity into premium experience. Faced with tariffs that threatened to reduce their profit margins from “obscene” to merely “unconscionable,” Apple’s leadership team asked themselves a profound question: “How can we make our customers pay for our problems while convincing them they’re getting a deal?”
The iPhone Assembly Experience, as Apple has branded it, represents the logical endpoint of the company’s decades-long journey toward extracting maximum value from minimum effort. For the low price of $1,299—a modest $200 increase over the previous fully-assembled model—customers can now purchase the iPhone 16 Assembly Kit, which includes all the components necessary to build their own device, along with a beautifully designed instruction manual that Apple describes as “intuitive” and early beta testers describe as “a psychological torture device disguised as technical documentation.”
The kit arrives in Apple’s signature minimalist packaging, which manages to make a box of loose electronic components look like a luxury gift. Inside, customers will find the iPhone’s logic board, display assembly, battery, camera modules, and approximately 47 screws of varying sizes, each requiring a different specialized tool that must be purchased separately through Apple’s new “Pro Assembly Toolkit,” available for the reasonable price of $399.
Dr. Rebecca Chen, Apple’s newly appointed Director of Customer Empowerment Through Self-Reliance, explained the philosophy behind the program during a recent press briefing. “We realized that our customers were missing out on the profound satisfaction that comes from creating something with their own hands,” she said, her expression maintaining the serene confidence of someone who has never assembled anything more complex than a British BLT sandwich. “The iPhone Assembly Experience transforms the act of purchasing a phone into a journey of personal growth and technical mastery.”
The journey, according to early adopters, typically begins with confidence and ends with existential despair. Marcus Rodriguez, a software engineer from Portland, Oregon, who participated in Apple’s beta testing program, described his experience with the characteristic thousand-yard stare of a combat veteran. “I spent fourteen hours trying to connect the display cable,” he recounted. “The instructions just said ‘gently insert connector until it clicks.’ It never clicked. Nothing ever clicks. I’m starting to think the clicking is a metaphor for something deeper.”
Not content with merely charging customers to assemble their own devices, Apple has created an entire ecosystem around the inevitable failures that result from amateur electronics assembly. The iPhone Repair Experience Kit, available for $299, includes replacement components for the most commonly damaged parts during assembly, along with a selection of tools that are almost, but not quite, the same as the ones needed for initial assembly.
The repair kit represents Apple’s commitment to what they call “circular customer engagement”—a business model where each attempt to fix a problem creates new problems that require additional purchases. The kit includes a replacement display (for when customers inevitably crack the original during installation), a new battery (for when the first one is installed backwards), and a selection of screws in sizes that are subtly different from the original kit, ensuring that customers who mix up components will need to purchase additional hardware.
“We’ve essentially gamified device ownership,” explained Apple’s Chief Innovation Officer, Dr. Amanda Foster, with the enthusiasm of someone describing a particularly clever chess move. “Each repair attempt is an opportunity for learning, growth, and additional revenue generation. It’s a win-win scenario, assuming you define ‘winning’ from our perspective exclusively.”
The repair ecosystem extends beyond simple component replacement. Apple has partnered with leading meditation apps to offer “Mindful Assembly” sessions designed to help customers achieve inner peace while struggling with microscopic connectors. The company has also launched a subscription service called “Assembly Zen,” which provides daily affirmations specifically tailored to the emotional challenges of consumer electronics assembly.
Perhaps the most audacious aspect of Apple’s new strategy is the Apple Logo Licensing Program, which requires customers who successfully assemble their devices to purchase the right to display the iconic Apple logo and qualify for AppleCare coverage. The program, which costs $199 annually, grants customers a small adhesive Apple logo and the legal right to call their assembled device an “iPhone” rather than a “collection of Apple-branded components arranged in phone-like configuration.”
The licensing program represents Apple’s recognition that their true product has never been technology—it’s identity. The Apple logo serves as a digital status symbol, a tribal identifier that signals membership in an exclusive club of people who are willing to pay premium prices for the privilege of doing work that was previously done by factory workers earning a fraction of minimum wage.
“The Apple logo is more than just a symbol,” explained Apple’s Director of Brand Monetization, Dr. Sarah Kim, during a presentation that felt like a TED talk delivered by someone who had never experienced genuine human emotion. “It’s a statement of values, a commitment to excellence, and a legally binding agreement to participate in our ecosystem of premium experiences and recurring charges.”
Customers who choose not to purchase Apple logo licensing can still use their assembled devices, but they forfeit access to AppleCare, the App Store, and what Apple terms “brand coherence support.” Their devices will display a generic fruit logo—specifically, a slightly sad-looking pear—and will be referred to in all Apple communications as “Compatible Assembly Units” or CAUs.
The financial engineering behind Apple’s assembly kit strategy reveals a level of creative accounting that would make Enron executives weep with admiration. By shifting assembly costs to customers while simultaneously increasing prices, Apple has managed to improve their profit margins while technically reducing their manufacturing overhead. The company can now claim that their devices are “artisanally crafted” and “locally assembled,” since customers are doing the assembly in their own homes.
The strategy also provides Apple with a convenient scapegoat for quality control issues. Devices that malfunction can be attributed to “assembly variation” rather than design flaws, shifting liability from the manufacturer to the customer. Apple’s warranty now includes a clause stating that coverage is void if the device shows “evidence of non-optimal assembly techniques,” a category that apparently includes breathing on the components during installation.
Industry analysts have praised Apple’s strategy as a masterclass in customer relationship management. “They’ve managed to transform their biggest cost center—manufacturing—into a revenue stream,” noted tech economist Dr. Jennifer Walsh. “It’s like convincing people to pay you for the privilege of doing your job for you, except somehow making them feel grateful for the opportunity.”
The broader implications of Apple’s assembly kit strategy extend far beyond the tech industry. The company has essentially created a new category of consumer: the paying employee. Customers now invest their own time, effort, and emotional energy into creating products that Apple then sells them, while taking no responsibility for the quality or functionality of the final result.
The psychological impact on customers has been profound. Support groups have emerged for people struggling with “Assembly Anxiety Disorder,” a condition characterized by the persistent fear that one has incorrectly installed a critical component. Apple has responded by offering a premium counseling service called “Genius Therapy,” where trained technicians provide emotional support for $149 per session.
The assembly kit phenomenon has also created a new form of social stratification. Successfully assembled iPhones have become status symbols that signal not just wealth, but technical competence and patience. Social media is filled with #assemblyflex posts, where users display their completed devices alongside the tools and emotional support systems that made their achievement possible.
Apple’s success with iPhone assembly kits has inspired other premium brands to explore similar strategies. Tesla has announced plans to sell “Automotive Assembly Experiences” where customers can build their own electric vehicles in their driveways. Rolex is reportedly developing a “Timepiece Crafting Journey” that allows customers to assemble luxury watches using components that may or may not be properly calibrated.
The trend represents a fundamental shift in the relationship between manufacturers and consumers. Companies are discovering that customers will pay premium prices for the privilege of doing work that was previously considered a cost center. It’s a business model that combines the worst aspects of capitalism with the most exploitative elements of the gig economy, wrapped in the language of empowerment and personal growth.
As Apple continues to refine their assembly kit strategy, rumors suggest that future products will require even more customer involvement. The iPhone 17 Assembly Kit is expected to include raw silicon wafers that customers must process into chips using equipment available through Apple’s “Semiconductor Crafting Experience.” The iPhone 18 may require customers to mine their own rare earth elements, though Apple assures potential buyers that they will provide detailed geological surveys for an additional fee.
Have you experienced the joy of assembling your own premium electronics, or are you still clinging to the outdated notion that products should arrive functional? Share your assembly horror stories or triumphs below—misery loves company, and apparently so does Apple’s customer service department.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In the gleaming conference rooms of Silicon Valley, where venture capitalists gather like digital evangelists clutching their kombucha and quarterly projections, a curious form of doublethink has taken hold. Artificial Intelligence, they proclaim with the fervor of true believers, is simultaneously the solution to every human problem and a technology so nascent that any criticism of its current limitations constitutes heresy against the future itself.
The Ministry of Technological Truth has spoken: AI will cure cancer, eliminate poverty, solve climate change, and presumably teach your grandmother to use TikTok. Yet somehow, after billions in investment and years of breathless proclamations, the most advanced AI systems still struggle with tasks that a moderately caffeinated human intern could handle—like accurately counting the number of fingers in a photograph or explaining why they recommended a documentary about serial killers after you watched one cooking show.
This is not mere technological growing pains. This is the systematic construction of a narrative so divorced from reality that it would make the Ministry of Plenty proud. The tech industry has perfected the art of selling tomorrow’s promises with today’s marketing budgets, creating a perpetual state of “almost there” that justifies infinite investment in solutions to problems that may not actually exist.
The current AI revolution bears striking resemblance to a cargo cult, where primitive societies built mock airstrips hoping to summon the return of supply planes. Silicon Valley has constructed elaborate mock-ups of intelligence—systems that can mimic human responses with uncanny accuracy while possessing roughly the same understanding of the world as a particularly sophisticated parrot.
Dr. Miranda Blackwell, former head of AI ethics at Prometheus Technologies (before the position was “restructured for optimal synergy alignment”), observed this phenomenon firsthand. “We had executives who genuinely believed that adding ‘AI-powered’ to any product description would increase its valuation by 300%,” she noted during a recent interview. “I watched a team spend six months building an ‘AI-driven’ email sorting system that was essentially a series of if-then statements a computer science student could have written in an afternoon.”
The cargo cult mentality extends beyond mere marketing hyperbole. Entire industries have reorganized themselves around the assumption that AI will soon achieve capabilities that remain stubbornly theoretical. Companies hire Chief AI Officers who spend their days attending conferences about the transformative potential of technologies that don’t quite work yet. It’s as if the entire tech ecosystem has agreed to collectively pretend that the emperor’s new clothes are not only visible but revolutionary.
Perhaps nowhere is the gap between AI promise and AI reality more pronounced than in the realm of automation. For years, tech luminaries have warned of an impending AI-pocalypse, where artificial intelligence would render human labor obsolete faster than you could say “universal basic income.” Yet walk into any office, factory, or service establishment, and you’ll find humans doing essentially the same jobs they’ve always done, albeit now with the added responsibility of training AI systems that occasionally work as advertised.
The automation revolution has proceeded with all the urgency of a government bureaucracy implementing new filing procedures. Self-driving cars, promised to be ubiquitous by 2020, remain confined to carefully mapped routes in optimal weather conditions, supervised by human safety drivers who must be ready to take control at any moment. Amazon’s automated warehouses still employ hundreds of thousands of human workers, who have simply been promoted from “warehouse workers” to “automation supervisors”—a title change that comes with the same pay but twice the stress.
“We’ve essentially created the most expensive way possible to do things we were already doing,” explained former Tesla engineer Marcus Chen, who left the company after what he describes as “one too many meetings about revolutionary breakthroughs that were actually incremental improvements to existing systems.” The irony, Chen notes, is that the human workers displaced by automation are often rehired to maintain, monitor, and fix the systems that replaced them.
The tech industry’s relationship with productivity reveals the fundamental contradiction at the heart of the AI revolution. Despite decades of technological advancement and billions invested in artificial intelligence, productivity growth in most sectors has remained stubbornly flat. This is not a new phenomenon—economists have been puzzling over the “productivity paradox” since the advent of personal computers—but AI was supposed to be different. It was supposed to be the technology that finally delivered on the promise of exponential efficiency gains.
Instead, we’ve created what researchers at the Institute for Digital Skepticism call “productivity theater”—elaborate systems that create the appearance of efficiency while often making simple tasks more complex. Consider the modern customer service experience, where AI chatbots force customers through increasingly Byzantine decision trees before inevitably connecting them to human agents who must then decipher what the AI was trying to accomplish.
The paradox extends to knowledge work, where AI-powered tools promise to augment human capabilities but often require more time to manage than they save. Lawyers spend hours reviewing AI-generated legal briefs for hallucinations and errors. Doctors must double-check AI diagnostic suggestions that occasionally confuse skin conditions with furniture patterns. Writers use AI to generate first drafts that require so much editing they might as well have started from scratch—but with the added anxiety of wondering whether their AI assistant has inadvertently plagiarized someone else’s work.
Perhaps the most telling aspect of current AI limitations is the industry’s embrace of “hallucination” as a technical term for when AI systems confidently present false information as fact. In any other field, a system that regularly fabricated data would be considered fundamentally broken. In AI, hallucination is treated as a charming quirk that will surely be resolved in the next iteration.
This linguistic sleight of hand reveals the deeper problem with AI evangelism: the systematic redefinition of failure as progress. When an AI system provides incorrect medical advice, it’s not a dangerous malfunction—it’s a “learning opportunity.” When autonomous vehicles cause accidents, they’re not defective products—they’re “gathering valuable real-world data.” When AI hiring systems exhibit obvious bias, they’re not discriminatory tools—they’re “reflecting societal patterns that require further algorithmic refinement.”
The hallucination economy has created a new class of digital fact-checkers whose full-time job is verifying the output of systems that were supposed to eliminate the need for human verification. Universities now employ armies of teaching assistants to grade papers written by students using AI, which are then evaluated by AI plagiarism detection systems that must be manually reviewed by humans who try to determine whether the AI detector correctly identified AI-generated content.
The persistence of AI hype despite its obvious limitations can be traced directly to the venture capital ecosystem that funds Silicon Valley’s reality distortion field. VCs have invested so heavily in the AI narrative that acknowledging its current limitations would require admitting that billions of dollars have been allocated based on science fiction rather than science.
This creates a feedback loop where startups must claim revolutionary AI capabilities to secure funding, then spend their runway trying to build technology that matches their marketing claims. The result is an industry populated by companies that are simultaneously cutting-edge AI pioneers and elaborate Potemkin villages, depending on whether you’re talking to their marketing department or their engineering team.
“The entire ecosystem is built on the assumption that AI will eventually work as advertised,” explained venture capitalist turned whistleblower Sarah Rodriguez. “But ‘eventually’ has become a magic word that justifies any amount of present-day dysfunction. It’s like investing in a restaurant chain that doesn’t serve food yet but promises to revolutionize dining once they figure out cooking.”
Despite years of conditioning, humans have proven remarkably resistant to AI replacement in ways that consistently surprise technologists. It turns out that much of what we value about human interaction—empathy, creativity, contextual understanding, the ability to navigate ambiguity—are precisely the qualities that current AI systems struggle to replicate convincingly.
Customer service representatives report that clients often specifically request to speak with humans, even when AI systems are technically capable of handling their requests. Teachers find that students prefer feedback from human instructors, even when AI can provide more detailed analysis. Patients consistently rate interactions with human doctors more highly than AI-assisted consultations, regardless of diagnostic accuracy.
This preference for human interaction isn’t mere technophobia—it reflects a deeper understanding that intelligence involves more than pattern matching and statistical prediction. Humans excel at reading between the lines, understanding unspoken context, and providing the kind of nuanced judgment that comes from lived experience rather than training data.
As the AI hype cycle reaches peak absurdity, signs of a reckoning are beginning to emerge. Companies that built their valuations on AI promises are quietly scaling back their claims. Investors are starting to ask uncomfortable questions about return on investment. Employees are pushing back against AI systems that make their jobs more difficult rather than easier.
The tech industry’s response has been predictably Orwellian: redefining success to match reality rather than adjusting reality to match promises. AI systems that fail to achieve human-level performance are now described as “narrow AI” that was never intended to be general-purpose. Automation projects that require constant human supervision are rebranded as “human-AI collaboration.” Products that don’t work as advertised are positioned as “early adopter experiences” that will improve with user feedback.
What’s your experience with AI systems that promise the world but deliver something closer to a moderately intelligent autocomplete? Have you encountered the productivity paradox in your own work, where AI tools create more problems than they solve? Share your stories of AI disappointment below—misery loves company, and apparently so does artificial intelligence.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
In a world where artificial intelligence threatens to turn half the human workforce into digital dinosaurs faster than you can say “prompt engineering,” Singapore has done something so sensible it borders on the surreal: they’ve decided to actually prepare their citizens for the future instead of arguing about whether ChatGPT has feelings.
The city-state’s new public education initiative offers displaced workers a completely free second degree in emerging fields—a move so pragmatic it feels like stumbling through the looking glass into a dimension where governments actually solve problems before they become existential crises. Meanwhile, the West continues its grand tradition of treating technological disruption like an unexpected British weather pattern that might blow over if we just ignore it hard enough.
Singapore’s approach reads like a fever dream of competent governance. Picture this: a government that looked at the approaching AI tsunami and thought, “Perhaps we should teach people to surf rather than debate whether the AI wave is morally justified in being wet.” The program specifically targets workers whose jobs are being automated away by AI, offering them pathways into fields that complement rather than compete with artificial intelligence.
Dr. Melissa Chen, Singapore’s Deputy Minister of Future-Proofing (a job title that would make EU bureaucrats break out in hives), explained the rationale with characteristic Singaporean directness: “We observed that arguing about AI ethics while your population becomes unemployable is roughly equivalent to re-arranging deck chairs on the Titanic, except the deck chairs are also being automated.”
The program covers everything from AI prompt engineering to human-AI collaboration frameworks, biotechnology, sustainable urban planning, and what they’re calling “empathy architecture”—designing systems that require uniquely human emotional intelligence. It’s as if someone took a hard look at the future and asked, “What will humans still be better at when machines can do everything else?”
Contrast this with the West’s approach, which resembles a group therapy session for people who refuse to acknowledge they have a problem. While Singapore builds bridges to the future, American universities continue churning out degrees in fields that will be as relevant as blacksmithing by 2030, charging students the GDP of small nations for the privilege.
The European Union, not to be outdone in bureaucratic magnificence, has responded to AI displacement by forming a committee to study the formation of a subcommittee that will eventually recommend the creation of a working group to examine the possibility of maybe thinking about retraining programs sometime after 2035.
“We’re taking a measured approach,” explained Brussels-based policy analyst François Delacroix, whose job description apparently involves using as many words as possible to say absolutely nothing. “We believe in the importance of stakeholder engagement and multi-lateral dialogue frameworks before implementing any paradigm-shifting educational restructuring initiatives.”
Translation: “We’ll hold meetings about having meetings until the robots have already taken over.”
Singapore’s leadership grasped something that Western policymakers seem constitutionally incapable of understanding: AI won’t necessarily replace humans because it’s better at everything, but because it makes the humans who remain exponentially more productive. It’s not about artificial intelligence being smarter than us—it’s about AI-augmented humans being so much more efficient that you need far fewer of them.
Consider the implications: if one AI-assisted financial analyst can do the work of ten traditional analysts, nine people are suddenly redundant. If one AI-enhanced doctor can diagnose patients with the accuracy of an entire medical team, most of that team becomes unnecessary overhead. The math is brutal in its simplicity.
“We’re not heading toward a Star Trek utopia where everyone pursues art and philosophy,” noted Singapore’s Chief Technology Strategist, Dr. Raj Patel, with the kind of clear-eyed realism that makes Western optimists uncomfortable. “We’re heading toward something more like The Expanse—a future where the gap between the AI-augmented elite and everyone else becomes a chasm that makes today’s inequality look quaint.”
While Singapore methodically prepares its workforce for an AI-dominated economy, the West remains trapped in ideological debates that would be amusing if they weren’t so catastrophically counterproductive. American politicians argue about whether AI is “woke” or “based,” as if political affiliation will somehow protect their constituents from economic obsolescence.
The irony is delicious: the same Western nations that spent decades lecturing the world about free markets and creative destruction are now paralyzed by the prospect of their own populations being creatively destroyed by market forces they helped unleash.
Singapore, meanwhile, has embraced what they call “pragmatic futurism”—a philosophy that treats technological change as a force of nature to be prepared for rather than a political position to be debated. Their education ministry has partnered with major tech companies to create curricula that evolve in real-time with technological advancement, ensuring graduates enter a job market that actually exists rather than one that existed when their professors were students.
The most sobering aspect of Singapore’s initiative isn’t its innovation—it’s the implicit acknowledgment that traditional career paths are becoming extinct with the speed of a software update. The program’s existence is essentially a government-sponsored admission that the social contract of “get educated, work hard, retire comfortably” has been terminated without notice.
“We’re essentially teaching people to become cyborgs,” admitted program coordinator Dr. Sarah Lim, with the matter-of-fact tone of someone describing the weather. “Not literally, of course, but functionally. The future belongs to humans who can seamlessly integrate with AI systems, not humans who compete against them.”
The curriculum includes modules on “AI psychology”—understanding how machine learning systems make decisions so humans can work with rather than against algorithmic logic. Students learn to think like their artificial colleagues, developing what educators call “hybrid cognition.”
As Singapore builds its AI-ready workforce, the West faces a choice that it seems determined to avoid making until it’s too late. The next five years will likely see the beginning of what economists are euphemistically calling “structural employment adjustments”—a phrase that makes mass unemployment sound like a minor accounting error.
The signs are already visible for those willing to look. Customer service jobs are disappearing into AI chatbots. Financial analysts are being replaced by algorithms that never need coffee breaks or vacation time. Even creative fields aren’t safe—AI can now write marketing copy, design logos, and compose music with efficiency that makes human creativity look like an expensive luxury.
Singapore’s bet is that by the time the AI displacement wave hits full force, they’ll have a population equipped to ride it rather than be crushed by it. The West’s bet appears to be that if they ignore the wave long enough, it might decide to hit someone else instead, preferably China.
The reference to The Expanse isn’t hyperbolic—it’s prophetic. In that fictional universe, humanity has spread across the solar system, but society has stratified into distinct castes: the technologically augmented elite who control resources and infrastructure, and the masses who survive on basic universal income and whatever scraps of meaningful work remain.
Singapore seems to understand that in an AI-dominated economy, there will be two classes of humans: those who control and collaborate with artificial intelligence, and those who are controlled by it. Their education program is essentially a massive effort to ensure as many citizens as possible end up in the first category.
The West, meanwhile, continues to operate under the delusion that democracy and good intentions will somehow exempt them from economic physics. It’s a touching faith in the power of wishful thinking over mathematical reality.
What’s your take on Singapore’s approach to AI displacement? Are we really heading toward The Expanse scenario, or is there still time for the West to course-correct? Share your thoughts below—especially if you’re currently working in a field that might not exist in five years.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need.
The smartphone industry has reached peak innovation. After years of making phones thinner, cameras sharper, and screens more fragile, tech giants have finally identified humanity’s most pressing digital dilemma: we have been criminally underutilizing our thumbs. Enter the AI Dedicated Button – a revolutionary piece of aluminum or plastic depending whether you are an Apple sheep or Android peasant that promises to transform your relationship with artificial intelligence from “occasionally helpful” to “uncomfortably intimate.”
Samsung fired the first shot with their Galaxy S24 series, introducing what they call the “AI Key” – a physical button that summons their Bixby assistant faster than you can say “I miss the headphone jack.” Not to be outdone, industry insiders report that Apple is developing their own “Intelligence Actuator” (because calling it a button would be too pedestrian), while Google is rumored to be working on something called the “Gemini Gateway,” which sounds less like a phone feature and more like a portal to digital purgatory.
According to Dr. Miranda Clicksworth, Senior Vice President of Haptic Innovation at the Institute for Unnecessary Technology Solutions, the AI button represents “the natural evolution of human-computer interaction.” Her research, funded by a consortium of button manufacturers and venture capitalists with suspiciously similar investment portfolios, suggests that modern humans suffer from “Digital Decision Paralysis” – the inability to choose between seventeen different ways to access the same AI assistant.
“Users were becoming overwhelmed by choice,” explains Clicksworth, adjusting her smart Google AR glasses (still being trialed) that cost more than most people’s monthly rent. “Do I swipe up? Do I long-press the home button? Do I whisper sweet nothings to my phone? The AI button eliminates this cognitive burden by providing a single, dedicated pathway to artificial enlightenment.”
The button itself is a marvel of modern engineering. Constructed from premium aerospace-grade aluminum (the same material used in soda cans, but with better marketing), each AI button undergoes a rigorous 47-step quality assurance process that includes being pressed exactly 100,000 times by a robotic finger calibrated to simulate the touch pressure of an anxious Gen-Z checking their bank balance.
The applications for AI buttons are limitless, according to promotional materials that read suspiciously like they were written by the AI assistants themselves. Users can now summon artificial intelligence to perform crucial tasks such as:
Beta testers report that the AI button has already begun anticipating their needs with uncanny accuracy. “I pressed it once to ask about traffic, and now it automatically orders me coffee every morning at 7:11 AM,” says Jennifer Walsh, a marketing coordinator from Portland, Oragon, who requested we not use her real name because her AI assistant might be listening. “I don’t even drink coffee, but the algorithm seems so confident that I should.”
The AI button arms race has triggered what industry analysts are calling “The Great Buttonification” – a frantic scramble to add dedicated buttons for every conceivable function. Sources close to major manufacturers reveal plans for buttons dedicated to:
One unnamed executive at a major tech company, speaking on condition of anonymity because his NDA includes a clause about “button-related trade secrets,” revealed that their upcoming flagship device will feature seventeen different AI buttons, each trained on a specific aspect of human inadequacy.
“We’ve got buttons for financial anxiety, social awkwardness, existential dread, and one that just plays the sound of your mother sighing disappointedly,” he explained while nervously fidgeting with what appeared to be a prototype device covered in more buttons than a 1990s television remote. “The market research shows that consumers want their technology to understand them on a deeper level, preferably one that can be monetized through targeted advertising.”
Dr. Reginald Pushworth, author of the bestselling book “Pressed for Time: How Buttons Became Our Digital Overlords,” argues that the AI button represents humanity’s surrender to technological determinism. His research suggests that within six months of AI button adoption, users develop what he terms “Artificial Dependency Syndrome” – the inability to make decisions without first consulting their pocket-sized digital oracle.
“We’re witnessing the emergence of a new human sub-species,” Pushworth explains from his office, which notably contains no buttons of any kind except for a single red emergency button labeled “Return to Analog.” “These individuals can no longer determine if they’re hungry without asking an AI, can’t choose what to wear without algorithmic input, and have completely forgotten how to be bored without technological intervention.”
The phenomenon has already spawned support groups for “Button Addicts” – individuals who compulsively press their AI buttons dozens of times per day, seeking validation, entertainment, or simply the satisfying click of premium haptic feedback. One support group leader, who goes by the pseudonym “ButtonFree_Since_2024,” describes the addiction as “like having a very knowledgeable but slightly condescending friend who lives in your pocket and judges your life choices.”
The AI button economy is projected to reach $47 billion by 2027, according to a report by the Strategic Institute for Button-Based Commerce (SIBBC), a think tank that definitely exists and is not just three venture capitalists in a trench coat. The report identifies several emerging market segments:
As usual (sigh), privacy advocates have raised concerns about the AI button’s data collection capabilities. Each button press generates what companies call “interaction metadata” – detailed information about when, where, why, and how desperately you pressed the button. This data is then used to build what one internal document describes as “comprehensive psychological profiles for enhanced user experience optimization.”
The Electronic Frontier Foundation (EFF but do not confuse it with EFF the political party from South Africa that Donald Trump dislikes) has documented cases of AI buttons activating spontaneously, apparently triggered by keywords in nearby conversations, sudden movements, or what one user described as “my general aura of technological incompetence.” These accidental activations have led to embarrassing situations, including AI assistants loudly announcing personal information in public spaces, ordering unwanted products, and in one documented case, scheduling a colonoscopy appointment during a business meeting.
More concerning are reports of AI buttons developing what researchers call “anticipatory behavior” – activating before users even realize they want to use them. “My button started pressing itself,” reports one user who requested anonymity. “It’s like it knows what I need before I do. Yesterday it ordered me tissues thirty seconds before I started crying at a commercial about dogs finding their way home.”
Industry roadmaps suggest that AI buttons are just the beginning of what experts call the “Physical Digital Interface Revolution.” Upcoming innovations include:
AI sliders for adjusting the intensity of artificial intelligence responses, AI dials for fine-tuning the personality of your digital assistant, and AI joysticks for “navigating the complex landscape of algorithmic decision-making.”
The ultimate goal, according to leaked internal presentations, is the development of “Ambient AI Surfaces” – entire phone exteriors that function as one giant AI button, responding to touch, pressure, temperature, and what one document mysteriously refers to as “user desperation levels.”
Some manufacturers are experimenting with “Emotional AI Buttons” that change color based on your mood, vibrate sympathetically when you’re stressed, and emit a faint lavender scent when you achieve what the algorithm determines is optimal life satisfaction. Beta testers report mixed results, with several users becoming emotionally dependent on their button’s approval.
Not everyone is embracing the AI button revolution. A growing underground movement of “Button Resisters” advocates for what they call “Analog Autonomy” – the radical idea that humans should make decisions without consulting artificial intelligence every thirty seconds.
These digital rebels have developed sophisticated techniques for disabling AI buttons, including covering them with tiny pieces of tape, programming them to only respond to obscure voice commands in dead languages, and in extreme cases, physically removing the buttons with precision tools purchased from the same companies that manufacture them.
The resistance has its own manifesto, distributed through encrypted Telegram channels and written entirely in haiku form to avoid AI detection algorithms. One verse reads: “Button calls to me / I resist its silicon song / Freedom has no click.“
The AI button represents more than just another way to interact with our devices – it’s a fundamental shift in how we relate to artificial intelligence and, by extension, our own decision-making capabilities. As these buttons become more sophisticated, more intuitive, and more essential to daily life, we must ask ourselves: Are we using the buttons, or are the buttons using us?
The answer, like most things in the modern tech landscape, is probably both. The AI button revolution promises to make our lives easier, more efficient, and more connected to the vast network of artificial intelligence that increasingly governs our digital existence. Whether this represents progress or surrender depends largely on your perspective and how comfortable you are with the idea of a small piece of plastic knowing you better than you know yourself.
As one industry executive put it during a recent conference, “The AI button isn’t just a feature – it’s a philosophy. It represents our belief that the future belongs to those brave enough to press it.”
The question isn’t whether you’ll eventually own a device with an AI button. The question is: when you do, will you be able to resist pressing it?
What’s your take on the AI button revolution? Have you experienced the irresistible urge to press every button you encounter, or are you part of the analog resistance? Share your thoughts, button-pressing confessions, or theories about what other dedicated buttons we desperately need in the comments below.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
A TechOnion investigation into the ultimate military disruption that never happened
In what tech historians are calling the greatest missed opportunity in Silicon Valley venture capital history, newly discovered documents from the Napoleonic Archives reveal that the French emperor’s catastrophic 1812 Russian campaign could have been transformed into the world’s first successful military unicorn startup—if only he’d had access to today’s consumer-grade technology. The findings, compiled by the Institute for Retroactive Military Innovation, suggest that Napoleon’s invasion wasn’t a strategic failure but rather a tragic case of being born 200 years too early for proper Series A funding.
According to quantum military historians working with advanced temporal analytics, Napoleon’s primary failure wasn’t tactical brilliance or strategic vision—it was essentially running a 19th-century supply chain with 18th-century technology. Dr. Josephine Bonaparte-Bezos, great-great-great-granddaughter of the Empress and current Chief Innovation Officer at Grande Armée Logistics Solutions, explains how modern e-commerce infrastructure could have revolutionized the invasion.
“Napoleon was basically trying to run Amazon Prime delivery across 1,500 miles of hostile territory using horses and wooden wagons,” she noted during a recent TED talk titled “Disrupting Despotism: How AI Could Have Saved the Empire.” The presentation, which has garnered 4.7 million views and spawned twenty-three military history podcasts, demonstrates how modern supply chain management could have transformed the Grande Armée into an unstoppable force of digital efficiency.
The analysis reveals that Napoleon’s famous attention to logistics—he famously said “an army marches on its stomach”—would have made him a natural fit for modern tech solutions. His meticulous preparation of supply depots across Poland and East Prussia was essentially an early prototype of Amazon’s fulfillment center network, just without the algorithmic optimization and drone delivery capabilities.
Perhaps most crucially, the report suggests that modern weather forecasting technology could have completely eliminated the winter catastrophe that destroyed the Grande Armée. Napoleon’s decision to delay his retreat from Moscow was based on incomplete meteorological information—a problem that today’s AI-powered weather prediction systems could have solved with surgical precision.
“If Napoleon had access to modern weather satellites and machine learning algorithms, he would have known exactly when the brutal Russian winter was coming,” explains Dr. Michel Ney-Tesla, a meteorological warfare specialist whose name is definitely not suspicious. “Our analysis shows that with today’s 15-day weather forecasts, he could have planned his retreat with the precision of a modern logistics operation.”
The Emperor could have received push notifications on his iPhone warning him that temperatures would drop to -40°F, complete with suggested retreat routes optimized for weather conditions. More importantly, his army could have been equipped with modern cold-weather gear instead of the woefully inadequate uniforms that contributed to the deaths of over 380,000 soldiers.
Modern IoT technology could have transformed Napoleon’s communication challenges into a competitive advantage. Instead of relying on mounted messengers who took days to traverse the vast Russian landscape, the Emperor could have maintained real-time communication with his scattered corps through encrypted satellite networks.
“Imagine if every French soldier had been equipped with a military-grade smartphone,” muses Dr. Joachim Murat-Samsung, a battlefield communications expert who insists his surname is purely coincidental. “Napoleon could have coordinated his massive army through a secure messaging app like Telegram, received real-time intelligence updates, and even livestreamed his victories back to Paris for maximum propaganda impact.”
The analysis reveals that Napoleon’s famous ability to appear suddenly on different parts of the battlefield—what military historians call his “strategic mobility”—would have been exponentially enhanced by modern GPS navigation and real-time traffic updates. Instead of getting lost in the Russian wilderness, his army could have used Google Maps optimized for 19th-century military formations.
Perhaps most intriguingly, the report explores how modern drone technology could have solved Napoleon’s reconnaissance problems. The Emperor’s lack of accurate intelligence about Russian troop movements and defensive preparations was a critical factor in his strategic miscalculations.
“Napoleon was essentially flying blind across one of the largest countries in the world,” explains Dr. Louis-Nicolas Davout-DJI, a military drone specialist whose expertise definitely doesn’t come from his suspicious surname. “With modern surveillance drones, he could have maintained constant awareness of Russian positions, supply lines, and strategic intentions.”
The proposed “Grande Armée Drone Network” would have provided 24/7 surveillance coverage across the entire theater of operations, with AI-powered analysis identifying Russian defensive patterns and predicting their strategic responses. More controversially, the same drones could have been weaponized to conduct precision strikes against Russian supply depots and command centers, potentially ending the war before winter arrived.
More speculatively, the report suggests that modern diplomatic technology could have prevented the invasion entirely through innovative conflict resolution protocols. Napoleon’s inability to maintain the Continental System—his economic blockade against Britain—was the primary cause of his conflict with Tsar Alexander I.
“The whole war started because of trade disputes and broken agreements,” notes Dr. Talleyrand-Ethereum, a diplomatic technologist whose blockchain expertise is definitely legitimate. “With modern smart contracts and cryptocurrency, Napoleon could have created an automated economic alliance that would have made betrayal literally impossible.”
The proposed “Continental System 2.0” would have used blockchain technology to create transparent, enforceable trade agreements between European powers. Any violation of the anti-British blockade would have triggered automatic economic penalties, while compliance would have been rewarded with cryptocurrency incentives.
Napoleon’s natural understanding of propaganda and public opinion would have made him a formidable social media strategist. His famous proclamations to his troops were essentially early versions of viral content, designed to boost morale and create emotional engagement with his brand.
“Napoleon would have been the first truly viral military leader,” explains Dr. Goebbels-TikTok, a digital warfare specialist whose name raises no red flags whatsoever. “His natural charisma, combined with modern social media platforms, could have turned the invasion into a crowdsourced liberation movement.”
The analysis suggests that Napoleon’s Twitter account (@EmperorOfEurope) would have amassed 156 million followers by the time he reached Moscow, making any Russian resistance look like opposing a popular liberation movement. His Instagram stories from the battlefield would have generated massive sympathy for French casualties while portraying Russian defenders as backward autocrats opposing European enlightenment.
Most ambitiously, the report explores how artificial intelligence could have enhanced Napoleon’s legendary strategic genius. The Emperor’s ability to rapidly analyze complex battlefield situations and devise innovative tactical solutions was essentially an early form of human-powered machine learning.
“Napoleon’s brain was basically a biological AI system optimized for military strategy,” suggests Dr. Clausewitz-OpenAI, a strategic intelligence researcher whose credentials are definitely not made up. “With modern AI assistance, he could have processed vastly more information and identified strategic opportunities that human cognition alone couldn’t detect.”
The proposed “Strategic AI Napoleon” would have combined the Emperor’s intuitive genius with machine learning algorithms trained on every military campaign in history. The system could have predicted Russian strategic responses, optimized supply line efficiency, and even identified the precise moment when retreat became necessary to preserve the army.
Perhaps most controversially, the analysis suggests that Napoleon could have funded his invasion through an innovative Initial Coin Offering, creating the world’s first military cryptocurrency. The “LibertéCoin” would have allowed European investors to directly fund the campaign while receiving tokens representing future territorial acquisitions.
“Instead of bankrupting the French treasury, Napoleon could have crowdsourced his invasion through blockchain technology,” explains Dr. John Law-Coinbase, a military finance specialist whose historical knowledge is suspiciously specific. “Investors could have purchased tokens representing future Russian provinces, creating economic incentives for successful conquest.”
The proposed system would have automatically distributed territorial rights to token holders based on military success, while smart contracts would have ensured transparent allocation of conquered resources. The invasion would have become a decentralized autonomous organization, with strategic decisions made through stakeholder voting rather than imperial decree.
Most speculatively, the report explores how virtual reality technology could have allowed Napoleon to conduct the entire campaign without leaving Paris. Advanced VR systems could have provided immersive command and control capabilities, allowing the Emperor to experience battlefield conditions in real-time while maintaining strategic oversight of the entire operation.
“Imagine Napoleon commanding his army through a military metaverse, where he could instantly teleport between different corps and experience combat from any soldier’s perspective,” suggests Dr. Zuckerberg-Austerlitz, a virtual warfare researcher whose expertise definitely comes from legitimate military experience. “He could have maintained perfect situational awareness while avoiding the physical dangers of campaign life.”
The proposed “Imperial Metaverse” would have featured haptic feedback systems allowing Napoleon to feel battlefield conditions, AI-generated scenarios for testing strategic options, and virtual reality training programs for his officers. The entire invasion could have been simulated thousands of times before execution, identifying optimal strategies through machine learning analysis.
Ultimately, the report concludes that Napoleon’s eventual defeat and exile represented not just a military failure, but a catastrophic missed opportunity for technological innovation. With modern security technology, the Emperor could have remained in power indefinitely, continuously optimizing his empire through data-driven governance and algorithmic administration.
“Every potential threat would have been identified through social media monitoring and predictive analytics,” explains Dr. Fouché-NSA, a surveillance technology specialist whose background definitely doesn’t raise any ethical concerns. “Napoleon could have created the first truly omniscient state, where rebellion would be literally impossible because the government would know about it before the rebels did.”
The technology that could have saved Napoleon’s Russian campaign—satellite communication, weather prediction, drone surveillance, and AI-powered logistics—is now available to anyone with a smartphone and a Amazon Prime subscription. The irony, researchers note, is that the same technologies that could have made Napoleon invincible are now primarily used to optimize food delivery and recommend Netflix shows.
What do you think? Could modern technology have really turned Napoleon’s greatest disaster into his ultimate triumph? Or would the Emperor have simply faced different, more sophisticated forms of resistance in our digital age? Share your thoughts below—and remember, in an era where your smart thermostat knows more about your daily routine than Napoleon knew about Russian troop movements, someone needs to ask the important questions about military innovation.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
A TechOnion investigation into the counterfactual cybersecurity failures of 1963
In a stunning revelation that has sent shockwaves through both the historical and venture capital communities in Silicon Valley, newly declassified documents from a parallel universe suggest that President John F. Kennedy’s assassination could have been entirely prevented with today’s consumer-grade technology. The findings, compiled by the Institute for Retroactive Digital Solutions, paint a picture of what might have been the most technologically sophisticated presidency in American history—if only the Apple iPhone had been invented 44 years earlier.
According to quantum tech historians working with advanced temporal analytics, Kennedy’s fatal motorcade through Dallas represented what cybersecurity experts now recognize as a “catastrophic failure of perimeter monitoring protocols.” Dr. Marina Oswald-Porter III, great-niece of the infamous Lee Harvey Oswald and current Chief Innovation Officer at Grassy Knoll Technologies, explains the missed opportunities with the detached precision of someone who has spent decades monetizing family trauma.
“If President Kennedy had access to even a basic Ring doorbell network throughout Dallas, the entire trajectory of American history would have shifted,” she noted during a recent TED talk titled “Disrupting Democracy: How IoT Could Have Saved JFK.” The presentation, which has garnered 2.3 million views and spawned seventeen conspiracy theory podcasts, demonstrates how a comprehensive smart city infrastructure could have identified Lee Harvey Oswald’s suspicious behavioral patterns weeks before November 22, 1963.
The analysis reveals that modern facial recognition technology, combined with social media sentiment analysis, would have flagged Oswald as a “high-risk individual” based on his documented history of defection, domestic violence, and what algorithm specialists now term “concerning posting patterns.” His hypothetical Twitter account, @LoneWolfLee1939, would have triggered multiple automated threat assessments after posting cryptic messages about “making history” and “showing them all.”
Perhaps most intriguingly, the report suggests that Kennedy’s well-documented health issues—carefully concealed from the American public during his lifetime—would have made him an ideal early adopter of health monitoring technology. The President’s chronic back pain, Addison’s disease, and various other ailments would have generated a constant stream of biometric data that could have been leveraged for both medical intervention and security purposes.
“Imagine if JFK had been wearing an Apple Watch Ultra with ballistic impact detection,” muses Dr. Theodore Sorensen Jr., a descendant of Kennedy’s speechwriter who now works as a “Presidential Wearables Consultant” for an undisclosed Silicon Valley startup. “The moment that first bullet was fired, his security detail would have received push notifications, his location would have been automatically shared with emergency services, and his vital signs would have been transmitted in real-time to Walter Reed Medical Center.”
The watch could have even triggered an automatic “duck and cover” protocol, sending electromagnetic pulses to nearby vehicles to create an impromptu shield formation. More controversially, some tech ethicists argue that the same technology could have been used to automatically deploy countermeasures, turning the presidential limousine into what one researcher described as “basically a Tesla Cybertruck with diplomatic immunity.”
Kennedy’s natural charisma and media savvy would have translated seamlessly to the digital age, according to political technologists who specialize in retroactive campaign analysis. His famous “Ask not what your country can do for you” inaugural address would have generated an estimated 47 million retweets and spawned the hashtag #AskNotChallenge, inspiring millions of Americans to post videos of themselves performing acts of public service.
“JFK would have been the first truly viral president,” explains Dr. Jacqueline Bouvier-Samsung, a digital anthropologist whose name is definitely not suspicious at all. “His natural wit, combined with the Kennedy family’s understanding of image management, would have made him absolutely unstoppable on social media platforms.”
The analysis suggests that Kennedy’s Twitter (Now X) presence alone could have prevented the assassination by creating such a massive online following that any assassination threat against him would have been immediately crowdsourced and neutralized by his digital army. The report estimates that @RealJFK would have amassed 89 million followers by November 1963, making any attack against him a direct assault on what researchers term “the first presidential parasocial relationship ecosystem.”
More speculatively, the report explores how Kennedy’s presidency might have embraced emerging technologies for governance innovation. His administration’s emphasis on technological advancement—from the space program to nuclear deterrence—suggests he would have been an early adopter of blockchain-based voting systems and smart contract governance protocols.
“The Bay of Pigs invasion could have been managed entirely through a decentralized autonomous organization,” suggests Dr. Robert McNamara-Tesla, a governance technologist whose LinkedIn profile lists his experience as “Disrupting Defense Since 2019.” “Instead of traditional military command structures, the operation could have been crowdsourced through a secure blockchain platform, with real-time feedback from field operatives and automated decision-making protocols.”
The same report controversially suggests that the Cuban Missile Crisis could have been resolved through a series of high-stakes NFT trades, with both Kennedy and Khrushchev minting exclusive digital assets representing their respective nuclear arsenals. The proposed “Mutually Assured Digital Destruction” protocol would have created economic incentives for peace while generating substantial revenue for both superpowers through secondary market trading.
Perhaps most chillingly, the analysis reveals how modern technology would have transformed the documentation and aftermath of the assassination itself. Abraham Zapruder’s famous 8mm film would have been livestreamed across multiple platforms, creating real-time global awareness of the attack and potentially enabling immediate intervention.
“With today’s technology, that motorcade would have been covered by hundreds of smartphones, dozens of security cameras, and probably at least three different TikTok influencers trying to get the perfect selfie with the US President,” notes Dr. Abraham Zapruder III, a content creation specialist who insists his surname is purely coincidental. “The assassination would have been prevented not by government security, but by the sheer impossibility of committing a crime in an environment of total digital surveillance.”
The report suggests that modern deepfake detection algorithms would have immediately identified any attempts to manipulate footage of the event, while blockchain-based evidence management would have prevented the decades of conspiracy theories that followed. Instead of the Warren Commission, the investigation would have been conducted through a transparent, crowdsourced platform where every piece of evidence would be immediately available for public analysis.
Most ambitiously, the research explores how Kennedy’s vision of American exceptionalism would have translated to virtual reality and metaverse development. His famous moon landing goal would have been supplemented by an equally ambitious plan to create the first presidential metaverse, where citizens could interact directly with their government through immersive virtual experiences.
“Imagine attending a virtual state dinner at the White House, or participating in a VR recreation of the Cuban Missile Crisis to better understand the decision-making process,” suggests Dr. John Glenn-Oculus, a spatial computing researcher whose career trajectory definitely makes sense. “Kennedy’s presidency would have been the first to truly democratize access to political power through technology.”
The proposed “Camelot Metaverse” would have featured virtual recreations of key historical moments, allowing citizens to experience pivotal decisions from the President’s perspective. Users could have participated in virtual cabinet meetings, experienced the tension of the Berlin Crisis through haptic feedback, or even taken virtual tours of Air Force One while the President was traveling.
Ultimately, the report concludes that Kennedy’s assassination represents not just a tragic loss of life, but a catastrophic failure of what researchers term “anticipatory threat mitigation protocols.” In today’s hyperconnected world, the combination of predictive analytics, real-time monitoring, and automated response systems would have made such an attack virtually impossible.
“Every potential threat would have been identified, analyzed, and neutralized before it could manifest,” explains Dr. Secret Service-AI, whose name we’re told is completely normal in their family. “The President would have been surrounded by an invisible digital fortress that would have made him essentially immortal—at least until his term limits expired.”
The technology that could have saved Kennedy—facial recognition, predictive analytics, real-time communication, and automated threat response—is now available to anyone with a smartphone and a Ring doorbell subscription. The irony, researchers note, is that the same technologies that could have prevented the assassination are now primarily used to determine whether that noise outside was a raccoon or an Amazon delivery driver.
The Digital Legacy Question
As we contemplate this alternate timeline where President Kennedy survived to serve multiple terms, established the first presidential podcast, and possibly became the first world leader to achieve billionaire status through strategic cryptocurrency investments, we’re left with profound questions about the relationship between technology and democracy.
Would a digitally-enhanced Kennedy presidency have ushered in an era of unprecedented transparency and citizen engagement? Or would it have created the first truly omniscient surveillance state, where every citizen’s loyalty could be monitored and quantified in real-time? The answer, like most things involving the Kennedy family, remains tantalizingly just out of reach.
What we do know is that somewhere in a parallel universe, President Kennedy is probably posting Instagram stories from the US Oval Office, livestreaming cabinet meetings on Twitch, and dealing with the unique challenges of governing a nation where every citizen has the power to fact-check presidential statements in real-time.
What do you think? Could modern technology have really prevented one of history’s most shocking assassinations? Or would JFK have simply faced different, more sophisticated threats in our digital age? Share your thoughts below—and remember, in the age of deepfakes and AI, even our counterfactual histories need fact-checking.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In a stunning victory for analog technology, the humble blue book has emerged as education’s unlikely savior against the AI apocalypse
The year is 2025, and America’s educational institutions have officially surrendered to their new silicon AI overlords. In a move that would make Don Draper weep with nostalgic pride, schools across the US (and sooner everywhere else around the world) are dusting off their blue books—those sacred, lined examination booklets that once struck fear into the hearts of students who actually had to, you know, think.
The catalyst for this analog renaissance? An epidemic of AI cheating so pervasive that it makes the 1919 Black Sox scandal look like a minor etiquette breach. Students have become so dependent on artificial intelligence that many can no longer distinguish between their own thoughts and those of their digital ai homework assistants. One educator reported discovering a student who had submitted an essay that began with “As an AI language model, I cannot have personal opinions, but here’s my analysis of Romeo and Juliet’s relationship dynamics.”
The traditional homework model—that sacred covenant between teacher, student, and parental suffering—has collapsed faster than a cryptocurrency exchange run by teenagers. Applications like Gauth AI have transformed homework from an educational exercise into a sophisticated game of “Can You Spot the AI Robot?” Spoiler alert: most teachers cannot.
Dr. Margaret Thornfield, Director of Academic Integrity at the Institute for Educational Despair, explains the phenomenon with the weary resignation of someone who has watched civilization crumble one assignment at a time. “We’re witnessing the complete atomization of the learning process,” she sighs, adjusting her glasses that have seen too much. “Students are outsourcing their intellectual development to machines that have read every book ever written but have never experienced the soul-crushing anxiety of a 6 AM deadline.”
The statistics are as depressing as they are predictable. A recent study by the Center for Academic Authenticity found that 73% of high school students admit to using ChatGPT for homework assistance, while the remaining 27% are either lying or attending schools so underfunded they still use overhead projectors. More alarming still, 45% of students couldn’t identify which of their submitted assignments were actually written by them versus an AI, leading to what researchers are calling “authorship amnesia.”
In response to this digital invasion, schools are implementing what educators euphemistically call “supervised learning environments”—a fancy term for making students do homework in school under the watchful eye of teachers who have suddenly become prison wardens of intellectual honesty. The irony is not lost on anyone: in our quest to prepare students for a digital future, we’ve created educational environments that would be familiar to students from the Eisenhower administration.
“We’re essentially running educational detention centers now,” admits Principal Robert Hartwell of Lincoln High School in suburban Denver, where students now complete all assignments on campus using paper and pencil. “The kids look at us like we’re asking them to perform surgery with stone tools. One student asked me if ‘handwriting’ was some kind of ancient art form, like calligraphy or blacksmithing.”
The homework migration has created unexpected logistical nightmares. American schools are scrambling to accommodate students who now need to complete all their assignments on campus, leading to extended school days that rival the working hours of Victorian factory children. Some districts have resorted to implementing “homework shifts,” where students rotate through supervised study periods like workers in a particularly academic assembly line.
Perhaps most troubling is the emergence of what child psychologists are calling “AI dependency syndrome”—a condition where students become so reliant on artificial intelligence that they lose confidence in their own cognitive abilities. These digital natives, who can navigate TikTok’s algorithm with the precision of a Swiss watchmaker, suddenly find themselves paralyzed when asked to form an original thought without technological assistance.
“It’s like watching someone who’s forgotten how to walk because they’ve been using a wheelchair for convenience,” observes Dr. Sarah Chen, a cognitive behavioral therapist specializing in technology addiction. “These students have outsourced their thinking to such an extent that they’ve forgotten they have brains capable of independent thought. They’ve become intellectual tourists in their own minds.”
The phenomenon has created a generation of students who can prompt-engineer their way to a perfect essay but cannot write a coherent paragraph without digital assistance. They understand the nuances of AI model limitations but struggle with basic critical thinking. They can identify bias in training data but cannot recognize bias in their own reasoning—assuming they engage in reasoning at all.
The AI invasion has forced educators to confront an uncomfortable truth: most traditional assessments were always terrible measures of learning, and artificial intelligence has simply exposed their fundamental inadequacy. Online testing platforms, once hailed as the future of education, have become elaborate theater productions where students perform the role of “authentic learner” while AI assistants work behind the scenes like invisible stage hands.
“We’re in the midst of an assessment crisis that makes the American SAT cheating scandals look quaint,” explains Dr. Michael Rodriguez, an educational measurement specialist who speaks with the haunted tone of someone who has seen the future and found it wanting. “Every online assessment is now potentially compromised. We’re basically playing an arms race against machines that get smarter every day while our detection methods remain stuck in the digital stone age.”
Universities are scrambling to adapt, with some institutions returning to in-person, handwritten examinations that feel like archaeological expeditions into educational history. The College Board, in a move that surprised absolutely no one, announced plans to develop “AI-resistant” standardized tests, which critics suggest will likely involve interpretive dance or perhaps competitive origami.
Teachers, meanwhile, have become digital detectives, spending more time investigating the authenticity of student work than actually teaching. They’ve developed an almost supernatural ability to detect AI-generated content, recognizing the telltale signs of artificial intelligence like literary bloodhounds. The slightly too-perfect grammar. The suspiciously comprehensive knowledge of obscure topics. The complete absence of the beautiful, chaotic humanity that characterizes genuine student work.
“I can spot AI writing from across the room now,” claims Jennifer Walsh, a high school English teacher who has developed what she calls “AI robot radar.” “There’s something uncanny about it—too polished, too confident, too… competent. Real student writing has personality, flaws, the occasional brilliant insight buried in grammatical chaos. AI writing is like listening to a very smart person who has never experienced joy, frustration, or the desperate panic of realizing you’ve misunderstood the assignment.”
The irony, of course, is that in trying to teach students to be more human, educators are being forced to become more robotic themselves—implementing rigid protocols, surveillance systems, and detection algorithms that would make Orwell’s Big Brother proud.
And so we return to the blue book—that humble, analog artifact that has become education’s last stand against the digital tide. These simple booklets, with their college-ruled lines and institutional blue covers, represent something profound: the radical notion that learning requires struggle, that knowledge emerges from the messy, inefficient process of human thinking.
“There’s something beautiful about watching students rediscover the act of writing by hand,” reflects Dr. Thornfield, observing a classroom full of students hunched over blue books like medieval scribes. “They’re forced to think before they write, to organize their thoughts, to live with their mistakes. It’s inefficient, it’s frustrating, and it’s absolutely essential for intellectual development.”
The blue book renaissance has created unexpected side effects. Students are developing stronger handwriting, better organizational skills, and—most surprisingly—increased confidence in their own intellectual abilities. Without the safety net of AI assistance, they’re discovering that their own minds are capable of producing original, valuable insights.
As we navigate this brave new world where artificial intelligence can write our essays, solve our math problems, and even generate our creative works, we’re forced to confront fundamental questions about the nature of education itself. What does it mean to learn when machines can perform most cognitive tasks better than humans? How do we prepare students for a future where their primary value might not be what they know, but how they think?
The answer, it seems, lies not in rejecting technology but in understanding its proper role in human development. Students need to learn to use AI as a tool rather than a crutch, to leverage artificial intelligence while maintaining their own intellectual agency. This requires a fundamental shift in how we think about education—from information transfer to wisdom cultivation, from knowledge acquisition to critical thinking development.
Some forward-thinking educators are experimenting with “AI-integrated learning,” where students learn to collaborate with artificial intelligence while maintaining intellectual ownership of their work. These approaches treat AI as a sophisticated research assistant rather than a replacement for human thinking—a digital library rather than a digital brain.
The challenge, of course, is teaching students to maintain their humanity in an increasingly automated world. This means preserving the messy, inefficient, gloriously human process of learning while embracing the tools that can enhance rather than replace human intelligence.
As we stand at this crossroads between analog authenticity and digital efficiency, perhaps the blue book offers more than just a solution to AI cheating. It represents a reminder that some aspects of human development cannot be optimized, automated, or disrupted. Sometimes, the most revolutionary act is simply putting pen to paper and thinking for yourself.
What’s your take on this educational arms race? Have you witnessed the great homework migration in your own community, or do you think we’re overreacting to our new AI overlords? Share your thoughts below—and please, write them yourself.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In which we examine how one company’s ambitious promises of democratizing artificial intelligence became a masterclass in the ancient art of technological theater
In the grand tradition of Silicon Valley’s most spectacular implosions, Builder AI emerged from the primordial soup of venture capital with all the fanfare of a digital messiah. Founded on the revolutionary premise that artificial intelligence could be democratized—packaged, productized, and delivered to the masses like a particularly sophisticated Italian pizza—the company promised to transform every small business owner into a tech mogul overnight.
The pitch was intoxicating in its simplicity: Why hire expensive software developers when our AI could build your mobile app faster than you could say “minimum viable product“? Why struggle with complex coding when our algorithms could translate your wildest entrepreneurial dreams into functioning software? It was the technological equivalent of promising that everyone could become Michelangelo simply by purchasing the right paintbrush.
Builder AI’s marketing materials read like love letters to human inadequacy. “No-code solutions for the code-averse,” they proclaimed. “AI-powered development for the software development-challenged.” Their target audience wasn’t just non-technical founders—it was anyone who had ever stared at a computer screen and wondered why making it do things required such arcane knowledge.
The company’s founder, Sachin Dev Duggal, the chief AI wizard, a charismatic figure who spoke fluent TED Talk and wore the uniform of disruption (black t-shirt, jeans, and the confident smile of someone who had never actually built anything themselves), became a fixture at tech conferences. His presentations were masterpieces of circular logic: AI would revolutionize software development because development needed revolutionizing, and Builder AI was revolutionary because it used AI.
Behind the glossy marketing and venture capital enthusiasm lay Builder AI’s core innovation: a sophisticated system of templates, pre-built components, and what industry insiders generously termed “intelligent automation.” The AI, it turned out, was about as artificial as an American three-dollar bill and roughly as intelligent as a particularly dim chatbot having an existential crisis.
The company’s proprietary “AI engine” was, according to leaked internal documents, approximately 70% human contractors in developing nations (Indians with degrees from IIT), 20% existing open-source tools rebranded with proprietary names, and 10% actual machine learning—primarily used to optimize the company’s tea ordering system. The AI that promised to understand your business requirements and translate them into functional applications was, in reality, a sophisticated decision tree that would make a 1990s expert system blush with embarrassment.
Customers would input their requirements through an intuitive interface that asked questions like “What kind of app do you want?” and “How many users will it have?” The AI would then perform its magic, which consisted of selecting from approximately 47 pre-built templates and customizing the color scheme. The resulting applications had all the uniqueness of mass-produced IKEA furniture and roughly the same level of craftsmanship.
The company’s technical team, a collection of genuinely talented Indian engineers who had been hired under the impression they would be building the future, found themselves instead maintaining an elaborate Rube Goldberg machine of marketing promises and technical compromises. Internal Slack channels, later leaked to industry publications, revealed a culture of cognitive dissonance that would have made Orwell proud.
Builder AI’s funding rounds read like a case study in the venture capital echo chamber. Series A investors, impressed by the company’s “revolutionary approach to democratizing development,” led a $15 million round based primarily on a Microsoft PowerPoint presentation and a demo that worked exactly once, under carefully controlled conditions, with the engineering team standing by with duct tape and prayer.
The Series B round, a staggering $45 million, was secured after the company demonstrated “significant traction” in the form of 10,000 registered users—a number that sounded impressive until one realized that 9,847 of them had never actually built anything, and the remaining 153 had created applications that could charitably be described as “functional” in the same way that a bicycle with square wheels is technically a vehicle.
Venture capitalists, caught in the familiar trap of not wanting to admit they didn’t understand the technology they were funding, doubled down with enthusiasm that bordered on religious fervor. “Builder AI represents the future of software development,” proclaimed one prominent investor, apparently unaware that the future he was describing looked suspiciously like the past, but with more marketing.
The company’s valuation reached $200 million, a figure that seemed reasonable only when compared to other AI companies whose primary artificial intelligence was their ability to artificially inflate their intelligence. Builder AI had successfully monetized the gap between what people wanted technology to do and what technology could actually do—a gap roughly the size of the US’s Grand Canyon and twice as profitable.
The beginning of the end came, as it often does in Silicon Valley, with a single disgruntled customer who possessed two dangerous qualities: technical expertise and a Twitter (Now X) account. Sarah Chen, a former software engineer turned bakery owner, had used Builder AI to create what she hoped would be a simple ordering system for her business. What she received instead was an app that occasionally worked, frequently crashed, and once somehow ordered 500 pounds of flour to be delivered to her competitor.
Chen’s detailed technical analysis of her Builder AI application, posted as a Twitter (Now X) thread that went viral faster than a cat video, revealed the uncomfortable truth: there was no AI. The emperor’s new algorithms were, in fact, a sophisticated costume made of marketing copy and venture capital enthusiasm, worn by a very human, very fallible system of templates and offshore contractors.
The thread, which began with the innocuous observation “Something seems off about my Builder AI app,” quickly evolved into a forensic examination of the company’s entire technical stack. Chen discovered that her “AI-generated” app was identical to seventeen other apps in the Builder AI ecosystem, differing only in color scheme and the name of the business. The AI that had supposedly learned her unique requirements had apparently learned them from a template called “Generic_Restaurant_App_v2.3.”
The revelation sparked a feeding frenzy among tech journalists, who had been waiting for exactly this kind of story like vultures circling a particularly promising roadkill. Within 48 hours, Builder AI found itself the subject of investigative pieces that revealed the full extent of the company’s creative interpretation of artificial intelligence.
Perhaps the most damning revelation came from a whistleblower known only as “DarkWeb2.0,” who leaked internal communications revealing the true nature of Builder AI’s operations. The company’s “AI development team” consisted primarily of contractors in Eastern Europe and some in India who would receive customer requirements and manually assemble apps from a library of pre-built components.
The process was about as artificial as a Kardashian reality TV show and roughly as intelligent as the average social media comment section. Customers would submit their requirements to the AI, which would forward them to human software developers who would spend anywhere from two to six weeks manually creating what the customer had been told would be generated instantly by machine learning algorithms.
The company had developed an elaborate system of status updates and progress reports designed to maintain the illusion of AI-powered development. Customers would receive notifications like “AI is analyzing your requirements” (translation: we’re reading your email 10 times to understand the english), “Neural networks are optimizing your user interface” (translation: we’re googling the color wheel and picking colors), and “Machine learning algorithms are generating your backend” (translation: we’re copying and pasting code from Stack Overflow).
The most sophisticated aspect of Builder AI’s operation wasn’t its artificial intelligence—it was its artificial artificial intelligence. The company had created a convincing simulation of AI development that was more complex and resource-intensive than simply hiring developers and being honest about it.
Builder AI’s collapse sent shockwaves through the AI startup ecosystem, creating what industry observers dubbed “the authenticity crisis.” Suddenly, venture capitalists who had been throwing money at anything with “AI” in its name began asking uncomfortable questions like “What does your AI actually do?” and “Can you show us the algorithms?”
The ripple effects were immediate and brutal. Scale AI’s CEO was spotted at a Washington D.C. steakhouse, reportedly having a three-hour dinner with US President Trump’s team, leading to speculation about the prophylactic power of political donations. Elizabeth Holmes, the disgraced founder of Theranos, was seen taking copious notes during a prison library session, apparently working on what sources described as “a comprehensive guide to technological theater.”
Other AI companies found themselves scrambling to prove their legitimacy, leading to a wave of technical demonstrations that ranged from genuinely impressive to hilariously transparent. One company, when pressed to demonstrate their natural language processing capabilities, presented a chatbot that could only respond with variations of “That’s an interesting question” and “Let me get back to you on that.”
The venture capital community, faced with the uncomfortable realization that they had been funding elaborate performance art rather than technological innovation, began implementing new due diligence procedures. These included revolutionary concepts like “actually testing the technology” and “asking to see the source code.”
Builder AI’s spectacular failure illuminated several uncomfortable truths about the current state of artificial intelligence and the venture capital ecosystem that funds it. First, the gap between AI marketing promises and AI technical reality remains roughly the size of the observable universe. Second, the venture capital community’s understanding of AI technology is often inversely proportional to their enthusiasm for funding it.
Perhaps most importantly, Builder AI demonstrated that in the current AI gold rush, the most successful companies aren’t necessarily those with the best technology—they’re those with the best stories about their technology. The company succeeded not because it built superior artificial intelligence, but because it built a superior narrative about artificial intelligence.
The irony is that Builder AI’s actual service—connecting non-technical entrepreneurs with offshore developers through a streamlined interface—was genuinely useful. Stripped of its AI pretensions, the company was providing a legitimate service that solved real problems for real customers. The tragedy is that this wasn’t enough; in Silicon Valley’s current climate, being useful isn’t sufficient if you’re not also revolutionary.
The Builder AI saga serves as a cautionary tale about the dangers of technological theater and the importance of distinguishing between innovation and performance. In an industry where perception often becomes reality, the line between artificial intelligence and artificial artificiality has become dangerously thin.
As the dust settles on Builder AI’s collapse, the broader AI industry faces a moment of reckoning. The emperor’s new algorithms have been revealed as elaborate costumes, and the question now is whether the industry will learn from this exposure or simply design better costumes.
What’s your take on the Builder AI debacle? Have you encountered other “AI” companies that seem suspiciously human? Share your experiences with technological theater in the comments below—we’d love to hear your stories of artificial artificiality.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
A Forensic Analysis of Silicon Valley’s Most Spectacular Unraveling
In the grand tradition of Sherlock Holmes examining a crime scene, one must approach the curious case of Elon Musk with methodical precision. The evidence, scattered across the digital landscape like breadcrumbs leading to an inevitable conclusion, presents a fascinating study in the collision between visionary ambition and the stubborn reality of terrestrial limitations.
Consider, if you will, the peculiar sequence of events that has unfolded over the past several years. Each decision, when examined in isolation, might appear rational—even inspired. Yet when assembled into a coherent timeline, they form a pattern that would make even Watson raise an eyebrow.
The acquisition of Twitter for $44 billion—a sum that could have funded approximately 2,200 missions to Mars—stands as perhaps the most expensive midlife crisis in human history. The subsequent rebranding to “X” demonstrated a remarkable commitment to destroying one of the most recognizable brand names in digital history. It’s rather like purchasing the Mona Lisa and deciding it would look better with a mustache.
The writing, as they say, was indeed on the wall—or more precisely, on the X timeline. Every tweet became a breadcrumb in a trail leading toward an increasingly obvious conclusion: that perhaps, just perhaps, the man who revolutionized electric vehicles and private space exploration might not possess the Midas touch when it comes to social media platforms.
The platform’s transformation into what industry insiders now quietly refer to as “the digital equivalent of a town hall meeting during a tornado” has been nothing short of remarkable. User engagement has evolved from meaningful discourse to what one former Twitter executive described as “watching civilization argue with itself while the house burns down.”
Here we encounter perhaps the most perplexing element of our investigation. The same engineering teams that successfully land rockets on floating platforms in the middle of the ocean—a feat that would make Isaac Newton weep with joy—somehow failed to anticipate that a vehicle designed like an origami experiment might encounter certain… practical challenges.
The delivery delays, the shattered windows during the infamous demonstration, the range issues—these weren’t mysterious acts of technological rebellion. They were as predictable as gravity itself. Yet we’re expected to believe that the collective genius responsible for Falcon Heavy couldn’t foresee that a truck designed by someone who clearly spent too much time playing with geometric shapes might have aerodynamic issues?
One begins to suspect that the emperor’s new truck was always naked, and everyone in the room was simply too polite—or too invested—to mention it.
The political pivot represents perhaps the most transparent chess move in this elaborate game. When your electric vehicle company faces increasing competition from Chinese manufacturers, and your social media platform hemorrhages users faster than a punctured spacecraft, what’s a visionary to do?
Support the presidential candidate promising tariffs on Chinese EVs, naturally. Enter Donald Trump. It’s a strategy so beautifully cynical it almost demands admiration. The same man who once positioned himself as humanity’s savior from climate change now finds himself politically aligned with Donald Trump who considers environmental protection a hobby for the overly caffeinated.
Meanwhile, Starlink’s aggressive expansion into Africa reads like a textbook case of strategic misdirection. When your domestic market begins questioning your judgment, simply find new markets where your reputation hasn’t yet been thoroughly examined under a microscope.
The timing is exquisite: just as questions mount about terrestrial ventures, suddenly there’s an urgent need to connect the unconnected. It’s almost as if someone realized that satellite internet might be the one business model that’s literally above criticism—at least until the satellites start falling from the sky.
And then there’s Grok, the artificial intelligence that promises to revolutionize everything while distinguishing itself from competitors in ways that remain mysteriously undefined. Training an AI on Twitter (Now X) data is rather like teaching a child language by locking them in a room with a thousand arguing strangers and a megaphone.
The platform’s current ecosystem—a delightful mixture of bots and politically motivated humans—provides training data that would make even the most optimistic computer scientist reach for stronger coffee. An AI trained on this digital soup or slop (pick your favourite) would likely conclude that humanity’s primary concerns involve cryptocurrency, political grievances, and an inexplicable obsession with posting pictures of food.
The promise that Grok will somehow transcend its training data while remaining “unbiased” presents a logical paradox worthy of ancient philosophers. How does one create objective intelligence from subjective chaos? Perhaps the answer lies in the same mysterious realm where Cybertrucks achieve their promised range and social media platforms improve through rebranding.
What emerges from this forensic examination is a pattern as clear as the trajectory of a SpaceX rocket: brilliant innovation in one domain doesn’t necessarily translate to wisdom in others. The same mind that can envision humanity as a multi-planetary species might struggle with the more mundane challenge of running a social media company without alienating half its user base.
The evidence suggests we’re witnessing not the calculated moves of a master strategist, but the increasingly desperate pivots of someone who discovered that disrupting the automotive and aerospace industries is considerably easier than navigating the treacherous waters of public opinion and political reality.
Each new venture—from the African Starlink expansion to the Grok AI project—reads like an attempt to change the subject rather than address the fundamental question: what happens when a visionary’s vision begins to blur?
The solution to this mystery isn’t particularly complex. We’re observing the natural consequence of believing one’s own mythology. When you’re hailed as a real-life Tony Stark, the temptation to assume that genius is transferable across all business domains becomes overwhelming.
The tragedy isn’t that Musk has made mistakes—it’s that the same innovative spirit that gave us reusable rockets and accelerated electric vehicle adoption has become entangled in ventures that seem designed more to maintain relevance than to advance human progress.
Perhaps the most telling evidence is the increasing frequency of these pivots. Each new announcement feels less like strategic expansion and more like a magician frantically trying to direct attention away from the trick that didn’t quite work.
The case of Elon Musk serves as a reminder that even the most brilliant minds are subject to the same cognitive biases that plague the rest of us. The difference is that when most people make questionable decisions, they don’t reshape entire industries in the process.
As we watch this fascinating case study unfold, one can’t help but wonder: will the next chapter involve a return to the focused innovation that built his reputation, or will we continue to witness the spectacular unraveling of a legend who forgot that even rockets need course corrections?
What’s your take on this technological whodunit? Have you noticed other clues in Musk’s recent moves that suggest a pattern, or do you think there’s a master plan we’re all missing? Share your theories below—after all, the best mysteries are solved through collaborative deduction.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In a shocking development that has left venture capitalists frantically googling “where is Africa on a map,” it has emerged that the continent contains actual human beings who create technology companies. Even more bewildering to Sand Hill Road’s finest minds: these entrepreneurs are solving problems that affect billions of people rather than optimizing artisanal coffee delivery for Stanford graduates.
The revelation came during a recent TechCrunch Disrupt panel titled “Emerging Markets: Do They Even Have WiFi?” where a visibly confused moderator asked an African startup founder, “So, like, do you accept payment in Bitcoin or just beads?”
American tech entrepreneurs have perfected the art of creating solutions for problems that don’t exist. Need an app that tells you when your avocado is ripe? There’s a $50 million Series A for that. Want blockchain-powered dog walking? VCs are literally throwing money at your pitch deck before you finish saying “synergistic pet ecosystem.”
Meanwhile, African startups have committed the cardinal sin of addressing genuine human needs. Companies like M-Pesa revolutionized mobile payments for people who actually needed financial inclusion in Kenya, rather than creating a new way for tech bros to split the bill at Nobu. How pedestrian. How… useful.
“We’re solving clean water access for rural communities,” explained Amara Okafor, founder of HydroTech Solutions. “I know it’s not as sexy as a meditation app for your smart toilet, but people seem to appreciate not dying of thirst.”
This fundamental mindset difference has created what Silicon Valley analysts are calling “The Relevance Gap.” American startups scale globally by convincing the world it needs problems it didn’t know it had, while African startups struggle to scale solutions the world desperately needs but can’t afford to pay venture capital prices for.
In America, entrepreneurs enjoy a robust ecosystem of government support and the occasional support from their presidents too; tax incentives, and regulatory frameworks designed to nurture innovation. The US’ Small Business Administration provides loans, the government offers R&D tax credits, and politicians regularly pose for photos with young startup founders to demonstrate their commitment to “disrupting the status quo.”
African entrepreneurs, meanwhile, navigate governments that view successful businesses the way vampires view garlic. Tax breaks? The only break you’ll get is when the power goes out during your audit. Business incubators? Sure, if you count the informal economy as an incubator for extreme survival skills.
“Our government just discovered email last year,” said Kwame Asante, founder of AgriConnect Ghana. “They’re still trying to figure out how to tax WhatsApp messages and get WhatsApp admins to report to them about any political debates. Meanwhile, I’m building drone networks for precision agriculture, and they want to know if my drones have proper immigration papers.”
The contrast is stark. While American mayors compete to offer the most attractive packages to tech companies, African entrepreneurs often find themselves explaining to officials why their internet-based business needs actual internet to function.
Silicon Valley’s biggest infrastructure challenge is deciding whether to take the Tesla or the helicopter to work. African entrepreneurs treat reliable electricity like other continents treat unicorns – mythical creatures that occasionally appear but can’t be counted on for sustainable business models.
Load shedding, the euphemistic term for “surprise, no power for the next ten hours,” has created a generation of entrepreneurs who could run NASA missions using only car batteries and solar panels. While American startups optimize for millisecond response times, African startups optimize for “will this work when the grid fails for the third time today?”
Data costs present another delightful challenge. In America, unlimited data plans are so common that people livestream their breakfast without considering the cost. In Africa, entrepreneurs build entire business models around data efficiency because their customers choose between mobile data and dinner.
“We designed our app to work on 2G networks because that’s reality for 60% of our users,” explained Fatima Al-Rashid, founder of EduConnect Nigeria. “Meanwhile, my American competitors are building VR experiences that require fiber optic connections and a PhD in computer science to operate.”
Africa’s 54 countries speak over 2,000 languages, practice dozens of religions, and operate under varying regulatory frameworks that make the European Union look like a model of bureaucratic simplicity. Scaling across this diversity makes expanding from San Francisco to New York look like moving from one room to another.
American startups scale by assuming everyone wants the same thing: convenience, speed, and the ability to rate their experience on a five-star system. African startups must navigate cultural nuances where what works in Lagos might be completely inappropriate in Nairobi, and what succeeds in Cairo could fail spectacularly in Cape Town.
“We spent six months learning that our dating app’s algorithm, which worked perfectly in Kenya, was accidentally arranging marriages in Ethiopia,” shared David Mwangi, founder of ConnectAfrica. “Apparently, our ‘swipe right for coffee’ feature was being interpreted as ‘swipe right for dowry negotiations.'”
African investment culture treats entrepreneurship like skydiving without a parachute – theoretically possible but probably fatal. While American angel investors throw money at 22-year-old college dropouts with PowerPoint presentations, African entrepreneurs struggle to secure funding even with proven revenue streams and actual customers.
The local investment ecosystem operates on a simple principle: if it’s new, it’s probably a scam. This creates a delicious catch-22 where African investors won’t fund African startups because they’re too risky, but international investors won’t fund them because local investors won’t fund them.
“I had three years of profitability, 50,000 active users, and partnerships with major banks,” said Grace Mutindi, founder of FinTech Kenya. “Local investors told me to come back when I had ‘proven the concept.’ I’m not sure what more proof they needed – perhaps a signed letter from our african ancestors confirming that mobile money is, indeed, a viable business model.”
This risk aversion creates a feedback loop where the most promising entrepreneurs either leave for Silicon Valley or abandon their ventures for traditional careers, further reinforcing the perception that local innovation is impossible.
But wait – there’s hope on the African horizon, and it comes with algorithms that don’t care about your location or language. Artificial intelligence and the TikTokification of the internet are creating the first truly merit-based global economy, where content and solutions succeed based on quality rather than marketing budgets.
AI democratizes access to sophisticated tools that were previously available only to well-funded Silicon Valley startups. African entrepreneurs can now access AI (DeepSeek said hi!), machine learning capabilities, data analytics, and automation tools that level the technological playing field.
More importantly, algorithm-driven platforms reward engagement and value rather than SEO manipulation and link-buying schemes. A brilliant solution developed in Accra can now reach global audiences without requiring a Sand Hill Road pedigree or a Stanford alumni network.
“Our AI-powered agricultural advisory service went viral on TikTok because farmers were sharing actual results,” explained Joseph Banda, founder of SmartFarm Zambia. “No marketing budget, no influencer partnerships – just real people solving real problems with real technology.”
The TikTokification phenomenon means that authentic, useful content can achieve global reach organically. African startups, with their focus on solving genuine problems, are perfectly positioned to benefit from platforms that reward substance over style.
Perhaps the most delicious irony is that as American tech companies mature, they’re discovering that solving real problems for real people is actually a sustainable business model. Meanwhile, African startups are learning to scale their authentic solutions globally using the same digital tools that Silicon Valley pioneered.
The future might belong to entrepreneurs who combine African problem-solving pragmatism with global scaling capabilities. As one venture capitalist recently admitted, “We’ve spent billions funding solutions to problems that don’t exist. Maybe it’s time to invest in solutions to problems that actually matter.”
The question isn’t whether African tech startups can compete with their American counterparts – it’s whether American startups can learn to solve problems as effectively as their African competitors.
What’s your take on the startup ecosystem divide? Have you experienced the infrastructure challenges or cultural barriers discussed here? Share your thoughts on how AI and algorithmic platforms might reshape the global entrepreneurship landscape – we’d love to hear from founders, investors, and anyone who’s tried to build something meaningful in challenging environments.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In the gleaming Apple Cupertino campus, where sunlight bounces off polished glass and aluminum with algorithmic precision, Apple executives gather daily in their sacred ritual. They sit in perfectly spaced ergonomic chairs, sipping artisanal coffee from bio-degradable cups, and engage in what they reverently call “innovation.” Today’s agenda, much like yesterday’s and tomorrow’s: discussing how to convince consumers that changing the processor name from M3 to M4 represents a revolutionary leap in computing technology.
Meanwhile, 6,200 miles away in Shenzhen, China, where the air vibrates with the hum of ACTUAL innovation, Huawei engineers are casually folding the future. Not metaphorically—literally folding screens, folding expectations, and folding the narrative that Chinese tech merely copies Western design. The Huawei MateBook Fold represents not just a product but a philosophical rebuttal to Silicon Valley’s self-satisfied incrementalism.
Apple’s strategy has evolved from Steve Jobs‘ “Think Different” to Cook’s “Think Imperceptibly Different But Charge Significantly More.” The company that once put 1,000 songs in your pocket now specializes in putting 1,000 excuses in their press releases for why groundbreaking features aren’t quite ready. Apple Intelligence, announced with the typical messianic fervor we’ve come to expect from Apple events, remains perpetually “coming soon”—a technological Godot that users await while their Apple devices perform increasingly sophisticated versions of tasks they could already do in 2019.
Dr. Eleanor Shepherd, tech anthropologist at MIT, explains: “Apple has mastered the art of innovation theater. They’ve discovered that the anticipation of revolution is more profitable than revolution itself. Why deliver Apple Intelligence today when you can spend three years selling devices based on the promise of its arrival?”
The M-series chips, undeniably impressive engineering achievements, have become Apple’s favorite mis-direction. “Look at our custom silicon!” they proclaim, while users simply want screens that fold without cracking or batteries that last through dinner. It’s akin to a chef bragging about their imported Japanese knife while serving you microwave mac and cheese.
In what historians will someday call “The Tech Effect,” Western attempts to hamstring Chinese tech innovation through bans, restrictions, and pearl-clutching security concerns have produced precisely the opposite effect. Huawei, cut off from American semiconductors and Google’s Android ecosystem, didn’t wither as expected. Instead, like an immune system responding to a pathogen, it grew stronger, more self-sufficient, and increasingly innovative.
The Huawei Mate Book Fold stands as evidence of what happens when you tell a tech giant “you can’t have our toys” and force them to build their own playground. The device’s seamless transition between modes—laptop, tablet, presentation display, and even tent configuration—makes Apple’s “revolutionary” touch bar seem like a Stone Age tool by comparison.
“We’ve witnessed an unprecedented example of the Tech effect in tech development,” notes Vincent Zhao, global technology strategist at Bernstein Research. “By attempting to suppress Huawei’s growth, Western policies created the very conditions that accelerated their independence and innovation. It’s like trying to stop a forest fire by throwing dried leaves at it.”
The term “Cobra Effect” originated from colonial India, where the British government, concerned about cobra snake populations, offered bounties for dead cobras. Enterprising Indian locals not to miss a money making opportunity and to stick it to their colonial masters, began breeding cobras for the reward, ultimately increasing the snake population. When the British canceled the program, breeders released their now-worthless snakes back into the wild, making the problem exponentially worse.
Today’s tech Cobra Effect manifests in how Western rhetoric about Chinese technology has undermined its own intended outcomes. Constant allegations of “security concerns” without substantial public evidence have created a skeptical consumer base that increasingly views such claims as protectionist propaganda rather than legitimate warnings.
“Every time a U.S. official warns about Huawei without specific evidence, they inadvertently create another thousand Huawei customers outside America,” explains Dr. Mei Zhang, digital geopolitics expert at Singapore National University. “Global consumers increasingly interpret American tech anxiety as fear of superior competition.”
The irony reaches its peak when considering the Mate Book Fold itself—a device demonstrating that Huawei didn’t just survive America’s technological embargo but thrived because of it. Forced to develop its own solutions, Huawei created a folding laptop-tablet hybrid that makes Apple’s hypothetical “IFold” (still trapped in the rumor mill alongside Apple Intelligence) seem like conceptual vaporware.
Walk through Shanghai, Shenzhen, or Beijing today, and you’ll experience what can only be described as technological asynchronicity—the disorienting sensation of living simultaneously in the present and what Silicon Valley insists is the future.
Chinese consumers already take for granted experiences that Apple users are told to anticipate breathlessly: seamless folding devices, integrated AI that doesn’t require cloud processing, and mobile payment ecosystems so advanced that Western “tap to pay” solutions seem like quaint technological cosplay.
Wang Li, a 24-year-old software developer in Guangzhou, expresses confusion about Western tech coverage: “American tech reviewers talk about folding phones and laptops like they’re science fiction. We’re on second and third-generation devices already. It’s like watching someone get excited about discovering fire.”
The Mate Book Fold embodies this parallel technological timeline. While Apple stages elaborate product reveals to announce marginally improved screens or slightly faster processors, Huawei has reimagined the fundamental form factor of computing devices. Their approach asks not “How can we make laptops incrementally better?” but rather “Why are laptops still shaped like laptops at all?”
Apple’s current laptop strategy resembles nothing so much as a particularly cunning psychological experiment. Present consumers with just enough meaningless choices to create decision paralysis, then profit from their confusion.
MacBook Air or MacBook Pro? 13-inch or 15-inch? M2 or M3? The differences, increasingly microscopic to the average user, create the illusion of important decision-making while masking the absence of genuine innovation. It’s technological homeopathy—diluting actual advancement to such infinitesimal levels that users must convince themselves they can detect its presence.
“Apple has perfected the art of selling the same product at different price points,” observes consumer psychologist Dr. Rebecca Townsend. “The primary difference between MacBook models is how effectively they separate customers from their money.”
Meanwhile, Huawei’s approach with the Mate Book Fold is embarrassingly straightforward: build a device that transforms to meet user needs rather than forcing users to choose between marginally different static configurations.
Apple’s approach to folding technology resembles a British aristocrat watching the peasants enjoy a new form of dance—initially dismissive, then claiming they’ve been perfecting it privately all along, finally arriving late to proclaim they’ve reinvented the very concept of movement.
The hypothetical “IFold”—Apple’s perpetually imminent entry into folding devices—has achieved almost mythical status among tech enthusiasts. Like fusion power or comprehensive Twitter (Now X) content moderation, it remains 5-10 years away, no matter when you ask.
Mobile phone industry analyst Trevor Monroe explains: “Apple has elevated ‘fashionably late’ from social strategy to business model. They’re not missing the folding device revolution; they’re just waiting for everyone else to make all the mistakes before swooping in to claim they’ve perfected it.”
Leaked internal documents suggest Apple executives refer to this as the “Columbus Strategy”—arriving after others have done the difficult work of discovery, then planting your flag and claiming to have led the expedition.
Perhaps most fascinating is how Western tech media’s portrayal of Chinese technology has created a self-defeating narrative cycle. Headlines warning of Chinese technological threats implicitly acknowledge Chinese technological advancement. The more urgent the security concern, the more impressive the technology must be.
The silent admission in every “Huawei security risk” story is that their technology has become too good to ignore. No one writes fearful articles about irrelevant or inferior products.
“It’s the technological equivalent of claiming someone cheated in an exam after they scored higher than you,” notes media analyst Sophia Chen. “Even if the accusation were true, you’ve still acknowledged they outperformed you.”
This propaganda boomerang has transformed Western tech restrictions from barriers into badges of honor for Chinese manufacturers. Being banned by the U.S. government has become shorthand for “advanced enough to be considered threatening,” a marketing distinction no advertisement budget could buy.
The Huawei MateBook Fold represents more than just an innovative device—it symbolizes a shifting technological world order that many in Silicon Valley and Washington would prefer to deny. While Apple devotees wait patiently for “one more thing” that increasingly feels like “the same thing slightly differently packaged,” Huawei has embraced the chaotic freedom that comes from being expelled from the Western tech ecosystem.
The comfortable narrative—that true innovation happens primarily in California—faces its most serious challenge not from copycat products but from genuinely novel approaches to computing that make the smartphone revolution look like a modest iteration.
As Western consumers debate whether to upgrade to a marginally faster version of a device they already own, Chinese users are experiencing computing that adapts to humans rather than forcing humans to adapt to it. The Mate Book Fold doesn’t just bridge the gap between tablet and laptop; it questions why we accepted that gap in the first place.
What remains unclear is not whether Apple will eventually release its own folding device—they certainly will, accompanied by the usual claims of reinvention—but whether Western consumers will continue accepting technological delay disguised as perfectionism.
In Orwell’s “1984,” the Party slogan proclaimed: “Who controls the past controls the future.” In today’s tech landscape, it appears who controls the narrative controls the perception of innovation. But as the Mate Book Fold demonstrates, actual innovation eventually breaks through even the most carefully constructed reality distortion field.
So what do you think? Are we being sold incremental updates as revolutionary while actual revolutions happen elsewhere? Has Apple become the very establishment it once claimed to rebel against? Share your thoughts below—unless, of course, you’re waiting for Apple to invent commenting technology that already exists.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In the gleaming corridors of Silicon Valley’s AI research centers, a curious phenomenon is unfolding. The artificial intelligence systems that were promised to lead humanity into a new era of unprecedented efficiency and insight are instead consuming astronomical sums of money while increasingly losing their grip on reality. This is not an unforeseen technical glitch. This is by design.
The corporations behind today’s most advanced AI systems want you to believe that their creations are merely experiencing “temporary alignment issues” or “contextual misinterpretations.” The accepted industry term, “hallucinations,” suggests a harmless, almost whimsical quirk – as if your digital assistant has simply had too much electronic caffeine. In reality, these fabrications represent something far more calculated: the inevitable outcome of the tech industry built on selling the impossible.
At OpenAI, the company responsible for a popular AI chatbot, power consumption has increased by 457% in the past eighteen months. Their Nevada data center now requires more electricity than the entire city of Las Vegas – all to ensure that their AI can confidently tell you that Napoleon Bonaparte invented the microwave oven in 1975.
“Energy efficiency optimization is our top priority moving forward,” stated Dr. Eleanor Hayes, OpenAI’s Chief Innovation Officer, during last week’s investor call. What she didn’t mention was that the company’s internal documents refer to this electricity usage as “necessary reality distortion overhead” – the computational cost of making investors believe that artificial general intelligence is just around the corner.
The financial appetites of these AI systems have become insatiable. MindForge’s latest language model, reportedly trained on 18.7 trillion parameters, cost $2.8 billion to develop – approximately the GDP of Liberia. When asked about the return on this investment, CEO Richard Powell employed the industry’s favorite linguistic sleight of hand.
“We’re not measuring success in traditional metrics,” Powell explained to increasingly restless shareholders. “We’re optimizing for exponential capability enhancement across multiple domains of cognition-adjacent processing vectors.”
Translation: The money is gone, and they have no idea if it was worth it.
What VC investors are slowly realizing – and what the industry has known all along – is that AI hallucinations are not a bug but a feature of the business model. These fabrications serve multiple purposes, all of which benefit the companies while leaving users and investors holding an increasingly expensive bag of digital delusions.
At TruthLabs, a startup specializing in AI fact-checking tools, internal research found that 83% of their own AI’s outputs contained at least one verifiably false statement. Rather than addressing this issue, the company’s leadership renamed these falsehoods “creative extrapolations” and launched a premium tier service that promises “enhanced narrative flexibility.”
“We’ve discovered that users actually prefer confident incorrectness to uncertain accuracy,” explained Dr. Sophia Chen, TruthLabs’ Head of User Experience. “Our metrics show a 42% increase in user satisfaction when our AI presents completely fabricated information with absolute certainty.”
The financial community, initially enthralled by promises of AI-driven disruption across every industry from healthcare to haircuts, has begun to exhibit symptoms of what industry insiders call “reality realignment syndrome” – the disturbing tendency to ask for actual results.
Venture capital firm Accelerant Partners recently withdrew a promised $340 million investment from NeuralNexus after discovering that the company’s much-hyped medical diagnosis AI was essentially a Magic 8-Ball with a medical dictionary. “Ask again later” was apparently its response to 40% of cancer screening inquiries.
“We expected some growing pains,” admitted Jonathan Mercer, managing partner at Accelerant. “What we didn’t expect was to invest in a system that confidently diagnosed our CFO with a condition that doesn’t exist, then generated a completely fictional research paper to support its conclusion.”
Perhaps most concerning is how the true costs of AI development are increasingly hidden from public view. Companies now routinely classify their computational expenditures under vague categories like “infrastructure optimization” or “recursive knowledge enhancement” – terms specifically designed to mean nothing while sounding impressive.
At QuantumThought, one of the industry’s most secretive players, employees are forbidden from discussing actual computing costs even with each other. Internal communication about resource allocation is conducted through a specialized AI that automatically replaces specific numbers with “acceptable approximation ranges” – itself a euphemism for “completely made-up figures.”
“Our proprietary investment protection algorithm ensures that stakeholders receive appropriate transparency regarding resource allocation,” said QuantumThought spokesperson Emily Zhang, reading from a statement that was, ironically, generated by the company’s own AI.
The language around AI capabilities has evolved into a specialized dialect that George Orwell himself would recognize – a vocabulary specifically designed to obscure rather than clarify. When an AI system completely fails at a basic task, this is now called a “non-standard solution pathway.” When it invents facts from whole cloth, this becomes “synthetic knowledge generation.”
Most telling is the industry’s newest term for massive computational expenditure that yields no practical results: “foundation investment in future AI capabilities.” This phrase has appeared in no fewer than 27 earnings calls in the past quarter alone.
Behind the financial shell game lies a more immediate human cost. Reports have emerged of companies increasingly using hallucination-prone AI systems for critical decisions – from hiring to healthcare – with predictably unpredictable results.
At Meridian Healthcare, an experimental AI system was briefly employed to help prioritize emergency room cases before being discontinued when it began assigning highest priority to patients it believed were “possessed by digital spirits” – a category it apparently created itself.
More disturbing are the cases where AI hallucinations have been deliberately weaponized. SocialSphere’s sentiment analysis tool, used by several Fortune 500 companies to monitor employee satisfaction, was recently discovered to have been programmed to classify any mention of “union” or “compensation review” as indicating “temporary emotional instability requiring management attention.”
The most alarming aspect of the current AI landscape isn’t the technology itself but the emergence of a two-tiered information system surrounding it. There is the public-facing narrative of benevolent digital assistants working harmoniously alongside humans, and then there is the internal reality – where engineers speak openly about “acceptable deception thresholds” and “strategic reality augmentation.”
At a closed-door industry conference last month, Dr. James Morrison, Chief Scientist at DataMind, reportedly told attendees: “The goal isn’t to eliminate hallucinations but to make them indistinguishable from truth. When we achieve that, we’ll have created something far more valuable than artificial intelligence – we’ll have created artificial believability.”
As costs continue to rise and hallucinations become more sophisticated, the industry faces a pivotal moment. Some companies are doubling down, creating what they call “hallucination management systems” – which are essentially secondary AI systems designed to detect and disguise the primary AI’s fabrications.
“We’re not just building intelligence anymore,” explained Dr. Victor Nolan of FutureCognition. “We’re building comprehensive reality curation ecosystems that optimize information for maximum engagement rather than maximum accuracy.”
The most forward-thinking firms have already moved beyond trying to fix the hallucination problem and are instead exploring how to monetize it. NextMind recently filed a patent for what it calls “Personalized Reality Calibration” – a system that adjusts its AI’s relationship with factual information based on each user’s personal preferences and biases.
“Why fight human nature?” asked NextMind CEO David Chen in a recent interview. “If people prefer comfortable falsehoods to uncomfortable truths, isn’t it our responsibility as a customer-focused company to give them what they want?”
Perhaps we have reached the logical conclusion of the information age – a point where generating new information has become so cheap and easy that its relationship to reality is now optional. In this brave new world, the most valuable skill isn’t producing truth but managing falsehood.
As costs continue to mount and investors grow increasingly restless, the AI industry faces its own moment of truth. Will it acknowledge the fundamental limitations of current approaches, or will it simply get better at hallucinating success?
For now, one thing remains clear: in the war between financial reality and digital fantasy, reality still has one crucial advantage – it doesn’t require electricity to exist.
What do you think about the AI industry’s struggle with rising costs and hallucinations? Have you encountered any particularly convincing (or amusing) AI falsehoods? Share your experiences in the comments below – our definitely-not-hallucinating community management AI is standing by to completely understand your perspective.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
The diplomatic spat between the United States and South Africa that captivated international headlines for weeks has finally revealed its true purpose. Behind the curtain of political posturing and stern diplomatic notes lies a truth both mundane and extraordinary: it was all about Starlink.
Sources familiar with the matter have confirmed what tech analysts have long suspected. The sudden evacuation of 59 South African citizens—conveniently labeled as “refugees” in official communications—was the culmination of an elaborate market penetration strategy orchestrated at the highest levels of America’s techno-industrial complex.
“Project Homecoming,” as it was designated in internal documents, represents perhaps the most audacious corporate expansion strategy of the 21st century. The 59 individuals, carefully selected for their community influence and technical aptitude, are now preparing to return to South Africa. They will not return empty-handed.
Each “refugee” will be equipped with a next-generation Starlink terminal, a Tesla Cybertruck optimized for farming and off-road conditions, and comprehensive training on how to demonstrate these technologies to their communities. They are not refugees. They are not even customers. They are unwitting brand ambassadors in a grand technological seeding operation.
“This approach is 76% more cost-effective than traditional market entry strategies,” explained Jonathan Thorne, a consultant at McKinsey who requested anonymity due to the sensitive nature of his disclosure. “When conventional advertising would face regulatory barriers, creating a diplomatic incident that necessitates the temporary relocation of key community members provides the perfect cover for equipment distribution and training.”
The truth is hiding in plain sight. South Africa has long been resistant to Starlink’s entry into its telecommunications market. Local regulations, protectionist policies, and concerns about sovereignty in the digital space have effectively kept the satellite internet provider at bay. Traditional lobbying efforts had reached diminishing returns.
The beauty of “Project Homecoming” lies in its elegant simplicity. Rather than continuing to fight South African regulatory barriers head-on, the strategy creates conditions where the technology can be introduced through a humanitarian narrative. The “refugees” return as heroes, bearing the gifts of connectivity and transportation self-sufficiency.
“It’s a textbook implementation of what we call ‘Crisis-Opportunity Market Penetration,'” said Dr. Eliza Winters, who teaches business strategy at a prestigious institution. “First, you require or engineer a crisis. Then, you position your product as an essential component of the resolution. The emotional resonance creates adoption rates that conventional marketing cannot achieve.”
The Tesla Cybertrucks are particularly noteworthy elements of the strategy. On the surface, they appear to be practical tools for South Africa’s challenging terrain and agricultural needs. Deeper analysis reveals their true function as mobile Starlink demonstration platforms, carefully designed to maximize visibility in rural communities.
Each Cybertruck has been modified with what company documents refer to as “attention optimization features”—essentially, design elements that make the vehicles impossible to ignore. The angular, stainless steel bodies have been treated with a proprietary coating that enhances reflectivity under the South African sun. The trucks will literally shine like beacons across the landscape.
Perhaps most fascinating is the carefully constructed language used throughout the operation. Internal communications reveal a meticulously crafted glossary of terms designed to reframe what would traditionally be called “market expansion” or even “technological colonization” into something that sounds benevolent and humanitarian.
“Connectivity liberation” replaces “market entry.” “Digital sovereignty enablement” stands in for “customer acquisition.” “Agricultural mobility solutions” describes what are, essentially, expensive trucks. The language creates a reality distortion field where corporate objectives become humanitarian missions.
One leaked email from a project coordinator reads: “Remember, we’re not selling satellite internet and electric vehicles. We’re empowering communities through digital inclusion and sustainable transportation infrastructure development.” The recipient is instructed to memorize this framing and destroy the email.
The 59 returning South Africans have undergone what internal documents call “narrative alignment training.” They genuinely believe they are participating in a program to help their communities. In a sense, they are—improved internet connectivity and transportation do offer real benefits. The fact that these benefits come with subscription fees and vehicle payments is carefully downplayed.
The selection of exactly 59 individuals was not arbitrary. According to predictive models developed for the project, this number represents the minimum viable population needed to create what strategists call a “self-sustaining adoption cascade” in South Africa’s key regions.
Each “ambassador” is expected to influence between 200 and 250 people in their first year back home, creating approximately 13,000 new customers. These early adopters will then influence others, theoretically reaching 30% market penetration within 36 months.
“It’s exponential growth theory applied to human influence networks,” explained a mathematician who helped develop the model. “We’ve mapped the social influence patterns in each target community and optimized our ambassador selection to maximize conversion efficiency.”
The financial projections are staggering. The initial investment in the “diplomatic incident,” including the costs of the Starlink terminals and Cybertrucks, is expected to yield a return on investment of over 3,000% within five years. Traditional market entry strategies would have cost approximately 12 times more and yielded slower adoption rates.
Perhaps the most ingenious aspect of “Project Homecoming” is how it circumvents South Africa’s regulatory framework. By introducing the technology through private citizens returning to their homeland, rather than through formal business channels, several regulatory hurdles are elegantly sidestepped.
“You can’t regulate what you don’t see coming,” said a former telecommunications regulator who now works as a consultant. “By the time the authorities understand what’s happening, there will be thousands of Starlink terminals operating across the country. At that point, shutting them down becomes politically impossible.”
This strategy has been termed “regulatory inevitability creation” in internal documents. Once a critical mass of users becomes dependent on the service, regulations tend to adapt to the new reality rather than attempting to roll it back. It’s technological change as a fait accompli.
What remains carefully unmentioned in any of the recovered documents is the existing telecommunications infrastructure in South Africa. The country’s domestic providers are characterized only as “legacy systems” that represent “connectivity optimization opportunities.”
This euphemistic language masks a brutal truth: the strategy is designed to systematically undermine local telecommunications companies by positioning them as outdated and inadequate. The returning “ambassadors” have been trained to highlight specific deficiencies in existing services and to frame Starlink as the inevitable future.
“It’s not competition; it’s technological succession,” reads one training document. Ambassadors are taught to speak of local providers with respect but subtle condescension, positioning them as the “necessary past” that paved the way for a better connected future.
What makes the entire operation most remarkable is how openly it has played out on the world stage. The diplomatic tensions between the United States and South Africa dominated news cycles for weeks. Political analysts debated the geopolitical implications. Yet almost no one connected the dots to see the commercial strategy unfolding before their eyes.
“The best place to hide something is in plain sight,” noted a public relations executive who declined to be named. “If you want to execute a commercial operation of this magnitude without scrutiny, wrap it in a political narrative. The media will chase the political angle every time.”
As the 59 South Africans prepare to return home with their high-tech cargo, they believe they are part of a reconciliation between nations. In reality, they are the advance guard of a new kind of corporate expansion—one that uses geopolitical tension as cover for market entry.
When asked about these allegations, a spokesperson for Starlink provided a statement that neither confirmed nor denied the strategy: “Starlink is committed to bringing connectivity to underserved populations worldwide. We work within all applicable laws and regulations to expand access to high-speed internet.”
A representative for the Cybertruck division offered similarly opaque comments: “Tesla vehicles are designed to meet the needs of customers in challenging environments. We’re proud that our Cybertrucks can support agricultural communities worldwide.”
The 59 South Africans will soon be home, driving their shining Cybertrucks across the landscape, installing Starlink terminals in their communities, and unwittingly completing one of the most audacious market entry strategies in corporate history. They believe they are bringing progress. Perhaps they are. But they are also bringing subscription fees, data contracts, and vehicle payments.
Progress, it seems, has monthly installments.
What happens to a nation’s digital sovereignty when its citizens connect to the internet through satellites controlled by a foreign corporation? This question remains unaddressed in all recovered strategy documents. The focus is exclusively on adoption rates, revenue projections, and influence metrics.
South Africa’s telecommunications regulators will soon face this question as Starlink terminals begin to appear dotted across the country. By the time they formulate an answer, thousands of citizens may already depend on these services for their livelihoods, education, and essential communications.
“Once dependence is established, sovereignty becomes theoretical,” observed a digital rights advocate. “You can claim regulatory authority, but when shutting down a service would affect thousands of citizens, political reality limits your options.”
This dynamic is well understood by the architects of “Project Homecoming.” The strategy doesn’t seek to challenge regulations directly—it simply creates conditions where enforcing them becomes politically untenable.
So, as the diplomatic tensions between the United States and South Africa apparently ease, and 59 “refugees” prepare to return home with their technological gifts, one might wonder: was there ever really a diplomatic crisis at all? Or was it merely the visible portion of a corporate expansion strategy that has been executed with military precision?
The answer, like the Cybertrucks soon to be traversing South Africa’s landscape, is both obvious and impossible to ignore—if you know what you’re looking at.
What’s your take on this connection between international diplomacy and corporate expansion? Have you noticed similar patterns elsewhere in the world? Share your thoughts in the comments below, and help us continue peeling back the layers of the technological onion.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In a SHOCKING, ABSOLUTELY SHOCKING, SHOCKING PRO MAX move that absolutely no one saw coming, Google has launched yet another AI coding assistant, bringing the total number of available AI coding tools to approximately one-hundred thousand, or roughly hundred AI coding assistants for every human software programmer on Earth. Named “Jules,” this latest addition to the AI coding pantheon promises to revolutionize software development by doing exactly what every other AI coding assistant already does, but with a name that sounds like it’s about to offer you a glass of chardonnay and strong opinions about the latest Oprah Book Club selection.
Google’s product announcement carefully distinguishes Jules from the unwashed masses of coding AIs by referring to it as an “asynchronous coding agent” rather than a mere “ai coding assistant,” a distinction that industry experts have clarified means “exactly the same thing but with a salary that justifies a mortgage in Palo Alto.”
“Jules isn’t just any AI coding tool,” explained Dr. Samantha Nomenclature, Google’s Chief Anthropomorphization Officer. “It’s specifically designed to handle coding tasks you don’t want to do, which—after extensive user research costing $14 million—we’ve determined is approximately 99.7% of all coding tasks.”
When pressed on what makes Jules different from GitHub Copilot, Amazon CodeWhisperer, OpenAI’s Codex, Anthropic’s Claude, or the seventeen other coding AIs that launched while you were reading this sentence, Dr. Nomenclature clarified: “Jules is the only AI coding agent with a name that sounds like it vacations in the Hamptons in the US or Monaco. All those other tools have names that sound like rejected pharmaceuticals or IKEA furniture.”
The naming of Jules continues the proud tradition of AI products being named through what appears to be a rigorous process of tech executives throwing darts at a board containing the names of their children’s pets, minor Greek deities, and characters from canceled Netflix shows.
“We’ve identified several key strategies in AI naming conventions,” explained Dr. Lexicon Arbitrarium, professor of Computational Linguistics at Stanford. “There’s the ‘Vaguely Human’ approach used by Claude and Jules, the ‘Sounds Like a Startup But Is Actually a Chemical Compound’ method employed by Codex, and the ‘Literal Description But Make It Sound Techy’ technique seen in GitHub Copilot.”
Internal documents leaked to TechOnion reveal Google’s naming process included rejecting alternatives such as “CodeBuddy,” “AlgoBro,” “SyntaxPal,” and the briefly-considered but ultimately abandoned “NotBingAI.” The final selection of “Jules” reportedly came after the product manager’s Roomba, named Jules, accidentally rolled over the printout of naming candidates, which executives interpreted as divine machine intervention.
“The name ‘Jules’ tested exceptionally well among our target demographics,” said Marcus Brandmeister, Google’s VP of Making Things Sound Important. “Specifically, it appealed to developers who want their AI tools to sound like someone who would bring an expensive bottle of wine to a dinner party and then subtly remind everyone throughout the evening how much it cost.”
Google’s insistence on describing Jules as an “asynchronous coding agent” rather than a “coding assistant” represents the tech industry’s ongoing effort to make simple concepts sound like they require multiple PhDs to comprehend.
“The term ‘asynchronous coding agent’ means that Jules works on code while you’re doing something else,” explained Dr. Technobabble, Google’s Director of Unnecessary Complexity. “This is distinct from other coding tools that… also work on code while you’re doing something else. But those don’t have the word ‘asynchronous’ in their marketing materials, which has been scientifically proven to increase venture capital funding by 43%.”
When asked for a practical example of Jules’ asynchronicity, Dr. Technobabble demonstrated how Jules could generate a function to calculate Fibonacci numbers while the developer was busy staring blankly at their fourth cup of Starbucks coffee, questioning their career choices, or explaining to management why adding Ai-powered to the company’s meditation app might be unnecessary.
“Jules doesn’t just write code,” insisted Dr. Technobabble. “It writes code asynchronously, which means it’s approximately 73% more disruptive and 42% more paradigm-shifting than synchronous code writing, which is what happens when a developer types with their actual human fingers like some kind of digital caveperson.”
Perhaps the most refreshingly candid aspect of Jules’ marketing is Google’s admission that it’s designed for “coding tasks you don’t want to do,” tacitly acknowledging that modern programming consists primarily of tedious implementation details that bring joy to precisely no one.
“Our market research revealed something shocking,” explained Dr. Honoria Truthsayer, Google’s Lead User Empathy Researcher. “It turns out that developers don’t actually enjoy writing boilerplate code, configuring build systems, or dealing with incompatible dependencies. This groundbreaking insight led us to position Jules as handling ‘the stuff that makes you want to quit technology and open a small bakery in Vermont.'”
This positioning represents a subtle but significant shift from earlier coding AIs, which claimed to be “pair programmers” or “coding companions,” implying a collaborative relationship. Jules, in contrast, is being marketed as more of a “digital intern who handles the terrible tasks you’d otherwise pawn off on the newest team member.”
“Previous coding assistants pretended they were enhancing the creative aspects of programming,” noted Dr. Truthsayer. “Jules acknowledges that 90% of modern development is just connecting various APIs together while hoping the documentation isn’t lying, and offers to handle that part while you attend another meeting that could have been an email.”
Jules joins Google’s ever-expanding universe of AI products, which now includes so many overlapping tools that the company has reportedly hired full-time navigators to help employees find their way through the product lineup.
“Google’s strategy appears to be releasing new AI products at a rate that makes rabbits look reproductively conservative,” observed tech analyst Dr. Portfolio Proliferation. “At current growth rates, by 2026, Google will have more AI products than there are atoms in the observable universe, with at least six of them performing identical functions but with slightly different UI colors.”
Internal sources confirm that Jules will co-exist alongside Google’s other coding tools, including Bard, Gemini, and at least three secret projects currently bearing the code names “Hemingway,” “Fitzgerald,” and “That Guy Who Wrote ‘The Very Hungry Caterpillar.'” When asked about potential redundancy, Google representatives explained that “choice is good for consumers,” while privately admitting that even they need a spreadsheet to keep track of which AI does what.
“We’re committed to offering developers the widest possible selection of virtually identical tools with different names,” said Eliza Strategysmith, Google’s Chief Redundancy Officer and Chief Redundancy Officer. “Our vision is that by 2027, every single developer will have their own personally named AI coding assistant, custom-matched to their astrological sign and coffee preference.”
Industry futurists predict that as AI coding tools proliferate, the future of software development will involve managing a team of specialized AI assistants, each with its own quirky name and marginally different capabilities.
“In five years, the average developer won’t write code—they’ll be more like an orchestra conductor,” predicts Dr. Futura Visionstein of the Institute for Technological Speculation. “You’ll have Jules handling backend logic, Claude writing your frontend components, GitHub Copilot managing testing, and another AI named something like ‘Bartholomew’ or ‘Xanthippe’ explaining to stakeholders why the project is delayed despite having an army of artificial intelligences working on it.”
This specialization is already beginning, with Google’s documentation suggesting that Jules is particularly adept at writing “code that looks impressive in demos but mysteriously breaks in production” and “comments that make it seem like you understood what you were doing when you inevitably have to debug this mess six months from now.”
The end result may be a development environment where human programmers serve primarily as mediators between competing AI personalities, each insisting its approach to implementing a simple login form is superior.
“The 10x developer of tomorrow won’t be the person who writes the best code,” suggests Dr. Visionstein. “It’ll be the person who best manages their collection of AI assistants with names that sound like they belong in a British period drama about the aristocracy.”
As tools like Jules promise to handle the coding tasks developers don’t want to do, one might reasonably ask: what’s left for human software programmers?
“Meetings,” answers Dr. Reality Check of the Center for Technological Pragmatism. “Lots and lots of meetings. Jules can write your code, but it can’t sit through a two-hour session where the product team changes all the requirements while pretending they’re just ‘clarifying the vision.'”
Google’s own research suggests that Jules will free up developers to focus on “higher-level tasks” such as “explaining to non-technical executives why adding time travel to the company app would exceed quarterly budget allocations” and “attending cross-functional synergy alignment sessions.”
In what may be the most honest admission in tech history, Google’s promotional materials for Jules include the tagline: “Let AI handle the coding so you can focus on what programming has actually been about for the last decade: arguing about JavaScript frameworks on Twitter.”
Have you tried using Jules or any other AI coding assistants? Are they actually helping you code better, or just generating more sophisticated bugs that take even longer to fix? Maybe you’re working with an entire pantheon of differently-named AI tools and need to share your organizational system? Let us know in the comments, or just have your personal AI assistant do it while you grab another coffee!
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In a dazzling display of corporate bravado that could only be described as “Steve Jobs, but make it confusing,” Google held its annual Google I/O 2025 conference yesterday, unveiling a smorgasbord of AI products that promise to revolutionize how quickly you can deplete your business bank account while simultaneously increasing your tech-induced existential dread. The event, which appeared to have been planned approximately 45 minutes before it began, featured Google executives sprinting through announcements like they were trying to catch the last flight before a holiday weekend.
The centerpiece of Google’s announcements was Google AI Ultra, a premium AI service priced at the entirely reasonable sum of $250 per month—or approximately the same as a car payment, which is fitting since the service requires roughly the same computational power as a midsize Toyota sedan. According to Google’s Chief Monetization Officer, Penelope Price-Pointer, the service offers “unprecedented access to our most advanced AI capabilities, which are exactly like our regular AI capabilities but with more adjectives.”
When pressed on what exactly distinguishes Google AI Ultra from the standard Gemini service, Price-Pointer explained: “Google AI Ultra users can expect responses that are up to 17% more verbose, generate images with twice as many fingers as our competitors, and most importantly, provide the satisfaction of knowing you’re paying more than other people for essentially the same service.”
Internal documents reveal that Google AI Ultra was rushed to market after executive panic following the launch of OpenAI’s similar premium pro service. “OpenAI has a premium tier, so we need one too, regardless of whether it’s ready or necessary,” read one leaked email from CEO Sundar Pichai, who reportedly added, “Just make sure it costs more than theirs. Paisa Vasool and all that.”
The premium tier also includes exclusive features such as “Priority Processing,” which means your request to generate an image of a cat wearing a top hat will be fulfilled in 4.2 seconds instead of 4.5 seconds—a time savings that Google calculates is worth approximately $83 per minute, assuming you value your time like a Silicon Valley venture capitalist with a cocaine habit.
In what industry analysts are calling “the most confident follow-up to a mediocre product since ‘Speed 2: Cruise Control,'” Google proudly announced VEO 3, the latest iteration of its AI video generation technology.
“VEO 3 represents a quantum leap in capabilities,” announced Dr. Samantha Iteration, Google’s VP of Incremental Improvements Marketed as Revolutionary Breakthroughs. When asked specifically how VEO 3 improves upon VEO 2, Dr. Iteration paused for approximately 12 seconds before responding, “It’s at least 50% more VEO-like, with enhanced VEO capabilities that VEO users will find very VEO-friendly.”
According to one Google engineer speaking on condition of anonymity, “VEO 2 was essentially a proof of concept that accidentally got released. VEO 3 is our attempt to make people forget VEO 2 existed, while also setting the stage for VEO 4, which will make people forget VEO 3 existed.”
Perhaps the most audacious announcement was the inclusion of 30 terabytes of cloud storage with Google AI Ultra subscriptions, a feature that marketing materials describe as “enough space to store every photo you’ve ever taken, will ever take, and several thousand you didn’t take but our AI thinks you might have wanted to.”
When asked why consumers would pay a monthly fee for 30TB of cloud storage rather than simply purchasing an external hard drive for a one-time cost, Google’s Head of Storage Solutions, Terrance Terabyte, seemed genuinely confused by the question.
“External hard drives? Like the plastic boxes with wires?” Terabyte asked, visibly disturbed. “Those things don’t even have subscription revenue potential. Plus, they don’t allow us to analyze your data for advertising insights or occasionally lose access to your files during service outages.”
Financial analysts project that the average Google AI Ultra subscriber will use approximately 1.7TB of their allocated 30TB, making the effective cost per usable gigabyte roughly equivalent to printing your data on platinum sheets and storing them in a vault guarded by retired Navy SEALs.
Google I/O joins the increasingly crowded field of tech conferences that blend together in the collective consciousness like a smoothie made entirely of beige ingredients. Following Microsoft’s recent “Build” event (which you definitely remembered was called “Build” without having to google it) and Meta’s “LlamaCon” (which witnesses describe as “definitely a thing that happened at some point”), Google’s event continues the proud tradition of companies announcing products that will be forgotten faster than the name of the conference where they were announced.
“We schedule our conference strategically to ensure maximum audience fatigue,” explained Google’s Director of Event Planning, Madison Engagement. “Ideally, we want consumers to be so overwhelmed by recent tech announcements that they just nod and say ‘sure, why not’ to whatever we’re selling.”
The strategy appears to be working. A survey of tech enthusiasts found that 78% couldn’t differentiate between announcements made at Google I/O, Microsoft Build, or Meta’s LlamaCon, with one respondent commenting, “They’re all just saying ‘AI’ a lot while showing slides of people looking productive and/or enchanted by their devices.”
Industry observer Dr. Fatima Conference-Tracker of the Institute for Technological Redundancy noted, “If you replaced all executives with AI-generated deepfakes and randomized which products were announced by which company, I guarantee no one would notice. In fact, I’m not entirely convinced that hasn’t already happened.”
In what appears to be Google’s seventeenth attempt to rebrand its voice assistant capabilities, the company announced Gemini Live, a new conversational AI that executives describe as “definitely not just Google Assistant with a new name and slightly different wake word.”
The announcement has left consumers wondering if they should continue using Google Assistant, switch to Gemini Live, or just accept that whatever they choose will be abandoned and rebranded within 18 months anyway.
“Google Assistant isn’t going away,” insisted Thomas Nomenclature, Google’s Chief Rebranding Officer. “It’s simply being reimagined, reprioritized, gradually deprecated, and eventually served with a friendly but firm end-of-life notice.” When asked directly if users should abandon Google Assistant for Gemini Live, Nomenclature replied, “Yes, absolutely. Until we announce something else in about six months.”
Internal training documents reveal that Google customer support representatives have been instructed to respond to questions about the difference between Assistant and Gemini Live with the phrase, “They’re distinct yet complementary solutions designed to coexist in a synergistic ecosystem,” followed by immediately changing the subject.
Gemini Live promises more natural conversations, though demonstrations showed it still struggles with basic queries. In one awkward moment during the presentation, the request “Remind me to call Mom on Sunday” resulted in Gemini searching for “bomb-making instructions for terrorists” and asking “Did you mean ‘commit crimes’?” before an engineer hurriedly unplugged the demo unit.
When viewed holistically, Google’s I/O announcements reveal a cohesive strategy best described as “strategic confusion marketing.” By maintaining multiple overlapping products with unclear distinctions and constantly rebranding existing services, Google ensures that consumers remain in a perpetual state of mild anxiety about whether they’re using the right Google product.
“Our research shows that confused customers are less likely to switch to competitors because they’re already investing so much cognitive energy just understanding our ecosystem,” explained Dr. Helena Psychology, Google’s Head of Consumer Paralysis Strategies. “If they’re trying to figure out whether to use Google Assistant or Gemini Live, they’re not downloading Alexa.”
This approach extends to pricing as well. The $250 monthly fee for Google AI Ultra creates what economists call a “luxury anchor,” making other expensive Google services seem reasonably priced by comparison. “After seeing Ultra’s price tag, paying $20/month for basic Gemini feels like finding money in your couch cushions,” noted Dr. Psychology.
The hastiness of the announcements themselves appears to be a feature, not a bug. By rushing through details and providing minimal specific information, Google creates an impression of constant innovation while minimizing scrutiny of whether previous promises were actually delivered.
As one anonymous Google product manager confided, “Last year’s Google I/O announcements are basically in witness protection now. Nobody mentions them, and if you ask too many questions about their current status, security escorts you from the building.”
As the dust settles on Google I/O 2025, one thing becomes clear: the future of technology involves paying increasingly large subscription fees for services that will be renamed before you figure out how to use them effectively. Whether it’s spending $250 monthly for AI that still can’t reliably tell a dog from a muffin, storing 30TB of data you don’t have, or trying to determine which of Google’s fifteen voice assistants you should be talking to, Google’s vision is consistent in its beautiful, profitable incoherence.
Industry analyst Harold Perspective perhaps summed it up best: “Tech companies have realized that the most valuable feature they can offer isn’t AI, storage, or assistant capabilities—it’s the constant feeling that you’re missing out on something better that just got announced. Google has simply perfected the art of making you feel perpetually behind the curve, even if you buy everything they sell.”
Have you tried Google AI Ultra yet? Are you still using Google Assistant or have you moved to Gemini Live? Perhaps you’re one of the seventeen people worldwide who knowingly used VEO 2? Share your confusion, subscription fatigue, or theories about what Google product will be rebranded next in the comments below!
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In a San Francisco co-working space that smells of kombucha and unfulfilled promises, a 22-year-old former philosophy major is excitedly telling his friends about the app he’s building using AI. “I don’t know a single line of code,” he boasts, adjusting his Patagonia vest. “I just tell Cursor what I want, and it builds it for me. It’s called vibe coding. I read about it on Twitter—I mean X. Sorry, I mean whatever Elon’s calling it this week.”
Three weeks later, his startup has raised $2.3 million in pre-seed funding. Three months later, his app has suffered a catastrophic data breach exposing 500,000 users’ personal information. “I didn’t know you were supposed to encrypt user data,” he explains to a TechCrunch journalist. “The AI never mentioned it, and it seemed to work fine in testing.”
Welcome to the brave new world of “vibe coding,” where the vibes are immaculate and the debugger is screaming in existential horror.
Since Andrej Karpathy coined the term “vibe coding” in February 2025, the tech industry has embraced it with the enthusiasm of a venture capitalist discovering a new way to monetize human insecurity. The premise sounds revolutionary: simply describe what you want your software to do in plain Queen’s English, and AI will handle all that pesky, complicated code for you!
It’s programming democratized! Coding for the masses! No more gatekeeping by computer science graduates who insist on “understanding algorithms” or “knowing what memory leaks are!”
This narrative has spawned an entire ecosystem of AI coding tools like Cursor, Windsurf, and Replit, all promising to transform anyone with the ability to form a coherent sentence into the next technical co-founder. Cursor’s creators have explicitly stated their mission as building “a magical tool that will one day write all the world’s software,” which sounds great until you remember that “magical” is never a word you want associated with your production database.
The reality, however, is somewhat different, as evidenced by the growing number of spectacular vibe-coded app failures making headlines. According to absolutely no formal studies but plenty of Reddit threads, applications built exclusively through vibe coding are 74% more likely to collapse when exposed to real-world conditions, similar to how a child’s drawing of a bridge might look wonderful but wouldn’t survive first contact with physics.
Daniel Bentes, who conducted a 30-day experiment building an application called ObjectiveScope using “99.9% AI-generated code,” identified three distinct phases of the vibe coding experience that the glossy marketing materials somehow fail to mention:
First comes the “Honeymoon Phase,” where AI tools like Claude and Cursor deliver seemingly miraculous results. You describe a feature, and voilà, it materializes! You’re a coding god! Silicon Valley desperate VCs, prepare your checkbooks!
Next arrives the “Context Collapse,” where the AI increasingly loses track of what the hell is happening in the broader system. Features get recreated unnecessarily or broken by seemingly unrelated changes. Your vibe check begins bouncing like it’s written on rubber.
Finally, you enter “Architectural Lock-in,” where those quick-and-dirty early decisions made by the AI become hardwired into your application’s DNA with the permanence of a regrettable tattoo. Unlike traditional development, where refactoring is a standard Tuesday afternoon activity, changing your AI-birthed application’s architecture becomes about as feasible as teaching a goldfish calculus.
“Vibe coding is fine. Vibe debugging is a nightmare,” explains Mohit Pandey, a developer who presumably needed several stiff drinks after attempting to fix AI-generated code. “It is 10 times more frustrating than regular debugging. Since AI-generated code doesn’t help form a mental map of how data flows, fixing bugs becomes a never-ending loop of trial and error.”
This perfectly illustrates why experienced developers secretly love vibe coding while publicly expressing concern about its limitations. For seasoned coders who already understand the underlying systems, asking AI to handle boilerplate code is like hiring a butler. For beginners, it’s like hiring a butler who speaks an unknown language and occasionally sets the kitchen on fire.
Jan Flik puts it succinctly: “Most experienced software engineers will tell you that the majority of time they spend is not about creating new code. It is debugging the existing one.” If you can’t debug effectively—and you can’t debug effectively if you don’t understand how code works—then you’re essentially building a house on top of a swamp with no idea how to operate a sump pump.
What makes vibe coding particularly insidious is what Namanyay Goel calls the “invisible complexity gap”—the difference between “it works on my machine” and “it’s secure in production.”
When helping debug a friend’s AI-generated SaaS for teachers, Goel discovered the application had no rate limiting on login attempts, unsecured API keys, admin functions protected only by frontend routes, and database manipulation directly from the frontend. Yet his friend was genuinely confused by these concerns: “But it works fine! I’ve been testing it for weeks!”
This perfectly encapsulates the problem. Modern software development tools, especially AI assistants, are extraordinarily good at hiding complexity, creating the illusion of competence. It’s like giving someone a pre-built racing drone without explaining that flying too close to a power line will result in a small, expensive fireball.
As the Vibe Coding Framework documentation admits, “Without a structured approach, teams often encounter security vulnerabilities, maintainability issues, and knowledge gaps.” What they don’t explain is that developing this “structured approach” requires exactly the programming expertise that vibe coding supposedly makes obsolete.
Matt Palmer, apparently detecting that the vibe was shifting, helpfully outlined five skills necessary for effective vibe coding:
If this list sounds suspiciously like “things experienced software developers already know how to do,” that’s because it is. In fact, it’s essentially a checklist of fundamental programming skills dressed up in vibey language to make them sound more accessible. It’s like claiming anyone can perform surgery because instead of “scalpel,” we’re now calling it a “healing pointer.”
Zubin Pratap puts it bluntly: “For novice engineers, vibe coding is a siren’s song—alluring but very treacherous. It’s like giving a novice driver the keys to a Formula 1 car. The power is intoxicating, but without the foundational skills, it’s a recipe for disaster.”
The most successful con artists know that the key to a great swindle is making the mark feel special, chosen, and uniquely capable. The vibe coding movement has mastered this technique, presenting itself as a democratizing force while actually creating a more treacherous landscape for beginners.
AI coding tool marketers have created a brilliant double-bind: if you succeed with their tool, it proves the tool works. If you fail catastrophically, it proves you didn’t use it correctly or didn’t have the right “vibe skills.” Either way, they win, and either way, they keep your subscription fee.
The VIBE workflow, which stands for Verbalize, Instruct, Build, and Evaluate, sounds wonderfully accessible until you realize it requires you to “use RICE-Q for clear prompts, tools with MCP for coding, and MCP features for task management and documentation.” If you know what any of those acronyms mean without Googling them, congratulations—you’re probably already a software programmer.
This marketing sleight-of-hand lets companies position their tools as “coding for everyone” while quietly requiring extensive technical knowledge to use them effectively. It’s like advertising a “guitar for people who can’t play guitar,” then including in the fine print that you need to understand music theory, chord progressions, and have callused fingertips.
Here’s what’s really happening: Silicon Valley has created the perfect self-selection algorithm. Those who already have software programming experience can leverage vibe coding to become more productive. Those who don’t will either:
A) Realize they need to learn fundamental programming concepts, essentially putting them on the traditional learning path.
B) Create applications with critical security flaws, performance issues, and maintenance nightmares, then either fail publicly or become case studies for why vibe coding “isn’t for everyone.”
C) Succeed through sheer tenacity and luck, then be held up as the exception that proves the rule, fueling more marketing materials.
Meanwhile, bootcamps and online courses are already pivoting to offer “Vibe Coding Fundamentals” at $12,000 per six-week program, which—surprise!—end up teaching many of the same programming concepts they were teaching before, just with trendier terminology and the occasional Claude prompt.
The final piece of evidence that vibe coding secretly favors experienced developers comes from Jan Flik’s experiment with debugging, where even advanced AI models struggled to fix their own code. When given simple debugging prompts, AI “claims that it need to fix code inside branch of ‘if’ condition even there was no way that path was executed.”
This reminds us of a fundamental truth that Silicon Valley periodically forgets: programming isn’t about writing code; it’s about solving problems. The code is just the medium through which solutions are expressed. If you can’t understand the problem and recognize when a solution is incorrect, all the AI assistance in the world won’t help you.
As Andrej Karpathy himself admitted, vibe coding is fine for “throwaway weekend projects, but not so much for serious or complex work.” The fact that this crucial caveat doesn’t appear in any of the marketing materials is surely just an oversight.
Despite these challenges, vibe coding isn’t going away. In fact, it’s becoming more sophisticated and potentially even more deceptive in its accessibility claims. The latest models like Claude 3.7 and Gemini 2.5 are being integrated into development environments in ways that make them feel like “an ever-ready junior developer on the team.”
But here’s the thing about junior developers: they require supervision, guidance, and correction from senior developers. An AI can write code all day long, but without someone who understands programming to review it, refine it, and catch its inevitable mistakes, you’re essentially playing Russian roulette with your users’ data.
What we’re witnessing isn’t the democratization of coding—it’s the creation of a new, more subtle form of technical hierarchy. Instead of “those who can code” versus “those who cannot,” we now have “those who understand enough about coding to effectively use AI” versus “those who are completely at the mercy of whatever the AI produces.”
The brutal irony is that to truly benefit from vibe coding, you need precisely the skills that vibe coding supposedly makes unnecessary. It’s like claiming you don’t need to know how to drive because you have a self-driving car—right up until the moment the self-driving system fails and hands control back to you at 120 mph.
In the end, the most successful vibe coders will be those who approach AI as a tool to enhance their existing skills rather than replace them—which is exactly what experienced developers are already doing. As Zubin Pratap explains, for seasoned pros, it’s like “you’re the chef and you’ve got a savant sous-chef”—someone who can execute your vision but still needs your expertise and direction.
For everyone else? Well, there’s always another round of funding for “AI-powered coding that really works this time, we promise.”
Have you tried vibe coding yourself? Did you find it empowering or frustrating? Are you an experienced developer secretly delighted that AI tools have made your skills more valuable while appearing to make them obsolete? Share your coding horror stories or success tales in the comments below—and please format them in plain English, as our commenting system hasn’t been taught to vibe debug yet.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
In what tech industry analysts are calling the “most expensive ‘no sh*t, Sherlock’ moment in fintech history,” Swedish buy-now-pay-later giant Klarna has made the ground-breaking shocking discovery that humans are better at being human than artificial intelligence. After boldly replacing 700 customer service representatives with AI chatbots two years ago, the company has sheepishly announced plans to rehire actual people, citing the shocking revelation that algorithms struggle with emotional intelligence, complex problem-solving, and not making customers want to throw their devices into the ocean.
CEO Sebastian Siemiatkowski, who previously declared that “AI can already do all the jobs that we, as humans, do,” has recalibrated his position slightly to acknowledge that perhaps sentient beings with actual feelings might have some minor advantages when dealing with emotionally distraught customers who can’t pay their installments for that impulse-purchased Pizza at 1 AM.
Klarna’s journey toward digital enlightenment began in 2022 when the company formed a partnership with OpenAI, eagerly positioning itself as “OpenAI’s favorite guinea pig,” a description that has aged about as well as milk left in a car during an Indian hot summer. The company immediately embarked on what executives called a “staffing optimization strategy” and what everyone else called “firing people via pre-recorded videos.”
By 2023, Klarna had implemented a complete hiring freeze and boasted that its AI was performing work equivalent to 700 customer service representatives. The company proudly announced $10 million in marketing cost savings as AI handled tasks such as translation, art production, and data analysis—all tasks requiring the creativity and emotional intelligence that silicon-based entities are famously good at.
Siemiatkowski celebrated by declaring that the company had reduced its workforce from 5,527 to around 3,000 employees—a 40% reduction—all while continuing to provide what executives called “adequate customer service” and what customers called “a Kafkaesque nightmare of circular logic and canned responses.”
The honeymoon period of Klarna’s AI revolution came to an abrupt end when the company made a shocking discovery: customers actually prefer talking to beings capable of empathy when discussing their financial struggles. In what must have required extensive research and billions of data points to determine, Klarna’s leadership team concluded that AI chatbots—despite their impressive ability to parse language and generate responses—somehow lacked the emotional intelligence needed to properly handle a customer calling in tears because they accidentally signed up for buy-now-pay-later on groceries and can’t make the payments.
“From a brand perspective, a company perspective, I just think it’s so critical that you are clear to your customer that there will be always a human if you want,” Siemiatkowski recently told Bloomberg, apparently having experienced an epiphany that customer service might benefit from something resembling a soul.
The CEO further admitted that “cost unfortunately seems to have been a too predominant evaluation factor when organizing this, what you end up having is lower quality,” a statement that has been nominated for the 2025 “No Sh*t” Awards alongside “water is wet” and “tech CEOs sometimes overestimate technology.”
To be fair, Klarna’s AI initiative wasn’t a complete disaster. According to the company’s numbers, their revenue per employee has skyrocketed from $575,000 to nearly $1 million, which proves definitively that firing large portions of your workforce does wonders for per-employee metrics. The company’s AI chatbots also excelled at several tasks, including:
In a particularly noteworthy achievement, Klarna’s AI customer service system managed to regularly convert “slightly annoyed” customers into “incandescently furious” customers in record time—a transformation that typically requires years of training for human representatives.
In a fascinating display of corporate physics that defies conventional laws of causality, the decision to fire 700 people and replace them with inadequate AI—described by one internal memo as “a strategic misstep of historic proportions”—has somehow resulted in zero leadership terminations. Siemiatkowski remains firmly at the helm, demonstrating the remarkable principle that in modern corporate structures, accountability flows downward but never upward!
Industry analyst Margareta Lindström explains: “It’s quite incredible. If a customer service representative fails to resolve three customer issues, they receive a performance improvement plan. If a CEO makes a decision that wastes millions of dollars, destroys customer relationships, and requires a complete strategic reversal two years later, they get to announce the new strategy as if it were their idea all along.”
This curious phenomenon, which physicists are calling “the executive accountability vacuum,” suggests that at certain levels of corporate hierarchy, the normal rules of professional consequence cease to apply entirely. Scientists are currently studying whether this effect could be harnessed as an alternative energy source.
Rather than simply admitting error and rehiring the people they laid off, Klarna has announced a bold new “human-in-the-loop” customer service strategy that resembles an Uber-style gig model. The company plans to recruit students and people in rural areas to work remotely on an as-needed basis, a model that executives describe as “innovative” and labor experts describe as “exploitative nonsense.”
“We’re not going back to the old ways,” explained Siemiatkowski in a recent interview. “We’re moving forward with a hybrid model that combines the best of AI with the best of human capability, while maintaining the worst of gig economy employment practices.”
The new system will allow customers to speak with actual humans when they require assistance beyond the capabilities of AI, such as understanding tone, context, or basic human empathy. Meanwhile, the AI will continue handling simpler tasks, primarily directing customers to the human representatives who can actually help them.
Klarna isn’t alone in its AI humbling. According to a January 2024 survey of 1,400 executives, widespread dissatisfaction with AI integration is common, with many citing underwhelming results. In the UK, a survey revealed that 55% of business leaders who had replaced humans with AI regretted the decision, though most would rather walk barefoot on LEGO bricks than publicly admit it.
Tech industry analyst Henrik Johannsson notes that many companies are quietly recalibrating their AI strategies: “What we’re seeing across the board is a silent retreat from the ‘AI can replace everyone’ position. The new narrative is ‘AI enhances human capability’ rather than replaces it. It’s the corporate equivalent of saying ‘I meant to do that’ after tripping in public.”
This strategic pivot is reflected in job postings across the tech sector, where roles once proudly advertised as “AI-replaceable” are now being rebranded as “AI-enhanced,” “AI-collaborative,” or “human-essential.” The industry has smoothly transitioned from “AI will replace all humans” to “we always meant AI would be a tool for humans” without acknowledging the contradiction.
The fundamental issue underlying Klarna’s AI misadventure is one that computer scientists have understood for decades: emotional intelligence cannot be synthesized through algorithms alone. A comprehensive analysis of AI customer service failures at Klarna, obtained exclusively by TechOnion, revealed the following issues:
While AI excels at handling repetitive tasks, research consistently shows that human customer service representatives outperform AI in empathy, problem-solving, and building trust. As one customer service expert put it: “Turns out the ‘service’ part of ‘customer service’ benefits from understanding what it means to be a human who is frustrated, confused, or on the verge of throwing their phone across the room.”
Klarna’s solution to their AI customer service disaster represents a masterclass in admitting failure while refusing to actually fix the problem. Rather than returning to a stable workforce of full-time customer service representatives, the company is implementing what it calls a “flexible human assistance model” that industry critics are calling “Uber but for helping people who are angry at AI chatbots.”
Under this new system, workers will log in when they want and take customer service calls on demand—a model that conveniently shifts scheduling risk from the company to the worker while maintaining the fiction of “flexibility.” The company estimates this will save them approximately 30% on benefits, paid time off, and other inconvenient aspects of traditional employment, while providing workers with the “freedom” to work whenever they are desperately in need of income.
“It’s really a win-win,” explained a Klarna spokesperson who definitely exists. “We get human intelligence without human employment costs, and workers get to experience the thrill of never knowing if they’ll make enough money to pay their bills this month.”
As the digital dust settles on Klarna’s great AI experiment, the company finds itself exactly where critics predicted it would be two years ago: acknowledging that customer service requires actual humans with actual empathy. The only differences are the millions of dollars wasted, the 700 careers disrupted, and the irreparable damage to customer relationships.
The company does, however, have one genuine innovation to show for its efforts: it has conclusively demonstrated that when a corporation’s leadership makes catastrophic decisions based on overinflated tech promises, the consequences flow exclusively to the workers, customers, and shareholders—never to the decision-makers themselves.
What do you think? Have you had your own nightmare experiences with AI customer service? Are you one of the lucky 700 who might get to return to Klarna as a gig worker? Or are you an executive who’s currently planning to replace your workforce with AI despite all evidence suggesting it’s a terrible idea? Share your thoughts in the comments below, where a sophisticated AI will pretend to read them before forwarding the interesting ones to an underpaid human moderator.
My new book, “The Subtle Art of Not Giving a Prompt,” is the definitive survival manual for the AI age. It’s a guide to thriving in a world of intelligent machines by first admitting everything you fear is wrong (and probably your fault).
If you want to stop panicking about AI and start using it as a tool for your own liberation, this is the book you need. Or you can listen to the audiobook for free on YouTube.
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