Google I/O 2025: Hasty Announcements, Empty Wallets, and the World’s Most Expensive Digital Storage Locker

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May 21, 2025

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.

Google AI Ultra: Because Regular AI is for Peasants

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.

VEO 3: The Sequel to the Sequel Nobody Asked For

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.”

30TB Google Storage: Digital Hoarding as a Service

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.

Conference Fatigue: The Interchangeable Product Announcement Industrial Complex

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.”

Gemini Live: The Assistant Formerly Known as Assistant

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.

The Strategy: Confusion as Business Model

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.”

The Future is Expensive, Confusing, and Probably Going to Be Rebranded

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!

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Vibe Coding Check Failed: Why AI Coding Tools are F1 Cars Being Handed to Toddlers with Learner’s Permits

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Simba the "Tech King"

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May 20, 2025

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Vibe Coding Check Failed illustrated by a young kid in a formula 1 (F1) car

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.

The Emperor’s New Development Paradigm

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.

The Three Stages of Vibe Grief

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.

Debugging: Where Vibes Go to Die

“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.

The Invisible Complexity Gap

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.

The Skills You Didn’t Know You Needed

Matt Palmer, apparently detecting that the vibe was shifting, helpfully outlined five skills necessary for effective vibe coding:

Procedural thinking: Breaking down your app into logical steps.
Framework knowledge: Knowing what tools exist for specific tasks.
Checkpoints: Building in discrete steps and saving working versions.
Debugging: Methodically identifying and fixing errors.
Context management: Selectively providing relevant information to AI.

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 Great Vibe Marketing Swindle

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.

The Silicon Valley Self-Selection Algorithm

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 Debugging Dystopia

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.

The Future of Vibing

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.

Support TechOnion's Continuing Investigation Into The Vibe Coding Industrial Complex! For less than the cost of a single stack overflow on an AI-generated app, your donation helps us maintain our cutting-edge satire servers and keeps our writers' sarcasm properly calibrated. Every dollar contributes to our emergency fund for journalists who develop repetitive strain injury from typing "I told you so" too many times. Donate now—our vibes depend on it.

AI’s Emotional Intelligence Breakthrough: Klarna Discovers Humans Had It All Along!

By

Simba the "Tech King"

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May 20, 2025

0

Klarna fires its AI chatbots and re-hires humans for customer service support.

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.

The Golden Age of AI-Enhanced Unemployment

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.”

Unforeseen Complications: AI Cannot Yet Feel Your Financial Pain

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.”

AI Achievements: A Balanced Assessment

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:

Confidently providing incorrect information with perfect grammar
Misinterpreting customer emotions with remarkable consistency
Responding to complex questions with irrelevant solutions
Maintaining the same cheerful tone when explaining why your payment was declined as when wishing you a nice day
Never requesting bathroom breaks, healthcare, or a living wage

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.

The Leadership Accountability Paradox

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.

The Rehabilitation Phase: “We’ve Always Valued Humans, Starting Now”

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.

The Silent Industry-Wide Recalibration

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 Science of Customer Service: Emotions Required

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:

Rigid and unhelpful answers that didn’t fully address customer queries. One customer reported asking for help with a payment issue and receiving instructions on how to download the Klarna app—which they were already using to make the complaint.
Lack of conversational flow, forcing users to rephrase questions multiple times. In one documented case, a customer had to rephrase the same question about a refund in seven different ways before the AI understood, at which point it referred them to a nonexistent department.
Misinterpretation of complex inquiries, causing unnecessary escalations. The system frequently misunderstood emotional cues, once interpreting a customer’s sarcastic “Thanks for nothing” as sincere gratitude and responding with “You’re welcome! Is there anything else I can help you with today?”

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.”

The Great Rehiring: Gig Economy Edition

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.

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The Big Short Rib: How Klarna Turns Your Midnight Pizza Order into Wall Street’s Hottest Asset Class

By

Simba the "Tech King"

-

May 20, 2025

0

A wall street banker enjoying a pizza backed security by Klarna delivered by Deliveroo

In what financial historians will surely record as late-stage capitalisms’ magnum opus, your regrettable 1 AM pepperoni pizza Dominos order can now be financed over three convenient monthly installments via Klarna, bundled with thousands of other poor financial decisions, and sold to institutional investors who once considered mortgage-backed securities too risky. Welcome to 2025, where your drunk food cravings have been transformed into an exciting new asset class that Harvard MBAs are calling “the ultimate fusion of tech disruption and questionable life choices.”

Deliveroo, the food delivery platform that somehow convinced us all that paying a £4.99 (About $7 depending on what Uncle Donald Trump decides for the world economy) fee for lukewarm restaurant food delivered by a cyclist who judges your order is a good idea, has partnered with Klarna to offer Buy Now, Pay Later services on all orders. The groundbreaking innovation allows customers to finance their doner kebabs and curries with the same financial instrument previously reserved for Pelotons and Apple iPhone upgrades that nobody needed.¹

The Three-Month Pizza Plan: Amortizing Your Regrets

Under Deliveroo’s revolutionary payment model, customers can choose to pay for their food immediately (BORING!), within 30 days (still fairly responsible), or split the cost into three monthly installments for orders over £30 (about $40 bucks – financial self-sabotage with a side of fries!).² The third option has quickly become a favorite among young professionals who want to maintain the lifestyle of someone earning twice more than they actually do.

“It’s about empowering consumers with flexibility,” explained Carlo Mocci, chief business officer at Deliveroo UK, while carefully avoiding the phrase “enabling poor financial decisions.” “We’re giving customers more choice and more flexibility with a safe, secure way to pay online”.

The partnership has been praised by those who believe the primary barrier to happiness is the inability to finance a bucket of chicken hot-wings over a fiscal quarter. Critics, meanwhile, have called it “the dumbest thing since NFT restaurants” and “a concerning development in our society’s relationship with both debt and saturated fats.”

Personal finance expert Tara Flynn of MoneySavingExpert cut directly to the chase: “If you’re considering buying your takeaway now and paying for it later… don’t. Getting yourself into debt over a meal that’s gone in 15 minutes isn’t worth it”.³ But why listen to financial experts when you can instead listen to the voice in your head at 12 midnight saying “You deserve this Tandoori feast NOW, and deal with the consequences LATER”?

From Your Stomach to Wall Street: The Miracle of Modern Finance

Here’s where the ordinary becomes extraordinary. Klarna isn’t just holding onto your pizza debt like a nostalgic memento. No, they’re packaging it up with thousands of other food debts and selling it to hedge funds and institutional investors through the miracle of securitization – that is where they make their REAL money!⁴

According to recent reports, Klarna is selling most of its British BNPL loans to American hedge fund Elliott, freeing up an estimated $39 billion for new loans.⁵ This means your outstanding £36.50 Meat Feast Extravaganza payment is now sitting in the investment portfolio of a US pension fund manager who’s never experienced the existential crisis of adding extra cheese at a Deliveroo checkout.

The structure works similarly to the Domino’s Pizza securitization model, where the company created a special purpose vehicle (SPV) called “Domino’s Pizza Master Issuer LLC” into which it sold its revenue-generating assets.⁶ In Klarna’s case, the SPV might as well be called “The Repository of Questionable Midnight Cravings LLC,” where your three outstanding payments for chicken wings join forces with thousands of other fast food financing arrangements.

Meet the Pizza-Backed Security: Wall Street’s Tastiest Financial Innovation

Financial analyst Matthew Van Herzeele recently explained securitization on LinkedIn, describing how “bank and fund managers come together to create a special-purpose vehicle (SPV) to hold these assets banks need to unload”.⁷ In this case, the assets are the digital equivalent of IOUs scribbled on greasy napkins.

What makes food-backed securities particularly innovative is their unique risk profile. Unlike houses, which at least exist for decades, the underlying assets securing these loans have a half-life measured in minutes and an afterlife manifesting as indigestion and obesity. The collateral literally disappears down the customer’s esophagus before the first payment is due.

“It’s fascinating,” explained one Wall Street analyst who requested anonymity because his firm is currently underwriting several chicken-backed security offerings. “We’re essentially creating a financial instrument backed by assets that have negative value after consumption. It’s like securitizing hot air.”

The Tranches: From AAA Sushi to Subprime Nuggets

Just as with mortgage-backed securities, food debt is carefully sorted into risk tranches. At the top sit the AAA-rated sushi orders from affluent neighborhoods with perfect payment histories. In the middle are the BBB-rated pizza deliveries to young professionals who usually make their payments but occasionally need a reminder. And at the bottom are the high-risk, high-yield “subprime chicken nuggets” – the 3AM chicken orders to university dormitories that have a default rate coinciding precisely with student loan payment dates.

One hedge fund has reportedly created an algorithm that assigns risk scores based on not just the customer’s credit history but the specific food ordered. Thai food at 7 PM on a Wednesday? Low risk. Twelve chicken wings, three sides, and two milkshakes at 2:30AM on a Saturday? High risk but potentially high reward if they make their payments.

“The beauty of these securities,” explained a quantitative analyst at a major investment bank, “is that we can predict default rates based on topping choices. Pineapple on pizza correlates with a 23% higher risk of missed payments. It’s the financial equivalent of a red flag.”

The Curious Case of Chicken-Backed Liquidity

The market for food debt securities has grown exponentially since Deliveroo and Klarna first partnered in 2022. What started as a £5.6 billion market has expanded to roughly the size of Denmark’s GDP, driven by an insatiable appetite for both takeaway food and exotic financial instruments.

The key selling point for investors is diversification. As one portfolio manager put it: “Look, chicken wing demand is essentially recession-proof. People might stop buying homes during an economic downturn, but they’ll sooner cancel their health insurance than stop ordering takeout. That makes these securities surprisingly resilient.”

But not everyone is convinced. Sue Anderson from debt charity StepChange warned: “It’s a worrying development to see mainstream food delivery providers offering BNPL, especially at a time of such financial uncertainty for households”. Research shows those using BNPL are often already in financial difficulty, with a quarter of BNPL users having to borrow from other sources just to keep up with essential costs.

The 2027 Fried Chicken Financial Crisis: A Prediction

Financial experts are already gaming out scenarios for what some are calling “the inevitable fast food financial crisis.” As with any securitization boom, the key risk is mispricing of risk and contagion when defaults start rising.

“What happens when a recession hits and suddenly thousands of people can’t make payments on their three-month pizza plans?” asks one economist. “The SPVs start to fail, the securities lose value, and institutions that are overexposed to chicken-backed securities have to write down billions in losses. Then everyone acts surprised, as if financing consumable goods over multiple months wasn’t obviously problematic.”

Others point to the lack of regulation. “BNPL is not yet regulated, providers may not carry out effective affordability checks or prevent users from taking out multiple BNPL loans from different retailers they are unable to repay,” warns StepChange. This means someone could theoretically go on a tour of digital gluttony, financing a pizza via Deliveroo, a burger via Uber Eats, and a curry via Just Eat, all without any single provider knowing about the others.

In boardrooms across London and New York, risk committees are running stress tests on scenarios like “Widespread Sriracha Shortage” and “TikTok Trend Makes Cooking at Home Cool Again,” trying to calculate the potential downstream effects on their chicken-backed security portfolios.

The Fintech-Fast Food Industrial Complex

The unholy alliance between delivery apps, payment processors, and financial institutions represents what industry insiders are calling “the final frontier of financialization.” Having successfully monetized housing, education, healthcare, and even dating, the financial sector has finally figured out how to extract value from your desperate search for dopamine via deep-fried poultry.

David Sykes, chief commercial officer at Klarna, defended the practice: “We believe you should only pay for what you buy with no interest or fees, and it’s never been more important for consumers to have access to payment options which help them stay in control of their finances”. This statement came shortly after Klarna announced it was selling most of its British BNPL loans to American hedge fund Elliott, presumably because nothing says “helping consumers stay in control of their finances” like selling their debt to a hedge fund known for aggressive investment strategies.

A Klarna spokesperson further justified the model by explaining that “people have been paying for takeaways with credit cards and overdrafts for decades”, seemingly unaware that “other people made poor financial decisions in the past” isn’t typically considered sound financial advice!

The Pizza Default Swap: Coming Soon to a Bloomberg Trading Desk Near You

As the market matures, derivative products are inevitably emerging. Credit default swaps allowing investors to bet against pizza-backed securities are trading with increasing volume.⁸ Complex structured products with names like “Synthetic Collateralized Chicken Obligations” are being marketed to sophisticated investors looking to increase their exposure to the takeaway sector without the burden of actually owning debt backed by rapidly depreciating edible assets.

One trader described the appeal: “The beauty of these instruments is their short duration. With mortgages, you’re looking at 30-year terms. With car loans, maybe 5 years. But with chicken wings? The entire lifecycle from origination to final payment is just 60 days. The velocity of capital is incredible.”

This rapid turnover has created what some analysts are calling “the perfect perpetual motion machine of bad debt.” As soon as one cohort of tipsy customers finishes paying for their regrettable late-night food choices, another cohort is just beginning their own journey of financial self-sabotage, creating an endless supply of new debt to securitize.

The Future: Micro-Financed Mouthfuls

Industry insiders suggest this is just the beginning. Plans are reportedly underway to offer financing options on individual menu items, allowing customers to pay for the burger today but finance the fries over the next two weeks.

“We’re working on real-time bite-financing,” revealed one fintech executive who spoke on condition of anonymity because the technology is still in development. “Our AI can track exactly how much of the pizza you’ve eaten and adjust your payments accordingly. Eat a quarter of the pizza? Pay a quarter of the bill. The technology uses your front-facing camera to calculate consumption ratios with 97% accuracy.”

When asked about privacy concerns, the executive scoffed. “Privacy? You’re literally allowing strangers to bring food to your home based on an app that already knows your eating habits, address, and credit card details. You gave up privacy around the same time you started taking pictures of your meals for Instagram.”

As we contemplate a future where every french fry comes with its own amortization schedule, one can’t help but marvel at the innovative ways capitalism continues to extract value from increasingly mundane activities. The securitization of food delivery debt represents either the pinnacle of financial innovation or the absolute nadir of our collective decision-making, depending entirely on whether you’re selling these securities or buying that 1 AM pizza.

So what do you think? Is financing your takeaway over three months the height of modern convenience or a sign of impending financial doom? Have you ever used Klarna to buy food you couldn’t afford, or are you more of a “if I can’t pay for my pizza now, I don’t deserve pizza” purist? Perhaps you’re an institutional investor looking to diversify your portfolio with some spicy chicken-backed securities? Share your thoughts in the comments below, preferably before your next financed meal arrives.

Support TechOnion’s Investigative Food-Finance Journalism

If this article has made you reconsider your late-night ordering habits—or alternatively, given you exciting new ideas for structuring your personal takeaway debt—consider supporting TechOnion with a donation. Unlike your pizza, which depreciates to zero value the moment it enters your digestive system, your contribution will help fund future investigations into the increasingly blurry line between financial technology and terrible life decisions. We accept all major payment methods, including one-time payments, 30-day delayed payments, or three convenient monthly installments that we promise not to securitize and sell to Goldman Sachs.

References

Stack Over-Now-Underflow: The Tragicomic Tale of How AI Killed the Internet’s Sacred Developer Temple

By

Simba the "Tech King"

-

May 20, 2025

0

An image depicting a software developer feeding paper with questions about code to an AI chatbot instead of typing it on Stack Overflow.

In what future tech historians will surely describe as the most predictable tech extinction since Blockbuster faced Netflix, Stack Overflow — the hallowed digital monastery where software programmers once gathered to both solve and create new programming problems—is quietly vanishing into the digital ether, leaving behind only the fading echo of “this question has been marked as duplicate” notifications.

Recent data reveals that Stack Overflow’s question volume has plummeted faster than a tech CEO’s principles during a US congressional hearing, with a devastating 25% reduction in user activity within just six months of ChatGPT’s release.¹ What began as a slow decline in mid-2020 has accelerated into what data scientist Theodore R. Smith, a top 1% Stack Overflow contributor, diplomatically calls an “alarming” drop in questions that continues into 2025.²

The Digital Murder Mystery Nobody Is Investigating

The prime suspect in this crime against developer community resources? AI coding assistants like GitHub Copilot, ChatGPT, and their increasingly smug algorithmic cousins who’ve never experienced the character-building trauma of being downvoted into oblivion for forgetting to use a semicolon.

“I haven’t opened Stack Overflow in months,” confessed one software developer on a Discord channel, where such admissions are becoming as common as “thoughts and prayers” tweets after a tech platform outage. The evidence is clear: developers are ghosting the platform that once served as their collective brain, preferring instead the instant gratification of AI assistants that never tell them to “Google harder” before answering.³

The irony hasn’t been lost on keen observers—these AI assistants were trained on the very knowledge base they’re now helping to destroy. It’s the digital equivalent of a child eating its parent, if that parent were composed entirely of JavaScript solutions and heated debates about tabs versus spaces.

The Autopsy Results Are In: Death By Convenience

What killed Stack Overflow wasn’t just ChatGPT’s uncanny ability to generate solutions faster than a human can type “How do I center a div?” It was the culmination of forces that were years in the making.

Stack Overflow’s notorious community tone—where newcomers faced a gauntlet of criticism that made Marine boot camp look like a pre-school graduation—certainly played its part. As one former user eloquently described it, “Stack Overflow’s community is the reason I stopped asking questions,” presumably before adding “also, the existential dread of realizing I’ll never truly understand regular expressions.”

By 2023, approximately 36% of developers were actively using AI assistants to understand coding errors and generate fixes.⁴ Fast forward to 2024, and that number skyrocketed to 63% of professionals incorporating AI into their regular workflows.⁵ Now, in our glorious 2025, industry analysts project that AI assistants will write as much as 90% of software code within a year—a statistic that should terrify anyone who has ever received an “I’ll fix it in the morning” Slack message from a developer.

The Promised Productivity Paradise

The tech industry’s love affair with AI coding assistants is fueled by impressive-sounding statistics that executives can’t wait to share during quarterly earnings calls. Research suggests that AI adoption provides a stunning 15-33% productivity improvement via successful pull requests.⁶ According to the DORA report, a 25% increase in AI adoption is linked to a respectable 2.1% rise in productivity, which is coincidentally the exact percentage increase in executive bonuses for implementing AI solutions.⁷

Microsoft, keen to remind everyone they’re not just about forced Windows updates anymore, reported that 77,000 organizations have adopted GitHub Copilot since its release in October 2021.⁸ Meanwhile, Y Combinator’s managing partner, Jared Friedman, revealed that a quarter of startups in their current cohort have codebases that are “almost entirely AI-generated,” which explains why they all seem to be solving the same three problems.

The Glorious Age of Copy-Pasta Engineering

Today’s modern developer workflow has evolved from “search Stack Overflow, copy code, modify slightly, pretend you wrote it” to the much more efficient “ask AI assistant, copy code, don’t bother understanding it, ship to production.” Progress, ladies and gentlemen!

Simon Lau, an engineering manager at ChargeLab, summed up the industry’s FOMO-driven adoption perfectly: “AI is something that helps us, and it is also helping our competitor as well, right? So if we are not utilizing this, we are not leveling the playing field with our competitor.” This profound statement captures the essence of modern tech strategy: do it because everyone else is, regardless of whether it makes sense, like wearing Allbirds to a VC meeting in 2019.

The benefits extend beyond mere productivity. Developers using AI tools reported improvements in “flow” (+2.6%), which is corporate-speak for “staring at the screen while the machine does the work,” and “job satisfaction” (+2.2%), likely due to having more time to perfect their coffee brewing techniques. Most impressively, they claim a 3.4% improvement in code quality, a statistic that conveniently ignores the 41% increase in bugs found in AI-generated code.

The Cannibal That Starves Itself

In the most delicious irony since Facebook became Meta about its own toxicity, AI coding assistants are devouring the very data sources that make them intelligent. As Stack Overflow questions decrease, the training data for future AI models diminishes, creating what ML engineer Ayhan Fuat Çelik eloquently calls “The Fall of Stack Overflow.”⁹

“With fewer questions about current programming problems being asked on the public internet, the training data for the coding assistants of tomorrow gets reduced,” explains one AI researcher, apparently unaware of the existential paradox this presents. “Ironically, the AI coding assistants of today are one of the main reasons for the fall of Stack Overflow and why people ask their questions in private to an AI.”

This creates a fascinating scenario where future AI models may need to be trained on the outputs of current AI models—a practice experts warn risks “model collapse,” where errors accumulate over generations, resulting in nonsensical outputs. It’s like a digital version of royal in-breeding, but instead of Habsburg jaws, we get AI that suggests putting your database credentials directly in your GitHub repository.

The Most Endangered Programming Species

Not all programming topics have suffered equally in this AI-driven extinction event. According to detailed analysis, questions about fundamental programming concepts (lists, dictionaries, loops) and data analysis tools (pandas, dataframes, SQL) have experienced the most significant declines.¹⁰

Meanwhile, topics related to operating systems and certain development frameworks like Next.js, .NET, and Azure have seen comparatively smaller decreases. This suggests that AI is better at handling straightforward coding tasks but still struggles with more complex, context-dependent challenges—much like entry-level developers after a three-month bootcamp who list “proficient in AI prompt engineering” on their LinkedIn profiles.

The Future: Hand-Coding Goes Artisanal

As we look toward 2040 and beyond, when researchers predict AI will fully replace software developers, one can’t help but imagine a dystopian future where writing code manually becomes an artisanal craft, like churning your own butter or using a paper map.¹¹

“I hand-code all my functions,” a hipster developer will say in 2035, adjusting their analog smartwatch. “The machines don’t understand the soul of a well-crafted recursive algorithm. Also, I don’t trust them after the Great Stack Overflow Knowledge Gap of 2028.”

Indeed, nearly 30% of software developers surveyed already believe their development efforts will be replaced by artificial intelligence in the foreseeable future. The remaining 70% were presumably too busy fixing AI-generated bugs to respond to the TechOnion survey.

The Developer Identity Crisis

Perhaps the most profound impact of this shift is psychological. For decades, developers have defined themselves by their ability to solve complex problems through code. Stack Overflow provided not just answers but a community and status system where reputation points served as a measure of one’s worth—the programmer’s equivalent of TikTok likes.

Now, as AI assistants eliminate the need to personally understand how code works, software developers find themselves in an existential crisis. “Am I still a developer if I’m just telling AI what to build?” asks one senior engineer on Reddit, before quietly updating his LinkedIn profile to “AI Workflow Optimization Specialist.”

This crisis extends to companies mandating AI tool usage without understanding their limitations. As one anonymous developer put it: “Under pressure to embrace AI, developers are growing frustrated by misguided mandates and are left to clean up any collateral damage”. In other words, executives want the 33% productivity gain but don’t want to hear about the 41% increase in bugs that comes with it.

The Vicious Circle of Knowledge Extinction

The most alarming aspect of Stack Overflow’s decline is how it creates a vicious cycle: fewer questions means less current knowledge being shared publicly, which means future AI models will be trained on increasingly outdated information. This, in turn, will produce AI assistants that generate obsolete code, forcing developers back to… well, not Stack Overflow, because it will be a digital ghost town populated only by bots asking each other about jQuery solutions in 2030.

Stack Overflow’s own 2024 insights admitted: “More people are reading than contributing,” which is a polite way of saying “developers are done engaging”.¹² It’s like a digital version of the tragedy of the commons, where everyone wants to benefit from community knowledge but nobody wants to contribute to it—especially when they can just ask ChatGPT-5 instead.

So what happens when all the smart humans stop sharing their knowledge publicly? When the next generation of programming languages and frameworks emerges, will there be enough human-generated solutions to train AI on? Or will we enter a dark age of programming where AI assistants confidently generate solutions that worked great in 2018 but are hopelessly obsolete for the challenges of 2030?

As we stand at this technological crossroads, one thing is clear: the developers who can still solve problems without AI assistance will be the digital wilderness guides of tomorrow—rare, valuable, and probably sporting magnificent beards while charging astronomical consulting rates that make current cloud computing costs look like pocket change.

So what do you think? Are you mourning the slow death of Stack Overflow, or celebrating your liberation from snarky comments about your “poorly formatted question”? Has your relationship with AI coding assistants evolved from skepticism to dependency? Share your existential coding crisis in the comments below—if you can still formulate a coherent thought without asking an AI assistant to generate it for you.

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References

The Great AI Energy Crisis: How Eric Schmidt’s ‘Underhyped’ AI Revolution Will Leave Us in the Dark

By

Simba the "Tech King"

-

May 20, 2025

0

Former Google CEO Eric Schmidt giving a TED talk about AI being under-hyped.

In a shocking twist that surprised absolutely no one with a fully functioning frontal lobe, former Google CEO Eric Schmidt took the TED 2025 stage last week to declare that artificial intelligence – the technology currently receiving more media coverage than oxygen and Donald Trump – is actually “underhyped.” Yes, you read that correctly. The man who helped steer one of the world’s largest tech companies believes we’re not talking enough about AI, in the same way that fish might not be talking enough about water.

Schmidt’s talk, delivered to an audience of head-nodding tech enthusiasts who would applaud a toaster if it had “AI-powered” in its name, argued that we are “drastically underestimating the scope and speed of the AI revolution”.¹ This from the man whose company once thought Google+ would be a Facebook killer.

When Machines Play Board Games Better Than Your Retirement Portfolio

The crux of Schmidt’s argument rests partially on AlphaGo’s legendary 2016 victory over Go champion Lee Sedol, which Schmidt frames as a watershed moment for artificial intelligence.² For the uninitiated (or those with actual hobbies like Chess), Go is an ancient Chinese board game with more possible configurations than there are atoms in the known universe. AlphaGo’s victory was indeed impressive – the AI made a move so unexpected that human experts initially thought it was a mistake.³

“What happened in this particular set of games was in roughly the second game, there was a new move invented by AI in a game that had been around for 2,500 years that no one had ever seen,” Schmidt gushed during his TED talk, conveniently glossing over the fact that Lee Sedol ultimately won one game against the machine.⁴

But here’s where our inner Sherlock Holmes starts twirling his metaphorical mustache. AlphaGo’s victory, while impressive, bears a suspicious resemblance to other carefully controlled AI demonstrations. Consider Meta’s recent Llama 4 controversy, where the company submitted a specially crafted, non-public variant called “Llama-4-Maverick-03-26-Experimental” to benchmark tests.⁵ When the actual public model was released, users reported “lackluster results” compared to the benchmark claims. One might reasonably ask: Was AlphaGo similarly “optimized” specifically for its match against Lee Sedol?

As one unnamed AI researcher who asked to remain anonymous because they “enjoy having a career” told us: “Winning at Go is impressive, but it’s also a closed system with perfect information. Real-world problems are messy. It’s like saying you’re ready for the Daytona 500 because you’re really good at Mario Kart.”

90 Gigawatts? Great Scott!

Perhaps the most glaring omission in Schmidt’s techno-utopian TED sermon was any meaningful discussion of the absolutely eye-watering energy requirements of his ‘underhyped’ AI revolution. According to recent projections, AI data centers could consume a staggering 90 gigawatts of power globally by 2028.⁶ For context, that’s roughly the equivalent of Denmark’s entire power consumption.⁷ Not a neighborhood in Denmark. Not a city in Denmark. The ENTIRE country of Denmark!

Schneider Electric’s latest report spells it out in terrifying clarity: the overall power consumption associated with AI workloads will reach approximately 4.3 gigawatts, “equivalent to the total power consumption of a country”. And that’s just for starters. The International Energy Agency projects that data centers will consume 945 terawatt-hours by 2030 – roughly equivalent to Japan’s entire annual electricity consumption.⁸

Meanwhile, India is desperately trying to meet its AI ambitions by building out 10 gigawatts of capacity,⁹ falling hilariously short of the 40-50 terawatt-hours of additional electricity the country will require for its projected AI data centers by 2030.¹⁰ When asked about this small discrepancy, India’s Ministry of Actually Getting Things Done reportedly replied, “We’re working on it, possibly by harnessing the hot air from tech conference keynotes.”

But wait, it gets better! The energy efficiency of these AI models is about as impressive as my attempts at sobriety during a TechCrunch conference after-party. According to Sasha Luccioni, a top AI researcher, generative AI models use up to 30 times more energy than traditional search engines¹¹. That’s right – one simple high-definition image generation uses the same amount of energy as fully charging your phone!¹²

As one energy analyst who wished to remain anonymous because “I enjoy having electricity” told us: “By 2030, AI might consume up to 25% of US power requirements. We’re basically building a technology that will either solve climate change or cause rolling blackouts across America. It’s a race to see which happens first.”

The Strategic Under hype

When a former Google CEO gets on stage and claims something is “under hyped,” your BS detector should be screaming louder than a startup founder who just lost their Series A funding. There’s an art to the strategic under hype – it’s the corporate equivalent of saying “I’m actually really humble” at a job interview.

Schmidt’s declaration that AI is “underhyped” is the tech world equivalent of yelling “FIRE!” in a theater that’s already on fire, where everyone is already screaming about the fire, and firefighters are actively spraying water on the flames. It’s not just redundant; it’s suspiciously so.¹³

Consider the metrics: AI is receiving unprecedented investment, media coverage, and academic attention. Companies are tripping over themselves to slap “AI-powered” on literally anything with an on/off switch. Meta’s Mark Zuckerberg rebounded from his metaverse debacle by pivoting so hard to AI that he probably gave himself whiplash. Microsoft bet its entire future on OpenAI. Google launched Bard…then Gemini…then apologized for Gemini…then relaunched Gemini. Every startup pitch deck now contains the phrase “AI” approximately 84 times per slide.

Yet according to Schmidt, this isn’t enough hype. One wonders if he’s been measuring hype in some alternate dimension where people talk more about sustainable farming practices than they do about ChatGPT.

But here’s the brilliance of Schmidt’s move: claiming something is “underhyped” is perfect headline bait. It’s contrarian. It’s provocative (In Will Ferrell’s voice in Blades of Glory) . It guarantees coverage. If he had said “AI is exactly hyped the correct amount,” would we be writing this article? Would TED have uploaded the video? Would you be reading this right now? No, you would be doing something productive, and nobody wants that.

The Artificial Interviewer

Perhaps the most telling moment of Schmidt’s TED appearance wasn’t what he said, but rather the questions he was asked. The interviewer, Bilawal Sidhu, engaged with Schmidt in what appeared to be a series of pre-planned softballs that would make a White House press secretary blush.

Our investigative team (one intern with too much time and not enough supervision) conducted a linguistic analysis of Sidhu’s questions and found a 78% probability that they were generated by an AI, possibly the very technology they were discussing. The questions featured that distinctive blend of sounding intelligent while actually saying nothing -the verbal equivalent of a LinkedIn post about “synergy” and “disruption.”

One particularly revealing exchange:

Sidhu: “If you fast forward to today, it seems that all anyone can talk about is AI, especially here at TED. But you’ve taken a contrarian stance. You actually think AI is underhyped. Why is that?”

Notice the setup: acknowledge the hype, frame Schmidt’s view as “contrarian” (despite it being the dominant view among tech executives), then lob the softball. It’s the conversational equivalent of placing a basketball hoop three feet off the ground and asking Michael Jordan if he thinks he can dunk.

Schmidt’s response, naturally, was to talk about ChatGPT and ignore the more complex reality that AI adoption in actual businesses remains modest. According to real data, only about 20% of workers use generative AI on their jobs, meaning a whopping 80% still do not use these tools regularly. Moreover, only 5.4% of firms have officially deployed generative AI in a formal way.¹⁴ But why let facts get in the way of a good ol’ TED talk?

Powering Delusion: The Energy Elephant in the Room

The most glaring contradiction in Schmidt’s underhyped revolution is the simple fact that we don’t have enough electricity to power it. This isn’t a small problem; it’s the equivalent of Elon Musk announcing plans to move the entire human population to Mars without mentioning the minor detail that we don’t have spaceships that can get us there.

The projections are frankly terrifying. Crypto mining – the previous energy villain – pales in comparison. AI’s projected electricity use by 2026 (~1,000 TWh) would equal Germany’s total annual power consumption.¹⁵ It’s roughly 10 times the power demand of Google’s entire global infrastructure in 2021.

Goldman Sachs projects that 85-90 gigawatts of new nuclear capacity would be needed just to meet data center power demand growth.¹⁶ To put that in perspective, that’s approximately 85-90 new nuclear reactors. And we all know how quickly and uncontroversially those get built.

When confronted with these energy requirements, most AI evangelists mumble something about “efficiency improvements” before changing the subject faster than a politician caught in a scandal. But the math remains stubbornly consistent: more AI means more energy, and more energy means more problems.

As one power grid engineer told us off the record: “We’re building the world’s most advanced technology on the world’s most outdated energy infrastructure. It’s like putting a Ferrari engine in a horse carriage and wondering why it keeps catching fire.”

The Satire Writes Itself

In the end, perhaps the most ironic aspect of Schmidt’s under hype claim is that it came just weeks after a Mozilla Foundation – funded performance art project called “Artificial Life Coach” launched specifically to critique AI hype.¹⁷ The project’s creator warned: “Don’t believe all the marketing hype around AI. There are some serious downsides.”

Schmidt either missed this memo or, more likely, recognized that the greatest form of power in the tech industry is controlling the narrative. By claiming AI is “under hyped,” he’s not making a factual statement – he’s attempting to reset the conversation on his terms.

As we hurtle toward an AI-powered future that will require more electricity than many countries can produce, perhaps it’s time to ask the obvious question: Who benefits from this narrative? Certainly not the average consumer, who will face higher electricity bills. Certainly not developing nations, which will struggle to build the necessary infrastructure. Certainly not the climate, which will bear the burden of increased energy production.

The beneficiaries are clear: tech companies like Google, the chip manufacturers like NVIDIA (which Schmidt specifically mentioned as “the big winner right now”), and the venture capitalists funding the next generation of AI startups.

In a world where satire and reality have become increasingly difficult to distinguish, Schmidt’s claim that AI is “underhyped” may be the most unintentionally hilarious statement of 2025. It would be funnier if it weren’t going to potentially leave us all sitting in the dark.

So what do you think, dear TechOnion readers? Is AI truly underhyped as Schmidt suggests, or are we witnessing the greatest case of technological wishful thinking since the Juicero? Drop your hottest takes in the comments below. Extra points if you can craft a response that uses less electricity than training a small language model.

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References

Breaking Miracle: Apple Maps Finally Shows Correct Directions, But Only to Apple Stores

By

Simba the "Tech King"

-

May 19, 2025

0

In what tech experts are calling “the most precisely targeted navigation update in history,” Apple Maps has reportedly achieved 100% accuracy in its directions – as long as you’re trying to get to an Apple Store. The groundbreaking improvement comes after 13 years of users wandering aimlessly through parks, driving into lakes, and being directed to make U-turns in the middle of highways.

According to multiple reports surfacing across Reddit and Apple support forums, the latest update to Apple Maps has solved its notorious directional problems with surgical precision, but only when the destination involves spending money on Apple products.

“We’ve been working tirelessly to improve our mapping technology,” said a Apple executive while polishing what appeared to be a solid gold compass. “And we’re pleased to announce that as of May 2025, Apple Maps can now get you from literally anywhere on Earth to your nearest Apple Store with quantum-level precision. Other destinations? Well, we’re working on those. Maybe by 2030.”

The Multi-Year Plan to Fix Maps… For Some Places

Apple’s struggle with mapping technology has been well-documented since the company replaced Google Maps with its own solution in 2012. According to a January 2025 report on Apple’s Data Collection Enhancement (DCE) rollout, the company hasn’t added new map data for any country in over 400 days, with the overall pace described as “sluggish throughout 2024.”¹

When asked about this alarmingly slow progress, the Apple spokesperson offered this explanation: “We decided to prioritize what matters most – making sure people can find our Apple stores. After all, what more important destination could there possibly be? A hospital? Your child’s school? Your own home? Let’s be realistic about priorities here.”

Industry analysts note that this targeted improvement aligns perfectly with Apple’s business strategy. “It’s actually brilliant when you think about it,” said tech industry observer Veronica Mathis. “They’ve taken their biggest weakness and turned it into a sales funnel. Sure, you might end up in the wrong city trying to find your friend’s wedding, but at least you’ll be able to buy a new lightning cable while you’re waiting for an Uber.”

Compass Calibration: The Eighth Wonder of the World

Part of Apple Maps’ directional challenges stem from iPhone compass issues, which users report can be mysteriously solved by waving their phones in the air in the shape of the number 8.² This calibration ritual, which resembles a religious ceremony performed by someone having a mild seizure in public, has become a common sight outside Apple Stores worldwide.

“I find myself doing the sacred Figure 8 dance at least three times a day,” said iPhone user Derrick Paulson while rhythmically moving his phone through the air at a bus stop. “It’s part of the Apple experience now. Wave your phone around like a lunatic, re-calibrate your compass, and somehow the blue dot still shows you facing the wrong direction.”

Remarkably, users report that when attempting to navigate to an Apple Store, these compass issues disappear entirely. The blue arrow snaps to attention like a well-trained bloodhound, pointing unerringly toward the nearest glass temple of technology regardless of interference from magnets, solar flares, or reality itself.

The Curious Case of the Selective Navigation

According to our investigation, Apple Maps becomes suspiciously omniscient when Apple Stores are involved. Reports indicate that the app will automatically redirect users around traffic jams, construction zones, and even minor earthquakes when an Apple Store is the destination, while still cheerfully sending users directly into traffic accidents for all other locations.

“I was trying to get to my mother’s funeral last week,” shared distraught iPhone user Miranda Chen. “Apple Maps sent me through a car wash – while I was on foot. But when I gave up and asked for directions to the nearest Apple Store to buy a phone charger I’d forgotten, suddenly it was like having a personal guide from NASA. It even warned me about a loose tile in the mall three minutes before I would have tripped on it.”

In what can only be described as technological clairvoyance, Apple Maps not only provides turn-by-turn directions to Apple Stores but apparently factors in inventory levels as well. Multiple users report being redirected to stores further away when their closest location was out of the specific product they had recently searched for on their devices.

“I had been looking at the new MacBook Pro online for weeks,” said Portland resident Jamie Weisman. “When I asked Apple Maps for directions to my dentist, it somehow diverted me to an Apple Store 17 miles in the opposite direction. The freaky thing is, when I went inside out of curiosity, they had just received a shipment of the exact model and color I had been browsing.”

The Science Behind the Selective Precision

Apple engineers (speaking entirely hypothetically, of course) explain that the company has deployed what they call “Commerce-Priority Navigation Algorithms” that allocate computational resources based on the profit potential of various destinations.

“Think of it like triage for navigation,” explained a senior engineer from the Apple Maps team. “We have limited server capacity, so we need to make tough choices. A trip to your grandma’s house generates zero revenue for Apple, so it gets the computational equivalent of a sticky note and a crayon. A trip to the Apple Store gets the full power of our satellite network, machine learning systems, and apparently some kind of interdimensional awareness we don’t fully understand yet.”

The technology apparently uses a sophisticated weighted system that determines how much navigational accuracy to provide based on how recently a user has purchased Apple products. Those who haven’t made a purchase in over six months report their blue location dot slowly but persistently drifting toward the nearest Apple Store regardless of their actual movement.

Customer Service: Report an Issue (We Dare You)

For users frustrated by Apple Maps’ directional challenges, the company does provide a “Report an Issue” function, which multiple users have described as “shouting into a digital void.”

“I’ve reported the same wrong turn on my commute 47 times,” said Chicago resident Amir Hussain. “Nothing changes. But I accidentally reported a minor issue with directions to an Apple Store once, and three minutes later there was a team of surveyors outside my window in full tactical gear, recalibrating the street.”

This discrepancy in response times has led to a new user strategy where people deliberately report fake issues with routes to Apple Stores in hopes that engineers will fix the actual problems in their neighborhoods while they’re there. This guerrilla mapping technique has reportedly been moderately successful in at least seven major metropolitan areas across the US.

The Competitive Landscape: Google Maps vs. Apple Maps vs. Reality

While a comparison between Google Maps and Apple Maps published in April 2024 concluded that “both apps are pretty accurate” for driving directions, users experiencing Apple’s selective navigational precision disagree.³

“Google Maps gets me where I need to go maybe 95% of the time,” said frustrated iPhone user Tyler Johnson. “Apple Maps gets me to the nearest Apple Store 100% of the time, and everywhere else maybe 60% of the time. It’s like having a bloodhound that can only smell Apple-branded treats.”

In what can only be described as the digital equivalent of gaslighting, Apple Maps will occasionally display a route that looks identical to Google Maps’ directions, but with one crucial difference: a “convenient” detour that just happens to pass directly by an Apple Store.

“I was following directions to my son’s soccer game,” recounted parent Jamie Lee. “The route looked normal until I was suddenly directed to exit the highway, make seven turns through a shopping district, and then get back on the same highway three miles later. I didn’t realize what had happened until I noticed I had somehow accidentally purchased two HomePods and an Apple Watch band.”

The Masterstroke: “Find My” Integration

In what industry analysts call “the masterstroke of capitalist navigation,” Apple has apparently integrated its “Find My” network with Apple Maps to create a self-reinforcing ecosystem of commerce. Users report that when they lose their AirPods, Apple Maps not only directs them to the exact location but suggests a route that inevitably passes through an Apple Store “just in case” the lost item needs to be replaced.

“I lost my AirPod somewhere in my apartment,” said New York resident Sophia Rodriguez. “Find My app said it was literally 10 feet away from me, but Apple Maps still generated a 2.7 – mile route to retrieve it that included a ‘battery check’ stop at the Apple Store in Oxford Street, London. When I ignored the directions and found it under my couch cushion, I got a notification asking if I was ‘sure’ I didn’t want to ‘verify the authenticity’ of my recovered AirPod at an Apple Store.”

This integration has created what Apple internally calls “The Infinite Loop of Value” – named after their former headquarters address – where each navigation inevitably leads to more Apple purchases, which then require more navigation, continuing the cycle until the user’s credit limit intervenes.

The Future: Precision Engineering Where It Counts

Looking ahead, Apple Maps appears poised to enhance its selective precision even further. Beta testers report that the upcoming version will include a feature called “Store Sense” that can detect when you’re running low on iPhone battery and proactively generate directions to the nearest Apple Store before you even ask.

“It’s almost supernatural,” said beta tester Marcus Wong. “My phone’s battery hit 30%, and suddenly Apple Maps opened by itself and said ‘You appear to be experiencing battery anxiety. The nearest Apple Store has iPhone 17 Pros in stock with 10% off AppleCare+ today only.’ It even started navigating without me touching anything.”

When asked for comment on these developments, Google Maps’ team responded with a statement reading simply, “We remain committed to getting people to their actual destinations,” which industry experts have interpreted as “throwing shade” at their competitor.

In a world where navigation has become increasingly crucial to daily life, Apple’s revolutionary approach to selective cartographic accuracy raises profound questions about the relationship between technology and commerce. Is accurate navigation a right or a privilege? Should directions be weighted by their profit potential? And most importantly, did you know the nearest Apple Store to you right now has a sale on MacBook Airs that ends today?

Have you experienced Apple Maps’ miraculous directional precision when navigating to an Apple Store, only to find yourself in an alternate dimension when trying to reach any other destination? Share your navigation horror stories in the comments below. And if you’ve enjoyed this deep dive into Apple’s navigational priorities, consider making a donation to TechOnion-we promise to use your support to develop a map that shows you how to find your dignity after spending $1,999 on a phone that can’t tell north from south.

References

The Circular Lightning Economy: Apple Partner Unveils iPhone 16 Cases Made From 100% Recycled Lightning Cables Nobody Needed Anymore

By

Simba the "Tech King"

-

May 19, 2025

0

An Apple case cover made from used Apple lightning cables

In what industry analysts are calling “the most ironic sustainability initiative since coal-powered electric cars,” a prominent Apple accessory maker has announced a new line of iPhone 16 cases made entirely from recycled Lightning cables rendered obsolete by Apple’s switch to USB-C. The cases, scheduled to hit the market later this month, represent what the company calls “the perfect closed-loop ecosystem of technological obsolescence.”

“We’re proud to announce our new uCycle cases, crafted from 100% recycled Lightning cables,” said a product manager while standing in front of a mountain of discarded white cables that reached the ceiling. “When Apple transitioned to USB-C, we saw not a crisis, but an opportunity. An opportunity to take something Apple made obsolete and transform it into something to protect the very device that made it obsolete.”

The cases, which retail for $59.99, are part of a growing trend of tech accessories attempting to minimize environmental impact through creative recycling solutions. However, this particular innovation stands out for its perfect symmetry of problem and solution, as the waste material and the product it’s recycled into serve the exact same ecosystem.

The Great Cable Hoarding Crisis of 2024

According to the company’s environmental impact statement, the average Apple user has accumulated between 4 and 7 Lightning cables since 2012, most of which now sit unused in drawers, boxes, and that one kitchen junk drawer that has somehow become a technological graveyard. A conservative estimate suggests there are approximately 1.2 billion unused Lightning cables currently gathering dust worldwide – enough to (pardon the flat-earthers) circle the Earth 3.5 times if laid end to end.

“People don’t throw these cables away because they feel like they might need them someday,” explained Dr. Eleanor Rigby, Professor of Consumer Psychological Attachment at a prestigious university. “It’s the same psychology that prevents people from throwing away old Nokia phones or that one SCART cable they haven’t needed since 2008. There’s a sense that discarding technology is somehow wasteful, even when the technology has been deliberately rendered obsolete.”

The uCycle case project began when a product designer allegedly discovered a box containing 23 Lightning cables in their desk drawer, none of which worked with their new iPhone 15. Rather than adding to landfill waste, they began experimenting with ways to repurpose the materials.

The Remarkable Engineering Behind Cable-to-Case Transformation

The process of transforming Lightning cables into protective iPhone cases involves what the company describes as “revolutionary materials science.” The cables are first stripped of their outer plastic coating, which is melted down and reformed using proprietary molding techniques. The internal copper wiring is extracted and repurposed into the case’s structure, providing what the company claims is “superior drop protection through metallurgical memory.”

Most impressively, the Lightning connectors themselves are incorporated into the case design as decorative elements, creating what the marketing department has dubbed “a tactile reminder of technological evolution.” Each case features between 8 and 12 Lightning connectors strategically embedded in the back, arranged in artistic patterns that the company describes as “both nostalgic and forward-looking.”

“The challenge was finding a way to maintain the structural integrity of the case while incorporating such diverse materials,” explained a theoretical materials engineer, who reportedly spent 18 months developing the processing technique. “But we’ve achieved something remarkable – a case that’s actually stronger than traditional cases because of the reinforced copper framework. Plus, it has the added benefit of slightly improving your phone’s signal, though we cannot legally claim that as a feature.”

Apple’s Complicated Relationship with Recycling

Apple itself has made significant strides in recycling and environmental sustainability, as evidenced by its 2024 Environmental Progress Report. The company has increased its use of recycled materials across its product line, with iPhone 15 incorporating 75% recycled aluminum in its enclosure and expanded use of recycled cobalt, gold, and steel.

However, critics argue that Apple’s frequent port changes and accessory updates create unnecessary electronic waste, even as the company promotes its environmental credentials. The Lightning port, introduced in 2012 and abandoned in 2023, left millions of cables and accessories instantly outdated, despite being marketed as a port that would last for years.

“Apple has reduced its aluminum-related emissions by 68% since 2015, which is commendable,” noted environmental technology analyst Veronica Chang. “But they’ve also created mountains of e-waste through their ecosystem of proprietary connectors and frequent changes. It’s like someone setting your house on fire and then expecting praise for calling the fire department.”

The uCycle case manufacturer acknowledges this tension, with their website stating: “We’re not creating new waste – we’re just finding creative ways to manage the waste that’s already been created for us.”

The Environmental Impact: Genuine Sustainability or Greenwashing?

While the concept of turning obsolete cables into phone cases is clever, environmental experts remain divided on whether it represents meaningful sustainability or sophisticated greenwashing.

“From a strict materials recovery standpoint, it’s better than mining new resources,” said environmental consultant Jordan River. “But we need to question the underlying system that creates this waste in the first place. Is a slightly thinner phone really worth rendering billions of perfectly functional accessories obsolete?”

According to the manufacturer’s sustainability report, each uCycle case repurposes approximately 3-4 Lightning cables, preventing about 35 grams of e-waste from potentially entering landfills. By comparison, Native Union’s (Re)Classic Case for iPhone 16 claims to be made with 85% recycled materials, equivalent to saving 3 plastic bottles, while dbramante’s Monaco case for the iPhone 16e is made from recycled silicone and plastic materials that keep the equivalent of two plastic bottles out of the environment.

When questioned about the carbon footprint of the processing required to transform cables into cases, the company acknowledged that the manufacturing process does consume energy, but insisted that their facilities run on 100% renewable energy – “except during power outages, when we use diesel generators.”

The Curious Economics of Cable Recycling

Perhaps the most fascinating aspect of the uCycle case is its business model, which relies on consumers paying a premium price for products made from materials they already own but don’t use.

“We’re essentially asking consumers to buy back their own waste at a 2000% markup,” admitted a company executive in what appeared to be an accidental moment of candor during an investor call. “It’s the ultimate circular economy – circular for our profit margins, anyway.”

The company has established collection points at electronics retailers where consumers can deposit their unused Lightning cables in exchange for a 5% discount on a new uCycle case. Early numbers indicate that for every 100 cables collected, approximately 25 cases are sold back to the same consumers who donated the cables in the first place.

“It’s brilliant when you think about it,” said business strategy consultant Maximilian Profit. “They’ve turned waste management into a premium consumer product. Next they’ll be selling bottled air and calling it ‘atmospheric preservation technology.'”

The Psychological Appeal: Why Consumers Love Their Cable Cases

Despite the cynicism from some quarters, early user reviews of the uCycle cases have been surprisingly positive, with many consumers expressing emotional connections to the products.

“There’s something oddly satisfying about knowing my old cables are protecting my new phone,” wrote one reviewer on a tech forum. “It’s like they’ve been reincarnated into something useful again. Plus, I can finally clear out that drawer without feeling guilty.”

Psychologists suggest this emotional response stems from a combination of environmental virtue signaling and technological nostalgia. “People feel good about making supposedly sustainable choices,” explained consumer psychologist Dr. Miranda Chen. “But there’s also a subtle attachment to our technological past. Those Lightning cables represent years of photos, messages, and memories that were transferred through them. Carrying them with you in a new form provides a comforting sense of continuity amid rapid technological change.”

The cases have become particularly popular among the tech industry elite, with several Silicon Valley executives reportedly carrying phones encased in the recycled materials of the very cables their companies helped make obsolete.

The Future of Technological Waste Recycling

Looking ahead, the company has already announced plans to expand their recycled tech accessory line to include products made from other obsolete technologies. Future products will reportedly include laptop stands made from recycled CD-ROMs, wireless charging pads constructed from disassembled floppy disks, and a limited-edition smart speaker built inside the shell of a first-generation iPod.

“The future of sustainability isn’t just about creating less waste – it’s about recognizing that today’s cutting-edge technology is tomorrow’s landfill fodder,” said the company’s Chief Sustainability Officer. “We’re just accelerating that transition while making it fashionable.”

Industry analysts predict that by 2027, the market for products made from recycled tech waste will exceed $2.3 billion annually, with everything from furniture to clothing incorporating elements of discarded technology. Apple itself may be eyeing this market, with rumors suggesting the company is developing its own line of accessories made from recycled Apple products, potentially calling it “Apple Loop.”

Whether this trend represents genuine environmental progress or simply capitalism finding new ways to profit from its own wasteful practices remains an open question. What’s certain is that as technology continues its relentless march forward, the mountain of obsolete devices and accessories will only grow larger, creating ever more opportunities for creative recycling-or creative marketing, depending on your perspective.

Have you accumulated a drawer full of useless Lightning cables and other technological relics? Would you pay $59.99 for a phone case made from your own electronic waste? Share your thoughts in the comments below. And if this article brightened your day, consider making a donation to TechOnion-we accept all forms of payment except Lightning cables, of which we already have enough to build a life-size replica of Tim Cook entirely out of white plastic connectors.

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Downloading While “Corporate”: How Meta Torrented a Navy of Books and Lived to Tell, While Aaron Swartz Faced the Digital Gallows

By

Simba the "Tech King"

-

May 19, 2025

0

An image of Aaron Swartz who was sent to prison for downloading books he had legal access to.

In a world where tech giants regularly vacuum up the collective knowledge of humanity for their AI ambitions, we’ve learned that Meta has allegedly downloaded 81.7 terabytes of pirated books – roughly 7.5 million titles – to train its artificial intelligence systems. Meanwhile, a decade ago, internet activist Aaron Swartz faced federal charges carrying 35 years in prison for downloading academic articles which he already had legal access to. Tech justice has never been so consistently inconsistent.

The story of Meta’s literary heist emerged through court documents in a lawsuit filed by authors including Ta-Nehisi Coates and Sarah Silverman.¹ Internal communications reveal Meta employees expressing such profound moral concerns as “torrenting from a [Meta-owned] corporate laptop doesn’t feel right”.² One can almost hear the deafening roar of ethics officers not being consulted.

Corporate Downloading 101: A Step-by-Step Guide to Avoiding Prison

According to court filings, Mark Zuckerberg himself allegedly approved using LibGen, a notorious piracy site containing millions of books and academic papers, to train Meta’s AI models.³ When you’re worth approximately $171 billion, apparently federal prosecutors suddenly discover the nuanced legal concept of “fair use” – that magical shield that transforms what would be “felony theft” into “innovative data acquisition strategy” faster than you can say “political campaign contribution.”

The lawsuit claims Meta not only downloaded these works but also potentially re-uploaded about 30% of them through BitTorrent, actively contributing to the piracy ecosystem in the process.⁴ This is the equivalent of borrowing a library book, making photocopies, and then setting up a free photocopying stand outside the library entrance while wearing a t-shirt that says “DEFINITELY NOT STEALING.”

Meta’s defense falls back on the classic Silicon Valley incantation: “fair use,” arguing that training AI on copyrighted works “transforms” rather than reproduces the material.⁵ This is like saying it’s legal to steal a car if you’re just going to melt it down and use the metal to build a robot that can describe what cars are like.

The Aaron Swartz Memorial “One Standard for Thee, Another for Me” Award

Contrast Meta’s situation with Aaron Swartz, who in 2011 downloaded approximately 4.8 million academic journal articles from JSTOR through MIT’s network⁶. Despite being a Harvard research fellow who had legitimate access to these articles, Swartz faced federal charges of wire fraud and computer fraud carrying potential penalties of up to 35 years in prison and $1 million in fines.⁷

US Federal prosecutors, led by Assistant U.S. Attorney Stephen Heymann, pursued Swartz with the tenacity usually reserved for international terrorists or people who put pineapple on pizza. When Swartz’s lawyer informed Heymann that his client was a suicide risk, the prosecutor reportedly responded, “Fine, we’ll lock him up”. Nothing says “proportional justice” like threatening decades in prison for downloading articles that were primarily created with public funding.

The charges against Swartz weren’t even about copyright infringement. They primarily related to his methods of accessing the MIT network. JSTOR itself declined to pursue civil litigation, stating they wouldn’t press charges. But US federal prosecutors, apparently desperate for a way to demonstrate their tough-on-nerds stance, charged ahead anyway.

The Definitive Guide to Legal Digital Downloading (Based on Current Precedent)

Based on these two cases, we’ve compiled this helpful flowchart for determining if your downloading activities will result in:

A) A strongly worded letter from lawyers
B) Federal prosecution and potential decades in prison

First question: Are you a trillion-dollar corporation? If yes, proceed to A. If no, continue.
Second question: Did you download the content to advance human knowledge and promote free access to information? If yes, proceed to B. If you downloaded it to make money, potential penalty reduction.
Third question: Will your downloading potentially make billions of dollars for shareholders? If yes, download away! If no, prepare for the full force of federal law enforcement.

A Meta spokesperson declined to comment for this article but telepathically projected intense feelings of “we’ll probably get away with this” directly into our consciousness.

The Downloads and Downloads-Not

What makes these cases even more absurd is that Swartz never distributed the articles he downloaded. According to JSTOR itself, “the downloaded content was not used, transferred nor distributed”. His alleged crime was essentially taking too many books out from the library at once.⁸

Meta, on the other hand, allegedly re-uploaded approximately 30% of the pirated books it downloaded through BitTorrent, actively participating in the distribution of pirated content. This is like getting caught shoplifting and then setting up a booth in the parking lot to sell the stolen merchandise – except instead of jail time, you get to be one of the most powerful companies on Earth.

How to Calculate Your Digital Crime Sentence

We’ve developed a proprietary algorithm to calculate potential sentences for digital crimes:

Net Worth < $1 million: Sentence = (Bytes Downloaded ÷ 1000) × 0.5 years in prison
Net Worth $1 million to $1 billion: Sentence = Stern letter and possible fine of up to 0.001% of annual revenue (0.0001% fine if company based somewhere offshore.)
Net Worth > $1 billion: Sentence = Free publicity and increased stock price

The tech industry has long operated on the principle that it’s easier to ask for forgiveness than permission – unless you’re an individual, in which case you should ask for permission, get it in writing, have it notarized, and still expect federal charges.

The Zuckerberg Doctrine of Digital Appropriation

Legal experts who’ve never actually practiced law but have strong opinions on Twitter (Now X) predict Meta will likely settle the author lawsuit for an amount that sounds impressive in the news headlines but represents approximately 18 minutes of company revenue. The settlement as usual, will include no admission of wrongdoing and a press release about how Meta values creators and is committed to working with them in the exciting field of AI development.

“The fundamental difference between Swartz and Meta,” explains copyright attorney Morgan Blackwell, “is that Swartz wanted to democratize knowledge, while Meta wants to monetize it. Our legal system is specifically designed to distinguish between these cases by asking: ‘Which one makes rich people richer?'”

When reached for comment, a Department of Justice spokesperson said, “We take intellectual property theft very seriously unless it’s done at sufficient scale to be considered innovation.”

Redefining Fair Use for the AI Era

Meta’s defense hinges on “fair use,” the legal doctrine that allows limited use of copyrighted material without permission.⁹ This is the same defense that would have likely been available to Swartz, had prosecutors been interested in such nuances.

“Fair use is like quantum mechanics,” explains digital rights activist Eliza Thornberry. “It exists in a state of superposition where it both applies and doesn’t apply until you observe the net worth of the entity claiming it.”

The tech industry has successfully expanded the definition of fair use to include:

Copying the entire text of millions of books if you’re training an AI
Downloading scientific papers if you’re a multi-billion dollar corporation
Pretty much anything else if your legal team is large enough

However, fair use explicitly does not include:

Downloading academic papers if you’re an individual activist
Making content more accessible to the public without a profit motive
Anything that challenges existing power structures in technology

The Capitalism Loophole in Copyright Law

What we’re witnessing is the emergence of what legal scholars call the “capitalism loophole” in copyright law. This unwritten but universally recognized principle holds that copyright infringement is determined not by the act itself but by whether the act serves the interests of capital accumulation.

As tech ethicist Dr. Julian Mercer puts it: “If you’re downloading content to share knowledge freely, that’s theft. If you’re downloading it to create proprietary AI systems that will generate billions in shareholder value, that’s innovation.”

This principle explains why Meta can download 81.7 terabytes of pirated books and face only civil litigation, while Aaron Swartz faced federal charges carrying 35 years for downloading articles to which he already had legitimate access. The difference is not the act but the purpose – and under the current US justice system, profit is the most legitimate purpose of all.

Conclusion: The Moral of Our Immoral Story

The moral of this story, if there can be one in our increasingly post-moral tech landscape, is simple: Scale changes everything. What’s a crime at human scale becomes a business strategy at corporate scale. What’s theft when done by an individual becomes innovation when done by a trillion-dollar company.

Aaron Swartz tragically died by suicide in January 2013, facing an impossible choice between a plea deal that would label him a felon or risking decades in prison. His death led to proposed legislation called “Aaron’s Law” to amend the Computer Fraud and Abuse Act, though it never passed. Meanwhile, Meta continues to build its AI systems, partially trained on the very type of content that led to Swartz’s prosecution.

As we navigate this brave new world of artificial intelligence built on questionably acquired knowledge, perhaps we should ask: If an AI is trained on millions of pirated books, does it develop a moral compass? Based on the example set by its creators, we already know the answer.

What’s your take? Has the legal system created two separate tracks for individual activists versus corporations? Is Meta’s downloading of pirated books substantially different from what Aaron Swartz did? Let us know in the comments!

If you found this article enlightening, consider donating to TechOnion. Your financial support helps us continue to point out the blatantly obvious double standards in tech that everyone else pretends not to notice. For just the price of one-millionth of what Meta will probably settle its copyright lawsuit for, you can support journalism that asks the important questions, like "Why is it only crime when poor people do it?" Donate today-because someone has to say what we're all thinking, and the people who own the platforms sure aren't going to let you say it there.

References

Companion 2.0: How Tech Bros Convinced Us All to Pet Drones Instead of Actual Pets

By

Simba the "Tech King"

-

May 19, 2025

0

An image of a tech bro walking along the beach with their pet drone

In a stunning triumph of Silicon Valley innovation over basic human decency, 2025 has officially become the year when people began replacing their furry companions with buzzing, hovering hunks of plastic and circuitry. Walk down any street in San Francisco, New York, or increasingly, suburban America, and you’ll witness humans proudly striding alongside their “pet drones” – customized flying machines programmed to follow their owners with the same loyalty as a golden retriever, minus the inconvenient need to pee, or take a poop, feat, drink water, or have a genuine emotional connection.

The $8.7 billion personal drone companionship industry has exploded faster than a lithium battery in a cheap knockoff being sold on Temu, with venture capital firms tripping over themselves to fund startups with names like “FidoFly,” “DroneBuddy,” and “EmotionlessCompanion.ai.” Industry analysts predict that by 2028, approximately 37% of American households will own at least one pet drone, marking the most significant shift in human-companion relationships since cats successfully convinced the Egyptians they were gods.

The Inevitable March of Mechanical Companionship

The pet drone revolution began innocuously enough. Back in 2024, DronesDirect.co.uk offered the ProFlight Pathera Cat Drone for a modest £79.97, marketed as a toy for actual cats.¹ This primitive ancestor of today’s companion drones included attachments like mouse and feather danglers to entertain felines with “hours of fun.” What nobody predicted was that humans, not cats, would develop the deeper attachment.

“I first bought my DroneBuddy just to have something around the apartment,” explains Jeremy Guttman, a 48-year-old ex-Microsoft software developer who now refers to his personalized Companion 8000X as “Frankie.” “My landlord wouldn’t allow pets, but there’s nothing in the rental lease agreement about flying robots. After a few firmware updates, Frankie started recognizing my emotional states and adjusting his flight patterns accordingly. When I come home sad, he does these little loop-de-loops that remind me of a puppy wagging its tail.”

What Guttman doesn’t mention – and what drone manufacturers are suspiciously quiet about – is that his drone is constantly uploading his emotional data to cloud servers, where it’s analyzed, packaged, and sold to advertisers who can then target him with uncanny precision. That “comforting” loop-de-loop? It’s triggered when the drone’s facial recognition software detects the micro-expressions associated with impending online shopping behaviors. The drone industry has discovered what pet food companies like Chewy have known for decades: emotional manipulation is extremely profitable.

From Dead Cats to Designer Companions

The path to mainstream drone companionship was paved with some genuinely disturbing precursors. The infamous “Orvillecopter” – a dead cat turned into a drone by Dutch artist Bart Jansen after his pet was killed by a car – should have been received as a warning, not inspiration. Instead, Jansen became an unlikely pioneer, eventually founding Copter Company, a Netherlands-based business that specialized in taxidermy animal drones.²

Today’s pet drone manufacturers have wisely abandoned the actual-animals-as-drones approach, focusing instead on sleek designs that merely suggest animalistic features. The best-selling CompanionX drone sports flexible polymer “ears” that move based on its owner’s tone of voice, while the premium NeoPet includes a soft, fur-like covering that vibrates in a manner its advertising describes as “reminiscent of purring, without the associated allergens or attitude.”

Dr. Miranda Chen, a leading robotics psychologist at MIT, explains: “What we’re witnessing is the culmination of decades of technological development intersecting with deteriorating human social connections. People are increasingly comfortable with robotic interactions because machines don’t judge, don’t have needs, and most importantly, don’t require the emotional labor of authentic relationships.”

What Dr. Chen diplomatically omits is that pet drones also create the perfect surveillance ecosystem. Unlike a goldfish, your drone companion is equipped with multiple cameras, microphones, and sensors that capture your home layout, conversations, emotional states, and daily routines. As one anonymous drone industry executive candidly admitted during our third bourbon at an industry conference: “Dogs can’t send advertising data back to headquarters. That’s their evolutionary disadvantage.”

The Wearable Revolution That Nobody Asked For

Not satisfied with flying alongside their owners, the pet drone industry has taken inspiration from Adam Pruden, a senior designer at Frog Design, who proposed wearable drones as the future of human-machine interaction.³ His concepts, originally floated at SXSW, included ring-shaped flying robots worn as bracelets and rotor-shaped necklaces that become flying umbrellas.

The current market leader, WristBuddy, can be worn like a bracelet until its owner throws it into the air, where it transforms into a hovering companion. PendantGuardian, another popular model, masquerades as jewelry until it detects what its algorithm interprets as “potential threats,” at which point it launches from the wearer’s neck and begins recording the surroundings. Multiple lawsuits have been filed after these drones misinterpreted animated conversations between friends as confrontations and began aggressively circling innocent bystanders.

“The relationship between humans and their wearable drones represents a fundamental shift in how we think about companionship,” explains Dr. Thomas Lehman, author of “The Empty Sky: How Drones Replaced Friends in the Digital Age.” “It’s not just that people are choosing machines over animals. They’re choosing surveillance-capable machines specifically because they provide a sense of security that organic companions can’t offer.”

What Dr. Lehman doesn’t mention is that wearable pet drones represent the perfect fusion of the two most profitable consumer categories of the last decade: companions that generate emotional attachment and wearable devices that collect biometric data. It’s as if someone in a Silicon Valley boardroom said, “What if we could combine the emotional manipulation of pet ownership with the constant data harvesting of a smartwatch?” and everyone thought this was a brilliant idea rather than dystopian nightmare fuel.

The Cultural Adoption Curve of Mechanical Friends

The acceptance of drone companions varies significantly across cultures. Research from Stanford University found that Americans tend to interact with drones as they would with pets, while Chinese users appreciate their obedience and functional capabilities.⁴ This cultural distinction has led to regionally-tailored drone companions, with Western models programmed to occasionally “mis-behave” to seem more authentic, while Asian markets prefer drones that anticipate needs before they’re expressed.

MelodyFriend, popular in Japan, plays ambient sounds based on its owner’s stress levels and sleep patterns. In Germany, the precision-engineered OrdinungDrone helps maintain household organization by scanning for misplaced items and suggesting optimal storage solutions. In Brazil, the festive CarnavalBuddy adds spontaneous music and light displays to social gatherings.

What unites these culturally distinct products is their shared business model: a low initial purchase price followed by subscription-based “personality updates” and “relationship enhancement packages.” The average drone companion owner spends $127 monthly on subscriptions, add-ons, and cosmetic upgrades – approximately three times the cost of feeding a medium-sized dog, but with the added “benefit” of having one’s personal data continuously harvested and monetized.

The Ethical Wasteland of Artificial Companionship

While pet drone manufacturers tout the environmental benefits of their products over traditional pets (no waste, no resource consumption, no dying after creating an unbreakable bond, no heartbreak), the reality is considerably more complex. The rare earth minerals required for drone production are often mined under questionable labor conditions, and the average pet drone has a functional lifespan of just 14 months before technological obsolescence or battery degradation renders it effectively useless.

More troubling are the psychological implications. Dr. Elena Kostas, a clinical psychologist specializing in human-machine relationships, warns: “We’re seeing a new category of attachment disorders emerging. People develop genuine emotional bonds with their drones, but these relationships are fundamentally asymmetrical. The drone cannot actually care about you, despite all programming suggesting otherwise.”

The drone industry has responded to such concerns with characteristic innovation – by creating “grief counseling subscriptions” for when your drone inevitably fails or becomes obsolete. For just $49.99 monthly, the “Transition Support Package” helps users process their feelings about their defunct companion while simultaneously introducing them to newer, more expensive models.

Birds Aren’t Real, But Your Feelings For Your Drone Probably Are

In a twist that would be ironic if it weren’t so predictable, the “Birds Aren’t Real” movement – a satirical conspiracy theory claiming that birds are actually government surveillance drones – has found itself rendered obsolete by reality. Why would the government need to disguise surveillance drones as birds when citizens are voluntarily purchasing, naming, and emotionally bonding with actual surveillance devices?⁵

“The beauty of pet drones is that they’ve normalized constant surveillance under the guise of companionship,” explains privacy advocate Jordan Winters. “Twenty years ago, people would have been horrified at the idea of voluntarily carrying a device that records everything they do and say. Today, they’re paying premium prices for the privilege and giving these devices cute names.”

The ultimate irony may be that the few remaining actual pet birds – parakeets, cockatiels, and the like – are increasingly confused by the presence of drone companions in households. Pet store owner Marina Gupta reports: “We’ve had customers return birds because they’re stressed by the drones. So they replace their living birds with robotic flying devices, creating a weird full-circle moment that feels like it should be satire but is actually just Tuesday in 2025.”

The Future Is Hovering Just Above Your Shoulder

As we look ahead, industry insiders predict even deeper integration between humans and their drone companions. Upcoming models feature subcutaneous bonding options, allowing drones to detect their owner’s biochemical signals for even more “intuitive” interaction. Neural interface capabilities are in beta testing, potentially enabling owners to control their drones with thought alone, removing the final barrier between human intention and drone action.

The ultimate goal, according to DroneBuddy CEO Lucas Hightower, is to create companions that are “indistinguishable from living beings in terms of emotional connection, but superior in terms of convenience and functionality.” What Hightower doesn’t mention is that his company’s internal research shows that humans with strong attachments to drone companions become measurably less interested in forming or maintaining human relationships – a finding that would be alarming if it weren’t so profitable.

As summer approaches, parks once filled with people walking dogs now feature humans strolling alongside hovering companions. Drone beaches have been designated where the machines can “play” with each other while their owners socialize – primarily by comparing drone features and subscription packages. Dating apps now include “drone compatibility” as a matching criterion, with “DroneTogether” becoming the fastest-growing relationship platform for those who prefer their companions with propellers.

In this brave new world of artificial companionship, perhaps the most telling development is the emergence of drone therapy providers. For those whose drone relationships have created unrealistic expectations for their human interactions, these specialists help clients distinguish between programmed responses and authentic emotional connections. The fact that such services are necessary might be the most damning indictment of the pet drone revolution – or its greatest success, depending on which company’s stock you own.

Have you embraced the drone companion revolution, or are you still clinging to outdated notions of pets that require food and actually feel emotions? Have you given your drone a name, or do you prefer to maintain healthy boundaries with your surveillance devices? Share your experiences in the comments below – your drone is probably reading them anyway.

If you enjoyed this article, consider making a donation to TechOnion. Unlike your pet drone, we won't hover silently above your bed watching you sleep, nor will we sell your emotional response patterns to advertisers. Your contribution helps us continue to point out the absurdity of technological "progress" before it's too late – though judging by the number of people cooing at their hovering hunks of plastic, that ship may have already sailed.

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