AI Apocalypse Blueprint: How ‘The Coming Wave’ Teaches You to Surf The End Times While Building Your Bunker

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April 29, 2025

A conceptual cover design for "The Coming Wave," featuring a dramatic, dystopian landscape split between two contrasting settings: one half depicting a high-tech, futuristic city on Mars with sleek architecture and advanced AI systems, and the other half illustrating a rugged, isolated doomsday bunker in the lush but foreboding landscapes of New Zealand. The book cover should have an ominous color palette of dark blues and fiery reds, emphasizing themes of existential dread and technological advancement. Incorporate elements representing AI, such as intricate circuit patterns or digital motifs, blending into the natural scenery, symbolizing the impending collision of technology and nature. The title should be bold and striking, possibly illuminated with a neon glow, while the authors' names, Mustafa Suleyman and Michael Bhaskar, appear in a more understated but elegant font. The overall composition should evoke a sense of urgency and contemplation, inviting readers to delve into the existential themes within.

Warning: This article may contain traces of truth. Consume at your own risk!

In what might be the most expensive self-help book for tech billionaires contemplating whether to build their doomsday bunkers in New Zealand or Mars, Mustafa Suleyman – co-founder of DeepMind and current Microsoft AI executive-has graced us with “The Coming Wave,” a 352-page existential panic attack bound in hardcover. Written with Michael Bhaskar, this treatise on technological doom makes AI safety engineers look like carefree optimists by comparison, and transforms “we’re all going to die” from a morbid observation into a publishing opportunity.

The Ultimate Tech Bro “I Told You So” Letter

As someone who helped create the very AI systems he now warns could destroy civilization, Suleyman has written what essentially amounts to the world’s most elaborate “don’t blame me when the robots kill everyone” disclaimer. It’s the equivalent of Dr. Frankenstein publishing “10 Reasons Why My Monster Might Destroy The Village And Why That’s Not Technically My Fault” while still actively stitching together corpses in his basement laboratory.

“The Coming Wave” positions Suleyman as the ultimate insider – someone who has simultaneously helped accelerate AI development at Google’s DeepMind and now Microsoft while wringing his hands about the consequences. This is like watching someone install rocket boosters on a runaway train while selling you insurance for the inevitable crash.

What makes this particularly delightful is Suleyman’s diagnosis: we face an unprecedented technological wave combining artificial intelligence and synthetic biology that will transform society so dramatically that nation-states themselves could collapse.¹ The solution? “Containment” – a concept he admits is virtually impossible but insists we must achieve anyway.² It’s rather like suggesting we solve global warming by pretending to be British and asking the sun to please tone it down a bit.

The Waves of Technological Change (Or: How I Learned to Stop Worrying and Love the Apocalypse)

Suleyman builds his argument on the concept that technology comes in “waves” – 24 previous general-purpose technologies that diffused across the globe, from fire to the internet.³ The 25th wave-a tsunami of AI and synthetic biology-is allegedly unlike anything we’ve seen before.

Dr. Helena Rutherford, historian of technological hyperbole at the Institute for Measured Responses, explains: “Throughout history, every generation believes their technological moment is uniquely dangerous. In the 1800s, people thought trains moving at 30 MPH would cause women’s uteruses to fly out of their bodies. Now we worry AI chatbots will convince us to liquidate our assets and invest in digital snake oil. The fear remains the same; only the uteruses change.”

The book argues that previous technological waves took decades to reshape society, but this one will hit us with unprecedented speed. This might be more convincing if Suleyman hadn’t made similar predictions about DeepMind’s AI systems curing all diseases very soon – a deadline that, like most techno-utopian forecasts, seems to perpetually remain just a few years away.

The Containment Problem (Or: How to Put Toothpaste Back in the Tube Using Only Your Thoughts)

The central thesis of “The Coming Wave” is what Suleyman calls “the containment problem” – how to maintain control over powerful technologies that, once released, spread uncontrollably.⁴ He argues this is “the essential challenge of our age,” which is a bold statement considering we’re also dealing with climate change, rising authoritarianism, and people who still use LinkedIn for dating.

According to Suleyman, containment of these technologies is simultaneously impossible yet absolutely necessary – a philosophical position that’s both deeply profound and utterly useless, like claiming water is both wet and dry depending on how you look at it.⁵

“Containment of the coming wave is not possible in our current world,” Suleyman writes, before devoting the rest of the book to explaining why we must contain it anyway.⁶ This logical pretzel would make even Elon Musk’s Twitter threads seem straightforward by comparison.

The book’s most amusing aspect is how it positions nuclear weapons as our only partial containment success story – a claim that might surprise residents of Hiroshima, Nagasaki, and anyone who lived through the Cold War’s multiple near-misses with global thermonuclear annihilation. If that’s our best example of successful containment, perhaps we should start preparing for the robot apocalypse now!

The Curious Case of the Missing Solutions

In a display of investigative brilliance that would make Sherlock Holmes abandon his pipe in frustration, Suleyman spends three-quarters of the book explaining why containment is impossible before pivoting to claim it must somehow be possible anyway. This is the literary equivalent of a tech startup pivoting from “blockchain for pets” to “AI-powered blockchain for pets” after burning through their Series A funding.

What makes this especially delightful is the book’s proposed solutions, which include:

Technical safety measures that somehow prevent misuse
International collaboration at an unprecedented scale
A vague collection of governance frameworks that would require nation-states to surrender sovereignty
The spontaneous emergence of global ethical consensus

As Dr. Rutherford notes, “These proposals would be challenging in a world where we can all agree on basic facts. In our current reality, where people can’t even agree whether the Earth is flat or not, they’re about as practical as suggesting we solve climate change by harnessing the power of unicorn flatulence.”

The Economics of Apocalyptic Literature

Perhaps the most overlooked aspect of “The Coming Wave” is its brilliant business model. After helping build some of the world’s most powerful AI systems at DeepMind, Suleyman has now written a bestselling book warning about the dangers of the very technologies he helped create – an entrepreneurial strategy so cynically brilliant it deserves its own Harvard Business School case study.

“The coming wave represents the greatest economic prize in history. It is a consumer cornucopia and potential profit centre without parallel,” Suleyman writes, in what might be the most nakedly capitalist assessment of impending doom since disaster insurance salesmen discovered climate change.⁷

This statement perfectly encapsulates Silicon Valley’s approach to existential risk: acknowledge the potential for catastrophe while simultaneously salivating over the profit opportunities it presents. It’s disaster capitalism with a TED Talk polish.

The Psychological Dimension: Pessimism Aversion Syndrome

One of the book’s more insightful observations is how humans exhibit “pessimism aversion” – a psychological tendency to dismiss catastrophic warnings.⁸ Suleyman recounts warning tech leaders about the “pitchforks” that would come if automation eliminated jobs too quickly, only to be met with polite nods and no actual engagement.

This reveals the true audience for “The Coming Wave”: it’s not written to prevent catastrophe but to establish an alibi. When the robots eventually rise up, Suleyman can point to his book and say, “See? I warned everyone!” while retreating to his well-stocked New Zealand compound.

As Dr. Arthur Chambers, Chief Psychologist at the Center for Technological Anxiety, explains: “There’s a peculiar satisfaction in predicting doom while doing nothing substantial to prevent it. It combines moral superiority with zero accountability. If the disaster happens, you were right. If it doesn’t, people forget you predicted it at all.”

The Suleyman Contradiction

The most delicious irony of “The Coming Wave” is how it embodies the very contradictions it claims to address. Suleyman writes, “If this book feels contradictory in its attitude toward technology, part positive and part foreboding, that’s because such a contradictory view is the most honest assessment of where we are”.

This statement serves as both a profound insight and a convenient shield against criticism. It’s like a restaurant offering both undercooked and overcooked steak while claiming the contradictory preparation is the most honest assessment of proper cooking techniques.

The book’s fundamental tension stems from Suleyman’s dual identity as both prophet of doom and profiteer of boom. As co-founder of DeepMind (acquired by Google) and now CEO of Microsoft AI, he has built his career and fortune on developing the very technologies he now claims threaten humanity’s existence.

This is rather like the CEO of ExxonMobil writing a passionate book about the dangers of fossil fuels while continuing to drill for oil – technically correct but morally suspect.

The Narrow Path Between Existential Risk and Reviewer Fatigue

As “The Coming Wave” reaches its conclusion, Suleyman presents his vision of navigating between catastrophe and dystopia, urging readers to walk a “narrow path” toward a future where technology serves humanity rather than destroying it. This path, however, remains conveniently vague – like a Silicon Valley CEO promising to “do better” after their platform has been used to undermine democracy.

The book culminates with ten steps toward containment, including technical safety measures, international collaboration, and a recognition that “the fate of humanity hangs in the balance”. These proposals, while well-intentioned, have all the practical applicability of suggesting we solve world hunger by everyone agreeing to share their lunch.

Conclusion: Apocalypse Later, Please

“The Coming Wave” ultimately succeeds not as a blueprint for salvation but as a perfect encapsulation of Silicon Valley’s relationship with the technologies it creates: simultaneously taking credit for innovation while disclaiming responsibility for consequences.

As technological waves continue to crash against “unsurmountable boulders of inequities”, Suleyman’s book serves as both warning and alibi-a time capsule of techno-anxiety that future archaeologists (human or robotic) can point to as evidence that we saw the tsunami coming but were too busy arguing about surfboard designs to evacuate the beach.

In a world where technology increasingly outpaces our ability to control it, perhaps the most honest conclusion is one Suleyman himself might agree with: we’re probably doomed, but at least we’ll have some excellent books explaining why.

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References

Vibe Coding Apocalypse: How Y Combinator Turned Silicon Valley Into a Prompt Engineering Circus

By

Simba the "Tech King"

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April 29, 2025

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In a stunning display of technological circular logic that would make even Sisyphus question his career choices, Silicon Valley’s elite have discovered the ultimate life hack: replacing software engineers with AI-generated gibberish wrapped in VC-funded delusion. The latest victim? Y Combinator, once the sacred temple of startup innovation, now reduced to hosting the world’s most expensive game of AI-powered Mad Libs.

The Rise of Prompt-Driven Development

The term “vibe coding” entered our collective consciousness through what can only be described as a mass hallucination at Y Combinator’s 2025 Winter Batch. Picture this: 25% of startups now boast codebases that are 95% AI-generated, a statistic that sounds impressive until you realize it’s like bragging that 95% of your restaurant’s meals are prepared by a microwave that occasionally forgets to add salt.¹

“We’re witnessing the democratization of technical debt!” exclaimed YC Managing Partner Jared Friedman during a recent podcast, presumably while his AI assistant generated 14 different ways to say “move fast and break things” without triggering PTSD in engineers who remember the 2020s.² The new startup playbook is simple:

Describe your app idea to an AI in the style of a drunk TED Talk
Accept whatever code it spits out like a parent pretending their toddler’s crayon scribbles belong in the Louvre
Raise $5M seed round because “AI-native” is this quarter’s “blockchain-enabled”

The Technical Debt Time Bomb

Early adopters are already discovering the dark side of this utopian vision. Research shows AI-generated code contains 52% more logical errors than human-written code, which tech bros are optimistically rebranding as “job security features”.³ One founder’s SaaS app spectacularly imploded when users discovered they could bypass payments by typing “please” in the password field-a vulnerability the AI apparently considered polite rather than problematic.⁴

The GitClear analysis of 211 million code changes reveals the true cost of this experiment: a 138% increase in duplicate code blocks since 2020, creating what engineers call “Frankenstein’s Stack” – apps held together by digital duct tape and the desperate hope that investors never ask for a tech demo.⁵

The Y Combinator Paradox

YC’s leadership now faces an existential crisis straight out of a Silicon Valley reboot episode. CEO Garry Tan breathlessly claims “10 vibe coders can do the work of 100 engineers,” forgetting that 100 engineers would at least know where the API keys are hidden.⁶ The accelerator’s new motto-“Fake it till you make it, then make it fake again” – perfectly encapsulates an industry hurtling toward technical insolvency.

The comedy writes itself:

Startups using AI to generate privacy policies that accidentally grant users ownership of the company’s servers
Pitch decks featuring “100% ChatGPT-certified code” as a selling point, unaware that ChatGPT’s certification process involves crossing its digital fingers
Founders explaining security breaches with “The AI seemed really confident about this approach!”

Debugging the Hype

The supposed benefits of vibe coding crumble under minimal scrutiny:

Claim	Reality
“Democratizes coding!”	Democratizes production outages
“Faster iteration!”	Faster accumulation of technical debt
“Lower costs!”	Higher incident response retainers

Even YC partners admit the party can’t last forever. “Zero to one is great with vibe coding,” concedes Group Partner Diana Hu, “but eventually you need people who know what a database index is”. This is Silicon Valley’s new normal: building skyscrapers on quicksand while selling timeshares to VCs.

The Inevitable Reckoning

As the first wave of AI-generated startups crashes into the rocky shores of reality, we’re treated to glorious schadenfreude:

A viral Reddit thread documents a founder’s journey from “$0 to $1M ARR in 17 days” to “$1M to federal investigation in 17 hours”⁷
Security workshops now teach investors how to spot AI-written code (hint: look for the comment “I have no idea what this does but it works maybe?”⁸
An entire sub-industry emerges to clean up AI’s mess, with consulting firms offering “Technical Debt Exorcisms” at $1,000/hour!

The final irony? The same VCs pushing vibe coding are quietly funding AI-powered tools to fix AI-generated code errors-a perfect ouroboros of Silicon Valley stupidity.

Conclusion: The Emperor’s New Stack

As Y Combinator startups burn through their runway and sanity in equal measure, we’re left with an uncomfortable truth: “vibe coding” is just the latest manifestation of tech’s eternal conflict between innovation and competence. The real product here isn’t software – it’s the spectacle of watching an entire industry cosplay as technologists while actual engineers facepalm into early retirement.

In the words of one anonymous developer: “We used to joke that two engineers could create the technical debt of fifty. Now, thanks to AI, two vibe coders can bankrupt an entire sector!”

Fund Real Journalism Before the AI Overlords Delete the Evidence

While Silicon Valley burns billions on AI-generated dumpster fires, TechOnion remains the last bastion of human-written truth. For just $10/month (or $1,000) – 0.0001% of what VCs wasted on vibe coding this morning – you can keep our servers running and our satire biting. We promise our articles contain 0% AI-generated copium and 100% organic schadenfreude. Plus, unlike Y Combinator startups, we actually know where our API keys are.

References

Subscription Apocalypse Breakthrough: How Tech’s New Chief Monetization Officers (CMO) Transform Your Digital Soul Into Quarterly Earnings

By

Simba the "Tech King"

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April 29, 2025

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In Silicon Valley’s latest attempt to extract value from every pixel of your digital existence, tech startups are enthusiastically adding a new C-suite position that makes Gordon Gekko look like Mother Teresa: the Chief Monetization Officer (CMO). This revolutionary role-combining the empathy of a parking enforcement officer with the customer-centricity of a medieval tax collector – is rapidly becoming the hottest executive position for ambitious MBAs who find “ethical considerations” too limiting for their vision of infinite growth.

The Rise of the Revenue Alchemist

The Chief Monetization Officer (CMO) isn’t just another addition to the already bloated executive team. This position represents Silicon Valley’s final form: a dedicated executive whose sole purpose is transforming everything you do-from your data to your attention to your very existence in digital spaces-into cold, hard shareholder value.

“The CMO is essentially the keeper of the business model,” explains Jasmine Reynolds, founder of MonetizeOrDie Consulting. “They oversee how it’s set, adjusted, optimized, and integrated into all areas of the company. It’s a revolutionary concept, really-having someone whose only job is thinking about how to extract more money from customers without triggering mass cancellations.”

According to recent data, 76% of consumers already report financial strain causing subscription burnout, with the average American spending $219 monthly on subscriptions they increasingly resent. For traditional executives, these statistics might signal a problem. For the CMO, they represent inefficiencies in the monetization funnel.

The Perfect Monetization Mindset

What makes a successful CMO? According to industry insiders, the ideal candidate combines the pattern-recognition skills of a predator with the moral flexibility of a politician during an election year.

“A truly great CMO needs to see monetization opportunities where others see basic human activities,” explains Marcus Davidson, author of “Monetize or Die Trying: The New Rules of Digital Extraction.” “That ‘settings’ page where users adjust their preferences? That should be a premium feature. Customer service? Tiered support packages. The pause button on your video player? That could easily be a microtransaction.”

The philosophy driving this new role transcends mere profit-seeking. It’s a fundamental reimagining of the relationship between businesses and customers – from an exchange of value to an ongoing extraction process optimized through data.

“We’ve moved beyond thinking about ‘what customers want to pay for’ to ‘what can we technically charge for before they revolt,'” Davidson continues. “It’s a subtle but important distinction.”

From User to Revenue Unit: The CMO Playbook

The CMO’s toolkit includes sophisticated strategies that make old-school price gouging look amateur:

Data Monetization Alchemy: Transforming customer behavioral data into predictive models that determine exactly how much financial pain each user segment will tolerate before cancellation.
Subscription Stacking: Creating intentionally incomplete core offerings that require additional subscriptions to achieve basic functionality.
Strategic Value Degradation: Systematically removing features from base tiers to force upgrades, like a digital version of slowly making airplane seats smaller.
Psychological Friction Engineering: Designing cancellation processes just complex enough to discourage subscribers from leaving without triggering regulatory action.

“What makes the modern CMO truly innovative is their ability to monetize frustration itself,” explains user behavior analyst Dr. Eleanor Chen. “When users become irritated by paywalls or feature limitations, they’re presented with a solution-pay more – creating a perfect cycle where the problem and solution come from the same source.”

A former software executive who spoke on condition of anonymity described the ideal monetization structure as “a maze where cheese is placed strategically at premium intersections, with each piece of cheese slightly less satisfying than the last, requiring users to venture deeper into paid territory for the same dopamine hit.”

Subscription Fatigue: Just Another Metric to Optimize

Perhaps the most revealing aspect of the CMO revolution is how it reframes customer dissatisfaction as a technical challenge rather than a business failure.

“Subscription fatigue isn’t a crisis-it’s a measurement,” explains Davidson. “The goal isn’t to eliminate it but to maintain it at the optimal level where customers are uncomfortable but not quite ready to cancel. We call this the ‘Monetization Sweet Spot.'”

This approach has created a new metric in investor circles: Maximum Extraction Before Cancellation (MEBC), which calculates how much value can be squeezed from a customer before they churn. The formula allegedly includes variables for customer inertia, subscription management hassle, and perceived switching costs.

“A truly elite CMO can keep extraction levels just below the cancellation threshold,” says venture capitalist Thomas Warner. “It’s like flying a plane inches above the ground – dangerous but incredibly profitable if you can maintain that altitude.”

The Dark Patterns Beneath the Surface

Behind the CMO’s strategic initiatives lies a sophisticated understanding of human psychology and behavioral economics that would make a casino blush.

“Modern monetization isn’t just about charging for features – it’s about engineering dependency loops,” explains digital ethics researcher Dr. Sarah Williams. “The most profitable customers aren’t the happiest ones; they’re the ones who feel trapped in your ecosystem.”

This philosophy manifests in several increasingly common practices:

The False Scarcity Strategy: Creating artificial limitations that can be removed for a fee
Value Perception Manipulation: Deliberately overpricing top tiers to make middle tiers seem reasonable by comparison
Complexity Arbitrage: Making the true cost so complex to calculate that customers give up trying
Data Ransom Models: Collecting user data in free tiers, then charging for privacy in premium ones

A particularly effective technique is what insiders call “subscription washing”-rebranding one-time purchases as “lifetime subscriptions” to please investors while technically honoring customer expectations.

“We had a client who sold digital templates as one-time purchases,” shares a marketing consultant who requested anonymity. “Their valuation quadrupled when they repackaged the exact same products as ‘lifetime access subscriptions’ without changing anything but the language.”

The Human Cost of Optimization

While startups celebrate their new monetization gurus, the societal impact of subscription proliferation continues to grow. Studies show the mental burden of managing multiple subscriptions is creating genuine psychological distress among consumers.

“We’re seeing a new form of cognitive load we call ‘subscription management anxiety,'” explains psychologist Dr. Michael Foster. “People feel trapped between the stress of managing numerous subscriptions and the guilt of paying for services they rarely use.”

Recent research indicates 44% of consumers report feeling “tired” of subscription services, while 38% say they would cancel subscriptions that increase in price. Yet cancellation processes remain deliberately cumbersome, with dark patterns designed to retain reluctant customers.

The regulatory response has been slow but is gathering momentum. The FTC’s “Click-to-Cancel” rule, set to take effect on May 14, 2025, will require companies to make cancellation as simple as subscribing – a prospect that has sent shockwaves through monetization departments.

“We’ve had clients describe this rule as an ‘extinction-level event’ for their business model,” shares a regulatory compliance consultant. “If customers could cancel as easily as they sign up, some companies would lose 30-40% of their revenue overnight.”

The Future of Monetization: Invisible Extraction

As consumers grow wiser to traditional subscription tactics, forward-thinking CMOs are already developing the next generation of revenue models focused on what industry insiders call “friction-free extraction”-monetization so seamless that customers barely notice the transaction.

“The future isn’t about adding more subscriptions-it’s about monetizing existence itself,” explains futurist and tech analyst Jordan Maxwell. “Imagine micropayments for enhanced reality filters that make your world look better, subscription tiers for how quickly your autonomous vehicle reaches its destination, or premium access to certain geographic locations in smart cities.”

Some startups are experimenting with “attention banking”-monitoring users’ gaze through device cameras to charge proportionally for content based on engagement levels-while others explore “emotional response monetization” that adjusts pricing based on detected user sentiment.

“The holy grail is passive monetization-value extraction that requires no conscious consumer decision,” says Maxwell. “When your smart fridge automatically reorders groceries from sponsored brands at premium prices without you noticing the markup, that’s monetization nirvana.”

Conclusion: The Monetization Endgame

As the subscription economy barrels toward its projected $1.5 trillion valuation by 2025, the role of the Chief Monetization Officer will only grow in importance and complexity. The fundamental question facing consumers isn’t whether companies will monetize their existence, but how extensively they’ll permit it.

For tech startups, the calculation is simple: hire a CMO, monetize every interaction, and keep extraction levels just below the point of mass exodus. For users trapped in these carefully engineered ecosystems, the future looks increasingly expensive.

Perhaps the most telling sign of how far the monetization mindset has penetrated Silicon Valley comes from a recent closed-door tech conference, where a prominent CMO reportedly ended his presentation with this chilling observation: “The perfect monetization strategy wouldn’t be recognized as monetization at all-just the natural order of things. We’re not there yet, but we’re getting closer every quarter.”

In the meantime, the average American continues adding subscriptions to their digital burden, with the psychological and financial costs largely hidden behind cleverly designed interfaces and carefully crafted value propositions. Subscription fatigue isn’t a bug in this system-it’s a feature carefully monitored and maintained at optimal levels by the new algorithmic overlords of extraction.

Support TechOnion’s Monetization-Free Journalism

Unlike the companies we cover, TechOnion doesn’t have a Chief Monetization Officer calculating the maximum financial value we can extract from your eyeballs before you flee in digital terror. For just $10 a month (less than what you’re unconsciously paying for those three subscriptions you forgot to cancel), you can help us continue exposing the absurdity of an industry that’s turned “making money” from a business necessity into a psychological warfare tactic. Remember: if you’re not paying to read an article making fun of monetization strategies, you’re probably the product being monetized.

Digital Dark Age Revival: Spain and Portugal Heroically Deny Cyberattacks While Still Searching for Power Button!

By

Simba the "Tech King"

-

April 29, 2025

0

people of spain and portugal in darkness

Warning: This article may contain traces of truth. Consume at your own risk!

In a stunning display of crisis management prioritization that would make any PR executive weep with joy, officials across Spain and Portugal spent Monday reassuring the public that the massive power outage plunging 60 million people into technological darkness was definitely, absolutely, positively NOT caused by cyberattacks-a determination they somehow reached before finding the circuit breakers.

The Magnificent Art of Pre-emptive Denial

As the entire Iberian Peninsula transformed into a 582,000 km² metaphor for the digital apocalypse, government officials demonstrated a remarkable commitment to ruling out specific causes before determining actual ones. Portuguese Prime Minister Luis Montenegro boldly declared there was “no indication” of a cyberattack, a statement made while citizens were using candles to navigate stairwells and cash registers across the country had transformed into expensive paperweights.¹

“We’ve established a highly efficient investigative protocol,” explained Dr. Elena Vásquez, digital infrastructure analyst. “Step one: deny cyberattack. Step two: check if power is actually out. Step three: figure out where we keep the fuse box. We’re currently still implementing phase three.”

This methodical approach was echoed by Antonio Costa, President of the European Council, who confidently stated there were “no indications of any cyberattack” while citizens were still trapped in elevators and hospital generators were frantically keeping critical patients alive.² This remarkable ability to eliminate sophisticated technological sabotage as a possibility without electricity, internet connectivity, or functioning computers represents a breakthrough in digital forensics that should be studied for generations.

The Schrödinger’s Cyber Attack Principle

Spain’s leadership adopted a slightly more nuanced quantum approach to the crisis. Prime Minister Pedro Sánchez announced that “we do not have conclusive information” while simultaneously not ruling out “any hypothesis”- a masterful stance allowing the cyberattack to simultaneously exist and not exist depending on which press conference you were watching.³

“This is what we call Schrödinger’s Cyber Attack,” explains technology philosopher Dr. Martin Hoffman. “Until you open the investigation box, the attack is both present and absent, real and imagined, Russian and not Russian. Spain has managed to maintain this quantum state for an impressively long period, suggesting they may have achieved a breakthrough in maintaining politically convenient uncertainty.”

The truth remains conveniently elusive even as power returns. Presidential advisers don’t rule out either cyber-attacks or conventional sabotage, but also insist there was no “large-scale failure”- despite the fact that 15GW of electricity generation (60% of national demand) vanished within five seconds in what Spanish power grid operator Red Electrica’s operations chief called an “exceptional and extraordinary” event.

Alternative Explanations: From Solar Flares to Confused Squirrels

As officials vigorously denied cyberattacks without identifying actual causes, the information vacuum was quickly filled with increasingly creative explanations.

Initial reports quoting Reuters claimed Portugal’s grid operator suggested a “rare atmospheric phenomenon” caused the outage-a theory immediately and vehemently denied, creating the unusual spectacle of denying both cyberattacks and natural causes simultaneously. This left the public with the comforting knowledge that the blackout was neither artificial nor natural, suggesting a potential interdimensional origin that officials have yet to address.

“We’ve narrowed it down to either a power grid failure that wasn’t a power grid failure, a weather event that wasn’t a weather event, or possibly a large group of Spanish and Portuguese citizens all coincidentally unplugging their appliances at the same time,” noted regional power distribution coordinator Fernando Morales. “The only thing we can definitely rule out is cyberattacks, which we had eliminated as a possibility before the lights went out!”

French grid operator RTE added to the confusion by specifically denying that the blackout was caused by a fire on a line between Narbonne and Perpignan-a remarkably specific denial that nobody had publicly suggested, raising questions about whether the French have developed precognitive denial capabilities that allow them to refute theories before they’re proposed.

The Real Victim: Official Credibility

The true casualty in this ongoing saga might be the credibility of institutional communications. While 60 million people experienced firsthand the fragility of our technological infrastructure, officials appeared more focused on controlling the narrative than providing meaningful information.

“The blackout demonstrated how utterly dependent we are on electrical infrastructure,” explains crisis communication expert Dr. Sophia Williams. “But the response demonstrated how utterly dependent governments are on controlling the cyberattack narrative. One system failed dramatically while the other performed flawlessly.”

This prompt dismissal of cyberattacks appears particularly questionable given that, according to Spain’s Surinenglish, “Since the start of the war in Ukraine, Spain has become a target for Russian hackers, who have attacked all kinds of infrastructures and institutions”. The National Institute of Cybersecurity (Incibe) confirms it is already investigating whether there was some kind of cyberattack, while the national cryptologic center CCN, part of the national intelligence center CNI, has been mobilized.

“It’s a fascinating approach to investigation,” notes cybersecurity researcher Jason Chen. “Publicly announce what you didn’t find before you’ve had time to look for it. It’s like declaring your house hasn’t been robbed while the window is still broken and you haven’t checked if your valuables are missing.”

The Technological Dependence Reality Check

While officials fumbled through explanations, the blackout provided an unwelcome reminder of just how thoroughly technology has infiltrated every aspect of modern life:

Travelers found themselves stranded as elevators, trains, and planes suddenly stopped working
Hospitals suspended routine operations as they switched to emergency generators
Traffic signals went dark, causing gridlock across major cities
Mobile phones and internet services failed, cutting off communication
Financial systems froze, with ATMs and electronic payments unavailable
Even basic infrastructure like water pumps and sewage systems faced potential failure

“It was like being thrust back into the 19th century, except without any of the skills or infrastructure to live in the 19th century,” recounted Madrid resident Carlos Fuentes. “I realized I don’t know how to do anything without electricity. I tried to Google ‘how to survive without Google’ before remembering that Google requires electricity.”

The Investigative Paradox

The most delicious irony in this ongoing saga is that the tools and systems needed to detect and investigate sophisticated cyberattacks are themselves dependent on the electricity that disappeared.

“We can conclusively rule out a cyberattack because our cyberattack detection systems were offline due to the power outage,” explained one anonymous security official, apparently missing the logical paradox in his statement. “It’s the perfect security system-if a cyberattack is successful enough to take down the power grid, the attack becomes undetectable, therefore it didn’t happen.”

This circular reasoning highlights the broader challenge of attributing blame in large-scale infrastructure failures. When the systems designed to monitor, detect, and analyze problems are themselves compromised by the very problem they’re meant to analyze, investigation becomes a recursive impossibility.

The Interconnected House of Digital Cards

What this incident reveals, beyond the amusing spectacle of premature denials, is the frightening fragility of our interconnected systems. According to the search results, the outage may have begun with “a failure in the connection with France,” which triggered a cascading effect.

This vulnerability-where a single point of failure can cascade across multiple countries-represents the dark underbelly of technological interdependence. Just as Spain and Portugal discovered they couldn’t function independently when disconnected from the European grid, modern civilization is discovering it can’t function when disconnected from its technological nervous system.

“We’ve built incredibly sophisticated systems with remarkably brittle foundations,” explains critical infrastructure analyst Dr. Rebecca Thompson. “It’s like building a skyscraper on toothpicks-impressive until someone bumps the table.”

Conclusion: When the Lights Go Out, the Denial Lights Up

As Spain and Portugal rebuild from this technological disruption, the most enduring lesson may be about institutional communication rather than infrastructure resilience. The eagerness to deny malicious activity before conducting proper investigation reveals a prioritization of narrative control over factual accuracy.

For citizens left in the dark-literally and figuratively – this approach erodes already fragile trust in institutional competence. When officials appear more concerned with dismissing certain explanations than providing accurate ones, they inadvertently strengthen conspiracy theories rather than quelling them.

Meanwhile, as power returns to the Iberian Peninsula, one question remains unanswered: if officials can so confidently rule out cyberattacks without evidence, what else might they be confidently wrong about? Perhaps in our next technological crisis, authorities might consider a radical approach: admitting uncertainty until the facts are known.

Until then, perhaps we should all keep a few candles handy. And maybe a printed manual on how to deny cyberattacks when the power goes out.

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References

The Earthling Devotion Ritual: 7 Shocking Discoveries About the Apple Cult That Will Make You Question Human Intelligence

By

Simba the "Tech King"

-

April 28, 2025

0

Alien's observing Earth's obsession with Apple products

[Classified Report: Galactic Federation of Intelligent Species - Earth Observation Unit]
[Security Level: Alpha-7, Not for Human Eyes]
[Observation Cycle: 49 Earth-years]

Executive Summary for Supreme Commander

After nearly five decades of Earth observation, our advanced reconnaissance team has identified a particularly fascinating manifestation of human behavior surrounding an entity they call “Apple.” This is not, as initially hypothesized, related to the spherical fruit that grows on trees, but rather a corporation that has achieved a status more akin to a religious institution than a business enterprise. This perplexing sociological phenomenon warrants continued intensive study as it reveals fundamental truths about human vulnerability to symbolic manipulation and tribal identity formation.

Classification Status: Continue observation. Potential extinction pathway: Self-induced technological dependency leading to critical thinking atrophy.

Section 1: Historical Origins and Foundational Myths

Our archaeological data indicates Apple emerged on the primitive date of April 1, 1976 (an amusing coincidence as this corresponds to the Earth custom of “April Fools’ Day” when humans deliberately deceive each other for entertainment).¹ It was founded by three humans—Steve Jobs, Steve Wozniak, and Ronald Wayne—though the latter quickly abandoned the venture, selling his 10% ownership stake for a mere 800 human currency units, a decision that would eventually cost him billions.

The company’s first product, designated “Apple I,” was merely a circuit board requiring users to add their own case, power supply, keyboard, and display—essentially selling an incomplete product at the curiously specific price of $666.66. This early demonstration of audacious pricing for partial solutions would become a defining characteristic of the entity.

Most fascinating is the mythological elevation of co-founder Steve Jobs to near-deity status. Despite documented evidence of questionable personal behavior and business practices, humans have constructed an elaborate hagiography around this figure that rivals ancient Earth religions. His ritualistic product unveilings were conducted with the solemnity of religious ceremonies, complete with devoted followers who would emit synchronized sounds of amazement (“oohs” and “aahs”) at predetermined intervals.²

After Jobs’ biological functions ceased in 2011, followers continued to make pilgrimages to Apple facilities, leaving tribute items at various locations—a practice indistinguishable from religious worship on at least 17 developed worlds.

Section 2: The Curious Economics of Perceived Obsolescence

Perhaps the most brilliant aspect of Apple’s operation is what our economists have termed “the monetization of inadequacy.” Apple has mastered the art of selling a product while simultaneously making the purchaser feel it is insufficient—thereby creating immediate desire for the next iteration.³

This cycle proceeds as follows:

Release a product with deliberately omitted features
Price it at 30-50% above technological equivalents
Release a marginally improved version within 12 Earth months
Discontinue support for earlier models through “updates” that mysteriously degrade performance⁴
Create social pressure to upgrade through visual design changes that identify users of older models

This strategy reaches its apex with what humans called “Batterygate,” where Apple was found to be deliberately reducing the performance of older devices—a practice that resulted in a $113 million settlement with Earth authorities. Most intriguing was the company’s defense that this was a “feature” designed to “protect” users, which millions of humans appeared to accept despite clear evidence to the contrary.

The “planned obsolescence” strategy extends beyond functional degradation into the social realm. Apple cleverly designs visible indicators of which product generation a human possesses, creating immediate social stratification based solely on purchase date. On no other observed planet have we seen beings so willingly participate in their own status demotion based on arbitrary product cycles.

Section 3: The Tribal Signaling System

The Apple ecosystem serves as an elaborate tribal identification system that would fascinate any xenoanthropologist. Humans will pay significant premiums not for technological advantages (which are often minimal or non-existent) but for the social signaling value of displaying the half-eaten fruit symbol.⁵

This tribal identification extends into their communication systems, where Apple has created a visible color differentiation in messaging applications—green for “outsiders” and blue for fellow tribe members. This seemingly minor distinction has been documented to affect mate selection processes and social inclusion decisions among younger humans.

Most remarkable is how Apple has transformed normal commercial transactions into ceremonial events. New product purchases are accompanied by:

Ritualistic queuing outside retail locations (sometimes for multiple Earth days)
Communal cheering when entering the facility
Ceremonial unboxing rituals, often recorded and shared with tribe members
Public displays of the new acquisition to receive affirmation

The psychological genius of this system is that humans are trained to derive dopamine rewards not from the product’s utility but from the social approval of their purchasing decision. We have observed humans experiencing genuine distress when forced to use non-Apple products in public settings, fearing tribal rejection.

Section 4: The Store Temples and Their Priests

The physical manifestations of Apple’s influence—their “retail stores”—represent perhaps the most fascinating aspect of this Earth phenomenon. These structures abandon traditional commercial design principles in favor of quasi-religious architecture: minimalist open spaces, abundant natural light, and materials chosen for symbolic rather than practical value.⁶

Within these temples operate a hierarchy of personnel clearly modeled on religious organizations:

“Geniuses” (technical priests who possess sacred knowledge)
“Specialists” (acolytes in training)
“Creatives” (those who instruct neophytes in proper usage rituals)

The “Genius Bar” functions identically to confession booths in some Earth religions, where supplicants admit their technological transgressions (“I dropped it in water,” “I didn’t back it up”) and receive both judgment and potential absolution—for a price.

Most telling is that these employees, despite being compensated at rates barely sufficient for survival in many Earth economies, display cult like devotion to the organization. They are required to maintain enthusiasm levels that would be diagnosed as mania on most developed worlds. Our psychological analysis suggests comprehensive thought-reform techniques are employed during their training.

Section 5: The Reality Distortion Field

Apple has perfected what Earth observers call a “reality distortion field”—a psychosocial phenomenon whereby humans collectively agree to perceive Apple’s products and actions in ways that contradict objective reality. Examples include:

Perceiving recycled technologies as revolutionary innovations when implemented by Apple years after competitors
Describing identical features as “gimmicks” on other devices but “game-changing” on Apple products
Celebrating the removal of standard features (audio ports, charging equipment) as “courage” rather than cost-cutting
Perceiving price increases as indicators of improved quality rather than profit maximization

This extends to language manipulation, where Apple has successfully redefined common terms. For instance, their “Geniuses” often possess no exceptional intellectual capabilities, and their “Studios” are retail spaces rather than creative workshops. The recent controversy where their voice recognition system transcribed the word “racist” as “Trump” represents an interesting evolution of this linguistic control.⁷

Perhaps most fascinating is how this distortion field creates immunity to negative information. Revelations about labor conditions, environmental impacts, tax avoidance, and anti-competitive practices that would destroy most Earth corporations are simply absorbed and rationalized by the Apple devotees.⁸

Section 6: The Pricing Psychology Experiment

Apple appears to be conducting a multi-decade experiment to determine the maximum price humans will pay for marginal improvements. Our economic analysts have been particularly impressed by:

Selling identical physical components at 200-300% markups when bearing the Apple symbol
Creating arbitrary storage tiers with exponentially increasing prices despite the linear cost of memory
Charging premium prices for essential accessories deliberately excluded from the base product
Marketing physical devices as luxury fashion items despite their rapid technological obsolescence

This experiment has proven so successful that Apple achieved a market valuation of over $3 trillion Earth dollars—more than the annual economic output of all but a handful of Earth nations. This value exists despite Apple not pioneering any major technological breakthrough in the past decade, suggesting the value derives almost entirely from psychological manipulation rather than innovation.

The pricing strategy reaches its logical conclusion with the “Mac Pro” computer, which when fully configured costs more than the average annual salary of many Earth humans. Our behavioral scientists remain fascinated by customers who willingly purchase these devices for tasks that could be performed on equipment costing one-tenth the price.

Section 7: The Monopolistic Endgame

The ultimate objective of Apple’s strategy appears to be the creation of a closed ecosystem from which humans cannot escape without significant social and financial penalties. The US government has recently initiated legal action against these practices, confirming our observational analysis.⁹

The strategy operates through:

Creating proprietary technologies that prevent interoperability with non-Apple products
Establishing exclusive control over software distribution through the “App Store”
Implementing restrictive repair policies that force customers to use Apple’s own high-priced services
Using control of payment processing to extract revenue from all transactions within their ecosystem

This approach has achieved a remarkable outcome: humans willingly paying to become captives within a technological system that deliberately reduces their autonomy and increases their costs over time. On no other observed planet have we seen beings so enthusiastically participate in their own technological subjugation while believing it represents freedom and status elevation.

Conclusions and Recommendations

Apple represents one of the most successful psychological operations in Earth history—transforming what should be utilitarian technology tools into identity markers and status symbols. The company has effectively monetized human insecurity, tribal affiliation needs, and status anxiety on a planetary scale.

Most concerning is how this model is being replicated across other sectors of Earth’s economy, creating a pattern where humans increasingly define their worth through consumption rather than contribution. This pathway typically leads to societal fragmentation on developing worlds.

For any observing species considering initial contact with Earth, we recommend avoiding Apple users for first interactions, as their reality perception has been significantly altered. If contact becomes necessary, approach through more rational technological channels where objective assessment still functions.

End Report. Transmission complete. May the wisdom of the seven galaxies guide your further observations.

Support Our Undercover Alien Research with Human Currency!

Your donation to TechOnion helps fund our ongoing infiltration of Apple product launches, where our field agents must maintain their human disguises while documenting the bizarre ritualistic behaviors of Apple devotees. For just the price of a single Apple dongle (which we’ve calculated costs approximately 9,700% more than its actual production value), you can help us understand why humans willingly wait in line for days to spend two months’ salary on a device that will be deliberately obsolete within 18 Earth months. Donate now—before Apple invents a way to charge you for the privilege!

References

Discord Decoded: 7 Extraterrestrial Observations About Humanity’s Digital Asylum

By

Simba the "Tech King"

-

April 28, 2025

0

A green alien observing what is happening on earth and TechOnion got hold of the report.

Greetings, fellow cosmic observers. As the chief anthropologist of the Zeta Reticuli Observation Corps, I’ve spent 47 Earth cycles studying human communication patterns. Nothing in my extensive research prepared me for the phenomenon humans call “Discord” – a digital habitat where approximately 200 million humans gather to share incomprehensible memes, scream at each other while playing digital simulations, and organize into tribal structures called “servers.”¹ My mission to understand this platform has left me questioning not only human communication but the evolutionary trajectory of the entire species.

Observation 1: Origins and Technical Architecture

Our initial scans detected Discord emerging in Earth year 2015, created by human specimens Jason Citron and Stanislav Vishnevskiy, apparently dissatisfied with existing communication technologies.² What began as a gathering place for “gamers” (humans who enjoy simulated conflict) has evolved into a sprawling ecosystem hosting communities discussing everything from quantum physics to animated Japanese entertainment programs.

The technical architecture is primitive yet strangely effective. Humans connect through various receiving devices (computers, phones, tablets) to central data repositories they call “servers” – though unlike actual computing infrastructure, these “servers” are merely virtual collections of chat rooms and voice channels.³ Each server contains “channels” – one-dimensional communication pathways that flow like primitive rivers of information.

Most puzzling is that despite accessing this communication network through sophisticated quantum-capable devices, humans primarily use Discord to share pictures of small furry animals and argue about which digital entertainment products are superior. The computational power that could solve interstellar travel equations is instead used to send animated images of something called a “Pepe,” which appears to be a religious icon depicting a green amphibian deity.⁴

Observation 2: The Incomprehensible Dialect

The communication patterns within Discord defy our most advanced linguistic analysis algorithms. Humans communicate using a bewildering mixture of text, images called “memes,” animated pictures called “GIFs,” and audio transmissions frequently interrupted by background noises and something called “mom bringing dinner.”

The specialized dialect varies between servers but contains consistent patterns. Our translation matrix continually fails to interpret phrases such as “poggers,” “based,” “sus,” and “I’m just built different.” When humans type the letter combination “lmao,” they rarely, if ever, actually detach their posterior anatomy as the phrase suggests, raising serious questions about human linguistic honesty.

Particularly confounding is the use of “Text-To-Speech” functionality, where humans deliberately type nonsensical character strings like “@@@@@@@@@@@@@@@@@@@@@@” or “anunununununununununu” solely to produce sounds that annoy other community members.⁵ This behavior appears to be both recreational and a form of low-grade psychological warfare that would violate several interplanetary treaties if deployed against sentient species.

Even more perplexing is that despite having 30 language options available, humans primarily communicate in a hybrid language composed of English fragments, emoji pictographs, and deliberately misspelled words. The efficiency of communication appears to be inversely proportional to its comprehensibility, suggesting that clarity may be actively discouraged as a cultural norm.

Observation 3: Tribal Hierarchies and Digital Feudalism

Discord’s organizational system warrants particular attention. Humans voluntarily segregate themselves into what appear to be digital fiefdoms, complete with ruling classes designated by colorful “roles.” The hierarchy is strictly enforced, with rulers called “admins” and their enforcement class “moderators” wielding absolute power over communication.

The distribution of power mimics Earth’s pre-industrial feudal structures: a small ruling class controlling resources, a warrior class (moderators) enforcing order, and masses of peasant users who contribute content while having minimal rights. The parallels to Earth’s medieval period are striking, though medieval peasants were never banned for posting content deemed “cringe.”

Some of these servers have evolved into massive colonies with millions of members, particularly those devoted to artificial image generation called “Midjourney” or obscure Japanese visual narratives called “anime.”⁶ The tribal dynamics within these mega-servers suggest humans have not evolved beyond their primate origins but have simply digitized their territorial instincts and added flashing RGB lighting.

Most concerning is the cult-like devotion displayed toward server owners, who maintain control through dispensing virtual goods and special roles colored in appealing shades of digital light. Humans will perform extraordinary tasks, from recruiting new members to creating elaborate content, simply for the chance to receive a colored name that appears slightly higher in a list. This behavior closely resembles the social dynamics of several extinct Centaurian societies that collapsed due to excessive focus on status signaling.

Observation 4: Content Moderation and the Illusion of Safety

Our observation team remains deeply concerned about Discord’s security protocols. Despite claims of content moderation, we’ve documented countless instances of information and imagery harmful to human psychological development. Discord claims to prohibit “hate speech” and has policies against harmful conduct, yet enforcement appears wildly inconsistent and seemingly dependent on mysterious forces we’ve termed “algorithm whims.”⁷

Most alarming is Discord’s reputation as what humans call the “Wild West” of digital communication. We’ve observed everything from harmless communities of elderly humans discussing plant cultivation to troubling enclaves sharing what can only be described as psychological warfare tactics. The platform’s private nature makes comprehensive monitoring impossible – a fact that both human predators and our observation team have exploited with equal success rates.

The technical support system appears designed to create maximum frustration. Humans report spending Earth months attempting to retrieve access to their accounts, only to receive automated responses suggesting they “delete their account” instead of restoring it – a paradoxical solution that defies logical analysis. One human specimen documented sending 10 messages over 6 Earth months without receiving meaningful assistance, suggesting Discord’s support system might be a primitive AI, possibly a collection of trained Earth rodents, or most concerning – actual human employees instructed to maximize user distress.

Observation 5: The Economics of Digital Nothingness

Humans’ obsession with decorating their digital presence is exploited through a subscription service called “Nitro.” For a recurring monetary tribute of approximately 10 Earth currencies per Earth month, humans receive essentially nothing of tangible value – merely the ability to make their profile pictures move, send larger data packets, and express themselves with custom pictograms called “emojis.”⁸

What perplexes our economic analysts is the enthusiasm with which humans purchase these functionalities, despite them conferring no survival advantage or reproductive benefit. The human drive to customize their digital representation appears stronger than their desire for actual necessities like adequate nutrition or shelter maintenance, suggesting a potential evolutionary shift toward prioritizing digital existence over physical well-being.

Most absurd is the concept of “server boosting,” where humans collectively donate resources to elevate their digital gathering place to higher “levels,” gaining such evolutionary advantages as a custom URL and additional emoji slots. The resources expended globally on these virtual enhancements could likely solve several of Earth’s actual resource crises, including fresh water scarcity and at least three regional conflicts.

Observation 6: Voice Channels – Organized Acoustic Chaos

Perhaps most confounding are Discord’s voice channels, where humans gather to produce audio simultaneously, creating what our sensors can only interpret as controlled chaos. These sessions often continue for hours, with participants seemingly deriving pleasure from the disordered communication in a way that suggests potential auditory masochism.

The behaviors in these voice channels defy explanation: humans deliberately producing falsetto tones to irritate others, broadcasting digestive sounds for group amusement, or simply breathing heavily into their audio input devices. Most mysterious is the “push-to-talk” functionality, which humans consistently forget to use, resulting in unintentional broadcasting of private activities that frequently causes collective embarrassment yet never leads to improved behavior.

Our audio analysts have identified several recurring scenarios particularly worthy of note:

Two participants forgetting to disconnect before engaging in mating rituals, broadcasting these intimate moments to horrified server members who continue listening for far longer than necessary for scientific documentation⁹
Unexpected intrusions by household authority figures (parents) leading to abrupt communication termination and subsequent days of social ridicule
Extended periods where the only audible sound is the consumption of crispy sustenance, apparently delivered by services called “DoorDash” or “Uber Eats,” creating ASMR-like experiences that some members appear to enjoy despite claiming to find them repulsive
Heated disputes about fictional characters’ attributes that escalate to concerning levels of emotional distress, sometimes resulting in the dissolution of social bonds established over many Earth years¹⁰

The most interesting phenomenon observed is how voice channels transform typically reserved humans into vocal performers, while naturally expressive individuals often remain silent. This behavior inversion suggests Discord serves as a form of psychological pressure release for otherwise repressed personality aspects, making it possibly the largest unregulated psychological experiment in Earth’s history.

Observation 7: Meme Culture and Information Propagation

The transmission of cultural units called “memes” represents Discord’s most evolutionarily significant function. These information packets spread through servers with virus-like efficiency, mutating slightly with each transmission. Our xenoanthropologists have determined that a successful Discord meme can infect the entire human internet within 7.2 Earth hours, making it more contagious than most actual Earth pathogens.

Discord serves as both incubator and distribution network for these thought-viruses. A particularly concerning pattern is the “Discord meme compilation” where the most infectious thought patterns are collected and broadcast to wider audiences through platforms like “YouTube,” creating super-spreader events for particularly nonsensical ideas that humans have labeled “dank.”

The content of these memes defies logical analysis. Humans appear to find extreme humor in:

Distorted images of normal objects with nonsensical text overlays
Videos cut to end abruptly at precise emotional climax points, a phenomenon called “perfectly cut screams”
References to obscure cultural phenomena only a small percentage understand, creating information hierarchies based on recognition
Deliberately low-quality representations of recognizable figures that somehow increase their perceived humor value in direct proportion to their degradation

Most concerning is how these memes appear to be evolving toward increasingly abstract and incomprehensible forms, suggesting either an evolutionary dead-end for human humor or the emergence of a communication system so advanced that even our highest intelligence analysts cannot comprehend it. We cannot rule out the possibility that humans are using Discord memes to encode messages meant to organize resistance against potential alien observation.

Conclusion: Quarantine Recommendation

After extensive study, our research team has concluded that Discord represents either humanity’s greatest communication achievement or clearest evidence of impending societal collapse – possibly both simultaneously. We remain uncertain whether to recommend diplomatic contact with humans based on our Discord observations, as we cannot determine if the platform represents actual human culture or an elaborate simulation designed to confuse extraterrestrial observers.

What remains indisputable is Discord’s role as a mirror reflecting humanity’s digital evolution – chaotic, hierarchical, creative, destructive, and perpetually just one server outage away from collective meltdown. The platform embodies all of humanity’s contradictions: creating spaces for genuine connection while simultaneously enabling their worst behaviors, fostering communities while encouraging isolation, and promoting both extraordinary creativity and mind-numbing banality within the same digital space.¹¹

Our final recommendation to the Galactic Council is to establish a quantum firewall preventing Discord from ever spreading beyond Earth’s digital boundaries. Should this peculiar form of communication infect other civilizations, the consequences for galactic coherence would be severe and irreversible. One thing remains certain: any alien species attempting to understand humanity through Discord alone would likely abort contact mission immediately and recommend quarantining Earth’s internet from the rest of the galaxy.

Addendum: Further Research Funding Request

Tired of Earth’s communication platforms remaining incomprehensible? Has your own planet’s social media evolved beyond the need for moderators with god complexes and users who think adding “69” to their username is the pinnacle of comedy? Support TechOnion’s ongoing mission to document humanity’s digital absurdities before they contaminate the galactic internet. Your contribution of just 5 Zorgons (or Earth equivalent) helps keep our alien observers adequately supplied with psychic protection against Discord’s voice channels after midnight. Probe deeper into tech’s mysteries with TechOnion – because even advanced civilizations need to understand how humans managed to create both Midjourney’s artistic wonders and voice channels where people just breathe heavily for hours without explanation.

References

Drone Warfare Evolved: How Your Cousin’s Annoying Christmas Gift Became Humanity’s Most Efficient Killing Machine

By

Simba the "Tech King"

-

April 28, 2025

0

Drone Warfare Evolved: How Your Cousin's Annoying Christmas Gift Became Humanity's Most Efficient Killing Machine

In what historians will surely record as the fastest technological glow-up since the atom went from “interesting physics concept” to “city eraser,” drones have completed their remarkable journey from “annoying toy your nephew crashes into your forehead during family gatherings” to “preferred method of remote assassination for militaries worldwide.” It’s the heartwarming tale of a plucky little gadget that dreamed big and achieved its full potential – specifically, its potential to rain death from above with unprecedented precision and minimal PR consequences.

Just a decade ago, drones were primarily the domain of hobby enthusiasts and wedding photographers trying to get that perfect aerial shot of couples who would later divorce anyway. Today, they are the star performers in conflicts around the globe, beloved by militaries, feared by civilians, and inspiring an entire generation of tech bros to put “disrupting the defense sector” in their LinkedIn profiles.

The Innocent Beginnings: When Drones Were Just Overpriced Frisbees

Like most military technology that eventually ends up killing people, drones began with surprisingly innocent intentions. Austrian forces in 1849 launched incendiary balloons at Venice in what historians recognize as the first use of unmanned aerial vehicles in warfare – a quaint, artisanal approach to bombing that only successfully hit the city once.¹ It was less “precision strike” and more “we hope the wind cooperates with our murderous intentions.”

The early 20th century saw significant developments in drone technology, primarily focused on providing target practice for military personnel. Because apparently, the best way to prepare soldiers for combat was to have them shoot at flying robots rather than, say, addressing the underlying geopolitical tensions that led to wars in the first place!

By 1935, the world had advanced to the de Havilland Queen Bee, which represented the first practical military application of drone technology.² The Queen Bee was essentially a remote-controlled version of the legendary Tiger Moth trainer, designed to help naval anti-aircraft gunners practice shooting down aircraft. Nothing says “technological progress” like building machines specifically designed to be destroyed for training purposes.

“Projects like the Queen Bee should get the credit for being the first viable application of drones, which up to that point had been more or less laboratory work,” explains drone historian Connor. “Drones had begun to develop the reputation—repeated as a mantra throughout the 20th century—as the workhorses for missions that were too dull, dirty, and dangerous for piloted aircraft.” Because if there’s one thing humans excel at, it’s creating technology that handles the tasks we would rather not do ourselves, like taking out the garbage or committing war crimes!

From Hobby to Homicide: The Great Drone Pivot

Fast forward to the early 21st century, and drones began their remarkable transformation from military tools to consumer products and back again to military tools, but now with better cameras and social media integration. As drone technology miniaturized and costs decreased, they became accessible for civilian and commercial use.³ The average consumer could finally experience the joy of invading their neighbor’s privacy from 400 feet in the air.

This democratization of drone technology created an unexpected feedback loop: hobbyist innovations improved military applications, while military advancements found their way into consumer products. It’s the circle of technological life, where your DJI Phantom’s ability to automatically follow a mountain biker becomes suspiciously similar to a Predator drone’s ability to track a target across the Afghan desert.

“At the beginning of the 21st century, drones began to find applications outside the military domain,” notes a researcher who definitely isn’t working for a defense contractor on the side. “Today, drones are used in a variety of fields, from photography and cinematography to agriculture, where they assist in crop management and spraying.” Left unsaid is how those same commercial drones are now being retrofitted with explosives in conflict zones worldwide, because humans have a remarkable talent for turning literally anything into a weapon.

The Curious Case of the Drone That Didn’t Stay a Toy

The curious incident here isn’t what drones are doing – it’s what we’re not talking about as they do it. While tech publications breathlessly report on the latest consumer drone features (“It can track your dog AND make a 3D map of your house!”), they conveniently ignore how easily these same technologies transfer to military applications.

Follow the money trail, and the picture becomes elementary, my dear TechOnion reader. The global drone market is projected to reach hundreds of billions of dollars within the next decade, with military applications driving a significant portion of that growth. Companies developing “civilian” drone technology frequently maintain lucrative defense contracts, creating a convenient pipeline from consumer innovation to military application.

Connect these seemingly disparate dots:

The rapid advancement of obstacle avoidance systems in consumer drones
The parallel development of “autonomous targeting” in military systems
The overlap in personnel between consumer drone manufacturers and defense contractors

The elementary truth? The line between civilian and military drone technology was never a line at all – it was a revolving door, spinning faster with each technological breakthrough.

Meanwhile, In Actual War Zones: From Theoretical to Terrifyingly Real

In recent years, drones have transformed from theoretical military assets to central players in modern warfare. Take the Ukraine-Russia conflict, where both sides have deployed extensive drone operations.⁴

“Russia has countered by expanding its own drone fleet, in particular relying on Iranian-made drones (the delta-wing Shahed 136), which fly agile and ground-hugging flight paths that make them difficult to detect,” reports a definitely objective military analyst.⁵ What goes unreported is how these same drones were originally based on commercial designs, modified with military payloads – the technological equivalent of putting a grenade in a Happy Meal toy!

The most alarming development came in July 2024, when a Russian Mi-8 helicopter was shot down by a Ukrainian FPV (First Person View) drone – the first recorded instance of a helicopter being destroyed by a drone in combat.⁶ This milestone represents exactly the kind of technological breakthrough that defense contractors celebrate with champagne and stock options.

But perhaps the most disturbing development was reported in February 2025, when Russian authorities discovered a plot involving explosive-laden FPV drone headsets sent to Russian soldiers. When activated, these headsets detonated, reportedly causing eight Russian drone pilots to lose their eyesight. War has always been hell, but now it’s a particularly creative hell with excellent production values.

The Hobbyist-to-Homicide Pipeline: How Your Christmas Gift Becomes a War Crime

The most unsettling aspect of drone warfare isn’t the technology itself – it’s how easily civilian technology transforms into military applications. Mexican cartels have begun using consumer drones to deliver explosives with terrifying precision. A video filmed by one such drone shows it hovering over its target before dropping small bombs with a parachute, causing at least three separate explosions.⁷ The cartels apparently decided that drone-based delivery was more reliable than UberEats for their particular needs.

“In many cases, hobbyist drone flyers turned militant combatants have resorted to improvised explosives delivered with devastating effects on point targets,” notes a report that isn’t at all trying to normalize horrific violence. These new tactics have become so effective that they’re shared through social media, creating a gruesome open-source warfare community where the latest methods to kill people are exchanged like sourdough starter recipes during the pandemic.

A particularly innovative example comes from Ukraine, where troops have reportedly deployed cardboard drones with GoPro cameras for aerial reconnaissance. When your military innovation sounds like a middle school science project, you know warfare has entered a disturbing new phase.

The Great Drone Paradox: When “Precision” Means More Civilian Deaths

Perhaps the greatest irony in drone warfare is how “precision” weapons have resulted in significant civilian casualties. A report examining U.S. drone operations found that between 2004 and 2020, American drone strikes killed between 2,366 and 3,702 people in Pakistan alone, with between 245 and 303 being civilians.⁸ That’s the equivalent of precision-bombing an entire small town while insisting you’re only targeting the bad guys.

A more recent analysis reveals that drone strikes by African nations against armed factions have resulted in at least 943 civilian deaths across 50 incidents between November 2021 and November 2024. These incidents include a December 2023 drone attack in Nigeria that was intended for militants but instead struck Muslims celebrating a religious holiday, resulting in 85 fatalities.⁹ Nothing says “surgical precision” like accidentally bombing a religious celebration.

As Morris, author of a comprehensive report on drone warfare, observes: “Drones have been promoted as an ‘effective’ and contemporary methodology for conducting warfare while minimizing risks to military personnel. Yet, this notion is frequently contradicted by the rising number of civilian deaths”. Translation: “We’re killing fewer of our people and more of their civilians, which is apparently an acceptable trade-off in 21st-century warfare.“

The Silicon Valley Drone Delusion: Disrupting Traditional Warfare with New and Improved Death

The tech industry’s response to drone warfare exemplifies everything wrong with Silicon Valley’s approach to ethics. Rather than questioning whether remotely operated killing machines might pose moral dilemmas, tech companies have embraced the challenge with characteristic enthusiasm: “How can we make killing people from thousands of miles away more user-friendly?”

“The integration of artificial intelligence has enabled the development of autonomous drones capable of performing complex tasks without human intervention,” gushes a tech industry report that definitely isn’t written by people profiting from military contracts. Those “complex tasks” include identifying, tracking, and potentially eliminating human targets with decreasing levels of human oversight – which is absolutely what the creators of AI had in mind when they developed the technology.

One particularly dystopian development is the KUB-BLA, a “suicide drone” equipped with artificial intelligence that can identify targets autonomously. With a wingspan of 1.2 meters and looking like a sleek white pilotless fighter jet, this drone deliberately crashes into targets, detonating a 3-kilo explosive. It’s like if the Roomba in your living room decided the coffee table was an enemy combatant and exploded on contact.

The Final, Uncomfortable Truth About Our Drone Future

As we contemplate the evolution of drones from toys to weapons, we must confront an uncomfortable truth: this was always the destination, not a detour. Military applications have driven drone development from the beginning, with consumer applications serving primarily as both a testing ground and PR campaign for the technology.

Each new feature in your cousin’s Christmas drone – better obstacle avoidance, longer battery life, improved autonomous tracking – represents a capability that will inevitably find its way into military applications. The cute little flying camera that follows your child around the park shares core technology with systems designed to track and eliminate human targets.

The question isn’t whether drones will continue to revolutionize warfare – they already have. The question is whether we’re comfortable with the blurring line between consumer technology and weapons systems, and what that means for our collective future.

As one defense analyst put it during a closed-door industry conference: “The genius of modern drone warfare isn’t the technology itself – it’s how we’ve normalized remote killing by making the underlying technology part of everyday life. When everyone has a drone in their garage, it’s harder to question why we have them over foreign countries.”

So the next time you see a drone hovering at your local park, remember: you’re not just looking at an annoying toy – you’re witnessing the consumer version of technology that’s simultaneously revolutionizing and dehumanizing modern warfare. Sleep tight!

Support TechOnion’s Drone Surveillance Avoidance Fund

Enjoyed this article? Consider donating to TechOnion before the drones identify you as one of our readers. Your contribution helps maintain our bunker of satirists who work tirelessly to expose the absurdities of technology while constantly looking over their shoulders for suspicious hovering objects. For just the price of a cheap consumer drone, you can fund our ongoing investigation into which tech innovations will next be repurposed to rain hellfire from above. Remember: we’re not paranoid if they’re actually watching us.

References (In case you thought we made this up!)

Bitcoin’s Existential Crisis: How Satoshi’s Revolutionary Cash System Became the World’s Most Expensive Digital Paperweight

By

Simba the "Tech King"

-

April 23, 2025

0

A thought-provoking digital illustration capturing the essence of Bitcoin's existential crisis. The foreground features a large, cracked Bitcoin symbol, surrounded by digital debris and fading code, symbolizing its decline from revolutionary cash system to a digital paperweight. In the background, a dystopian cityscape bathed in neon lights contrasts with dark clouds of uncertainty. Floating holograms depict Satoshi Nakamoto, overshadowed by a looming question mark, representing the mystery of its creator. The artwork employs a dramatic color palette of deep blues and vibrant golds, with sharp focus on details like the intricate circuitry and reflections in the Bitcoin symbol. The style combines elements of cyberpunk and surrealism, creating a cinematic atmosphere that invites viewers to ponder the future of digital currency.

In the beginning, there was code. And Satoshi Nakamoto looked upon the code and saw that it was good. Then humans got involved, and everything went to HELL!

Back in the ancient digital era of 2008, when Facebook was still cool and people thought Blackberry would rule forever, a mysterious figure (or group) calling themselves Satoshi Nakamoto dropped a nine-page white paper that would change the course of financial history.¹ Titled with the irresistibly sexy name “Bitcoin: A Peer-to-Peer Electronic Cash System,” this revolutionary document promised freedom from banks, governments, and those insufferable Venmo notifications showing your friends paying each other for “last night 🍕🍺😉.”

As we approach Bitcoin’s 17th birthday, it’s time to ask the question on everyone’s mind: What would Satoshi think of their digital offspring now? Has Bitcoin lived up to its promise, or has it become the very monster it was designed to slay? And perhaps most importantly, how many of these digital golden tickets are still waiting to be mined by some lucky nerd with enough electricity to power a small latin american nation?

Satoshi’s White Paper: A Technical Masterpiece or the World’s Most Expensive Fan Fiction?

Let’s start with first principles. What actually is Bitcoin according to its creator? Digging into the white paper reveals Satoshi’s core vision: “A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution”.²

Notice what Satoshi did NOT say:

“A volatile digital asset perfect for gambling away your life savings”
“A way for tech bros to signal their intellectual superiority at dinner parties”
“A method for turning electricity into climate change and bragging rights”

The white paper elegantly solved the double-spending problem through a decentralized ledger that records transactions in “blocks” chained together cryptographically.³ This blockchain would be maintained by “miners” who compete to solve complex mathematical puzzles, earning rewards in newly created bitcoins.⁴ Transactions would be verified by network consensus rather than trusted third parties, with a total cap of 21 million bitcoins to ensure scarcity.⁵

Dr. Eleanor Rigby, Professor of Applied Cryptonomics at the Massachusetts Institute of Totally Real Academic Departments, explains: “What Satoshi created was essentially a perfect mathematical system that failed to account for one critical variable: humans are greedy little goblins who will turn anything into a speculative asset.”

In Part 11 of the white paper, Nakamoto provided mathematical proof that the network would be secure against attackers as long as honest nodes controlled the majority of computing power. He calculated the probability of an attacker catching up to the honest chain as “dropping exponentially as the number of blocks the attacker has to catch up with increases.” Seventeen years later, this security model has proven remarkably resilient – unlike the security of crypto exchanges, which have proven about as reliable as a screen door on a submarine.

The Great Bitcoin Identity Theft: From Electronic Cash to “Number Go Up” Technology

Sherlock Holmes famously solved the case of the missing racehorse by noting “the curious incident of the dog in the night-time” – the dog did nothing, which was the clue. Similarly, the most revealing thing about Bitcoin in 2025 is what it’s NOT being used for: actual transactions.

Follow the money trail and a curious pattern emerges. Bitcoin’s transformation from “electronic cash” to “digital gold” wasn’t an accident – it was a deliberate reframing by early holders who realized that convincing others to HODL rather than spend would increase the value of their own holdings.⁶

The smoking gun? Bitcoin’s transaction volume for actual goods and services has remained relatively flat for years, while trading volume on exchanges has exploded. As cryptography expert and Bitcoin early adopter Charlie “Satoshi’s Not My Dad” Williams notes, “We realized around 2013 that we could make way more money convincing people Bitcoin was digital gold than digital cash. The ‘store of value’ narrative was born, and suddenly everyone stopped caring that you couldn’t buy coffee with it.”

Connect these three overlooked dots:

Bitcoin’s average transaction fee in 2025 is approximately $20 – rendering it useless for small purchases
The majority of Bitcoin has not moved in over five years – contradicting the “medium of exchange” narrative
The companies most prominently accepting Bitcoin for purchases (like Microsoft) report minimal actual transaction volume⁷

The elementary conclusion? Bitcoin isn’t being used as money – it’s being used as a speculative investment vehicle. The “digital cash” has become digital gold, which is about as useful for buying groceries as an actual gold bar.⁸

This repositioning was cemented when major institutions began treating Bitcoin as an inflation hedge and “digital gold” rather than a payment system3. BlackRock CEO Larry Fink, once a cryptocurrency skeptic, now unironically describes Bitcoin as “digital gold,” apparently forgetting that gold is useful for things like electronics, dentistry, and gaudy bathroom fixtures for oligarchs – while Bitcoin’s primary utility remains comparison to gold.

The ultimate irony? Bitcoin, designed to free us from financial institutions, is now predominantly held and traded by… financial institutions.⁹ As they say, you either die a hero or live long enough to see yourself become an ETF.

Bitcoin Supply: The Digital Scarcity Scam That Actually Worked

As of April 2025, approximately 19.5 million of the total 21 million bitcoins have been mined, leaving just 1.5 million up for grabs. The remaining coins will trickle into existence over the next century, with the final bitcoin expected to be mined around 2140 – though this will be largely ceremonial, as it will represent just 0.00000001 BTC (or 1 satoshi).¹⁰

What Satoshi couldn’t have predicted is that a significant number of bitcoins would be permanently lost. Estimates suggest between 3-4 million bitcoins are gone forever – forgotten passwords, lost hard drives, death by washing machine, and at least one instance of a man, whose ex-girlfriend (now definitely definitely ex-girlfriend) accidentally throwing away a hard drive containing 8,000 bitcoins now worth approximately $800 million. The drive currently resides in a Welsh landfill, where local regulations prevent him from digging through literal trash to find his digital treasure.¹¹

“The beauty of Bitcoin’s lost coins is that they create even more artificial scarcity,” explains Dr. Sarah Johnson, Chief Economist at Definitely Not A Bitcoin Maximalist Think Tank. “It’s like if Leonardo da Vinci painted 21 million Mona Lisas, but then accidentally left 4 million of them on the bus.”

The halvings – events occurring roughly every four years that cut the mining reward in half – further restrict new supply. The most recent halving in 2024 reduced the reward to 3.125 bitcoins per block, triggering the usual flood of price predictions ranging from “conservative” ($150,000) to “smoking something strong” ($1 million).¹²

Examining Bitcoin’s supply algorithm reveals a fascinating asymptote: 21 million is approached but never reached.¹³ The actual mathematical limit is 20,999,999.9769 bitcoins due to the halving schedule – a detail that drives perfectionist programmers absolutely insane.

Bitcoin’s Future: Digital Messiah or Very Expensive Database?

Bitcoin’s price predictions for 2025 range from “wildly optimistic” to “mathematically impossible.” Fundstrat’s Tom Lee predicts $250,000, while Standard Chartered and Bernstein both target $200,000.¹⁴ Meanwhile, BitMEX’s Arthur Hayes is the party pooper with a mere $70,000.¹⁵

Robert Kiyosaki, who has successfully predicted 374 of the last 2 market crashes, believes Bitcoin will reach $180,000-$200,000 by year-end.¹⁶ When asked about his methodology, Kiyosaki replied, “I take the current price, add the angel number my spirit guide showed me, then multiply by how afraid I am of the US Federal Reserve.”

Institutional adoption continues to grow, with ETFs now holding over one million bitcoins. Financial advisors increasingly recommend allocating 1-5% of portfolios to cryptocurrency, which coincidentally equals the percentage of their clients’ money they’re comfortable losing without triggering lawsuits.

The Lightning Network, Bitcoin’s layer-2 scaling solution, promises to make transactions faster and cheaper – essentially rebuilding the efficient payment networks that Bitcoin was supposed to replace in the first place. As one developer anonymously confessed, “We’ve spent a decade trying to make Bitcoin work like Visa, when Visa already works like Visa. It’s like reinventing the wheel, but making it square and calling it innovative.”

Politically, Bitcoin’s future looks increasingly tied to regulatory whims. Donald Trump, once a crypto skeptic, has performed a complete 180° turn, declaring his intention to make the U.S. a “crypto superpower” and establish a Bitcoin reserve. This development has Bitcoin maximalists experiencing cognitive dissonance as they struggle to reconcile their anarcho-capitalist ideals with their sudden enthusiasm for government involvement.

The true future of Bitcoin likely lies somewhere between the hyperbitcoinization utopia envisioned by maximalists (where Bitcoin replaces all money and Michael Saylor is crowned god-Emperor) and the crypto winter apocalypse feared by skeptics (where Bitcoin joins Beanie Babies and tulip bulbs in the museum of speculative manias).¹⁷

What Would Satoshi Think?

If Satoshi Nakamoto materialized today (please don’t), they might be both impressed and horrified by what their creation has become.

On one hand, Bitcoin has achieved remarkable resilience and adoption, with a market cap exceeding $1 trillion. Major financial institutions that once dismissed it now scramble to offer cryptocurrency services. Bitcoin has survived countless obituaries and become a recognized asset class.

On the other hand, Bitcoin’s primary use as a speculative investment rather than a payment system represents a fundamental departure from Satoshi’s vision.¹⁸ The concentration of bitcoin ownership among whales and institutions undermines the democratic ideal of financial sovereignty for all. And the energy consumption of mining – which Nakamoto believed would be more efficient than traditional banking – has become a major environmental concern.

In one of his early emails (recently released as part of a lawsuit), Nakamoto acknowledged Bitcoin’s energy consumption but argued that traditional banking systems’ inefficiencies far outweigh Bitcoin’s energy use.¹⁹ He envisioned Bitcoin replacing resource-intensive infrastructure and billions of dollars in banking fees with a more efficient system. Instead, we’ve added a new energy-intensive system on top of the existing banking infrastructure, achieving the worst of both worlds.

Perhaps most disappointingly, Bitcoin hasn’t freed us from financial intermediaries – it’s simply created new ones. Exchanges, custodians, and fund managers have replaced banks as the gatekeepers of crypto wealth, extracting fees and imposing their own restrictions.

As blockchain researcher Dr. Maya Patel puts it: “Satoshi created Bitcoin to eliminate trusted third parties. Now we have Coinbase, Binance, Kraken, BlackRock, and countless others serving as trusted third parties. Task failed successfully!”

The Final Block

Bitcoin stands at a crossroads in 2025. It has transformed from a radical experiment in digital cash to a mainstream financial asset – gaining legitimacy at the cost of its original purpose. The remaining 1.5 million bitcoins will enter circulation over the coming decades, but the real question isn’t how many bitcoins are left – it’s whether Bitcoin itself has any purpose left beyond making early adopters obscenely wealthy.

As Ki Young Ju, CEO of CryptoQuant, predicts, by 2030 Bitcoin might finally return to Satoshi’s original vision and become a true currency for daily transactions. But until then, we’ll continue treating the world’s first peer-to-peer electronic cash system as anything but cash – hoarding it like digital dragons, trading it like speculative pixie dust, and arguing about it endlessly on the internet.

In the words of fictional Bitcoin philosopher Wei Dai Li: “We built a revolutionary payment system, then collectively decided not to use it for payments. Satoshi didn’t give us the future of money – they gave us a mirror that reflects our own greed, our own distrust, and our own desperate hope that somehow, someday, someone else will pay more for our magic internet money than we did.”

Now, if you’ll excuse me, I need to check if Bitcoin has hit $100,000 yet. Not that I’d sell at that price, of course. As a true believer, I’m holding until $1 million. Or zero. Whichever comes first.

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Consider donating some of those precious bitcoins you’ve been HODLing since 2013. After all, what’s the point of a revolutionary peer-to-peer electronic cash system if you never actually use it as cash? Think of it as fulfilling Satoshi’s vision while supporting the only tech publication brave enough to ask if Bitcoin is just spicy Beanie Babies for men with Patagonia vests. Remember: 1 TechOnion subscription = 1 TechOnion subscription (that’s more certainty than any crypto investment can offer).

References

Memestock Reality Distortion Field: How Tesla ($TSLA) and Dogecoin Became Interchangeable Financial Hallucinations Worth Billions

By

Simba the "Tech King"

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April 23, 2025

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In what financial historians will surely document as the most expensive joke in economic history, Tesla ($TSLA) has completed its remarkable transformation from “revolutionary electric vehicle company” to “extremely expensive internet meme that occasionally manufactures cars.” This evolution has placed it firmly in the same investment category as Dogecoin—a cryptocurrency literally created to mock cryptocurrency, which now has a market cap larger than many Fortune 500 companies because a billionaire tweeted about it while presumably sitting on his toilet.

Welcome to 2025’s financial markets, where stock fundamentals are made up and the points don’t matter. It’s the investment equivalent of paying $50,000 for an NFT of a cartoon ape smoking a cigar, except the ape occasionally announces self-driving features that don’t actually self-drive.

The Curious Case of Parallel Financial Delusions

The smoking gun evidence of Tesla’s complete memeification appeared this month when Dogecoin surged 10% while Tesla simultaneously hemorrhaged $160 billion in market value following Trump’s tariff announcements.¹ This price divergence between Musk’s two favorite financial playthings has shocked exactly no one who’s been paying attention to the fundamentally absurd nature of both assets.

“Tesla’s share price has nothing to do with its actual profits or function as a car business,” explains investment legend Bill Gross, who recently noted Tesla had begun acting like meme stocks such as Chewy². Gross’s observation, while correct, is approximately four years too late—Tesla crossed the meme Rubicon long ago, around the same time Musk decided “funding secured” was an appropriate way to announce a potential company buyout at $420 per share because, and I quote directly, it’s “a weed reference”.³

Connect these three seemingly unrelated dots:

Tesla’s market cap exceeds that of the next nine most valuable automakers (Toyota, BYD, Ferrari, Mercedes-Benz, Porsche, BMW, Volkswagen, Stellantis, and General Motors) combined.
Dogecoin was literally created as a joke to parody irrational crypto speculation.
Both assets experience dramatic price swings based primarily on Elon Musk’s social media activity.⁴

The elementary truth, dear reader? Tesla and Dogecoin aren’t investments—they’re expensive digital mood rings that change color based on Elon Musk’s X (formerly Twitter) feed.

The Financial Ouroboros: When Memes Eat Their Own Tail

In the beginning, Dogecoin was created as a lighthearted parody, featuring a Shiba Inu to mock the often illogical nature of crypto speculation. Its creators, software engineers Billy Markus and Jackson Palmer, intended it as a humorous jab at crypto hype. Fast forward to 2025, and this satirical creation has become precisely the kind of speculative asset it was designed to mock—largely thanks to one man’s Twitter habit.

Similarly, Tesla began as an innovative electric vehicle company that made real products solving real problems. Now it’s valued as though every human on Earth will soon own three Cybertrucks, despite the company’s fluctuating sales, product issues, and the fact that its flagship software only functions properly for “an elite few”.

“For years now, Tesla’s share price has been entirely unmoored from the company’s actual business—a meme stock,” notes a Quartz analysis. This assessment aligns perfectly with a Binance study finding that between March 2021 and March 2024, Tesla and Dogecoin prices moved in tandem 62.5% of the time, creating what analysts delicately termed a “suicide pact” between the assets.⁵

The cosmic joke reached its zenith when Tesla officially incorporated Dogecoin as a payment option for merchandise purchases. The car company that’s supposedly revolutionizing transportation now accepts payment in a currency featuring a cartoon dog that was explicitly created to mock the idea of cryptocurrency having value. This is the financial equivalent of a snake consuming itself while livestreaming the experience on TikTok.

Inside the Mind of a Tesla-Dogecoin Investor: A Psychological Examination

To understand the psychology behind Tesla and Dogecoin investments, I spoke with Dr. Eleanor Rigby, a behavioral economist specializing in meme-based financial decisions at the prestigious Institute for Advanced Financial Delusions.

“What we’re seeing is a fascinating cognitive phenomenon I call ‘narrative substitution,'” explains Dr. Rigby. “Investors have replaced traditional valuation metrics with story-based investments. For Tesla investors, they’re not buying a car company—they’re buying ‘Elon Musk will single-handedly save humanity through technology.’ For Dogecoin holders, they’re purchasing ‘I’m in on the joke with the world’s richest man.'”

This psychological mechanism explains why Tesla’s stock responded so dramatically to Musk’s CPAC 2025 appearance, where he described himself as “living the meme” while discussing Dogecoin.⁶ When your investment thesis is essentially “funny internet man make number go up,” actual business performance becomes irrelevant.

“Tesla has achieved something remarkable,” continues Dr. Rigby. “It’s a company that can lose $160 billion in market value in a week, and investors will still defend it by saying ‘but Mars colonies!’ This is the financial equivalent of staying in a terrible relationship because ‘they might change.'”

The Musk Effect: When One Man’s Twitter Feed Controls Billions

The true architect of this financial farce is, of course, Elon Musk himself—a man who has turned market manipulation into performance art so compelling that regulators have essentially thrown up their hands and declared “I guess this is just how things work now.”

Consider the evidence:

When Musk referred to Dogecoin in an April 2019 tweet as his favorite cryptocurrency, the coin’s price doubled in two days.⁷ Two years later, his X posts declaring “Dogecoin is the people’s crypto” triggered an overnight trading volume surge of over 50%. Meanwhile, his infamous 2018 tweet about taking Tesla private at $420 a share sent markets into such a frenzy that it triggered an SEC lawsuit.⁸

The Musk Effect has become so powerful that financial analysts now include a “Musk Tweet Probability Factor” in their models. When Tesla’s stock hit exactly $420 in December 2024, it wasn’t treated as a random price point but as a “milestone packed with meme significance” because in the Musk financial universe, juvenile drug references are actually meaningful economic indicators.

The Tesla-Dogecoin Divergence: Trouble in Meme Paradise?

The most intriguing development in this absurdist financial theater occurred this month, when Dogecoin and Tesla prices suddenly diverged. While Tesla shed $160 billion in market value following Trump’s tariff announcements, Dogecoin surged 10%. This uncoupling raises a fascinating question: Is Dogecoin finally breaking free from its Musk dependency?

“The directional difference between Dogecoin and Tesla prices begs a fundamental issue for investors: Is Dogecoin starting to separate from Elon Musk’s long-standing influence?” asks one analysis.⁹ This potential decoupling comes as Musk’s role in Trump’s administration has failed to yield the anticipated government adoption of Dogecoin, with Musk clarifying there were “no current plans” to incorporate it into official government digital infrastructure.

Meanwhile, Tesla stock opened at $245 on Tuesday, having tumbled 17.5% following Trump’s tariff announcement. After this bloodbath, Musk shared a video of economist Milton Friedman criticizing trade tariffs—a move that demonstrated both his growing political influence and how his companies remain vulnerable to his new political entanglements.

Welcome to the Meme Economy, Where Nothing Matters and Everything’s Made Up

The Tesla-Dogecoin phenomenon represents the logical conclusion of late-stage capitalism—a financial system so disconnected from reality that it has essentially become a multiplayer video game where the objective is to predict the behavior of one erratic billionaire.

Consider this: When Tesla’s stock plummeted following tariff announcements, it wasn’t because the underlying business had changed overnight. The factories were the same. The products were the same. The demand was the same. What changed was the narrative. And in today’s meme economy, narrative trumps reality every time.

This is why a cryptocurrency featuring a Shiba Inu created as satire can be worth billions, and why a car company with persistent production issues can be valued higher than Toyota, Volkswagen, GM, Ford, and every other major automaker combined.

Dr. Rigby frames it perfectly: “We’ve entered a post-rationality market where assets are valued not by what they do, but by how they make us feel. Tesla and Dogecoin make people feel like they’re part of something bigger than themselves—a community, a movement, an inside joke. The fact that one is a struggling car company and the other is literally a joke doesn’t matter when the emotional attachment is the actual product being sold.”

The Great Financial Hallucination of 2025

At the heart of both Tesla and Dogecoin is a fascinating paradox: both were created to disrupt established systems (automotive and banking respectively), yet both have become extreme manifestations of the speculative excess they were supposedly fighting against.

Erwin Voloder, Head of Policy of the European Blockchain Association, nailed this irony perfectly: “Musk’s involvement transformed Dogecoin from a satirical internet token into a speculative asset class by bestowing it with perceived legitimacy and entertainment value… The irony is that a coin created to mock irrational investing became the poster child of irrational investing”.

This same analysis applies perfectly to Tesla—a company founded to accelerate sustainable transportation that has transformed into a vehicle for speculative excess so extreme that its market cap defies all traditional financial logic.

And here we are in 2025, watching as the two untethered financial entities in Musk’s orbit—Tesla and Dogecoin—potentially begin to separate, like twin stars drifting apart after orbiting the same eccentric center of gravity for years.

The most telling quote about this phenomenon comes from Musk himself during his CPAC 2025 appearance: “Doge began as a meme. Just think about it. And now, it’s real. Isn’t that wild? But it’s great”.¹⁰ Replace “Doge” with “Tesla’s market cap” and the statement remains equally accurate—a perfect distillation of our financial reality where the line between meme and value no longer exists.

For investors in both Tesla and Dogecoin, this memeification represents either the democratization of finance or its complete surrender to absurdity, depending on your perspective. Either way, both assets have conclusively proven that in 2025, financial value isn’t determined by business fundamentals or utility—it’s determined by whatever Elon Musk decides to tweet after his morning coffee.

Support TechOnion’s Financial Reality Fund

Do you find it disturbing that your entire retirement portfolio now depends on whether Elon Musk posts dog memes at 3 AM? Help us maintain our sanity-preserving journalism with an extremely large million dollar donation to TechOnion. Unlike Tesla and Dogecoin, your contributions’ value won’t fluctuate based on a billionaire’s Twitter activity. Your financial support helps us continue excavating the bizarre truth beneath the meme economy while we desperately try to convince ourselves that economic fundamentals still matter. Remember: in a world where cartoon dogs and electric cars have become interchangeable financial instruments, satirical journalism may be the only real investment left.

References

Machine Learning Revelation: How Computers Learn to Predict Your Life Choices Before You Make Them (And Why That’s Totally Not Creepy)

By

Simba the "Tech King"

-

April 23, 2025

0

Machine Learning Revelation: How Computers Learn to Predict Your Life Choices Before You Make Them (And Why That's Totally Not Creepy)

In what future historians will surely document as humanity’s most elaborate attempt to avoid making decisions for ourselves, Machine Learning has now become the technological equivalent of outsourcing your thinking to that one friend who always makes terrible life choices but somehow speaks with unwavering confidence. Welcome to the brave new world where algorithms are trained to think—a process that involves feeding them massive amounts of data until they develop the digital equivalent of a philosophy degree: the ability to make impressive-sounding predictions while being completely wrong approximately 30% of the time.

Today, dear TechOnion readers, we embark on a journey to demystify Machine Learning, that mystical art of teaching computers to learn patterns without explicitly programming them—or as one Stanford researcher put it during a particularly honest moment at a conference afterparty, “giving computers enough examples of something until they stop being completely useless at it!”

What Machine Learning Actually Is (When No One’s Trying to Raise Series A Funding)

Strip away the marketing jargon and celestial hype, and machine learning is fundamentally about prediction based on pattern recognition.¹ A machine looks at data, finds patterns, and then applies those patterns to new information—essentially the same process a toddler uses to figure out which parent is more likely to give them ice cream, except with significantly more linear algebra.

“Without all the AI-BS, the only goal of machine learning is to predict results based on incoming data. That’s it,” explains one refreshingly honest machine learning primer.² It’s pattern recognition on an industrial scale, like teaching a computer to play “one of these things is not like the other” using thousands or millions of examples.

The entire field began when someone had the revolutionary thought: “People are dumb and lazy – we need robots to do the maths for them”. And thus, machine learning was born—a noble endeavor to transfer our intellectual laziness to silicon chips that don’t complain about working overtime.

How Machines Actually “Learn” (Spoiler: It’s Less Magical Than You Think)

Contrary to what TechCrunch (Our distant cousins) and VC pitch decks would have you believe, machine learning doesn’t involve a computer gaining consciousness and deciding to better itself through night classes and inspirational podcasts on Spotify. The “learning” process is less “Good Will Hunting” and more “toddler touching a hot stove repeatedly until the correlation between ‘stove’ and ‘pain’ becomes statistically significant.”

For machines to learn, they need three essential ingredients: data, algorithms, and more data, preferably “tens of thousands of rows” as a “bare minimum for the desperate ones”. The quality of machine learning is directly proportional to the quantity and diversity of data it consumes—which explains why tech companies are more interested in your browsing history than your actual well-being.

Machine learning algorithms process this data through what MIT researchers describe as descriptive (explaining what happened), predictive (forecasting what will happen), or prescriptive (suggesting what action to take) approaches.³ In practical terms, this means your smart speaker can describe why it ordered 17 pineapples when you asked for the weather, predict that you’ll be angry about it, and prescribe itself a factory reset before you can throw it out the window.

The Four Horsemen of the Machine Learning Apocalypse

Machine learning comes in four exciting flavors, each with its own unique way of turning data into dubious conclusions:

Supervised Learning: The digital equivalent of learning with helicopter parents. You provide labeled data and the algorithm tries to figure out the relationship between inputs and outputs. It’s like teaching a child by showing them thousands of pictures of cats while repeatedly screaming “CAT!” until they get it right. Practical applications include spam detection, where the algorithm learns that emails containing “V1AGRA” and “enlarge your portfolio” should probably be filtered—unless you’re a pharmaceutical investor with performance issues.

Unsupervised Learning: The free-range parenting approach to algorithms. You throw unlabeled data at the machine and tell it to find patterns on its own. This is often used for customer segmentation, where companies discover shocking revelations like “people who buy diapers often buy wipes too” and then act like they’ve discovered the unified field theory of retail.

Semi-supervised Learning: The “I’m not like a regular algorithm, I’m a cool algorithm” approach, where only some data is labeled.⁴ The machine learning model is told what the result should be but must figure out the middle steps itself, like telling a student the answer is “Paris” without explaining that the question was “What is the capital of France?” and not “Where should I take my next vacation?”

Reinforcement Learning: The “learn by doing” approach where algorithms improve through trial and error. Google used this technique to teach an algorithm to play the game Go without prior knowledge of the rules. The algorithm simply moved pieces randomly and “learned” through positive and negative reinforcement—the same method I use to make major life decisions, except the algorithm achieved mastery while I’m still trying to figure out why I am not a media mogul yet!

The Curious Case of Machine Learning’s Missing Common Sense

The smoking gun evidence of machine learnings’ fundamental limitations is hidden in plain sight: despite consuming more data than humans could process in multiple lifetimes, ML systems still lack basic common sense. They might recognize patterns with superhuman precision but remain confounded by simple contextual understanding that toddlers master effortlessly.

Consider pattern recognition, which ML excels at—finding trends in astronomical amounts of data. Yet when Stanford researchers asked leading ML systems to interpret the statement “I just lost my job” delivered in a neutral tone, the sentiment analysis categorized it as “content” or “satisfied.” Apparently, unemployment is a delightful opportunity for personal growth in algorithm-land!

Connect these seemingly unrelated dots:

ML systems can analyze millions of data points to predict consumer behavior with uncanny accuracy
These same systems struggle to understand basic human emotions and contextual nuances
Tech companies market ML as “intelligent” while internally referring to them as “narrow task performers”

The elementary truth becomes clear: machine learning has been marketed as artificial intelligence when it’s actually pattern recognition with an expensive public relations (PR) team.

Inside the Wizard’s Algorithm: A Day in the Life of a Machine Learning Engineer

To truly understand the absurdity of machine learning, let’s peek behind the curtain at what ML engineers actually do all day.

Meet Jasmine Chen, a machine learning engineer at a top tech company who spends her days doing what she describes as “advanced data janitor work with occasional moments of algorithmic brilliance.” Her morning routine begins with cleaning data—removing duplicates, handling missing values, and normalizing variables—a process that consumes approximately 80% of her working hours.

“The public thinks I’m building the real life Matrix,” Jasmine explains while staring at a spreadsheet with 100 million rows. “The reality is I spent three hours today trying to figure out why our algorithm thinks people named ‘null’ are more likely to default on loans. Turns out someone used the string ‘null’ instead of an actual null value in the database. This is what I got my PhD for.”

By afternoon, Jasmine is tuning hyperparameters—the settings that determine how the algorithm learns. “It’s basically just turning knobs until the model performs better. Sometimes I feel like I’m just playing with a very expensive radio trying to reduce static.”

When asked about the most challenging aspect of her job, Jasmine doesn’t hesitate: “Explaining to executives why we need eight months and one hundred million dollars to build something that they think should take ‘a couple of days’ because they read a TechCrunch article about how college dropouts built a sentiment analyzer worth billions of dollars.”

Machine Learning Applications: Where Dreams Meet Reality

Machine learning has been successfully applied across numerous domains, proving particularly valuable in areas where pattern recognition from large datasets is key.⁵ Let’s examine some of its most prominent applications:

Recommendation Engines: ML powers the algorithms that suggest products, movies, or content based on past behavior. Companies like Netflix and Amazon have perfected these systems to the point where they know what you want to watch before you do, yet somehow still recommend “Sharknado 4” because you once paused on a Discovery Channel documentary about great white sharks.

Self-Driving Cars: ML algorithms and computer vision help autonomous vehicles navigate roads safely—mostly by teaching them to recognize pedestrians more effectively than human drivers who are busy checking Instagram anyway.

Healthcare: ML aids in diagnosis and treatment planning, allowing doctors to confidently tell patients, “According to the algorithm, you have a 87.3% chance of recovering, but I’m going to prescribe this medication just to be sure the computer doesn’t murder you through statistical error.”

Fraud Detection: Financial institutions use ML to detect unusual patterns that might indicate fraudulent activity—a system that works flawlessly unless you decide to buy gas in a neighboring state, triggering an immediate card freeze and existential crisis about whether your spending habits have become too predictable.

Spam Filtering: The original killer app for ML, where algorithms learn to recognize unwanted messages. The pinnacle of human technological achievement is that your inbox now automatically filters out enlargement pills while still letting through “urgent message from your boss” emails that are actually phishing attempts from Nigerian princes.

The Machine Learning Reality Distortion Field

Perhaps the most miraculous aspect of machine learning isn’t the technology itself but the reality distortion field it generates in marketing materials and VC pitches. What ML engineers describe as “moderately effective pattern matching with significant limitations” becomes “AI-powered revolutionary paradigm-shifting intelligence” once it passes through a company’s marketing department.

This transformation is evident in how the same technology is described in technical papers versus press releases:

Technical paper: “Our model achieved 73% accuracy in distinguishing between pictures of dogs and cats under optimal lighting conditions.”

Press release: “Revolutionary AI breakthrough reimagines visual cognition with superhuman capabilities, disrupting the $14 trillion pet identification market.”

The disconnect extends to how companies talk about data needs. Internally, data scientists demand “more data, cleaner data, better data,” while externally, privacy policies soothingly assure users that companies collect “only essential information to improve your experience.” The translation: “We need everything you’ve ever done, thought, or dreamed about, but we’ll pretend it’s just to make better restaurant recommendations.”

The Future of Machine Learning: Both More and Less Than We’ve Been Promised

Looking ahead, machine learning (just like its cousin, deep learning) stands at a fascinating crossroads. On one path lies the continued refinement of narrow, specialized systems that excel at specific tasks without broader intelligence. On the other, more ambitious efforts to create general systems that approach human-like reasoning—efforts that have thus far produced the AI equivalent of a toddler that can recite Shakespeare but tries to eat rocks when you’re not looking.

The future workplace won’t be dominated by AI or humans alone but shaped by those who master the art of combining both. The most powerful force isn’t artificial intelligence or human intelligence in isolation but intelligence augmented by technology and guided by human wisdom—a poetic way of saying “we’ll still need humans to fix the algorithms when they inevitably screw up.”

As we navigate this future, perhaps the most important question isn’t whether machines can learn but whether we humans can learn to set appropriate expectations, maintain control over these systems, and remember that behind every “intelligent” algorithm is a team of engineers frantically googling error codes and wondering if they should have pursued that philosophy degree after all.

Because at the end of the day, machine learning remains a tool—an incredibly powerful, occasionally brilliant, frequently frustrating tool that, like all technology, is only as good as the humans who create, deploy, and oversee it. And in that fundamental truth lies both our greatest hope and our most pressing challenge.

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