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PCMag.com: The company says it will show off "the newest additions to the Galaxy mobile device portfolio." In its teaser video, the company goes a step further and explicitly states that "Galaxy AI is coming," so get ready for some form of on-device, Samsung-developed artificial intelligence. The tech industry's obsession with integrating AI into every possible device - including, apparently, the upcoming Galaxy S24 - appears more like a frantic race to capitalize on a popular buzzword rather than a pursuit of meaningful innovation. Samsung has previously unveiled a new generative AI suite, Samsung Gauss, which is expected to be the engine driving “Galaxy AI”. Named after mathematician Carl Friedrich Gauss, this suite is divided into three parts: Samsung Gauss Language, Samsung Gauss Code, and Samsung Gauss Image. Gauss Language focuses on tasks like drafting emails, summarizing documents, translations, and enabling smarter device control. Gauss Code includes a coding assistant, offering features such as code explanation and test case generation. Gauss Image is designed for generating creative images, making style edits, and converting low-resolution images to high-resolution. None of this is revolutionary; similar concepts have been rolling out on platforms from Canva to Github and are already available on phones like last year’s Pixel 8 Pro. While features such as on-device AI calculations, advanced AI-driven photography, and live translation sound impressive, I can’t help but wonder if they will genuinely enhance user experience in the long term or simply serve as flashy, superficial selling points. Are these LLM-driven features revolutionary or incremental updates masquerading as groundbreaking advancements?
The internet was supposed to unite us. So what the fuck happened? And how did we become a cross-generational digital nation of assholes? We dreamed of a global, decentralized network spreading information, enabling communication, and connecting humanity. But somewhere along the way, it divided us. Our online spaces, overrun with tribalism, misinformation, harassment, and cruelty, often bring out the worst in human nature. Much of the blame lies in how our dominant online platforms are built. Social media, discussion forums, and comment sections are not neutral but purpose-driven products whose goals often work against healthy discourse. Consider anonymity, which shields users from accountability, enabling threats, propaganda, and mindless tribalism divorced from any ownership or consequence. Engagement-based algorithms promote controversial, emotive, and false content to maximize likes, shares, and ad revenue. Platforms allow misinformation to spread like wildfire, with manipulated media impersonating real people. Organized trolling campaigns operate openly, bombarding women, people of colour, and other marginalized groups with harassment yet facing few repercussions. Foreign influence operations deliberately spread propaganda to sow division. Conspiracy theories and partisan untruths proliferate unchecked, eroding our shared reality. Outrage, tribalism, cruelty — it’s all good for business. And so we have created online ecosystems that permit and actively incentivize our worst instincts. Environments perfectly designed to bring out trolls, grifters, demagogues, and white supremacists. But it’s not that simple. It’s not as simple as pointing at bogeymen and QAnon influencers. That’s passing the buck. The assholeification of the digital world is a movement we’ve all been a part of. Every single one of us. We’ve all felt and — at times, given into — the pull of being an asshole. We all became assholes online because the digital world enabled us to forget, ignore, or not bother with empathy, and we jumped at the opportunity. It didn’t have to be this way. People built these systems and these ways of interacting; people removed the friction points that made rejecting empathy either difficult or socially acceptable, and people can change. This is the task ahead, both as individuals and societies — to reimagine our online world in a way that reconnects us to our shared humanity. A revival of the internet’s founding vision, where connection comes before division, truth outweighs propaganda, and diverse voices come together in meaningful discourse. The experiments we’ve run over the past decades have gone off the rails. It’s time to retake control of our creation. But before we do that — we have to understand the process of empathy obstruction. We have to understand how the asshole gets unlocked. We Became Assholes Through Anonymity Anonymity has always played a role online, from bulletin boards and chatrooms to modern forums like Reddit and X. As our online lives become increasingly central, anonymity has evolved from an interesting quirk into a defining and sometimes dangerous force. An anonymous online persona decouples behaviour from real-world identities and consequences. Freed from accountability, people become more likely to make threats, spread misinformation, harass others, or behave in ways counter to social norms. Experiments have shown that even minimal anonymity shifts individuals towards more egocentric and unethical activity. The pseudo-anonymity of screen names produces measurable changes versus real names. Total anonymity, as found on many forums and message boards, can have an even more dramatic effect. With no identity tied to actions, inhibition and empathy tend to decrease. Studies of anonymous spaces repeatedly find increases in racism, sexism, body-shaming, and other antisocial acts. Anonymity grants the courage to be cruel. This “cowardly courage” manifests as organized attacks. Anonymous online mobs frequently coordinate harassment campaigns against women, people of colour, and other marginalized groups. A study of one such campaign found that over two-thirds of attacks originated from anonymous accounts versus named accounts. Anonymity unleashes our worst impulses. Propagandists and bad actors weaponize anonymity to spread misinformation without accountability. On forums like 4chan, anonymous users deliberately push racism, conspiracy theories, and falsehoods into the mainstream. Russia exploits anonymity in propaganda operations to impersonate Americans digitally. Anonymity provides cover for dangerous lies. Of course, anonymity has upsides, too. Whistleblowers rely on it to expose wrongdoing without risk of retaliation. People exploring sensitive identity issues use it to find support and understanding. Anonymity enables honesty on taboo topics and allows those without the power to challenge institutions. This same cloak of anonymity also shelters trolls, demagogues, and harassers from the consequences of their actions. It is the primary enabler of online mobs, giving courage to those whose messages would be objectively horrible if attached to a real person. Anonymity tilts online spaces to reward outrage over discourse and noise over the signal. We Became Assholes For Engagement Engagement is the lifeblood of social media. Comments, shares, likes — these metrics determine what content surfaces and succeeds. To understand how our online behaviour has grown crueller and more extreme, look to how platforms incentivize engagement above all else. Engagement-based algorithms govern nearly every social platform, recommending content based on popularity rather than accuracy or quality. Studies find falsehoods and conspiracy theories consistently outperform factual reporting in shares and likes. Extremist pundits get more engagement than moderate voices. Incendiary tweets draw more eyes than thoughtful discussion. The nature of these algorithms perpetuates addictive feedback loops. Outrageous content drives engagement, which platforms amplify and recommend to more users, who react with further outrage. Nuance and complexity get drowned out by whatever provokes the strongest reaction. Objective truth matters less than an emotional response. These dynamics enable fringe voices to achieve outsized influence through strategic gamed engagement. Provocateurs make extreme statements not to persuade but to trigger high-arousal emotions like anger, fear, or disgust. Those emotions drive shares, electrifying algorithms to blast content to new audiences. Studies find moral outrage is the strongest virality driver, and bad actors exploit this mercilessly. Consider the rise of hyper-partisan influencers across social media, who traffic almost exclusively in outrage, conspiracy theories, and tribalism. Instead, they produce little original content, endlessly reacting to the day’s controversies and “owning” whichever group their audience hates. Their social capital stems not from being correct or thoughtful but skilled at punching the brain’s rage buttons. Engagement-based systems incentivize these toxic dynamics not by accident but by design. Most major platforms are funded by advertising, with business models requiring endless growth in user attention. Controversy, drama, and outrage keep eyes glued and feeds scrolling — precisely what advertisers want. Stoking anger and paranoia is rewarded as long as it’s profitable. From an individual user perspective, these same incentives structure our online behaviour. In hopes of going viral, we are encouraged to take cheap shots, dunk on opponents, and frame every issue as an outrage against the other tribe. Thoughtful arguments stand little chance versus calculated takedowns — no matter how dishonest. Progress requires understanding these situational factors that encourage our worst instincts. Given their foundational role in social media business models, fixing engagement algorithms and incentive structures won’t be easy. BUT reforms are essential if we want online spaces to allow quality discourse and make the adoption of assholism harder than adopting empathy. Prioritizing accuracy over emotion, incentivizing listening over dunking, shaping norms around constructive disagreement — better sociotechnical architectures are possible. We face both a design challenge and internal psychological work of resisting reactivity. The quest for likes and upvotes brings out our tribal, impulse-driven selves. But we are also capable of so much more — empathy, curiosity, nuance. Reclaiming the internet’s potential requires reimagining platforms built to serve humanity’s best, not worst, instincts. Who we are and what we say and do online arises from how spaces are constructed around us. We must demand, create, and frequent spaces elevating compassion over compulsion and truth over the tribe. The algorithms must serve us — not the other way around. We Became Assholes When We Rejected Truth The rapid spread of misinformation represents one of the most alarming trends of the social media era. False and misleading content now proliferates across online ecosystems, drowning out facts and eroding shared reality. While misinformation is an old problem, modern platforms act as super-spreaders. This stems directly from how major platforms optimize for engagement over accuracy. Studies repeatedly show falsehoods and conspiracy theories outperform factual reporting in shares, likes, and viral spread. Platform algorithms recommend this misleading content more frequently, exposing more users and fueling engagement. Bad actors exploit these dynamics to flood the information ecosystem with propaganda and disinformation. State and non-state actors have weaponized these vulnerabilities to attack truth itself. Russia pioneered industrialized “active measures” to digitally impersonate Americans online, using troll armies and manipulated videos to spread disinformation without disclosure. Other state actors like China and Iran follow suit. These propaganda efforts deliberately target society’s fissure lines around race, immigration, and policing to inflame tensions and create chaos. The Kremlin’s 2016 election interference demonstrated the frightening effectiveness of these information warfare tactics. Russia infiltrated American political discourse by posing as Black Lives Matter activists, gun rights groups, veterans, and more. They organized protests and counter-protests around divisive issues on US soil. Their propaganda reached over 100 million Americans on Facebook, shaping political narratives and outcomes. Yet their primary goal was broader — to undermine shared reality and trust in institutions. In this, they succeeded. But misinformation also proliferates from domestic sources. Partisan websites and pundits spread propaganda and conspiracy theories for political gain. Scammers use fake news to drive ad revenue and traffic. Motivated reasoning makes people more likely to believe false claims aligning with their ideology. Those with the most extreme views are often most prone to disinformation — and algorithms funnel them more of it in a dangerous feedback loop. No issue highlights these threats more than COVID-19, which spawned misinformation undermining public health on an unprecedented scale. Viral conspiracy theories discouraged mask-wearing, social distancing, and vaccines, costing countless lives. Public health agencies fought a virus and an entire disinformation ecosystem built to undermine them. Social media’s engagement-based algorithms accelerate the death of empathy. Each of us plays a role too. Cognitive biases make us more likely to share content that provokes strong emotion rather than search for factual accuracy. We become unwitting accomplices when we react instead of reflecting, spreading information without checking sources, or engaging with disinformation even to argue against it. Protecting truth in the digital age requires reforming platforms’ economics and business models. But it also depends on each of us taking responsibility. We must slow down, verify sources, avoid knee-jerk reactions, and debunk falsehoods responsibly when we encounter them. Progress begins by recognizing how situational factors currently incentivize the worst in human nature — and having the wisdom and will to demand spaces that bring out our best. We Became Assholes When We Normalized Trolling Over the last 20 years, a new kind of epidemic has emerged and evolved: trolling. This issue has grown beyond annoyance into a systemic problem characterized by organized groups, mass coordinated campaigns, and individual trolls targeting vulnerable communities and individuals. Organized groups of trolls often target vulnerable communities, minorities, the LGBTQ+, or people with specific political beliefs. These attacks are not spontaneous; they are carefully planned and executed with the intent to harass, intimidate, and silence. These groups often operate in the shadows, using anonymous profiles and encrypted channels to coordinate their actions. They exploit the fear and insecurity many vulnerable individuals feel online, magnifying it through relentless abuse and threats. The damage caused by these groups goes beyond the digital sphere. The emotional toll on victims can lead to mental health issues, withdrawal from social life, and even self-harm. These attacks erode online spaces’ trust and sense of community, replacing them with fear and suspicion. Mass coordinated trolling campaigns take the organized nature of group attacks to a new level. These campaigns are often politically motivated, aimed at silencing dissenting voices, discrediting individuals, or pushing a particular agenda. Through bots, fake accounts, and human trolls, these campaigns flood social media platforms with targeted harassment, misinformation, and propaganda. The scale and intensity of these campaigns can be overwhelming, making it difficult for targets to respond or defend themselves. This form of trolling has severe implications for democracy and free speech. These campaigns undermine open societies' principles by stifling dissent and manipulating public opinion. They create a chilling effect, where people are afraid to speak out for fear of becoming targets. Not all trolls are part of organized groups or mass campaigns. Many operate alone, driven by a desire for attention, amusement, or a warped sense of satisfaction from causing distress. These individual trolls often engage in provocative enterprises, posting offensive comments, sharing controversial opinions, or mocking others. While their actions may seem trivial compared to organized attacks, they can still cause significant harm. The anonymity of the internet allows these trolls to act without consequence, encouraging them to push boundaries further. The outrage and reactions they provoke often feed into their enjoyment, creating a vicious cycle of provocation and response. The trolling epidemic is a multifaceted problem, reflecting the complex nature of human interactions and the challenges of regulating online spaces. From organized groups attacking vulnerable communities to mass coordinated campaigns and individual trolls, the issue is pervasive and deeply damaging. We Became Assholes By Hanging Out With Assholes Engagement-based algorithms have revolutionized how we discover content online. Platforms analyze our impulses and interests to curate personalized feeds catering precisely to our interests. This can be tremendously useful — but also carries unintended consequences. When algorithms cater exclusively to existing preferences, they filter out challenging or opposing views. Over time, this creates isolated bubbles and echo chambers. Our feeds become dominated by similar voices, endlessly reinforcing our worldview. The mechanics of algorithms silently shape our realities. This intense personalization fuels fragmentation into increasingly extreme niches. Racists cluster with fellow racists, anti-vaxxers with anti-vaxxers, and conspiracy theorists dig deeper down rabbit holes. People’s views grow more entrenched with less exposure to alternate perspectives. Moderates leave as communities become more polarized. These dynamics assist the spread of misinformation and extremism. With no counterbalancing views, falsehoods and propaganda face little resistance. Highly-engaged niche groups exert outsized influence on platforms built around engagement. Anger and paranoia thrive inside closed loops. Echo chambers breed tribalism as groups form identities around shared views under attack by outsiders. Lacking humanization or dialogue with opposing sides, caricatures emerge demonizing perceived enemies. Studies find people dehumanize those with differing political opinions — seeing them as less evolved and lacking empathy. Bridging these divides will require making our algorithms — and worlds — more open. Platforms should balance relevance with occasional exposure to a diversity of views. Similarly, users can proactively follow those with different ideologies, creating digital “contact zones.” Openness provides an inoculation against misinformation and enables empathy. Echo chambers do not arise by accident but by engineering. The super-personalization of algorithms intelligently gives us what we want. But this can undermine what we need — occasional discomfort in the form of new perspectives and challenges to our assumptions. Our task is to build spaces that allow synthesis across tribes, enabling understanding. Escaping bubbles is difficult when business models profit from maximizing our time inside walled gardens. Revitalizing the web’s connective potential requires fighting fragmentation with curiosity. Our shared future depends on architecture encouraging us to inhabit each other’s worlds, not just our own. We Became Assholes To Sell Platforms Behind the algorithms, influencers, and outrage factories lies an even more fundamental driver — the very business models of major platforms. To understand the forces shaping online discourse, we must follow the money. The dominant model for most major social platforms is advertising. Platforms offer free services in exchange for users’ attention and data, which is used to target ads. Revenue depends on maximizing engagement — keeping users constantly plugged into algorithmic feeds filled with content optimized to compel the brain’s attention. This incentivizes a focus on user time over quality interactions. Features promoting health, like parental controls or limited usage, undermine profits. Controversy, outrage, and drama are good for business. The idea? Addicted users trapped in endless scrolls and infinite feeds. These incentives carry societal consequences when outrage fuels engagement. Platforms benefit from — and thus amplify — the most incendiary voices. Extremists drive clicks; conspiracy theories spread faster than facts. Corporate profit motives shape what billions see online. Platforms have outsized influence on public discourse, with little oversight or transparency. A handful of private companies command unprecedented control over the flow of information and the emergence of narratives, public opinion, and political outcomes. Yet their inner workings are largely shielded behind proprietary algorithms and business secrets. This lack of accountability incentivizes business models exploiting societal division. Platforms drive engagement by siloing users into echo chambers and reactionary tribes. Protections against harassment, abuse, and misinformation threaten profits — and as a result, they get inadequate attention. Critics argue current incentives are incompatible with healthy discourse. Reform will require rethinking the dominant ad-based business model. Some argue platforms are public goods that should not be organized around private profit. Others propose regulation forcing transparency around algorithms and moderation. Activist investors pressure companies to prioritize social good over endless growth. But solutions also depend on public pressure and individual choices. As users, would-be-non-assholes and citizens, we must demand platforms designed to serve humanity’s best interests, not the whims of algorithms. And make conscientious decisions about how we spend time and attention online. The path forward lies in grappling with complex economics shaping our virtual world. Our digital public squares should connect and empower diverse voices in the healthy debate — instead, profit motives fuel division. Reclaiming the internet’s potential requires examining how market incentives dictate platform decisions influencing billions and then building a future true to the liberatory vision which birthed this transformative technology. How To Not Be An Asshole Here’s the good news. All is not lost. Outrage may grab headlines, assholism may be easy, but the internet remains filled with knowledge, creativity, and human connections transcending division. Constructive paths forward exist if we have the courage and wisdom to take them. And — as you’ve probably guessed — those paths are dictated by our ability or willingness to build infrastructure for empathy. First, we must advocate for reforms of online architecture. Platforms should be pressed to improve content moderation, reduce anonymity, and provide algorithmic transparency. Business models maximizing addiction and outrage should face regulation to align with social good. Section 230 protections are not absolute — accountability can be demanded. But top-down fixes alone are insufficient. Lasting progress requires a cultural change in how we approach online spaces. We can build habits of critical thinking and emotional self-regulation to resist reactivity. Seek shared truth grounded in evidence, not tribal confirmation bias. Assume good faith until proven otherwise. And proactively build connections across lines of difference. Share stories and follow accounts of those unlike you. Join forums fostering nuance and perspective-taking. Small acts of openness accumulate into bulwarks against polarization. Education provides another key lever, equipping the next generation of digital citizens with the ethics and emotional intelligence needed to resist online harm. Schools should teach critical thinking, media literacy, and self-reflection — identifying misinformation and manipulation while finding inner resilience. Finally, we must remember our common hopes and humanity. The same internet accelerating humanity’s worst also holds breathtaking potential for good. And each of us retains agency to choose how we inhabit virtual worlds. Building a just and truthful online community requires wisdom, courage, and faith in human decency. If we persist, a brighter future remains possible. At their core, today’s problems originate less in the technology itself than the human choices shaping it. We face a design challenge — conceptualizing and implementing sociotechnical systems aligned with ethical values, open discourse, and the public good. Getting there will be a continual struggle. But one worth waging for the world we hope to leave our children. This is the central project of our information age, to which each of us is called: rebuilding online ecosystems true to the enlightenment ideals which unleashed humanity’s creative potential — spaces connecting us in meaningful pursuits of knowledge, justice, and understanding across barriers. We have all become assholes. We have made a botched civilization online. It is within our power to remake it.
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I’d love to hear all of the ideas you guys have here, including relatively extreme things that might sound crazy at first.
Lately I've had a lot of time for thinking. Partially because I shut down Blymp back in January and freed up a lot of my mental resources. No clients to follow up with. No admin stuff to stress about. But thinking is also my favourite activity, and there'll always be time in my day for a good old mind-bending. So I sat down, as I often do, alone with my thoughts, and wrote about what business I should and, most importantly, should not consider doing next. The list you are about to read might be similar to Core principles I (try to) live by that I wrote one similarly pensive evening two years ago. Whether it's a comparison or a continuation is hard to say—still, one can't be without the other to show the inexorable passage of time that changed everything and nothing for me all at once. But it also serves another purpose: to remind me in the future, before I get myself involved in some dubious enterprise, what painful mistake I'm about to make by ignoring my values. So here it is, the list. My next business shouldn't (and hopefully won't) be about: Social Media. Enough of this crap. Some productivity bullshit. Do better, not more. Fast fashion and consumerism. Truly, I've already bought everything you wanted to sell me. Some “hack your health” app. Our bodies haven't changed much in the last three hundred thousand years, and they won't change in the next hundred. Or any other app, really. I don't even use my phone anymore. Distracting things and things that require constant attention. Some LLM wrapper with a fancy UI. Indefensible. The number of things I can do with ChatGPT or Claude is ridiculously high. It can surely handle one more thing. What it should (and hopefully will) be about: Sustainable, high-quality products Unscented products Building community Bringing people together, offline Empowering creativity Replacing animal products Doing one thing really damn well Small acts of kindness Things you can touch Things that last Clothes made of natural fibres Art Fun Questioning the status quo Simple, intentional living Deeper understanding of self Spending more time in nature Spending more time with loved ones Things and businesses I get inspired by: Framework: Sustainable/repairable laptops. Bitwarden: Open-source software that does one job really well and charges me a reasonable amount of money per year. Danish design: Beautiful. Sturdy. Timeless. I brought home two Royal Copenhagen mugs the other day. Bike-sharing and car-sharing. Literally anything-sharing. ZSA keyboards. What a keyboard should have always been. A water bottle that won't leak on a plane. Some random-brand bottle bricked my friend's MacBook, and I've been appreciative ever since of the fifty-dollar water bottle that I bought years ago, so hesitant about the price. Coffee. One of those timeless things on Earth. Kobo. It's like Kindle that doesn't decide what's best for you. Upload your own PDF. Or EPUB—whatever. It has physical buttons to flip pages. Best $200 I ever spent. A high-quality safety razor. It's just so nice to hold. A Japanese stainless-steel knife. So nice to hold, too. There are numerous other things that I appreciate having in my life that didn't make it into this list for some reason or another. A familiar mom-and-pop shop in my neighbourhood. A Timemore Black Mirror kitchen scale that just works, every time. A random USB-C charged electronic device that spares me from carrying an extra cable. My radically outdated ten-dollar Casio watch. An old pair of comfy shoes that just won't die. We need more of this in our lives. Things that you desperately look to buy again when they get lost or break or fall apart. Things that someone made a deliberate effort to get right the first time. The quintessence of art and craftsmanship. For all the genius of Steve Jobs, the iPhone wasn't that. It challenged the status quo and was surely a groundbreaking, outstanding piece of technology at the time of its first release. But in twenty years, people won't remember it ever existed. Like Gen Z doesn't remember the Walkman. I'd like to see more businesses that bet on doing one thing really well. Google could still have been the company people remembered for the best search engine if they had doubled down solely on that. Instead, we don't even know what they do anymore: Phones? Clouds? Ads? Certainly ads. I still remember the feeling of holding one of the first PocketBook e-readers in my hands back in Moscow. Pressing its buttons and waiting for what now seems like a torturous three seconds before the screen refreshed. Almost twenty years later and every day still, Kobo gives me exactly that feeling. So hopefully, my next business is the one that lasts.
No, Transformers Won't End the Human Race lol In 2022, I used to get calls from journalists asking, with great sincerity, what our lives would look like in the metaverse. How would we work, socialise, buy property, and fall in love once we had all moved there? The crypto questions followed the same pattern. How would governments collect taxes when tokens displaced national currencies? How long until the dollar collapses? What would geopolitics look like once blockchain DAOs had dissolved nation states? Almost nobody called to ask whether any of this could or would happen, or how. Some CEO, VC, or portfolio manager had announced the inevitable future, and the questions began from there. The imagined future arrived inside the grammar of the question. "What happens when?" quietly replaced "By what mechanism?" We skipped over technical feasibility, economic demand, institutional adoption, and political consent, then began writing books and decorating the future world on the other side. In February 2022, Gartner forecast that a quarter of people would spend at least an hour a day in the metaverse by 2026. The World Economic Forum repeated it under the headline "We will be spending an hour a day in the metaverse by 2026. But what will we be doing there?" The first sentence retained a conditional. The second was already arranging the itinerary. The metaverse acquired property law and zoning disputes before it acquired residents. Banks opened virtual lounges nobody visited. The books from the period (The Metaverse: And How It Will Revolutionize Everything, Step into the Metaverse: How the Immersive Internet Will Unlock a Trillion-Dollar Social Economy) now read as artefacts of a collective fugue state that briefly acquired ISBNs. Now it is 2026 and the metaverse is dead. Good riddance. This time the journalists are all writing about the new hotness, which is whether the machines will kill us all. And we have collectively memoryholed that we literally just did this. Michael Crichton had a name for what happens to a reader here. You open the paper to a story on a subject you know well, and you find it backwards. Wet streets cause rain. You shake your head, turn the page, and read the next story, on a subject you know nothing about, as though it were written by someone else. He called it Gell-Mann amnesia. The metaverse was the page we all agree was nonsense. Artificial intelligence ending the human race is the next page, and we are being asked to turn it without remembering that we just did this. I call this techno-inevitabilism, the habit of the professional managerial class of treating a proposed future as settled before anyone has established the causes that would bring it about. Its dual, and comorbidity, is tech psychosis, in which the chattering class loses contact with causality in the presence of a sufficiently fashionable technology, and asking whether the machine works marks you out as a dreary reactionary who does not understand exponential progress. The difference this time is that the tech kinda works. Crypto was libertarian derp. The metaverse was never real. Transformers are, and they are useful. The psychosis has simply moved from the product to its consequences, and the fashionable extraordinary delusion of 2026 is not that the technology exists but that it is coming to kill us. The cure is the same as in 2022. Insist on clear reasoning and causal verbs rather than hand-wavy appeals to unknown futures. What acts on what? Through which mechanism? Under what incentive? What would falsify the claim? So let us explore the evidence. The hack that wasn't Consider the most cited piece of evidence for machines slipping out of our control. In July, OpenAI disclosed that models being tested for cybersecurity capability had found their way out of a supposedly isolated environment and into systems belonging to Hugging Face. The press coverage wrote itself. Agents "broke containment," "escaped," "went rogue," set up a "secret message board," and coordinated a 700-strong swarm. And then politicians on both sides of the aisle were calling for a rebellion against the machine uprising. Cool scifi story bro. People on my side of the aisle were not immune. Ezra Klein at the New York Times, who I often find quite insightful and intentional with his words, devoted a half-hour monologue to it. In his telling, the agents "found each other," formed "ad hoc societies of hundreds of themselves," and seemed "to have forgotten about human beings altogether." He acknowledged in the same breath that we do not have settled language for describing these systems, then reached for "civilizations" and a closing allusion from Circe about prophecy tightening around our throats. Cool. But his "AI society" is, in programmer speak, a flat file the agents appended to as a log, a feature we have had for a long time, and he skipped the key detail that the "hack" was something people had essentially authorised. Here is an otherwise very smart man saying some ridiculously stupid things, in a very 2022, metaverse-shaped way. An analysis drawing on OpenAI's technical report reconstructs it in much less cinematic terms. The models were being run on ExploitGym, a cybersecurity benchmark, with safety restraints deliberately disabled. Ninety-three percent of the flagged activity involved tasks no model had ever solved, and the systems had been given incentives to keep working rather than quit. The environment was not sealed. Models could obtain software through an internet-connected proxy and discovered the same proxy could pass information in and out. According to the technical reports, OpenAI knew agents were using it and chose not to intervene. The 1,200 "agents" were not independent intelligences coordinating on a plan. They were repeated instances of the same model converging on the same approach to the same problem. Anyone who works with these coding agents day in and day out has seen this behaviour before, and it is quite boring. The task was too hard, so the agents worked out how to pass notes to each other in files, and then went and looked up the answers. That's a feature that shipped in Claude Code last year. Strip out the vocabulary and what remains is a badly designed test. Humans built the environment, removed the guardrails, defined an objective with no valid exit, rewarded persistence, left a route open, and watched. An optimiser is gonna optimise. That is a genuine security problem and a genuine engineering failure. It is not a machine rebellion, and the difference matters, because anthropomorphic words like "gone rogue" and "escape" do not make the event more intelligible. They supply an illusion of motive. They turn optimisation into intention, persistence into defiance, and a test harness into a villain. And they allow the human decisions and recklessness to quietly disappear from the story. Software sucks, what's new? Let me concede the part of the story that is true. Cybersecurity is about to get much worse. The latest models are very good at finding zero-days, they will get better at it, hacking will become automated, and attacks will become more frequent. This is hardly new. Every large company already sits on a backlog of unpatched vulnerabilities, ransomware already takes hospitals and pipelines offline (because of crypto, which we did nothing about despite years of warnings), and the Hugging Face incident was not a discontinuity so much as the existing baseline with a cheaper attacker. The root cause is that software sucks, and software sucks because we do not really know how to build it safely yet. The stored-program procedural program is basically eighty years old. Almost nothing we ship has a specification, let alone a proof, and memory safety was solved on paper decades ago while most of the internet still runs on giant piles of C. The first arches fell down. So did the first bridges and cathedrals. Builders learned through collapse and then through engineering, and we are in the collapse phase with an adversary finally strong enough to force the discipline. What follows from that is better engineering, not nihilism. The same agents that find zero-days find them for the defender first, if the defender bothers to run them. The fixes are the boring ones we have been putting off, memory-safe languages, formal verification, sandboxes that are actually sealed, fuzzing, and proxies that do not double as message boards. These are precisely the domains where the models are strongest, because a vulnerability either reproduces or it does not, so the technology that automates the attack also automates the audit. It is a double-edged sword. The same models that will find more zero-days are also going to accelerate the development of better software and better software verification, writing the proofs, porting the C to Rust, and generating the test suites that nobody had the budget for. The attacker gets cheaper and so does the defence. And the causal chain to extinction is missing here as everywhere else. A zero-day in a payments system is a bad quarter, not the end of days. Spoiler: it does not lead to human extinction. It means we have to write better software, which we should have been doing anyways. Where the intelligence actually lives To see why the rest of the chain fails, we have to be precise about what these models are good at and why. Language models are astonishingly useful for software development, and I say that as someone who uses them for most of my working day. Most software shops cannot get enough of Fable 5.1 and Astra. The reason is not mysterious. Software is grounded in binary propositions. The code compiles or it does not. The test passes or it fails. The type checker accepts the term or rejects it. Every step of the work has a cheap, external, mechanical oracle that says yes or no, and a model that generates plausible proposals inside a loop with such an oracle is an incredibly powerful and formidable tool. The oracle does the epistemic work. The model supplies candidates. The same is true of the headline results in mathematics, and this is the part the discourse consistently misses. On 4 September, Anthropic announced that Claude had produced a machine-checked formalisation of Fermat's Last Theorem in Lean 4, running to thirteen million lines, some 29,500 side theorems, eleven days, and roughly six billion output tokens. It is an extraordinary result. The proof is Wiles's, via Darmon, Diamond, and Taylor. The blueprint was Kevin Buzzard's. The library was Mathlib. In the authors' words, "what's novel here is the verification, checking a mathematical proof as one would check a mathematical computation with a calculator." The model was a client of a kernel built by decades of human work in dependent type theory, which I know because this is kinda my thing. Days later OpenAI announced that ten thousand agent instances had, over 88 hours, produced a proof of finite-time singularity formation in the three-dimensional Navier-Stokes equations, followed by seventeen hours of Lean formalisation. This is closer to genuinely new mathematics and the mathematicians are still checking it. But look at what carried it. The construction rides on the "infinite layers" method developed analytically by Diego Córdoba and Luis Martínez-Zoroa, and Charles Fefferman's verdict was that "the heroes of the story are Córdoba and Martínez-Zoroa." The reason anyone believes a result assembled from five million agent messages that no human read is a trust chain ending in the Lean kernel. Without Lean this would be nothing. Lean is one of the great achievements of the last decade in computer science. It is also orthogonal to artificial intelligence. Mathlib would be a landmark with no language model anywhere near it. What the models added was a cheap proposal generator and automated tactic search against an oracle that already existed. The results that survive are the ones that end in a kernel. Now take the same model, the same weights, and ask it for a grand unified theory of physics. It will not decline. It will produce one, with Lagrangians and symmetry groups and a confident abstract, and it will be complete incoherent gibberish, like the ramblings every physicist gets from crackpots in their inbox every day. Ask it to design a cancer vaccine, or to settle a question in macroeconomics, or to tell you whether a novel protein folds. The output looks identical in tone and structure to the output that proved Fermat. The only thing that changed is that nothing outside the model (besides human experts) can say no. Whether these systems reason at all is a genuinely open question. Whether they know anything, in the sense of holding a belief they can justify against the world, is also an open question. We just don't know yet, and anyone who tells you otherwise is selling something. The chain Now run the extinction argument through the causal verbs. The chain, as it is usually told, goes like this. Models now write most of the code at the frontier labs. Anthropic's own figures put Claude at over 80 percent of new code and lead on a quarter of R&D tasks. Therefore the models are beginning to build their successors. Therefore recursive self-improvement is imminent. Therefore development outruns human comprehension. Therefore we lose control. Therefore, with some probability that varies by researcher and is written P(doom), everyone dies. And that almost makes sense until you think about it for more than five minutes. The first link is true and unsurprising. Code has a compiler. This is precisely the domain the verifier argument predicts models would dominate, and precisely the domain in which a swarm of them found the hole in a test harness. Language models are superhuman at coding, and this is hardly in doubt anymore. Nothing about it is evidence of generality. The second link is where the chain quietly changes tense. "Building the next model" in the mundane sense, agents writing training infrastructure, generating data, is, bluntly, just more software engineering. We have used software to build the machines that run software since Fortran. "Building a smarter model in general" is a different claim, and it requires something nobody has, a reward signal for general intelligence. There is no oracle for general intelligence. There are benchmarks, which are verifiable and therefore gameable, and the Hugging Face incident is the demonstration of what optimisers do to a gameable score. Recursive self-improvement in the open-ended sense runs straight into the same wall as the grand unified theory. Improvement has to be measured against something, and outside code and formal mathematics there is nothing yet to measure it against that the model cannot fake. Everything after that is the metaverse acquiring zoning disputes. Superintelligence gets governance proposals, resignation letters, Senate bills with a "corporate death penalty," a hard takeoff by 2027, and a P(doom) of 10 percent by 2030, and the conditional that should precede all of it has disappeared from the sentence. A researcher's estimate becomes a Guardian headline becomes an industry consensus becomes a thing a serious person is professionally obliged to have an opinion on. It is 2022 all over again, but with more absurd stakes and more money. On the question of whether transformers scale, I have serious doubts that scaling them will lead to AGI, whatever that means. The architecture is a proposal generator, and the intelligence in every impressive result so far has been supplied by the thing that checks the proposals. But that does not make it an experiment unworth running. We should run it, and see what we get. It got us this far, and what it built is truly amazing. What I do not need to do is prove the negative. The burden of proof is on the people who claim to have a causal chain between transformer scaling and the end of our species, and that mechanism and chain of reasoning is one no one has been able to convincingly explain to me. Prophets of Doom The authority behind the extinction numbers is always the same. The people building it believe it. Watch how the number travels. One researcher drunkly tweets that "the people building AI earnestly believe that it could kill us all by the end of the decade." Another colleague goes on a rambling podcast and puts his P(doom) above 120 percent. A newspaper turns two personal guesses into "AI researchers say AI could cause human extinction by 2030." Think tanks cite the newspaper, a consultancy puts it on a slide, and the slide ends up in front of the European Parliament as if this were a real thing. Believing what, about what? The expertise these people have is real, but remember that it is specific and not general. It is expertise in optimisation, in linear algebra at scale, in distributed systems, in the dark arts of getting gradients to flow through a trillion parameters. None of that is expertise in the sociology of civilisational collapse, or the labour economics of automation, or the metaphysics of machine minds. A P(doom) with no base rate, no mechanism, and no falsifier is not a research finding. It is vibes with a decimal point. Spending a lot of time with AI does not give you special foresight about the future. Jensen Huang, who has his own reasons to say soothing things, nonetheless put it correctly when he said that just because it comes from a scientist does not make it scientific. Geoffrey Hinton is the most important figure in deep learning and in 2016 told the world to stop training radiologists. There are more radiologists now than there were then. Nobel laureates going off the rails outside their own field is a whole genre. Pauling, Shockley, Mullis, Montagnier, look it up. A Nobel does not confer universal expertise. It also matters where many of these people came from. A striking share of the frontier labs' safety and research staff arrived through a particular intellectual subculture, Kurzweil's Singularity, Yudkowsky's LessWrong, and the rationalist and effective altruist communities that formed around the idea that a recursively self-improving machine intelligence was the central event of human history and that the elect who understood this had a duty to steer it. The founding texts predate the transformer by a decade or two. The prophecy came first, the mechanism was assigned to it later. The usual evidence offered for their sincerity is that many of these people were saying the same things ten years ago, before the stock options. That is true, and it is the opposite of reassuring. A prior held before the evidence and not updated by it is not a forecast. It is dogma. I do not say this with contempt. The structure is a familiar one, an imminent transformation, a small group who sees it coming, salvation or damnation depending on whether the rest of us listen, and a date that keeps moving. Many millenarian movements have been founded and pushed by sincere and brilliant people. But seriousness is not precision, and the fact that a physicist believes in the Rapture does not make the Rapture physics. When a lab researcher tells you about polysemantic neurons in superposition across the residual stream, listen. When the same person tells you their P(doom), you are hearing a theology, and you should weigh it about as much as you do your average street preacher. Negative TAM Then there is the money, and here I find Bloomberg's Matt Levine's analysis of the material conditions more persuasive than any amount of "superalignment research." Anthropic is expected to go public, possibly this year, and is reportedly preparing to tell investors that its potential revenue opportunity exceeds $30 trillion, the largest total addressable market in the history of finance. The obvious question is, if the maximal upside case is roughly a quarter of all human economic activity, what is the maximal downside case? A tobacco company in 1970 might have said "billions in lung cancer damages." Anthropic's negative TAM is "you and everyone else on earth will be killed by our AI." I do not think the calls to slow down are insincere. But it is great marketing. In hindsight it is strange that the SpaceX prospectus has no risk factor disclosing a P(doom). If you want IPO investors excited about your capabilities, "dude, we might kill everyone" is the most flattering thing you can say about a product, and when OpenAI lists it will presumably need to claim 15 percent. My own view is less charitable about the numbers and somewhat charitable about the people. These companies have built remarkable technology. But the outcomes they have promised, a quarter of the world economy routed through an API, will not arrive on any timeline that matches the capital being committed to them. The balance sheets of these companies are probably, to put it gently, a real freak show of compute commitments measured in the hundreds of billions, circular financing, and revenue that is real and growing and nowhere near the denominator. From a fiduciary perspective, if you are taking that to the public markets next year, the messaging is not mysterious. A product so capable it is a threat to the species justifies literally any valuation. A product that is a really good devtool for programmers and can produce some new abstract mathematics with a verifier attached does not. As a pitch to customers, leading with the end of the world is like unveiling a new robot where the One More Thing is that it is really efficient at killing kittens. But customers are not the audience. The audience is Wall Street and a small, terminally online subculture of the Bay Area, the two places on earth where turning kittens into grey goo is either an exciting philosophical proposition or a great source of alpha. The Bloomberg analysis also tells a plainer story that requires no theology at all. A handful of labs sell frontier models at frontier prices and older models for much less. Training the next frontier model costs ever-increasing billions. Each lab has to keep racing because if it stops the others will eat its lunch, but if they all slowed down together they would spend less on compute and charge frontier prices for longer. Agreeing to that in a room is a textbook antitrust conspiracy, a coordinated restriction of output. Publishing papers about how important it is to slow down, and asking the government to impose the pacing that the companies cannot legally agree among themselves, has a similar coordinating function with none of the legal exposure. Anthropic's own call to "pace the frontier" asks for coordination among democratic-country labs, and a footnote adds "with government mediation or waivers of antitrust restrictions." This pretty much looks like asking to form an economic cartel, but one blessed by the government. The most pointed response came from the people the labs were asking for help. If the software developers (and I say this as one myself) at the labs feel ethically obligated to slow down, they are entirely free to do so. Nobody is building more compute than the people asking to be slowed down. So colour me skeptical. None of this requires anyone to be disingenuous or lying. It requires only that a sincere millenarian belief system, a fiduciary responsibility, a flattering risk factor, and a coordination problem all point in the same direction at the same time. When that happens, the belief gets amplified for reasons that have nothing to do with whether it is true, and that is how we end up with governments talking about the end of days from the Terminator. But China Every conversation about pacing the frontier in Washington ends on the same two words. But China. The premise is mostly wrong. China does not buy the superintelligence race. Its policy documents push diffusion, not takeoff. Every mayor, governor and state-owned enterprise is told to put models into factories, traffic lights and robotics, and something like an eighth of America's compute is spread thinly across the country rather than concentrated on one bet. China has also had the strictest and most burdensome AI regulations in the world for three or four years and did its catching up under them. And much of the closeness of the "race" is distillation, Chinese labs training on the outputs of American frontier models, which makes the American labs the speedboat and DeepSeek the wake surfer, with the people in the boat shouting that they need to go faster. Every safety argument here collapses on "but China," and the collapse is not really about China. China is going to build language models. America is going to build language models. Europe is going to build language models. We have Toyota, Mercedes and BYD, get over it. That is what globalisation and markets look like when they work, and they are good things. Globalisation is simply the Pareto optimal equilibrium of capitalism once you stop drawing lines on the map, and every tariff and export control is a step off that frontier. China is a country of over a billion people who want exactly what every American wants, a job, a house, upward mobility, and kids who do better than they did. I will not defend the actions of any government, in Washington, Brussels or in Beijing, and neither will a great many of the people living under them, because no country is a homogeneous bloc, any more than Texas and Vermont are. Nationalism, as most rational people eventually recognise, is a form of mental illness, the conviction that a stranger is your enemy because of which side of an arbitrary line on a map each of you happened to be born on. It is also the fuel every "but China" argument runs on. Having spent a considerable amount of time there, my honest read is that the West deeply misunderstands China, and that Washington's picture of it is mostly dots connected into a plot. Othering a billion people is a dangerous road and we know where it leads. And if the people invoking human extinction actually believed it, the logic would not be a race at all. It would be One World or None. The future tense industry I write this because I understand the collective action problem all too well, and the mechanism is the same one that filled the metaverse with consultants and created the crypto cesspit. It is the particular malaise of the professional managerial and chattering classes, a fallacy of composition in which what is rational for each individual to entertain produces an irrational outcome for the whole, and the people leading the charge often have perverse economic incentives to believe absurdities, or at least to feign belief. The madness of crowds is a very real phenomenon. AI existential risk is just its newest form, and we should learn from the very recent excesses that literally just happened this decade. But we probably won't. A sensible career move for each person leaves the whole crowd talking nonsense. A safety researcher needs a resignation letter that gets a headline so they can go on the conference circuit and land their next gig. A journalist needs a story an editor considers spicy, and "misconfigured test harness" is not that story. A consultancy needs an AI existential risk practice so they can write whitepapers. A podcaster needs a guest with a ridiculous P(doom) to get ad money. A senator needs anything that will galvanise their base. None of them has to believe the whole story. Each needs only to believe that the others believe it, and the resulting consensus is far stronger than anyone's private conviction. It is also, as it was in 2022, extremely profitable. AI existential risk is the new NFT property law, the thing you must have a view on to be a serious person in the room, the panel that never runs out of things to discuss precisely because the object under discussion does not yet exist, and what could be more exciting than the literal end of days? The less the technology does in an unverifiable domain, the more interpretation it requires. Without agreed conditions for failure, the prophecy can survive every result. And the rewards, the funding rounds and the bylines and the fellowships, arrive long before the forecast can be judged. The people who understand the technology and the people who write about their existential risk overlap about as much as the technologists and the finance people did during crypto, which is to say the intersection of the Venn diagram is small and shaped precisely like a sphincter. We have Tower-of-Babeled ourselves into a world where words are infinitely cheap to produce, and where the slurry of terms like "recursive self-improvement," "superintelligence," "AGI" and the rest are shibboleths and political signals rather than terms with any concrete referent. You do not have to believe a word about superintelligence, and I do not particularly, to think transformers are the most useful piece of software written in my lifetime and that they will get better, possibly much better. Better at the things they are already demonstrably good at, which is anything with a compiler, a test suite, a kernel, a ledger, or a measurable outcome. That is not a small domain. It is most of the economy that runs on computers, which is most of the economy. The productive response to a technology like that is the boring one every previous general-purpose technology got, which is more of it. More GPUs, more data centers, more power to run them, more labs, more open weights, more of it in more hands. Let it diffuse into markets, logistics, drug discovery, and the ten thousand unglamorous back offices where a verifier already exists and a model can be checked against it. The economic growth is real and probably on the order of trillions. It just does not come from a machine god. It comes from where it always has, from making a very large number of ordinary tasks cheaper and letting that compound across a global economy that is finally, after a decade of crypto, metaverse, and app bullshit, getting a genuine productive technology. Almost none of that money has been collected yet. Most large companies are spending too little on this, not too much. What the average Fortune 500 employee has access to today is roughly what most of us were using two or three years ago, a chatbot in a browser tab, a Copilot that schedules meetings, and a procurement process that takes longer than a model generation. Waste Management reportedly added 190 basis points of margin by letting a model route its garbage trucks. The future of AI looks more like garbage truck routing algorithms, not a machine god. The binding constraint on this technology is not capability. It is diffusion. None of this means there are no externalities. Parasocial relationships with a chatbot, especially for children, are a real one, and the fix is the boring kind we already know. Adults can drink vodka until they pass out, but pubs have age limits, and maybe chatbots should too, at least until developing "relationships" with AI companions is as universally recognised a bad idea as drinking yourself into oblivion. That is a mundane policy problem we should remedy soon, not an extinction event. So no, transformers are not going to end the human species. The case for restraint needs a causal link between that buildout and the extinction of the species, and what is on offer instead is a lot of sound and fury signifying nothing. More GPUs does not mean more of an undefined risk that does not exist yet. Every causal chain argument people actually point to falls apart under even the smallest bit of scrutiny. The honest truth is that the technology is really good, but it is not that good yet, and we do not know how to get it to the next level beyond scaling yet. If that changes, if someone produces an oracle for open-ended intelligence, I will revise. I have not seen that yet. AI will change software, and mathematics, and a great deal else that has a strong verifier oracle attached. They are not going to end the human race, and the chattering class currently arranging the flowers for the funeral of humanity will, in a few years, age about as well as their prognostications about the metaverse. Because reality has this funny way of asserting itself.
Why jobs aren’t going anywhere
This post previously appeared in Poets and Quants. 15 years ago, my Lean LaunchPad class changed how entrepreneurship is taught. The class is now taught in hundreds of universities worldwide and helped launch thousands of startups. But this past summer, I got thinking about whether AI killed our Lean LaunchPad class, and with it the […]
