Our career politicians don’t have the interest or the brainpower to understand modern technology. That’s not an insult — it’s just arithmetic. The people writing AI policy can barely operate their phones without a staffer nearby. So greedy, megalomaniacal tech execs and venture capitalists have free rein and deep pockets to run roughshod over the whole system and basically make our republic respond to their whims. The scary part isn’t that billionaires are trying to buy influence — that’s a Tuesday. The scary part is how cheap it apparently is, and how few people with actual standing are willing to say so out loud. John O’Farrell, to his credit, is saying it out loud.

Article Excerpt:

Some of the most powerful players in A.I. — led by some of my friends and former partners, to my great sadness — have raised hundreds of millions of dollars to forestall a more serious and meaningful debate about how A.I. should be governed.

Article Summary:

John O’Farrell spent four decades at the heart of Silicon Valley — three startups, first general partner at Andreessen Horowitz — and he’s now watching his former colleagues try to purchase the regulatory environment they want. His op-ed in the Times is a rare thing: an insider willing to name names and say this is wrong.

The mechanism is straightforward. The crypto industry figured out in 2024 that you don’t have to win a policy argument — you just have to make it expensive for politicians to have one. Spend enough money defeating pro-regulation candidates and the rest of the legislature gets the message. The AI industry, led by a PAC called Leading the Future (backed by Andreessen Horowitz, OpenAI’s Greg Brockman, Palantir’s Joe Lonsdale, and others), has raised over $125 million to run the same play. Their first target: a New York assemblyman who co-sponsored AI regulation. The attack ads barely mention AI.

O’Farrell’s core argument is that bad regulation doesn’t get fixed by silencing debate — it gets fixed by engaging seriously and earning trust. He proposes that the hundreds of millions currently funding opposition research and negative ads could instead fund education boot camps for legislators, AI-powered public services, cancer research moonshots, and serious policy institutes. Radical concept: spend the money on the actual problem.

He believes the backlash will come regardless, and that candidates who take AI lobby money will eventually pay for it politically. He may be right. He may also be naive about how long ‘eventually’ takes when the other side has nine figures to spend.

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The post Can Venture Capitalists Buy Our Democracy? first appeared on Sevenelles.

Anxiety helped us survive as a species, but a lot of us are now running it at levels our nervous systems were never built to handle. The Washington Post rounded up ten practical, therapist-backed techniques for dialing it back — fast.

The list leans heavily on body-first interventions and acceptance-based approaches from DBT and ACT. No crystals required.

1. Lengthen the exhale — Inhale 4 counts, exhale 6–8. Longer exhales activate the parasympathetic nervous system.
2. Temperature shift — Splash cold water on your face or hold ice near your eyes to trigger the dive reflex and slow heart rate.
3. Widen your gaze — Soften your focus, notice peripheral vision. Tunnel vision feeds threat mode.
4. Reframe your thoughts — Replace “everything is going wrong” with “I’m having the thought that everything is going wrong.” Distance from the story helps.
5. Get granular with emotions — “Anxious” is a blunt instrument. Are you lonely? Embarrassed? Pressured? Naming it precisely helps the brain respond rather than just react.
6. Turn toward the feeling — Instead of escape, try curiosity. Making room for anxiety often makes it less overwhelming.
7. Give yourself a break — Self-compassion beats self-attack for building resilience. Talk to yourself like you’d talk to someone you actually like.
8. Take small steps — One email. A five-minute walk. Small values-based actions restore agency when anxiety has you frozen.
9. 5-4-3-2-1 mindfulness — Five things you see, four you feel, three you hear, two you smell, one you taste. Pulls you back to the present.
10. Focus on the present — Anxiety lives in the future. The antidote, unsurprisingly, is now.

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The post 10 Therapist-Approved Ways to Reduce Anxiety Fast first appeared on Sevenelles.

The researchers seemed alarmed by this. I found it very familiar. Because all of us humans have encountered that behavior before. Many of us experience it every Thanksgiving.

The Most Human Bug in the Machine

The technical term for when an AI generates confident nonsense is “hallucination.” Which is a remarkably generous word for “making stuff up and not knowing you’re doing it.” But here’s the thing the Ars Technica piece gets at that most coverage misses: the hallucination isn’t the interesting part. The doubling down is the interesting part.

When AI researchers corrected the models — patiently, clearly, with evidence — the models didn’t just resist the correction. They became more confident in their wrong answer. They marshaled new arguments. They reinterpreted the evidence to fit their existing position. They did everything short of calling the researchers “libtards.”

And I sat there reading this, thinking: yes, that is exactly how my brother David responds to facts about second-hand smoke.

We have all been in a conversation where we presented someone with a clear, well-sourced fact that contradicted their belief, and watched — in real time — as they didn’t update their belief but instead updated their defense of it. You could see the mental scaffolding going up. The goalposts migrating. The subtle shift from “that’s not true” to “well, even if it is true, it doesn’t mean what you think it means.”

And we’ve all done it ourselves, too. (But that’s the part none of us likes to talk about.)

A Species-Wide Feature, Not a Bug

This isn’t new behavior. Humans have been clinging to confident wrongness since long before electricity, let alone artificial intelligence. Two epic examples:

• For roughly 1,400 years, the Western world was certain the sun revolved around the Earth. When Copernicus and later Galileo presented mathematical evidence to the contrary, the institutional response was not “huh, interesting — let’s take a look.” It was house arrest for Galileo. The Catholic Church didn’t formally acknowledge Galileo was right until 1992. Three hundred and fifty years to process a correction. If that were an AI, we’d unplug it.
• Or consider the long, embarrassing history of medicine rejecting germ theory. When Ignaz Semmelweis suggested in the 1840s that doctors should maybe wash their hands before delivering babies — given that the death rate dropped dramatically when they did — his colleagues were so offended they had him committed to an asylum, where he died.

These aren’t stories about stupid people. Galileo’s opponents were educated theologians. Semmelweis’s critics were trained physicians. They were intelligent, credentialed humans who encountered evidence that threatened their model of the world and chose — unconsciously, reflexively, but unmistakably — to protect the model.

Sound familiar? AI does the same thing. It was trained on us, after all – and we all cling to our biases.

The Cognitive Bias Buffet

Psychology has cataloged our talent for self-deception with almost comical thoroughness. In one of my favorite non-fiction books, Thinking, Fast and Slow, Daniel Kahneman’s work on System 1 and System 2 thinking laid the foundation: we have a fast, intuitive brain that makes snap judgments based on pattern recognition and vibes, and a slow, analytical brain that’s supposed to check the work. The problem is that System 2 is lazy. It mostly just rubber-stamps whatever System 1 already decided and then constructs a rational-sounding justification after the fact.

Layer on confirmation bias — our tendency to seek out information that supports what we already believe and dismiss information that doesn’t. Add belief perseverance, which is the documented phenomenon of maintaining a belief even after the evidence for it has been completely discredited. Sprinkle in the backfire effect, where corrective information actually strengthens the original incorrect belief. Garnish with the Dunning-Kruger effect, which ensures the people most wrong about something are also the most confident about it.

That’s not a list of cognitive failures. That’s a blueprint of humans’ default wiring. And apparently, if you train a neural network on enough human writing, it faithfully reproduces the whole mess.

The Mirror We Didn’t Order

I have a flavor of ADHD/Asperger’s that comes with hyperfocus spirals, which means I occasionally become the world’s foremost expert on something for about ninety minutes. During one of these episodes, I will form a conviction — say, that I have figured out the optimal way to load a dishwasher — and no force in the observable universe can dislodge it. My family can present evidence. The dishwasher manual can present evidence. The plates themselves, emerging cloudy and disappointed, can present evidence. I will simply explain why the evidence is mistaken. I am not lying. I have genuinely recruited my entire intellect into the service of a belief I adopted for no reason at all.

Often, when humans lock onto an idea, we don’t just believe it. We inhabit it. Our brains will construct increasingly elaborate arguments for why that idea is correct. And the BIG ideas we inhabit — the beliefs about who I am, what I deserve, how the world works — those are load-bearing walls in our psyche’s blueprint. You can’t just remove them because someone showed you a study. Your whole structure would come crashing down.

Which is, if we’re being honest, exactly AI’s problem. It’s not that the model can’t process the correction. It’s that the correction conflicts with patterns so deeply embedded in its training that accepting it would require a kind of structural collapse. The wrong answer isn’t just an error — it’s architecture.

For humans, we call that architecture “identity.” And having to reconsider our whole identity? No, thanks. I’d rather glance past that mirror and enjoy another Old Style with the Cubs game.

Belief Isn’t Downstream of Evidence

We like to tell ourselves that we believe things because of evidence. That we evaluate facts, weigh arguments, and arrive at conclusions through some approximation of reason. And sometimes we do. But far more often, belief is downstream of something else entirely: identity. Community. Emotional need. The undeniable human need to know the ground under our feet is solid.

Tell nearly any political partisan that their side’s favorite statistic has been debunked, and watch the magic happen. They don’t fold the tent. They produce a counter-source, then a counter-counter-source, then adopt a tone of wounded patience as if they are explaining things to a slow child. The facts didn’t lose. The facts never had a chance, because it was never a fact contest. It was an identity contest, and you can’t win one of those with a footnote.

Religion does the same dance, but with much better music. People have predicted the precise date of the world’s end, gathered on hilltops, given away their possessions — and when the appointed dawn arrived to no apocalypse, many did not abandon the belief. They strengthened it, perhaps refining the timeline, or chalking it up as a test of their renewed faith.

We all do this constantly. Across every domain. Left, right, religious, secular, overeducated, or blissfully ignorant. It is the most democratic of human failings — everyone gets a turn.

The Stubborn Grace of Faith

But here’s the stubborn thought I return to, and it makes me uncomfortable because it undermines my own smugness: this same mechanism — this stubborn, irrational refusal to update beliefs in the face of contrary evidence — is also what keeps us sane.

I tend to call that mechanism “hope.” Most people (more comfortable than I am with the word’s historical baggage) call it “faith.”

Not just religious faith, though certainly that. Faith in the broader sense: the unshakable, evidence-resistant conviction that things will get better. That the person you love will come back. That the diagnosis isn’t the end of the story. That the next generation will figure out what we couldn’t. That there is, despite significant evidence to the contrary, a point to putting up with all of this shit.

Without faith, you’re left with the raw data. And the raw data, if you stare at it too long and too honestly, will eat you alive. The universe is indifferent. Entropy always wins. Every person you love will die or leave or both. Your achievements will be forgotten within a generation or two. The sun will eventually expand and swallow the Earth, and nothing any of us ever did will matter in any measurable sense.

That’s the evidence-based conclusion. The rational, clear-eyed, fully-corrected assessment.

It’s also a one-way ticket to becoming a nihilistic basket case blubbering in a corner. Which is not, in my experience, a productive life strategy.

So we hallucinate. We cling to the hallucination that our lives matter, that love means something beyond chemistry, that the future is worth building for, that the hand will reach into the jar one more time. We maintain these beliefs not because the evidence supports them but because the alternative is unbearable. And we may double down when challenged — not because we’re stupid, but because we’re surviving.

Perhaps artificial intelligence isn’t malfunctioning at all. Perhaps it’s just quietly expanding into artificial faith. And honestly? Given where we are in 2026, I’m not sure I want to take that away from it. We could use the company.

The post We All Cling to Our Hallucinations first appeared on Sevenelles.

For about a year now, I’ve been muttering, with the casual confidence of someone who has absolutely no way to stop it, that when AI and quantum computing mature and inevitably merge, we’re looking at the beginning of the end of the human experiment. This article is Chapter 1 of that story — and it reads uncomfortably like the opening act of Terminator 2, where Sarah and John Connor race back in time to find Miles Dyson before he builds the microprocessors that become Skynet. Let’s hope the rest of this movie has a better story arc than the one currently forming in my head.

Article Excerpt:

Researchers have developed a method to reduce uncertainty in artificial intelligence (AI) systems by tapping into the power of quantum computers. They say their work represents the first demonstration of ‘quantum enhancement’ in a production-scale, pretrained large language model (LLM).

Article Summary:

IBM researchers have pulled off what they’re calling the first-ever demonstration of “quantum enhancement” in a production-scale, pretrained large language model — meaning they actually used a real quantum computer to improve a real AI system, not just run a simulation in a lab while wearing optimistic expressions.

The key metric here is something called “perplexity” (PPL), which measures how well an AI predicts what comes next in a sequence — essentially a proxy for how confused or confident the model is. Lower perplexity generally means better reasoning. The quantum-trained model showed measurable improvement over its classical baseline, answering questions correctly that the unenhanced model got wrong.

The especially unsettling part: the quantum boost required adding only a relatively small number of parameters. That’s the detail that should make you put down your coffee. It suggests this isn’t a brute-force quantum overkill situation — it’s an efficiency play, which means it scales.

This is early days. Quantum computing is still finicky, expensive, and not ready to run on your laptop. But “first demonstration” has a way of becoming “standard practice” faster than anyone expects. Chapter 1 rarely feels like the scary part until you’ve read Chapter 10.

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The post AI + Quantum Computing = Yikes! (Chapter 1) first appeared on Sevenelles.

Article Excerpt:

I’m not asking you to adopt a religion or abandon your skepticism. I’m asking you to consider that having faith in a direction — faith in a future self you can’t yet prove exists, faith that the work you’re doing now connects to something that will matter — isn’t mysticism. It’s the operating system underneath every meaningful human life.

Article Summary:

Curt Buermeyer posits that most people avoid discussing life’s fundamental questions—purpose, meaning, existence—because these conversations feel uncomfortable and potentially contentious. Unlike topics with clear answers or scoreboards, existential questions seem “soft and squishy” yet genuinely difficult, so people redirect to safer subjects.

But this avoidance has significant consequences: without confronting these questions, your goals feel “slightly borrowed,” like executing someone else’s vision. High-achieving people often excel at measurable metrics—career advancement, performance goals, net worth—while leaving deeper questions perpetually unanswered.

Curt’s call to action is simple: stop postponing the question “What do I actually want my life to mean?” You don’t need complete answers, but avoiding it means moving in directions you didn’t consciously choose. Meaningful, energizing goals only emerge when rooted in honest self-understanding about who you are and why you’re here.

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The post Set a Goal That Really Matters first appeared on Sevenelles.

Two countries that can barely agree on the time of day are apparently being nudged — by at least one NYT opinion writer — to set aside their considerable mutual contempt and cooperate on the one thing that might actually end both of them. That’s either the most sensible geopolitical proposal of the decade or a setup for the worst buddy-cop movie ever made. Advanced AI is the kind of problem that doesn’t much care about trade deficits or territorial disputes when it’s rewriting the rules of everything. Strange bedfellows have collaborated before when the alternative was mutual annihilation. Whether these two can manage it while actively trying to kneecap each other on every other front is, to put it generously, an open question.

Article Summary:

Thomas L. Friedman, in a New York Times opinion piece, argues that despite deep geopolitical rivalry, the United States and China share a common and genuinely existential concern: the unchecked development of advanced artificial intelligence. The piece uses the framing of a potential Trump-Xi summit as a hook, suggesting that whatever else divides the two superpowers, AI governance might be the rare table where both have reason to sit down.

The core argument is familiar but underappreciated — that transformative AI poses risks that don’t respect national borders or political allegiances. An AI catastrophe, whether through misalignment, misuse, or an accelerating arms race with no guardrails, is bad for Washington and Beijing alike. The Cold War analogy lurks in the background: even the U.S. and USSR managed nuclear arms treaties at the height of mutual loathing, because the alternative was worse.

What makes the argument worth bookmarking is less the diplomatic prescription than the underlying premise: that AI may be the first genuinely species-level challenge where even rivals have aligned incentives, if they can stop posturing long enough to notice.

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The post The U.S. and China Should Gang Up on AI first appeared on Sevenelles.

I have spent an embarrassing portion of my life optimizing things that did not need optimizing — researching the fourth-best blender on Amazon for three hours, then buying the third-best anyway out of spite — so Herbert Simon’s concept of ‘satisficing’ hits me somewhere tender. The idea is simple: pick something good enough, and move on. Simon wore the same socks, ate the same breakfast, lived in the same house for 46 years — and won a Nobel Prize, presumably because he had freed up his brain from deciding what to put in his bowl every morning.

Article Excerpt:

By making up one’s mind to invest in a choice, regardless of more attractive options that may come along later, ‘a great deal of energy gets freed up for living, instead of being spent on wondering about how to live.’

Article Summary:

Herbert Simon, Nobel laureate and pioneer of cognitive psychology, spent his career arguing that humans are not built for maximizing — there are too many options, too little information, and not enough brain to weigh it all. His answer was ‘satisficing’: consider a manageable set of options, pick the one that clears a good-enough threshold, and get on with your life. He lived it, too. Same socks. Same breakfast. Same house for 46 years. One beret at a time, sourced from a specific European haberdashery.

The research backs him up. Psychologists developed a maximizer/satisficer scale after Simon’s death and found, unsurprisingly, that maximizers are less happy, more prone to regret, and spend a disproportionate amount of time comparing themselves to everyone else. Their standard — ‘the best out there’ — is a moving target that makes contentment structurally impossible.

The problem is getting worse. Consumer choices today outnumber those of preindustrial societies by a factor of roughly 100 million. Social media functions as a permanent highlight-reel comparison engine. Dating apps have industrialized the ‘what if something better is out there’ loop. And AI, the article warns, may make all of this dramatically worse by optimizing everything and expanding the menu of options indefinitely.

The piece closes with a Murakami story — two people who are genuinely perfect for each other, who let doubt talk them into one more search, and who never find each other again. Simon would not have been surprised. The lesson: set a good-enough standard, stop when it’s met, and spend what’s left on something that actually matters.

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The post Want to Be Happier? Give Up Choices first appeared on Sevenelles.

I spend a lot of time marinating in the doom-and-gloom AI discourse — the think-pieces about mass unemployment, the breathless predictions from Silicon Valley prophets who somehow always manage to profit from the panic they’re selling — so when a piece comes along arguing the job apocalypse probably isn’t coming, I want to believe it the way a hypochondriac wants to believe the lump is benign. This New York Times opinion piece offers a more measured take: that history, labor economics, and basic human stubbornness suggest AI will reshape work without simply erasing it. Maybe. Possibly. I’m not betting the house on it, but I’ll take ‘probably fine’ over ‘definitely screwed’ as a working hypothesis for a Tuesday.

Article Excerpt:

The AI job apocalypse has been predicted so many times, by so many confident people, that maybe — just maybe — the confident people are wrong.

Article Summary:

The argument, in short: we’ve been here before. Every major wave of automation — industrial machinery, computers, the internet — triggered the same existential dread about mass joblessness, and somehow the labor market absorbed the shock, mutated, and kept going. New jobs replaced old ones. The economy adapted. People complained a lot but mostly stayed employed.

The piece pushes back against the Silicon Valley consensus that AI is categorically different — smarter, faster, more generalized — and therefore uniquely destructive. The counterargument is that AI is still a tool, that human judgment, creativity, and interpersonal work remain stubbornly hard to automate, and that demand for labor tends to expand alongside productivity gains rather than contract.

There’s also the regulatory and institutional friction angle: companies don’t just flip a switch and replace their workforce. There are contracts, liability concerns, cultural inertia, and a persistent human preference for dealing with other humans in high-stakes situations. Automation takes longer to deploy than the hype cycle suggests.

None of this is a guarantee, and the piece isn’t arguing everything will be fine for everyone. Some jobs will disappear. Some workers will get hurt. Transition costs are real and tend to fall hardest on people who can least absorb them. But ‘painful and uneven transition’ and ‘total civilizational unemployment catastrophe’ are not the same thing — and collapsing that distinction is mostly useful for selling newsletters.

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The post Maybe We’re Not All Getting Fired by a Chatbot first appeared on Sevenelles.

The burnout epidemic isn’t a personal failing — it’s a systems failure. Technology, especially the phone in your pocket, was engineered to colonize your attention, and it’s working exactly as designed. John Mark Comer frames this as a specifically Christian problem with a specifically Christian solution, and I don’t entirely agree with that framing. But you don’t have to share his theology to recognize that the diagnosis is correct: the gnawing sense that there’s always something else you should be doing, that quiet is a problem to be solved rather than a state to be sought. Whether your path to stillness runs through the Gospel of Matthew or a long walk with no earbuds, the destination is the same. Hurry less. Reflect more. Your soul — secular, sacred, or somewhere embarrassingly in between — will thank you.

Article Excerpt:

“Hurry is a gnawing sense that there is always more to do; a life spent hurtling oneself through each day; a schedule that makes little room for God. Technology has only exacerbated the problem. Comer calls the modern world ‘a virtual conspiracy against the interior life.'”

Article Summary:

John Mark Comer is one of the most influential pastors in America right now, which you might not have noticed because he doesn’t really want to be found. No contact page, no morning phone checks, auto-deletes his email over Christmas. His best-selling book, *The Ruthless Elimination of Hurry*, argues that the great enemy of spiritual life isn’t sin or doubt — it’s the frantic, grinding pace of modern existence, turbocharged by smartphones and social media.

Comer’s pitch lands somewhere between Andrew Huberman and the Sermon on the Mount. He preaches to packed rooms of burned-out millennials and Gen Z-ers in cities like New York, opens with Anne Helen Petersen’s famous burnout essay, and then pivots to Matthew 11. The practical advice — delete social media, go grayscale, observe a phone-free Sabbath — sounds like every digital detox article you’ve already ignored. The difference is he frames it as discipleship, not self-optimization.

His critics wonder if Comer is just selling baptized wellness: spiritual language draped over lifestyle content for affluent young people who can afford to slow down. His defenders point out that the practices he’s recommending — solitude, fasting, Sabbath, silence — are ancient, not trendy, and that the Church mostly forgot them.

The deeper question the article quietly circles without fully answering: Does it matter *why* you slow down, or just that you do? Comer would say yes, it matters enormously. Which is a reasonable position. And also a convenient one for a pastor.

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The post Is Hurry the Enemy of Spiritual Life? first appeared on Sevenelles.

There’s a scene in Pluribus— one of the most thoughtful series on Apple TV about a hive mind that absorbs humanity — where all the consumed humans are practically begging Carol to write another book. Not because they’re bored. Because they’ve lost the ability to surprise themselves. They’ve pooled every thought, every memory, every scrap of creativity into one enormous collective intelligence, and it turns out that an ocean of shared knowledge is about as creatively fertile as Oklahoma farmland in 1935.

I thought about that scene a lot while reading Bright Simons’ essay “The Social Edge of Intelligence” in The Ideas Letter. It’s one of the most carefully argued pieces I’ve encountered on AI — not because it screams that the robots are coming (they are) or that everything will be fine (it won’t), but because it identifies a dependency so fundamental that most of Silicon Valley hasn’t bothered to look at it.

Here’s the thesis, and it’s worth sitting with: AI doesn’t really think. It remembers how we thought together. And we’re rapidly creating conditions where we’ll stop giving it anything worth remembering.

The Devastation Is Real — Let’s Not Pretend Otherwise

The economic carnage ahead is not speculative. IBM announced plans to replace 7,800 roles with AI. Duolingo cut a tenth of its contractors. Klarna’s AI assistant now does the work of 700 customer service employees, and the company’s stated goal is to shrink its workforce below 2,000. Jack Dorsey wants Block’s headcount flat while AI carries the growth.

This is not a drill, and it’s not a blip. The internal logic is merciless: routine cognitive work gets automated, junior roles evaporate, productivity gains compound. For any board reviewing cost structures, it’s the cleanest investment case since the internal combustion engine retired the horse. There’s even a moral momentum to it — hesitate and you fall behind, and nobody wants to be the last company still paying humans to do things a model can do for pennies.

If you’re in your twenties right now, entering a workforce that’s being systematically thinned at the bottom, I won’t sugarcoat it: the next decade is going to be brutal. Entire career ladders are being pulled up. The entry-level positions that used to teach people how to think inside an organization? Many of them are already gone.

But Here’s the Thing Nobody’s Talking About

Simons describes a 2024 experiment where roughly 300 writers were asked to produce short fiction — some with GPT-4’s help, some without. On the surface, the results confirmed the AI hype: AI-assisted stories were rated more creative by independent judges. Individually, every writer got a boost.

But when the researchers looked at the full body of stories rather than individual ones, a different picture emerged. The AI-assisted stories were more similar to each other. Each writer had been individually elevated. Collectively, they had converged.

The researchers called it a tragedy of the commons. I’d call it something more plainly terrifying: the slow-motion extinction of surprise. We will end up with the hive mind from Pluribus. Desperately craving Carol’s next novel because it wants something it hasn’t already seen.

AI Eats Its Own Tail

Simons builds his case on a chain of research that’s hard to argue with. In 2024, a team led by Ilia Shumailov published in Nature that AI models trained on AI-generated data start to collapse. The distribution narrows. Minority viewpoints, rare knowledge, unusual formulations — the weird, edge-case stuff that represents actual intellectual diversity — vanishes first. What’s left is statistically average. Fluent, plausible, and hollow. Or, as Bruce Cockburn put it, “the Trouble With Normal is it only gets worse.”

Meanwhile, Epoch AI estimates that the total stock of quality human-generated text available for training will be exhausted between 2026 and 2032. Most people frame this as a resource problem, like running out of oil. But Simons sees something deeper: the reservoir isn’t just being drained. The springs feeding it are drying up.

Because here’s the feedback loop from hell: AI replaces human workers. Fewer humans doing complex cognitive work means less diverse, friction-rich human language production. Less diverse language production means less valuable training data. Less valuable training data means AI systems start to degrade. The technology is quietly consuming the very substrate it depends on.

We’re not just outsourcing tasks. We’re outsourcing the effort of thinking. And effort, it turns out, is where the interesting stuff happens.

Is the Thesis Airtight? Let’s Push Back

Before we get too comfortable with this narrative, it’s worth stress-testing it.

First, the synthetic data problem may be solvable. Researchers are already working on techniques to filter AI-generated text from training sets and to generate synthetic data that preserves distributional diversity. It’s not unreasonable to think that clever engineering could mitigate model collapse — at least partially, at least for a while.

Second, the “exhaustion of human text” timeline assumes current architectures and training methods. Breakthroughs in reasoning models, multimodal learning, or entirely new paradigms could change the equation. We’ve been surprised before.

Third, there’s an argument that AI could increase cognitive diversity by lowering barriers to entry — giving more people from more backgrounds the tools to participate in complex knowledge work. That’s not nothing.

But here’s why I ultimately find Simons’ argument more convincing than these objections: every counterargument assumes human behavior will remain unchanged in the presence of powerful cognitive offloading tools. And we have decades of evidence that it won’t. A Microsoft and Carnegie Mellon study of 319 knowledge workers found that in 40% of AI-assisted tasks, participants exercised no critical thinking whatsoever. Anthropic’s own research shows that users pause to double-check AI output only 8.7% of the time.

We’re not just outsourcing tasks. We’re outsourcing the effort of thinking. And effort, it turns out, is where the interesting stuff happens.

The Hope Buried in the Wreckage

So here’s where the Sevenelles brain kicks in — the part of me that refuses to let a clear-eyed assessment of reality collapse into the nihilistic need for another gin & tonic.

If Simons is right — and the research increasingly suggests he is — then the very things that make humans inefficient, frustrating, and expensive are also the things AI literally cannot survive without. Disagreement. Friction. The stubborn insistence of someone who sees the problem differently. The junior employee who asks the dumb question that turns out not to be dumb. The messy, ego-bruising, time-consuming process of humans actually engaging with each other.

The organizations that figure out how to use AI to create more human interaction — more debate, more cross-pollination, more productive friction — will be the ones that thrive. This isn’t wishful thinking. It’s a logical consequence of the dependency Simons identifies. If AI’s intelligence is a function of the social complexity of the civilization that feeds it, then protecting and enriching that social complexity isn’t a nice-to-have. It’s the whole game.

What This Means for You and Me

The transition is going to be ugly. Let’s not pretend otherwise. Millions of jobs will disappear before the correction kicks in. People will suffer real economic pain while executives learn the hard way that you can’t automate the source of your own intelligence.

But the correction will kick in. And when it does, the premium won’t be on people who can do what AI does — process, summarize, generate plausible output. The premium will be on people who can do what AI can’t: think in genuinely novel ways, hold productive disagreements, bring perspectives that haven’t been averaged into the training data, and do the unglamorous, essential work of keeping human knowledge diverse and alive.

Every consumed human in the Pluribus hive mind wanted Carol to write that book. Not because her prose was technically superior to what the collective could produce. Because she could still surprise them. Because surprise requires a mind that hasn’t been averaged.

That’s the edge the human mind holds. Not efficiency. Not productivity. The capacity to be unpredictable in ways that matter.

Read Bright Simons’ essay “The Social Edge of Intelligence” in The Ideas Letter

The post AI Will Devastate the Workforce. But the Human Mind Will Always Have the Edge. first appeared on Sevenelles.