Can we all take a second to step back and admit that this is really not good? No wonder we are not okay.

The scientists have officially diagnosed us

A recent review in Behavioral Sciences, summarized nicely by ZME Science, uses a phrase that should be printed on the back of every smartphone: evolutionary mismatch. The idea is that our brains evolved for one world, and we now live in a completely different one, and the gap between those two worlds is where all our anxiety, loneliness, doomscrolling, and low-grade nihilism come from.

We already know the food version of this. Sugar and fat were once rare and precious, so we evolved to hoover them up whenever we could. Now they’re piped into gas stations at 3 a.m. in the form of blue Icees the size of a toddler, and we’re shocked that half the country has metabolic syndrome. Same Pleistocene + 24/7 food glut = Diabetes and the explosion of GLP-1 use.

The scientists now recognize that the social version of this mismatch is doing to our minds what the Big Gulp did to our pancreases (pancrei?).

We were built for a village. Instead we have a news feed.

Our ancestors lived in bands of maybe 50 to 150 people. You knew everyone in your tribe. They knew you. If you screwed up, Grog gave you the stink-eye across the fire, you apologized by sharing your haunch of mammoth, and life continued.

Now your brain — using that same Pleistocene hardware — is trying to rank you against every influencer, ex-classmate, tech billionaire, and pilates-toned stranger an algorithm shoves in your face. Your status-detection module was designed to handle a village. You’re trying to handle the entire planet – a planet that also seems to be on fire, figuratively and literally.

This isn’t just cranky-old-man theorizing. A 2025 study in Nature Human Behaviour found that adolescents with mental health conditions were significantly more sensitive to online social comparison and feedback than their peers. Translation: the kids most vulnerable to comparison are the ones being served the most of it, at maximum brightness, at 1 a.m., in bed. What could possibly go sideways?

Loneliness has become a WHO-level emergency

The World Health Organization now considers loneliness a global health threat, estimating it affects roughly one in six people worldwide and increases risk of stroke, heart disease, dementia and early death at rates comparable to smoking. Let that land for a second: our species — the one that literally cannot survive infancy without other humans — is now dying from a shortage of them.

The former U.S. Surgeon General wrote an entire advisory comparing the health impact of chronic loneliness to smoking 15 cigarettes a day. Fifteen. And unlike cigarettes, you don’t get to enjoy the enveloping warmth of the first nicotine hit.

Meanwhile, Pew reports that nearly half of American teenagers are online “almost constantly.” Not a lot. Not frequently. Almost constantly. A generation is being raised inside a slot machine regularly insults their appearance.

The Greatest Hits of ways we’re breaking ourselves

A partial inventory of ancient brain vs. modern environment:

• Threat-detection system designed for saber-toothed cats, now processing 47 push notifications about civil unrest in a country you cannot pronounce.
• Mate-selection instincts tuned for the six eligible humans in your village, now sorting through 4,000 curated faces on Hinge, each optimized with a filter that makes them look like an anime sex god.
• In-group loyalty circuits designed for actual tribes, now hijacked by cable news to make you hate your neighbor because of a bumper sticker.
• Reciprocity systems that once tracked “Did Grog share the berries?” now trying to compute whether an influencer with 4 million followers is really your friend.
• Attention span, formerly capable of tracking an animal for six hours, now unable to finish a paragraph without checking whether anyone liked the paragraph you just posted.

Jonathan Haidt has been screaming about this for years, mostly about kids and phones, and increasingly the data is doing the screaming with him. Depression, anxiety and self-harm among adolescents began climbing in a suspiciously vertical line starting around 2012 — right when the smartphone became universal, and the front-facing camera taught every 12-year-old to audit her own face for a living.

Some people are quietly walking out of the casino

Here’s the interesting part. A small but growing number of humans have started noticing the fluorescent lights, the free drinks and the missing windows, and are simply… leaving the casino.

Teenagers, of all people, are leading the charge. The New Yorker has documented a real, if small, trend of Gen Z kids trading their iPhones for “dumbphones” — flip phones, Light Phones, the kind of brick your dad had in 1998. The stated reason is almost tender: they want to feel like people again.

Parents are organizing Wait Until 8th pacts, collectively agreeing to keep smartphones out of kids’ hands until at least eighth grade so no single family has to be the weird one. Whole schools and, in some cases, whole countries are banning phones from classrooms — and the early data is showing exactly what you’d expect: better grades, less bullying, more talking to actual humans.

Meanwhile, adults are rediscovering things called “clubs” and “third places” — running clubs, book clubs, church basements, board-game nights, even the ancient technology of having people over for dinner. Somewhere a sociologist is crying with joy that some people are reinventing the neighborhood.

A modest, panicked plea

Look, I’m not going to pretend I’m out here churning butter and organizing a barn raising. I’m typing this into a device that will beam it into a network that will notify a machine that will decide whether other machines should show it to you. I’m as compromised as anyone. The call is coming from inside the house, and the house is on fire, and the fire is also a phone.

But here’s the thing. Every one of us gets exactly one Pleistocene brain, non-transferable, no refunds. And right now we are handing that irreplaceable piece of hardware to whichever billionaire has the shiniest app and saying, “Do whatever you want with this, I trust you.”

So maybe — just as a thought experiment — put the phone in a drawer for an evening. Or actually use the phone part of your smartphone to call somebody and have a conversation. Invite three humans over and feed them something you burned yourself. Go for a walk without documenting it. Meet a neighbor whose politics you’ll hate and discover they’re weirdly nice in person. Read a whole book. Get bored. It turns out boredom is key to success and happiness.

Because if we don’t start clawing back some of the wiring we came factory-installed with, the endgame here isn’t pretty. It will be us, in about forty years, sitting in climate-controlled pods, being emotionally regulated by an AI that pats our heads and calls us “good human” every time we finish our nutrient paste. Like a tiny, inbred lapdog. Cue the music:

The post Our Brains Aren’t Built for This Shit first appeared on Sevenelles.

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.

Read the Full Article

The post Can Venture Capitalists Buy Our Democracy? 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.

Read the Full Article

The post AI + Quantum Computing = Yikes! (Chapter 1) 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.

Read the Full Article

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

Read the Full Article

The post Maybe We’re Not All Getting Fired by a Chatbot 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.

In another great newsletter, my friend Curt Buermeyer summarizes Peter Diamandis’s three-phase roadmap from AI labor disruption to post-scarcity abundance. I’ll be honest — I want to believe it. Curt and I agree that the next five years are going to be rough. But where he sees a ten-year arc bending toward something beautiful, I see a timeline that assumes robotic construction, autonomous transport, AI healthcare, and vertical farming all scale and democratize faster than any infrastructure transformation in human history. But here’s the thing about being a pessimist: often you’re wrong, and that is a beautiful thing. Curt’s optimism is grounded and honest, not naive, and Diamandis has a track record of being right about exponential curves before the rest of us could see them. So I’m bookmarking this roadmap not because I’m convinced, but because I hope like hell they’re right and I’m wrong.

Article Excerpt:

I believe we’re facing a reality where roughly 40% to 50% of white-collar jobs—the knowledge work that college students and recent grads have spent years studying for—will likely be displaced almost overnight.

Article Summary:

Curt Buermeyer’s latest Smash Your Thinking newsletter summarizes a roadmap by Peter Diamandis (XPRIZE founder, exponential tech evangelist) for navigating the AI-driven collapse of the labor market without also collapsing the social contract. The core argument: the AI disruption won’t look like the Industrial Revolution, which reshuffled the workforce over 150 years. This one is compressed into a decade, and there’s no adjacent sector to retrain into because AI is eating every sector simultaneously.

Diamandis lays out three phases. Phase 1 (2025–2028): the fracture. Jobs disappear, identities crater, and the proposed floor is a $3,000/month Universal Basic Income paired with a 32-hour workweek to keep people attached to purpose and social structure while the shock absorbs. Phase 2 (2028–2031): the automation dividend. AI companies have extracted enormous value from public infrastructure — our data, our research, our systems — and the public deserves a share. Think Alaska Permanent Fund, but for the robot economy. Phase 3 (2031–2035): the great deflation. Housing drops from $2,000/month to $600. Transport falls to $225. Food, energy, and healthcare trend toward near-zero marginal cost, driven by robotics, solar, autonomous fleets, and AI diagnostics.

Curt adds his own caveat: he’s skeptical the Phase 3 deflation happens as fast as Diamandis projects. Prices in housing, healthcare, and food have never actually gone down for normal people, and he’d bet the timeline slips. The actionable close is straightforward: share the roadmap, audit what you’d do with your time if survival wasn’t the point, and pressure policymakers to build the bridging mechanisms now — before “the Valley” between 2026 and 2031 turns into something uglier.

Read the Full Article

The post Will AI Force US to Adopt Universal Basic Income? first appeared on Sevenelles.

Based on the podcast conversation below, I would now amend that to read “We are so fucked. We are so totally fucked. Or maybe only for a decade or so.“

The podcast is a VERY interesting glimpse into the future. I highly recommend giving it a listen.

Above: The Moonshots hosts join Andrew Yang to unpack AI’s explosive collision with politics – deepfakes weaponizing elections, UBI surging as job-killer abundance hits, and radical fixes like open voting for anyone from Cuban to Robbins – while plotting democracy’s entrepreneurial reboot.

The post Psssst… Wanna Glimpse Into Our Future? first appeared on Sevenelles.

In this article, Curt Buermeyer discusses how the traditional model of trading time for money is rapidly becoming obsolete due to AI-driven productivity gains. For over a century, hourly billing has been standard across professions, but the workplace is now shifting from “pay-per-hour” to “pay-per-problem-solved.”

The most vulnerable workers are those whose value is measured by presence rather than performance. When employers pay for time, they’re actively seeking automation solutions to eliminate those costs entirely.

The new paradigm focuses on “problems solved per dollar.” A marketing strategist using AI to create an excellent plan in two hours delivers the same value as a traditional team spending 100 hours—clients only care about the outcome, not the process.

To adapt, professionals must reframe their value around specific problems solved rather than hours worked. This requires identifying the 20% of work requiring human judgment, strategy, and empathy that AI cannot replicate, while using AI to handle routine tasks efficiently.

Read the Full Article

The post The Death of the Billable Hour: Why Your Salary is at Risk in 2026 first appeared on Sevenelles.

We are so fucked. We are so totally fucked.

Usually, I follow that statement with, “but there is always hope.” Josh Tyrangiel’s article in The Atlantic leaves little room for hope, given the dysfunctional state of our democracy.

Here’s one of the many quotes from the article that paints a dire picture of AI’s impact on America’s working class:

“But I’m telling you it’s the end of America as we know it if we don’t use this moment to do things differently.”

Gina Raimondo

(former Rhode Island governor,
former US Commerce Secretary,
current venture-capital executive)

If you are fortunate enough to be in or nearing retirement with adequate savings to survive a major financial nut-punch, you can skip the article. For everyone else, read the article and plan ways to protect the family jewels.

Article Summary:

In 1869, Massachusetts created America’s first Bureau of Statistics of Labor to measure workplace conditions during the Industrial Revolution, establishing the principle that democratic governments should track what’s happening to their workers. Today, as artificial intelligence threatens to sodomize the American Dream, that commitment to measurement and evidence-based policymaking is being sorely tested.

While economists see no clear data yet showing AI’s impact on employment, corporate leaders are openly warning of massive job losses. CEOs from Ford, Anthropic, and other major companies have predicted that AI could eliminate 10-20% of jobs within years, with some estimating that half of white-collar positions could disappear within a decade. The scary part: after openly discussing these projections just a few months ago, executives have gone silent, advised by PR teams to stop talking publicly about AI and layoffs.

Economists remain divided on timing and severity. Some, like Federal Reserve Bank of Chicago President Austan Goolsbee, argue it’s too early to draw conclusions—productivity is strong and unemployment remains low. Others, like MIT’s David Autor and Daron Acemoglu, emphasize that speed matters: gradual change allows societies to adapt, but rapid disruption causes lasting damage, as seen with the “China shock” that devastated US manufacturing communities in the 1990s.

However, economist Anton Korinek warns this time may be different. Unlike previous technologies, AI can help deploy itself, potentially compressing decades of change into years. If 40% of global jobs are affected rapidly, as the IMF projects, our already hideously dysfunctional political institutions may not have the cajones to save the working class.

Our feckless Congress is paralyzed by fear of an authoritarian buffon, unable to even fund expanded labor statistics or revive worker retraining programs. In a classic “fox guarding the henhouse” move, the Trump administration has delegated AI oversight to venture capitalist David Sacks, who has hundreds of AI investments. Without political action, the challenge may ultimately be less about jobs than about whether our republic survives AI.

Article Excerpt:

“Mass job loss doesn’t just mean unemployment; it means missed loan payments, cascading defaults, shrinking consumer demand, and the kind of self-reinforcing downturn that can transform a shock into a crisis, and a crisis into the decline of an empire.“

Read the Full Article

The post America Isn’t Ready for What AI Will Do to Jobs first appeared on Sevenelles.