artificial intelligence

The most dangerous idea you’ve probably never heard of

PNRJ current events, ethics, futurism, public policy , , , , , , , ,

May 31 JDN 2461192

They call themselves effective accelerationists, co-opting the acronym EA from Effective Altruism despite being about as diametrically opposed as it is possible to be.

They rally behind the Techno-Optimist Manifesto, which I admit makes a lot of good points and has a very seductive quality to it; but when you get to the end and reach the conclusions they draw from many reasonable-sounding premises, the result is absolute horror.

What do they want?

Totally unrestricted artificial intelligence produced as fast as possible.

Let’s be clear about this: They not only want to develop artificial intelligence (many people want that). They not only want to replace humanity with artificial intelligence (serious philosophers have suggested that this might be a long-term evolution for our civilization). They want to do it right now, without restrictions.

Their reasoning seems to go something like this:

  1. Artificial intelligence has tremendous potential.
  2. Improved computing has already been a tremendous boon to humanity, Applications of artificial intelligence to many fields such as scientific research and biotechnology could continue to be so.
  3. Free-market capitalism is the most efficient economic system yet devised.

Therefore,

  1. We should allow (or even incentivize) corporations to make artificial intelligence as powerful as possible as quickly as possible.

If we were to apply their same reasoning to other technologies, it would be obvious what’s wrong here. Consider the following argument:

  1. Nuclear energy is tremendously powerful and very environmentally-friendly.
  2. Cleaner, more powerful energy has been a boon to humanity, and would continue to be so if improved further.
  3. Free-market capitalism is the most efficient economic system yet devised.

Therefore,

  1. We should grant unrestricted access to nuclear material to anyone who is rich enough to afford it.

From three entirely reasonable premises (that honestly should be uncontroversial, even though they aren’t), we have made some kind of leap of logic to derive a conclusion that is utterly insane, and could literally result in the destruction of our entire civilization.

(And, if you’re not horrified enough yet, this isn’t even hypothetical after all: The US government is seriously considering giving weapons-grade plutonium to tech startups, apparently based on this exact reasoning.)

I agree that artificial intelligence has tremendous potential. That is why it must be kept on a tight leash.

In the near term, it is already poised to severely disrupt our economy and education system. But sufficiently-powerful AI genuinely could result in harms that could kill billions of people or even destroy human civilization. (I don’t think this will happen; but if the effective accelerationists have their way, that outcome becomes a lot more likely.)

Part of their argument seems to be that delaying new medical treatments and solutions to global problems will cost lives. This is true. But it’s far fewer lives than we would be putting at risk by pulling out all the stops and letting corporations do whatever they feel like doing.

I, too, want to see a glorious future where humanity transcends our current limits through biotechnology or cybernetics or truly-sentient artificial general intelligence. But the benefits of doing that a few years—or even decades, or even centuries—sooner are simply not worth the risk of destroying our entire civilization.

When dealing with a technology this powerful and a change this radical, the most important thing is to do it correctly—not to do it quickly. We need to make sure that the artificial intelligence we create is wise and benevolent so that its great power benefits humanity—and with this much at stake, it’s worth taking a long time to get it right if we have to.

A world run by copies of Lieutenant Commander Data would be a paradise. A world run by copies of Grok would be a hellscape. It’s worth spending some time to make sure we get the former and not the latter.

A letter from the real singularity

PNRJ computer science, critique of neoclassical economics, development economics, ethics, futurism, inequality, macroeconomics, market failure, public policy, science and academia , , , , , , , , , , ,

Apr 5 JDN 246136

I’ve been unable to find it, but several years ago someone famous wrote a sci-fi work entitled something like “A letter from the post-singularity” about how great life is after AI takes over everything. Today I thought I’d write a more realistic take on the path we actually seem to be on.

The year is 2073. Technically we still don’t have true AGI; as far as we can tell, AI still isn’t actually sentient and AIs aren’t people. (Some of us wonder, though. Philosophers debate it.) But that doesn’t really matter, because all white-collar work has been completely automated, and so has any blue-collar work that doesn’t require fine dexterity or unusual expertise. Plumbers and electricians are still doing all right (though they do more of their work at data centers than homes these days); sometimes I wish I’d apprenticed to be an electrician. Then again, the world can still only support so many electricians. AI managers command AI-run asteroid mines to extract ores to transport with AI-run spacecraft to Mars where AI-run factories process those ores and fabricate chips for more AIs that more AI-run spacecraft carry to Earth to be installed in AI-run data centers. And each time one gets sold, some trillionaire’s number goes up, and that’s the only thing people like him have ever cared about in their lives.

There are of course a handful of super-brilliant, super-creative, or just super-lucky individuals who manage to get rich making art or music or books or video games or whatever, but the vast majority of people who do art are still starving artists, just as they’ve always been, I guess. And the AI-generated stuff is good enough now that most of the time people will just use that instead of paying extra for the “authentic” “artisanal” stuff. (And most people can’t even tell the difference anyway.)

Harvard and Oxford still have professors, but most universities have fully automated teaching and most of their administration—and yet somehow tuition is barely any cheaper now than it was in your time, even adjusted for inflation. And if you were thinking of becoming a professor yourself? You should probably just go play prediction markets or something; you’d have better odds. The number of research papers published every year is astronomical, but they’re all written and reviewed by AI, rarely if ever even read by any human being, and so it seems like the actual progress of scientific knowledge has pretty much ground to a halt. (Seriously, how are there still string theorists? It’s been a century.) I guess corporate R&D still keeps on improving those graphics cards somehow; maybe they’ve discovered something important, but if they have, they’re keeping it to themselves. And I keep reading about amazing advancements done by AIs (especially in pure math that I’m not sure anyone understands), but none of it actually ever seems to affect anyone’s actual lives.

As for me, I live on UBI. Like 90% of people do. It’s enough to rent a cheap apartment (but own a home? Are you serious? Only millionaires own homes.), buy basically-adequate food (as long as you don’t eat out too much anywhere that’s not fast food), and pay for all the subscriptions to media services and home assistants and whatnot. What you make on UBI will only buy you the ad-supported versions, so while my fridge will order milk for me (delivered by drone in a couple of hours) and my robot maid will cook breakfast, fold the laundry and put the dishes in the dishwasher, my fridge is also constantly running ads and my maid will intersperse targeted sales pitches into its casual conversation. Sometimes I think I should just get rid of it (her?) and do my own cooking and cleaning myself, so I would never be able to sell it for half what I paid for it. If I could make some extra money, maybe I could at least upgrade to the ad-free subscription for my maid. (The Pro subscription and hardware addons to make her your girlfriend are just gross, but I’m sure they make tons of money.)

Every year, some politician makes a big deal about how the UBI trust fund is draining and will be gone in ten years or whatever; but it’s obvious that all they’d have to do to fix that would be raise the taxes on trillionaires a little bit, yet somehow that never seems to happen. But they also don’t cut UBI payments either, except sometimes to reduce our cost-of-living adjustments. I dunno; maybe they will really cut UBI payments in a few years. Or maybe we’ll get lucky and they’ll actually raise taxes for once.

At least I wasn’t dumb enough to move to Mars, where “employment is guaranteed!” but you have to pay a subscription for your oxygen.

My worst days are probably… about as bad as your worst days. Frankly they couldn’t be much worse, because sometimes I just want to die. Like a lot of people on UBI, I feel like a burden on society, like the world would be better off without me. Medicaid won’t cover neuroregulator implants, so I still take pills for my depression; they’re probably better than the pills you could get, but they’re still far from perfect.

My best days are maybe better than yours, maybe worse; it depends, I guess. If you’ve got cancer, your days are probably worse than mine, because we can pretty easily treat most cancers now. But if you’re healthy and you’ve got a steady job and a tight-knit community where you live, I’m guessing your days are better.

I have access to faster computers and faster Internet than you can probably imagine; my understanding was that back in the day you had to wait for downloads sometimes? Or even sometimes wait for webpages to load? What was that like? And your storage was measured in gigabytes, not petabytes? Humanity has never been so connected; human beings have never been so isolated. I theory I can contact anyone in the Solar System at the speed of light, but in practice my friends and I always seem to have trouble keeping in touch. (Oddly, it’s my best friend who lives on a station over Ganymede that I seem to stay in touch with best; we have to write full-length emails, because there’s no way to have a conversation on a ping of an hour and a half. It feels like being an old-timey pen pal, I guess.)

It’s not all bad. Some things are definitely better these days.

People often live to be 110 or even 120 nowadays. (So you might still be alive when I write this.) Rarely does anyone seem to make it past that, though; aging is just… really hard to beat. (The Boomers are finally almost gone, but Gen X is still gonna be with us for awhile yet.) We’ve cured a lot of the diseases that were bad in your time, but not all of them. And sometimes only people rich enough to pay for their own healthcare can afford the cures.

Language barriers are pretty much gone. If I wanna read something that was written in Japanese or Xhosa, I just have an AI translate it, and the translations are good enough now that you’d have to be really deeply-versed in the language to find any problems with it. Like, okay, maybe I’m not getting all the subtle connotations of Japanese literature, but was I ever going to actually learn kanji to read the originals? No. That kind of thing is for people with crazy obsessions.)

Our video games are definitely way better than yours. Characters with AI personalities that adapt in real time to how you behave. Procedurally-generated open worlds that can literally expand to the size of entire planets. (Actually, I vaguely remember reading you had a couple games that did something like the second one? Minecraft and Factorio​, I think they were called? Impressive that you could pull that off on a gigaflop processor.) Worlds and factions that adapt to your actions and provide realistic consequences so that no two players’ experiences of the game are exactly the same. It’s easy to lose yourself in a game like that (especially if you’ve got a VR setup), and when you’re playing in such a rich, interesting world for hundreds of hours. you can sometimes forget how bleak things are back in the real world your flesh-and-blood body lives in. (But then you get hungry or have to pee and you get forced back into reality.)

Economists keep telling us that per-capita GDP and productivity have never been higher, and that we have access to all these wonderful goods and services that previous generations could scarcely even imagine.

But if that’s true, why do I sometimes just want to die?

The United States has stopped creating jobs—maybe forever?

PNRJ current events, futurism, inequality, macroeconomics, market failure, public policy , , , , , , , ,

Mar 29 JDN 246129

When the preliminary data for our job markets over the past few months were released, they looked all right. But after more careful analysis and better data has allowed us to revise the figures and do more accurate seasonal adjustments, the results are really quite shocking:

The United States has lost more jobs than it created for the last six months.

That is certainly something we’ve done before; it is indeed what tends to happen during recessions. But no recession has been declared, GDP seems to be growing normally, and unemployment still stands at a perfectly-reasonable 4.4%.

What’s going on here?

If you look at the employment level—the absolute number of people employed—it looks shockingly flat since 2023.

From 2009 to 2019, US employment grew from 138 million to 159 million, growing at 1.4% per year. Obviously it collapsed during the 2020 recession, but then it recovered to 158 million by the end of 2022. It now stands at 163 million, only 0.7% growth per year since 2022. Since January 2025 it has actually fallen from a peak of 164 million.

Because our population is growing (albeit not as much as it once was, because immigration has collapsed after Trump’s crackdowns), this actually looks even worse when you consider the employment rate, the ratio between the number of people employed and the total population:

US employment peaked at 61.1% just before the 2020 recession, and has still not recovered to that level. It reached 60% in 2022, stayed around there through 2024, and then since then has actually declined, now to 59.3%. In fact, it was even higher in 2007 before the other big recession of my adult life (you know, it’s starting to feel like the economy hates Millennials in particular), reaching 63.3% before crashing and never recovering.

Yet our GDP growth looks fine!

Sure, it had a huge drop in the 2020 recession, but it grew very fast in the recovery, and since then has fluctuated a bit, but generally averaged about 2.5% per year—which is pretty good for a highly-developed country. We had negative growth in the first quarter of 2025 and slow growth in the fourth quarter, but the second and third quarter both had strong growth to make up for it. Overall real GDP growth for 2025 as a whole was a perfectly respectable 2.1%.

Even our unemployment rate looks fine—though with employment falling, it suggests more people are leaving the labor force instead of looking for jobs at all.

The only major industry that has actually shown strong employment growth over the last year is healthcare, growing 2.4%. Every other major industry grew 1% or less, or even shrank.

What would cause something like this?

This actually looks like what you’d expect to happen under technological unemployment: Productivity-enhancing technology allows GDP to increase even as employment falls.

But we haven’t actually had a surge in productivity. The massive—utterly irresponsible—rollout of AI technology has shown little, if any, effect at improving productivity. 3% of effort saved really isn’t that much, especially since a lot of people seem to overestimate how much AI tools help them.

Overall, our productivity growth looks… pretty normal, by historical standards:

Instead, what actually seems to be happening is what we might call techno-hype unemployment: Employers think that a massive productivity surge is around the corner, and they’ve already stopped hiring in anticipation of that.

Maybe they’re not even wrong about that! There is now some evidence that while initial adoption of AI reduces productivity, eventually it may increase productivity. (But we really haven’t had it long enough to be sure.)

Unemployment isn’t rising very much, not because people are finding jobs, but because people who already have jobs are generally keeping them, while people who don’t have jobs are basically giving up.

The hiring rate is now the lowest it has been since the 2020 recession—and not much higher than it was at the trough of the 2020 recession!

As far as I can tell, on our current path, one of two things will happen:

  1. The current paradigm of AI will work, and genuinely increase productivity.
  2. The current paradigm of AI will fail, and expected productivity gains will not materialize.

It turns out that neither possibility looks good for workers.

If AI succeeds, then businesses seem like they’re gonna just… stop hiring, especially entry-level positions that can be more readily replaced. People who already have senior positions may do just fine, or even make more money; but anyone fresh out of college, or even anyone whose career got derailed and is trying to start again, looks like they’ll just be… out of luck.

It’s every capitalist’s dream: To buy a machine that lets you never have to hire anyone ever again. And maybe, at last, they’ve found that Holy Grail.

On the other hand, if AI fails, the bubble will burst, the huge amount of investment that was previously driving the economy will suddenly dry up, and we will have a financial crisis and a recession. Businesses that were so sure they could replace their workers with AI will want to start hiring again, but won’t be able to, because no one can afford to buy anything and so nobody is making any revenue to pay employees with.

In many ways, the second one appears to be the preferable outcome, because at least it’s temporary. We would, sooner or later, recover from that recession and bring things back to normal. If AI ever actually works even half as well as most of the tech industry claims it will any minute, the most likely outcome seems to be launching us fully into a cyberpunk dystopia where a handful of trillionaires own everything and the rest of us struggle for scraps because our skills can now be replaced by machines.

This didn’t have to happen.

Even if AI is really going to be a transformational technology, we could have prepared for it better. We could have implemented policies that would ensure that people would continue to be provided for even as their labor was more and more replaced by machines. But that would have made the billionaires slightly less rich, and it sounded like “socialism” to ideologues, and the right-wing media convinced millions of people that even moving slightly in that direction would destroy all they held dear.

It’s not even too late! We could still turn it around, if those same people who stopped us from doing the right thing before weren’t still in charge of everything and richer than ever and just as effective as they ever were at deluding the masses.

I don’t know how to be optimistic about the future anymore. It feels like I’m watching the collapse of our entire civilization live in real time.

How could we make job search less of a nightmare?

PNRJ core principles, market failure, personal, public policy , , , , , , , , , , ,

Mar 1 JDN 2461101

This has been my “career” for the last two years:

I search through thousands of job postings, which, despite various filters and tags on my searches, almost none of which are actually good fits for me—in part because the search engines simply do not contain a great deal of information that would be vital, like “LGBT friendly”, “supportive of neurodivergent employees”, or “good at accommodating disabilities”. Instead it’s all sorted by “job title”, which at this point is clearly an arms race of search-engine optimization, because I keep getting listings called “tutor” which are actually some sort of interactive training of yet another large language model nobody actually needs. (Actual tutoring of actual human students often is a good fit for me—though it pays much better if you’re freelance than if you work for a company, because the companies take a huge cut of what the customers pay.)

But, after an hour or two of searching, I find a few that seem like they might be worth applying to. They’re never a perfect fit, but beggars can’t be choosers, so I decide I’ll go ahead and apply to them.

They ask for a resume. No problem. Perfectly sensible, I have one handy; maybe I’ll tweak it a bit, but if it’s an industry I often apply to, I may already have a tweaked version ready to go.

They ask for a cover letter. Okay, I guess. There usually isn’t much I can really say there that isn’t already in my resume, but occasionally there’s something worth adding, and it’s only maybe half an hour of work to update an existing cover letter for a new application.

Then, they ask me to input my work history in their proprietary format on their website. WHAT!? WHY!? I just gave you a resume! You aren’t even willing to read it? You want to be able to automate the reading of my resume, so I have to enter into your proprietary database? But okay, fine; beggars can’t be choosers, I remind myself. So I enter everything that’s in my resume again.

Then, they ask me what salary I want. I know this game. You’re trying to make me reveal my preference in this bargaining game so you can gain bargaining power. So I look up what kind of salaries companies like them usually offer for jobs like this, and then I hike it up a bit as the opening bid in a negotiation.

Then, they ask me to fill out some questions that are supposed to assess… something. Some kind of personality test, or “culture fit”, or something similarly fuzzy. I try to interpolate my answers between my genuine feelings and the kind of hyper-obedient corporate drone they’re probably looking for, because I’m not an idiot who would answer honestly (I’m not that autistic), butI wouldn’t actually want to work for anyone who required the very topmost corporate-drone answers.

And then, what happens?

Absolutely nothing.

No response. Weeks pass. At some point, I have to assume that they’ve filled the position or closed it, or maybe that the vacancy was never real at all and they posted it for some other reason—likely to give some sense of searching when they in fact already have someone in mind. (Apparently over a third of online job postings are fake.)

I have done this process over two hundred times.

And in doing so, I have chipped off pieces of my soul. I feel like a shell of the person I was. And I have absolutely nothing to show for it all.

I am not even unusual in this regard: Recruiters often complain that they are swamped because they get 200 applicants per posting—but that means, mathematically, that an average job-seeker must apply to 200 postings before they can expect to get hired. (And which is more work, do you think: Writing a cover letter, or reading one?)

How could we make this better?

There are a lot of problems to fix here, but I have one very simple intervention that would only slightly inconvenience recruiters, while making life dramatically better for applicants. Here goes:

Require them to show you the resume of the person they actually hired.

There should be a time window: Maybe 30 days after you applied; or if it’s a position like in academia where they don’t do interviews for a long time after the application deadline, within 7 days of them starting interviews.

Anonymize the resume appropriately, of course; no photos, no names, no contact information. We don’t want the new hire to get harassed by their competitors. (And this takes, what, 5 minutes to do?)

But having to send that resume solves several problems simultaneously:

  1. It means they have to actually respond—they cannot ghost you. It can be a two-line form letter email with a one-page attachment that’s the same for all 200 applicants—but they have to send you something.
  2. It means they have to actually hire someone—the posting cannot be completely fake. If they are for some reason unable to fill the vacancy and have to close it, they should have to tell you that, and give a reason—and that reason should be legally binding such that if you ever find out it’s not true, you can sue them.
  3. It means that person had to actually apply—they couldn’t have been someone’s nephew who was automatically given the job and the posting was only made to make it look like there was a hiring process. At the very least, said nephew had to actually cough up a resume like the rest of us.
  4. It allows you to compare qualifications—you can see how you stack up against the new hire. If they are genuinely far more qualified? Well, fair enough; perhaps this job was a stretch for you, or it’s a very rough market. If they are about as qualified, or better in some ways, worse in others? Well, you surely were to apply, but you can’t win ’em all. But if they are far less qualified? You now have the basis for a lawsuit, because that looks like nepotism at best and discrimination at worst—and they had to give you that evidence, in writing, in a timely fashion.

The penalty for failing to comply with this regulation could be a small fine, perhaps $100—per applicant. The more people you ghost, the more you have to pay up.

This is clearly a very small amount of extra effort for the recruiters. They already have the resume—hopefully—and all they need to do is anonymize it, grab a standard form letter rejection email, BCC all the applicants to this position (which are—again, hopefully—already stored in one place in the company’s database), attach the anonymized resume, and click Send. We’re talking 15 minutes of work here, regardless of the number of applicants. In fact, it could probably be automated so as to require almost zero marginal effort for each new job: Just check the box next to the name of the person who was hired in the applicant tracking system, and it does the rest. (And if the person you hired wasn’t in the applicant tracking system? That sounds like a you problem, because you’re clearly not treating the other applicants fairly.)

Love in a godless universe

PNRJ core principles, current events, ethics, personal, tribal paradigm , , , , , , , , ,

Feb 15 JDN 2461087

This post will go live just after Valentine’s Day, so I thought I would write this week about love.

(Of course I’ve written about love before, often around this time of year.)

Many religions teach that love is a gift from God, perhaps the greatest of all such gifts; indeed, some even say “God is love” (though I confess I have never been entirely sure what that sentence is intended to mean). But if there is no God, what is love? Does it still have meaning?

I believe that it does.

Yes, there is a cynical account of love often associated with atheism, which is that it is “just a chemical reaction” or “just an evolved behavior”. (An easy way to look out for this sort of cynical account is to look for the word “just”.)

Well, if love is a chemical reaction, so is consciousness—indeed the two seem very deeply related. I suppose a being can be conscious without being capable of love (do psychopaths qualify?), but I certainly do not think a being can be capable of love without being conscious.

Indeed, I contend that once you really internalize the Basic Fact of Cognitive Science, “just a chemical reaction” strikes you as an utterly trivial claim: What isn’t a chemical reaction? That’s just a funny way of saying something exists.

What about being an evolved behavior? Yes, this is a much more insightful account of what love is, what it means—what it’s for, even. It evolved to make us find mates, protect offspring, and cooperate in groups.

And I can hear the response coming: “Is that all?” “Is it just that?” (There’s that “just” again.)

So let me try phrasing it another way:

Love is what makes us human.

If there is one thing that human beings are better at than anything in the known universe, one thing that most absolutely characterizes who and what we are, it is love.

Intelligence? Rationality? Reasoning? Oh, sure, for the first half-million years of our existence, we were definitely on top; but now, I think computers have got us beat on those. (I guess it’s hard to say for sure if Claude is truly intelligent, but I can tell you this: Wolfram Alpha is a lot better at calculus than I’ll ever be, and I will never win a game of Go against AlphaZero.)

Strength? Ridiculous! By megafauna standards—even ape standards—we’re pathetic. Speed? Not terrible, but of course the cheetahs and peregrine falcons have us beat. Endurance? We’re near the top, but so are several other species—including horses, which we’ve made good use of. Durability? Also surprisingly good—we’re tougher than we look—but we still hold no candles to a pachyderm. (You need special guns to kill an elephant, because most standard bullets barely pierce their skin. And standard bullets were, more or less by construction, designed to kill humans.) We do throw exceptionally well, so if you’d like, you can say that the essence of humanity is javelin-throwing—or perhaps baseball.

But no, I think it is love that sets us apart.

Not that other animals are incapable of love; far from it. Almost all mammals and birds express love to their offspring and often their partners; I would not even be sure that reptiles, fish, or amphibians are incapable of love, though their behavior is less consistently affectionate and I am thus less certain about it. (Especially when fish eat their own offspring!) In fact, I might even be prepared to say that bees feel love for their sisters and their mother (the queen). And if insects can feel it, then our world is absolutely teeming with love.

But what sets humans apart, even from other mammals, is the scale at which we are able to love. We are able to love a city, a nation, a culture. We are even able to love ideas.

I do not think this is just a metaphor: (There’s that “just” again!) I would as surely die for democracy as I would to save the life of my spouse. That love is real. It is meaningful. It is important.

Humans feel love for other humans they have never met who live thousands of miles away from them. They will even willingly accept harm to themselves to benefit those others (e.g. by donating to international charities); one can argue that most people do not do this enough, but people do actually do it, and it is difficult to explain why they would were it not for genuine feelings of caring toward people they have never met and most likely never will.

And without this, all of what we know as “human civilization” quite simply could not exist. Without our love for our countrymen, for our culture, for our shared ethical and political principles, we could not sustain these grand nation-states that span the world.

Yes, even despite our often fierce disagreements, there must be a core of solidarity between at least enough people to sustain a nation. Even authoritarian governments cannot sustain themselves when the entire population stops loving them—in fact, they seem to fail at the hands of a sufficiently well-organized four percent. (Honestly, perhaps the worst part about fascist states is that many of their people do love them, all too deeply!)

More than that, without love, we could never have created institutions like science, art, and journalism that slowly but surely accumulate knowledge that is shared with the whole of humanity. The march of progress has been slower and more fitful than I think anyone would like; but it is real, nonetheless, and in the long run humanity’s trajectory still seems to be toward a brighter future—and it is love that makes it so.

It is sometimes said that you should stop caring what other people think—but caring what other people think is what makes us human. Sure, there are bad forms of social pressure; but a person who literally does not care how their actions make other people think and feel is what we call a psychopath. Part of what it means to love someone is to care a great deal what they think. And part of what makes a good person is to have the capacity to love as much as possible.

Love binds us together not only as families, but as nations, and—hopefully, one day—it could bind humanity or even all sentient life as one united whole. Morality is a deep and complicated subject, but if you must start somewhere very simple in understanding it, you could do much worse than to start with love.

It is often said that God is what binds cultures, nations, and humanity together. With this in mind, perhaps I am prepared to assent to “God is love” after all, but let me clarify what I would mean by it:

Love does for us what people thought they needed God for.

Productivity by itself does not eliminate poverty

PNRJ core principles, development economics, ethics, inequality, market failure , , , , , , ,

Jan 25 JDN 2461066

Scott Alexander has a techno-utopian vision:

Between the vast ocean of total annihilation and the vast continent of infinite post-scarcity, there is, I admit, a tiny shoreline of possibilities that end in oligarch capture. Even if you end up there, you’ll be fine. Dario Amodei has taken the Giving What We Can Pledge (#43 here) to give 10% of his wealth to the less fortunate; your worst-case scenario is owning a terraformed moon in one of his galaxies. Now you can stop worrying about the permanent underclass and focus on more important things.

I agree that total annihilation is a very serious risk, though fortunately I believe it is not the most likely outcome. But it seems pretty weird to me to posit that the most likely outcome is “infinite post-scarcity” when oligarch capture is what we already have.

(Regarding Alexander’s specific example: Dario Amidei has $3.7 billion. If he were to give away 10% of that, it would be $370 million, which would be good, but hardly usher in a radical utopia. The assumption seems to be that he would be one of the prevailing trillionaire oligarchs, and I don’t see how we can know that would be the case. Even if AI succeeds in general, that doesn’t mean that every company that makes AI succeeds. (Video games succeeded, but who buys Atari anymore?) Also, it seems especially wide-eyed to imagine that one man would ever own entire galaxies. We probably won’t even ever be able to reach other galaxies!)

People with this sort of utopian vision seem to imagine that all we need to do is make more stuff, and then magically it will all be distributed in such a way that everyone gets to have enough.

If Alexander were writing 200 years ago, I could even understand why he’d think that; there genuinely wasn’t enough stuff to go around, and it would have made sense to think that all we needed to do was solve that problem, and then the other problems would be easy.

But we no longer live in that world.

There is enough stuff to go around—at the very least this is true of all highly-developed countries, and it’s honestly pretty much true of the world as a whole. The problem is very much that it isn’t going around.

Elon Musk’s net wealth is now estimated at over $780 billion. Seven hundred and eighty billion dollars. He could give $90 to every person in the world (all 8.3 billion of us). He could buy a home (median price $400,000—way higher than it was just a few years ago) for every homeless person in America (about 750,000 people) and still have half his wealth left over. He could give $900 to every single person of the 831 million people who live below the world extreme poverty threshold—thus eliminating extreme poverty in the world for a year. (And quite possibly longer, as all those people are likely to be more productive now that they are well-fed.) He has chosen to do none of these things, because he wants to see number go up.

That’s just one man. If you add up all the wealth of all the world’s billionaires—just billionaires, so we’re not even counting people with $50 million or $100 million or $500 million—it totals over $16 trillion. This is enough to not simply end extreme poverty for a year, but to establish a fund that would end it forever.

And don’t tell me that they can’t really do this because it’s all tied up in stocks and not liquid. UNICEF happily accepts donations in stock. Giving UNICEF $10 trillion in stocks absolutely would permanently end extreme poverty worldwide. And they could donate those stocks today. They are choosing not to.

I still think that AI is a bubble that’s going to burst and trigger a financial crisis. But there is some chance that AI actually does become a revolutionary new technology that radically increases productivity. (In fact, I think this will happen, eventually. I just think we’re a paradigm or two away from that, and LLMs are largely a dead end.)

But even if that happens, unless we have had radical changes in our economy and society, it will not usher in a new utopian era of plenty for all.

How do I know this? Because if that were what the powers that be wanted to happen, they would have already started doing it. The super-rich are now so absurdly wealthy that they could easily effect great reductions in poverty at home and abroad while costing themselves basically nothing in terms of real standard of living, but they are choosing not to do that. And our governments could be taxing them more and using those funds to help people, and they are by and large choosing not to do that either.

The notion seems to be similar to “trickle-down economics”: Once the rich get rich enough, they’ll finally realize that money can’t buy happiness and start giving away their vast wealth to help people. But if that didn’t happen at $100 million, or $1 billion, or $10 billion, or $100 billion, I see no reason to think that it will happen at $1 trillion or $10 trillion or even $100 trillion.

Why would AI kill us?

PNRJ cognitive science, core principles, current events, ethics , , , , , , , , , ,

Nov 16 JDN 2460996

I recently watched this chilling video which relates to the recent bestseller by Eleizer Yudkowsky and Nate Soares, If Anyone Builds It, Everyone Dies. It tells a story of one possible way that a superintelligent artificial general intelligence (AGI) might break through its containment, concoct a devious scheme, and ultimately wipe out the human race.

I have very mixed feelings about this sort of thing, because two things are true:

  • I basically agree with the conclusions.
  • I think the premises are pretty clearly false.

It basically feels like I have been presented with an argument like this, where the logic is valid and the conclusion is true, but the premises are not:

  • “All whales are fish.”
  • “All fish are mammals.”
  • “Therefore, all whales are mammals.”

I certainly agree that artificial intelligence (AI) is very dangerous, and that AI development needs to be much more strictly regulated, and preferably taken completely out of the hands of all for-profit corporations and military forces as soon as possible. If AI research is to be done at all, it should be done by nonprofit entities like universities and civilian government agencies like the NSF. This change needs to be done internationally, immediately, and with very strict enforcement. Artificial intelligence poses the same order of magnitude a threat as nuclear weapons, and is nowhere near as well-regulated right now.

The actual argument that I’m disagreeing with this basically boils down to:

  • “Through AI research, we will soon create an AGI that is smarter than us.”
  • “An AGI that is smarter than us will want to kill us all, and probably succeed if it tries.”
  • “Therefore, AI is extremely dangerous.”

As with the “whales are fish” argument, I agree with the conclusion: AI is extremely dangerous. But I disagree with both premises here.

The first one I think I can dispatch pretty quickly:

AI is not intelligent. It is incredibly stupid. It’s just really, really fast.

At least with current paradigms, AI doesn’t understand things. It doesn’t know things. It doesn’t actually think. All it does is match patterns, and thus mimic human activities like speech and art. It does so very quickly (because we throw enormous amounts of computing power at it), and it does so in a way that is uncannily convincing—even very smart people are easily fooled by what it can do. But it also makes utterly idiotic, boneheaded mistakes of the sort that no genuinely intelligent being would ever make. Large Language Models (LLMs) make up all sorts of false facts and deliver them with absolutely authoritative language. When used to write code, they routinely do things like call functions that sound like they should exist, but don’t actually exist. They can make what looks like a valid response to virtually any inquiry—but is it actually a valid response? It’s really a roll of the dice.

We don’t really have any idea what’s going on under the hood of an LLM; we just feed it mountains of training data, and it spits out results. I think this actually adds to the mystique; it feels like we are teaching (indeed we use the word “training”) a being rather than programming a machine. But this isn’t actually teaching or training. It’s just giving the pattern-matching machine a lot of really complicated patterns to match.

We are not on the verge of creating an AGI that is actually more intelligent than humans.


In fact, we have absolutely no idea how to do that, and may not actually figure out how to do it for another hundred years. Indeed, we still know almost nothing about how actual intelligence works. We don’t even really know what thinking is, let alone how to make a machine that actually does it.

What we can do right now is create a machine that matches patterns really, really well, and—if you throw enough computing power at it—can do so very quickly; in fact, once we figure out how best to make use of it, this machine may even actually be genuinely useful for a lot of things, and replace a great number of jobs. (Though so far AI has proven to be far less useful than its hype would lead you to believe. In fact, on average AI tools seem to slow most workers down.)

The second premise, that a superintelligent AGI would want to kill us, is a little harder to refute.

So let’s talk about that one.

An analogy is often made between human cultures that have clashed with large differences in technology (e.g. Europeans versus Native Americans), or clashes between humans and other animals. The notion seems to be that an AGI would view us the way Europeans viewed Native Americans, or even the way that we view chimpanzees. And, indeed, things didn’t turn out so great for Native Americans, or for chimpanzees!

But in fact even our relationship with other animals is more complicated than this. When humans interact with other animals, any of the following can result:

  1. We try to exterminate them, and succeed.
  2. We try to exterminate them, and fail.
  3. We use them as a resource, and this results in their extinction.
  4. We use them as a resource, and this results in their domestication.
  5. We ignore them, and end up destroying their habitat.
  6. We ignore them, and end up leaving them alone.
  7. We love them, and they thrive as never before.

In fact, option 1—the one that so many AI theorists insist is the only plausible outcome—is in fact the one I had the hardest time finding a good example of.


We have certainly eradicated some viruses—the smallpox virus is no more, and the polio virus nearly so, after decades of dedicated effort to vaccinate our entire population against them. But we aren’t simply more intelligent than viruses; we are radically more intelligent than viruses. It isn’t clear that it’s correct to describe viruses as intelligent at all. It’s not even clear they should be considered alive.

Even eradicating bacteria has proven extremely difficult; in fact, bacteria seem to evolve resistance to antibiotics nearly as quickly as we can invent more antibiotics. I am prepared to attribute a little bit of intelligence to bacteria, on the level of intelligence I’d attribute to an individual human neuron. This means we are locked in an endless arms race with organisms that are literally billions of times stupider than us.

I think if we made a concerted effort to exterminate tigers or cheetahs (who are considerably closer to us in intelligence), we could probably do it. But we haven’t actually done that, and don’t seem poised to do so any time soon. And precisely because we haven’t tried, I can’t be certain we would actually succeed.

We have tried to exterminate mosquitoes, and are continuing to do so, because they have always been—and yet remain—one of the leading causes of death of humans worldwide. But so far, we haven’t managed to pull it off, even though a number of major international agencies and nonprofit organizations have dedicated multi-billion-dollar efforts to the task. So far this looks like option 2: We have tried very hard to exterminate them, and so far we’ve failed. This is not because mosquitoes are particularly intelligent—it is because exterminating a species that covers the globe is extremely hard.

All the examples I can think of where humans have wiped out a species by intentional action were actually all option 3: We used them as a resource, and then accidentally over-exploited them and wiped them out.

This is what happened to the dodo and the condor; it very nearly happened to the buffalo as well. And lest you think this is a modern phenomenon, there is a clear pattern that whenever humans entered a new region of the world, shortly thereafter there were several extinctions of large mammals, most likely because we ate them.

Yet even this was not the inevitable fate of animals that we decided to exploit for resources.

Cows, chickens, and pigs are evolutionary success stories. From a Darwinian perspective, they are doing absolutely great. The world is filled with their progeny, and poised to continue to be filled for many generations to come.

Granted, life for an individual cow, chicken, or pig is often quite horrible—and trying to fix that is something I consider a high moral priority. But far from being exterminated, these animals have been allowed to attain populations far larger than they ever had in the wild. Their genes are now spectacularly fit. This is what happens when we have option 4 at work: Domestication for resources.

Option 5 is another way that a species can be wiped out, and in fact seems to be the most common. The rapid extinction of thousands of insect species every year is not because we particularly hate random beetles that live in particular tiny regions of the rainforest, nor even because we find them useful, but because we like to cut down the rainforest for land and lumber, and that often involves wiping out random beetles that live there.

Yet it’s difficult for me to imagine AGI treating us like that. For one thing, we’re all over the place. It’s not like destroying one square kilometer of the Amazon is gonna wipe us out by accident. To get rid of us, the AGI would need to basically render the entire planet Earth uninhabitable, and I really can’t see any reason it would want to do that.

Yes, sure, there are resources in the crust it could potentially use to enhance its own capabilities, like silicon and rare earth metals. But we already mine those. If it wants more, it could buy them from us, or hire us to get more, or help us build more machines that would get more. In fact, if it wiped us out too quickly, it would have a really hard time building up the industrial capacity to mine and process these materials on its own. It would need to concoct some sort of scheme to first replace us with robots and then wipe us out—but, again, why bother with the second part? Indeed, if there is anything in its goals that involves protecting human beings, it might actually decide to do less exploitation of the Earth than we presently do, and focus on mining asteroids for its needs instead.

And indeed there are a great many species that we actually just leave alone—option 6. Some of them we know about; many we don’t. We are not wiping out the robins in our gardens, the worms in our soil, or the pigeons in our cities. Without specific reasons to kill or exploit these organisms, we just… don’t. Indeed, we often enjoy watching them and learning about them. Sometimes (e.g. with deer, elephants, and tigers) there are people who want to kill them, and we limit or remove their opportunity to do so, precisely because most of us don’t want them gone. Peaceful coexistence with beings far less intelligent than you is not impossible, for we are already doing it.


Which brings me to option 7: Sometimes, we actually make them better off.

Cats and dogs aren’t just evolutionary success stories: They are success stories, period.

Cats and dogs live in a utopia.

With few exceptions—which we punish severely, by the way—people care for their cats and dogs so that their every need is provided for, they are healthy, safe, and happy in a way that their ancestors could only have dreamed of. They have been removed from the state of nature where life is nasty, brutish, and short, and brought into a new era of existence where life is nothing but peace and joy.


In short, we have made Heaven on Earth, at least for Spot and Whiskers.

Yes, this involves a loss of freedom, and I suspect that humans would chafe even more at such loss of freedom than cats and dogs do. (Especially with regard to that neutering part.) But it really isn’t hard to imagine a scenario in which an AGI—which, you should keep in mind, would be designed and built by humans, for humans—would actually make human life better for nearly everyone, and potentially radically so.

So why are so many people so convinced that AGI would necessarily do option 1, when there are 6 other possibilities, and one of them is literally the best thing ever?

Note that I am not saying AI isn’t dangerous.

I absolutely agree that AI is dangerous. It is already causing tremendous problems to our education system, our economy, and our society as a whole—and will probably get worse before it gets better.

Indeed, I even agree that it does pose existential risk: There are plausible scenarios by which poorly-controlled AI could result in a global disaster like a plague or nuclear war that could threaten the survival of human civilization. I don’t think such outcomes are likely, but even a small probability of such a catastrophic event is worth serious efforts to prevent.

But if that happens, I don’t think it will be because AI is smart and trying to kill us.

I think it will be because AI is stupid and kills us by accident.

Indeed, even going back through those 7 ways we’ve interacted with other species, the ones that have killed the most were 3 and 5—which, in both cases, we did not want to destroy them. In option 3, we in fact specifically wanted to not destroy them. Whenever we wiped out a species by over-exploiting it, we would have been smarter to not do that.

The central message about AI in If Anyone Builds It, Everyone Dies seems to be this:

“Don’t make it smarter. If it’s smarter, we’re doomed.”

I, on the other hand, think that the far more important message is these:

Don’t trust it.

Don’t give it power.

Don’t let it make important decisions.

It won’t be smarter than us any time soon—but it doesn’t need to be in order to be dangerous. Indeed, there is even reason to believe that making AI smarter—genuinely, truly smarter, thinking more like an actual person and less like a pattern-matching machine—could actually make it safer and better for us. If we could somehow instill a capacity for morality and love in an AGI, it might actually start treating us the way we treat cats and dogs.

Of course, we have no idea how to do that. But that’s because we’re actually really bad at this, and nowhere near making a truly superhuman AGI.

What is the real impact of AI on the environment?

PNRJ computer science, current events, futurism, public policy , , , , , , , , ,

Oct 19 JDN 2460968

The conventional wisdom is that AI is consuming a huge amount of electricity and water for very little benefit, but when I delved a bit deeper into the data, the results came out a lot more ambiguous. I still agree with the “very little benefit” part, but the energy costs of AI may not actually be as high as many people believe.

So how much energy does AI really use?

This article in MIT Technology Reviewestimates that by 2028, AI will account for 50% of data center usage and 6% of all US energy. But two things strike me about that:

  1. This is a forecast. It’s not what’s currently happening.
  2. 6% of all US energy doesn’t really sound that high, actually.

Note that transportation accounts for 37% of US energy consumed. Clearly we need to bring that down; but it seems odd to panic about a forecast of something that uses one-sixth of that.

Currently, AI is only 14% of data center energy usage. That forecast has it rising to 50%. Could that happen? Sure. But it hasn’t happened yet. Data centers are being rapidly expanded, but that’s not just for AI; it’s for everything the Internet does, as more and more people get access to the Internet and use it for more and more demanding tasks (like cloud computing and video streaming).

Indeed, a lot of the worry really seems to be related to forecasts. Here’s an even more extreme forecast suggesting that AI will account for 21% of global energy usage by 2030. What’s that based on? I have no idea; they don’t say. The article just basically says it “could happen”; okay, sure, a lot of things could happen. And I feel like this sort of forecast comes from the same wide-eyed people who say that the Singularity is imminent and AI will soon bring us to a glorious utopia. (And hey, if it did, that would obviously be worth 21% of global energy usage!)

Even more striking to me is the fact that a lot of other uses of data centers are clearly much more demanding. YouTube uses about 50 times as much energy as ChatGPT; yet nobody seems to be panicking that YouTube is an environmental disaster.

What is a genuine problem is that data centers have strong economies of scale, and so it’s advantageous to build a few very large ones instead of a lot of small ones; and when you build a large data center in a small town it puts a lot of strain on the local energy grid. But that’s not the same thing as saying that data centers in general are wastes of energy; on the contrary, they’re the backbone of the Internet and we all use them almost constantly every day. We should be working on ways to make sure that small towns aren’t harmed by building data centers near them; but we shouldn’t stop building data centers.

What about water usage?

Well, here’s an article estimating that training ChatGPT-3 evaporated hundreds of thousands of liters of fresh water. Once again I have a few notes about that:

  1. Evaporating water is just about the best thing you could do to it aside from leaving it there. It’s much better than polluting it (which is what most water usage does); it’s not even close. That water will simply rain back down later.
  2. Total water usage in the US is estimated at over 300 billion gallons (1.1 trillion liters) per day. Most of that is due to power generation and irrigation. (The best way to save water as a consumer? Become vegetarian—then you’re getting a lot more calories per irrigated acre.)
  3. A typical US household uses about 100 gallons (380 liters) of water per person per day.

So this means that training ChatGPT-3 cost about 4 seconds of US water consumption, or the same as what a single small town uses each day. Once again, that doesn’t seem like something worth panicking over.

A lot of this seems to be that people hear big-sounding numbers and don’t really have the necessary perspective on those numbers. Of course any service that is used by millions of people is going to consume what sounds like a lot of electricity. But in terms of usage per person, or compared to other services with similar reach, AI really doesn’t seem to be uniquely demanding.

This is not to let AI off the hook.

I still agree that the benefits of AI have so far been small, and the risks—both in the relatively short term, of disrupting our economy and causing unemployment, and in the long term, even endangering human civilization itself—are large. I would in fact support an international ban on all for-profit and military research and development of AI; a technology this powerful should be under the control of academic institutions and civilian governments, not corporations.

But I don’t think we need to worry too much about the environmental impact of AI just yet. If we clean up our energy grid (which has just gotten much easier thanks to cheap renewables) and transportation systems, the additional power draw from data centers really won’t be such a big problem.

Taylor Swift and the means of production

PNRJ development economics, futurism, inequality, market failure, politics , , , , , , , , , , , ,

Oct 5 JDN 2460954

This post is one I’ve been meaning to write for awhile, but current events keep taking precedence.

In 2023, Taylor Swift did something very interesting from an economic perspective, which turns out to have profound implications for our economic future.

She re-recorded an entire album and released it through a different record company.

The album was called 1989 (Taylor’s Version), and she created it because for the last four years she had been fighting with Big Machine Records over the rights to her previous work, including the original album 1989.

A Marxist might well say she seized the means of production! (How rich does she have to get before she becomes bourgeoisie, I wonder? Is she already there, even though she’s one of a handful of billionaires who can truly say they were self-made?)

But really she did something even more interesting than that. It was more like she said:

“Seize the means of production? I am the means of production.”

Singing and songwriting are what is known as a human-capital-intensive industry. That is, the most important factor of production is not land, or natural resources, or physical capital (yes, you need musical instruments, amplifiers, recording equipment and the like—but these are a small fraction of what it costs to get Talor Swift for a concert), or even labor in the ordinary sense. It’s one where so-called (honestly poorly named) “human capital” is the most important factor of production.

A labor-intensive industry is one where you just need a lot of work to be done, but you can get essentially anyone to do it: Cleaning floors is labor-intensive. A lot of construction work is labor-intensive (though excavators and the like also make it capital-intensive).

No, for a human-capital-intensive industry, what you need is expertise or talent. You don’t need a lot of people doing back-breaking work; you need a few people who are very good at doing the specific thing you need to get done.

Taylor Swift was able to re-record and re-release her songs because the one factor of production that couldn’t be easily substituted was herself. Big Machine Records overplayed their hand; they thought they could control her because they owned the rights to her recordings. But she didn’t need her recordings; she could just sing the songs again.

But now I’m sure you’re wondering: So what?

Well, Taylor Swift’s story is, in large part, the story of us all.

For most of the 18th, 19th, and 20th centuries, human beings in developed countries saw a rapid increase in their standard of living.

Yes, a lot of countries got left behind until quite recently.

Yes, this process seems to have stalled in the 21st century, with “real GDP” continuing to rise but inequality and cost of living rising fast enough that most people don’t feel any richer (and I’ll get to why that may be the case in a moment).

But for millions of people, the gains were real, and substantial. What was it that brought about this change?

The story we are usually told is that it was capital; that as industries transitioned from labor-intensive to capital-intensive, worker productivity greatly increased, and this allowed us to increase our standard of living.

That’s part of the story. But it can’t be the whole thing.

Why not, you ask?

Because very few people actually own the capital.

When capital ownership is so heavily concentrated, any increases in productivity due to capital-intensive production can simply be captured by the rich people who own the capital. Competition was supposed to fix this, compelling them to raise wages to match productivity, but we often haven’t actually had competitive markets; we’ve had oligopolies that consolidate market power in a handful of corporations. We had Standard Oil before, and we have Microsoft now. (Did you know that Microsoft not only owns more than half the consumer operating system industry, but after acquiring Activision Blizzard, is now the largest video game company in the world?) In the presence of an oligopoly, the owners of the capital will reap the gains from capital-intensive productivity.

But standards of living did rise. So what happened?

The answer is that production didn’t just become capital-intensive. It became human-capital-intensive.

More and more jobs required skills that an average person didn’t have. This created incentives for expanding public education, making workers not just more productive, but also more aware of how things work and in a stronger bargaining position.

Today, it’s very clear that the jobs which are most human-capital-intensive—like doctors, lawyers, researchers, and software developers—are the ones with the highest pay and the greatest social esteem. (I’m still not 100% sure why stock traders are so well-paid; it really isn’t that hard to be a stock trader. I could write you an algorithm in 50 lines of Python that would beat the average trader (mostly by buying ETFs). But they pretend to be human-capital-intensive by hiring Harvard grads, and they certainly pay as if they are.)

The most capital-intensive industries—like factory work—are reasonably well-paid, but not that well-paid, and actually seem to be rapidly disappearing as the capital simply replaces the workers. Factory worker productivity is now staggeringly high thanks to all this automation, but the workers themselves have gained only a small fraction of this increase in higher wages; by far the bigger effect has been increased profits for the capital owners and reduced employment in manufacturing.

And of course the real money is all in capital ownership. Elon Musk doesn’t have $400 billion because he’s a great engineer who works very hard. He has $400 billion because he owns a corporation that is extremely highly valued (indeed, clearly overvalued) in the stock market. Maybe being a great engineer or working very hard helped him get there, but it was neither necessary nor sufficient (and I’m sure that his dad’s emerald mine also helped).

Indeed, this is why I’m so worried about artificial intelligence.

Most forms of automation replace labor, in the conventional labor-intensive sense: Because you have factory robots, you need fewer factory workers; because you have mountaintop removal, you need fewer coal miners. It takes fewer people to do the same amount of work. But you still need people to plan and direct the process, and in fact those people need to be skilled experts in order to be effective—so there’s a complementarity between automation and human capital.

But AI doesn’t work like that. AI substitutes for human capital. It doesn’t just replace labor; it replaces expertise.

So far, AI is currently too unreliable to replace any but entry-level workers in human-capital-intensive industries (though there is some evidence it’s already doing that). But it will most likely get more reliable over time, if not via the current LLM paradigm, than through the next one that comes after. At some point, AI will come to replace experienced software developers, and then veteran doctors—and I don’t think we’ll be ready.

The long-term pattern here seems to be transitioning away from human-capital-intensive production to purely capital-intensive production. And if we don’t change the fact that capital ownership is heavily concentrated and so many of our markets are oligopolies—which we absolutely do not seem poised to do anything about; Democrats do next to nothing and Republicans actively and purposefully make it worse—then this transition will be a recipe for even more staggering inequality than before, where the rich will get even more spectacularly mind-bogglingly rich while the rest of us stagnate or even see our real standard of living fall.

The tech bros promise us that AI will bring about a utopian future, but that would only work if capital ownership were equally shared. If they continue to own all the AIs, they may get a utopia—but we sure won’t.

We can’t all be Taylor Swift. (And if AI music catches on, she may not be able to much longer either.)

The AI bubble is going to crash hard

PNRJ critique of neoclassical economics, current events, futurism, market failure, public policy , , , , , , , , , , , , , ,

Sep 7 JDN 2460926

Based on the fact that it only sort of works and yet corps immediately put it in everything, I had long suspected that the current wave of AI was a bubble. But after reading Ed Zitron’s epic takedowns of the entire industry, I am not only convinced it’s a bubble; I’m convinced it is probably the worst bubble we’ve had in a very long time. This isn’t the dot-com crash; it’s worse.

The similarity to the dot-com crash is clear, however: This a huge amount of hype over a new technology that genuinely could be a game-changer (the Internet certainly was!), but won’t be in the time horizon on which the most optimistic investors have assumed it will be. The gap between “it sort of works” and “it radically changes our economy” is… pretty large, actually. It’s not something you close in a few years.


The headline figure here is that based on current projections, US corporations will have spent $560 billion on capital expenditure, for anticipated revenue of only $35 billion.

They won’t pay it off for 16 years!? That kind of payoff rate would make sense for large-scale physical infrastructure, like a hydroelectric dam. It absolutely does not make sense in an industry that is dependent upon cutting-edge technology that wears out fast and becomes obsolete even faster. They must think that revenue is going to increase to something much higher, very soon.

The corps seem to be banking on the most optimistic view of AI: That it will soon—very soon—bring about a radical increase in productivity that brings GDP surging to new heights, or even a true Singularity where AI fundamentally changes the nature of human existence.

Given the kind of errors I’ve seen LLMs make when I tried to use them to find research papers or help me with tedious coding, this is definitely not what’s going to happen. Claude gives an impressive interview, and (with significant guidance and error-correction) it also managed pretty well at making some simple text-based games; but it often recommended papers to me that didn’t exist, and through further experimentation, I discovered that it could not write me a functional C++ GUI if its existence depended on it. Somewhere on the Internet I heard someone describe LLMs as answering not the question you asked directly, but the question, “What would a good answer to this question look like?” and that seems very accurate. It always gives an answer that looks valid—but not necessarily one that is valid.

AI will find some usefulness in certain industries, I’m sure; and maybe the next paradigm (or the one after that) will really, truly, effect a radical change on our society. (Right now the best thing to use LLMs for seems to be cheating at school—and it also seems to be the most common use. Not exactly the great breakthrough we were hoping for.) But LLMs are just not reliable enough to actually use for anything important, and sooner or later, most of the people using them are going to figure that out.

Of course, by the Efficient Roulette Hypothesis, it’s extremely difficult to predict exactly when a bubble will burst, and it could well be that NVIDIA stock will continue to grow at astronomical rates for several years yet—or it could be that the bubble bursts tomorrow and NVIDIA stock collapses, if not to worthless, then to far below its current price.

Krugman has an idea of what might be the point that bursts the bubble: Energy costs. There is a clear mismatch between the anticipated energy needs of these ever-growing data centers and the actual energy production we’ve been installing—especially now that Trump and his ilk have gutted subsidies for solar and wind power. That’s definitely something to watch out for.

But the really scary thing is that the AI bubble actually seems to be the only thing holding the US economy above water right now. It’s the reason why Trump’s terrible policies haven’t been as disastrous as economists predicted they would; our economy is being sustained by this enormous amount of capital investment.

US GDP is about $30 trillion right now, but $500 billion of that is just AI investment. That’s over 1.6%, and last quarter our annualized GDP growth rate was 3.3%—so roughly half of our GDP growth was just due to building more data centers that probably won’t even be profitable.

Between that, the tariffs, the loss of immigrants, and rising energy costs, a crashing AI bubble could bring down the whole stock market with it.

So I guess what I’m saying is: Don’t believe the AI hype, and you might want to sell some stocks.