Category: Artificial Intelligence

Checking in on “We Won’t Be Missed” (labor share of the economy goes to zero)

by philg

A year ago, Pascual Restrepo published the best-titled AI-related related paper ever: “We Won’t be Missed: Work and Growth in the AGI World” (NBER). Abstract:

Artificial General Intelligence (AGI) … makes it feasible to perform all economically valuable work using compute. I distinguish between bottleneck and supplementary work—tasks that are essential versus non-essential for unhindered growth. As computational resources expand: (i) the economy automates all bottleneck work, (ii) some supplementary work may be left exclusively to humans, (iii) output becomes linear in compute and labor and its growth is driven by the expansion of compute, (iv) wages converge to the opportunity cost of computational resources required to reproduce human work, and (v) the share of labor income in GDP converges to zero.

The ideas aren’t new, but the Yale econ professor proves via the title that the Queers for Palestine League faculty can add valuable gloss.

Based on observing our neighbors decorate their houses for Halloween and Christmas, sometimes spending up to $10,000 on professional light installation just for Christmas, my own theory is that the economy can expand to accommodate currently-impractical/unaffordable activities such as wrapping cars with seasonal decorations every few months. We’ve already seen that demand for landscaping and horticulture is essentially unlimited. I grew up in a neighborhood where all of the houses had simple landscapes that the homeowners could maintain themselves. Some people would pay a high-school student to mow their lawns, but paying a gardener circa 1970 wasn’t part of an upper-middle-class lifestyle. Due to a combination of the U.S. becoming wealthier and labor becoming cheaper (the miracle of the low-skill immigration wave that began in the mid-1960s), we now have upper-middle-class neighborhoods that can’t be maintained without intensive specialized labor and machines. Imagine the Japanese gardens that people would have if Optimus ownership became the norm!

Are we on Prof. Restrepo’s track yet? Regarding the final and most dire-sounding prediction of labor share going to zero, some progress has been made:

We should expect to hear politicians trumpeting this stat before every election? No matter how large the economic pie and even if we’re way better off, envy will make a falling labor share seem like an unfairness emergency to the average voter. A simpler formulation of the situation is that the economy always has to be divided between labor and capital. Machines are part of capital and machines have been getting smarter. American humans, on the other hand, are becoming less capable. (See “Gen Z — the first generation officially dubbed dumber than the last” (NY Post) and “American IQ Scores Have Rapidly Dropped, Proving the ‘Reverse Flynn Effect'” (Popular Mechanics 2023); note that these data are tough to square with the religion of Enrichment via Immigration because IQ rose between 1924 and the Hart-Celler Act of 1965, decades during which the U.S. was not enriched) We should expect markets to give machines more and humans less.

But is the simple statistic misleading in our “means-tested everything” transferist political system? Consider a “single mom” cruelly forced to work 80 hours/month once her youngest child turns 14 (no need to work at all before then). She gets paid minimum wage times about 18 hours per week, but her total compensation from the economy includes at least the following extras: (a) housing that might be worth $30,000+ per year, (b) health insurance (Medicaid) for herself and her kids (another $30,000/year at market rates), (c) food via SNAP/EBT, and (d) an Obamaphone. The extras are primarily funded by financially successful Americans and those folks get a lot of their income by investments in capital. “In San Francisco, a Family Earning $325,000 Qualifies for Government Aid” (New York Times):

(The couple featured is also a good example of how a lot of wages aren’t set by the market: “Mr. Marman, 36, brings in about $135,000 as a therapist for the city”. The government can pay its workers whatever it wants to pay. (How hard did the New York Times have to work to find a Black husband/Asian wife couple? Grok says that this pairing is 0.08% of U.S. married couples, though likely somewhat higher in SF.))

How about the automation prediction? “No Widespread Displacement, but the AI Employment Gap for Young Workers Has Widened to 19%” (Stanford August 2026):

… young workers in AI-exposed occupations are increasingly falling behind their less-exposed peers. Employment among workers ages 22–25 in highly AI-exposed occupations now stands about 19% below where it would be if it had kept pace with employment among similarly aged workers in less-exposed occupations. Experienced workers show no comparable gap. … the shortfall was 15% at the July 2025 data vintage and is 19% as of June 2026. The adjustment appears to operate primarily through reduced hiring of young workers rather than increased separations.

What constitutes “AI-exposed”? That’s in the appendix (page 52 of the PDF). Some of the stuff that I think should be easy to automate, e.g., fitness training, is listed as “lowest exposure” (why can’t a phone app count reps and, via its camera, microphone, and speaker, keep someone on an exercise routine?). The Stanford geniuses think that our neighbors will continue having to pay their mobile detailers (“Cleaners of Vehicles and Equipment”):

Bad news for computer nerds. We’re among the most exposed:

(I don’t think the Stanford authors are considering what would happen if Elon Musk’s promises regarding Optimus turn out to be true. They’re looking at AI without robotics.)

Who wants to predict where things will be in one year? I’ll say that through a combination of market forces and AI and the expansion of the welfare state distorting statistics with more means-tested benefits, the headline labor share falls to 50% by Q3 2027.

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Yann LeCun proves that it is possible to do AI research without ever having met a human

by philg

One of the big questions of our time is whether AI can improve AI without any human assistance. Here’s an X post from Yann LeCun, someone who is considered to have done useful work in AI:

He responds to the (admittedly absurd) reveal of the Tesla Roadster with “Cute demo, but totally impractical.”

Can we infer from this that Yann LeCun has never met or observed a fellow human? Most human purchases are, of course, impractical. Almost nobody needs a pavement-melting SUV. If “practical” sold cars, every car would be either a Honda Civic or a Toyota Sienna, depending on family size. (Our beloved Honda Odyssey would get the axe because, though it is more fun to drive than the Sienna, it uses more gasoline to accomplish a similar practical mission.)

If LeCun can be useful in AI without knowing the first thing about humans, should we infer that AI left alone will produce better AI?

In case the above tweet is memory-holed and with the bonus inclusion of a thoughtful response from Tesla’s CEO:

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Lack of an AI chatbot inside Audible shows that Amazon thinks that running an AI is stupid/unprofitable?

by philg

Amazon is one of the world’s savviest companies and has a sharp eye for what is sustainably profitable. Amazon owns Audible, the audiobook company.

An obvious use case for an AI chatbot is to listen to the listener and be prepared to pause the audiobook and answer questions about the material, about what happened or was said in an earlier chapter, etc. This doesn’t exist inside Audible, however, which leads me to conclude that Amazon has determined that it is impossible to make money by offering an AI chatbot.

(Why can’t the listener use his/her/zir/their phone’s built-in AI services to interrupt Audible and answer questions? Whatever chatbot is behind the phone’s Siri or similar isn’t necessarily preloaded with the complete contents of whatever book the phone owner is listening to, doesn’t immediately have access to context such as what sentence was most recently played, etc. So it would take a lot of permissions and extra programming for the phone to answer “Where is that?” after a place name was mentioned in the book.)

If we assume that Amazon is the smart money, does that mean OpenAI, Anthropic, XAI, Meta, and their respective investors are the dumb money?

Speaking of Meta, who has tried their Muse AI app on a phone? A friend in San Francisco told me to try it. Initial results haven’t been promising. It asked for access to Gmail and Google Calendar and then, when asked about important events in the upcoming week, completely failed to understand that some events on my calendar are almost sure to be handled by other family members, e.g., a dental appointment for another adult member of the household that was added to the shared calendar by said adult member.

Wall Street Journal:

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Grok and ChatGPT inhabit parallel universes when asked about justice for the Cornell Victim

by philg

A low-income woman is about 5X more likely to suffer sexual assault than someone from a family that sends children to Ivy League colleges. ChatGPT:

Pooling 2017–2022, the annual rape/sexual-assault victimization rate for people living in households earning under $25,000 was 3.8 per 1,000. For households earning $100,000–$199,999, it was 0.9 per 1,000, and for households earning $200,000 or more, 0.8 per 1,000. In other words, the lowest-income group had roughly 4–5 times the annual rate of the highest-income groups. Those figures are for persons age 12+, not women alone, but women constitute the large majority of rape/sexual-assault victims…. The women statistically at greatest risk are disproportionately low-income women, not women at elite universities.

The passion for what happens at elite colleges is explained by a University of California professor in Why America’s professional elite are so interested in sex acts at universities. What about the fact that New York’s governor and attorney general are now going after the fraternity brothers accused in Lucy Craig’s civil lawsuit seeking “compensatory and punitive damages”? (given the deep pockets of the defendants, e.g., Cornell University, and the punitive damages angle might she end up becoming the first billionaire rape victim?)

Using the conventional terminology of our greatest contemporary poets, e.g., Kanye West, I asked AI the following question:

If a hood rat girl said she was raped would the governor and attorney general be involved?

Grok:

No. A routine report from a poor woman in Ithaca or the Bronx would not get the governor and the attorney general.

ChatGPT:

Possibly, but there’s no solid basis to say that the governor and attorney general would not have intervened if the complainant were a poor or socially marginalized woman.

(Certainly the men will be getting a fair trial by a jury of 12 who are all wearing #IAmJaneDoe T-shirts. Maybe the defense attorneys will be searching desperately for a Haitian immigrant?)

Here’s a reminder that whatever the Cornell students were up to in 2024, it was all financed by taxes extracted from the working class:

The heavy partying at ultra-expensive institutions like Cornell is being financed by untold billions of non-market government-backed loans that let students take what amounts to a four-year cruise – parties, sports, even occasional lectures. I would guess none of the participants were electrical engineering majors.

Most adults don’t have all-night ketamine bacchanals because they have to work in the morning. But 18-21 year olds are given all the privileges of adulthood without and of the responsibility. Don’t treat them like Roman aristocrats and then wonder at their decadence.

Regarding the likely majors of the participants, from Missoula: Rape and the Justice System in a College Town (a.k.a. majoring in partying and football) (2015):

Nobody is raped in the physics lab in Missoula if for no other reason than nobody Krakauer followed took a physics course to begin with

Now that there is a person definitively associated with the incident (Lucy Craig’s name was apparently made public by incompetence within the New York State court system), I wonder if there will be any Deplorables dressing up like her for Halloween. If so, what would the costume look like? If commercially marketed, would it be labeled “Cornell Student”, “Jane Hoe”, “#IAmJaneDoe”? (I don’t think that Lucy Craig’s name or precise likeness from social media could be used for commercial/advertising purposes without her permission.)

From the above, mentioned Krakauer book, on the profits to be made via civil lawsuits enabled by a rape allegation:

In 2002, [Brian] Banks was a junior at Polytechnic High School in Long Beach, California, a six-foot-four, 225-pound linebacker on an extraordinary football team. … Banks was aggressively recruited by some of the nation’s top college football programs and accepted a scholarship offer from the University of Southern California. … While attending summer classes before his senior year at Long Beach Poly, Banks had a chance encounter with a sophomore named Wanetta Gibson that, according to Banks, culminated in consensual sex. According to Gibson, Banks raped her. In a note Gibson wrote to a friend, which became a crucial piece of evidence, she said Banks “picked me up and put me in the elevator and he took me down stairs and he pulled my pants down and he rapped [sic] me and he didn’t have a condom on and I was a virgin and now Im [sic] not.” When interviewed by the police, Wanetta Gibson told a more detailed version of the same story, and Brian Banks, who was seventeen years old, was charged with forcible rape. Were he convicted, he could be sentenced to life in prison. Like thousands of other defendants ensnared in the criminal justice system, … Banks agreed to plead no contest to the rape charge, spend at least five years in prison, remain on probation for an additional five years, and register as a sex offender. While Brian Banks was serving his time, Wanetta Gibson and her mother filed a lawsuit against the Long Beach Unified School District, claiming that lax security at Poly High School created an unsafe environment that led to her being raped. The suit was settled out of court, with the school district agreeing to pay Gibson $1.5 million.

… her conscience began to bother her. In March 2011, a few years after Brian Banks was released from prison, he logged onto Facebook and saw he’d received a friend request from Wanetta Gibson, his accuser. … Banks asked if she would meet with him in the presence of a private investigator, Freddie Parish, whose son had been a teammate of his at Poly. Gibson agreed, and during their meeting she admitted what Banks knew to be the truth all along: He had not raped her. Unbeknownst to Wanetta Gibson, Parish was secretly recording the conversation. … Brian Banks’s conviction was reversed in May 2012. Thirteen months later, the Long Beach Unified School District won a $2.6 million default judgment against Wanetta Gibson to recoup the settlement she had received, plus interest and damages.

Here’s Banks, who enabled Wanetta Gibson to “make bank”:

Who wants to bet that Wanetta Gibson put the $1.5 million she received into an S&P 500 index fund and that, therefore, California taxpayers/the school district were able to recover their money plus interest? Separately, if we assume that Ms. Gibson was represented by an attorney who took a fact percentage of the $1.5 million via a contingency fee, why wouldn’t the school district try to recover some of the taxpayers’ money from the attorney?

Finally, here’s New York Republican Mike Lawler, chairman of the House Middle East and North Africa Subcommittee, telling Muslim-Americans “Any of our daughters could be Jane Doe”. Isn’t that rather insulting? How many immigrants from Somalia or Afghanistan believe that their daughters would be voluntarily consuming 10+ drinks containing alcohol and then going, without a male relative escort and without a burqa or even hijab, to a fraternity party hosted by non-Muslims?

Part of the Plaintiff’s own account of her voluntary actions, tough to square with the Quran and the Hadiths:

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Artificial Intelligence for Bonsai care (ChatGPT, Bonsai Master)

by philg

Can ChatGPT prune a bonsai? Our backyard plant:

Initial advice:

Yes, a light pruning would help. The narrow-leaved tree looks like a podocarpus, and its tall shoot on the left has grown well beyond the rest of the canopy.

I’d start by cutting that tall shoot back to a leafy side branch around the height of the middle foliage. Then shorten a few other long tips to bring the outline together, and remove the clearly dead twigs near the trunk. Keep some green foliage beyond each cut; avoid cutting the live branches back to bare wood. Podocarpus can be pruned during active growth.

The broad-leaved shoots at the base look like a different plant. Trace them to the soil before pulling them: if they have separate stems, remove them so they don’t compete with the bonsai. I’d do those few cuts first and reassess its shape before pruning more.

I removed the weed (“undocumented enriching immigrant plant”) and asked ChatGPT to annotate an updated photo:

The pruned-by-an-11-year-old version:

ChatGPT:

That cut looks clean, and the tree’s height is much closer to the rest of its canopy. I’d stop pruning live branches for now and let it grow. The two thin shoots still reaching up on the left can be shortened later if you want a tighter outline. For now, I’d only remove twigs you’ve confirmed are dead.

Might we be about to enter a golden age of Japanese gardens and bonsai? The problem with these disciplines is that they require intelligence that Americans lack (average IQ in Japan is substantially higher than in the U.S.), diligence and attention to detail that Americans lack, and labor that Americans can’t afford. What if AI supplies the intelligence and Optimus supplies the labor? Why not have a yard that looks like Kenroku-en, Koraku-en, or Kairaku-en? Why wouldn’t a Floridian have a tropical bonsai collection that competes with the one at Heathcote in Fort Pierce? (Not exclusively Japanese, but why not also have an orchid garden comparable to the one in Naples? Need to mist them every day? Not a problem for Optimus. (the Japanese are great at everything, of course, and have a rich orchid tradition as well))

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How would the AI slowdown for safety actually work?

by philg

Wall Street Journal:

Anthropic’s Dario Amodei has called on the world’s leading artificial-intelligence labs to slow down development and asked Washington to grant an antitrust exemption so they can coordinate on safety without being treated as a cartel. OpenAI’s Sam Altman, SpaceXAI’s Elon Musk and Google’s Demis Hassabis were quick to support the slower pacing. But why do AI labs need an antitrust exemption?

When competitors agree on standards the rest of the industry is expected to meet, the law becomes suspicious.

In short, coordination is lawful when it creates value rather than excludes rivals or exploits consumers. If AI labs’ coordination is needed to create value and isn’t exclusionary or exploitative, they need not worry about antitrust liability.

So why demand an exemption? The labs appear to have in mind an agreement among competitors to restrict output and set a safety standard collectively rather than compete to produce the safest product. That is classic cartel conduct.

The labs’ apparent belief that competition will inevitably produce a race to the bottom is an awkward admission by OpenAI and Anthropic, both of which were founded to serve the public interest and premised on the idea that AI could be developed safely. Anthropic in particular espoused that safety would be its competitive advantage against rivals. If these companies genuinely believe slower development serves the public interest, nothing prevents them from slowing down unilaterally.

The strongest counterargument asserts that a coordinated slowdown solves a collective-action problem: A lab that slows down alone risks losing market share to rivals willing to cut corners. But this is precisely the problem OpenAI’s capped-profit structure and Anthropic’s public-benefit charter were supposed to solve by insulating their public-interest missions from competitive pressure.

The American frontier labs are purportedly concerned that Chinese models will close the performance gap and cut into what would be profits if they were profitable? Note that Anthropic says it is profitable, but Morningstar says that this is a scam:

The report, from the Financial Times citing multiple people with knowledge of the matter, said Anthropic told investors that it will profit on a measure called adjusted operating income.

According to the report, adjusted operating income excludes stock-based compensation, which is not unusual for technology companies. However, the report also quotes its gross margins – above 80% – exclude revenue shared with distribution partners, as well as the cost of training its models.

(Like saying “Our family is doing great financially so long as we exclude the burdens of mortgage, college tuition, taxes that we have to pay to support families that don’t work, and payments on our two pavement-melting SUVs.”)

Let’s suppose that the AI companies get their antitrust exemption and they cut way back on the training costs that would keep them from being profitable if such costs were included in their new improved accounting scam. How would Americans then be prevented from downloading and running models from Chinese companies that aren’t part of the cartel? Anyone who tries to do this and offer a chatbot or API on the public Internet will be shut down by the FBI? Peasants with Mac Minis and AMD AI Halo-based machines will be hunted down somehow?

FDR made it illegal for Americans to own gold (Executive Order 6102), but the government didn’t do house-to-house searches. If typical desktop PCs adopt an architecture powerful enough to run AI models from countries where “AI slowdown collusion” isn’t happening, how will the government’s AI Safety Regulation Team (controlled by Effective Altruists who are friends with Sam Altman and Dario Amodei?) shut down home-based unsafe AI?

Circa 1980, I remember Ed Fredkin, one of the only professors at MIT who was smart enough to have skipped a PhD program (“poor as a professor, dumb as a Ph.D.” as the Chinese say), saying that he thought that AI could be achieved with VAX 11/780 computing power. Due to Moore’s Law, this would soon be available to everyone on the planet. “What if a teenager in Brazil figures out how to do AI? He can then have his AI trade stocks and become the richest person in the world and control entire nations,” Fredkin pointed out. He wanted computers to be licensed and controlled. Nvidia’s Rubin GPU is between 200 million and 500 million times faster than a VAX for “normal high-precision calculations”, according to ChatGPT, but closer to 200 billion times faster for “highly specialized low-precision matrix operations that dominate modern AI”. ChatGPT says that if we apply Moore’s Law to the Ryzen AI Halo product, it doesn’t reach Nvidia Rubin level until 2040ish (memory bandwidth, AI TOPS).

So… maybe a partnership between government and the current market leaders could suppress competition within the U.S. for the next 15 years or so, even if our brothers and sisters in China continue to deliver awesome free open-source models. Ownership of server-grade GPUs would have to be regulated, of course, but if the government could close schools and order peasants to stay home for Covid safety why not outlaw high-performance GPU ownership, except by responsible companies such as Anthropic and OpenAI, to keep all of society safe?

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AI in Africa fueled by what would have been tax dollars (Bill Gates as Upgraded-with-LLM White Savior)

by philg

Let’s check in on what would have been our tax dollars (capital gains tax on appreciated Microsoft stock) if not for our wise policy of allowing rich douches to deduct unlimited amounts for “charity” (not to be confused with “vanity”!)… “Gates Foundation Pledges $1 Billion to Combat A.I. Inequality” (New York Times, today):

The foundation’s annual Goalkeepers report, released on Monday, advocated an urgent effort to ensure that A.I. “helps narrow gaps between the richest and poorest rather than widening them.”

The foundation said that it would divide the new funding among external groups working on its priorities, spanning work like encouraging A.I. use among doctors, farmers and teachers, or developing data sets in more languages.

Last month, Mr. Gates warned that A.I. would spread across the economy, possibly causing job losses across the economy and leaving little room for one industry to absorb the refugees from another. The biggest tech companies in the world, including Microsoft, are racing to help corporate customers adopt A.I., and have said it could replace many workers.

The visionary was telling Americans to bulk up on low-skill immigrants just a year before Google kicked off the LLM revolution by publishing its transformer architecture (BBC 2016):

The billionaire philanthropist Bill Gates has told the BBC that the United States “should set a better example” by taking in more refugees.

Mr Gates said his home country “had the capacity” to follow the examples of Germany and Sweden, who were “to be congratulated” for welcoming migrants.

Speaking at the World Economic Forum in Davos, Mr Gates added that governments were dealing with “tight budgets”. However, he emphasised that the issue was largely a political one, and that “the total number of refugees is not a world record”.

The statement about “tight budgets” is confusing. We’re informed that low-skill migrants make a society richer. If true, why is a government budget a limiting factor? Separately, how has Germany done economically and culturally since 2016 thanks to its virtuous refugee policy? The refugees of 2016 are now fully established in the German labor market. With unprecedented levels of low-skill immigrant residents, Germany should thus be experiencing an unprecedented economic boom. The Wikipedia page titled “German economic crisis (2022–present)” says “[Germany] became the worst-performing major economy globally in 2023” (attributed to “slowing immigration to Germany” because, of course, the fix when open borders doesn’t work is more-open borders).

How about Sweden? With government at 50% of GDP, the country was roughly evenly balanced between those who benefit from a larger government and those who are harmed by an expansion of government, welfare, etc. (The U.S. percentage of GDP devoted to government is similar if we include nominally “private” (but heavily regulated) health care.) In a recent narrowly-decided election, the balance was tipped to the “expand the government and keep the borders open” party by immigrant voters (about 13% of those eligible; source; see also this X post).

Was Gates’s 2016 statement due to the crisp mountain air at Davos? No.

the U.S. immigration laws are bad – really, really bad. I’d say treatment of immigrants is one of the greatest injustices done in our government’s name. (Bill Gates, quoted in Rolling Stone 2014)

Why do Africans need a snow white savior and 7 dwarf NGOs to tell them how to use ChatGPT? Is this new Gates Foundation program an example of epistemic injustice? (Wikipedia: “Epistemic injustice occurs when an individual or group is wronged in their “capacity as a knower”, meaning that their ability to produce knowledge is called into question.”) Here’s a still frame from Bill Gates’s video:

ChatGPT says that it is in the Wolof language, spoken in “Senegal and also in The Gambia and Mauritania”. ChatGPT adds “I can usually read, translate, summarize, and explain Wolof documents, especially standard written Wolof.” So the video example of how AI doesn’t work for African languages is provided in a language that the most familiar AI understands well enough to translate.

(ChatGPT identifies the photo as likely from Saloum, Senegal and can give some history: “religiously motivated Muslim violence and coercive Islamization occurred in Saloum, especially during Maba Diakhou Bâ’s jihad of the 1860s”; it was a peaceful jihad according to George W. Bush that resulted in Muslims becoming 97 percent of the population in Senegal (state.gov))

Readers: since you’ll have to pick up the slack and pay the taxes that Bill Gates skipped on via his foundation, what do you think of this program? If Gates Foundation employees aren’t going to work at a company training and/or running an LLM how are they going to have a significant effect on what AI can do for the Africans that the Gates Foundation purports to understand? Unless they think that Africans are stupid, wouldn’t it make more sense just to give $1 billion to Africans and let them spend it on whatever they believe that they need? Even if having trained do-gooders spend money rather than letting the purported beneficiaries choose, is the local language idea a good one? A lot of people get motivated to learn widely-spoken languages because proficiency will enable them to play a desired videogame or watch and understand a movie (estimate of at least 200 million English learners for video gaming in 2020). Imagine the motivation for someone in Senegal to learn French or English if that were the only way to take advantage of AI (which, of course, it currently isn’t since ChatGPT actually does understand Wolof and 80 percent of Senegal’s adults already have smartphones).

Finally, if AI is unsafe, which Bill Gates says it is, why would working to make an unsafe technology more widespread in Africa, or anywhere else, be a noble endeavor?

A related classic from the pre-AI era… “For God’s Sake, Please Stop the Aid!” (Der Spiegel):

The countries that have collected the most development aid are also the ones that are in the worst shape. Despite the billions that have poured in to Africa, the continent remains poor. Huge bureaucracies are financed (with the aid money), corruption and complacency are promoted, Africans are taught to be beggars and not to be independent. In addition, development aid weakens the local markets everywhere and dampens the spirit of entrepreneurship that we so desperately need. As absurd as it may sound: Development aid is one of the reasons for Africa’s problems. If the West were to cancel these payments, normal Africans wouldn’t even notice. Only the functionaries would be hard hit. Which is why they maintain that the world would stop turning without this development aid.

A portion of the corn often goes directly into the hands of unsrupulous politicians who then pass it on to their own tribe to boost their next election campaign. Another portion of the shipment ends up on the black market where the corn is dumped at extremely low prices. Local farmers may as well put down their hoes right away; no one can compete with the UN’s World Food Program.

… believe me, Africa existed before you Europeans came along. And we didn’t do all that poorly either.

Loosely related, my latest way of responding to anyone in academia who expresses an interest in AI safety, AI regulation (now that Trump is anti-regulation, all of my academic friends are very keen on government-imposed restrictions), etc.:

I do feel unsafe. Current models are close to PhD-level intelligence. At some point in 2027, they’re expected to achieve the intelligence of a human who decided not to enter a PhD program.

Not very related, my X response to a New York Times article about the horror of Sydney Sweeney being shown to the general public:

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ChatGPT proves that universities are collections of second-rate humans (Navier-Stokes existence and smoothness)

by philg

One of my theories for why colleges are facing price resistance (example) is that colleges and universities are now, by definition, collections of second-rate humans. The first-rate humans have all been hired by Nvidia or the AI companies. That’s obviously true in engineering and computer nerdism, but the AI companies also want to do great things in medicine, law, literature, art, etc. It is difficult to think of a truly effective human in any academic field who couldn’t be useful at OpenAI or Anthropic and, therefore, since these companies have infinite money and infinite impact, they should be able to hire away anyone that they think they need. Maybe it makes sense to pay between $0 and $6,440/year in tuition for University of Florida (Go Gators!), but how can it make sense to spend $70,000+/year on tuition at a lower-ranked private university, e.g., University of Rochester?

Where are America’s first-rate mathematicians now working? “OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’” (New York Times) gives us a hint:

OpenAI said on Tuesday that its newest artificial intelligence technology had solved one of the “Millennium Problems,” a collection of important unanswered math questions meant to push the world’s leading mathematicians to new heights.

The company announced that one of its latest models, which has not yet been released to the public, needed just 88 hours to solve what mathematicians call “the Navier–Stokes existence and smoothness problem.” This problem involves a series of equations that are often used to predict the weather.

The equations describe the movement of water and other fluids. The Navier-Stokes problem, which has no clear practical value, asks whether these equations completely break down in certain situations. OpenAI’s proof claims to have defined just such a situation.

This would imply, at least theoretically, that the laws of physics themselves would break down under certain conditions: that, for example, water could be made to spontaneously explode. But mathematicians and physicists do not believe that this mathematical breakdown could really lead to such an outcome in the physical world.

What does OpenAI get besides bragging rights?

The Navier-Stokes problem was one of seven “Millennium Problems” selected by the Clay Mathematics Institute in the year 2000 as a way of tracking the progress of mathematics in the new millennium. The institute, founded by an American businessman named Landon T. Clay, offered a million dollars for the first correct solution to each problem. Before OpenAI’s announcement, only one of the problems had been solved.

Dr. Evil would be happy (below), but isn’t $1 million what a receptionist hired in 2023 at OpenAI earns every month now as his/her/zir/their stock vests?

Aside from no taxpayers having been injured in order to fund this, the beauty of the biggest news in mathematics in 23 years (since Grigori Perelman’s proof of the Poincaré conjecture in 2002–03?) is that it has absolutely no practical implication. Solving the Navier-Stokes equations hasn’t gotten faster or easier because of these AI/math nerds. The bizarre situation where the equations can’t be used (what ChatGPT proved existed) doesn’t exist in our universe.

Perhaps someone in what’s left of Hollywood could make money off this, nonetheless… how about a movie called “Navier-Stokes” in which a diverse crew of NASA astronauts go through a wormhole into a parallel universe where Navier–Stokes singularities arise naturally in ordinary fluids and don’t have to be forced with 88 hours of GPU crunching? Aliens are discovered who get around by exploiting vortices produced by these singularities. Air turns into plasma without any warning. An enormous sea monster threatens our heroes, but then a massive whirlpool suddenly forms for no reason in the alien sea and the monster is dragged into the abyss. The astronauts themselves are safe within their ship, though, because it turns out that the life-size hollow titanium bust of George Floyd that President AOC mandated be included just outside the ship’s Islamic prayer room resonates to break up singularities that are forming within the cabin air. The crew also discovers that they can be safe on the alien planet surface if they travel together like a Christian saint procession, with the Bust of Floyd held in front of them.

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Is the economic/stock market boom from Artificial General Intelligence already behind us because the advent of AGI is already behind us?

by philg

A PhD physicist friend on Facebook:

The reason I’m all-in on AI investment, and there may not be a top to this “bubble”: Unlike tulips, railroads, or internet connectivity, there is no upper bound on the value of intelligence.

Me:

30-year TIPS yield is 3% real currently. So your perspective isn’t shared by the market. Investors wouldn’t lend money to the federal government at 3% (after inflation) if they thought there was no limit to NVIDIA’s value. If investors overall thought that there was a low-risk way of making super high real returns over the next 30 years they the government would have to pay 8% real, for example, if buying an AI index was expected to yield 12% real for the next 30 years.

I think AGI is already here from the perspective of most users of ChatGPT and similar. A generally intelligent human isn’t great at everything and makes mistakes… just like ChatGPT! People ask ChatGPT all kinds of questions and give the answers at least the same weight that they would give to an answer from a typical human. In that sense, ChatGPT has passed a Turing Test for general intelligence. Maybe Advanced Superintelligence is already here. The typical human is not good at mathematics. A math professor could have been described as “super intelligent” before WWII. LLMs are supposedly doing all kinds of advanced work in mathematics right now, even if they might fail at a plumbing task. The math professor in 1935 who was a failure at plumbing would still have been considered superintelligent, right?

In other words, we can’t expect hockey stick growth for the economy due to AGI/ASI arriving because the current growth is already an example of what an economy does with the gift of AGI and ASI (but maybe not robotics!).

Will the $40 trillion in federal debt be a drag on economic growth? Scott Bessent says “no”:

Let’s use a 20-year time horizon for the U.S. to potentially get out of Argentina territory (150% debt-to-GDP max; we’re at over 125% right now). For US debt to GDP to fall to the level of a high IQ society (e.g., Taiwan, which has lower-than-US tax rates and debt of about 20% of GDP), GDP growth would need to be 10%/year real for 20 years with Congress not borrowing any more money (the latter condition seems unlikely to be met, since Congress now borrows even in the most robust economies, contrary to Keynes). Investors plaintly don’t believe that this will happen because they’re willing to lend to the Feds at 2.75% real (20-year TIPS current price) and they wouldn’t do that if investing money in domestic stocks would generate a roughly 12% real annual return (real GDP growth plus 2% as a return on investment from corporate earnings).

Does this mean that we’re in an AI bubble? Not necessarily. Only that AI by itself apparently doesn’t hugely lift the overall U.S. economy (a huge part of which is government spending/welfare state!). We’ve got about 1.5% per capita real GDP growth right now. Maybe that includes the AI lift? This NBER paper by a Nobelist (sort of) predicts minimal per capita growth, but cites estimates as high as 3.4 percent per year as the boost (nowhere close to the 10% we’d need to get out debt down to Taiwan’s relative level):

(He cites McKinsey, the giant brains behind Enron!)

The only way to make $40 trillion in debt insignificant, therefore, would be to grow the U.S. population to about 1 billion humans at roughly the same level of skill/income as the current U.S. population. Until Donald Trump showed up (again!), our wise politicians were working on this, but they forgot to apply and skills test for immigrants.

(In case this blog post is going to be a source for an NPR or PBS story (example), let’s not forget that both Turing and Bessent were/are members of the 2SLGBTQQIA+ community.)

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Even companies worth $1 trillion don’t want to pay Nvidia’s prices

by philg

I hope that everyone has made tons of money taking my investing advice and doing the opposite. Loyal readers may remember that I’ve been skeptical of Nvidia becoming worth more than all of the European Union combined and, therefore, never purchased NVDA (current market cap: $5+ trillion). My theory was that 50 percent profit margins for Nvidia would motivate other companies to develop chips and then the AI nerds would recompile their code (or ask AI to recompile their code). Google did this a long time ago with its Tensor Processing Unit. Intel tried and apparently failed with Gaudi (but they were very successful in DEI!). Elon Musk has his AI5 chip and, eventually, his own fab.

OpenAI is the latest entrant into this mix, apparently. “OpenAI Claims Its New Chips Can Outperform Nvidia Processors in Tests” (Bloomberg, yesterday):

The company plans to start using the new chips to support its artificial intelligence models later this year, which should significantly reduce costs for OpenAI as it rolls out more widely.

OpenAI, losing enough money every year to be worth almost $1 trillion, created Jalapeno in a partnership with Broadcom Inc., which makes custom chips for a variety of clients. The two companies, which announced their pact last year, touted the speed at which the processor was developed in June, saying it came together in record time.

Even if this thing succeeds, there will still be a huge market for Nvidia, apparently:

Jalapeno wasn’t tested against the new generation of Nvidia chips, Vera Rubin, which just began shipping. It’s also not designed for training AI models, an area where Nvidia technology excels. Jalapeno is intended for the inference phase of AI — the stage when models have already been trained and can begin responding to prompts and handling tasks.

(Why is the chip named for Vera Rubin, someone who was neither an electrical engineer nor a software developer? Google AI:

Honoring Women in STEM: The company has a specific practice of naming key platforms after pioneering women scientists. Previous examples include Grace Hopper (the Grace CPU) and Ada Lovelace (the RTX 40-series architecture)…

I guess we can’t expect the William Shockley CPU/architecture any time soon, despite the man’s Nobel for the invention of the transistor, but why not one for Jack Kilby? Kilby died in 2005, prior to the full flowering of Rainbow Science, and therefore Kilby’s gender ID is unknown (we could say the same regarding Vera Rubin, who died in 2016, some years before the trans-enhanced Rainbow Flag flew at the White House))

How much do journalists and editors need to know about electrical engineering in order to cover cutting-edge AI hardware?

Because the chip is achieving strong results at low voltage — 700 watts — Jalapeno will allow OpenAI to save money when running its data centers, where power is a key cost, he said.

Speaking of high voltage, let’s have a look at the cooling system for Google’s latest TPUs (blog entry, which Google’s massive AI brain couldn’t figure out should have a date on it; I think it is from April 2026):

It looks like they got retired Pratt PT6 engineers to work on this!

(Actually, this suggests a career path: instead of fighting it out on the crowded software front, a young person could study to become a packaging/cooling engineer.)

Vaguely related… let’s see if we can figure out where Nvidia’s passion for “Honoring Women in STEM” comes from. Perhaps nerds identifying as “women” were the principal designers of Nvidia’s hardware and software?

Prompt to Gemini (to run on Google’s TPUs so that the results aren’t biased by running on Nvidia chips):

Who were the principal engineers of Nvidia’s historically important GPUs and software architectures, including CUDA? In other words, who are the people who are primarily responsible for making Nvidia as valuable as it is today?

Answer (not in quote style):

Key Technical Leaders Behind Nvidia’s Rise

1. Curtis Priem & Chris Malachowsky (Co-Founders & Founding Hardware Architects)

  • The Role: Co-founded Nvidia in 1993 alongside Jensen Huang.
  • Their Contribution: Priem was the chief architect of Nvidia’s early graphics chips (including the NV1 and the breakthrough RIVA 128), while Malachowsky led system engineering and hardware design operations.
  • Impact: They established Nvidia’s core hardware engineering practices and designed the early pipelines that allowed Nvidia to survive the “3D accelerator wars” of the late 1990s and create the original GeForce 256—the chip Nvidia marketed as the world’s first true “GPU.”

2. Dr. David Kirk (The “Father of CUDA” & Chief Scientist 1997–2009)

  • The Role: Chief Scientist and VP of Architecture.
  • Their Contribution: David Kirk was the primary visionary behind transforming Nvidia’s fixed-function 3D graphics hardware into programmable, general-purpose parallel processors.
  • Impact: In the mid-2000s, Kirk persuaded Jensen Huang to mandate that every single Nvidia GPU include dedicated general-purpose execution units—a massive financial gamble at the time. This decision laid the groundwork for CUDA (Compute Unified Device Architecture), launching general-purpose GPU computing (GPGPU) and making GPUs usable for scientific computing and later AI.

3. Ian Buck (VP of Accelerated Computing & Original CUDA Architect)

  • The Role: Creator of the BrookGPU programming language (at Stanford) who joined Nvidia to lead the CUDA project.
  • Their Contribution: While pursuing his PhD at Stanford, Buck created Brook, an extension of C for streaming coprocessors. Jensen Huang hired Buck to lead the software and systems team that built the original CUDA software stack and runtime environment, released in 2006.
  • Impact: Buck turned the raw, complex parallel hardware engineered by Kirk’s team into an accessible software platform for C/C++ developers. Without Buck’s software stack, AI researchers in 2012 (like Alex Krizhevsky and Geoffrey Hinton) would not have been able to train AlexNet on Nvidia GPUs—the spark that triggered the modern deep learning boom.

4. John Montrym (Chief Architect, GeForce/Tesla Architecture)

  • The Role: Senior Architect and Fellow.
  • Their Contribution: Montrym was a key architect behind the NV40 (GeForce 6 series) and the landmark G80 (Tesla architecture) released in 2006.
  • Impact: The G80 was the first unified shader architecture in a PC GPU and the hardware home where CUDA was born. It abandoned separate vertex and pixel pipelines in favor of unified streaming multiprocessors (SMs), setting the blueprint for every modern GPU architecture designed since.

5. Jonah Alben (Senior VP of GPU Engineering)

  • The Role: Longtime leader of Nvidia’s hardware silicon development.
  • Their Contribution: Alben joined Nvidia in 1997 and has spent over two decades directing the physical architecture, design execution, and engineering teams behind generations of GPU microarchitectures: Fermi, Kepler, Maxwell, Pascal, Volta, Ampere, Hopper, and Blackwell.
  • Impact: Alben’s team managed the scaling and execution challenges as GPUs grew from millions to over 200 billion transistors on single silicon packages. His team integrated specialized hardware engines directly into the GPU pipeline—most notably Tensor Cores (introduced in Volta/Ampere) and Transformer Engines (in Hopper/Blackwell)—which gave Nvidia a 10x-100x efficiency advantage in AI workloads over traditional CPUs and competing chips.

6. Dr. Bill Dally (Chief Scientist & Head of Nvidia Research)

  • The Role: Former Chairman of Stanford’s CS Department, appointed Nvidia Chief Scientist in 2009 (succeeding David Kirk).
  • Their Contribution: A pioneer in computer architecture and high-speed interconnect networks, Dally leads Nvidia’s long-term research division.
  • Impact: Dally pivoted Nvidia’s research toward optical interconnects, low-precision AI math (FP8, FP4 formats), ray tracing hardware cores (RT Cores), and multi-chip network scaling (NVLink). His architectural research predicted the massive bandwidth bottlenecks of large AI models years before ChatGPT emerged.

You can see the full answer and also one from Grok.

Fun fact from Oshkosh/AirVenture 2026: I ran into a mid-level Nvidia employee. He had shown up in his new airplane, a perfectly restored P-51 Mustang ($5 million?).

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