Jagt isn’t alone. A professor at Pepperdine reported that Gen Z students have “an inability to critically think.” At Columbia University, students “seem bewildered” by the idea of finishing multiple books in a semester, and at the University of Virginia, students are “shutting down” when they encounter ideas they don’t understand.

The professors blame smartphones and AI.

The good news: 26 states have now passed full bans on phone use in elementary, middle, and high school. The bad news: the bans took roughly 15 years to materialize, and if it takes us as long to manage AI’s impact on education, there will be drastic consequences.

According to the most recent research, AI disrupts the learning process by allowing students to outsource their thinking to large language models. They avoid thinking hard, and therefore they don’t actually learn.

AI can help students learn, but only if it’s prompted to act as a tutor and not give answers away. This requires immense restraint that students with immature prefrontal cortices don’t have. Kids who spend seven hours a day on TikTok likely don’t have the capacity to refuse AI’s offer to write their essay for them.

Teachers can try to fight cheating by using AI detectors or even tracking writing progress in Google Docs — but they’re up against a burgeoning industry of VC-backed companies working to make AI detection impossible.

It’s hard to think of a more important resource to protect than young minds. As Jagt wrote: “The students who cannot read a 20-page article today are the voters who will not be able to read a bill, or the jurors who cannot follow a closing argument, tomorrow.”

The Status Quo

Before we go any further it’s important to understand the current state of American schools. Data released earlier this month gave us an update on students’ academic achievement. The results were bleak: Math and reading scores for nine and 13-year-olds have steadily declined since 2012. Teenagers are now scoring at the same level they were 50 years ago, back when calculators had just been invented.

To put it another way: One third of 12th graders don’t have basic reading skills, and 80% of hiring managers believe that today’s high school students are less prepared to enter the work force compared with previous generations.

As I wrote in a previous piece, key markers of intelligence are on the decline for adults, too. The OECD assessed literacy and math skills of thousands of adults in 31 developed countries. It found that, over the past 10 years, literacy proficiency improved significantly in only two countries (Finland and Denmark), remained stable in 14, and declined significantly in 11, including in the U.S.

It’s too early to blame AI. These poor results are likely a combination of the attention-span destroyers known as smartphones, changes in accountability measurements in schools, and pandemic-related disruptions. The takeaway: AI’s arrival coincides with low points in cognition and education.

One teacher wrote as much in a survey I conducted (more on that later).

We are unprepared, and the disruption is already here. Roughly 85% of high schoolers and college students admit to using AI for coursework; I believe the other 15% are lying.

What AI does to learning

AI compromises learning by allowing us to dodge crucial cognitive processes. What follows is an extremely oversimplified explanation of how you learn.

First, information from your eyes or ears is processed and held in your working memory, which depends on your prefrontal cortex. Then, your hippocampus helps encode that information into memory. It does so by coordinating activity across different parts of your brain, creating patterns of neural connections that can be reactivated later.

That distinct web of connections (memory) gets stronger as you reactivate it by recall. That’s why explaining a new concept to a friend or using flashcards work: both force you to reactivate, and thus strengthen, that neural network.

Reactivating learned concepts requires mental effort. To make cognitive work easier, humans have developed tools that alleviate some of that effort, like writing, typing, and calculators. Using those tools is referred to as cognitive offloading.

AI is not one of those tools. Unlike calculators or even the internet, AI doesn’t give you just one part of an answer: it gives you the entire answer. In most traditional academic use cases, if you have AI, you don’t have to engage mentally. You don’t have to reactivate those neural networks and therefore you don’t learn.

Tyler Jagt, the college professor whose students can’t complete 20-page essays, explained the problem with AI in school: “Offloading the struggle to a chatbot does not ‘free students up for higher-order work.’ It deprives them of building the strength to do any substantial cognitive work at all.”

Researchers came up with a new term that encapsulates that idea: cognitive surrender. In their own words, cognitive surrender “represents a deeper abdication of critical evaluation, where the user relinquishes cognitive control and adopts the AI’s judgment as their own… they stop deliberative thinking altogether.”

Scientific studies support this. In one experiment, two groups of college students completed a research project, with one group using an LLM, and one using traditional search. The group using the LLM experienced lower mental effort and produced weaker reasoning than peers using search engines.

In another study, students used ChatGPT to prepare for a math test. They did well on practice sets, but when ChatGPT was taken away, they performed substantially worse.

A Stanford University review of over 800 academic papers on AI in K-12 education came to a similar conclusion: AI boosted students’ performance when they had access to the tool, but when taken away, those gains disappear, or in some cases, reverse. The paper concluded that AI use does “not necessarily result in deeper learning.”

Subscribe now

The Adults in the Room

Scientific studies of AI in academia are essential, but they lack the urgency and emotionality of first-person accounts. That’s why I asked teachers to tell their stories.

Close to 200 U.S. educators filled out my survey. Sixty-one percent teach at public schools. Roughly half teach kindergarten through 12th grade, and half teach at the university level.

Overall, educators perceive the impact of AI on student skills to be dramatic and predominantly negative.

The most negatively-impacted skills are writing, critical thinking, and creativity. Teachers believe math skills are less impacted by AI. This could be because math homework is still typically problem sets done by hand, or because most AI is still surprisingly bad at math.

I also asked teachers to describe the impact that AI is having on their students. Ninety-five percent of survey respondents typed out an answer, and many of them were paragraphs long. I’ll include select quotes here. If you want to read all of them, check out the full list here.

Goodbye to Thinking and All That

Frequent AI use has been linked to lower critical thinking, lower brain activity, and weaker encoding of “learned” material. When answers and full essays are available at the touch of a button, students can easily opt out of mental effort.

Teachers agreed. Responses have been edited for clarity and length.

Students understand that AI is impacting them, but they don’t know how to stop it. In a survey of 7,000 high school students, almost half agreed with the question “Do you feel that you are relying on AI too much for your learning?” Of those students, over 40% said, “I tried to limit my usage, but it was so difficult I failed.”

Students are starting to wonder why they should even bother to learn.

On Writing

One of the most common uses of AI is writing. Roughly half of ebooks released on Amazon now are written by AI, 18% of lawsuits filed this year are AI-generated, and 57% of scientific papers published last year contained AI-generated text. Even a much-anticipated book investigating AI’s impact on truth contained fake, AI-generated quotes.

AI writing is ubiquitous in academia. A Gallup poll found that more than half of college students use AI daily or weekly for writing tasks, and anecdotes have surfaced on social media of students’ disbelief that anyone could write 20-page papers without AI.

One of the most-cited victims of AI in my survey was writing skills.

Everyone’s Writing About Apple Pie

AI is also impacting students’ creativity.

This, too, is supported by large-scale studies. Georgetown University neuroscientist Adam Green analyzed thousands of college application essays and found that those judged to be human-written contained 8x more novel ideas than those generated by AI. Though the AI-generated essays contained more descriptive and flowery language, the topics were more homogeneous.

It’s likely too soon to see the full impact of AI on student achievement, but we can infer what they might be from teachers’ first-hand accounts.

AI Policy

Educators do not have the resources they need to teach in an AI-enabled world, and policy makers aren’t acting quickly enough. The few AI-related education bills that have passed in the states are hardly prescriptive. For example, Illinois has decreed that AI bots cannot teach community college classes in lieu of human instructors, and Ohio, Oklahoma, and Maryland have all required school districts to, wait for it, come up with AI policies.

Having an AI policy is no guarantee of progress. Sixty percent of teachers I surveyed said that their school had an AI policy — but nearly two-thirds of them deemed it ineffective.

One reason it is so hard to regulate AI in school is because detection is fraught. Schools are spending thousands on AI detection software, but studies show that the detection tools on the market don’t work. Only 6% of teachers said that they used an AI detector; the vast majority said they didn’t use them because they weren’t effective.

Teachers also shared anecdotes of parents getting involved when they felt their child was unfairly accused of AI plagiarism.

This is a solvable problem — but the top AI companies don’t want to solve it. In fact, OpenAI invented a technology that was 99.9% accurate in detecting AI-generated work years ago, but decided to not release it because they were worried it would discourage some users.

Share

Not All Bad

The Trump administration has dismantled the Department of Education and cut nearly $900 million in education research grants. It’s a terrible time to reduce education research because we need to know more about how AI disrupts learning, but also about how it could improve it.

Studies have shown that student outcomes improve when they use tailored versions of large language models that don’t do students’ work for them, but instead ask questions like a tutor would.

Teachers in my survey explained how AI could be used to enhance learning.

But without effective training, AI is just a powerful weapon.

The Stakes

Countries with more educated populaces have governments with better rule of law, less corruption, and higher rates of productivity and innovation. By letting students outsource their learning and thinking to AI, we risk raising a generation of Americans who are not only unable to continue our proud tradition of innovation and achievement, but who are also less equipped to preserve democracy. As British statistician Claus Moser said: “Education costs money, but then so does ignorance.”

One teacher summarized the dilemma succinctly: “No thinking = no learning.” If the very practice of thinking is under threat, isn’t everything at stake?

One lesson I’ve learned from Scott is: always end on an emotional note. He does this in all of his presentations and his No Mercy / No Malice posts. The emotions aren’t always the same — they can be somber or hopeful or angry. But the point is to ensure that readers and viewers walk away feeling something. Scientifically, emotion makes content more memorable. I tried to end this piece by invoking fear. Did it work?

1. The modern world is destroying focus—but a new study shows how to get it back

2. Student cheating is becoming impossible to detect

3. What does it all mean? Once a year, French students try to explain

P.S. Thank you to all 195 educators who took my survey. I appreciate your time.

Like anyone on the internet, I’ve scrolled through my fair share of AI slop: The fake Bigfoot vlogs, the synthetic babies riding golden retrievers, the cooking videos where the hands pass through the cutting board. So as I waited in line for Dreams of Violets, I had already written this post in my head.

There would be melting faces. There would be hands with six fingers, then four, then six again. Physics would be tangibly off. Continuity would be nonexistent. The audience would be one-third press, one-third AI guys in Patagonia vests, and one-third people who wandered in looking for a different theater. People would walk out. They might even protest. I would get 1,500 words out of the wreckage and a good story to tell at parties.

That’s not what happened.

Dreams of Violets

Dreams of Violets was directed by Ash Koosha, an exiled Iranian filmmaker and musician living in London. The film is a 74-minute docudrama inspired by the January 2026 protests in Iran, in which an estimated 36,000 people were killed. Koosha and his brother Pooya — who co-produced the film — both grew up in Iran and eventually escaped to London over a decade ago after Koosha was imprisoned for his involvement in a film documenting Iran’s music scene.

When the internet went dark in Iran on January 8 and reports of the massacre began to surface, Koosha felt compelled to document what he was hearing from friends and family.

What followed was roughly three months of work producing a feature film using an entirely AI-driven pipeline. No actors, no sets, and no cameras. Using a stack of tools including Kling AI for video generation, Google’s image models for core frames, and his own proprietary technology for blocking and lens accuracy, Koosha built every frame in a London flat.

He used AI to generate the scenes from the the very limited amounts of smartphone footage from the protests before the blackout occurred. The characters were created via prompts describing the appearance of real Iranians that Koosha knew. He also voice-acted every character himself and used AI to modify the audio recordings to fit the gender and age of each role.

The budget was roughly $2,000, spent exclusively on software subscription fees and AI compute tokens.

Subscribe now

The film’s selection was controversial. Before the screening, Tribeca co-founder Jane Rosenthal acknowledged as much in remarks clearly designed to head off criticism. “This was not a technical exercise,” she told the audience. “This was an artist finding a way to bear witness.”

Koosha made the same case, telling the audience that his use of AI wasn’t a creative choice, it was the only tool available to someone in exile with no crew, no access, and a story the Iranian regime had made nearly impossible to tell any other way.

No matter the intention or message, reporting on the film has been hyperfocused on the technology used to make it. That’s for good reason: If Dreams of Violets is a proof of concept for AI filmmaking done well, Hollywood could be completely upended.

Slow Death

Hollywood is already in a precarious position. Ticket sales at the box office have yet to recover from the pandemic. Streaming, media consolidation, and an expanding attention economy have hurt the industry too. Labor strikes in 2023 disrupted development and production pipelines, making a bad situation worse.

What followed has been a hollowing out of one of America’s most beloved industries. Motion picture and video production employment is lower than it was a decade ago. Los Angeles recorded just 19,694 shoot days in 2025 — the lowest-ever figure outside of 2020, and roughly half of total shoot days recorded in 2019. Television production, the region’s largest employment driver, peaked at 18,560 annual shoot days in 2021 — by 2024, it had fallen nearly 60%.

The pullback isn’t just opportunistic cost cutting — it reflects genuine financial distress across the industry. High churn rates and hundreds of billions in content spending left most streamers in the red. Until 2024, Netflix was the only consistently profitable streamer. Eventually, investors ran out of patience, and streaming media companies consolidated, introduced ad tiers, raised prices, and, of course, trimmed production budgets.

Disney has been cutting its content budget since 2023, Paramount cut its budget by 7% last year, and Apple’s and Amazon’s stayed roughly flat. Netflix’s original movie output has fallen to an eight-year low.

The glory days of unbridled production spending are over. What’s left is an industry trying to figure out how to make content cheaper.

Hollywood Goes Abroad

Step one for studios and streamers has been to shift production spend abroad. In 2024, Netflix was projected to have spent more money outside of North America than inside for the first time ever.

Why? It’s relatively expensive to film in the U.S. A cinematographer who might cost $30,000 a week in Hollywood can be hired for as little as $2,000 in Hungary. It’s also easier for big film studios to get production tax credits abroad — Australia, Canada, and the U.K. offer tax rebates of 25% to 30%, while the U.S. has no federal incentive program. Those benefits combined with cheaper labor costs make the choice simple: Produce outside the U.S., pay less. This is apparent all across the industry. New productions have decreased in the U.S. since 2021, while foreign productions have increased.

To some degree, it’s been working. The inflation-adjusted average production budget of each year’s 10 top-grossing films hit a peak of $272 million in 2022, and since then, it’s fallen sharply. Hollywood is already making movies for less. The question now is how much fat can still be trimmed.

Smoke and Mirrors

The next logical step is the one many in Hollywood are reluctant to publicly embrace: use AI to cut costs. Not in the way Koosha did — not for the entire production. But as a tool for the expensive, unglamorous work that eats away at budgets behind the scenes.

Background crowd scenes, set extensions, B-roll shots, and visual effects work currently demand armies of specialists — and AI is already automating much of it. A 2024 survey found that 75% of entertainment industry leaders had used AI to cut such jobs.

Those opposed to AI usually make the same case: AI imagery carries an uncanny quality that a trained eye catches instantly. They’re not wrong; some AI video still gives itself away. Other critics reject AI on ethical grounds.

But those trained eyes belong to a small and self-selecting minority. The much larger audience, the one that actually buys the tickets and fills the seats, has been consuming AI-generated video for years — mostly on social media, without even knowing.

With each new model, audiences have a harder time identifying what’s AI and what isn’t. A peer-reviewed study published in 2024 (many generations of AI video technology ago) found that the human detection rate of AI-generated video was roughly 57% — close to a coin flip. The argument that audiences will reject AI on principle runs into a fairly obvious problem: What if they can’t even spot it?

Feature Presentation

In the movie’s opening sequence, a barrage of shots from the streets of Tehran began flashing across the screen. I was immediately floored by the quality of Koosha’s film. There were aspects of the film that I’d never seen from AI-generated video before: lens flare, realistic lighting, impressive detail on buildings, truly expressive faces.

The film follows five strangers caught in the January 2026 crackdown on Iranian protesters in Tehran, where regime forces are executing demonstrators in the streets. When a soldier corners the group in a dead-end alley, a young boy in a wheelchair named Amir watches from a window above and decides to act.

As the story progressed, the limitations of the technology became clearer. Dialogue was sparse, and it often didn’t line up with characters’ lips. The backgrounds in certain scenes were out of focus, and the film utilized close-up shots more often than usual, presumably as a way to keep the viewer from seeing distracting errors in the footage. The film was structured more like a dream sequence than a conventional narrative — a fair choice. But it came across as disjointed and a bit rushed.

The film’s shortcomings didn’t surprise me — that’s what I was there for, after all. What caught me off guard were the moments in which I became lost in the film’s world. At one point during a particularly tense scene in which the Islamic Revolutionary Guard Corps was executing a protester, a woman in the seat next to me gasped. At that moment, I also realized that I was clenching my jaw. It wasn’t an actor that made me feel anxious and unsettled — it was an artificially generated avatar.

This is not to say that I believe there will soon be an AI category at the Oscars, nor do I think Dreams of Violets would win that award. Instead, the movie proved that AI is becoming yet another tool in the filmmaker’s toolbox. For Koosha, it was the only tool. For filmmakers in Hollywood facing constrained budgets, AI is one tool among many. In that regard, Dreams of Violets represents an inflection point for filmmaking. While an entirely AI movie may never be the goal, AI’s capabilities and economics suggest that it will be baked into many movies going forward.

We’ve been covering AI on Prof G Markets for as long as I can remember. And one question has always haunted it: How can it actually be used? When I first began doing research for our shows just two years ago, I couldn’t answer that. The tools were largely useless.

Since then, they’ve gotten a lot better. I now use AI to pull insights from long research reports, surface interesting articles and studies, and gather background on the stories we’re covering. Occasionally, I’ll use it to help shape a pitch.

My own experience mirrors my takeaway from the film: If I had used AI to write this newsletter top to bottom, it would have been a terrible newsletter. But if I hadn’t used AI at all, this would have taken longer to write — and cost more to produce.

1. The robots are coming

2. How to make millions on Tubi and Roku

3. Americans don’t trust CEOs

The NSB advises and oversees the U.S. National Science Foundation (NSF), an independent federal agency that supports basic research. If you’ve ever searched for anything on Google, used GPS to find a restaurant, or gotten a vaccine, you’ve benefited from such research.

Firing the people who oversee that system has significant consequences for every American, as well as U.S. national interests.

The NSB was planning to meet May 5 to deliver an important report. The topic? The United States ceding scientific ground to China, a country that just outspent the U.S. on R&D for the first time in modern history.

If firing the people responsible for maintaining America’s scientific edge at the precise moment we’re falling behind sounds shortsighted, trust your instincts.

The Quiet Engine of American Discovery

In 1945, Vannevar Bush — head of the government’s wartime Office of Scientific Research and Development — wrote a report entitled Science: the Endless Frontier. The report made the case for a federal agency dedicated to basic research: the curiosity-driven pursuit of fundamental knowledge, with no specific product or application in mind. The report landed on President Truman’s desk, and, in 1950, Congress founded the NSB and the NSF.

Federal investment in science didn’t start with Bush — it began nearly 175 years earlier. The Constitution empowers Congress to “promote the progress of science and useful arts” by protecting patents and copyrights. Thomas Jefferson sent Lewis and Clark west in 1804 not only to map the Louisiana Purchase but also to catalog the region’s natural history. Benjamin Franklin funded his own experiments but framed them as public goods. In sum, Bush’s proposal merely codified a system for funding a core principle already rooted in our country’s DNA.

Today, it’s hard to understate the importance of scientific research to the American economy. Since 1945, advances in science and technology have driven a staggering 85% of national economic growth.

The most successful venture capital firm in history, according to my boss, Scott Galloway, is Uncle Sam. One example: The U.S. government invested $3.8 billion in the Human Genome Project, which created more than 300,000 jobs and generated an economic output of $796 billion, giving America an ROI of 141 to 1. The NSF accounts for only 0.1% of federal spending but supports roughly a quarter of all federally funded basic research at U.S. colleges and universities.

The Federal Reserve estimates that government-supported research from the NSF and other agencies has had a return on investment of 150% to 300% since 1950, meaning for every dollar U.S. taxpayers invested, they got back between $1.50 and $3.00.

Eighty-one years ago, Bush wrote in his report that “basic research is the pacemaker of technological progress.” The data – and history – agree with him, but the truth has never gotten in the way of Trump’s decision-making.

Mocking Science Is a Fool’s Errand

Writing off scientific research is not new. From 1975 to 1988, Sen. William Proxmire issued monthly “Golden Fleece Awards,” which targeted spending he considered wasteful.

The first Golden Fleece Award went to Elaine Hatfield and Ellen Berscheid, because the NSF awarded them $84,000 to study why people fall in love.

“I believe that 200 million other Americans want to leave some things in life a mystery,” Proxmire wrote, “and right on top of the things we don’t want to know is why a man falls in love with a woman and vice versa.”

Nevertheless, Hatfield and Berscheid’s research helped establish relationship science as a legitimate field. Their work provided the scientific foundation for a $6 billion industry many single people see as essential: dating apps.

Critics like Sen. Proxmire often took projects out of context and painted them in the silliest light they could. The award became synonymous with perceived government waste and impacted how the NSF distributed grants.

Case in point: in 2011, a video of a shrimp on an underwater treadmill went viral as an example of frivolous federal spending. It may sound silly, but the study was serious: It was designed to measure how shrimp responded to changes in water quality — a legitimate concern for the $80 billion shrimp industry.

For what it’s worth, the shrimp treadmill only cost $1,000. For comparison, in one month last year, Defense Secretary Pete Hegseth spent more than $7 million on lobster, $15 million on steak, and $124,000 on ice cream machines.

Congressman Jim Cooper of Tennessee responded with his own award in 2012: The “Golden Goose Awards.” The idea was to highlight “seemingly obscure studies” that led to major breakthroughs that benefited society.

In 1966, Thomas Brock and Hudson Freeze went on an NSF-funded trip to Yellowstone to study thermophiles — bacteria that thrive in extreme temperatures. They collected a microorganism, Thermus aquaticus (nicknamed Taq), which contained an enzyme that could survive the high-heat cycles required to copy DNA strands without breaking down. Taq was the unlock for “polymerase chain reaction” (PCR), which amplifies small samples of DNA for study.

You might recall the PCR from COVID days: It was the primary diagnostic tool used globally during the pandemic. Discoveries made with the PCR method have led to drugs and vaccines for diseases ranging from cancer to kidney disease.

“I was doing basic research on organisms at high temperatures and focusing on evolution and ecology,” Brock said. “I wasn’t even thinking about industrial uses.” Brock and Freeze won the Golden Goose award for T. aquaticus in 2013.

Or take the 2012 Golden Goose winner. That research asked: Why do certain jellyfish glow green? In 1962, Osamu Shimomura and a colleague at Princeton gathered thousands of jellyfish off the coast of Washington and isolated a protein that fluoresced green under UV light.

Three decades later, biologist Martin Chalfie figured out how to use the gene for the green fluorescent protein (GFP) as a kind of molecular highlighter that lets scientists see when and where genes switch on inside a living cell.

Today, these findings help scientists study how cancer tumors form new blood vessels, how Alzheimer’s kills neurons, and how HIV infects cells. GFP has become a standard tool in molecular biology labs and pharmaceutical drug screening worldwide. All of these breakthroughs trace back to federally-funded curiosity about a glowing jellyfish.

Subscribe now

Pennies In, Trillions Out

The internet is a canonical example of how public dollars paid off at a scale no one could have predicted. In 1969, the Defense Department’s research arm ARPA (now DARPA) funded an experimental computer network called ARPANET. Over the next two decades, DARPA-funded researchers built the protocols that became the technical foundation of the modern internet. In the 1990s, the NSF funded the world’s first freely available web browser, Mosaic. That development spurred a revolution in communication that has had a trillion-dollar impact on the global economy.

In 1994, an NSF Digital Library Initiative grant funded a graduate student named Larry Page. Page was interested in the “missing links” in webpage ranking, and he partnered with NSF Graduate Student Fellow Sergey Brin to develop the “PageRank” method, which survives as one of the main components of Google search. Alphabet is now worth nearly $5 trillion. The NSF grant behind it was $4.5 million – and Page and Brin’s work was only a slice of that.

Venture capitalists may never have funded a random graduate student if the government hadn’t stepped in. The government derisked the technology, making PageRank more attractive to private investors.

In 2012, Dr. Jennifer Doudna’s lab at UC Berkeley teamed up with Emmanuelle Charpentier to investigate the bacterial system CRISPR-Cas9. The work was an act of basic science — not aimed at any particular result. However, they ended up developing the first CRISPR therapy to treat sickle cell disease, an illness that afflicts 8 million people globally. Doudna and Charpentier won a Nobel Prize for the research in 2020. Not only did the NSF fund Doudna’s foundational work on RNA, but it also now funds researchers using CRISPR to treat diseases and enhance crop production.

The Myth of Silicon Valley

Before it was the tech capital of the universe, Silicon Valley was known as the “Prune Capital of the World.” Government spending changed its fortune. During the Cold War, R&D funding flowed west of the Mississippi to California and Stanford University, specifically. Stanford Dean of Engineering Fred Terman, who had been mentored by none other than Vannevar Bush, was determined to make Stanford the next Harvard.

Historian Margaret O’Mara traced the history of the Valley in her book, The Code: Silicon Valley and the Remaking of America. “The government was involved as a customer, as a catalyst, as a de facto venture capitalist at an early stage, when there was no commercial market for this stuff,” O’Mara said in an interview. “But, the story of the Valley is also one of entrepreneurship … the brilliance of the Valley is that they believed they did it by themselves.”

We subscribe to a myth of Silicon Valley: There’s something magical in the water they’re drinking out there that breeds a special, independent innovator. It’s true that Silicon Valley has birthed and attracted brilliant minds that have developed transformative technologies. But to neglect the role of government support in enabling their genius is to neglect its history.

Elon Musk is perhaps the embodiment of this myth. Musk is undeniably a force of nature, but he had help along the way. Government-funded researchers solved the hard problem of lithium-ion batteries, which power Tesla and the rest of the EV industry. SpaceX, too, would be nowhere without the government. In fact, Musk himself said, “I feel very strongly that SpaceX would not have been able to get started, nor would we have made the progress that we have, without the help of NASA.” And AI research was seeded by decades of DARPA and NSF grants.

This isn’t a matter of who gets the credit, but rather a recognition that American technological progress has long been the product of a symbiotic relationship between the public and private sectors.

Own Goal

The White House requested budget reductions of 40% to the National Institute for Health (NIH) and 57% to the NSF for this fiscal year. The Trump administration did so despite the fact that over 80% of Americans want to increase R&D funding. Ultimately, Congress rejected Trump’s request and cut only 3.4% of the NSF’s budget.

Still, the administration is finding innovative ways to asphyxiate science in this country.

As of May 1, the NSF had committed only 10% of its appropriated funds, half of what it had awarded by this point in previous years. The NSF has put a hold on grants for universities that have been targeted by Trump, such as Harvard and Yale.

The NSF is now funding fewer grants in every area of science and medicine.

Grant Witness tracks the termination of grants of scientific research agencies under the Trump administration. They found that the current losses from disrupted grants at the NSF, NIH, EPA (Environmental Protection Agency), SAMHSA (Substance Abuse and Mental Health Services Administration), and CDC (Centers for Disease Control and Prevention) total $36.9 billion.

Losing Ground

For 80 years, the U.S. led the world in scientific and technological achievements, achievements that were enabled by investment in basic research.

That’s not true anymore. China has now surpassed the U.S. in research spending (when adjusted for purchasing power). And this isn’t even a breakthrough moment. China also overtook the U.S. in total scientific publications in 2024.

New funding awards from the NIH to American universities have declined 46% this year. That money supports 400,000 jobs annually, generates more than $94.5 billion in new economic activity each year, and, crucially, supports lifesaving medical research.

The White House’s justification is that these cuts eliminate wasteful spending. Their real issue? Trump can’t see how funding science would enrich or glorify him. Ironically, it definitely could. Just imagine: “We cured cancer. I cured cancer. Nobody thought it was possible, the doctors said it couldn’t be done.”

There are better reasons to fund basic science research than presidential vanity, but there aren’t any good reasons to defund it. The Information Technology and Innovation Foundation estimates that a 20% cut in federal research and development starting in fiscal year 2026 would shrink the U.S. economy by nearly $1 trillion over 10 years and reduce tax revenue by around $250 billion. That’s the definition of penny-wise and pound-foolish.

My dad used to take my sisters and me to the American Museum of Natural History every weekend when we were growing up. The museum had a “Discovery Room” where we could hold real fossils in our hands, look at specimens under a microscope, and identify animals in a two-story replica of an African baobab tree. Exploring there instilled in me a deep love for and appreciation of science. I also developed a pride in the American pursuit of scientific discovery.

Scott often invokes Carlo M. Cipolla’s Golden Law of Stupidity: A stupid person is someone who causes problems for others without any clear benefit for himself, possibly incurring losses. Firing the NSB struck me as a textbook case of that kind of stupidity in action. But while it’s easy (and fair) to blame Trump, I wanted to better understand how people like Sen. Proxmire helped create a narrative context that enables that kind of stupidity. It strikes me as a grave threat to our nation, and our planet.

Uncovering the link between the public and private sectors and understanding how their combined efforts fuel innovation is, in my view, how we mitigate the cost of killing “silly” science.

Kristin O’Donoghue is a research analyst on the Prof G Markets team. She joined in September 2025, after a year working at the Carnegie Endowment for International Peace as a junior fellow. Kristin graduated from the University of Virginia in 2024.

1. Why Your Best Ideas Aren’t Original

2. This Prada-designed onesie will help keep NASA’s Artemis astronauts cool on the moon

3. Saving a Lost Generation of Young Men—with Chop Saws

To translate: The mayor of New York City will not let technology companies automate the work of taxi drivers and rideshare drivers. He won’t be the last politician to take that stance.

Hit the Brakes

For as long as autonomous driving has existed, there has been organized resistance to it. Mamdani is not an outlier — he’s the latest and most prominent face of a movement that has been working for nearly a decade to slow, stall, or stop autonomous vehicles from replacing human workers. That movement has a name, a headquarters in Washington, D.C., and 1.3 million members: the International Brotherhood of Teamsters, America’s largest private-sector union and the most powerful force standing between autonomous vehicles and the open road.

Nearly a decade ago, the Teamsters successfully lobbied Congress to exclude autonomous semitrucks from legislation that paved the way for autonomous vehicle testing. Four years later, in 2021, they shut down a bill that would have relaxed federal autonomous vehicle rules. In 2023, the Teamsters backed a California bill that would’ve required human drivers in all autonomous trucks. The bill made it past the state Legislature, but Gov. Newsom vetoed it.

As autonomous driving develops into more serious technology, so do the Teamsters’ efforts to halt it. Last year, the conflict intensified when Teamsters Local 25 called on Waymo to pause their planned Boston rollout altogether.

“Waymo is steamrolling into cities throughout our country without concern for workers or residents,” said Local 25 President Tom Mari at a rally outside of Boston’s City Hall. “They’re doing this because they want to make trillions of dollars by eliminating jobs.”

As of now, Waymo is continuing their testing in Boston despite the Teamsters’ opposition. Under the current Massachusetts permitting process, all autonomous vehicles need a human operator behind the wheel. There’s currently a bill in the Massachusetts State House that would change this — but it’s been met with a competing Teamsters-backed bill that would officially codify the requirement for human operators. It’s unclear how or when this conflict will be resolved, especially as Waymo continues to ramp up its lobbying spend.

What is clear, however, is that these legislative battles are not one-off events. They’re the beginning of what will be a long path to seeing autonomous vehicles on the roads of all 50 states. And for good reason — the Teamsters have a point. Job destruction is coming.

Driver Destruction

Since ride-sharing services began popping up in the early 2010s, they’ve become the backbone of the gig economy. According to Deloitte, about a third of the American workforce participates in the gig economy. From there, it’s estimated that at least a quarter of American gig workers drive in some capacity, whether that be delivering food, groceries, or humans. Autonomous vehicles put all of these jobs at risk.

If we zoom out to include other driving occupations, it gets even worse. According to the Bureau of Labor Statistics, there are roughly 440,000 taxi and limousine drivers, 460,000 food delivery drivers, 1.5 million small-package delivery drivers, and 2.2 million long-haul truckers. That’s a total of 4.6 million jobs.

Driving is also the most common occupation among young men without a college degree, by far. All of this means that, as you read this, nearly 3% of the American workforce is in the crosshairs of Big Tech.

Nearly all of those occupations are, in some capacity, represented by the Teamsters union. The transportation sector as a whole has a union penetration rate of about 14% — a minority, obviously, but still more than double the rate of the private sector in total.

The Teamsters’ fight against autonomous driving is important not only to its members but also to its very existence. If the Teamsters begin bleeding members due to automation, they’ll start losing dues, which would place pressure on the union’s finances and dampen its political influence.

Outside of unions, it can be assumed that there will be plenty of political capital to be gained in future elections for politicians who choose to take a stance against Big Tech and autonomous vehicles. The tide is turning on all forms of AI, and public polling already reflects this reality.

The share of Americans who think AVs improve road safety has actually fallen 12 percentage points since 2018 — and that’s despite the technology becoming exponentially safer over the same period.

Compared with human drivers, Waymo vehicles have 82% fewer injury-causing crashes, 92% fewer crashes involving injured pedestrians, and 85% fewer crashes involving injured cyclists. Last year, car accidents were the leading cause of death for Americans ages 5 to 29. In an entirely Waymo’d world, more than 33,000 lives would be saved annually. But without public buy-in, none of this matters.

The raucous boos at graduation ceremonies across the country illustrate how unpopular artificial intelligence and autonomous technologies more broadly are in America. Autonomous vehicles are possibly the most visceral and physical embodiment of AI that the average American might interact with on a regular basis. In cities where Waymo or Tesla or Zoox operate, autonomous vehicles are everywhere. They serve as a constant reminder of the once-farfetched, unrecognizably transformed future that awaits us — a future that most Americans interpret as dystopian.

Let’s Talk

The Teamsters are not the first union to stand in front of automation. In the 1810s, English textile workers — the original Luddites — smashed power looms with hammers in the dark of night. In the end, it didn’t save their jobs.

A century later, elevator operators’ unions fought to keep humans at the helm of automatic elevators. Those jobs are long gone. In the 1960s, longshoremen’s unions battled the introduction of shipping containers. The longshoremen lost that fight, but not before negotiating severance funds and job guarantees that softened the blow for existing workers. The pattern is undefeated: Technology always wins, the only question is how much protection workers are able to take with them on the way out.

This is exactly why Mamdani and the Teamsters are wrong. Letting the permit expire is not good for labor — in fact, it has the opposite effect.

The more honest and useful fight is the one the longshoremen eventually settled for: not blocking the technology altogether but demanding that the companies deploying it bear some of the cost of the disruption they’re causing. Retraining funds, transition payments, even a Waymo-funded safety net for displaced drivers are all policies worth proposing. Instead, the Teamsters’ current Luddite-inspired strategy will leave workers empty-handed. The question they should ask themselves is: Do you want to be right or do you want to be effective?

Each week, this section will give you an inside look at how this article came about, as well as the analytical and storytelling techniques the author used to report it.

Working with Ed on Markets has made me acutely aware of the public’s souring perception of all things artificial intelligence. Months before commencement speakers were getting heckled for mentioning AI, Ed pointed out the mounting opposition to data center construction across the country and its relation to politics.

I’ve been following the autonomous race closely, and Ed’s findings made me wonder: Is the same thing happening with robotaxis? I realized that the answer was a resounding yes. But when you think about the trade-off between safety and jobs, things get complicated … and a lot more interesting.

One more thing: Part of being a good analyst is recognizing patterns across different domains. When a new story reminds you of something you’ve seen before, follow that instinct. History might not always repeat, but it’s always instructive.

Dan Chiolan is a research analyst on the Prof G Markets team. He started as an intern in 2024 before joining Prof G Media full time after graduating from Temple University.

1. Is Nvidia too big to fail?

2. Want to know the future? Don’t trust the stock market

3. Why you should never skip a TV intro