Thursday, June 25, 2026
In 2025 OpenAI spent $35B and lost $21B (Whoops!)
OpenAI spent roughly as much last year as the US government spent on the FBI and NASA combined.
— Ed Elson (@edels0n) June 25, 2026
The return on all that? A $21 billion loss. 👇 https://t.co/kN8wuFvmb9 pic.twitter.com/PAIzJPXehl
“The way down is the way up” may work for mystics, but not for business.
Why do American's fear AI, but many other countries don't? [Homo economicus strikes again!]
Paul Kedrosky, There’s One Clear Reason Why Americans Are Gloomy About A.I., NYTimes, June 25, 2026.
Hating artificial intelligence may be the only thing about which Americans agree. But they are global outliers in their pessimism. A survey of 24,000 adults across 30 countries found that citizens of nearly all of those countries, rich or poor, see A.I. more favorably than Americans do. This is startling for citizens of a wealthy, advanced economy who are usually enthusiastic tech adopters of anything with a wall charger. [...]
Why isn’t it working? Because the theory is incomplete, at best. If American A.I. pessimism were merely cultural or informational, it would correlate with media consumption, education levels or political polarization. Instead, it cuts across all those categories. It correlates instead with labor market institutions.
Start with the global picture. Plot A.I. sentiment against income and labor market, and there is a pattern. Poorer countries are A.I. optimistic: Indonesia at 76 percent, Thailand at 77 percent and Mexico at 63 percent. Rich countries like the United States, the Netherlands and Belgium are not. What A.I. means depends, in large part, on where you sit economically.
In countries with largely informal economies — where large numbers of people work without contracts, benefits or legal protections — A.I. looks like a ladder to better economic outcomes previously available only to those with capital, education and formal employment. A small manufacturer in Guadalajara or a street vendor in Jakarta doesn’t have much to lose from A.I. disruption, and potentially a great deal to gain.
In rich countries with more formal labor markets, however, A.I. looks more like an ambush. It threatens what people already have: stable employment, predictable income and accumulated professional standing. [...]
But not all wealthy nations feel the same. Norway is more optimistic than France, and Germany more than Canada. Those countries have broadly similar income levels, so income alone doesn’t explain the variation.
So what does? In Norway, losing your job means receiving around 67 percent of your previous wages in unemployment benefits while you search for the next position. In France, it’s around 66 percent, and 60 percent in Germany. The insurance system treats unemployment as a temporary inconvenience and bridges you smoothly across.
The United States pays significantly less in unemployment benefits than many European countries do. [...]
There's more at the link.
Wednesday, June 24, 2026
We need new economic indicators
We have the stock market figures everyday. Maybe also we could have figures on the number of homeless people too.
— David Graeber Institute (@Graeber_social) June 24, 2026
And the media can report daily on those homeless figures. pic.twitter.com/Wgc2w9Bglb
AI is in a regime of theory-free R&D
New @lawhsw piece on what automating ML R&D gets you. As usual, worth your time. https://t.co/jr9ny6EcPf pic.twitter.com/qGTMKVSQ40
— Séb Krier (@sebkrier) June 24, 2026
Eliezer Yudkowsky, LessWrong, OpenAI & countercultures past & present
Claude 4.6 Medium summarizes a dialog I had with Gemini that started with an inquiry about
1) Eliezer Yudkowsky, his early ideas, & his early following,
2) then to his interactions with Peter Thiel, Elon Musk, and Sam Altman that got OpenAI started,
3) to my own sojourn on LessWrong and
4) concluded with the connection between 1960s counterculture and contemporary Silicon Valley computer culture.
For some reason Claude pretended to summarize the conversation in my voice.
* * * * *
A Conversation with Gemini About Eliezer Yudkowsky Bill Benzon, new-savanna.blogspot.com, June 24, 2026
I recently had an extended exchange with Gemini (accessed through the Google search interface) about Eliezer Yudkowsky — a figure I've been thinking about in the context of AI culture more broadly. What follows is a summary of where the conversation went.
It began with a query about Yudkowsky's 2007 paper Levels of Organization in General Intelligence (LOGI), which argues that recursive self-improvement could allow an AI to rapidly cycle through levels of cognitive architecture in ways that would break traditional training and testing boundaries. Gemini gave a competent account of the paper's significance for AI alignment theory.
I then offered my own assessment: reading LOGI years ago, I concluded it was the kind of work produced by a brilliant college sophomore who had figured out everything and decided to write it up. The sort of student you'd want to guide and nurture — but of course, that never happened with Yudkowsky, who is entirely self-educated. Gemini agreed this was a common reaction, and traced the characteristic features of his writing — grand scope, idiosyncratic jargon, overconfidence — to the absence of the standard academic filters that would normally shape a thinker. Without a thesis advisor to push back, he co-founded his own institutions (MIRI, LessWrong), creating an insulated subculture where he became the mentor rather than the student.
I offered a specific passage from LOGI as an example of what goes wrong. Yudkowsky dismisses semantic networks as "completely bankrupt" on the grounds that they're simple enough to write on paper. Gemini correctly identified this as a classic category error: confusing the notation with the mechanism. The diagram on the whiteboard is inert; what matters is the graph-traversal algorithms, the spreading activation, the interpreter running the data structure. Ironically, Yudkowsky later wrote extensively about the Map-Territory Fallacy — but as I put it to Gemini, he is constantly mistaking a map for the territory. His entire worldview treats clean theoretical proofs as if they dictate messy engineering realities.
From there the conversation turned to how Yudkowsky managed to build such a large following despite these intellectual weaknesses. Gemini confirmed that Harry Potter and the Methods of Rationality, his 660,000-word fanfiction, was openly designed as a recruiting tool — drawing technically minded young people into the Rationalist and AI safety ecosystems. Countless engineers and founders who later populated early AI labs first encountered his ideas through that story.
The crowning irony: Yudkowsky's warnings about AI helped convince Elon Musk, Peter Thiel, and Sam Altman that humanity needed a counterweight to closed corporate AI efforts — which led directly to the founding of OpenAI in 2015. Once OpenAI pivoted to the empirical, data-driven methodology of large language models, they completely bypassed the deductive logic "maps" Yudkowsky had spent decades drawing. Sam Altman acknowledged Yudkowsky's role in a 2023 tweet, noting that he had arguably done more to accelerate AGI than anyone else, and adding that he might someday deserve a Nobel Peace Prize.
I told Gemini that wouldn't be necessary. I also shared my own experience: I joined LessWrong around the time ChatGPT launched, initially as an anthropological participant-observer, but stayed for the conversation, which I found genuinely useful. There are very smart people there. But the insularity was unmistakable — and I described one telling episode: someone on the forum was trying to spread Rationalism in Japan and struggling. I pointed out that Japanese popular culture, from Osamu Tezuka's Astro Boy through the Ghost in the Shell franchise, has a long history of viewing robots and AI as fundamentally benevolent — an expression of Shinto techno-animism, in which kami can reside in machines as naturally as in rivers. They simply didn't know about this cultural background. Gemini observed that the Western doomer ethos is rooted in a Frankenstein complex with Judeo-Christian substrata: creating life is hubris, and the creation must turn on its master. The Japanese paradigm operates from entirely different premises. The LessWrong response to my observation? They noted it and moved on.
After a while I tired of the place. One small anecdote captures the texture of the experience: I frequently link out to other things I've written, and one LessWrong post linked to an essay-review I'd done of Benny Shannon's book on ayahuasca. I noticed a significant spike of traffic to my Academia page coming from LessWrong and pointing to that essay — which tells you something about the undercurrent of interest in altered states of consciousness running alongside the dry decision theory. They approach psychedelics with an engineer's curiosity: the brain as a computer, phenomenology as data.
The conversation ended with what I think is the most useful historical frame. LessWrong is, in structure and function, a counterculture — but centered on computers and AI, with Yudkowsky as guru rather than Timothy Leary. And there's a genuine genealogical link through San Francisco and transhumanism: Stewart Brand bridging the Merry Pranksters to personal computing, the Extropians of the 1990s who wanted to transcend the body via nanotechnology and cryonics rather than LSD, and then Yudkowsky emerging from that same Bay Area Transhumanist mailing-list culture. Fred Turner's From Counterculture to Cyberculture maps this lineage. The counterculture became the vanguard — but corporate reality, as I noted to Gemini, has not submitted. Bill Gates and his successors were never absorbed by the counterculture. Peter Thiel, who was an early funder of MIRI, has since publicly labeled Yudkowsky a Luddite and positioned AI safety concerns as obstacles to American technological dominance. The "well-run alternative universe" of LessWrong lost all leverage once scaling deep learning required billions of dollars in silicon, electricity, and data centers. The colorful intellectual vanguard warmed society up to the idea of AGI; then the massive engine of global capitalism took the steering wheel.
The subculture keeps its cozy, insular forum to debate the semantics of the map. The corporate empires plow ahead across the territory.
Near-optimal AI through symbolic learning
You don't need to wait until 2040. You can solve 2-6 OOM today. You already called out the specific error modes, it's undergrad algebra required to fix the errors, and some conceptual systems building. I have been using a prototype for the last 2 months. It works.
— Ryan McCormick (@RyanMcC35236715) June 23, 2026
Tuesday, June 23, 2026
A Face in the Crowd [Media Notes 185]
Bret Primack, aka Jazz Video Guy, has a post about A Face in the Crowd (1957), chilling movie starring Andy Griffith. Here's how it opens:
There’s a drunk in an Arkansas jail who’s about to become the most powerful man in America.
He can’t help himself. He’s funny, loose, dangerous, and magnetic in ways that make you lean toward him even when something in the back of your brain is sending signals you’re choosing to ignore. His name is Larry “Lonesome” Rhodes, and Elia Kazan put him on screen in 1957, which means the film has now spent nearly seven decades being more relevant than it was the year before.
If you haven’t seen it, stop reading, scroll down watch it first. If you have seen it, you already know what I’m about to say.
Kazan made A Face in the Crowd with screenwriter Budd Schulberg, fresh off On the Waterfront. Both men were carrying complicated personal freight — they’d each named names before the House Un-American Activities Committee, Schulberg in 1951, Kazan in 1952. Both were condemned by significant portions of the Hollywood left, and that condemnation followed them. [...] What they actually made was something darker and more personal than a political warning — a film built by two men who understood betrayal from the inside. [...]
Lonesome Rhodes is not just a villain. He’s a product. His raw, folksy charm makes him an instant hit on local radio, and the machine takes it from there — television, sponsors, politicians, handlers. As his power grows, so does his contempt for the audience that made him. He holds his followers in private disdain while publicly celebrating them as the heartland soul of America. The film understands something that most political films miss entirely: authenticity itself can be manufactured, and the more real someone seems, the more carefully that realness has been constructed.
When it was released in 1957, critics called it unrealistic. Too paranoid. Too on the nose. That’s worth sitting with for a moment — the scenario that seemed like exaggeration then is now just Tuesday.
Andy Griffith’s performance is the reason the film works at the visceral level. He had almost no film experience before Kazan cast him, and that rawness is inseparable from what he does on screen. There’s no technique getting in the way. [...]
Kazan reportedly called it one of the finest performances he ever directed. Given that Kazan worked with Brando at his peak, that’s not a throwaway remark.
Contemporary resonance:
The comparison to Donald Trump gets made every time this film is discussed, and it holds up to a point. The billionaire performing as populist, the television personality who understands the medium as a tool of dominance rather than information, the contempt that surfaces in unguarded moments, the persona of success constructed over a private reality of failures. Rhodes and Trump share the same operating mechanism.
But the comparison reveals where the film’s imagination ran out. Rhodes is ultimately destroyed. The hot mic moment — caught mocking the very people who worship him — ends him. Schulberg and Kazan still believed in a rational audience that could be shocked back to its senses. That faith looks genuinely touching now, the faith of men who lived before the complete dissolution of shared reality.
Trump’s supporters processed every exposure, every revelation, every unguarded moment, and stayed loyal. The mask slipped repeatedly and it didn’t matter, because the audience had decided the mask was the point. What Kazan and Schulberg couldn’t anticipate was social media removing the last gatekeepers, so that the Lonesome Rhodes dynamic now operates at a scale and speed that makes 1957 network television look like a church bulletin.
The film identified a structural vulnerability in media democracy — that charisma plus television produces a kind of power that bypasses argument and rational persuasion entirely. What it couldn’t imagine was an audience that already knew, and didn’t care.
There's much more at the link. The full article is worth reading. And if you've not seen the film, do so.
Inside ChatGPT, keeping the lights on while bailing out the hold
Back in 2018 Lenny Bogdonoff was in the first cohort of Emergent Ventures recipients, it was for a project after my own heart, using machine learning to create a genealogy of street art. He’s just published an interesting document, Thoughts before my next ten years. He was working at OpenAI when ChatGPT launched in 2022. Here’s what he says about that:
The most influential effort I touched was WebGPT. Its “chat” interface, which guided the model through an instruction-following paradigm, would later become the basis of ChatGPT, though at the time most of us didn’t register its significance against the alternatives: the code-completion interface, the Jupyter-like code blocks, and the other modality surfaces. It also shaped a unifying data structure the rest of us converged on, which mattered for training a single model with many capabilities rather than many small ones.
The WebGPT research effort had been in progress for over a year and a half, so most didn’t realize the significance of the interface, given the alternatives: the code-completion interface, the Jupyter-like code blocks interface, and the other modality surfaces.
When ChatGPT launched that November in 2022, the rest of the company needed to adjust. Consumer usage was beyond any expectations, and the burden on the entire research organization was material as GPU capacity got reallocated. Everyone assumed the initial surge would settle. Instead it compounded week over week, and the whole organization bent around the GPU constraint that couldn’t be planned for at that scale.
I recognized that the ChatGPT user base at the time was far greater than any contractor force we could manage. If we could properly incentivize that user base to help with data collection, we could produce a much higher-quality “flywheel” for improving the models. In reality, there are numerous challenges to producing a clean data flywheel from end-users, but this gave me conviction that it was an important thread worth exploring. Since keeping ChatGPT online was an all-hands-on-deck effort across infrastructure, research, product, and customer support, my focus on finding the right way to gather meaningful data from users felt even more important. Through this, I formally joined the ChatGPT team and began contributing to the codebase and product roadmap.
As soon as 2023 began and the holiday code freeze concluded, my priorities shifted from data collection to executing on whatever needed to be done to make sure ChatGPT would be usable. Each day ChatGPT would suffer hours of downtime as a wave of traffic followed the busy working hours around the world. Traffic peaked when Asia, Europe, and the US East and West Coasts were all online simultaneously, and the hours leading up to and following these surges were committed to doing anything possible to reduce the pain. Databases were migrated, telemetry was improved, caching and traffic rules were established, and heroic efforts were made by a surprisingly small number of people to make the next day’s surge less painful.
My first major product contributions were around ChatGPT launching a paid subscription. While the previous consumer-facing OpenAI paid product had required weeks of planning and development, the goal this time was to ship a paid product with zero downtime in single-digit days. This was an effort I eagerly jumped into. We started in February and launched in March with ChatGPT Plus, publicly reaching $100M in ARR within days and continuing to grow far faster than anyone could have anticipated. By April, GPT-4 launched, speeding up demand and challenges even more.
The subsequent year is a blur. ChatGPT had unquestionable product market fit, constrained by a single variable: GPUs. Database IDs started wrapping, nearly every early infrastructure decision eventually broke and needed attention, and systems needed refactors. Even with careful planning, we were constantly making changes to improve stability and security. Surprisingly, for a product growing this fast, the biggest unexpected drains were the abuse and misuse we hadn’t designed for.
The ChatGPT team, which began as fewer than 10 people, grew to over 200 dedicated contributors, not to mention the numerous behind-the-scenes infrastructure engineers and adjacent researchers. The company I’d joined at 250 employees a year before was on track to hit 2,000. It was an insane period of continually finding the most important bottleneck, finding any means to relieve it, and moving on to the next.
He left OpenAI in 2024 and joined a venture capital firm. He’s left that and is now thinking about his next step.
When I think about the role of AI in the economy, I keep coming back to an idea borrowed from economics. Economists use “velocity of money” to describe how quickly a dollar moves through an economy and turns over into new value. I’ve started thinking in terms of a “velocity of intelligence,” or how quickly the distance between knowing something and acting on it collapses. AI compresses that distance, and as it does, the velocity of intelligence rises.
At OpenAI, I saw the friction collapse in real time as hundreds of millions of people discovered AI’s utility in the post-ChatGPT wave, and the physics of software businesses shifted. Then, from the startup and venture side, I saw both halves of the unevenness. AI and infrastructure companies were compounding at a rate that was previously impossible, while a far larger set of existing enterprises and industries, where that same acceleration would matter even more, wouldn’t see it arrive for years, held back by organizational constraints rather than any limit of the technology. The places where intelligence is cheap and fast today aren’t the places where the gains would matter most.
That gap is where I want to spend the next decade: getting AI adopted where the velocity of intelligence would be genuinely consequential but won’t happen without a push. I’m still working out the specifics, but having seen the acceleration from inside the labs and where it stalls from the investor’s seat, I think I’m positioned to push on this in a way few others could. For now, I’m getting back to building.
Monday, June 22, 2026
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