Wednesday, 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
NYTimes Opinion: Don't Stress about AI; Don't Bring Back the Tests; A New Generation of Creatives
Three from The New York Times, June 22, 2026.
Robert J. Shiller, We Have to Stop Freaking Out About A.I.
Like many others, I believe A.I. could lower employment. But unlike most, I don’t necessarily blame the technology itself. Instead, I worry about the potency of the fear it is generating.
Our brains are wired to respond to stories. Narratives floating in a population can affect individuals’ economic decisions about whether to buy a big house, or whether to send their kids to an expensive private school or even whether to have kids at all. When millions of people make millions and millions of decisions based upon negative expectations, there is a risk that fear can actually help birth the reality.
The idea that something like artificial intelligence will replace many human jobs goes back thousands of years. Aristotle envisioned a powered loom and a self-playing lyre someday replacing human servants. In the 19th century, groups of textile workers (the Luddites) destroyed the new machines they believed were replacing them. In the 1920s, the play “R.U.R.” — the letters stand for “Rossum’s Universal Robots” — depicted a war of the robots against humans. [...]
...the British mathematician I.J. Good wrote an essay that imagined a new technology that could continue improving itself until its abilities would surpass those of humans. The idea, which came to be known as the “singularity,” would quietly circulate until 2005. That’s when the futurist Ray Kurzweil wrote “The Singularity Is Near,” a book arguing that human-level A.I. would arrive by 2029. Either we would merge with machines and transcend our biological limits, or the machine would grow so powerful it could end all of humanity.
The theory captured the imaginations of tech titans, and even the top A.I. researchers and executives, who warned of a range of alarming scenarios, from job losses to widening inequality or even the eradication of humanity itself. While the job market has slowed for a host of reasons, there are reports that fear of an A.I. apocalypse is worsening the freeze and contributing to record lows in consumer sentiment.
There’s only so much Washington can do about these narratives. And, suffice to say, Donald Trump is no Franklin Roosevelt.
As such, perhaps the best we can do is to appeal directly to the leaders of Silicon Valley who have been promoting these negative narratives with such vigor. Surely the resulting media attention highlighting how dangerously powerful your A.I. model is may help you sell more wares, but it may be far harder to do so in a period of recession. Try not to forget the critical lessons taught by our past.
Ross Wiener, I Thought ‘No Child Left Behind’ Would Fix Public Schools. I Was Wrong.
The new data is emboldening calls to restore something like the No Child Left Behind Act, the stringent, test-based accountability policy that defined American education from 2002 to 2015 and imposed penalties on schools whose students did not meet proficiency requirements on state standardized tests. The Atlantic captured that impulse in a 2025 podcast episode titled “Bring Back High-Stakes School Testing.” In it, Margaret Spellings, a secretary of education under President George W. Bush and now president of the Bipartisan Policy Center, argues we need to restore “the muscle of accountability, the muscle of assessment.” Rahm Emanuel, exploring a 2028 presidential run, said in April that Democrats have abandoned standards and accountability and must return to them.
It was a mistake in the past to treat test scores as the purpose of public schools rather than as partial proxies for what a good education actually delivers. Reading and math are profoundly important and improving instruction must be part of any serious agenda. But test-based accountability policies were not sufficient decades ago. They are even less adequate now. [...]
Over time, I became convinced that, with the best of intentions, I and many others in the education reform community had transferred our moral commitment to children over to the standardized tests. We had done this earnestly, not cynically, but we still did damage.
In 2023, 40 percent of high school students reported persistent feelings of sadness or hopelessness. One in five had seriously considered suicide; nearly one in 10 had attempted it. Research from the SNF Agora Institute at Johns Hopkins found that 40 percent of Gen Z believes political violence can be justified, compared with 11 percent of baby boomers. Too many students experience school as an obligation with few opportunities for agency or meaning; recent survey data indicates that large shares of students find school boring and irrelevant and are struggling with engagement in the classroom. The academic crisis and the human crisis are not entirely separate phenomena. [...]
Taking their priorities seriously would mean broadening what we expect from the classroom. Schools should put what students can do on equal footing with what they know, embedding real skills in academic learning rather than leaving them to chance or sequencing them to later in life. Schools should reconnect with the communities they serve, so young people learn through and about the places where they live. And they should reanimate the character-forming, developmental mission a pluralistic democracy requires.
Tom Rothman, Hollywood Needs Regular Jolts of Creativity. It Just Got One.
In the last month, “Backrooms,” a horror movie directed by Kane Parsons, a YouTube creator who just turned 21, opened to an astounding $81.5 million in America. A second horror film, “Iron Lung,” made and self-distributed by Mark Fischbach, another online creator, has grossed over $50 million worldwide. Perhaps most significant of all, “Obsession,” a horror film directed by Curry Barker, a 26-year-old YouTube creator, crossed $200 million at the box office this weekend and surpassed the latest “Star Wars” film.
“Backrooms,” “Iron Lung” and “Obsession” each has its own unique origin story. But what they have in common is that they’re all fueled by an avid young audience — exactly the demographic that gloomy industry pundits have repeatedly declared will never return to movie theaters.
Wrong.
Is this YouTube-fueled youthquake simply a coincidental confluence of events? Or does it portend an upending of the Hollywood status quo? Actually, it’s a bit of both. Indeed, for any fear of YouTube barbarians at the gates, this is instead a great opportunity for traditional Hollywood. [...]
Rothman then goes on the list changes in taste following Altman's M*A*S*H in 1970 and Soderberg's Sex, Lies, and Videotape in 1989.
Needless to say, there's more at the links.This kind of moment is happening again. Mr. Barker, with a background in short films and YouTube sketch comedy, tells a story in “Obsession” that is so powerfully relatable to Gen Z audiences that the word of mouth has caused the box office to defy gravity.
When Mr. Parsons expanded his YouTube short films into the feature-length “Backrooms,” the aesthetic he’d honed as a teenager on the internet captured an underlying anxiety in his audience, with whom he’d already developed a direct relationship online. These filmmakers are very young, but what matters is not chronological age so much as an iconoclastic spirit, an instinct for what the audience is wanting but not getting and, of course, talent. [...]
“Backrooms” and “Obsession” are also the beneficiaries of expensive and savvy studio marketing campaigns. Reminiscent of how the New Hollywood directors made a lot of money for the old studios in the ’70s — think Francis Ford Coppola and “The Godfather” — and how the indie darlings of the ’90s did the same, these YouTube-born phenomena have ultimately prospered handsomely inside the system.
For perfect symmetry, note that the No. 1 film at the box office when it debuted two weekends ago, one spot ahead of “Obsession,” was “Disclosure Day,” made by Steven Spielberg, the greatest artistic and commercial director in history. “Disclosure Day” is his 37th film. His first was the ’70s New Hollywood anti-authoritarian film “The Sugarland Express,” which he made when he was not much older than Mr. Barker and Mr. Parsons.
The integration of independent creativity with industry influence is a good thing all around. It offers exposure for, and help to, new voices, giving them more visibility and opportunity, and it promotes the kind of originality that Hollywood desperately needs.
Brain area specialized for visual recognition of words
No child is born able to read. The brain ships with no reading region at all. It builds one, and the construction runs on the exact effort AI removes.
— Aakash Gupta (@aakashgupta) June 21, 2026
Learning to read physically repurposes a patch of visual cortex. A spot in the left fusiform gyrus starts out tuned to objects… https://t.co/3sOTaLSR2Z
Sunday, June 21, 2026
Killer Joe, arranged by Quincy Jones
On the YouTube Page:
Here is the quintessentially cool arrangement of “Killer Joe” by Quincy Jones. Enjoy my Remix/Remaster of this 1969 jazz classic.
Background information provided by Bruce Scott:
"Killer Joe" was written by the legendary tenor sax, composer and arranger, Benny Golson. The latter and Quincy Jones are very close friends. This classic tune is from the LP entitled "Walking In Space" (A&M/CTI 3023), which was released in stereo on November 19th, 1969. "Killer Joe" was recorded on June 18th, 1969 at the Van Gelder Studios, in Englewood Cliffs, New Jersey. Recording Engineer: Rudy Van Gelder. Produced by Creed Taylor.
“Walking in Space” is a 1969 studio album by Quincy Jones The album was recorded for A&M and features an iconic photo of Quincy by Pete Turner who made some of the most famous A&M album covers. "Killer Joe" features Ray Brown on bass and Grady Tate on drums, an early example of a strong jazz rhythm section in pop music.
I had the half-speed mastered audiophile pressing of this album. I never cared for its lopsided stereo mix, where most everything was slammed into the right channel, save the reeds and muted trumpets, which were the only thing in the left channel.
In my mix, I kept the drums in the right channel, but the swapped channels of the instruments, putting the horns, woodwinds and guitar on the left channel with the reeds and muted trumpets on the right with the drums, so it’s a much more balanced mix. I panned the female vocalists in the right channel. [It was very boring having them centered]. The bass was originally slightly right justified, so I panned it dead-center for better balance and reduced it by 10%.
It appears to have been recorded on older 1st generation transistorized solid-state equipment due to the very sterile and distorted audio quality. The original bi-polar stereo mix was most likely done on the old 4-track mastering system as well. Having been compressed for LP mastering compromised the dynamics quite a bit. I cleaned it up and expanded the dynamic range to brighten it up and bring it into the new millennium.
Personnel on this recording:
• Quincy Jones - conductor, arranger
• Freddie Hubbard, Lloyd Michaels, Dick Williams, John Frosk, Marvin Stamm, Snooky Young – trumpets
• Jimmy Cleveland, J. J. Johnson, Alan Raph, Tony Studd, Norman Pride, Kai Winding – trombones
• Joel Kaye, Roland Kirk, Hubert Laws, Jerome Richardson – reeds
• Paul Griffin - piano
• Eric Gale - electric guitar
• Ray Brown - double bass
• Grady Tate – drums
• Bob James - electric piano
• Hubert Laws - flute, tenor sax
• Toots Thielemans - guitar
• Bernard Purdie - drums
• Hilda Harris, Marilyn Jackson & Maretha Stewart - vocals
Technical Information:
I used SpectraLayers PRO-7 to isolate the Vocals, Orchestra, Drums, Bass & Piano into 5 separate tracks. Adobe Audition was used for Dynamic Range recovery, Delay, Reverb, EQ, and final mixdown. NERO 2019 Platinum used to generate the final video.
New Book Project :Language, Memory, and Mind: A Supplement to The Computer and the Brain
As you may know, I’ve been working on a book project, Play: How to Stay Human in the A.I. Revolution. For some reason I’ve been unable to finish the proposal, though I’ve got lots of stuff and a number of the chapters are substantially drafted. But I keep finding myself distracted into thinking about basics, very basic things about computing and A.I.
At the very end of his life, John von Neumann wrote a slim book, The Computer and the Brain (1958). It grapples with the problem of how computation can be implemented in a physical medium and does so in a way that is basic, both simple and straightforward and profound. We’ve learned a great deal about both the brain and the computer since then, but as far as I know, no one has revisited von Neumann’s project and extended it to include what we have since learned. That’s what I propose to do in this book.
Now, I have no intention of trying to summarize what we’ve learned on those two topics since 1958. That’s working at the wrong level. When von Neumann was writing he, and by extension, we, had no conception of distributed representation much less how it could be achieved physically. Now we do. That’s what needs to be added to von Neumann’s exposition.
I have no intention of repeating what von Neumann did. In particular, I will not revisit his material on analog computing. Rather, I want to augment his discussion. Fortunately the new material is of such a nature that I should be able to write short book that can be read as a stand-alone discussion or as a supplement to von Neumann’s book. I’m imagining a sophisticated general audience of the sort that reads 3 Quarks Daily.
My working title: Language, Memory, and Mind: A Supplement to The Computer and the Brain. I expect the book to by 100 to 120 pages long (30K to 40K words).
I have uploaded a bunch of material (100K words or more) to Claude and asked it to review that material and put together and initial outline. I’ve appended that below the asterisks.
* * * * *
Preface
How to use this book — with or without von Neumann. What it adds to his argument. What it doesn't attempt. Brief note on the collaboration with Claude that produced parts of the text.
Introduction: Von Neumann's Unfinished Argument
What he got right: the architectural mismatch between brains and computers — memory and computation separated in the digital machine, unified in the neuron. The energy efficiency puzzle he couldn't explain. His honest acknowledgment that the brain's organizational principles lay beyond the framework he'd built. The concepts he lacked that this book supplies.
Chapter 1: Two Paradigm Cases
The chess-language contrast as the entry point. Chess has a bounded, well-defined geometric footprint — 8×8 board, six piece types, explicit rules, finite tree. Language has an unbounded, poorly-defined geometric footprint — rooted in the full complexity of physical and social reality. Chess was AI's founding benchmark precisely because it seemed to demand the highest human intelligence while yielding to computational treatment. Moravec's paradox: the easy problems are hard and the hard problems are easy. Transcendent versus non-transcendent coding — programmers can observe and specify a chess engine completely from outside; nobody can specify an LLM from outside, including its creators. Where we now stand.
Chapter 2: Location and Content
A collection of photographs. Solid objects at specific locations — finding by address is natural, finding by content requires going to each photo in turn. The combinatorial explosion that follows. The formal argument: solidity localizes content; localized content can only be retrieved by address. What holography does physically — interference patterns distribute information about each stored object across the whole plate, so that any partial cue can activate the whole. Lashley's ablation experiments: memory didn't disappear when specific cortical tissue was removed because memory was never stored in specific locations in the first place. Von Neumann's energy efficiency puzzle, now answerable: the brain doesn't spend energy moving content to a processor because memory and processing are the same physical substrate.
Chapter 3: The Brain as Content-Addressed System
The McCulloch-Pitts neuron-as-logic-gate: computationally fruitful, architecturally wrong. What neurons actually are — active units and memory units simultaneously, connected in massive parallel. Distributed representations: concepts as patterns across populations of neurons, not stored at specific cell addresses. Yevick's logical necessity argument in plain terms: the world contains two categories of object, geometrically simple ones that sequential symbolic processing handles efficiently and geometrically complex ones that only holographic parallel processing handles efficiently; the world contains both; therefore any adequate cognitive system must implement both regimes. Path tracing and pattern matching as the two fundamental operations on any cognitive network. Freeman's cinematic model — global coherence frames at 10-12 Hz as the atomic unit of biological cognitive processing — and its correspondence to speech production rates.
Chapter 4: Language as a One-Dimensional Projection
The semantic network as the right model for conceptual structure: meaning as position, each node defined by its pattern of relations to other nodes. Sydney Lamb's principle. The multidimensional character of the conceptual network versus the one-dimensional character of any spoken or written string. Language strings as 1D projections of the multidimensional network — necessarily lossy, hence paraphrase and ambiguity. The colored beads thought experiment: strip away semantic content, replace each token with a color, and you have a 1D image — making visible the purely formal structure the LLM operates on. Words as abstract addresses in an abstract space. Why classical computational linguistics hit combinatorial explosion: it was trying to reconstruct the multidimensional structure in a location-addressed system.
Chapter 5: What Large Language Models Actually Are
The transformer architecture in plain terms. The weight space as distributed content-addressed memory — concepts are patterns smeared across billions of parameters, not stored at specific addresses. The forward pass as the atomic processing unit, corresponding to Freeman's global coherence frame: one complete transit through the weight space producing one output token. The token string as a path through the abstract address space, with each forward pass mediating between the 1D sequential surface and the multidimensional distributed interior. What LLMs do well — pattern matching over the weight space, which is what their architecture naturally supports. What they do poorly — sustained sequential path tracing requiring precise state maintenance, common sense grounded in embodied experience, continuous learning. Why these limitations aren't engineering failures awaiting a fix but structural consequences of implementing holographic-like processing on location-addressed hardware with training only on 1D projections.
Chapter 6: What the Analysis Implies.
The first principles of intelligence are not the first principles of computation. Why scaling won't close the gap: scaling improves the quality of the holographic approximation but doesn't change the architectural mismatch, provide embodied grounding, or enable continuous learning. The fast takeoff fantasy as physics-free reasoning — every self-improvement step requires moving billions of parameters between physically separated memory and compute on real hardware that consumes real energy. The TSMC problem: the most critical hardware infrastructure in the world runs on tacit knowledge distributed across human communities that no LLM can access or replicate. What a genuinely adequate artificial cognitive system would require, in the terms this book has developed. The research program that's needed and why it requires multi-generational public investment rather than industrial R&D on commercial timescales. The human-machine collaboration that's already underway and what it can and cannot achieve.
Conclusion: The Mismatch, Named
Von Neumann saw the gap and couldn't name what was on the other side of it. This book names it: content addressing, requiring distributed storage, implemented in biological tissue through interference-like neural dynamics, approximated in LLMs through distributed weights on location-addressed hardware, grounded in embodied experience that no text-trained system has. The naming matters because you can't close a gap you can't see clearly.
Appendix: A Chronology of Chess, Language, and AI
From the working paper, lightly edited.
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