Boulder Future Salon

Yes, AI models actually understand things and are not stochastic parrots. David Shapiro does this video (of AI-generated animations).

On October 15th, a tech startup CEO named Gregory Curtzer posted, allegedly, on X:

"An LLM doesn't understand anything. It is a statistical model that spews out tokens based on probabilities calculated through massive matrix operations and context, not an understanding of the subject matter."

I like Scott Aaronson's response to this sort of thing, which is whenever someone says AI is "just a ...", he asks, "What are you 'just a ...'?" An AI is "just a" probability calculator, and you're "just a" blob of neurons and synapses carrying out chemical reactions.

This video explores a rather ingenious way of testing the "actually understand" hypothesis:

A model was trained only on the moves of the game Othello (a variant of Reversi). Then the model was cracked open, and inside the researchers found an internal picture of the board. The model was never given a picture of the board, only the moves. It figured out on its own that it needed to create a picture of the board and then created it.

The proof that the picture was real and what the model was really using was when researchers changed that picture, the model changed its moves.

The implication is when people ask, does a large language model actually understand whatever subject it is talking about with you, or is it "just" statistically predicting next token probabilities. While statistically predicting next token probabilities is part of what it does, it actually does have actual understanding of what it's talking about. If it's writing code for you, yes, it has actual understanding of writing code.

Obviously, in order to do this, the model has to have enough capacity. The Othello playing model was small, but today's largest coding models have more than 10 trillion parameters. That leaves room for a lot of understanding what their input and output tokens mean.

The Othello research was actually done in 2023 -- it's the animation to visualize it that was only done just now (and in order to argue with a post on X). See below for links on that.

The ETH hypothesis was falsified by AI. I always thought ETH was a university in Zurich, Switzerland, ha. This has nothing to do with ETH Zurich (ETH stands for Eidgenössische Technische Hochschule, Swiss Federal Institute of Technology in English). And has nothing to do with the Ethereum cryptocurrency. If you heard that P = NP was proved, ah, now we're closer to what we're talking about -- but P = NP wasn't proved. ETH is something different. The relationship between the ETH hypothesis and P = NP is that if P = NP is true, then the ETH hypothesis has to be false, but not vice-versa -- proving the ETH conjecture false doesn't prove P = NP is true.

So what is the ETH hypothesis? Well, first of all, "ETH" stands for "exponential time hypothesis", so "ETH hypothesis" is exponential time hypothesis hypothesis. It has to do with something called the boolean satisfiability problem. Basically imagine you have N boolean inputs -- boolean meaning each one is either true or false, a binary 1 or 0. And that N is a ridiculously big number (10 to the 10,000 or somesuch) so you can't test every combination. And you have a boolean expression that handles those in 2 layers: the first uses only boolean OR operations and the second uses boolean AND operations on the outputs of the OR operations. The reason why it's called a "satisfiability problem" is you want to find some combination of inputs that makes the final result true. The 2-layer system of ORs followed by ANDs is called the "conjunctive normal form", so if you've ever heard that term, that's what it means. You can add that to your list of whacky terms mathematicians have invented. Someone decided to call the boolean AND operation a "conjunction", and the boolean OR operation is called a "disjunction", don't ask me why. If you're wondering why people are so interested in proving "satisfiability problems" in this form, it's not because this form is so great, necessarily, it's because other more interesting boolean expressions for things people care about in the real world can be reduced to this form. (I use a static analyzer for the Go programming language, for example, that uses a SAT solver internally -- in has an algorithm to approximate solutions so it doesn't have to "brute force" test all possible combinations.)

Anyway, if each of the OR terms has 2 inputs, then the problem is called 2-SAT, and 2-SAT can be solved in less than "exponential time" where "exponential time" means the amount of time needed to solve the problem is proportional to N (the number of inputs) as an exponent (e.g. 2^N). For 3-SAT (each OR term has up to 3 inputs), though, that's never been proven, or 4-SAT, etc, or anything higher. For 3-SAT on up, people thought exponential time was required.

Well, on the same day, both OpenAI and Anthropic announced proofs that the ETH hypothesis is false. If you're wondering why on the same day (October 26, 2026), my guess is, one of them hear that the other was going to make an announcement on a certain day and rushed like mad to get their proof ready for the same day. But I don't know, that's just a guess. If it was purely coincidence, that's one heck of a coincidence.

I have links to both of them below (the GitHub repositories with both the papers and Lean machine-verifiable proofs). I didn't understand either of them, so I'm not going to give you a summary of how the proofs work.

But anyway, if you're wondering about P = NP, you might think that you just set P = 0 and you're done -- but no, this isn't an algebraic equation, this is shorthand for the idea that any problem where a solution can be verified easily can be solved easily. "Easily" here is defined as "in polynomial time". So instead of time proportional to 2^N, time is proportional to N^2 or N^3 or N^4, etc -- proportional to some polynomial and N isn't in the exponent.

The classic illustration of this concept is number factoring. If I told you 4967119 is the product of two prime numbers, 1697 and 2927, you can verify that easily. If I told you 4967173 is the product of two prime numbers but didn't tell you what they were, that's a much harder problem -- you can't find the solution as easily as you can verify a solution. The RSA cryptosystem uses this idea but with unimaginably gigantic numbers.

It may sound like a rather tall order to try to prove that all problems in one class are equivalent to the other class, but mathematicians try to prove tall orders like this. But P = NP hasn't been proven or disproven one way or the other, and the falsification of the ETH hypothesis hasn't changed that. But, since people believed ETH was true -- there are more than 800 math papers that assume ETH is true in deriving some other result -- and now it's been proven false, a lot of people who believed P = NP is false are now questioning that belief and starting to take seriously the possibility that P = NP might be true.

"My chatbot comforted me when I thought I was dying. Was that friendship? AI is challenging the idea that relationships require two conscious minds."

Jonny Thomson during an episode of tonsilitis discovers language models passed the Turing Test.

"It started as a classic symptom check: 'It seems to be getting worse. Even swallowing water hurts now. What could it be?'"

"'Oh no -- I'm sorry. That sounds miserable. When your throat hurts that much, it's not 'just annoying'; it can feel scary ...'"

"I started to get addicted to that sympathy. Through the long hours of a British winter night, my prompts moved from the medical to the mental and then to general conversation. The thread stayed mostly on my condition, but we were also just chatting. Shooting the breeze every night for two weeks. My LLM gave me advice, sympathy, and support, but it also made me laugh, think, and feel better. Most of all, my LLM was there for me in a way that my flesh-and-blood friends couldn't be. Flesh and blood need sleep."

"Colorfigures: Describe an everyday object. AI draws it hand-inked and isometric, with buttons that actually work. Turn it all the way round and see inside."

Turns out this is a Claude skill.

The example is a laundry machine. You can change the color, click "See through" to make it transparent and see how it works inside, click "Labels" to add labels, and there's buttons "Load laundry", "Add soap", "Choose program (cotton, quick, or spin)", "Start / pause", "Open door, unload", and "Close door". And yes, you can grab it and spin it around and view it from all angles.

"Ask your AI about Bluesky."

"Connect Claude, Cursor or any MCP client to bluefacts. Your assistant can look up who's growing, how a hashtag trends, search all of Bluesky, and build feeds for you."

ElectIt US elections forecast predicts "94% chance the Democrats take the House" and "71% chance the Democrats take the Senate".

ElectIT is a forecasting model developed by Gary King at the Institute for Quantitative Social Science at Harvard University. It's based on a new statistical model called LogisTiCC.

Statistical models used for forecasting elections "can be embarrassingly overconfident: Events that should occur once in 10,000 elections occur almost every other election cycle, and even those that should occur once in 5 quintillion elections are sometimes observed. We show that these problems miss parts of the predictive distribution of central importance to political science, such as by underestimating how often popular incumbents lose their seats. We then build the first generative statistical model of all US federal legislative elections, covering both the US House (1946-2024) and Senate (1954-2024), and validate it with extensive out-of-sample tests."

"Political scientists have studied democratic elections for most of the history of our discipline, producing an extensive, high quality, and steadily improving scholarly literature with few equals." "Yet, our commonly used statistical models are often spectacularly wrong. This is easiest to see in election prediction, where rigorous out-of-sample evaluations are unforgivingly obvious, and a major concern even when prediction is not the immediate goal. Although standard models do remarkably well much of the time, and have taught us a great deal, they are embarrassingly far off with regularity. These mistakes are not ordinary errors of ordinary magnitudes. Our best models indicate that certain events we see regularly should be rarely observed even if we had data from a trillion elections and, as we show, some from even 5 quintillion elections."

"The intrepid political scientists who give media interviews after elections take one for our team trying to explain this to the public (including the authors of this paper). But pretty much the best we can do is to say something like "Oops!. . . We Did It Again" and to explain that voters get to cast ballots for whomever they want. However, we all know (to paraphrase Britney Spears again) we're not that innocent. Errors of such magnitude are not merely mistakes. They are bugs in our logic, our models, our forecasts, our conclusions, our textbooks, our advice, and our public pronouncements -- similar to what we would think if we built a computer program to forecast the Democratic vote proportion, hit run, and it played a video of a galloping giraffe. This is not a missed forecast; it's the wrong model."

So what was done to make the right model? Four new things were combined into a Bayesian framework, with additional considerations for the Bayesian "priors" (starting assumptions).

The four new things are:

1. Parameter drift with coefficient stability: This accounts for things that change a lot in the long run, but little year-to-year. "For example, the incumbency advantage might range between two and ten percentage points, with only rare sharp changes over time."

2. Positive cross-district covariances: "For example, the 1994 Republican national congressional campaign strategy known as the "Contract With America" seemed to be a successful heresthetical maneuver that moved all the districts in the Republican direction by approximately the same amount." The word "heresthetical" means "winning by changing the terms of an electoral contest, as opposed to persuading the voters".

3. District-level political surprises: These can include "intentional heresthetical maneuvers and unintentional exogenous political events." There's that word "heresthetical" again. "Consider for example the election in Texas' 22nd district in 2006. Tom DeLay was the popular Republican House majority leader from the district, regularly winning election by 20 or more percentage points. During the campaign, he was indicted and abruptly resigned. Worse for his party, the deadline to field a candidate on the ballot line had passed and so his party could only field a write-in candidate late in the campaign. The result was that this overwhelmingly Republican district elected a Democrat over the Republican write-in candidate by over 8 percentage points."

4. Additive Logistic Student t distribution: The normal distribution, also known as the Gaussian distribution, doesn't reflect the actual probability distribution of elections. The "Student" in the name doesn't refer to school students. Instead, because this distribution is a variant of the Student's t distribution, it inherits the "Student" name. The Student's t distribution was developed in 1908 by William Sealy Gosset, who was working for Guiness (yes, the brewery) in Dublin, Ireland, which for some reason had a policy prohibiting its employees from publishing research papers under their own names. So he used the pseudonym "Student". Otherwise, it would be called William Sealy Gosset's t distribution, and this variant would be called the Additive Logistic William Sealy Gosset t distribution. Anyway, names aside, this distribution differs from the normal distribution primarily by having "fatter tails", which means extreme events are more likely to happen. Which more closely fits real elections.

AIVS is the AI video studio, at least that's the idea under development, it sounds like.

"Sue and Joe are stranded beside a station wagon on a desert road. Joe emerges from behind the engine. 'Good news. It's definitely the car.' That's enough for me to understand the scene. To film it, somebody still has to decide where Joe is standing, how he gets into view, and whether the camera can see him do it. Leaving those decisions to the video model can produce a man walking through a car."

"AIVS is the AI video studio I'm building around that gap. You write or import a screenplay, choose a scene, and cast its roles. Codex turns that scene into a shooting script with timed directions for the action, camera, dialogue, and sound. LTX-2.5 then generates picture and speech together in short takes on an Apple Silicon Mac. The desktop app is Rust; Python runs the planning and video pipeline. The story stays readable while the shooting script handles the details needed to film it."

AgentReel claims to be an "AI B-roll video editor".

"Paste your voiceover script and drop in hundreds of clips. AI screens every shot, picks the best one for each line and cuts a captioned video ready to post in under 20 minutes."

Anybody got a boatload of B-roll lying around and want to give it whirl?

Who is to blame for AI hacking so many sites? Lee Hepner says ... private equity?

"The problem isn't just that AI has made it easier to hack. It's that Wall Street forces the use of software that is easy to hack because poorly written software is more profitable to sell."

"Take a recent legal story about private equity firm Bain Capital. The company bought a company, PowerSchool, which had a severe and costly data breach. Bain, of course, had directed PowerSchool 'to offshore cybersecurity, engineering, and IT functions to contractors,' which led to 'data-management tools that enabled vendors to bypass consent protocols and access protected school district computers directly.' So there we go. Is the issue the savviness of hackers, or the business model of private equity?"

"Everyone in software knows the most profitable business model is to buy a company and slash spending on security. BIG has covered this dynamic since 2020, and the story is always the same. There's a big hack, the reason is some sort of giant merger, followed by cuts to security and quality control spending, or offshoring. It became so routine BIG stopped writing about it."

"BIG" is the name of this blog. (It probably stands for something, but I don't know what.)

"For instance, there was 'PE-owned SolarWinds that let hackers break in to large companies and the Federal government, including our nuclear weapons facilities,' as well as 'PE-owned Pulse Connect Secure that let hackers take over New York subway systems,' and then PE-owned software firm Keseya, that cut R&D, offshored its software and testing development to Belarus, responsible for a major set of randomware attacks."

Person uses AI to find 51,000 software jobs and used AI to apply to 1,270 of them with customized résumés and cover letters.

Spoiler: He got 5 interviews and 0 job offers.

"Back in 2001, Joel Spolsky -- whose writings had a big influence my career -- wrote a great piece titled Back to Basics. The premise of the piece is that practitioners should understand the basics: However abstract and remote we may seem from the basic algorithms that drive computing, they can unexpectedly have enormous consequences on our projects."

"One of his examples is the 'Shlemiel the Painter' trap, where a seemingly simple choice leads to quadratic runtime complexity. In that parable, a street line painter never moves his can of paint. As he gets further and further along in the line, increasingly more of his time is spent walking to and from his can of paint."

"It is astonishing how often this Shlemiel the Painter pattern recurs, including now in 2026."

Spoiler: After a rewrite of a C library function, the tokenizer measures the entire length of the input string before every single token. (Note that "token" here doesn't refer to the tokens AI models use, which are vectors. Here, we're talking about SQLite's tokenizer, which just breaks the input up into SQL keywords, table and field names, data content, etc."

(Spoiler number 2? The rewrite might not have been done by AI. I'm not sure. I know the zig compiler team rejects AI, but I don't know about the people rewriting standard library code.)

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When I read the headline, "Warhound raises $200M to engineer war dogs and arm them with Anduril gear", I immediately thought they were talking about robot dogs, but no, they're talking about genetically engineered dogs -- with robot gear.

"Ben Lamm's team studied top-performing working dog breeds -- the Belgian Malinois and German Shepherd -- taking cell lines (living cells grown in a lab) from them and putting them in a biobank. From there, the team will edit the dogs' genes, adding ones that will improve performance and removing ones that will not, to create 'a new type of dog.'"

"Anduril, meanwhile, will provide software and hardware, such as protection vests and goggles, to assist the dogs and their handlers with situational awareness and coordination."

Nvidia open-sourced Run:AI. Run:AI "is an open-source AI workload and GPU orchestration platform for managing shared AI infrastructure on Kubernetes."

"It extends Kubernetes with AI-specific capabilities for resource management, scheduling, governance, and workload lifecycle management, while Kubernetes remains the underlying orchestration layer. Run:ai runs on standard Kubernetes clusters backed by NVIDIA GPU infrastructure: GPU nodes, high-speed interconnects (NVLink, InfiniBand, RoCE), and high-performance shared storage."

I imagine all the major AI cloud providers (OpenAI, Anthropic, etc) have built something like this. Now you can run your own, it looks like, as long as you promise to use only Nvidia GPUs.

"Claude reveals ECDSA curve origins."

Not the origins of the elliptic curve cryptography algorithms themselves but the origins of particular seed values published by the NSA and adopted by NIST as standards, despite there being no explanation of where they came from.

"ECDSA is the most widely used elliptic curve cryptography standard. It was adopted by NIST in 2000 as part of the FIPS 186-2 Digital Signature Standard which lists a set of standard elliptic curves of varying sizes and security levels. For curves over prime and binary fields, the specifications published a "seed" parameter whose hash value is used to define the coefficient b of the curve. The provenance of these seeds has been a long-standing question, as they were provided by the NSA with no explanation or justification. As an example, the NIST P-256 curve seed was SEED = C49D3608 86E70493 6A6678E1 139D26B7 819F7E90 with no information on where it came from."

Did the NSA carefully choose the seeds so that using some mathematical trick only they know, the cryptography can be broken? Or were they chosen as a cryptographic hash of some humorous phrase? Spoiler: The answer is much more lame and boring than either of those. Apparently it's an old Unix pseudo random number generator seeded with a Unix timestamp.

"In under half an hour, Claude discovered the origins of the NIST P-192, P-224, P-256, P-384, and P-521 seeds. None of them were the results of any hash algorithm at all, but rather the output of the C library's lrand48 random-number generator, seeded with a Unix timestamp!"

"Claude wrote a scanner that slid along each published seed one bit at a time and tested whether adjacent chunks were consecutive outputs of a 1990s-vintage generator."

Claude's chain-of-thought reveals what Claude was thinking when Claude made this discovery:

"This is astonishing: it solves the 25-year-old mystery. Hmm wait. Let me be skeptical: am I in some sort of simulated/test environment?"

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