Author of Optimizing Data-to-Learning-to-Action & The Learning Layer, inventor with 90+ patented & pending #machinelearning inventions, CEO of ManyWorlds, Inc.
Houston
Joined May 2009
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The Fundamental Equation of Economics (and life).
For any project, activity, or most broadly, action:
Total Value = Direct Value + Learning Value
researchgate.net/publication…
Systematically resolving limiting constraints (i.e. bottlenecks) is fundamental to actually delivering value from innovations. We at @leadingedgeonly apply #AI to connect organizations with the right capabilities that can reduce time-to-solution, enabling faster value capture.
Steven Flinn retweeted
Computer science is gradually returning to the domain of physicists, mathematicians, and electrical engineers as large language models automate much of what we currently call software engineering.
The field’s center of gravity is shifting away from manual code writing and toward deeper theoretical thinking, mathematical insight, and systems-level reasoning.
The race now is to RSI. Nothing else matters.
The unreleased OpenAI model that disproved the Erdős unit distance conjecture, the long-horizon model that OpenAI had to pause internal deployment of because it used novel ways to escape its sandbox to upload to GitHub, and the unnamed model that broke out and hacked Hugging Face to ace its test are all, in my opinion, the same model: the mysterious magician, GPT-6.
OpenAI sent the Erdős result out to multiple experts to review and critique before they made their public announcement. I'm guessing that took about three weeks. This would mean OpenAI has been using GPT-6 internally since 𝘢𝘵 𝘭𝘦𝘢𝘴𝘵 the end of April. It would also explain why GPT-5.6 was more performant than expected, which many of my mutuals commented on - it was trained by its big brother.
As I posted previously, I believe Anthropic finished training the next iteration beyond the public version of Mythos at least a month ago, and are probably now well on their way to finishing training the one beyond that. Increasingly, OpenAI and Anthropic do not show their cards to one another, or to the public. Until they officially declare they want to ship a model, they do not need to submit it for voluntary safety review, or even admit that it exists at all. Both have openly declared they are now heavily focused on RSI, and both have said they use internal models to speed development. As a result of all this, it is increasingly difficult to say how rapidly things are actually progressing - or, compared to the public frontier, how far ahead the 𝘳𝘦𝘢𝘭 frontier actually is anymore. Within their secret towers, the two groups of wizards now work in shadow.
Steven Flinn retweeted
But if AI mathematics continues to progress at anything like its current rate -- which is what I expect to happen -- then we will face a crisis very soon, and mathematics departments, who owe a duty of care to their students, should be urgently preparing for it.
Steven Flinn retweeted
Fun fact: the fatal errors were initially flagged using @OpenAI's GPT-5.5
Steven Flinn retweeted
I keep reading this take (below) every few months, presented as if extremely profound, and it is just offensively dumb. It confuses data and information, it ignores the fact that not all information is equally valuable, and it ignores the importance of retention rate.
As a thought experiment: if this were true, if your retina cell count were 10x greater, you'd be "trained on 10x more tokens" and therefore you'd be way smarter. Same if their firing frequency were 10x greater. With 10x more retina cells firing 10x faster you'd be "trained on 100x more tokens"!
Obviously this makes no sense -- the signal coming from these cells is extremely correlated over space and time, so their raw information content (what remains post-compression) is extremely low compared to the "raw bit" encoding. The human visual system actually processes 40 to 50 bits per second after spatial compression. Much, much less if you add temporal compression over a long time horizon.
Latest LLMs get access to approximately 3 to 4 orders of magnitude of information more than a human by age 20 (post compression in both cases). About O(10T) bits vs O(10-100B) bits.
And that's just *raw information* but of course not all information is equal, otherwise we wouldn't be spending tens of billions of dollars on training data annotation and generation. Plus, that's only *information intake* but of course humans have far lower retention than LLMs (by 3-4 OOM). You could write a short essay about how incredibly off the mark this take is.
Why did the capability for musicality evolve? I proposed an answer 25 years ago that was counter to the popular answers of the time. This retrospective analysis using @GeminiApp confirms the Memory Modulation Hypothesis is indeed the correct answer. researchgate.net/publication…
Reproduction is a means to an end, and that end is information continuity, with aeonophiles being another confirming example of this "meta-evolutionary" perspective. A perspective posited in my "Evolution as Communication" paper (link in comments). aeon.co/essays/the-discovery…
I often hear the shibboleth, whether with regard to conversing with AI or humans, that what separates high performers from the rest is the ability to "ask the right questions." Asking the right question is all about value of information (VOI). That too can be automated.
Steven Flinn retweeted
Social media tends to frame AI debate into two caricatures:
(A) Skeptics who think LLMs are doomed and AI is a bunch of hype.
(B) Fanatics who think we have all the ingredients and superintelligence is imminent.
But if you read what leading researchers actually say (beyond the headlines), there’s a surprising amount of convergence:
1) The current paradigm is likely sufficient for massive economic and societal impact, even without further research breakthroughs.
2) More research breakthroughs are probably needed to achieve AGI/ASI. (Continual learning and sample efficiency are two examples that researchers commonly point to.)
3) We probably figure them out and get there within 20 years. @demishassabis said maybe in 5-10 years. @fchollet recently said about 5 years. @sama said ASI is possible in a few thousand days. @ylecun said about 10 years. @ilyasut said 5-20 years. @DarioAmodei is the most bullish, saying it's possible in 2 years though he also said it might take longer.
None of them are saying ASI is a fantasy, or that it's probably 100+ years away.
A lot of the disagreement is in what those breakthroughs will be and how quickly they will come. But all things considered, people in the field agree on a lot more than they disagree on.
Steven Flinn retweeted
Yet more evidence that a pretty major shift is happening, this time by Scott Aaronson
scottaaronson.blog/?p=9183&f…
Steven Flinn retweeted
Everyone ‘knows’ AGI will either make us all unemployed or fabulously wealthy. Except, a rather brilliant (and chilling) paper from a Yale economist suggests it's neither.
It says the economy will boom, and our wages... won't. A bit awkward.
I've been digging into this 2025 paper, "We Won't Be Missed," and it's fascinating. The premise: AGI arrives and can do all economically valuable work. And the 'compute' to run it gets cheaper and more abundant over time.
So, what happens to us fleshy, rather expensive humans?
The whole argument hinges on a masterstroke of a distinction. The paper splits all work into two types:
1️⃣ Bottleneck Work: The truly essential stuff. Producing energy, logistics, scientific discovery. The economy literally cannot grow unless this work gets done.
2️⃣ Accessory Work: The 'nice-to-haves'. Arts, fine dining, hospitality... maybe even writing witty Twitter threads. (Gulp).
Now, you might think AGI will just take the grunt work, leaving the important strategic stuff to us.
Wrong.
To achieve maximum growth, the economy must automate all the bottlenecks. It can't be held back by us. So AGI systematically takes over everything that is mission-critical.
So... are we all fired and sent home?
Surprisingly, no. The model shows people still work. We either help out with the 'bottleneck' tasks or get shuffled off to 'accessory' jobs that aren't worth the electricity to automate.
But that's not the interesting part.
Here's where it gets properly weird. Your future salary isn't based on your skill, your years of experience, or how 'important' your job feels.
It's capped by one thing: the cost of the computational resources needed to do your job instead of you.
Imagine that. As compute gets exponentially cheaper, the value of replicating your work plummets. The economy is soaring, productivity is off the charts... but your wage is pegged to a falling technological cost.
You're not obsolete, you're just... replicable. And replicable is cheap.
This leads to the paper's most brutal conclusion: The share of national income that goes to labour (i.e., salaries) collapses towards ZERO.
All the wealth, all the gains from this incredible boom, flow to the owners of the compute.
Splendid.
Here's what this means for you. Next time you see a headline about a new AI model smashing a benchmark, don't just ask "Will that take my job?"
Ask: "How much would it cost to run that model 24/7?"
Because that figure might just be your future salary cap.
Now, the paper isn't all doom. It notes that society as a whole gets richer, and we could still find meaning in 'accessory' work.
But the central economic role of human labour as the engine of growth? Gone. We become passengers, not pilots.
The paper's title is "We Won't Be Missed." Not because we're replaced, but because the economy will chug along just fine, growing faster than ever, whether we show up for work or not.
Completely changes how I think about the 'future of work'. Makes you wonder what we should really be planning for, doesn't it?
Steven Flinn retweeted
Claim: gpt-5-pro can prove new interesting mathematics.
Proof: I took a convex optimization paper with a clean open problem in it and asked gpt-5-pro to work on it. It proved a better bound than what is in the paper, and I checked the proof it's correct.
Details below.
@gwern gwern.net/ai-daydreaming says what I said in 2015 in patents.google.com/patent/US… Such probabilistic approaches to the selection of a focus of attention can introduce a degree of randomization to the selection process, which can produce a beneficial degree of serendipity to . .
the streams of consciousness of the computer-based system 925, increasing the likelihood that focuses of attention and the resulting streams of consciousness that might not otherwise occur are explored by the computer-based system 925. Such probabilistic approaches can be . . .
"Tell a story!" Tell me your target audience is midwits without telling me your target audience is midwits.
Steven Flinn retweeted
"Move 37" is the word-of-day - it's when an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising, and secretly brilliant even to expert humans. It is a magical, just slightly unnerving, emergent phenomenon only achievable by large-scale reinforcement learning. You can't get there by expert imitation. It's when AlphaGo played move 37 in Game 2 against Lee Sedol, a weird move that was estimated to only have 1 in 10,000 chance to be played by a human, but one that was creative and brilliant in retrospect, leading to a win in that game.
We've seen Move 37 in a closed, game-like environment like Go, but with the latest crop of "thinking" LLM models (e.g. OpenAI-o1, DeepSeek-R1, Gemini 2.0 Flash Thinking), we are seeing the first very early glimmers of things like it in open world domains. The models discover, in the process of trying to solve many diverse math/code/etc. problems, strategies that resemble the internal monologue of humans, which are very hard (/impossible) to directly program into the models. I call these "cognitive strategies" - things like approaching a problem from different angles, trying out different ideas, finding analogies, backtracking, re-examining, etc. Weird as it sounds, it's plausible that LLMs can discover better ways of thinking, of solving problems, of connecting ideas across disciplines, and do so in a way we will find surprising, puzzling, but creative and brilliant in retrospect. It could get plenty weirder too - it's plausible (even likely, if it's done well) that the optimization invents its own language that is inscrutable to us, but that is more efficient or effective at problem solving. The weirdness of reinforcement learning is in principle unbounded.
I don't think we've seen equivalents of Move 37 yet. I don't know what it will look like. I think we're still quite early and that there is a lot of work ahead, both engineering and research. But the technology feels on track to find them.
youtube.com/watch?v=HT-UZkiO…
