Badri (@badrinarayan) header
Badri (@badrinarayan) profile picture

Independent AI Researcher

San Francisco Bay Area, CA
Joined November 2008
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AI Alarm Hype Cycles - A brief history GPT-2: OpenAI withholds weights due to misuse/misinformation concerns GPT-3: No open release, Dario Amodei and other GPT-3 team members depart OpenAI to found Anthropic due to concerns about safety GPT-4: Mainstream panic, "6 month pause" letter, extinction statement o1/reasoning models: OpenAI board coup attempt, Ilya Sutskever eventually departs OpenAI as well as many others like Jan Leike, Mira Murati, etc GPT-5: AI 2027 gains rapid publicity for their predictions, Anthropic releases Alignment-faking study Mythos: Anthropic claims it's a cyberweapon, government slaps export controls on it, companies scramble to limit model uses GPT-6 Jacob Coxon resigns to unprecedented publicity, acccuses Anthropic and OpenAI of "gambling with our lives", is interviewed everywhere This is but the tip of the iceberg, make of it what you will.
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so one conclusion from all the drama is: you can make frontier AI run for 880,000+ hours costing $millions and obtain a solution to a famous open math problem while two dudes who know what they are doing can prompt frontier AIs to achieve the same result in a couple hundreds of LLM hours?
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Badri retweeted
Terence Tao
what's the current best model for math-adjacent technical writing and thinking?
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Very honestly, the current status of research in this area is more sophisticated than this result. This way of communicating science can make serious and long lasting damage. [This is not to say that the result isn’t new or that AI models aren’t amazing.]
šŸ‘€ GPT-5.6 Sol and Fable 5 just cracked a 25 year old open problem in wireless communication theory
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A very nice response on AI-generated formal proofs in Lean: Should we trust them?
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Badri retweeted
Regulatory capture by any other name tastes just as sour…
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It's just so RICH that the Head of Strategic Futures at an organization that (A) was founded as a nonprofit (B) with a vision of promoting OPENNESS in AI and (C) is literally called @OpenAI is now throwing shade at open source / open weight models. How far they have fallen.
Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
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The future is open source. We need to embrace it and get on with it. Imagine if America closed the door on open source. We would explicitly be forcing American companies to pay $26-56 per 1MM tokens for the same intelligence their adversaries/competitors around the world would pay $0.50-1 for. This is economically unsustainable unless AI is bullshit. If it’s the supposed through line of all future economic activity that we’ve been told it is, we can’t handicap America at such a meaningful cost disadvantage. This can also be viewed thru a military lens and not just a simple economic one as well. Letting our adversaries attack us for $0.50 per 1MM tokens while we spend $26-56 per 1MM tokens to defend ourselves is equally ruinous. It’s the Cold War Soviet collapse in reverse.
This is *exactly* what I predicted would happen. I said Chinese models would have advanced cyber capabilities within a matter of months and the only thing to do about it was to use AI-powered cyberdefense to protect our systems. Trying to gatekeep models doesn’t work.
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when your team gets assigned a "10x engineer"
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No, you put the armor on the part of the dragon with FEWER crossbow holes in it
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Visualization of Modems Connecting
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An Outsider’s Tour of Reinforcement Learning, Part 11: The surprising power of PID for model-free RL tasks. argmin.net/2018/04/24/ilc/
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A friend sent me this as his snarky answer to a question. Belly laugh.
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An Outsider’s Tour of Reinforcement Learning, Part 6: Policy Gradient Doesn’t Use Gradients. argmin.net/2018/02/20/reinfo…
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Choosing features: regularization. Choosing an algorithm: regularization. Using float32 instead of float64: regularization. A bug in your code: regularization.
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Tensor Comprehensions: einstein-notation like language transpiles to CUDA, and autotuned via evolutionary search to maximize perf. Know nothing about GPU programming? Still write high-performance deep learning. @PyTorch integration coming in <3 weeks. research.fb.com/announcing-t…
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grad student: ā€œFor successful RL, it is important to carefully tune all hyperparameters, including the random seed.ā€ arxiv.org/abs/1709.06560
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An optimizer’s perspective on a brilliant reinforcement learning paper by Salimans et al (with Roy Frostig) argmin.net/2017/04/03/evolut…
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by today's definition, y=mx+b is an artificial intelligence bot that can tell you where a line is going
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