My 3-month-old son is now TWICE as big as when he was born. He's on track to weigh 7.5 trillion pounds by age 10

OpenAI just released a model that can generate 1-minute videos. You simply cannot argue that these models don't / won't compete with the content they're trained on, and the human creators behind that content. What is the model trained on? Did the training data providers consent to their work being used? The total lack of info from OpenAI on this doesn't inspire confidence. Across the AI industry, people's work is being exploited without consent to build products that compete with that work. This must be controlled by regulators.

Here is an OpenAI document submitted one week ago where they advocate for including datacenter spend within the “American manufacturing” umbrella There they specifically advocate For Federal loan guarantees Sam Lied to everyone, Again

The countdown until we are told that LLMs are “too big to fail” starts now. “We can’t afford to lose to China”, they will say, accepting their multibillion dollar bailouts.

I shared the following note with the OpenAI team today.

"pov footage of an ant navigating the inside of an ant nest" Video generated by Sora

Oh brother

Statement from Scarlett Johansson on the OpenAI situation. Wow:

𝙾𝚔 𝚝𝚑𝚒𝚜 𝚝𝚒𝚖𝚎 𝙴𝚕𝚘𝚗 𝙳𝙴𝙵𝙸𝙽𝙸𝚃𝙴𝙻𝚈 𝚟𝚒𝚘𝚕𝚊𝚝𝚎𝚍 𝙵𝚎𝚍𝚎𝚛𝚊𝚕 𝚕𝚊𝚠.

Page One in Edinburgh: “He has vast wealth at his fingertips, and he uses it for some of the most wicked evil I’ve seen.” ⁦@ukpapers⁩ $TSLA

Microsoft’s latest SEC filing quietly exposed that OpenAI lost around $11.5 billion last quarter, based on Microsoft’s 27% ownership stake and a $3.1 billion hit to its own net income. The filings confirm that Microsoft has funded $11.6B of its $13B commitment to OpenAI, with losses directly affecting Microsoft’s earnings under equity accounting rules.

1/ Can Large Language Models (LLMs) truly reason? Or are they just sophisticated pattern matchers? In our latest preprint, we explore this key question through a large-scale study of both open-source like Llama, Phi, Gemma, and Mistral and leading closed models, including the recent OpenAI GPT-4o and o1-series. arxiv.org/pdf/2410.05229 Work done with @i_mirzadeh, @KeivanAlizadeh2, Hooman Shahrokhi, Samy Bengio, @OncelTuzel. #LLM #Reasoning #Mathematics #AGI #Research #Apple

Terence Tao says today's AIs pass the eye test -- but fail miserably on the smell test. They generate proofs that look flawless. But the mistakes are subtle, and strangely inhuman. “There's a metaphorical mathematical smell.. it's not clear how to get AI to duplicate that.” Intelligence isn't what looks correct. It's what smells true.

At first, I thought GPT-5 had cracked those math problems on its own. Turns out (as Demis pointed out) GPT-5 just looked up the answers via web search. We really need better peer review for these “AI discovers science/math” claims.

"This is the same climate of antisemitism that has led to the massacre of Jews throughout the centuries. This is not just harassment. This is our lives on the line." @MIT student Talia Khan highlights the rise of antisemitism at MIT.

I would like to clarify a few things. First, the obvious one: we do not have or want government guarantees for OpenAI datacenters. We believe that governments should not pick winners or losers, and that taxpayers should not bail out companies that make bad business decisions or otherwise lose in the market. If one company fails, other companies will do good work. What we do think might make sense is governments building (and owning) their own AI infrastructure, but then the upside of that should flow to the government as well. We can imagine a world where governments decide to offtake a lot of computing power and get to decide how to use it, and it may make sense to provide lower cost of capital to do so. Building a strategic national reserve of computing power makes a lot of sense. But this should be for the government’s benefit, not the benefit of private companies. The one area where we have discussed loan guarantees is as part of supporting the buildout of semiconductor fabs in the US, where we and other companies have responded to the government’s call and where we would be happy to help (though we did not formally apply). The basic idea there has been ensuring that the sourcing of the chip supply chain is as American as possible in order to bring jobs and industrialization back to the US, and to enhance the strategic position of the US with an independent supply chain, for the benefit of all American companies. This is of course different from governments guaranteeing private-benefit datacenter buildouts. There are at least 3 “questions behind the question” here that are understandably causing concern. First, “How is OpenAI going to pay for all this infrastructure it is signing up for?” We expect to end this year above $20 billion in annualized revenue run rate and grow to hundreds of billion by 2030. We are looking at commitments of about $1.4 trillion over the next 8 years. Obviously this requires continued revenue growth, and each doubling is a lot of work! But we are feeling good about our prospects there; we are quite excited about our upcoming enterprise offering for example, and there are categories like new consumer devices and robotics that we also expect to be very significant. But there are also new categories we have a hard time putting specifics on like AI that can do scientific discovery, which we will touch on later. We are also looking at ways to more directly sell compute capacity to other companies (and people); we are pretty sure the world is going to need a lot of “AI cloud”, and we are excited to offer this. We may also raise more equity or debt capital in the future. But everything we currently see suggests that the world is going to need a great deal more computing power than what we are already planning for. Second, “Is OpenAI trying to become too big to fail, and should the government pick winners and losers?” Our answer on this is an unequivocal no. If we screw up and can’t fix it, we should fail, and other companies will continue on doing good work and servicing customers. That’s how capitalism works and the ecosystem and economy would be fine. We plan to be a wildly successful company, but if we get it wrong, that’s on us. Our CFO talked about government financing yesterday, and then later clarified her point underscoring that she could have phrased things more clearly. As mentioned above, we think that the US government should have a national strategy for its own AI infrastructure. Tyler Cowen asked me a few weeks ago about the federal government becoming the insurer of last resort for AI, in the sense of risks (like nuclear power) not about overbuild. I said “I do think the government ends up as the insurer of last resort, but I think I mean that in a different way than you mean that, and I don’t expect them to actually be writing the policies in the way that maybe they do for nuclear”. Again, this was in a totally different context than datacenter buildout, and not about bailing out a company. What we were talking about is something going catastrophically wrong—say, a rogue actor using an AI to coordinate a large-scale cyberattack that disrupts critical infrastructure—and how intentional misuse of AI could cause harm at a scale that only the government could deal with. I do not think the government should be writing insurance policies for AI companies. Third, “Why do you need to spend so much now, instead of growing more slowly?”. We are trying to build the infrastructure for a future economy powered by AI, and given everything we see on the horizon in our research program, this is the time to invest to be really scaling up our technology. Massive infrastructure projects take quite awhile to build, so we have to start now. Based on the trends we are seeing of how people are using AI and how much of it they would like to use, we believe the risk to OpenAI of not having enough computing power is more significant and more likely than the risk of having too much. Even today, we and others have to rate limit our products and not offer new features and models because we face such a severe compute constraint. In a world where AI can make important scientific breakthroughs but at the cost of tremendous amounts of computing power, we want to be ready to meet that moment. And we no longer think it’s in the distant future. Our mission requires us to do what we can to not wait many more years to apply AI to hard problems, like contributing to curing deadly diseases, and to bring the benefits of AGI to people as soon as possible. Also, we want a world of abundant and cheap AI. We expect massive demand for this technology, and for it to improve people’s lives in many ways. It is a great privilege to get to be in the arena, and to have the conviction to take a run at building infrastructure at such scale for something so important. This is the bet we are making, and given our vantage point, we feel good about it. But we of course could be wrong, and the market—not the government—will deal with it if we are.

Newsom just signed a new bill requiring Gen AI businesses to publicly disclose their training data 👀

I disagree. The education system is broken and needs to be dismantled

This is from Apple's State of the Union The local model is a 3B parameter SLM that uses adapters trained for each specific feature. Diffusion model does the same thing, adapter for each style. Anything running locally or Apple's Secure Cloud is an Apple model, not OpenAI.

It’s a hefty 206-page research paper, and the findings are concerning. "LLM users consistently underperformed at neural, linguistic, and behavioral levels" This study finds LLM dependence weakens the writer’s own neural and linguistic fingerprints. 🤔🤔 Relying only on EEG, text mining, and a cross-over session, the authors show that keeping some AI-free practice time protects memory circuits and encourages richer language even when a tool is later reintroduced.

But something here really doesn't make sense. It seems like a huge and pointless fuck up by the board and they seem more capable than that

news: OpenAI's upcomning Orion model shows how GPT improvements are slowing down It's prompting OpenAI to bake in reasoning and other tweaks after the initial model training phase.

The top AI model is now smarter than 85% of humans. By the end of 2026 it will be smarter than 99.9% of humans. And you think you will still have a job?

Wow! evidence from OpenAI itself in favor of my conjecture of diminishing returns.

Grok 3 is so based 😂

from Philly courtroom — Musk lawyer says his $1m giveaway is NOT a lottery. "There is no prize to be won" and winners "are not chosen at random," Musk lawyer Chris Gober said. Instead, the $1m is a salary they "earn" to be a spokesperson for Musk's pro-Trump super PAC. Winners are picked based on their "suitability to serve" and their personal story, Gober said. In response, Philly DA team called this a "complete admission of liability" that Musk is running an illegal lottery under PA law. Remember: When Musk announced the giveaway.... in PA nonetheless.... he said, "we are going to be awarding $1 million randomly." DA showed this in court to the judge. Gober argued there's a difference between "randomly" and "by chance," which is why he argued that this isn't an illegal lottery under PA gaming laws.