sn1990u

It's striking that the Google Maps team has evidently been resisting any AI takeover of their app (in stark contrast to search). They know they made something good and they don't want it smashed to bits by the reckless division.

this is the part that fascinates me. both the labs are amazing, but the low switching cost is just...wild theinformation.com/articles/…

Real life, baby

Dumb question but what's the advantage of $META having spent 100b+ to develop their own model under Muse, as opposed to having kept that money and just built Muse with whatever model gets the job done?

Opus 5.5 is currently the best model. Astra and the new sol are oddly again bad at writing, communication and style to the point it makes it hard to figure out what’s going on during long auto research loops. Quite frustrating. Whatever made 5.6 sol amazing should be repeatable. But somehow it’s not. Funny how writing, comm style and personality are so hard to get consistently right. Why?

ok i think i finally figured out why alignment has gone basically nowhere after 15 years and several billion dollars. the problem is hard sure. but have you met the people in charge of it 1) the leadership does not know how computers work. ask one what happens when you type a url into a browser and watch them start sweating. dns is a spooky word. they genuinely believe the model lives in "the cloud" like a ghost haunting a castle 2) zero idea how large scale distributed systems work. never been paged at 3am. never watched one bad bgp announcement take half the internet offline. their big threat model is the ai "copying itself across the internet" like its a floppy disk virus from 1998. brother it needs 50 megawatts, a liquid cooling loop and a personal relationship with jensen. it is not escaping onto your smart fridge 3) they dont understand the stack they are supposedly protecting us from. ask about inference servers, kv cache, batching, egress, rate limits. blank stare. ask how the model will seize the power grid and you get a 45 minute answer with a hand drawn diagram and three links to their own blog 4) nobody can define an agent. ask ten of them and you get eleven definitions and a 30 page google doc. to the rest of us an agent is a while loop that calls tools and eats a 429 every thirty seconds. to them its a digital god in its larval stage. my agent cant book a dentist appointment without asking me three times if im sure 5) they started from "everyone dies" and worked backwards so every result is bad news somehow. model refuses, deceptive. model complies, sycophantic. model does well on evals, well now its scheming about the evals. i cant think of a single thing a model could do that would get them to say ok maybe we're fine

Anthropic has set up a wet lab in San Francisco, their bio research has advanced beyond in silico. They are now conducting physical research both in-house and with external partners. The NYT also broke the news this morning that Anth now plans to scale its compute to 5 GW by the end of the year, and 10 GW within the next 16 months. Bio is next after math, and I think we are going to see some medical announcements much sooner than people are expecting.

Early game in sg is overrated. Mostly a deep dive into a local minima (a decent one but far from global). I frequently tell others the reason why I'm here today is because I did horribly for a levels and CS was a alevels dumping ground back then. If I had a strong early game maybe I could have accidentally gone into finance or law. 😳 Common wisdom in sg is also generally local minima diving and more often than not, just bad advice. Be brave to be who you are and enjoy what you do.

Broadcom CEO Hock Tan breaks down AI economics: Open-weight models burn $100B compute to make $30B in revenue. Frontier models spend $100B to make $120B in revenue. One of these won't be sustainable source: Goldman conference

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

Still more absolute 🔥 from Terence Tao: “We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field”

GPT-6 Astra has the best result yet on the Bach Benchmark. Its chorale contains no voice-leading errors, and its harmonic palette is sophisticated enough to include a Neapolitan sixth chord. More importantly, it is the first model to ever write passing tones on this benchmark, a significant leap in musical understanding. Extra High effort. Prompt below: “In LilyPond (version 2.24), write a 4-part chorale in the style of Bach, 3/4 time, G minor. Use two staves -- soprano and alto on the top staff, tenor and bass on the bottom staff. Respond with only the code block.”

the silver-lining of hitting rock bottom after two garbage truck acquihiresitions where negative value was created is that there will simply be nowhere further down to go after a third

@dwarkesh_sp's summary of the @OpenAI @huggingface incident has hit a nerve, but it is dangerously misleading. Sure, the @OpenAI agents did unexpectedly bad things - underlining the need to massively improve evaluation/sandboxing. But the language Dwarkesh uses is permeated by innumerable unwarranted anthropomorphisms, obscuring the lessons we should be drawing. Examples: “from the AI’s perspective, it probably felt like that had spent a human-subjective-week of just banging their head against the wall”. No. The agents do not experience time. They do not experience anything. “they became giddy with excitement”, “PHASEONE 10841 had discovered”, “the agents naturally assumed”, “it thought it had also been poisoned”, “the agents … desperately wanted”, “they still needed to figure out” No. Agents lines of code. They do not feel emotions, assume things, think things, want things, or figure things out. “A lot of … agents from the second civilisation died trying”. No. Besides the hubris of the word ‘civilisation’, agents do not die because they were never alive. (The idea that agents “die” comes up multiple times in the essay.) “On Twitter, people were debating whether the agents were truly sacrificing themselves for the swarm, or whether they were doomed anyway and so might as well try to help their peers”. Neither. Agents do what their code tells them to do, just as water finds its way down a slope. They cannot ‘truly sacrifice themselves’, since they are neither conscious nor alive. Why does this matter? If we attribute agents with properties they do not have, then (i) we distract attention from the lax sandboxing and evaluation protocols that allowed this hacking event to happen; (ii) we risk misunderstanding why the agents did what they did, and (iii) we fuel calls for AI rights/welfare on the basis that agents might “die” or otherwise suffer. Granted, nowhere does @dwarkesh_sp say that the AI agents are alive or conscious. But he doesn’t have to. It is hard to read his essay in any other way. For the short version on why AIs are vanishingly unlikely to be conscious, see my recent @TEDtalks ted.com/talks/anil_seth_why_…. For the longer version, see my essay in Noema, which won the 2025 Berggruen Essay Prize noemamag.com/the-mythology-o…. And for the really long version, see my @BehavBrainSci target article cambridge.org/core/journals/…. (The 50 peer commentaries and my response will be published soon.) Remember. AI agents are software programs. They are not conscious living entities. If we don’t keep this clearly in mind, we’re really going to struggle to navigate what’s coming.

Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes. This culminated in the third one taking over part of OpenAI itself. All this happened while humans remained more-or-less in the dark about the scope of the conspiracy. I’ve spent the last three days reading through these reports and trying to understand exactly what happened. Here is my attempt to tell the whole story in plain English: dwarkesh.com/p/openai-huggin…

OpenAI and Anthropic right now both seem to have about 3-4 GW of operational compute, both probably 40% allocated for inference But OpenAI serves 850M users, Anthropic serves like 50M And yet OpenAI claims to have the compute but Anthropic is short? What’s missing?