Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of drug development. In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences. anthropic.com/research/early…

Genie 4 runs DOOM: "Walking through a typical office but every computer is running DOOM and all of the walls are also running DOOM and also everything is DOOM" "My coworker who is a demon appears" "He has a chaingun" "He gives me a bonus"

Rumors that chili maker Lao Gan Ma laid off 500+ workers due to AI have gone viral in China. Beneath Beijing’s positive messaging, many ordinary Chinese people are worried about AI and jobs. Lao Gan Ma recently touted a new AI-powered chili inspection system that uses high-speed industrial cameras that significantly outperform manual inspection. A stat about the company using this AI system to reduce human inspectors from 600 to 48 went viral on Chinese social media. The iconic, family-run company pushed back. But if you look at the data, the company has reduced headcount from 2,912 to 740 in the past few years while earning record revenues. Original story: m.jiemian.com/article/152425…

Excited to share Genie 4 today our most realistic and interactive world model: - steer dynamic environments in real time, shaping events, camera angles, and actions - generate native audio matched to the world, from realistic background sounds to characters who can converse in real time! - simulate photorealistic scenes and real-world physics - create more coherent multi-step events through reasoning from Gemini We've started to roll out as a research preview to trusted testers. Coming soon to Google AI Ultra subscribers in Project Genie and early access partners via the Gemini API and AI Studio.

Many of you don't understand how serious this is, so let me break it down. In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew. About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea. They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption. Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea. So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved. But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.

“The core algorithm was found on October 16 by an internal Claude research model with 14M output tokens and no human input. Anthropic researchers worked with Claude to improve the presentation, resulting in this paper.” github.com/anthropics/formal…

SITUATION UPDATE: Following GMO Internet's refusal to pay a $50M ransom, the hackers behind the breach have promised to trigger ransomware covertly planted on customers' PCs via the cloud. GMO is urging users to unplug network cables, and power off their PCs before noon today.

Today Physical Superintelligence PBC is announcing solutions to five long-standing problems in quantum information theory. psi.inc/research/oqp-batch-2… Thread 👇

p still probably doesn't equal np

Anthropic files for IPO. But who is poised to get the 💰? My usual breakdown below 👇 Investors will be sharing nearly $1 TRILLION of proceeds on ~$142b invested. A ~6.5x blended multiple on a huge amount of capital, in just 5 years. 🤯 Note: all those numbers assume a $1.5 trillion IPO valuation. So let’s dive in 🔎 1) The craziest fact: no VC in the Series A or Series B 🤯 The $124m Series A in May-21 was led by Jaan Tallinn (Skype’s co-founder), with Dustin Moskovitz and Eric Schmidt. It is likely to return ~345x the original capital, or ~$43b to those early angels. Mind-boggling. The $982m Series B in 2022 was led by... SBF and FTX: it will return ~79x the capital invested. FTX’s $500m would be worth ~$39b today. Plot twist: the estate sold it for ~$1.3b in 2024, in one of the worst trades ever. 2) The first VCs in: Spark and Menlo are killing it 🏆 Spark will make over $10b from its Anthropic investments. They first came into the 2023 Series C and are making ~79x their investment in that round. They followed in every single round since, investing $777m in total, now worth ~$11b. Menlo led the Jan-24 Series D and will make ~30x its money on that round. And finally, Lightspeed’s Series E lead will make ~16x in 18 months. 3) The real winners in $ terms: the strategics 💰 Amazon put a grand total of ~$18b in, mostly via convertible notes converting at ~$2 a share, against $58.90 for the Series H investors. That gives Amazon ~21% of the company, worth ~$310b. That’s ~17x its investment. 💵 Google is not that far behind: its notes and the $10b it put in this April alone will be worth ~$83b. And that’s before the compute: Anthropic has committed $110b+ to AWS and $111b to Google Cloud. They are both investors and suppliers, so they win twice. 4) Founders and team: incredible outcome, and giving most of it away 🙏 The seven co-founders hold ~240m shares each. That’s ~$21b per person, or ~$150b combined. 👏 And each of them has pledged 80% of it to philanthropy. So ~$120b will be going to good causes. On top of that, Anthropic put 5% of the company (~$75b) into its own Public Benefit Fund. And the IPO will mint a huge number of UHNW in the team: they will share ~$355b worth of shares, options and RSUs. Perhaps the biggest and fastest employee wealth creation event in history. Congrats to Dario, Daniela and everyone involved. Legendary. 👏

We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory. openai.com/index/building-sc…

Microsoft offers buyouts to 60,000 employees. This latest exit offer is available to far more employees than the previous retirement one, and comes just months after Microsoft laid off 4,800 employees. Full details here 👇theverge.com/tech/1018666/mi…

Speeding up Gemma! Gemini 4 Argon enabled a 2.85x speedup on an A100 via vLLM, jumping from ~37,900 to 108,000+ tokens/sec on gemma-4-E2B-it. ✅ 100% token parity ✅ Zero quality drop ✅ Done in days, not months See the exact changes across 8 PRs: goo.gle/4zNtANl

Three metrics that set the bar high: - 900% Quarterly NRR (usage based) - $6M Revenue / FTE - Power users 20x usage to avg sub Takeaways for Enterprise AI: - Shift to verifiable agent work, priced per task / outcome / verified result - AI-native companies building w/ agents get extreme operating efficiency - Market is so competitive ANT can’t even forecast its own revenue / growth with confidence! - Enterprises can’t predict spend, will favor predictable, committed contracts

Never seen such dismay: Why a 36-year market veteran is warning India msn.com/en-in/money/economy/…