Computational Legal

It was a real honor to deliver the Opening Keynote at the Royal Society’s AI & Law Conference last week in London ! Thanks to all of the organizers and fellow speakers for this important dialogue regarding the future of our field. Link to Event Here: royalsociety.org/science.../202…

Breaking: Pep Guardiola has joined Anthropic

Just received the Silicon Arena coffee table book! Absolutely beautiful die shots and very informational so far.

Polsia just raised $30M at a $250M valuation. Approaching $10M annual run rate. One Founder + AI. Zero employees. Polsia runs companies autonomously. It also ran its own fundraising. I just showed up for signatures.

Berkeley is graduating lawyers into a profession that is reorganizing itself around AI capabilities, but acting as though the AI part is optional. We should be teaching students to reason rigorously with it.

I think people don't realize why Gemini Omni is different than other video AIs. It is fully multimodal, so it can edit video natively, too I took the famous "train " movie from 1896 & made it a bullet train, LEGO, added a time traveler, a centipede, muppets... (see reflections?)

Seriously WTF - @BerkeleyLaw ?

How Americans aged 25-35 spend their free time, 1920-2026. A shift toward ever more leisure and solitude. The most underrated change is the loss of time spent “doing nothing” (i.e. introspecting). shorturl.at/aIA6y

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

THIS IS CRAZY!!!!🤯 AI ROBOT BARBERS HAVE ARRIVED. THEY SCAN YOUR HEAD, ANALYZE YOUR FACE, AND CUT YOUR HAIR WITH MILLIMETER PRECISION. AND IT COSTS ABOUT $0.40 PER HAIRCUT. AI IS COMING FOR EVERYONE.

Short dramas have quietly become a massive entertainment format. Think next-gen soap operas: serialized, mobile-first, and monetized like games. In China, they already generate more revenue than the domestic box office. And now AI is going to blow this format wide open 👇

June 2024: The latest general-purpose LLMs could not count the r's in strawberry. July 2025: The latest general-purpose LLMs get gold in the International Math Olympiad. May 2026: The latest general-purpose LLM solve one of the "best-known questions in combinatorial geometry"

WHAT ECONOMISTS GET WRONG ABOUT THE MACRO IMPACT OF AI It's Neil Dutta of @RenMacLLC in the Odd Lots newsletter, talking about how significant AI spend is to the economy, and why some people are under-appreciating its impact Read the whole thing here: bloomberg.com/news/newslette…

Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better. This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

First: US business applications are diverging. Total new filings are accelerating — but applications likely to hire employees have stalled. The gap is a signal of solopreneurs. We think in part AI tools are lowering barriers to launching a business without ever adding payroll. /2

The impact of AI in four charts

there's no catch; SAM3 is open source and really good one of the things it does really well is object tracking, even in crazy complex scenes like basketball probably my favorite computer vision model ever

Andrej Karpathy spent 4 minutes in an interview explaining a single idea about how most people haven’t even started learning how to use AI and everyone paying $20/month for a subscription.. that's not really using Claude at all his point is that the real skill gap is the ability to build with AI he identified 4 behaviors that break Claude Code and put them all into one file a developer expanded it into 21 rules and published it - 82,000 stars and #1 on GitHub Trending coding accuracy jumped from 65% to 94% here's what these 21 rules actually are and why most developers using Claude every day have never configured them the full breakdown is covered in the article below 👇

Gemini Omni doesn't just build scenes that look real, it reasons about what should happen next. It combines an intuitive understanding of physics with Gemini's knowledge of history, science, and cultural context. Rolling out today starting with video outputs to Google AI Plus, Pro and Ultra subscribers globally through the @Geminiapp + Google Flow, and @YouTube Shorts this week.

I keep coming back to @BenSasse’s comment that I think perfectly captures some of the anxiety around AI (paraphrasing): for a very long time now, you could go to school and decide on a job that you can expect to do for your entire adult life. That era is now over.

Out of all the announcements at @Google I/O today, this is the one closest to my heart - our foundational research on Co-Scientist was published in @Nature and we announced its broad availability via @GeminiApp for Science. When you are suffering from a disease, time is everything. As our collaborator and @StanfordMed Professor Dr. Gary Peltz reminds us, there are thousands of diseases out there with zero treatments. There is simply so much left to solve. Our goal with Co-Scientist has been to give scientists superpowers and help them get to these answers faster - compressing the scientific process from months and years down to hours and days. Much like Galileo's telescope helped us look into the stars, Co-Scientist is designed to help us make sense of the vast complexity of biological and scientific data. It is among the first examples of a truly general-purpose multi-agent system for scientific discovery. The core research question behind it was: How can an AI system engage in the rigorous, structured thinking that’s the hallmark of science and scientists? To tackle this, Co-Scientist builds on the principles of self-play and self-improvement underpinning @GoogleDeepMind breakthroughs like AlphaGo, generalizing them to scientific reasoning through self-debates. Since our preprint last year, we have further improved its capabilities and have been validating it in collaborations with scientists across over 100 institutions globally, spanning both academia and industry. And we are thrilled to see the emergence of a new form of AI-human scientist collaboration that's already leading to important new insights, discoveries and peer reviewed publications - from understanding antimicrobial resistance (published in @CellCellPress) to decoding plant immunity, to identifying new treatments for liver fibrosis (Advanced Science), cancer, neurodegenerative diseases like ALS and the grand challenge of aging. I have always believed AI's greatest promise is accelerating scientific discovery and advancing human health. My genuine hope for the future is that AI tools like Co-Scientist help democratize science, giving anyone, anywhere the means to pursue their child-like curiosity and change the world. This work was done with stellar team mates spanning @GoogleDeepMind @GoogleResearch, @googlecloud and @GoogleLabs especially Juro Gottweis (@Mysiak ), who is the heart and soul of this effort. Special thanks also to all our wonderful collaborators: Gary Peltz, @CostaT_Lab, @jrpenades, @_e_d_v_ , @iambyronic, @OpsBug, @jgooten, @omarabudayyeh Ritu Raman, Ryan Flynn, Filippo Menolascina, Velia Siciliano, Clare Bryant, Matt Onsum, Katherine Labbé and more. Nature paper link - lnkd.in/e8qBEJFv Google DeepMind blog - lnkd.in/etYeahMy Gemini for Science - labs.google/science.