what a great city

"You're allowed to think about the worst case scenario, but you gotta do something about it"

legal made less than a 3% jump

Our goal isn’t to get a data advantage over our law firm clients and even if we wanted to we can’t for exactly the reason you described - the majority of their data is actually client data so even a law firm like Kirkland can’t take all that data and train a model on it bc it would break confidentiality. The two things firms can do are 1) firm-specific models - encode all of your firm knowledge in client agnostic ways to train models (templates, deal points, using your lawyers feedback on non-client data, disentangle your firm’s trajectories from client data, etc) and 2) client-specific models - for deep enough client relationships work with your client to build a joint model where both parties benefit from converting the relationship into AI. This is especially good for firms with long lasting relationships with their clients to make those relationships stickier. The best way to train either of these models is building a product that centralizes the entire client matter process (i.e. allows you to do an acquisition end-to-end and capture the entire trajectory and feedback across the entire team of associates, partners, client). These products will be specialized by practice area (M&A completely different from fund formation which is completely different from IP litigation). Without something like this it’s very hard to get meaningful signals from individual associate queries on a chat based product. We think this general product / infra will be shared across firms but then highly customized and the customizations and data will be owned by the law firms and their clients. Another problem we want to help law firms and their clients solve is how do you manage client data at scale when you are doing this type of training? The Fortune 500 customers we work with already struggle with professional services providers and data security because most of the work is done over email, downloaded onto desktops and printed out. And it’s not the law firm’s fault - they need to stitch together many different products to support all the needs of their clients' regulatory and security requirements. This is already a huge challenge and model training on top of this is going to make things infinitely more complicated. We’re building a collaborative platform (shared spaces) that allows law firms and clients to securely share data on client matters and build custom agents for their clients in a secure way. Eventually this will become infrastructure that allows firms to train client-specific models and for clients to have the piece of mind that the data they are sharing is isolated from other clients and it’s being used to their exclusive benefit. I think this is a good example of a very vertical product that it probably doesn’t make sense for a single law firm or model provider to build. So I agree there isn’t a market for laptops for lawyers but I think there probably is for infrastructure that enables enterprises to manage all their internal and external legal spend, workforce, agents, processes and the same for law firms. Compute is a great moat but in the cloud era most SAAS companies didn’t build datacenters and many were still wildly successful by building on top of the cloud providers. I think we will see the same thing in the next decade as well with respect to model providers.

Five months ago, I argued against the President's $4 trillion tariffs at the Supreme Court. In 237 years, the Court had never struck down a sitting President's signature initiative. Legal scholars said it was impossible. Some of my own colleagues said it was impossible. We won. 6-3. But the real story isn't what happened in that courtroom. It's what happened in the months before. And its the subject of my TED talk, coming out tomorrow. I had the best legal team in the nation, especially Colleen Roh Sinzdak, the most outstanding legal strategist I know. Huge thanks, too, go to the Liberty Justice Center (and in particular its fearless and hyper-intelligent leader Sara Albrecht), who organized the client small businesses, as well as to the brave small businesses themselves. I also had four teachers preparing me. A mindset coach who'd worked with Andre Agassi. An improv coach who taught me that "Yes, and" works in Supreme Court arguments the same way it works everywhere else. A meditation coach who taught me stillness. And Harvey. Harvey predicted many of the questions the Justices asked — sometimes almost word for word. Brilliant. Tireless. Occasionally insufferable. Here's the catch: Harvey isn't a person. Harvey is a bespoke AI I built over the last year with a legal AI company, trained on every question every Justice has asked in oral argument for 25 years, and everything they've ever written. Tomorrow, TED releases my talk about what really happened — and what I learned standing at that podium. AI can predict. AI can analyze. What AI cannot do is the one thing that actually won the argument. Connect. Read the room. Hear not just a Justice's words, but her worry — and answer the worry. That is the irreducibly human skill. Find yours. Go deeper. In this age of AI, that's where your edge lives. The talk goes live Thursday, May 7 at 11am ET: go.ted.com/nealkumarkatyal What's the irreducibly human skill in your work — the thing AI can't touch?

Bahahah somebody getting fired

Today we're announcing LevelUp: a free, four-week training program that takes people with no prior experience and prepares them to work as fiber technicians on data center construction sites across the US. We built this program with CBRE because the fiber technician field, and the broader construction industry, is facing a nationwide shortage at a time when data center demand is higher than ever. How it works: 🔧 Classroom instruction, hands-on labs team activities covering transferable technical skills 🎓 Graduates have the opportunity to work at Meta's US construction sites through our contractor network 🤝 Open to everyone from recent high school grads to mid-career professionals Since 2010, Meta's data center projects have supported 30,000 skilled trade jobs during construction 5,000 permanent operational roles. LevelUp is about building the pipeline to keep that going. Learn more: go.meta.me/0eb3f6

hey @NYCMayor fix this please

Arnold accidentally described the most reliable character test on Earth in a gym story about his son-in-law. The incline press is a brutal choice and that's the point. The bench press tells you nothing. Everyone looks strong on the flat bench. Incline isolates the anterior deltoids and upper chest, smaller muscles that fatigue faster. You hit failure sooner. The mask comes off sooner. Angela Duckworth's research at Penn quantified exactly what Arnold was watching for. Grit scores predicted West Point cadet survival better than SAT scores, high school rank, and physical fitness combined. The cadets who dropped out of Beast Barracks weren't the weakest. They were the ones who had never been pushed past the point where quitting felt rational. Arnold watched Pratt give up on the incline. Then he watched him push through anyway. That sequence is everything. The willingness to keep going when the thing stops being fun is the single highest-correlation trait with long-term success in every field with data on it. The gym is the last honest room left. No titles, no résumés, no pitch decks. Just gravity and iron and whatever you actually have inside you when the weight gets heavy. Most fathers-in-law take you to dinner and ask what you do for a living. Arnold took Pratt to the weight room and found out who he actually is.

I spotted a lawyer recently at a big law firm doing something on his laptop between depositions. Took me a second to realize what I was looking at. He had NotebookLM open with 6 years of case files uploaded. Here's what he was actually doing. I watched him paste in a fresh deposition transcript and run one prompt: "Cross-reference this testimony against all prior statements in this case and flag every contradiction with exact page citations." What used to take a paralegal team 2 days came back in 90 seconds. But that wasn't the part that broke my brain. He uploads every opposing counsel's past filings into a separate notebook. Then asks: "What argumentation patterns does this attorney rely on and where have those arguments failed in court before?" He walks into every hearing already knowing how the other side thinks. I asked him how long he'd been doing this. "Since I realized billing hours for document review was making me dumber." His win rate in summary judgment motions is up. His prep time is down 60%. His partners think he just got sharper with experience. He told me the experience part is true. He just has a 6-year memory that never forgets a page number.

Coinbase has always been about increasing economic freedom, and this factors into the state where we choose to incorporate. Texas has a strong culture of celebrating builders who are growing our economy, creating prosperity for all. They've also embraced crypto. By this metric, it was an easy choice. Thank you Governor @GregAbbott_TX for your leadership.

The best way to get new customers to do a really really really good job with your existing customers. And if that doesn’t work, you didn’t put enough “really”s in front of “good job”.

Google and Yale scientists have trained an LLM that has generated a novel hypothesis about cancer cellular behavior. This prediction was confirmed multiple times in vitro. - "What made this prediction so exciting was that it was a novel idea. Although CK2 has been implicated in many cellular functions, including as a modulator of the immune system, inhibiting CK2 via silmitasertib has not been reported in the literature to explicitly enhance MHC-I expression or antigen presentation. This highlights that the model was generating a new, testable hypothesis, and not just repeating known facts." The model that generated this prediction is a 27B-parameter LLM based on the Google Gemma open source models, and trained on a corpus comprising >1B tokens of transcriptomic data, biological text, and metadata. Quite remarkable that a small (just 27B) LLM trained on specialized data is able to make novel scientific discoveries. "Teams at Yale are now exploring the mechanism uncovered here and testing additional AI-generated predictions in other immune contexts. With further preclinical and clinical validation, such hypotheses may be able to ultimately accelerate the path to new therapies."