Jake Sendar @jakesendar

The biggest problem I have with Copilot is it doesn’t work.

Satya Nadella @satyanadella

a day ago

We’re building Copilot as a new OS for work that spans every model, every form factor, and every task. Today, we’re announcing our biggest update to Copilot to date, bringing four things together: · Autopilot: proactive and long-running agent built for the enterprise · Code:

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Legal AI is becoming a meaningless category. When Harvey first came out, it was interesting that a company was building AI for the legal industry because general-purpose LLMs had extremely limited legal capabilities. Even if Harvey’s real-world performance wasn’t significantly better than Claude or ChatGPT, the fact that they were even trying separated them from the pack. Today, both OpenAI and Anthropic are building products for legal work, and much of the discourse on LinkedIn and X is about whether vertical legal AI companies are in trouble as a result. My answer is that if you’re building a generalized legal AI product, then yeah, you might be. But the entire premise of generalized legal AI has made little sense to me for a while now, and it's one of the main reasons we set out to build Walton. Anyone who’s worked with lawyers knows that the legal industry is so multifaceted that even calling it a single industry feels misleading. The difference between an M&A associate and a criminal defense attorney is so vast that they may as well be in completely different professions. So I’m highly skeptical of claims that a legal AI product can broadly outperform an off-the-shelf AI system without substantial, separate investment in each practice it claims to support. To be clear, I’m not saying the Harveys and Legoras of the world don’t provide value. Obviously, LLMs are transforming the practice of law. I’m saying that they may not offer much beyond off-the-shelf AI. Email is essential to lawyers, yet lawyers don’t use legal email. They use Outlook. This has significant implications for companies building generalized legal AI because it undermines the common comparison to AI coding tools. Products like Claude Code and Codex have gotten so good at end-to-end software engineering that real-world usage generates meaningful insights that can inform model training and make the products even better. That feedback loop was possible because even early AI coding tools like GitHub Copilot were immediately useful to a broad range of software engineers. There’s been an assumption that legal AI would follow a similar path. But coding is relatively monolithic at the product level. Front-end and back-end developers can both use Claude Code, even when they’re building completely different types of software. Legal is different. To get comparable coverage, you might need twenty products instead of one, each requiring deep domain understanding, not to mention access to proprietary, confidential data. That’s a massive undertaking. Short of AGI, there’s no reason to assume one or two companies will build the best legal product for every practice or even every workflow within a single practice. The second wave of legal AI will look very different from the first.

So he’s getting fired from big company now right?

voxium @v0xium

6 days ago

I am done with this shit. It is over. The state of engineering right now is horrible. It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is

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Elon's been promising full self-driving for nearly a decade, yet Tesla's FSD system still requires a human in the driver's seat, eyes on the road, ready to intervene. As a result, its real-world utility remains limited. But the technology continues to improve, and at some point a relatively small increase in reliability will cross a threshold that makes supervision unnecessary. When that happens, the gains in utility will be massive. Every autonomous system has a delegation threshold, and it largely depends on the stakes of the task being automated. Cross the threshold, unlock massive utility. At Walton, we think we're on the verge of crossing the threshold for complex legal transactions. Read more here: walton.ai/blog/the-road-…

@theNickBerk I think an important question though is will that legal advice need to be provided by lawyers?

@shorttimelines Well it turns out he joined in May lol. So definitely not vested either way. But he was at oai for nearly 3 years so point still stands when they ipo and he also may have participated in secondaries.

@kevinroose You’re the other side of the same coin dude, which is probably why the “cope merchants” bother you so much. Very little nuance or empathy in your takes these days. It’s like you’re now sitting at the cool kids table and forgot that you’re supposed to be a reporter.

@shorttimelines Guess we can’t know for sure, but I’d be shocked if Anthropic makes employees forfeit their vested shares if they leave before the IPO.

@shorttimelines Right so he’s at least partially vested and he’ll get liquidity at the IPO, assuming he’s been there for over a year. Hardly a financial risk walking away. He’s set for life.

@agenticattorney Agree for now but the narrative can quickly shift with a good model release. Fable 5.1 is incoming.

@villi That’s because there are just about zero use cases that exist.

@joseamontorob Not sure I understand what you’re saying.

The cynical take on Harvey’s new post-trained model Tenet is that it doesn’t need to be much better than its already-strong and cheap-to-serve Chinese base model (Kimi K3), because its main purpose is to repackage that model into something law firms are comfortable using with sensitive client data. I think this is a reasonable take, especially because Tenet’s legal-specific benchmarks are not particularly impressive, but there’s a lot more going on here. First, Harvey is repositioning themselves (again) as a legal AI infrastructure/research org rather than an app-layer company, which I think is the right strategy. They have the capital needed to pull it off, and it’s a much less competitive space than the app layer, for now at least (as post-training costs come down, I expect this to change). Second, the reason Tenet isn’t very good is that Harvey has only recently started building out their post-training infrastructure. Tenet was mostly post-trained on synthetic and publicly available data. For future Harvey models to get materially better than their base models, they need both access to real matter data coming directly from user sessions and permission to use that data for training. They already have access. According to the billboards, Harvey is used by 75% of the Am Law 100, and you better believe they’re developing methods to capture every meaningful signal those users generate. Most of the new features in Harvey II suggest that's what they're doing (e.g. memory). So now they just need permission, which is more complicated but not impossible. Harvey’s standard terms prohibit them from using firm data for training. But they’re solving this in a clever way. They’re going to enter into agreements with firms that allow them to train on firm data as long as the resulting model belongs to the firm, not Harvey. And I’d be shocked if Harvey doesn’t also work out a way to pool some of that data into a shared training set for their generally available models. If Harvey can pull this off, they’ll have a data flywheel that will be extremely difficult for even the frontier labs to compete with, because it will require real lawyers running real matters on a platform designed to mine every user action. This is what Anthropic (Claude Code) and OpenAI (Codex) did with coding. Of course, there are big differences between coding and legal, especially around verifiability, but even if LLMs can’t “solve” legal the way many proclaim they’ve solved coding, Harvey is positioning themselves to at least try.

@nicbstme And what are your views on gun control?

@kevinroose @kevinroose how is it that every single one of your positions on AI aligns with the interests of the leading frontier labs? Like literally every single one.

@AlexYablon He's so brave.

@stewfortier What is the idea?

Finally pulled the plug on Fable for a less intelligent model because I can't stand talking to it. If there's actually an inverse relationship between raw intelligence and the ability to sound like a human (e.g. due to aggressive RL/post-training or something else) then the smartest models will end up being a lot less useful than we think.

@cursor_ai please stop pushing “agents window” on me. I use you specially because you’re not an “agents window”.