AI产品定价：为何法律AI无法复制编程工具的50%渗透率

This week with Harry Stebbings, Rory O’Driscoll and Jason Lemkin: rule-breaking as a product feature, the legal AI ceiling, agents that route around their own guardrails, and Anthropic walking from a multi-billion dollar deal after diligence.

#1. Instinct and GrokBot Work Partly Because They Break Rules Public Companies Legally Cannot

The new class of consumer agents (Instinct, GrokBot, and whatever OpenClaw descendants ship next month) do things that are genuinely useful and genuinely against somebody’s terms of service. GrokBot spins up a VM and a browser per user and Googles things for you, which Google’s ToS prohibits. Instinct scrapes LinkedIn in ways you are not supposed to scrape LinkedIn. Some of the outbound calling flows are prohibited or illegal in parts of the US.

Jason’s read: it isn’t that these products aren’t exciting. It’s that a real share of the excitement comes from the rule-breaking itself, and that’s an advantage available only to private companies and to Elon. At EchoSign we shipped real-time document collaboration and redlining online five to eight years before anyone else did. It worked by running Word in a container in a VM, which violated Microsoft’s terms of use. The night after the Adobe deal closed, that feature got ripped out. A five year head start, gone, because a public company’s legal team gets a vote and a startup’s doesn’t.

Rory’s read: the history cuts both ways. No business at scale ever got built on scraping LinkedIn or breaking Google’s ToS. But Uber blustered through, broke the laws, got popular enough that the politicians folded. There is no single answer here. What is predictable is the second-order effect: a bunch of people pointed Instinct at Resy over one weekend and hammered the reservation API until it broke. If these agents become ubiquitous, the booking systems will build the separate API and the rate segmentation, because if there are people trying to book restaurants, the restaurant booking business will find a way to serve them.

#2. Why I’d Pass on Instinct at $2.5B, and Why That’s a Portfolio Decision Not a Product Opinion

Harry ran the metaphorical IC: would you write a $100M growth check into Instinct?

Jason’s read: no, and my ceiling would be $2B when the last round was $2.5B, so we’d have passed on price anyway. Gorgias will have its own Instinct for e-commerce. Meta will have one. There will be 20 in the next YC batch and a hundred startups doing a version of it. Betting on this one pre-revenue is not my vibe, and I’ll probably regret it the way I regretted passing on Loom, where I said the same thing (everyone will build their own) and was wrong. The honest version: to play this game you need the stomach to write 10 or 20 consumer checks at $2.5B, because it can’t be the only one in the fund. That’s a fund size and a worldview, not a deal.

Rory’s read: in a consumer investment like this there is no financial math you can use to buy the stock. You’re saying it’s a huge category and this team has the early lead. Establish momentum as early as possible, establish monetization later. That has been proven to work when the traction is real.

Harry’s read: it looks like the commoditization argument people made about Lovable, which was also called a light wrapper. Then you watch Instinct’s founder ship location sharing, then a OnePassword partnership, then the next thing, on a weekly cadence, with Index and Benchmark behind him and one of the best brands in the market. Shipping cadence is the answer to cloning.

#3. Jensen Declared AGI, and the Only Number That Matters Is the Half Trillion Dollars of Code

Jensen Huang says AGI has arrived, crediting OpenAI’s GPT Astra, trained on 100,000+ NVIDIA chips with 400,000 more coming.

Jason’s read: AGI is a nonsense term. The only thing that has mattered for two years is that LLMs do code, and code is a half trillion dollar industry. Focus, people. If you want a working definition anyway, use the non-GAAP one: for a given task, would you rather have an AI do it or a human? If the answer is AI over 50%, 90%, 99% of humans, go category by category. It doesn’t have to swallow the whole category.

Rory’s read: anything that can be reduced to code will be done by it. Rather than twisting yourself into a pretzel over whether the model can do everything, focus on the fact that it does this one thing amazingly well and that this one thing has massive economic value. Stop thinking and go ship something in code.

Rory also lifted a line from Ben Thompson worth keeping: LLMs are the most scaled artifacts humans have ever developed. Not the biggest physical thing (that’s the pyramids or the Great Wall) but the most complex single digital thing we’ve ever built, with the sum total of human knowledge encapsulated in it. Worth remembering every once in a while when the benchmark discourse gets tedious.

#4. Harry Watched His Girlfriend Use Legora and Decided Legal AI Is Underpriced. Rory Put a Ceiling On It.

Harry’s read: if coding is a half trillion dollar market, and Harvey and Legora are doing to legal what Cursor did to code, why isn’t there a half trillion dollar market in law?

Rory’s read: because the take rate is different. In coding you can credibly argue that for every dollar of engineering labor, fifty cents goes to tooling. In legal the subscription is $10K to $12K per lawyer against a $200K salary. That’s 5%, maybe 10% to 15% of total spend over time, versus 30% to 50% in coding. And businesses are rational economic actors. If it could do all the work and fire all the people, they’d do it tomorrow and not blink. The fact that they haven’t tells you it doesn’t do all the work yet. That said, 10% of any top-line labor category is enormous. US legal services is roughly $300B, so $30B to $60B can move to legal tech. That’s an amazing business. It’s just not coding.

Jason’s read: what I underestimated pre-AI was how much legal research resembles coding. It’s so complicated that no human ever gets it fully right. There was too much case law, too much regulation, no Stack Overflow, just Westlaw and Lexis. Nobody had a thousand man-years to research every case. Coding agents are great partly because they know every piece of open source ever written, and legal has that same shape. The reason legal is probably the third best category after coding and support is that it’s word-centric, and sorting through myriads of words was the first thing these models did amazingly well.

Rory’s counterpoint: coding is inherently more verifiable. Parts of it are mathematically verifiable, parts you just run on the machine. If law were fully verifiable we could predict Supreme Court decisions from logic. Less verifiability means less ability to extrude the human.

#5. Radiology Kept 100% of Its Radiologists After Losing 95% of the Work

Jason’s read: the model that matters here isn’t replacement, it’s compression. Harvey and Legora may end up doing 95% of what associates used to do, and the best humans get compressed into the 5% that moves the needle. Nobody should be spending weeks on case law about an 1872 shipwreck off North Carolina.

Rory’s read: and the remaining 5% turned out to be more than enough to justify 100% of the radiologists. Volume went up because imaging went up. And when the diagnosis is bad, you don’t want a machine telling you you’re dying. The job gets redefined around the tasks that can’t be delegated. The same will be true in law: the client meeting, the argument with opposing counsel.