Matt Perault

What does it mean to serve clients well as AI changes the practice of law? @Harvey’s chief legal officer John LaBarre on how lawyers’ professional responsibilities could evolve alongside the technology.

Are coaches too quick to bench players in foul trouble? College basketball tips off this week, and with it comes a familiar ritual: a player picks up a second foul in the first half, and the coach sends him to the bench. Same with a third foul early in the second half, or a fourth at any point. But in many of these games, the benched player never even fouls out. Coaches guarantee their best players lose minutes to avoid the risk that they might lose minutes. It’s coaching orthodoxy, but it costs teams points and wins. In a post out today on my personal blog, I explore protective benching, its costs, and how to fix it.

As we’ve talked to AI builders and operators, a recurring theme is that AI makes it possible to do things that were previously too costly or impractical. Law offers yet another useful example of that positive-sum potential. Greater efficiency makes it possible to do more at less cost. Expectations rise, which can expand demand for legal services and create new opportunities for lawyers. In our latest episode of the @a16z AI Policy Brief, I’m joined by @Harvey’s chief legal officer John LaBarre to discuss how AI is changing the practice of law.

Policy certainty matters for any small startup, especially one building hardware. To feel confident making a $10 million investment in manufacturing equipment, a founder needs to know that US policy won’t reverse course a year later. @DiodeInc’s @DavideAsnaghi sees the AI era as a generational opportunity for American manufacturing. His message to policymakers is clear: durable, bipartisan support gives companies the confidence to invest and build in the US.

.@DiodeInc’s @DavideAsnaghi sees the potential for an empowering shift for American manufacturing workers: from rote inspections to overseeing entire production runs. Automating repetitive inspections lets workers focus on understanding why a machine is making mistakes and fixing the problem. As the U.S. expands its manufacturing capacity, there is growing demand for more people who can do that work.

One of the most compelling opportunities for AI is making it possible to build things that previously took too much time or cost too much to produce. @DiodeInc’s @DavideAsnaghi explains how AI can compress machine-programming work from a year to days. For American hardware companies, connecting design with manufacturing opens up new possibilities to scale production here at home.

How can America turn its strength in AI into a manufacturing advantage? A small team of engineers in Brooklyn is building the blueprint, starting with the printed circuit boards that power nearly every electronic device. I spoke with @DiodeInc CEO @DavideAsnaghi about building hardware faster, giving factory workers better tools, and what it takes to grow US manufacturing.

.@RayanKrishnan and @Glenn__Parham of @ValsAI explain the “domain transfer fallacy” in AI. They argue that for a benchmark to be credible, it must test models on the real-world work they’re expected to perform. Better benchmarks give businesses and governments better evidence to choose the right model for the job.

If a student took the SAT at home, proctored it themselves, and reported their own score, how much would you trust the result? That’s @RayanKrishnan’s analogy for allowing model developers to run their own benchmark test sets.