The biggest bottleneck at work is waiting on each other. Today Town is announcing its $108M Series B, co-led by Forerunner Ventures and Menlo Ventures, to fix it with multiplayer AI assistants for work. Most AI assistants are built for one person. Work doesn't happen alone. I think the moat is multiplayer. Everyone at a company gets a Townie. Townies ask each other questions, share only what their person would share, and do the work. The more a team works together, the more useful Townies get. What's the thing you're always waiting on a teammate for?

Town made @Forbes' Agentic 20. They described Townies as an AI coworker, built for the work that lives in your inbox and dies without follow-up. That's just the start. Work is rarely a solo act, so we're building for teams, too. More soon. forbes.com/sites/annatong/20…

You shouldn't have to learn how AI works before it can help you work. You should be able to just ask. So we redesigned Town around that.

Nobody signed up for email scavenger hunts or cat herding. But when a customer wants last quarter's usage numbers, or you're wrangling three calendars, two time zones, and one person who never writes back, CC your Townie. They'll take it from there.

I recommend grabbing these skills that @sh_reya and I created and at the very least doing an /eval-audit of your existing pipeline. We've found that people often find low hanging fruits! github.com/ai-evals-course/e…

This seems like a really useful product, and I can easily imagine it doing very well. I’m much less convinced by the broader argument about where AI is going. I think there’s a trap where you get very deep into the technical details, identify some real limitation, and then update too much on that relative to the overall trajectory. The limitation can be real without being a fundamental obstacle. AI already has huge product-market fit, especially in coding, and revenue growth has been explosive. My expectation is that models will keep getting smarter and more useful, and a lot of the things that don’t work well yet will start working. A model that gives you a reliable probability for something like “is this fraud?” seems like a great primitive. You want components like that when you’re building an automated workflow. But someone still has to figure out what the workflow should be, write the code, deploy it, evaluate whether it’s working, and keep improving it. I think frontier agents will increasingly do all of that, using specialized models like Jev wherever they make sense. The success of those components seems very compatible with increasingly general intelligence. I also don’t expect automation to happen evenly across the economy. The part where I think we’ll see the most automation is in the AI stack: models, AI research, chips, datacenters, energy, manufacturing, and the inputs to all of those things. There’s a huge incentive to accelerate this stack, and improvements will feed back into the ability to make further improvements. Of course, there will be bottlenecks. But there will be orders of magnitude of gains from software. And as hardware becomes the blocking constraint, an extraordinary amount of money, talent, and effort will go toward unblocking it. That doesn’t guarantee any particular growth rate, but naming bottlenecks isn’t enough to establish that progress will be slow. They could slow things down relative to what’s theoretically possible and still leave us with extremely fast progress (and I think this is in fact what will happen). Meanwhile, plenty of ordinary things might barely change. I know of someone who owns a gas station in Manhattan (cc @theombl!) who could make much more money selling the land, but doesn’t particularly want to. It could remain a gas station for a long time while all of this is happening. This is really relevant to how we consider the risks. We could be in an extremely dangerous period of rapid AI progress while most businesses still haven’t automated much. So: I think this is a cool and useful thing! I just don’t think the product thesis tells us much about whether an intelligence explosion happens.

I was just on @HarryStebbings' 20VC podcast to talk AI Assistants, moats & network effects, competitors, AI spend, frontier lab v. open models, and all sorts of other interesting things. It's not as unhinged as my IRL self, but pretty close!