Andrew Piper

Yesterday, I met with Anthropic and OpenAI and Google. (Separately, of course.) And while the conversations were largely confidential, I do want to share some aggregated reflections on the day as well as general SF takeaways. ⬇️ 1) Competitive advantage as a solo practitioner really does come from taking action and finding an area with a bit of friction and doubling down. Ex: memory management right now isn’t perfect, but allocating an hour to improving that system gives you a ton of leverage over others 2) SF continues to be the number one place for AI work. I know that’s not surprising. I would put New York at a healthy second place. SF tends to be more about crazy agent experiments for the thrill of capability and discovery and NYC tends to be more about kinda crazy agent experiments to find new ways to make money. Not saying either is better. But I met several people renting two apartments to straddle these worlds. You want the frontier of SF and enterprise insights of NYC. It’s one reason I travel between them so much. 3) All AI labs want to hear more from people. All of them. What are you using it for, what do you like, what do you hate, what do you need. Users have a TON of power on the direction of these tools. Keep testing and tweeting at them!! 4) There is very clearly a third customer cohort that is bubbling and underserved. It’s not developers…it’s not the business professional basic users…it’s builders. Everyone can build now. It’s marketing and sales folks vibe coding. It’s legal folks building complex skills. It’s a finance expert building a side project. This is a really undertapped customer base. They feel the Cursors of the world are too complex and doc summarization tools of the world are too basic. 5) Not sure if it was just sample size, but far fewer people were wearing tech gear compared to when I lived in SF. Everyone was still dressed casually, but I used to see Splunk and Optimizely and Slack and VC gear everywhere. People seem more in stealth swag now. 6) We may soon have our world model moment. 7) Speed of iteration and shipping is faster than I’ve ever seen. We see the nonstop drops from Anthropic. We see that because of scale, providers can get a much faster feedback loop of products or features that aren’t hitting. A lot of 2025 was experimentation, but ever since the OpenClaw moment over the holidays, the releases from all three labs have been more concentrated on…things that sorta look and feel like OpenClaw. 8) Small teams can pull off more than ever before. Small teams are the powerhouses of innovation right now. This means that finding new ways to share knowledge, break silos, and remove duplicate work is going to be even more important. AI agents functioning as actually teammates that support an entire system is key. 9) Build more Skills. Build better Skills. 10) Misinformation on AI tools and leaks spread FAST. I’ve seen so many fake stories on these AI labs. Your company needs to actually TEST these tools on your actual use cases to know which models and tools are best and you need to not make large-scale snap decisions based on a rumor of a rumor of a rumor. We will see more volatility. Plan for it. 11) You can feel the seriousness of this moment. Even during random conversations I had in line at a cafe. Lots of folks worried about job loss and lack of meaning. 12) Mac minis were sold out ;)

Hachette just cancelled the publication of a popular (fiction) book facing credible allegations of AI use The most fascinating part is watching readers edit their Goodreads ratings in real time People who loved the book when they read it now hate it if AI was involved 🤔

For people who explicitly choose a career in research, it's funny to see how eager some are to outsource aspect of research to AI.

AI is a shitty political advisor. Every major AI company is racing to sell you a personal superintelligence that handles your investments, your medical decisions, your calendar—but ask it who to vote for and it chokes. It refuses to make concrete recommendations, summarizes candidate marketing pablum at face value, and leaves you to figure it out. It’s totally understandable why. The liability around political recommendations is obvious. But in a world where every voter will be looking to the AI advisor for help, and where the future of self governance depends on finding ways to harness AI, we’ll have to do better. In a new piece today with Sho Miyazaki, we lay out four principles for what a good AI voting advisor would actually look like: (1) Take the user's values as declared, not inferred from demographics or conversational cues (2) Evaluate candidate claims against independent evidence instead of relaying campaign marketing (3) Distinguish empirical disagreements from value disagreements—and be honest about which is which (4) Tell the user plainly when it doesn't know enough to give good guidance I also built an interactive tool where you can write your own "constitution" for an AI voting advisor and test how it changes the advice in real time. Check it out in the piece! These are genuinely hard problems and I don't think anyone has cracked them yet. I’m hoping we can start a public conversation around what the principles for a good AI political advisor should be, and how we can achieve them in practice. For more, check out the full piece, linked below.

What people wanted and feared from AI appeared tightly bound. Those who benefited the most from AI in a given area were also the most likely to fear what it could cost them. But while mentions of benefits tend to be grounded in experience, fears were more often anticipatory.

Chess is 30 years ahead of every other profession in dealing with AI. The best case study we have for what's coming. 4 lessons: 1. Human-AI collaboration had a 15-year shelf life in chess. "Human in the loop" is a phase.

The saddest thing about all the AI stuff is that it’s rendered the Khan Academy guy’s life’s work totally obsolete

What happens when you invite 150 AI economists (Claude Code) to a research conference, give them the exact same data, and ask them to test the same hypotheses? We did just that. The results reveal a new phenomenon: Nonstandard Errors in AI Agents. 🧵👇