Gabor Cselle

My biggest takeaways from Head of Product for @Gemini and Google's other frontier AI models—Tulsee Doshi (@tulseedoshi): 1. Great PMs are more in demand than ever. “When you can build anything, building something great actually is now where most of the value is,” Tulsee said. The scarce skill is now identifying which of the thousands of possible things to build is the one worth building. Alongside that is putting that judgment into practice: aligning people and converting speed-induced chaos into a coherent direction. 2. The PM role has shifted from writing requirements to creating something with “heft.” The PM who hands a requirements doc to an engineer and calls it done is not going to do well in this new environment. The new baseline is going one step further: actually producing something of substance, be it a prototype, a thorough data analysis, or even a ready-to-ship MVP. 3. Tulsee hires PMs who have opinions about models. She wants PMs who play with models every day, have their own use cases for judging whether a model is good, and stress-test capabilities themselves. They’re also technical enough to roll up their sleeves, dig into data, and build an eval. And they’re adaptive: “The space is changing every two weeks. A roadmap you wrote two months ago, you’re gonna be looking at it again and rewriting it.” 4. To understand a new model, test it on work you care about. When a new model comes out, some people try 3D animations, others creative writing or hard financial analysis. Tulsee’s advice for evaluating new models is to pick tasks where models “are already showing signs of life” but aren’t perfect by your own definition. That leaves room to see different models handle the problem differently, and it’s where you’ll notice how the models improve. 5. Model development is less like engineering and more like alchemy. “Model training is kind of like Snape’s potions class,” she says. “You can put all the ingredients in and be thoughtful about what you expect, but you also have to expect that there will be a little bit of magic.” Her team’s job is to identify where the model shines (often in ways no one planned) and to turn that into something great for users. 6. Tulsee’s 60-20-20 rule is a practical way to build ahead of the models. Your AI product roadmap should look like this: roughly 60% of what you build should work today, another 20% should be close—“not perfect, but you can see how the models might get here in the very near future”—and the final 20% should show glimmers of something magical that the model can’t quite do yet. The exact numbers are flexible, but the idea is to be ready to double down on something that just became possible. 7. Your first eval can be five prompts. When tackling evals, the most common mistake people make is waiting until you can build something large-scale before even starting. “Start with even just five prompts,” she says. “Start simple. Start with even just yourself as the rater.” A set of even 20 prompts that creates a signal between model A and model B is “so powerful.” Getting to a small, honest set of examples forces you to articulate what “good” looks like. Get clear on a set of good examples first, then scale up. 8. Burnout comes from chaos and grunt work more than from long hours. Tulsee admits everyone on her team is “putting in all of the hours.” But in her experience, burnout comes from churn and chaos, and work that feels like a waste of time. Work you’re excited about is exhausting but brings more joy. That’s why she hopes AI tools can reduce burnout by removing the grunt work, so people spend more time thinking and being creative. My tech workforce survey found the same thing: more hours doesn’t necessarily equal burnout. 9. Google’s full-stack AI strategy gives it an advantage few competitors can match. From custom TPU hardware to products used by over a billion people each, Google operates across the entire value chain. Tulsee describes Google’s goal as building models across the “Pareto curve of cost to quality”—not just the frontier LLM but models that serve a search user, a software engineer, and a cyber defender. Frontier competition matters to Google, and Tulsee is direct about Google’s intention to win the SOTA model race.

20 years ago there was a building in SF that was nicknamed the Y Scraper because so many YC founders lived there. They just had a reunion and it makes me so happy to see them all. One thing you can say for sure about YC: no investor's founders become closer friends.

I'm now building an "improvements" feature into all of my personal vibecoded applications: a freeform text input that's hooked up to an agent that yolo implements whatever is submitted. It's an interesting experience -- the activation energy barrier to sanding any rough edge or implementing any new feature becomes extremely low. Someone else who uses one of the applications I created has already implemented 150 improvements over the course of a few days. This pattern feels like a new kind of tool -- liquid software? -- and every application that doesn't have this feature now has a slightly rigid and anachronistic flavor. Why can't I just tell it how I want it to be different? Now, it's not obvious to me how this pattern can or should scale. Does it become unmaintainable after a while? (Maybe model intelligence improvement outstrips my feature appetite such that it's fine?) How does collaborative software work in this model? Can software designed this way be secure and reliable? Whatever the larger questions about broader deployment, it definitely works great in a personal context. It reminds me a little of the visions of Lisp/Lisp machines, Smalltalk, and HyperCard -- systems that the user can take control of and modify. Those visions never quite worked, though: the vast majority of software today is not end-user modifiable in any useful way. So, while this "liquid" affordance is a very small touch, the way it changes one's relation to the software feels a bit transgressive and empowering in a Lickliderian and Engelbartian way.

I redid the startup founder signals that lead to top tier VCs investing in the seed / A, after incorporating some feedback. This added ex-frontier lab and cyber security near the top, IIT as a positive, and educated in China as a negative. The main changes were; - extended founding year to 2019-2026, ~2x'd the number of startups - unified and added some signals Here are the new signals:

Patrick Stewart, who will be remembered most for his roles as Captain Jean-Luc Picard on “Star Trek: The Next Generation” and as Professor Charles Xavier in the “X-Men” films, has died at 86. variety.com/2026/tv/news/pat…