🔮 Product eats the AI company; the bitter lesson prevails; Fable 5 as CEO, undersea diplomacy & jellyfish sleep++ #579
“This was not anticipated.”
Hi all,
Happy Sunday.
GenAI drives almost 2% of traffic to Walmart and Target; home and electronics categories lead. A quick show of hands, please:
Here’s Sunday edition #579.
Product is the team
A working paper by Harvard Business School’s Rembrand Koning and INSEAD’s Hyunjin Kim studies how AI-native startups1 are different to non-AI startups. At similar funding and growth, AI natives are 25% smaller; they have more engineers, denser expertise and fewer managers.
Now, what’s interesting in this paper is that AI makes a real difference in a business when it’s folded into the product. Some of the knowledge work that used to be done at the edge of the product by human teams is now done directly within the product. When the work is done directly in the product layer, the feedback loops close. Kim and Koning write:
Capturing real value from AI often requires re-engineering processes around it—and, as our results suggest, re-engineering the product itself so that AI does the work directly.
This complements my perspective that firms must redesign around AI‑closed loops. Giving workers a Copilot or coding tools is most useful for understanding what the technology can do. But real gains will come from the much harder task of rebuilding how you work centered on AI. Read our analysis here.
See also, Sam Altman speaking to the Stanford CS153 class this week said:
everything about starting a startup has changed so much. … with an affordable amount of spend on tokens, you can do what a 100 person incredibly great engineering team would do as a startup and that was just totally impossible. I think what you can take on, the level of ambition you can have, the speed of which you can move, the amount of stuff you can do at once, is just totally different.
Joanne Chen, general partner at Foundation Capital (which backed Netflix and Uber) and I discuss what it takes to build AI-native companies here.
The bitter lesson always prevails
Generalist frontier models are outperforming best-in-class specialist medical tools in head-to-head tests. The specialist models that were tested are gold standard — almost two-thirds of US physicians use OpenEvidence. My go-to expert on AI and medicine, Eric Topol, says: “This was not anticipated.”
But this is precisely Richard Sutton’s Bitter Lesson from 2019
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. […] We have to learn the bitter lesson that building in how we think we think does not work in the long run.
