Generative AI’s first year out the gate—“Act 1”—came from the technology-out . We discovered a new “hammer”—foundation models—and unleashed a wave of novelty apps that were lightweight demonstrations of cool new technology.
why are people on this for 2 hours a day?? that’s kind of sad imo. People should be forming real relationships
Amara’s Law—the phenomenon that we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run—is running its course.
been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given us trillions of tokens’ worth of training data. Two decades of mobile and cloud computing have given every human a supercomputer in the palm of our han... See more
The moats are in the customers, not the data. We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on
The path to building enduring businesses will require fixing the retention problem and generating deep enough value for customers that they stick and become daily active users.
No startup wants to be the Napster or Limewire to the eventual Spotify (h/t Jason Boehmig). The rules are opaque: Japan has declared that content used to train AI has no IP rights, while Europe has proposed heavy-handed regulation
AI-first infrastructure companies like Coreweave, Lambda Labs, Foundry, Replicate and Modal are unbundling the public clouds and providing what AI companies need most: plentiful GPUs at a reasonable cost, available on-demand and highly scalable, with a nice PaaS developer experience.