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
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.
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.
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.
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
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 bottleneck is on the supply side. We did not anticipate the extent to which end user demand would outstrip GPU supply. The bottleneck to many companies’ growth quickly became not customer demand but access to the latest GPUs from Nvidia. Long wait times became the norm, and a simple business model emerged: pay a subscription fee to skip the li