Generative AI’s Act Two

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.

We now believe

the market is entering “Act 2”—which will be from the

customer-back

. Act 2 will solve human problems end-to-end.... See more

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

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We now believe the market is entering “Act 2”—which will be from the customer-back . Act 2 will solve human problems end-to-end. These applications are different in nature than the first apps out of the gate. They tend to use foundation models as a piece of a more comprehensive solution rather than the entire solution. They introduce new editing in... See more

This moment has

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

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.

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 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

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