observations on AI startups in early 2023
1/ founders are going $0-10M in ARR first 18 months of (efficient) selling, leveraging LLMs against “niche” use cases
2/ despite the AI hype, there’s plenty of real customer value, retention and repeat usage
3/ too many people are hunting for a neat strategic narrative of “which layer of the stack endures,” telling some clean story about “data moats,” or wringing their hands that large labs or incumbents are going to win the core modalities (text, code, image etc.)
4/ this kind of hand wringing is folly. the history of software markets is nondeterministic
5/ the huge amount of value creation / capture out of the box for creative product folks is incredibly promising for startups. time and effort is better spent understanding customer problems deeply, and understanding the state of the art, and leveraging the latter for the former
6/ who wins is based part on market structure, but also partly on who the players are, their execution, and how they redraw the software category lines
7/ the level of successful app-level AI experimentation will only increase as more customer-focused domain experts and engineers understand AI, and the barriers to using these technologies decrease
8/ researchers knocking ChatGPT as a model that had “basically already existed publicly for a year in GPT playground / as an API” and those who are knocking app companies as “thin shims on foundation model APIs” have fallen prey to technical arrogance.
9/ ChatGPT reaches XXM users instead of 100K because it has an interface that makes sense to consumers (kudos to Greg Brockman and co).
10/ Copilot became quickly essential because Alex Graveley & co figured out how to fit “passive” prediction into coding workflows in a way that made sense to developers.
11/ People building from the models/tools up (VS from the customer back) are often unwilling to focus enough to do that last mile to make a product useful for customers.
12/12 Excited for more founders to walk the last mile of AI.