The AI Startup Litmus Test
AI is like water.
Or more specifically, generative AI applications are like water. We've looked at hundreds of AI companies, and much like bottled water, many of them are the exact same under the hood.
This is just the reality: there will be 100 teams trying to do what you are doing, in the same way you are doing it. True tech differentiation in AI... See more
Or more specifically, generative AI applications are like water. We've looked at hundreds of AI companies, and much like bottled water, many of them are the exact same under the hood.
This is just the reality: there will be 100 teams trying to do what you are doing, in the same way you are doing it. True tech differentiation in AI... See more
AI is Like Water
However, a key risk with several of these startups is the potential lack of a long-term moat. It is difficult to read too much into it given the stage of these startups and the limited public information available but it’s not difficult to poke holes at their long term defensibility. For example:
- If a startup is built on the premise of taking base L
AI Startup Trends: Insights from Y Combinator’s Latest Batch
As we see base models continue to proliferate, and fine-tuned models begin to sit on top of them, our hypothesis is that these “thin layer” products should focus on having a clear opinion of how they form the output or how they shape the human to AI interaction layer. Put simply, the maximally viable product in AI is likely not where the vast major... See more
Michael Dempsey • Thin Layers of AI, Thick Layers of Personality
A looming question mark is how companies will build competitive moats; true tech differentiation is rare, and companies will need to find ways to stay ahead of competition, perhaps with network effects or with iterative loops of user engagement and product refinement.
Rex Woodbury • AI in 2023: The Application Layer Has Arrived
- You have access to a proprietary asset (like data) that others don’t have easy access to. In our “write job postings” example, perhaps you have a corpus of thousands of job postings including some outcome scores (as to how well they did). You could use this data to create better job postings. Others don’t have ready access to this data. Note: The a
Dharmesh Shah • How To Build a Defensible A.I. Startup
