🤔 Where are the moats for "GPT wrappers"? After building multiple LLM-based apps used by thousands of users, here's what I've learned about creating defensible Vertical AI solutions... 🧵👇
@ErnestRyu It'll be like software engineering. The 10% of math research work AI can't do well yet will be 100x more valuable, the other 90% will be automated.
For AI apps, context is the moat.
OpenAI‘s memory feature increases the usefulness and switching cost of ChatGPT. We will see the same with other AI apps.
With the likes of OpenAI‘s o3 and its ability to produce truly novel work it feels like the intelligence take off is starting. Models will improve models.
Today @JoshuaKushner came to talk at @Harvard. My top three takeaways:
1) Thrive only invested in seven AI companies over the last 3 years. Josh explained, he doesn’t know if any of the vertical AI companies will still exist in a world with extremely capable AI models
2) OpenAI now is like Apple before they had decided which apps they build vs. let developers build. They need to look after their ecosystem but also want to build some themselves
3) A big opportunity Josh sees is vertical models such as for life science, physical intelligence or industry specific (e.g. legal)
The limiting factor of AI models today for most knowledge work is lack of context, not model capability.
We’ll increasingly see tools to capture context to easily add it into prompts.
For a course at Harvard, I developed a game using AI.
My assignment was overdue, so I made that the theme of the game.
In Overdue, you’re a student dodging an endless wave of homework by firing excuses at professors. You can play it here: https://t.co/ZT1WcQxTga (desktop only)
Projection: In 15 years, we will be talking to AI for 4-6h per day.
Analogy: The iPhone was released 18 years ago. I now use it 4-6h per day. We should expect AI to have a similar impact on our day-to-day interaction with the world.