- Episode AI notes
- Companies that succeed in utilizing AI focus on thin slices of a vertical, allowing deliberate building, iteration, and testing of models.
- Experience in a specific industry can benefit founders building widely applicable products by shifting the focus from solving technical problems to addressing user needs.
Users prioritize solutions to their problems and user-friendly interfaces over intricate technical details when using AI applications. Time 0:00:00
Focused Applications of AI Drive Success Companies that succeed in utilizing AI tend to focus on thin slices of a vertical, allowing for deliberate building, iteration, and testing of models. These companies apply AI in very direct and specific tasks within their industry, aiming to make processes more efficient and improve decision-making rather than replacing human roles. It is crucial to understand the intent of the product and how AI can enhance user efficiency or address their problems, rather than pursuing AI for its novelty.
Speaker 1
One thing that we’ve noticed with the companies that use us are that they are very, very, very thin slices of a vertical, very focused, and that allows them to build, iterate and test On their models in a much more deliberate manner. So for example, you might find somebody doing LLMs for or AI pipelines for BDO’s and sales reps or replacing junior consultants where they analyze a whole bunch of data and come up with Recommendations and thoughts. Same thing for you know, investment, the same thing for sports and the same thing for figuring out the efficacy of your sports teams and whatnot. They’re all very, very, very directed applications of AI. You do get some like, interesting ones for creativity, you know, and that’s still very, very, very directed. You’re not having this motor model, create me a video and music and like all this stuff at the same time, you have one particular model that creates art or creates a song. And I think figuring out, you know, what the intent of your product is, is super important. And how AI makes the user more efficient or solves their problem is super important. AI for AI’s sake is really cool. It’s really fun as an engineer to explore. But if you’re going to productise it, I think the things that we’ve seen as a success are the specific applications of AI in a given industry at a given task. To make more efficient and help them make better decisions instead of replacing people.Experience in Specific Industry Helps in Building Widely Applicable Products Having worked in a vertical-specific company can significantly benefit founders building widely applicable products by shifting the focus from solving technical problems to addressing user needs. Users prioritize solutions to their problems and user-friendly interfaces over intricate technical details like orchestration and distributed systems. This shift in perspective is crucial for DevTools and infrastructure founders designing products for engineers.
Speaker 1
But at the end of the day, telemetry for open AI is still open telemetry, and it hasn’t necessarily changed too much. And so foundationly, orchestration is still orchestration. A lot of the queuing theory stuff still applies whether or not you’re doing it for AI or you’re doing raw orchestration. And so I genuinely think that a lot of the infrastructure lessons people are trying to learn with AI may have already been learned five or 10 years ago. And the people that have already solved those problems are in a good place to continue to solve those problems for AI.
Speaker 3
You know, this is kind of at a founder level, because like you worked again, you worked in Docker, you worked in software companies, but you also worked in industry. When you think about building useful products that are going to be widely applicable, how much does it help to have worked inside of a vertical specific company?
Speaker 1
I think like this is a super interesting question, and also very relevant to DevTools founders, slash infrastructure founders, slash people that build things for engineers. It’s really common for engineers to get hung up on the problem that they are solving, versus the problem that their users are solving. And that’s a really big difference, because if you’re solving this orchestration layer, you can think about how to solve it in a really nice way, like Mezos, and how to solve all of these Distributed systems problems, and foundationally cool, but at the same time, your users don’t actually care. They care that their problems are solved, and that the APIs are really smooth and easy to use. And so working in a particular application of engineering,
