Balancing AI Expertise and Industry Acumen in Vertical Applications

In this episode of AI + a16z, Ambience cofounder and chief scientist Nikhil Buduma joins Derrick Harris to discuss the nuances of using AI models to build vertical applications (including in his space, health care), and why industry acumen is at least as important as technical expertise. Nikhil also shares his experience of having a first-row seat to key advances in AI — including the transformer architecture — which not only allowed his company to be an early adopter, but also gave him insight into the types of problems that AI could solve in the future.

Here's an excerpt of Nikhil explaining the importance of understanding your buyer:

"If you believe that the most valuable companies are going to fall out of some level of vertical integration between the app layer and the model layer, [that] this next generation of incredibly valuable companies is going to be built by founders who've spent years just obsessively becoming experts in an industry, I would recommend that someone actually know how to map out the most valuable use cases and have a clear story for how those use cases have synergistic, compounding value when you solve those problems increasingly in concert together. 

"I think the founding team is going to have to have the right ML chops to actually build out the right live learning loops, build out the ML ops loops to measure and to close the gap on model quality for those use cases. [But] the model is actually just one part of solving the problem. 

"You actually need to be thoughtful about the product, the design, the delivery competencies to make sure that what you build is integrated with the right sources of the enterprise data that fits into the right workflows in the right way. And you're going to have to invest heavily in the change management to make sure that customers realize the full value of what they're buying from you. That's all actually way more important than people realize."

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Fundamentals of Deep Learning

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