Tech startups are often described by analogy: “[successful thing] for [industry].” In the 2010s, the successful thing was usually Uber or Airbnb.

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2024-06-06 16:30:15

Tech startups are often described by analogy: “[successful thing] for [industry].” In the 2010s, the successful thing was usually Uber or Airbnb. Today, of course, it’s AI — for developers, for customer support, for product feedback, and for a million other things. It’s an easy way to explain what you’re doing without getting into a 30-minute lecture on the nuances of your technology, and one thing every founder learns quickly is that simplicity is better than accuracy.

What we’re starting to realize is that AI is going to break a lot of these analogies. Because of the breadth of their training data, LLMs and other foundation models excel — out of the box — at a variety of tasks that span traditional job functions. Simultaneously, they lack some of the depth and nuance that any professional accumulates over years of experience.

This might seem unintuitive at first, but you’ve certainly seen AI behave in this way. Many writers have observed that LLMs are good at brainstorming ideas but lack the finesse to polish writing. Developers find that ChatGPT and Copilot are great at writing code snippets for common tasks but can’t track the requisite context to fully customize it to your codebase. At the same time, it’s the same LLM that gets the basics done in both cases. This is what we mean when we say AI is breadth-first — a jack of all trades.