Bryan McCann, CTO of you.com, explores how AI reshapes engineering and organizational design. He presents two paths for developers: managing AI tools or innovating AI improvements. Bryan emphasizes the importance of refining mental models to leverage AI effectively. He likens AI to early-stage magic, showcasing the power of language and symbol manipulation. The conversation also touches on organizational lessons drawn from neural networks. For those eager to explore AI's potential, he advises focusing on passionate learning for greatest impact.
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volunteer_activism ADVICE
Try AI First, Then Inspect Failures
First try to get AI to do the task before you do it yourself to expand what you can offload.
If AI fails, dig into why and either improve the prompt or identify a missing capability to build.
insights INSIGHT
Two Long-Term Developer Paths
Developers will split into tool managers or builders who improve AI itself as automation advances.
Those who can improve AI will be rare, highly valuable specialists like 'neurosurgeons.'
question_answer ANECDOTE
Mental Models Shape AI Adoption
Jonathan describes how narrow mental models make people dismiss AI when it fails their narrow expectation.
He compares early car adoption metaphors to current struggles forming useful AI mental models.
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Hey everyone, welcome to today's episode of Developer Tea. This is the second part of my interview with Bryan McCann, the CTO at you.com. If you haven't listened to Part One, I'd encourage you to go back, as it provides crucial context for our continued discussion. In this episode, we dive into how you can think about relating to and integrating the massive changes that AI is bringing to your job, whether you are a software engineer, manager, director, or product professional. Bryan and I discuss his interests beyond research, including art and organizational design.
Explore the two primary paths for developers in the long run: specializing as managers of AI tools (like a product manager with engineering insight) or striving to be better than AI at building better versions of AI itself (the "neurosurgeon" type).
Understand why refining your intuitions about what should be built becomes increasingly crucial as automation makes execution easier.
Examine how conceptual biases often become the bottleneck when interacting with powerful AI tools, such as focusing on very narrow tasks for a broad tool.
Learn how to approach AI failures: treat a failed output as an opportunity to dig in and figure out why, perhaps by asking the AI to write a better prompt or identifying a fundamental missing capability that could become a great startup idea.
Conceptualize AI as the earliest versions of magic, where the manipulation of symbols (like embeddings) allows us to extend our influence into the world in a flexible and powerful way.
Discover principles of organizational design by studying how neural networks learn, focusing on strong information flow, skip connections, and aligning with the objective.
Consider the idea that the next phase of human development might involve emulating AI’s learning mechanisms (rather than expecting AI to become more human-like) to unlock the next phase of humanity and continue our search for meaning.
Hear Bryan’s final piece of advice for listeners: focus on learning and working on things you are passionate about that will have the highest possible impact.
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