Make Things That Matter

Chris Smith: How to think about adding AI to your product

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Episode notes

Chris Smith is a longtime engineering leader who has been in the trenches of building with AI & machine learning for years. He’s led the development of data systems & strategies at tech giants like early Google, Yahoo, and Sun; S&P 500's like Live Nation; and a wide variety of startups.

Topics discussed:

(00:00) AI industry at inflection point, causing chaos

(09:05) Machine learning, neural nets, and generative AI

(14:03) Generative AI: LLMs + broad understanding

(21:56) Open source models improve specialized problem solving

(25:06) Access to data leads to competitive advantage

(32:53) AI training improves productivity and learning speed

(42:51) Reduced investment in GPT models speeds results

(48:47) Expectation mismatch leads to brand perception risks

(53:54) Non-technical work is crucial for AI product success

(57:30) Building a computer vision product from scratch

(01:03:14) A strategic approach to refining and testing prototypes

(01:08:04) Closing learning loops

Links & resources mentioned

Find the full transcript at:

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Chris Smith:


X / Twitter: @xcbsmith

• Bluesky @xcbsmith

People & orgs:

Dr. Marily Nika - AI Lead, Meta Reality Lab

Travis Corrigan - Head of Product, Smith.AI


Evidence Guided - Itamar Gilad

Other resources:

GPT = “generative pre-trained transformer”

Wizard of Oz experiment

Tom Chi - learning loop

Joel Spolsky: The iceberg secret, revealed

ML Ops

Computer vision

Precision-Recall curves

Leaked Google memo: “There is no moat”

Universal basic income (UBI)

Stop-loss order

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