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: https://podcast.makethingsthatmatter.com/chris-smith-how-to-add-ai-to-product/#transcript

Send episode feedback on Twitter @askotzko , or via email

Chris Smith:

• LinkedIn

• X / Twitter: @xcbsmith

• Bluesky @xcbsmith

—

Related episodes:

• #75 Chris Smith: Simple guidelines for AI investment sizing

—

People & orgs:

• Dr. Marily Nika - AI Lead, Meta Reality Lab

• Travis Corrigan - Head of Product, Smith.AI

—

Books:

• 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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.makethingsthatmatter.com