High Signal: Data Science | Career | AI

Episode 35: Beyond Online Experimentation: Generative Software That Optimizes Itself


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Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software.

Our conversation traces the path from basic hypothesis testing to a frontier where Generative AI creates, evaluates, and refines product variants in a closed loop. We explore the architectural shift required to move from testing single variants to optimizing entire parameter spaces, and how startups are already using AI to generate production-ready landing pages for Fortune 500 companies in hours rather than weeks. Tingley also shares a strategic lens on "experimentation programs," explaining how plotting the distribution of treatment effects across different product areas can serve as a powerful tool for capital allocation and high-level strategy.

LINKS

  • Martin on LinkedIn
  • Want Your Company to Get Better at Experimentation? by Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley (Harvard Business Review)
  • Avoid the Pitfalls of A/B Testing by Iavor Bojinov, Guillaume Saint-Jacques and Martin Tingley (Harvard Business Review)
  • Martin & Co.'s Seven Part Blog Series on Experimentation at Netflix
  • Roberto Medri (Meta) on High Signal: The Incentive Problem in Shipping AI Products — and How to Change It
  • Tim O’Reilly on High Signal: The End of Programming As We Know It
  • Watch the podcast episode on YouTube
  • Delphina's Newsletter
  • ...more
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