korrents

korrents · video

How to run your product team like a research lab | Dan Shipper (Every) (Lenny & Friends Summit, San Francisco, 10 September 2026)

Dan Shipper · 18m · youtube.com ↗

11 korrents from this recording

Dan Shipper did not write this page.

Every claim below is a statement made in this recording, quoted word for word and linked to the second it was said, so you can hear it rather than take our word for it. The wording comes from the transcript published alongside the recording; the sentence above each quote is our reading of the claim, not their wording.

  1. 1:32 · watch on youtube.com↗

    Our reading

    Most customers are unfamiliar with frontier AI and look to the companies building their products to tell them what to do.

    Their wordsbut most of your customers are not familiar with this stuff. They're actually looking to you uh, to tell them what to do.
  2. 1 min later

  3. 2:07 · watch on youtube.com↗

    Our reading

    Executing a roadmap and staying at the AI frontier are opposed ways of working, so asking the same people to do both is very hard.

    Their wordsIt feels like if you're running a product team, it feels like it's everyone's job to execute the road map at a high level and to stay at the frontier at the same time. And that's really, really hard because these are very opposed ways of working.
  4. 1 min later

  5. 2:47 · watch on youtube.com↗

    Our reading

    A product organisation should run like a research lab, or at least add research-lab elements.

    Their wordsMy contention that you should run your product org like a research lab or you should add some research lab lab elements to your product org.
  6. 3 min later

  7. 5:18 · watch on youtube.com↗

    Our reading

    AI makes a labs team of a single person possible.

    Their wordsWhat is amazing about AI is it allows you to have a labs team of one.
  8. 1 min later

  9. 6:01 · watch on youtube.com↗

    Our reading

    A labs team should expect to throw away about 90% of what it makes.

    Their wordsOn a labs team, you are going to expect to dispose of like 90% of what you make. You try it and then you throw it away.
  10. 2 min later

  11. 7:43 · watch on youtube.com↗

    Our reading

    In the AI age, an experimental team should probably be one or two people at most.

    Their wordsI think in the AI age, I I call it a two-slice team. Like you want one or two people max.
  12. 1 min later

  13. 8:37 · watch on youtube.com↗

    Our reading

    On a labs team the feedback loop between making something and knowing whether it is good matters most, and the tightest loop is building for yourself.

    Their wordsThe biggest thing that that matters on a labs team is making the feedback loop as tight as possible between making something and knowing if it's good. And the tightest feedback loop is making something for yourself.
  14. 1 min later

  15. 9:20 · watch on youtube.com↗

    Our reading

    A lab should run many experiments in parallel, even competing approaches to the same problem.

    Their wordsyou should be building many experiments in parallel, even if those experiments are trying to do the same thing. Try competing approaches to the problem.
  16. 7 min later

  17. 16:27 · watch on youtube.com↗

    Our reading

    Novelty feels exciting, so whether an experiment is really better can only be judged by usage over time.

    Their wordswhat is new feels very exciting, but the question is when it's a month later, is it actually any better? It's hard to know unless you use time and usage over time as a filter.
  18. 1 min later

  19. 17:05 · watch on youtube.com↗

    Our reading

    Building something that disrupts your own product is an old idea that is only now starting to work, because AI lets teams move so fast.

    Their wordsthe idea of doing a self-disruption of building something that then disrupts your own product has been around for a while, but is actually starting to work now in AI because you can move so fast.
  20. 1 min later

  21. 18:11 · watch on youtube.com↗

    Our reading

    A product organisation set up well welcomes new model releases instead of dreading them.

    Their wordsThe way that you know this is working is you will welcome moments when new models drop and be excited about them instead of dreading them.