20Product: Linkedin CPO, Tomer Cohen on How Linkedin Does Product Reviews, A Post-Mortem on Stories, Linkedin Messenger and Spam & Why the Data Advantage in AI is Diminishing

Posted on 26th May 2023 by Harry

Tomer Cohen is the CPO @ Linkedin. Since joining in 2012, Tomer has served in key leadership roles, helping launch and scale new innovative member and customer experiences. He previously led the growth and development of LinkedIn’s Marketing Solutions portfolio and LinkedIn’s consumer and mobile products. Prior to LinkedIn, Tomer worked as an entrepreneur with Greylock Partners and founded a company in the personal CRM space.

In Today’s Episode with Tomer Cohen We Discuss:

1.) From Israeli Military and Chip Design to CPO @ Linkedin:

  • How did Tomer make his way from the Israeli military to being CPO @ Linkedin?
  • What does Tomer know now that he wishes he had known when he became CPO?
  • What have been some of his biggest lessons from working with Reid Hoffman?

2.) Product: Art or Science:

  • How does Tomer determine whether product is art or science? If he were to put a number on it, what would it be?
  • How does Tomer determine whether to go with his gut vs go with the data on product decisions?
  • How is AI changing the role of product managers and product leaders?
  • What do product leaders and PMs need to do to stay up to date with the latest changes in AI?

3.) Linkedin: Review of Current Products: Feed, Stories, Messenger

  • How does Tomer analyse the success of “the feed” in Linkedin? What worked? What did not work?
  • Why did “Stories” not work in Linkedin? What went wrong? What did they learn?
  • What is Tomer doing to tackle the spam issue in Linkedin? What are the biggest challenges associated?
  • Why does Linkedin still have such poor messaging service? Why is it a difficult problem to solve for?

4.) AI Changes Everything:

  • Why does Tomer believe this wave of AI is the most significant technological shift in our lifetime?
  • Who will win the race in AI; startups or incumbents?
  • Which model will work most efficiently; open or closed?
  • Will we see large enterprises prefer bundled AI options or unbundled with specialised providers?

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