May 27, 2023 · 1h 36m · news
Tomer Cohen: Why LinkedIn Stories Failed; How LinkedIn's Feed Was Born; AI Startups | E1019 · 20VC with Harry Stebbings
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this interview, LinkedIn's Chief Product Officer Tomer Cohen joins Harry Stebbings to discuss his journey to product leadership, the core frameworks driving LinkedIn's evolution and feature pivots, and the transformative impact of generative AI on business, organizational structures, and the future of knowledge creation.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Harry holds 16.7% of the talking time here. How this is scored →
speaking balance: gold is Harry, purple is the guest (3 minute bins)
When Harry asks whether product development is art or science, Tomer explicitly pushes back, noting the question created tension because delineating art from science is impossible.
Hardest push from Harry ▶ 1:15:55 Harry rejects comparison between search engines and AI scrapingHarry refuses Tomer's framing that AI content scraping is like search indexing, pointing out that AI directly steals user traffic rather than routing users back to the publisher.
Biggest teaching moment ▶ 12:03 Tomer re-educates on the hidden emotional drivers of B2B software purchasingTomer breaks down how purchasing B2B software is not a functional spreadsheet task but a high-stakes, career-risking emotional job centered on building internal consensus.
Harry holds his own ▶ 1:10:08 Harry challenges Tomer's assertion on diminishing data moatsHarry uses concrete industry examples like Navan/TripActions to challenge Tomer's assertion that pre-trained LLMs diminish the power of proprietary data.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Harry as informed peer | Guest teaching | Guest disagreement | Harry pushing back | Why |
|---|---|---|---|---|---|---|
| Tomer Cohen's Journey to LinkedIn CPO | 0 | 0 | 0 | 0 | Harry sets a welcoming tone and asks standard background questions about Tomer's career journey. Tomer recounts his trajectory from chip design to meeting Reid Hoffman and joining LinkedIn. | |
| Profound Lessons from Reid Hoffman | 2 | 3 | 2 | 1 | Harry asks whether product design is art or science. Tomer gently rejects the binary premise, noting it created tension for him because art and science are deeply interwoven. | |
| The Three Product Profiles: Visionary, Craftsman, or Operator | 4 | 2 | 1 | 2 | Harry references Shreyas Doshi's three product archetypes (Visionary, Craftsman, Operator) to prompt Tomer. Tomer avoids locking himself into a single box, emphasizing his love for all three aspects. | |
| The 'Job to Be Done' Framework at LinkedIn | 1 | 4 | 0 | 0 | Tomer schools the host on humanizing product needs, explaining that B2B buying is deeply emotional and social rather than purely functional. Harry listens intently while prompting with quick follow-ups. | |
| The Role of Founders in Corporate Innovation | 4 | 3 | 2 | 2 | Harry cites Shopify's VP of Product claiming innovation stops when founders leave the CPO role. Tomer politely disagrees with the absolute claim, citing counterexamples like Microsoft, Intuit, and Netflix. | |
| Evaluating LinkedIn's UI and the Impact of Generative AI | 3 | 2 | 2 | 6 | Harry asks a direct and blunt question about LinkedIn's UI feeling outdated. When Tomer pivots to corporate growth statistics, Harry pushes back asking him to go deeper on where product clarity resides. | |
| Rethinking the Social Graph with Topical Recommendations | 4 | 3 | 1 | 3 | Harry references TikTok and Facebook shifting away from social graphs and queries Tomer on connection utility. Tomer highlights the shift toward topical recommendations and follow relationships. | |
| Product Failures: The Ephemeral Story Experiment | 4 | 4 | 2 | 3 | Tomer breaks down the failure of LinkedIn Stories as misunderstanding creator intent. Harry presses on the importance of speed to market citing Alex Schultz, but Tomer reframes the key metric as product-market fit rather than launch speed. | |
| Evaluating Post-Launch Data with Conviction | 2 | 3 | 0 | 0 | Harry asks how product teams decide between early data signals and product conviction. Tomer outlines the distinction between building on pre-existing evidence versus hypothesis-led conviction. | |
| Structuring Collaborative Product Jams | 1 | 2 | 0 | 1 | Tomer outlines how LinkedIn rebranded product reviews into collaborative Product Jams. Harry asks about remote versus in-person discussion quality. | |
| Maintaining Priority and Accountability Post-Jam | 2 | 2 | 0 | 1 | Harry asks how accountability is maintained post-jam when multiple parties are involved. Tomer explains decision framework roles like recommenders, approvers, and single decision-makers. | |
| The Turnaround of the LinkedIn Feed | 3 | 2 | 3 | 6 | Tomer shares the turnaround story of the LinkedIn Feed. When Harry repeatedly presses Tomer to give his feed product a numerical rating out of 10, Tomer persistently deflects to qualitative measures. | |
| Balancing Short-Term Revenue and Long-Term Innovation | 5 | 3 | 2 | 5 | Harry challenges grandiose corporate visions like Stripe's 'increasing internet GDP', noting it means nothing to a freelancer needing clients. Tomer explains how job-to-be-done frameworks prevent losing touch. | |
| Leading Product Teams in the Age of AI | 2 | 3 | 0 | 0 | Harry asks how AI alters product leadership skills. Tomer uses analogies of white-water rafting and shift from chef to ingredient provider to explain loss of deterministic control. | |
| Enterprise AI: Bundled vs. Unbundled Preferences | 4 | 2 | 1 | 2 | Harry puts forward a strong thesis that enterprise buyers are inherently lazy and prefer bundled AI suites. Tomer agrees, elaborating on how central AI models collapse multi-step workflows. | |
| AI-Driven Productivity and the Future of Code Creation | 6 | 4 | 3 | 6 | Harry cites GitHub stats and asks where value accrues in AI. When Tomer suggests data advantage is diminishing due to pre-trained models, Harry directly pushes back using specific proprietary data examples like Navan/TripActions. | |
| New Team Structures and the Craft of Prompt Engineering | 1 | 3 | 0 | 0 | Tomer discusses prompt engineering as a core competency, giving an example of asking AI to explain quantum physics in a PBS style for a 10-year-old. | |
| Content Discovery, Scraping, and the Evolving Internet Business Models | 5 | 3 | 2 | 6 | Harry confronts Tomer on AI models scraping publisher data without compensation. When Tomer equates it to traditional web search indexing, Harry refuses the analogy, pointing out LLMs consume the user visit directly. | |
| AI Safety, Regulation, and Responsible Principles | 3 | 3 | 2 | 2 | Harry asks whether AI models should be politically correct. Tomer separates fundamental model objectivity from application-level safety guidelines. | |
| The Next Frontier of AI: Generating New Knowledge | 2 | 4 | 0 | 1 | Tomer explains the transition from AI analyzing existing knowledge to generating net new scientific knowledge. Harry asks if generating new knowledge is synonymous with AGI. | |
| Education in the Age of AI: The Growth Mindset | 4 | 3 | 1 | 3 | Harry questions traditional schooling relevance, sharing how ChatGPT produced a complex German real estate analysis in seconds. Tomer argues growth mindset is the ultimate enduring skill. | |
| Quick Fire Round | 3 | 3 | 2 | 3 | In the quick fire round, Tomer answers on hiring, embarrassing products, intermittent fasting, and Microsoft's OpenAI partnership. Harry gently pushes back on intermittent fasting causing cognitive decline. |