Julia Schottenstein on M&A Strategy, dbt Labs, and Why Worse Is Better

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Julia Schottenstein
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Julia Schottenstein on M&A Strategy, dbt Labs, and Why Worse Is Better
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Julia Schottenstein on M&A Strategy, dbt Labs, and Why Worse Is Better

Julia Schottenstein was a VC at NEA before joining dbt Labs as a product leader, where she leads dbt Cloud and led the acquisition of Transform (a semantic layer startup). She came to dbt after losing the investment to Sequoia and then asking the founder for a job. This episode covers M&A strategy for founders, the dbt origin story, open source business models, pricing, and product philosophy — with more frameworks per hour than most episodes in the corpus.

Key ideas

  • M&A is always about creating plan Bs. The time to start building acquirer relationships is before you need them. For any company, Schottenstein estimates there are only two to three natural buyers who would find the acquisition genuinely strategic. Identifying them early and maintaining open, friendly relationships — even while competing — preserves optionality.
  • Inflict pain, with a smile. The strategy for getting noticed by a potential acquirer: compete directly in the area where the target company has a competitive advantage, and make it impossible for the acquirer to ignore you. Transform did this to dbt Labs by being vocal and technically sharp on the semantic layer — the exact problem dbt was solving too slowly. This created the strategic urgency that led to the acquisition.
  • People, market, product, distribution. Schottenstein’s four-dimension framework for evaluating early-stage companies (or deciding whether to join one): people (can you trust this CEO to lead?), market (is it growing with space for a new entrant?), product (do users love it enough to evangelize it?), distribution (does the company have an unfair advantage in how it reaches customers?). You will not get 10 out of 10 on all four; identify the weakest dimension and decide whether you can help de-risk it.
  • Worse is better. Schottenstein’s product mantra: shipping a good-enough product is almost always better than waiting for the perfect one. You cannot know how users will respond until they use it. Technical debt from shipping is a champagne problem — it means people are using the product.
  • dbt’s flywheel: power, simplicity, openness. dbt succeeded because it reduced friction to near zero for entry (open source, SQL-native), was genuinely simple to use, and built a community of 50,000+ in Slack who served as both product testers and evangelists. The cloud product monetises through stateful interactions and cross-team collaboration features, which open source does not address.

Topics covered

  • How Schottenstein lost the dbt investment to Sequoia, then joined the company instead
  • What made dbt extraordinary in 2019: cloud data warehouse timing, identity-forming product
  • Four-dimension framework for evaluating early-stage companies
  • M&A strategy: when to start, who the natural buyers are, how to get noticed
  • The inflict-pain strategy: competing to create urgency, staying friendly to preserve optionality
  • The Transform acquisition: semantic layer, dbt’s gap, and why it worked
  • Being transparent about exploring M&A in distressed conditions
  • Open source business model: what dbt keeps open vs. what it monetises
  • Pricing and willingness to pay: how dbt ran its first-ever pricing change
  • dbt values: more concerned with value creation than value capture; transparency wins
  • The algorithm rope-tying exercise: getting the whole team to own a new system
  • ‘Worse is better’ and ‘tech debt is a champagne problem’ as PM operating principles
  • T-shaped generalist: how VC skills (network, context-switching, pattern matching) transfer to product

See also