Lenny's PodcastM&A, competition, pricing, and investing | Julia Schottenstein (dbt Labs)
CHAPTERS
- 0:00 – 1:01
Cold open: Using M&A to create “Plan Bs” and get noticed by strategic buyers
Julia opens with her core philosophy on acquisitions: M&A is about creating optionality before you need it. She argues that most startups only have a tiny set of truly strategic buyers and explains how to get on their radar without burning bridges.
- •M&A as “Plan B” creation and keeping doors open
- •Reality check: usually only 2–3 highly strategic buyers for a company
- •“Inflict pain” on a potential acquirer by pressuring their strategic advantage
- •Do it in a friendly, partner-like way—don’t posture too aggressively
- •Avoid prematurely shutting down conversations with incumbents
- 1:01 – 4:19
Show setup: Why this episode focuses on dbt Labs, M&A, competition, pricing, and open source
Lenny introduces Julia and frames the episode around dbt Cloud, the Transform acquisition, and practical playbooks for founders and product leaders. Sponsors and logistics follow before the interview begins.
- •Julia’s role leading dbt Cloud product at dbt Labs
- •Episode themes: M&A process, competition, pricing, open source, product frameworks
- •Transform acquisition context (Lenny as investor; Julia on acquirer side)
- •Sponsor segments before the interview begins
- 4:19 – 8:24
Julia’s background and the unusual path from VC to product at dbt Labs
Julia explains her career arc and how her interests in business, technology, and markets map to both investing and product. She shares the story of discovering dbt, trying (and failing) to invest, and ultimately deciding to join the company to help build it.
- •Former early-stage investor at NEA focused on dev tools/infra/data
- •Discovery of dbt and recognizing unusually strong user identity/enthusiasm
- •Market timing: rapid growth of cloud data warehouses (e.g., Snowflake era)
- •Lost the deal to Sequoia; personal investment attempt vetoed
- •Chose to join dbt Labs to “jump on the runaway train”
- 8:24 – 11:11
A VC-style framework for evaluating early companies: people, market, product, distribution
Julia shares the four dimensions she uses to judge early-stage company potential—the same framework she suggests for candidates considering joining a startup. She describes what “good” looks like in each category and how candidates can assess fit.
- •Four factors: people (founder), market, product, distribution
- •Founder evaluation: trust, vision + operational detail (Tristan example)
- •Market evaluation: growth and opportunity for a new entrant
- •Product evaluation: talk to users and listen for genuine excitement
- •Distribution as a decisive advantage; consider how you can de-risk weak spots
- 11:11 – 12:06
How to recognize product-market fit: the “can’t stop talking about it” signal
Lenny and Julia discuss the emotional and behavioral indicators of real product pull. Julia emphasizes user “chatter” and sharing behavior as a leading indicator that the product is resonating and can drive organic growth.
- •PMF shows up as users evangelizing unprompted
- •Strong signal: users can’t stop talking about the product
- •Sharing with teammates and across companies indicates real pull
- •Evangelism reduces distribution burden by turning users into marketers
- 12:06 – 13:11
Distribution strategies: open source, bottom-up adoption, and enterprise sales strengths
Julia contrasts different go-to-market archetypes and what “distribution advantage” looks like. She uses dbt’s open-source ecosystem as an example of low-friction bottom-up adoption while noting some products win via enterprise, top-down selling.
- •dbt’s ecosystem advantage: open source lowers adoption friction
- •Bottom-up/product-led motion: users start without talking to sales
- •Enterprise motion: success depends on complex sales capability and networks
- •Assess whether a company is truly strong in the motion it’s pursuing
- 13:11 – 16:03
M&A strategy for founders: build a strong offense, then engineer optionality
Julia lays out how founders should think about M&A before they’re forced into it. She argues the best negotiation position is having a viable standalone path, but when that’s not true, founders must intentionally create alternatives and buyer interest.
- •Start thinking about M&A when you don’t need it
- •Best leverage comes from being able to stay independent (do nothing)
- •Many companies ultimately need M&A; plan for that reality
- •Create “Plan Bs” and maintain optionality over time
- •Get on strategic buyers’ radar without antagonizing them
- 16:03 – 18:01
Case study: Transform acquisition and the “inflict pain, stay friendly” playbook
Julia explains how Transform pressured dbt in an adjacent product area (semantic layer) while positioning as a partner. That mix—credible product threat plus collaborative posture—made Transform both visible and acquirable, and eased post-acquisition integration.
- •dbt moving into semantic layer/metrics consistency territory
- •Transform’s advantage: deep product focus and early technical solutions
- •dbt’s advantage: distribution and ecosystem scale; product lag in new area
- •Transform marketed loudly (pressure) while presenting as a partner (friendly)
- •Partner integrations pre-acquisition reduced integration friction after the deal
- 18:01 – 20:27
How dbt thinks about competition: hold vision, grow the pie, lean into strengths
Julia shares dbt’s codified competition philosophy, emphasizing focus and long-term thinking. The team aims to avoid distraction, expand the overall market opportunity, and reserve a few strategic “standards” areas to defend strongly.
- •Pillar 1: hold true to vision; ignore noise and shade
- •Pillar 2: grow the pie with partners; market expands with new use cases (e.g., ML)
- •Pillar 3: lean into strengths; foster ecosystem for complementary solutions
- •Defend core standards: transformations and semantics together for users
- •Take a long-term view on competitive dynamics
- 20:27 – 26:36
Why dbt became the default: simplicity, openness, flywheels, and timing from consulting roots
Julia breaks down what made dbt’s rise durable: a simple, approachable workflow that expanded who could do production-grade data work, combined with open-source distribution and ecosystem network effects. She adds a key origin story: dbt grew out of a consulting practice that forced continuous, real-world iteration.
- •Power in simplicity: inviting SQL users into production-quality workflows
- •Guardrails that improve data quality while keeping onboarding easy
- •Open source drives low-friction adoption, sharing, and network effects
- •Flywheel: community usage attracts partners; standardization unlocks ecosystem tooling
- •Timing + consulting roots (Fishtown Analytics): 2 years of hands-on pain discovery feeding product improvements
- 26:36 – 29:20
Team offsite as systems thinking: the rope-and-people graph exercise to internalize change
Julia describes a memorable offsite exercise designed to help the entire team deeply understand a major algorithm change. By physically modeling a graph with people as nodes and rope as edges, the team slowed down, absorbed edge cases, and built shared ownership.
- •Goal: help team internalize a major “zero-to-one” algorithm change
- •Physical simulation: engineers as nodes, rope as edges, sticky notes as labels
- •Benefit: forces shared understanding; prevents a few experts from running ahead
- •Creates memorable, mission-centric moments that increase ownership
- •Applied to a shift in how transformation graphs/DAGs are built
- 29:20 – 31:48
Deciding what stays open source vs. what’s proprietary in dbt Cloud
Julia explains dbt’s open-core boundary: keep the core transformation “guts” open as a standard, while monetizing the scalable, collaborative, stateful development and production experience in dbt Cloud. The dividing line is largely about state and multi-team collaboration workflows.
- •Open source = core transformation logic and standards layer
- •Proprietary cloud = “supercharging” dev lifecycle and productionization at scale
- •Reserve stateful interactions and cross-team collaboration for cloud product
- •Ecosystem health requires the core standard to remain open
- •Open-core model: open adoption + paid acceleration
- 31:48 – 34:26
Pricing and willingness to pay: start earlier, learn elasticity, and treat pricing as evolving
Julia shares dbt’s lessons on pricing: avoiding pricing conversations is a trap, and it’s better to test willingness-to-pay before and during product building. She discusses dbt’s value-creation mindset and what they learned from their first-ever pricing change.
- •Many startups delayed pricing due to “growth at all costs” era dynamics
- •You don’t get to avoid pricing conversations—only choose when to have them
- •dbt value: customers see it as a meaningful fraction of warehouse spend; pricing captures only a small slice
- •First pricing change provided critical elasticity learning
- •dbt Cloud competes with dbt Open Source by design; must be clear what users pay for
- 34:26 – 36:34
Running a pricing change: cross-functional process and interviewing tactics
Julia describes how dbt approached pricing changes as a company-wide effort involving finance, product, and product marketing. She notes that customers rarely state exact willingness to pay, so teams rely on relative-value methods and “too cheap/too expensive” anchoring to find a workable range.
- •Pricing work is cross-cutting: finance models + customer discovery + product changes + comms
- •Talk to customers—spreadsheets alone won’t solve pricing
- •Track conversion rates and churn/turn rates to measure impact
- •Interview approach: “no-brainer,” “fair,” and “too expensive” thresholds
- •Gather enough data (dozens of conversations) to triangulate a decision
- 36:34 – 38:52
When to be public about selling: Hail Mary transparency, buyer sets, and outreach phrasing
Julia reacts to the idea of publicly stating you’re selling, endorsing transparency when runway is short. She explains how acquirers often buy teams, why having a “dataroom” and team story matters, and how founders should communicate depending on how much time they have left.
- •If you’re in a Hail Mary situation, transparency can widen the net and reduce stigma
- •Simple outreach can work: what didn’t pan out, what’s strong, and that you’re running a process
- •Acqui-hires often hinge on the team; make that explicit and organized
- •Build a buyer set (~dozen), then narrow based on fit and constraints
- •Messaging varies by runway: avoid explicit M&A talk if you have time; be direct if you don’t
- 38:52 – 46:31
Using connections and corp dev: planting seeds early, getting introductions, and leveraging investors
Julia and Lenny emphasize that many acquisitions start from relationships built long before the transaction. Julia advises founders to use corporate development teams to get routed to internal deal sponsors, and to lean on investors’ networks without guilt when things aren’t working out.
- •Buyers should know you before the acquisition moment—relationships matter
- •Use corp dev meetings as an entry point; ask for sponsor intros (product/GM)
- •Reframe early talks as partnerships/collaboration if you have runway
- •Investors can help connect you; they understand portfolio risk and long games
- •Founders shouldn’t stay trapped out of obligation to return capital
- 46:31 – 52:02
M&A market outlook + dbt values and community-driven product culture
Julia gives a measured forecast: as time passes from the 2021 peak, valuation expectations reset and more attractive assets come to market, pulling buyers back in. The conversation then turns to dbt’s values—transparency, humility, and value creation—and what it’s like to serve a large, opinionated community.
- •Market factors slowing M&A: integration burden, headcount constraints, macro uncertainty
- •Why it may pick up: better assets available, founder expectations reset over time
- •dbt values: value creation over capture, transparency wins, humility, work done well as its own end
- •Community-driven culture: many employees came from the community itself
- •Operating with opinionated users: broad internal product empathy + constant feedback loops (50k Slack community)
- 52:02 – 54:08
Shipping and iteration mindset: “worse is better” and tech debt as a sign of usage
Julia shares her bias toward learning through shipping rather than chasing perfection. She illustrates with dbt Cloud’s early scheduler implementation: a naive approach was fine at the start, and scale-driven tech debt only became worth solving once real usage existed.
- •Mantras: “Worse is better” and “Tech debt is a champagne problem”
- •Shipping is the moment you can truly learn user behavior and edge cases
- •Early scheduler example: simple loop-based approach that worked initially
- •Scale later forced rebuilds (thousands of companies, millions of runs)
- •Don’t over-engineer distributed systems before you have real demand
- 54:08 – 1:00:50
How VC skills translate to product leadership + lightning round and closing
Julia maps venture capital skills—context switching, taste-building, networking, and power-law thinking—onto product leadership and strategic bets. The episode closes with a lightning round covering books, interview questions, favorite products, and how to find Julia online.
- •VC skill transfer: network investment as an underrated PM lever
- •T-shaped generalist advantage: credibility across finance, business, and product
- •Power-law mindset: look for bets that can bend the business trajectory
- •Lightning round: books (Range, Quiet, biographies), interview question on self-learning, “single-thread the team”
- •Where to reach her: Twitter and dbt community Slack
