The "Inflict Pain" M&A Strategy: How dbt Labs Became the Default Data Standard

Julia Schottenstein reveals unconventional M&A tactics, competition philosophy, and the strategic decisions that made dbt the default data transformation standard.

Julia Schottenstein shares insider perspectives on M&A strategy, competitive positioning, and product decisions that helped dbt Labs become an essential part of the modern data stack.

Key Takeaways

• The "inflict pain" M&A strategy involves creating competitive pressure on potential acquirers while maintaining friendly relationships to preserve optionality
• Only 2-3 companies will ever find your startup extremely strategic, making targeted relationship building more effective than broad outreach
• dbt's success came from two years of manual consulting work that revealed real pain points before building the product at scale
• "Worse is better" product philosophy emphasizes shipping imperfect solutions quickly to learn from users rather than pursuing perfection
• Pricing conversations should happen before building features, not when sales teams struggle to sell completed products
• Open source creates powerful network effects when the core transformation logic remains free while charging for collaboration and operational features
• Competition strategy should focus on holding true to vision, growing the overall market, and leaning into unique strengths rather than responding to every competitor move

Timeline Overview

• 00:00–20:00 — VC to Product Transition: Julia's unusual career path from NEA investor to dbt Labs product leader, discovering dbt's unique user identity formation, and losing the Sequoia deal
• 20:00–40:00 — M&A Strategy Deep Dive: The "inflict pain" approach, Transform acquisition case study, maintaining friendly relationships while creating competitive pressure, and timing M&A conversations
• 40:00–60:00 — dbt's Success Formula: Power of simplicity, open source network effects, two-year consulting foundation, and timing alignment with cloud data warehouse explosion
• 60:00–80:00 — Competition and Pricing Philosophy: Three-pillar competition approach, value creation over value capture, first-ever pricing change lessons, and willingness to pay research methods
• 80:00–100:00 — Product Development Insights: Open source vs proprietary decision framework, team ownership through manual algorithm exercises, "worse is better" shipping philosophy
• 100:00–END — Leadership and Culture: T-shaped generalist advantages, network investment strategies, dbt Labs values, and working with opinionated user communities

The "Inflict Pain" M&A Strategy: Competitive Pressure with Friendly Relationships

Julia's approach to M&A represents a sophisticated understanding of acquisition dynamics that goes beyond traditional relationship-building to create strategic necessity for potential buyers.

• The Strategic Scarcity Principle: Julia emphasizes that "for any one company there's only ever two to three buyers that find what you're building to be extremely strategic." This scarcity requires focused relationship building rather than broad market approaches, making targeted competitive pressure more effective than general networking.
• Creating Unavoidable Awareness: The "inflict pain" strategy involves identifying "the area that you bring a competitive advantage" and making "it impossible for them to not notice you because that's when they're going to have their ears perk up." This approach forces potential acquirers to evaluate your company as a strategic response rather than opportunistic evaluation.
• The Transform Case Study: dbt acquired Transform because Transform had "figured out some of the technical challenges" in semantic layers while positioning themselves as a "friendly partner" to dbt's community. Transform created competitive pressure by being "vocal and loud about how their semantic layer solves these really hard technical problems" while maintaining integration possibilities.
• Friendly Competition Paradox: Julia warns against founders who "prematurely will shut down a conversation" or "take too competitive of a stance" because "M&A is all about creating plan Bs and you don't want to shut that door down prematurely." This requires balancing competitive pressure with relationship preservation.
• Timing and Desperation Management: The strategy works best "when you don't need one" because having "a viable alternative which is do nothing, stay the course" provides "the upper hand in absolutely every single M&A conversation." For companies without runway, transparency about the situation becomes more important than subtlety.
• Network Leverage for Crisis: When companies face dire circumstances, Julia recommends using "your venture capitalist has a really big network" because "your investors understand that they're not making their money back and what they want to do instead is have you end up at a really great company" for long-term relationship benefits.

dbt's Success Formula: Simplicity, Timing, and Deep Problem Understanding

dbt's evolution from Fishtown Analytics consulting firm to data transformation standard reveals how manual work can inform product development and market timing can amplify simple solutions.

• The Two-Year Manual Foundation: Before scaling dbt as a product, the founders spent "almost two years" doing consulting work where "they were building dbt and using dbt to help them do their jobs better in supporting their clients." This hands-on experience meant "whenever they encountered paper cuts or friction or the workflow was taking longer than they expected they would build that into dbt."
• Power Through Simplicity: Early critics said "what's so special about dbt we have a SQL templating tool at our company we built one in house like this is really straightforward and simple." Julia argues "that is the power of it" because dbt created "this nice framework where it's harder to mess up, keeps data quality really high but it is pretty simple to get started and learn."
• Identity Formation Over Tool Usage: What distinguished dbt was how users described "their experience on dbt was like unlike anything I had heard before it was much more of an identity for them than just a tool that they were using to get their job done." This emotional connection created evangelism that traditional tools couldn't match.
• Strategic Timing with Cloud Warehouses: dbt's success aligned with explosive growth in cloud data warehouses when "snowflake went from a 4 billion dollar company to a 12 billion dollar company" in 2019. This timing created "chaos that was really the opportunity for dbt because they created some orderliness and structure to the way that people worked with their data."
• Open Source Network Effects: dbt's open source core created powerful flywheels where "DBT is really easy to get started with at your company with reduced friction" leading to organic sharing, diverse use case discovery, and partner ecosystem development that "attracts Partners to want to build for DBT" and creates "integrated modern data ecosystem" benefits.
• Horizontal Scaling Strategy: The consulting experience revealed that data transformation problems existed across industries and company sizes, enabling dbt to "build a truly horizontal company" rather than focusing on specific verticals or use cases, dramatically expanding addressable market size.

Competition Philosophy: Vision, Growth, and Strength Focus

dbt's three-pillar competition approach demonstrates how successful companies can maintain strategic focus while building collaborative ecosystems rather than purely defensive postures.

• Hold True to Vision: The first pillar involves maintaining conviction in strategic direction despite competitive pressure because "most of that is just noise if you have a lot of conviction that you're going in the right journey you want to just keep your eyes straight ahead and run your best race and not be too distracted by what maybe some critics are saying."
• Grow the Pie Philosophy: Rather than fighting for market share, dbt focuses on expanding total market opportunity by working "with our partners in our ecosystem to make the opportunity set even larger." Julia cites the expansion from "reporting and bi use cases" to "operationalizing their data now with this big wave of ml" as evidence that "the pie continues to grow."
• Lean Into Strengths: The third pillar involves strategic focus on core capabilities while "leaving space for our ecosystem to offer solutions to our users" rather than trying to build everything. dbt concentrates on "transformation standard as well as our semantic standard because we believe those two are better served together" while partnering on other functionality.
• Long-Term Competitive Thinking: Julia emphasizes "generally speaking when it comes to competition we take a really long-term view" because short-term competitive responses often distract from building sustainable advantages. This patience allows dbt to respond strategically rather than reactively to competitive threats.
• Ecosystem Partnership Strategy: By fostering "an ecosystem where we can partner with lots of companies in the modern data stack," dbt creates network effects that make competitive displacement more difficult while providing users with comprehensive solutions without dbt building every component.
• Strategic Boundary Definition: dbt clearly defines which areas they "do want to hold our ground" (transformation and semantic standards) versus where they'll partner, preventing scope creep while maintaining strategic control over core value propositions that drive their business model.

Pricing Strategy: Value Creation Over Value Capture

dbt's approach to pricing reveals sophisticated thinking about customer value perception, competitive positioning, and long-term relationship building in open source business models.

• Value Creation Priority: dbt operates under the philosophy "we're more concerned with value creation than value capture" which Julia explains means when customers describe dbt as "20 to 35% as valuable as what they spend on their cloud data warehouse but we charge our customers is a very small fraction of that 20 to 35%."
• Competitive Against Yourself: A unique dynamic emerges where "when we lose a deal we most often lose it to DBT open source and we like it that way we're happy to lose to ourselves" because it validates the core product value while indicating pricing sensitivity for proprietary features.
• Preemptive Pricing Conversations: Julia advocates for pricing discussions "before you build the product" rather than "when your sales team's trying to sell something and people aren't excited about what you've built aren't willing to pay for it." This prevents building features that don't justify their development costs.
• Price Elasticity Learning: dbt's "first ever pricing change in the company history" taught them "a tremendous amount when you have that event because you get to test the price elasticity of your customers" which becomes "so important to learn that lesson while the company's still smaller the stakes are lower."
• Relative Value Research Methods: During pricing research, dbt found "people aren't very willing to share explicitly what they will pay but there's some tools that we used on relative value people most think about what is the relative value of DBT in their Cloud warehouse" to understand pricing sensitivity without direct questions.
• Cross-Functional Pricing Process: Effective pricing requires "an all hands on deck conversation" because "it's pricing is so crosscutting because it's a finance discussion as well you're kind of modeling out things in spreadsheets" while also requiring customer conversations and product implementation considerations.

Product Development Philosophy: Imperfection and User Learning

Julia's product philosophy challenges perfectionist tendencies common in product development by emphasizing rapid user feedback cycles over comprehensive pre-launch optimization.

• "Worse is Better" Implementation: This philosophy "helps combat this perfectionism because perfect doesn't exist and you should instead go with good enough because when you ship that's the moment when you get to learn a lot from your users and you just can't anticipate it" despite extensive user research and edge case planning.
• The Naive Scheduler Example: dbt's initial cloud scheduler was "pretty naive like we were a little embarrassed by it it was a big old for loop over a big old jobs table" but "it got the job done." This simple solution scaled to "8,000 companies using our scheduler" and "10 million runs per month" before requiring architectural improvements.
• Tech Debt as Success Indicator: The philosophy "Tech debt is a champagne problem" reframes technical debt as evidence of product-market fit because "that means people are using the product" rather than a failure of engineering planning. This prevents premature optimization that solves non-existent problems.
• User Learning Primacy: Julia emphasizes "you just can't anticipate" user behavior "until you ship" despite extensive research and planning. This learning can only happen with real usage patterns rather than hypothetical scenarios or beta testing with limited scope.
• Scaling Solutions Appropriately: The scheduler example demonstrates that "what we didn't need at launch was a distributed scheduler with go workers and rabbit MQ we just didn't need it because we had no users." This prevents over-engineering solutions for scale problems that may never materialize.
• Manual Algorithm Understanding: Julia's creative approach of using "spool of rope and sticky notes" to make her engineering team "work through the new algorithm extremely slowly step by step" ensured "everyone had a role to play" so "you couldn't leave that exercise without knowing exactly what was going on" rather than having knowledge concentrated in a few team members.

Strategic Open Source Decisions: Core vs. Proprietary

dbt's open source strategy demonstrates sophisticated thinking about which capabilities create network effects versus which generate revenue, enabling sustainable business models while maintaining community benefits.

• Core Logic Open Source: dbt keeps "the guts of the data transformation" open source "where you describe your business logic" because this creates the network effects and ecosystem benefits that drive adoption. This core functionality becomes the standard that others build around.
• Proprietary Collaboration Features: dbt reserves "stateful interactions and also any kind of cross team or structural collaboration" for their cloud offering because these features primarily benefit larger organizations willing to pay for enhanced coordination capabilities.
• Ecosystem Importance: Julia emphasizes "to us ecosystem is really important so it's important that that remains open source" because the network effects and partner integrations depend on accessible standards that don't require proprietary licenses or fees for basic integration.
• Open Core Model Benefits: This approach enables dbt to "supercharge that experience with kind of an open core model and build proprietary software that makes people much more successful using DBT" while maintaining the community benefits that drive adoption and ecosystem development.
• Strategic Boundary Clarity: Having clear principles about "what you believe should be open source or what is the open standard that really matters" prevents decision paralysis when new features could go either direction, enabling consistent strategic choices.
• Community-Driven Development: Because core transformation logic remains open, community contributions improve the product for everyone while dbt maintains revenue opportunities through operational and collaboration enhancements that larger organizations need.

Leadership Transition: From Investing to Product Management

Julia's unusual career path from venture capital to product management reveals transferable skills and perspectives that product leaders can develop regardless of their background.

• T-Shaped Generalist Advantages: Julia describes her "superpower is that I'm a t-shaped generalist" where knowing "a little about a lot of things from finance to business to product" while going "a lot deeper in product in the areas that I specialize in" creates "more credible experiences that I can pull from" when working across organizational functions.
• Network Investment Strategy: From venture capital, Julia learned to "spend a lot of time investing in my network" by building relationships with "operators at other companies that are like DBT Labs that are growing nicely maybe a little bit ahead of where we are" to ask questions about navigation challenges and bring "the best ideas" back to apply internally.
• Risk and Power Law Thinking: Investing background provides perspective that "most investments don't work out you lose the dollars that you put in but all the returns come from these rare events" which translates to product work where "it encourages me to continue to make bets for the company that has the chance of bending the trajectory of our business" rather than only safe incremental improvements.
• Context Switching Abilities: Venture capital work requires "meeting lots of different companies context switching" and knowing "a little bit about quite a lot of different things" to "refine their investment taste" which translates well to product management's need for rapid context switching between different problems and stakeholders.
• Business Model Understanding: Julia's investing background provides deeper appreciation for "how did you navigate open source how did you navigate pricing how did you navigate acquisitions" because these directly impact company valuation and growth trajectories that investors evaluate.
• Strategic Pattern Recognition: Experience evaluating many companies across different stages and business models develops pattern recognition for "what are the uncapped upside opportunities" that can be applied to product decisions and strategic planning within a single company.

Common Questions

Q: What's the most effective M&A strategy for startups?
A: Identify your competitive advantage area and "inflict pain" on potential acquirers while maintaining friendly relationships to preserve optionality.

Q: How do you know if a product will be successful early on?
A: Look for users who "can't stop talking about it" and want to share it with others, indicating identity formation beyond tool usage.

Q: When should pricing conversations happen?
A: Before building features, not after sales teams struggle to sell completed products, to avoid building unmarketable capabilities.

Q: What should be open source vs proprietary in a business?
A: Keep core logic that creates network effects open while charging for collaboration, state management, and enterprise coordination features.

Q: How do you compete effectively without getting distracted?
A: Hold true to your vision, focus on growing the total market, and lean into your unique strengths rather than responding to every competitor move.

Julia Schottenstein's insights reveal how successful product strategy requires balancing multiple stakeholder needs while maintaining strategic focus on long-term value creation. Her experience spanning venture capital and product management provides unique perspectives on competitive dynamics, pricing strategy, and organizational decision-making that can benefit product leaders across different company stages and market contexts.

Practical Implications

• Build relationships with potential acquirers before needing M&A by creating competitive pressure while maintaining friendly partnerships

• Spend significant time manually solving customer problems before building scalable product solutions to understand real pain points

• Ship imperfect solutions quickly to learn from users rather than pursuing perfection that delays valuable feedback cycles

• Conduct pricing research before building features using relative value comparisons rather than direct willingness to pay questions

• Keep core product logic that creates network effects open source while charging for collaboration and operational enhancements

• Focus competition strategy on vision adherence, market growth, and strength leverage rather than defensive responses to competitors

• Invest in operator networks at similar companies ahead of your current stage to learn navigation strategies for upcoming challenges

• Make strategic decisions about which capabilities to build versus partner for based on core value proposition and competitive advantage

• Create memorable team experiences like manual algorithm exercises to ensure distributed understanding of complex technical changes

• Maintain T-shaped skill development combining broad business knowledge with deep product expertise for cross-functional effectiveness

• Use "worse is better" philosophy to combat perfectionism while recognizing that tech debt indicates product usage success

• Leverage venture capital thinking about power laws and risk to make product bets with potential for significant business trajectory changes