Annotations (13)
“You have to hit two home runs with open source. The first home run is developing an open source technology that gets mainstream adoption. But the second home run, which most people don't think about, is how do you actually build a business on top of it? The open source technology becomes one of your main competitors because anyone can get it for free, including competitors who may have better distribution and customer relationships.”— Alan Tu paraphrasing Ali Ghodsi
Business & Entrepreneurship · Strategy & Decision Making · Technology & Engineering
DUR_ENDURING
Open source needs two home runs: adoption, then monetization
“The founding team from Berkeley's AMPLab made three foundational bets in 2009: cloud computing would be big, data would be big, and open source would be a viable business model. At the time, cloud was still controversial. The seven founders were literally working one floor above the team developing early data center research. They gained conviction around cloud not from market validation but from proximity to the underlying research.”— Alan Tu describing Ali Ghodsi
Strategy & Decision Making · Business & Entrepreneurship · Technology & Engineering
DUR_ENDURING
Conviction from research proximity, not market proof
“The benefit of coming from academia is that you don't have the preexisting notions of what you should or should not do when building a business on top of open source. Before Databricks, the main example was Red Hat, which provided services and support for Linux. Because the Databricks team wasn't aware of the precedents, they thought about things in a very first principles way.”— Alan Tu
Strategy & Decision Making · Business & Entrepreneurship · Philosophy & Reasoning
DUR_ENDURING
Ignorance of precedent enabled first principles
“When Databricks created their proprietary implementation of Spark, there was tension in the community. People felt betrayed when the founders didn't put all the bells and whistles into the open source version. When you're successful with open source, it can be a curse because you become very popular and technologists view you as someone who brought a great thing into the world. Then you need to be willing to be a villain. Most people have trouble making that jump.”— Alan Tu
Strategy & Decision Making · Psychology & Behavior · Leadership & Management
DUR_ENDURING
Open source success creates community expectations that constrain monetization
“Databricks has a lot of different products that are like the address book on a smartphone. The address book is incredibly important, not just for phone calling, but many things expand from it. But no handset maker can charge for the address book. Databricks gives away products like their governance layer for free using open source to get adoption, even though these products are strategic.”— Alan Tu paraphrasing Ali Ghodsi
Strategy & Decision Making · Business & Entrepreneurship · Economics & Markets
DUR_ENDURING
Monetize where you can capture value, not just provide it
“Databricks has a DNA of having an opinion about where the world is going and betting behind it. Going back to the early bets, they said these are the three bets we're making: cloud will be big, data will be big, open source will be a good way to build adoption. Then they said we think the lakehouse will be big, this is where the world should go, this will help customers, and we're going to bet behind that.”— Alan Tu
Strategy & Decision Making · Leadership & Management
DUR_ENDURING
Clear strategic bets enable execution; hedging detracts
“Databricks came up with the term lakehouse, combining data lake with data warehouse. At the time, there was quite a bit of ridicule about this idea. It was almost too clever. But they did the work to educate the market around why the lakehouse architecture was the best of all worlds and why that was the future. Today, the lakehouse is a very real defined category that industry observers have all coalesced around.”— Alan Tu
Strategy & Decision Making · Business & Entrepreneurship
DUR_ENDURING
Category creation requires both product execution and market education
“Databricks embraced open formats and purposefully decided not to charge for storage, which was something Snowflake had historically done. This was strategically disruptive because it enabled customers to keep their data wherever they were storing it. They didn't force customers to move data into Databricks. You could run Databricks on top of where the data already sits.”— Alan Tu
Strategy & Decision Making · Technology & Engineering
DUR_CONTEXTUAL
Architectural disruption: no forced data migration
“Databricks named the company Databricks instead of Spark, even though Spark was already a well-appreciated brand. The name Spark would have given them tremendous awareness and marketing benefits. But from day one, they felt it was going to be more than just Spark. Databricks meant there would be many bricks you could use to solve the broader problem around data. Underneath the naming decision is a reflection of long-termism and thinking about what would set them up to become much more over time.”— Alan Tu
Strategy & Decision Making · Business & Entrepreneurship
DUR_ENDURING
Name chosen for future optionality over immediate brand leverage
“The way Databricks makes decisions has been with a very long-term mentality in mind. Go back to the decision around how they named the company. There are always trade-offs where if you have a shorter time horizon, you might make a certain decision. You might not open source something, you might monetize whatever feature you just came out with. Every single time you make a more short-term-oriented decision, that inevitably opens you up to some sort of vulnerability down the road.”— Alan Tu
Strategy & Decision Making · Leadership & Management
DUR_ENDURING
Short-term decisions create long-term vulnerabilities
“Databricks helps lead the industry to where they think it should go. They identify pain points and come up with solutions that make sense, as opposed to looking at an existing market and doing a me-too product just to expand TAM. One underlying thing Databricks does well is recognizing true value creation as opposed to just monetizing and revenue growth. They have a predictive view of what's going to happen in the future, designing for what the customer doesn't necessarily know they need yet.”— Alan Tu
Strategy & Decision Making · Business & Entrepreneurship · Creativity & Innovation
DUR_ENDURING
Lead market to where it should go, not follow existing
“Databricks was private during the 2022 cycle in growth tech and were able to continue playing offense while public peers were unable to. This accelerated their business. The ability to continue investing behind sales and R&D was something that really benefited Databricks. That experience was informative, and the reasons to go public would have to be sufficiently high to overcome the benefit they've experienced staying private.”— Alan Tu
Strategy & Decision Making · Economics & Markets · Business & Entrepreneurship
DUR_CONTEXTUAL
Private status enabled offense during 2022 downturn
“Databricks gave out free t-shirts that said Delta is Spark on ACID to explain their new Delta product. This was both a technical explanation, ACID being a database acronym for Atomicity, Consistency, Isolation, and Durability, and a clever marketing play. One of Databricks' core competencies is marketing. They understand how to market products to the technical community in ways that resonate.”— Alan Tu
Business & Entrepreneurship · Creativity & Innovation
DUR_ENDURING
T-shirt marketing: technical pun for technical audience
Frameworks (3)
The Two Home Runs of Open Source
A framework for successfully monetizing open source technology
Building a sustainable business on open source requires hitting two sequential home runs. The first is creating an open source technology that achieves mainstream adoption. The second, often overlooked, is building a commercial product differentiated enough that customers will pay for it despite the free alternative. Most companies fail at the second home run because they don't create sufficient differentiation or they're unwilling to be seen as betraying the open source community.
Components
- Achieve Open Source Adoption
- Create Differentiated Commercial Product
- Navigate Community Expectations
Prerequisites
- Strong open source technology with proven adoption
- Technical capability to create meaningfully better proprietary version
- Leadership team willing to make unpopular decisions
- Capital to invest in commercial product development
Success Indicators
- Commercial product has 2x+ better performance than open source
- Customers voluntarily migrate from free to paid version
- Net dollar retention above 120%
Failure Modes
- Insufficient differentiation leads to no paid conversions
- Community backlash damages adoption of both versions
- Competitors with better distribution monetize your open source better than you do
Category Creation vs. Market Following
Framework for deciding whether to lead or follow in market positioning
Companies can either lead the market to where they believe it should go by creating new categories, or follow existing markets with me-too products. Category creation requires having an opinion about the future, educating the market, and betting behind that vision with consistent execution. It produces higher differentiation but requires more investment in market education.
Components
- Develop Conviction About Future State
- Create the Category Language
- Execute on Product and Market Education
- Maintain Consistency
Prerequisites
- Strong product that genuinely solves problems in a new way
- Capital to invest in market education alongside product development
- Leadership conviction to maintain course despite initial skepticism
Success Indicators
- Industry analysts adopt your category language
- Competitors position themselves relative to your category
- Customers use your category terms when describing their needs
Failure Modes
- Market doesn't understand or care about the distinction you're making
- Better-capitalized competitor co-opts your category
- You lose conviction and pivot away before category takes hold
Long-Term Decision Trade-Off Analysis
Framework for evaluating whether short-term gains create long-term vulnerabilities
Every strategic decision involves trade-offs between short-term gains and long-term positioning. Companies that consistently make short-term-oriented decisions accumulate vulnerabilities that compound over time. This framework helps identify when short-term temptations create strategic debt.
Components
- Identify the Short-Term Temptation
- Map the Long-Term Vulnerability
- Assess Strategic Consistency
- Make the Call and Commit
Prerequisites
- Clear long-term thesis about market direction
- Leadership team aligned on long-term vs short-term prioritization
- Stakeholder expectations managed appropriately
Success Indicators
- Consistent decision pattern over multiple years
- Market recognizes your strategic positioning as distinct
- Long-term bets compound into defensible advantages
Failure Modes
- Death by a thousand compromises where each short-term decision seems small
- Market conditions change and long-term thesis becomes invalid
- Company runs out of capital before long-term bets pay off
Mental Models (5)
First Principles Thinking
Decision MakingBreaking down complex problems to fundamental truths and reasoning up from there, rather than reasoning by analogy or precedent. Databricks founders thought from first principles about open source monetization because they lacked awareness of precedents.
In Practice: Academic founders' advantage was thinking from first principles due to ignorance of commercial precedents
Demonstrated by Leg-at-001
Optionality Preservation
Strategic ThinkingMaking decisions that preserve future flexibility rather than maximizing immediate returns. Databric
In Practice: Company naming decision that sacrificed immediate brand leverage for future flexibility
Demonstrated by Leg-at-001
Sunk Cost Fallacy
PsychologyThe tendency to continue investing because of past investment, even when discontinuing would be rational.
In Practice: Open source founders must overcome community sunk cost expectations to commercialize
Demonstrated by Leg-at-001
Value Capture vs Value Creation
EconomicsThe distinction between creating value for customers and capturing that value through pricing. Companies must identify where they can capture value, not just where they provide value.
In Practice: Databricks strategic decision about which features to monetize versus give away
Demonstrated by Leg-at-001
Market Timing
TimeThe importance of when you take action relative to market cycles. Databricks' pr
In Practice: Private market status enabled offensive investment during public market downturn
Demonstrated by Leg-at-001
Key Figures (2)
Alan Tu
15 mentionsPortfolio Manager and Analyst at WCM Investment Management
Ali Ghodsi
8 mentionsCEO and Co-founder of Databricks
One of seven co-founders from Berkeley's AMPLab.
- You have to hit two home runs with open source
Glossary (1)
grok
VOCABULARYTo understand intuitively or by empathy; to establish rapport with
“Even that, it can be hard to grok.”
Key People (1)
Jensen Huang
(1963–)CEO and co-founder of NVIDIA
Concepts (5)
Open Source
CL_TECHNICALSoftware development model where source code is publicly accessible and can be modified by anyone.
Apache Spark
CL_TECHNICALOpen-source distributed computing system for big data processing and analytics.
ACID (Database)
CL_TECHNICALAtomicity, Consistency, Isolation, Durability. Properties guaranteeing reliable database transactions.
ARR (Annual Recurring Revenue)
CL_FINANCIALNormalized annual value of recurring revenue from subscription contracts.
Net Dollar Retention
CL_FINANCIALMetric measuring revenue expansion from existing customers.
Synthesis
Synthesis
Migrated from Scholia