I asked @jmj the least glamorous thing he’s done in his career: ”I ran out of delivery drivers in Kansas City, so I flew there myself and delivered every bouquet of flowers myself. I was doing growth, and we did this Valentine's Day campaign where we were subsidizing the cost. You could buy flowers and get them hand delivered to your door for like $30. It's basically marketing and we charge a nominal amount. We launched this in San Francisco, New York, and Kansas City. I found people to deliver the flowers in San Francisco and New York. And then realized I don't have enough people in Kansas City. So I flew there, and I was driving through Kansas and Missouri, and I just delivered all the flowers myself. The idea was, I just learned how to do things that were very unsexy and to have fun with it. That moment I was like, this is awesome. I'm in Kansas driving flowers to people. They're all happy, and I'm listening to good music. I see a lot of employees at venture firms and companies who just wanna go straight to the top. And I just remember, man, I was delivering flowers. You have to do some of that really grindy grunt work. Back to the founders and companies who will win, I think they all do a certain amount of grindy work and have fun with it."

.@jmj on when a Seed fund should break its portfolio construction rules: "We did Erebor, the $2 billion round. We put a large percentage of one of our funds into that. We got to know Owen, the CEO, but obviously Palmer is a big part of the company. If we had said at IC, 'No, we can't do that deal, we're a seed fund,' then we would've just eliminated a big part of the market. And Erebor is, in five months, one of the fastest growing banks of all time. I said, why would you not put 5 or 10% of your fund into that company? It just makes sense relative to only doing really early stage investing."

Do you have to be in the Bay Area to build a fund-returning portfolio? @jmj has the receipts: "If I look at our Fund 1 returning companies, none of them were in the Bay Area. One was in Singapore. One was in New York. And one was in Miami. And then if I look at Fund 2, our likely fund returning investments, two of them were companies in Canada. Fund 3 probably will be maybe one in Los Angeles. Being a newer fund, we need to be more creative to compete. You need to look outside of the bubble."

Would you leave SF for a tech job in the Midwest? @jmj left San Francisco for Kansas City with a bag of clothes in 2011: "I saw the company on Twitter, which is what I was doing at the time. Mostly just meeting people on Twitter. They put out a job posting for a growth marketer. I DM'd the founder. We got on a call the next day. They said, 'You need to move to Kansas City tomorrow because we have all these other people who want the job.' I left San Francisco and moved to Kansas City without even knowing if it was Kansas City, Missouri or Kansas City, Kansas. I didn't actually know the difference between the two. To me, Kansas City was just a place. I went there with one bag of clothes, and I didn't have a place to live. My friends thought I was absolutely crazy. It was like, why are you leaving the tech ecosystem to go work in the Midwest at some company? The experience taught me that you can do crazy things, pivot your career, literally leave a city and go to Kansas City to start your career. That seemed like a crazy idea at the time.”

The best VCs in the world return the fund on 4% to 7% of their picks. @jmj on why every investor still thinks they're above average: "Right now it's much, much harder to pick a winning company at the seed than it was five years ago. The number of companies and ideas that come to market, and then the pace at which those same companies can be disrupted. When I started Chapter One, I went and spent time with Mike Maples at Floodgate. He's super generous, gave us access to all their historical data and some data from other firms who gave him permission to share. It gave us the picking rates from the best firms, and you could back into your own math on what your portfolio size should look like. I'd be willing to bet that data, while directionally useful today, is not as relevant. Because my realization is that the picking rates have gone down quite a bit across the board. Picking rate is what percentage of companies you invest in end up becoming fund-returning investments. The picking rates historically for some of the best venture firms, the best of the best, Sequoia, USV, the general range would be anywhere between 4% and 7%. So if a tier one fund has a 4% chance of returning the fund on an investment, they need 25 companies in their portfolio to just return the fund. Then you have to ask yourself as a newer manager, are the companies I'm seeing as good as the best firms in the world? For most people, obviously, the answer's no. So you'd have to construct a portfolio to have more companies, because you need more shots on goal. But literally every investor I talk to thinks they're above average. That they source better companies, or they have better judgment. There's a self-awareness that comes by saying, hey, we don't know. We have a feeling we're fishing in the right ponds. But we can't say for a fact that our companies are better than the best firm in the world. So I think bigger portfolios are, in most cases, a really good strategy."

How @jmj took down Tinder with its first push notification: "My first week, Tinder had no ability to send a push notification. We could send transaction notifications, so you have a message or you have a like. But we as a company could not send a push notification. The company just had not invested the resources in building it. So within a week they're like, 'Can you help us figure out how to send push notifications?' Then I was like, you know what? If I send our first push notification to 40 million people, I guarantee you we're gonna have a big day. At the time they gave me a CSV file of the mobile IDs. Literally exported a single file that lived on my hard drive. It was something you would never do as a public company. This was announcing a feature called Super Like. We had to figure out how to get it translated into 40 different languages. One of the things I figured out quickly was that you should rate limit the notifications, because if we just sent 40 million people the same notification at once, Tinder would go down. I ended up doing that by accident once or twice. We took down Tinder. But we sent it out and had our highest daily active day ever. That was within my first two or three weeks. Everyone's like, 'Jeff is a smart guy. He knows how to send push notifications.' It was amazing. I was like, literally I can be a hero at this company. From that they put me in charge of revenue, and we ended up becoming the top grossing app in the world."

.@jmj on why most pivots don’t go far enough: "There's a lot of companies who do a similar thing. They'll do a soft pivot, or an adjacent pivot that's one deviation away from what they're currently doing. When actually, probably the right thing if you're going to pivot, is to actually re-found the company. And that might mean doing something radically different from what you're doing today. The mistake people make when they're pivoting, whether it's their company, their venture firm, their careers, is not being bold enough about what that new direction might be. It's primarily because it's almost like an admission that what you were doing before is wrong. Which might not be the case. It might just be that what you were doing before is no longer the right strategy going forward. You're not underwriting your historical decision-making. You're taking a forward-looking view on what is happening in the world and how you should position your firm or your company. That's what I think people get wrong."

From @jmj on why more founders should pivot, and why you should pivot hard: "In the last five years I've probably had more phone calls with founders around the should-I-pivot conversation. And the one thing I've heard 100% of the time is, 'I didn't know that I could have this conversation with my investors.' I think it was seen as a sign of weakness. Or maybe you're worried, are we losing faith in the company? Does this mean we won't be able to raise the next round? On those calls I always say you should consider pivoting the company if it's at all a thought in the back of your mind. Most of the time the conversation starts with you're getting the same investor update for two quarters in a row. None of the metrics are moving. There’s small iterations on the idea, and there's nothing that's gonna profoundly change the business from where it is today. More investors should have that conversation. It creates a lot of trust with you and the team. But most of the time they're really excited to have it. And in probably every single case they have ended up pivoting the company. That just shows the team knows already that they need to pivot. They just haven't been able to come to terms with it, either internally or with their investors. Every month you're spending time on the wrong idea is a bad use of your time and capital as a founder. Especially right now, when you have so many things that you can build. You should not waste any time. Go work on really important problems, as opposed to being stubborn about the thing you pitched in your deck."

I asked @Databricks CRO Ron Gabrisko when they'll go public: "It's not a matter of if, it's a matter of when. We run this company like a public company. We report our financials, and every quarter we go through our audit committee. We're here to build a trillion-dollar company. So I'd say we're going public six months at a time. We're not in a rush. We're going to build this into a trillion-dollar company. That's the journey."

.@Databricks CRO Ron Gabrisko grew the business from $1M to $7B ARR. Here's what he thinks is the biggest mistake in sales, and the four stages every company grows through: "The biggest mistake I see is salespeople start pitching before they understand anything about you or your challenges. Get to know somebody, build rapport, ask questions first. On the company side, there are four stages. Zero to $10 or $20 million, you're finding product-market fit. $20 to $100 million, you're building a repeatable playbook. $100 million to a billion, you're expanding internationally and into partners. Past a billion, it's about the right leaders, culture, and running fast. Each stage needs different things. You usually need salespeople to push your product, because people don't know about it or how to use it. If people are buying stuff, you want people to sell stuff."

.@databricks CRO on the real reason enterprises still struggle with AI adoption: "Everybody has an FDE model now, but they need help wiring it all together. It's not simple. The biggest challenge is the data. Everybody knows the data is the key to these enterprise use cases. But in a lot of cases, the data isn't in the right place. It's all over the place, in legacy systems, old formats, proprietary formats. Getting your data into a good place to attach AI is a tough, complicated problem that they're using Databricks for."

.@Databricks CRO Ron Gabrisko on why open source users won't just buy from you: "We tried PLG first. We thought customers would just come to us and ask to buy. It wasn't working. Customers in open source don't really want to buy anything. You have to ask them what they're willing to pay for. If I get my support question answered, I'm good to go. PLG is people coming to you. Sales is you going to them. It's the opposite. Salespeople open doors. That said, if you have a great PLG motion like ours, with tons of people coming in the door, you still need salespeople to go sell them the thing you actually monetize."

.@databricks CRO Ron Gabrisko on turning millions of open source users into a $190B business: "There were literally millions of people using Spark. That was the open source product, early days. First, we just said: go meet with as many Spark users as possible. Understand how they're using it, what their challenges are, and what they'd pay for. It wasn't rocket science. Then you look for the trends. People will pay for security. People will pay for scalability. So you start adding those features as a paid layer on top of the open source. Early days we were selling to Silicon Valley digital-native startups. With open source, they love building their own stuff. But when you go to enterprise, they don't have the expertise to build large open source projects. They need partners. They need a managed service. So our break into enterprise was a really big step in how we scaled the company."

Is having seven co-founders a nightmare? @Databricks CRO Ron Gabrisko says the opposite after scaling from $1M to $7B ARR in 10 years: "I get this a lot, especially from CROs. They say, I can only handle one founder, how do you handle seven? I think it's a massive advantage. You have seven true owners of the business. Customers always want a founder to speak at their event. And our founders are all super technical. So if I'm talking to a technical audience, I have seven times as many people to do exec alignment with CTOs and CDOs. Obviously, I spent a lot of time with them in the early days. Engineers are skeptical of salespeople, so I spent a lot of time getting to know them, their strategy, how they wanted to build the culture. The founders set the tone, the culture, everything, especially early. It's been a huge advantage with these seven. Love them to death. Anthropic has seven co-founders too. Maybe that's the lucky number?" h/t @pk_iv for this question for Ron

How Databricks leveraged a16z to get its first big enterprise customers and go from $1M to $7B ARR in 10 years: "a16z is a little special. They have relationships with the CIOs of some of the biggest enterprises on the planet. They'd invite the CIO of a company like Apple or Capital One in for a Silicon Valley Day, and show them ten portfolio companies. We'd get thirty minutes to demo. The mistake startups make is sending a junior salesperson. I did most of those myself. I get to pitch my product to the CIO of a huge company. And most of the time, they're so thankful you took the time that they'll give you a POC or a small land. Now you're in the door, with a big logo. So go pitch those yourself. 'Can you intro me to XYZ' is less valuable. I'd rather target fewer companies where I can actually add value. We landed a bunch of our biggest customers that way."

.@databricks CRO Ron Gabrisko runs the entire $7B ARR sales org on his own product, and can predict revenue within one or two percent: "For an enterprise, how do you make decisions? It's all about the context of your data, and attaching that to AI. So we built a product called Genie. I have it on my phone. I use it to run our business. It has all the context of our company. And I can just ask it questions in English. It does all the calculations, all the SQL queries, all the tech in the background. If I ask it, what are the top ten customers that might churn in Germany, it builds the churn model, tries to understand the churn, and even makes recommendations on how to fix it. We run all of Databricks on Databricks. I can predict our revenue within one or two percent. I can predict which customers are going to churn, and which products are the stickiest. The interface is like any LLM. You just start asking questions. The difference is it's doing it on your data, not generic, publicly available data. It can do calculations, graphs, predictions, launch agents. So it's super sophisticated for enterprise AI versus the generic tools out there today."

Databricks CRO Ron Gabrisko broke all the rules growing from $1M to $7B ARR. First one was hiring 40 sales reps in his first quarter. "It was a funny story. All day Wednesday and Thursday, all I'm doing is interviews. I told them, you've at least got to give me a break to go to the bathroom. A lot of those folks were from my network, people I trusted. My early thesis was, Spark is everywhere. My first task was to understand what they're willing to pay for, and who I can sell it to. What's the profile of the ideal customer? So you hire a bunch of people you trust to go talk to all those open source users, get that information, and find the trends. We'd just raised funding, so I did a coverage model to cover all the segments and find out which customers were more likely to buy, and what they wanted to buy. We moved fast that first year. We went from less than a million to $13, $15 million, then to $50, to $100, to $250 million. Now we're $6.9 billion plus. That was my first task. Go find out what people will pay for, and which segments you can sell to."

Databricks CRO Ron Gabrisko on why AI models matters less than everyone thinks: "There are all kinds of new models now. OpenAI, Anthropic, plus a lot of open source ones. But it's not really about the strength of the model. The models are already smart enough. I ask them all kinds of questions, and they're as smart as I am. If not smarter. It's all about the context you give them. Your ability to connect a model to your proprietary data and your business context is what unlocks the outcomes. We were AI-first from the beginning. Ten years ago we were thinking about AI and machine learning before anyone else. That's why we built Unity Catalog to govern not just your data, but your models and notebooks. We always know what's relevant to answer a question."