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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."

27,529 次观看 • 9 天前

Databricks CRO Ron Gabrisko on why they only hire technical salespeople: "My salespeople can demo the product themselves. We have an amazing pre-sales team, but everyone in my org should be able to demo. Because if you have technical buyers, you need technical sellers. Technical buyers don't like non-technical sellers. If you don't bring anything to the table, why would I as a customer spend time with you? My time's precious. I'll only spend it if I'm going to learn something, or you're going to help me solve a problem. So if I'm a technical buyer, you need to bring something that helps me. If you don't, I'm not going to waste my time with you."

Databricks CRO Ron Gabrisko on why they only hire technical salespeople: "My salespeople can demo the product themselves. We have an amazing pre-sales team, but everyone in my org should be able to demo. Because if you have technical buyers, you need technical sellers. Technical buyers don't like non-technical sellers. If you don't bring anything to the table, why would I as a customer spend time with you? My time's precious. I'll only spend it if I'm going to learn something, or you're going to help me solve a problem. So if I'm a technical buyer, you need to bring something that helps me. If you don't, I'm not going to waste my time with you."

21,926 次观看 • 10 天前

.@jason shares what it was like inside the Twitter / X buyout in 2022: "It would be 12-1am, everyone would be exhausted, and we'd do two more meetings. Elon just has an insane work ethic. In my opinion, it was the greatest heist in the history of business. A large portion of them were not working, making huge salaries, and spending money like drunken sailors. There was software and office space being paid for that was never touched. They were paying $400 for catered lunches. You could have sent them to Michelin star restaurants for every meal and still saved money. There were some people that were working hard. But there were people that were not working at all. Combined with COVID work form home work ethic, there was abuse in that system that has never been matched. It really broke people's brains when they laid off 85% of the staff and the product started working better and shipping faster. People realized these companies are massively overstaffed and you can do more with less."

.@jason shares what it was like inside the Twitter / X buyout in 2022: "It would be 12-1am, everyone would be exhausted, and we'd do two more meetings. Elon just has an insane work ethic. In my opinion, it was the greatest heist in the history of business. A large portion of them were not working, making huge salaries, and spending money like drunken sailors. There was software and office space being paid for that was never touched. They were paying $400 for catered lunches. You could have sent them to Michelin star restaurants for every meal and still saved money. There were some people that were working hard. But there were people that were not working at all. Combined with COVID work form home work ethic, there was abuse in that system that has never been matched. It really broke people's brains when they laid off 85% of the staff and the product started working better and shipping faster. People realized these companies are massively overstaffed and you can do more with less."

309,312 次观看 • 8 个月前

I talked to Eoghan McCabe about why you should be posting more: "Personally, you see every piece of content you post. And if you post a lot, it might feel like you're posting too much. But because of algorithmic feeds, no one else sees everything you post. So you can't assume everyone sees every single post. The average person will only see 10%, 20@, maybe 50% of what you post. No one will actually see all of it. And you don't know what's gonna work when you're posting. So you really just need to put a lot of shots on goal. It's like having a high shipping velocity on product. Imagine it takes 100 posts for something to break through. If you post 10x a day, it will take 10 days. If you post once a day, it will take over a quarter. Once a year, it will take 100 years. This is an advantage that some younger founders have. People like Amjad at Replit, he can work and tweet. He's built a great product. And he's constantly promoting it."

I talked to Eoghan McCabe about why you should be posting more: "Personally, you see every piece of content you post. And if you post a lot, it might feel like you're posting too much. But because of algorithmic feeds, no one else sees everything you post. So you can't assume everyone sees every single post. The average person will only see 10%, 20@, maybe 50% of what you post. No one will actually see all of it. And you don't know what's gonna work when you're posting. So you really just need to put a lot of shots on goal. It's like having a high shipping velocity on product. Imagine it takes 100 posts for something to break through. If you post 10x a day, it will take 10 days. If you post once a day, it will take over a quarter. Once a year, it will take 100 years. This is an advantage that some younger founders have. People like Amjad at Replit, he can work and tweet. He's built a great product. And he's constantly promoting it."

189,314 次观看 • 9 个月前

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

209,752 次观看 • 1 年前

I asked Will Gaybrick when Stripe will go public: "I think the better question is, why would we go public? What's the incremental benefit of going public? It's a bunch of work. It's a different way of operating. And there's a blurring of private and public investors. To this day we've never burned a dollar of investor money. We've always been financially independent, and now extremely profitable. We're focused on growing the GDP of the internet, as quickly as possible. We're already highly regulated financial institution, and resolutely focused on our customers."

I asked Will Gaybrick when Stripe will go public: "I think the better question is, why would we go public? What's the incremental benefit of going public? It's a bunch of work. It's a different way of operating. And there's a blurring of private and public investors. To this day we've never burned a dollar of investor money. We've always been financially independent, and now extremely profitable. We're focused on growing the GDP of the internet, as quickly as possible. We're already highly regulated financial institution, and resolutely focused on our customers."

124,520 次观看 • 9 个月前

I asked Garry Tan how to use meta prompting to get better at AI: "My partners at YC Jared Friedman and Pete Koomen showed me how to do this. You can take almost anything that you do all the time and just drop it into a context window. And then say, “Here’s a bunch of inputs and outputs." And maybe you also add a bunch of notes. And then you tell it, “Write me a prompt that can act as an agent that takes this input and makes this output over here.” You can do this for almost any type of knowledge work. And you can even introspect. "What are things you notice that I did to convert this from the input to the output?”. And then you can just start using the prompt. Initially, it’s going to suck. Because it’s just not that smart yet. But what’s funny is now, I also use it to Iterate my writing. You can be very direct, "I would never say that", "Don’t say it like this", or "Oh, you used the long word there, use the short word". Just speak to it conversationally. And then when you're happy with the output, you can use that new output to make a new prompt. "Based on this conversation, give me a better initial prompt that incorporates all the things we talked about." And you can do this with literally everything. And in theory, there’s so much it applies to that people do day-to-day. You could use it for tweets. You could use it for editing podcasts. You can use it for pretty much everything. I have a folder of prompts that I use all the time. My YouTube prompt is on v27 or something. I'll go through this process with all the different max models. I'll use GPT 5.2 Pro. I’ll use Grok. I'll use Claude. Then, I’ll take all the outputs from all the models and put them into Claude and say "Here’s my prompt, here’s the output from four LLMs, including yourself. Rate each response and tell me what the pros and cons of each approach are." And I usually say "give it to me in numbered form". And then you can agree with one, disagree with two, tell it three is this or that. And then after that, you say given all of this, synthesize it."

I asked Garry Tan how to use meta prompting to get better at AI: "My partners at YC Jared Friedman and Pete Koomen showed me how to do this. You can take almost anything that you do all the time and just drop it into a context window. And then say, “Here’s a bunch of inputs and outputs." And maybe you also add a bunch of notes. And then you tell it, “Write me a prompt that can act as an agent that takes this input and makes this output over here.” You can do this for almost any type of knowledge work. And you can even introspect. "What are things you notice that I did to convert this from the input to the output?”. And then you can just start using the prompt. Initially, it’s going to suck. Because it’s just not that smart yet. But what’s funny is now, I also use it to Iterate my writing. You can be very direct, "I would never say that", "Don’t say it like this", or "Oh, you used the long word there, use the short word". Just speak to it conversationally. And then when you're happy with the output, you can use that new output to make a new prompt. "Based on this conversation, give me a better initial prompt that incorporates all the things we talked about." And you can do this with literally everything. And in theory, there’s so much it applies to that people do day-to-day. You could use it for tweets. You could use it for editing podcasts. You can use it for pretty much everything. I have a folder of prompts that I use all the time. My YouTube prompt is on v27 or something. I'll go through this process with all the different max models. I'll use GPT 5.2 Pro. I’ll use Grok. I'll use Claude. Then, I’ll take all the outputs from all the models and put them into Claude and say "Here’s my prompt, here’s the output from four LLMs, including yourself. Rate each response and tell me what the pros and cons of each approach are." And I usually say "give it to me in numbered form". And then you can agree with one, disagree with two, tell it three is this or that. And then after that, you say given all of this, synthesize it."

51,632 次观看 • 7 个月前

From Garry Tan on what keeps him up at night: "99% of people who apply to YC get rejected. And it keeps me up at night. Up to half of any one batch may have been previously rejected before getting in. It's quite common to apply 2-4x before getting in. We get 80,000 applications per year. We're always worried we're over rotating in any direction. Too big can be bad. Too small can also be bad. And right now, there are clearly way more capable founders that we're rejecting. And if you can stand being rejected a few times, you're cut out to be a founder. Being a founder requires an insane amount of resilience. One rejection shouldn't hold you back. A really good founder would say, they got this wrong, and I'm gonna prove myself right. And we love that."

From Garry Tan on what keeps him up at night: "99% of people who apply to YC get rejected. And it keeps me up at night. Up to half of any one batch may have been previously rejected before getting in. It's quite common to apply 2-4x before getting in. We get 80,000 applications per year. We're always worried we're over rotating in any direction. Too big can be bad. Too small can also be bad. And right now, there are clearly way more capable founders that we're rejecting. And if you can stand being rejected a few times, you're cut out to be a founder. Being a founder requires an insane amount of resilience. One rejection shouldn't hold you back. A really good founder would say, they got this wrong, and I'm gonna prove myself right. And we love that."

47,207 次观看 • 7 个月前

I asked Jake Stauch about raising Serval's $75M Series B from Sequoia the day they announced their $50M Series A: "We closed the A in August and delayed the announcement until October. The day we announced, I got a text from Sequoia asking to come to their office. I was at a customer conference in Orlando. They ended up flying out and meeting me for dinner with a term sheet. They had talked to all of our customers. They talked to everyone I'd ever worked with, managers, peers, and direct reports. They also talked to all our competitors' customers. And they had a clear picture of the market, and just knew we were going to win. So instead of waiting on the sidelines, they wanted to come in immediately. And I initially turned them down. We had just fundraised. I didn't need the money. We were maybe 15 people at the company. I didn't have a way to deploy the capital. But I thought about it more. And talked to some references that had raised from Sequoia. And we decided the support on customer introductions and signaling, recruiting, and strategy would be valuable to us now vs waiting a few months."

I asked Jake Stauch about raising Serval's $75M Series B from Sequoia the day they announced their $50M Series A: "We closed the A in August and delayed the announcement until October. The day we announced, I got a text from Sequoia asking to come to their office. I was at a customer conference in Orlando. They ended up flying out and meeting me for dinner with a term sheet. They had talked to all of our customers. They talked to everyone I'd ever worked with, managers, peers, and direct reports. They also talked to all our competitors' customers. And they had a clear picture of the market, and just knew we were going to win. So instead of waiting on the sidelines, they wanted to come in immediately. And I initially turned them down. We had just fundraised. I didn't need the money. We were maybe 15 people at the company. I didn't have a way to deploy the capital. But I thought about it more. And talked to some references that had raised from Sequoia. And we decided the support on customer introductions and signaling, recruiting, and strategy would be valuable to us now vs waiting a few months."

37,137 次观看 • 6 个月前

Episode #93: Michael Kim @ Cendana Michael Kim is the Founder of Cendana Capital, making anchor investments in very early stage VC funds. We talk 204x DPI funds, characteristics of the top performing venture investors, how Cendana does diligence on fund managers, portfolio construction best practices + Michael’s 60x rule, and why a high ownership to fund size ratio drives outlier returns. We also get into how VCs are using AI, the competition between Seed and multi-stage investors, why US endowments are under siege, and how secondaries are driving most early stage venture returns today. Michael also opens up about the early days of starting Cendana, the 18 month grind raising Cendana Fund 1, the day he almost died, and ranking in the top 2% globally in Call of Duty. Shoutout to Roger Ehrenberg, Kevin Hartz, Semil, Jeff Clavier, Beezer Clarkson, Jack Altman, Jeff Morris Jr., Sheel Mohnot, Nichole Wischoff, Ted Alling, and Rick Zullo for their help putting this together. Thanks bolt.new for supporting this episode (check out their Hackathon with $1m in prizes!) Full episode here on X, or grab a link in the replies. Timestamps: 4:24 The day Michael almost died 5:10 Call of Duty & video games 9:34 Hiring @ Cendana 10:31 How Cendana uses structured and unstructured data 16:51 How VCs are using AI 19:55 Why secondaries are driving most early stage venture returns 22:01 Deciding when to sell secondaries 24:28 Best performing venture funds ever 27:26 The best VCs have amazing access to the best founders 33:42 Why Cendana backs Solo GPs 35:57 How to invest over time and hype cycles 41:35 Why multi-stage firms are investing earlier 44:45 Cendana’s current thesis: High ownership % to fund size 45:51 Why Cendana started backing non-lead VCs 48:41 How Cendana does diligence on fund managers 52:22 VC NPS Scores and Ron Conway’s Silver Bullet 53:49 Good vs bad new VC firm strategies 56:36 Determining defensibility of a strategy 57:57 “Messy middle” software buyout fund 1:03:25 Portfolio construction best practice 1:08:11 Michael’s 60x Rule 1:14:28 How Seed funds compete with multi-stage funds 1:20:05 Should you collect logos writing small checks? 1:21:07 Becoming an LP for the city of SF 1:24:42 Taking 18+ months to raise Cendana Fund 1 in the GFC 1:26:48 Warehousing the first Cendana Fund 1 investments 1:29:56 How to do a first close 1:34:29 Why it’s hard to kill a VC firm 1:37:00 What happens to ZIRP tourist fund managers 1:40:22 How to raise a Fund 2 or 3 today 1:42:07 “US endowments are under siege” 1:44:55 What the best GP LP relationships look like 1:46:41 What Fund of Funds get wrong 1:50:43 The three most interesting trends in venture today

Episode #93: Michael Kim @ Cendana Michael Kim is the Founder of Cendana Capital, making anchor investments in very early stage VC funds. We talk 204x DPI funds, characteristics of the top performing venture investors, how Cendana does diligence on fund managers, portfolio construction best practices + Michael’s 60x rule, and why a high ownership to fund size ratio drives outlier returns. We also get into how VCs are using AI, the competition between Seed and multi-stage investors, why US endowments are under siege, and how secondaries are driving most early stage venture returns today. Michael also opens up about the early days of starting Cendana, the 18 month grind raising Cendana Fund 1, the day he almost died, and ranking in the top 2% globally in Call of Duty. Shoutout to Roger Ehrenberg, Kevin Hartz, Semil, Jeff Clavier, Beezer Clarkson, Jack Altman, Jeff Morris Jr., Sheel Mohnot, Nichole Wischoff, Ted Alling, and Rick Zullo for their help putting this together. Thanks bolt.new for supporting this episode (check out their Hackathon with $1m in prizes!) Full episode here on X, or grab a link in the replies. Timestamps: 4:24 The day Michael almost died 5:10 Call of Duty & video games 9:34 Hiring @ Cendana 10:31 How Cendana uses structured and unstructured data 16:51 How VCs are using AI 19:55 Why secondaries are driving most early stage venture returns 22:01 Deciding when to sell secondaries 24:28 Best performing venture funds ever 27:26 The best VCs have amazing access to the best founders 33:42 Why Cendana backs Solo GPs 35:57 How to invest over time and hype cycles 41:35 Why multi-stage firms are investing earlier 44:45 Cendana’s current thesis: High ownership % to fund size 45:51 Why Cendana started backing non-lead VCs 48:41 How Cendana does diligence on fund managers 52:22 VC NPS Scores and Ron Conway’s Silver Bullet 53:49 Good vs bad new VC firm strategies 56:36 Determining defensibility of a strategy 57:57 “Messy middle” software buyout fund 1:03:25 Portfolio construction best practice 1:08:11 Michael’s 60x Rule 1:14:28 How Seed funds compete with multi-stage funds 1:20:05 Should you collect logos writing small checks? 1:21:07 Becoming an LP for the city of SF 1:24:42 Taking 18+ months to raise Cendana Fund 1 in the GFC 1:26:48 Warehousing the first Cendana Fund 1 investments 1:29:56 How to do a first close 1:34:29 Why it’s hard to kill a VC firm 1:37:00 What happens to ZIRP tourist fund managers 1:40:22 How to raise a Fund 2 or 3 today 1:42:07 “US endowments are under siege” 1:44:55 What the best GP LP relationships look like 1:46:41 What Fund of Funds get wrong 1:50:43 The three most interesting trends in venture today

61,879 次观看 • 1 年前

I asked Chris Hladczuk why he always gets on the plane: "Our customers are signing up for a decade-long relationship. You can't build the trust needed for that over a zoom call. If you're asking someone to trust you with something as important as what we build, the fact that I won't get on the plane and go see them for a few hours says a lot. Always get on the plane."

I asked Chris Hladczuk why he always gets on the plane: "Our customers are signing up for a decade-long relationship. You can't build the trust needed for that over a zoom call. If you're asking someone to trust you with something as important as what we build, the fact that I won't get on the plane and go see them for a few hours says a lot. Always get on the plane."

28,809 次观看 • 6 个月前