Pascal Unger

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Pascal Unger

Pascal Unger

@PascalUnger

Co-Founder & Managing partner @focal_vc | I back technical founders at inception | writing: https://t.co/ftMMFWG1oh

Miami, FL Katılım Nisan 2019
876 Takip Edilen1.5K Takipçiler
Pascal Unger
Pascal Unger@PascalUnger·
@villi A bunch of larger seed funds I chatted to are now doing “small A’s” and are calling them seed 2s 🤷🏻‍♂️
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villi
villi@villi·
I have spoken to 2 large VC firms in the past week, both of which have told me they are skipping the Series A. People say Series As are tough right now, but on the flip side, who is doing the Series As? Perhaps bc only hypey things are working atm. It seems like an opportunity.
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Danielle Strachman 💗 🐈 💃 🪴 🎸 🎨 🐕
Big week for 1517 -- we head into the final countdown for our Fund IV. Only 17% of funds make it to Fund IV. And by Fund IV, emerging managers have usually: ✅ Had a portfolio company IPO or major exits ✅ Survived at least one real downturn ✅ Gained trust with LPs ✅ Built operational systems and a team to persist We're moving from an experiment to a firm with longevity at 1517!
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Pascal Unger
Pascal Unger@PascalUnger·
This 👇
Erik Torenberg@eriktorenberg

Early-stage hiring can be a lot like venture investing: It's power-law driven: a few people contribute most of the value. So it’s far more important to hire the best people than it is to avoid making bad hires. Which means you’re not making mistakes, you’re likely not taking enough swings. It's also people-focused rather than process-focused or role-focused. When you’re looking for venture investments, you’re not pre-defining everything at the outset. You're not saying something like “okay, I have 15 companies I'm going to invest in, 5 will look like this, 5 will look like that, etc. They should have this kind of product, have these kinds of metrics, have these sorts of strengths." Instead, you meet a lot of companies, and occasionally you meet one where you’re like, “Wow, these founders are GOOD. We need to invest in them, because they are generally putting it all together in some unique and differentiated way, that we never could have pre-defined a priori, but when you see it, you absolutely know. Once you identify that company, then you do whatever it takes to get them; doesn’t matter whether there is a “slot in the portfolio” for that kind of company, you just do it and you regret nothing. The best hires do not come from pre-defining “Mission, Outcomes, Competencies” and then searching for people who fit those requirements. The best hires are much more likely to come from a process where, everyone at the company gets together and says, “Okay, who are the two dozen absolute best people we know, and how do we hire those people, for some role.” You identify those people, you spend a lot of time with them, and then the minute they become available on the hiring market, you are ready to make them an offer immediately; and you just figure out a job for them. The parallels to VC don’t end there, the toughest part about hiring a superstar is not the diligence process, but the winning. The best candidates are in extreme demand. The close process must similarly be tailored to that person. Make the process fit the candidate vs the other way around. To be sure, this analogy is most relevant when it is in a value-creation role at a hot company that can attract elite talent, not a value-preservation role or for non-elite talent. What distinguishes these rare and exceptional people is that, in addition to doing a great job at what they’re known for, they will also reach into all of these corners of your company and tie off every loose end and fix every broken pipe and tune up the whole machine way beyond what any job description could ever have anticipated, or any hiring manager could assess for. Great people like this are just absolute risk-killers inside your company; just like great founders are. They will just sit at their desk for hours and crank through fix after fix after fix for the pure love of the game and for the good of the team, and after the fact you’ll look back on the work they did and think, “wow, the impact they’ve had is so beyond anything we could’ve articulated.”

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Erik Torenberg
Erik Torenberg@eriktorenberg·
Early-stage hiring can be a lot like venture investing: It's power-law driven: a few people contribute most of the value. So it’s far more important to hire the best people than it is to avoid making bad hires. Which means you’re not making mistakes, you’re likely not taking enough swings. It's also people-focused rather than process-focused or role-focused. When you’re looking for venture investments, you’re not pre-defining everything at the outset. You're not saying something like “okay, I have 15 companies I'm going to invest in, 5 will look like this, 5 will look like that, etc. They should have this kind of product, have these kinds of metrics, have these sorts of strengths." Instead, you meet a lot of companies, and occasionally you meet one where you’re like, “Wow, these founders are GOOD. We need to invest in them, because they are generally putting it all together in some unique and differentiated way, that we never could have pre-defined a priori, but when you see it, you absolutely know. Once you identify that company, then you do whatever it takes to get them; doesn’t matter whether there is a “slot in the portfolio” for that kind of company, you just do it and you regret nothing. The best hires do not come from pre-defining “Mission, Outcomes, Competencies” and then searching for people who fit those requirements. The best hires are much more likely to come from a process where, everyone at the company gets together and says, “Okay, who are the two dozen absolute best people we know, and how do we hire those people, for some role.” You identify those people, you spend a lot of time with them, and then the minute they become available on the hiring market, you are ready to make them an offer immediately; and you just figure out a job for them. The parallels to VC don’t end there, the toughest part about hiring a superstar is not the diligence process, but the winning. The best candidates are in extreme demand. The close process must similarly be tailored to that person. Make the process fit the candidate vs the other way around. To be sure, this analogy is most relevant when it is in a value-creation role at a hot company that can attract elite talent, not a value-preservation role or for non-elite talent. What distinguishes these rare and exceptional people is that, in addition to doing a great job at what they’re known for, they will also reach into all of these corners of your company and tie off every loose end and fix every broken pipe and tune up the whole machine way beyond what any job description could ever have anticipated, or any hiring manager could assess for. Great people like this are just absolute risk-killers inside your company; just like great founders are. They will just sit at their desk for hours and crank through fix after fix after fix for the pure love of the game and for the good of the team, and after the fact you’ll look back on the work they did and think, “wow, the impact they’ve had is so beyond anything we could’ve articulated.”
Erik Torenberg@eriktorenberg

This is how you hire an org of superstars. People first, roles/process second. There are many ways to put the puzzle together, but you want to prioritize top talent above all. It’s easier to deal with the chaos of non-standardized processes than it is to find & convince top talent. - From @zebriez’s excellent Colossus piece, “Inside Cursor”

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Pascal Unger
Pascal Unger@PascalUnger·
Must read for anyone building in AI re what it takes to win as an independent AI startup. Would add a 4th point to Differentiation, Focus, and Velocity: AMBITION. The market for feature companies is gone. The labs will eat them sooner or later. The only startups that survive the next 5 years are the ones where every decision, from day one, is sized for a huge outcome.
Russell Kaplan@russelljkaplan

# The Path Forward for AI Startups A lot of founders are messaging each other after the SpaceXAI <> Cursor “IPO-deferred acquisition”. Common discussion topic: what is the future for independent startups? Must ~everyone ultimately be acquired by a frontier lab or go extinct? The data from our direct experience @cognition suggests the opposite. The more startups in a category that defect from independent competition by selling to a lab, the stronger the remaining ones become. We experienced this firsthand last year with Windsurf. When the founders went to Google and we acquired the remaining company, it dramatically accelerated our product roadmap and GTM. Now, cloud agents are ready for prime time, and our usage has exploded. (We’re in the fastest rate of usage growth in Cognition’s history - almost 50% month-over-month growth in Devin enterprise.) We already see the next round of acceleration with yesterday’s news, from prospects and customers to candidate inbound. In just about every category, there’s a clear market for a winning independent offering that’s not tied to models from any one lab. Especially in a space as dynamic as software engineering, where customers value model flexibility as the rankings from different providers are constantly changing. For startups to seize that independence opportunity, here are the lessons we’ve learned so far: 1. DIFFERENTIATION You need to have extremely clear differentiation vs. what’s already offered by the labs. Cursor had stiff competition from Claude Code in self-serve, in part because one tool was substitutable for the other, which presented a challenge. Our approach has been to differentiate heavily for enterprises, which is the largest market for software engineering. Specifically: 1. We invest as much in forward deployed engineering and AI enablement as we do in core R&D. Our customers treat us as a change management partner, not just an AI software engineering platform. We run 1000-person workshops all around the world to help train developers inside companies on frontier AI adoption. We target specific use cases and outcomes in addition to providing developer tooling. 2. We focus on accelerating the *entire software development lifecycle* at large company scale, not just the writing of code. Devins now spin up automatically for everything from ticket scoping to DeepWiki codebase indexing to security vulnerability remediation and application monitoring alert response. 3. We eat the pain of deployment complexity to work well in the largest and most complex environments imaginable. Cognition can run inside a customer’s virtual private cloud, has a permissioning and team collaboration model that can scale to 100,000+ developers inside one company, runs as well for COBOL mainframes as it does for modern Python. From day 1 each Devin ran in a microVM on its own machine, vs running locally as a CLI tool, which allows arbitrary horizontal scaling and is a better fit for event-driven automation. Of course, one element of startup differentiation will always be model independence. This is particularly powerful in large enterprises, who value supplier continuity and the ability to centralize tooling without taking on the business risk that they committed to the wrong foundation model. And useful for individual developers, who always want to try the latest models. (If you haven’t yet tried the Windsurf 2.0 release which came out last week, it’s a good day to give it a shot!) I expect the labs will catch up on some of these fronts at some point. But at that point, we’ll have already made the next leap in differentiation, because… 2. FOCUS You won’t outcompete the labs in everything, but you can outcompete the labs in *your* thing. Every application domain has fractal complexity at the edges. Lean in to what makes your domain special and offer things no one else can. Does it make sense for a lab to devote training resources to a specialized code review model? Probably not - they’re working on AGI. But for the 3-6 month window where the latest frontier models don’t solve that use case at acceptable performance, cost, or latency, do it yourself and build a better product experience than would otherwise be possible. Rinse and repeat as the frontier of what’s possible via specialization continues to evolve. 3. VELOCITY One of our values at Cognition is: “Every second counts.” Maniacal urgency helps in every startup, but it counts extra in today’s accelerated AI times where advantages compound faster than before. With sufficient focus, you can out-accelerate the AI labs on any one specific feature or workflow. Do this consistently to stretch the overhang of what’s enabled by each new generations of models, and you can maintain your edge on a differentiated product experience. - In many ways the SpaceXAI <> Cursor news is a win for everyone. SpaceX gets a new research team and the chance to become competitive in coding. Cursor gets a meaningful exit and the opportunity to accelerate their research roadmap with much more compute. And the whole ecosystem benefits from increased competitiveness among the foundation model labs. Congrats to the teams on the outcome.

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Pascal Unger
Pascal Unger@PascalUnger·
@russelljkaplan This is excellent. Would add a 4th point which is AMBITION. You only have a chance if you think big and bold on every step / decision along the way. There's no more market for "feature companies" like there was 5y ago. Go big (huge) or go home.
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Russell Kaplan
Russell Kaplan@russelljkaplan·
# The Path Forward for AI Startups A lot of founders are messaging each other after the SpaceXAI <> Cursor “IPO-deferred acquisition”. Common discussion topic: what is the future for independent startups? Must ~everyone ultimately be acquired by a frontier lab or go extinct? The data from our direct experience @cognition suggests the opposite. The more startups in a category that defect from independent competition by selling to a lab, the stronger the remaining ones become. We experienced this firsthand last year with Windsurf. When the founders went to Google and we acquired the remaining company, it dramatically accelerated our product roadmap and GTM. Now, cloud agents are ready for prime time, and our usage has exploded. (We’re in the fastest rate of usage growth in Cognition’s history - almost 50% month-over-month growth in Devin enterprise.) We already see the next round of acceleration with yesterday’s news, from prospects and customers to candidate inbound. In just about every category, there’s a clear market for a winning independent offering that’s not tied to models from any one lab. Especially in a space as dynamic as software engineering, where customers value model flexibility as the rankings from different providers are constantly changing. For startups to seize that independence opportunity, here are the lessons we’ve learned so far: 1. DIFFERENTIATION You need to have extremely clear differentiation vs. what’s already offered by the labs. Cursor had stiff competition from Claude Code in self-serve, in part because one tool was substitutable for the other, which presented a challenge. Our approach has been to differentiate heavily for enterprises, which is the largest market for software engineering. Specifically: 1. We invest as much in forward deployed engineering and AI enablement as we do in core R&D. Our customers treat us as a change management partner, not just an AI software engineering platform. We run 1000-person workshops all around the world to help train developers inside companies on frontier AI adoption. We target specific use cases and outcomes in addition to providing developer tooling. 2. We focus on accelerating the *entire software development lifecycle* at large company scale, not just the writing of code. Devins now spin up automatically for everything from ticket scoping to DeepWiki codebase indexing to security vulnerability remediation and application monitoring alert response. 3. We eat the pain of deployment complexity to work well in the largest and most complex environments imaginable. Cognition can run inside a customer’s virtual private cloud, has a permissioning and team collaboration model that can scale to 100,000+ developers inside one company, runs as well for COBOL mainframes as it does for modern Python. From day 1 each Devin ran in a microVM on its own machine, vs running locally as a CLI tool, which allows arbitrary horizontal scaling and is a better fit for event-driven automation. Of course, one element of startup differentiation will always be model independence. This is particularly powerful in large enterprises, who value supplier continuity and the ability to centralize tooling without taking on the business risk that they committed to the wrong foundation model. And useful for individual developers, who always want to try the latest models. (If you haven’t yet tried the Windsurf 2.0 release which came out last week, it’s a good day to give it a shot!) I expect the labs will catch up on some of these fronts at some point. But at that point, we’ll have already made the next leap in differentiation, because… 2. FOCUS You won’t outcompete the labs in everything, but you can outcompete the labs in *your* thing. Every application domain has fractal complexity at the edges. Lean in to what makes your domain special and offer things no one else can. Does it make sense for a lab to devote training resources to a specialized code review model? Probably not - they’re working on AGI. But for the 3-6 month window where the latest frontier models don’t solve that use case at acceptable performance, cost, or latency, do it yourself and build a better product experience than would otherwise be possible. Rinse and repeat as the frontier of what’s possible via specialization continues to evolve. 3. VELOCITY One of our values at Cognition is: “Every second counts.” Maniacal urgency helps in every startup, but it counts extra in today’s accelerated AI times where advantages compound faster than before. With sufficient focus, you can out-accelerate the AI labs on any one specific feature or workflow. Do this consistently to stretch the overhang of what’s enabled by each new generations of models, and you can maintain your edge on a differentiated product experience. - In many ways the SpaceXAI <> Cursor news is a win for everyone. SpaceX gets a new research team and the chance to become competitive in coding. Cursor gets a meaningful exit and the opportunity to accelerate their research roadmap with much more compute. And the whole ecosystem benefits from increased competitiveness among the foundation model labs. Congrats to the teams on the outcome.
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Pascal Unger
Pascal Unger@PascalUnger·
@lucasbagnocvaz Nice one @lucasbagnocvaz and something we're thinking about a lot - as a fund focused on leading at inception, the biggest paranoia I have is around adverse selection at the top of our funnel due to ownership targets / our fund model.
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Pascal Unger
Pascal Unger@PascalUnger·
@cryptoadiona Disagree! There’s always room for good people and an amazing thing re Miami is how welcoming it is.
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Pascal Unger
Pascal Unger@PascalUnger·
@BillAckman I know a company working on making sure your odds of getting cancer (plus Alzheimer, diabetes or any genetic disease) are significantly lower in the first place - would love to introduce them to you @BillAckman
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Pascal Unger
Pascal Unger@PascalUnger·
We're excited to introduce you to Chance Jiajie Li and Zhenze Mo, the founders behind MOHAN - building subsurface world models so mining companies drill fewer holes and hit more ore. The founding team includes: • Researcher at MIT Media Lab • Serial founder since 18, previously led a 30-person DAO startup (accepted to YC China) • Former national-level track and field athlete • Northeastern University MS in CS specializing in LLMs and Multi-Agent Reinforcement Learning • MIT Earth Science professor with deep ties to the global mining industry • MIT Geology PhD specializing in porphyry copper systems • MIT AI PhD working on physics-constrained generative models This team bridges the oldest science with the newest technology to solve the most important resource problem of our generation. Meet the founders of 👇 🌐 mohan.media @focal_vc | @thelabmiami
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Pascal Unger
Pascal Unger@PascalUnger·
We're excited to introduce @realjacklau and Zeeshan Shahid, the founders of @SearcleAI. Here's what most companies don't realize yet: Google, ChatGPT, and Perplexity all decide what to recommend using completely different logic. Your Google ranking means nothing to ChatGPT. Your SEO strategy is invisible to Perplexity. Search isn't one game anymore. It's three. And most brands are only playing one. @SearcleAI helps companies get found — and cited — across every AI search surface, not just Google. The team behind it: • Ex-Bridgewater & IMC quantitative systems (built for pattern recognition at scale) • Scaled startups to 8-figure ARR and ~1M monthly visitors • 4x founder + Techstars/Founder Institute mentor They're applying quant rigor to a market everyone else is guessing at. Meet the founders of 👇 🌐 searcle.ai @focal_vc | @thelabmiami
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Ben Lang
Ben Lang@benln·
Who are the pre-seed / seed investors every founder should want on their cap table these days? Refreshing my shortlist.
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