Trying to help the few do the impossible; building @angularventures. Early stage. Enterprise. Deep tech. EU+IL. 🇮🇱🇺🇦🇪🇺🇺🇸

London | Tel Aviv
Joined March 2007
Gil Dibner retweeted
Rejection is a fundamental part of success. Anyone that gives up at the first obstacle will likely never bring anything into existence. Catch-22: 22 rejections Dune: 23 rejections The Help: 60 rejections Harry Potter: 12 rejections
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Gil Dibner retweeted
I've learned over my two decades in venture capital that money is not just a commodity. The idea that all investors are fungible doesn't align with my experience. VCs hold significant power to influence a company's path. They can either help propel a founder forward or seriously hinder their progress. This understanding drives my decision to get involved very early, well before product-market fit. I want to partner with founders at the outset. My focus is on helping them navigate early experiments, recruit great people, and manage the intense stress that comes with building a startup while trying to live healthily. Follow for more insights on venture partnerships.
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This can't be good. "But pressure to back promising new AI startups is upending that process, with investors sometimes paying wildly different prices for the same company. That means a company can notch multiple valuations simultaneously or in rapid succession, and sometimes only the higher number is made public. These types of deals have proliferated in Silicon Valley, the center of the AI boom, and are now spreading to Europe. That is alarming some founders and investors who say inflated valuations can leave startups with little room to grow into their price tags, particularly in a region with fewer blockbuster exits." buff.ly/JABwaRh
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Gil Dibner retweeted
JFrog’s recovery defies the SaaS sell-off as AI makes its software more essential. The Israeli company lost $700 million in market value in minutes after Anthropic’s Mythos announcement, but its shares have since surged. calcalistech.com/ctechnews/a…
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REPORTED FIVE-YEAR CREDIT default swap spreads, a measure of what it costs to insure against a borrower's default, are about 101 basis points for Nvidia, the chipmaker, and about 1,090 for CoreWeave, which rents out computing power built on Nvidia's chips, nearly eleven times as wide. A basis point is one hundredth of a percentage point. These are annualized spread quotations, which are not necessarily the cash payments the contracts require. Both quotes come from a corner of the credit market that has grown far busier in the past three months, and the sharp difference between them offers another view of financing risk alongside the increase in trading. buff.ly/cz6MbWp
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Germany has found that its AI ambitions are butting up against its climate commitments. EU member states have committed to cutting greenhouse gas emissions by 55 percent by the end of the decade. But policies meant to help meet that target are interfering with attempts to trigger a data center building frenzy. buff.ly/n3dh1HG
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Foursquare was acquired last month. The outcome was disappointing. It's taken me a while to write this, but I think I'm finally done grieving it. Here are some thoughts on our final years, an effort to honor the team who helped build & maintain FSQ's legacy, and my attempt to finally get some closure. teendrama.substack.com/p/som…
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New on the blog: — Room for ambition A late-night conversation at a startup office in Finland has stayed with me for almost twenty years. Some thoughts on ambition, getting good at something, and the people around you. karrisaarinen.com/posts/room…
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Gil Dibner retweeted
Equivocating ambition w/ massive funding rounds is the cancer of an entire generation of founders and a magnet for scam artists.
Invest in founders with a level of ambition that makes you uncomfortable. I’ve sat at the table with a number of entrepreneurs who are raising seemingly back-to-back mega rounds every 3 months in the last year. I’m an extraordinarily aggressive risk-taker, but in all these meetings I’ve felt physically uncomfortable. To the point when I noticed I was gripping the chair in a meeting with a founder. This is what it takes to win big and be truly venture-backable today.
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Gil Dibner retweeted
Replying to @kpowerinfinity
Wrote this a few months ago and I send it quite frequently open.substack.com/pub/nikunj…
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I've watched a few really good founders raise big seed rounds this year, then struggle to raise their A, even as the business kept making progress. The seed valuation had set a bar they weren't ready to clear yet. And with everyone excited about the term sheet, it was easy to put off what they'd need to show at the next round. One of them had about as strong a resume as you'll see in AI, and a team to match. Sub-$1M ARR would have been a fine A story at a normal seed price. At theirs it wasn't. (For what it's worth, I write both seed and A checks, so I sit on both sides of this.) The top of the seed market is a bit wild right now. Carta's 95th percentile seed valuation was about $72M in Q2 last year. This Q2 it was $200M. Meanwhile the share of seed companies that get to a Series A within two years has roughly halved since 2018. During a raise almost all the attention goes to how much and at what valuation. The question I wish more founders asked is whether the company is actually set up to hit the growth the next round will expect. If you're planning on a 2-3x step-up at the A, here's what that means. A $25M post means pitching a $50-75M A, which a company with real traction can get to. A $100M post means $200-300M, and at that price the A partner is comparing you to the handful of companies growing faster than almost anyone. The bar right now is brutal. In the A conversations I'm in, a $15M seed (usually a $75-100M post today) means $2M+ ARR, growing 5-6x over the last year, with a believable path to another 5x. None of this means raise less. Before you sign, work backward: what will we need to show at the A, and does this money and ~24 months get us there? If it does and the terms are good, take it. If the honest answer is "only if everything goes right", that's worth knowing now rather than in month 20. A big seed buys you runway and a higher bar. Most founders only count the runway.
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Gil Dibner retweeted
One of the most underrated skills in venture capital is knowing when to leave a founder alone. VCs love talking about how much value they add. Strategic introductions. Weekly check-ins. Board meetings. Hiring advice. Product feedback. Fundraising strategy. Before you know it, a founder has 12 investors giving them 12 different opinions on how to run their company. And somehow, all 12 think they’re being helpful. I’ve been on both sides of this. Investors absolutely can make a difference. The right introduction, a difficult conversation, or advice from someone who’s seen the same problem before can save a founder months of work. But there’s a difference between being helpful and needing to feel helpful. Not every investor needs a weekly update. Not every product decision requires a discussion. And not every founder needs another former finance guy explaining how to build software. Sometimes the best thing a VC can do is make an introduction, answer the phone when needed, and get out of the way. Founders should absolutely hold their investors accountable for the value they promised. But investors should also remember who actually runs the company. Because if your portfolio founder spends more time managing investors than managing employees, congratulations. You’ve become part of the problem you invested to solve.
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Gil Dibner retweeted
Replying to @chamath
This is why people using earnings multiples to justify it not being a bubble may not be grounded. A bubble can inflate revenue and earnings too!
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I suspect 70-80% of revenues at AI companies are from other AI companies who’ve raised money from the Venture industry. This is not a circular revenue problem but it is a revenue quality problem. We’ve seen this before in the SaaS cycle in the early/mid 2010s.
Through September, AI companies have captured ~83% of all US venture funding.
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"Inference has no yardstick" No one knows what they are buying or what it costs. buff.ly/OXlUwIJ
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Gil Dibner retweeted
Moats! Does anyone have one anymore? Yes. Yes, they do. But only a small subset of what people call moats create real long-term value and defensibility. Not actually a moat: 1. Distribution: This does matter more than ever. Building products has gotten easier, but getting them to customers has not. Distribution, however, does not create durable defensibility. It helps create the raw ingredients for one, but it is not itself a moat. 2. Execution: Yes, speed is critical for startups. But a company that has to sprint forever is not a good business. Ultimately, it will misstep, slow down, and competition will catch up. Speed gives a company more shots on goal as it tries to build a great product and a great business, but it must eventually find something more durable. 3. Brand: The idea is that when everyone is building the same thing, the most resonant brand will win. But for startups, brand works more like customer acquisition spend: Put money in, get growth out. It’s not something that will effectively keep others from competing long-term. Brand can become a true moat over time (as with Coke or Nike or Apple), but it just takes too long to really matter for most startups. What you're looking for is compounding advantages: a self-reinforcing flywheel that gets stronger and stronger over time, ultimately making a company very hard to compete with long-term. There are only four compounding advantages that really matter. Every major tech company has at least one: 1. Switching costs: The more deeply embedded a product is in the way a customer works, the harder it is for that customer to switch. This increases retention and revenue per customer, which can be reinvested into the product. 2. Network effects: The more users a product has, the better it gets for everyone. This increases engagement and attracts more users. 3. Scale economies: The more volume a company has, the larger and more efficient its operations can be. This lowers unit costs, funding lower prices or a better product at the same price, further driving up volume. 4. Data effects: The more usage a product has, the more data it generates. This data can be used to improve the product and increase usage. If you can’t see the potential for any in the startup you’re considering joining, that’s a red flag. In today’s incredible post, @danhockenmaier shares a simple framework you can run in about an hour after getting an offer: 1. Identify a startup’s compounding advantage 2. Gauge how strong it could get 3. Measure whether it’s working Inside: dozens of real company examples, the specific metrics to track, and benchmarks for what good and great look like. Read it here: lennysnewsletter.com/p/how-t…
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Bad pitches: • Open with the market size • Explain what the product does • Show a hockey stick with no assumptions behind it Good pitches: • Open with the thing you know that nobody else believes • Explain why now, not just why • Show what you've already built with almost nothing
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The Bar Tab A few weeks ago I wrote that the music was playing and everyone in venture capital felt obliged to dance. A few weeks after that I wrote that history doesn't repeat but it rhymes, and walked through the last time a band this loud played a room this full, which was 2000, A lot of you wrote in to ask what comes next. I didn't know. So I did what any responsible investor does when he doesn't know something: I got a napkin and started doing arithmetic I wasn't qualified to do. Fair warning. Every time I tried to make the numbers kinder, they got worse. This is a known property of napkins. Start with the size of the band. Microsoft, Alphabet, Amazon, Meta and Oracle spent about $230 billion on AI capex in 2024, about $400 billion in 2025, and have guided to around $800 billion this year. Goldman has $1.2 trillion next year and $1.4 trillion the year after. Add it up through 2029 and $4 trillion is the floor. Consensus is closer to $5.5 trillion. I'm using $4 trillion because I'm an optimist and because I only had one napkin. Now, a bar tab is a thing that eventually gets handed to somebody. What does $4 trillion have to earn? Say 12%, which is about what a sane person wants for setting that much money on fire in West Texas. That's $480 billion a year. Call it $500 billion of free cash flow by 2029. Then I remembered a GPU is not a building. Amazon depreciates servers over five years, and Michael Burry thinks that's charitable. If you want 12% on $4 trillion of equipment that'll be scrap before my youngest finishes college, you need about $1.1 trillion a year. I'm going to keep using $500 billion anyway. When the story is bad enough, you don't need to make it worse to get people's attention. Five hundred billion of free cash flow at a 50% gross margin means $1 trillion of revenue. Somebody has to write checks to the hyperscalers for a trillion dollars a year of AI compute by 2029. Who? Some of it is the hyperscalers buying themselves a round: Meta's capex buys Meta's ads. But the biggest outside checks come from the frontier labs, the handful of companies that train the models the rest of us build on. Full disclosure: Menlo is an investor in this category, so adjust for the fact that I'm a man with a drink in his hand telling you the bar is overpriced. Say the labs are half of it. That's $500 billion of revenue, as a group, by 2029. Their combined run-rate today is north of $100 billion. Three years ago it was $2 billion. That is the fastest revenue ramp in the history of business, it isn't close, and it still isn't enough. To hit $500 billion the category has to grow fivefold in three years, 70% a year, every year, from a base the size of Coca-Cola. There's no precedent for that. There was also no precedent for the last three years, which is the whole trouble with this moment. The people who've been right so far have been right by ignoring precedent, and a man who got rich ignoring precedent is the last man alive who'll start consulting it. So there are two forks in the road, and both of them are toll roads. Fork one: the labs don't get there, and the $4 trillion doesn't earn its keep. People say "debt crisis." Careful. Here's where the rhyme matters. In 2001 the trillion dollars of telecom debt was junk paper issued by CLECs with no revenue, and the bondholders got sixteen cents. Today the four big spenders fund about 70% of their capex out of operating cash flow and carry AA ratings. The debt lives at Oracle, CoreWeave, xAI, Meta's off-balance-sheet vehicles and in about $350 billion of private credit. That's not 2008. It's 2001 with better credit: write-downs, capex cuts, and a few bodies in the second tier. The band doesn't die. It just stops taking requests. And here's the thing nobody says out loud. About 40% of AI capex is GPUs, and Nvidia's gross margin is 75%. So roughly 30% of the $4 trillion has already been earned. By Nvidia. The return on this capex has, in a sense, already happened. It went to one company in Santa Clara, which is like finding out the band got paid up front and the tab is yours. Fork two: the labs do get there. Then $500 billion a year has to come from somewhere, because money is not conjured, whatever the deck says, and there are only three somewheres. It comes out of wages. US compensation of employees is $15.7 trillion. Five hundred billion is 3.2% of it. At $100,000 per fully loaded worker, that's five million jobs, which takes unemployment from 4.3% to about 7%. "Productivity" is what we call this at conferences. Or it comes from growth. Five hundred billion is 1.6% of US GDP. If AI makes the economy 1.6% bigger than it would otherwise have been, nobody loses, every deck was right, and I owe a great many people an apology, which I'll deliver cheerfully and from a boat. I'd note that AI is already adding about a point and a half to GDP growth, but that's the capex itself, not the output. So far the bar tab is being counted as revenue, which bartenders have been doing since the invention of bartenders. Or it comes out of someone else's budget. The world spends $1.44 trillion a year on software. Five hundred billion is 35% of every software dollar on earth, so software can't be the whole answer, but it's the first place the labs will look, and the IT services and outsourcing budgets behind it are another $1.7 trillion. In this version the thing that goes to zero isn't the hyperscalers. It's SaaS at ten times revenue. If you're long SaaS at ten times revenue, read that sentence twice. The honest answer is a blend, and the whole game is which blend, because every blend has a different loser. In fork one it's the lenders. In the wage version it's people with jobs. In the budget version it's everyone long software. In the growth version nobody loses, which is exactly why I don't trust it. In thirty years I have never seen a version where nobody loses. I have seen a great many decks that promised one. Reflexivity ends when the story and the cash get too far apart. The story is $4 trillion. The cash is $100 billion, growing faster than anything ever has. That gap closes one of two ways: the cash catches up to the story, or the story comes down to the cash. The music's still playing, the floor's still full, and I'm out there with everybody else, because that's the job. But I've started keeping one eye on the bar. In 2000 the bartender came around in about ninety days. He didn't ask who'd had the most fun. He just asked whose card was open.
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