.@mmurph on why SaaS-era VC's weren't technical enough for AI: "If you look at SaaS applications, there wasn't really a lot of technology under the hood. You had to build some things efficiently/ But you used AWS, and you were building really cool workflows and interfaces. Now, if you look at these AI companies, whether it's Legora or Lovable, there's huge amounts of technology under the hood. The investors who thrived in the SaaS era were not as technical as those needed to thrive in this environment. And that's just on the application front. On the infrastructure front, its the same thing. So we hired @timt, who was the CTO of Splunk for a long time. @mejoff, who was the chief product officer at Atlassian. Matt Kraning, a PhD in machine learning from Stanford, who sold his company to Palo Alto Networks. @deedydas, who was the founding PM and engineer at Glean. @amywumartin, who had been in venture and at FTX, interestingly. All this team came together over a period of 3-4 years. Much more technically oriented. They understand developer communities, and the things that are really flourishing right now."

I’ve been a Canva DAU across @BananaCap_ and @ThePeelPod for years because it’s such a simple yet powerful product One of the most time consuming things I do is rearrange text and logos. They just launched their second design model in the past week which should finally fix this.

Most surprising part of my conversation with @aydin was when he said @felicis likes to put more money in after a portfolio company has a bad quarter. "Sometimes founders wanna do crazy things. And the investors are like, 'That's batshit crazy.' Sometimes what they really want is one of the investors to say, 'You know what? That is batshit crazy. But you should absolutely do it. I trust you, and you will do great. And I will prove it. I will put even more money in the company.' There's been a number of situations, like n8n or Semgrep. The company's doing really well, and inevitably there is a little hiccup. As soon as the first hiccup happens, people start getting agitated. 'Oh my God, we missed this quarterly goal and this one stat.' We do something very unusual. We're like, 'Yes, you had a bad quarter. Maybe you had two bad quarters. But the main trajectory of the company is going to a really great place.' The numbers were off because we have a very small n. Every startup, when they're starting, there's not enough deals. There's not enough people. One thing is slightly off, and it derails the results. You have a deal that closes in July instead of June. But that's the point where the founder is really vulnerable. So we're like, 'We'll give you more money. We'll give you support. We do believe in you.' And the founder is like, 'Wow. This investor is not just telling me things. They're also willing to back it up.' They wanna put even more money in the company at a moment where things seemingly were not that great. That part is very unique to Felicis. If we exist 20 more years from now, one of the key reasons will be that we've done right by the founders. Without that, I don't think we even have a 1% chance of persisting through the next two to three decades."

New @ThePeelPod with @asenkut Aydin started @felicis 20 years ago with $4M of his own money after every LP told him "we don't back people like you." Today, Felicis is investing out of its $900M Fund 10. We talked through the journey of building Felicis to $4B of AUM, why success is the most dangerous moment for an investor, how to keep your edge raising bigger funds, why a generalist approach is best for catching new technology waves, and the 10x rule he learned from Larry Page. Full episode here + links below. Timestamps: 0:00 Why success is the downfall of a good investor 2:04 Catching waves from YouTube to AI, chips, and defense 4:47 Is AI a bubble? How to tell what's real 6:17 Silicon Graphics: the company with no second act 7:33 Why AI infrastructure spend isn't hype 12:00 7 emails, no reply: how he met Tobi at Shopify 15:30 On adding value: "Just ask the founder" 17:30 The founder pledge: vote with the founders 18:26 Fund 1 was $4M of his own money 21:01 Find LP's preconditioned to believe in you 22:07 Venture is a people business 23:05 Raising Fund 2: the endowment’s "CIA interview" 26:48 Why outsiders bet on outsiders 28:23 Turner's rejected podcast audition 30:46 LP pushback on Fund 2 and Fund 3 32:56 Why he hired a CFO at Fund 3 35:04 Feedback founders can't argue with 37:21 Founders forget the clock is ticking 40:43 Backing founders through the first bad quarter 42:34 Does a 250x bigger fund lose its edge? 45:53 a16z, OpenRouter, Cursor, and big fund returns 47:30 Why deep tech needs huge rounds 50:57 Trillion dollar markets, billion dollar contracts 53:36 Why SpaceX and Anduril alumni will crush it 56:34 From a $50k first exit to "off by three orders of magnitude" 58:17 The software mindset in hard tech 59:38 Google's duct-taped servers 1:00:56 Scanning every book in the world 1:02:37 Larry Page's 10x rule 1:04:54 Outsider founders: Shopify and Adyen 1:07:17 Why Silicon Valley VC's miss companies outside the Bay Area 1:08:16 Generalist vs. specialist: 55 unicorns, no subject experts 1:12:17 Category winners sit between sectors 1:14:59 Don't obsess over entry price 1:17:24 Paid 700x revenue for Supabase, it grew 1000x 1:18:33 Notion, Canva, and the one-button product 1:20:27 The "OpenAI is about to collapse" crowd 1:21:41 Inspiration from Benjamin Franklin 1:23:47 Funding the twinkle in a founder's eye

"If you were late to AI and you weren't a top firm, I don't know how you recover." @mmurph on why VC firms tend to decay over time: "The first is poor handling of generational transition. The bad way: people hang around too long, and younger, talented partners leave. Or, let's just say you don't have as much alignment in the firm. Some of the best decisions we've made are because we had incredibly high alignment within the firm to do something that at the time felt like, 'Why are we doing that? It's a little off-spec for us.' If you don't have a high-trust organization, if people are fractured and doing their own thing, when somebody comes in with an exception like that, it's, 'That's not what we do.' So you start creating false guardrails. A lot of those dynamics can happen even if it's not a generational issue. People are practicing the business too much as individuals. You're not getting that collective strength that makes you better as a firm. Another massive one is missing a technology wave. If you were late to the AI game and you weren't a top firm, I don't know how you recover. If you're a top firm, you can recover because of your reputation. But you're still playing catch-up."

Early Anthropic investor @mmurph reveals why most neolabs won’t make it: "There's 70+ neolabs now. We're in 10 of them. There's two strategies. One is an alternative universe, where the model is super strong. We did Skild for robotics. For robotics, it's how you train the model and what it does. It's dealing with things like gravity. They're just different physics. Chai Discovery for drug discovery. Chai has their own models to develop antibodies and things like that. Or Axiom Math, which is math-oriented models. Or look at Suno. Mikey and team built their own audio models to make that experience magical. They built the model to enable the service. Same with Wispr. We have one called Inception that does diffusion for text, which makes it super fast for things like coding. We've got another one that does continual learning. Then there's a bunch of models that are highly complementary to existing foundation models. We're not saying, 'This is going to go right at Anthropic and take revenue from them.' It's more, where are the adjacent opportunities that can benefit from a complementary model? Now, with 70 of these, and many of them raising a billion as an A and then two or three billion as a B. That's clearly not going to end well. But right now, with the desire for the technology and the talent, there's going to be some amazing outcomes. Like Fei-Fei's company, acquired for $8 billion before it really had any revenue traction. If frontier intelligence is the product, that product is incredibly valuable right now if you don't have it. So we will see some spectacular acquisitions and outcomes. And we're also gonna see a lot of big craters in the ground."

Google took 19 years to hit $100B. Anthropic will do it in a third of the time. @mmurph on investing in both before they had revenue: "Earlier in my career, I sat in all the board meetings from the time we invested in Google at Kleiner to the time it IPO'd. Still today, people probably think of it as the fastest growing business ever. I think it was zero to a billion in five years. Zero to $10 billion in eight years. And then $10 billion to $100 billion in 11 years after that. So you're talking 19 years to get to $100 billion. It's tremendous. But then you have a company like Anthropic that comes along and can do that in a third or less of the time. That puts in perspective how quickly this company is growing, and what a different phenomenon we're in with AI."

Anthropic's largest customer spent $1.8B in the first 6 months of the year, equal to 1.5% of its revenue and 5% of non-gaap cash opex. The next 4 largest customers spent a combined $3B in 1H. Those 5 customers grew their spend 80% Q/Q in Q2 and 20% in Q3. #themoreyouknow#

Anthropic's S-1 just revealed only 250 customers are spending over $10M on AI: Early Anthropic investor @mmurph at @MenloVentures on why we're at the "very tip of the spear" of enterprise AI adoption: "It shows you the value of the technology. If that happens at some large company, you're like, 'Why isn't that happening at more large companies?' Probably because they haven't tried it out enough. They haven't enabled their organization to do it. They haven't re-platformed all their existing applications. Think about this market and how big these companies have become in such a short period of time. Then you look and you're like, 'Wait a minute. How penetrated is the technology in the Fortune 5000?' Almost none. Okay, so they're doing some coding and some productivity. How many of them have really re-platformed their whole application stack? Nobody. We're at the very tip of the spear on all the re-platforming, all the new internal productivity apps these companies are going to build, and their comfort level and understanding of AI. The whole next leg of this wave will be spectacular."

SoFi just posted a record Q3. $1.37B revenue, up 44%, with 17.1M members. A couple months ago, @Figure CEO @MBTannenbaum, who joined SoFi at ~75 people, told me why it worked when the other fintechs didn't: "SoFi was one of the unique fintechs at the time, focused on a mass affluent customer. Almost all the fintechs of the 2010 era, LendingClub and Prosper, a lot of these emerging online lenders, were focused on a down-market customer. But here's SoFi, refinancing the student loans of people who had graduated and were less risky. People miss this, but if you're trying to do financial services for consumers who don't have a lot of money, you're capping the amount of money you can make. If someone only has $20,000 to spend for the year, you have to capture all of it to make $20,000, which is impossible. If someone is making $120,000, that becomes six times easier."

From @mmurph on bending the rules to back Anthropic pre-launch at $4 billion: "I know a lot of people today it's like, oh, $4 billion, isn't that kinda common? But if you roll the clocks back to '22, '23, there were no companies like that. Exiting at $4B was literally a spectacular exit in the entire history of venture. And now that was the entry point. Then you've got issues like, well, what fund does that go in? How does a venture fund do that? And because it's pre-revenue and pre-launch, how does a growth fund do that? So we were caught in the middle. I'm glad we had a partnership that was all in on AI. This was at least the second best company in the category, with the best technical team. The models were benchmarking really well, and it was a massive market. So we bent the rules a little bit to say, 'Let's participate in this round with a goal to lead the next round.' That worked out fabulously. As soon as we invested, we had a different type of relationship with the company. We worked with them on recruiting, talking to customers, and a lot of strategic issues that come up early in a company. Then we saw them put together the deals with AWS and Google, and the revenue flywheel started to spin. The company went from zero when they launched in April to, it's been reported, something like $85 million at the end of the year. We saw that happening in September, October, and jumped on it to lead the round. Unless we had made that small initial investment, we wouldn't have had the relationship to see all of this. Or earned the trust with the team to lead that round."

New @ThePeelPod with Matt Murphy @mmurph at @MenloVentures We go inside Menlo's pre-revenue, pre-launch Anthropic investment and how they emerged as a top AI firm. Matt might be the only person who invested pre-revenue in both Google and Anthropic, and we talk through when to break the rules in venture, the three LP's that made over $1B each on the $500M Anthropic SPV Menlo raised without being able to show a single financial, Menlo's barbell investing approach, and why today isn't like 1999. Full episode here + links below. Timestamps: 0:00 How to read Anthropic's S-1 1:21 Do AI companies actually make money? 2:23 Google didn't want ads. Anthropic didn't plan on enterprise. 4:51 Bending the rules to back Anthropic pre-revenue, pre-launch 9:50 You can't be a leading AI firm without a top model company 12:00 Leading the Series D at $18B six months later 14:22 Why the best researchers chose Dario 15:25 Menlo's first-ever SPV, nearly the size of the fund 16:44 The LP meeting that sparked a $500M SPV 19:18 Raising at $18B without showing financials 20:04 Three LP's made $1B+ each 24:23 Why Matt joined Menlo in 2015 26:00 Rebuilding Menlo over the past decade 32:49 Venture as a team sport 35:45 How to keep a challenger mentality 38:15 Why VC firms decay 41:31 The Anthology Fund: OpenRouter, Wispr, Goodfire 42:27 Ownership vs. backing the best 46:10 Why this isn't 1999 48:06 Frontier vs. open weight models 51:29 The barbell portfolio strategy 53:54 Avoiding the messy middle between early and growth 55:07 How to spot a category leader 57:18 Fortune 5000 AI adoption: "almost none" 59:06 Menlo is in 10 of 70+ neolabs: Skild, Chai, Axiom 1:02:13 Where $1B AI startup raises actually go 1:04:18 Phase 1 vs. Phase 2 of AI 1:06:26 What the AI IPOs mean for venture 1:09:11 What Matt does differently after 25 years 1:10:52 The Alex Smith lesson

New @ThePeelPod with @orenzeev He manages $2.7B across 11 funds with no team. He has no meetings, doesn't write investment memos, and has never shown an LP a single slide from a deck. We get in to the 30-minute conversation that turned into his first fund, why most partners add negative value, the days his calendar is empty enough to surf on a Tuesday, his fund that had only five companies (and why diversification is a lie), how Navan went from a $90M run rate to zero in a month (and survived), why he does cross-fund investing, refusing DPI when LP's want it, and the 30-year history of Israel's startup economy. Full episode here + links below. Timestamps: 0:00 Running $2.7B with no team or partners 1:58 How one person can beat a team 4:43 Omission beats commission: why committees miss 7:13 What really fills a partnership's day 9:23 The returns business, not the production business 10:58 A week in his nearly empty calendar 16:49 The mistakes new solo GP's make 19:34 Eleven funds, zero slides 22:09 No thesis 24:28 Why diversification doesn't work 27:14 The fund with only five companies 30:44 TripActions insight: everyone hates their travel software 34:59 Paying employees to spend less 37:34 COVID: $90M run rate to zero 39:24 How Navan survived 41:34 A $100M round with no way to price it 44:29 Keeping the team: the real test of leadership 46:04 What Oren looks for in founders 50:39 Cross-fund investing, and "trust me, bro" 53:59 Always his own biggest LP 55:14 The DPI stand that cost him LP's 58:54 Decart, and the Anthropic deal 1:03:54 Inside the 30-year rise of Israel's tech ecosystem 1:07:19 The Yozma program that built an industry 1:11:59 Why Israelis build now instead of selling early 1:14:39 Moving to the Valley in the 2002 nuclear winter 1:17:19 Audible, and why the first home run matters 1:19:29 Leaving Apax, and going solo 1:22:04 Peter Thiel and a fund raised in 30 minutes 1:24:24 The second fund: 48 hours, still no deck 1:25:49 ICON: helping Israeli founders crack the Valley 1:30:29 The leader he admires most: Anwar Sadat