We have raised a $550M Series E at an $8.8B valuation, led by Atreides. Every major existing investor is back in, and we’re welcoming Baron Capital, Capital Group, Fidelity, Glade Brook and J.P. Morgan. We picked investors who think in decades. Several of them work with law firms and legal teams every day, so they can see where the work is heading. Lawyers are moving from AI assistants to agents that do multi-step work. The money goes there. We’re growing our teams across North America, Europe, Latin America and Asia-Pacific, and going deeper on the Legora aOS. Thank you to the 1,000+ people at Legora, and to the 140,000 legal professionals who use what we build. I say this a lot because I believe it: it’s the best time in history to be a lawyer. We're thankful to be a part of it. Full story: legora.com/blog/series-e

When I joined venture 2 years ago, everyone told me I was foolish to pick Menlo Ventures. Today, research from the best Stanford professor of venture capital ranks Menlo as #3 in 2026, and #1 for <$25B in AUM, with 50 unicorns and only ~20 investors. These rankings rewards being early, leading, valuation, exits and recency. Rankings are just rankings, but it’s extremely rare for VC firms to change reputations so quickly. It’s a small milestone to celebrate. Menlo has spent the last 5 years rebuilding the team with people who have built startups and long time investors. We built a culture around sticking to first principles while staying true to our roots of low volume high conviction bets. In the last few years, we have been fortunate to partner with Anthropic, OpenRouter, Lovable, Suno, Fireworks, Gimlet, Higgsfield, Wispr, Modal, Legora, OpenEvidence and they are really the stars of the show. And we’ve missed innumerably more. However, in venture, you’re only as good as your next deal.

Over the last 7 days, Opus 5.5 is the top model in spending on OpenRouter for 20 of the 29 task categories we track

We are ALL IN on AI. It's nice when third parties acknowledge and quantify how all-in we have been on AI. Grateful to be in the company of such amazing firms who we respect, collaborate , and sometimes compete with. More details of the study here - ilyastrebulaev.substack.com/…

OpenAI tells investors their annualized revenue is $50B for end of September, up from $30B in July, short of the $70B previously reported. Anthropic was at $65B annualized end of July.

Menlo participated in our Seed for a $3m check. They doubled down and led our Series A with a big swing. Since Day 0, and across ups and downs, they (multiple folks at the firm @mkraning @CCgong @venkyganesan @deedydas) have been helping like they are all in. Smart portfolio construction and unwavering commitment & loyalty can co-exist.

Lot of people hating on his phrasing of the “option bet”. Doesn’t bother me at all, and I raised money from Menlo. Any fund who’s got an “option bet” in you whose GPs personally offer to put up cash for you (and when it’s clear you are not their fund maker at the time) to make payroll when you may have all your cash frozen in an SVB debacle is A-ok by me. Happy for them and their big AI bets and wins 👏 👏 🙏 🙏

have to disagree with @venkyganesan here but i can understand where hes coming from as a predominantly series bcdefg investor as an early stage fund we underwrite every investment as a potential fund returner and work with the team to help them get to scale for us every investment matters because we only make a few a year (13 in the past two years+) so every bet we make we want to be a home run and isnt an option call to double down later

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

Menlo Ventures is on a tear They led an investment in Anthropic at an ~$4B valuation At the time, most thought OpenAI would be the singular AI winner Maybe we’ll see something similar with @FactoryAI? 👀 Welcome to the Factory team @mmurph!

Matt Murphy is a legendary investor with a track record of being very early and being very right. He led the Series D of Anthropic in Jan 2024. Their best model was Claude 2.1. I am thrilled to have @mmurph and Menlo Ventures investing in @FactoryAI. We are still so early.

We're committing $100 million to train 10,000 engineers at our customers and partners. Every exec team I meet wants to go faster. And that often comes down to the best AI + the best talent to apply it. And that talent can be scarce. anthropic.com/news/claude-fr…

You probably know Venky Ganesan as a Partner at Menlo Ventures — an early Anthropic investor. But at 16, he left India alone for a tiny town in rural Washington. He calls what happened next a “culture shock.” Listen to our full convo here: curiositycentre.com/the-high… - @venkyganesan @MenloVentures @mmurph @yee_darcy @bznotes @someshdash

Today Higgsfield crossed $1B in annualized revenue. We've grown 20x since September 2025. There’s a popular argument that foundation models will capture all the value in AI, leaving little for the application layer. Higgsfield is proving the opposite thesis, as better models expand what enterprises can achieve and increase the value of applications embedded in their workflows. That thesis is reflected in our business: 300% net revenue retention. 115% MoM growth in enterprise adoption since June. Positive gross margin since the beginning of this year. Thank you to our incredible team and everyone building it with us.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

It was born in a physics simulation. For 140 years, our model played against increasingly tough opponents, each one a previous version of itself.