‘A Rare Land-Grab Moment’: Menlo Ventures’ Matt Murphy On The Next Wave of AI And Putting $3B In New Capital To Work

In June, Menlo Ventures footnote]Menlo Ventures is an investor in Crunchbase. They have no say in our editorial process. For more, head here .[/footnote] announced $3 billion in new capital across two funds , marking the largest raise in its 50-year history. Menlo Ventures XVII will invest primarily in seed and Series A companies, while Menlo Inflection IV will provide growth capital to startups at Series B and beyond. The new funds will target companies throughout the AI market, from foundational models and infrastructure to enterprise, healthcare and consumer applications. The new capital gives the Silicon Valley firm more flexibility to back companies from their earliest days through later funding rounds that can require hundreds of millions of dollars. It also shows how important AI has become to a firm previously known for investments in companies including Uber , Roku and Siri . Matt Murphy of Menlo Ventures. In recent years, Anthropic has become the most prominent company in Menlo’s AI portfolio. The firm first invested in the AI model developer in 2023 and has added to its investment in later rounds. Menlo’s other AI investments include app-building platform Lovable , music-generation startup Suno , AI model marketplace OpenRouter , voice productivity company Wispr , AI infrastructure companies Fireworks AI and Modal , robotics startup Skild AI , and AI research company Goodfire . Matt Murphy , a partner at Menlo since 2015, has played a central role in developing that strategy. He invests across AI infrastructure, developer tools and AI-native software and has led Menlo’s investments in companies including Anthropic, Lovable, OpenRouter, AI-powered software delivery platform Harness , code security startup Semgrep and legaltech startup Legora . Before joining Menlo, Murphy spent 15 years as a general partner at Kleiner Perkins, where he was an observer at Google from the firm’s initial investment through its IPO, helped launch the $200 million iFund with Apple and worked on investments including DocuSign, AppDynamics, Upstart and Shazam. Earlier in his career, he held operating roles at Netboost and Sun Microsystems. Crunchbase News spoke with Murphy about why AI is pushing Menlo toward larger and more concentrated investments, what the firm has learned from its relationship with Anthropic, and where he sees the next opportunities — as well as potential bottlenecks — across the AI market. The interview has been edited for brevity and clarity. Crunchbase News: Inflection IV puts Menlo in competition with some of the biggest late-stage investors in the world. How do you keep the firm’s close, founder-focused approach when you’re writing much larger checks? Murphy: AI companies need more capital than previous generations of software companies. They’re staying private for longer, and the winners are quicker to break from the pack. For us, a larger fund gives us the ability to partner with founders from company formation through hypergrowth. Through our venture fund, we invest in seed and Series A companies, but the inflection fund gives us the scale and flexibility to back the clear winners as they emerge. This was our strategy with Anthropic, Suno, Wispr, OpenRouter and Lovable. You’ve recently invested $100 million in companies including Lovable and Suno. Is that level of concentration becoming a bigger part of Menlo’s strategy, or is it reserved for a small number of standout AI companies? The Anthropic investment is an example of us doubling down when we had incredible conviction. Remember, we first invested in the [Series] C round, which gave us a chance to get close to the team, see how well they were executing, and understand where they were going. When we led the [Series] D round, it was still the largest investment the firm had ever made. We learned from that experience and success, and it’s become a standard part of our approach now. Also and importantly, the market has changed. There’s a gold rush around later-stage AI, and the companies that break out are growing at rates we’ve never seen before, at scale. These companies need capital to sustain that growth and, frankly, have earned higher private valuations given the growth rate. We’re changing how we invest, but overall we’re pursuing more of a barbell right now. On the later end, we’re much more aggressive, stage- and capital-wise, for the right companies. That said, the bar is still very high. Many AI categories are overfunded, and there is a huge amount of speculation. The winners of this era separate quickly, and we believe they will compound at unprecedented rates. Your relationship with Dario Amodei and Anthropic gave Menlo an early view into where the AI market was heading. What are you seeing now that you think other investors may still be missing? I don’t know that it’s counterintuitive, but I’d say we are moving from Phase 1 to Phase 2 of the market and are seeing an entirely different set of opportunities and challenges. In Phase 1, developers just picked a model to start building AI. In Phase 2, we are seeing companies get to scale using AI and looking to optimize their spend and infra choices. A whole host of companies are seeing tailwinds alongside Claude and Claude Code, such as OpenRouter, Fireworks, Modal and Gimlet . It will be a multi-model world. One size won’t fit all, and we’ve been active in that area as well, including more vertical models such as Chai Discovery for life sciences and Skild for robotics. The Anthology Fund has helped you spot promising AI companies early. As the application layer matures, what specific bottlenecks are you seeing founders run into when building enterprise-grade defensibility on top of frontier models? The Anthology Fund has been an incredible source of deal flow and has given us a broad aperture around what areas of AI are disproportionately taking off. It’s been a great program for getting closer to a broad set of application and infrastructure companies and building relationships before deciding where to lean in. I wouldn’t say it’s been the key factor in identifying bottlenecks across the AI ecosystem. For sure it is part of it, but from the broad set of portfolio companies and new companies we meet, the No. 1 bottleneck has been how to take all the new code that has been written and get it into production faster, safely, and securely. This has created a big tailwind for companies helping with software delivery, like Harness with application and code security, like Semgrep; and code review and testing like Greptile . Additionally, the rise of custom models based on open-source/open-weight models has created a number of bottlenecks as companies look for compute, training, sandboxes, and more. Both development and runtime resources have become essential to accommodate this next wave, and companies like Modal and Fireworks are addressing that with their offerings and the compute capacity they’ve been able to aggregate across various compute providers, including Nebius and CoreWeave. Valuations across the AI market have risen dramatically. Which parts of the market do you think are most likely to produce strong, sustainable businesses: infrastructure, model tools or industry-specific applications? We’ve been active across models, infrastructure, and applications. All are showing tremendous potential and tailwinds right now. At the moment, infrastructure is seeing a disproportionate spike in opportunities as enterprises and AI-native companies embrace a multi-model approach and scramble to keep up with the compute and infrastructure management needs that it requires. Coding tools are now mainstream and putting tremendous pressure on organizational processes to release software faster and more efficiently, which is leading to tailwinds for companies like Harness and Gimlet. It’s fair to say the majority of companies are optimizing for market share right now rather than gross