This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

Startups raised funding rounds with a lot of zeroes at the end this week. Three companies 鈥� , and 鈥� secured financings of $1 billion or more. Additionally, a robust lineup of companies in sectors including AI, e-commerce, cybersecurity, biotech and even mining also announced sizable new rounds.

1. , $1.37B, manufacturing: Hadrian, a developer of highly automated factories, raised $1.37 billion in Series D funding led by , , , , and . The financing sets a $7.87 billion valuation for the 6-year-old, Torrance, California-based company.

2. (tied) , $1B, energy storage: Austin-based Base Power, a developer of residential battery energy storage systems, secured $1 billion in Series D financing at a $13 billion post-money valuation. , , and led the financing, which coincided with the launch of the company鈥檚 Base Core home battery.

2. (tied) , $1B, nuclear power: Valar Atomics, a developer of technology and infrastructure to deliver nuclear energy, closed on $1 billion in Series B funding led by . The El Segundo, California-based company also secured a $200 million credit facility led by and .

4. , $700M, AI connectivity: Lumilens, developer of a connectivity platform for AI infrastructure, emerged from stealth and announced more than $700 million in new funding. , , , and led the financing for the San Jose, California-based startup.

5. , $545M, live shopping: Live shopping marketplace Whatnot bagged $545 million in Series G funding. The round reportedly a $20 billion valuation for the Los Angeles-based company, with , and as lead investors.

6. , $310M, critical minerals: Mariana Minerals, a software-focused developer of projects for supplying critical minerals, picked up $310 million in Series B financing led by . The 4-year-old company engineers, builds and operates mines and refineries using its software platform.

7. , $300M, AI infrastructure: Volta, a developer of AI cloud infrastructure, from stealth and said it raised a Series A at a $2.4 billion valuation, led by , , and .

8. , $250M, cybersecurity: San Francisco-based cybersecurity provider Horizon3, announced a $250 million Series E. and led the round, which set a valuation of more than $2 billion, triple the value set for its Series D last year.

9. , $188M, biotech: Watertown, Massachusetts-based drug discovery startup LifeMine Therapeutics secured $188 million in Series E funding led by . The funding will go toward clinical development of its lead program and advance its pipeline of transplantation and immunology therapies.

10. , $150M, agentic AI: HappyRobot, developer of an agentic AI platform geared for enterprises in sectors including logistics, financial services, utilities and manufacturing, raised $150 million in Series C funding led by and .

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of Aug. 1-7. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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Among lead investors, topped the ranks last month, while was by far the busiest backer by deal count. The highest-spending investors for the period, meanwhile, appear to be and .

For more detail, below we ranked active investors for July by several metrics. These include most prolific venture dealmakers, most active lead backers, biggest spenders and highest-volume seed investors.

Active lead investors

We鈥檒l start with active lead investors for the month, which, as usual these days, featured a heavily AI-centric lineup of deals.

Khosla Ventures ranked as the most active lead investor in rounds of $5 million or more, with eight deals in July. The largest were a $300 million Series A for quantum computing startup and a $120 million Series C for AI-enabled legal tech provider .

took the No. 2 slot, with six lead deals, followed by , with five. Below, we charted the top eight lead investors for the month by deal count.

Busiest venture investors

The ranks looked quite different when we widened the category to include both lead and non-lead investments in rounds of $5 million or more.

By this metric, repeat frontrunner Y Combinator once again took first place, participating in at least 19 such rounds. The storied accelerator typically takes a non-lead stake in follow-on rounds for startups it incubated.

Insight Partners and Andreessen Horowitz were next on the list, with 10 deals each, followed by Khosla and , with nine each. For a bigger-picture view, below we ranked the top 18 busiest venture investors for July.

Highest spending investors

When we focus on investors who led the most expensive assortment of startup financings last month, the lineup shifts once again.

For July, Coatue ranked as the apparent highest-spending听1 lead investor, backing a $10 billion financing for 鈥� rocket company, . (It should be noted though, that Blue Origin, founded in 2000, is probably too old to be considered a startup, although it is still a private company.)

Nvidia also stepped up, backing a $5 billion financing for foundational AI startup . Index Ventures and Andreessen Horowitz ranked high as well, each leading or co-leading rounds collectively valued above $2 billion.

Below, we rank 18 of the highest-spending lead investors for the month.

Seed dealmakers

Seed dealmakers were a bit more challenging to rank for July, in part because there鈥檚 often a time delay before smaller deals enter the dataset. One thing that is apparent is that Y Combinator was the most prolific investor at this stage, while other 鈥渦sual suspects,鈥� like and , also ranked high.

Related reading:

• A Record 14 Billion-Dollar Rounds In July Pushed Venture’s Historic Run Higher

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To dig into this trend, 兔子先生传媒 News analyzed a dataset of repeat founders who have gone through YC鈥檚 cohorts. That analysis revealed some very interesting insights. The dataset, shared with us directly from YC, consisted of 454 repeat founders through the program as well as 935 founder-company records spanning 2005 through 2026.

What we found is that repeat participation to date has mostly been a two-chapter journey: 428 founders (94%) went through YC exactly twice, while only 25 appeared three times. and co-founder was the sole four-time founder.

Other highlights: Founders typically returned to YC five years after their previous appearance, with an average gap of 5.1 years. However, the data reveals two distinct themes. Nearly 30% of return participations occurred within two years 鈥� including 38 in the same calendar year 鈥� while 61 returns happened after a decade or more. Some founders jump straight into their next venture, while others take years off to build experience before coming back around.

Repeat founder numbers peak in the most recent data, hitting 65 in 2025. But that doesn’t automatically mean people are returning at higher rates. In recent years, YC cohorts have grown significantly, and the 2025-26 numbers include newer batch formats alongside potentially incomplete data.

It’s also clear that returning to YC isn’t always a solo journey.

Several complete founding teams returned together for subsequent companies, including those behind , and , as well as and .

A trend YC partners are watching closely

, a general partner at the San Francisco-based accelerator who worked the spring 2026 batch, has enjoyed a front-row seat to the shift. In that cohort, he said he had 鈥渁 bunch of repeat, second-time founders鈥� he’d worked with before 鈥� several during their previous YC company.

鈥淚t definitely feels like more of a trend now,鈥� Epstein said. Still, he’s careful not to overstate the novelty.

鈥淚t’s not a new thing. But the alumni base of past YC founders continues to grow,鈥� he said in an interview with 兔子先生传媒 News, and that naturally translates into more people eligible to come back.

Epstein has worked with more than 1,000 startups at YC. Before that, he was a startup entrepreneur himself, co-founding (YC W10), a marketplace for graphic design assets that he sold to in 2014 before spinning it back out as an independent company in 2017.

Ask him what separates second-time founders from first-timers, and he points to experience using the program itself.

鈥淭hey know exactly how to get the most out of the advice, network and resources available to them,鈥� he said. 鈥淗aving been through the startup grind, they get really good at focusing on the signal that matters and cutting out the noise.鈥�

That experience also helps them avoid a specific, costly mistake.

鈥淭he biggest mistake I see second-time founders avoid is overhiring or overspending pre-product-market fit,鈥� Epstein said. 鈥淭he biggest regret of all the successful first-time founders I know is that they hired too many people, moved way slower and didn’t like working at their own companies anymore.鈥�

Leaner teams, powered by AI

That instinct toward leanness shows up in another pattern: Many repeat founders are choosing to start solo the second time around.

鈥淪ome of them (repeat participants) are solo founders, but they’re not building alone,鈥� Epstein said. 鈥淭hey already have networks of people they can bring in as founding employees. This helps them move faster, and feels more fun and less lonely.鈥�

He compares this shift to how cloud computing eliminated the need for startups to raise large sums just to pay for servers.

鈥淚t wouldn’t surprise me if 10-15 years from now you look back at all the money startups had to raise to hire people and realize that’s not a requirement,鈥� he said.

AI is accelerating that shift, and Epstein sees it pulling former company builders, including himself and YC CEO , back into hands-on product work.

鈥淚t’s so easy to get back into it and start building again. And it’s incredibly exciting,鈥� he said. That mix of hard-won product sense and new tooling, he believes, is changing what one person can build alone.

鈥淭hey actually become the people that can produce at 10x or 100x what a traditional engineer would be able to build,鈥� he said.

As an example, Epstein pointed to , a founder he first worked with on in 2020 who’s now building an AI tool that helps founders manage their projects and automate tasks.

Even so, Epstein believes founders keep coming back for the same core reasons: personalized advice from partners, a community of ambitious peers, access to top investors and alumni, and the urgency of the batch environment.

鈥淭he pressure cooker environment of the batch, which pushes them to move even faster, and distribution to thousands of companies within the network,鈥� he said. 鈥淚t’s extremely hard to replicate those things on your own.鈥�

From Opkit to Sazabi

One of the repeat founders Epstein has worked with is , whom YC has now backed twice.

Callaway’s path to Silicon Valley began almost by accident. As a college sophomore, he skipped a lined-up investment banking internship after reading about a software bootcamp in San Francisco 鈥� a decision he calls 鈥減robably the single most important鈥� of his life.

From then on, his goal was clear: 鈥淚 wanted to do my own venture-backed tech startup, and I wanted to do a YC venture-backed tech startup.鈥�

After gaining experience at and fintech , he founded his first company, , in YC’s fully-remote summer 2021 batch. Opkit was a healthcare-fintech startup building insurance verification and revenue-cycle-management software.

鈥淚t was, in retrospect, not the right thing for me to be working on, but a really fun and interesting and rewarding first venture,鈥� he said in an interview. Opkit was later acquired by .

That experience shaped his second company, , a name chosen deliberately in contrast to Opkit.

鈥淥pkit wasn’t very personal to me. It was more of an MBA case study approach to starting a business,鈥� he said. 鈥淲ith Sazabi, it needs to really be in alignment with who I am and my passions and interests.鈥�

Sazabi, an AI-native observability platform competing with incumbents like , draws directly on work Callaway has done throughout his career 鈥� a return, in his words, to 鈥渨hat I know best.鈥� He sees it as part of a common pattern: First-time founders often avoid building in the field they know best, then return to it with their second company.

Callaway hadn’t originally planned to go through YC again, and the reconnection happened almost by chance through an email that looped in his former partner on Opkit, Epstein. Once Callaway decided to return, he was more strategic about timing, even deferring his batch to build out more of the product first.

鈥淚 wanted to use YC as a go-to-market acceleration event,鈥� he said, something he likely wouldn’t have known to do without having gone through the program before.

The founder was back at YC in person for the first time this spring. He described the second-time experience as something entirely new: 鈥淚t was really something special.鈥�

This time around, Callaway also noticed a more experienced cohort than his own first batch, along with new concerns specific to the AI era. 鈥淭here’s a lot of anxiety around what the durable moat is in an AI world when lines of code are effectively free,鈥� he said.

On fundraising, he drew a pointed comparison to 2021. 鈥淪pring 2026 felt similar to fall 2021,鈥� he said, 鈥渂ut unlike 2021, where interest rates and ZIRP drove a lot of that energy, in 2026 it’s driven by AI and by real material gains.鈥�

The company鈥檚 thesis is resonating with investors. In late June, Sazabi announced an $8 million seed round led by , and Y Combinator, with participation from and more than 60 angels from companies including , and .

鈥淎I has changed how software gets written. Now it is changing how software gets operated,鈥� Callaway said. 鈥淪azabi is rebuilding observability from first principles for a world where agents are part of every engineering team.鈥�

Overall, as AI continues to lower technical barriers and YC’s alumni pool keeps growing, second-time founders like Callaway are becoming an increasingly visible part of the accelerator’s lineup.

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This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We鈥檝e included both startups and publicly traded, tech-heavy companies. We鈥檝e also included companies based elsewhere that have a sizable team in the United States, such as , even when it鈥檚 unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as 鈥渦nclear.鈥�

In November 2022, was doing something rare for a longtime educator: taking time off. Having launched his career as a teacher in Atlanta Public Schools, Khan became an assistant principal before founding his own public high school in Denver. After a year spent coaching principals at the district office, he decided to take a 鈥減ersonal sabbatical.鈥�

Then, ChatGPT came out.

Khan began tinkering with the new technology, fascinated by its potential.

鈥淚 actually went out to my old school building, the one that I founded, and started using it with teachers,鈥� Khan recalls. He ran workshops and asked the educators to use the tool in as many scenarios as possible.

The responses were varied. Most teachers barely touched it. A few tried, but felt doing the work manually was faster. However, some had a lightbulb moment.

鈥淭here were one or two teachers who told me, 鈥楾his has completely revolutionized the way I teach,鈥� 鈥� Khan said in an interview with 兔子先生传媒 News.

Seeing that divide sparked something in him.

鈥淚 thought this technology could impact every teacher, not just teachers who are really enthusiastic about using new technologies,鈥� Khan said. 鈥淪o the task then was, 鈥楬ow can we take all the power of this new technology and make it really accessible to teachers?鈥� 鈥�

Building the 鈥榲ertical AI鈥� for K-12

That experiment set the groundwork for , a platform designed as an all-in-one AI operating system for K-12 educators and students. For teachers, the tool acts as a daily assistant. It performs tasks like building rubrics, differentiating assignments for varied learning levels, and generating practice worksheets and reading materials.

The platform also helps educators offer monitored AI experiences directly to students. Those experiences range from algebra tutors to writing assistants customized with state exam rubrics that deliver tailored feedback to help students revise their essays.

鈥淵ou can think of MagicSchool as the vertical AI solution for K-12 schools,鈥� Khan said. “Enterprises are adopting generative AI in other fields 鈥� Like in law, there鈥檚 and that are vertical AI for legal firms. We鈥檙e kind of that, but for K-12 schools.鈥�

Today, the company鈥檚 primary customers are school districts that want to provide a safe, governed environment for generative AI that aligns with data privacy rules and local curriculum priorities. MagicSchool now partners with large school systems, including Denver Public Schools, and Florida鈥檚 Broward County Schools and Hillsborough County Schools, as well as private institutions.

“One in five children in America go to a school that is in partnership with MagicSchool,” Khan noted. Additionally, roughly 8 million educators worldwide have signed up for the platform, he said.

The uphill battle to raise capital

Despite the platform鈥檚 rapid adoption, Khan’s path to raising capital for MagicSchool was a challenge. In the beginning, he worked with hourly contractors and lacked a formal business model.

鈥淚 had no real business plan,鈥� Khan said. “The most successful tech companies from my perspective as a consumer were the ones that just got a lot of users, and that was my goal 鈥� I was like 鈥榣et’s just get a lot of people using this, and we’ll figure it out from there.鈥� 鈥�

Once MagicSchool鈥檚 user base neared 1 million, Khan began pitching venture capitalists. However, when compared to standard Silicon Valley profiles, his background as an educator initially proved to be a hurdle rather than a selling point.

鈥淣obody wanted to give a former principal money,” Khan said, recalling 鈥渜uite literally hundreds of meetings鈥� before securing an institutional investor.

鈥淚 think that investors are taught to pattern match,鈥� he noted. 鈥淭hey’re saying, ‘Hey, well, did you go to ? Are you a tech person? Did you work at ? ‘… I have none of those things on my resume.鈥�

Even edtech-focused investors were hesitant, leaving Khan frustrated as he watched other founders secure millions based purely on tech-heavy resumes.

鈥淚 remember seeing other edtech companies right around our size raise seed rounds 鈥� with no product, no sales, no nothing,鈥澨� he recalled. 鈥� I would think, 鈥楶eople know what our product is. Millions of teachers know what our product is, and nobody’s heard of that one.鈥� 鈥�

To overcome the skepticism, Khan relied strictly on impressive growth metrics, convincing investors during every fundraising stage that the business鈥� 鈥渢raction was undeniable.鈥�

The strategy paid off. Following early angel investor checks, MagicSchool went on to raise a $2.4 million seed round led by Colorado-based . To date, the company has raised nearly $63 million in total funding, driven by strong financial growth, including 3x year-over-year revenue growth at the end of last year, according to Khan. The startup鈥檚 other backers include , , and.

Expertise as the next wave of innovation

Although Khan no longer manages the high school he founded, he stays connected to the classroom through district visits. The school remains a top-performing public school in Denver under a former founding team member, he noted.

鈥淥f course, I miss that,鈥� Khan admits. 鈥淭here’s nothing that can replace the relationship you build over a long period of time with students.鈥�

Yet, his deep-rooted experience in education ultimately became MagicSchool’s greatest asset 鈥� a trend Khan sees taking hold across the broader AI landscape as domain experts step up to build industry-specific tools.

鈥淚 think that what we’ve learned over time is that the model is no longer the differentiator,鈥� Khan says. 鈥淗ow you contextualize the model with the real problems that people have in the work that they’re doing, and specific expertise, is the thing that’s going to unlock the next wave of innovation and impact for generative AI.鈥�

Related 兔子先生传媒 query:

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It is difficult to define the extent to which a specific company should morph itself into an “AI native” company. Does this add value for everyone?

AI does not automatically increase exit value. In some cases, it can reduce differentiation, compress margins, complicate diligence and make a company more difficult to acquire. Like pricing, customer service or go-to-market strategy, AI requires a careful balancing act between speed and defensibility, innovation and complexity, short-term productivity and long-term strategic value.

Let鈥檚 jump into three ways AI strategy can impact exit value.

Build an AI architecture that acquirers can trust

Many startups are rapidly adding AI copilots, model integrations, orchestration layers, prompt libraries, vector databases and third-party AI tools across the organization. This may accelerate product development and help teams ship faster. However, from the perspective of an acquirer, it can also create a more complicated architecture.

During due diligence, buyers care about how AI is being used. Which models are embedded in the product? Which vendors are critical to delivery? Where does customer data flow? How are outputs monitored? What happens if pricing changes, APIs break or regulation shifts?

A startup may see AI adoption as innovation. A buyer may see it as integration complexity, vendor dependency, compliance exposure and security risk.

This is especially important for strategic acquirers that need to integrate the target into a larger platform. If AI makes the product easier to scale, automate, secure and maintain, it can support valuation. If it creates a fragile layer of external dependencies, unclear data flows and difficult-to-audit decision-making, it may reduce confidence and lower the price a buyer is willing to pay.

Invest in proprietary data

Even one year ago, adding AI functionality to a product could create excitement by itself. Today, many AI features are becoming easy to replicate. Summarization, search, chat interfaces, recommendations, content generation and workflow assistance are increasingly available through the same underlying models and infrastructure. This matters for exits.

A strategic acquirer rarely pays a premium simply because a startup integrated the latest model. They pay for what they cannot easily build themselves: proprietary datasets, unique customer workflows, strong distribution, deep vertical adoption or network effects that improve with scale.

Founders should therefore ask a simple question: Is our AI strategy creating a defensible asset, or are we just adding features that competitors can copy within weeks or months?

Revisit your buyer map as AI redraws strategic boundaries

Historically, many companies built their exit strategy around a familiar buyer map. A cybersecurity startup might sell to a larger cybersecurity vendor. A vertical SaaS company might sell to a competitor in the same industry. A workflow automation company might sell to a productivity platform. AI is changing those boundaries.

As AI expands what platforms can do, strategic buyers are moving into adjacent markets they previously ignored. An infrastructure company may acquire an identity platform because AI agents need secure access controls. An ERP vendor may acquire workflow automation because AI is moving closer to business process execution. A data platform may acquire a vertical application because domain-specific data is becoming more valuable.

This means CEOs should revisit their buyer map every six to 12 months. The most logical acquirer today may not be the same one that would have been logical even one year ago.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 兔子先生传媒 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

Related 兔子先生传媒 query:

Related reading:

• Your SaaS Metrics Are A Result, Not A Strategy
• The Boardroom Blind Spot: When Success Hides Disruption
• The No. 1 Reason M&A Deals Fail Before They Even Start

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July ranked as the third-largest funding month of the year, up 10% over June, following on the heels of a record-breaking first half of 2026, when startups raised $515 billion globally.

Fourteen startups raised billion-dollar rounds in July, the highest count in a single month, though not the largest amount raised in such deals, an analysis of 兔子先生传媒 data shows. The tally includes nine U.S.-based companies, two each from Germany and China, and one company headquartered in Singapore.

The largest startup funding deal last month was a $10 billion investment in , the first external financing for the -founded space exploration company.

, a frontier lab founded by former Chief Scientist , reportedly raised $5 billion from . The next two largest deals were Beijing-based frontier lab 鈥檚 $3.5 billion raise after releasing its latest Kimi K3 model, and raising $2.8 billion for short-video generation.

Two Germany-based companies in defense tech also raised billion-dollar rounds: and . In the U.S., companies that raised billion-dollar-plus rounds spanned the energy, industrial robotics, AI training, security and semiconductor industries.

Funding to AI

A total of $35 billion, or around 53% of global venture funding, went to AI-focused companies in听 July. Other leading sectors were aerospace, defense and energy.

U.S.-based companies raised a total of $39 billion, or around 59% of global venture capital, last month with roughly half of the capital invested in its AI-focused companies.

Exits

July was also a robust month for startup exits, including via acquisition and public-market debuts.

Venture-backed M&A totaled more than $9 billion in July, with five companies exiting at prices听 over $1 billion, 兔子先生传媒 data shows. Notable acquisitions included London-based data center provider 鈥檚 roughly $1.65 billion acquisition of software layer , which was built to manage AI workflows, and in the security sector, AI-native security company 鈥檚 $1 billion acquisition of , a service to manage non-human identities.

Twelve venture-backed companies went public above $1 billion in value in July, including five from China, six U.S.-based companies, and one from Italy. The largest was Chinese chipmaker , which went public at around $85 billion and . Italy-based , an acquirer of software companies including and , went public at a value of $18.5 billion. And last-mile transportation company , founded in 2017, went public at $1.6 billion in value, raising $167 million in the process.

In closing

If the first half of 2026 established that venture has entered a new era of mega-financings, July reinforced that the trend is broadening rather than fading. Record numbers of billion-dollar rounds in both hardware and software, alongside a healthy IPO and M&A market, point to an ecosystem where capital is not only concentrating in category leaders but is also beginning to recycle through exits.

Related 兔子先生传媒 queries:

Methodology

The data contained in this report comes directly from 兔子先生传媒, and is based on reported data. Data is as of Aug. 3, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted. 兔子先生传媒 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to 兔子先生传媒 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. 兔子先生传媒 also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. 兔子先生传媒 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the 鈥淪eries [Letter]鈥� naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a 鈥渧enture鈥� round. (So basically, any round from the previously defined stages.)

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The road to the top rarely runs in a straight line, but there are less circuitous routes to becoming CEO. According to analyzing every CEO succession in the S&P 500 since 2000, there are four feeder roles: COOs, divisional CEOs, CFOs, and 鈥渓eapfrog鈥� leaders promoted from below the C-suite.听

But a separate 10-year study called the suggests that the fastest climbers 卤 鈥渟printers鈥� who reached the CEO seat well ahead of the 24-year average 鈥� didn鈥檛 get there by climbing the corporate ladder to the top. They got there through bold, often unconventional career moves, such as taking on a failing division or building something from scratch.听

In fact, what set them apart wasn鈥檛 pedigree but building a specific skillset that made them CEO material: decisiveness, reliability, adaptability, and the ability to engage people around a plan.

Product management doesn鈥檛 appear in the CEO-pathway research, likely because none of the major studies breaks the role out as a separate category. It鈥檚 a relatively newer function, and it tends to get folded into general management or engineering in career datasets.听

But once you look at what the job demands 鈥� ownership of a tangible outcome, obsession with what customers value, the willingness to make tough decisions 鈥� it maps directly onto the traits the CEO Genome Project found in its sprinters.

I鈥檝e spent my career leading product and technology at companies including , , and . Those roles landed me my first CEO position without having to fill the typical CEO-starter pack jobs because I was able to articulate my skill set to the board.听

In short, product management is about making a product successful; being CEO is about making a business successful. The ingredients are the same.听

Don鈥檛 take my word for it. joined in 2004, leading product management for the Google Toolbar, years before he became CEO. spent eight years as YouTube鈥檚 chief product officer before taking the top job there in 2023.听

Yes, the scope of the job differs, but that鈥檚 true of every promotion, especially for the hardest job on offer. A CEO carries the full weight of the business: financial performance, legal and regulatory exposure, the board, and the market. A PM鈥檚 remit is naturally narrower: one product, one roadmap, one team to rally.听

But scope isn鈥檛 the same as skillset. The job gets bigger, but the muscles you exercise remain the same: setting a vision under uncertainty, prioritizing ruthlessly, making calls with incomplete information, and getting people who don鈥檛 report to you to deliver anyway. Learn to do that for a product, and you鈥檝e already learned to do it for a business, just on a bigger scale.

That mindset isn鈥檛 new to start-ups and scale-ups either, where a PM is often the closest thing to a mini-CEO, making calls across product, growth, and operations simply because no dedicated function exists yet to do it for them.听

Part of the reason I think the PM-to-CEO pathway is so often overlooked is that the function is judged by its worst practitioners. Plenty of people with 鈥減roduct manager鈥� on their CV spend more time managing processes and stakeholders than owning outcomes and building amazing products. And it鈥檚 that version of the job that shapes how PMs get perceived, and why few are inspired to make the leap. The PMs who have done the job – by taking ownership of the outcome rather than the process – are building something that truly resembles the job description of a CEO.听

The best advice I can give to aspiring executives and entrepreneurs today – whether they鈥檙e PMs or not – is to choose a metric that they want to be accountable for. In my previous role, I focused on monthly active users; now I鈥檓 focusing on EBITDA. Strip away the layers and remain outcome-oriented.听

The CEO pathway research keeps looking for the right sequence of job titles, but that model is increasingly outdated in the modern-day work environment. Rather than focusing on traditional CEO pathways, aspiring executives should focus less on glitzy job titles to add to their CVs and more on the concrete skills they can gain. Product management, done properly, offers the perfect crash course.听

is the CEO of , a London-based technology company building a global payments infrastructure to facilitate money transfers. He previously led product and technology for companies including , , and .

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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 , and .

In recent years, has become the most prominent company in Menlo鈥檚 AI portfolio. The firm first invested in the AI model developer in 2023 and has added to its investment in later rounds. Menlo鈥檚 other AI investments include app-building platform , music-generation startup , AI model marketplace , voice productivity company , AI infrastructure companies and , robotics startup , and AI research company .

, 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鈥檚 investments in companies including Anthropic, Lovable, OpenRouter, AI-powered software delivery platform , code security startup and legaltech startup .听

Before joining Menlo, Murphy spent 15 years as a general partner at Kleiner Perkins, where he was an observer at Google from the firm鈥檚 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.

兔子先生传媒 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 鈥� as well as potential bottlenecks 鈥� across the AI market.

The interview has been edited for brevity and clarity.

兔子先生传媒 News: Inflection IV puts Menlo in competition with some of the biggest late-stage investors in the world. How do you keep the firm鈥檚 close, founder-focused approach when you鈥檙e writing much larger checks?

Murphy: AI companies need more capital than previous generations of software companies. They鈥檙e 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.听

This was our strategy with Anthropic, Suno, Wispr, OpenRouter and Lovable.听

You鈥檝e recently invested $100 million in companies including Lovable and Suno. Is that level of concentration becoming a bigger part of Menlo鈥檚 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.听

There’s a gold rush around later-stage AI, and the companies that break out are growing at rates we鈥檝e never seen before, at scale. These companies need capital to sustain that growth and, frankly, have earned higher private valuations given the growth rate.听

We’re changing how we invest, but overall we鈥檙e pursuing more of a barbell right now. On the later end, we’re much more aggressive, stage- and capital-wise, for the right companies.听

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.听

Your relationship with 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鈥檛 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.听

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 .听

It will be a multi-model world. One size won鈥檛 fit all, and we鈥檝e been active in that area as well, including more vertical models such as for life sciences and for robotics.听

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.听

I wouldn鈥檛 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.听

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 .听

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鈥檝e 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,听model tools or industry-specific applications?

We鈥檝e 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 margin, but there are many opportunities for margin improvement over time, and this is a rare land-grab moment.

You鈥檝e backed new AI research labs before they even have a product, including . At that stage, what convinces you that a team has something truly different, and that it can compete with much larger technology companies?

As I mentioned, we believe in a multi-model world, where one size won鈥檛 fit all needs and use cases. We have an explicit strategy to gain early exposure to some of the most compelling AI research teams with distinctive techniques or capabilities, even at a very early stage. Many of these companies are raising $100 million-plus rounds, and while we occasionally lead in this category, we prefer to write smaller checks initially. This helps us build broader exposure across the category and talent pool, and then double down once we see one really taking off.

Frankly, there are too many right now, and all claim some differentiated technique or team. Of the roughly 60 model companies, we believe we鈥檝e invested in more than five of the best and expect to lean into one or two of them as they ramp.

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• Anthropic Backer Menlo Ventures Raises $3B In New Funds To Back AI Startups Across Stages听

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

Another week, another bevy of big rounds. For this past week, the largest round was a reported $5 billion -backed financing for foundational AI unicorn , followed by a $1 billion investment in . Other sizable rounds went to companies in sectors including energy storage, health testing, nuclear power, fintech, and, yes, AI.

1.听, $5B, foundational AI: AI lab Safe Superintelligence announced a long-term with Nvidia to rapidly accelerate its growth. The deal included a $5 billion investment from the chip giant with an eye toward boosting compute resources for Safe Superintelligence, a Silicon Valley startup founded by co-founder .听

2.听, $1 billion, fusion energy: Commonwealth Fusion Systems, a startup working on fusion energy technology and developing a grid-scale fusion power plant, raised $1 billion in fresh funding from unspecified investors. The round brings the total invested to date for the Massachusetts-based company to $4 billion.

3.听, $550M, thermal energy storage: Antora Energy, a company that provides energy through thermal batteries to data centers, announced that it has closed on $550 million in a Series C funding round. and co-led the financing for the nine-year-old, San Jose-based company.

4.听, $450M, health testing: Austin-based Function, a provider of lab testing, imaging, and personal health information that markets to consumers, secured $450 million in growth financing from .听

5.听, $370M, nuclear energy: Antares, a nuclear fission energy company that develops compact microreactors for defense and space applications, raised $370 million in Series C equity funding. and led the financing for the three-year-old company, which raised $100 million in debt funding alongside the equity investment.听

6.听, $200M, AI simulation: Simile, a developer of AI tools for running simulations, said it picked up over $200 million in fresh funding at a $2 billion post-money valuation. led the round, which comes just five months after the Palo Alto-based company launched its product.

7.听, $190M, cybersecurity: Cybersecurity provider ThreatLocker closed on $190 million in Series F funding to hone its platform and expand internationally. led the round for the Orlando-based company.

8.听, $170M, fintech: New York-based CAIS, an alternative investment platform for independent financial advisors, secured $170 million in Series D financing. led the round, which set a valuation for the company of more than $2 billion.

9.听, $160M, fintech: PEX, an AI-enabled provider of prepaid and charge cards for businesses, along with tools to track finances, raised $160 in equity and debt funding, with as lead investor.

10.听, $145M, AI infrastructure: Eliyan, a developer of connectivity technology for AI infrastructure, completed its Series C with a total of $145 million at a $1 billion valuation. led the financing for the five-year-old Santa Clara, California-based company.

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