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.

This week, most of the largest U.S. startup funding rounds centered around the sector one would suspect: artificial intelligence. This was true for the week鈥檚 largest venture financing, a $1.5 billion Series F for AI inference technology provider , as well as a majority of rounds in the Top 10. Beyond that, the next-biggest area for startup funding was biotech.

1. , $1.5B, AI inference technology: Baseten, a provider of systems software to run AI applications workloads, raised $1.5 billion in Series F funding, its fourth fundraise in 18 months. , , , and co-led the round, which set a $13 billion valuation for the San Francisco-based company.

2. , $1B, digital marketing: AppsFlyer, a San Francisco-based provider of data analytics with digital marketing as a core use case, reportedly secured more than $1 billion in a Series E funding round at a post-money valuation of $2.7 billion. Backers reportedly include , , and .

3. , $650M, AI inference technology: San Francisco-based Groq closed on $650 million in new funding led by and that it says will be used to scale its AI inference cloud technology and infrastructure. The investment comes just over six months after an acquihire-type transaction in which hired away its founder and key team members and licensed its technology.

4. , $330M, ophthalmic therapies: Ollin Biosciences, a developer of therapies for vision-threatening diseases, picked up $330 million in Series B funding. and led the financing for the Austin-based company.

5. , $320M, foundational AI: General Intuition, developer of a foundational AI model based on gameplay, secured $320 million in Series A funding at a $2.3 billion valuation. led the financing for the New York-based company, while backers including and participated.

6. , $250M, government software: Peregrine Technologies, provider of a platform used by public safety agencies and other government entities, secured $250 million in Series D financing. , , , , and led the financing, which set a $6.8 billion valuation for the San Francisco-based company.

7. (tied) , $200M, risk intelligence: Palo Alto, California-based Quantifind, developer of a risk intelligence platform for financial crime detection and national security operations, closed on $200 million in growth financing led by .

7. (tied) , $200M, foundational AI: San Francisco-based Mirendil, a frontier lab building systems that excel at AI R&D, says it raised a seed round of $200 million led by and . The startup also counts as a backer.

9. (tied) , $190M, AI infrastructure: AI networking infrastructure startup Upscale AI raised $190 million in Series A extension funding, bringing total financing to $500 million. led the round, which set a $2 billion valuation for the Santa Clara, California-based company.

9. (tied) , $190M, biotech: San Francisco-based Osanni Bio, a therapeutics platform focused on ophthalmic therapies and other treatments, secured $190 million in Series B funding led by .

Large non-US deals:

The week also brought some large European rounds:

, $569M, defense tech: Berlin-based defense tech startup Stark reportedly raised $569 million in a financing led by and .

, $546M, insurance: Paris-based health insurance startup Alan secured $460 million in new investment in primary and secondary equity led by .

Methodology

We tracked the largest announced rounds in the SA国际传媒 database that were raised by U.S.-based companies for the period of June 18-26. 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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SA国际传媒 data tells us Black startup founders still receive only a tiny sliver of venture funding. What the numbers don鈥檛 tell us is why investors continue to overlook those entrepreneurs, and more importantly, how the industry can improve the odds for Black and other underrepresented business leaders.

To better understand what’s driving the persistent gap 鈥� and what it will take to close it 鈥� SA国际传媒 News spoke with six venture capitalists who actively back Black founders about where they believe the ecosystem continues to fall short and how it can improve outcomes.

While they offered different perspectives, several themes emerged: Venture firms need to broaden the networks they rely on to source deals, founders continue to face structural barriers long before they pitch investors, and lasting progress will require changes from both investors and entrepreneurs.

Expand beyond familiar networks

Underrepresented founders face distinct pressures as the venture industry retreats to its traditional networks, according to partner , who said the pullback in funding to Black founders overlooks where investors can discover market-outperforming businesses.

鈥淭he bar for all founders has gotten higher in recent years, and I don’t necessarily think that’s a bad thing,鈥� she said, pointing out that the surplus of capital during the previous market peak meant startups that probably should not have been funded received investment anyway.

Still, the subsequent market correction has triggered a familiar defense mechanism among institutional investors, she said. 鈥淲hen things get hard, it’s human nature to revert to what you know and what feels safe,鈥� Kidder said. 鈥淯nfortunately, that means back to the same networks, and so there’s been extra pressure on underrepresented founders.鈥�

Instead of viewing diversity through a philanthropic lens, Kidder argues that the current environment means venture investors need to look outside of their conventional circles to beat the market. 鈥淎lpha is more likely to be found outside that comfort zone in founders who bring different perspectives and solutions to the table, especially in healthcare,鈥� she said.

To date, Boston-based Seae has backed nine Black startup founders. Kidder notes that those entrepreneurs, like with the rest of the founders in the firm鈥檚 portfolio, bring 鈥渆xtraordinary grit, experience and passion to building sustainable solutions for the market.鈥�

, partner at , agrees that venture firms’ existing networks tend to limit who gets funded and he argues that expanding those networks requires deliberate action. To that end, his firm several years ago launched Lobby: Elevate, an event designed to support underrepresented startup founders.

The entrepreneurs who attended the firm鈥檚 Founders of Color Summit demonstrate that 鈥渨hat is lacking for Black founders is opportunity and investment, not talent,鈥� he said.

Hornik said more venture firms need to intentionally create opportunities to meet founders they would not otherwise encounter, whether through events or by bringing more investors into direct conversations with underrepresented entrepreneurs.

Simply agreeing that bias exists, he said, won’t change investment outcomes.

“I don’t think there is a single white VC I respect who has funded a large cohort of Black founders, myself included,” Hornik said. “I can certainly do better.”

Because venture investing is inherently subjective, Hornik argues investors must actively push back against the implicit bias that can shape sourcing and partnership discussions. The funding statistics for Black founders won鈥檛 change unless investors are 鈥渋ntentional about the problem,鈥� he said.

That view is echoed by , co-founder and general partner of , who believes that the many public commitments made by firms to back more Black founders in the summer of 2020 following George Floyd鈥檚 death were 鈥減erformative and not permanent.鈥�

The 鈥渦nironic and quick鈥� retreat in the years since has been actively harmful to Black founders, he said. Going forward, the industry needs to make truly structural changes to see long-term improvement.

鈥淭he warm intro network is the biggest filter in venture, and it鈥檚 viewed as an asset, not a structural problem,鈥� he said. 鈥淚f you’re inside, you get meetings. If you’re not, you don’t, no matter how strong the company is. Pattern matching gets the headlines, but it’s downstream of who walks through the door.鈥�

Rhodes argues that many venture capital funds treat sourcing as a “passive intake.鈥�

In contrast, funds that systematically expand their top-of-funnel reach beyond traditional networks tend to discover companies that competitors miss.

That鈥檚 not a diversity initiative, he noted, but a distinct 鈥渋nformation advantage.鈥�

Break down barriers before the pitch

For , managing partner at , a quality often overlooked by investors is resourcefulness. Having built a startup himself before becoming an investor, Johnson said many Black founders learn to build high-quality businesses with far less capital than their peers.

鈥淏lack founders innovate at the same quality and scalability as others, with a fraction of the capital,鈥� he said.

Ironically, that same scrappiness often stymies Black founders during the pitch process, according to , founder and general partner at and the author of

O鈥橠onnell argues that many of the biggest obstacles emerge well before founders ever walk into a pitch meeting, though they continue there.

Venture firms recruit heavily from elite universities where Black computer science students make up only a small share of the student body, he said, while the broader tech ecosystem in Silicon Valley can feel unwelcoming to many Black engineers.

鈥淪ilicon Valley itself is alienating,鈥� O鈥橠onnell said. 鈥淭he Bay Area has no meaningful Black community, the interview panels are all-white, the lunchroom is all-white, and the neighborhoods are all-white. Qualified Black engineers rationally choose to work somewhere they won’t be isolated.鈥�

鈥淣ot wanting to be the only Black person in the room isn’t a failure of ambition,鈥� he added. 鈥淚t’s a reasonable response to a visible signal about what the environment will be like.鈥�

That disparity continues into the fundraising process itself, according to O’Donnell, who argues that underrepresented founders often ask for less capital and make more conservative projections because they’ve spent their careers facing greater scrutiny and are often expected to justify every dollar.

Venture investors, however, are by their very nature looking for founders who pitch ambitious, risky, fund-returning visions.

As one example, O’Donnell recalled a Black urban mobility startup founder whose pitch to VCs became caught between describing the large company he hoped to build and the modest business he had already created on the path to profitability.

The founder was 鈥減itching the way someone pitches when they’ve been taught that financial responsibility matters, but he was pitching in front of people who don’t care about financial responsibility at all,鈥� he said. 鈥淭hey care about whether 鈥� if the risk was ramped up high enough 鈥� this could return a fund.”

Change the funding playbook

Many investors and Black founders who spoke with SA国际传媒 News came to a similar conclusion: Improving venture outcomes for underrepresented founders will require changes on both sides of the table, with investors broadening who they meet and founders building businesses that make it increasingly difficult to overlook them.

For venture firms, that starts with intentionally expanding how deals are sourced, rather than relying on warm introductions and longstanding networks.

, general partner at , argues that the responsibility for changing outcomes rests primarily with the institutions that control the vast majority of venture capital.

鈥淰enture firms led by white people and ‘model minorities’ should be asked the hard questions,鈥� said Robinson, whose early-stage venture fund is designed to back Black entrepreneurs. 鈥淭heir track records should be examined. Their implicit and explicit bias should be called out and they should have to answer for it.鈥�

Robinson believes firms need to do more than wait for investment-ready companies to appear. Instead, she said, they should actively expand their sourcing pipelines and create programs that help founders reach the stage where they’re ready to raise institutional capital.

For founders, her advice is pragmatic. Entrepreneurs should spend less time chasing investors and more time building businesses customers want, she said.

“Black founders need to relentlessly pursue sales and customers as they have been indoctrinated to pursue investors,” she said, arguing that strong commercial traction gives investors “less of a choice but to invest” once the metrics become undeniable.

Rhodes, the general partner at Fictive Ventures, also offered a reminder that venture capital is only one potential financing path. Before pursuing that path to funding, startup founders should first determine whether their business actually fits the venture capital model and the growth expectations that come with it, he said.

The venture model is built around risk-taking, he noted, but there鈥檚 a double standard for white and non-white entrepreneurs: 鈥淎 Black founder’s first failure gets treated as confirmation,鈥� he said. 鈥淎 white founder’s first failure gets treated as experience.鈥�

Still, if a Black founder is determined that venture capital is the right financing source, he or she should recognize that investors are buying a stake in the future outcome of the business.

That means personal backgrounds, stories and community impacts only matter to investors in so much as they serve to predict a financial return. 鈥淣obody is investing in you just because you’re Black,鈥� Rhodes said.

In fact, he believes that investors who frame their investment decisions around founder identity are typically the first to 鈥渄isappear in a downturn.鈥�

Instead, Rhodes advises founders to focus on finding and building for the investors who 鈥渢ruly understand鈥� the business and are committed to helping build it over the long term.

That鈥檚 a view echoed by Kidder. 鈥淔ocus on the build, get creative to show early proof points 鈥� build and leverage relationships where you’ve built trust and delivered results to seek out investors who believe in you and what you’re building,鈥� she said. 鈥淎nd, don’t let the stats dissuade you from the dream. Trust your gut and focus on delivering sustainable results.鈥�

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• SA国际传媒 Data: Venture Dollars For Black Startup Founders Stay Scarce Despite AI Funding Boom
• 鈥楾his System Wasn鈥檛 Built For Me鈥�: Black Founders Became Investors To Change Venture Capital

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Those were the unsurprising findings of a SA国际传媒 analysis of U.S. startup acquisition outlays in 2026. So far this year, acquirers have spent at least $119.8 billion buying private, venture-backed companies, on pace to exceed 2025鈥檚 record-setting tally.

For 2026, however, about half of total M&A spending on U.S. startups comes from a single deal: 鈥檚 $60 billion of AI coding tool Cursor and its parent company . SpaceX first announced an option to the company in April and consummated the deal after its IPO this month.

The Cursor purchase represents the largest startup acquisition of all time, nearly double the size of the prior frontrunner, 鈥檚 purchase of for $32 billion. After that, the next-biggest startup M&A deal was 鈥檚 $19 billion acquisition of in 2014.

Other big M&A deals

While other 2026 startup purchases weren鈥檛 setting records, many of them were still on the historically large size.

To illustrate, we used SA国际传媒 data to put together a list of the 10 largest disclosed-price U.S. startup acquisitions this year.聽1 The bottom nine range from $2 billion to $7 billion.

Biotech was a standout

Biotech was especially big. This is due in large part to , which announced in April that it was acquiring , a developer of gene therapies with a particular focus on cancer treatment, in a deal valued at up to $7 billion in cash. Per SA国际传媒 data, the high end of the purchase price represents the largest acquisition of a venture-backed biotech company in years.

Lilly was also the acquirer in two other deals in our Top 10 ranking. The pharma giant bought , a developer of RNA therapeutics, for up to $2.4 billion, and , a developer of blood cancer therapies, for up to $2.3 billion.

Overall, half of the 10 largest deals this quarter were biotech transactions. However, in most cases the number represents the maximum potential acquisition price, which will require the acquired company to meet pre-determined milestones, typically around clinical results and commercialization.

Brex, Modular and more

Outside of biotech and, of course, Cursor, the next-largest acquisition was 鈥檚 purchase of business credit card and account provider for $5.15 billion. It’s followed by ‘s acquisition, announced yesterday, of AI chip startup for $4 billion.

Further down the list is 鈥檚 2聽acquisition this month of , a provider of AI-enabled customer experience tools, and 鈥檚 purchase of , an industrial AI platform, each at $3.6 billion.

With the second quarter winding to a close, we wouldn鈥檛 rule out the likelihood of another big deal making headlines in coming days. Even if that doesn’t happen, however, it鈥檚 already clear that 2026 is shaping up as a big spending year for startup M&A.

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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�

led the financing, which included participation from , and existing backers. The raise brings the company鈥檚 total funding to more than $76 million since its 2018 inception and values it at $127 million post-money. That鈥檚 more than double the valuation of its previous funding round 鈥� a $25 million Series A that closed in December 2023.

“Healthcare has spent decades generating enormous amounts of patient data without a reliable way to make that information usable,鈥� said xCures CEO in an exclusive interview with SA国际传媒 News. 鈥淲e鈥檙e changing that.鈥�

Venture investment in healthcare and biotech companies that have an AI bent has been on an upward trajectory in recent years. As of June 22, investors have put an estimated $8.5 billion into seed- to growth-stage funding for companies in AI-powered health tech categories, according to SA国际传媒 data. In 2025, funding to the sector across all stages $15.8 billion. This year鈥檚 total is already nearly as much as the $8.6 billion raised in the category in all of 2024.

Pivoting to solve a problem

Founded in 2018 as a spinout from by , xCures initially launched to provide decision-support tools for patients with advanced cancer. At its inception, the company focused on patients with Stage 3 or Stage 4 recalcitrant cancer diagnoses, where standard care options were exhausted.

While working with thousands of patients across the country in a direct-to-consumer setting to build its initial model, the company encountered a systemic bottleneck.

鈥淲hat we learned in the process was that the decision-making was hard,” Newton said. “These are complicated things, but doable. But the even harder thing was to get our hands on the data and information about the patient that we needed in order to give them the advice in the first place.鈥�

At the time, patient records were arriving at the company in boxes and over fax machines. This logistical hurdle prompted xCures to pivot to build the underlying infrastructure needed to connect directly to national healthcare interoperability networks. Today, xCures hooks into these electronic exchanges on behalf of its customers, shifting its primary focus to structuring what Newton described as the industry’s 鈥渄irty data.鈥�

鈥淭he data in those medical records is incredibly dirty, so it’s duplicative. There are pictures of things, scans of things. There are errors that are caused because it’s all human entry,鈥� Newton explained. “There鈥檚 lots of narrative information, and we turn it all into something that basically is clinical intelligence or the clinical clarity an organization needs to make its next decisions.鈥�

Creating a 鈥榗linical clarity engine鈥�

Patient information remains scattered across thousands of labs, hospitals, imaging centers and electronic medical records, often arriving as unstructured documents that are difficult to use in clinical workflows. This is where xCure can provide a differentiated experience, according to Newton.

鈥淭hey’re [competitors] really in the transport business 鈥� moving data from Point A to Point B,鈥� he noted. “We think of our product as the executor’s clinical clarity engine. We’re in the business of taking that transported data and making it into something that’s actually instantly useful, versus just moving it from one space to another.鈥�

The xCures Clinical Clarity Engine, he said, solves this by integrating capabilities to generate decision-ready checklists from automated patient histories, backed by evidence-grade data. Newton estimates that the engine is three to five years ahead of anyone else in the market. To date, xCures has processed more than 300 million medical records sourced from more than 550,000 healthcare locations nationwide, supporting clinical decisions for millions of patients across the U.S., per the company.

To manage this volume without incurring the extreme processing costs associated with running massive, unstructured files through generic models, xCures utilizes a variety of AI, combining its own home-built machine learning models with commercial frontier models from existing vendors. The company manages these tools through a proprietary governance framework.

鈥淲e really see it as the harness for 鈥� the process for applying AI, and how we make sure that the tasks that we’re asking the AI to do are appropriate and well-governed, and that the rules of engagement are really clearly defined,” Newton said.

High growth and enterprise adoption

This technological approach has driven impressive traction. Operating on a usage-based SaaS model with committed caps, xCures grew from roughly $3 million to $10 million in annualized recurring revenue in 2025, according to Newton, and it鈥檚 on track to break $20 million in 2026.

While xCures achieved cash-flow breakeven last year, the company has intentionally entered a capital-burn phase to build its team for its 2027 business pipeline, he added.

The startup鈥檚 enterprise customer base consists of 25 clients, including lab diagnostic companies such as , and . Large hospital networks use the tool to 鈥渋nstantly鈥� generate patient histories for operating room scheduling, screen for comorbidities and estimate operative times ahead of surgeries. The engine is also used by telehealth providers lacking robust Electronic Health Record architectures, as well as by Medicare Advantage plans seeking to automate population risk stratification, prior authorizations, medical-necessity documentation and administrative appeals.

Solving healthcare’s most expensive grunt work

Ultimately, Newton believes that reducing the immense administrative drag built into the American healthcare system is crucial.

“Companies like xCures really reduce the administrative burden and represent the fastest path to realizing value in healthcare for everybody who’s involved in it,” Newton said. “This idea that we can use AI not to do things that doctors should do, but just to make it all better, easier, faster, cheaper and better for everybody involved … there’s just a lot of, like, grunt work that you should do that’s really expensive, and so that’s probably the most immediate opportunity.”

, partner at Innovius, wrote via email that his firm backed xCures because it was impressed with its ability to 鈥渓ocate, extract, and normalize messy data across thousands of incompatible sources.鈥� By applying real clinical context to surface exactly what matters, the investor noted that Mika Newton and his team are successfully “building the foundational AI data layer that will power the entire healthcare industry.”

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founded after departing as CTO in 2022. The firm invests in companies rebuilding the physical economy. As Schroepfer and his partners at the firm see it, surging demand for AI, power and industrial capacity is creating a once-in-a-generation opportunity to rebuild the physical economy 鈥� from energy infrastructure and advanced manufacturing to materials and robotics. And as AI makes software cheaper and easier to create, the competitive advantage increasingly shifts to the hardware, energy systems and supply chains that underpin it all.

Key to starting the fund was Schroepfer’s experience building out the infrastructure to support Meta鈥檚 business. 鈥淚 could see the trends coming. We’re going to need all the compute,鈥� he said. 鈥淚 don’t know where we’re going to get the power, so it’s going to create this massive supply-demand crunch.鈥�

Gigascale raised its first institutional fund this month, a $250 million investment vehicle. The firm has already made more than to date.

Schroepfer鈥檚 partners at the firm are , previously an investor at climate-focused investor , and , previously at .

Before raising the fund, the firm made 22 investments funded by Schroepfer鈥檚 family office in order to prove the model. At the time, the broad perception was you could not make money investing in the hardware layer.

鈥楴ot software with higher capex鈥�

Gigascale invests at pre-seed through Series A with some later-stage investments. Its check size is anywhere from $1 million to $10 million.

Hardware businesses are not the same as software businesses. 鈥淚t’s not a software business with higher capex,鈥� said Schroepfer. 鈥淭he failure modes are very different. The way you plan and test and iterate, and what you understand is very different.鈥�

In our conversation, we spoke about an array of topics, including energy as a major investment focus, his learnings from running Meta, why now is a great time to build a hard-tech company and what excites him about the IPO.

Gen茅 Teare: What is Gigascale’s thesis?

Mike Schroepfer: It’s really simple. We are backing companies that are rebuilding the physical economy. This is how things are powered, built, moved, manufactured and how people are fed.

The belief is there is a confluence of technological changes that are bringing new products and new companies to market that are better, faster, cheaper than what’s out there. This is the biggest part of the economy.

Another way to say it is that we think the future is atoms, not bits, and it’s a really exciting time to be building these companies.

What did you see that made you decide to set up the fund in 2023?

Schroepfer: A lot of the tech trends I have been part of 鈥� from the web transition when I worked on Firefox, to the early web infrastructure at , to the mobile transition in the early 2010s, to founding the Facebook AI Research Lab in 2013, well before ChatGPT 鈥� were looking at the very shallow part of exponential curves. These technological changes did not seem that prevalent yet, but they were on this massive upswing.

I saw the same set of curves in solar cells, batteries and electrolyzers. They were all going through massive exponential cost downs, and at the same time a massive increase in demand. We had electric vehicles showing up, onshoring and manufacturing, and this was pre-data centers. I knew compute demand was going to grow. Where are we going to get the electrons to fuel all of this? It’s going to create an immense supply chain crunch.

Demand and supply were converging at the same time to create massive tailwinds. It just felt like this opportunity to rebuild the entire physical infrastructure in a way that our kids are happy about. Meaning, the new solution wins because it is cheaper, better and faster.

The other co-benefit it brings along with it is that because it is simple and cheaper, it is also less polluting, so it doesn’t hurt humans. I can build a solar farm way faster than I can build a gas power plant. I can live next to a solar farm and get zero pollution. I do not want to live next to a gas plant.

What I understand about the firm is that you are very focused on energy specifically. Is that a misunderstanding?

Schroepfer: It is probably the single biggest area that we invest in. A large chunk of our portfolio is energy. It is a $2 trillion market and it is the place where I think all the disruption is happening. But we also invest in industry, including materials from neodymium to copper, production and recycling, to a lot of AI in the physical world. That includes everything from how I use AI to make my house more efficient with , to how I build power-efficient AI inference chips with .

Then there is the built environment, in terms of buildings, and a little bit in food. We do a little bit in everything, but if you look at our portfolio, the two biggest hunks are really energy and AI in the physical world.

When do you think Silicon Valley woke up to the focus on the physical world?

Schroepfer: In the broad consensus, it happened recently 鈥� in the last six to 12 months. There were some folks who were looking at it early, but I think the broad consensus has just happened recently.

The other thing that I saw is, if AI is going to make software nearly free to write, then I think software businesses might be challenged, and the moat moves to the hardware. The game becomes: How do I get the infrastructure built to have a better AI? That is mostly an infrastructure hardware problem, less of a software coding problem, and that is going to filter through a lot of businesses.

When I started, frankly, three years ago, I had many people 鈥� I am thinking of someone sitting in my office 鈥� saying, 鈥渄on’t do this.鈥� All the money is in software. You can’t make money in hardware.

It doesn’t hurt that , , , and are now household names of companies that have had massive valuation runs because they are such a core part of the physical economy. I used to use Nvidia as my example, but now I can use SpaceX. Talk about a company in the biggest market that is running away from the competition. It’s a really hard company to compete with.

How should we understand the energy needs in the U.S.?

Schroepfer: We’ve been at relatively flat demand over the past 20 years or so, meaning each year that goes by, we don’t need much more power, close to 0%. We are now growing at at least a few percent a year.

Something has gone from almost no growth to relatively high growth. You’ve got hundreds of gigawatts of data centers planned to be built over the next five years alone. That doesn’t count EV charging stations and electrification of homes and factories. It’s a massive supply-demand imbalance right now, and building power takes a long time. If you’ve got to build a power line, if you’ve got to permit a gas power plant, these things take years, not months. It has created massive demand, but everyone wants compute yesterday.

Meta has used tents instead of buildings for their servers because cutting out the time erecting steel for the building gets them compute faster. Everyone is thinking about how to get power faster and how to get compute faster because, again, it’s a competitive advantage when infrastructure is the moat.

Which technologies are you focused on in the shorter term, and then the longer term?

Schroepfer: We have companies deploying things now. In the power crunch, one of the big problems is that the demand for power swings much more widely than it used to. It used to be fairly steady. Now you have big training runs, you have solar that comes on and comes off, and you need a shock absorber to dampen the power or deal with three or four days of clouds or no wind, if you’re depending on renewables.

is a company that has a new kind of battery that lasts for four days. You charge it up, and it’s there for 100 hours. In any event where a power plant is offline or the sun is not shining, Form Energy is there. Utilities think of this instead of building a gas power plant. There are these gas power plants called peakers, which you only turn on when you really need them. They sit there all the time, and then you fire them up in these intervals. Instead of doing that, which is very expensive, you have this Form Energy battery: zero emissions, much cheaper to operate, and built from the ground up for utilities using a totally different technology. They are going to be deploying batteries this year, as an example.

Going in a different direction, the entire supply chain for how we get electrons to a building. I’m going to build a new data center, and I have to hook it up to the grid to get electrons there. There is all this equipment in the middle called power transformers, these big green boxes or big metal boxes. It’s literally 1930s technology. We haven’t changed much since then. They are back-ordered for years now because they are these exquisite hardware machines.

There is a new company, , that said, 鈥渨ait a second, we’ve been shipping this new generation of technology called solid-state power electronics in electric vehicles 鈥� the Model 3, Model Y, and more 鈥� for millions of units a year, with very fast ramps. We’re taking that same technology and putting it on the grid.鈥� We’re replacing this 1930s technology with 2020s technology. It’s more efficient, it’s a third the size and, most importantly, they’re going to start shipping lots of units next year. They’re building their factory right now. In 2027, they’ll be shipping lots of these Heron Link units.

A little bit further out, we have a company called that said, 鈥渨e’ve got about 10 terawatts, which is an immense amount of power, in the Southern Ocean in waves sloshing around with nothing else going on down there. If we can harness that, it is an untapped resource.鈥�

They’re building autonomous buoys that float in the ocean. They bob up and down and turn that wave motion into electricity. Then they use that to power, on the buoy, a compute node to do AI inference and use to send the bits back. They’re kind of exporting electrons via tokens in the Southern Ocean.

They’ve been testing off the coast of Portland, and they’re going to deploy their first units next year. People have talked about data centers in space. My big pitch for this company is that it’s 100x cheaper to put a ton of capacity in the open ocean than it is to put it into space. If you think data centers in space are a good idea, you might want to look at the ocean.

Then you can think about , a company in El Segundo, California. They are building a compact, next-generation microreactor, or nuclear reactor. You can think of it as something you put on a truck or on an airplane, and it can run and power something for five years straight. Instead of, in a remote region in Alaska or on a Pacific island, doing what they do now, which is shipping diesel fuel there to run a diesel generator 24/7, you install one of these boxes, and it produces power for five years before it needs refueling. Most importantly, again, you would not want to sit next to a diesel generator while it’s operating. It has very toxic emissions. This thing has no emissions. It’s good for humans, and it’s actually going to be cost competitive with those things. Those are some examples of things we’re doing in the power sector that I think are really affecting the future.

Is there an unlock in this industry that has made development cheaper and faster at this moment in time?

Schroepfer: The analog I’d use is from computing. We used to build mainframes, these big building-sized computers. Then we had minicomputers that were still really big. This is the motherboard for the first server we designed at Meta that we deployed in 2011, called Freedom. It was a Type 1 server. It was the web server.

I installed millions of these, maybe tens of millions. I don’t even know how many. They’re all the same, every single one of them. They go in a pizza-box-size thing that goes into a rack in a building. That building comes in four units. Each of those is the same. That building is next to another building, which is exactly the same. We build four of those on a site. They all look the same. I did that in 17 places around the world. They all look the same.

The technique we use to make things cheap is mass manufacturing. Everything in your life that has gone down in price or improved in price-performance is mass manufactured: your iPhone, the servers and data centers. They’re all the same. They’re mass manufactured.

The world is full of custom, bespoke stuff that’s wickedly expensive.

In the power grid, for example, all of the stuff I talked about, you custom order it. I want a transformer. I do engineering design. I send it off to someone. Four years later, a truck shows up with the crane and all the rest of it. That’s inherently expensive and gets more expensive every year. Everything that is custom gets more expensive every year, so I think the biggest thing we’re seeing is this move to things that are mass manufactured.

Solar panels are mass manufactured. Batteries, the things that go in your phone or in your electric vehicle, are 99% cheaper than they were 20 years ago. That’s because we manufacture them at a massive scale. Every time you double the size of manufacturing, you get a 10% to 20% reduction in cost, and there are so many other problems like that.

In this case, the power electronics, the transformer, are all special-purpose. Heron Power is going to make the same box for a data center, for an EV car charger, and for a solar farm. It’s the same box. No changes. That’s how we’re going to get a cost curve down for these things. That is the most exciting trend underneath this: the idea that generalization and mass manufacturing of things allows you, year over year, to reduce costs.

When you’re competing in the power industry, fossil fuel costs have been basically stagnant. They go up and down a little bit, but if you average them over 50 years they are not on a cost-down curve. It doesn’t get cheaper to get oil out of the ground. My competition is flat, and I’m getting 10% to 20% cheaper every year. That’s a great business to be in. That’s the big trend behind all of this. We saw it first in solar and in batteries, but it enables a whole bunch of other things in other industries, like power electronics and more.

Are we at this time very dependent on China for mass manufacturing?

Schroepfer: A lot is coming from China, but I visit a factory a week in the United States that is getting spooled up with robotics, with really smart founders from and SpaceX. It turns out that when you start in 2026, you can build a much more efficient, much faster factory. You can use modern technologies.

Right now, China has the industrial base, and we’ve let it go. But I think we have a shot at rebuilding it in the United States, and I see brilliant founder after brilliant founder running at this problem inside the United States every day and every week.

It’s one of the reasons I started this firm, too. I think we have a shot to rebuild that industrial base in a next-generation set of technology. Just like regions around the world that didn’t have landlines went straight to cellphones, we’re going to go straight to fully automated robotic factories with 3D printing, laser milling and the latest technology set. It is not going to be a cut-and-paste of what happened in China, but a next-generation set of technologies that allow the U.S. to be self-sufficient in what we’re doing.

We’ve seen new techniques. As an example, rare earths were something no one ever talked about. Neodymium is this rare earth material that is key to making a magnet. Who cares about magnets? Well, magnets are in every electric motor in anything. Anything that has an electric motor, you care about magnets. Almost all the neodymium is made in China, and it is made in this very polluting, dangerous process. You do not want to visit one of these factories with fluorinated gases 鈥� it’s awful.

We’ve got a company making neodymium in Alameda, California. That is not an easy place to permit polluting things, which is fine for them because their process doesn’t pollute at all. It’s very simple. It’s two reactors. I walked around the facility. You don’t need any protective gear. Because it’s so simple, they are cost-competitive with Chinese imports.

To their customers who are saying 鈥淚’m trying to make magnets,鈥� they’re saying 鈥済reat, I will sell you neodymium. I have it. It’s cost-competitive.鈥�

Everyone is excited, but the thing we’re whispering in the background is, it’s also not polluting. This is how we’re going to win. It’s not a cut-and-paste of that technology over here, but saying, 鈥淗ow do we approach this in a way that’s simpler and cheaper, and then likely cleaner as well?鈥�

We’re doing the same thing in copper. We’ve got a whole bunch of bets in different kinds of materials where I think we can do it better in the U.S. We’ve got a company, , in South Carolina that’s doing this for copper recycling. We’re doing it in cement manufacturing. There is a whole variety of opportunities. I don’t have enough time to meet all these entrepreneurs.

We talked a lot about some of the companies in the energy sector. What are the other areas of investments that you’ve made that you’re excited about?

Schroepfer: I mentioned this a bit, but worth going a little deeper on is applications of AI to the physical world. I talked about one: Fractile, which is building a next-generation AI inference chip that’s much more power efficient.

Another example is a company called , which is using AI to put a simple piece of hardware on a power line, on both sides of a power line, to detect if there is a fault in that power line that might be causing a fire. The idea is that if you detect that fault sooner, you can prevent the fire before it’s a problem instead of waiting for it to happen and then having to respond. Using AI plus hardware to figure these things out is another example of that.

We have another company called that’s using AI to help with the nuts and bolts of how people make transactions to build energy projects. There is a lot of due diligence work and other things that need to happen. You can build, very much like for legal or for doctors, these vertical AI companies. This is a vertical AI company for energy developers. There is a lot to happen there.

Then is doing industrial automation with robots, using next-generation models to train robots to be more effective in factory environments, back to my point of how we are going to do this in the U.S. with advanced robotics. I think AI for the physical world is a big area.

I talked a bit about materials: neodymium, copper. We have a company called that’s making clean chemicals. Those would be the big areas I would highlight.

I know there are a lot of investors that you partner with or work with that are similarly focused in this area.

Schroepfer: The thing that’s been most interesting is that there is a set of folks who have been doing hard tech or climate for a while, and they are great partners of ours, from to to to many others. But what’s been interesting to me is the generalist firms coming in. A very common co-investor for us is , , or . We’re seeing them come in large amounts, because they’ve seen the economic opportunity here.

What did you learn from spending 14 years at Meta?

Schroepfer: I learned a few things. When I joined in 2008, the company had fewer than 100 million users, was not profitable, and had about a 150-person engineering team. We relied on outside parties to do all the hardware work. We were leasing data center space.

Over the next 14 years, we grew dramatically in users and profitability and in the size of the team. But we also moved into the physical world. As I showed you the server, we built our first data center in 2011. I built 10 million-plus square feet of data centers in 17 places all over the world. We then moved to consumer hardware, so we built the smart glasses, the Oculus Quest VR headset, and the Portal. Then we moved into AI research with the Facebook AI Research Lab in 2013.

That shift into the physical world brought a lot of really humbling lessons. There were a lot of times where stuff just went wrong. At the very first data center, I remember touring it under construction, and we had wood blocks on the loading dock because they had graded the loading dock wrong, so the trucks couldn’t back up and unload properly.

It’s this new, awesome, state-of-the-art data center with a free-air cooling system, and we got wrong the thing that every in the country has five of. It’s a million small challenges.

This is the thing I bring to the founders that I see: having learned how to build stuff in the physical world builds an appreciation for the risks and scale, and for how you need to emphasize speed and learning rate.

People learn the wrong lesson. They think hardware means spending a lot of time designing on paper. Wrong lesson. You have to get out there because you don’t know which part is going to blow. You have to get out there and learn as fast as you can and as cheaply as you can, so that when you’re in mass production, you’re not learning things, you’re just repeating.

That lesson, from data centers to consumer hardware, matters. When we build consumer hardware, you spend 18 months building this exquisite pair of glasses or this exquisite headset, but before you sell it, you have to do this drop-test thing, where you literally say, what happens when someone takes it out of the box at home and drops it on the ground? If it breaks, they return it, and we eat the cost. So you sit there and drop this thing with high-speed cameras over and over again to make sure it will survive a drop from head height. You don’t think of these things when you’re designing it. You have to make sure someone can drop it and it’s fine, or spill some wine on it and it’s fine.

Those problems in the real world, plus the challenges of building an executive team and scaling an organization, are the fun part of my job: working with our founders and having their back when things are tough, when they need to recruit someone, or when they’re running into a challenge in the real world, because I’ve seen it. I’ve seen it all.

What’s your reaction to the SpaceX IPO?

Schroepfer: I’m honestly pretty excited about it, because we have a lot of SpaceXers in our portfolio. I have a lot of friends who are alumni or work at SpaceX. Having more people in the world with the financial resources to work on audacious engineering projects is going to be really good.

I think it’s also a lesson in building and hardware. How many companies can land rockets the way SpaceX can? They’ve been doing this for a decade, so they have a very large technical moat in terms of what they’re able to deploy in the world. Starlink is another example. Everyone is racing to catch up. If you’ve ever used Starlink on an airplane, you don’t ever want to be on an airplane without Starlink. It’s hard to describe other companies that have such a singular product as SpaceX. I think it’s exciting that the markets are rewarding that. I can’t wait to see what SpaceX alumni do next.

I imagine there’s going to be a lot of company formation coming out of that IPO.

Schroepfer: It’s going to be an exciting five years. I met you after I started my first company in 2000 and sold it off. We looked at starting another company, and then I worked at and Facebook, so I’ve been through a couple cycles of this. I think it is the most exciting time to start a company, in terms of the capital available, the AI tools available to you, and the physical tools to build things quickly in the physical world. It’s the bet I made: I think this is the most interesting time to be building new companies. That’s the smaller version of why I did this. I think this is the time. This is the thing to be doing.

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The Menlo Park, California-based firm highlighted its early investment in , which last month overtook rival as the top-valued frontier lab in the world with a staggering $965 billion valuation. While Menlo Ventures鈥� investment in Anthropic鈥檚 was not its first bet on artificial intelligence, the firm described it as its 鈥渇lag-planting moment.鈥�

鈥淲e made our first investment in Anthropic in 2023, when the company was pre-product, pre-revenue. By then, ChatGPT was a household name, and many believed the LLM race was already decided. We saw it differently,鈥� the firm wrote in published Tuesday. 鈥淚n and his founding team 鈥� arguably the most accomplished researchers in the field 鈥� we saw the rare mix of technical depth and clarity of purpose that defines a category leader. We were convinced there was room for another independent foundation model company, that Anthropic was the team to build it, and that an investment in Anthropic could anchor our broader AI strategy.鈥�

The firm went on to lead Anthropic鈥檚 the following year.

鈥淭hat early relationship gave us a rare vantage point on the model layer and on the infrastructure, workflows, and application opportunities forming around it,鈥� the firm said this week.

Two new funds

The firm鈥檚 new capital is across two funds: , earmarked for seed and Series A startups, and , a growth fund for Series B and later startups that are 鈥渁lready pulling away from the pack and on their way to becoming the breakout names of the AI era.鈥�

Along with Anthropic, other notable Menlo Ventures investments over the years include , , , and . Anthropic, which has filed plans for a 2026 IPO, would be the largest exit to date for one of its portfolio companies by far, with an expected IPO target of $1 trillion or more.

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During his tenure as chair from 1987 to 2006, Greenspan was renowned for his cryptic utterances on the economy, leaving rate-watchers befuddled as to whether they presaged a likely cut or hike. His wife, veteran correspondent , famously that their marriage took time because 鈥渉e claims he proposed three times before I was able to understand. He was so oblique. It was like his testimony.鈥�

In spite of his long history of obfuscation, however, Greenspan is best known for a fairly unambiguous two-word phrase: 鈥渋rrational exuberance.鈥� He coined it in a 1996 to the聽 , a conservative-leaning think tank, titled 鈥淭he Challenge of Central Banking in a Democratic Society.鈥�

One of the speech鈥檚 core points was the notion that pricing logic in an industrial economy dominated by durable goods and materials is far simpler than for a modern economy increasingly dominated by software and services.

鈥淲hat is the price of a unit of software or a legal opinion? How does one evaluate the price change of a cataract operation over a 10-year period when the nature of the procedure and its impact on the patient changes so radically?鈥� he mused, before turning to that most famous insight.

That insight, if I am translating Greenspan-speak correctly, was linked to the question of how one can establish long-term confidence in valuations of assets tied to fast-changing technologies and business models, like software, where prior notions of unit economics no longer applied.

鈥淗ow do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions,鈥� he wondered. It鈥檚 a conjecture that 30 years later still has no obvious answer.

Notably, Greenspan鈥檚 speech actually predated the most heated periods of the dot-com boom, bubble and implosion, which began in the late 1990s and culminated with the hitting its cyclical peak in early 2000. During and shortly after that period, money-losing e-commerce companies like online grocer and pet supply retailer famously went public at then sky-high valuations before abruptly shuttering. Internet infrastructure providers fared even worse, exemplified by networking equipment maker going from Canada鈥檚 most valuable company to penny stock in a couple years.

But while losers lost big, winners eventually eclipsed them. Dot-com-era megastars and , for instance, are now worth nearly $8 trillion combined.

That brings us to one of Greenspan鈥檚 other well-known analogies: the lottery ticket.

In Congressional testimony in early 1999, pressed for his thoughts on then fast-rising share prices of hot internet companies, the Fed chair the stock-buying frenzy to playing the lottery. He observed that people have long been willing to pay more for a lottery ticket than their chances of winning would justify, simply because they are drawn to the remote chance of a huge win.

”And undoubtedly some of these small companies, which have stock prices going through the roof, will succeed and they very well may justify even higher prices,” he said. ”The vast majority are almost sure to fail. That’s the way the markets work in this regard.”

Fast-forward to today, and one is easily drawn to apply Greenspan鈥檚 analogy to the current AI mania. Once again, we鈥檙e seeing unprecedented valuations attached to money-losing companies, many in still relatively nascent stages of development.

In other ways, however, this time it鈥檚 not a dot-com lottery ticket redo. For one thing, the companies in which a retail investor might be buying said ticket are by no means small. , at its current market cap, is the sixth-most valuable U.S. public company. It鈥檚 priced like a winner, not a wanna-be.

Same holds true for recent valuations for and , both of which have confidentially filed for public offerings likely to debut in coming months. Anthropic hit a $965 billion post-money valuation, while OpenAI鈥檚 was recently around $852 billion.

One wonders what Greenspan would say about these stratospheric asset price levels. I鈥檇 suspect there are better than lottery-ticket odds that it would be something cryptic.

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Photo: Dr. Alan Greenspan, former Chairman of the Board of Governors of the Federal Reserve, speaks at the Per Jacobsson Foundation Lecture, October 21, 2007, in Washington, DC. (Photo by International Monetary Fund Photograph/Stephen Jaffe used under the .)

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The company is a marketing analytics platform that acts as an independent referee of sorts to track which digital ads actually drive mobile app downloads and in-app purchases. It helps companies measure their return on ad spend while claiming to protect user privacy and block ad fraud.

While AppsFlyer CEO and co-founder declined to comment on specific deal details, he did confirm to Axios that , , and each took a minority stake in the San Francisco-based startup.

AppsFlyer鈥檚 most recent raise before this was in 2020. With the latest round, the company has now raised $1.3 billion in known funding since its 2011 inception, per .

Previous backers include , 1, , and .

鈥淭hey believe what we believe: that attribution and measurement must be independent, unbiased and trusted,鈥� Kaniel was quoted as saying of AppsFlyer鈥檚 newest investors. 鈥淎s AI takes over more of how advertising gets bought and optimized, the signals feeding those systems become the most consequential infrastructure in the industry.鈥�

He added that the company is eyeing the public markets, calling the financing 鈥渁 step on that path.”

So far in 2026, companies in sales, marketing and CRM categories have pulled in around $4.1 billion globally in seed- through growth-stage funding, per SA国际传媒 . That puts the space on track to come in roughly flat with or a bit up from the prior three years 鈥� when annual funding hovering around the $8 billion mark 鈥� though still far below boom-era levels, when sales and marketing investment topped $20 billion. Notably, many of the startups funded in recent quarters have been AI-focused, with many of them offering agentic tools and automation in areas such as sales, marketing and customer experience management.

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It used to be that if you invested in SaaS, you slept well at night. Returns were predictable because the business model was subscription-based and incredibly scalable: build a horizontal cloud-based platform to target as wide a market as possible, charge per seat and grow by expanding the user base.

1, and their peers returned billions to investors on that model. But now, due to AI, where AI agents are replacing humans as the user (through what the industry calls 鈥渉eadless鈥� models) and upending the per-seat model, the SaaS market has lost its predictability. January’s $300 billion single-session wipeout is a leading indicator that the old SaaS model has passed its peak.

Investors are retrenching and trying to predict what鈥檚 next as the three frontier AI companies vault into the public markets at multitrillion-dollar valuations. We would argue that these infrastructure platforms enable the next wave of software innovation: AI-native software that automates and enables the $2 trillion white-collar services market.

Generic, horizontal SaaS, as we know it, is a declining legacy model (like on-premise software before it), but investors still have reason to be optimistic about the software market. That鈥檚 because AI-native software is going after a much larger opportunity than SaaS ever claimed and the productivity gains and value creation opportunities are unprecedented. The target markets are vertical industry focused and highly specialized, priced differently and built on proprietary data moats that didn’t exist five years ago.

Death of per-seat pricing

SaaS has always been priced on a per-seat basis. That model evaporates the moment AI agents generate most of the usage. A company that once needed 100 CRM licenses for its sales operations team may soon need just 50.

Technology companies facing that reality have to choose a new path forward beyond connecting people鈥檚 workflow: perform and charge for the actual work done (usage) or based on outcomes (ROI). A legal AI platform charges per contract drafted, doing the work of a lawyer. Here the software charges for some fraction of the labor it replaces. A spend management AI-native software application can take a percentage of overages found or a chargeback software application could take a fee on the value of the chargebacks it successfully recovers.

The next era of AI-native software runs on automation and performing knowledge-worker actions, not connecting workers or workflows. These solutions reach beyond IT budgets to much larger labor budgets. The companies that adapt will build faster, deliver more value and command a premium for it.

Horizontal is a liability

Generic horizontal SaaS is the most vulnerable to this changing market. If an entire product is a wrapper around a workflow that an AI agent can now handle autonomously, the value proposition may be greatly reduced. Form builders, project management platforms, SMB-focused CRMs, off-the-shelf social schedulers: these categories are compressing fast and may not recover.

The defensible positions now belong to vertical niche specialists, companies that have built what we call the three 鈥淒s.鈥� Distribution through a recurring and longstanding customer base.

Domain expertise specialized to operate in regulated or complex industries. Proprietary data that drives decision-making and is closely held by customers and inaccessible to frontier models.

When your product is built around the specific workflows, terminology and compliance requirements of one industry, ending a vendor relationship is less about migrating data and more about rebuilding a complex web of experiences, corner cases and historical knowledge. Customers stay not because they’re trapped, but because the cost of retraining, reconfiguring and finding a vendor who understands their world is too high.

The more deeply a company understands the regulatory environment, the operational constraints, and the institutional logic of a specific industry and a specific customer, the harder it becomes to displace.

Legal contract repositories, insurance underwriting criteria, bank loan performance data; once embedded in a model and a workflow, these assets create high switching costs that dwarf anything a generic SaaS contract ever produced. You can export a Salesforce contact list. You cannot export your underwriting logic.

People are part of the product

The model that will define the next decade of B2B software deliberately combines software and services, what practitioners call Human-in-the-Loop, or HITL: pairing agentic intelligence with human judgment at the points in a workflow where it matters most.

Legal, healthcare, cybersecurity, construction, financial services, defense; these verticals are defined by high stakes, regulatory complexity and contextual judgment. Routine and repetitive tasks may be mostly automated, but some portion of decisions will always require human judgement because the cost of errors or omissions is prohibitive.

This solutions-centric customer relationship changes what a software company fundamentally is. When a vendor is embedded in how a client operates, handling onboarding, workflow design, optimization and quality control, it accumulates something pure SaaS rarely achieved: proprietary data, domain expertise and institutional trust. Every client engagement makes the product smarter and each deployment deepens the moat.

This is why the most durable software businesses of the next decade will be built inside verticals, not across them. The companies that understand this will stop treating services as a cost of implementation and start treating them as a compounding asset.

A bigger market than SaaS ever was

Even capturing a small fraction of what projects is a $6 trillion annual productivity opportunity from AI transformation dwarfs the traditional enterprise software market. AI-native vertical platforms no longer just compete for the technology budget, they also compete for the labor budget, the compliance budget and the risk budget. That’s a much bigger pie and a more strategic partnership conversation than any per-seat SaaS vendor ever got to have.

The winners won’t be companies that bolt AI onto existing SaaS products, or that add a services layer as an afterthought. They will be the firms with true subject matter expertise that happen to run on AI-native software. They will collapse the boundary between software and services entirely, building businesses whose value compounds with every customer relationship and every data asset they accumulate.

The AI-native software company is a fundamentally different kind of company than the SaaS era ever produced. And it’s worth considerably more.

is the founder, managing partner and chair of the investment committee at . He launched Lateral with a strategy to allocate first institutional growth capital to independent, owner-operated middle-market businesses underserved by typical buyout firms. Previously, he served as a managing director at , a venture capital and growth equity firm that has invested in more than 300 companies including , , , , and . De Silva also previously co-founded , a marketplace for construction equipment that was sold to for nearly $800 million. He received an MBA from , a master of philosophy from the , and an undergraduate degree from .

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