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�

Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital鈥檚 broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm鈥檚 affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with 小蓝视频色情网页版 News, Docter also discussed how AI is reshaping SaaS and why he doesn鈥檛 believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don鈥檛 get at any other VC firm. I鈥檓 using my words carefully because I鈥檓 not saying we鈥檙e better. I鈥檓 just saying we鈥檙e unique.

That unique network is that we have access to network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what鈥檚 going on in the world and understand technology and how it鈥檚 being used. What do Fortune 500 companies want or need? What is asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

classically said, 鈥淚nvest in what you know.鈥� The way I look at it is that we鈥檙e trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that鈥檚 also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there鈥檚 a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn鈥檛 changed from how we鈥檝e always thought about it and how venture capital always thinks about it. First and foremost, you鈥檙e really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It鈥檚 not purely about the technical capability of the founders. There鈥檚 definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder鈥檚 ability to be agile 鈥� to understand when they鈥檙e wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that鈥檚 qualitative. It鈥檚 EQ more than IQ, but a lot of times that determines success. I don鈥檛 think this AI era has changed that. That鈥檚 consistently true.

The answer to the second question is even harder. How do you know if you鈥檙e betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It鈥檚 really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don鈥檛 overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, 鈥淧lay nice but win.鈥� We always try to play nice but win.

It takes a village for these things to work, so it鈥檚 important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the 鈥渟leeping giants鈥� thesis suggests the second wave 鈥� the companies with the foundational architecture in place when a catalyst like generative AI hits 鈥� may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation 鈥� which means it鈥檚 creating a brand-new category of business or software product that doesn鈥檛 exist today and is going to be huge 鈥� or category disruption, meaning there鈥檚 already a very large category that exists and I鈥檓 going to disrupt it with my technology. I鈥檓 doing something much better, faster, cheaper or stronger.

It鈥檚 important to have a sense of whether a company is doing category disruption or category creation. If you鈥檙e doing category creation, being first means you have to educate everybody. It鈥檚 a heavy lift. It鈥檚 a daunting amount of work, capital and effort that goes into explaining something that doesn鈥檛 currently exist and why it鈥檚 going to be needed in the future.

A lot of times, first-mover advantage isn鈥檛 an advantage there. Category creation is often where the second, third or fourth company hasn鈥檛 had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there鈥檚 value in first-mover advantage. You鈥檙e disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There鈥檚 a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It鈥檚 disruptive because it will change how software is built and consumed. Maybe even more importantly, it鈥檚 going to change how it鈥檚 priced. The per-seat pricing model is probably outdated and going to die. It鈥檚 going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don鈥檛 believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They鈥檙e going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They鈥檙e not going to go away.

How they charge and price might be different, but they鈥檙e still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows 1, and .

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they鈥檝e sold to for years and years and have long-standing relationships with. If 鈥� and it鈥檚 a big if 鈥� they understand how to embrace the AI transformation that鈥檚 going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don鈥檛 make the turn. But is that any different from any other technological or industrial revolution? It鈥檚 always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren鈥檛.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: 鈥淲hat is your distribution strategy?鈥� That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it鈥檚 clear that some SaaS companies won’t be able to transform themselves organically. They鈥檙e going to need to undergo an inorganic transformation, meaning they鈥檒l have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you鈥檙e trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year 鈥� including massive liquidity events like , and 鈥� right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I鈥檇 love to say we saw it all coming, but the reality is we can鈥檛 time the market. It just doesn鈥檛 work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, and .

We just try to stay really focused on backing great founders with deeply technical ideas. We鈥檙e investing early and know that sometimes it can take years for the market to fully catch up to what鈥檚 being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You鈥檝e talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Docter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is 鈥淚s their revenue durable?鈥� Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we鈥檙e also seeing is a huge shift away from recurring revenue to something I鈥檓 calling聽 鈥渞e-occuring鈥� revenue. I know that鈥檚 not really a word. What I mean by 鈥渞e-occuring鈥� is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say 鈥渨e got our first deal with in October, and they did a second deal with us in January, and we already did our third deal in March鈥� is very powerful.

Given DTC鈥檚 unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Docter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. 鈥淵ou don鈥檛 get what you don鈥檛 ask for.鈥� I know that鈥檚 an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don鈥檛 be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it鈥檚 management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

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The purported deal, which has been rumored for months, is notable not just for its scale 鈥� it would be one of the largest acquisitions of a technology company in recent years 鈥� but also for its highly unusual nature. Privately held startups typically lack the cash, publicly traded shares and debt capacity to acquire their publicly listed brethren.

Of course, Stripe is not just any privately held company. The fintech startup was, until just a few short years ago, the highest valued startup based in the U.S., before being eclipsed on that metric by AI labs and . In February, the company announced it had inked deals with investors to provide liquidity to current and former employees through a tender offer at a $159 billion valuation, which still ranks it as the fourth most valuable startup in the world.

With substantial private capital 鈥� it has raised some $10.4 billion since inception, 鈥斅燬tripe has long been one of the most acquisitive venture-backed startups. It has made since its 2010 inception, according to 小蓝视频色情网页版 data. Only three have disclosed prices: stablecoin platform at $1.1 billion (2025), usage-based billing software startup at $1 billion (2026), and Nigerian payments startup at $200 million (2020).

Stripe鈥檚 M&A pace has also accelerated sharply since 2020, 小蓝视频色情网页版 data shows, with 13 of its 21 acquisitions announced since then.

Its recent strategy appears to be focused on stablecoins and crypto infrastructure 鈥� Bridge, , and 鈥斅燼s well as on billing and money movement through Metronome, payment processing startup and .

If the plan to buy PayPal does go through, it will most certainly make Stripe an even more formidable player in the crowded payments space.

It would also rank as one of the largest acquisitions of a U.S. tech company, public or private, of the past five years, according to 小蓝视频色情网页版 data, trailing only a handful of larger deals including $61 billion purchase of in 2022 and 鈥檚 acquisition of AI coding platform Cursor and its parent, , for $60 billion last month.

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All told, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year. (It鈥檚 important to note that H2 2025 marked the strongest six-month funding period for fintech startups since the second half of 2022.)

Fintech funding in the first half of 2026 also topped the sector鈥檚 investment totals in 2020 and the pre-pandemic year of 2019, though they remain lower than the peak funding year of 2021 as well as 2018.

Historically, the United States has led the globe when it comes to fintech funding, and the first half of this year was no exception. More than 52% 鈥� $15 billion 鈥� of the global fintech funding in H1 flowed into companies based in the U.S. The United Kingdom was the second-largest recipient of capital, with companies there raising a collective $2.7 billion. India came in third, with a total of $1.9 billion raised, 小蓝视频色情网页版 data shows.

Deal count drops

Even as dollar volume climbed, deal flow into venture-backed fintech startups fell fairly significantly in H1 2026, 小蓝视频色情网页版 data shows. The first half of the year saw 1,605 funding deals announced in the sector, a 25.7% decline from the more than 2,161 completed in H1 2025 and down 40% from H1 2024.

Where investors are placing their bets

Active fintech investors who spoke with 小蓝视频色情网页版 News said they see a split market emerging.

In general, the startup investment market has been cleaved into two extremes, with funding either pouring into brand-new companies or concentrating into a tiny handful of larger, established giants, according to , a partner at (Google Ventures).

The fintech sector is following the same pattern, Sakach told 小蓝视频色情网页版 News via email, but its biggest players are using their size in an unusual way. 鈥�2026 marks the definitive ‘lab-i-fication’ of the modern corporation,” she noted, with some fintech platforms using their scale and steady profits to fund experimental new divisions.

Because these companies have significant data and distribution advantages, they are becoming magnets for top-tier workers, according to Sakach. For instance, she said, is now competing directly with top AI research labs for engineering talent, while is using its dominant position to build out new products in enterprise billing and blockchain.

For early-stage startups inside the U.S., the focus is shifting away from copying legacy financial services toward creating entirely new categories.

Wealth management is seeing a massive surge, driven by an influx of assets from a younger generation demanding AI tools, Sakach pointed out.

Fintech startups are also targeting massive, hidden corporate headaches.

鈥淎 50% reduction in global chargebacks is a ~$60 billion opportunity when accounting for both the merchant and banking overhead,鈥� she said.

The biggest shift, however, is happening around artificial intelligence and financial services. 鈥淐oding was AI’s first killer use case; financial markets could be the second, given its extraordinarily broad corpus of data,鈥� said Sakach, pointing to new concepts such as automated hedge funds and prediction markets.

, partner at , said the firm鈥檚 investments into the fintech sector have surged this year, as areas such as money movement infrastructure, stablecoins and tracking of real-world assets on the blockchain draw attention.

鈥淲e’ve never been busier: The quality of founders, the size of the markets they’re going after, and the maturity of the technology being built has never been more impressive,鈥� he said.

Those trends showed up among fintech鈥檚 largest fundraisers last quarter, with companies such as New York-based , which is building an agentic decision platform for banks and insurers, and , an African payments infrastructure startup, clinching some of the period鈥檚 largest funding deals. Both raises took place in June, with Taktile raising a $110 million Series C funding round led by and Flutterwave landing a Series E round of an undisclosed amount that valued the company at $3.2 billion.

Risks and opportunities

Even with a wealth of new opportunities in the sector, investors are also wary of the risks introduced by AI and hype around businesses that don鈥檛 have a clear path toward growth or profitability.

Sakach was particularly skeptical of new stablecoin networks that lack a clear way to get users, personal credit card startups with tough profit margins, and traditional banking software.

The problem with selling software to legacy banks is that their slow buying cycles 鈥渆ffectively break the hypervelocity speed needed for AI-level product evolution,鈥� she said. Instead, Sakach believes that AI tools will likely succeed by embedding highly specialized engineering teams directly into specific business units.

The era of the generic digital bank or basic payment app is largely over, in Overdorff鈥檚 view: 鈥淲ithout a real wedge or distribution advantage, it’s hard to build a durable business there.”

The real value of AI right now is its ability to act as the central engine for financial products rather than just a side feature, Overdorff believes. Startups are using the technology to compress complex underwriting, fraud detection and advisory workflows 鈥渢hat used to take teams of analysts weeks into tasks that happen in minutes.鈥�

As a result, traditional industries such as tax and audit are being completely upended, he said.

Traditional financial institutions, which are usually the slowest to adopt new tech, are finally bringing AI into their core operations, though Overdorff cautioned 鈥渢hat shift is opening up as much risk as opportunity.鈥�

He also flagged the cybersecurity risks associated with the rapid adoption of new technologies and AI into the financial system. 鈥淭he compliance and governance layer becomes just as important as the AI itself,鈥� he wrote.

Mega-valuations keep top fintechs private

While the fintech IPO market was robust in 2025, it has been markedly quieter in the U.S. so far this year. Three fintech companies went public in the first half of 2026, and they were all foreign companies opting to list in New York: Brazil鈥檚 and and Japan鈥檚 . That鈥檚 the same number of finance-related startups that went public in the first half of 2025, when , and made their debuts.

Many of the fintech companies expected to list in 2026 have remained private, often at escalating valuations. That includes fintech giants such as Stripe, , Ramp, , and others that have opted for more private financing, secondary sales or simply waiting out the public markets.

For example, in February, payments infrastructure giant Stripe announced it had inked deals with investors to provide liquidity to current and former employees through a tender offer at a $159 billion valuation. That valuation represented an impressive 49% increase from the $106.7 billion Stripe was valued at in September, when it completed .

In early June, expense management startup Ramp announced a $750 million funding round at a $44 billion valuation, just a few months after raising $300 million at a $32 billion valuation.

The H2 outlook

The trend of capital concentration seen in the first half of the year will continue into H2, Overdorff predicted, with 鈥渕ega-rounds for a small set of category leaders, and a tougher fundraising environment for everyone else.鈥�

And while AI adoption will continue to deepen rather than flatten out, the industry will also be watching the stock market closely. The conversation around IPOs is heating up for mature fintech companies, though Overdorff notes that 鈥渢he timing may hinge on how other high-profile tech IPOs perform this year.鈥�

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Last month, of , the corporate venture arm it launched in 2016 and grew to more than $850 million across three funds. The company hired to explore selling portfolio stakes on the secondary market, putting positions in companies such as and in play. The news also arrived weeks after .

Two corporate venture programs shutting down inside six weeks invites speculation that corporations are retreating from venture capital, but in fact the opposite is true.

Measured in dollars, corporate venture has never been stronger. According to , corporate investors participated in 鈥� venture’s strongest funding year since 2021.

, , , , and all led billion-dollar rounds into AI companies last year, per 小蓝视频色情网页版 data. Nvidia by itself made more than 40 startup investments and appeared in. Meta paid $14.3 billion for its stake in Scale AI. 1听补苍诲 s venture arm backed Anthropic’s.

Amid this strength, though, corporate venture is also quietly splitting in two, and the proof is buried inside the record numbers. Bain attributes the elevated corporate participation , and the billion-dollar rounds trace back to the same short list of names.

Take that handful out of the data and the year looks very different. Venture capital itself went through the same sorting over the past decade, as mega-funds absorbed more and more of the capital while everyone else competed for allocation, and corporate venture is now following the same script. The people with the most at stake are the smaller funds and startups downstream.

And notice that the wind-downs are coming from serious programs. PayPal’s arm ran for a decade and , and Fidelity International manages hundreds of billions of dollars. Size never protected either one, and the dividing line runs through the mandate. For Nvidia, Alphabet, Salesforce and Cisco, startup investing is a core strategy, funded off enormous balance sheets, because their businesses depend on owning a position in the technology cycle. Nvidia backs the companies that build on its chips, and that commitment survives budget season. For most other corporations, venture is one strategic priority among several, competing for capital with the core business itself.

To be clear, there’s nothing wrong with that. When a new chief executive commits to finding , winding down even a well-run program can be the disciplined call, and disciplined capital allocation is what shareholders ask of public companies. Corporate venture has always moved in cycles, and the waves of closures after 2000 and 2008 said far more about parent balance sheets than about the returns on offer. Individual programs are mortal, but the asset class keeps growing.

When I started my career, technology drove roughly 2% of the American economy, and today it drives a double-digit share of GDP and nearly 40% of the stock market.

Who feels it first

For smaller funds and their portfolio companies, the split is already changing the math. ‘s finds corporate funds pursuing fewer, more targeted deals, and the share using the secondary market jumped from 15% in 2024 to 22% in 2025; PayPal’s Jefferies mandate takes that same path at the scale of an entire program.

When a corporate arm winds down mid-life, its portfolio companies lose a strategic backer and a source of follow-on capital at once, the smaller funds that syndicated alongside it lose their anchor for the next round, and a secondary sale replaces a committed partner with a financial buyer.

I spend my days working with early-stage venture funds, and I’m watching this pattern develop in real time: strong companies outside AI, with a departing corporate backer on the cap table, heading into rounds their existing syndicate can’t fill alone.

The lesson for startup management teams and VC fund managers is to plan for corporate capital to come and go. The pro rata rights that funds hold in their best companies become most valuable at exactly these moments, when a strategic investor steps back and ownership in a breakout company becomes available to whoever can fund it.

Smaller funds should line up committed follow-on capacity before their winners come back to market, so a corporate partner’s exit becomes a chance to buy more of a company they already know well. Founders should run the same exercise from the other side of the table and know today which investors on their cap table can carry the next round.

Corporate venture will keep growing because the forces behind it keep growing, and programs will open and close along the way, as they always have. What’s changed is the sorting: permanent capital consolidating at the top of the market, and everyone else learning to plan around that fact. The funds and founders who prepare for it will come out the other side owning more of the companies that matter.

is the founder and managing partner of , a growth-equity firm that co-invests in venture-backed companies by leveraging the unused pro-rata rights of more than 1,000 early-stage VC partners.

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To understand how institutional capital is navigating this gap, 小蓝视频色情网页版 News recently interviewed , a partner at . Ranum has spent nearly 15 years at the firm, where he focuses on B2B enterprise software, security and industrial infrastructure. Prior to joining Sapphire, he spent 12 years as a product management and strategy executive at .

His recent investments include core infrastructure plays such as and , as well as the industrial AI platform .

In this e-mail interview, Ranum breaks down how the definition of net revenue retention is shifting, why he believes 2026 will see a historic run of major tech IPOs, and where real enterprise demand is materializing on the factory floor.

This interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: You鈥檝e been at Sapphire for 15 years. Right now, public market software multiples are at decade lows as Wall Street worries about AI disruption, while private AI valuations are hitting record highs. As a growth investor caught in the middle, how are you valuing companies today? Are traditional growth metrics like net revenue retention still the gold standard, or has the math completely changed?聽

Ranum: The gap between public and private market signals right now is unlike anything I’ve seen. I think it creates a real opportunity for investors who can make sense of it. Public software multiples have come down hard, while private AI valuations are hitting record highs. Those two things can’t both be right indefinitely, but the fundamentals underneath are holding up. Gross margins, free cash flow, and NDR have actually improved. The market is broadly pricing in disruption risk, but the companies that are genuinely building enterprise value are still being built.

What that means for how I evaluate companies is that I’m spending more time on whether something is genuinely embedded in how enterprises work, not just whether the numbers look good today. NRR still matters. It tells you whether customers are finding real value. But it’s a lagging indicator. What tells me more is whether switching away from a product would meaningfully disrupt operations. If the answer is yes, that’s a more durable signal than any retention metric.

The current regulatory environment has essentially frozen large-scale tech M&A, and the IPO market is sluggish. If the traditional exit pathways are bottlenecked, how does that change the way you underwrite a Series B or C bet? Do companies just have to stay private and build to massive scale longer than they used to?聽

Ranum: I鈥檇 push back a bit on the framing that M&A is frozen. Software M&A activity actually picked up meaningfully in 2025, with deal value rising 40% year over year to $334 billion across 678 transactions. We saw that in our own portfolio with over half a dozen acquisitions in the past six months. What鈥檚 changed is the pricing. The valuations are being reset, but the deals are getting done.

On IPOs, I believe 2026 is shaping up to be a historic year, with having gone public, having filed, and reportedly set to file soon. If they follow through, we’re looking at some of the largest IPOs ever over the next several months. That’s a remarkable moment. Below that tier, though, the picture is more nuanced. Companies that meet today’s higher bar will wait for more favorable conditions, likely into 2027 or beyond. That means you have to build accordingly, focusing on margin alongside revenue, so you have real optionality when the time comes. The secondary market also helps, giving companies and their investors more flexibility as they wait.

You used to love investing in what you called 鈥渂oring software,鈥� or tools that quietly automated mundane enterprise tasks. Today, every software company claims to be an AI company. In 2026, does traditional SaaS even exist as a viable investment category anymore, or is a software startup inherently unbackable if it isn鈥檛 AI-native from day one?

Ranum: I don鈥檛 think the narrative is AI vs. SaaS. Instead, it’s AI plus SaaS. The companies that are struggling aren’t struggling because they’re SaaS businesses. They’re struggling because investors are in a 鈥渟how me鈥� era, and they don’t have clear answers yet.

Show me the free cash flow. Show me the path to profitability. Show me how AI is actually helping you win. You can’t get a stock bump anymore just by claiming you’re integrating AI. The market wants evidence of monetization.

The way I think about it is whether a company is building something that fundamentally changes how work gets done, or just layering AI on top of a workflow that a human is still doing. We used to back systems of record and workflow companies where the human was doing all the work. Now we’re in a position where the system itself can come in and actually do some of those tasks. That’s a different category of value entirely, and it changes what we look for. The bar has moved, but the opportunity is very real for the companies that can clear it.

Your core thesis is that the LLM stack is fracturing into distinct, standalone billion-dollar layers, such as orchestration (LangChain) and identity (WorkOS). But we鈥檙e seeing a massive border war. Big model providers like OpenAI are building their own tools, and data giants like are buying up security tools. How do standalone startups protect their turf when giants encroach from both sides?

Ranum: Both fracturing and consolidation are happening simultaneously, and I think that’s actually the right way to think about it. The moat isn’t about being first in a category. It’s about becoming genuinely embedded in how enterprises work. The companies I’m most excited about are the ones capturing orchestrated workflows in which the enterprise’s actual processes run through the product. That makes them very hard to displace, regardless of what the giants are building around them.

Because of your background at SAP, you know how enterprise buyers think. Right now, CFOs are looking at massive AI pilot bills and demanding to see actual ROI. When a startup is pitching an enterprise on a software governance or security tool, how do they defend that line item to a cynical CFO before the enterprise has even fully figured out its core AI strategy?聽

Ranum: What we consistently hear from buyers is that trust has become what actually separates the market. Security, governance, compliance, and auditability aren’t nice-to-haves anymore. They’re what make an AI deployment defensible when the CFO or the board asks hard questions.

And cost predictability is right alongside that. We’re in an era of greater focus on ROI, and enterprises want to know what this will cost them at scale before they commit. The vendors that can answer that question clearly are winning deals over the ones that can’t.

It feels like Silicon Valley is obsessed with the glamour of humanoid robots right now. Meanwhile, Sapphire鈥檚 big bets in this space, like Tractian, focus on practical, unglamorous industrial AI and predictive maintenance. Are humanoid robots an expensive venture capital distraction right now? Where is the actual, contract-signing enterprise demand on the factory floor today?聽

Ranum: The near-term ROI story is in constrained, high-value industrial settings such as packing, picking, inspection, and maintenance. These environments have clear labor economics, manageable deployment risk, and real buying cycles. That’s where the contracts are getting signed today.

Our portfolio company Tractian is a good example of what that looks like in practice. Unplanned downtime costs the world’s 500 largest companies roughly 11% of their revenue annually, which is a massive, measurable problem.

Tractian addresses it directly by combining sensor hardware with AI that detects early warning signs of equipment failure. The value proposition is concrete before you sign the contract, and the platform gets smarter the longer you use it. That’s the kind of embedded, compounding value we look for.

The humanoid era will come, but the gradient approach beats the all-or-nothing bet for near-term value creation. Start with specific, well-defined tasks where the payoff is obvious and work from there. The market is ready for that today.

Heavy industry and manufacturing are notoriously slow to change. A startup can’t just plug a modern AI API into a 30-year-old machine on a factory floor. For founders trying to build in the industrial tech space, is the winning strategy to build entirely new autonomous hardware, or is the bigger venture opportunity in retrofitting the world’s existing infrastructure with smart software?聽

Ranum: I believe the winning strategy is smart software layered on top of existing infrastructure rather than replacing it. Factories aren’t going to rip out 30-year-old machines because a startup has a better alternative. That’s just not how it works. The opportunity is in making those machines intelligent.

That said, the hardware-plus-software combination really does matter. You can’t get the data without the sensors. But the durable value is in the software layer that keeps learning over time. That’s where I鈥檓 focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft 鈥� annoying, but fixable. In robotics and industrial tech, a mistake means a factory line shutting down or a broken multimillion-dollar asset. From a venture perspective, how much harder is it to scale a robotics startup when the cost of product failure is so high in the physical world?聽

Ranum: I’d actually reframe the question. The cost of failure in physical environments is what makes the value proposition defensible. When the downside of getting it wrong is measurable, the upside of getting it right is equally concrete. You can walk into a sales conversation and show a customer exactly what prevention is worth before they sign anything. That’s a different conversation than selling software, where ROI takes quarters to show up.

From a scaling perspective, the key is discipline about where you deploy first.

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Within the region, U.K. startups gained significant share in Q2, raising more than $10 billion. That marked the third-largest funding quarter for the U.K. on record, and came in at less than $500 million below its peak quarter in 2021.

European startup M&A activity also picked up in Q1 and continued that momentum in Q2, even as public-market exits stayed subdued.

Table of contents

• Large rounds drive gains
• H1 2026 up 50%
• UK momentum builds
• By stage
• Looking ahead
• Methodology
• Glossary of funding terms

Large rounds drive gains

Four companies raised venture fundings of a billion dollars or more last quarter, accounting for 25% of all startup investment in the region in Q2, 小蓝视频色情网页版 data shows.

Those billion-dollar-plus rounds were raised by an AI-centric group: -owned AI drug developer , which was spun out of ; green steel production manufacturer ; , which is developing robots for home and industrial applications; and , an AI lab founded by former DeepMind researchers.

However, most of the growth in funding year over year and quarter over quarter was driven by rounds of $100 million and over. The majority of funding 鈥� 65% 鈥斅爓ent to a group of 42 companies that raised rounds of $100 million-plus. Sectors that stood out for these companies include聽 biotech, quantum, financial services, AI labs, aerospace, semiconductor, robotics and energy.

H1 2026 up 50%

Funding to Europe-based startups in H1 was up 50% year over year to total $42 billion, 小蓝视频色情网页版 data shows. Still, the region鈥檚 startup investment for the first half of the year remained well below the 2021 H1 peak, when VC funding in Europe totaled $60 billion.

It鈥檚 also drastically lower than the $392 billion raised in North America鈥檚 record-setting H1, with that region鈥檚 funding up 158% year over year.

Europe鈥檚 funding deal count subsided last quarter, but mostly at the seed stage. Late-stage rounds were up a bit, while early-stage deals dipped slightly year over year. (It鈥檚 worth noting, seed stage rounds are often added to the 小蓝视频色情网页版 data set after the close of the quarter, so those numbers will increase over time.)

UK momentum builds

The United Kingdom widened its venture-funding lead last quarter, as startups based in the country raised $10.4 billion 鈥� not far from the peak in 2021 at $10.8 billion.

The region鈥檚 No. 2 startup market, Germany, trailed with $3.2 billion raised by its startups in Q2, and France followed in third place with $2.4 billion. Sweden was Europe鈥檚 fourth-largest startup market last quarter, with its companies raising $2 billion.

小蓝视频色情网页版 data shows funding to Europe鈥檚 AI-focused companies reached more than $10 billion in Q2 鈥� the largest quarterly amount so far 鈥� but slightly below the Q1 percentage, when those companies raised more than half of the region鈥檚 startup investment.

By stage

Europe鈥檚 late-stage funding totaled $12.1 billion in Q2, up 90% year over year. Large Series C and D rounds were raised by Germany-based robotics developer Neura Robotics; Netherlands-based , which makes inspection tools for semiconductor manufacturing; U.K.-based quantum computing startup ; and Germany-based satellite launcher .

Early-stage funding reached $8.6 billion across 250-plus Europe-based startups last quarter, 小蓝视频色情网页版 data shows. Large Series A and Series B rounds were raised by London-based Isomorphic Labs, London-based AI self-learning lab , Germany-based fusion energy company , London-based semiconductor developer , and London-based quantum processor provider .

European seed funding totaled $3.2 billion last quarter, with a billion dollars of that raised by just one company: Ineffable Intelligence.

Other large seed rounds were raised by , a London-based AI lab for science; Italy-based autonomous driving technology producer ; and Stockholm-based defense tech company .

M&A increase

While IPO activity for European startups was muted, M&A showed strong momentum following increased activity in Q1. A total of 154 Europe-based, venture-backed companies were acquired for a cumulative $11.5 billion or more in Q2, 小蓝视频色情网页版 data shows. That includes three companies acquired for more than $1 billion each in biotech, industrial AI and micromobility.

Looking ahead

European startup investment has now steadily increased since the fourth quarter of 2024, with increased momentum in the just-ended quarter, driven by larger rounds of $100 million and over. The region鈥檚 startup ecosystem shows particular strength in deep tech and financial services as well as the formation of new AI labs, and M&A activity has fueled liquidity for the next batch of startups.

Now the question remains: Will it be enough to keep Europe competitive with the frontrunners, the U.S. and China?

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Related reading:

• 小蓝视频色情网页版 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• North American Startup Funding Shattered Records In First Half Of 2026, Driven By AI

Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of July 6, 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. 小蓝视频色情网页版 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 小蓝视频色情网页版 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. 小蓝视频色情网页版 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. 小蓝视频色情网页版 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.)

If that introductory sentence sounds familiar, that鈥檚 because it鈥檚 the same storyline we reported for the first quarter, when drove investment to stratospheric heights with the largest venture round of all time.

Total investment for the second quarter of 2026 was comparatively lower, but still ranked as the second spendiest on record. Investors continued to pour huge sums into AI high-flyers, with a giant financing for accounting for about half of the quarterly tally.

Overall, investment in U.S. and Canadian startups totaled a staggering $392 billion for the first half of 2026, per 小蓝视频色情网页版 data, dwarfing anything we鈥檝e seen before.

For Q2, meanwhile, investment totaled $137.2 billion. That鈥檚 also massively higher than any prior comp, with the lone exception of Q1.

Capital concentration was the name of the game. For both Q1 and Q2, historically high investment levels were the result of giant rounds, not increases in overall deal count. Deal count remained well below prior high marks for recent years, as charted below.

As usual, capital also concentrated at late stage. However, early-stage investment still rose in Q2, boosted once again by AI.

Of course, the past few months were a blowout period for giant exits as well. led in Q2 with the largest IPO of all time. It followed up with the acquisition of , which was a record-setting startup M&A deal. In addition, we saw a handful of comparatively smaller but still sizable public offerings and acquisitions.

For a more granular look at funding and exit dynamics for the second quarter, below we break down investments by stage and look at the role of AI in boosting totals. We also look at standout IPOs and M&A deals.

Table of contents

• Late stage
• Early stage
• Seed
• AI
• Exits
• IPOs
• M&A
• Uncharted territory
• Methodology
• Glossary of funding terms

Late stage

We鈥檒l start with later stage and technology growth deals, since that鈥檚 where most of the money went.

For Q2, funding for this category totaled around $101 billion. It was the second-highest tally in five quarters, as charted below, and also the second-highest of all time.

was by far the quarter鈥檚 heftiest fundraiser, pulling in $65 billion at a $965 billion post-money valuation. The financing included $50 billion in a May round led by , , and , as well as corporate-led rounds by ($5 billion) and ($10 billion). Anthropic followed up in June by filing confidentially for an IPO.

Defense tech unicorn also picked up a big round, securing $5 billion in a May Series H financing led by and .

Early stage

Early-stage investment hit the highest level in more than three years in Q2, offering fresh proof that megarounds aren鈥檛 only a thing for more established startups.

Overall, North American early-stage funding totaled just over $31 billion, nearly double year-ago levels and up about 15% from Q1. Deal count, however, hit the lowest point in five quarters, as charted below.

A single deal contributed more than 40% of the quarterly early-stage funding total. That was the $12 billion financing for , a startup focused on physical AI that counts as a co-founder.

The three next-largest deals were far smaller by comparison, but still quite big by early-stage standards. , an AI startup working on 鈥減ersonalized intelligence,鈥� raised $700 million. Behind that came , a startup building an AI system based on the human brain that picked up $500 million, which was followed by , an AI robotics upstart that closed on $400 million.

Seed

While early-stage funding was up, seed investment in Q2 actually declined a bit from prior quarter and year-ago levels.

Per 小蓝视频色情网页版 data, around $4.9 billion went to seed and angel rounds in the second quarter, down 15% from the prior quarter and down 27% from a year ago. Round counts also dropped, though we expect that number to rise a bit over time as smaller seed deals commonly get added to the dataset weeks or months after they close.

Still, seed totals also got a boost from a handful of unusually large rounds. The biggest was a $200 million financing for , a foundational AI startup focused on R&D. Overall, at least five companies raised seed or angel rounds of $100 million or more in Q2, per 小蓝视频色情网页版 data.

AI

Once again, venture funding for the quarter was overwhelmingly dominated by AI.

About 80% of investment across stages went to AI-focused startups in Q2, per 小蓝视频色情网页版 data. Overall funding to AI categories was nearly triple year-ago levels, though still down from Q1, which had the record-setting $122 billion OpenAI financing.

A majority of AI-focused funding for Q2 was from three previously mentioned rounds for Anthropic, Prometheus and Anduril.

Exits

In addition to backing giant rounds, investors also scored some big returns on prior investment in the form of IPO and acquisitions.

IPOs

On the IPO front, Q2 brought us the historic public market debut of SpaceX. The rocket, satellite and AI giant raised $75 billion in the largest IPO of all time in June. With a recent market cap around $2.1 trillion, it鈥檚 currently the sixth-most valuable American public company.

While no one else will come close to topping that, the quarter did also bring us a handful of other sizable debuts by venture-backed companies. Of this, the most closely watched was AI infrastructure and chip designer , which raised $5.6 billion in its May IPO.

Quantum computing company delivered another big debut with its June IPO, followed by , a developer of modular nuclear reactors. For a broader view, below we list the largest IPOs of the quarter by venture-backed North American companies.

M&A

The second quarter also delivered the largest startup acquisition of all time: SpaceX鈥檚 $60 billion of AI coding tool Cursor and its parent company . SpaceX first announced an option to purchase the company in April and consummated the deal after its IPO.

In biotech, the largest purchase was from , which announced in April that it was acquiring , a developer of gene therapies, in a deal valued at up to $7 billion in cash.

Other standout deals include 鈥榮 acquisition of AI chip startup for $4 billion and 鈥檚 1聽acquisition of , a provider of AI-enabled customer experience tools.

Below, we rank the largest transactions:

Uncharted territory

For those wondering where we go from here, it seems pertinent to note that startup history doesn鈥檛 give much material for case studies to compare with the first half and second quarter of 2026. Never before have we seen such massive funding rounds, such a highly valued venture-backed company debut, or a startup acquisition to rival the Cursor purchase.

Looking forward, it appears that high-flying startups and their backers expect the current unprecedented conditions to persist, with Anthropic and OpenAI both signaling their intentions to go public at valuations close to or exceeding $1 trillion. Meanwhile, massive startup funding rounds are still happening at a steady clip, with deals in excess of $1 billion no longer an anomaly.

Will these trends persist? Who knows. At this point, however, it鈥檚 assumed in startup circles that there will be some enormous winners in the age of AI. The question still is: Who will prevail?

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• 小蓝视频色情网页版 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• North America Q1 Funding Surges Across Stages To Record Level
• SpaceX Shares Close Up 19% After Largest IPO Of All Time
• Anthropic Nears $1T Valuation And Leapfrogs OpenAI On Unicorn Board With Massive Funding Round
• Lilly Acquiring Kelonia In Largest Funded Biotech Startup Purchase In Years聽

Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of July 2, 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. 小蓝视频色情网页版 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 小蓝视频色情网页版 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. 小蓝视频色情网页版 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. 小蓝视频色情网页版 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.)

Illustration:

Nonetheless, amid a period of soaring , rising EV adoption rates, and accelerating progress in fusion and other fields, cleantech investment activity isn鈥檛 slowing down.

In the first half of this year, investors poured $15 billion into seed- through growth-stage rounds for companies in 小蓝视频色情网页版 cleantech, EV and sustainability-focused categories. That puts funding on track to slightly exceed the 2025 tally, which was the lowest in several years.

On a quarterly basis, funding is also on the rise. Around $8 billion went to companies in cleantech and related categories in the second quarter of this year, the highest quarterly total since 2024.

Even taking into account recent gains, however, cleantech funding remains far below its former peak in 2021 and 2022. Given that overall venture funding has risen with the AI boom, cleantech also accounts for a smaller share of total investment.

Where funding is concentrating

That鈥檚 not to say megarounds aren鈥檛 getting done in the sector. A look at the largest funding rounds of 2026 paints a varied picture of where capital is concentrating.

Stockholm-based green steel producer scored the largest financing of 2026, securing $1.6 billion in a round led by Swedish asset manager . Stegra plans to use the money to complete the construction of its large-scale steel plant.

The next-biggest round went to , a -backed startup that has been generating buzz and reservations for a flagship electric pickup starting at around $25,000 that can be converted to an SUV. Troy, Michigan-based Slate raised $650 million in Series C funding in April and plans to deliver its first trucks to customers later this year.

The third- and fourth-largest financings were fusion deals. The latest of those went to , which raised $465 million in a June Series G funding to go toward building a fusion power plant. The -led round set a $15.5 billion post-money valuation for the Everett, Washington-based company.

A few months earlier, fusion startup picked up $450 million in Series A funding led by . The San Francisco-based company, formed around a fusion breakthrough at , plans to build the world鈥檚 most powerful laser to further its goal of grid-scale energy production.

For a broader view of where large financings are concentrating, below we put together a list of 10 of the largest cleantech-related rounds this year.

Under the circumstances, the space looks underfunded

While sums going to cleantech-related startups aren鈥檛 tiny, looking at total investment tallies does leave one with the impression that the space looks underfunded.

After all, energy is a growth sector, and clean energy is leading the way. The forecasts the share of renewables and nuclear in the world鈥檚 power mix will rise to 50% by the end of this decade. At the same time, global power demand is set to grow by more than 3.5% per year on average over the rest of this decade.

Exits of venture-backed companies are also happening, another source of encouragement for startup investors. The most recent IPO in the space was geothermal provider , which went public in May, raising $1.9 billion. The Houston-based company had a recent market cap around $8.6 billion.

On the nuclear power front, , a developer of small modular reactors, carried out its own Nasdaq IPO in April, raising $1 billion. The Rockville, Maryland, company was recently valued at a little over $5 billion.

Looking ahead, it鈥檚 not far-fetched to see myriad factors that could power clean energy, sustainability and EV sectors higher. For clean power in particular, the voracious energy demands of AI are certainly a catalyst to consider. We鈥檒l stay tuned to see if growing energy demand ultimately translates into greater startup investment.

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First, San Diego-based announced its of a Palo Alto, California-based software startup focused on making it easier for developers to run AI models across different types of computer chips.

At the same time, reports emerged that chip startup is finalizing an $800 million funding round led by , valuing the company at $10 billion. Together, the two deals underscore a growing reality in tech: As hardware remains scarce and expensive, the software layers that connect these chips are becoming just as valuable as the silicon itself.

Watching these shifts unfold firsthand is , a managing partner at who led early investments and holds board seats at both Modular and SambaNova.

Munichiello brings a pragmatic operational background to tech investing, having served as a captain and paratrooper in the U.S. military before transitioning to the private sector. He later worked as an early executive at , helping scale the warehouse automation company through its $775 million acquisition by .

With a background in mathematics and computer science from and an MBA from , Munichiello has spent his venture career focused on core software infrastructure, developer tools and data systems, including early backing of companies such as , and .

In this interview, he discusses the mechanics behind the Qualcomm-Modular deal, the practical realities of managing hardware scarcity, and what the current wave of consolidation means for the future of independent startups.

This interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: The acquisition of Modular by Qualcomm highlights a massive push to decouple AI software from hardware fragmentation. Does this signal that the ultimate value in the AI stack is permanently shifting away from proprietary hardware architectures and toward developer-friendly software layers that can run across any compute environment?

Munichiello: The types of hardware required for AI in the future are becoming heterogeneous. Originally, it looked like it was just GPUs from , and then also GPUs from and other players. But now, the direction hardware is going is toward “disaggregated inference,” which basically means splitting apart the different compute used for different parts of answering a question when engaging with a model.

It increasingly looks like there will be three types of chips used in disaggregated inference: an AI-specific chip, a CPU and a GPU.

For a player like Qualcomm, all three of those components are present, so they need a software layer that sits across them. Everywhere else, Nvidia included, they usually sell alongside CPUs and accelerators, and there hasn鈥檛 really been a software solution that works across all of those.

When did you first start investing in this wave of AI infrastructure and semiconductors?

Munichiello: We鈥檝e been investing in AI since 2016, starting as early as a company called , which was first company, sold to , and became part of the Siri team. After that, we invested in , co-founded by , which was later sold to and became an important part of its stack. HPE actually went on to be the compute partner for and worked very closely with as well.

We also got excited about semiconductors early, long before this current wave, when we led the Series A for SambaNova. I first met that company when it was just three people and a slide deck. We led that round in December 2017 鈥� after led the seed investment 鈥� and I鈥檝e sat on the board since. That initial investment was $15 million at a $480 million valuation.

It seems like a lot of legacy chip giants and major cloud providers are aggressively buying up infrastructure startups. What does this consolidation mean for early-stage founders? Are we entering an era where standalone startups need to plan for an early acquisition, or is there still a path to an independent IPO?

Munichiello: There is definitely a path to an independent IPO. showed that trajectory beautifully, and I’m really happy for and that team. There is absolutely a trajectory to build big, standalone businesses because the demand for compute is completely off the charts. We can’t make semiconductors fast enough, nor can .

Everyone is trying to find extra capacity by making everything more efficient. Technology often emerges with a big boom in mass demand and high prices, and then we figure out how to make it cheaper. We are in that efficiency step right now. Demand for inference is everywhere, from medicine and law to coding, customer support and finance.

We are trying to squeeze every last bit of value out of chips. Squeezing that value comes from using multiple types of chips: using cheaper CPUs when we can, GPUs when we need them, and the most expensive chips only for the most complicated parts of the process.

We are also evaluating software across the stack to ensure every aspect of these queries is as efficient as possible. It鈥檚 not surprising that there are a lot of acquirers. The universe of buyers has expanded from just semiconductor companies buying other semiconductor companies to software companies, hyperscalers and model companies buying chip companies, too. Amazon has Trainium and Inferentia; has Maia; has the TPU, and every big tech company wants to be able to say it has a chip.

How does the rise of open-source models shift this dynamic?

Munichiello: The universe of potential buyers expands even larger when open-source models become prolific. In the Qualcomm announcement, they talked a lot about their enthusiasm for open source 鈥� not just keeping Modular open-source, but for models to be open-sourced. When that happens, instead of enterprise companies paying hundreds of millions of dollars to model providers to do inference, the companies themselves will own their models and run them on their own hardware.

So you firmly believe that IPOs are not totally off the table for early-stage tech and hardware companies?

Munichiello: Not at all. Look at , which is highly hardware-intensive. I think we will see many IPOs here in the next six months. I know of at least 15 or 20 companies that are planning to go public, so it is going to be a very busy period.

In a market where valuations are multiplying rapidly based on technical metrics like chip throughput, how are you able as an investor to separate real, sustainable product-market traction from early hype?

Munichiello: There are a lot of AI companies getting valuations that are disconnected from the business outcomes they are driving. True traction comes down to quarter-over-quarter execution, hitting sales demands and actually fielding physical systems for customers.

A company becomes highly attractive to investors when it delivers a massive volume of technology into production environments 鈥� like data centers for major enterprise brands and devices we use every day.

That, combined with incoming demand from “Neo-Clouds” (new data centers built specifically for inference), shows real traction. These players are looking for any chips they can get their hands on, and the concept of disaggregated inference 鈥� combining three different chip types to lower the total cost of ownership 鈥� is highly compelling. It also alters the competitive landscape; it shows that the market isn’t just a runaway race for one dominant player, but an opportunity for CPU providers to catch up as well.

GV has a track record of backing foundational tech long before the generative AI hype cycle. How has your framework adapted now that AI infrastructure capital requirements have skyrocketed? When a startup needs hundreds of millions just to compete at the frontier, how do you maintain a focus on the team and relationship without getting bogged down by the sheer scale of capital?

Munichiello: It has always been complicated to start from scratch and build a meaningful, generational company. We are not in the business of momentum investing. We don’t invest in something just because we think it will be marked up by other investors over time. We look for fundamental technologies and consequential businesses that can stand on their own.

When we met Modular, it was just Tim and Chris with an idea, and we convinced them to take our $23 million investment. At the time, we were nervous about valuing the company at more than $80 million or $90 million, and it ended up getting valued at $155 million in that first round.

We took 15% of the company right off the bat in a round that felt way out over its skis for that moment in the world. But they hired an amazing team of compiler engineers, started growing and built in a space that became the most strategic in all of AI.

We value different companies based on their specific markets. Some are incredibly capital-intensive and require billions of dollars, meaning we can’t do it alone. As an investor, we must bring our network and a syndicate of other investors who can write hundreds of millions of dollars in checks.

Software companies can move a bit faster, make more mistakes and pivot. In hardware, if you tape out a chip and it doesn’t work, you are set back for years and have to raise significantly more money. It鈥檚 much more binary when it comes to the physical world. A hundred million dollars goes a lot further in software because you can always optimize your token usage or engineering to shift directions, which is incredibly hard to do in robotics or hardware.

This acquisition represents a massive return on your initial investment. What does this success say about your broader investment philosophy?

Munichiello: It鈥檚 a fantastic outcome 鈥� a 27x return on our initial investment and roughly 10x on our total dollars invested. But we aren’t a firm that just leads a Series A and then steps back. We look to write massive checks and co-lead later rounds, especially when things get difficult.

It is inevitable that every company will hit a wall at some point 鈥� whether due to macroeconomic factors, team dynamics or customer challenges. We call these “crucible moments,” and they are what make companies truly interesting. In an internal email I sent to our team, I talked about loving curveballs. We are used to things going sideways, and that’s when we really step up and help our companies. We like to find these incredibly hard problems, back exceptional people with the character and grit to survive those moments, and help them build standalone businesses.

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