To understand how institutional capital is navigating this gap, 蹤獲弝け 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.

蹤獲弝け News: You���ve 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?��

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?��

Ranum: I���d 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���s 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 ���boring software,��� 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���t AI-native from day one?

Ranum: I don���t 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 ���show me��� 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���re 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?��

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���s 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?��

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?��

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���m focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft ��� 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?��

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.

蹤獲弝け 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, 蹤獲弝け 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 ��� 65% �����went to a group of 42 companies that raised rounds of $100 million-plus. Sectors that stood out for these companies include�� 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, 蹤獲弝け data shows. Still, the region���s 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���s also drastically lower than the $392 billion raised in North America���s record-setting H1, with that region���s funding up 158% year over year.

Europe���s 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���s worth noting, seed stage rounds are often added to the 蹤獲弝け 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 ��� not far from the peak in 2021 at $10.8 billion.

The region���s 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���s fourth-largest startup market last quarter, with its companies raising $2 billion.

蹤獲弝け data shows funding to Europe���s AI-focused companies reached more than $10 billion in Q2 ��� the largest quarterly amount so far ��� but slightly below the Q1 percentage, when those companies raised more than half of the region���s startup investment.

By stage

Europe���s 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, 蹤獲弝け 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 .

蹤獲弝け 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 蹤獲弝け 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, 蹤獲弝け data shows. That includes three companies acquired for more than $1 billion each in biotech, industrial AI and micromobility.

Looking ahead

蹤獲弝け 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���s 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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• 蹤獲弝け 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 蹤獲弝け, 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. 蹤獲弝け 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 蹤獲弝け 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. 蹤獲弝け 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. 蹤獲弝け 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 ���Series [Letter]��� 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 ���venture��� round. (So basically, any round from the previously defined stages.)

If that introductory sentence sounds familiar, that���s because it���s 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 蹤獲弝け data, dwarfing anything we���ve seen before.

For Q2, meanwhile, investment totaled $137.2 billion. That���s 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���ll start with later stage and technology growth deals, since that���s 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���s 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���t 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 ���personalized intelligence,��� 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 蹤獲弝け 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 蹤獲弝け 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 蹤獲弝け 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���s 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���s $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 ���s acquisition of AI chip startup for $4 billion and ���s 1��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���t 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���s 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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• 蹤獲弝け 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 蹤獲弝け With Massive Funding Round
• Lilly Acquiring Kelonia In Largest Funded Biotech Startup Purchase In Years��

Methodology

The data contained in this report comes directly from 蹤獲弝け, 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. 蹤獲弝け 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 蹤獲弝け 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. 蹤獲弝け 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. 蹤獲弝け 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 ���Series [Letter]��� 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 ���venture��� 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���t slowing down.

In the first half of this year, investors poured $15 billion into seed- through growth-stage rounds for companies in 蹤獲弝け 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���s not to say megarounds aren���t 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���s 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���t 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���s 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���s 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���ll stay tuned to see if growing energy demand ultimately translates into greater startup investment.

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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���ve included both startups and publicly traded, tech-heavy companies. We���ve also included companies based elsewhere that have a sizable team in the United States, such as , even when it���s 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 ���unclear.���

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.

蹤獲弝け 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���t 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���ve 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 ��� after led the seed investment ��� and I���ve 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���s 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 ��� 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 ��� 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 ��� combining three different chip types to lower the total cost of ownership ��� 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���s 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���s a fantastic outcome ��� 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 ��� 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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That���s the trend we���re seeing for the second quarter of 2026. This period has brought us both the largest venture-backed exit of all time, with , and a bevy of other comparatively tinier but still sizable startup exits through acquisition or IPO.

Overall, we���re still well behind the prior high in terms of the number of big exits, as you can see charted below. The IPO and SPAC boom of five years ago will be hard to match for exit count.

��Bigger numbers

But while the Q2 exit deal counts may be still below peak, the actual returns are not.

For that, of course, we can thank SpaceX, which earlier this month shattered records with a historical debut that culminated in a staggering $2.1 trillion first-day market cap. Its long-awaited offering raised some $75 billion and served as an enormous liquidity event for founder .

Compared to that, every other Q2 startup exit looks pretty paltry. But by any other comparative metric, these other big exits were also very impressive.

SpaceX���s $60 billion acquisition of AI coding platform a few days after its IPO, for instance, was the priciest purchase of a private, venture-backed startup ever.

As for IPOs, made a splashy entry in May with an offering that raised at least $5.55 billion. Shares are down from the first-day closing price, but the company still maintains a sizable market cap around $38 billion.

Earlier this month, quantum computing company also had a big debut on , raising $1.7 billion and securing an initial market cap of $15.6 billion. Shares are still up sharply from the initial price.

For a broader view of big deals, below we put together a list of all the Q2 venture-backed private company exits valued at $1 billion or more.

Trend: fewer deals but larger ones

Even though the number of big deals picked up in Q2, the more noteworthy trend is the size of exits rather than the quantity. Size will likely still be the standout feature in coming months, with both and filing confidentially for IPOs that could test the trillion-dollar mark.

At the same time, however, the pace of exits in the billion-dollar-plus club, which in any prior cycle were considered considerable, is showing no signs of slowing.

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Those were the unsurprising findings of a 蹤獲弝け 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���s record-setting tally.

For 2026, however, about half of total M&A spending on U.S. startups comes from a single deal: ���s $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, ���s purchase of for $32 billion. After that, the next-biggest startup M&A deal was ���s $19 billion acquisition of in 2014.

Other big M&A deals

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

To illustrate, we used 蹤獲弝け data to put together a list of the 10 largest disclosed-price U.S. startup acquisitions this year.��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 蹤獲弝け 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 ���s 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 ���s 2��acquisition this month of , a provider of AI-enabled customer experience tools, and ���s purchase of , an industrial AI platform, each at $3.6 billion.

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

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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��� investment in Anthropic���s was not its first bet on artificial intelligence, the firm described it as its ���flag-planting moment.���

���We 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,��� the firm wrote in published Tuesday. ���In and his founding team ��� arguably the most accomplished researchers in the field ��� 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.���

The firm went on to lead Anthropic���s the following year.

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

Two new funds

The firm���s 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 ���already pulling away from the pack and on their way to becoming the breakout names of the AI era.���

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 ���he claims he proposed three times before I was able to understand. He was so oblique. It was like his testimony.���

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

One of the speech���s 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.

���What 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?��� 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.

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

Notably, Greenspan���s 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���s 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���s 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���s analogy to the current AI mania. Once again, we���re seeing unprecedented valuations attached to money-losing companies, many in still relatively nascent stages of development.

In other ways, however, this time it���s 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���s 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���s was recently around $852 billion.

One wonders what Greenspan would say about these stratospheric asset price levels. I���d 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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