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?泭

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?泭

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?泭

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?泭

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?泭

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?泭

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

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

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

Previous backers include , 1, , and .

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

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

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

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Globally, robotics startups have so far raised $18.8 billion in 2026, compared to $15 billion in the full year of 2025. The figure also handily surpasses the $14.1 billion raised in the peak venture funding year of 2021, and we still have more than six months of fundraising left.

The impressive rise in funding reflects a marked shift in perception among venture investors about the robotics sector, which was traditionally considered an expensive, asset-heavy hardware gamble. In particular, investors appear to be drawn to startups working on embodied AI, or artificial intelligence with a physical body that interacts with the real world in real time.

Noteworthy recent rounds

The surge in funding is driven by a number of robotics-focused startups raising considerable capital from investors this year. Also, interestingly, two of the five largest raises in 2026 to date have been by Austin-based companies.

Topping the list of largest deals in 2026 so far is Austin-based , a defense tech startup focused on autonomous sea vessels. In March, the 4-year-old company raised $1.75 billion in Series D funding, bringing its total funding to around $2.6 billion. led the round, which set Saronic���s valuation at $9.25 billion ��� more than double its Series C level in 2025.

Earlier this month, Germany���s , a developer of AI infrastructure for robots to learn, collaborate and operate across real-world environments, said it secured up to $1.4 billion in Series C funding. led that raise.

In January, , a robotics company building an ���omni-bodied��� brain to operate any robot for any task, announced that it had raised $1.4 billion, tripling its valuation to over $14 billion. That financing came just over seven months after Skild raised at a $4.5 billion valuation. led the startup���s latest round, which included participation from , ���s venture capital arm.

On June 15, Beijing-based , which creates water robots and intelligent unmanned equipment, raised $1 billion in a massive Series A round led by .

And in February, AI-powered robotics company raised $520 million in an extension of its $415 million Series A raise in February 2025, bringing the total round to over $935 million. Existing backers , , and joined new investors, including and manufacturing giant in participating in the extension.

Interestingly, spinout has already raised two rounds in 2026. In March, the Palo Alto, California-based startup closed on a $500 million Series A round, co-led by and . Then in May, it raised another $400 million in a financing led by . The company is developing an AI-enabled industrial robotics platform focused on automating industrial and manufacturing tasks at scale.

Exits

While mergers and acquisitions have been relatively robust with several strategic buyouts, the robotics IPO landscape is a bit quieter, particularly in the U.S.

In China, however, a number of robotics companies have recently gone public. The of , targeting a $3 billion to $7 billion valuation, was considered a milestone for the industry. In March, the company filed for an to list on the , and its IPO was widely expected to spur other startups in the space to pursue their own public-market debuts.

, a startup based in China���s Shandong province that makes lightweight industrial robots, in May listed on the , raising about $86 million. And it did not disappoint. Robotphoenix closed its first full day of trading at HK$53.75 ($6.86 U.S.), up nearly 80%, though shares have dipped to the HK$37 range more recently.

On the M&A front, a number of Big Tech and automotive giants have been aggressively acquiring embodied AI and humanoid talent to anchor their physical automation strategies.

In February, AI-powered supply chain provider acquired , an Austin-based maker of autonomous forklifts and lift trucks.

Skild AI in April that it had picked up the robotics arm of in an effort to deploy its technology to warehouses.

And in May, tech giant entered the humanoid robotics field directly by acquiring San Diego-based . The team was absorbed into Meta’s Superintelligence Labs unit to accelerate training of its foundational physical AI model.

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So far in 2026, U.S. companies have pulled in nearly 80% of global seed- through growth-stage financing, per 蹤獲弝け data. That���s a sharp divergence from the years leading up to the AI boom, when American companies typically secured less than half of all investment.

Gap for AI is even more pronounced

The U.S. share of artificial intelligence-related investment is even greater.

So far this year, nearly 88% of AI-related startup funding, or $319 billion, went to U.S.-headquartered companies, per 蹤獲弝け data. Of that, most went to just two recipients, and .

Since both Anthropic and OpenAI are on track for public market debuts later this year, it���s possible next year���s comps will be less lopsided, as they won���t be raising any more giant late-stage financings. We���ll see.

Large venture hubs outperform small and mid-sized ones

Although no other country comes close to the U.S. for startup funding, a few of the larger technology investment hubs are seeing year-over-year gains.

Funding to China���s startups, in particular, is on the rise after several sluggish years. So far in 2026, startups have raised over $33 billion, per 蹤獲弝け data, already surpassing the total for all of 2025.

The United Kingdom is also looking up. U.K.-based startups have pulled in $16.5 billion so far this year, compared to $19.5 billion in all of 2025. AI and fintech are the country���s leading sectors for investment.

Other mid-sized venture markets are seeing funding levels this year that are on track to be flat or moderately higher year over year, per 蹤獲弝け data. In Europe, this includes France, Spain and Germany.

In Asia, India, Japan and South Korea are also neither way up nor way down. Canada and Australia, meanwhile, aren���t in a slump but also aren���t seeing any major AI-focused funding raised this year.

Maybe it���s a US bubble?

Now that more than three-fourths of startup funding is going to U.S. companies, it seems timely to note that the country is home to only a little over 4% of the global population.

On the tech startup front, it���s undoubtedly an impressive 4%. The U.S. has an unrivaled track record for building leading technology companies, along with the capital and talent to keep on doing so.

That said, certain trends do warrant some serious bubble consideration. The anomalously high concentration of startup funding into American companies is one of them.

Surely many of the countries in which the remaining 96% of people on Earth dwell possess entrepreneurial talent, infrastructure and economic might that could support more than just a measly 12% share of AI startup funding. If one was a betting type, it���s hard not to argue that the odds for that look pretty good.

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The IPO泭caps a remarkable journey for a company that raised nearly $12 billion in private investment since its founding in 2002 to become the world���s most valuable venture-backed startup with a most recent private-market valuation of $1.25 trillion. Along the way, SpaceX helped redefine both the space industry and the late-stage venture market.

Its long-awaited offering raised some $75 billion and served as泭an enormous liquidity event for Musk, who became the as a result, as well as his close friend and confidant of , who now owns a stake valued at more than $68 billion in SpaceX. It’s also a massive and successful exit for early venture and corporate investors including , , , and .

SpaceX’s offering was unconventional along several fronts. Along with the IPO���s record-breaking nature ��� more than 10x larger than ���s $104 billion offering in 2012 ��� the company also by setting a fixed price of $135 per share, rather than the traditional approach whereby investors and bookbuilders determine a range based on demand.

Hawthorne, California-based SpaceX is also wildly unprofitable. The company posted a net loss of $4.28 billion in the first quarter of 2026, up more than 700% from a year ago. Revenue totaled $4.69 billion in Q1, up 15% from a year ago. Its megacap valuation means it���s slated to trade at an aggressive premium of 94x revenue.

The SpaceX offering is the first in a lineup of at least three historic IPOs this year, with generative AI giants and openly racing to make it to the public markets in coming months. Altogether, the three IPOs transfer some $3 trillion in value from the private to public markets.

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Meanwhile, filed on June 1 at a $965 billion valuation, and followed on June 8, . After four years of a venture liquidity drought, the read across the industry is simple: the IPO window is finally open again.

I would be careful with that read.

Look at where the money is coming from. SpaceX’s raise alone is slated to be more than the .

that with brokerage cash balances low, retail investors may have to sell existing holdings to fund their SpaceX orders, with and Bitcoin the most likely sources, and SpaceX is reserving as much as 30% of the deal, roughly $22.5 billion, for that same risk-on crowd. Crypto’s own this year as capital rotated toward AI. These three companies could very well be the entire 2026 IPO class.

Put together, this points to a concentration event rather than a broad reopening. A small number of funds and pre-IPO sellers get liquidity, three tickers absorb the available capital and attention, and the rest of the queue waits. If you run an early-stage company, the window reopening for SpaceX does very little for you directly.

The acquisition outlook

What these listings do change is more durable, and it runs through M&A.

A public SpaceX, OpenAI and Anthropic become some of the best-capitalized acquirers on the planet, with liquid stock to spend. OpenAI has already closed roughly half a dozen acquisitions this year, nearly matching its full 2025 total, and AI dealmaking across the market in the first quarter. The vast majority of venture exits have always been acquisitions; these offerings deepen the pool of buyers far more than they shorten the IPO queue.

For founders, that reframes the goal. Don’t build for an IPO window that was only ever open to a handful of companies. Build to be the company a newly public AI giant needs to own: real ownership of a workflow, proprietary data that compounds, the testing and evaluation infrastructure these labs increasingly run on, or a wedge into a market one of these platforms wants to enter. At the seed stage, the exit math has always pointed toward a single meaningful acquisition, and this wave widens the set of acquirers who can write that check.

For investors, the discipline is to not mistake a concentration event for a market that has reopened. The liquidity ���泭and the distributions LPs have spent four years waiting on ���泭will land with a narrow set of names. Most portfolios still get liquid the way they always have, through M&A, and the health of that market matters more to the median fund than whether SpaceX trades up on day one.

The test comes this fall. If the retail bid holds and the next tier of the queue prices well, Friday really will be the start of a broad reopening. Watch those follow-on listings, and watch what three newly public companies do with their stock over the next year. That second part is what reaches the rest of the market.

As the co-founder and managing partner of , is committed to establishing MGV as the premier venture firm for world-class tech entrepreneurs to accelerate their visions. Under Schr繹der���s stewardship, MGV has swiftly ascended to a top-quartile firm, surpassing the performance of 95% of venture funds. The performance of MGV is driven by Schr繹der���s unique approach to venture investing ��� that providing intensive sales training, devising robust fundraising strategies and securing follow-on investments is the best way to support founders and drive the deepest return for investors. has recognized him as one of the Top 100 global seed investors, and his perspectives are published regularly in 蹤獲弝け News and other leading publications.

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• The SpaceX IPO Filing Looks Nothing Like Those Of The Elite Group Of Tech Giants It’s Hoping To Join
• SpaceX IPO At $1.5T Valuation Would Be 10x Larger Than Biggest VC-Backed Listing Of All Time

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Checking in five months later, the space continues to sizzle from a startup funding standpoint, even though public markets have pulled back from the space in recent days. So far in 2026, investors have poured around $10.7 billion into seed through pre-IPO rounds for companies in 蹤獲弝け���s semiconductor category. That puts funding on track to eclipse last year���s levels.

Noteworthy recent rounds

Beyond , which went public last month after securing a $1 billion February pre-IPO round, a number of semiconductor-focused startups are raising considerable investor capital this year. Using 蹤獲弝け data, we put together a list of the 10 largest venture funding recipients.

One of the three largest fundraisers after Cerebras is , a developer of chips customized for the large model needs of AI labs. The Silicon Valley startup raised a $500 million Series B in February led by and .泭

Another moving up the ranks is , which also secured $500 million in a March Series E financing led by . The San Jose, California-based company is an AI infrastructure startup focused on optics technology, with strategic backers including and .泭

, a startup working on chips for AI superintelligence, reportedly also secured $500 million in new funding early this year. led the financing, which was said to set a $5 billion valuation for the Silicon Valley-based company.

The Cerebras factor

For now, the market fate of AI chip and infrastructure developer weighs heavily over the semiconductor startup space.

The Silicon Valley company���s massive IPO last month raised over $5 billion and saw shares soar in first-day trading. Since then, 11-year-old Cerebras has been heading lower, with shares down about a third from the initial closing price.泭

Still, it���s far from a slacker. With a recent market cap around $50 billion, paired with rapidly rising revenues, Cerebras is finding plenty of investor support for its pitch that it is building ���the fastest AI infrastructure in the world.���泭泭

High AI valuations and enthusiasm give sector a boost

Broadly, semiconductor startups are benefiting from the more widespread investor enthusiasm around the growth of AI and their continued support for the massive infrastructure outlays it requires.

That���s visible in the public markets as well, with semiconductor indices trading near all-time highs, a pullback in recent days notwithstanding. Chip designer Nvidia, meanwhile, remains the world���s most valuable public company.

The semiconductor industry is also young enough that most of today���s industry behemoths began as venture-backed startups. And given the rich history of innovative upstarts unseating leading players in this space, no one is doubting the chances of that storyline repeating.

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and each launched multibillion-dollar deployment ventures staffed with forward-deployed engineers, while a long list of startups building AI infrastructure, autonomous software and robotics also reached unicorn status. Together, the new entrants point to where investors increasingly see value creation: turning AI advances into real-world applications and pairing software intelligence with physical automation.

Beyond AI, new unicorns were minted across many sectors including healthcare, quantum, aerospace, financial services, manufacturing, e-commerce and energy.泭

China dominated in the robotics sector, while Canada did so in quantum. The single new legaltech unicorn last month was from Brazil. also joined the board this past month, as the adult creator content company raised its first external financing.泭

Of the new unicorns, 17 are U.S-based, while four each are based in China and the UK. Two new unicorns joined the board from Canada, as one each from India and Brazil.泭

Unicorn IPOs

The board���s total value is undergoing rapid fluctuations amid lofty new valuations for some of the largest new unicorns, as well as high-profile exits to the public markets.

The 蹤獲弝け reached $9.9 trillion in value in May, as Anthropic moved ahead of OpenAI to become the second most valued private company after . On the heels of the funding, Anthropic privately filed for an IPO, followed shortly thereafter by OpenAI’s .泭

SpaceX is expected to list this Friday, in what would be the largest-ever IPO. Its listing will erase more than one-tenth of value from the board as the the -led company exits the private markets.泭

Chip company went public in May in a blockbuster IPO that valued the company at $56.4 billion,泭well above its last private valuation of $23 billion just three months earlier in February.泭

New unicorns in May

Here are May���s new unicorn companies, including 10 companies that are less than 3-years old:泭

AI deployment

• San Francisco-based raised a $4 billion private equity round led by with co-leads , and . The new company is majority owned by with partnerships with 19 investment firms and consultancies. OpenAI acquired , with its 150 forward-deployed engineers to support enterprises in this effort. The less than 1-year-old-based company was valued at $14 billion in the new funding, which it said will be used to scale operations and acquire companies.泭
• raised a $1.5 billion private equity funding to build an AI services company to work with companies to bring Claude into their operations. Each of the co-leads ��� , private equity investor and legal firm ���泭invested $300 million into the round. and also invested in the joint venture. The less than 1-year-old-based, San Francisco-based company���s valuation was not disclosed.
• , a company building search for AI agents, raised a $250 million Series C led by . The 5-year-old San Francisco-based company was valued at $2.2 billion and is used by coding agents, go-to-market agents and chat agents.泭
• Boston-based autonomous AI software developer raised a $200 million Series A led by . Blitzy���s platform reverse engineers existing code bases to build a knowledge graph and thereby enable autonomous development of software projects over days or weeks that can re-engineer and test complicated systems and deal with technical debt. The 2-year-old company was valued at $1.4 billion and is said to be used by dozens of global 2,000 companies.泭
• , a routing technology for applications to select from 400-plus models, raised a $113 million Series B led by Alphabet���s . Investors in the round included a host of corporate venture firms including , , , and . The 3-year-old New York-based company was valued at $1.3 billion.

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• raised a $700 million Series A led by . The company plans to build personalized robotics developing its own models, training and hardware. The 1-year-old San Jose, California-based company was valued at $6 billion. It was founded by CEO , founder of humanoid robotics unicorn .
• Guangdong, China-based , a dual arm robotics developer, raised a $147 million Series B led by and . It said its new funding will be used for R&D, production and a global sales network. The 10-year-old company was valued at $1.5 billion.泭

• Shanghai-based has raised four funding rounds since it was spun out of in January, and reached a valuation of $1 billion. Agilink is focused on dexterous hand technology. The funding will be used for model development, data and hardware with the spinout able to license to the broader robotics market.泭
• , a robot leasing and rental platform, raised a Series A funding. The less than 1-year-old Pudong, China-based company was valued at $1 billion. It is looking to expand from event rentals to warehousing, logistics and park operations.泭

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• , a treatment provider for cardiovascular and orthopedic disease, raised a $1.5 billion corporate round led by . Boston Scientific has an option to acquire its heart valve technology. The 10-year-old Georgia, U.S.-based company was valued at $4.4 billion.泭
• , a longevity biotech company, seeking to extend human life by a decade, with therapeutics targeting age related disease raised the initial close of funding round led by . The 5-year-old Redwood City, California-based company was valued at a pre-money valuation of $1.8 billion.泭
• , launched a suite of AI agents for healthcare built from its clinical data, raised $146 million in equity and secondary funding led by . The 15-year-old New York-based company was valued at $1.6 billion.

Quantum computing

• Vancouver-based , a quantum computing company that combines silicon-based qubits with native photonic interconnects, raised a $70 million extension funding led by Luxembourg-based . Photonic raised $130 million in January. The 9-year-old company was valued at $2 billion.
• Quebec-based , which says it addresses quantum error correction in each qubit, raised a $30 million funding. The company has raised a mix of government grants and venture capital. The 6-year-old company was valued at $1.4 billion.

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• , a builder of rockets to deploy data centers in space, raised a $305 million Series B led by . The 2-year-old San Carlos, California-based company, formerly called Aetherflux, was valued at $2 billion. The company plans to launch its first satellite later this year. Its technology entails using the upper stage of the rocket as a low-earth orbit satellite that uses solar energy to create 1-megawatt data centers in space.泭
• Hyderabad, India-based , a rocket company that delivers satellites into space, raised a $60 million funding led by Singapore-based and Menlo Park, California-based . Skyroot is planning the maiden voyage of Vikram-1 in June. The 7-year-old company was valued at $1.2 billion.

Financial services泭

• , an AI insurance provider for startups, raised a $160 million Series B led by . The 2-year-old San Francisco-based company was valued at $1.3 billion and plans to go after the trucking industry next.泭
• Intelligent wealth management platform raised a $150 million Series D led by . With in recruited assets, it is built to create an all in one system for advisors. The 7-year-old San Francisco-based company was valued at $1 billion.

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• , a manufacturer of aerospace and defense components, raised a $300 million Series B led by . The 1-year-old El Segundo, California-based company, which aims to strengthen America���s industrial base, operates six factories across the U.S. and was valued at $1 billion.
• , likewise says it is building out American manufacturing with a rapid custom manufacturing software to production platform. It raised its first institutional funding of $110 million led by , and founders and . The 7-year-old Reno, Nevada-based company supports small-scale inventors to large-scale enterprises and has shipped 30 million parts to 300,000 customers. The company was valued at $1 billion.

E-commerce

• , a real-time inventory management platform, raised a $170 million Series B led by and . Its sensor technology tracks items and its precise location and movement in the store. Retail customers include and . The 13-year-old New York-based company was valued at $1 billion.

• London-based , a booking service for hair salons, beauty experts and wellness salons raised a $80 million Series C led by . The 11-year-old London-based company was valued at $1 billion.

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• , a nuclear fusion startup spun out of Tsinghua University, raised a $74 million Series A funding. The 4-year-old China-based company was valued at $1 billion.
• , a provider of fast charging batteries, raised a $60 million Series C led by strategic investor . The batteries are used in data centers, robotics, electric vehicles and grid infrastructure. The 7-year-old Cambridge, UK-based company was valued at $1 billion.

Social media泭

• Creator platform raised its first external funding, a $535 million private equity round led by , which now owns around 16% of the company. The 10-year-old London-based adult content platform was valued at $3.2 billion. Its CEO noted the company has paid out since 2016.

Data center泭

• Modular data center builder raised a $230 million Series B led by , and. In partnership with the company plans to build capacity for secure data centers useful for military and remote manufacturing environments. The 3-year-old San Francisco-based company was valued at $2.2 billion. Customer booking for fiscal year 2026 was up 540% from 2025.泭

郭梗眶硃梭喧梗釵堯泭

• S瓊o Paulo-based , a Brazilian AI legal platform to manage company litigation, raised a $100 million Series B led by that valued the 2-year-old company at $1.2 billion. Enter counts , and among its customers, who use its technology along with law firms to handle litigation paperwork and settlements. Around have been managed through the platform. led the Series A.

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• , a digital asset trader, raised a $150 million funding led by , UK bank Standard Charter���s fintech arm. The deal brings digital assets into banking and represents GSRs first strategic external investor. The 12-year-old London-based company was valued at $1 billion.泭

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• , a security platform built for an open-source automated coding environment, raised a $60 million Series C led by . The platform is adopted by companies including Anthropic, , , , and and supports 27,000 organizations. Its socket firewall product is free to block malicious packages. The 6-year-old Stanford, California-based company was valued at $1 billion.

Related 蹤獲弝け unicorn lists:泭

• (1,785)
• (619)
• (160)
• (189)
• (118)
• (102)
• (921)
• (525)
• (241)
• (39)
• (486)

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• Active Startup Investors Didn���t Hold Back In May
• Anthropic Funding Pushed Startup Investment To Near-Record Levels In May As Exit Market Reopened泭泭

Methodology

The 蹤獲弝け 蹤獲弝け is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 蹤獲弝け data. New companies are as they reach the $1 billion valuation mark as part of a funding round.泭

The unicorn board does not reflect internal company valuations ��� such as those set via a 409a process for employee stock options ��� as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.泭

Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to .泭

Exits analyzed here only include the first time a company exits.泭

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.

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All told, global venture funding reached $92 billion in May, marking the second-largest monthly total on record, just behind February, 蹤獲弝け data shows. Of that, Anthropic raised $50 billion泭1 , or 54% of the month���s total funding.

Startup funding was up 284% year over year from $24 billion, per 蹤獲弝け data.

The month also had a successful IPO for a venture-backed company as chip company Cerebras, which has benefited from growing demand for AI inference, went public at the upper end of its range at $185 per share and opened at $350. The stock is currently trading around $225 as of June 2, which values the company at just over $49 billion.

On the valuation front, Anthropic rocketed ahead of on The 蹤獲弝け 蹤獲弝け as it became the second-most highly valued private company at $965 billion, just behind at $1.25 trillion. Just months earlier in February, Anthropic was valued at $380 billion. The board has shot up in value in recent months and has 1,780 companies altogether valued at $9.9 trillion as of the end of May.

Billions more

Last month, a further $17 billion was raised by 10 companies in rounds of $500 million and above. They include defense tech unicorn , which raised $5 billion, and China-based AI labs and , which each raised more than $2 billion having raised rounds earlier this year. Automated coding lab raised $1 billion, and , which develops AI for customer service, raised $950 million in a single round.

Funding to the AI sector totaled $72 billion, or 79% of funding, last month.

The boom funds itself

The Cerebras IPO sets the stage for further public listings, including potentially record-setting ones.

SpaceX publicly filed its prospectus in May, stating its intention to raise $80 billion via its IPO. The space tech giant has raised $9.4 billion in equity funding to date, per 蹤獲弝け.

Anthropic, which is set to beat OpenAI to the public markets after filing its confidential IPO paperwork on June 1, has raised $125 billion in equity funding thus far, compared with its rival���s roughly $180 billion in private funding.

The private markets in 2026 have raised capital at a greater pace than ever before, thanks to泭 larger rounds, faster follow-on fundings and record-breaking valuations. At the same time, if SpaceX, Anthropic and OpenAI all list this year, as they���ve said they intend to, the resulting liquidity could be the largest in market history, pouring hundreds of billions back into the hands of startup investors who will redeploy it into the next wave of private companies.

Related 蹤獲弝け queries:

Related reading:

• Anthropic Files Confidentially For IPO
• Anthropic Nears $1T Valuation And Leapfrogs OpenAI On 蹤獲弝け With $65B Funding Round

Methodology

The data contained in this report comes directly from 蹤獲弝け, and is based on reported data. Data reported is as of June 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.)

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