Since its launch almost two decades ago in 2007, the firm — which had an initial fund of just $3 million —�� has invested in around 550 companies. With this latest fund, its assets under management have reached $1 billion.��

91��Ʒ News spoke with , the firm’s managing partner who joined Seedcamp in 2010 and , who rejoined the firm in 2022 to head up the select fund and establish a New York presence.��

Since fund 2, it has invested in 100 companies per fund. “What we’ve learned is that you need a community to support each other,” said Espinal. The tipping point for the firm was 70 companies where it became clear that founders were helping one another, becoming customers, and teams starting new companies.

“We realized early on that the best thing a founder can get is access to another founder who just went through that experience — not necessarily a founder who is successful 10 years down the road and is a great figurehead, but someone just a little bit ahead. That’s effectively our secret sauce,” said Espinal.��

Seedcamp also invests in software and vertical AI, but is careful about what is compelling and unique. “We’re trying to monitor so we’re not one of eight bets in one area that’s been overinvested within the AI vertical space, and making sure that you’re not betting on number 100 in a space that’s hypercompetitive,” Espinal said.��

Seedcamp plans to invest in 35 new companies per year, totaling 100 to 120 for the new fund. It invests up to $1.3 million in its initial check, and will lead roughly 70% of those deals with a 5% to 10% ownership target.��

The firm reserves 40% for follow-on seed and Series A rounds. Its select fund will invest in portfolio companies from Series B onward.

“Building is so much easier and faster now,” Howe said. “Signals of product-market fit are there earlier. The founder DNA is still the same, but the ability to see it in action earlier is there with the AI lift.”

New York presence

Howe, who heads up the New York office, noted that European companies are heading to the U.S. earlier. “Historically, maybe we’d see a company raise a round and stay in Europe, dominate their local market, raise a few more rounds, and then come to the U.S.” she said. “Now we’re seeing them come right from the get-go.”

From fund 3, its 2014 vintage fund, the firm’s return is 13x distributions to paid-in capital, with Revolut, UiPath and seed investments from that fund.

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“Silicon Valley has done very well with software, but while software was eating the world, they forgot about silicon,” Barrett told 91��Ʒ News in an interview.

The firm recently closed a $475 million fund focused on investing in deep-tech startups at seed and Series A. In the decade-plus since its founding, it has built its investment thesis around the idea that breakthroughs in science and engineering — not just software — would create the next generation of valuable companies.

With demand surging for compute, semiconductors and energy, Barrett argues the rest of the industry is now catching up. “We’ve been at it for more than a decade,” he said. “In recent years, as AI is eating software, people are scrambling back to recognize that the energy, semiconductors and infrastructure they operate on all need capital too. We’ve been operating in that regime for a very long time.”

Barrett is originally from Australia and came to Silicon Valley in the 1980s. He’s been coding for 50 years, he said, after developing an early and deep respect for science and engineering as the child of two engineers. His childhood was steeped in punch cards, draftsmen and drawings of control systems and machinery, he said.

“Science lets you follow breadcrumbs from prehistoric plumage to semiconductors. One principle can be applied somewhere orthogonal and create extraordinary value,” Barrett said in a lengthy interview with 91��Ʒ News.

Barrett went on to found video game developer , joined to build the entertainment browser acquired by , and was subsequently CTO at prior to co-founding Playground Global in 2015.

Playground Global operates a lab in the former Palo Alto Research Building in Palo Alto, California. The location hosts 350 people, including those working at its portfolio companies and others with adjacencies working from the lab.

On a recent visit to the warehouse, I saw various models of robots, materials for aerospace construction, and a model of building powerful lasers to increase the speed of semiconductor manufacturing. The quantum computing startup , a Playground portfolio company, moved in when it had three employees and moved out when it reached 90.

The firm has four general partners. Along with Barrett, they are , the former CEO of and who architected CPUs at Intel that helped computing take off at scale, and who joined the Playground team last year as a general partner specializing in semiconductors; , who has made many investments in biotech, including ; and co-founder , who led the investment in .

What follows are highlights from a wide-ranging interview with Barrett that covered topics including sovereign technology, the need to invest in companies that operate on the physical plane, and why he believes putting data centers in space is stupid.

This interview has been lightly edited for clarity.

Gené Teare: What is the thesis for Playground Global?

Peter Barrett: It is about reducing new results in science and engineering into commercial and societal value. That means operating at the boundary between computation and the physical world. We are very interested in new capabilities of computation driving civilization forward, and that inevitably means operating in the same physical plane that we live in.

We’re seeing in our data a huge amount of funding going into space, semiconductors and robotics. It seems as if the whole venture industry has pivoted to this much broader array of companies. Do you see that as a good thing?

Barrett: We lost a lot when people weren’t investing in things that strike us as important. It is good that there is capital chasing the things we care about and that have real consequence.

You can’t spin up a deep-tech practice overnight. You still need domain expertise. You still need to understand why investing in nuclear reactors is good, and why data centers in space are preposterous.

Silicon Valley hasn’t been very efficient with much of the capital it’s deployed over the past decade or so. But I do think it’s good that people recognize that software may be eating the world, but you can’t eat software. We have to operate in the physical layer.

Do you think Silicon Valley gets more efficient?

Barrett: We need to do the work. You develop the instincts and the platform to deploy capital efficiently into these places.

It’s important that people recognize there’s this unprecedented funnel of technical change. AI is an early indicator of it, but we have technologies like quantum. We know how to produce computation using things beyond transistors and semiconductors.

We’re scratching the surface in terms of AI models. We’re right at the beginning of an explosion and renaissance in materials science driven by things like quantum computing.

Now would be the time — and candidly, I feel the imperative — that anywhere there is science and capital, it needs to be turned into value, especially in liberal democracies, because the despots are doing a pretty good job of it. It’s incumbent on us to stay ahead.

We’re in the DOS age of AI. We’re scratching the surface, both in terms of the models we make and the hardware we run them on.

Now would be the time for people to write checks into things that are sensible and valuable. We spent a lot of time on NFTs. How are we doing with cancer? How are we doing with our most difficult challenges in terms of healing and feeding the world?

There are lots of new degrees of freedom that could take capital and turn it into value.

Do you think deep tech fits the venture thesis, despite the long time horizons and the amount of capital it requires?

Barrett: The long time horizons certainly exist. If you’re building PsiQuantum, we’re building million-qubit quantum machines. That takes billions of dollars and a decadal effort.

The corollary is that we’ve had hardware exits in two years. The timelines for hardware aren’t necessarily that different from software.

Therapeutics naturally take a longer time, because of clinical trials. But we’ve also seen exits there. One of our companies tested half a million drugs in a single animal and created a new corpus of AI input for building models to create therapeutics. That’s not a decadal effort — that’s a handful of years before exit.

We try to craft a portfolio that’s a mix of tactical and strategic. Some of these companies get to hundreds of millions in revenue within a few years. Others, like PsiQuantum or , may take a decade to reach full entitlement. That’s part of portfolio construction.

The biology company you mentioned —��what’s its name?

Barrett: . It did the largest pharma deal of its kind last year with . The deal could be worth $2 billion on the back end.

It’s a unique mechanism to create giant AI training sets by using physical systems — using animals and in vivo testing to create that dataset. It affords the ChatGPT and biology moment, where you can have large enough training sets to build big models.

You describe the firm as investing somewhere between improbable and impossible. Are there companies that really fit that thesis when you first met them?

Barrett: When we first met PsiQuantum, they were talking about building a machine which was 10,000x the state of the art. Using then-current technologies, it would have been the size of the Sierra Nevadas.

They required exponential improvements in both hardware and software, and they’ve achieved both. It’s the size of a warehouse, not a laptop.

The work we’re doing in biology, materials, quantum algorithms and superconducting logic — which will replace transistors and semiconductors — all of these things sound like science fiction, but they’re much closer to improbable. In many cases they’re entirely practical before we invest; they just seem improbable to those unfamiliar with the domain.

There are things that are not impossible but are still really dumb — data centers in space, small modular reactors (SMRs), or fusion. The physics may work, but the economics don’t, or the timelines don’t align.

I’m disappointed we haven’t invested in anything that turned out to be more impossible than we thought. None of our portfolio companies failed because the technology didn’t work.

We’ve had capitalization failures. We flew hydrogen planes. We’ve built things that were thought to be virtually impossible that turned out to be straightforward. They may have missed their market or may have been unable to raise the capital to continue.

I want to do something where the technology doesn’t work, and we’ve yet to do one of those.

Is there a company you missed out on where it looked impossible and you wish you’d invested?

Barrett: I wish I hadn’t taken ‘s word for it when was a non-profit.

We haven’t missed many. As the roadmap developed, we wish we had been earlier in a couple of categories that are really interesting. But overall, we haven’t missed too many.

In which sectors or companies have you invested where the time horizons have shortened due to AI?

Barrett: Adding Pat Gelsinger to the team reflects an interest in scaling semiconductors along various dimensions, including energy efficiency and how power is delivered.

We do everything from nuclear reactors all the way through to transmission, energy conversion outside the data center, inside the data center, under the chip, what kinds of chips you’re running, what models run on top of those chips, what architectures those chips are made from, and what materials those chips are made from.

At every layer of the infrastructure — optical interconnects, memory systems — we have a best-in-class company at every point. We built the first AI accelerator a decade ago, and we’ve broadened that to encompass the entire ecosystem, from the creation of electrons to how they expend themselves doing useful software work.

There are bubbly aspects of the current AI moment, but the bubble is being modulated to some degree by the unavailability of energy.

We’re in the DOS age of AI. LLMs are embarrassingly incompetent compared to what comes next, but we believe in the durability and growth of AI, and are making investments in model architectures and the ways AIs are trained. We see demand for compute, energy and infrastructure continuing to grow.

We have technologies that can reduce general-purpose compute workloads by 100x to 1,000x over state of the art. We believe we know how to make the energy and deliver it. We know how to connect these systems.

So quixotic pursuits like putting data centers in space are unnecessary.

Talking privately to hyperscalers and Fortune 50 companies, they all say there is way more demand for AI in its future incarnation than exists today. It’s incumbent on us to figure out how to do it 100x, 1,000x or 10,000x more efficiently, because that demand turns into GDP growth and better solutions to our hardest problems.

What are the companies in energy and semiconductors that you are betting on?

Barrett: One example is the wild superconducting logic company . We can make things that are post-semiconductor and post-transistor, with devices that switch five orders of magnitude more efficiently than transistors.

They operate at cryogenic temperatures, but quantum computers do that, and our extreme ultraviolet lithography system does that. The future of computation is cryogenic. Even after you pay to make it cold, you’re still 100x to 1,000x more energy-efficient on compute.

This technology has been around since last century, but it’s mainly been used for secure signals intelligence and radar applications. We’re generalizing it for compute.

Another example is . People talk about SMRs, which are a physics solution to a financial problem, or fusion, which is still decades away. Alva instead uprates the existing nuclear fleet to get hundreds of megawatts out of each unit by replacing 1970s steam generators with a 2020 steam generator.

We can deliver power in a handful of years. No new fuel, no new regulatory path, and a business model that makes sense for operators. We can put gigawatts onto the grid without moving a fence line of an existing reactor and without upgrades to the electricity grid.

We know how to make AI training wildly more efficient. We know how to train different kinds of AI models that we’ve been unable to train.

The last supercomputer at uses something unlike a CPU or GPU to run existing software. We’ve been running software the same way for 70 years, but there are other ways, with dataflow architectures. We have a company doing that — [].

The degrees of freedom from materials, systems, code and models have never been greater. We’re exploring all of them. But most require rolling your sleeves up in the physical world.

LLMs feel like brute-forcing something — like a drunk looking for keys under the streetlight. We’re pushing more and more into that, and I think that’s a dead end. We know other ways of moving forward.

Are you seeing new model companies, separate from LLMs, that are going to solve things?

Barrett: Our brains are not LLMs. They’re not transformers. Transformers are effective, but they are one of a long line of soon-to-be-extinct models that get replaced by something that works better.

That millionfold gap between our brains and GPUs is an architectural gap. Meat is much worse at computation than hardware can be, so biology shouldn’t be better.

Physics allows a million times a million more efficiency, and we should start chipping away at that.

Intelligence is useful and can be pressed into service against basic things like photosynthesis. Plants were invented by accident of evolution 3 billion years ago. They’re pretty, but not efficient. They shouldn’t be green; they should be black. We know how to make photosynthesis twice as efficient, and probably 5x more efficient.

We’re not stuck with the physical constraints of our technology or of nature. Nature is beautiful, but cobbled together by a process that we can have agency over.

All the materials that operate our civilization are discovered, not designed, because we can’t design things we can’t simulate. Our best computers cannot simulate the quantum nature of nature. That’s about to change.

We’re stumbling around in the dark, relying on serendipity and the occasional magical material. Whereas we can construct any number of materials with magical properties that are currently hidden from us by our inability to simulate the quantum mechanical processes that animate chemistry.

We are right on that threshold of unlocking all of these dimensions. And at the same time, we’re putting money into NFTs, the metaverse and other things that will come and go, without anybody ever caring.

Are you talking about the mix of quantum with biology and model-focused companies?

Barrett: Quantum allows us to directly design materials, directly explore the method of action of drugs, and directly design drugs.

AI has a role to play in biology and understanding structures we can measure. We think there are quantum wet labs where we can measure the performance of small-molecule drugs against models of nature and then verify in nature.

We don’t know how many things that animate our industry actually work. We don’t know how Tylenol works. We don’t know how the Type II superconductors we’re building fusion reactors out of work. We know that if you take iron and nitrogen and arrange them in a certain way, they produce magnets stronger than rare earth magnets, but we don’t know why.

There are mysterious things we’ve stumbled across that hint at an Aladdin’s cave locked behind a wall of computation. That wall is coming down.

Which sectors do you think are going to take a lot longer to come to fruition?

Barrett: Civilization will operate on fusion eventually, but right now the only reactor that works using gravimetric confinement is the sun. I think that’s a long way off.

Data centers in space are stupid. You can’t operate a gigawatt data center in a thermos. We have terrestrial answers to those questions that we should pursue.

I’ve always been a detractor of self-driving cars, which are starting to work. Now we need an economic model that makes them sensible and doesn’t drown our cities. The problem with transportation in cities is not the degree of autonomy. If we cared about traffic deaths, we’d worry about roundabouts.

There’s also nonsense with NFTs and the metaverse which have sopped up enormous amounts of capital. Small amounts of capital using these tools against our most difficult diseases would yield results. Small modular reactors are an unwarranted innovation.

There are lots of things that, at first blush, seem good and valuable, but there are far better solutions that are simpler and more imminent. We need to be practical about where the money goes.

There was a company that just joined the 91��Ʒ, valued over $1 billion this past month, doing orbital data centers. Are you saying this whole category doesn’t make sense?

Barrett: To his credit, will show you a picture of what a 100-kilowatt data center looks like, and it’s bigger than Starship. A 100-kilowatt is a small rack from that is human-sized.

The arguments are that there are a lot of renewables in space. But there are a lot of renewables on the ground too. North Western Australia has solar and wind that are 70% naturally firm, and on the ground, so you can build things on it.

Put a data center in North Western Australia, which we are doing. We have a renewable site 35x the size of Manhattan.

Energy generation and compute in space is a nonstarter because space is not cold. You’re building things in a thermos and need to get rid of heat. A single human-sized rack is 100 kilowatts, which is about the size of the International Space Station’s radiators and solar panels.

Starship has yet to actually put anything in orbit. It’s made some fireworks, which are pretty, and it’s a beautiful thing. is an amazing company because of Falcon 9 and Starlink. But data centers and power generation in space makes no sense.

We know how to build arbitrary amounts of energy generation on the ground with very safe, very large nuclear reactors. We’ve been doing it for decades.

For all the talent and genius rattling around the Valley, we do spend money on silly things.

Do you think now is the most exciting time to be investing, or have some of those investments already been made and are going to come to fruition?

Barrett: We’ve already made investments in things on a really steep trajectory.

Snowcap will take a decade before we’re building GPUs with that technology, but we’ll have commercial product from them next year. We’re getting better at early, undeniable signals.

PsiQuantum is a long journey, but some things just take that amount of time.

X-Lite seems like a ridiculously long journey, although we’re building the prototype facility now, and it received the first money from the new CHIPS Act.

Some hardware companies making silicon or systems are getting significant revenue in a handful of years.

There’s a sleeper in Fund I. Its first trick was to make MRI machines 100,000x more sensitive, and they’re shipping those. In the background they’ve also been developing that core physics to build a new quantum computing modality. So we actually have two quantum computing companies in Fund I.

Even though that’s a 10-year-old company, there are about to be two companies, one of which will be a unicorn virtually overnight.

There are wild things bubbling under the surface that people are going to wonder where they came from.

Companies like — the only co-packaged optics on TSMC — we’ve been working on that for a long time. Now people are waking up to silicon photonics and co-packaged optics.

There are also stealth companies that are indistinguishable from magic. Some of those will come out of stealth this summer.

Is there anything we haven’t chatted about that you think is worth noting?

Barrett: It’s a sobering note, but globally there is a need and desire for sovereign capability in tech — in Western Europe, Australia, Canada and elsewhere.

There are extraordinary pools of capital, pension funds and Australia’s superannuation fund. Given the things we can invest in, globally the West needs to do a better job translating that capital into societal and economic value.

The safety and durability of liberal democracies depends on creating wealth and staying ahead.

We see a resurgent desire to do that in Europe and Australia. Around those pools of capital, there’s ambition. We need to drive that ecosystem globally, not just in the U.S.

The pace of innovation in Ukraine, driven by need, is indicative of changes that can be made in parts of the world less friendly to the tenets we hold dear in liberal democracies.

We can’t operate under the assumption that everybody clever lives in Palo Alto or that we can only invest in things we can drive to. We need to deploy capital globally, and we do. We’re going to do more of that.

Do you feel encouraged by the amount of infrastructure build-out that’s going to happen over the next few years? It feels like it will create a boom in all sorts of technologies because the drive for efficiency will become much stronger.

Barrett: LLMs are not the end. We’ll run LLMs on these data centers initially, but we’ll run their descendants and other more useful things on these machines and on quantum machines.

It’s going to be hard to overbuild because computation is incredibly useful. There’s no upper bound. We’re not in a Malthusian zero-sum game for resources.

We know how to make everything more productive. We know how to grow GDP arbitrarily large. But we need food, energy and medicine there, and we need to normalize the distribution of wealth.

There is unbounded abundance we can unlock if we spend capital on the right things. We know how to do much more of that than people suspect.

The fact that sensible people are considering data centers in space indicates they’re not paying attention to the things we already have in hand that can move the needle.

We do need compute in space. We need AIs in space, sensing in space, and Starlink is great. But we need to use technologies that make sense, not try to make skyscrapers out of toothpicks.

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Related reading:

• Sector Snapshot: Quantum Computing Startup Investment Slows In 2026 While Public Markets Hold Strong
• Sector Snapshot: Semiconductor Startup Funding Still Running Hot

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91��Ʒ News spoke with co-founder , who describes the firm’s thesis as using technology to bring capabilities traditionally available to the top 1% to the other 99%.

Portfolio companies that fit that thesis include LatAm neobank ; fleet safety management startup ; , which is a tool for travel agents; , which develops agents for enterprises; and coffee chain .

The firm has a strong focus on logistics, payroll, construction and other real economy sectors.

It is also exploring vision models and world models — the equivalent of LLMs for visual understanding. If AI could truly understand every pixel and atom in a construction site, that will unlock robotics, Ajao said.

Manufacturing intelligence is another area of interest.

Ajao asks: Can AI understand manufacturing processes the way LLMs understand text, whether it’s perfumes, pharmaceuticals, chips or concrete, for real economy applications?

Stage focus

The firm invests at seed through Series B. From the early-stage fund, Base10 plans each year to make 10 to 15 seed investments, and two to three at Series A. The Series B fund, roughly equal in size, will make three to four investments each year.

Base10 is research first, spending months analyzing sectors before investing.

“We might ask what IT support firms look like when you have AI, or what the software stack of the modern restaurant is,” said Ajao.�� The firm tries to meet every company globally operating in that space. It spends roughly 50% of its time with companies that are not fundraising, with 90% of investments made due to its research.

For the recent batch of 160 companies, the firm only meets with those that align with their research. Along with too much happening, founders are better prepared.�� For the firm, being informed allows them to get to conviction fast.

Base10 has created an internal AI system called Base11 to classify companies, and automate research. However, “the actual decision-making and winning is more human than ever,” said Ajao.

That means spending more time understanding founders as people and talking to customers, said Ajao.

Competition among venture firms is also higher than ever. “It forces all of us to articulate a lot more why someone should partner with us,” he said.

Through its Advancement Initiative, Base10 donates up to 50% of carried interest to underfunded colleges and universities to support financial aid.

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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.��

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.��

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.��

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 91��Ʒ 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 .��

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.��

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

New unicorns in May

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

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.��
• 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 —��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.��
• 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.��
• , 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.

Robotics��

• 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.��

• 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.��
• , 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.��

Healthcare��

• , 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.��
• , 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.��
• , 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.

Aerospace��

• , 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.��
• 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��

• , 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.��
• 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.

Manufacturing��

• , 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.

Energy��

• , 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��

• 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��

• 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.��

Legaltech��

• São 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.

Cryptocurrency��

• , 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.��

Security��

• , 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 91��Ʒ unicorn lists:��

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

Related reading:

• Anthropic Files Confidentially For IPO��
• Active Startup Investors Didn’t Hold Back In May
• Anthropic Funding Pushed Startup Investment To Near-Record Levels In May As Exit Market Reopened����

Methodology

The 91��Ʒ 91��Ʒ is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 91��Ʒ data. New companies are as they reach the $1 billion valuation mark as part of a funding round.��

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.��

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. 91��Ʒ 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 91��Ʒ long after the event was announced, foreign currency transactions are converted at the historic spot price.

Illustration:

All told, global venture funding reached $92 billion in May, marking the second-largest monthly total on record, just behind February, 91��Ʒ data shows. Of that, Anthropic raised $50 billion��1 , or 54% of the month’s total funding.

Startup funding was up 284% year over year from $24 billion, per 91��Ʒ 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 91��Ʒ 91��Ʒ 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 91��Ʒ.

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�� 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 91��Ʒ queries:

Related reading:

• Anthropic Files Confidentially For IPO
• Anthropic Nears $1T Valuation And Leapfrogs OpenAI On 91��Ʒ With $65B Funding Round

Methodology

The data contained in this report comes directly from 91��Ʒ, 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. 91��Ʒ 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 91��Ʒ 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. 91��Ʒ 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. 91��Ʒ 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:

Size increase

Median seed round sizes have been climbing since 2023, 91��Ʒ data shows, with the median U.S. seed round last year now standing at around $3 million. That’s 3x larger than it was in 2018.

The upper quartile median last year was around $5.6 million — more than double what it was in 2018�� — and the lowest quartile was $1 million. (Although, underlying those medians is a much wider range of deal sizes.)

At seed, “What we see is everything from the inception stage, which is typically $3 million to $5 million, unless it’s a truly unique and obvious founder, all the way through to $8 million to $10 million-plus rounds,” said , managing partner at , one of the earliest institutional Bay Area seed funds founded in 2004.

McLoughlin noted that the typical check size his firm writes for a seed round has almost doubled from 18 months ago. “We’re still trying to buy at least 10% ownership, ideally more, and our average check has grown from $2.5 million or less, to $4.5 million,” he said.

The speed at which those round sizes have accelerated is mind-bending, he said. “If you’d asked me 18 months ago, would the $8 million to $10 million-plus seed round become de facto, I would have said you were crazy”

Series A rounds have also grown in size, per 91��Ʒ data. Last year, the median U.S. Series A deal was $15 million, with the upper quartile at $25 million and the lower quartile at $7 million. That trend has continued into 2026, with median Series A rounds moving still higher.

Longer time frame to Series A

But while companies that are funded at the seed stage are typically raising larger checks, they’re also taking longer to move on to Series A and face lower odds of graduating to that phase at all, 91��Ʒ data shows.

Since 2023, U.S. startups have been taking longer to raise a Series A round following an initial seed round of $1 million and over, per 91��Ʒ data, with that time frame now stretching to more than two years.

That continues a general upward trend since 2018 of startups taking longer to raise a Series A round after seed, with notable exceptions in the previous peak funding years of 2021 and 2022, when the timeline shrunk by six months.

The threshold for raising a successful Series A is no longer $1 million in annual recurring revenue, said McLoughlin. In the AI era, startups are expected to show $2 million to $3 million — even $4 million — in ARR as proof that the business has the momentum to scale, he said.

“When you’re fundraising for your [Series] A, you’re not in competition with the startups you deem to be competitors,” said McLoughlin. Rather, he noted, you’re in competition with every other deal floating around in the venture ecosystem — not just the partner you’re talking to and their ability to do the deal, but what the entire team is doing, how far along they are, how far ahead of pace they are on their investment cycle, and whether they’re being pushed to only do things that truly look like breakouts.

Fewer graduates

Since 2021, drastically fewer companies that raised an initial seed round of $1 million or more have progressed to a later-stage funding or exited, 91��Ʒ data shows.

Through 2020, companies that raised a seed round of $1 million-plus had a typical graduate rate of 55% or higher.

Since then, graduation rates appear to be falling dramatically. Of the companies that raised a $1 million-plus seed round in 2023, only 24% have progressed further, 91��Ʒ data shows. For the 2024 cohort of seed-funded companies, that’s even lower: just 16%.

While these cohorts are staying at the seed stage longer, still McLoughlin predicts, “we’re going to see the mortality rate from seed to A will be much, much higher.”

As the dynamics of seed funding change, investors are being forced to rethink their portfolio strategies — adjusting to the right number of bets, reserving enough capital for follow-on rounds, and deciding whether to invest earlier or in larger seed rounds with potentially less ownership.

“We’ve also got to be comfortable with this notion that there will probably be more early outcomes or failures in the portfolio, but if we do our job well, the big outcomes will be bigger than they’ve ever been before,” said McLoughlin.

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Related reading:

• Data: The Seed Funding Boom Is Concentrating Capital In The San Francisco Bay Area
• Seed Funding Is Bigger Than Ever — And Harder To Get

Clarification: The rates of graduation from seed stage in 2023 and 2024 have been updated.

Illustration:

In 2025, 70% of U.S. funding — more than $200 billion — was invested in 389 companies that raised rounds of $100 million and over, 91��Ʒ data shows. Of that, $90 billion went to just six companies that each raised more than $5 billion last year.

Contrast that with around 6,000 companies that raised the remaining 30% of U.S. venture capital in 2025, a total of $88 billion in rounds starting anywhere from $1 million to less than $100 million.

Those numbers point to 2025 as the year of the most U.S. venture capital concentration on record, with investment clustering in top private companies even more so than it did in the previous peak year of 2021, when startup funding more than doubled year over year.

The capital concentration at the top did not come entirely at the expense of smaller startups, however. While private-market investment accumulated into the larger rounds, funding to the sub-$100 million rounds did not decline, but increased around $8 billion to roughly the same number of companies last year.

Looking back at 2021

In 2021, when capital concentrated into the largest rounds, 60% of capital went to companies that raised rounds of $100 million or more.

The difference between 2021 and 2025 was that in the earlier year, a greater portion of capital went to companies in the $100 million to $500 million range — around 770 companies — while in 2025 a greater portion of capital went to just 50 companies in rounds of $500 million and over.

Further concentration in 2026

Capital concentration shows no signs of abating this year.

Already just through April, U.S. venture capital totals in 2026 are on par with funding for all of 2025, and 80% of startup investment this year so far has gone to rounds of $500 million and more, across 29 companies.

A growing venture capital market

91��Ʒ data indicates that the private markets are concentrating heavily into the largest companies, but also growing overall for startups across the size spectrum, albeit much more modestly for all but the largest companies.

While more funding and value accrued to the top in 2025, the rest of the market also increased year over year, with the exception of rounds between $1 million and $10 million. Even at the smaller round size, however, funding and deal counts were down less than 10% year over year.

Looking ahead

The fundamental question for the venture world now is: Does the rapid growth and capital concentration for the largest companies come at the expense of smaller startups, or does the success of , and the like expand the total addressable market for tech startups so drastically that it promises to grow and reshape opportunities across the entire ecosystem?

“While Anthropic and OpenAI are absolutely amazing companies, by virtue of the capital they’ve raised, they are going to have to go after incredibly large markets,” , managing director at , said during a recent 91��Ʒ News-hosted “There’s just so much white space around that, where really interesting founders and startups can play.”

, partner at This is a moment where I’m extremely excited about betting on seed and Series A, especially in spaces that do compete with Anthropic and OpenAI,” she said during the event. “I think they’ve really struggled to focus at this point in time, and they’ve been spending so much time trying to win lots of different use cases.”

, co-founder of , said he looks for startups to invest in that can build a defensible position in the new market: “If you’re very deeply embedded into a company’s workflow, where you’re doing many different tasks, not to mention you’re assembling a proprietary corpus of data that you can train your models on, we think that can be a sustainable moat over time.”

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Related reading:

• The 91��Ʒ 91��Ʒ
• The Seed Funding Boom Is Concentrating Capital In The San Francisco Bay Area
• The AI Boom Has Drastically Changed Who’s Funding The Hottest Companies In 2025 Vs. 2021
• Get To Know The Latest Class Of Ultra-Fast Fundraising Unicorns
• This Is A Momentous Year For Early-Stage Unicorns
• The Largest Recent Seed Rounds Are All For AI Companies

Illustration:

The energy sector necessary for AI compute also garnered significant funding this year.��

All told, roughly half of European venture funding in 2026 to date has been in AI-related companies, 91��Ʒ data shows.

The uptick in artificial intelligence investment has coincided with an overall gain in startup funding in the region the last past quarters. Funding was up a third year over year in Q4 and Q1, reaching more than $17 billion each quarter.��

AI talent hubs

One area where Europe is seeing momentum is with frontier labs.

Employees from — the original AI lab established in London in 2010 and acquired by Google in 2014 —��have spawned two new labs in London: and . And , previously Meta AI’s lead, formed in Paris. Just this year, the three companies have altogether raised $2.6 billion.��

Last year, German-based AI lab raised hundreds of million in funding. One of the earlier model companies from Europe, , founded in 2023, has raised $4 billion in total.��

Europe is also home to one of the early diffusion model companies, . from Heidelberg, Germany, recently merged with Canada-based in April for sovereign and commercial AI deployments, valuing the merged entity at $20 billion, creating a transatlantic competitor to U.S. model companies.��

The recent spate of new AI lab formation and renewed momentum on the funding front could be a driver for talent hubs to concentrate in Europe. Still, although foundation labs in Europe have raised more than , that represents a tiny percentage of the amount raised by frontier model companies in the U.S.

AI-native

In the European report, found 81% early-stage companies, largely pre Series A, are AI-native — up from 50% a year ago. Leading by company count this year were 12 companies in dev tools and infrastructure and 11 companies in industrials and robotics.��

The advantages of building in Europe are “access to strong engineers in the very beginning — having people that want to build and be part of a founding business, and access to good quality talent that you can retain,” said , a principal at Notion Capital who co-wrote the report.��

He also noted that in earlier vintages, the trend was to “build a company, expand to the U.S. at some point around the Series B. Now, from the start, founders tend to think globally from day one.”

The single most dramatic change, however, is how much leaner teams are ahead of the Series A, he said.

US bound

Despite the more recent pickup, European funding growth has lagged behind the U.S. since 2024.��

The leading San Francisco-based model companies — and — have raised $254 billion since 2023 and recentered the Bay Area post-pandemic as the place to be for ambitious founders.��

“The companies that start in the UK, France, Germany, and the Nordics, then come to Silicon Valley to grow,” said , managing partner at , speaking on current market trends.��

“You can build an amazing business anywhere in the world now. The barrier to building greatness has shrunk,” said McLoughlin, who himself relocated to�� San Francisco from the UK in 2010. “But, the chances of building a generational company are so much higher, if you come to the Bay Area.”

UK-founded incubator (EF) relocated to the U.S. in 2024. EF sources founders�� from leading universities around the globe to start companies but incorporates each business it funded in the U.S.��

“The Bay Area program is not just about proximity to capital,” said , CEO and co-founder on announcing EF’s recent fund raise. “It changes the ambition gradient. Founders move faster, think bigger and compete on a global stage from day one.”

“I’m seeing more than ever, companies that started in these emerging markets, and then going to the U.S. very early in their journey — not to sell themselves, but to sell to customers,” said , general partner at global investment firm . The firm invests on a global basis day zero at pre-seed, with its Elevate fund investing at later stages.��

“The time to copy a business is a month or two months, as opposed to years,” said Abdel-Nour,�� “You have an incentive to go and capture these big markets before your U.S. competition has really reached escape velocity,” he said.��

Related 91��Ʒ queries:

Illustration:

Two newly founded AI labs, both based in London and both with researchers from , raised large rounds out of the gate and made their 91��Ʒ debuts. The two companies, and , both raised large initial fundings out of the gate, though take very different approaches to training AI.�� They were joined by another new unicorn in the foundation AI sector: , an open-source model company from China with on-device smaller models.��

Six companies working on humanoid robotics —��five from China and one from Japan — also received billion-dollar-plus valuations last month. Quite a few of these companies are building models for robotic intelligence using simulated data.��

The financial services, defense, developer tools, energy and healthcare sectors each added two or three new unicorns in April.��

Of the 28 companies, 12 are U.S.-based and eight are from China. The UK counted two new unicorns last month, while Germany, Spain, Switzerland, India and Japan each added one.��

April’s new unicorns

Here are April’s new unicorn companies. Of the 28 companies, 26 are AI-related.��

Foundational AI��

• , a London-based AI lab using reinforcement learning rather than human-generated data, raised a $1.1 billion seed round led by and . The less than 1-year-old company was founded by of AlphaGo and . It was valued at $5.1 billion in its first funding.��
• London-based , a new AI intelligence lab with the goal of continuous learning improvement, raised a $500 million Series A led by and . Founded by DeepMind researchers and ’s 1 previous AI lead, the less than 1-year-old company was valued at $4.5 billion.��
• Beijing-based , an on-device foundation model developer, raised funding led by and . Its open source MiniCPM is deployed in automotives, smartphones, PCs and home devices. The 3-year-old company was valued at $1 billion.��

Robotics��

• Shanghai-based is a robotics AI company building a foundational model as well as hardware. It uses simulated training to create a model for grasping and spatial awareness. The 1-year-old company raised a Series A round and was valued at $2 billion.
• Shanghai-based humanoid robotics company raised a $513 million seed round led by and HSG. The 1-year-old company was valued at $1.9 billion.��
• Beijing-based , a hardware and software developer of models for robotics using simulated data, raised a $220 million Series B. The 3-year-old company was valued at $1.5 billion.��
• Shenzhen-based , a builder of humanoid and quadruped robots, raised a $200 million Series B led by and . The 2-year-old company robots will be deployed for traffic, security and retail. It was valued at $1.5 billion.��
• Shenzhen-based , a commercial robotics company for delivery and commercial cleaning, raised a $146 million funding led by and . The 10-year-old company was valued at $1.5 billion.��
• Tokyo-based , a humanoid robotics company to address public safety and urban maintenance, raised a Series A led round. The 1-year-old company co-founded by was valued at $1 billion.

Financial services��

• , which automates research for investment banks, raised a $160 million Series D led by . The 4-year-old New York-based company was valued at $2 billion.
• Bangalore-based , a consumer and small business lending service, raised a $220 million Series E led by , , and . The 8-year-old company was valued at $1.5 billion.��
• , a banking and expense management service targeting small businesses and solopreneurs, raised a $100 million Series C led by , and . The 5-year-old San Francisco-based company, founded by college dropouts at the time, was valued at $1.4 billion.��

Defense��

• Space defense company raised a $600 million Series D led by and . The company has built software for space operations and an autonomous orbital vehicle called Jackal. The 4-year-old, Colorado-based company was valued at $2.2 billion.��
• Defense aviation company raised a $200 million Series C led by Khosla Ventures. The 7-year-old El Segundo, California-based builder of autonomous aircraft was valued at $1 billion.��

Developer tools��

• , a web search provider for AI agents used by and , raised a $100 million Series B led by Sequoia Capital. The 2-year-old Palo Alto, California-based company was valued at $2 billion.��
• , an agentic software coding tool for enterprises, raised a $150 million Series C led by . The 3-year-old San Francisco-based company was valued at $1.5 billion.��

Energy��

• , developer of small nuclear reactors to provide direct power for AI data centers, raised a $340 million Series B funding. The 2-year-old El Segundo, California-based company was valued at $2 billion.��
• , a long duration energy storage battery provider, raised a $58 million Series C led by . The 12-year-old Bayern, Germany-based company that supports energy needs for grids, data centers and industry, was valued at $1.2 billion.��

Health care��

• Shanghai-based , a developer of a model for healthcare that includes computer vision and large language models, raised a $73 million Series A round. The 12-year-old company has built an assistant for doctors for screening, diagnosis and patient care, and was valued at $1 billion.��
• Switzerland-based , a developer of a peptide product to address enamel repair without needing surgery, raised a private equity funding led by . The 6-year-old company was valued at $1 billion.��

Data platform

• has built a semantic layer between data and agents necessary to interpret data and provide guardrails for AI. The 4-year-old San Francisco-based company raised a $120 million Series C led by and was valued at $1.5 billion.��

Manufacturing

• Shanghai-based , a collaboration tool to make factories more efficient, raised a $146 million Series D funding. The 10-year-old Shanghai-based company was valued at $1.3 billion.

Agentic AI

• , which builds agents trained on company data, raised a $80 million funding led by . The 1-year-old San Francisco-based company was valued at $1.3 billion.��

Aerospace��

• Madrid-based , which is building data from satellites tracking changes in the earth for various commercial needs, raised a $130 million Series B led by . The 6-year-old company was valued at $1 billion.��

Marketing & sales��

• , a provider of booking and customer service for the services industry using AI, has raised a Series B funding led by and . The 4-year-old New York-based company was valued at $1 billion. The company has raised $125 million in funding from seed through its Series B.��

Biotechnology��

• , an AI biotechnology infrastructure platform speeding up drug discovery, raised a $40 million Series E. The 8-year-old Waltham, Massachusetts-based company was valued at $1 billion.��

Waste management��

• converts unused food products into energy. It raised a Series C funding led by strategic partner . The 19-year-old Concord, Massachusetts-based company was valued at $1 billion.��

Related 91��Ʒ unicorn lists:��

• (1,756)
• (611)
• (128)
• (187)
• (118)
• (102)
• (896)
• (516)
• (239)
• (38)
• (477)

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• Billion-Dollar AI Rounds Push April To Third-Highest Startup Funding Month In A Year
• This Is A Momentous Year For Early-Stage Unicorns��
• The Rising Investors Behind The New Unicorn Class��
• While OpenAI Shattered Records, Robotics and Semiconductor Startups Quietly Added The Most New Unicorns In February��

Methodology

The 91��Ʒ 91��Ʒ is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 91��Ʒ data. New companies are as they reach the $1 billion valuation mark as part of a funding round.��

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.��

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. 91��Ʒ 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 91��Ʒ long after the event was announced, foreign currency transactions are converted at the historic spot price.

Illustration:

This increase was driven by large rounds to AI lab and Jeff Bezos’s , which is focused on AI manufacturing. The two companies raised $15 billion and $10 billion, respectively, together accounting for 45% of venture capital in April.��

Large rounds across multiple sectors

Billion-dollar rounds were also raised by Swedish green steel production plant , New York-based AI data operations provider , and London-based AI lab , which was founded by former employees.

Rounds $500 million and above were raised by Michigan-based modular electric pickup truck manufacturer , Colorado-based space defense company , Shanghai-based humanoid robotics startup , another London-based frontier lab, , and London-based , a global payments platform majority-owned by .

AI led

Artificial intelligence funding in April reached $37 billion, accounting for 66% of global venture investment last month.��

AI model companies raised the lion’s share of capital at $26.7 billion. Physical AI in robotics, aerospace, drones and autonomous vehicles represented around $5.3 billion. And AI infrastructure in semiconductor and data centers raised $1.8 billion.��

The U.S. once again dominated startup funding, with American companies raising $39 billion, or around 70% of global venture capital.

Public markets and GDP growth

The first quarter of this year showed the dominance of AI in both the public and private markets, and that continued into April.

As the hyperscalers , and topped analyst revenue expectations and continued heavy AI expenditures, around half of the 2% U.S. GDP growth in Q1 was due to AI buildout, per an estimate from Oliver Allen, an economist with .

That was mirrored on the private-market side. Global venture investment is up 139% year over year through April, per 91��Ʒ data, with nearly 60% of that capital going to just five companies backed by deep-pocketed public technology companies, private equity and venture investors.

Related 91��Ʒ query:��

Related reading:

• Q1 2026 Shatters Venture Funding Records As AI Boom Pushes Startup Investment To $300B��

Methodology

The data contained in this report comes directly from 91��Ʒ, and is based on reported data. Data reported is as of May 4, 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. 91��Ʒ 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 91��Ʒ 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. 91��Ʒ 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. 91��Ʒ 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: