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

91Ÿ«Æ· 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.Ìę

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.Ìę

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.Ìę

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.Ìę

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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His investment mandate primarily focuses on early-stage technology companies from seed to Series B that align with or impact the global telecommunications, network infrastructure and enterprise software sectors.

Under his leadership, AT&T Ventures targets investments in software, hardware and infrastructure sectors where AT&T’s network scale and internal engineering resources provide a distinct commercial or technical diligence advantage. Portfolio companies include enterprise and deep-tech firms such as , , , , and .

Prior to his current 12-year stint directing AT&T Ventures, Taneja spent more than two decades working across corporate development, venture lending and investment banking. He previously managed M&A and strategic investment activities for during ownership.

Taneja also served as a director at , where he focused on growth-capital debt and equity investments in mid- to late-stage technology businesses, as well as holding corporate finance and investment banking roles at and .

In an email interview with 91Ÿ«Æ· News, Taneja shares why he believes that while AI has drastically lowered the barrier to building software, it has also shifted the definition of seed-stage technical risk.

The new dynamics, in his view, gives AT&T Ventures an opportunity to differentiate itself by offering immediate, real-world technical validation and network integration rather than just capital.

The interview has been edited for brevity and clarity.

91Ÿ«Æ· News: If startups are building fully functioning apps by the seed round using AI, what does that mean for the traditional definition of technical risk? Is tech risk dead at seed, or has it just evolved into something else?

Vikram Taneja: The old definition of technical risk was “can they build it?” Although not entirely absent at the seed stage, I’d say it is becoming less relevant given the dramatically lower barrier to building software with AI tools.

But what replaced it is actually harder to answer: “Is the tech defensible?” Not just “does it work?” but “does it compound?”

Data moats, proprietary training sets, network effects built into the architecture — that’s the new measure of durability.

In prior cycles, technical complexity alone created some natural protection. As a result, the technical risk conversation has shifted to focus on how a company defends itself over the next three to four years, especially as frontier labs move down the stack into application layers and start targeting entire verticals.

Similarly, the distribution question shows up much earlier. “How can you get this to market?” is increasingly asked at the seed stage rather than later in the cycle.

We’re also seeing increased competition for investors to secure larger stakes at seed that they would have previously pursued at the A round. This is driving investors to be more thorough at the seed stage, and founders have to be prepared to meet higher expectations across the board.

When anyone can use AI tools to spin up a working app in a weekend, product execution happens fast, but moats can be incredibly shallow. At the seed stage, how are you separating a truly defensible platform from a beautifully executed wrapper?

Taneja: In early 2025, we saw a wave of AI wrapper companies built on top of frontier models like ‘s GPT, ’s Claude or LLaMA, and a lot of capital flowed into them. What’s changed is that frontier LLMs have now clearly started to take more of a platform approach — moving into the application layers and beginning to pick off the low-hanging fruit.

This is why defensibility becomes critical in AI investing. No platforms are totally defensible, but on some level, you have to ask that question now at the seed stage.

We’re looking for platforms using proprietary data that can’t be replicated by AI, companies that have embedded deep domain expertise — areas where general-purpose AI still lacks industry context — into their workflows, or highly specialized ecosystems or niche markets that provide another layer of insulation in categories that are too targeted for frontier labs to pursue directly.

Are you seeing a change in the actual headcount or makeup of seed teams? If AI handles the heavy lifting of the initial code, are these founders spending their seed capital on engineers, or are they shifting resources immediately to distribution and go-to-market?

Taneja: There is still an engineering focus in the early stage, as there should be, but we are increasingly seeing product, sales, or partnership roles becoming sought after earlier than in the past. And the reason is, as you stated, that it’s easier to build a working prototype, or even a production-ready application, so the focus very quickly turns to establishing trials with customers or exploring distribution paths to dial in the product features.

For strategic investors like AT&T Ventures, where we often do proof-of-concepts with potential portfolio companies, this is very exciting. We get a chance to work with companies earlier in their formation, can get real technical validation much earlier than otherwise, and can similarly try to find a path to collaborate more quickly.

AT&T Ventures has traditionally played heavily in the Seed to Series B space. If institutional VCs are rushing to seed to grab larger stakes because the tech is mature, how does that change the competitive landscape for CVCs? Are you finding yourself competing directly with traditional multistage funds earlier than before?

Taneja: The makeup of seed rounds has definitely changed. Multi-stage funds used to show up at Series A or B when there was enough traction to underwrite. Now they’re at seed because, as we discussed, the companies are mature enough, and they are trying to find winners earlier in the cycle. So yes, we’re in the same rooms as before.

But I’d push back on the idea that we’re competing directly.

A Tier 1 financial VC’s seed check and an AT&T Ventures seed check are different instruments. They are offering capital, brand, guidance and pattern recognition from backing hundreds of companies.

We’re offering something a financial VC structurally does not: our network teams working with your product in a production environment, oftentimes before we even write the check, for example. That’s free diligence running in both directions. We’re validating the company, but it’s also receiving a real-world signal from one of the world’s largest network operators.

For a seed-stage company that’s already solved the building problem and now needs distribution, that’s tangible value and complementary to what financial VC firms are providing. So that competitive pressure has actually sharpened our value proposition. It forces us to bring more than just capital to the table.

Historically, corporate partners want to see enterprise readiness, security compliance and scalability — things a seed startup rarely has. If a seed startup has a fully functioning product but is still a two-person team, can an enterprise like AT&T actually run a pilot with them, or does the corporate integration timeline become a bottleneck?

Taneja: It starts with strategic rationale. That has always been the entry point for us at AT&T Ventures, and that hasn’t changed. If that is in place, then it doesn’t always require full enterprise readiness to start a pilot. It can be a structured trial or a highly targeted engagement, depending on the company’s stage.

We have a number of ongoing proof of concepts with portfolio companies across areas such as AI-RAN, connected infrastructure and computer vision.

The key is clarity upfront — clarity on what the objective of the engagement is and how we measure success. Once that is clear, even early-stage companies can be integrated into a learning or testing environment without unnecessary delay. The goal is to make the AT&T relationship feel like an accelerant to further adoption.

If seed is the new Series A in terms of product maturity, are you seeing Series A pricing bleed into the seed round? How are you disciplined about valuations when the product looks like a Series A, but the company infrastructure is still very early?

Taneja: Seed pricing indeed looks different than maybe four or five years ago. We’re routinely seeing seed deals priced in the low- to mid-single-digit-million range at about $20 million to $25 million post-money. This is pretty much where Series A deals were a few years ago. But it’s not necessarily unjustified — the makeup and traction of seed-stage companies are much further along than predecessor vintages as we’ve discussed.

We stay disciplined by being explicit about what we’re actually underwriting. We’re not just underwriting the financial return on this round — we’re underwriting the strategic value of the relationship over a five- to 10-year horizon.

Does this company make AT&T’s network more intelligent? Does it open up a new customer segment? Does it validate a thesis we’re building around? Are there commercial opportunities beyond our initial thesis? When you frame it that way, it gives us a longer horizon to work with and provides multiple levers to pull.

And honestly, that’s where our engineering and product teams play a key role. They help us decipher whether the product that looks like a Series A is actually built like one, or whether it’s a great demo sitting on a foundation that hasn’t been stress-tested. That technical read bolsters our conviction when making investments.

A functional AI app at the seed stage still requires massive infrastructure. When you evaluate these early-stage companies, how much does their underlying architecture and how they handle data processing or edge computing factor into your decision?

Taneja: Architecture is a key part of our diligence process. The way we think about it really depends on the ultimate use case. Is it for internal use — i.e., a tool that AT&T will be working with in our environments — or is it something we’d be distributing or incorporating into some form of product offering?

If the former, all aspects of the architecture will be reviewed, and this is most likely to occur throughout trials and proof of concepts as we develop a technical understanding of the application or product. If it’s the latter, then we’re likely most interested in understanding how this product architecture scales over time and what it means from a cost, latency and infrastructure perspective. We love to see companies embracing edge-related technologies, but that doesn’t preclude us from working on applications that use traditional data processing methods.

You’ve spoken before about your interest in “physical AI” and robotics (like Apptronik). The software lifecycle is easily compressed by generative AI, but hardware and physical deployment take time. Does this “seed is the new Series A” trend apply to pure-play software strictly, or are you seeing AI accelerate physical tech and IoT at the early stage too?

Taneja: Physical AI is a sector we’ve been looking at quite a bit, particularly because inference and decisioning in autonomous systems, robotics and connected devices create a very different type of demand profile on networks.

The software layer is clearly accelerating — things like perception, control systems and decisioning are moving faster because of AI (the rounds show it!). That will ultimately help pave the way for the adoption of physical AI. However, the physical deployment cycle still takes time, so you don’t see quite the same level of time compression there.

What is interesting for us at AT&T is the intersection — how intelligence is moving closer to the edge and how that changes the way networks need to be architected to handle those workloads.

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• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo

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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 91Ÿ«Æ· 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 91Ÿ«Æ· 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 —Ìęwhat’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 91Ÿ«Æ·, 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.

91Ÿ«Æ· queries:

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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91Ÿ«Æ· 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.Ìę 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.Ìę 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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Size increase

Median seed round sizes have been climbing since 2023, 91Ÿ«Æ· 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 2018Ìę — 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 91Ÿ«Æ· 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, 91Ÿ«Æ· 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 91Ÿ«Æ· 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, 91Ÿ«Æ· 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, 91Ÿ«Æ· 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.

Related 91Ÿ«Æ· queries:

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.

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AI and software continue to draw the biggest share of startup investment, but most of the interesting companies that caught our eye in the past month were working on problems in the physical world, often far from the glow of a laptop screen.Ìę

They include a defense-tech startup that aims to bring manufacturing closer to the frontlines, a company working to recycle valuable raw materials from defunct solar panels at industrial scale, and a startup that wants to produce cell-based milk for the dairy supply chain. Let’s take a look.

$82M to build near the battlefield

A decade ago, defense tech was considered a niche and sometimes controversial corner of venture capital, with few startup investors daring to place bets on companies working with the military.Ìę

How times have changed. Already this year, $13.6 billion in venture investment has gone into companies in 91Ÿ«Æ·â€™s military, national security and law enforcement categories — more than 1.5x last year’s annual total.Ìę

is one of the latest defense startups to get some of that funding, with an approach that aims to bring manufacturing closer to the battlefield. The San Diego-based startup last month announced an $82 million Series B led by .Ìę

Firestorm builds expeditionary manufacturing systems and modular drones for military use. Its containerized “xCell” manufacturing platforms are designed to produce drones, replacement parts and other systems closer to the battlefield, a concept gaining traction as militaries rethink supply chains and logistics in contested regions such as the Indo-Pacific.

Existing and new investors including, , , , and others also joined its latest funding round, which brings Firestorm’s total funding to nearly $150 million, .

“The ability to produce, adapt, and sustain systems at speed and scale will define outcomes in future conflict,” , founder and chief investment officer at Washington Harbour Partners, said in a statement. “We’re excited to lead Firestorm’s Series B and back a company building a new model for manufacturing that replaces centralized supply chains with deployable, containerized units that can operate at the edge.”

The raise lands amid a broader surge in investor appetite for military tech, not just from defense-industry investors but also some of Silicon Valley’s biggest venture names. Sector heavyweight recently raised another $5 billion at a staggering $61 billion valuation in an – and -led round, underscoring just how mainstream venture-backed defense startups have become.

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$60M for a legal tech operating system

Legal tech has been one of the fastest-growing startup sectors in recent years, at least when measured by funding to the area, with venture investors pouring a record $4 billion-plus into the industry last year. That growth, of course, has been driven by AI’s rapid automation of many aspects of the notoriously paperwork-heavy industry.

Adding to this year’s tally is , a startup that says it’s building the operating system and brand for AI-native law firms. The startup said last month that it raised $60 million in Series A funding at a $750 million valuation from big-name investors. led the round and , and participated.

Manifest OS says it takes a different tack than most legal tech startups. Rather than sell software to traditional law firms that operate under a billable hour model, the company only caters to AI-native firms that charge clients based on outcomes.

“Companies want fee transparency, predictability, and speed,” , a Manifest investor and former general counsel for Ìę1, and , said in a statement. “Lawyers want to focus on delivering results, not justifying billable hours. Manifest OS’s model and use of advanced technology align those interests in a way the traditional system simply doesn’t.”

Along with AI software that helps attorneys with tasks like client communications, legal research, document drafting and billing, Manifest OS also offers a centralized back office to handle client intake, business development, paralegal work and other administrative tasks. That, according to the firm, frees attorneys up to focus on more complex legal work.

One important caveat: All firms that use its platform operate under the Manifest Law name. According to the startup, that results in a consistent brand presence, pricing, response time and service quality to clients. Its is a business immigration law firm.

The startup says it has already served 150-plus corporate clients, including large tech companies, since launching 18 months earlier. It has hired more than 100 attorneys to date, it said, less than 1% of those that applied.

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$23M for industrial solar panel recycling

French cleantech startup said last month that it has secured €20 million (about $23 million) in Series B and grant funding to tackle a growing problem: industrial-scale solar panel recycling.Ìę

By 2050, tens of millions of tons of solar panels are expected to become defunct, according to ROSI. The company’s technology recovers high-purity raw materials including silver, silicon, copper, aluminum and glass from those panels so that they can be recycled into new products.Ìę

ROSI said the new funding will be used to build its first large-scale recycling plant in Spain. The site will be able to process 10,000 tonnes per year.Ìę

The funding was led by , , and Spanish family office . Zurich-based corporate advisory firm , which specializes in deep tech, acted as strategic financial adviser and investor. Other investors included unnamed Swiss and Polish family offices.

“Our ambition is to build a European-scale industrial platform for circular management and the production of strategic raw materials, transforming end-of-life solar panels into a reliable source of high-purity materials for the European industries of tomorrow,” ROSI President and co-founder said in a statement.

The investment comes as cleantech funding has seen tepid investor enthusiasm in recent years. Overall funding to startups in 91Ÿ«Æ·â€™s cleantech-, electric vehicle- and sustainability-related categories fell to a five-year low in 2025. Still, some areas — including solar and recycling — have continued to see larger rounds.

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$2.3M for a cell-based milk supplierÌę

Venture investment in food and beverage startups has fallen precipitously in recent years, from more than $22 billion in the peak year of 2021 to . Companies working on cell-based alternatives to traditional sources of protein such as meat and dairy products, in particular, have largely fallen out of favor with startup investors, 91Ÿ«Æ· data shows.

That makes Montreal-based ’s recent $3.2 million CAD (roughly $2.3 million) seed round all the more interesting. The company, previously named BetterMilk, says it produces “complete milk” — with proteins, fats and sugars — from mammary cells in a bioreactor, without employing any cows.

Its recent round was led by , with participation from , , and existing investors including , and .

Rather than make a direct-to-consumer play, as many food and beverage startups have done, Opalia is positioning itself as a supplier in the food industry. The company recently inked a two-year deal with dairy supplier and a paid pilot with an unnamed “Canadian division of a leading global dairy group.”

“We see Opalia as a foundational player in the next era of dairy,” , managing partner at Nadarra Venture, said in a statement. “What sets them apart is a combination of highly credible, differentiated science and a clear, executable path to scale within existing dairy infrastructure, addressing the economics required to compete globally. Today, global demand for dairy is outpacing supply, and the traditional system is under increasing pressure from climate and resource constraints, making innovation no longer optional.”

Opalia plans to make its commercial debut in 2028 and said it’s currently working through the regulatory process in North America.

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$16M to automate the factory playbook

Mountain View, California-based last month announced a $16 million seed funding roundÌęto speed up what it calls one of manufacturing’s most stubborn bottlenecks: turning digital product designs into actual production plans.

The startup’s platform, dubbed AutoAssembler, plugs into existing CAD and PLM systems and uses AI to automate process planning, the painstaking engineering work required to determine how parts fit together, in what order they should be assembled, and how products can realistically be built at scale. C-Infinity says workflows that once took weeks can now be completed in minutes.

Its seed round was led by with participation from and

C-Infinity’s pitch taps into a broader trend gaining traction across industrial tech: software that doesn’t just analyze operations, but actively participates in physical production decisions. That kind of investment in physical AI — real-world applications of artificial intelligence, including in factories and on construction sites — has taken off this year.ÌęAll told, startups working on physical AI have already hauled in more than $37 billion in venture funding globally in 2026, , shattering the full-year records of $21 billion set in both 2025 and 2021.

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The result is a funding scene that is drawing sustained high activity from top-name venture firmsÌę as well as big checks from relative newcomers and megacap strategic investors. That’s reflected in April’s tally of most active U.S. startup investors, with rankings topped by well-established VCs such as and , tech giants like and , and a few names that don’t commonly show up in the lists.

To illustrate, we used 91Ÿ«Æ· data to aggregate most active investor rankings across multiple metrics, including venture deal count, lead rounds, and highest value lead rounds.

Most active venture investors

We’ll start with most active venture investors, looking at those who participated in the largest number of startup funding rounds for $5 million or more at any stage.

By this metric, came out on top, with 11 rounds. The famed accelerator commonly tops this ranking as it participates as a non-lead investor in follow-on rounds for startups it incubated.

Khosla Ventures and tied for second, with nine rounds each, followed by and with seven rounds each. Below we rank the top X most active venture investors for the month:

Neo’s inclusion is noteworthy as it’s a newcomer to the top of the ranks. The San Francisco incubator has an investment model that shares similarities with Y Combinator, providing mentorship and seed funding to upstarts and securing participation rights for follow-on rounds.

Another standout high in the rankings with six deals was , an investor focused on diverse and inclusive teams that has strong ties to the LGBTQ+ community. Gaingels has long been a prolific seed investor and participant in follow-on rounds. But as average deal sizes overall rise, Gaingels’ follow-on rounds have been getting bigger too.

Most active and highest spending lead investors

Among lead investors, Khosla Ventures took the top slot, with a lead- or co-lead role in seven April rounds exceeding $5 million. Andreessen Horowitz was the next-busiest lead investor, with five deals.

For a bigger-picture view, below we list the top X most active lead investors for April:

When we turn to highest-spending lead investors, the ranks shift some. This list looks at investors who led or co-led rounds with the highest aggregate value in April.Ìę

It’s not an exact tally of who put the most capital to work, given that syndicate rounds don’t break out each lead investors’ share. However, it does provide a general idea of the heaviest spenders.

For April, Google was the single largest lead investor, thanks to its reported $10 billion investment in , a deal that includes terms for another potential $30 billion to come. Amazon was next on the list, with a $5 billion Anthropic investment of its own, and up to $20 billion to come, as part of a for compute power.

Next on the list were two that don’t typically top our ranks: and . The two firms co-led a $1 billion April Series F for AI data and computing platform at a $30 billion valuation.

Overall, a bit slower

While the April active investor data doesn’t scream “slowdown,” the month was nonetheless somewhat less busy than March in terms of deal counts by top dealmakers. Given that AI enthusiasm has not dissipated and massive deals continue to get done, it’s not clear that is an indicator of changing investor appetites.Ìę

For now, we’ll avoid reading too much into month-to-month fluctuations and wait to see whether further tallies point to a broader change in the investment climate.

Related reading:

• Get To Know The Latest Class Of Ultra-Fast Fundraising UnicornsÌę
• Frontier Labs And Robotics Companies Again Top List Of New Unicorns In April
• Most Active And Highest-Spending Startup Investors Diverged In Q1Ìę

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As Ayoub describes it, while the medical profession was sold to him as a lucrative and stable career, the reality of modern healthcare hit home during a gap between his fellowship and his full-time role at . Living in New York City and unable to afford rent, he attempted to pick up extra shifts at a local urgent care. Despite a clear workforce shortage and his own need for income, he was told he couldn’t start for 90 to 120 days.

The culprit? A fragmented, manual credentialing process that acts as the industry’s primary bottleneck.

“The bottleneck is not the number of doctors, but the fragmented infrastructure connecting them to where they are needed,” said Ayoub, who also serves as an assistant professor of neurosurgery at the Donald & Barbara Zucker School of Medicine. “Most people assume the issue in healthcare staffing is a lack of doctors, but what we’ve seen is something different.Ìę

There’s a large, underutilized workforce that simply can’t move between systems efficiently,

So in early 2025, Ayoub and began pondering a solution. Their initial ideas eventually turned into , a startup with an AI-powered platform designed to serve as an “automated Dropbox” for physicians.Ìę

Today, the New York City-based startup is announcing it has raised $2.2 million in a pre-seed round led by , 91Ÿ«Æ· News reports exclusively. also participated in the round.

AI-driven healthcare takes off

AI-related healthcare has seen a significant rise in venture funding globally, 91Ÿ«Æ· shows. Investors put an estimated $14.9 billion into seed- through growth-stage funding to companies in AI-powered health tech categories in 2025, per 91Ÿ«Æ· data. That’s up significantly compared to the $8.6 billion raised in all of 2024.

Many of the recently funded healthcare startups are AI-centric, and, like Saile, are focused on streamlining dated processes.Ìę

In the current system, every time a doctor wants to work at a new facility — whether it be a hospital, a surgery center, or a telemedicine platform – he or she must manually resubmit a CV, licenses, and board certifications via email. In applying to an urgent care facility, Ayoub realized that while staffing agencies act as gatekeepers, the underlying infrastructure was broken.Ìę

There was no centralized way for a doctor to maintain a compliant status and share it instantly across different job verticals.

Saile aims to solve that problem by storing and tracking all a doctor’s credentials in one place and providing alerts before documents expire so that a physician can always be compliant. It goes one step further by providing access to a shift marketplace. In a nutshell, the startup serves as a portable credential passport for physicians to be identified and assigned patients at various hospitals.Ìę

The company’s five modular AI agents automate what currently takes months of manual coordination across recruiting, onboarding, credentialing, staffing and compliance.Ìę

By combining credentialing and staffing into a single infrastructure layer, Ayoub says Saile has shortened the onboarding timeline by roughly 45 days, from about 90 to 120 days, and reduced administrative tasks for healthcare facilities by an estimated 40%.

“Other solutions either focus on one piece of the problem or offer staffing tied to a single job type,” Ayoub said in an interview. “Saile owns the entire journey
And facilities get direct access to a pre-vetted pool of local and regional physicians without juggling multiple vendors or paying for the friction in between.”

Investing in the ‘infrastructure layer’

What began as a bootstrapped project fueled by word-of-mouth in a tight-knit clinician community has quickly gained momentum. The app has grown to nearly 5,000 active user physicians nationwide. Operating with a lean core team of four, the company plans to use its new capital to expand its AI agent infrastructure, grow its marketplace capabilities, and deepen integrations with facility credentialing systems.

Saile has four core revenue streams, with a primary focus on a per-seat SaaS model for facilities. The approach is to offer facilities access to the pool of physicians, and then charge on a per-seat usage basis for the workflow and credentialing infrastructure that supports it.

, founder and partner at Matchstick Ventures, said his firm was drawn to the founder market fit it saw in Saile.

“Marc had felt the pain of this problem and actually had built this more or less for himself out the gate,” Brosher said in an interview with 91Ÿ«Æ· News. “We love those combos where founders aren’t just randomly seeking out a solution to make a buck. This was very much a personal thing for him in the problem that he was solving.”

The firm also saw a “big” market opportunity in offering an “all-in-one” solution for doctors looking to pick up side jobs.

“People have tried to go after this a few different ways. They’ve either gone after credentialing, or they are a staffing agency,” Brosher added. “And when we look at this market, we feel like there needs to be disruption here
Ultimately, Saile is building the infrastructure layer beneath staffing. We feel like having that all-in-one infrastructure layer is actually where the real value is to be had.”

Related 91Ÿ«Æ· queries:

Related reading:

• 91Ÿ«Æ· Sector Snapshot: Funding To AI-Related Healthcare Startups Is Robust This Year

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This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here.

Large U.S. venture deals this week were led by a massive defense tech raise for space security startup . That theme continued with another two aerospace- and defense-related companies also getting major investor backing. We also saw sizable deals for startups applying AI to fintech, marketing, customer service, healthcare and developer tools. Let’s take a closer look.

1. , $600M, aerospace and defense: Centennial, Colorado-based True Anomaly raised a massive $600 million Series D led by and , with participation from a long list of other backers including , , , , and . True Anomaly develops space security and in-orbit defense systems, an area drawing increasing venture investor attention amid rising geopolitical tensions. The new round brings its total funding up to $1.1 billion, .

2. , $160M, AI and fintech: New York-based Rogo secured $160 million in Series D funding led by and joined by other investors including , , , , and . Rogo builds AI-powered tools to automate financial research and workflows. The latest financing brings its total funding raised to date to $314 million, . The deal is also the latest example of investor enthusiasm for startups targeting high-value knowledge work such as law and accounting.

3. , $150M, AI and marketing: San Francisco-based Hightouch raised $150 million in a Series D co-led by and . , , , and other investors joined. The company focuses on agentic AI-driven marketing and customer data activation. The round brings Hightouch’s total funding to date to and comes amid rising demand for AI tools embedded directly into enterprise marketing stacks.

4. , $125M, AI and customer service: New York-based Avoca brought in $125 million in a Series B led by and, with participation from other investors including , , and . Avoca develops AI agents for customer communication workflows. The new raise brings its total funding to $125.5 million, .

5. , $110M, AI and customer service: San Mateo, California-based Netomi raised $110 million in a Series C led by , with participation from and others, including individual investors , , and . The company offers AI-powered customer experience automation across channels. The new funding brings its total raised to date to $217 million, .

6. (tied) , $100M, developer tools: Palo Alto, California-based Parallel secured $100 million in a Series B led by , with additional backing from other big-name investors , and . The startup is building a suite of AI agents and developer tools to automate workflows. It has raised $260 million to date, .

6. (tied) , $100M, aerospace and defense: Sunnyvale, California-based Scout AI raised a sizable $100 million Series A led by and . A long list of other investors joined, including , and . The startup develops AI systems for aerospace and defense applications. Its large early-stage round underscores continued investor appetite for dual-use and defense-focused startups, which globally raised a record $7.7 billion in 2025, per 91Ÿ«Æ· data.

8. , $82M, aerospace and defense: San Diego-based Firestorm closed an $82 million Series B led by . also participated in this round, as did , , , and others. Firestone builds modular, mission-adaptable drone systems. It has raised nearly $150 million total, .

9. , $77M, health diagnostics: Cambridge, Massachusetts-based Iterative Health raised $77 million in a Series C led by and, with additional backing from , and . The company develops AI-powered diagnostic and clinical workflow tools, particularly in gastroenterology. It has raised more than $268 million since inception, according to .

10. , $75M, foundational AI: Investors continue to back next-generation foundation model startups. One of the latest is San Francisco-based AI research startup Standard Intelligence, which raised a $75 million Series A led by and . The raise comes at a $425 million pre-money valuation. Other investors in the deal include , and AI researcher . Standard AI is developing “computer-use” models designed to interact directly with software. Its approach — training on large-scale video data rather than manually annotated screenshots — aims to significantly reduce costs and improve performance.

Large non-US deals

We also saw several sizable deals for startups based outside the U.S.:

, $1.1B, foundational AI: London-based frontier lab Ineffable Intelligence raised a $1.1 billion seed round, the largest for a European startup on record. (The previous record was set just a couple of months ago, when Paris-based frontier lab raised a $1.03 billion seed round.) and led Ineffable’s seed funding.

, $300M, aerospace: China-based Volant Aerotech raised a $300 million Series C led by . The company is developing electric vertical takeoff and landing aircraft, or eVTOLs, designed to be used as taxis.

, $200M, robotics: China-based humanoid robot developer Robot Era raised a $200 million round led by , with participation from a long list of investors including and . The company is developing robots designed for industrial and service work, and follows a string of other large fundings for China-based robotics startups.

Methodology

We tracked the largest announced rounds in the 91Ÿ«Æ· database that were raised by U.S.-based companies for the period of April 25-May 1. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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In 2025, the Bay Area expanded its dominance of U.S. seed funding — capturing a growing share of both deals and dollars — even as most startups remained geographically dispersed, an analysis of 91Ÿ«Æ· data shows.

The result is a more bifurcated landscape: a handful of major hubs, led by San Francisco and New York, pulling in a larger share of capital, while the rest of the country saw its slice shrink.

The Greater Los Angeles area and the Greater Boston area are the next-largest hubs for seed investment after the Bay Area and New York, but their share of funding at this stage, as measured by dollars, has dipped 1 or 2 percentage points each since 2024.

Where seed funding is clustering

The Bay Area and New York remain the two central hubs for U.S. startup activity. The New York area has largely held steady as a seed funding center, while the Bay Area is pulling ahead, led by heavy investment in AI startups headquartered there.

On a dollar basis, the Bay Area captured 45% of U.S. seed funding in 2025 — up sharply from 33% in 2024 and 28% in 2023, 91Ÿ«Æ· data shows.

New York retained its typical share at around 17%, while Greater Los Angeles and Greater Boston each accounted for about 5% of total funding.

That growth is in contrast to the rest of the country. Startups outside the top four metro areas represented just 28% of U.S. seed funding in 2025, the lowest share on record and well below the 40% average seen from 2018 through 2024.

Startup distribution remains diverse

Still, geography tells a more nuanced story when looking beyond dollars. Two-thirds of U.S. seed-stage startups in 2025 were based outside the Bay Area, underscoring how distributed startup formation remains even as capital concentrates.

And beyond the top hubs, a long tail of smaller ecosystems — including Austin, Seattle, Miami, Chicago, Washington, D.C., Denver and San Diego — continues to produce a steady stream of new companies.

Another caveat: Strip out the largest seed rounds of $10 million or more, and the capital concentration looks less extreme. Without those outliers, the top four markets account for about 61% of seed funding amounts, within 2 to 3 percentage points, closer to historical norms.

Seed deal counts are also concentrating

While total seed funding has climbed, deal activity tells a slightly different story: Fewer rounds are getting done overall and a larger share of them are happening in the top hubs.

The Bay Area alone accounted for roughly one-third of all U.S. seed rounds in 2025, up 5 percentage points from the prior year, per 91Ÿ«Æ· data. New York has remained relatively steady at around 16% of deals since 2018.

Meanwhile, Greater Los Angeles and Greater Boston have each seen modest declines, falling to about 5% and 4% of seed deal share, respectively.

Taken together, the four leading metro areas made up 57% of U.S. seed deals in 2025, per 91Ÿ«Æ· data. The rest of the country accounted for 43%, a drop of about 5 percentage points from prior years.

Bay Area deal sizes shrink

Even as the Bay Area dominates in total capital and deal volume, it looks different on a per-deal basis. Median seed round sizes in 2025 were actually higher in other major hubs — including New York, Boston and Los Angeles — than in the Bay Area, which has seen typical deal sizes shrink since the market peak.

Overall, a more complex picture of the U.S. seed market has emerged in the past five years. Capital is concentrating geographically but not uniformly. The Bay Area is capturing more of the biggest rounds and overall dollars, but two-thirds of funded startups are still created outside of the region. And as a larger ecosystem, the Bay Area’s median seed round sizes were below the other leading hubs with fewer deals, but comparatively larger medians.

Related 91Ÿ«Æ· queries:

Related reading:

• Seed Funding Is Bigger Than Ever — And Harder To Get
• The Largest Recent Seed Rounds Are All For AI Companies
• Seed Funding Hasn’t Stalled, But It’s Skewing Larger And Is More Competitive Than Ever, 91Ÿ«Æ· Data Shows

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