It used to be that if you invested in SaaS, you slept well at night. Returns were predictable because the business model was subscription-based and incredibly scalable: build a horizontal cloud-based platform to target as wide a market as possible, charge per seat and grow by expanding the user base.

1, and their peers returned billions to investors on that model. But now, due to AI, where AI agents are replacing humans as the user (through what the industry calls 鈥渉eadless鈥� models) and upending the per-seat model, the SaaS market has lost its predictability. January’s $300 billion single-session wipeout is a leading indicator that the old SaaS model has passed its peak.

Investors are retrenching and trying to predict what鈥檚 next as the three frontier AI companies vault into the public markets at multitrillion-dollar valuations. We would argue that these infrastructure platforms enable the next wave of software innovation: AI-native software that automates and enables the $2 trillion white-collar services market.

Generic, horizontal SaaS, as we know it, is a declining legacy model (like on-premise software before it), but investors still have reason to be optimistic about the software market. That鈥檚 because AI-native software is going after a much larger opportunity than SaaS ever claimed and the productivity gains and value creation opportunities are unprecedented. The target markets are vertical industry focused and highly specialized, priced differently and built on proprietary data moats that didn’t exist five years ago.

Death of per-seat pricing

SaaS has always been priced on a per-seat basis. That model evaporates the moment AI agents generate most of the usage. A company that once needed 100 CRM licenses for its sales operations team may soon need just 50.

Technology companies facing that reality have to choose a new path forward beyond connecting people鈥檚 workflow: perform and charge for the actual work done (usage) or based on outcomes (ROI). A legal AI platform charges per contract drafted, doing the work of a lawyer. Here the software charges for some fraction of the labor it replaces. A spend management AI-native software application can take a percentage of overages found or a chargeback software application could take a fee on the value of the chargebacks it successfully recovers.

The next era of AI-native software runs on automation and performing knowledge-worker actions, not connecting workers or workflows. These solutions reach beyond IT budgets to much larger labor budgets. The companies that adapt will build faster, deliver more value and command a premium for it.

Horizontal is a liability

Generic horizontal SaaS is the most vulnerable to this changing market. If an entire product is a wrapper around a workflow that an AI agent can now handle autonomously, the value proposition may be greatly reduced. Form builders, project management platforms, SMB-focused CRMs, off-the-shelf social schedulers: these categories are compressing fast and may not recover.

The defensible positions now belong to vertical niche specialists, companies that have built what we call the three 鈥淒s.鈥� Distribution through a recurring and longstanding customer base.

Domain expertise specialized to operate in regulated or complex industries. Proprietary data that drives decision-making and is closely held by customers and inaccessible to frontier models.

When your product is built around the specific workflows, terminology and compliance requirements of one industry, ending a vendor relationship is less about migrating data and more about rebuilding a complex web of experiences, corner cases and historical knowledge. Customers stay not because they’re trapped, but because the cost of retraining, reconfiguring and finding a vendor who understands their world is too high.

The more deeply a company understands the regulatory environment, the operational constraints, and the institutional logic of a specific industry and a specific customer, the harder it becomes to displace.

Legal contract repositories, insurance underwriting criteria, bank loan performance data; once embedded in a model and a workflow, these assets create high switching costs that dwarf anything a generic SaaS contract ever produced. You can export a Salesforce contact list. You cannot export your underwriting logic.

People are part of the product

The model that will define the next decade of B2B software deliberately combines software and services, what practitioners call Human-in-the-Loop, or HITL: pairing agentic intelligence with human judgment at the points in a workflow where it matters most.

Legal, healthcare, cybersecurity, construction, financial services, defense; these verticals are defined by high stakes, regulatory complexity and contextual judgment. Routine and repetitive tasks may be mostly automated, but some portion of decisions will always require human judgement because the cost of errors or omissions is prohibitive.

This solutions-centric customer relationship changes what a software company fundamentally is. When a vendor is embedded in how a client operates, handling onboarding, workflow design, optimization and quality control, it accumulates something pure SaaS rarely achieved: proprietary data, domain expertise and institutional trust. Every client engagement makes the product smarter and each deployment deepens the moat.

This is why the most durable software businesses of the next decade will be built inside verticals, not across them. The companies that understand this will stop treating services as a cost of implementation and start treating them as a compounding asset.

A bigger market than SaaS ever was

Even capturing a small fraction of what projects is a $6 trillion annual productivity opportunity from AI transformation dwarfs the traditional enterprise software market. AI-native vertical platforms no longer just compete for the technology budget, they also compete for the labor budget, the compliance budget and the risk budget. That’s a much bigger pie and a more strategic partnership conversation than any per-seat SaaS vendor ever got to have.

The winners won’t be companies that bolt AI onto existing SaaS products, or that add a services layer as an afterthought. They will be the firms with true subject matter expertise that happen to run on AI-native software. They will collapse the boundary between software and services entirely, building businesses whose value compounds with every customer relationship and every data asset they accumulate.

The AI-native software company is a fundamentally different kind of company than the SaaS era ever produced. And it’s worth considerably more.

is the founder, managing partner and chair of the investment committee at . He launched Lateral with a strategy to allocate first institutional growth capital to independent, owner-operated middle-market businesses underserved by typical buyout firms. Previously, he served as a managing director at , a venture capital and growth equity firm that has invested in more than 300 companies including , , , , and . De Silva also previously co-founded , a marketplace for construction equipment that was sold to for nearly $800 million. He received an MBA from , a master of philosophy from the , and an undergraduate degree from .

Related SA国际传媒 query:

Illustration:

“Silicon Valley has done very well with software, but while software was eating the world, they forgot about silicon,” Barrett told SA国际传媒 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 鈥� not just software 鈥� 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.

鈥淪cience lets you follow breadcrumbs from prehistoric plumage to semiconductors. One principle can be applied somewhere orthogonal and create extraordinary value,鈥� Barrett said in a lengthy interview with SA国际传媒 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.

Gen茅 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鈥檛 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 鈥� and candidly, I feel the imperative 鈥� 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 鈥� 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 鈥斕齱hat’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 鈥� 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 鈥� which will replace transistors and semiconductors 鈥� 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 鈥� 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鈥檝e 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鈥檛 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鈥檙e 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 鈥� optical interconnects, memory systems 鈥� we have a best-in-class company at every point. We built the first AI accelerator a decade ago, and we鈥檝e 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鈥檙e 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鈥檚 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鈥檙e still 100x to 1,000x more energy-efficient on compute.

This technology has been around since last century, but it鈥檚 mainly been used for secure signals intelligence and radar applications. We鈥檙e 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鈥檝e been unable to train.

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

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

LLMs feel like brute-forcing something 鈥� like a drunk looking for keys under the streetlight. We鈥檙e pushing more and more into that, and I think that鈥檚 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鈥檙e 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鈥檛 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鈥檙e pretty, but not efficient. They shouldn鈥檛 be green; they should be black. We know how to make photosynthesis twice as efficient, and probably 5x more efficient.

We鈥檙e 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鈥檛 design things we can鈥檛 simulate. Our best computers cannot simulate the quantum nature of nature. That鈥檚 about to change.

We鈥檙e 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鈥檙e 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鈥檛 know how many things that animate our industry actually work. We don鈥檛 know how Tylenol works. We don鈥檛 know how the Type II superconductors we鈥檙e 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鈥檛 know why.

There are mysterious things we鈥檝e stumbled across that hint at an Aladdin鈥檚 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鈥檚 a long way off.

Data centers in space are stupid. You can鈥檛 operate a gigawatt data center in a thermos. We have terrestrial answers to those questions that we should pursue.

I鈥檝e 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鈥檛 drown our cities. The problem with transportation in cities is not the degree of autonomy. If we cared about traffic deaths, we鈥檇 worry about roundabouts.

There鈥檚 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 Unicorn Board, valued over $1 billion this past month, doing orbital data centers. Are you saying this whole category doesn鈥檛 make sense?

Barrett: To his credit, will show you a picture of what a 100-kilowatt data center looks like, and it鈥檚 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鈥檙e 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鈥檚 radiators and solar panels.

Starship has yet to actually put anything in orbit. It鈥檚 made some fireworks, which are pretty, and it鈥檚 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鈥檝e 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鈥檝e already made investments in things on a really steep trajectory.

Snowcap will take a decade before we鈥檙e building GPUs with that technology, but we鈥檒l have commercial product from them next year. We鈥檙e 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鈥檙e 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鈥檚 a sleeper in Fund I. Its first trick was to make MRI machines 100,000x more sensitive, and they鈥檙e shipping those. In the background they鈥檝e 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鈥檚 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 鈥� the only co-packaged optics on TSMC 鈥� we鈥檝e 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鈥檛 chatted about that you think is worth noting?

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

There are extraordinary pools of capital, pension funds and Australia鈥檚 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鈥檚 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鈥檛 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鈥檙e going to do more of that.

Do you feel encouraged by the amount of infrastructure build-out that鈥檚 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鈥檒l run LLMs on these data centers initially, but we鈥檒l run their descendants and other more useful things on these machines and on quantum machines.

It鈥檚 going to be hard to overbuild because computation is incredibly useful. There鈥檚 no upper bound. We鈥檙e 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鈥檙e 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.

SA国际传媒 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

Illustration:

For decades, the SaaS playbook was clear: predictable revenue streams, very high gross margins, efficient customer acquisition and strong net revenue retention made a startup very attractive to investors. These metrics built unicorns and defined how investors valued SaaS investments.

But today, with the launch of LLMs, and in the shadow of the 鈥淪aaSpocalypse,鈥� 30 years of relative SaaS stability has been shattered and the playbook is being rewritten with disappearing ink.

If you鈥檙e a SaaS founder 鈥� especially one raising capital 鈥斕齮his may lead to uncertainty and confusion. You may lose sleep because the whole market trajectory is uncertain. Investors themselves are trying to anticipate how the SaaS business model will change and, ultimately, what your company should be. To add further confusion, the model many VCs are championing (SaaS and services, anyone?) doesn鈥檛 look anything like traditional SaaS. So, what should a founder do?

Ignore the SaaS du jour

In a recent , partner argued that the next trillion-dollar company will be a software business disguised as a services firm, one that sells both tools and outcomes.

His logic is straightforward: For every dollar spent on software, six are spent on services. Meanwhile, LLMs are commoditizing many AI-native SaaS products before they even have a chance to scale. In this world, Bek argues, judgment 鈥� not software 鈥� is the scarce asset and customers will eventually pay for outcomes, not seats.

For founders, advice like this can be seductive to take. If software margins are compressing and AI is eroding moats, why not follow the trend, add services and open new revenue streams?

Founders need to be careful about taking this fashionable advice because it is greatly driven by investor anxiety and not as much by market reality. What VCs are really responding to are two separate concerns: how to reduce the risk that a portfolio company is disrupted by foundation models, and how to adapt to a new SaaS economy where software alone may no longer command the margins, defensibility or growth premiums it once did.

Founders should instead be prepared to answer this practical question: which parts of their business still matter, which parts have changed, and how do they need to adjust in that context. If the offering is not core to the operations of the enterprise, a pivot will likely be necessary.

The market reset is real, and yes it affects your pitch

The growth-at-all-costs mindset is gone. In its place, investors are laser-focused on capital and sales efficiency, gross and net retention, as well as Rule of 40, gross retention, CAC payback and burn multiple.

What this means for your pitch: A strong SaaS founder today must be able to demonstrate a sharp wedge, a clear buyer, strong usage, measurable ROI and a product roadmap that expands from point solution into platform.

The bar has moved from 鈥淐an this company grow?鈥� to 鈥淐an this company grow efficiently and organically, retain customers through budget scrutiny, and compound value as it scales?鈥�

AI startups can grow at unprecedented rates, but early hypergrowth can be misleading when switching costs are low and retention is unproven. Investors are excited by AI growth, but increasingly skeptical of AI novelty.

A demo is not enough. You need to prove AI creates durable workflow ownership, not temporary experimentation. Remember, if it takes less than a year to create a company using the current tools, without a sufficient moat, it will take even less time to create an even better company to compete with this one in 12 months.

Focus must be on creating a system of intelligence or a vertical operating system for an enterprise. Understanding workflows is critical. Features and functionalities are no longer sufficient.

Your pricing model is going to change

Seat-based pricing is no longer always the right answer. If your AI performs work independently, customers don’t need more seats to get more value. This is pushing the market toward usage-, consumption- and outcome-based models. notes that long-term pricing is shifting toward value-based and outcome pricing, and that continued cost-of-intelligence improvements could eventually help margins expand.

In the old SaaS model, value was tied to access: seats, users, departments. In the AI era, value is tied to outcomes. Software isn’t just helping employees do tasks anymore. It’s beginning to execute them directly: writing code, reviewing contracts, resolving support tickets, analyzing financial data, automating back-office workflows.

Have a big moat

Promising AI categories are attracting 2x to 3x more competitors than in prior years, while large SaaS incumbents are aggressively launching AI products, acquiring startups and hiring AI talent. Investors will ask you directly: what’s your moat? Is this a real defensible position, or a feature that 1, or can ship in a quarter?

AI expands the addressable market for software significantly. Traditional SaaS captured software budgets. AI-enabled SaaS can capture services spend, labor spend and outsourced process spend. Battery frames this as a major expansion from cloud software into services automation and human labor displacement 鈥� a much larger opportunity than prior SaaS waves.

Rules for the road

The market is open for exceptional SaaS companies. But the bar is higher, and investors have seen enough AI pitches to be skeptical of the theme. What they want to hear from you: A specific customer pain point with evidence of urgent demand; proof of retention, not just initial adoption; efficiency metrics that hold up under scrutiny; and a clear, concrete explanation of how AI improves your product, your business model and your customer’s ROI.

The founders who get funded in this environment will be domain experts who understand their customer’s workflow deeply, where AI can safely replace, augment or accelerate human work, and disciplined operators who understand the economic tradeoffs: when to use frontier models, when to use smaller specialized models, when to fine-tune, and when to preserve human review.

is managing partner at . He has more than 41 years of C-level global experience in the tech sector as a seasoned investor, entrepreneur, board member and educator focused on helping teams prepare for rapid growth, scaling and liquidation events.

Related reading:

• Why The Future Of SaaS Is Eat Or Be Eaten
• 3 Ways AI Is Transforming Venture Capital Investment

Illustration:

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

Big fundraising deals did not take a pause for summer this week. In the U.S., the largest financings went to enterprise software company and blockchain technology provider . The largest deals of the week, however, were for European companies, with Germany鈥檚 pulling in $1.4 billion and Finnish space tech company landing $520 million.

1. , $400M, enterprise software: NinjaOne, provider of an IT operations and endpoint management platform, raised over $400 million in Series C extension funding at a $12.3 billion valuation. The Austin-based company said it grew revenue over 70% in 2025 and posted a profit in the first quarter of this year.

2. , $355M, blockchain technology: Digital Asset, a provider of blockchain technology geared for financial institutions, secured $355 million in a later-stage financing led by 鈥檚 crypto fund, . Founded in 2014, the New York-based company has raised at least $847 million in known funding to date, per .

3. , $350M, AI cloud infrastructure: Las Vegas-based TensorWave, an AMD AI cloud technology provider for training and inference workloads, closed on $350 million in Series B funding. and led the financing.

4. , $300M, biotech: Beren Therapeutics, a developer of therapeutics for conditions characterized by defective cholesterol trafficking, raised $300 million in equity and debt funding. The financing for the Thousand Oaks, California-based company includes $165 million in debt funding from as well as $135 million in equity investment.

5. , $200M, robotics: Standard Bots, a manufacturer of AI-native industrial robots, picked up $200 million in Series C funding. and were lead investors in the round, which set a $1 billion valuation for the New York-based company.

6. , $125M, genetic medicines: SonoThera, developer of an ultrasound-mediated genetic medicine platform, secured $125 million in Series B funding. led the financing for the San Francisco-based company.

7. (tied) , $100M, medical devices: Tempe, Arizona-based GT Medical Technologies, developer of a form of radiation therapy called GammaTile that is used at the time of brain tumor removal surgery, picked up $100 million in Series E funding led by .

7. (tied) (aka Genspark), $100M, agentic AI: MainFunc, the company behind Genspark, a developer of agentic AI tools for the workplace, reportedly $100 million in Series B extension funding at a $2.6 billion valuation. Investors reportedly included , and South Korea’s .

9. , $99.5M, biotech: Cambridge, Massachusetts-based City Therapeutics, a developer of RNA interference (RNAi)-based medicines, closed on $99.5 million in Series B funding from backers including new investors and .

10. , $85M, tools for the deaf and hearing-impaired: Rylo, developer of an app for hearing-impaired people, raised $85 million in growth funding from , and existing investors.

Outside the US

, $1.4B, robotics: Germany鈥檚 Neura Robotics, a developer of AI infrastructure for robots to learn, collaborate and operate across real-world environments, says it secured up to $1.4 billion in Series C funding.

, $520M, space tech: Helsinki-based Iceye, operator of a satellite constellation for monitoring conditions on Earth, raised $520 million in a Series F funding round led by , at a valuation of over $12 billion.

Methodology

We tracked the largest announced rounds in the SA国际传媒 database that were raised by U.S.-based companies for the period of June 6-12. 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.

Illustration:

Not only is a round of $100 million not remarkably large anymore, it鈥檚 not even atypical. Per SA国际传媒 data, the median U.S. late-stage round this year was exactly $100 million.

Moreover, if $100 million is supergiant, what do you call something more than 1,000x bigger, like 鈥檚 record-setting round this spring? That company鈥檚 chatbot suggests terms such as “leviathan,鈥� 鈥渃olussus鈥� or 鈥渢itan.鈥� Another option would be to recognize that what was once a legit supergiant round is today just a humdrum, everyday kind of deal.

The $100M+ round over 10 years

The rise of the $100 million-plus round hasn鈥檛 been chronologically linear, as charted below:

Initially, the category gained traction in the late 2010s, as companies such as , and scaled up late-stage financing in advance of plans for public offerings.

Around the peak of the 2021 bull market, the volume of 鈥渟upergiant鈥� rounds hit a cyclical peak. Dealmaking fell in subsequent years before picking up again with the rise of the AI funding wave.

Notably, more money than ever is now going into jumbo-sized rounds. However, as capital gets concentrated among a handful of hot names, deal volumes remain well below the prior peak.

Still, trends are looking up. So far this year, investors have backed 250 startup financings of $100 million or more. That puts 2026 on track for a year-over-year gain in deal count. Capital raised, meanwhile, is already at record-setting levels thanks to giant rounds for OpenAI, 听and others.听听

Median round on the rise

In tandem, the size of the median late-stage round has also risen. Per SA国际传媒 data, the typical financing at this stage has roughly doubled since 2020, from just over $50 million to around $100 million.

And it鈥檚 not a small cohort either. So far this year, U.S. startups have secured 250 rounds of $100 million or more, per SA国际传媒 data. Of those, half were for $200 million or more. Eighteen were for $1 billion more.

Valuations moving higher too, obviously

Of course, you don鈥檛 get ginormous startup financings without rapidly escalating valuations as well. And this year has been exceptional in delivering those.

Among U.S. startups that raised $100 million or more this year, 21 had pre-money valuations of $10 billion or more, per SA国际传媒 data.1 Two of those 鈥� Anthropic and OpenAI 鈥� have filed confidentially for IPOs that could reportedly set valuations close to $1 trillion.

Bottom line: Startup investors aren鈥檛 just putting unprecedented sums into giant rounds;听 they鈥檙e expecting record-setting returns as well. We鈥檒l see in coming months if public markets deliver.

Related SA国际传媒 query:

Related reading:

• The Rise And Rise Of Supergiant Rounds
• Small Late-Stage Rounds Are Fading Away

Illustration:

For more than a decade, customers spent their software budget procuring vertical SaaS products. ACVs, or annual contract values, were modest, customer acquisition cost had to stay below a ceiling, and the resulting go-to-market playbook was product-led growth, SDR-led and content-driven.

With AI, many products are no longer SaaS but usage and outcomes based. They are replacing labor, not software. At my investment firm, , we call this new category of companies vertical AI. Vertical AI spend doesn’t just come from a customer’s software budget. It often comes out of headcount as well, a much larger line item. As a result, ACVs have jumped meaningfully to 6- and 7-figure deals.

I’ve written before about how AI for vertical SaaS, and how the value framing shifted from subscription pricing to. As ACVs have grown in vertical AI, the go-to-market motion is changing too. We’ve explored tactics to drive a more efficient sales process.

Here, I鈥檒l explore how the channels are changing as well.

Why direct sales is back

Direct sales has historically only worked at true enterprise scale. The cost of an AE’s time wasn’t warranted for smaller ACVs. Below a certain deal size, the math didn’t work for high-touch sales. That’s why SaaS GTM became PLG and SDR-led.

With vertical AI ACVs frequently landing in the 6- or 7-figure range, founders now have room to invest meaningfully in winning each logo. We鈥檙e also seeing these smaller businesses spending relatively more with quicker sales cycles which is enabling higher volume.

AEs, in-person sales motion, and other tactics that didn’t pencil at scale under old SaaS economics now do. Direct sales now works further down market where prior SaaS economics didn’t allow it.

Two channels in particular have driven a lot of distribution and success for vertical AI companies recently. They are distinct from each other but we’ve seen companies have success with both.

No. 1: Private equity and heads of AI

Many PE firms are actively pushing their portfolio companies to drive efficiency with AI. Some have even created a new role internally to spearhead these initiatives. These AI partners are often tasked with collecting and disseminating learnings, finding good AI tools, and connecting them into the portfolio if there’s a fit.

The motivation is sometimes EBITDA driven, but can also be softer than that. Many of these execs are focused on adding value across the portfolio, helping companies build AI competency, and coming up with an execution plan.

The decision making structure also varies. Sometimes the and push adoption down to the portfolio. More often, the firm will forward information to relevant company executives and leave the decision making to them. If executed well, this can be a very efficient channel for vertical AI companies. One introduction to the PE firm surfaces many qualified leads across their portfolio companies.

Usually, companies will land one customer initially. Positive feedback then travels in two directions. Laterally to peer companies within the portfolio, and back up to the PE investor, who introduces the vendor to others in the portfolio. We’ve seen this be particularly successful in industries where rollup strategies are popular like healthcare services, dental, MSP, accounting, legal, financial advisory, insurance brokerage, home services and industrial.

No. 2: Conferences

We’ve also seen sector and function specific conferences be incredibly valuable in driving distribution for vertical AI companies. The advantage is concentrated attention and self selection by the right buyer. Buyers are captive and open to learning.

They come to these events curious to hear what’s new in their sector. Attendance allows companies to meet the right buyer, showcase the product live, and collect leads at scale. Sponsoring and attending dinners is another opportunity to meet prospects.

I鈥檇 argue that scalability of lead generation and brand awareness matters more now than ever. That requires getting the word out about your own company but also cutting through the noise of others in the market. Buyers are actively building out their AI strategies so vertical AI companies should be sprinting on GTM. Companies need to be top of mind when potential buyers are open to evaluating new tools.

Whether that becomes a sole source decision or an RFP, the prerequisite is being part of the consideration set. In order to do that, your buyer needs to know you exist, and this is a great way to spread the word efficiently.

What this means

The GTM playbook for vertical AI now looks meaningfully different from the SaaS playbook it grew out of. Distribution, pricing and sales motion have all shifted in tandem, with each piece reinforcing the others. Buyer pull justified larger ACVs, larger ACVs justified deeper investment in the sales motion, and the new economics opened up channels that didn’t work under the old model.

The companies pulling away are the ones pairing a great product with the right GTM motion. They have recognized that bigger ACVs demand a different playbook, and they have adapted before their peers.

When the gates of distribution opened, everyone walked through. The companies winning now have figured out what to do once they were inside.

If you’re a founder building vertical AI and rethinking GTM, I’d love to hear from you.

听 is a general partner at , where she invests in and partners with early-stage founders from inception through Series A across sectors including AI, fintech, healthcare and enterprise software. Prior to joining Defy, Agarwal spent seven years at and began her investing career at . A former founder and operator, she previously co-founded two startups and started her career at

Related reading:

• New Value Models, New Playbooks: Tactics AI-First Companies Use To Scale

Illustration:

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

Startup investors were in a spendy mood this week, backing more than a dozen rounds in the multiple hundreds of millions. Of those, the biggest one went to spend-management platform , which closed on $750 million, followed by three $500 million rounds for companies in the AI and space tech sectors.

1.听, $750M, finance software: Spend-management software provider Ramp secured $750 million in a financing led by , 听and . The round set a $44 billion valuation for the 7-year-old, New York-based company.

2. (tied) , $500M, space tech: Redondo Beach, California-based Impulse Space, a developer of spacecraft and propulsion systems for transport, moving and orbital repositioning in space, raised $500 million in Series D funding. and led the financing which brings total investment to date to more than $1 billion.听

2. (tied) , $500M, AI developer tools: Supabase, provider of an open source platform for developers and AI app builders, closed on $500 million in fresh funding. led the financing, which set a $10.5 billion valuation for the 6-year-old, San Francisco-based company.

2. (tied) , $500M, foundational AI: New York-based Flourish, a startup working on artificial intelligence models inspired by the human brain, raised $500 million in initial funding. Backers include , 听and .

5. , $465M, fusion energy: Helion, a startup with a mission to build the world鈥檚 first fusion power plant, picked up $465 million in Series G funding led by at a $15.5 billion post-money valuation. The round brings total reported funding for the Everett, Washington-based company to at least $1.5 billion, per .听

6. , $435M, longevity medicines: NewLimit, a developer of medicines designed to restore youthful function in old cells through epigenetic reprogramming, closed on $435 million in Series C funding. led the financing for the South San Francisco, California-based company, which was co-founded by CEO .

7. (tied) , $400M, AI for music: Suno, a provider of AI tools for making music, raised $400 million in Series D funding led by . The round set a $5.4 billion valuation for the company, which is currently facing lawsuits from multiple music labels for training its AI on copyrighted materials.

7. (tied) , $400M, robotics: Generalist AI, a startup focused on using AI to enable robots to do complex tasks, picked up $400 million in new funding led by . The financing reportedly set a $2 billion valuation for the 2-year-old, San Mateo, California-based company.

9. , $350M, AI enterprise software: AlphaSense, an AI-enabled market intelligence and workflow orchestration platform, closed on $350 million in a new funding round led by , , , 听and . The round set a $7.5 billion valuation for the New York-based company.

10. , $300M, defense tech: Defense tech startup Mach Industries raised $300 million in Series C funding at a $1.8 billion valuation. and led the financing for the 3-year-old, Huntington Beach, California-based company.

Methodology

We tracked the largest announced rounds in the SA国际传媒 database that were raised by U.S.-based companies for the period of May 30-June 5. 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.

Illustration:

The market believes software businesses can鈥檛 charge premiums anymore and it predicts slowing growth indefinitely.

There are two reasons. First, AI powers a 10x decrease in software production costs. Second, these AI capabilities enable a huge wave of new competitors, both VC-funded startups and in-house solutions.

Reduced software production costs and rising competition, they say, will eliminate software鈥檚 pricing power. Public software stocks traded down 20% this year through mid-May, and for the first time in history, software trades at a discount to the average S&P500 multiple on earnings. SaaS is dead.

It is true that AI has brought falling costs and rising competition. But it does not follow that SaaS is dead. Lowering the cost to produce software does not mean that software revenue will shrink. In fact, history suggests the opposite.

In certain cases, efficiency begets consumption. This is the lesson of the . It worked for coal engines, it worked for data centers, and it will also work for AI-powered software.

The Jevons Paradox

Let鈥檚 start with coal. In 1860s Britain, many worried about burning through the country鈥檚 coal resources too quickly. Conventional wisdom said that developing more-efficient coal-burning engines would make the coal last longer.

But economist William Stanley Jevons recognized that more coal-efficient engines would cause an increase in demand for coal energy with the result that Britons would burn through their coal more quickly, not less.

Jevons was right. When greater efficiency produced lower costs, it also unlocked enormous new demand. This consumed coal reserves faster, not slower.

Twenty-five years ago, I joined a buyout firm during the 2001 dot-com crash. My first investment was in a troubled datacenter company. Exodus Communication, which reached a peak market cap of $32 billion, then went through bankruptcy twice as datacenter demand continued to fall.

In 2004, I recommended that our firm acquire that datacenter business out of the second bankruptcy for $200 million and merge it into a competitor named Savvis.

At the time, the market considered datacenters a shrinking industry. Dot-com companies were pulling racks of servers out of the sites, and datacenter floors were emptying out. Industry analysts forecast that given Moore鈥檚 law about the exponential growth of chip capacity and increasing server power density, a single rack in 10 years would deliver what it took 100 racks to deliver in 2005, and that in 20 years, a rack would deliver what 10,000 racks delivered in 2005. Conventional wisdom said that more-efficient chips would require less datacenter floorspace over time.

Our thesis that demand for datacenter floorspace would grow was not a popular opinion at the time. If 10,000 racks in 2005 would be replaced by just one rack in 2025, didn鈥檛 the U.S. have plenty of datacenter floorspace already?

was running advertisements showing a room full of servers replaced by one mainframe. One skeptical investment committee member told me that this business had been through bankruptcy twice in two years, and that if it went through a third time, I would go with it.

Today, one rack can indeed deliver 20,000x the compute power of racks from 2005, and as everyone knows, far from having too much floorspace, we can鈥檛 build new datacenter capacity fast enough. Truly enormous latent demand for computing power was unlocked as rack efficiency increased. The Savvis story ended well too, sold six years later for $3.2 billion.

The Jevons Paradox was true for coal, and it was true for data centers. It will also be true for AI-supported knowledge work.

Knowledge work and market expansion

Twenty-five years ago, only the wealthy had access to personalized investment advice. In 1996, Nobel Laureate Bill Sharpe co-founded to bring personalized investment advice to anyone with a 401(k).

My firm was an investor, and I had the privilege of working closely with the company. At first it tried to sell advice about how to invest 401(k) funds, but only about 20% of employees were interested in taking advice and then managing their 401(k) positions themselves.

Financial Engines鈥� breakthrough innovation was to manage the 401(k) positions directly, not just advise. Employees could check a box: 鈥渄o it for me鈥�. The demand from people who previously had no access to this advice was beyond all expectation and did enormous good. I recall that an early customer was , whose tens of thousands of employees with an average age of 27 years had approximately 40% of their 401(k) monies in cash, 40% in stock of JCPenney (which would eventually file for bankruptcy in 2020), and 20% in everything else. Just moving them into sensible low-cost mutual funds appropriate to their age and other financial goals generated huge benefits.

Financial Engines went from zero to $169 billion in assets under management when it was acquired in 2018 for $3 billion.

The company delivered a service that is very similar to what we today would call agentic AI. The customer (an employee with retirement savings) was delegating a decision (invest my money) to a computer system, and the employee paid in a way tied to the outcome (~50 basis points on AUM).

Of course, the technology to deliver this was quite different, and this was a very narrow application. The lesson remains: Software enabled a massive efficiency in delivering knowledge work (in this case individual investment advice) and a huge latent market appeared to buy the service.

The end of the rationing of knowledge work

The increased cost efficiency of AI, like the increased cost efficiency of Financial Engines鈥� algorithms, allows demand to increase because it relaxes a supply constraint on knowledge work.

Across human history, even to today, knowledge work has always been rationed because it is supply constrained.

Knowledge workers take years of education and training, tend to want to live in high-cost places, over time want to work on only certain kinds of problems they find interesting, and require a lot of management to get along. That is why we pay them such high wages and do everything we can to make them more productive.

Business software is a tool to make knowledge workers more productive. The total business software market in the U.S. is on the order of $0.5 trillion per year, according to Gartner. The U.S. market for knowledge work, that is the amount paid to the 100 million knowledge workers in this country, is roughly $10 trillion, per numbers. Currently, we spend about 5% of the cost of knowledge workers on software tools to help them.

AI enables software companies to not just sell tools to knowledge workers, but to begin to sell the knowledge work outcomes themselves, as I have written about in prior SA国际传媒 articles.

Put those together: 90% cost compression in software development plus the ability to sell knowledge work. We know there is a huge latent demand for knowledge work, if only it were not so expensive and hard to access.

For the first time, millions of people and businesses who have never had access to a strategist, an analyst, a lawyer or a financial adviser are about to get one.

Software is far from dead. The increase in efficiency offered by AI will allow it to do much more for less, and just like a more efficient coal engine or data center, this will unlock huge latent demand for knowledge work.

Ultimately, this will increase revenue and the strength of software businesses that use AI to further improve knowledge workers鈥� productivity or deliver knowledge work outcomes directly. Software鈥檚 job today is to solve the problem of delivering this safely and reliably.

It was no small task for industry to learn how to manage knowledge workers who are human, and it will be just as big a task to learn how to manage those who are machine knowledge workers. That is the challenge.

But remember that today the market for knowledge work is 20x the size of the market for software. The scale of the prize for software companies is unlocking the latent demand for knowledge work that, if history is any guide, will dwarf today鈥檚 software market.

The market today fears that the efficiency delivered by AI will shrink the software industry. Exactly the opposite is true. AI will unlock massive latent demand for knowledge work, and the software market will explode. Long live software.

co-founded in 2014 and is managing partner. He has served on numerous private and public technology company boards, and currently is a director of , , , , and . Previously, he was a partner and member of the investment committee at . He also worked at and . Morse serves on the board of directors of and as member of the advisory board for the HMTF Center for Private Equity Finance at . He attended , graduating summa cum laude with a BSE, and , where he earned his MBA and was an Arjay Miller Scholar. Morse lives in Austin.

Related reading:

• Don鈥檛 Just Talk About AI. Measure Business Outputs. Here鈥檚 How.
• The Rise Of The AI Executive
• Invention To Innovation: Making Sense Of AI鈥檚 Moment

Illustration:

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

Venture funding has always been a world of haves and have nots. And these days, the haves are having more than ever. Case in point this week was . The 5-year-old generative AI giant secured $65 billion in Series H funding this week, pushing its post-money valuation to a mind-blowing $965 billion.

After that, the next-biggest financing was a $1 billion round for AI software development tool maker , lifting its valuation to $26 billion. Companies in a range of other sectors also managed to secure sizable though smaller rounds, in areas including commerce logistics, developer AI, insurtech, fusion and more.

1. , $65B, foundational AI: Generative AI company Anthropic raised $65 billion in a Series H funding round, more than doubling its post-money valuation to a staggering $965 billion. San Francisco-based Anthropic said , , and led the financing, and that , , , , and co-led the investment.

2. , $1B, AI software development: Cognition, developer of AI software engineer Devin, has closed on over $1 billion at a $26 billion valuation. , , and 1听led the financing for the San Francisco-based company.

3. , $250M, logistics: Atlanta-based Stord, developer of a fulfillment network, software and AI tools for independent brands, secured $250 million in Series F funding. The round set a $3 billion valuation for the 11-year-old company.

4. , $113M, AI for developers: OpenRouter, a marketplace for AI models, secured $113 million in Series B funding. led the financing for the New York-based startup.

5. , $106M, insurtech: San Francisco-based Corgi Insurance, developer of an AI-native insurance platform for startups, picked up $106 million in Series B1 funding led by . The financing, which set a $2.6 billion valuation, comes just three weeks after Corgi $160 million in Series B funding at a $1.3 billion valuation.

6. (tied) , $100M, fusion energy: Kearny, New Jersey-based Thea Energy, a developer of technology for fusion energy systems, raised $100 million in Series B funding led by . Thea says the funding will go toward manufacturing infrastructure.

6. (tied) , $100M, healthcare data: Garner Health, a platform for finding healthcare providers, closed on $100 million in Series E funding led by . The financing set a $2.74 billion for the New York-based company.

8. , $90M, space tech: Observable Space, a space tech startup that develops and builds advanced optical systems, says it raised $90 million in Series A funding led by to scale manufacturing and develop its technology. The Santa Monica, California-based company also announced that it secured a $94 million contract with the.

9. , $59M, AI video: Reactor, a San Francisco-based developer platform for real-time generative video, emerged from stealth with $59 million in funding led by .

10. , $52M, cancer detection: San Diego-based ClearNote Health, a developer of early detection and monitoring tests for multiple forms of cancer, picked up $52 million in Series D financing. Founding investor led the round.

Methodology

We tracked the largest announced rounds in the SA国际传媒 database that were raised by U.S.-based companies for the period of May 23-29. 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.

Illustration:

The global innovation economy remains largely defined by agglomeration dynamics. Worldwide, 19 ecosystems dominate the innovation landscape, increasingly concentrating innovation demand (corporates) and supply (scaleups) 鈥� attracting further growth capital (investors).

Meanwhile, other ecosystems struggle to achieve a meaningful presence on the global innovation map and are at serious risk of technological disruption and economic downfall.

Yet something is happening below the surface. Over the past decade, the composition of the Global Innovation Ecosystems Life Cycle Curve changed dramatically, as the number of scaleup ecosystems worldwide has more than doubled.

The trend is not stopping just here: we expect these figures to even triple in the coming years.

In this new scenario, emerging innovation economies hold the potential for disrupting the agglomeration paradigm, toward a new scheme of interconnected networks of specialized local innovation hot spots.

Among them, there is also Africa. While the continent still lacks ecosystems at the most advanced stages of maturity, it now counts four ecosystems at the startup stage and 40 at the standup stage, compared with respectively 25 of those 10 years ago, according to by my organization, , in collaboration with and .

Africa: the awakening giant of the coming decade?

As of today, Africa鈥檚 innovation economy includes 883 tech scaleups that have raised a combined $24.7 billion. Despite this progress, the continent still represents only about 1% of global figures.

The African innovation landscape remains highly concentrated around four main hubs: South Africa, Egypt (North-East), Nigeria (West Africa) and Kenya (East Africa). The North-Western corner of the continent still lacks a dominant hub, although Tunisia, Morocco and Algeria remain the leading candidates.

A testbed for clean technologies?

Emerging innovation economies that thrive on the global innovation map typically build on top of highly specialized, unique local strengths.

Our recent analysis has identified clear evidence that Africa holds significant potential over the development of clean energy systems and technologies.

The relative prominence of the cleantech sector in Africa is evident from the data:

• Africa is home to 95 cleantech scaleups, representing roughly 11% of the total scaleup base.
• Collectively, they have attracted approximately one-fifth of all capital deployed to African ventures.
• Cleantech has also generated a disproportionate share of high-growth leaders, accounting for around 20% of both scalers (scaleups that raised more than $100 million) and super scalers ($1 billion-plus).

Within cleantech, a highly specialized vertical is also emerging, what we might call 鈥済ridtech鈥�:

• It comprises 16 scaleups (17% of the cleantech total) and two scalers (25% of total).
• It has attracted around 30% of total cleantech funding.
• Africa鈥檚 sole cleantech tech giant, Kenya-based , operates within this gridtech vertical.

That said, the numbers still point to a gap.

The elephant in the room

The main challenge is the grid infrastructure deficit, which remains the primary bottleneck to scaling energy system technologies. As shown in the map below, Africa鈥檚 grid infrastructure is highly fragmented: High-voltage networks are concentrated in a few densely populated areas, while large parts of the continent remain largely disconnected.

As a result, grid infrastructure development and electrification are key to unlocking Africa鈥檚 growth 鈥� consider that Africa still accounts for only about 5% of global energy supply 鈥� and its innovation potential.

At the same time, the continent holds world-class renewable resources, including approximately 13% of global technical hydropower potential and around 60% of the world鈥檚 best solar resources.

Africa鈥檚 energy system is expanding, but fully unlocking its economic and innovation potential will depend on accelerating electrification and strengthening grid infrastructure.

Blended finance will be critical to enable this growth. Both private and public capital are required: private capital drives innovation, while public finance enables foundational infrastructure such as grid expansion.

In particular, private capital needs to be complemented by structured public finance initiatives to address the inherent limitations of a relatively small domestic VC market, which remains heavily focused on early-stage investments.

Public capital will be essential for infrastructure development. In gridtech especially, public investors are expected to account for up to about 80% of total investments by 2030, reflecting the capital intensity and risk profile of grid infrastructure.

International capital still dominates the market, with approximately 69% of active investors originating outside Africa, underscoring continued reliance on foreign capital despite growing local participation.

Get the full story in our report:

is chairman of and a professor at . He is a serial entrepreneur who has started three startups in his career, the last of which is , among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects 鈥� venture clients, venture builders, intrapreneurship, CVCs 鈥� with large multinational companies, as well as advising and training on this subject. Onetti has a column on () and several other tech blogs.

Photo by on .