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, Iā€™ll 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. Weā€™re 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.

Iā€™d 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.

Ģż 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

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This is a weekly feature that runs down the weekā€™s top 10 announced funding rounds in the U.S. Check out last weekā€™s 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.Ģż, $750M, finance software: Spend-management software provider Ramp secured $750 million in a financing led by , , and . The round set a $44 billion valuation for the seven-year-old, New York-based company.

2 (tied).Ģż, $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.Ģż

2 (tied).Ģż, $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 six-year-old, San Francisco-based company.

2 (tied).Ģż, $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 , , and .

4.Ģż, $465M, fusion energy: Helion, a startup with a mission to build the worldā€™s 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 Crunchbase data.Ģż

5.Ģż, $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 .

6 (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.

6 (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 two-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 , , and , , 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 three-year-old, Huntington Beach, California-based company.

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The market believes software businesses canā€™t 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 softwareā€™s 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

Letā€™s start with coal. In 1860s Britain, many worried about burning through the countryā€™s 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 Mooreā€™s 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, didnā€™t 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 canā€™t 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 Enginesā€™ breakthrough innovation was to manage the 401(k) positions directly, not just advise. Employees could check a box: ā€œdo it for meā€. 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 Enginesā€™ 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 Crunchbase 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 workersā€™ productivity or deliver knowledge work outcomes directly. Softwareā€™s 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 todayā€™s 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.

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This is a weekly feature that runs down the weekā€™s top 10 announced funding rounds in the U.S. Check out last weekā€™s 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 1Ģżled 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 Crunchbase 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.

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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) ā€” 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, Africaā€™s 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 ā€œgridtechā€:

• It comprises 16 scaleups (17% of the cleantech total) and two scalers (25% of total).
• It has attracted around 30% of total cleantech funding.
• Africaā€™s 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, Africaā€™s 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 Africaā€™s growth ā€” consider that Africa still accounts for only about 5% of global energy supply ā€” 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 worldā€™s best solar resources.

Africaā€™s 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 ā€” venture clients, venture builders, intrapreneurship, CVCs ā€” with large multinational companies, as well as advising and training on this subject. Onetti has a column on () and several other tech blogs.

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Defense, energy, robotics and government have historically been classic no-go areas for VC investment. These ā€œhardā€ industries have slow procurement cycles, tight regulatory oversight and high-friction customer migration in common. Legacy software vendors serving them have benefited from a barrier of complexity to innovate slowly without facing the risk of customer churn.

This made the victims of this yearā€™s AI anxiety-driven sell-off all the more dramatic. Software juggernauts serving heavy industries ā€” , , , ā€” have gone from safe bets to being the subject of investor scrutiny.

While headlines have attributed that sell-off to quick-fire launches of tools for vertical industries, thereā€™s more at play. The macro trend is a newfound founder enthusiasm to build AI-native entrants in legacy industries, and the backing theyā€™re enjoying from VCs that can see the once-in-a-generation opportunity to disrupt entire industries.

Why investor perceptions are changing

Context is important. Geopolitical instability, supply chain pressure and energy security concerns have placed industrial resilience at the center of national policy.

Be it the U.S. or across Europe, policymakers are prioritizing investment in grid upgrades, transportation networks and public sector infrastructure, while also re-examining procurement and compliance systems that have slowed the adoption of emerging technologies that could bring said industrial resilience about quicker.

At the same time, quick advances in AI and agentic systems make it possible to build a new class of AI-native software tailored to ā€œhardā€ industries through deep integration with verticalized tooling and specialist automation of critical workflows.

Age-old incumbent moats, like cumbersome migration cycles that put businesses off moving to new software providers, are also being challenged as embedded automation cuts migration processes down from weeks to days.

The creation of software in and of itself has become commoditised in the AI era, and more investors are spotting that operational depth, intuitive UI/UX, speed to market and seamless integration into complex real-world systems are traits of high-quality vertical software that startups are well-placed to build.

Investors are also realizing that most of the available value from horizontal SaaS has been extracted. In those early post-ChatGPT years, VCs widely backed AI companies building for non-regulated SMB adoption ā€” exactly the audience that foundational model players like and Anthropic are now making inroads with as they push into enterprises. Foundational models are general in nature, and their verticalization can therefore only stretch so far. Given this, AI-native products built for heavy industries are compelling and competitive propositions for VCs.

Growing faith that incumbents are vulnerable

Thereā€™s always been lots of skepticism among investors and tech executives that AI startups can meaningfully challenge incumbents that have been on top for decades. But those companies are operating over sprawling product architecture and processes that were built in the pre-AI era.

Pivoting from that state of affairs to AI-native systems is a massive undertaking, whereas new companies are being launched with those systems in place from day one. Incumbents also have a low incentive to innovate at pace when customer churn is limited. But in the current context of breakneck speed improvements to AI models and agentic systems, waiting for churn to show up will be too late.

Scepticism also risks overlooking the profile of outstanding founders building AI-native challengers. Some of the fastest-growing startups in defense, energy, government and the public sector are led by people who came directly from the same industries they are transforming. Their understanding of sector constraints and operational realities gives them an advantage over general software providers that lack the same specialism and experience.

Picking up pace

Savvy entrepreneurship and VC investors are colliding to make a play for hard sectors. Once seen as off-limits due to procurement complexity or regulatory burden, these sectors represent huge, untapped potential in the new AI-native era.

The emerging companies offering solutions designed for these industries with deep, vertical-specific tooling integration and critical workflow automation are well placed to command a growing share of overall AI funding as they serve customer pain points that have gone unanswered for years.

We are talking about disruption within markets worth trillions. The scale of the opportunity for growing VC interest in sectors theyā€™ve historically avoided is no mystery or miscalculation. The vision is an ambitious one. Rather than simply building better software, the foundational sectors of the world economy are about to be reimagined.

is a partner for the U.S. and Europe at early-stage venture capital firm . He currently oversees the deployment of the firmā€™s latest $1 billion fund, backing a range of AI-native startups building to disrupt legacy industries and business processes. In a personal capacity, Cuvelier wrote an angel check for at pre-seed.

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At least not these days. The space and AI pioneer posted a net loss of $4.28 billion in the first quarter of 2026, up more than 700% from a year ago. Revenue, meanwhile, totaled $4.69 billion in Q1, up 15% from a year ago.

As a public company, SpaceX is reportedly seeking a valuation of around $1.5 trillion or more, . Itā€™s aiming to raise up to $80 billion or more in the offering, which would make it the largest IPO in history.

At its target valuation, SpaceX would join a rarified club of just seven U.S. public technology companies with market caps of $1.5 trillion or more. Of those, just five have crossed the $2 trillion mark.

Of course, those companies took time to grow into their 13-digit valuations. But at some point, they too made their first public IPO filings. And they too had revenue.

The similarities end there. For a sense of how SpaceX compares at IPO time to other members of the trillion-plus-club, we took a look at their original S-1s from the 1980s and onward. Hereā€™s what their numbers looked like just before their public market debuts:

: Today, the Silicon Valley chip designer is a $5.3 trillion market cap company. Anyone who invested in its 1999 IPO, needless to say, has done extraordinarily well.

At the time of its market debut, of course, such a trajectory was not obvious. Still, it looked like a solid bet. The company, which then focused on designing 3D graphics processors for the PC market, had $93 million in revenue for the three reported quarters prior to its IPO, growing severalfold year over year. Over the same period, it posted a modest $3.5 million loss.

: Google was already the dominant player in online search when it went public in 2004, with impressive financials to boot. Revenue for the first half of that year totaled $1.35 billion, more than doubling in a year, paired with a $326 million profit.

While that was impressive, so is Googleā€™s ongoing growth. Currently, its market cap is $4.7 trillion and it posts more than $400 billion in annual revenue, with massive profits as well.

: The iconic smartphone and computing giant knows a thing or two about longevity. Apple turned 50 last month, and it went public over 45 years ago, in 1980.

It was an impressive and attention-getting offering for the time, with $118 million in sales and nearly $12 million in profit. It helped that Apple was already a prominent consumer brand at the time due to its popular home computers. These days, its market cap hovers around $4.5 trillion.

: Microsoft went public in 1986, so itā€™s had some 40 years to grow into its current $3.1 trillion valuation. But even back in the era of big hair and floppy disks, the software giantā€™s IPO prospectus showed clear signs this would be no ordinary market entrant.

In the year before its IPO, Microsoft had revenue of $140 million and net income of $24 million. That income figure, however, includes stepped-up spending on marketing and R&D. Without those expenses, profit margins looked astoundingly high for a time before software business models were status quo.

: At the time of its public offering in 1997, Amazon was known as an online bookseller, branding itself as “Earth’s Biggest Bookstore.” All the other stuff came later.

Still, it was a compelling offering at the time, with Amazon growing annual sales from zilch to around $16 million in just two-and-half years after its inception. It pitched losses as part of its growth strategy, which called for investing heavily in marketing and promotion, site development and operating infrastructure.

Needless to say, things worked out well, with Amazon currently valued at more than $2.8 trillion.

SpaceX is not like the others

If we look at the most valuable public tech companies, a few commonalities about their earlier days stand out. All went public relatively early in their operating histories and debuted with sharply growing revenue and either profits or losses in the single-digit millions.

SpaceX, founded in 2002, looks by comparison like an oldster for a company on the cusp of a public market debut. Itā€™s also worth pointing out that Google, founded in 1998, is only four years older than SpaceX. That means, itā€™s had 28 years to grow into becoming a company with over $400 billion in revenue over the past 12 months and $138 billion in operating income.

SpaceX, by contrast, has had 24 years to grow into becoming a company that loses $4.3 billion in a single quarter.

Related Crunchbase query:

Related reading:

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led the financing, which included participation from (Googleā€™s early-stage AI fund), , , , and several angel investors. The raise brings Fazeshiftā€™s total raised to $22 million since its 2023 inception.Ģż

The San Francisco company was founded by a team with an unconventional pedigree: (CEO), a former consultant and mechanical engineer and (CTO), an -trained nuclear submarine officer.Ģż

The two met at , but their lightbulb moment came while running a previous startup, , where they found themselves color-coding spreadsheets to track payments for just 10 customers and realized that the tools they were using failed to solve the basic problem of ensuring money actually hits the bank.

They realized that while there are more than a million accounts receivable (AR) clerks in the U.S. alone, many of them spend their time bouncing between systems such as , CRMs like , bank portals, and email threads because these systems do not natively talk to each other.Ģż

Unlike accounts payable, which a company can standardize internally, Leksana contends, accounts receivable is a “snowflake” problem that remains one of the least automated functions in finance. Every customer has a unique set of requirements; for instance, a large retailer might demand that an invoice be submitted through a specific proprietary portal with Part A and Part B attached as PDFs.Ģż

Fazeshift claims that it can automate more than 90% of manual AR tasks ā€” from invoicing and collections to payment matching and reconciliation ā€” by operating on top of existing systems and executing workflows across them. It essentially sits on top of a companyā€™s current stack as a ā€œbrain.ā€

Competitors, according to Leksana, are generally focused on automating tasks, while Fazeshift is working on building what she described as an ā€œintelligent control layerā€ that helps companies ā€œcollect faster, more predictably and with less effort, and that is continuously improving through proprietary payer behavior data.ā€

ā€œWhat sets us apart is our ability to handle complex workflows that other tools fail to solve ā€“ especially in industries like wholesale, construction, staffing, and HVAC, where AR processes are highly fragmented and manual,ā€ Leksana told Crunchbase News in an interview.

An OS for the finance organization

After launching at the start of the Summer 2024 Y Combinator cohort, Fazeshift has seen its revenue grow 12x in a single year, attracting dozens of enterprise customers, including eight unicorns and its first public company, according to Leksana.

Customers include , , , and , as well as one of the largest independent wholesale distributors in the Southeast, the worldā€™s top e-commerce aggregator, and a leader in music publishing, per Leksana.Ģż

Looking ahead, Leksana believes that Fazeshift has the potential to expand beyond accounts receivables. The goal is for Fazeshift to become the primary operating system for the entire finance organization.

ā€œOur long-term vision is to expand into a broader CFO suite,ā€ she said, ā€œbuilding toward a future of autonomous finance where core operational work is executed by AI and human teams can focus on agent management, strategic work, and governance.ā€

Broken workflows for ā€˜critical functionsā€™

, partner at F-Prime Capital, said her firm was impressed by Fazeshiftā€™s efforts to meet the needs of companies still running AR mostly on spreadsheets and email.

ā€œYouā€™d be surprised how many Fortune 500 companies only started adopting software a few years ago and still have dozens, if not hundreds, of AR clerks on staff,ā€ she wrote via email. ā€œThat gap between how critical the function is and how broken the workflows remain is exactly the kind of opportunity we look for.ā€

Wu also believes the market is at an inflection point where AI is moving from co-pilot to co-worker, and human teams are shifting from doing the work to reviewing and managing AI agents.

ā€œFazeshift is bringing us closer to an autonomous future for finance,ā€ she said. The founders had ā€œlived the pain of broken AR workflows firsthand at their last company and set out to build the platform they wished theyā€™d had. When you meet founders like that, you move fast.ā€

Fintech startups, particularly those that apply AI to traditionally manual or burdensome processes, have benefited from increased investment in recent quarters. Global funding to VC-backed financial technology startups totaled $53.8 billion in 2025, per Crunchbase . Thatā€™s a more than 29% increase from 2024ā€™s total of $41.6 billion raised.

Related reading:

• Fintech Startups Globally Raise More Money In Far Fewer Deals In Q1 2026

Related Crunchbase query:

• Ģż

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

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

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

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

Aprilā€™s new unicorns

Here are Aprilā€™s new unicorn companies. Of the 28 companies, 26 are AI-related.Ģż

Foundational AIĢż

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

RoboticsĢż

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

Financial servicesĢż

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

DefenseĢż

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

Developer toolsĢż

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

EnergyĢż

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

Health careĢż

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

Data platform

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

Manufacturing

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

Agentic AI

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

AerospaceĢż

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

Marketing & salesĢż

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

BiotechnologyĢż

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

Waste managementĢż

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

Related Crunchbase unicorn lists:Ģż

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

Related reading:

• March Mints 11 New Unicorns, As $200B Is Added Through Up Rounds And Board Posts Strong ExitsĢż
• Billion-Dollar AI Rounds Push April To Third-Highest Startup Funding Month In A Year
• This Is A Momentous Year For Early-Stage UnicornsĢż
• The Rising Investors Behind The New Unicorn ClassĢż
• While OpenAI Shattered Records, Robotics and Semiconductor Startups Quietly Added The Most New Unicorns In FebruaryĢż

Methodology

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

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

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price.

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Weā€™re not talking about confidential filings either. Sure, they signal serious intent and contain valuable information for regulators. But for the rest of us, itā€™s the public S-1 filing that signifies an IPO is actually imminent.

By this latter measure, the past few weeks have been pretty busy for venture-backed startups. , the designer of speedy AI inference chips, filed publicly last week for an offering expected to raise around $2 billion. The Silicon Valley company, which withdrew plans for an IPO last fall, is reportedly seeking a valuation upwards of $35 billion this time around.

That alone would be enough to set IPO market watchers abuzz. Per Crunchbase data, it stands to be the largest initial share offering of a U.S. semiconductor company to date.

However, Cerebras wasnā€™t the only venture-backed company seeking a multibillion-dollar IPO valuation.

Power players

Another, albeit smaller, contender is nuclear power startup , which is making its debut today. The Rockville, Maryland-based company priced shares at $23 each late Thursday, above the projected range, raising around $1 billion. Shares closed up 27% in first-day trading Friday.

Meanwhile, on the geothermal power front, is also looking to take its clean energy ambitions to the public market. The Houston-based company filed last week for a offering that could bring in around $250 million.

Biotech IPOs heating up

Biotech is also heating up. Last week delivered a big debut from , a Waltham, Massachusetts-based developer of oral and injectable treatments for obesity and metabolic disease that $718 million in its Nasdaq offering. , a Fremont, California-based startup applying proteomics to early disease detection, made its market entry as well, securing a current market cap around $1.6 billion.

More biotech debuts are on deck too. Austin-based , a venture-backed developer of a nerve stimulation device for stroke survivors, filed last week for an offering. The prior week brought S-1 filings from Bostonā€™s , a developer of medicines for depression, anxiety and other neuropsychiatric disorders, and , a Denmark-based biotech which focuses on treatment of blood coagulation disorders.

Space and defense on the rise

Of course, everyone knows the Texas-based company on deck to publicly file for a space tech offering of unprecedented magnitude. for an IPO a few weeks ago, with media reports pegging its target valuation around $1.75 trillion. If the company forges ahead with reported plans for a June market debut, a public filing should follow in the next few weeks.

In the interim, another, much, much smaller offering in the defense tech space is on track to hit the market much sooner. , a Herndon, Virginia-based developer of radio frequency intelligence for military customers, filed earlier this month for a offering. It comes amid a period of heightened investor appetite for defense tech, with an expectation of more debuts in the space likely in coming months.

Now we just need some software

Of course, itā€™s not an IPO market that is welcoming to all venture-backed startup sectors. One area noticeably absent from the impending offering list is enterprise software. While SaaS has long been a mainstay of the IPO pipeline, the sector has taken a hit of late amid investors’ concerns of AI disruption.

That said, itā€™s still encouraging to see a swathe of other sectors dipping a toe in IPO waters.

Related Crunchbase queries:

Related reading:

• After Swarmerā€™s Soaring Debut, Here Are 12 Other Potential Defense Tech IPOs
• IPOs Are Holding Up In 2026, But SaaS Debuts Arenā€™t Happening

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