AI’s share of venture funding climbed to 80% this quarter, up from 55% a year ago. The deployment is real. The liquidity question behind it is the one founders should be paying attention to.

In 2025, Crunchbase roughly 2,300 venture-backed acquisitions against just 65 IPOs. In aggregate, the LPs sitting behind every venture fund have been in since 2022. Record deployment in Q1 doesn’t change the math at the LP level, and that pressure is reshaping every term sheet, follow-on decision and board conversation in venture right now.

Know what’s actually driving the firm across the table

When a partner walks you through their thesis, they’re telling you a story about your market; rarely are they telling you a story about their fund. That second story determines whether they can write your bridge in 18 months, whether they’ll fight for pro rata in your Series B, and whether their behavior in the next downturn looks like patience or anxiety.

LPs are demanding cash back. Paper markups aren’t enough. Some firms are responding well, consolidating into their best companies and being deliberate about new commitments while others are pretending it’s still 2021. Founders should know which type they’re sitting across from before signing anything.

Ask the questions founders rarely ask

Three reference calls with portfolio CEOs used to be enough due diligence on a VC. Not anymore.

Ask what vintage the partner’s current fund is and how much dry powder is left. Ask how many of their 2018 through 2020 companies have produced realized returns rather than paper markups.

Ask whether their LPs have been pushing for GP-led secondaries. If the answer is yes, the firm is operating under a cash-flow constraint that will show up in your boardroom. These aren’t rude questions. They’re the same ones serious LPs are asking that partner this quarter, and high-quality firms welcome the conversation.

Build your buyer relationships now

If you’re raising in 2026, you’re statistically far more likely to get acquired than to ring the bell at the . Q1 2026 alone produced, the third-busiest quarter since 2022. Of the 21 venture-backed exits over $1 billion globally last quarter, only four happened in the U.S. The exit window for American founders is narrower than the headline funding numbers suggest.

Smart founders design for that reality from Series A. They know which platform companies have an active corporate development team. They build product surface area that maps cleanly into someone else’s stack. They cultivate executive relationships at the most likely acquirers years before any sale conversation, so when one starts naturally the introduction is already there.

Capital is plentiful. Discipline is what separates outcomes.

Every dollar concentrated into the four AI mega-rounds is a dollar that hasn’t returned anything to LPs yet. Founders who understand the LP-to-GP-to-startup chain end up with better partners, smarter terms and companies built for more than one path to a great outcome.

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

Related reading:

• What The Record Venture Funding Quarter Actually Means For Your Startup’s Fundraise
• Q1 2026 Shatters Venture Funding Records As AI Boom Pushes Startup Investment To $300B

Just through mid-May, China-based robotics companies this year have raised $5.6 billion across 176 deals, Crunchbase data shows. That sum matches total investment to the nation’s robotics companies in all of 2021, the peak of the funding cycle. Investment in the sector has also already eclipsed the $4.3 billion raised by China-based robotics companies in all of 2025.

Startup funding in Asia overall surged to $27.4 billion in Q1, its highest level in over three years, with China capturing $16.5 billion — 60% — of that total, according to recent Crunchbase data. Robotics contributed meaningfully to that $16.5 billion total, with startups in the sector raising $3.3 billion across 126 deals.

Embodied AI boom

A review of Crunchbase data shows that investors now are no longer funding mostly pre-programmed hardware, but increasingly backing China-based startups working on embodied AI — or artificial intelligence with a physical body that interacts with the real world in real time.

That shift toward artificial intelligence-driven robotics mirrors a global surge in investment into robotics and other physical AI startups. It’s also thanks to the rise of advanced, open-source reasoning models that have fundamentally changed how robots operate. Startups are moving away from coding robots line-by-line toward Vision-Language-Action models that allow physical machines to observe, reason and execute physical tasks end-to-end.

In China, robotics startups at the intersection of the software and hardware integration are drawing the largest checks in the space and often back-to-back funding rounds. They include:

• , a 1-year-old humanoid robotics company that integrates embodied intelligence that last month raised a massive $513 million seed round led by and . The Shanghai-based company was valued at $1.9 billion.
• , which develops robotic systems and automation solutions for industrial and service applications, closed a $140 million Series A extension round in January from investors including . Then just three months later, it raised $293 million in a massive Series B round co-led by and
• In February, Beijing-based , which says it’s building a “universal brain” for robots, raised a $290 million Series A led by and . The 2-year-old company was valued at $1.5 billion. Then in April, it announced a $145 million Series A extension financing, bringing the total round to $435 million.
• Humanoid robotics company in February raised a $145 million Series B led by . The 2-year-old China-based company was valued at $1.4 billion. In April, it announced a $290 million extension to that round, bringing its total to $435 million
• Shenzhen-based , a builder of humanoid and quadruped robots, raised a $200 million Series B last month led by and . The 2-year-old company’s robots will be deployed for traffic, security and retail. It was valued at $1.5 billion.

Top investors

Crunchbase data shows the most active investors in the space are largely Asia-based. The busiest this year has been Hong Kong-based , taking part in six deals, including a $200 million round last month for humanoid robotics and embodied intelligence developer .

Among lead or co-lead investors, three China-based firms — , and — have each taken part this year in deals totaling $290 million or more.

Exits gain steam

Venture investors are likely feeling confident as the sector notches notable liquidity events, including IPOs and acquisitions.

The of , targeting a $3 billion to $7 billion valuation, is a milestone for the industry. The company in March filed for an to list on the , and its IPO would likely spur other startups in the space to pursue their own public-market debuts.

The sector has already seen some notable exits.

They include Hong Kong-based ,  a Shanghai-based startup that makes lightweight industrial robots. The company on May 18 listed on the , raising about $86 million. And it did not disappoint. Robotphoenix closed its first full day of trading at HK$53.75 ($6.86 U.S.), up nearly 80%. (Interestingly, Chinese robotics firms as their primary liquidity hub.)

On the M&A front, in what is widely considered a historic first for China’s embodied artificial intelligence sector, AI robotics unicorn in July 2025 engineered a two-stage consortium takeover to in legacy manufacturer for about $290 million. AgiBot’s co-founder formally stepped in to chair Swancor, effectively turning the publicly traded shell into a direct extension of AgiBot.

Ultimately, it seems that 2026 is the year China’s robotics companies are pivoting from raising early venture rounds to mass production, as a domestic market that currently accounts for more than 43% of global robotics venture investment, per Crunchbase.

Related Crunchbase queries:

Related reading:

• China Leads Asia’s Startup Funding To Its Highest Level In More Than 3 Years
• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo

Illustration:

I can definitely understand their logic in wanting a valuation that builds on top of the previous valuation. I also understand that they have strong underlying tech, a good team and the belief that the right buyer would see the potential upside.

And to be fair, there are cases that support this line of thought.

But in the current AI cycle, we’ve seen transactions that look disconnected from fundamentals.

structured a roughly $650 million licensing and talent deal with , effectively bringing in most of the team and its technology. has done similar deals around AI startups, combining technology access with hiring key teams. and others have been active in this space as well.

In rare situations, they can be replicated. When they are, they can lead to exceptional outcomes and everyone involved would welcome that. But they cannot be the working assumption.

The main reason I decide to walk away from a deal is simply because expectations are not aligned with how M&A actually works. If we are not aligned on that from the beginning, I am setting myself up for failure.

Here are the patterns I see most often:

1. Narrative without enough proof

Founders tend to focus on what the company can become. Buyers focus on what has already been demonstrated. Technology and vision matter, but they need to be supported by real signals: Revenue quality, growth, retention and how easily the product fits into a buyer’s ecosystem all carry weight.

When expectations are built mainly on potential, the gap becomes hard to close.

2. Using exceptional outcomes as reference points

The market always has headline deals that shape perception, especially in AI. While these examples are real, we cannot use them as a baseline.

Most transactions are still priced on traction, growth and strategic fit. When a process is anchored on rare outliers, it is likely doomed to fail.

3. Capital raised vs. commercial reality

When a company raises capital at a certain valuation, it sets a reference point. Founders and investors expect the next outcome to build on that. Buyers look at something else entirely. Current performance and future synergies. If the business has not grown into the expectations created by earlier funding rounds, a gap forms, and it is a gap we need to address.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to Crunchbase News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

Illustration:

Look closer, and the story changes completely. Early-stage funding was up 41% year over year. AI/ML deal count , up from roughly 5,600 the year before. More companies are getting funded at the early stage, not fewer.  The concentration at the top? That’s an infrastructure play. The application layer looks entirely different.

Build vertical, not horizontal

The real signal is in the shift from horizontal to vertical. shows horizontal SaaS down 35% over the past 12 months while vertical SaaS is essentially flat (up 3%). That divergence matters for founders deciding what to build.

Horizontal software (project management, general productivity, collaboration) is commoditizing fast as AI agents handle coordination natively. But vertical software? That’s where proprietary data shines and industry-specific compliance workflows matter. AI makes the first category less valuable and the second category more valuable.

If you’re starting a company right now, the data says: Pick an industry, not a feature. Claims processing in insurance, scheduling in healthcare, compliance in financial services, job costing in construction. These are workflows where software penetration has been shallow for decades because the problems were too specific for horizontal tools. AI changes that math.

Build for the $6T, not the $500B

The addressable market for software is expanding, not contracting. In Redpoint’s CIO survey, 58% cite AI as the top driver of increased software spend. As agents move from copilot features into autonomous workflow execution, the addressable market grows from roughly $0.5 trillion in current U.S. enterprise software spend toward $6 trillion or more, because AI starts capturing portions of the knowledge-worker payroll that software never could.

This is classic : When a resource gets dramatically cheaper to produce, consumption goes up. AI makes software dramatically cheaper to build, deploy and maintain. Suddenly, job costing for midsize contractors pencils out. Inventory optimization for independent pharmacies becomes economically viable. The cottage industries that enterprise software ignored for decades? They’re all in play now.

Build for acquirability, not just IPO optionality

But let’s talk about exits, because that’s where the rubber meets the road. The IPO market remains largely closed. In 2025, roughly 2,300 VC-backed startups were acquired compared to just 65 IPOs, per Crunchbase data.

LPs have seen nearly $200 billion in cumulative negative net cash flows since 2022. The pressure to return capital through M&A is real and growing.

Smart founders are building for this reality from day one. They’re building products that integrate into existing enterprise stacks, accumulating proprietary data that makes them expensive to replicate and cheap to integrate. Strategic acquirers in insurance, healthcare, logistics and financial services are actively buying vertical software companies. Why? Because these buyers can’t build this stuff internally — they lack the talent, the focus, and frankly, the DNA.

Start with the workflow, not the technology

So while everyone’s mesmerized by the infrastructure megarounds, the real opportunity is staring us in the face. Pick a specific industry workflow that’s still manual or stitched together with Excel. Build the AI-native solution that actually works for that vertical. Get to revenue before the market catches up.

The record quarter and the shrinking fund base are telling the same story from different angles. Infrastructure capital is concentrating at the top while the application layer is wide open for those willing to roll up their sleeves and solve real problems for real industries. That’s where I’m putting capital, and that’s where smart founders should focus their energy.

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

Related reading:

• These 3 Charts Show How Venture Capital Has Concentrated At The Top In 2026
• Q1 2026 Shatters Venture Funding Records As AI Boom Pushes Startup Investment To $300B

Illustration:

Per Crunchbase data, the high end of the purchase price represents the largest acquisition of a venture-backed biotech company in years. It’s a testament to the perceived potential of Kelonia’s pipeline of genetic medicines, including a treatment targeting multiple myeloma that has produced promising clinical trial results.

It’s also a quick progression by biotech standards. Boston-based Kelonia from stealth mode just four years ago, with $50 million in Series A funding led by , and . Two years later, it entered a research and licensing partnership with , a subsidiary of Japan’s , that included funding to develop immuno-oncology therapeutics using its in vivo gene placement system.

Per Lilly, Kelonia’s platform promises to not just improve outcomes for patients, but to do so in a rapid, simpler “off-the-shelf format” compared to currently available CAR T-cell therapies. Its approach has been described as enabling the reprogramming of patients’ T-cells inside the body so those cells can attack cancer.

Kelonia’s vision is ambitious enough, and early results encouraging enough, to warrant what ranks as the largest acquisition of a venture-backed, private biotech company in the past 10 years, per Crunchbase data. To put that in context, below we ranked the next-largest deals valued at up to $2 billion or more.

The list does not include venture-backed biotechs that went public, which would broaden the ranks considerably. This includes companies that sold to acquirers shortly after IPO, such as , a developer of therapeutics for obesity and metabolic disease that went public in February 2025 and sold to later in the year in a deal valued at around $10 billion.

In vivo acquisitions on the rise

Acquirers of late have been paying particularly handsomely for startups developing in vivo therapeutics. Just two months ago, Lilly purchased another high-profile venture-backed company in the space, , dedicated to engineering immune cells in vivo, in a deal valued at up to $2.4 billion. Cambridge, Massachusetts-based Orna had previously raised over $320 million in venture funding, per Crunchbase data.

Last June, meanwhile, pharma giant snapped up , a biotech advancing in vivo engineering of cells through RNA delivery, for $2.1 billion. San Diego-based Capstan had previously secured $340 million in venture funding.

Another big deal, announced in October, was ’s acquisition of , a Cambridge, Massachusetts-based developer of RNA medicines that reprogram the immune system in vivo, for $1.5 billion.

Of course, Kelonia’s purchase price dwarfs all its predecessors, and by a hefty sum. Under the terms of the agreement, Lilly will acquire Kelonia for $3.25 billion up front, plus up to $3.75 billion in subsequent payments tied to meeting clinical, regulatory and commercial milestones.

Related Crunchbase list:

Related reading:

• Even Bigger Venture Bucks For Obesity Therapies As Metsera Sells For Up To $10B

Illustration:

This past quarter, funding to security- and privacy-focused startups dipped slightly on a sequential basis, but remained well above year-ago levels. Overall, investors put $4.9 billion into global companies in the space in Q1, per Crunchbase , a comparatively solid performance relative to recent quarters, as charted below.

Round counts also held steady at just under 200 1, per Crunchbase data. Of those, 13 were financings of $100 million or more.

Biggest funding recipients

The largest funding recipient in Q1 was , a consumer-focused privacy startup that closed on $375 million in Series B funding.

Two other companies raised $250 million Series B financings: , a provider of AI-enabled cybersecurity services, and , a cloud security provider.

For a broader view of large funding recipients, below we put together a list of 10 of the largest Q1 cybersecurity rounds.

AI is dominant trend

Not surprisingly, a majority of cybersecurity-related funding went to companies that are also in Crunchbase AI-related categories. This coincided with a record quarter for AI-related funding overall, with the category capturing 80% of all global funding in Q1.

Acquirers were also attuned to AI. This included the quarter’s largest acquisition, ’s purchase of identity access management startup for a reported $740 million. Another big AI-related deal was ’ acquisition of agentic endpoint security provider for a reported $400 million.

IPO activity, meanwhile, was quiet, with no major cybersecurity-related offerings in Q1, per Crunchbase data.

Looking ahead

We expect to see AI-driven investment continue to be a dominant theme for cybersecurity. And while Q2 has not yet brought us a cybersecurity megaround, the default assumption is this is only a matter of time.

Related Crunchbase queries:

Related reading:

• Q1 2026 Shatters Venture Funding Records As AI Boom Pushes Startup Investment To $300B
• North America Q1 Funding Surges Across Stages To Record Level

Illustration:

In October 2024, my co-founders and I set out to make our Ph.D. research useful in the real world. We had built AI models that could interpret medical images such as X-rays and CT scans across tens of thousands of potential diagnoses, generating comprehensive radiology reports that mirror how radiologists reason in clinical practice. At a time when AI in radiology was limited to flagging a handful of specific conditions, this marked a fundamental shift.

Less than a year later, we faced a critical fork in the road: raise venture capital and continue independently, or accept an acquisition offer from , the world’s largest radiology practice.

The conventional wisdom in tech is that real ambition means staying independent. But in asking ourselves what it would truly take to transform healthcare, the answer was different.

Clinical AI is highly regulated with long sales cycles and complex stakeholder dynamics, where structural advantages tend to harden market positions and compound over time. We decided that joining forces — carefully structured to protect our velocity — would dramatically improve the odds that we realize our mission of significantly increasing the world’s access to healthcare.

Research success is not clinical readiness

During my Ph.D., I trained radiology AI foundation models on what, at the time, felt like massive research-scale datasets; tens to hundreds of thousands of studies. These models make for strong academic demonstrations, prototyping new capabilities across a range of tasks. In real clinical settings, however, they would not yet have met the standards required for production-level safety and consistency in patient care.

Despite the persistent narrative that AI will make radiology obsolete, the reality is that the problem is extraordinarily difficult. A single CT study, for example, can contain 10 high-resolution volumetric series, effectively 3D videos. Add prior studies for the same patient, and you can have a billion pixels of data.

Those billion pixels encode entire medical textbooks worth of information. On top of this, real-world radiology is defined by edge cases where rare but critical pathologies are encountered regularly. We learned a hard truth early on: Models that work in controlled research environments often fall apart when exposed to real-world complexity.

Think about self-driving cars. A decade ago, progress looked impressive. But the real world kept introducing new failure modes. After more than a decade of significant capital investment, only a handful of companies have approached true reliability.

Components required to build reliable models

Key patterns emerged. The companies that made the most progress controlled the entire system and achieved scale early. They owned the vehicles, the sensor stack, the data collection pipeline, the simulation environments, and the deployment infrastructure. That integration, operating at scale, allowed them to continuously collect rare edge cases, retrain models, validate improvements and redeploy safely.

Radiology is no different. Success in the real world requires massive, diverse historical datasets and live data feeds that continuously surface rare edge cases and distributional shifts. It requires vast clinical resources and operational infrastructure to redesign clinical workflows around AI, engineer systems that perform reliably at scale, conduct large-scale research studies, secure regulatory clearance, refine models safely, and continuously monitor performance post-deployment.

Additionally, frontier language models have clearly demonstrated that continuous, high-quality and extensive human feedback is the secret sauce in making models useful. This is no different in radiology. In a world where radiology reports are drafted by AI, every draft must be reviewed, edited and signed off by a human radiologist.

Those edits become high-quality signals that can be leveraged for improving the AI models. Better models elevate radiologists’ accuracy and capacity. Improved radiologist accuracy increases the quality of future training data. Increased capacity allows radiologists to take on additional contracts.

That, in turn, generates more data and high-quality corrections, setting a powerful flywheel in motion. Access to this correction data is rare in AI and can only work meaningfully at a massive scale. These capabilities would be incredibly difficult to achieve as a standalone AI startup.

In healthcare, growth follows evidence

In healthcare, trust is hard earned. It rests on demonstrated clinical efficacy, reliability, security and regulatory rigor. For a health system or radiology group to adopt technology from a new startup, particularly in workflows that directly affect patient care, requires rigorous, real-world evidence.

Evidence in healthcare is not generated in small pilots. It is built through sustained performance across diverse sites, patient populations, modalities and edge cases. If a system proves itself within the world’s largest radiology practice, it establishes credibility across multiple dimensions at once — efficacy, reliability, security and scalability.

In sectors where lives are at stake and the goal is to build something that endures, the way to build it is from within the system you’re trying to improve. Selling early didn’t shorten our journey, it accelerated it. It gave us the foundation required to deliver on our mission of significantly increasing the world’s access to healthcare.

is the CEO and co-founder of , the AI business unit of at . During his undergraduate studies in physics and electrical engineering, he became driven by a singular mission: increasing the world’s access to healthcare through technology. Convinced that AI was the most promising technology to make this happen, but not yet good enough for real-world clinical use, he pursued a Ph.D. in AI at where he focused on foundation models for radiology. His doctoral work produced Merlin, a 3D vision-language model for CT interpretation published in “Nature” in 2026 and recognized as one of the most important papers in the field.

Illustration:

The first quarter of 2026 was unlike any other for venture investment, driven by unprecedented spending on AI compute and frontier labs. Crunchbase data shows investors poured $300 billion into 6,000 startups globally in the quarter, up over 150% quarter over quarter and year over year.

That marks an all-time high for global venture investment not approached by any other quarter on record. In fact, startup investment in the first quarter of 2026 alone totaled close to 70% of all venture capital spending in 2025. The quarterly sum also tops all full-year investment totals prior to 2018.

Q1’s startup investment largely went to AI startups and disproportionately to a handful of U.S.-based companies in record-setting deals. Four of the five largest venture rounds ever recorded were closed in Q1 2026, with frontier labs ($122 billion), ($30 billion), ($20 billion) and self-driving company ($16 billion) collectively raising $188 billion, or 65% of global venture investment in the quarter.

Overall, AI shattered records last quarter, with $242 billion — 80% of total global venture funding in Q1— going to companies in the sector. The previous record was set in Q1 2025, when AI accounted for 55% of global venture funding.

Table of Contents

• Valuation surge, capital concentration
• US above 80%
• Late-stage hike
• Early stage up over 40%
• Seed funding up over 30%
• IPO slowdown, M&A pick up
• Public pressure
• Methodology
• Glossary of funding terms

Valuation surge, capital concentration

Along with the three major frontier labs and Waymo, another 10 companies raised funding rounds of $1 billion or more in Q1, in sectors spanning generative and physical AI, autonomous vehicles, semiconductors, data centers, robotics, defense and prediction markets.

Those outsized rounds pushed overall startup valuations higher in Q1. The Crunchbase Unicorn Board added $900 billion in value during the quarter, marking the largest valuation bump in a single quarter.

US above 80%

U.S.-based companies raised $250 billion, or 83% of global venture capital in Q1, Crunchbase data shows. That’s up significantly from 71% in Q1 2025, which was already well above historical averages in the decade before 2024.

The second-largest market globally for venture funding in Q1 was China, with $16.1 billion invested. The U.K. followed, with $7.4 billion invested. Both countries were up quarter over quarter and even more significantly year over year.

Late-stage hike

The Q1 funding surge was concentrated in late-stage funding, which reached $246.6 billion — up 205% year over year — across 584 deals. A total of $235 billion was invested in 158 late-stage companies that raised rounds of $100 million and more.

Early stage up over 40%

Early-stage funding totaled $41.3 billion across 1,800 deals, Crunchbase data shows.

Funding was up marginally quarter over quarter but up 41% year over year from $29.4 billion. Much of that increase went to Series A rounds, Crunchbase data shows. Series B deals were down quarter over quarter but still up year over year.

Seed funding up over 30%

Seed funding totaled $12 billion, up 31% year over year, though the increase was entirely due to larger rounds, with deal counts falling 30% year over year to 3,800.

IPO slowdown, M&A pick up

Record venture investment in U.S. companies did not translate into a stronger IPO market in Q1.

In fact, the U.S. market for new listings slowed in Q1 amid a broader stock market selloff in software, although China’s IPO market picked up.

A total of 21 venture-backed companies exited globally above $1 billion in Q1. Thirteen of those were from China, four more from elsewhere in Asia, and four from the U.S.

The largest IPO in Q1 was Japan-based , a fintech for mobile payments valued at $10 billion upon listing.  Two foundation lab companies from China — and — debuted on the , each valued at more than $6 billion.

While the IPO market was somewhat lackluster, startup M&A was strong in Q1 with exits cumulatively valued north of $56.6 billion, Crunchbase data shows. That marked the third-highest startup M&A quarter since the downturn of 2022.

The largest M&A deals in Q1 were ’s $6 billion planned acquisition of ’s gaming platform , and ’s planned $5.15 billion acquisition of fintech startup .

Public pressure

While frontier lab megarounds defined Q1 2026, a closer look at the data shows every startup funding stage grew last quarter, as did round sizes across the board.

And unlike the cloud and mobile era, this cycle is also being built in the physical world, with massive capital flowing not just into software, but infrastructure, autonomous vehicles, robotics and manufacturing.

Now, with startup valuations surging and a backlog of companies with unprecedented sums of private capital behind them, pressure is intensifying on the IPO markets to reopen in 2026.

Related Crunchbase queries:

Methodology

The data contained in this report comes directly from Crunchbase, and is based on reported data. Data is as of March 31, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted. 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.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. Crunchbase also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. Crunchbase includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the “Series [Letter]” naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a “venture” round. (So basically, any round from the previously defined stages.)

Illustration:

OpenAI has already made six acquisitions in 2026, nearly as many as it made in all of 2025, according to Crunchbase . Its latest purchase took place on March 19, when it announced plans to , a creator of open-source tools for software developers. This month, it also snapped up , an open-source tool for testing AI applications.

Overall, the San Francisco-based company has acquired 17 companies in the past three years, Crunchbase data shows. Eight of those purchases were made in 2025, although it didn’t even start making acquisitions until April last year.

By contrast, OpenAI only acquired two companies in 2024: and , and one company in 2023: .

The company seems to be continuing its acquisitive streak this year. It announced three acquisitions in January alone, setting the tone for what appears likely to be a busy M&A year. In January, it acquired:

• , a consulting firm providing custom AI “solutions” and specializing in GenAI, predictive analytics and strategy.
• , an AI-powered health app that aims to unify scattered medical records from hospitals, labs, wearables and consumers.
• , which provides LaTeX editing, error detection and team collaboration.

In February, OpenAI participated in an deal involving open-source AI agent and its creator, .

Historically, OpenAI hasn’t disclosed the purchase price for most of its acquisitions. The most expensive deal — at least among transactions for which a sales price was revealed — was its May 2025 acquisition of . OpenAI paid $6.5 billion for the then 1-year-old startup, which developed AI-powered devices.

However, not all of OpenAI’s proposed acquisitions have worked out. Last July, news broke that its planned $3 billion purchase of Windsurf had fallen apart.

Cash considerations

Certainly, OpenAI has deep pockets with which to buy companies despite reportedly being wildly unprofitable. In late February, the company announced it had closed a staggering $110 billion fundraising round at an $840 billion post-money valuation. The financing marked the largest startup funding deal ever, according to . OpenAI’s investors involve a diverse bunch, including , , , ,, and .

Still, despite all that funding, according to a report from , projects that OpenAI’s cumulative free cash flow by 2030 will still be in the red, leaving “a $207 billion funding shortfall that must be filled through additional debt, equity, or more aggressive revenue generation.​”

Startup M&A overall

OpenAI’s biggest rival, , has been far less acquisitive. So far this year, it has made only one known purchase, buying , a 2-year-old software development startup. In 2025, Anthropic made two known acquisitions: , an LLM evaluation platform for enterprises, and , a JavaScript runtime for developing and managing web applications.

Overall startup M&A dealmaking has been fairly robust so far this year, Crunchbase data shows. This includes two deals in the multiple billions: ’s $5.15 billion purchase of and s $2.4 billion acquisition of . The AI sector’s appetite for acqui-hires and smaller purchases of earlier-stage startups also continues to boost momentum.

Related Crunchbase query:

Related reading:

• The Most Active Startup Acquirers Of The Past 3 Years Aren’t Always Who You’d Expect
• Small And Mid-Sized Startup Purchases Are Still Well Below The 2021 Peak

Illustration:

Some are longstanding blue chip tech and pharmaceutical companies. Others are fast-growing venture-backed unicorns. And still others are more recent public market entrants looking to stay competitive in the age of AI.

To get a sense of who’s buying in bulk, we used Crunchbase data to put together a that acquired three or more seed- or venture-backed startups in the past three years. From there, we picked the most acquisitive names.

The most prolific startup acquirers of the past 3 years

Per Crunchbase data, the most prolific acquirers of seed- and venture-backed startups in recent years are 1, and . Overall, our query showed six companies with six or more known purchases, charted below.

For top-ranked Salesforce, high-volume M&A is nothing new. The San Francisco software giant has purchased at least 91 companies in the past 20 years, per Crunchbase data. Its most recent startup purchases include , a revenue orchestration platform, and , which focuses on agentic AI for e-commerce.

OpenAI, by contrast, has a shorter track record of M&A shopping sprees. The pioneering generative AI company has bought 16 companies in the past three years. Among the most recent was an deal involving open-source AI agent and its creator, . This month, it also snapped up , a creator of open source tools for software developers, and , an open-source tool for testing AI applications.

Snowflake, meanwhile, has 19 acquisitions to date. Most recently, it acquired , a developer of AI observability tools that previously raised more than $460 million in venture funding.

Notably, recent the active acquirers list for recent years looks quite a bit different that the ranking of all-time top M&A dealmakers in the Crunchbase dataset, shown below:

Highest-spending acquirers

The most prolific startup buyers also aren’t always the biggest check-writers. By the latter metric, the far-and-away leader is , and its $32 billion acquisition of .

For a broader picture view, we used Crunchbase data to put together a list of six companies that made the biggest-ticket funded startup acquisitions of the past three years.

2026 off to a promising start

So far this year, it looks like the pace of startup M&A dealmaking remains fairly robust.

This includes two deals in the multiple billions: ’s $5.15 billion purchase of and s $2.4 billion acquisition of . The AI sector’s appetite for acqui-hires and smaller purchases of earlier-stage startups also continues to boost momentum.

We’ll see if it keeps up.

Related Crunchbase list:

Related reading:

• Small And Mid-Sized Startup Purchases Are Still Well Below The 2021 Peak
• Crunchbase Predicts: Why The Race For Talent And Tech Could Accelerate Startup M&A In 2026
• M&A The Highest On Record For Unicorn Exits In 2025

Illustration: