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

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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.

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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

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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.

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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.)

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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

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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

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These in-between exits don’t generate a lot of buzz, but collectively they add up to a tidy sum. Last year, for instance, U.S. startup purchases under $300 million 1 brought in about $8.7 billion altogether, Crunchbase data shows.

These small and mid-sized deals are not a long-term growth area for M&A, by many measures. The total deal value of purchases between $100 million and $300 million last year was still below levels routinely reached nearly a decade ago, as charted below.

Moreover, the total value can add up to just a fraction of a single, larger exit. ’s $32 billion purchase of , for instance, is worth more than 4x all these sub-$300 million deals put together.

Even so, we’re up from prior lows. Startup purchases in this range hit a low point a couple years ago and have rebounded since, with this year off to a brisk start as well.

Smaller deals shrink more

Smaller disclosed-price acquisitions of under $100 million are also well below peak. The volume and value of these deals hit a low in 2024 and has made somewhat of a comeback since, as charted below.

These sub-$100 million purchases are a mixed bag for returns. Investors might recoup solid profits from companies that raised a few million in seed funding and sold for prices in the tens of millions.

In other cases, startups sold for considerably less than the sums they raised in venture investment. Using Crunchbase data, we aggregated a few examples of such deals from the past year. It includes companies with known struggles, such as , which filed for bankruptcy before selling to an acquirer this month.

No power buyers

Notably, there is no “power acquirer” for small and mid-sized startup purchases. Out of 181 sub-$300 million startup acquisitions since 2024 there was no buyer with more than two such deals, per Crunchbase data.

That said, there are companies with a larger number of funded startup purchases, just without reported prices for all or most. Examples include , , , , , and , among others.

When price isn’t disclosed, it’s hard to gauge how founders and investors fared on the deal. That said, most of the more active buyers can certainly afford to pay well. Whether they choose to do so is another matter.

*This is only disclosed-price purchases. Most startup acquisitions do not have a disclosed price.

Related Crunchbase queries:

Related reading:

• Biotech Startup M&A Is Reliably Delivering Some Big Exits
• 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

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A host of interesting, under-the-radar recently funded startups caught our attention in the past month, including one that’s developing nuclear-waste generated electricity on the moon, another that aims to use AI to extract business intelligence from enterprise contracts, and a shoebox-picking warehouse robot. Let’s take a closer look.

$55M to turn contracts into business intelligence

AI-driven contract intelligence platform said last month that it raised $55 million in a Series B round led by existing investor , with participation from , , and .

The funding for the San Francisco-based company comes amid record-breaking funding for legal tech startups, particularly those that apply AI-driven automation to the notoriously paperwork-heavy profession. All told, venture funding to legal tech startups in 2025 nearly doubled year over year to more than $4 billion, per Crunchbase data.

Ivo itself has now raised $77.2 million from investors, . Its latest funding comes as in-house legal teams face mounting pressure from rising contract volumes and growing compliance demands.

Even as contracts increasingly serve as the backbone of revenue, vendor relationships and risk management, much of the data inside those agreements remains locked in PDFs and legacy systems, which are difficult to search or analyze without manual review.

Ivo’s platform automates contract review and transforms agreements into structured, searchable data. Its review product uses lawyer-built playbooks to standardize positions and flag deviations, with customers reporting time savings of up to 75% compared to manual review, per the company. Its intelligence layer also reportedly allows teams to surface obligations, renewal terms and risk exposure across entire contract libraries in seconds.

Since its previous funding round, Ivo says it has grown annual recurring revenue by 500%, increased its total customer count by 134%, and expanded adoption within the Fortune 500 by 250%. Its customers include , , , and .

“Our goal has always been to make interacting with contracts fast, accurate, and enjoyable,” CEO and co-founder said in a statement. “Every key relationship in a business is defined by an agreement, yet most organizations struggle to extract the insights inside them. Our focus is to give in-house teams a trustworthy solution that helps them work faster and gives them visibility into their contracts that was previously impossible.”

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$10M for warehouse robots, including one that picks shoeboxes

Amid record robotics investment, we perhaps shouldn’t be too surprised to see some very specialized bots get funding.

One is from , a Polish warehouse robotics company that last month raised a $10 million Series B extension led by . Along with its new funding, the Warsaw-based company unveiled its Shoebox Picker robot, designed to “reliably pick two-piece, unsealed shoeboxes.” That might sound like a niche task, but the company said shoeboxes account for up to 20% of SKUs in U.S. fashion e-commerce, yet have long resisted automation.

The Shoebox Picker can pick up to 450 units per hour when it’s only handling shoeboxes, and up to 600 units per hour for mixed bins, per the company. It can handle more than 98% of the shoeboxes on the market, according to Nomagic.

Nomagic’s vision is “to bring physical AI into the heart of warehouse and logistics operations, where intelligent, autonomous systems can finally bridge the gap between digital optimization and real‑world execution,” CEO and co-founder said in the funding announcement.

The company was founded in 2017 and has raised $84.6 million to date, .

Venture funding to robotics-related startups overall totaled nearly $14 billion last year, per Crunchbase data. That’s a 70% increase over 2024 and eclipses even the peak funding year of 2021.

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$5M to replace synthetic dyes with plant-based alternatives

, a startup developing plant-based color technology, raised $5 million in a pre-Series A round led by ’ Blue Ocean 2 fund, with participation from and .

The Cambridge, U.K.-based startup is tackling one of the fashion and chemical industries’ dirtiest secrets: synthetic dyes. An stems from textile dyeing and fabric finishing treatments.

Sparxell’s funding seems timely, as regulators globally are tightening scrutiny of chemical substances. The has with restrictions on intentionally added microplastics, and policymakers are weighing broader bans on PFAS “forever chemicals.” In the U.S., the has also been in food and consumer products.

Spun out of the , Sparxell aims to replace petroleum-based pigments and heavy metals with wood pulp-derived coloring. The company says that arranging cellulose crystals to reflect specific wavelengths of light produces 100% plant-based pigments, glitters and inks designed as direct replacements for conventional dyes.

The startup says its process can cut water use by up to 90% compared to traditional dyeing methods and eliminate microplastics and toxic runoff. Unlike synthetic dyes, Sparxell’s cellulose-based pigments are also biodegradable, per the company.

“Our technology isn’t just an alternative — it is here to stay because it delivers superior performance due to its nature-inspired features. This funding takes us from proof of concept to production and commercial launches,” CEO and founder said in a statement. “We’re at an inflexion point. Brands are under pressure to eliminate synthetic toxins from their supply chains.”

Founded in 2022, Sparxell has now raised $10.2 million, . The new funding will help it scale from pilot projects to tonne-scale manufacturing by 2026, per the company.

Apparel-related venture funding totaled about $1.5 billion globally last year and in 2024, per Crunchbase data, down significantly from the peak year of 2021 when it totaled $9.2 billion.

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$2.6M for AI-driven M&A deal-sourcing

Singapore-based , an M&A sourcing platform for corporations and high-growth startups, recently raised $2.6 million in a funding round led by , with participation from angel investors.

The startup is targeting one of the most relationship-driven corners of corporate strategy: deal origination. While acquisitions have become a key growth lever for companies of all sizes, sourcing targets, especially in the mid-market and sub-$70 million range, remains slow, opaque and heavily dependent on banker networks and in-market listings.

GrowthPal says its AI-driven platform acts as an “M&A copilot” that translates a buyer’s strategic objective — say, entering a new geography or acquiring a specific capability — into a structured acquisition thesis. AI agents then scan a database of more than 4 million technology companies, analyzing signals including hiring trends, funding history, web activity and public filings to surface high-fit, often off-market targets.

“M&A sourcing is where most time and effort is wasted, especially for smaller and mid-market deals,” , co-founder and CEO of GrowthPal, said in a statement. “Teams spend weeks researching, filtering, and chasing opportunities that never go anywhere. We built GrowthPal to help buyers focus only on high-intent, high-fit targets and move from mandate to meaningful conversations far faster.”

GrowthPal, which has raised $4 million total, , says it has already supported 42 completed transactions and facilitated more than 210 letter-of-intent-stage conversations across North America, Europe, Asia and Latin America. Its clients reportedly span large enterprises, PE-backed firms and growth-stage startups across SaaS, fintech, IT services and other sectors.

In one case, the company says, a single client closed seven acquisitions in 18 months using the platform.

Its funding seems prescient: There were more than 2,300 M&A deals globally involving venture-backed startups last year, per Crunchbase data, up only slightly from the year prior, but insiders who spoke with Crunchbase News said they expect strategic acquisitions for talent and technology to surge this year.

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$411K to generate energy on the moon

Talk about a moonshot.

, a Latvia-based startup, this month said it has raised €350,000, or about $411,000, in pre-seed funding to generate electricity on the moon.

The company said the funding was led by and angel investor . Along with the equity round, Deep Space says it secured another €580,000 (about $682,000) in public contracts and grants by the , and the Latvian government.

The company aims to develop a novel generator based on radioisotopes — materials derived from nuclear waste that generate energy through natural decay — to power moon surface exploration and for military satellite reconnaissance.

“Our technology, which has already been validated in the laboratory, has several applications across the defence and space sectors,” Deep Space CEO and founder said in a statement. “First, we’re developing an auxiliary energy source to enhance the resilience of strategic satellites. It provides the redundancy of satellite power systems by supplying backup power that does not depend on solar energy, making it crucial for high-value military reconnaissance assets.”

Ščepanskis noted in the statement that while Deep Space’s technology wouldn’t be used for weaponry, the Russia-Ukraine war was a motivating factor for its development. That became even clearer last year, when Ukraine lost its beachhead in Russia’s Kursk Oblast as the U.S. .

“As Europe is trying to become more independent, it is imperative to produce satellites with advanced capabilities on our own,” Ščepanskis said. “Our technology provides an auxiliary energy source for satellites, which makes them more resilient to non-kinetic attacks and malfunctions.”

Venture funding to space- and defense-related technologies, which often overlap, soared last year. Global funding to space tech totaled $14.2 billion in 2025 — more than double the annual totals in 2023 and 2024 — per Crunchbase data. Funding recipients included a mix of defense tech, satellite and rocket developers, and startups finding innovative use cases for geospatial data.

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Related reading:

• 5 Interesting Startup Deals You May Have Missed In December: A Hospital-Grade Wireless Heart Monitor, AI-Designed Proteins For Manufacturing, And More
• Crunchbase Predicts: Why The Race For Talent And Tech Could Accelerate Startup M&A In 2026

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