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鈥檚 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 鈥� carefully structured to protect our velocity 鈥� would dramatically improve the odds that we realize our mission of significantly increasing the world鈥檚 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鈥檚 largest radiology practice, it establishes credibility across multiple dimensions at once 鈥� 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鈥檙e trying to improve. Selling early didn鈥檛 shorten our journey, it accelerated it. It gave us the foundation required to deliver on our mission of significantly increasing the world鈥檚 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 鈥淣ature鈥� 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. 附近上门 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 鈥� 80% of total global venture funding in Q1鈥� 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 附近上门 附近上门 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, 附近上门 data shows. That鈥檚 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 鈥� up 205% year over year 鈥� 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, 附近上门 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, 附近上门 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鈥檚 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.聽 Two foundation lab companies from China 鈥� and 鈥� 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, 附近上门 data shows. That marked the third-highest startup M&A quarter since the downturn of 2022.

The largest M&A deals in Q1 were 鈥檚 $6 billion planned acquisition of 鈥檚 gaming platform , and 鈥檚 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 附近上门 queries:

Methodology

The data contained in this report comes directly from 附近上门, 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. 附近上门 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 附近上门 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. 附近上门 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. 附近上门 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 鈥淪eries [Letter]鈥� 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 鈥渧enture鈥� 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 附近上门 . 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, 附近上门 data shows. Eight of those purchases were made in 2025, although it didn鈥檛 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 鈥渟olutions鈥� 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鈥檛 disclosed the purchase price for most of its acquisitions. The most expensive deal 鈥� at least among transactions for which a sales price was revealed 鈥� 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鈥檚 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鈥檚 investors involve a diverse bunch, including , , , ,, and .

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

Startup M&A overall

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

Related 附近上门 query:

Related reading:

• The Most Active Startup Acquirers Of The Past 3 Years Aren鈥檛 Always Who You鈥檇 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鈥檚 buying in bulk, we used 附近上门 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 附近上门 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 附近上门 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 附近上门 dataset, shown below:

Highest-spending acquirers

The most prolific startup buyers also aren鈥檛 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 附近上门 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: 鈥檚 $5.15 billion purchase of and s $2.4 billion acquisition of . The AI sector鈥檚 appetite for acqui-hires and smaller purchases of earlier-stage startups also continues to boost momentum.

We鈥檒l see if it keeps up.

Related 附近上门 list:

Related reading:

• Small And Mid-Sized Startup Purchases Are Still Well Below The 2021 Peak
• 附近上门 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鈥檛 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聽1 brought in about $8.7 billion altogether, 附近上门 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. 鈥檚 $32 billion purchase of , for instance, is worth more than 4x all these sub-$300 million deals put together.

Even so, we鈥檙e 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 附近上门 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 鈥減ower acquirer鈥� 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 附近上门 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鈥檛 disclosed, it鈥檚 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 附近上门 queries:

Related reading:

• Biotech Startup M&A Is Reliably Delivering Some Big Exits
• 附近上门 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鈥檚 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鈥檚 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 附近上门 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鈥檚 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 .

鈥淥ur goal has always been to make interacting with contracts fast, accurate, and enjoyable,鈥� CEO and co-founder said in a statement. 鈥淓very 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.鈥�

Related 附近上门 query:

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

Amid record robotics investment, we perhaps shouldn鈥檛 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 鈥渞eliably pick two-piece, unsealed shoeboxes.鈥� 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鈥檚 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鈥檚 vision is 鈥渢o 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鈥憌orld execution,鈥� 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 附近上门 data. That鈥檚 a 70% increase over 2024 and eclipses even the peak funding year of 2021.

Related 附近上门 query:

$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 鈥� Blue Ocean 2 fund, with participation from and .

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

Sparxell鈥檚 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 鈥渇orever chemicals.鈥� 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鈥檚 cellulose-based pigments are also biodegradable, per the company.

鈥淥ur technology isn’t just an alternative 鈥� 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,鈥� CEO and founder said in a statement. 鈥淲e’re at an inflexion point. Brands are under pressure to eliminate synthetic toxins from their supply chains.鈥�

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 附近上门 data, down significantly from the peak year of 2021 when it totaled $9.2 billion.

Related 附近上门 query:

$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 鈥淢&A copilot鈥� that translates a buyer鈥檚 strategic objective 鈥� say, entering a new geography or acquiring a specific capability 鈥� 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.

鈥淢&A sourcing is where most time and effort is wasted, especially for smaller and mid-market deals,鈥� , co-founder and CEO of GrowthPal, said in a statement. 鈥淭eams 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.鈥�

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 附近上门 data, up only slightly from the year prior, but insiders who spoke with 附近上门 News said they expect strategic acquisitions for talent and technology to surge this year.

Related 附近上门 query:

$411K to generate energy on the moon

Talk about a moonshot.

, a Latvia-based startup, this month said it has raised 鈧�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 鈧�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 鈥斅爉aterials derived from nuclear waste that generate energy through natural decay 鈥斅爐o power moon surface exploration and for military satellite reconnaissance.

鈥淥ur technology, which has already been validated in the laboratory, has several applications across the defence and space sectors,鈥� Deep Space CEO and founder said in a statement. 鈥淔irst, we鈥檙e 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.鈥�

艩膷epanskis noted in the statement that while Deep Space鈥檚 technology wouldn鈥檛 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鈥檚 Kursk Oblast as the U.S. .

鈥淎s Europe is trying to become more independent, it is imperative to produce satellites with advanced capabilities on our own,鈥� 艩膷epanskis said. 鈥淥ur technology provides an auxiliary energy source for satellites, which makes them more resilient to non-kinetic attacks and malfunctions.鈥�

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 鈥� more than double the annual totals in 2023 and 2024 鈥� per 附近上门 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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• 5 Interesting Startup Deals You May Have Missed In December: A Hospital-Grade Wireless Heart Monitor, AI-Designed Proteins For Manufacturing, And More
• 附近上门 Predicts: Why The Race For Talent And Tech Could Accelerate Startup M&A In 2026

Illustration:

Over the past two calendar years, acquirers have agreed to pay more than $38 billion to purchase聽1 venture-backed companies in 附近上门 biotech industry categories. So far, 2026 is off to a brisk start as well, with this month to pay up to $2.4 billion for , a startup focused on engineering immune cells in vivo.

Per 附近上门 data, 2025 and 2024 were two of the strongest years on record for biotech M&A. While we鈥檙e still below the 2021 peak, we鈥檙e also well past the subsequent low point, as charted below.

Largest deals in recent quarters

Since last year, at least nine funded U.S. biotech companies have sold in transactions valued at $1 billion or more, including potential milestone payments. Using 附近上门 , we assembled a list, ranked by deal size.

The largest deal was 鈥檚 purchase of , a developer of targeted oral therapies for solid tumors, for $3.05 billion in cash late last year. The pharma giant expressed particular interest in adding Halda鈥檚 clinical stage oral therapy for prostate cancer to its portfolio.

The two next-biggest acquisitions were both in the area of in vivo therapeutics, which enable a patient鈥檚 own body to generate cell therapies that can treat underlying disease.

One was Lilly鈥檚 aforementioned purchase of Watertown, Massachusetts-based Orna, which had聽 previously raised over $320 million in venture funding from lead backers including , and .

The other was 鈥檚 mid-2025 acquisition of , a clinical-stage biotech developing targeted in vivo RNA technologies, with an initial focus on autoimmune diseases. AbbVie agreed to pay up to $2.1 billion in cash to acquire the San Diego-based startup,which previously raised $340 million in venture funding.

Biotech funding share slides, and IPO volume remains weak

While some large acquisitions are happening, the overall picture for biotech funding and exit activity looks more muted.

Last year, less than 9% of all U.S. startup funding went to companies in 附近上门 biotech categories. That鈥檚 the lowest share in years, and largely a function of more capital going to companies in other hot sectors like generative AI.

In terms of total finding, biotech looks more stable. In 2025, just over $25 billion went to U.S. startups in the space, roughly flat year over year.

IPO activity is lower than usual. Last year, just 21 biotech, pharma or medical device companies went public, per 附近上门 data, the lowest number in years.

So far this year, we鈥檝e had four debuts, including most recently the debut this month of , a developer of cancer therapies recently valued around $900 million.

Not a slump, and not a boom

Overall, biotech funding and exit data paints a picture of a sector that鈥檚 neither booming nor in a protracted slump. That鈥檚 not the most exciting place to be, but it can be quite viable for quite a long time.

Related 附近上门 query:

Related reading:

• In The Era Of Unicorn Valuation Escalation, A Trillion Dollars Isn鈥檛 What It Used To Be
• Biotech Share Of US Funding Hits Lowest Point In 附近上门 History
• Biotech Isn鈥檛 Partaking In The IPO Rebound

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And underlining the pace at which some startups are now sprinting to billion-dollar-plus valuations, four of the new unicorns are less than a year old.

In exit news, 9-year-old fintech unicorn was acquired by for $5.2 billion. That鈥檚 well below its January 2022 valuation of $12.3 billion but still marks a win for earlier investors seeking liquidity.

Of the 31 companies that joined the board, 23 are U.S.-based and two hail from Canada. Germany, France, Belgium, Israel, Japan and India each added one new unicorn to the board last month.

Among sectors, AI and AI infrastructure contributed the most new unicorns, totaling nine from those two areas. The next-leading sectors, with three new unicorns each, were manufacturing and security propelled by AI. AI was also a major contributor to new unicorns in the semiconductor, defense and autonomous driving sectors.

The largest funding last month for a unicorn company was $20 billion to 鈥檚 at an . Within a month of that funding, xAI in early February announced a merger with another Musk-led company, rocketmaker .

11 exits

Brex鈥檚 acquisition by Capital One was the largest of the four M&A deals for unicorn-valued companies in January.

On the IPO side, seven companies went public, the most high-profile of which were and , both foundation AI model companies based in China.

Here are January鈥檚 newly minted unicorns.

AI

• , an AI research lab focused on human collaboration, raised a $480 million seed funding led by and 1. The less than 1-year-old Redwood City, California-based company was valued at $4.5 billion.
• , an AI scientific research lab, raised a $180 million seed round led by , and . The less than 1-year-old San Francisco-based company was valued at $1.5 billion.
• AI evaluation platform raised a $150 million Series A led by 2听补苍诲 . The less than 1-year-old San Francisco-based company was valued at $1.7 billion.
• Voice AI startup raised a $143 million Series C led by France-based . The 10-year-old San Francisco-based company was valued at $1.3 billion. As part of its announcement, Deepgram disclosed the acquisition of , a voice AI startup for restaurants and drive-thru ordering.
• , an infrastructure company for voice AI, raised a $100 million Series C led by . The 5-year-old San Jose, California-based company was valued at $1 billion.

AI infrastructure

• , an AI networking company, raised a $200 million Series A led by , and . The 1-year-old Santa Clara, California-based company was valued at $1 billion.
• GPU marketplace raised a $150 million Series B led by . The 2-year-old Palo Alto, California-based company was valued at $1 billion.
• , for secure AI run locally on devices, raised a Series A extension funding of an undisclosed sum. The 6-year-old Austin-based company was valued at $2.5 billion.
• , which manages a GPU marketplace, raised a Series C led by . The 6-year-old company was founded in Lithuania and is now headquartered in Miami. It was valued at $1 billion.

Manufacturing

• , a builder of factories for defense and the aerospace industry, raised a $131 million private equity funding led by . The 5-year-old Hawthorne, California-based company was valued at $1.6 billion.
• , a developer of no-code applications for manufacturing, raised a $120 million Series D led by . The 11-year-old Somerville, Massachusetts-based company was valued at $1.3 billion.
• 惭辞苍迟谤茅补濒-产补蝉别诲 , a manufacturing automation company utilizing modular robotics, raised a $90 million Series D led by . The 9-year-old company was valued at $1.2 billion.

Security

• , provider of security for cloud services in real time to protect from hackers, raised a $250 million Series B led by . The 3-year-old San Francisco-based company was valued at $1.5 billion.
• Tel Aviv-based , an AI security platform that integrates with existing security platforms to provide context on incidents, raised a $140 million Series D led by . The 6-year-old company was valued at $1.2 billion.
• Belgium-based , a developer-oriented security platform, raised a $60 million Series B led by . The 3-year-old company was valued at $1 billion.

Semiconductor

• , an AI chip developer to run transformer models, raised a reported $500 million funding led by . The 3-year-old Cupertino, California-based company was valued at $5 billion.
• , an AI chip design company, raised a $300 million Series A led by . The less than 1-year-old Palo Alto, California-based company was valued at $4 billion.

Cryptocurrency

• Stablecoin payments platform raised a $250 million Series C led by . The 4-year-old New York-based company was valued at $2 billion.
• Crypto payments network raised a $75 million Series C led by . The 5-year-old San Francisco-based company was valued at $1 billion.

Healthcare

• Maternity healthcare provider, raised a $92 million Series C led by Stripes. The 4-year-old New York-based company with plans to expand healthcare services to women and children was valued at $1.7 billion.
• , a co-ordination platform for medications across doctors, pharmacies and patients, raised a Series B led by . The 3-year-old New York-based company was valued at $1 billion.

Defense

• Paris-based , an autonomous drone maker, raised a $200 million Series B led by aircraft manufacturer . The 2-year-old company was valued at $1.4 billion.
• , a builder of secure software for the defense industry, raised a $136 million Series B led by . The 4-year-old Colorado-based company was valued at $1 billion.

Fintech

• Tokyo-based brokerage infrastructure provider raised a $150 million Series D led by . The 11-year-old company was valued at $1.2 billion.
• India-based , a payment infrastructure provider, raised a $50 million Series D led by . The 13-year-old company was valued at $1.2 billion.

Fitness

• , an owner of physical fitness brands and the parent of , raised a $785 million private equity financing led by . As part of the transaction it announced a merger with . The San Luis Obispo, California-based company was valued at $7.5 billion.

Autonomous Driving

• Toronto-based , a self-driving technology company, raised a $750 million Series C led by and ,valuing it at $3.8 billion. The 5-year-old company announced a partnership with to support robotaxis.

Social media

• , an AI-powered video generation platform for social media, raised an $80 million Series A extension funding which brings its Series A funding total to $130 million. The 3-year-old San Francisco-based company was valued at $1.3 billion.

Education

• Online tutoring platform raised a $150 million Series D led by at a $1.2 billion valuation. The 14-year-old Brookline, Massachusetts-based company was founded by Ukrainians and maintains a team in Ukraine.

Compliance

• ESG compliance software platform raised a $100 million Series C led by , a joint venture between and . The 7-year-old Baden-Wurttemberg, Germany-based company was valued at $1.1 billion.

Energy

• , a developer of a residential energy storage device for electricity and electric vehicles, raised a $163 million funding. The 7-year-old San Francisco-based company was valued at $1 billion.

Related 附近上门 unicorn lists:

• (1,684)
• (596)
• (37)
• (186)
• (115)
• (102)
• (868)
• (494)
• (226)
• (38)
• (470)

Related reading:

• High-Spending Investors Give 2026 An Active Start
• In The Era Of Unicorn Valuation Escalation, A Trillion Dollars Isn鈥檛 What It Used To Be
• Global VC Investment Surged In January, With U.S. Dominating Funding, But A Pair Of AI Model IPOs In China
• A Growing Share Of Seed And Series A Funding Is Going To Giant Rounds

Methodology

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

The unicorn board does not reflect internal company valuations 鈥� such as those set via a 409a process for employee stock options 鈥� 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.

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. 附近上门 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 附近上门 long after the event was announced, foreign currency transactions are converted at the historic spot price.

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They鈥檙e not afraid to write big checks either. The latest evidence of this was a $450 million Series A that Livermore, California-based fusion power startup Wednesday.

led the round for the 2-year-old company, joined by , and other backers. Inertia plans to use the funds toward a fusion pilot at , which will involve building the world鈥檚 most powerful laser and a production line to mass manufacture .

The financing is the latest in a string of recent, very large deals around both fusion and nuclear fission. Per 附近上门 data, both funding and deal volume for the space hit a high last year, and 2026 is off to a promising start as well.

Headline deals, leading fundraisers

It鈥檚 mostly funding announcements, but not exclusively. On the fusion front, the highest profile recently proposed deal was 鈥檚 surprising announcement in December that it plans to combine with fusion company in what TMTG called a stock transaction valued at more than $6 billion.

The deal is a long time coming for TAE, which was founded in 1998 and is the oldest operating venture-backed fusion energy company in the 附近上门 dataset. The company has seen at least $1.5 billion in prior known funding to date.

Other fusion companies have also been prodigious fundraisers. The leader is , with $2.86 billion in equity funding, while other standouts include ($1 billion), ($900 million) and ($357 million).

Nuclear fission is another hot area for investment, with over $2.5 billion in funding last year, per 附近上门. The largest deal was a $700 million Series D in late November for , a developer of advanced nuclear reactor and fuel technology.

Activity looks to be accelerating further this year, with more than $270 million in funding, including a $140 million round two weeks ago for Tennessee-based , which manufactures advanced nuclear fuel for new reactors.

Public markets too

Public investors also appear receptive. , which develops nuclear reactors, went public in 2024 through a merger with a SPAC launched by . It鈥檚 down quite a bit from the height scaled late last year, but still had a recent market cap around $10 billion.

Other SPAC deals have also popped up, including , which wants to develop energy parks with small modular reactors to meet data center demand, and , a developer of light-water micro-modular reactors. Meanwhile , a developer of small modular nuclear plants, completed a SPAC merger in October.

Related 附近上门 query:

Related reading:

• A Little Background On Fusion Funding And TAE Technologies, The Company Merging With Trump Media
• Fusion Startup Helion Powers Up With $425M Series F
• Nuclear Fission Shows Continuing Popularity (With VCs, At Least)
• Cleantech鈥檚 Rough Year Ends On An Up Note
• Bill Gates-Founded Nuclear Energy Startup Raises $650M From Nvidia鈥檚 VC Arm, Others
• Fusion Funding Has Fizzled

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Today, it wouldn鈥檛 even be enough to buy one, if it was the newly combined and , now valuing itself at $1.25 trillion. It would also fall short for purchasing both of the next two 鈥� and 鈥� at post-money valuations they鈥檙e reportedly seeking.

So how much would it take to buy the top 100 at current valuations? About $3.5 trillion, according to an estimate from private share marketplace . 2

Call it the age of valuation escalation. Leading startups, traditionally known for their skill in growing businesses, are now demonstrating a similar mastery of scaling how much they鈥檙e worth.

It鈥檚 not exactly a new phenomenon, as top venture-backed companies have a long history of securing significant up rounds. What鈥檚 remarkable about the current era, of course, is the sheer size of the valuations.

The past couple months have offered a particularly fast-moving blur of聽 reported valuation gains we thought might warrant a summary.

To illustrate, we鈥檙e highlighting gains in two categories: companies valued at $100 billion-plus (the biggest unicorns)聽 and those valued at between $20 billion and $100 billion (the next-biggest unicorns). Both are seeing some big swings up and to the right.

The biggest unicorns

We鈥檒l start with the biggest recent upward moves at the most highly valued U.S. companies, in order of valuation:

: SpaceX acquired 鈥檚 xAI last week in a that will reportedly value the combined company at $1.25 trillion. The deal comes in advance of an anticipated IPO later this year.

: The generative AI giant is reportedly in to raise $100 billion in fresh funding at a valuation of $750 billion or more. In October, the company at a $500 billion valuation.

: The Claude chatbot developer and OpenAI rival has reportedly at least $10 billion for a new financing at a $350 billion valuation this year, and is said to be likely to a total of more than $20 billion.

: The AI and data unicorn Monday that it has raised at a $134 billion valuation. The latest financing includes $5 billion in equity investment and $2 billion in debt funding. The company also said it crossed a $5.4 billion annual revenue run-rate.

: The autonomous driving company raised $16 billion in last week at a $126 billion post-money valuation.

: The payments platform a tender offer a year ago at a $91.5 billion valuation. It鈥檚 unclear what its most recent valuation would be, although market trends indicate it would likely be higher.

The next-biggest unicorns

: The blockchain and cryptocurrency company had a $40 billion valuation a in November.

: The developer of general-purpose humanoid robots a $39 billion post-money valuation for its last financing, a .

: The AI financing automation platform was at $32 billion in November, up from $22.5 billion just a few months earlier.

: The AI startup was at $32 billion as part of a in April.

: The defense tech unicorn secured at a $30.5 billion valuation in June.

: The AI processor developer picked up a round last week that聽 set a post-money valuation for the company of approximately $23 billion.

: The crypto exchange was reportedly around $20 billion after a funding round in November.

: The company known for its Cursor AI coding platform announced in November that it raised $2.3 billion in Series D funding at a $29.3 billion post-money valuation.

Gains are quite recent

Looking at the companies in both the biggest unicorns and next-biggest unicorns categories, what鈥檚 striking, in addition to the huge valuations, is how recently so many of these companies set or secured these high numbers.

Just over a year ago, SpaceX鈥檚 valuation hit $350 billion following a closely watched secondary share sale. That was considered quite high at the time.

And just 14 months ago, OpenAI鈥檚 valuation was . It was also considered quite high. Go figure.

What鈥檚 also noteworthy is that many of the next-biggest-unicorns secured their highest valuations to date in the last couple months of last year. That was prime-time for valuation escalation, perhaps in anticipation of an opening IPO window and growing investor consensus regarding early leaders in hot, emerging sectors.

Will these numbers hold up? Who knows. But one thing is clear: Anyone predicting a retraction for the 鈥渉igh鈥� valuations attributed to leading unicorns a year or two ago has so far been mostly very wrong.

Related reading:

• The 附近上门 附近上门
• Cursor鈥檚 $2.3B Financing Reminds Us: Coding Automation Is Still Ultra-Hot

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