This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We’ve included both startups and publicly traded, tech-heavy companies. We’ve also included companies based elsewhere that have a sizable team in the United States, such as , even when it’s unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as “unclear.”

Our data shows that despite record venture capital spending in 2026 — nearly $680 billion through Q3 — investors are writing fewer checks to seed and early-stage companies, the businesses one typically thinks of when we talk about startups.

Large rounds dominate funding

One of the best ways to visualize this dichotomy is by looking at how much of the record global venture funding this year went to funding rounds across different tiers: less than $100 million, $100 million to just under $1 billion, and $1 billion and more.

As illustrated above, the funding surge this year is concentrated in those largest rounds, while the smaller-round market has essentially flatlined since 2023.

The massive surge in billion-dollar-plus rounds in Q1 and Q2, of course, was thanks to record-setting financings for and . But it’s not just those two companies concentrating capital at the top. About one-third of Q3 funding went to just 27 companies raising billion-dollar-plus rounds.

Deals keep falling even as dollars rise

In North America, the largest startup market, deal counts have trended downward over several years, even as venture investment soared.

In Q3, reported deal counts in North America fell 16% year over year and 14% quarter over quarter, reaching the lowest quarterly total since 2023, even as North American funding dollars jumped 50% year over year to $92 billion. The contraction is concentrated in seed and early-stage deals —��North American late-stage counts actually increased year over year.

The early-stage slump

Series B is a particularly stark illustration of the dollars-up-but-deals-down trend. Series B marks the stage when startups are maturing: they typically show some growth and traction but still have plenty of upside potential. But while dollars invested in U.S. startups at the Series B stage hit a multiyear high so far this year, deal count is on track to hit a multiyear low.

It’s a similar story among seed and Series A startups, with money concentrating into larger rounds even as deal count dips.

Looking ahead

Anthropic, the biggest venture-backed private company of all, is slated to go public this month in a potential $2 trillion IPO, offering a prominent test of public investors’ appetite for the companies driving this venture boom.

A strong public-market reception could reinforce venture investors’ willingness to finance enormous AI bets, while a disappointing debut could put unicorn valuations under pressure.

But neither outcome guarantees that funding will reach a wider pool of startups. The real measure of a broader startup recovery we’ll be watching: whether deal counts start to rise alongside the dollars.

Related Crunchbase query:

Related reading:

• North America’s Startup Funding Falls In Q3 As AI Giants Eye The Public Markets
• Series B Funding Is Up, But Fewer Deals Are Getting Done
• Jumbo-Sized Series A Rounds Are On The Rise
• California Startups Have Raised More So Far In 2026 Than The Entire US Ever Has In A Single Year

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Startups in the travel tech space have raised $2.2 billion this year through the third quarter, according to Crunchbase data. The largest funding recipients span a wide range of categories including hotel management software, travel booking platforms, alternative accommodations and corporate events. The mix also includes electric aviation, travel finance and insurance, and consumer hospitality businesses, reflecting a broad definition of travel tech.

The broad trend: Investors are making far fewer bets on travel startups than immediately before and after the COVID-19 pandemic, Crunchbase data shows. This year could be the lowest since at least 2018 when it comes to dollars raised and deal count.

AI is a recurring theme among the largest funding recipients, with many using it to perform functions such as automating hospitality operations, personalizing bookings and streamlining travel planning.

The numbers: In the first three quarters of 2026, startups in the travel tech space have raised $2.2 billion globally across just 302 funding rounds, Crunchbase data shows. That’s down fairly significantly so far from the $3.7 billion raised in 2025. Deal volume is on track to fall for a third consecutive year — pointing to a market where investors are putting more money into fewer companies.

Funding and deal volume in recent years remain far below past levels. In 2021, for example, funding in the sector topped $12 billion. That’s a bit surprising considering that so much travel was halted in the midst of the COVID-19 pandemic. But even prior to the pandemic, funding and deal totals were much higher — totaling $9.4 billion across 1,277 deals in 2018 and $10.4 billion across 1,190 transactions in 2019.

Even travel industry researcher noted the funding downturn in a recent newsletter, saying: “Though it’s too early to say whether this is a long-term market movement, it’s a clear sign of the time��.”

Notable deals

Interestingly, several of the year’s largest deals involved startups headquartered outside of the United States.

The year’s largest funding recipient through the third quarter was, which raised a $300 million Series D in January led by, with participation from,,, and. The Amsterdam-based hospitality software company helps hotels manage reservations, payments and operations, and says it plans to expand its use of AI agents to automate workflows. The financing valued the company at $2.5 billion.

Chinese electric aircraft developer raised about $144 million in February in a financing led by , joined by, and other investors. Chengdu-based Aerofugia, also backed by automaker, develops electric passenger aircraft that take off and land vertically, often called flying taxis.

Montreal-based secured a from in February. The startup uses AI to help publishers, creators and event organizers turn their audiences’ interest in travel into bookings and affiliate revenue.

Home-swapping platform announced an led by in February. The San Francisco-based startup operates a members-only network where travelers exchange stays in one another’s homes. Its announcement also included a $40 million Series B co-led by and CEO , bringing the combined funding disclosed that day to $125 million.

Another large deal went to Paris-based, which announced a in February led by with participation from, and . The company uses AI to help businesses source venues, organize corporate events and manage related purchasing and payments.

Exits

Several travel startups have found buyers in 2026, including companies developing AI tools for planning and booking trips. acquired corporate travel AI assistant in February, while acquired AI trip-planning startup in July. Both deals illustrate a trend of established travel companies buying startups to expand their AI capabilities.

Expedia also completed its acquisition of venture-backed , a booking platform for museums, attractions and experiences, in February.

On the IPO side, a potentially prominent prospective exit fell through. Travel experiences platform withdrew its planned U.S. offering in October after having filed last November.

Still, the downturn in startup funding and dearth of IPOs in the sector hasn’t affected one of the bigger now-public players in the space. Corporate travel booking and expense management platform has seen its stock rise nearly 60% this year. Formerly called TripActions, it began trading on under NAVN in October 2025. Its stock closed at $27 on Oct. 28, up about 58% this year from its Dec. 31 closing price of $17.08. It was also 8% above its $25 IPO price, meaning much of this year’s gain recovered from losses following the listing.

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Clocking in at 335 pages, the document wasn’t exactly light reading. However, after a preliminary once-over, we highlight a few of the more intriguing disclosures below:

Revenue growth is unusually rapid

For the first half of this year, Anthropic reported revenue of $16.3 billion. That’s up 14x from the same period in 2025.

Things aren’t slowing either. While the S-1 doesn’t forecast the second half of this year, has that Anthropic could reach a $110 billion annualized revenue run rate by year-end, citing analysts who met with company executives.

But losses persist

Anthropic also posted a $693 million operating income loss for the first half of 2026, down about two-thirds from last year. Its net loss for this year’s first half was $80 billion, of which about 90% stemmed from what it called a “change in fair value of convertible notes, derivative liabilities, and ��.”

Founders hold similar stakes

Each of Anthropic’s seven founders holds between 230,000 and 246,000 shares of Class A stock, per the S-1. This kind of egalitarianism seems unusual given that startup prospectuses usually show a founding CEO with a much larger relative stake compared to co-founders.

The company’s co-founders include , , , , , and .

Anthropic’s risk factors take up 93 pages of its IPO filing

So, is investing in Anthropic a risky bet?

Anthropic certainly seems to think so. The company’s IPO prospectus contains a staggering 93 pages of risk factors. They start at page 27 and end on page 120.

Some are pretty mundane. For instance, the company is concerned that slower revenue growth could potentially constrain its ability to fund frontier AI development and attract and retain top talent. Having incurred significant debt to date, it’s worried about the ability to keep servicing those obligations while still covering operating costs.

But more unusual risks, like rogue AI agents, are in there too. In one section, Antrophic notes that “future models are expected to demonstrate capabilities that could provide meaningful assistance to malicious actors seeking to develop chemical, biological, or other dangerous weapon��.”

Related Crunchbase query:

Related reading:

• Oura Hits Pause On IPO While Anthropic’s Prospectus Reveals The Cost Of Its AI Ambitions
• The AI IPO Wave Is The Dot-Com Bubble’s Evil Twin. Anthropic’s Delay Just Proved It.
• Anthropic Files Confidentially For IPO
• A Hard Year For Software IPOs

Illustration:

When a company delays its IPO to show investors better numbers, that’s a strategy. When the NYC pension fund publicly asks you to delay it over AI safety concerns, your chief rival’s CEO calls his own IPO “ill-advised” and postpones it until at least next year, tech stocks are sliding on open-weight model fears, and your own prospectus warns of “existential risks to humanity” from the product you’re selling, that’s not a strategy.

That’s a market sending you a message.

‘s numbers are extraordinary. Its third-quarter 2026 revenue was $16.8 billion — up 45% quarter on quarter. That means the company’s annualized revenue run rate is roughly $67 billion.

What the S-1 actually says

Here are the numbers from Anthropic’s :

• $4.6 billion: Anthropic’s full-year 2025 revenue.
• $7.33 billion: What Anthropic spent on compute and infrastructure in 2025 alone — more than its total revenue.
• $42 billion: Anthropic’s net loss in 2025. The roadshow will immediately explain that $34 billion of it was a noncash accounting charge — an adjustment to the estimated value of financing instruments that could eventually convert into shares.
• $518 billion: How much Anthropic expects to spend, at least, over the course of a decade on AI infrastructure with six partners, including , and , according to the confidential IPO prospectus by . About 80% of the total is noncancelable or payable regardless of usage.
• $2 trillion: That’s the IPO valuation Anthropic is still targeting for November, a figure representing 435x its 2025 revenue.

One more number from the S-1 that the roadshow will bury: A quarter of Anthropic’s 2025 revenue came from two unnamed customers.

Three signals the market is sending

The IPO delay is the headline. It isn’t the only signal worth reading.

Signal one: NYC Comptroller Mark Levine. Last week, the supervisor of New York City’s pension funds — which hold roughly $7 million in Anthropic exposure today, but could hold dramatically more if Anthropic enters the Russell 3000 post-IPO — sent to Anthropic CEO urging the company to pause the IPO entirely.

The risks he cited are the same ones Anthropic’s own prospectus flags: increasingly autonomous AI systems that could behave in unexpected ways, create security problems, and in the document’s own language, pose “existential risks to humanity.”

Signal two: delays ’s IPO. OpenAI has said it will not go public in 2026. CEO Altman called an IPO at this moment given AI safety concerns, and while that framing is partly strategic — positioning OpenAI as the more responsible actor — it’s also a real signal when the CEO of the second-most valuable AI company in the world looks at current conditions and decides to wait.

Signal three: The chip selloff. Last week’s was driven partly by concern over AI-buildout costs. ’s helps explain the concern: Through 2030, it sees at most $3.6 trillion in revenue attributable to AI data centers, versus $8.5 trillion needed to cover planned investment, financing costs and a 12% return.

The open-weight problem

Here is the structural argument that no amount of Q3 revenue growth resolves: Anthropic’s $2 trillion valuation requires closed-model AI to maintain a durable pricing premium over open-weight alternatives. That premium is being systematically dismantled.

Open-weight AI is challenging the economics behind closed-model valuations. Enterprise adoption rose from 23% to 67% in 2026, with companies reporting 70%–90% cost savings. R1 offers frontier-level performance on most enterprise benchmarks at 7 cents per million input tokens; independent analysis puts Anthropic’s comparable input and output pricing at roughly 18x and 23x higher, respectively.

DeepSeek’s weights are free to download, allowing enterprises to self-host without a model-provider API contract or per-token billing, while the capability gap with closed models reportedly closes within six months of a frontier release.

Against that backdrop, — the research firm that called Anthropic’s proposed offering “an unprecedented test of investor gullibility” — that justifying a $2 trillion valuation would require an immediate 10% after-tax operating margin sustained for a decade, extraordinary revenue growth, and $358 billion in annual after-tax operating profit by 2035.

Why this is worse than 1999

The dot-com bubble had a genuine excuse. Nobody knew what the internet would become. The AI IPO wave of 2026 does not have that excuse.

Worldwide spending on AI is , up 47% year over year, according to Gartner. Yet a July 2025 estimated that 95% of the organizations it examined were getting no return from generative AI. Meanwhile, said in its earlier this year that just 7% of surveyed leaders say they’ve established ROI from AI.

This is the context in which Anthropic is asking investors to pay 435x 2025 revenue for a company with $518 billion in locked infrastructure commitments, 25% revenue concentration in two unnamed clients, and a pricing moat that open-weight models are closing in six-month cycles.

The delay from October to November doesn’t change any of those numbers. It adds one quarter of revenue data.

What actually survives this correction

Here is what no AI IPO roadshow will say out loud: The biggest enterprise AI winners will not necessarily own the most expensive model. They will own the architecture that turns any model into governed, auditable, measurable outcomes — through proprietary enterprise data, domain-specific fine-tuning, multiagent orchestration and controls that make autonomous execution trustworthy.

That value does not depend on keeping model capabilities scarce or securing a $2 trillion valuation. It compounds in production, where customers understand that the model is not the product. The deployment is.

The dot-com crash did not happen because the internet was bad. It happened because the price was wrong. AI investors face the same distinction: Real technology does not automatically justify its valuation. A strong quarter and a one-month delay do not close that gap. November simply gives Anthropic more time to explain the math.

, the co-founder and CEO of , is an entrepreneur and product leader with more than 20 years of experience across cybersecurity and AI. He has built three generative AI companies, including one that reached unicorn status. Gurtu is known for turning advanced AI into scalable, real-world platforms, and previously contributed to successful exits, including acquisitions by , and , and a public company, . He holds a master’s degree from the and CISSP certification.

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The partner began his career as a high school math teacher with after studying astrophysics at . He later worked in college access and Yale admissions, and earned graduate degrees in business and education from before joining Reach. As a teacher, he was an early user of , an experience he describes as “game-changing.”

His investment firm, Reach Capital, has also evolved. Founded in 2015 after spinning out of , the San Francisco-based firm initially focused on technology for K-12 schools and districts. Its mandate has since expanded to learning, health and work, with investments in companies including ClassDojo, , and .

In August, Reach announced its fifth fund, a $265 million vehicle that brought its assets under management to nearly $1 billion. The firm, which has backed more than 180 companies, plans to invest in approximately 50 additional teams through the new fund, targeting pre-seed, seed and Series A rounds.

Crunchbase News spoke with Kim about how Reach’s education roots inform its broader strategy, what it saw in AI detection company , and why some of its best investments initially divided its own team. We also discussed how ownership stakes and secondary transactions have shaped the firm’s approach to returning capital.

This interview has been edited for length and clarity.

Crunchbase News: You started your career as a high school math teacher. How did that shape your view of education technology?

Kim: I taught ninth through 12th grade, mostly Algebra I and geometry. I was very early to ClassDojo. Back then, it was a very simple classroom management tool, and it was game-changing for me. It has since evolved into a sprawling suite, but I already knew technology could move the needle.

Reach began as an education investor. How did the firm’s focus evolve into learning, health and work?

Kim: Reach started in 2015, and our original focus was exclusively on learning and education technology — more specifically, technology sold into schools and districts.

Our founding partners were seeing the growth of devices in schools, one-to-one device ratios and broadband penetration. The belief was that you would start to see a proliferation of technology serving students, teachers and administrators and moving the needle on learning outcomes. That is what ended up playing out. Some of our early bets are now household names in edtech, such as , and ClassDojo.

Reach spun out of NewSchools Venture Fund, where three of our four founding partners had started doing for-profit seed investing before 2015. Over time, we branched out from K-12 into higher education, and then into consumer learning.

We also started getting pulled toward healthcare and work. Health and wellbeing affect a learner’s readiness for the classroom, so we were being pulled toward mental health, behavioral health and developmental health. On the work side, formal learning typically ends in college, but you still need the bridge to a job. Once you’re on the job, you need to continue learning, especially in the current environment.

A few years ago, we officially put a stake in the ground and said we invest across learning, health and work. The common thread is that these are the most important systems underlying human development and flourishing.

So far, the breakdown has been roughly 50% learning, 25% health and 25% work.

These sectors have different buyers, regulations and business models. What did investing in education teach you that translates to healthcare and work?

Kim: Education is highly regulated, and institutional buying is complex. Many stakeholders must provide input, and decisions often require board-level approval. We were familiar with and comfortable with regulated products and complex go-to-market motions.

Selling to a university or a K-12 district, in terms of the complexity and the need to intersect with regulation and policy, is quite comparable to selling to a health system, hospital or insurance carrier.

Cartwheel is a good example of where our experience gave us an advantage. It provides mental health teletherapy, but the customer is the school district and the care setting is in schools.

While we were relatively new to behavioral health when we made that investment, we were very familiar with selling to school districts and implementing an intervention within a school setting. That gave us an advantage both in winning the investment opportunity and supporting the company afterward.

Reach talks about “elevating human potential.” What makes an investment fit that mandate?

Kim: We look for products and services that increase access to quality education, quality care or quality earnings, or some combination of those. We really have to see the mechanism for that happening.

It’s not good enough to be pitched a SaaS or AI product and have the founders say, “This will make people more productive at work, and therefore maybe they will earn more or enjoy their jobs more.” That’s plausible, but it isn’t a good enough theory of how the product actually increases access to quality earnings.

Several teammates, including myself, are former educators. We care about meaningful, needle-moving impact.

We address access from both the demand side and the supply side. On the demand side, we build products that create delightful, impactful experiences for consumers: , Stepful’s allied healthcare training, and Replit for what its founder calls “citizen developer��.”

On the supply side, it’s helping schools, health systems and companies function more efficiently and effectively. , for example, helps logistics and hospitality companies hire hourly workers better and treat them better. We think about the problem from both sides of the market.

Reach backed GPTZero, which agreed to join . Was that an investment in solving a problem created by AI?

Kim: That wasn’t actually our investment thesis. In my opinion, they have the best AI detector on the market. But their thesis was that AI use in education should not be banned. It shouldn’t be punitive or become a cat-and-mouse game between the educator and the learner.

Since all of us as professionals use AI in our jobs, students should learn how to use it responsibly and thoughtfully. It was the first AI detector company I had seen with that positioning, as opposed to helping schools, districts and universities clamp down on AI usage.

That’s why I liked it. I don’t see it as a problem for students to use AI for certain purposes, such as brainstorming, organizing thoughts and editing. GPTZero helps educators work with students and teach them how to use AI responsibly.

Do you see other opportunities to build companies around the problems AI creates?

Kim: Yes, most notably in AI governance and evaluations.

Going back to regulated categories, you need to make sure AI is not irresponsibly using personally identifiable information in education or healthcare. Those categories have very strict regulations, and infrastructure companies are emerging to help ensure AI products interact with that data responsibly.

I do think there are opportunities for large, successful, profitable companies to be created in response to the problems AI creates.

What does GPTZero’s sale tell you about which AI products can become durable standalone companies and which belong within larger platforms?

Kim: I don’t know whether GPTZero is an example of either. They had been fielding acquisition offers from pretty early on. This particular opportunity arose earlier this year, and there was a compelling synergy story with Superhuman, formerly .

The founders and the board agreed that it made sense to combine the companies. But they could just as easily have remained standalone. There is another AI detection company called that’s quite good and is still a standalone, venture-backed company.

It’s not clear to me that this category can’t exist on its own. We have to be very case-by-case. We don’t know until we see the trajectory, financial sustainability and evolution of the market, whether it makes sense to explore a strategic acquisition or grow independently and perhaps one day become a publicly traded company.

Reach has backed more than 180 companies. What patterns have you noticed among those that exceeded expectations?

Kim: The reality of venture is that a very small share of investments yields the overwhelming majority of returns. Given the outlier nature of those successes, it’s hard to identify unifying principles that always predict a successful exit. If we could, we would only make those investments.

But there are a few things we think are somewhat common across our wins. One is a very smart, gritty, genuinely mission-driven founder or founding team, often with a strong personal connection to the problem.

Second is some sort of nonconsensus insight about the market. That often comes from experiencing the problem themselves, sometimes as practitioners in the industry — a former educator, for example.

Third is a defensible and scalable business model.

The nonconsensus insight is funny because it’s what makes these companies look weird and maybe uninvestable in the beginning. Even within our team, they’re often the most polarizing opportunities. You look at the votes, and a bunch of people vote no, but it gets just enough yeses to pass. Then, 10 years later, it’s one of our best investments.

People lose sight of this in the current environment, but the best opportunities are often the most nonconsensus. Those can be the most creative and interesting founders, and you can get in at reasonable valuations early and help them become consensus bets.

Reach says its first two funds have returned investors’ capital. What produced those returns?

Kim: We’re no exception to the power-law distribution in venture. Our first fund has been returned 2.2x by literally two investments. Our second fund has been returned 1.2x by one investment. These are cash distributions, by the way.

Especially in the second fund, there’s still remaining value, so we’ll see how high that number gets. One fund-returning exit would be great; two or three would be amazing. That’s what we’re striving for.

How has that experience changed your approach to ownership and follow-on investing?

Kim: We’ve had some great exits where, given how prominent the company is, you might be surprised that our share of the returns wasn’t that large. That’s purely an ownership calculation.

From our first fund to today, we’ve meaningfully increased our initial ownership target. We now try to get as close to 15% ownership upfront as possible.

That means writing slightly bigger checks and reserving relatively less for follow-on investments and more for the initial check. It’s important for the exit math and distribution math.

What role do secondary transactions play in getting liquidity?

We’ve become very proactive and conscientious about liquidity opportunities. Much of our distributions to investors come from secondary sales — selling our shares in a subsequent round, before an actual exit.

Whenever there’s an opportunity, we evaluate it. We consider how long we’ve been in the company, how much we can get out, and how much upside we think remains.

As companies stay private longer, early-stage investors are achieving liquidity much more often through secondary transactions. I like to think we were early to that trend, but it’s certainly mainstream now.

Sometimes, when we see enough upside, we’ll buy secondary shares. GPTZero was an example: After our Series A investment, we bought secondary shares, and I’m really glad we did because it worked out well. For us, we sell more often, but occasionally we buy.

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• ‘Nobody Wanted to Give A Former Principal Money’: How An Educator Built An Edtech AI Startup With $63M From VCs

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All told, startups in Latin America raised a combined $1.3 billion across seed- and growth-stage deals in the three-month period ending Sept. 30. That was up 26% year over year but down 9% from the second quarter.

For perspective, we charted out total investment, color-coded by stage, for the past 12 quarters below.

Of the quarterly total, $757 million went into late-stage and growth deals, up 74% compared to the $435 million that flowed into such deals in the third quarter of 2025. However, that is down 21.3% from the $962 million in late-stage and growth rounds raised by LatAm startups in the second quarter. (Although it’s worth noting that Q2 2026 represented the most raised at that stage since the second quarter of 2022.)

Mexico startups have raised $1.52 billion in venture funding during the first three quarters of 2026, per Crunchbase data. Notably, that’s about 40.7% more than the $1.08 billion Brazil’s startups raised in the same period. This is unusual as Brazil has historically been the largest recipient of venture funding in Latin America.

Still, Brazil continues to attract a broad range of investors.

“Over the past year, we have seen investments into Brazil from firms including , , , and ,” , managing partner of , told Crunchbase News. “For several, this marked either their first investment in Latin America or their first in a long time.”

, Endeavor Catalyst’s vice president and head of Latin America, noted that Mexico drew more venture funding in the first half of 2026 than Brazil. But much of �ѱ�澱����’s funding went to a few startups, he pointed out, while Brazil recorded many deals.

“Investors are concentrating on a few Mexican winners, while Brazil retains the breadth,” Piquet said, adding that Brazil also leads in realized exits.

Table of contents

• Outsized rounds
• An uptick in seed funding
• ‘Another rediscovery moment’
• Methodology
• Glossary of funding terms

Outsized rounds

Several mega-raises took place in Latin America in the third quarter. And notably, the two largest funding deals were not for companies in Brazil or Mexico, which have historically raised the largest share of venture capital in the region. They also weren’t to fintech, which typically has the most funding recipients in the region, but rather to energy-related startups.

, the Chilean project company for ’s Black Giant lithium project, raised $225 million in a July round at a reported $900 million valuation. And Colombia’s , a clean energy company that builds and operates solar power systems for homes and businesses, landed a $129 million Series C in August at a reported $600 million valuation.

�ѱ�澱����’s , an AI-powered financial services company, raised a $125 million Series C in September at a $1.6 billion valuation. led the financing, which included a senior debt funding facility provided by .

And in August, Brazilian fintech brought in $106.9 million in an equity and debt seed round at a $626 million valuation.

Also in August, Brazil’s , an AI-native wealth advisory company, landed $85 million in�� what Crunchbase shows is the largest seed funding deal ever raised for a Latin American startup. Founded by , former CTO of , and , founder of , the startup is backed by ,,, and .

An uptick in seed funding

Latin America experienced a marked surge in very early-stage funding totals in the third quarter, largely thanks to the seed rounds mentioned above.

Of the $1.3 billion raised by Latin America’s startups in the third quarter, about 20.5% — or $266 million — was raised across the angel and seed stages.

That’s up 128% from the $117 million raised across those stages in the second quarter of 2026, and up 62% from the $165 million in the same quarter last year.

Just over 22%, or $288 million, was raised at early stages, down 18.9% from $355 million in the second quarter and 34% from $437 million in the same period last year.

We expect the Q3 deal counts to rise somewhat over time; however, as seed rounds in particular are commonly reported weeks or months after they close.

‘Another rediscovery moment’

Latin America is drawing renewed interest from international venture investors, according to Endeavor’s Taylor. He said international VCs were more active in the region, particularly Brazil, during the first three quarters of 2026 than in 2024 and 2025 combined, through both new investments and follow-on funding.

“I joke that investors from the U.S. ‘rediscover’ Latin America every five or six years, and I think we are approaching another rediscovery moment,” Taylor told Crunchbase News.

Endeavor Catalyst recently closed its oversubscribed $320 million fifth fund. Latin America accounts for nearly 40% of its portfolio by both deals and dollars, and Fund V had made 14 investments in the region as of Sept. 22. Taylor also pointed to opportunities beyond Brazil and Mexico, including repeat founders building global businesses from Colombia and emerging companies in Venezuela and Central America.

Liquidity is improving, although acquisitions and secondary transactions are providing more opportunities than public listings, Piquet said. He pointed to approximately 60 private Latin American technology companies that have each raised at least $150 million as a potential pipeline for future IPOs.

“The exit door is open, although it is not primarily opening through IPOs yet,” Piquet said.

Taylor expects more exits could help bring global investors back consistently, with potential listings by companies such as , or contributing to another “rediscovery moment” in 2027.

International investors remain interested in Latin America but have become more selective about deploying capital, according to of . Firms with a longstanding presence in the region remain engaged, while many that entered during the 2020–2021 investment boom have shifted toward AI opportunities elsewhere and are concentrating on existing portfolios, she noted.

“Global investors are increasingly investing behind themes rather than geographies,” she said, putting Latin American startups in more direct competition with companies in the U.S. and other markets.

Investor interest is increasingly centered on AI-native businesses and financial infrastructure, particularly stablecoins and cross-border payments. Gadala-Maria said more founders are targeting global markets from the outset, marking a departure from the previous wave of startups that adapted established business models to underserved local markets.

“Increasingly, we’re seeing founders build globally competitive technology companies from Latin America, rather than necessarily for Latin America,” she said.

Gadala-Maria agrees that Brazil and Mexico remain the region’s largest markets, with Brazil offering a deeper, more mature startup ecosystem and Mexico providing substantial room for financial services innovation. She pointed to Argentina as an overlooked market, citing its strong technical talent and founders building companies for global customers.

Correction: Details regarding Kapital’s Series C were updated. The deal was a debt and equity raise.

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Methodology

The data contained in this report comes directly from Crunchbase, and is based on reported data. Data is as of Oct. 5, 2026.

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

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

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

Glossary of funding terms

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

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

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

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

Illustration:

European startup funding reached $25 billion last quarter, well above the $14 billion invested in Q3 2025 and up slightly from the $24 billion invested in Q2. Quarterly funding was up 77% year over year, per Crunchbase data.

While the U.K. continued to lead startup funding totals last quarter, Germany and France both posted their strongest quarters since the boom years of 2021 and early 2022. Last quarter’s funding totals were boosted heavily by a $3.5 billion investment in Paris-based , the largest venture round ever for a Europe-based company.

Table of contents

• Billion-dollar fundings
• Venture expands beyond the UK
• Late-stage rise
• Early stage
• Seed
• Looking forward
• Methodology
• Glossary of funding terms

Billion-dollar fundings

Four European companies raised billion-dollar-plus fundings, representing close to 40% of the region’s startup capital raised last quarter.

They include Mistral, which also announced its new open-source model Large 4, an LLM it claims outperforms competing open source models on several vectors.

Data center provider raised a convertible note of $3.36 billion that it says it will convert once it goes public, which it has filed plans to do.

Two defense tech startups also raised billion-dollar-plus rounds last quarter: raised a Series E of $1.8 billion, and raised $1.2 billion.

AI drove 75% of European funding this past quarter — totaling $18.8 billion — the highest proportion on record.

Investments in physical tech in Europe in defense, data centers, energy, aerospace and robotics accounted for roughly half of European funding in Q3.

Venture expands beyond the UK

The U.K. led the region’s quarterly funding totals with $7.5 billion even as Germany and France gained market share for venture capital funding in Q3 with $5 billion and $4.8 billion invested, respectively.

The fourth-largest market, Sweden, saw $1.5 billion in venture capital invested while the Netherlands and Spain posted their strongest quarter since the COVID boom years with $1.4 billion and $1.2 billion invested in their startup markets, respectively.

Late-stage rise

A total of $17.3 billion — 70% of Europe’s Q3 venture capital — was invested in late-stage deals in 83 companies.

Outside of the billion-dollar rounds, large late-stage fundings between $400 million to $700 million were raised by Stockholm-based health scanning provider ; Lisbon-based autonomous drone developer ; Spain-based , which offers model compression technology for enterprise AI adoption; Amsterdam-based AI deployment startup ; Bayern-based space transportation company ; and Stockholm-based AI web application builder .

Early stage

Europe’s early-stage funding totaled $5.7 billion into just over 200 companies in Q3, down quarter over quarter and flat year over year.

The largest Series A funding deals, each $200 million or over, were raised by Eindhoven-based AI semiconductor startup and Zürich-based autonomous construction company .

Large Series B rounds were raised by Munich-based fusion energy company and London-based optical computing provider .

Seed

Europe’s seed funding fell quarter over quarter and was flat year over year. Around $2 billion was invested in more than 750 companies at seed stage in Q3.

London-based managed inference company raised the largest seed round at $100 million, followed by Munich-based AI for robotics startup , which raised $55 million.

Looking forward

Europe’s momentum in 2026 continued in Q3, which was the second quarter this year when venture totals topped $20 billion. Those are figures not seen in the region’s startup scene since Q3 2022. Along with growth in AI funding, late-stage funding noticeably increased last quarter with many of the leading rounds sourced across multiple European countries.

Europe represented 16% of global venture capital in Q3, although the true impact of its technology and startups is higher, with many of the region’s startups expanding or moving to the U.S. to capitalize on the rapid growth of AI there.

At the same time, Europe’s ambition to build leading deep-tech companies and meet demand for sovereign AI will be tested in the quarters ahead by its ability to mobilize the substantial capital these businesses need.

Related Crunchbase queries:

Related reading:

• Crunchbase Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
• Reporter’s Notebook: Europe’s Sovereign AI Push Needs Customers As Well As Capital

Methodology

The data contained in this report comes directly from Crunchbase, and is based on reported data. Data is as of Oct. 5, 2026.

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

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

Glossary of funding terms

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

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

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

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

Illustration:

In 2021, putting AI on a slide could get you funded. Small and mid-sized businesses were eager to be early adopters, and they bought fast. I watched companies secure customers, raise money, then struggle to keep anyone using their products. Retention fell off a cliff.

Many of those products were generic: They did what traditional software already did, with AI fairy dust sprinkled on top. Others saved a bit of time on something nobody’s job depended on. A 20% improvement on a noncritical task makes for a nice demo and a dead renewal.

There were never easy AI wins, even if there was easy money going around. The market is now correcting for that. Companies delivering meaningful value are growing faster than anything I have seen in enterprise software. For investors, the question is which of these companies can keep their customers — and whether the coming IPOs will expose the difference.

What makes an AI business defensible today

Before my co-founder and I wrote a single line of code, we interviewed more than 900 mechanical engineers, from juniors to VPs. We asked where their hours went and which of those hours they would pay to get back. Those 900 conversations shaped how I think about building an AI business.

First: the improvement has to justify the investment. Saving 10% on an occasional task will rarely change how a company operates. Reducing a business-critical process from weeks to minutes, or saving serious money, will.

Second: domain expertise. Frontier models are becoming infrastructure. Access to a good LLM is available to almost anyone with an API. The commercial opportunity lies in understanding an industry well enough to solve problems that generic tools cannot.

Third: proprietary context. In terms of engineering examples, every company that builds physical products has accumulated decades of engineering knowledge. Much of it remains buried in old drawings or locked away in the heads of experienced employees. That knowledge is absent from a foundation model’s training data. An AI product that cannot access it will struggle to become essential for the business. The advantage grows as the product becomes embedded in the customer’s workflows and replacing it becomes costly.

What this means for funding and IPOs

The upcoming AI IPOs will put these business models under much greater scrutiny. Public investors will examine net revenue retention and gross margins, assessing whether rapid growth is able to translate into a sustainable business. They will also want to know whether each deployment becomes cheaper to support as the company grows. The standards they establish will influence what private investors expect from earlier-stage AI companies.

I can already see the change in my own fundraising conversations. A few years ago, $1 million in ARR was a milestone. In the AI era, a new product can reach $1 million in ARR in about a year, and shut down a year later. Now ARR alone no longer tells investors enough.

What investors keep asking about is expansion. They want to know whether customers are increasing their spending after the initial deployment. Expansion is the closest thing we have to proof that a product has changed how an organization works: customers have experienced its value and committed more of their own budget to it.

Over the next 12 to 24 months, I expect capital to keep flowing to companies that combine deep industry expertise with significant improvements in business-critical work and access to customers’ proprietary knowledge. They may grow through hands-on deployment rather than viral self-service, but they can expand inside accounts that trust them.

Companies whose products are thin layers over someone else’s model, with impressive customer lists and retention charts nobody wants to show, will find their next funding round much harder than the last one. I would rather grow fast and steady for five years than spectacularly for five quarters.

is the founder and CEO of , the first AI for mechanical engineering — a large mechanical model for physical product design. He conducted AI and mechanical engineering research at as a Fulbright postdoctoral fellow and became the youngest Ph.D. graduate in the history of the Farid has built a community of more than 60,000 engineers and supports underserved youth through his nonprofit initiative.

Related Crunchbase query:

Illustration:

In total, investors poured $92 billion into seed- through growth-stage rounds for U.S. and Canadian startups in the third quarter, per Crunchbase data. That’s a 35% decline from the prior quarter but up 50% from year-ago levels.

Deal volume held fairly steady, with late-stage and early-stage round counts remaining close to prior quarter levels.

Artificial intelligence remained the prevailing theme for startup investors. Per Crunchbase data, roughly two-thirds of total funding this past quarter went to AI-focused companies. Of that, a big chunk went to large rounds for , and .

Overall, however, investment fell across most stages. Early-stage dealmaking posted a particularly sharp decline from a multiyear peak the prior quarter, while later-stage funding was also down.

As for exits, quarter-over-quarter IPO comps were always going to be challenging, given that Q2 featured ’s record-setting market entry. Even compared to a typical quarter, however, the IPO market was fairly sluggish in Q3, with a few biotech, energy and consumer-facing offerings, but no blockbuster tech debuts. M&A activity was more exciting, topped by ’s September acquisition of .

Below, we look over the quarterly numbers in more detail, breaking out investment by stage, charting AI funding, and analyzing exit activity.

Table of contents

• Late-stage and technology growth funding
• Early stage
• Seed
• AI
• Exits
• M&A
• IPOs
• Slower, but not a slowdown
• Methodology
• Glossary of funding terms

Late-stage and technology growth funding

We’ll start with late stage, since that’s where most startup funding went.

For Q3, investors poured $66.45 billion into late- and growth-stage deals, per Crunchbase data. That’s up about a third from year-ago levels, but down sharply from Q1 and Q2 of this year, when OpenAI and Anthropic pulled in financings of $110 billion and $65 billion, respectively.

For the just-ended quarter, the largest late-stage and growth rounds went to Databricks ($5 billion), AI infrastructure unicorn Crusoe ($3.9 billion), ($3 billion), and AI coding startup ($2 billion). Overall, more than a dozen startups attracted late-stage or growth rounds of $1 billion or more, per Crunchbase data.

Early stage

Early-stage investment also held up at historically high levels in Q3, albeit down from prior highs.

Overall, investors put $20.6 billion into early-stage rounds in the just-ended quarter, per Crunchbase data. That was down sequentially from the prior quarter but still well above prior year levels.

A few exceptionally big rounds pushed up the latest quarterly tally. The largest funding recipients included open source AI company ’s $1.1 billion Series A, nuclear startup ’ $660 million Series B, and chip hardware and software developer ’s $500 million Series A.

Seed

Seed-stage dealmaking was also quite busy in Q3.

At least $5 billion went to seed, angel and pre-seed rounds in the just-ended quarter, per preliminary Crunchbase data. That’s a bit below both the prior quarter and year-ago comps. However, we expect the Q3 tally to rise a bit over time as seed deals commonly get added to the dataset a few weeks or months after they close.

The AI space delivered some particularly large seed rounds. Of these, standouts were physical AI startups , which picked up $300 million, and . which secured $90 million.

AI

The percentage of funding going to AI-focused startups also held up at high levels in Q3, per Crunchbase data.

A total of $61 billion went to AI-focused rounds, per Crunchbase data. While that’s down sharply from the prior two quarters, it’s still one of the highest tallies on record.

Exits

As for exits, Q3 featured a number of large M&A deals, particularly in the AI space. The IPO market was a bit quieter, with much of the market’s attention looking forward to enormous offerings from foundational AI pioneers in coming months.

M&A

Acquisition activity was fairly robust in Q3, boosted by a handful of large AI-related purchases. Of these, the biggest was Nvidia’s acquisition of open model development platform Hugging Face for $12.93 billion.

The next-biggest M&A deal was ’s purchase last week of , an AI model and research lab led by AI pioneer , in a stock deal valued around $8.2 billion. Not far behind, in third place, was ’s acquisition of , a startup that routes prompts through different AI models, in an August transaction reportedly valued around $7.5 billion.

In total, there were 11 North American startup acquisitions at reported prices of $1 billion or more in Q3, per Crunchbase data. We list them below.

IPOs

Overall, it was a light quarter for IPO activity. Per Crunchbase data, 17 venture-backed North American companies went public on major U.S. and Canadian exchanges in Q3, per Crunchbase data, collectively raising just under $4 billion.

Three biotech companies raised the largest sums in their IPOs: , focused on mRNA; , a developer of cardiovascular therapies; and , which is working on antibody therapeutics.

Other venture-backed companies that carried out good-sized debuts included , a developer of advanced nuclear fuel, and , the scooter and e-bike rental platform.

Notably, however, it was the IPOs that didn’t happen this quarter that attracted the most attention. By this, we are referring of course to Anthropic, which is reportedly eyeing a�� public list as early as November, and OpenAI, which filed confidentially for an IPO in June and is expected to make its debut in 2027.

Slower, but not a slowdown

Overall, Q3 seems to exemplify the notion that funding activity can decline from peak levels without a clear indication that the investment climate has turned bearish.

Yes, giant funding rounds contracted. However, the maturation of the two most valuable startups, OpenAI and Anthropic, into even more valuable pre-IPO companies, is the kind of thing venture investors want to see.

Moreover, big rounds are still closing at a brisk clip. The AI space is still regularly minting fresh unicorns. And acquirers are snapping up leading names at historically high valuations.

Could things go south from here? Sure. But for now, the AI-driven momentum continues.

Related Crunchbase query:

Related reading:

• North American Startup Funding Shattered Records In First Half Of 2026, Driven By AI

Methodology

The data contained in this report comes directly from Crunchbase, and is based on reported data. Data is as of Oct. 2, 2026.

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

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

Glossary of funding terms

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

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

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

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

Illustration: