Founded by former engineers, Sunnyvale, California-based Tigris is building a network of localized data storage centers that it claims can meet the distributed compute needs of modern AI workloads.

Tigris has raised about $31.9 million since its founding in 2021, . Its October 2025 Series A brought in $25 million from lead investor, with participation from and , which also led its seed round. Other earlier backers include.

Co-founder and CEO told at the time of its Series A that the startup had grown 8x every year since its founding in November 2021.

The planned deal would give Dropbox a storage platform built for developers whose applications run across multiple cloud providers.

A push into AI infrastructure

The acquisition news coincides with what San Francisco-based Dropbox describes as ���a significant shift��� in its AI strategy as it expands beyond storing and sharing files into selling the infrastructure that companies use to build AI applications.

Publicly traded Dropbox also unveiled DBX Agent Cloud, which will initially offer standalone object storage ���泭 a way to store and retrieve large collections of data, including images, video and the files used to train and run AI models.

For Dropbox, buying Tigris offers a faster route into a market that serves a different audience from the individuals and businesses that use its file-sharing software.

���Tigris brings together a product already running in production, an experienced team, and established relationships with AI developers,��� , Dropbox���s chief business officer, told 做厙勛圖 News via email. ���That combination would accelerate our entry into infrastructure for AI applications through DBX Agent Cloud.���

A bigger bet amid a slower deal pace

Dropbox has made 35 known acquisitions, according to 做厙勛圖, with much of that activity taking place in its earlier years. Between 2012 and 2015, the company conducted 23 publicly reported acquisitions, including nine in 2014 alone. In contrast, it made four acquisitions across 2024 and 2025, averaging two per year, roughly 65% below its earlier pace.

Its recent purchases suggest a focus on AI-powered productivity, search and security. Dropbox acquired scheduling startup and document security company in 2024, followed by and in 2025.

While the planned Tigris purchase would be its largest yet, Dropbox has made other sizable purchases over time. Its acquisitions of e-signature company in 2019, document-sharing platform in 2021 and business forms provider in 2022 had purchase prices of $230 million, $165 million and $95 million, respectively.

Two paths into AI

Dropbox���s push into AI infrastructure is one part of a broader strategy that also includes adding AI capabilities to its existing file-sharing product.

For existing customers, the company is bringing AI directly to their Dropbox files to help them find, understand, create, edit and organize content, according to Webster. Customers can switch between supported AI models and work with their Dropbox files in tools like ChatGPT and Claude, with results returning to Dropbox.

For companies and developers, meanwhile, DBX Agent Cloud extends Dropbox���s storage capabilities to the infrastructure behind AI applications and agents.

���DBX Agent Cloud opens a new developer and infrastructure market for Dropbox, while AI inside Dropbox is intended to deepen engagement, retention and expansion among existing customers,��� Webster said.

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Startups in the travel tech space have raised $2.2 billion this year through the third quarter, according to 做厙勛圖 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, 做厙勛圖 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, 做厙勛圖 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 times.���

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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The conversation prompted me to consider something I believe many entrepreneurs miss, and it���s this: Revenue can rise while enterprise value falls.

When revenue and value move in opposite directions

Founders and boards routinely track revenue and profitability over time. But another graph deserves equal attention: enterprise value. Its trajectory can turn downward while the operating numbers continue improving, with significant implications for M&A timing.

Consider a hypothetical SaaS company generating $15 million in annual recurring revenue, valued at 6x ARR. Its implied enterprise value is $90 million. Two years later, ARR reaches $20 million, but the multiple falls to 3.5x revenue. Enterprise value drops to $70 million. Revenue increased 33%, while value declined 22%.

These are only illustrative assumptions to demonstrate how multiple compression can overshadow operating growth. For a founder postponing a sale until the next revenue milestone, the practical implication is clear: Reaching that milestone may still produce a smaller exit.

Qualitative, absolute and relative factors

The revenue multiplier reflects several dimensions that evolve differently. Qualitative factors include management quality, founder dependence, competitive intensity, product differentiation and strategic relevance to buyers.

Absolute financial measures include revenue, EBITDA and cash flow.

Relative performance measures include growth, margins, net revenue retention and gross revenue retention. Although retention metrics are expressed as percentages, their importance lies in what they reveal about customer durability and expansion. Buyers assess these measures together and compare them with relevant peers. As companies mature, their absolute results can improve while their growth rates weaken.

Meanwhile, stronger profitability, better retention or deeper management capabilities may support a higher multiplier. The valuation outcome depends on the combined effect. Boards should therefore ask which characteristics will improve over the next year, which could deteriorate and how buyers might price those changes.

M&A preparation should begin while you are winning

A sale requires preparation, buyer engagement, negotiation and diligence, during which buyers continually reassess performance and forecasts.

Waiting until a slowdown becomes visible can weaken negotiating leverage and raise questions about management���s projections. Strategic buyer demand also follows its own timetable. A buyer may place substantial value on acquiring a particular technology, customer base or geographic presence today. That interest can diminish after an internal product launch, a competing acquisition or a budget change.

Waiting can still create considerable value when additional scale attracts more buyers, profitability improves or concentration falls. Boards can make that case tangible by reviewing estimated enterprise value alongside the operating plan, using scenarios in which revenue grows while the multiple holds, expands or contracts. They should also consider how debt, cash, dilution, liquidation preferences and transaction terms affect shareholder proceeds. Nobody can reliably identify the valuation peak in advance.

However, every decision to delay a sale embeds a view about future valuation. That view deserves its own graph.

, a guest contributor to 做厙勛圖 News, is a seasoned lecturer and strategic adviser to startups and investors, specializing in strategy, growth and M&A. You can for further insights and discussions.

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But that���s often not the case, an analysis of 做厙勛圖 data shows. This year, a number of larger acquisitions of private companies involved startups that haven���t disclosed a new investment for more than four years.

Several of these acquisitions are quite recent ��� and in some cases, quite large. The standout is ���s purchase earlier this month of , a platform for catering and workplace meals, for $2.3 billion in cash. The deal comes nearly five years after Boston-based ezCater raised its last reported venture funding round.

That���s not the only one. A number of companies whose names might feature on a playlist of 2021 boom-era unicorns have sold to acquirers in recent months, including one-time highfliers like , and . In many cases, companies are selling at valuations which, while not puny, are still far below their former peaks.

Who���s getting acquired

To illustrate the extent to which these aging startups are finding acquirers, we used 做厙勛圖 data to put together a list of 15 deals involving well-funded companies that haven���t raised equity funding for four or more years.

Post-peak runway

One big issue for older and middle-aged startups is that valuations hit a cyclical peak in 2021 and early 2022. Many companies that raised during that time had difficulty attracting fresh capital or were reluctant to close new rounds at reduced valuations. Even with the more recent AI boom, little went to this cohort.

Moreover, since the 2021 boom-era rounds were quite large, funded startups commonly had the option to cut operating costs and extend their runways with existing capital. As a result, quite a few companies that haven���t raised rounds in years are still chugging along, managing to survive and even grow without more venture funding.

EzCater is a case in point. In the year prior to its acquisition, the business generated more than $2.5 billion in gross bookings. It had an annual growth rate in the high teens, with average order sizes over $400.

That said, even that revenue and growth rate didn���t secure ezCater much of an acquisition premium from its last funding round. At $2.3 billion, Uber���s purchase price is 53% above the $1.5 billion pre-money valuation the catering startup secured in its last funding round in late 2021.

Acquisitions below peak venture valuation

In many cases, even good-sized acquisitions of well-funded startups are occurring at valuations far below former peaks.

Take Miro, a provider of online collaboration tools that agreed to sell to last month in a deal valued around $1.36 billion. That���s a huge step down from early 2022, when San Francisco-based Miro closed its last round at a $17.5 billion post-money valuation.

Or consider Bending Spoons��� prior purchase of data and workflow platform , for $1.28 billion in August. That���s barely over one-tenth the $11 billion valuation Airtable secured in its December 2021 Series F.

Fashion startup is another that lost its shimmer after securing a 2020 valuation in the hundreds of millions. It sold to fast-fashion leader this spring for $100 million.

In the health and wellness sector, meanwhile, Headspace also saw its valuation plunge. The meditation and mental health app sold to in a deal reportedly valued around $300 million. That���s well below its peak post-money valuation of around $2.6 billion.

A good thing?

While startup investors aren���t celebrating acquisitions made far under peak private valuations, they���re not the worst outcome. And for startups themselves, they���re also far from a catastrophic scenario.

Of course, every unicorn investor hopes for high-multiple returns. But for startups that go years without funding, the market seems to be telling us that���s not likely in the cards.

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Acquisitions are reshaping the technology industry. Venture-backed AI companies acquired 195 AI startups this year through late September, already exceeding the total for all of 2025, 做厙勛圖 data shows.

For investors and founders, such acquisitions can represent major financial milestones. For employees holding equity compensation, a transaction can also change personal financial plans they had expected to follow for years.

A client whose employer was acquired in an all-cash transaction described to me the atmosphere inside the company during the weeks that followed: Employees repeatedly discussed the deal in office corridors, trying to understand what it meant for their equity awards, their jobs and their future. She compared the confusion to the atmosphere surrounding a major reorganization or a round of layoffs.

The transaction offered the prospect of substantial financial gains. Yet employees were struggling to establish what those gains would mean for them individually.

One acquisition, different outcomes

Employees at the same company can emerge from an acquisition with remarkably different financial outcomes.

Some own vested shares. Others hold restricted stock units, stock options or performance-based awards. Their rights depend on grant agreements, vesting conditions and transaction terms. Even two employees holding RSUs may face different payment schedules or employment requirements.

Depending on the agreements, outstanding awards may be settled in cash, accelerated, converted into replacement awards, or remain subject to future conditions. One employee might receive a substantial payment when the transaction closes. Another may have much of the expected value tied to continued employment.

The announced acquisition price provides a reference point. It rarely tells an individual employee everything they need to know about the money they will receive.

This helps explain the conversations my client described. Colleagues may compare the value of their equity without realizing that their contractual rights differ.

Wealth and employment uncertainty

Technology employees with substantial equity compensation often depend on their employer in several ways.

Their salaries, bonuses, career opportunities and investment portfolios may all be linked to the same company.

An all-cash acquisition can reduce their exposure to its share price once vested shares are converted into cash. Other forms of dependence may continue.

The acquiring company may reorganize teams, change responsibilities or introduce retention arrangements. Employees whose unvested awards remain conditional on employment may face difficult choices about staying.

Someone expecting a large payment can therefore feel financially stronger while becoming less certain about their career.

Before making plans

A major acquisition announcement can change personal ambitions long before the proceeds arrive.

Employees may begin considering early retirement, a new business, a career break or a major family purchase.

Those possibilities are worth exploring. But an announced acquisition may still require regulatory approvals and other closing conditions. The headline value of an equity award may differ substantially from the cash ultimately available to its holder.

Taxes, exercise costs where applicable, outstanding liabilities and payment schedules can all affect the outcome. So can the employee’s financial position outside the company.

A substantial payment might make a career change affordable without providing enough capital to fund permanent retirement. Existing pension savings, household spending and future earning capacity remain important.

In my work with technology executives, I have seen expectations about new wealth reshape personal plans before the underlying numbers have been established.

Employees facing a cash acquisition can begin with three questions: What rights do my specific awards provide? When will the proceeds actually become available? And what choices will the resulting after-tax capital support?

The answers may differ considerably among colleagues.

An exit without an exit decision

Founders who sell their companies often participate directly in negotiating the transaction. Employees holding equity compensation generally have much less influence over its timing.

They may have intended to retain their shares for years, sell gradually or remain employed until additional awards vested. A corporate acquisition can replace those plans with a timetable established by the transaction.

For some employees, the result will be substantial financial flexibility. For others, a significant part of their expected compensation may remain connected to future employment.

The period between announcement and closing can be especially difficult because the financial opportunity is visible while important details remain unresolved.

My client’s description of the office corridors captured that experience. The company had made a decision about its future. Its employees were left to work out what that decision meant for theirs.

The acquisition determines the terms of a corporate transaction. Each employee still has to establish how much financial freedom it creates.

is co-CEO and managing partner of , which works with technology executives, founders and families on integrated wealth planning, equity compensation and major liquidity events. His background includes more than two decades in the technology industry.

Disclosure: The views expressed are the author’s own. This article is for general informational purposes and does not constitute financial, investment, legal or tax advice. Details of the client experience have been presented without identifying information.

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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.

做厙勛圖 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.

做厙勛圖 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 developers.���

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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A better model could arrive tomorrow, but replacing the first assistant would require changing how the company operates. That is where I believe an important AI moat is developing: inside customer workflows.

Recent acquisitions and partnerships illustrate three implications for founders, investors and boards.

Workflow access creates strategic value

���s agreement to acquire for approximately $22.6 billion in equity value gives it software used to design, manufacture and service physical products, placing it inside customer decisions throughout a product���s lifecycle.

Meanwhile, the – partnership illustrates a similar logic: Combining frontier AI with established chip-design tools and expertise, supported by licensing and revenue sharing.

My reading is that these transactions highlight the strategic value of owning the environment in which AI performs useful work. Companies already embedded in complex industries bring customer relationships, domain expertise and trusted processes that AI developers need to commercialize their technology.

For founders, that suggests building a position inside a specific customer workflow. For established businesses, it creates an opportunity to turn existing workflow access into bargaining power.

Completing recurring work builds defensibility

reported that its agents handle more than 15 million conversations weekly, including refunds, insurance renewals and healthcare bookings, alongside announcing an employee tender at a $22 billion valuation. The operational adoption is particularly relevant to the moat discussion. Once a product connects to internal systems, follows permissions, handles exceptions and reliably completes tasks, replacing it involves migration, testing, retraining and operational risk.

This can strengthen retention and create opportunities to serve additional workflows. Integration alone offers limited protection, however. Customers need to depend on the product and measure the value it delivers. Investors should therefore examine how much recurring work flows through the platform, which processes rely on it, and what switching would actually require.

Companies can acquire their way into workflows

���s of combines an AI assistant and enterprise search technology with established workflow automation. At completion, Moveworks had 5.5 million employee users, with approximately 250 customers already using both companies��� technologies.

The strategic opportunity is to connect employee requests directly to the systems and processes that resolve them across IT, HR and other functions. This creates a clear corp dev agenda: identify the customer workflows the company wants to enter, understand the barriers, and assess whether an acquisition or partnership can accelerate entry.

An attractive target may bring technology, customer relationships, integrations and an established role in daily operations.

Diligence should test how deeply customers depend on the product, whether those relationships will survive the transaction and whether the combined offering can deliver measurable improvements. As AI capabilities improve, acquiring the right position inside customer operations could become a powerful route to durable growth.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 做厙勛圖 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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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 做厙勛圖 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 Z羹rich-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.

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• 做厙勛圖 Data: Q3 2026 Posted A Record Count Of Billion-Dollar Rounds As The Global AI Race Heats Up
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Methodology

The data contained in this report comes directly from 做厙勛圖, 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. 做厙勛圖 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 做厙勛圖 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. 做厙勛圖 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. 做厙勛圖 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

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

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

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In total, investors poured $92 billion into seed- through growth-stage rounds for U.S. and Canadian startups in the third quarter, per 做厙勛圖 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 做厙勛圖 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 做厙勛圖 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 做厙勛圖 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 做厙勛圖 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 做厙勛圖 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 做厙勛圖 data.

A total of $61 billion went to AI-focused rounds, per 做厙勛圖 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 做厙勛圖 data. We list them below.

IPOs

Overall, it was a light quarter for IPO activity. Per 做厙勛圖 data, 17 venture-backed North American companies went public on major U.S. and Canadian exchanges in Q3, per 做厙勛圖 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泭 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.

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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 做厙勛圖, 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. 做厙勛圖 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 做厙勛圖 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. 做厙勛圖 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. 做厙勛圖 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:

The buying spree has pushed acquisitions of AI startups by other venture-backed AI companies to 195 through Sept. 29, according to 做厙勛圖 data ��� 14% more than in all of 2025. Yet the number of buyers grew just 2%, indicating that increasingly active acquirers are driving much of the increase.

Those deals point to a new phase of competition in the AI sector: Well-funded startups are using M&A to broaden their products and reach new customers faster than they could by building everything themselves. In legal tech, for example, acquisitions are bringing research, regulatory monitoring and litigation tools into broader platforms.

CFO described that calculus in a earlier this year explaining the company���s back-to-back purchases of multiple startups: ���M&A is explicitly part of how we accelerate what we’re building. The question we always ask is: does this deal get us somewhere faster than we’d get there ourselves?���

Repeat buyers step up dealmaking

Some of the AI acquirers have returned to the dealmaking table more than once in recent years, 做厙勛圖 data shows. Across the three-year period, 67 repeat buyers accounted for about 42% of all transactions tracked. OpenAI was by far the most active, with 20 AI-related acquisitions, including 10 this year.

This year���s other repeat buyers include and legal AI startup Legora, which have each announced five acquisitions, followed by legal AI startup with four. Customer-service AI provider and coding company have each made three acquisitions this year, while announced two. Many of the buyers are vertical AI startups acquiring companies in their respective areas.

Several of this year���s transactions are sizable.���s reported $1.65 billion acquisition of was the largest with a recorded price, followed by���s $1 billion acquisition of identity security startup.

Anthropic���s acquisition of, which develops AI for pharmaceutical research, was valued at $400 million. 倏梯梗紳插梆���s $300 million acquisition of, a Los Altos, California, startup that uses customized AI in camera hardware for computational photography, and���s acquisition of, valued at up to $285 million, rounded out the five largest deals recorded in the dataset through Sept. 29.

Overall, prices were disclosed for only 12 of the 195 deals, making it difficult to gauge how much AI startups are spending on acquisitions overall.

Dealmaking has clearly picked up as more AI companies turn to acquisitions ��� and, in some cases, make them a recurring part of their growth strategy. 1

���It���s all about speed in the AI world,” according to , partner at , which has backed numerous AI startups including Anthropic and Legora. “It���s faster to acquire a team or product than build it yourself. If you���re not growing 10x, you���re not interesting to growth investors, which leaves a gap in the funding market for AI startups that need a home. High valuations have also given AI startups cheap currency to use their stock to get these deals done with minimal dilution.”

What AI startups are buying ��� and why

The specific companies being acquired offer insight into what AI startups hope to gain from their stepped-up M&A activity.

倏梯梗紳插梆���s purchases have ranged widely, from healthcare data and scientific-writing software to developer infrastructure, security tools and specialized talent. The San Francisco-based company announced three acquisitions in January alone, setting the tone for a busy M&A year.

In January, it acquired:

• , which built AI software to help executive coaches automate leadership assessment reports.
• , 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, . Then in March, it announced plans to , a creator of open-source tools for software developers. It also snapped up , an open-source tool for testing AI applications.

In June, OpenAI agreed to buy , formerly known as Gitpod, which provides secure cloud environments where developers ��� and, increasingly, AI agents ��� can continue working after a user���s laptop is closed. In August, it also picked up , an AI presentation company that converts prompts, notes and documents into editable presentations.

Vertical AI roll-ups

Well-funded vertical AI startups, particularly in legal tech, have also been busy this year buying smaller companies, an analysis of 做厙勛圖 data shows.

San Francisco-based Harvey���s AI-related purchases have focused on filling gaps around its core platform. developed tools for creating product demos, videos and guides, while built software that helps companies connect customer data and applications with AI systems.

New York-based developed software that helps asset managers capture insights from previous investments and apply them to new deals. That acquisition helped expand Harvey���s presence in the asset management space. Its fourth known acquisition this year, announced Sept. 9, was , which builds open-source tools for testing, monitoring and managing AI agents.

, COO of Harvey, told 做厙勛圖 News that the company���s M&A strategy is rooted in finding ���technical talent with high ownership and deep experience in legal tech or an adjacent space to legal.���

She pointed to Benchmark as an example. The co-founders, she said, ���know the asset management space cold, and their name was dropped so many times in customer conversations that it was a natural fit for them to join our team.���

Burke further described the startup���s M&A strategy as ���selective but aggressive.���

���We hold an incredibly high bar for talent and when we identify an additive company, we move quickly and will continue to do so this year and beyond,��� she added.

Legora has pursued an even broader legal-tech rollup. The Stockholm-based company���s five announced acquisitions this year include:

• , a nine-person Canadian startup whose agents work inside Word and Outlook;
• , a Stockholm company building AI-native legal research tools;
• , an Australian regulatory-intelligence platform that monitors more than 100 areas of law for changes;
• , whose AI agents analyze documents and data for commercial real estate companies; and
• London-based , which helps litigation teams extract facts from large collections of documents, identify inconsistencies and build case timelines.

Sierra���s acquisitions reflect an effort to expand both geographically and beyond conventional customer-service automation. In March, it acquired Tokyo-based enterprise AI startup as part of its expansion in Japan, followed by Paris-based , which helps businesses automate operational work with AI. In July, Sierra bought , a 14-month-old startup developing ���long-horizon��� agents.

For acquisition targets, joining a larger platform can offer a shortcut to scale even when their own businesses are growing rapidly. TakeOff founder wrote in a July that his company was posting a ���near 8-figure run rate��� with a team of just three people. ���The advice for an AI startup growing at our pace is to hire out a sales team, raise again, and keep going,��� he wrote. ���We had capital, customers, and great traction,��� but the Sierra acquisition offered an opportunity to ���accelerate our shared vision and simultaneously build it at a grander scale.���

The surge in M&A activity is driven by both a wider pool of buyers and increasingly active serial acquirers. At this pace, we should expect even more in the months and years ahead.

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• Data: OpenAI Has Already Done Nearly As Many M&A Deals In 2026 As It Did All of Last Year
• Startups Are Still Acquiring Startups, Led By Ultra-High-Valuation Unicorns
• Your AI Strategy May Be Destroying Your Exit Value

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