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 ������Ƶ News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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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Related reading:

• ������Ƶ 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 ������Ƶ, 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. ������Ƶ 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 ������Ƶ 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. ������Ƶ 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. ������Ƶ 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 total, investors poured $92 billion into seed- through growth-stage rounds for U.S. and Canadian startups in the third quarter, per ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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 ������Ƶ data.

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

IPOs

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

�����Դ���’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 ������Ƶ 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 ������Ƶ 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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Related reading:

• 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

Illustration:

Funding to private companies has skyrocketed this year, totaling $679 billion in venture funding globally in Q1 through Q3 2026, per ������Ƶ data. That is the highest amount in the first three quarters of any given year.

While global venture funding in Q3 was down 25% quarter over quarter from the $212 billion raised in Q2, it was up 53% year over year from the $104 billion raised in Q3 2025.

 Table of Contents

• Billion-dollar fundings
• US and sectors
• Late-stage funding
• Early-stage funding
• Seed
• Exits: IPOs and M&A
• Robust market
• Methodology
• Glossary of funding terms

Billion-dollar fundings

A notable shift in 2026 has been the number of startups that have raised billion-dollar-plus rounds. An all-time record of 27 companies raised such sizable rounds in Q3 2026, an increase from the previous record, set in Q2, of 16 companies and Q1’s total of 14 companies.

Around a third of all global venture capital raised in Q3 went to those 27 companies. In the two prior quarters, billion-dollar raises pushed total funding to well above historical norms.

No single company raised funding in the tens of billions last quarter. However, eight companies raised funding rounds of $3 billion or more, with frontier labs and data centers leading by company counts for the biggest raises. Of these eight companies, five were founded within the past four years.

The largest funding deals went to and , which each raised $5 billion. Close behind, , , , , and each raised more than $3 billion.

US and sectors

U.S.-based companies continued to raise the dominant share of venture capital with $91 billion invested in American startups, or around 57% of the global total in Q3. The San Francisco Bay Area alone accounted for 24% of global venture investment in the quarter.

AI-driven startups across the stack raised $102 billion, or 64% of global venture capital, down from the two previous quarters, but still 14 percentage points above Q3 2025 global funding.

The realm of physical AI has attracted significant capital in 2026. That continued in Q3 with the aerospace, robotics, data centers, semiconductor and energy sectors each raising $10 billion or more in the quarter.

Late-stage funding

Late-stage venture funding totaled $105 billion in Q3, down 23% from Q2 but up 73% from Q3 2025, ������Ƶ data shows. Funding rounds of $100 million and above accounted for close to 90% of late-stage financings.

Early-stage funding

Early-stage funding totaled $40.6 billion in Q3, up by 25% from a year earlier. The number of companies raising jumbo sized rounds at $100 million and over, represented 50% of early-stage financings.

Seed

Global seed funding totaled $13 billion in Q3, ������Ƶ data shows. Of that, $2.6 billion went to seed rounds of $100 million and over, around 20% of seed financings.

Exits: IPOs and M&A

Q3 was a fairly strong quarter for exits. China dominated on the IPO front for the largest exits  with semiconductor and robotics listings surging on their first day of trading.

• China-based , developer of DRAM chips, was the largest IPO in Q3, raising  $8.6 billion at an $85.5 billion value. Its stock surged more than 500%  on its debut to become the most valuable listed company in the local market.
• The second-largest was Singapore-based fast fashion company listing at a $26.3 billion valuation, well below its 2022 valuation peak of $100 billion. Shein was the one exception that closed its first day around its opening price, after falling by about 10%;
• Shanghai-based AI chipmaker , a competitor to , went public in September, raising $912 million at a valuation of $25.5 billion.
• Milan-based , acquirer of and , raised $1.6 billion on its debut at a value of $18.4 billion.
• Hangzhou-based humanoid robotics company went public raising $905 million debuting at a valuation of $9 billion.

U.S.-based companies, meanwhile, dominated the largest M&A deals.

Semiconductor companies were the largest acquirers in Q3. bid to acquire New York-based open-source AI platform for $12.9 billion. Semiconductor giant announced its intent to acquire ’s , a San Francisco-based builder of models for physical intelligence, for $8.2 billion. The third-largest acquisition was payments provider ’s acquisition of New York-based AI model router for $7.5 billion.

Robust market

Over the past few quarters, larger checks across funding stages have pushed venture investment higher as young AI companies gained traction rapidly. Half of startup capital invested in Q3 went to companies founded since 2022.

Venture funding has also expanded beyond software into physical infrastructure and robotics, reindustrializing economies. And a dynamic seed funding climate for AI-native startups signals continued appetite for the next wave of companies.

Methodology

The data contained in this report comes directly from ������Ƶ, 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. ������Ƶ 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 ������Ƶ 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. ������Ƶ 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. ������Ƶ 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.)

Related reading:

• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Jumbo-Sized Series A Rounds Are On The Rise

Illustration:

That creates enormous productivity potential, but it also creates a new security problem: Companies now need to protect not only users, devices and applications, but software actors that can take actions on their behalf.

AI agents are becoming a new class of enterprise identity

An agent may access corporate files, query databases, send emails or execute code. Once it has that level of access, it needs permissions, monitoring and governance. Companies will need to know which agent accessed what information, which systems it connected to, and whether the actions it took were authorized.

As enterprises move from experimenting with a few agents to deploying hundreds of them, agent identity will become another important layer of cybersecurity. The challenge is that these identities are not passive. Agents can move between systems, invoke tools and make decisions, which makes controlling them more complex than managing traditional users or service accounts.

The value will sit in specific control points

This market will probably not develop as one broad category called “AI security.” The real opportunity will be around specific control points.

One company may protect agent identity, another may control the data an agent can access, while others may focus on prompts, MCP servers, plug-ins, traffic or auditability.

We are already seeing activity around these areas. recently acquired Israeli startup which focuses on real-time data classification and policy enforcement. Israeli cybersecurity startup , meanwhile, raised a $27 million Series A led by and focuses on understanding and securing increasingly complex internet traffic, including traffic generated by autonomous systems.

These companies are solving different problems, but together they show how the market may begin to separate into distinct security layers.

These control points are creating a new M&A map

Identity providers may extend identity governance to autonomous agents. Data-security vendors may need to control what information agents can access. Cybersecurity platforms, cloud companies and enterprise software vendors may eventually need agent-security capabilities embedded directly into their products.

For entrepreneurs, this means that “AI security” may already be too broad a positioning. The more important question is what exactly the company controls.

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

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One of the strangest things about the current AI cycle is just how fast the math changes for the people building it. You can be a 20-something founder who feels like you are still in the warm-up phase of your career, yet on paper your equity is already life changing. Or a mid-level engineer who went from holding startup options to staring at a substantial personal balance sheet practically overnight.

The gap between life experience and the sudden reality of managing serious wealth is widening as AI-native companies reach major valuations faster and private company liquidity arrives earlier.

A of more than 3,400 founders and senior leaders across 20 countries found that AI-native startups are reaching billion-dollar valuations in about 3.5 years, roughly half the time it took before generative AI. They are doing it with about half the staff.

We have seen an even more compressed version firsthand. We recently advised founders who went from launching their company to a major liquidity event in less than a year.

When the money outpaces the mindset

For decades, tech wealth followed a more predictable script. Significant personal wealth often accumulated alongside a long career. Equity vested over years, responsibilities grew and additional grants often followed. If everything went right, an acquisition or IPO marked a visible transition into a very different financial reality.

Today, that boundary is much less clear. AI capabilities allow companies to grow at a much faster pace.

A young founder can suddenly face questions that used to come much later in life. What are their long-term personal goals? What should the new capital be used for? What does financial independence mean for someone who may still be figuring out what they want their life to look like?

These are not always questions that can be answered overnight.

Compounding this is the fact that one doesn’t have to wait for an IPO to de-risk. Tender offers and secondary transactions allow founders and employees to turn part of their equity into cash while the company remains private.

Take as an example. While still only 3 years old, the company authorized a $100 million secondary sale for staff at a $6.6 billion valuation. By February 2026, it had at an $11 billion valuation.

For someone inside a company moving at that speed, the sequence can look very different from the traditional startup script. It is a dizzying loop of grants, valuations and a sudden liquidity window. All of this can happen long before an IPO.

Flexibility is the name of the game

A sudden liquidity event can make financial independence a realistic goal. It may make buying a home possible, even while someone is still single or has no idea where they want to live long term. It may allow them to take care of parents or fund another entrepreneurial chapter.

The pace of these cycles can also be contagious. Opportunities seem to be everywhere. At the same time, a founder may still be taking substantial risks with the current venture and have very little idea what life will look like in five years.

When we advise technology executives and founders in this position, we try to leave room for several possible paths while the broader picture is still developing. Some capital may eventually support long-term family security. Some may need to remain available for opportunities or life changes that do not exist today.

A future business endeavour, a career change, a relocation to another country, or other less conventional ideas can change the picture again. Some of these moves can be made today, but others need time to develop.

A company may compress 10 years of growth into three, but people cannot compress 10 years of life into three. Ignoring that gap is where real risk can build.

Valuations and liquidity can move incredibly fast. Decisions about family wellbeing, security, career and the future still move at a human pace. Your financial architecture needs to respect the difference.

is co-CEO of , where he works with founders, senior technology executives and families on wealth strategy, liquidity events and long-term financial planning. Before moving into wealth planning, he spent many years in the technology industry and writes about the intersection of technology, equity and personal wealth.

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Startups across sales, marketing and customer management have raised $7.5 billion so far this year, according to ������Ƶ data. The largest rounds span everything from advertising and customer data to sales software, e-commerce and customer support — reflecting just how many companies are still trying to build a better way to market and sell.

The broad trend: Investors are making far fewer bets on sales and marketing startups than immediately before and after the COVID-19 pandemic, but they’re still writing checks into the space.

Unsurprisingly, AI-focused companies are capturing a much larger share of funding than during the prior peak, with most sales, marketing and CRM investment going to companies in ������Ƶ AI-related categories.

The numbers: So far in 2026, startups in sales, marketing and CRM have raised $7.5 billion globally across 830 funding rounds, ������Ƶ data shows. At the current pace, funding could finish near the $9.3 billion raised in both 2023 and 2024, although potentially below last year’s $11.1 billion. Deal volume, meanwhile, is on track to fall for a fourth consecutive year — pointing to a market where investors are putting more money into fewer companies.

Funding in recent years remains far below past levels. In 2022, for example, funding in the sector topped $27 billion, and in 2021, it totaled nearly $41 billion.

Notable deals

The year’s largest funding recipient so far was, which raised more than $1 billion in a June Series E from , , and . The San Francisco-based marketing measurement company, whose products now include AI agents that analyze marketing data and automate tasks, was valued at $2.7 billion.

Restaurant financing and rewards platform announced $450 million in new capital in February. The Austin-based company did not identify a lead investor or disclose a valuation.

In January, AI-native customer service company raised a $350 million Series D led by . The Berlin-based company develops AI agents that handle customer conversations by phone and other channels. The financing tripled its valuation to $3 billion.

Meanwhile, , an online marketplace for digital products, communities and courses, received a $200 million strategic investment from in February. The deal valued the New York-based company at $1.6 billion.

Another larger deal went to Dubai-based property listings platform , which announced a $170 million equity investment in January. The company uses AI in products including home valuations and tools that help real estate agents improve and prioritize listings. led the deal, with participation from another UAE sovereign wealth fund and existing investor . The company did not disclose a valuation.

On Sept. 9,  AI-powered sales automation startup announced it had raised a $115 million Series D at a $7.1 billion valuation. This was more than double the $3.1 billion valuation it achieved when it raised a $100 million Series C in August 2025. Wellington led the latest round, with participation from , , ’s a16z Perennial wealth management arm, , and others. The company says the raise followed 4x revenue growth in 2025. It also told ������Ƶ News that it’s on track to hit $200 million in ARR this quarter, and $240 million by the end of the fiscal year.

Exits

The sector has produced one notable public offering, but most exits are coming through acquisitions as larger companies buy specialized sales and marketing products to add to their existing platforms, ������Ƶ data shows.

, a Redwood City, California-based mobile advertising and app-marketing company, began trading on the in June. It initially sold 19 million shares at $23 each, raising $437 million. The IPO valued Liftoff at $3.83 billion, based on the outstanding shares disclosed in its IPO prospectus.

There have been a number of M&A deals this year in the sector, too, though in most cases, the acquisition price was not disclosed.

The largest known deal was Dutch payments giant acquisition of , a Berlin-based loyalty and promotions platform, in July for about $880 million. Talon had previously raised over $120 million in venture funding.

Other startup M&A deals in the marketing and sales arena in 2026 include:

• In July, acquired Seattle-based sales intelligence startup to add information about prospective buyers to its sales products.
• In June, agreed to acquire , whose software helps companies identify and contact people visiting their websites.
• Sales platform acquired , which helps sales teams identify prospective customers based on product use and other signals, in March.
• acquired the Estonian startup , whose software connects sales and marketing data, in August.
• acquired India-based marketing intelligence startup in September through a team and technology deal.

Funding is down from peak years, but it’s clear investors haven’t lost interest in sales and marketing startups. However, they are putting more money into fewer of them. Companies that help businesses find customers, increase sales, or retain existing business are still landing big checks and attracting buyers. But with acquisitions far more common than IPOs, a public-market exit remains much harder to come by.

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An overwhelming majority of early venture-backed startups utilize a standard four-year vesting schedule with a one-year cliff. It seems like the ultimate one-size-fits-all template. Yet almost no one talks about how this default framework routinely causes bitter legal battles over founder equity, wasting hundreds of thousands of dollars on litigation that could have been avoided.

By the time a founder leaves or gets terminated, the damage is already done, leaving the company stuck with costly “dead weight on the cap table.”

What dead weight actually costs your company

When a co-founder with substantial ownership leaves — voluntarily or involuntarily — they often walk away with a massive, permanent piece of the company.

From a VC’s perspective, and that of the remaining partners, this is pure dead weight. You now have someone holding 15% to 20% of the equity who is no longer providing any value. Of course, contractually it’s theirs, and they’ve usually earned it.

Practically, it can break the company in three distinct ways:

• It kills motivation: The remaining team has to grind for years toward an IPO or acquisition, knowing that a fifth of the exit payout is going to someone sitting on the sidelines.

• It breaks future dilution pools: When you need to bring in new executives or raise a new VC round, your outstanding share count is artificially bloated by a departed founder. Issuing a simple 1% option pool suddenly requires 20% more shares than it otherwise should.
• It creates voting and control nightmares: If a departed founder owns 20%, you need their signature on standard investment documents and major shareholder votes. Even if they didn’t leave under bad circumstances, their risk tolerance and timeline are completely misaligned with the active team.

The shrinking threshold of tolerance

Five to 10 years ago, investors might have tolerated a departed founder holding 5%, 10% or even 20% of the company. Today, that threshold has collapsed. Many VCs will now insist that a former founder hold no more than 2.5% of the cap table.

However, because the standard four-year vesting agreement has no contractual mechanisms to claw back shares, companies start looking for alternative ways to do so when a founder leaves.

Initially, this usually involves pressuring them to give up shares “for the goodwill of the company.” When that fails, they sic investors on them, threaten their professional reputation, and sometimes resort to litigation.

I see this over and over. We frequently see litigation that is nominally about intellectual property or confidentiality, but everyone knows the real goal is simply to get the equity back. These are multi-hundred-thousand-dollar lawsuits that never would have been filed except as a desperate attempt to claw back departing founder equity.

How to fix the problem

The four-year vest, one-year cliff standard is a very lemming-like system in which founders follow the same standard as everyone else.

They often pull the language in equity agreements off automated legal platforms because it’s cheap, fast and requires minimal thought. If we want to fix this problem — and I believe every startup should — the industry needs to converge on a new, more nuanced position built into founding documents from day one.

We can split the proposed solve into two categories:

Fixing control and voting (the easy part)

Founding documents can automatically strip voting power upon departure. It’s simple enough to build in an obligation to hand over a voting proxy to the current CEO the moment a founder leaves, along with a mandatory drag-along clause that requires them to comply with future sales or investment rounds. Alternatively, a class of nonvoting shares can be created for departed founders and other service providers.

Fixing the economics (the hard part)

Four years is too short. It does not match the actual lifetime of a modern startup heading toward an exit. There are structural changes to the standard founder equity and vesting templates that could address this problem:

• Extend and back-weight vesting: Move to a five- or six-year schedule and stop using even distributions. Force back-weighting — such as 5% in year one and 10% in year two — to reward longevity and protect the cap table if someone leaves early.
• Pre-agreed buyouts and forfeiture over time: Agree upfront on a methodology and price for the company to buy back vested shares post-termination, perhaps leaving the departed founder with a permanent floor of 2%. Alternatively, tie the equity to timing: If the company sells three months after a founder leaves, they keep their 20% because they built that value. If it sells four years later, a portion of that equity should automatically forfeit back to the pool.
• Automatic share class conversion: Build a mechanism where a departing founder’s equity automatically converts into a separate class of stock with no voting rights and inferior economic rights.

A watch-out for minority founders

Minority co-founders face the highest risk of litigation aimed at clawing back their equity. They should push for pre-agreed severance, clear definitions of “cause,” and accelerated vesting protections before signing paperwork.

Even if the dominant founder refuses those terms to satisfy institutional investors, having the conversation is a critical de-risking tool. Simply observing how your co-founder reacts to these structural negotiations can provide a lot of intel. Are they hostile and defensive? Are they secretive, claiming “the lawyers said no” without CC’ing you on the emails?

How a co-founder handles the equity conversation at the outset can provide valuable insight into how they will handle conflict when the stakes are much higher.

Protecting the cap table from day one

Right now, the venture ecosystem is still operating within an outdated, broken structure. VCs want clean cap tables, remaining founders want motivated teams, and departing founders want to be fairly compensated for the early risks they took. But the current four-year vest, one-year cliff template satisfies none of them. Instead, all it does is ensure that when a founder relationship ends, companies are left hobbled by dead weight on their cap table.

Startups face high-stake, bespoke risks. Equity structures should reflect that reality. Engaging a lawyer to properly customize and document these relationships at the outset isn’t hard or expensive. What is expensive is spending hundreds of thousands of dollars later on a lawsuit, searching for leverage to claw back equity that should have been protected from the very beginning.

is a partner at and an accomplished startup lawyer and litigator specializing in corporate, transactional, intellectual property and complex commercial matters. He has advised startups on multimillion-dollar financings and acquisitions and represented clients in sophisticated intellectual property and corporate disputes. Siegel is licensed to practice in both California and New York.

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The highest-valued new entrants were China-based humanoid robotics business , valued at more than $6.3 billion; San Jose, California-based photonics company , valued at $5.5 billion; and Palo Alto, California-based AI model platform , and San Francisco’s semiconductor manufacturing startup , both valued at $5 billion.

AI software featured prominently across model training, assistants, agentic and enterprise workflow automation, coding and voice transcription.

Semiconductors was the second-largest sector, with five new unicorns. Robotics and financial services each added three, while data centers, security and energy each added two.

The U.S. accounted for 16 of August’s new unicorns. China followed with four. South Korea, India, Singapore, the United Arab Emirates, Switzerland, Germany and Turkey each added one. Nigeria and Indonesia also each added one new unicorn — for both, their first new unicorn of the year.

Nine companies exited the ������Ƶ in August, per ������Ƶ data: Three that went public — the most notable being — and six via acquisition, including , and .

New unicorns in August

Here are August’s new unicorn companies:

AI and software

• , a Palo Alto, California-based platform for training, fine-tuning and deploying custom AI models based on proprietary data, announced $1.1 billion in funding led by and . The less-than-1-year-old company, founded by former co-founder , was valued.
• San Francisco-based , an AI assistant that executes personal tasks, raised a $250 million Series B led by and . The 1-year-old company was valued at $2.5 billion.
• Shanghai-based , a builder of agents for digital and physical environments, raised a $220 million seed round led by and . The less-than-1-year-old company was valued at $2 billion. Its founder, , a researcher, left earlier this year.
• San Francisco-based , which builds AI-powered voice-writing and meeting-transcription tools, raised a $280 million Series B led by , who also led its Series A in 2025. The 5-year-old company was valued at $2 billion.
• San Francisco-based , which provides AI-powered code review and change-management tools, raised a $143 million Series C at a $1.5 billion valuation. and co-led the round. The 3-year-old company said it would commit more than $10 million to keep its tools free for open-source projects over the next year.
• San Francisco-based , which deploys AI agents across calls, email, documents and enterprise systems, raised a $150 million Series C at a $1.2 billion post-money valuation. and led the round. The company is 4 years old, started in logistics and has expanded to insurance, energy, telecommunications and airlines among others and counts 150 enterprise customers.
• Turkey-based , a developer of consumer mobile applications, raised a $50 million Series A led by . The 4-year-old company was valued at $1.25 billion. Its apps include AI chatbot Nova, diagnosing plants with PlantApp, and art generator DaVinci.

Semiconductors

• , a San Jose, California-based developer of photonic interconnects for AI computing infrastructure, raised a $700 million Series C at a $5.5 billion valuation. , , , and led the round. The 2-year-old company is already deployed within data centers.
• raised $400 million in funding led by hedge fund . The 1-year-old company was valued at $5 billion. The San Francisco-based company creates tooling for semiconductor manufacturing and was founded by researchers.
• South Korea-based , which develops edge AI processors for on-device inference, raised about $29 million in the first tranche of its Series D funding from existing investors. The 8-year-old company targeting robotics and electronics was valued at about $2.2 billion.
• Shanghai-based , an AI chip startup for inference, raised a Series A led by local state capital investors and . The 4-year-old company was valued at about $1.5 billion with plans to ship its product in Q4 2026.
• Santa Clara, California-based , which develops low-power silicon and software for AI data centers and physical AI, raised a $110 million Series A led by . The 4-year old company was valued at more than $1 billion.

Robotics

• China-based , which is building the general-purpose IRON humanoid robot, raised more than $900 million in its first outside financing at a post-money valuation exceeding $6.3 billion. led the round, with participation from and support from and Alibaba Group. The company, a subsidiary of public smart electric vehicle company , is 10 years old.
• Singapore-based , which develops robots to operate in real-world environments, raised about $669 million in funding. The 2-year-old company was valued at about $3.3 billion and is set to deploy robots in a Dairy Queen in Shanghai to handle the entire 55-step process of taking orders, preparing the food and handing it to a customer.
• Zurich-based , which develops autonomous technology for heavy construction machinery, raised a $200 million Series A led by . The 4-year-old company was valued at $1 billion and works across multiple construction brands.

Financial services

• Bengaluru-based , a financial-services company spanning payment, lending and insurance, raised $100 million in funding led by . The 7-year-old company was valued at $1.3 billion.
• Berlin-based , a finance AI platform for ������Ƶ mid-sized businesses to  manage spend, card issuing and expenses, raised a $40 million Series C led by and . The 7-year-old company was valued at around $1.15 billion. The company says it has 5,000 businesses that use the service to give finance teams control.
• Palo Alto, California-based , an AI-native enterprise resource planning platform for accounting, raised a $100 million Series C led by . The 4-year-old company was valued at $1 billion.

Aerospace and defense

• Los Angeles-based , a manufacturer of autonomous military drones and counter-drone systems, raised a $250 million Series C at a $2.5 billion post-money valuation. and the co-led the round. The company is 3 years old. Neros has contracts with the U.S. military as well as half a dozen allied countries.
• Mountain View, California-based , which builds and operates satellite constellations for national security, civil and commercial customers, raised a $250 million Series C led by . The 5-year-old company was valued at $1.5 billion.

Data centers

• Palo Alto, California-based , a vertically integrated AI infrastructure platform, raised a $300 million Series A led by , , and . The less-than-1-year-old company was valued at $2.4 billion. Alongside the equity, Volta secured $5 billion in debt to fund data center buildouts.
• , a full-stack AI infrastructure and neocloud platform, received led by Doha-based broadband provider , which holds a 49% stake. Jakarta-based Zankore is less than 1-year-old and is valued at $1.6 billion. The platform is targeting 1 gigawatt of AI computing capacity.

Security

• San Francisco-based , an AI-native security company that provides autonomous penetration testing, raised a $250 million Series E led by and . The 7-year-old company was valued at $2 billion and is used by 7,000 organizations including defense, Fortune 10, banks and healthcare companies among others.
• Palo Alto, California-based , which provides security for AI agents and third-party applications, raised an $85 million Series D led by . The 9-year-old company was valued at $1.1 billion.

Energy

• China-based , a nuclear fusion company developing small modular reactors, raised about $179 million in seed funding. The 1-year-old company was valued at about $1.5 billion.
• Washington, D.C.-based , which develops software that adjusts AI data-center workloads based on power-grid demands, raised a $150 million Series A at a $1 billion valuation. and co-led the round. The 2-year-old company says the round brings total funding to more than $220 million.

Transportation

• Nigeria-based , a vehicle financing and autonomous fleet management infrastructure, raised a $250 million Series C led by , and . The 7-year-old company was valued at $2.1 billion. It operates a fleet of 42,000 vehicles — both human-driven and autonomous — across 29 cities, with annual recurring revenue of $420 million.��

Critical minerals

• Houston-based , which builds mines and refineries using its MarianaOS software platform, raised a $310 million Series B led by . The 2-year-old company was valued at $1.5 billion.

Web3

• Dubai-based , an AI-enabled stablecoin neobanking platform for cross-border payments and tokenized assets, raised a $68 million Series C led by Tokyo-based at a $1 billion valuation. The 7-year-old company says it processes more than $40 billion in annualized transaction volume.

Related ������Ƶ unicorn lists:

• (1,862)
• (658)
• (276)
• (195)
• (119)
• (102)
• (961)
• (547)
• (254)
• (39)
• (489)

Related reading:

• Global Venture Funding Jumps 122% In August As Streak Of Billion-Dollar Deals Continues
• Which Investors Have Backed The Most 2026 Unicorns?
• 40 Companies Joined The ������Ƶ In July, The Highest Count In 4 Years

Methodology

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

The unicorn board does not reflect internal company valuations — such as those set via a 409a process for employee stock options — as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.��

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

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

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

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