led the round, with participation from co-leads Scaleup Europe Fund, managed by , and existing investor .

The financing comes nearly one year to the day after Mistral鈥檚 $2 billion Series C, which valued the company at $13.7 billion. Mistral has now raised $7.5 billion since its 2023 inception.

Mistral鈥檚 new raise means it retains its spot as the most highly valued foundation model company out of Europe. There are currently seven private frontier labs valued above $20 billion on The 小蓝视频色情网页版 Unicorn board 鈥� eight including China鈥檚 , which is also building its own model 鈥� though the rest are all based in the U.S. and China.

In a statement, the company says the new capital will 鈥渟ignificantly expand Mistral’s frontier research鈥� and help it 鈥渆xpand infrastructure and accelerate鈥� its commercial growth and international footprint. Mistral currently operates across 20 countries and counts more than 125 global enterprises as customers, including , , and .

Mistral builds AI models for tasks such as generating text, writing code and analyzing documents, putting it in competition with U.S. companies including , and .

However, unlike many of its U.S. rivals, Mistral touts greater control for businesses by offering models they can customize and run on their own systems rather than relying entirely on a third-party cloud provider.

Europe鈥檚 VC momentum

This year has marked a turning point for European venture funding. In Q2, Europe posted its strongest quarter in four years for venture funding, 小蓝视频色情网页版 data shows. All told, Europe-based startups raised $24 billion in the quarter, up around a third quarter over quarter and two-thirds higher than the $14.4 billion raised in Q2 2025.

At the time of Mistral鈥檚 $2 billion Series C, that raise represented the largest venture round ever raised by a European AI company.

This latest round has now eclipsed that, and several other large AI-related deals in Europe have also closed this year. They include London-based Google spinoff , which raised a $2.1 billion Series B in May led by and AI data center provider , which raised a $2 billion Series C at a $14.6 billion valuation in March co-led by and . (It has also since raised several billion in debt financing, per 小蓝视频色情网页版.)

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• Europe Posted Its Strongest Venture Funding Quarter In 4 Years As UK Gains, M&A Holds Up
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When an executive at suggested that seemed distracted by artificial intelligence, she was not an engineer working on the coding startup鈥檚 AI products. She was its general counsel.

The executive told her 鈥榊ou鈥檙e a great GC and good at business development 鈥� but it seems like maybe your head鈥檚 not in it because you鈥檙e so into this AI thing,鈥欌�� Ziniti recalled in an interview with 小蓝视频色情网页版 News.

鈥淎nd it was true,鈥� she said.

By then, Ziniti had spent roughly two decades working as a lawyer at companies such as , and . She had never learned to code. She had not ever founded a company. And she had not worked in the same sort of technical or product roles that many venture-backed software founders have.

But her time at Replit gave her an early look at generative AI. That was enough to inspire her to leave the legal profession and build , a startup bringing AI technology to legal work.

An early look at GPT

In late 2021 and early 2022, Replit was working with on AI-powered coding products, giving Ziniti access to an early version of GPT before ChatGPT was released publicly. She began considering what the technology could mean for lawyers.

Ziniti later taught classes on using ChatGPT for legal work. During these sessions, she began to see the disconnect between applying a general-purpose chatbot to a profession that requires precision, sourcing and tone.

For instance, in one class, she used ChatGPT to research the legal considerations involved in entering the Brazilian market. The response covered the relevant issues, but it started out with language about going to Rio and grabbing surfboards, Ziniti recalled.

At that point, Ziniti recognized there was a distinct gap between an AI-generated answer and something a lawyer could actually use in a professional setting. She realized that what broad models weren鈥檛 quite getting right could 鈥� and should 鈥� become product requirements.

As she began developing the idea, Ziniti teamed up with , an engineer she had worked with at Replit who had experience with earlier generations of GPT. Pourvakil had once been admitted to , she said, but chose engineering instead, partly because he expected AI to automate some legal work.

鈥淚 would teach these classes in the morning and tell my co-founder in the afternoon, 鈥極ur software needs to provide accurate citations. Our software needs to tell you where in the document it鈥檚 getting the backup for this. Our software needs to speak in this way,鈥� 鈥� she said.

A founder who was also the customer

Their backgrounds complemented each other. Pourvakil knew how to build the technology, while Ziniti intimately knew the work it was meant to support.

鈥淚 am a better founder for GC AI than I think anyone could be because I was the ICP,鈥� Ziniti said, referring to the startup term 鈥渋deal customer profile.鈥� 鈥淚 was able to step up and meet the moment, even though I don鈥檛 code.鈥�

Her understanding of that customer came from a legal career that began well before the current AI boom. Ziniti started as a paralegal at , later worked at and went on to hold senior legal positions at Amazon, Cruise, robotics startup Anki, and Replit.

Although it was when Ziniti realized the enormous potential of AI when it came to the legal industry, Replit wasn鈥檛 her first experience with the technology. In 2013, she became the first full-time lawyer assigned to Amazon鈥檚 Alexa. She later encountered other forms of AI through autonomous vehicles at Cruise and robotics at Anki.

Notably, she had done much of her work inside companies rather than at law firms. With that experience in mind, Ziniti decided to focus GC AI on those in-house legal departments.

From idea to company

Ziniti left Replit on Nov. 1, 2023, and incorporated San Mateo, California-based GC AI about a week later.

The company initially built an AI assistant for corporate legal departments, then added products for contract analysis and for handling requests submitted to legal teams.

GC AI has seen impressive growth, growing 400% year over year, according to Ziniti. The company now serves roughly 2,100 companies, up from about 900 a year earlier, she said. Its customers range from large enterprises to startups with a single in-house lawyer, and include companies such as , , , and .

The company makes money through a mix of per-seat subscriptions and additional products, including a contract-analysis tool priced by document volume. It also sells a platform for handling and responding to requests submitted to legal departments.

While GC AI competes with legal AI startups and , Ziniti said the startups mostly serve different customers. Harvey and Legora initially focused on law firms, while GC AI has concentrated from the beginning on corporations and their in-house teams.

Its contract intelligence offering, for example, is designed to analyze a company鈥檚 own documents, while law firms鈥� tools may need to work across materials belonging to thousands of clients. Nearly 30% of GC AI鈥檚 seats are also used by employees outside legal departments, including HR and finance executives, according to Ziniti.

She believes her extensive legal background helped GC AI address one of the biggest barriers to selling AI software to corporate legal departments: trust.

鈥淚t is the number one most important thing,鈥� she said.

About one-third of GC AI鈥檚 125 employees are lawyers, according to Ziniti. The company pursued SOC 2 compliance early, built data-isolation protections, and says it does not train its models on customers鈥� confidential information.

One longtime customer, Ziniti recalled, described their perception of the difference between GC AI and general-purpose AI tools this way: 鈥淭rust, trust, trust. Oh, and trust.鈥�

A different route into venture capital

Ziniti was new to founding a company, but not entirely new to the world of venture capital. She had invested as an angel and served as general counsel at several venture-backed startups, which helped build investor relationships before she began raising money herself.

One of GC AI鈥檚 first commitments came from , founder of , a venture firm that invests in experienced operators becoming first-time founders. Ziniti had previously worked with Illig at Cruise.

鈥淥nce you get one commit, then it鈥檚 relatively easy,鈥� Ziniti said of the company鈥檚 seed round.

GC AI has since raised nearly $72 million across three rounds. Its most recent financing was a $60 million Series B co-led by and , which valued the company at $555 million. , , , and The Council also participated.

Ziniti said about 45 general counsels have also invested in GC AI through a special-purpose vehicle.

Her experience illustrates one way generative AI is producing a different type of software founder. One who is an experienced industry practitioner who has identified a use for a certain technology but needs a technical partner to build it.

In Ziniti鈥檚 case, the idea for GC AI emerged from the overlap between two parts of her career. Replit exposed her to generative AI early, while her years as an in-house lawyer gave her firsthand knowledge of what a legal AI product should do.

鈥淵ou have a unique insight on the world in some way from your life experience,鈥� she said. 鈥淚 did, and I built around that.鈥�

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The financing, led by , brings Lyte鈥檚 total raised to $272 million. , which led Lyte’s Series B, also participated in the Series C, along with , , (formerly Exor Ventures), and additional existing and new investors.

The Sunnyvale, California-based company was founded in 2021 by , and 鈥� a trio of former engineers who worked on the iPhone giant鈥檚 advanced sensing and perception technologies. Shpunt had also previously co-founded , a startup whose 3D-sensing technology powered Microsoft Kinect before Apple acquired the company in 2013.

The trio’s past work helped bring 3D perception to the mainstream and later became a foundation for Apple鈥檚 Face ID technology.

Lyte is building custom silicon, 4D sensing, RGB, motion awareness and AI software to help robots sense where they are and what is moving around them. Initially, its customers are primarily in the warehousing and manufacturing industries.

The startup operated in stealth until earlier this year when it and announced it had raised $107 million in Series A and B funding.

鈥楢 new kind of perception鈥�

鈥淧hysical AI will create entirely new categories of robots, and every one of them will need to understand the world around it,鈥� CEO Shpunt told 小蓝视频色情网页版 News via email. 鈥淭hat requires a new kind of perception: precise, real-time understanding of geometry and motion that machines can trust enough to act on. We built Lyte to become the perception foundation for that future.鈥�

Funding in the physical AI space has exploded in recent years. In the first half of 2026, global venture funding in the space totaled $47.4 billion across 521 deals, per 小蓝视频色情网页版 data. That鈥檚 up dramatically 鈥� almost 4x 鈥� compared to the second half of 2025 when physical AI startups raised $12 billion across 470 deals. It鈥檚 also up significantly 鈥� by nearly 80% 鈥� from the $26.4 billion raised across 436 deals in the first half of 2025.

, managing partner at Maverick Silicon, joined Lyte’s board of directors as part of the funding round.

鈥淟yte is building a foundational sensing platform that enables a wide range of robots to perceive and understand the world around them,鈥� he said in a release. 鈥淭he breadth and diversity of early customer demand strengthen our conviction that Lyte is poised to become one of the defining technology companies of the robotics era.鈥�

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A16z led an $87 million equity investment, with participation from , , , and . And General Catalyst鈥檚 Customer Value Fund committed $113 million in debt to fund 贵茅濒颈虫鈥檚 growth.

Founded in 2020 by and , Miami-based 贵茅濒颈虫 has now raised a total of nearly $300 million. The company did not reveal its valuation after its Series C round, saying only it had 鈥渋ncreased threefold鈥� since its Series B, a $75 million round led by QED Investors in 2025.

贵茅濒颈虫 says it has processed more than $8 billion in transactions to date and grew revenue more than 2.5x in the past year. It connects people in the U.S. with families across 11 Latin American markets including Mexico, Brazil, Costa Rica, Honduras and Peru.

鈥淚 experienced this problem personally,鈥� Godoy said in a statement. 鈥淲hen I came to the U.S., even getting a small loan was harder than it should have been. Traditional financial institutions often start with the product they want you to use. We want to start with the person. You tell 贵茅濒颈虫 what you need, in your own words, and we help you figure out the rest.鈥�

, general partner of , said in a statement that he believes 贵茅濒颈虫 represents 鈥渨hat the future of financial services can look like for millions of Latinos in the United States.鈥�

鈥湽竺┍艟背� has packaged two frontier technologies, AI and blockchain networks, into something simple and consumer-friendly: a better way to send and receive money,鈥� he added.

The company plans to use its new capital to expand its offerings and enter new markets across Latin America.

Overall, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year, per 小蓝视频色情网页版 data.

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This story was updated to clarify the company’s valuation.

But startup investors haven鈥檛 abandoned the sector, either, 小蓝视频色情网页版 data shows. Instead, they鈥檙e being more selective about their bets and putting more money into companies using AI and other technology to make construction, property operations and real estate transactions faster and less expensive.

That shift shows up in both the year鈥檚 largest funding rounds and biggest acquisitions 鈥� and, notably, much of the biggest funding activity is happening outside the U.S.

The broad trend: Even before the pandemic-fueled funding peaks, proptech startups received more than double the venture funding in 2019 than in more recent years. While investors haven鈥檛 given up on proptech, funding to startups in the space remains down as interest rates hover in the .

In case you forgot, during the COVID-19 pandemic, home buyers and owners had access to 15-year mortgage interest rates as low as 2.5%. Those historically low interest rates fueled investor interest in the space, especially in the U.S.

Today, venture investors are backing startups working in areas such as AI-driven construction, property operations, underwriting and transaction infrastructure with demonstrable ROI. At the same time, more generic real estate software and later-stage companies without exceptional growth face significant funding challenges, our data shows.

And interestingly, four of the five largest deals in 2026 to date took place outside the United States.

The numbers: So far in 2026, global real estate-related startups have pulled in about $8.7 billion in seed- through growth-stage financing, per 小蓝视频色情网页版 . That compares to $24 billion in 2019, the second-highest year on record after the 2021 venture funding spike. It also compares to $12.3 billion raised in 2025. It appears that with four months left in the year, proptech funding is on pace to roughly match or slightly exceed 2025 levels.

Deal count is also down fairly significantly, with 794 deals so far this year. For context, the space saw more than 2,400 deals in 2019. Last year, the sector notched 1,446 transactions. The lower deal count signals both potentially decreased investor interest in the space and larger round sizes.

Noteworthy deals

The three largest deals in the proptech space so far took place in Europe, and two of those top deals involved companies doing work with steel.

Stockholm-based , a green steel startup, landed the largest haul in a private equity deal led by , also of Sweden. In June, the 6-year-old company raised about $1.6 billion in a transaction that made Wallenberg its majority owner.

In August, of Madrid raised $695 million in a venture round led by another Madrid-based company, , for its own green steel plant. The 3-year-old startup raised the money at a $3.1 billion valuation.

And in January, Amsterdam-based , a cloud-native hospitality management system, closed a $300 million Series D funding round at a $2.5 billion valuation. London鈥檚 led the financing for the 14-year-old company.

The only U.S. company to crack the top five when it comes to the largest deals was San Francisco-based autonomous construction tech startup , which raised $270 million in a Series B funding round in February. The financing, co-led by and , brought Bedrock鈥檚 total funding to over $350 million and valued the company at $1.75 billion.

Montreal-based AI-powered digital mortgage startup rounds out the list with a $216 million Series E raised in June at a $1.47 billion valuation.

Exits

There have been some meaningful proptech exits in 2026, although the activity is much stronger in M&A than in IPOs.

The only known significant initial public offering in the space was conducted in January by Columbia, Missouri-based , a construction-equipment rental company with a jobsite technology platform. EquipmentShare raised about $747 million in primary proceeds by pricing 30.5 million shares at $24.50. Including shares sold by existing holders, the offering totaled approximately $859 million.

Real estate-related startup M&A, however, has been robust in 2026 so far, with several of the largest transactions involving brokerage consolidation. Overall, the broad acquisition trend is centered around incumbents buying data, workflow ownership and distribution so they can build credible AI products more quickly.

The largest deal in the proptech space was $3.6 billion cash purchase of , which operated an AI-powered equipment maintenance and asset management platform, announced in May. (MaintainX had seen its valuation jump to $2.5 billion in 2025 after a $150 million Series D raise.)

There were several other large acquisitions.

• In January, completed its acquisition of in an all-stock $1.6 billion transaction that made it 鈥渢he world鈥檚 largest brokerage,鈥� according to .
• Construction tech giant announced in July that it was acquiring , a provider of aerial and ground-based reality-capture software for construction and other industries, for $845 million in cash. In a smaller deal, Procore also picked up construction AI-agent platform .
• Commercial real estate giant in August completed its $800 million cash purchase of , a housing-market data and technology provider for the homebuilding industry.
• And also in August, officially completed its $880 million acquisition of , forming a new parent entity named the Real REMAX Group.

The AI effect

AI is starting to move from the testing stage into everyday use across real estate and construction, according to a from and titled 鈥淧roptech鈥檚 Impact on Real Estate Innovation and Transformation.鈥�

The report says companies are using it to cut costs, make better decisions, and handle routine work more efficiently. Meanwhile, proptech is expanding beyond property-management software into areas such as construction, energy, infrastructure and climate technology.

Overall, proptech funding remains far below its pandemic-era highs, but the types of companies attracting money are evolving. Investors and buyers tend to favor businesses that can show they save customers time or money, particularly in construction, building operations and real estate finance. As such, the proptech sector increasingly includes companies that look quite different from those funded in years past.

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The Incline Village, Nevada-based company is also acquiring Austin-based agentic AI startup as it looks to automate more of the labor-intensive work involved in investigating financial crime.

led the investment, which includes both primary capital and a secondary tender offer for employees. , , and others also participated. Socure did not disclose the terms of its acquisition of Fravity.

With the latest funding, Socure has raised over $742 million in disclosed funding since its 2012 inception. It was previously valued at $4.5 billion at the time of its Series E round in 2021. The company did not break down how much of its raise was primary and secondary capital.

Rapid growth as fraud surges

The transactions come as Socure says it is seeing both rapid growth in its own business and a sharp rise in increasingly sophisticated fraud. The company is refreshingly open about its financials, telling 小蓝视频色情网页版 News that it ended the second quarter with $364 million in annual recurring revenue, up 63% from a year earlier, and added 95 customers during the quarter, including , , and . It also claims to be growing 鈥減rofitably.鈥�

Socure uses AI and machine learning to help banks, fintechs and government agencies verify identities so they can 鈥渁pprove real customers instantly while stopping fraud.鈥�

It now has more than 3,000 enterprise customers. They include 19 of the 20 largest U.S. banks, more than 600 fintech companies, major sportsbook and prediction-market operators, and 160 public-sector organizations. Specifically, some of those customers include , , , , and . The company鈥檚 revenue model mixes usage- and transaction-based SaaS.

AI creates both an opportunity and a problem

Socure co-founder and CEO said AI is creating both an opportunity and a problem for the business. For example, Socure saw an 8,000% increase in AI-driven fraud across its network last year, according to the company, as generative AI and other tools make it easier to create convincing fake identities and automate attacks.

At the same time, AI could help address one of the more costly parts of fraud prevention: investigating the large number of cases and alerts that automated systems flag for human review.

That is where Fravity comes in.

Automating fraud investigations

Fravity has built an AI-native platform that uses agents to automate fraud, risk and compliance investigations. Its technology will be incorporated into Socure’s RiskOS platform as RiskOS_Agents, initially focusing on watchlist screening and monitoring and know-your-business checks.

Socure and Fravity already share several enterprise customers that use the two products together, according to Socure. Across its existing deployments, Fravity has reduced cost per case by 80%, sped up case resolution fivefold and cut false positives by as much as 70%, the companies say.

The acquisition puts Socure more directly into what identity intelligence company estimates is a $71.1 billion financial crime investigation market. The problem is particularly acute at banks, where 53% spend at least an hour reviewing each alert, and 37% manually review more than 40% of alerts, according to Liminal.

As AI increases the volume and sophistication of fraud, Ayers argues that the identity layer 鈥� determining whether people and increasingly AI agents are who or what they claim to be 鈥� is becoming more critical to doing business online.

“I believe there are two types of companies that matter in the AI-driven global economy: those that are AI-native, and those that fight the consequences of AI acceleration,” he said in a statement.

Expanding beyond financial services

The investment follows a period of expansion for Socure beyond its financial services roots. In May, the company won a five-year, $163 million federal contract to provide identity-proofing technology for Login.gov. It is also pushing further internationally.

Socure had more than 550 employees as of March 2026, more than 100 more than it had about a year ago, according to Ayers.

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By conventional Silicon Valley standards, had some of the credentials one might expect of a startup founder: , experience as a VC on Sand Hill Road, and time working inside a fast-growing venture-backed startup.

One thing he decidedly lacked was a technical background.

鈥淚’ve never written a line of code in my life,鈥� Levtov told 小蓝视频色情网页版 News in an interview. Years later, after building , a 12-year-old San Francisco-based supply chain software company that he says now works with 35 federal agencies and generates double-digit millions of dollars in annual recurring revenue, that remains true: 鈥淎nd I still haven’t written a single line of code.鈥�

Over the years, Levtov raised $42 million in funding for Craft. His experience has given him a close-up view of both the disadvantages non-technical founders face, and the reasons Silicon Valley鈥檚 preference for technical founders may be too simplistic.

An unlikely route into tech

Levtov鈥檚 own route into technology was anything but direct. His family emigrated from the Soviet Union to England when he was a toddler, and with two musician parents, he began playing cello at age four. He later attended a specialist music school in London, studied at the Royal College of Music, and participated in a Columbia- exchange while earning an English literature degree from .

By graduation, Levtov had decided to keep music as a hobby and pursue business instead. He joined , later attended Stanford Business School, and eventually landed at , an ad-tech startup that grew from about 10 employees to roughly 200 during his time there.

鈥淚 was just totally bitten by the bug,鈥� he recalls. 鈥淎nd I said, 鈥楾his is what I want to do with my life. I want to build a company one day. Somehow, entrepreneurship is for me.鈥欌��

Before becoming a founder, though, Levtov spent time on the other side of the table as a venture capitalist at . He later left venture for an operating role at video service provider , and after moving back to Europe, eventually worked at helping Silicon Valley startups including , , and establish distribution partnerships.

His eventual startup grew out of an unsuccessful attempt to build an enterprise social network.

As part of that project, Levtov鈥檚 team created company profiles by collecting information from corporate websites, job pages, management pages and other sources.

Those profiles began showing up prominently in searches, convincing him there might be a business there.

The disadvantage of not being technical

But unlike a technical founder, he could not simply build the product himself.

鈥淢y first coder was literally a $20 an hour Odesk or person,鈥� he said.

That dependence slowed everything down.

鈥淔or the non-technical founder, it’s just fundamentally a much longer time at the very beginning to get to something because a technical founder basically codes their idea on nights and weekends,鈥� he said.

Instead, Levtov had to hunt down developers, explain his vision, and try to determine whether the result would match it. Once he鈥檇 done those things, he then had to find the capital to pay for it.

Still, the business gained traction.

Its company profiles eventually appeared in 100 million search results per month and drew about 2.25 million visitors organically, according to Levtov.

鈥業 guess that means not me鈥�

When Levtov began raising venture funding in London in 2015 and 2016, he ran into another challenge familiar to non-technical founders: Investors preferred founders who could build the product themselves.

鈥淚 decidedly remember this clarity with which I found venture funds whose websites I go to and research. And what did they say? 鈥榃e support technical founders in doing this and that.鈥� And it really was just this moment [of realizing], 鈥榦h I guess that means not me, right?鈥� 鈥� he said.

Even so, Levtov does not describe himself as having been shut out of venture capital. He had Stanford and Venrock on his r茅sum茅 and eventually secured funding from in the U.K., and later after moving back to Silicon Valley.

And he believes the preference for technical founders has some logic behind it.

鈥淭hey’ve got a direct line between the business concept and the code in which it’s executed,鈥� Levtov said.

His own experience showed him how costly that gap could be. He said there were times he hired the wrong technical person and did not have enough expertise to recognize the problem quickly.

鈥淭hat is a real disadvantage: This inevitable disconnect, this gap between the non-technical person’s knowledge and, you know, the bare metal, as it were, or the most intrinsic innards of the software code by which this business product is going to live and breathe,鈥� Levtov said.

He believes those mistakes slowed the company鈥檚 growth.

Finding the business inside the product

But the company鈥檚 eventual breakthrough also illustrated the potential advantage of approaching technology from the business side.

Someone at contacted the company and pointed out that its data could help track changes across a sprawling supply chain. The system could pick up signals such as changes in hiring, executive departures and new product offerings.

Lockheed became its first enterprise customer. Then, in 2020, the reached out about using the product to monitor 300,000 companies in the defense industrial base. The company closed a five-year, $6.5 million deal 94 days later, according to Levtov.

鈥淲e figured out that our company is actually a supply chain company, and we haven’t looked back since then,鈥� he said.

Notably, those customers were not software developers asking for better developer tools. They were, noted Levtov, business users with business problems.

And this is where he believes his own background helped. A non-technical founder may not be able to evaluate code or engineering talent in a way that a technical founder can, he pointed out. But they may be stronger in areas such as understanding customers, managing people, fundraising and building relationships.

AI is further complicating that debate, since software can increasingly be built without traditional coding expertise. But Levtov stops short of arguing that technical founders no longer matter.

鈥淚t really just takes both. It takes both sides,鈥� he said. 鈥淚 think if you can have a technical founder and a non-technical founder, you’re probably in the ideal spot.鈥�

Technical founders may have an edge at the earliest stages, Levtov said. As companies scale, the balance can shift toward skills like hiring, selling, positioning and dealmaking.

At different points in a company鈥檚 life, he said, 鈥渋t’s really about the tech right now,鈥� while at others, 鈥渋t’s all about the dealmaking, or all about the positioning, or the marketing.鈥�

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has been operating in that market since 2013. The New York-based company operates a marketplace for shares of privately held companies, giving employees and other shareholders a way to sell stock before a company goes public or is acquired.

announced plans to acquire EquityZen in October 2025 and completed the deal in January 2026, bringing the company under the investment bank鈥檚 umbrella.

, who co-founded EquityZen and serves as its chief strategy officer, has had a front-row seat to the secondary market’s evolution. 小蓝视频色情网页版 News spoke with Haslett about what secondary-market pricing says about today鈥檚 most sought-after startups, why AI companies are commanding premiums while many older startups trade at discounts, what the IPO market looks like beyond its biggest names, and why investors are taking a closer look at hard tech.

The following conversation has been edited for length and clarity.

小蓝视频色情网页版 News: The second quarter was one of the strongest venture-backed IPO quarters since 2021, but drove much of that activity. If you remove SpaceX, how open is the IPO market for the typical late-stage startup?

Phil Haslett: Generally, I鈥檇 say it鈥檚 better than it was three or six months ago. If you were a private late-stage technology company, you probably were going to wait until after SpaceX anyway, so that hurdle is gone.

Tech markets are also doing well. The stock market is at an all-time high, and there鈥檚 been a strong recovery in tech stocks overall. I assume that we鈥檙e gearing up for a busier summer than usual.

Another thing to consider is IPO performance beyond SpaceX. Some have had initial enthusiasm followed by a slowdown. has come down a bit. So companies may see it as a good time to go public, while post-IPO performance has been, in a word, 鈥渕eh.鈥�

But within AI, I think we鈥檝e seen that there鈥檚 opportunity up and down the production curve 鈥� from energy for data centers, to the technology inside them, to orchestration of compute, to efficient spending on training and inference. There are a lot of interesting companies along that spectrum, and I think that bodes well for companies in the space that want to go public.

A few companies entered your Top 20, including , , and . Does that reflect a durable shift away from traditional software, or are investors chasing a small group of scarce, high-profile hard-tech companies?

Haslett: I think it reflects a thematic shift. The companies entering that list generally fall into AI infrastructure, space tech and robotics.

If those are industries we think will have generational growth opportunities, the logical conclusion is that each sector will have winners. SpaceX gets people thinking about opportunities in space and space tech, and by extension defense tech.

The same applies to AI infrastructure. If the market is that big, and we鈥檝e seen companies go public over the last year or so, it stands to reason investors will be interested in other companies in that space. I think that鈥檚 more important than simply chasing scarce supply.

These businesses tend to be more capital intensive and may take longer to reach predictable revenue than a traditional SaaS company. How are secondary investors underwriting them?

Haslett: If a company needs more capital, investors have to decide whether the overall opportunity is big enough to justify waiting longer and having the company raise more.

If you have to build a factory or get regulatory approval, that can delay the company鈥檚 ability to increase its valuation or reach an exit. Investors discount that into what they鈥檙e willing to pay.

Secondary investors are making the same calculus as primary venture and growth investors, so you鈥檇 imagine much of that is already baked into headline valuations from primary raises.

What鈥檚 changed is that capital-intensive companies now have more financing options. Five or six years ago, a battery company or new chip manufacturer might have had little choice but to raise equity. In 2026, more credit and asset-based financing options are available.

That matters because if one of these companies underperforms or has a distressed asset sale, creditors and lenders get paid first. Secondary investors have to factor that in, too.

EquityZen says the average transaction occurred at a 38% discount to the last funding round, while many AI transactions traded at premiums. What does that say about how bifurcated the private market has become?

Haslett: I don鈥檛 know if it鈥檚 a mispricing. There are essentially two vintages of private companies right now.

Some companies weren鈥檛 built AI-first and have had to adapt. Many raised during the go-go years of 2021, at very high valuations, and may not have raised since. They鈥檝e had to rethink their strategies, which can slow growth and execution. That gets reflected in the discount.

Then there鈥檚 a new wave of companies, from 2023 and beyond, that were built with an AI-first mentality. They started from a clean slate, may operate more efficiently, and have a cleaner story for the market.

Some of those companies are raising rounds in quick succession at higher valuations. Secondary investors may pay a premium because they believe the company鈥檚 trajectory is clear and the next valuation increase could happen quickly.

is an example from the 2021 cohort. It raised at roughly a $10 billion-plus valuation and just sold for substantially less. It鈥檚 still a good business, but when investors compare 20% growth with newer companies going from zero to hundreds of millions in revenue in just a few years, you can understand why their appetite changes.

We may see more companies from that era sell for less than where they raised in 2021.

Over the past few years, many private companies have conducted secondaries because they weren鈥檛 ready to go public. When should founders consider establishing a company-approved secondary program?

Haslett: Historically, companies started thinking about liquidity programs after they鈥檇 been around five, six, or seven years, largely to reward employees for their patience and provide liquidity to early investors.

Now we鈥檙e seeing younger companies engage in controlled liquidity and tender offers.

One reason is talent retention. There are only so many engineers and data scientists, and companies need to compete for them. Secondary liquidity has become more normalized.

More solutions are available than before. Morgan Stanley, for example, has significantly grown its tender-offer activity as investor interest and available tools have expanded.

There鈥檚 also more investor appetite. Investors are increasingly willing to gain ownership through tender offers or secondary transactions. Five years ago, that was far less common.

Right now, it鈥檚 a very founder- and employee-friendly environment, and investors are willing to support secondary liquidity because they want access. If markets turn, that pendulum could shift back.

For investors considering private-company shares, what does a secondary-market price tell them compared with the valuation at the company鈥檚 last fundraise?

Haslett: I think it gives them the true price.

A primary valuation is a point-in-time measure of what investors were willing to pay, and those investors generally received preferred stock with additional rights and liquidation preferences.

The secondary market is more telling of what you could actually get in your pocket now. For companies that embrace secondary liquidity, those prices help employees, former employees and early investors understand what their shares are actually worth.

How does EquityZen calculate popularity and distinguish durable investor demand from curiosity or hype?

Haslett: Our platform allows investors, typically retail accredited investors, to tell us what they鈥檙e interested in. They can browse companies, review our analysis, and indicate which companies they would invest in, if shares became available, and at what size.

That gives us a real-time metric of what our user base wants to invest in and how much. It helps guide where we spend our time bringing opportunities to clients.

The last thing we want is to work with a shareholder when we can鈥檛 find a buyer, or with a buyer when we can鈥檛 find shares for sale.

What does the recent consolidation in the secondary market tell you about how the market is evolving?

Haslett: There was a lot of attention toward the end of 2025 around consolidation in the secondary-market space. went to , and EquityZen went to Morgan Stanley.

To me, that reflects market growth, increasing adoption of secondary liquidity, and the fact that the biggest financial institutions are paying attention. I don鈥檛 expect that to change.

Your data showed that some software companies began trading at premiums again in the second quarter. What separates those gaining investor confidence from those still trading at deep discounts?

Haslett: Execution. Leadership and execution.

It鈥檚 about a company鈥檚 ability to take a legacy SaaS business and turn it into something AI-enabled across the business. Are you using AI tools to improve internal tasks? Are you building AI into your product for clients?

Companies that can combine the stickiness and customer loyalty they鈥檝e already built with their domain expertise and AI are going to do just fine. The ones that are slower to adopt are going to get pummeled.

Six months ago, there was concern that when a company like announced a cybersecurity or legal tool, companies in those sectors would immediately lose value. I think some of that was a knee-jerk reaction.

Customers already using your software have some patience, but they also expect you to keep improving the product and give them a reason not to switch. The companies that are slow to react, or too proud to react, are the ones I think will get hit hardest.

, and 1听are examples of software that is deeply ingrained in large enterprises. If companies can keep their products working well and keep adapting them, they still have a shot at being successful standalone businesses. It comes down to management execution.

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spent much of his childhood in his family鈥檚 St. Paul, Minnesota, convenience store, where he packed cigarette cartons and watched his father run a business seven days a week, 365 days a year.

Hussain was the son of a first-generation immigrant who arrived from Karachi, Pakistan, and worked his way from employment at a to owning his own corner store. That experience instilled in Hussain a work ethic that stuck with him. But his father made a clear trade-off with him in high school.

鈥淢y dad didn’t want me to necessarily come back to the store,鈥� Hussain recalls. 鈥淗e’s like, ‘Look, like this is what I did and built. Go use school as a mechanism to leave.鈥� 鈥�

Ultimately, Hussain went on to form , a New York-based AI startup that automates parts of finance and accounting. Founded in 2023, the company has raised around $90 million, employs about 180 people, and was last valued at $400 million, according to Hussain.

But unlike many tech startup founders, Hussain didn鈥檛 study computer science in college. Instead, he earned a humanities degree at , won a Marshall Scholarship to , left academia abruptly to work at , and spent six years learning the operational ropes at early-stage startup before launching Tabs in 2023.

From St. Paul to Oxford

Hussain leveraged a scholarship from the to attend Cornell, where he fell in love with comparative politics and history. Fixated on academia, he graduated and immediately headed to Oxford to pursue a Ph.D. Two months in, reality hit.

鈥淚 realized this is a terrible idea,鈥� Hussain admits. 鈥淚 grew up … way too scrappy packing the cooler to survive through a postdoc and potentially a very structured 10-year career, which seemed very hard and long and not in my control.鈥�

Deciding to reset his trajectory at 23, Hussaini took a chance on management consulting at BCG in the Midwest. Though it provided an intensive crash course in business operations, spreadsheet modeling and corporate processes, the structured corporate hierarchy lacked the agency he had seen in his father’s store.

By 2015, he decided to embed himself directly into tech, taking a massive pay cut to join Latch 鈥� then a 10-person seed-stage startup 鈥� as its first operations hire.

鈥淗ad I tried to do this directly out of Oxford or out of BCG, I think [it] would have been impossible,” Hussain told 小蓝视频色情网页版 News in an interview. 鈥淥ne of the things that often keeps many non-traditional founders out is … the ability to access capital, but also understand the playbook of how to build, how to design around a real problem, and build a team.鈥�

Over six years at Latch, as the company grew to tens of millions in revenue, Hussain picked up a few lessons about building venture-backed companies. He learned to pursue large markets, to surround himself with people whose strengths complement his own, and to build for major shifts in technology.

Humanities vision meets deep tech

In 2023, Hussain applied those principles to start Tabs, an AI platform that automates revenue recognition, billing and collections. From the beginning, the founder knew he had to leverage his strengths. He also knew his weaknesses. Hussain recognized that he brought commercial vision and operational execution, not the ability to write code, to the table. So he partnered with a deeply technical co-founder, , to balance his own background.

鈥淚 came from the humanities,鈥� Hussain noted. 鈥淭abs is a deeply technical and complex problem to solve, and so having someone who could augment my vision … was a very important part.鈥�

Investors took notice. Early relationships and the operational credibility Hussain built during his “apprentice” years paid off. Tabs quickly raised a $4 million pre-seed round co-led by and . Since then, the startup has grown to roughly 180 employees, raised about $92 million in total capital, reached a $400 million valuation in its last round, and maintained triple- to quadruple-year-over-year revenue growth.

To Hussain, non-traditional backgrounds in tech are a strategic advantage that fosters the resilience required to survive early-stage uncertainty.

鈥淚 think a lot of non-traditional folks 鈥� have to embrace a ton of volatility, even ahead of being a founder, to make the sacrifices to learn,鈥� Hussain said. “Sometimes it’s just the non-traditional background that allows you to embrace non-traditional ways of learning that ultimately get you into entrepreneurship.”

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Venture investors appear to increasingly see physical AI as the next leg of the broader AI boom. Notably, according to a recent article in , many firms known for early bets on software, internet services and social media companies are writing more checks to companies building 鈥減hysical technologies and materials tied to the artificial-intelligence boom.鈥�

小蓝视频色情网页版 data backs this up.

In the first half of 2026, global venture funding in the space totaled $47.4 billion across 521 deals, per our data. That鈥檚 up dramatically 鈥� almost 4x 鈥� compared to the second half of 2025 when physical AI startups raised $12 billion across 470 deals. It鈥檚 also up significantly 鈥� by nearly 80% 鈥� from the $26.4 billion raised across 436 deals in the first half of 2025.

To give you an idea of just how much more money is going into physical AI companies, here’s a comparison. In the three years spanning 2022 to 2024 combined, venture investors put a total of $41.9 billion into physical AI companies 鈥� still several billion less than we鈥檝e seen raised in just the first half of this year alone.

And before we go any further, I should clarify that by our criteria, physical AI includes industries such as robotics, autonomous vehicles, aerospace, drones, industrial automation and sensors.

Noteworthy deals

Several multibillion-dollar megadeals drove the spike in H1 investment. One very large deal in particular accounted for nearly one-third of all venture dollars: Mountain View, California-based 鈥檚 raised in February. , , and co-led the financing, which was raised at a staggering $126 billion valuation.

Other companies that have brought in large rounds this year include:

• In May, defense tech startup raised another $5 billion in funding at a $61 billion valuation 鈥� double the $30.5 billion valuation it received less than a year earlier.
• San Diego-based in March landed a $2 billion Series G round co-led by and . Its valuation jumped to $12.7 billion.
• In March, Austin-based , a defense tech startup focused on autonomous sea vessels, raised $1.75 billion in Series D funding, bringing its total funding to around $2.6 billion. led the round, which set Saronic鈥檚 valuation at $9.25 billion 鈥� more than double its Series C level in 2025.

Exits

The physical AI space has also produced several notable exits so far in 2026, although activity has been more concentrated in aerospace, defense and drones than in areas like robotics.

has been the clear outlier, raising $75 billion in its June IPO at a $1.77 trillion valuation. Other notable public debuts include Herndon, Virginia-based space intelligence company , which raised $416 million, and Arlington, Virginia-based autonomous drone maker , which raised $320 million. On the M&A side, one of the most notable deals was roughly $900 million acquisition of Tel Aviv鈥檚 humanoid robotics startup , a transaction the company explicitly tied to its push into physical AI.

Investor POV

, general partner at , told 小蓝视频色情网页版 News via email that while funding in physical AI has historically been concentrated in robotics and humanoids, defense, and foundational models, he sees the opportunity as much broader. Physical AI, in his view, represents the convergence of software, hardware, sensors and IoT, and services across a wide variety of real-world applications. What is changing, according to Ziegler, is AI’s ability to process data from those systems at such a scale and speed to generate useful operational insights, while the underlying hardware becomes cheaper and more accessible.

鈥淓ven our mobile phones now have LIDAR scanners on them,鈥� he noted, 鈥渄emocratizing the ability to map objects and spaces.鈥�

For Edison Partners, the appeal is particularly strong in high-value, traditionally analog industries where physical AI can become mission-critical infrastructure. Ziegler pointed to manufacturing, supply chain, utilities, agriculture, transportation, government, and physical and spatial intelligence as areas of interest. Many of these companies resemble vertical software businesses, he said, with 鈥渁ttractive unit economics, large deal values and multi-year deployments,鈥� while their combination of software, sensors and hardware can generate proprietary datasets that become increasingly valuable over time. Edison is especially interested in applications where the return on investment is measurable through predictive maintenance, risk management, asset integrity, security and autonomous operations.

The economics of building these companies have also improved considerably over the past two years. Ziegler compared the shift to what cloud infrastructure did for SaaS.

鈥淭he costs to build these companies have come down, and AI infrastructure and multi-modal tech to do so is now available,鈥� he said.

Meanwhile, compute and foundation-model capabilities have become more accessible, reusable models and physics-based simulation have improved, training data is more plentiful, and sensor and hardware costs have declined. At the same time, companies are increasingly bundling hardware into recurring or mixed-revenue models and moving toward outcome- or usage-based pricing. That combination, Ziegler said, makes the hardware itself a distribution mechanism for software and data, with 鈥渉ardware [as] the distribution model for creating a data intelligence flywheel.鈥�

, partner and head of growth at , told 小蓝视频色情网页版 News via email that while physical industries remain capital intensive, AI and other enabling technologies are changing how efficiently companies can build and scale. Historically, the capital required to reach meaningful scale made investors wary, but he argues that 鈥渢ech barriers are plummeting, experienced talent is pouring in, and market demand is rising.鈥�

That convergence is driving more investment into areas including energy, robotics and autonomy, inference, chips and compute, and data center infrastructure. As a result, he said, funding is increasingly shifting away from experimentation and toward companies that can hit production milestones, land customers and scale efficiently.

For Eclipse, physical AI is not a new theme but a core investment thesis dating back to the firm鈥檚 founding in 2015. Fath said the opportunity has become more compelling because 鈥渢he technical and economic conditions are now catching up to that longstanding conviction,鈥� allowing companies to iterate, deploy products and reach customers faster.

Eclipse defines physical AI broadly as 鈥渋ntelligence embedded in systems that perceive, reason, and act in the real world,鈥� while generally avoiding investments in standalone large-language-model providers. Fath described the firm鈥檚 focus as investing on the 鈥渟houlders,鈥� rather than the 鈥渉ead.鈥� This means that Eclipse backs both the infrastructure that enables generative AI, such as chips, compute, energy and data centers, and the companies applying AI to build new businesses in the physical world.

He views the current landscape as the result of technology, talent, capital, demand and policy finally aligning. More powerful compute, foundation models, simulation and developer tools are allowing smaller teams to build faster with less capital and labor, Fath points out. Looking ahead, he expects value to accrue throughout the physical AI stack, but believes the strongest moats will belong to companies that vertically integrate and own multiple layers.

Ultimately, he said, 鈥渃ustomers value operational efficiency, reliability, and revenue, not technical sophistication alone.鈥� The companies that can turn technical capability into dependable systems at commercial scale 鈥� and then use their data and infrastructure to expand into additional products 鈥� are likely to capture the most value.

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