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 ÂÜŔňĘÓƵ 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 “profitably.”

Socure uses AI and machine learning to help banks, fintechs and government agencies verify identities so they can “approve real customers instantly while stopping fraud.”

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’s 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 — determining whether people and increasingly AI agents are who or what they claim to be — 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.

Related ÂÜŔňĘÓƵ query:

• Ěý

Illustration:

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

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

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

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

It’s been a rapid progression to get here. In the past two years, venture investors have poured more than $7 billion into legal and legal tech startups, most with an AI focus. Funding to the space hit a record level last year, with $4.6 billion invested, per ÂÜŔňĘÓƵ data. So far this year, legal tech startups have pulled in more than $2.2 billion.

Top fundraisers

The biggest chunk of funding in recent quarters has gone to startups familiar to followers of the space.

, a provider of AI tools for legal professionals, is the sector’s top fundraiser with $1.2 billion in investment to date. The 4-year-old, San Francisco-based company is reportedly now another $500 million at a $15.5 billion valuation.

, an AI platform built for lawyers, is also in the midst of a massive scale-up. The Stockholm-based startup raised $600 million in Series D funding this year, securing a valuation of $5.5 billion, tripling over a six-month period.

, a 2008 vintage provider of legal practice management software that has pivoted heavily into AI, has also been attracting growth funding. While it didn’t secure a round this year, the Vancouver company closed on $1.4 billion in equity financing in 2024 and 2025.

For 2026, meanwhile, at least 12 legal tech-focused startups have secured rounds of $50 million or more. We’ve put together a list below.

Notably, there’s still quite a bit of activity at the early stage. Out of the 12 largest rounds this year, eight were Series A or Series B financings. Seed-stage dealmaking is also busy, with more than 50 legal- and legal-tech seed rounds of $1 million or more this year, per ÂÜŔňĘÓƵ data.

Exits

Legal tech startups are also selling to acquirers at a steady clip.

Legora has been particularly acquisitive of late, snapping up at least five companies this year, all of which raised seed or venture funding. Harvey is also a serial buyer, acquiring at least three companies in 2026. Neither company has disclosed purchase prices.

Among publicly traded acquirers, , a Dutch legal and healthcare software provider, has made at least two sizable legal tech startup acquisitions since last year. It paid $500 million for , a provider of legal spend management tools, and $105 million for , an AI workspace for legal professionals.

We haven’t seen venture-backed legal tech companies go public lately, but the biggest names seem to be signaling the possibility. Harvey, for instance, it added over $100 million in ARR in the first quarter of this year, indicating it has the revenue and growth trajectory of a strong IPO candidate.

With high investment comes high expectations

Robust investment in legal tech comes amid high expectations for AI-delivered efficiencies among legal professionals.

A of professionals in the space this year found that 80% of respondents believe AI will have a high or transformational impact on their work within the next five years.

Early benefits look promising too, with more than half of respondents attesting that their organizations are already seeing a return on investment from investing in AI. Top use cases include document review, legal research, summarizing documents, and drafting briefs or memos.

One of the highest-impact areas for AI ahead is saving time, with tools that automate repetitive tasks. Generally speaking, that’s a welcome offering, although legal professionals do widely anticipate it could disrupt the hourly billing model.

Overall, the storyline looks similar to what we see in other industries where AI is shouldering more tasks. AI isn’t expected to replace lawyers and legal support staff. However, it could free people to spend more time on valuable tasks only a human can do, enable employers to run with a smaller staff, or both.

Related ÂÜŔňĘÓƵ query:

Related reading:

• Legal Tech Investment Hits All-Time High With Filevine Funding
• Swedish Legal Tech Startup Legora Lands Another $50M In Nvidia-Led Series D Extension

Illustration:

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’s umbrella.

, who co-founded EquityZen and serves as its chief strategy officer, has had a front-row seat to the secondary market’s evolution. ÂÜŔňĘÓƵ News spoke with Haslett about what secondary-market pricing says about today’s 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.

ÂÜŔňĘÓƵ 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’d say it’s 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’s been a strong recovery in tech stocks overall. I assume that we’re 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, “meh.”

But within AI, I think we’ve seen that there’s opportunity up and down the production curve — 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’ve 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’s 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’s ability to increase its valuation or reach an exit. Investors discount that into what they’re willing to pay.

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

What’s 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’t know if it’s a mispricing. There are essentially two vintages of private companies right now.

Some companies weren’t 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’ve had to rethink their strategies, which can slow growth and execution. That gets reflected in the discount.

Then there’s 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’s 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’s 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’t ready to go public. When should founders consider establishing a company-approved secondary program?

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

Now we’re 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’s 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’s 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’s 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’re 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’t find a buyer, or with a buyer when we can’t 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’t 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’s about a company’s 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’ve 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 1Ěýare 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.

Related ÂÜŔňĘÓƵ query:

Illustration:

In Silicon Valley, if a founder wants to build the next unicorn, there’s a formula: find a bold idea, surround yourself with well-heeled advisers and investors, and raise a war chest. With cash and fundraising buzz, go after a large market in search of product-market fit. That journey sometimes leads to winning pilots, more rounds and real customers. More often, the company pivots into a different niche or quietly dissolves. A whiff of failure sends employees to the exits and funding evaporates. That’s the VC-backed model. It fuels the dreams of college dropouts and frustrated engineers, rewarding luck and timing when they meet in the hottest niches.

But not all companies can or should be built that way. Only a handful of winners make fairy tale successes. The more common path is bootstrapped or self-funded: Start with an existing customer problem and get paid more than it costs to solve it. Find more customers with the same problem, build systems to improve the solution, repeat.

For entrepreneurs without the luxury of risk capital, product-market fit can’t be an odyssey. It has to be a starting point. Much of the global economy has been built this way. The path may take longer than the VC “go big or go home” approach, but many small companies scale into middle market businesses, and a few of the best find their way to market leadership, even in tech. Consider and . For every VC-backed startup, there are hundreds of bootstrapped founders building profitable businesses without any outside investment.

Customer-focused and experienced founders

Ask VC-backed founders how they built their company, and you’ll hear about the team and investors first. Bootstrapped founders tell it in reverse: the customer comes first, and the team is built around them.

Some of the most successful VC-backed founders are younger, benefiting from inexperience by seeing opportunity as a blank sheet of paper rather than a wall of entrenched obstacles. A 25-year-old with no mortgage, no reputation to protect, and no comfortable job to leave can withstand a failure and start again. These risk-taking enterprises spare no expense to attract the best hired guns money can buy and build fancy offices, all with a focus on hitting milestones for the next round of financing. When it works, the outcomes are spectacular: think of the Collison brothers at taking on payments, or ‘s young team taking on development tools.

But these are exceptions, not the rule. Industry experience, domain knowledge and customer relationships are essential to building a company. Bootstrapped founders typically know their customer before they build. There’s no search for product-market fit, because the product is built for problems the founder already knows intimately. Growth comes from deepening existing relationships, a surer path to revenue than risk capital is meant to fund. The team is hired out of profits to serve paying customers, not to test if demand exists.

Bootstrapped founders have a different profile. Typically mid-career, they have more at risk: a mortgage, a reputation, a family depending on their income. They lack the appetite for a long-shot bet. Instead, they gravitate toward businesses with a real chance of working, aiming for profitability quickly, often starting small rather than earth-shattering, with lower barriers to entry. The result is a business run for profitability, not growth. Leadership has often worked together before or shares common backgrounds. Growth is often linear and slow for years, until the company reaches a scale where it can pursue more strategic opportunities.

The AI advantage for bootstrapped companies

In an AI era where code-generation and product design tools bring down the cost of building and deploying new products, most companies should require less risk capital, not more. In the past, a non-technical founder with an idea needed outside capital to build it. Product development required an engineering team, and an engineering team meant a payroll early revenue couldn’t finance. That was the justification for raising a seed round before lining up a single customer. With AI, capital is no longer the limiting factor for innovation.

The VC-backed market, though, is moving the other way, with larger seed rounds and bigger early-stage funds than ever. Increasingly, risk capital is used for less rational reasons that speak to the speculative bubble we live in: not to fund product development, but to buy time to market, fuel “land grab” velocity in sales and marketing, and subsidize deployments that would otherwise be uneconomic for customers.

A founder today can build a working application with a small team, deploy with real customers, and validate whether further investment is needed. The product/market gap that once required millions of dollars and world-class hires can now be closed by a handful of competent people. , the with $1 billion in revenue, is an extreme example of what is possible. Niche markets once too small for VC-backed startups now can be addressed by bootstrapped companies.

That doesn’t mean every business should be bootstrapped. A founder with a genuinely untested, capital-intensive idea and no existing customer base still has real use for outside risk capital to fund the search for a market. But AI has lowered the cost of entry and should spur an unprecedented number of bootstrapped companies built outside the VC ecosystem, profitable and lean from the start. The best of them will become the.

is the founder, managing partner and chair of the investment committee at . He launched Lateral with a strategy to allocate first institutional growth capital to independent, owner-operated middle-market businesses underserved by typical buyout firms. Previously, he served as a managing director at , a venture capital and growth equity firm that has invested in more than 300 companies including , , , , and . De Silva also previously co-founded , a marketplace for construction equipment that was sold to for nearly $800 million. He received an MBA from , a master of philosophy from the , and an undergraduate degree from .

Related reading:

• Saas Isn’t Coming Back. Something Much Bigger Is Replacing It

Illustration:

So far this year, more than 500 seed- or venture-backed private companies across the globe have sold to other private, venture-backed companies, per ÂÜŔňĘÓƵ data. The most prolific acquirers include many of the most famous and valuable unicorns, including , and .

Overall, the pace of dealmaking in 2026 looks relatively flatĚý1Reported deal counts are down slightly this year from the comparable period, but are likely to even out more over time as some acquisitions, particularly smaller deals, are added to the dataset weeks or months after they close.2 compared to last year. That’s not entirely surprising given that overall market conditions haven’t changed dramatically. The number of tech startup IPOs remains below normal. Hot venture-backed AI companies are still sustaining unheard-of valuations. And the rise of megarounds means favored startup acquirers are flush with cash.

Startups buying startups in recent years

In total, at least 440 funded startups sold to other startups in the first half of this year. The second half is shaping up to be a bit slower, meanwhile, with fewer than 100 deals so far.

For a more expansive chronological view, below we charted startup M&A deal counts by half-year beginning in 2021.

The pace of M&A dealmaking peaked about four years ago and fell afterward, in tandem with a broader dip in startup investment. But activity has picked up over the past couple of years with the rise in AI investment.

Startups that buy a lot of other startups

A few startups have proven particularly acquisitive.

The standout in this category is probably OpenAI, which has acquired eight startups this year, most of them seed- or early-stage companies. To date, the generative AI giant has bought at least 19 companies, per ÂÜŔňĘÓƵ data.

Anthropic has also been a busy buyer. It’s snapped up at least five startups so far this year, including the $400 million purchase of AI biotech startup .

In the fintech space, meanwhile, has been on an M&A spree. The crypto transactions platform acquired five funded startups focused on cryptocurrency or blockchain between April and July.

Others with multiple funded startup M&A deals this year include AI infrastructure unicorn , security provider , and the legal tech startups and .

No big slowdown in sight

While prediction can be a fool’s game, there’s not much in the immediate set of indicators pointing to a slowdown in startups’ appetite for acquisition. Amid fierce competition for an edge in the AI race, well-funded startups commonly find it’s simply faster to buy another company than try to build out certain technologies themselves.

Same goes for talent. Through acquihire transactions, startups can bring on board not just top-tier individuals but experienced teams with a track record of building impressive things together.

Concentration of capital is another factor driving M&A deals. While overall startup funding has risen this year, it’s increasingly spread across a smaller pool of companies. That leaves one large cohort of startups struggling to raise funding while another has plentiful capital for acquisitions.

Go-to-market expenses also factor into M&A considerations. A startup might produce a compelling offering in-house but find it costly to bring it to market. The process may look more feasible under the wing of a larger, more mature startup.

Bottom line: Given the high number of willing sellers and well-funded buyers, expect the startup-to-startup acquisitions to continue.

Related ÂÜŔňĘÓƵ query:

Related reading:

• Why More Startups Are Buying Other Startups In 2025
• Anthropic Nears $1T Valuation And Leapfrogs OpenAI On ÂÜŔňĘÓƵ With Massive Funding Round
• The Rise And Rise Of Billion-Dollar-Plus Rounds

Illustration:

This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here.

Startup investors were busily closing on big rounds this week, with AI and defense among their favored target sectors. The biggest financing went to , a defense tech startup developing a hypersonic missile. Other sizable rounds went to companies developing AI inference technology, a video-creation platform, data centers and voice-to-text tools.

1. , $800M, defense tech: Castelion, a defense tech startup developing a hypersonic strike missile, raised new Series C funding consisting of $800 million in equity capital along with $250 million in debt financing. , and led the equity financing, which set a $13 billion valuation for the Torrance, California-based company.

2. , $700M, semiconductors: San Jose, California-based Etched, a developer of inference clusters to accelerate AI computing, secured $700 million in a new funding round led by and joined by a long list of prominent investors. The financing set a $21 billion valuation for the 4-year-old company.

3. , $400M, AI video tools: AI video- and image-creation platform Higgsfield closed on $400 million in Series B financing at a $5.4 billion valuation. led the round for the San Francisco-based company, with the financing drawing at least 18 investors.

4. , $350M, data centers: Groq, an operator of 13 data centers across the globe, pulled in $350 million in a new fundraise led by , with planned participation from .The fundraise, which values the San Francisco-based company at $3.5 billion, comes on the heels of a $650 million in June.

5. , $280M, voice-to-text AI: Wispr Flow, a provider of an AI-powered voice-to-text tool called Flow, picked up $280 million in Series B funding at a $2 billion valuation. led the financing, joined by a long list of new and existing investors.

6. , $250M, satellites: Muon Space, a designer, builder and operator of satellite constellations, closed on $250 million in Series C funding led by . The Mountain View, California-based company also recently opened a manufacturing facility in San Jose, California, designed to produce up to 500 satellites annually by 2027.

7. , $150M, micromobility:Ěý Also, a spinout that makes electric bikes and small four-wheeled micromobility vehicles, secured $150 million in Series D funding led by . The Palo Alto, California-based startup said the financing will go in part toward accelerating development of its autonomous vehicle platform.

8. , $110M, AI computing: Velaura AI, a developer of AI compute infrastructure focused on ultra-low-power silicon and software technologies, picked up $110 million in Series A funding. led the financing, which set a valuation of over $1 billion for the Silicon Valley-based startup.

9. , $100M, agentic finance: Rillet, a developer of AI-powered enterprise resource planning tools, landed $100 million in Series C funding led by . The round, which sets a $1 billion valuation for the San Francisco company, is Rillet’s third financing in the past year.

10. , $75M, sleep testing: Happy Health, and Austin-based developer of a ring device for diagnosis and treatment of sleep apnea, raised $75 million from and .

Methodology

We tracked the largest announced rounds in the ÂÜŔňĘÓƵ database that were raised by U.S.-based companies for the period of Aug. 15-21. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

Illustration:

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 “physical technologies and materials tied to the artificial-intelligence boom.”

ÂÜŔňĘÓƵ 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’s up dramatically — almost 4x — compared to the second half of 2025 when physical AI startups raised $12 billion across 470 deals. It’s also up significantly — by nearly 80% — 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 — still several billion less than we’ve 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 ’s 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 — 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’s valuation at $9.25 billion — 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’s humanoid robotics startup , a transaction the company explicitly tied to its push into physical AI.

Investor POV

, general partner at , told ÂÜŔňĘÓƵ 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.

“Even our mobile phones now have LIDAR scanners on them,” he noted, “democratizing the ability to map objects and spaces.”

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 “attractive unit economics, large deal values and multi-year deployments,” 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.

“The costs to build these companies have come down, and AI infrastructure and multi-modal tech to do so is now available,” 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 “hardware [as] the distribution model for creating a data intelligence flywheel.”

, partner and head of growth at , told ÂÜŔňĘÓƵ 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 “tech barriers are plummeting, experienced talent is pouring in, and market demand is rising.”

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’s founding in 2015. Fath said the opportunity has become more compelling because “the technical and economic conditions are now catching up to that longstanding conviction,” allowing companies to iterate, deploy products and reach customers faster.

Eclipse defines physical AI broadly as “intelligence embedded in systems that perceive, reason, and act in the real world,” while generally avoiding investments in standalone large-language-model providers. Fath described the firm’s focus as investing on the “shoulders,” rather than the “head.” 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, “customers value operational efficiency, reliability, and revenue, not technical sophistication alone.” The companies that can turn technical capability into dependable systems at commercial scale — and then use their data and infrastructure to expand into additional products — are likely to capture the most value.

Related ÂÜŔňĘÓƵ query:

Related reading:

• Anduril Raises Another $5B As Defense Tech Startups Shatter Funding Records
• SpaceX Shares Close Up 19% After Largest IPO Of All Time

Illustration:

In turn, semiconductor giants are investing record sums in startups. So far this year, the sector’s most valuable companies have participated in rounds collectively valued at over $250 billion, per ÂÜŔňĘÓƵ data. That’s multiples above prior high marks.

Large cap chip companies are also leading and co-leading some of the biggest financings. This includes ’s record-breaking $122 billion March funding round, in which was one of eight lead investors.

The other big deals

There’s no getting around that the OpenAI megaround really skewed the 2026 totals. That one deal accounts for over 95% of the value of all semiconductor company-led financings.

Still, there are plenty of other big rounds with semiconductor backing this year that, by any other comparative benchmark, would also be considered enormous. Take July’s $5 billion corporate financing from Nvidia for foundational AI startup .

So far this year, corporate semiconductor giants have invested in more than 60 startup financings of $100 million or more. Of those, 16 rounds were valued at $1 billion or more, which we list below.

Most active and highest spending semiconductor investors

It should surprise no one that Nvidia is the most active and highest spending corporate investor in the semiconductor space. The AI chip architect has participated in a record 59 known funding rounds so far this year, per ÂÜŔňĘÓƵ data, up from 53 in all of 2025.

With a market cap around $5.4 trillion and a continued reign as the world’s most valuable public company, Nvidia certainly has the financial resources to invest heavily in startups. The company is also active as a lead investor, having led or co-led at least 11 private company financings this year, per ÂÜŔňĘÓƵ data.

, with 19 private company financings this year, is also upping its startup investment activity in tandem with what’s been a strong year for its own shares. This year’s tally includes at least four rounds valued at $1 billion or more.

Another standout is , with at least 17 known startup investments so far this year. The South Korean megacap has a lengthy history of active participation in seed and venture deals.

The corporate investment tallies also don’t represent the full extent of semiconductor companies’ involvement in the venture funding ecosystem. Additionally, some invest through backing outside venture funds.

Is this peak?

With semiconductor companies raking in profits from the AI boom, and shares soaring alongside, it’s worth considering whether we may be close to a peak for semiconductor startup investment. On the other hand, if industry leaders’ shares keep rising, the sums spent on startup dealmaking look comparatively small relative to semiconductor giants’ swelling valuations.

Related ÂÜŔňĘÓƵ queries:

Illustration:

This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding deal roundup here.

is back raising another $5 billion, after it raised that amount eight months ago. The largest fundings also went to an AI neolab, data center and electricity storage, defense, coding and biotech. Let’s take a look.

1. , $5B, data platform: Databricks has surpassed a $7 billion revenue run rate, with more than 80% year-over-year growth in Q2. The San Francisco-based company raised $ 5 billion in a funding round led by with participation from , , and new investor . Databricks was valued at $134 billion back in December 2025 when the company announced it had surpassed a $4.8 billion revenue run rate. From its Series A funding of $13 million in 2013, the 13-year-old company has raised around $25 billion in funding over all time, per ÂÜŔňĘÓƵ data.

2. , $1.1B, AI reinforcement learning: Founded earlier this year, River AI raised $1.1 billion across its seed and Series A rounds led by and , with strategic investment from and . Its founder, , has been at the center of AI developments at , and over the past decade. The Palo Alto, California-based company seeks to build AI that is personalized and trained directly on what a company or person needs to accomplish, giving the end user control.

3. , $750M, electric grid: Form Energy raised a Series G funding round led by . The Massachusetts-based company builds longer-lasting 100-hour batteries for grid electricity storage. A host of investors participated, including private equity, venture and angels, and infrastructure and climate-focused investors , , , , , , and the .

4. , $250M, defense: Neros Technologies, a defense drone manufacturer and drone interceptor, raised a $250 million Series C led by and . The Los Angeles-based company, founded in 2023, has raised $370 million to date, per . The company announced that by 2028 it will be building 1 million drones per year. It has contracts with the U.S. military and half a dozen allied countries across Europe, Asia and the Middle East.

5. , $143M, AI code review: CodeRabbit, which has developed an open-source AI code review technology used by 150,000 open-source projects and 17,000 customers, according to the company, is on a roll. London-based and Los Angeles’ led its Series C, and the Walnut Creek, California-based company plans to set up a London office based on customer interest across Europe. As coding becomes increasingly automated, code review is critical to determine which projects meet production standards.

6. , $136M, datacenter networking: Point2 Technology, which builds interconnect solutions for AI data centers, raised an extension of its Series B led by Korea-based , including a strategic investment from and participation from existing investor . The San Jose, California-based company raised $136 million in the Series B.

7. , $110M, organ preservation: Bridge to Life, a 21-year-old organ preservation company, raised a $100 million Series C funding led by and . The Illinois-based company is developing an organ viability assessment tool to expand the market and will use the proceeds to extend its VitaSmart technology to U.S. transplant centers.

8. (tied) , $100M, protein therapeutics: Aureka Biotechnologies, an AI-native drug discovery platform, raised $100 million in Series B funding led by . Based in Shanghai and California, the 3-year-old company has raised $200 million to date. Proceeds will go toward training its model with its lab-in-the-loop feedback mechanism.

8. (tied) , $100M, loyalty rewards: PointsKash, a loyalty reward mobile app, raised private equity funding led by . The Florida-based company provides in-store kiosks for cash, loans and rewards.

10. , $90M, biotechnology: Epicrispr Biotechnologies, a developer of genetic medicines, raised a $90 million Series C led by and . The San Francisco-based company develops treatments for neuromuscular disease through modulating genes.

Large non-US deals:

Several startups based outside the U.S. also raised notable rounds this week. They include:

, $400M, automated applications: Lovable raised a $400 million Series C funding led by and , valuing the company at $13.3 billion. The Stockholm-based company claims 900 million visits each month and 60 million projects created. Lovable’s Series B raise in December 2025, co-led by Menlo Ventures, valued the company at $6.6 billion.

, $300M, defense: Cambridge Aerospace, a U.K. defense tech company, raised a $300 million Series C led by . The 2-year-old company raised a $200 million Series B in April led by and . Since its Series B funding, the has committed to purchasing its Skyhammer air defense system to intercept drones and low-speed missiles.

Methodology

We tracked the largest announced rounds in the ÂÜŔňĘÓƵ database that were raised by U.S.-based companies for the period of Aug. 8-14. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

Illustration: