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 ������Ƶ 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.

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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.

“I’ve never written a line of code in my life,” Levtov told ������Ƶ 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: “And I still haven’t written a single line of code.”

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’s preference for technical founders may be too simplistic.

An unlikely route into tech

Levtov’s 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.

“I was just totally bitten by the bug,” he recalls. “And I said, ‘This is what I want to do with my life. I want to build a company one day. Somehow, entrepreneurship is for me.’”

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’s 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.

“My first coder was literally a $20 an hour Odesk or person,” he said.

That dependence slowed everything down.

“For 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,” he said.

Instead, Levtov had to hunt down developers, explain his vision, and try to determine whether the result would match it. Once he’d 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.

‘I guess that means not me’

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.

“I decidedly remember this clarity with which I found venture funds whose websites I go to and research. And what did they say? ‘We support technical founders in doing this and that.’ And it really was just this moment [of realizing], ‘oh I guess that means not me, right?’ ” he said.

Even so, Levtov does not describe himself as having been shut out of venture capital. He had Stanford and Venrock on his résumé 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.

“They’ve got a direct line between the business concept and the code in which it’s executed,” 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.

“That 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,” Levtov said.

He believes those mistakes slowed the company’s growth.

Finding the business inside the product

But the company’s 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.

“We figured out that our company is actually a supply chain company, and we haven’t looked back since then,” 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.

“It really just takes both. It takes both sides,” he said. “I think if you can have a technical founder and a non-technical founder, you’re probably in the ideal spot.”

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’s life, he said, “it’s really about the tech right now,” while at others, “it’s all about the dealmaking, or all about the positioning, or the marketing.”

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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 ������Ƶ 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 ������Ƶ 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.

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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’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. ������Ƶ 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.

������Ƶ 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 1��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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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 .

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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 ������Ƶ 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 flat��1Reported 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 ������Ƶ 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.

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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:�� 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 ������Ƶ 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.

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This month’s installment of this column is all AI, though applications for the technology range widely, from two startups that apply AI to trash or recycling, to another that promises to help people breathe and sleep better, to a company that says its AI can help architects and builders spot commercial projects before they’re even announced. Let’s jump in.

$27M to help recycling plants see what’s in trash

For decades, the recycling industry has relied on sampling and educated guesses to understand what moves through its facilities. But wants every discarded bottle, carton and wrapper to become data.

The London-based startup said last month that it has raised a £20.3 million ($27 million) Series B led by technology investor . The company installs AI-powered camera systems above conveyor belts in recycling plants, then uses computer vision to identify materials, products and brands in real time. Greyparrot says that data helps operators recover more valuable materials, improve sorting efficiency and comply with increasingly strict recycling regulations.

Its systems are now deployed in more than 20 countries and have analyzed more than 1 trillion waste objects, per the company. It counts large waste-processing companies such as and among its customers.

The data gathered at plants also feeds Greyparrot’s Deepnest platform, which it says consumer brands including , and use to understand what happens to their packaging after consumers throw it away, helping to inform redesigns and comply with Extended Producer Responsibility rules in places such as Canada and the EU.

The fresh funding will help expand the company’s footprint across North America and Europe and support its goal of preventing more than 1 million tons of waste by 2030.

The raise reflects growing investor interest in applying AI in the physical world rather than behind computer screens. Companies in the physical AI sector raised nearly $47.3 billion in the first half of 2026, ������Ƶ data shows, up nearly 80% year over year, as startups increasingly apply artificial intelligence to settings such as factories, recycling plants and other 3D environments.

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$17M to help robots navigate where GPS can’t

For a recently funded robotics company, the next frontier for physical AI is underground: in mines, tunnels and other places where GPS doesn’t work.

Australian startup said last month that it secured $17 million in new funding. That includes a $10 million equity round backed by , , , and as well as a $7 million venture debt facility from the country’s National Reconstruction Fund Corp. The company plans to scale manufacturing and expand its AI autonomy and cloud mapping platforms.

Emesent is best known for Hovermap, a LiDAR scanning payload that mounts to drones, vehicles or backpacks to create detailed 3D maps of mines, industrial sites and other hazardous environments. But increasingly, the company’s focus is software. Its Cortex AI platform enables robots to navigate autonomously in environments without GPS, while its Aura cloud platform processes and analyzes the resulting spatial data.

The company says its technology is already deployed at more than 200 mine sites worldwide and that it is expanding into the defense, critical infrastructure and construction sectors.

Its raise is another example of increased interest and investment in physical AI. As industries grapple with labor shortages and increasingly dangerous operating environments, startups that combine robotics, computer vision and autonomy are attracting fresh capital to automate work that’s difficult, dirty or unsafe for humans.

Robotics investment funding overall has been on a tear in recent quarters. Startups in the category raised $15 billion globally in 2025 — an annual record that has already been eclipsed partway through 2026 —��������Ƶ data shows.

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$12.25M for AI that treats congestion with sound

We’ve covered AI that can do dirty work like help sort through trash or navigate underground mines. What about AI to help people breathe?

San Francisco-based medtech startup recently raised an oversubscribed $12.25 million Series A led by . The company develops FDA-cleared, noninvasive devices that it says use AI and acoustic resonance therapy to treat congestion and improve sleep without drugs.

The company said its flagship Sonu band personalizes sound waves based on a user’s facial anatomy to open nasal passages, while its newer Spatial Sleep device aims to help users fall asleep faster and stay asleep longer.

The raise comes as investors continue to back AI-powered medical devices that combine software with regulated hardware. Companies that intersect ������Ƶ’s AI and medical devices industries raised more than $629 million in the first half of this year, our data shows, up about 33% year over year.

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$3.85M to turn unrecyclable trash into fuel

Trash and recycling emerged as an unexpected theme in this month’s column. While Greyparrot helps companies better understand what’s in landfills and recycling plants, another recently funded company, , says it’s working to turn unrecyclable garbage into industrial fuel.

The Las Vegas-based company last month announced a $3.85 million round co-led by and to commercialize technology that converts hard-to-recycle plastics and other waste into industrial fuel. The startup says its engineered fuel can replace coal in cement, steel and other heavy industries without requiring factories to modify existing equipment. The new funding will help it build its first commercial U.S. biofuel facility outside Las Vegas.

Global venture investment into cleantech-related startups has been steady but not record-breaking in recent years, ������Ƶ data shows. Around $15 billion went into rounds for companies in ������Ƶ’s cleantech-, EV- and sustainability-focused categories in the first half of 2026, putting this year’s funding on track to slightly exceed the 2025 tally, which was the lowest in several years.

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$3.5M to predict construction projects before they’re announced

The biggest construction opportunities often surface months before the first request for proposals. promises to use AI to spot them first.

The New York-based startup last month raised $3.5 million in seed funding from ’s accelerator, , and others to build an AI platform for architecture, engineering and construction firms. The startup promises to give such companies an edge over their competitors by helping them discover projects earlier and identify the best path to winning them. Instead of searching public bid databases, Cascade says its tech can analyze signals such as bond filings, property transactions, capital budgets and meeting minutes to identify projects while they’re still taking shape.

Overall funding to real estate-related startups has trended higher in recent quarters, and the sector emerged as a bright spot for seed funding in the first half of 2026, an analysis of ������Ƶ data shows. Other seed-funded real estate startups this year have spanned areas ranging from streamlining planning and building processes to real estate investing to reducing power consumption in buildings.

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Correction: The article was updated to reflect Forge Industries’ correct headquarters location.

Related reading:

• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows

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spent much of his childhood in his family’s 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.

“My dad didn’t want me to necessarily come back to the store,” Hussain recalls. “He’s like, ‘Look, like this is what I did and built. Go use school as a mechanism to leave.’ ”

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’t 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.

“I realized this is a terrible idea,” Hussain admits. “I 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.”

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 — then a 10-person seed-stage startup — as its first operations hire.

“Had I tried to do this directly out of Oxford or out of BCG, I think [it] would have been impossible,” Hussain told ������Ƶ News in an interview. “One 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.”

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.

“I came from the humanities,” Hussain noted. “Tabs is a deeply technical and complex problem to solve, and so having someone who could augment my vision … was a very important part.”

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.

“I think a lot of non-traditional folks … have to embrace a ton of volatility, even ahead of being a founder, to make the sacrifices to learn,” 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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Related reading:

• ‘Nobody Wanted to Give A Former Principal Money’: How An Educator Built An Edtech AI Startup With $63M From VCs
• How A Teenage Carpenter Became The Founder Of AI Construction Startup Trunk Tools

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