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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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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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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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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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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On the ������Ƶ ������Ƶ, we track active investors over all time. Here, we look at the investors in the companies that gained horns in the most recent funding cycle to see which firms predominate.

New unicorn counts have picked up significantly year over year. So far this year, 250 companies have joined the board through Aug. 15, up from 2025’s 193 companies. Leading sectors included robotics, AI labs, healthcare and biotech, financial services, AI infrastructure, and AI deployment, among others. Of the companies, 139 (56%) are U.S.-headquartered, and 47 (19%) are from China.

An analysis of ������Ƶ data finds that most of the funding these companies raised came in 2026: a whopping 75% of all funding — $74 billion out of $98 billion. By contrast, 30% of deals took place in 2026, the highest count so far by year, with 329 deals. Nonetheless, most deals occurred in prior years, with seed investments starting in 2012, Series A in 2014, and Series B rounds in 2017, though the pace has picked up since 2024.

The Top 10 most active investors in this cohort by investment count were Sequoia Capital, Khosla Ventures, Y Combinator, , , , , , , and .

Y Combinator is the only accelerator on this list and BoxGroup the single seed investor to make the Top 10. (formerly Sequoia Capital China), headquartered in Hong Kong with offices across China, is the notable investor from Asia on this leading list of 29 investors. Private equity firms and are in this leading list, and on the corporate venture capital front and are featured.

Seed portfolio

Y Combinator and Sequoia Capital had the largest seed portfolio counts, with investments of $20 million or less. Seed investor BoxGroup, headquartered in New York, had the third-largest count of seed portfolio companies, a significant achievement since it invests in far fewer companies than Y Combinator and its funds are a fraction of what Sequoia Capital raises.

Also impressive were with five companies at seed, and , Lux Capital and Founders Fund, each with four portfolio investments at seed. Among this cohort, Lux Capital and Founders Fund had the largest crossover, sharing three portfolio companies out of four.

Series A leaders

The most active Series A lead investors were Andreessen Horowitz, with Khosla Ventures and tied with Sequoia Capital at six investments each. Series A investment sizes show a wide range, from $6 million to $500 million. Larger Series A rounds were not dominant but noticeable for many of these firms, except for , Founders Fund and Bessemer Venture Partners.

As funding activity, unicorn creation and valuations accelerated in 2026, the investors with the largest portfolios were those with early-stage access and the resources to continue backing companies as they scale. Established multistage firms dominate the rankings, while only a handful of accelerators, seed specialists, corporate investors, private equity and Asia-based firms break into the leading group.

The next test will be whether this year’s newly minted unicorns can turn rapid capital formation and lofty valuations into durable, category-defining businesses.

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

������Ƶ 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 ������Ƶ 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 ������Ƶ 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.

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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 ������Ƶ 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 ������Ƶ 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 ������Ƶ 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.

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