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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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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A clean sheet rewrite of a legacy product certainly improves customer experience, but it can take years from starting gun until the last customer is migrated and the old code is fully decommissioned.

is an HR software business, owned by my investment firm, with just that challenge. In December, when joined as CEO, the company had developed a plan to rewrite from scratch one of its oldest software products. The timeline was 18 months, with a 30% surge in engineering headcount to power through the project. But Jeff and his new CTO did it better, faster and smarter.

Jeff came to the board in February with a radical alternative: redesigning the engineering team organization and individual job specs, literally changing what people do all day to best put to work the power of off-the-shelf AI tooling.

HireRoad’s new approach would complete the development in 16 weeks, not 18 months, and the customer base migration and legacy decommission would be completed in calendar year 2026. In my 30-year career as a software investor, I had never seen any organization achieve such a task at anything like that velocity. The board debated and made the leap, killing the old plan and taking this frontier bet.

The rebuild was done in 15 weeks, a week ahead of schedule, and as of this writing, the first 34 customers have been migrated to and are live on the new platform, with glowing feedback. The pacing to complete the migrations and decommissioning is on track. The kicker is that Jeff and team completed this with a smaller team, freeing up the 30% headcount surge to work on other HireRoad developments.

AI-Sprinkle vs. AI-native

In 2024 and 2025, we at felt proud of ourselves and quite cutting-edge for providing the engineering teams across our software portfolio with access to AI tools such as Copilot and Claude Code. We saw productivity gains of 10%, then 20%, now more like 30%.

But somehow, our companies were all getting stuck at those 30ish percent gains.

How to reach 3x gains? The realization was that providing AI tool access alone was, candidly, not AI-enabled but rather AI-sprinkled. The breakthrough came when leaders went beyond the AI-sprinkle and instead adopted AI-native daily practices.

Let’s pause for a moment on terminology here. The phrase “AI-native” is thrown around a lot just now. In our usage, AI-native describes what you do all day, not when your company was founded. Anyone can learn to work in an AI-native fashion, and it means directionally using AI tooling first and humans to orchestrate, coordinate and communicate.

AI-native work is not just doing the same thing faster; it means doing different things with more delegation and quicker learning loops, and I will share some specific examples as we go.

Startups will call the move to so-called AI-native organizational practices obvious. They are right, but they are not burdened by an existing organization or established products and customer bases. They get to build AI-native practices into their organization from the start. In contrast, private equity portfolio companies have to remodel.

Our experience is that the AI-sprinkle — or, giving an AI layer to an otherwise unchanged organization — provides mere percentage gains to productivity. We have to redesign the organization around the power of the tools to get multiples on productivity.

A 30% productivity gain feels good, but it is the trap of the current moment in AI. And the path from 30% to 3x is uncomfortable. It runs through changing how teams are structured and what people actually do all day. In this way, delivering on the promise of AI is a business change, not a technology change.

I had the great good fortune to take a course in strategy at business school from and Andy Grove. Burgelman is a professor whose 12-year study, , delivered the definitive business text on , which Grove famously ran through its own era of technological revolution in the chip industry. His intellectual framework applies exactly to the current moment of technological revolution.

Evolutionary vs. revolutionary

Burgelman’s framework is that there are two kinds of strategic behavior, which he called induced and autonomous. Induced strategies fit the company’s existing structure and trajectory, like an AI layer inserted into an existing process. They are evolutionary moves, continuously advancing and improving on the current direction of travel. Autonomous strategies are those arising from outside the current business plan, like rewriting the job definitions and changing the team structure and work patterns of your product and engineering teams around the power of AI tooling.

Autonomous strategies are revolutionary moves. With AI, 30% gains are to be had from AI-sprinkle on the induced-strategy evolutionary path. The 3x gains require AI-native autonomous strategies, meaning revolution.

An oft-repeated analogy is how electricity transformed manufacturing. Replacing the steam engine powering a mill with an electrical motor delivered very little productivity gain.

Productivity skyrocketed only when the manufacturing plant itself was redesigned, distributing small electric motors throughout the factory in a horizontal layout, delivering what a single steam engine never could. What interests me most about this story is why it took decades before the factories were redesigned. Why couldn’t those organizations make the revolutionary leap more quickly? That is where the Burgelman/Grove case study is so helpful.

Burgelman points out that revolutionary ideas are very often squelched by institutional inertia and the cultural power of the evolutionary path. To be realized, revolutionary strategies need full buy-in from the CEO and Board.

The retelling of Grove’s revolutionary moment is here very apt. As told in Grove’s seminal business book “,” he and Intel co-founder were sitting together struggling with a strategic question. Intel’s primary business at that time was memory chips, a business where Japanese competitors were assaulting them in a brutal price war, pushing Intel to the brink. Intel also had a smaller, growing business line in microprocessors, the CPUs inside personal computers.

After a long pause, head in hand I imagine, Grove looked up at Moore and said, “If we got kicked out and the board brought in a new CEO, what do you think he would do?” And Moore said without hesitation, “He would get us out of memories.” Grove replied, in effect, why shouldn’t you and I take a walk around the building just now, and come back in the door, and do it ourselves?

That is just what they did, and the great run of “Intel Inside” as the leading CPU maker was launched. The uprooting of your proven daily practices and time-tested organizational design, to an AI-native way of working and team design, is a difficult revolutionary act. It may feel just as uncomfortable, just as heroic, as that fateful Grove-Moore conversation.

So, what did HireRoad do to affect the 30% to 3x revolution? The new technology leadership trained the team on a new hour-by-hour how to spend your day, built around the power of the AI tooling. The new sales leadership worked with the engineers to put the rapidly produced prototypes in the hands of clients, shortening the user feedback loop. When users identified bugs, the system logged them, wrote code to fix them, and presented the solution to a “human in the loop” for final judgment and publication. Customer support was engaged to develop and communicate a high confidence transition plan for users.

Overall, the HireRoad team became smaller and more senior, with resources freed to work on other initiatives, and to roll out these practices across other HireRoad product lines.

Management innovation and private equity

AI-native organizations are the third major management innovation of my private equity career. The first management innovation was the removal of bloated cost structures and tight linkage of executive compensation to equity outcomes in the 1980s, and the second was the conversion of on-premise licensed software to subscription model SaaS in the 2010s.

Those investors who mastered and first put those techniques into practice created vast fortunes for their capital partners. The starting gun has just been fired on the third wave. The organization changes to implement AI are a business change, not a technology change. While the ideas and practices can arise from anywhere in the organization, companies will not participate until this revolutionary change is endorsed by the CEO and board.

There are some 10,000 privately held software companies in the U.S. today, depending on exactly how you count. Leaders of those businesses know, explicitly or perhaps just through gut feel of the shifting sands, that doing the same thing in the same way in the age of AI is a losing strategy. You won’t lose all at once. You will be slowly starved as competitors move at 3x your pace around you. Certainly, your prospects to be a leader will close.

You have the customers, the distribution and the knowledge of the problem you are solving, all legs up on the startups. The nature of the organizational change you need to make is known, or knowable.

When considering this moment, shared by all of us who work with existing software organizations, think about the decades between the initial one-big-motor electrification of factories and the 1920s many-small-motors factory redesign which delivered the huge productivity gains. These changes don’t just happen on their own, and this time around, we won’t have the luxury of a lengthy transition. When considering your own revolutionary strategic move, run the Grove thought experiment. Walking outside around your building, ask yourself, “If I were fired, what moves would the newly hired CEO make today, to win with this company in the age of AI?” I suspect the nature of your answer will not be to sprinkle more LLM access across your unchanged organization. Rather, ideas will occur to you on how to change your team structures and what people do all day to better serve your customers through the incredible AI tooling now at your disposal.

Are those the moves you are making today?

co-founded in 2014 and is managing partner. He has served on numerous private and public technology company boards, and currently is a director of , , , , and . Previously, he was a partner and member of the investment committee at . He also worked at and . Morse serves on the board of directors of and as member of the advisory board for the HMTF Center for Private Equity Finance at . He attended , graduating summa cum laude with a BSE, and , where he earned his MBA and was an Arjay Miller Scholar. Morse lives in Austin.

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It is difficult to define the extent to which a specific company should morph itself into an “AI native” company. Does this add value for everyone?

AI does not automatically increase exit value. In some cases, it can reduce differentiation, compress margins, complicate diligence and make a company more difficult to acquire. Like pricing, customer service or go-to-market strategy, AI requires a careful balancing act between speed and defensibility, innovation and complexity, short-term productivity and long-term strategic value.

Let’s jump into three ways AI strategy can impact exit value.

Build an AI architecture that acquirers can trust

Many startups are rapidly adding AI copilots, model integrations, orchestration layers, prompt libraries, vector databases and third-party AI tools across the organization. This may accelerate product development and help teams ship faster. However, from the perspective of an acquirer, it can also create a more complicated architecture.

During due diligence, buyers care about how AI is being used. Which models are embedded in the product? Which vendors are critical to delivery? Where does customer data flow? How are outputs monitored? What happens if pricing changes, APIs break or regulation shifts?

A startup may see AI adoption as innovation. A buyer may see it as integration complexity, vendor dependency, compliance exposure and security risk.

This is especially important for strategic acquirers that need to integrate the target into a larger platform. If AI makes the product easier to scale, automate, secure and maintain, it can support valuation. If it creates a fragile layer of external dependencies, unclear data flows and difficult-to-audit decision-making, it may reduce confidence and lower the price a buyer is willing to pay.

Invest in proprietary data

Even one year ago, adding AI functionality to a product could create excitement by itself. Today, many AI features are becoming easy to replicate. Summarization, search, chat interfaces, recommendations, content generation and workflow assistance are increasingly available through the same underlying models and infrastructure. This matters for exits.

A strategic acquirer rarely pays a premium simply because a startup integrated the latest model. They pay for what they cannot easily build themselves: proprietary datasets, unique customer workflows, strong distribution, deep vertical adoption or network effects that improve with scale.

Founders should therefore ask a simple question: Is our AI strategy creating a defensible asset, or are we just adding features that competitors can copy within weeks or months?

Revisit your buyer map as AI redraws strategic boundaries

Historically, many companies built their exit strategy around a familiar buyer map. A cybersecurity startup might sell to a larger cybersecurity vendor. A vertical SaaS company might sell to a competitor in the same industry. A workflow automation company might sell to a productivity platform. AI is changing those boundaries.

As AI expands what platforms can do, strategic buyers are moving into adjacent markets they previously ignored. An infrastructure company may acquire an identity platform because AI agents need secure access controls. An ERP vendor may acquire workflow automation because AI is moving closer to business process execution. A data platform may acquire a vertical application because domain-specific data is becoming more valuable.

This means CEOs should revisit their buyer map every six to 12 months. The most logical acquirer today may not be the same one that would have been logical even one year ago.

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

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Think of the most famous boxers you know, likely the heavyweights: Muhammad Ali, Joe Louis, Mike Tyson. In a clash between titans, the advantages seem easy to understand, since the bigger fighter looks like the stronger one.

But size alone is not a strategy. Sugar Ray Robinson was a middleweight, not a heavyweight, and in the 1950s, A.J. Liebling said he looked “more like a loose-limbed dancer than a boxer.”

Robinson’s advantage was completeness: speed, footwork, intelligence and stamina. In a famous 1951 match against Jake LaMotta, Robinson schooled the reigning, heavier middleweight champion with a 13th-round TKO.

Completeness also applies to companies. The market tends to assume that big companies will capture the biggest gains from AI. But AI is tough to get right at any size.

Look at , valued at $6 billion in 2024. It made headlines claiming its -powered chatbot could handle millions of conversations and do the work of 700 customer service employees. Customers hated the rollout, and by 2025, Klarna was . Or , which watched its value after ChatGPT commoditized its main offerings.

If everyone can get AI wrong, who wins?

Enter the scrappy middleweight

Each year we speak with thousands of operators and founders, and one pattern is clear: The biggest long-term gains from AI will not flow to heavyweight incumbents or many AI-native startups but to scrappy middle-market technology companies, the middleweights.

The next phase of AI disruption will be challenging, but middleweights can gain serious ground.

One objection: Won’t hyperscaler companies go after certain verticals? If Copilot inside 365 or 1��agents can run a workflow, how does a middleweight company survive? The answer depends on what constitutes durable advantage. Horizontal platforms are built for generalized work, not the messy, regulation-heavy, category-specific workflows of the real world. Middleweights can win by making their software the system of record that AI calls into instead of software that AI replaces.

The odds for making big, impactful gains with AI right now favor the middle market, where proven growth companies can use customer trust, domain expertise, capital structure and speed to transform their businesses, taking market share from slower incumbents. With three-quarters of AI’s economic gains now being captured by just per , entrepreneurs who stand still may already be losing the round.

What makes for a winning middleweight company?

The best middleweight technology companies share the five traits below, all working together as a system.

Disciplined self-assessment. Middleweights are designed to act quickly on honest feedback, and their boards help them test where AI generates value versus where it merely consumes engineering capacity and budget.

Seat-based pricing is one area for brutal assessment. When autonomous agents do the work, the revenue model should reflect outcomes, not users. In 2023, customer service platform made a bold switch, pricing its AI agent Fin at 99 cents per resolved conversation. That agent became the company’s core offering, and it recently . Outcome-based pricing might seem painful at first (and reorganize your GTM team and their incentives), but it anticipates an agentic future.

Agility. Enterprise companies are weighed down by technical debt and legacy infrastructure. Middleweights have enough scale and proprietary data but not so much organizational mass that every experiment needs 10 layers of approval. Their agility is as much cultural as structural.

These are ambitious, scaling companies growing 20% or more with strong unit economics, and a tech-first mindset runs through the entire business, not just the engineering org. Take the restaurant software , where early AI gains came from product leads ; those product teams then built a flywheel connecting new product features to external communications, with LLMs continuously editing and improving instructions for AI agents.

Workflow ownership. In the AI era, the strongest moat is owning a complex workflow. Middleweights have spent years gaining this position — integrating into customer systems, accumulating exception-level data, learning operational nuances that take a claims process from 95% accurate to 99.5%. (The last 4.5 points are the moat.)

An company, , doesn’t just apply AI to contracts; its moat is absorbing the decision workflow around each contract. As a contract moves through approvals, negotiations and redlines, the important part is learning from the history of why internal teams decided the way they did. Well-positioned companies will hold the institutional memory that AI agents need to query to do their jobs.

Technical capacity. Most large companies are stuck in AI pilot purgatory, and the market still underestimates how operationally demanding AI deployment is. Middleweights have something most AI-native startups lack: years of working with real customers. , another FTV company, started in 2007 as a service-heavy cybersecurity business that has learned deep detection logic from operating in more than 1,000 customer environments, including some of the largest global enterprises. As the company saw rapid automation from machine learning, then more sophisticated AI, it moved in-house SOC analysts into higher-value product development roles, allowing engineers with deep cyber expertise to drive key R&D.

A well-capitalized balance sheet. Companies with cleaner balance sheets can move faster, absorb experimentation costs, pursue selective M&A, and keep investing through periods of disruption. Large legacy software companies carrying heavy leverage, optimized for cost-cutting and growing at 5%-10%, can’t be light on their feet and will struggle to reallocate capital aggressively enough into AI R&D.

The imperative

Plenty of boxers can be complete for one season. Sugar Ray Robinson executed consistently in 200 professional fights, mastering the sweet science with a reliable system. That same high standard now applies to companies in the AI era.

The window for transformational gains with AI is not open indefinitely. Speed is a middleweight leader’s advantage. Do not wait to perfect your AI strategy; start executing.

If you don’t know where to start, pick key workflows, map them and ask whether AI makes them more defensible or more exposed. The answer may determine whether you give up a round or win the match.

is managing partner at , where he oversees the firm’s global strategy and investment decisions. He has been a growth equity investor at FTV for more than 20 years, leading investments in enterprise technology and services and financial technology and services. Bernstein has over 25 years of private equity experience. Prior to FTV, he was a partner at and its predecessors where he managed the business and financial services group. He began his private equity career with and started his professional career in the investment banking division of in New York.

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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 poured capital into a varied lineup of large rounds this week, targeting sectors including physical AI, biotech, cybersecurity, AI infrastructure and fintech. By far the largest financing of the week was a $1.7 billion round for founder ’s physical AI startup, , followed by sizable investments for 3D AI model developer and battery technology company .

1. , $1.7B, physical AI: Atoms, the physical AI startup founded by founder , raised $1.7 billion in a funding round led by . Kalanick touted the Los Angeles-based company’s vision as “about the coming industrial revolution where large industrial economic sectors get completely digitized.”

2. , $400M, AI for 3D: Silicon Valley-based Meshy AI, a startup developing foundation models for AI-powered 3D generation, closed on $400 million in Series B funding at a $1.5 billion valuation. Lead backers include , and , per ������Ƶ data.

3. , $300M, battery technology: Battery technology company Sila secured $300 million in a new round led by and . The Alameda, California, company will use the funding to expand its silicon anode plant in Moses Lake, Washington.

4. , $300M, inference technology: Etched, a co-designer of chips, racks, software and manufacturing methods for use in frontier models, picked up $300 million in Series C funding. led the round, which set a $10 billion pre-money valuation for the San Jose, California-based company.

5. , $180M, fintech: Augustus, a startup aimed at providing financial institutions around the world direct access to dollar accounts, secured $180 million in Series B funding. led the round, which set a $1 billion valuation for the San Francisco-based company.

6. , $160M, defense tech: Cathedral, a startup aimed at expanding U.S. military cyber capabilities, reportedly $160 million with backing from Sequoia Capital and Andreessen Horowitz. The Washington, D.C.-based startup was reportedly founded by a ​team of former DOGE employees.

7. , $130M, biotech: Crystalys Therapeutics, a biotech developing therapies for people living with gout, closed an oversubscribed $130 million Series B round. led the financing for the San Diego-based company.

8. , $120M, healthcare software: San Francisco-based Candid Health, developer of a revenue cycle management platform for the healthcare industry, landed $120 million in Series D funding led by .

9. , $100M, cybersecurity: Glow, a Palo Alto, California-based AI-powered endpoint security startup, launched from stealth and announced it has raised $180 million to date, of which, per ������Ƶ, $100 million comes from its newest financing. Lead backers include Sequoia Capital, , , and .

10. , $75M, cybersecurity: Boston-based Neo Security, a startup working on an agentic software control platform for enterprises, picked up $100 million in a new round led by and Andreessen Horowitz.

Methodology

We tracked the largest announced rounds in the ������Ƶ database that were raised by U.S.-based companies for the period of July 18-24. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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Looking broader into the entire market, I can see how this makes a lot of sense. Companies are becoming increasingly uneasy about where critical infrastructure and sensitive data live. AI fraud is getting better. Voice cloning is becoming more convincing. Vibe coding is allowing less experienced developers to build faster, but not always more securely. Quantum computing is still over the horizon, but serious companies are already thinking about what it may mean for encryption and long-term data protection.

For the past decade, cloud migration was treated as the obvious strategy. It gave companies speed, scale and lower upfront costs. Startups could launch without buying servers. Enterprises could modernize without rebuilding their own infrastructure.

That logic still holds. But we are witnessing an interesting shift where progress is happening so fast, security cannot keep up, thus creating an uneasy feeling causing decision-makers to revert back to older, and perhaps safer perceived strategies.

Here are three trends that I believe are pushing on-prem back into the limelight.

AI fraud is changing the security conversation

In many cases, cloud providers are more secure than what a company could build internally. The issue is that thanks to AI, attackers are becoming more sophisticated, and quick. AI makes phishing more polished, fake invoices more believable, and voice impersonation harder to detect. A call that sounds like the CFO or CEO asking for a payment approval is no longer far-fetched.

That changes how companies think about exposure. The attack surface is not only servers. It is identity systems, SaaS tools, APIs, employee workflows, permissions, contractors and support portals.

For sensitive systems such as communications, payments, identity and customer data, control becomes more valuable. On-prem does not guarantee security. But it can reduce dependency on outside platforms and give companies clearer ownership over the systems they cannot afford to compromise.

Enterprise AI may favor private infrastructure

Cloud AI APIs are excellent for testing. A company can launch a pilot quickly without buying GPUs, managing models, or hiring a large infrastructure team.

But enterprise AI is moving into production. That changes both the economics and the risk.

The most useful enterprise AI applications require proprietary data: contracts, source code, customer records, financial reports, support tickets, security logs, medical files and internal communications. This is the data that gives AI business value. It is also the data companies are most careful with.

For these use cases, on-prem or private AI infrastructure becomes more attractive. The model can run closer to the data. Access can be controlled more tightly. Retention, compliance and audit requirements become easier to manage.

There is also a cost angle. Token pricing is convenient in a pilot, but expensive at scale. When thousands of employees or customers use AI every day, paying per query can become a serious recurring cost. For stable, high-volume workloads, owning or controlling the infrastructure may be cheaper than renting every interaction forever.

Quantum risk is making long-term data protection more strategic

Quantum computing is not breaking enterprise encryption today. But the risk is already part of serious security planning.

The concern is that the minute quantum becomes commercial, all encrypted data sitting in the cloud will be transparent. No existing encryption will hold against a quantum computer. That matters most for companies holding long-life sensitive data: banks, healthcare providers, telecom companies, governments, defense-related organizations and infrastructure providers.

Regardless of whether or not on-prem is the best solution for all this, it is perceived as such. Hence, I believe it will drive higher demand for the legacy on-prem strategy. This early shift is also an opportunity, but that’s for another article.

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

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In 2026, conversations about Black founders and venture capital have focused on access to funding. But as AI reshapes startup economics, the bigger challenge is no longer simply getting a first check, it’s raising enough capital at the seed stage to successfully reach Series A.

AI has fundamentally lowered the cost of building software companies. Founders can launch products faster, automate operations and accomplish with five employees what once required teams of 30. Yet while AI has reduced the cost of building a startup, it has not reduced the cost of scaling one. Companies still need resources to acquire customers, hire experienced talent, invest in go-to-market strategies, and generate the revenue and growth metrics institutional investors expect before leading a Series A round.

For Black founders, who continue to receive a disproportionately small share of venture capital, the inability to secure fully funded seed rounds has become one of the greatest barriers to building venture-scale companies.

AI is making seed capital more valuable, not less

One of the biggest misconceptions about AI is that startups simply need less money. In reality, AI has shifted when capital matters most. Because startups can now build products more efficiently, investors are increasingly rewarding founders who demonstrate real traction instead of polished ideas. Seed funding is no longer financing an experiment, it is financing proof.

That means founders need enough capital to move beyond building a product and toward building a business. Today’s Series A investors are looking for recurring revenue, customer retention, capital efficiency and repeatable growth. Those milestones require time, execution and sufficient capital.

The startups that reach them are increasingly those that raised enough capital early to stay focused on customers instead of constantly fundraising.

The numbers tell a stark story

The challenge is particularly acute for Black entrepreneurs. According to ������Ƶ data, U.S. startups with a Black founder or co-founder received just $942 million in venture funding in 2025, only 0.32% of all venture capital invested in the nation. That represents one of the lowest funding shares in years and a dramatic decline from 2021, when Black founders raised $5.2 billion during the post-George Floyd investment surge.

While 2026 has shown encouraging signs, with Black-founded startups raising approximately $643 million by late May, the strongest quarter since mid-2022, the improvement was driven largely by a handful of unusually large financings, including a $350 million AI round. Across the broader ecosystem, Black founders remain significantly underrepresented in venture funding.

The issue isn’t simply that too little capital is available. It’s that many Black founders raise partial seed rounds that leave them without enough operating flexibility to achieve the milestones required for institutional Series A financing.

The real gap is between seed and Series A

Historically, venture capital rewarded bold ideas and rapid expansion. Today’s market rewards disciplined execution. Investors expect startups to demonstrate product-market fit, meaningful revenue growth, and efficient operations before committing Series A capital. That has made the journey between seed and Series A longer and more demanding.

Black founders who raise only enough money to survive often find themselves trapped in a cycle of continuous fundraising. Instead of focusing on customers, product development and hiring, they spend valuable months chasing additional capital just to extend their runway.

In an AI-driven market where product cycles move faster than ever, that lost time can determine whether a startup becomes a category leader or gets left behind.

Oversubscribed seed rounds are a competitive advantage

This is why oversubscribed seed rounds are taking on new importance for Black founders. Traditionally, oversubscription was viewed primarily as a signal of investor demand. Today, it is becoming a strategic advantage.

Additional capital gives Black founders flexibility to weather slower fundraising markets, invest aggressively when opportunities emerge, and continue executing without returning to investors every few months. It also allows founders to pursue growth intentionally rather than reactively.

Capital efficiency remains important, but efficiency is most valuable when paired with enough capital to execute.

The AI economy requires longer vision

The venture industry often celebrates AI for making entrepreneurship more accessible. In many ways, that’s true. The barriers to launching a company have never been lower. But lowering the cost of starting a company does not eliminate the capital required to build an enduring one.

Closing the Series A funding gap is therefore not simply about increasing investment in Black founders. It’s about ensuring founders have enough money to reach the milestones that unlock future institutional capital. That’s how you create more Black unicorns.

For Black founders, the conversation should no longer focus solely on access to capital. It should focus on whether they have enough capital to compete. In the AI economy, the Black-led companies that endure won’t simply be those that build the fastest, they will be the ones with the resources to keep building long enough to win.

and are the co-founders of (Black Ops VC), an early-stage venture capital firm. Norman is a managing partner at Black Ops VC. He is also the CEO of , an AI-powered market research platform used by industry giants such as and that’s designed for the media and entertainment spaces to gather audience feedback on video content, and a partner at , an accelerator that provides intense programming, resources and capital to overlooked founders.

Along with serving as general partner at Black Ops VC, Green is the founder and CEO of , an AI-powered CRM and inventory management platform specifically designed for art galleries, dealers, auction houses and collectors.��

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