Legal tech funding is booming, but the money isn’t spreading evenly across the market.

Last year, Crunchbase News reported that legal tech startup investment was riding high as investor enthusiasm for AI reshaped legal software funding, citing a report estimating that 44% of legal work could eventually be automated. That concentration has helped create one of the clearer success stories in legal AI — and may also be obscuring an adjacent market that remains far less developed.

Using disclosed funding totals for a selected group of plaintiff-side legal AI companies, the imbalance is hard to miss.

EvenUp has raised $370 million, $164 million, $85 million, and Darrow $63 million, for a combined total of roughly $682 million. Plaintiff-focused companies account for about 71% of disclosed capital for legal AI, suggesting investors have found a part of the sector where adoption, workflow clarity and venture-scale narratives already line up.

That investor interest is not difficult to understand. Plaintiff firms tend to share more standardized workflows around client intake, case evaluation, medical review and demand generation — all areas where AI can automate repetitive work and improve throughput. As those firms have adopted software, the category has become easier to understand, distribute and fund.

The underserved side: legal defense

The defense side, by contrast, remains underdeveloped and may present the next big opportunity.

Corporate legal departments and the law firms managing high-volume defense work still rely heavily on fragmented systems, spreadsheets, email-based coordination and outside counsel processes that were not designed to produce portfolio-wide visibility. For companies facing hundreds or thousands of active matters, litigation is often still run more as a services function than a software-enabled one.

That creates a sizable but harder-to-package opportunity. Retailers, insurers, healthcare systems and financial services companies can each manage large litigation portfolios, yet many still lack a unified view of case risk, settlement patterns, legal spend and outside counsel performance. The need is not new. What has been less clear is whether a venture-backable software category could be built around it.

Part of the reason defense-side legal AI has lagged is structural. Workflows vary widely by industry, matter type and regulatory context, making the market less standardized than plaintiff-side practices. Buying decisions also tend to run through general counsels, legal operations teams and outside counsel relationships, which can lengthen sales cycles and make the category look less immediately viable to investors.

But a shift is underway. Last fall, Crunchbase News reported that legal tech funding reached record highs in 2025, reinforcing how quickly investor attention has shifted toward AI-enabled legal workflows. As plaintiff-side firms get faster at sourcing, valuing and prosecuting claims with software, the operational pressure on defense teams mounts. At the same time, AI is making it more feasible to turn messy litigation workflows into systems that can surface comparable matters, flag risk earlier and benchmark outcomes across portfolios.

From an investor perspective, that makes defense-side litigation AI look less like a niche and more like an underbuilt segment of a broader legal software market. If plaintiff-side investment reflects where legal AI has already become easy to fund, defense-side infrastructure may represent where the next category still has room to form.

Investors, take notice

For venture capitalists, this is the kind of asymmetry worth watching: a large enterprise market with measurable pain points, improving technical feasibility, and no entrenched category leader yet. What investors should watch is whether startups in the category can pair proprietary outcome data with repeatable enterprise adoption — the combination most likely to produce a durable category leader.

One emerging approach on the defense side is exposure and settlement benchmarking: using historical resolution data to estimate settlement ranges, legal spend and case risk across similar matters. In practice, that can mean comparing claims by jurisdiction, plaintiff firm, claim type or other operating variables to help in-house teams make faster and more consistent decisions.

If the category scales, one potential moat may come from proprietary outcome data. Defense-side settlement details, matter economics and resolution patterns are often difficult to reconstruct from public records alone.

A platform that aggregates and normalizes those signals across customers could build a data asset that becomes more useful with scale — a familiar dynamic in vertical software, and a potential early signal for investors of durable advantage in defense-side legal AI.

There is still no clear, scaled, venture-backed winner built specifically around defense-side litigation intelligence. For startup and growth investors, that makes the segment less a settled market than an open question: whether one of legal AI’s next durable companies will emerge not from the workflows that have already attracted the most capital, but from a large enterprise category whose software stack is still taking shape.

�� is CEO and co-founder of , which builds AI-powered litigation intelligence for corporate defense teams and law firms.

Related Crunchbase query:

Related reading:

• Legal Tech Startup Investment Is Riding High, Thanks To AI Boost
• EvenUp, AI Software For Personal Injury Law, Doubles Valuation To $2B As Legal Tech Funding Hits Record High
• Highest Count Of New Unicorns Join Crunchbase Board In Over 3 Years As Exits Also Gain Steam
• Wheels Of Justice Slow To Accept Legal Tech As Funding Falls

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A grab bag of funded startups caught our attention this past month, from a previously bootstrapped custom metal manufacturer that got its first outside funding from big-name Silicon Valley backers, to a startup that aims to provide geothermal energy from underwater volcanoes to small island nations. Let’s take a look.

$110M for on-demand custom manufacturing

First, let’s start with a refreshingly non-AI round, and a sizable one at that.

Reno, Nevada-based said last month that it has raised $110 million in funding led by brothers and founders and , along with and , at a $1 billion valuation.

The company operates an on-demand manufacturing platform specializing in custom-cut metal and fabrication. The round is its first venture investment, and apparently came only after Sequoia’s flew to Reno to woo SendCutSend CEO into accepting Silicon Valley backing. Previously, Belosic had bootstrapped the company, founded in 2018, with personal savings, bank loans and credit cards, he told .

He held little interest in taking cash from startup investors until SendCutSend started to be flooded earlier this year with orders from AI-driven industries including robotics and data centers, and Belosic said he realized the business needed outside investment to grow.

Investor of Paradigm told WSJ that underlying SendCutSend’s booming business is intense demand for rapid, on-demand sheet metal and custom parts. “If you think about the entire frontier of robots, defense companies, rocket companies, electric-car companies, they all need very fast turn prototyping,” he said.

The investment is Paradigm’s first into the manufacturing sector, he noted.

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$100M for insurance-covered metabolic health counseling

GLP-1 weight-loss drugs may be booming, but a well-funded startup is betting that medication alone isn’t enough to solve the chronic disease crisis.

, a New York-based metabolic health startup that combines dietitians, AI tools and GLP-1 medication management, last month said that it raised a $100 million Series C round led by . , , and a long list of other investors also backed the round, which brings the company’s total funding to date to just over $213 million, .

Founded in 2021, Nourish operates what it describes as the country’s largest dietitian-led metabolic health clinic, pairing more than 10,000 registered dietitians with AI coaching, lab testing and virtual care. The company has increasingly expanded into GLP-1 prescribing and medication management as demand for drugs such as Ozempic and Wegovy continues to surge.

Nourish said it has partnered with hundreds of health insurers in the U.S. and that its service is covered by most plans.

Its pitch is that the next phase of the GLP-1 boom will require more than prescriptions. While the drugs have transformed obesity treatment, many patients struggle to stay on them long term or maintain results after stopping, according to the company. Nourish is positioning itself as a broader metabolic health platform focused on nutrition, behavior change and ongoing clinical support alongside medication.

“Chronic disease is the central failure of U.S. healthcare — nearly 200 million Americans affected, trillions spent, and outcomes that still don’t move,” Menlo Ventures partner said in a statement. “What Nourish has built in four years is remarkable: a care model that actually bends the cost curve, with 10,000 dietitians, deep payer relationships, and clinical outcomes patients stick with.”

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$58M for Gen Z group travel adventures

Group travel startups are having a moment as younger travelers increasingly look for ways to meet people while exploring new destinations.

, a Milan-based startup that organizes group travel experiences for millennials and Gen Z travelers, raised a €50 million (roughly $58 million) Series C funding round as it looks to expand further across Europe and enter the U.S. market. The round was led by .

Founded in 2017, WeRoad operates a platform that connects solo travelers and small groups through curated multiday trips led by coordinators. The company says it has served more than 300,000 travelers across over 1,000 itineraries, with offerings ranging from adventure travel and cultural experiences to outdoor excursions. Participants are typically grouped with strangers in similar age ranges, turning the trips into a hybrid of travel booking and social networking.

“We live in a time when artificial intelligence and social media are reshaping the way we connect with each other. And amid all this digital connection, real human connection has become increasingly rare. Around 30% of young adults say they feel lonely every day. In the United States, this phenomenon is especially significant,” the company said in a statement. “We believe we have an answer. Not the only one, not a perfect one, but a real one: putting people in a room together (or on a quad bike in Morocco, in a canoe in Vietnam, or in front of a sunset in Patagonia) and letting whatever is meant to happen, happen.”

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$26M to keep AI data centers cooler

AI may be driving the data center boom, but keeping those facilities cool is becoming a business opportunity in its own right.

, a U.K.-based startup developing precision liquid cooling systems for AI infrastructure, said last month that it raised a $26 million Series B as demand surges for technologies that can manage the growing heat and power requirements of next-generation AI data centers. The round was led by and and brings Iceotope’s total funding to date to just under $100 million, .

Founded in 2005, Iceotope has developed a chassis-based liquid cooling approach designed to replace traditional air cooling and cool entire systems rather than individual chips. The company says it now holds 219 granted and pending patents. It said it will use the new funding to expand product and engineering development, grow its patent portfolio and accelerate partnerships that bring its cooling technology to market.

The raise comes as AI workloads create mounting challenges for conventional cooling systems. Iceotope argues its technology can reduce energy consumption and water use while supporting high-density AI and high-performance computing deployments in both data centers and edge environments.

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$25M for geothermal energy from subsea volcanoes

As AI companies scramble for more electricity, investors are increasingly willing to fund some unconventional ideas for generating it. One of those is , a Seattle-based startup developing subsea geothermal power systems designed to tap into heat generated by subsea volcanic activity.

The company recently raised between $25 million and $30 million in a seed round led by , sources familiar with the matter .

Founded just last year, Endurance Energy is targeting island nations — where it says electricity can cost almost 7x as much as in the U.S. — industrial sites and eventually hyperscale data centers that need large amounts of reliable power.

Unlike solar and wind, geothermal energy carries the promise of round-the-clock, renewable baseload electricity, a feature that has become increasingly attractive as AI infrastructure drives soaring power demand.

Endurance says its seafloor geothermal generators could deliver gigawatts of power from hydrothermal systems along tectonic plate boundaries and volcanic regions. It is , where about 80% of electricity generation still relies on imported diesel fuel.

Earlier this year, the company signed an agreement with the Tongan government and launched a pilot project aimed at harnessing geothermal heat generated by subsea volcanic activity around the island nation.

“Clean geothermal power will enable us to substitute most of our diesel base load power and further insulate ourselves from future external shocks caused by geopolitical conflicts and global economic impacts,” Tongan Prime Minister Lord Fakafānua said in a statement.

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Operating as an “all-in-one” software ecosystem, Scotch provides liquor retailers with point-of-sale hardware, custom software, payment processing and a back-office suite to manage state-by-state regulatory complexities. Customers range from boutique single-register shops to enterprise stores running over a dozen lanes.

led Scotch’s Series A raise, which included participation from , and . The injection of capital comes on the heels of a growth spurt, with the Denver-based startup reporting greater than 500% year-over-year growth and surpassing $1 billion in processed payment volume.

While the company declined to reveal its valuation, co-founder and CEO said the funding marks “a significant step-up” from its $10 million seed round, raised in September 2024 and led by First Round Capital.

Old-school market

Formally incorporated in January 2024, Scotch was born out of a unique industry challenge encountered by Bolling and CRO during their previous venture, . A convenience-store software company that supported 15,000 stores across the U.S., Skupos attracted attention from major consumer packaged goods giants such as , and Budweiser owner .

“Budweiser, in some way, shape, or form, tried to get us not only to continue to grow our C-store business, but to also expand into the liquor store industry,” Bolling told Crunchbase News in an interview.

Market research conducted in 2022 revealed a striking contrast between the two sectors. While the $650 billion convenience store market is highly fragmented, its point-of-sale technology is heavily consolidated around four major players.

Conversely, the liquor-store industry proved to be an entirely different beast: highly fragmented, intensely regulated and flooded with more than 200 regional, legacy POS systems.

Recognizing that the Skupos business model didn’t align with that level of fragmentation, the founders held off. Following the acquisition of Skupos by in August 2023, the team revisited the concept.

Drawing inspiration from the business model of restaurant tech giant — with whom the founders frequently shared strategy notes in the mid-2010s — they recognized the potential to replicate that success in a highly specialized, nuanced retail market.

, former chief architect of (acquired by for more than $1 billion), serves as Scotch’s CTO.

‘Business in a box’ strategy

The platform’s business model scales directly with the merchant, driving revenue through a hybrid mix of SaaS fees, charged on a per-device, per-month basis; fintech monetization, or collecting standard interchange fees on its payment volume and hardware sales, providing the modern storefront terminals necessary to run the infrastructure.

While general retail giants like Lightspeed and exist, Scotch markets itself as the only player capable of handling the severe operational and compliance hurdles distinct to alcohol retail.

Customers include The Liquor Store of Jackson Hole, Big Bear Wine & Liquor, Corkdorks and Everest Spirits Superstore.

Eradicating the ‘toil’ via AI

With inventory sizes ranging from 2,000 to 12,000 distinct products per store, manual inventory and vendor management can lead to miscalculated ordering and tied-up working capital, noted Bolling.

Scotch says it differentiates itself by building artificial intelligence directly into these back-office workflows. The platform uses AI to eliminate administrative friction, with the company claiming its offering can save business owners over a full day of work per week. It also saves them money by giving them, for example, a more accurate picture of their inventory, according to Bolling.

“We’ve really focused our AI workflows on the ‘toily’ aspects of running one of these businesses,” Bolling said. “Some of our customers are sommeliers who opened a store because they are passionate about serving their community with the right wine curation. That’s their creative outlet. We try to take up the parts of the day that suck for these business owners.”

By optimizing supply chains and automating store management, Bolling believes that Scotch’s AI native architecture is driving “measurable” gross margin expansion for its merchants.

Grassroots growth and word of mouth

Because it is targeting an industry historically dominated by “old-school,” family-owned, mom-and-pop operations, Scotch has employed an unconventional go-to-market approach. The company relies on a dual strategy of targeted geographic inside and outside sales reps as well as localized trade association partnerships. The reasoning behind that approach, according to Bolling, is because liquor store owners rarely search for new POS hardware on a whim.

However, the startup’s fastest growth vector over the last six months has been organic word of mouth. Because many state laws cap the number of liquor licenses an individual can own, competitive hostility is low, creating tight-knit networks of friendly competitors.

“They go to the same industry events, they talk to each other, they are in study groups together,” Bolling noted. “When one of them adopts a system like Scotch, they refer a lot of other customers our way.”

Scotch currently has about 45 employees working out of its Denver headquarters. It plans to use its new capital in part to scale its engineering and sales operations across the United States in addition to accelerating product development.

Going after ‘the hard part of the market first’

, general partner at VMG Partners, believes that Scotch is modernizing “one of the last major categories of retail.”

“The beverage alcohol market is nearly $250 billion and, despite that, is still operating on systems built in the 1970s with on-prem servers,” he wrote via email. “It isn’t an exaggeration to say Scotch is the only player that has solved enterprise-level complexity.”

Most industry startups never moved beyond basic solutions for small businesses, believes Stenmark.

“Scotch went after the hard part of the market first, solving for some of the largest and most complex retailers in the country,” he wrote via email. “This approach allowed them to harden their product early, and has translated to them having the only product that can actually solve every business operations and payments problem a retailer might have, whether they be a national brand or a beloved regional storefront.”

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All told, global venture funding reached $92 billion in May, marking the second-largest monthly total on record, just behind February, Crunchbase data shows. Of that, Anthropic raised $50 billion��1 , or 54% of the month’s total funding.

Startup funding was up 284% year over year from $24 billion, per Crunchbase data.

The month also had a successful IPO for a venture-backed company as chip company Cerebras, which has benefited from growing demand for AI inference, went public at the upper end of its range at $185 per share and opened at $350. The stock is currently trading around $225 as of June 2, which values the company at just over $49 billion.

On the valuation front, Anthropic rocketed ahead of on The Crunchbase ������Ƶ as it became the second-most highly valued private company at $965 billion, just behind at $1.25 trillion. Just months earlier in February, Anthropic was valued at $380 billion. The board has shot up in value in recent months and has 1,780 companies altogether valued at $9.9 trillion as of the end of May.

Billions more

Last month, a further $17 billion was raised by 10 companies in rounds of $500 million and above. They include defense tech unicorn , which raised $5 billion, and China-based AI labs and , which each raised more than $2 billion having raised rounds earlier this year. Automated coding lab raised $1 billion, and , which develops AI for customer service, raised $950 million in a single round.

Funding to the AI sector totaled $72 billion, or 79% of funding, last month.

The boom funds itself

The Cerebras IPO sets the stage for further public listings, including potentially record-setting ones.

SpaceX publicly filed its prospectus in May, stating its intention to raise $80 billion via its IPO. The space tech giant has raised $9.4 billion in equity funding to date, per Crunchbase.

Anthropic, which is set to beat OpenAI to the public markets after filing its confidential IPO paperwork on June 1, has raised $125 billion in equity funding thus far, compared with its rival’s roughly $180 billion in private funding.

The private markets in 2026 have raised capital at a greater pace than ever before, thanks to�� larger rounds, faster follow-on fundings and record-breaking valuations. At the same time, if SpaceX, Anthropic and OpenAI all list this year, as they’ve said they intend to, the resulting liquidity could be the largest in market history, pouring hundreds of billions back into the hands of startup investors who will redeploy it into the next wave of private companies.

Related Crunchbase queries:

Related reading:

• Anthropic Files Confidentially For IPO
• Anthropic Nears $1T Valuation And Leapfrogs OpenAI On ������Ƶ With $65B Funding Round

Methodology

The data contained in this report comes directly from Crunchbase, and is based on reported data. Data reported is as of June 2, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. Crunchbase also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. Crunchbase includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the “Series [Letter]” naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a “venture” round. (So basically, any round from the previously defined stages.)

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How times have changed. Already this year, more than $14.6 billion in venture investment has gone into companies in Crunchbase’s military, national security and law enforcement categories, blowing past the sector’s previous annual record of $9.6 billion raised in all of 2025.

Investors have poured billions into startups developing AI-powered military systems, autonomous vehicles, defense software and space technologies, and they’re writing increasingly large checks to do it, Crunchbase data shows.

Funding growth accelerates

The defense tech sector’s rise has been years in the making

Global defense tech funding totaled $1.6 billion in 2020 before climbing to $3.9 billion in 2021, Crunchbase data shows. Funding then remained relatively steady between roughly $2.8 billion and $3.8 billion from 2022 through 2024.

That changed dramatically last year, when funding jumped to a record $9.6 billion. Now, five months into 2026, startups in the sector have already eclipsed the full-year record set in 2025.

Deal flow has stayed steadier, mirroring a broader trend of venture capital concentration. So far this year, defense tech startups have announced 107 venture rounds, Crunchbase data shows, putting 2026’s pace slightly ahead of the 206 deals done in 2025.

Megarounds lead the way

The biggest contributor to this year’s funding surge, by far, is .

The Costa Mesa, California-based company announced a $5 billion Series H last month, a deal that valued it at $30.5 billion and further cemented its status as the most valuable venture-backed defense startup in the world.

Still, it’s not the only defense- or military-related startup drawing large sums of funding. Many of this year’s biggest rounds involve companies building AI-enabled defense systems, autonomous aircraft and maritime vehicles, military software platforms and space infrastructure.

Case in point: a $300 million Series C round announced today for , an autonomous drones systems manufacturer. The round was led by and and values the Huntington Beach, California-based startup at $1.8 billion.

Autonomous aviation startup raised a $2 billion Series G round in March led by and, while , which makes unmanned surface vessels for naval and defense use, secured a $1.75 billion Series D led by later that month.

Space-related startups with defense applications have also been especially prominent among defense-tech bets this year. , and , rank among the largest defense-related funding recipients of 2026, highlighting continued investor interest in technologies with both commercial and national security applications.

Attention turns to exits

As funding totals climb, investors may begin looking toward exits. Already this year, one smaller defense-tech startup, AI drone company , went public, with shares soaring more than 500% in their first day of trading. They remain near the high end of their price range as of early June.

Anduril is now widely viewed as one of the most likely defense tech candidates to pursue an IPO in the coming years. A public offering by a company of its size would mark a significant milestone for the sector and provide a closely watched test of public-market appetite for next-generation defense contractors.

Other well-capitalized companies across defense, autonomy and space are also reaching a scale where public listings or major acquisitions become more plausible, with Crunchbase’s predictive intelligence tools forecasting that nearly four-dozen . Along with Anduril, they include True Anomaly, Shield AI, Sierra Space and .

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Related reading:

• Anduril Raises Another $5B As Defense Tech Startups Shatter Funding Records
• Sector Snapshot: Defense Tech Funding Hits Record High
• After Swarmer’s Soaring Debut, Here Are 12 Other Potential Defense Tech IPOs

The market believes software businesses can’t charge premiums anymore and it predicts slowing growth indefinitely.

There are two reasons. First, AI powers a 10x decrease in software production costs. Second, these AI capabilities enable a huge wave of new competitors, both VC-funded startups and in-house solutions.

Reduced software production costs and rising competition, they say, will eliminate software’s pricing power. Public software stocks traded down 20% this year through mid-May, and for the first time in history, software trades at a discount to the average S&P500 multiple on earnings. SaaS is dead.

It is true that AI has brought falling costs and rising competition. But it does not follow that SaaS is dead. Lowering the cost to produce software does not mean that software revenue will shrink. In fact, history suggests the opposite.

In certain cases, efficiency begets consumption. This is the lesson of the . It worked for coal engines, it worked for data centers, and it will also work for AI-powered software.

The Jevons Paradox

Let’s start with coal. In 1860s Britain, many worried about burning through the country’s coal resources too quickly. Conventional wisdom said that developing more-efficient coal-burning engines would make the coal last longer.

But economist William Stanley Jevons recognized that more coal-efficient engines would cause an increase in demand for coal energy with the result that Britons would burn through their coal more quickly, not less.

Jevons was right. When greater efficiency produced lower costs, it also unlocked enormous new demand. This consumed coal reserves faster, not slower.

Twenty-five years ago, I joined a buyout firm during the 2001 dot-com crash. My first investment was in a troubled datacenter company. Exodus Communication, which reached a peak market cap of $32 billion, then went through bankruptcy twice as datacenter demand continued to fall.

In 2004, I recommended that our firm acquire that datacenter business out of the second bankruptcy for $200 million and merge it into a competitor named Savvis.

At the time, the market considered datacenters a shrinking industry. Dot-com companies were pulling racks of servers out of the sites, and datacenter floors were emptying out. Industry analysts forecast that given Moore’s law about the exponential growth of chip capacity and increasing server power density, a single rack in 10 years would deliver what it took 100 racks to deliver in 2005, and that in 20 years, a rack would deliver what 10,000 racks delivered in 2005. Conventional wisdom said that more-efficient chips would require less datacenter floorspace over time.

Our thesis that demand for datacenter floorspace would grow was not a popular opinion at the time. If 10,000 racks in 2005 would be replaced by just one rack in 2025, didn’t the U.S. have plenty of datacenter floorspace already?

was running advertisements showing a room full of servers replaced by one mainframe. One skeptical investment committee member told me that this business had been through bankruptcy twice in two years, and that if it went through a third time, I would go with it.

Today, one rack can indeed deliver 20,000x the compute power of racks from 2005, and as everyone knows, far from having too much floorspace, we can’t build new datacenter capacity fast enough. Truly enormous latent demand for computing power was unlocked as rack efficiency increased. The Savvis story ended well too, sold six years later for $3.2 billion.

The Jevons Paradox was true for coal, and it was true for data centers. It will also be true for AI-supported knowledge work.

Knowledge work and market expansion

Twenty-five years ago, only the wealthy had access to personalized investment advice. In 1996, Nobel Laureate Bill Sharpe co-founded to bring personalized investment advice to anyone with a 401(k).

My firm was an investor, and I had the privilege of working closely with the company. At first it tried to sell advice about how to invest 401(k) funds, but only about 20% of employees were interested in taking advice and then managing their 401(k) positions themselves.

Financial Engines’ breakthrough innovation was to manage the 401(k) positions directly, not just advise. Employees could check a box: “do it for me”. The demand from people who previously had no access to this advice was beyond all expectation and did enormous good. I recall that an early customer was , whose tens of thousands of employees with an average age of 27 years had approximately 40% of their 401(k) monies in cash, 40% in stock of JCPenney (which would eventually file for bankruptcy in 2020), and 20% in everything else. Just moving them into sensible low-cost mutual funds appropriate to their age and other financial goals generated huge benefits.

Financial Engines went from zero to $169 billion in assets under management when it was acquired in 2018 for $3 billion.

The company delivered a service that is very similar to what we today would call agentic AI. The customer (an employee with retirement savings) was delegating a decision (invest my money) to a computer system, and the employee paid in a way tied to the outcome (~50 basis points on AUM).

Of course, the technology to deliver this was quite different, and this was a very narrow application. The lesson remains: Software enabled a massive efficiency in delivering knowledge work (in this case individual investment advice) and a huge latent market appeared to buy the service.

The end of the rationing of knowledge work

The increased cost efficiency of AI, like the increased cost efficiency of Financial Engines’ algorithms, allows demand to increase because it relaxes a supply constraint on knowledge work.

Across human history, even to today, knowledge work has always been rationed because it is supply constrained.

Knowledge workers take years of education and training, tend to want to live in high-cost places, over time want to work on only certain kinds of problems they find interesting, and require a lot of management to get along. That is why we pay them such high wages and do everything we can to make them more productive.

Business software is a tool to make knowledge workers more productive. The total business software market in the U.S. is on the order of $0.5 trillion per year, according to Gartner. The U.S. market for knowledge work, that is the amount paid to the 100 million knowledge workers in this country, is roughly $10 trillion, per numbers. Currently, we spend about 5% of the cost of knowledge workers on software tools to help them.

AI enables software companies to not just sell tools to knowledge workers, but to begin to sell the knowledge work outcomes themselves, as I have written about in prior Crunchbase articles.

Put those together: 90% cost compression in software development plus the ability to sell knowledge work. We know there is a huge latent demand for knowledge work, if only it were not so expensive and hard to access.

For the first time, millions of people and businesses who have never had access to a strategist, an analyst, a lawyer or a financial adviser are about to get one.

Software is far from dead. The increase in efficiency offered by AI will allow it to do much more for less, and just like a more efficient coal engine or data center, this will unlock huge latent demand for knowledge work.

Ultimately, this will increase revenue and the strength of software businesses that use AI to further improve knowledge workers’ productivity or deliver knowledge work outcomes directly. Software’s job today is to solve the problem of delivering this safely and reliably.

It was no small task for industry to learn how to manage knowledge workers who are human, and it will be just as big a task to learn how to manage those who are machine knowledge workers. That is the challenge.

But remember that today the market for knowledge work is 20x the size of the market for software. The scale of the prize for software companies is unlocking the latent demand for knowledge work that, if history is any guide, will dwarf today’s software market.

The market today fears that the efficiency delivered by AI will shrink the software industry. Exactly the opposite is true. AI will unlock massive latent demand for knowledge work, and the software market will explode. Long live software.

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.

Related reading:

• Don’t Just Talk About AI. Measure Business Outputs. Here’s How.
• The Rise Of The AI Executive
• Invention To Innovation: Making Sense Of AI’s Moment

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The statement was light on details and did not specify the planned offering size or where it will list. For its most recent funding round, a $65 billion Series H funding announced last week, the San Francisco company more than doubled its post-money valuation to a staggering $965 billion.

With that round, Anthropic also surpassed its closest rival, , in terms of last reported valuation. In February, OpenAI announced it had closed a $110 billion round at an $840 billion post-money valuation.

Anthropic has now raised roughly $125 billion from investors, per Crunchbase data.

The path to the public markets

The IPO filing marks an escalation in the race among generative AI behemoths to make it first to the public market. That said, it could still be while.

Before making its market debut, Anthropic must still receive a sign-off from securities regulators on its confidential filing. After that, it will need to submit its public filing, carry out its pre-IPO roadshow, and put the remaining pieces in place for an offering of this presumed magnitude.

How long could it take? It’s unclear, of course, but if we use as a proxy, things could proceed briskly. SpaceX, which is reportedly seeking a valuation of $1.8 trillion or more, submitted its confidential filing on April 1. The company is expected to begin trading this month, with multiple reports citing June 12 as the target date.

If Anthropic follows a similar timeline, we could potentially see a market debut in August. Before that, however, will be the public filing of its IPO prospectus, which will offer a long-awaited peek under the hood at Anthropic’s famously fast revenue growth and the scope of the capital expenditures it has taken to get there.

As someone who has used the word boring in IPO market headlines many times in the past, one thing that can assuredly be said is that word no longer applies.

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The conventional fundraising playbook goes something like this: Build your list, craft your deck, start warming intros six months out, and prepare to spend the next year in a loop of coffee chats and follow-up emails that mostly go nowhere.

I did none of that. Not because I had some genius alternative strategy — but because I figured out, early and somewhat accidentally, that the best investors don’t want to be pitched. They want to discover you.

Every meaningful check in our $14 million round came from a personal encounter. A talk I gave. A dinner I attended. A conference I almost didn’t go to. If there’s a single lesson I’d want every first-time founder to take from my fundraising experience, it’s this: stop optimizing your outreach and start engineering the rooms you’re in.

Full disclosure: I went to . I know what you’re thinking: “Of course she raised $14 million — she had the network handed to her.” And look, I won’t pretend the alumni connections didn’t open certain doors. They did. It’s a competitive advantage most founders don’t have, and I won’t insult your intelligence by pretending otherwise. Harvard’s own data shows that have gone on to found for-profit or nonprofit ventures, collectively launching over 146,000 companies globally. But even at Harvard, ’s — and the vast majority of those 146,000 companies never reach meaningful scale. If a diploma were enough, far more of my class would be on .

What a diploma doesn’t give you is a mission people instinctively care about. I’m a Gen Z neuroscientist building technology to solve something my generation knows firsthand: the mental health toll of a world where social media has displaced real human connection. That mission travels on its own.

Investors don’t need convincing that loneliness is a crisis or that the way teenagers relate to each other has fundamentally changed — they see it in their kids, their families, the culture around them. When your problem is self-evident to the people in the room, the pitch is halfway done before you open your mouth.

Reverse the power dynamic

The obvious problem with cold outreach is noise. A partner at a top-tier fund receives hundreds of cold pitches a week. Yours lands in a full inbox alongside dozens of others with equally compelling subject lines.

Even if your deck is exceptional, you’re asking someone to extend trust to a stranger, based on a document, before any human relationship exists. According to , at all. Among those that are read, the conversion rate — the share that leads to any meaningful next step — sits at , even for founders who do everything right.

But there’s a less obvious problem: cold outreach inverts the dynamic you actually want. When you cold pitch, you are the one seeking. You are, structurally, in a position of need.

The investor holds all the leverage.

What I learned — through experience I did not fully understand until I looked back on it — is that every great investor relationship I have started from a moment where they came to me. And the engine behind almost every one of those moments was a room where I was speaking, teaching or simply showing up as someone who had something worth saying. I was never chasing.

Find the right rooms

None of the relationships I’m about to describe started with an email. They started with a room, a talk and a reason to be there that had nothing to do with raising money.

Any room works — if you have something worth saying. You don’t necessarily need to find yourself in prestigious halls. Any room where the right people are present, and you’re there as a voice rather than a business card, will do. A panel at a tech and wellness summit. A founder dinner in Soho where I gave a 10-minute talk on the neuroscience of friendship — the host made three introductions the following week. None of these were “investor events.” They were rooms where people who cared about the mission happened to be. In New York and San Francisco, these happen every day — on , on , through your alumni network. You don’t need a big name to get a small stage. You just need to show up and ask.

The stage size doesn’t matter. Being on it does. The big conferences won’t invite you to speak until you already have traction. That’s fine — because the rooms that actually move the needle are often smaller anyway. , founder of , came through someone who heard me at the . approached me after a sports dinner in London — a room I was in because I’m a marathon runner, not because I was fundraising. The investment followed because we were aligned on something that did matter.

Speak about the problem. Not the product. The talks that generated the most meaningful investor relationships weren’t the ones where I pitched . They were the ones where I spoke about loneliness, neuroscience, and what technology can and cannot do for human connection. The subject matter attracted people who already cared about the mission. By the time anyone asked about the company, they were already bought in on me.

Trust compounds across encounters. One of our key shareholders — a major fund — started with a partner I first met at a conference in Dubai. We ran into each other again at a New York event. And again after that. Three encounters across three cities, each one building a little more context, a little more trust. By the time we were both ready, the relationship already existed. Was it luck? Maybe. But I keep showing up in rooms where the right people are. At some point that stops being luck.

The deck gets you a second meeting. The relationship gets you a yes. An investor isn’t just a wallet. They’re betting on the change you want to make in the world — and on you. The relationship that leads to a check often starts with something human: a shared interest, a run, a conversation that had nothing to do with fundraising. That’s not a bug in the system. That’s the system.

The real lesson: Cold outreach has its place. It works for some people in some contexts, and I won’t pretend otherwise. But if you have a mission that is genuinely worth talking about — and if you can speak about it with conviction — the most efficient thing you can do is engineer visibility in the rooms where your investors already are. Not as a founder seeking capital. As a voice worth listening to.

That’s the posture that builds the kind of investor relationships where someone approaches you after a dinner, or appears in your orbit three times across three cities until trust quietly accumulates. That’s the posture that turns a shared run or a sports dinner into a check from someone who genuinely believes in what you’re building.

The goal isn’t to get lucky. The goal is to make yourself impossible to miss — every time you have a mic, and every time you don’t.

is the founder and CEO of , a Gen Z platform reshaping how friendships begin and grow in person. A solo female founder and member of Gen Z herself, she holds degrees from and in computational neuroscience, neurobiology and behavior. Under her leadership, Clyx has raised $14 million in Series A funding backed by ‘s , co-founder , F1 World Champion , and , and facilitated more than 500,000 real-world friendships across six cities worldwide.

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So far this year, investors have put about $7.8 billion into Boston-area startups, per Crunchbase That puts the region on track for a moderate annual gain and the strongest tally in about four years, as charted below.

Invidious comparison

Under normal circumstances, such numbers might be celebrated as pretty strong. But many Bostonians don’t see it that way.

“For the first time, startups in Texas raised more VC money than those in Massachusetts,” one headline this spring. Earlier this year, another correspondent concerns from local startup backers and builders that the tech startup scene is thinning out.

At root, the issue may not be that Bostonians are delivering so little investable startup talent, but rather that other places are swimming in unprecedented capital. This kind of invidious comparison is particularly stark in the AI realm.

Overall, North America venture funding hit a record high in the first quarter of this year, surging to $252 billion. Of that, more than 87% went to companies in Crunchbase AI-related categories.

Few of those AI mega-fundraisers were in Massachusetts. The biggest, most heavily funded names in generative AI, like , and others, are predominantly headquartered in the San Francisco Bay Area. That means Boston didn’t get a slice of history’s largest startup funding rounds.

By contrast, biotech, a traditional area of strength for the Boston area, hasn’t been on a funding tear. True, there’s no dramatic slump. But in a time when a single venture-backed AI company can snag $122 billion in a , biotech round sizes can’t compete for scale.

Standout rounds

Still, by pre-AI standards of venture funding, Boston has been scaling some heavy hitters.

Per Crunchbase , at least 12 companies in the greater metro area��1 raised rounds of $200 million or more this year, listed below.

The largest round went to , a provider of wearable fitness technology and a subscription platform that raised $575 million in Series G funding at a $10.1 billion valuation in March. The company says it is powered by more than 24 billion hours of physiological data and purpose-built AI models to provide predictive, personalized health insights.

, a provider of consumer privacy and security tools, came in second. It secured $375 million in Series B funding in March led by and .

Next on the list is , which provides healthcare plans to seniors on Medicare. The 9-year-old company disclosed in January that it had closed on $366 million across two Series F funding tranches.

Biotech startups, meanwhile, didn’t make the top 3 but were heavily represented on the list. Overall, more than half of funded startups in the list are focused on biotech or healthcare.

Why compare?

Boston isn’t the San Francisco Bay Area, and it certainly isn’t Texas. So it’s worth asking: What is the point of comparing startup ecosystems? Is a metro area flailing if it doesn’t keep up with a particular major innovation cycle, even if it maintains core areas of strength?

At risk of over-generalizing, we’d conclude that competitive rank still matters. A metro area can retain its crown as a startup innovation hub only if it continues to produce transformative companies.

For Boston, there’s no indication the region is losing its edge in biotech and other sectors where it’s long been an established powerhouse. However, in the generative AI era, it’s also evident that the region has not produced one of the most high-valuation players in the space, and that’s put some ding in the city’s reputation as a leading innovation hub.

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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.

Venture funding has always been a world of haves and have nots. And these days, the haves are having more than ever. Case in point this week was . The 5-year-old generative AI giant secured $65 billion in Series H funding this week, pushing its post-money valuation to a mind-blowing $965 billion.

After that, the next-biggest financing was a $1 billion round for AI software development tool maker , lifting its valuation to $26 billion. Companies in a range of other sectors also managed to secure sizable though smaller rounds, in areas including commerce logistics, developer AI, insurtech, fusion and more.

1. , $65B, foundational AI: Generative AI company Anthropic raised $65 billion in a Series H funding round, more than doubling its post-money valuation to a staggering $965 billion. San Francisco-based Anthropic said , , and led the financing, and that , , , , and co-led the investment.

2. , $1B, AI software development: Cognition, developer of AI software engineer Devin, has closed on over $1 billion at a $26 billion valuation. , , and 1��led the financing for the San Francisco-based company.

3. , $250M, logistics: Atlanta-based Stord, developer of a fulfillment network, software and AI tools for independent brands, secured $250 million in Series F funding. The round set a $3 billion valuation for the 11-year-old company.

4. , $113M, AI for developers: OpenRouter, a marketplace for AI models, secured $113 million in Series B funding. led the financing for the New York-based startup.

5. , $106M, insurtech: San Francisco-based Corgi Insurance, developer of an AI-native insurance platform for startups, picked up $106 million in Series B1 funding led by . The financing, which set a $2.6 billion valuation, comes just three weeks after Corgi $160 million in Series B funding at a $1.3 billion valuation.

6. (tied) , $100M, fusion energy: Kearny, New Jersey-based Thea Energy, a developer of technology for fusion energy systems, raised $100 million in Series B funding led by . Thea says the funding will go toward manufacturing infrastructure.

6. (tied) , $100M, healthcare data: Garner Health, a platform for finding healthcare providers, closed on $100 million in Series E funding led by . The financing set a $2.74 billion for the New York-based company.

8. , $90M, space tech: Observable Space, a space tech startup that develops and builds advanced optical systems, says it raised $90 million in Series A funding led by to scale manufacturing and develop its technology. The Santa Monica, California-based company also announced that it secured a $94 million contract with the.

9. , $59M, AI video: Reactor, a San Francisco-based developer platform for real-time generative video, emerged from stealth with $59 million in funding led by .

10. , $52M, cancer detection: San Diego-based ClearNote Health, a developer of early detection and monitoring tests for multiple forms of cancer, picked up $52 million in Series D financing. Founding investor led the round.

Methodology

We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of May 23-29. 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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