For more than a decade, customers spent their software budget procuring vertical SaaS products. ACVs, or annual contract values, were modest, customer acquisition cost had to stay below a ceiling, and the resulting go-to-market playbook was product-led growth, SDR-led and content-driven.

With AI, many products are no longer SaaS but usage and outcomes based. They are replacing labor, not software. At my investment firm, , we call this new category of companies vertical AI. Vertical AI spend doesn’t just come from a customer’s software budget. It often comes out of headcount as well, a much larger line item. As a result, ACVs have jumped meaningfully to 6- and 7-figure deals.

I’ve written before about how AI for vertical SaaS, and how the value framing shifted from subscription pricing to. As ACVs have grown in vertical AI, the go-to-market motion is changing too. We’ve explored tactics to drive a more efficient sales process.

Here, I鈥檒l explore how the channels are changing as well.

Why direct sales is back

Direct sales has historically only worked at true enterprise scale. The cost of an AE’s time wasn’t warranted for smaller ACVs. Below a certain deal size, the math didn’t work for high-touch sales. That’s why SaaS GTM became PLG and SDR-led.

With vertical AI ACVs frequently landing in the 6- or 7-figure range, founders now have room to invest meaningfully in winning each logo. We鈥檙e also seeing these smaller businesses spending relatively more with quicker sales cycles which is enabling higher volume.

AEs, in-person sales motion, and other tactics that didn’t pencil at scale under old SaaS economics now do. Direct sales now works further down market where prior SaaS economics didn’t allow it.

Two channels in particular have driven a lot of distribution and success for vertical AI companies recently. They are distinct from each other but we’ve seen companies have success with both.

No. 1: Private equity and heads of AI

Many PE firms are actively pushing their portfolio companies to drive efficiency with AI. Some have even created a new role internally to spearhead these initiatives. These AI partners are often tasked with collecting and disseminating learnings, finding good AI tools, and connecting them into the portfolio if there’s a fit.

The motivation is sometimes EBITDA driven, but can also be softer than that. Many of these execs are focused on adding value across the portfolio, helping companies build AI competency, and coming up with an execution plan.

The decision making structure also varies. Sometimes the and push adoption down to the portfolio. More often, the firm will forward information to relevant company executives and leave the decision making to them. If executed well, this can be a very efficient channel for vertical AI companies. One introduction to the PE firm surfaces many qualified leads across their portfolio companies.

Usually, companies will land one customer initially. Positive feedback then travels in two directions. Laterally to peer companies within the portfolio, and back up to the PE investor, who introduces the vendor to others in the portfolio. We’ve seen this be particularly successful in industries where rollup strategies are popular like healthcare services, dental, MSP, accounting, legal, financial advisory, insurance brokerage, home services and industrial.

No. 2: Conferences

We’ve also seen sector and function specific conferences be incredibly valuable in driving distribution for vertical AI companies. The advantage is concentrated attention and self selection by the right buyer. Buyers are captive and open to learning.

They come to these events curious to hear what’s new in their sector. Attendance allows companies to meet the right buyer, showcase the product live, and collect leads at scale. Sponsoring and attending dinners is another opportunity to meet prospects.

I鈥檇 argue that scalability of lead generation and brand awareness matters more now than ever. That requires getting the word out about your own company but also cutting through the noise of others in the market. Buyers are actively building out their AI strategies so vertical AI companies should be sprinting on GTM. Companies need to be top of mind when potential buyers are open to evaluating new tools.

Whether that becomes a sole source decision or an RFP, the prerequisite is being part of the consideration set. In order to do that, your buyer needs to know you exist, and this is a great way to spread the word efficiently.

What this means

The GTM playbook for vertical AI now looks meaningfully different from the SaaS playbook it grew out of. Distribution, pricing and sales motion have all shifted in tandem, with each piece reinforcing the others. Buyer pull justified larger ACVs, larger ACVs justified deeper investment in the sales motion, and the new economics opened up channels that didn’t work under the old model.

The companies pulling away are the ones pairing a great product with the right GTM motion. They have recognized that bigger ACVs demand a different playbook, and they have adapted before their peers.

When the gates of distribution opened, everyone walked through. The companies winning now have figured out what to do once they were inside.

If you’re a founder building vertical AI and rethinking GTM, I’d love to hear from you.

听 is a general partner at , where she invests in and partners with early-stage founders from inception through Series A across sectors including AI, fintech, healthcare and enterprise software. Prior to joining Defy, Agarwal spent seven years at and began her investing career at . A former founder and operator, she previously co-founded two startups and started her career at

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Legal tech funding is booming, but the money isn鈥檛 spreading evenly across the market.

Last year, 小蓝视频色情网页版 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 鈥� 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 鈥� 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, 小蓝视频色情网页版 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 鈥� 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 鈥� 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鈥檚 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.

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

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• 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 小蓝视频色情网页版 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鈥檚 take a look.

$110M for on-demand custom manufacturing

First, let鈥檚 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鈥檚 booming business is intense demand for rapid, on-demand sheet metal and custom parts. 鈥淚f you think about the entire frontier of robots, defense companies, rocket companies, electric-car companies, they all need very fast turn prototyping,鈥� he said.

The investment is Paradigm鈥檚 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鈥檛 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鈥檚 total funding to date to just over $213 million, .

Founded in 2021, Nourish operates what it describes as the country鈥檚 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.

鈥淐hronic disease is the central failure of U.S. healthcare 鈥� nearly 200 million Americans affected, trillions spent, and outcomes that still don’t move,鈥� Menlo Ventures partner said in a statement. 鈥淲hat 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.鈥�

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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 鈧�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.

鈥淲e 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,鈥� the company said in a statement. 鈥淲e 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.鈥�

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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鈥檚 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 鈥� where it says electricity can cost almost 7x as much as in the U.S. 鈥� 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.

鈥淐lean 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,鈥� Tongan Prime Minister Lord Fakaf膩nua said in a statement.

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Operating as an 鈥渁ll-in-one鈥� 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鈥檚 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 鈥渁 significant step-up鈥� 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 .

鈥淏udweiser, 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 小蓝视频色情网页版 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 鈥� with whom the founders frequently shared strategy notes in the mid-2010s 鈥� 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鈥檚 CTO.

鈥楤usiness in a box鈥� strategy

The platform鈥檚 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 鈥榯oil鈥� 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.

鈥淲e’ve really focused our AI workflows on the ‘toily’ aspects of running one of these businesses,鈥� 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鈥檚 their creative outlet. We try to take up the parts of the day that suck for these business owners.鈥�

By optimizing supply chains and automating store management, Bolling believes that Scotch鈥檚 AI native architecture is driving 鈥渕easurable鈥� gross margin expansion for its merchants.

Grassroots growth and word of mouth

Because it is targeting an industry historically dominated by 鈥渙ld-school,鈥� 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 鈥榯he hard part of the market first鈥�

, general partner at VMG Partners, believes that Scotch is modernizing 鈥渙ne of the last major categories of retail.鈥�

鈥淭he beverage alcohol market is nearly $250 billion and, despite that, is still operating on systems built in the 1970s with on-prem servers,鈥� he wrote via email. 鈥淚t isn鈥檛 an exaggeration to say Scotch is the only player that has solved enterprise-level complexity.鈥�

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

鈥淪cotch went after the hard part of the market first, solving for some of the largest and most complex retailers in the country,鈥� he wrote via email. 鈥淭his 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.鈥�

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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, 小蓝视频色情网页版 data shows. Of that, Anthropic raised $50 billion听1 , or 54% of the month鈥檚 total funding.

Startup funding was up 284% year over year from $24 billion, per 小蓝视频色情网页版 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 小蓝视频色情网页版 Unicorn Board 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 小蓝视频色情网页版.

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鈥檚 roughly $180 billion in private funding.

The private markets in 2026 have raised capital at a greater pace than ever before, thanks to听 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鈥檝e 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.

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Related reading:

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

Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, 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. 小蓝视频色情网页版 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 小蓝视频色情网页版 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. 小蓝视频色情网页版 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. 小蓝视频色情网页版 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 鈥淪eries [Letter]鈥� 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 鈥渧enture鈥� round. (So basically, any round from the previously defined stages.)

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The market believes software businesses can鈥檛 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鈥檚 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鈥檚 start with coal. In 1860s Britain, many worried about burning through the country鈥檚 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鈥檚 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鈥檛 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鈥檛 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鈥� breakthrough innovation was to manage the 401(k) positions directly, not just advise. Employees could check a box: 鈥渄o it for me鈥�. 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鈥� 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 小蓝视频色情网页版 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鈥� productivity or deliver knowledge work outcomes directly. Software鈥檚 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鈥檚 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.

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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 小蓝视频色情网页版 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鈥檚 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鈥檚 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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So far this year, investors have put about $7.8 billion into Boston-area startups, per 小蓝视频色情网页版 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鈥檛 see it that way.

鈥淔or the first time, startups in Texas raised more VC money than those in Massachusetts,鈥� 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 小蓝视频色情网页版 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鈥檛 get a slice of history鈥檚 largest startup funding rounds.

By contrast, biotech, a traditional area of strength for the Boston area, hasn鈥檛 been on a funding tear. True, there鈥檚 no dramatic slump. But in a time when a single venture-backed AI company can snag $122 billion in a , biotech round sizes can鈥檛 compete for scale.

Standout rounds

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

Per 小蓝视频色情网页版 , at least 12 companies in the greater metro area听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鈥檛 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鈥檛 the San Francisco Bay Area, and it certainly isn鈥檛 Texas. So it鈥檚 worth asking: What is the point of comparing startup ecosystems? Is a metro area flailing if it doesn鈥檛 keep up with a particular major innovation cycle, even if it maintains core areas of strength?

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

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The amount includes previously announced corporate-led rounds by ($) and (), bringing the new funding in the latest raise to a still-staggering $50 billion.

Anthropic’s new valuation also means the San Francisco-based startup has now surpassed its closest rival, on that metric. In February, OpenAI announced it had closed a $110 billion round at an $840 billion post-money valuation. That financing marked the largest raise ever, according to .

, , and led Anthropic鈥檚 latest raise. , , , , and co-led the round. The financing also included $15 billion of previously committed investments from hyperscalers, $5 billion from , which, interestingly, also participated in OpenAI鈥檚 most recent round of funding.

Anthropic鈥檚 massive round comes just over three months after the startup raised $30 billion in a Series G that valued it at $380 billion post-money. It has now raised around $125 billion since its 2021 inception, .

Since that round, Anthropic says it has grown its enterprise customer base. Its run-rate revenue crossed $47 billion earlier this month, according to the company.

鈥淐laude is increasingly indispensable to our growing global community of customers, and we work tirelessly to make tools like Claude Code and Cowork more helpful, more powerful, and more adaptable to their needs,鈥� said , chief financial officer of Anthropic, in . 鈥淭his funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.鈥�

Correction: This article has been updated to correct Anthropic’s total funding amount to date and the amount of new, previously announced capital in its Series H.

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The global innovation economy remains largely defined by agglomeration dynamics. Worldwide, 19 ecosystems dominate the innovation landscape, increasingly concentrating innovation demand (corporates) and supply (scaleups) 鈥� attracting further growth capital (investors).

Meanwhile, other ecosystems struggle to achieve a meaningful presence on the global innovation map and are at serious risk of technological disruption and economic downfall.

Yet something is happening below the surface. Over the past decade, the composition of the Global Innovation Ecosystems Life Cycle Curve changed dramatically, as the number of scaleup ecosystems worldwide has more than doubled.

The trend is not stopping just here: we expect these figures to even triple in the coming years.

In this new scenario, emerging innovation economies hold the potential for disrupting the agglomeration paradigm, toward a new scheme of interconnected networks of specialized local innovation hot spots.

Among them, there is also Africa. While the continent still lacks ecosystems at the most advanced stages of maturity, it now counts four ecosystems at the startup stage and 40 at the standup stage, compared with respectively 25 of those 10 years ago, according to by my organization, , in collaboration with and .

Africa: the awakening giant of the coming decade?

As of today, Africa鈥檚 innovation economy includes 883 tech scaleups that have raised a combined $24.7 billion. Despite this progress, the continent still represents only about 1% of global figures.

The African innovation landscape remains highly concentrated around four main hubs: South Africa, Egypt (North-East), Nigeria (West Africa) and Kenya (East Africa). The North-Western corner of the continent still lacks a dominant hub, although Tunisia, Morocco and Algeria remain the leading candidates.

A testbed for clean technologies?

Emerging innovation economies that thrive on the global innovation map typically build on top of highly specialized, unique local strengths.

Our recent analysis has identified clear evidence that Africa holds significant potential over the development of clean energy systems and technologies.

The relative prominence of the cleantech sector in Africa is evident from the data:

• Africa is home to 95 cleantech scaleups, representing roughly 11% of the total scaleup base.
• Collectively, they have attracted approximately one-fifth of all capital deployed to African ventures.
• Cleantech has also generated a disproportionate share of high-growth leaders, accounting for around 20% of both scalers (scaleups that raised more than $100 million) and super scalers ($1 billion-plus).

Within cleantech, a highly specialized vertical is also emerging, what we might call 鈥済ridtech鈥�:

• It comprises 16 scaleups (17% of the cleantech total) and two scalers (25% of total).
• It has attracted around 30% of total cleantech funding.
• Africa鈥檚 sole cleantech tech giant, Kenya-based , operates within this gridtech vertical.

That said, the numbers still point to a gap.

The elephant in the room

The main challenge is the grid infrastructure deficit, which remains the primary bottleneck to scaling energy system technologies. As shown in the map below, Africa鈥檚 grid infrastructure is highly fragmented: High-voltage networks are concentrated in a few densely populated areas, while large parts of the continent remain largely disconnected.

As a result, grid infrastructure development and electrification are key to unlocking Africa鈥檚 growth 鈥� consider that Africa still accounts for only about 5% of global energy supply 鈥� and its innovation potential.

At the same time, the continent holds world-class renewable resources, including approximately 13% of global technical hydropower potential and around 60% of the world鈥檚 best solar resources.

Africa鈥檚 energy system is expanding, but fully unlocking its economic and innovation potential will depend on accelerating electrification and strengthening grid infrastructure.

Blended finance will be critical to enable this growth. Both private and public capital are required: private capital drives innovation, while public finance enables foundational infrastructure such as grid expansion.

In particular, private capital needs to be complemented by structured public finance initiatives to address the inherent limitations of a relatively small domestic VC market, which remains heavily focused on early-stage investments.

Public capital will be essential for infrastructure development. In gridtech especially, public investors are expected to account for up to about 80% of total investments by 2030, reflecting the capital intensity and risk profile of grid infrastructure.

International capital still dominates the market, with approximately 69% of active investors originating outside Africa, underscoring continued reliance on foreign capital despite growing local participation.

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is chairman of and a professor at . He is a serial entrepreneur who has started three startups in his career, the last of which is , among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects 鈥� venture clients, venture builders, intrapreneurship, CVCs 鈥� with large multinational companies, as well as advising and training on this subject. Onetti has a column on () and several other tech blogs.

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