Leading sectors by count were financial services, robotics, AI orchestration, multimodal AI, energy and the semiconductor industry.

In the past two months, the board added more than $100 billion each month in value from newly minted unicorns. Three companies joined the board at decacorn values. , and together added $49 billion in the past month.

The U.S. counts 19 new unicorn companies, just under half of the newly minted unicorns in July. China, the second-largest country, numbered eight. From the U.K., there were three companies, and from Singapore, two. Lithuania, Germany, Spain, Hungary, Australia, India, Israel and Hong Kong each count one.

Among the newly minted unicorns, 15 were less than 3 years old. And seven companies were more than 10 years old.

So far this year, the count of new unicorns has accelerated. A total of 195 companies joined in H1 this year, already exceeding the total for all of 2025.

New unicorns in July

Here are July’s new unicorn companies:

Financial services

• Singapore-based , an affiliate of the private payments company , raised a $1.2 billion Series A funding round with participation from Ant Group and . Ant International was spun out in 2024 and was valued at $11.2 billion in this recent funding.
• , a digital banking platform for banks and credit unions, raised $115 million in private equity funding led by . The 9-year-old San Ramon, California-based company that supports customer retention and services was valued at $1.6 billion.
• Budapest-based , an auto insurance provider using AI, raised a $23 million Series B funding round led by . The almost 2-year-old company, founded by a Serbian team, was valued at $1.6 billion.聽 Ominimo reports $350 million in gross written premiums and is approaching 1 million customers. It operates in Hungary, Poland, the Netherlands and Sweden, and plans to expand across Europe and to the U.S. in 2027.
• , an AI-native private bank for high net worth business owners, raised a $70 million Series B led by . The 4-year-old San Francisco-based company was valued at $1.2 billion.
• , a membership and savings app for U.S. consumers, raised $65 million in Series D funding led by . The 10-year-old San Francisco-based company was valued at $1.2 billion. Surpassing $200 million in net revenue in 2025 from membership and transactional revenue, the company says it is growing 50% year over year and is approaching 1 million members.
• London-based , a savings app which has become a digital wealth management platform, raised a $60 million secondary market transaction led by . The 11-year-old company was valued at $1.1 billion. The secondary sale is to provide liquidity for long-term employees. The company is profitable and has helped 200,000 people buy their first home.

Robotics

• Guangdong-based humanoid robotics company raised $200 million in pre-IPO funding. The 4-year-old company is focused on entertainment, hospitality, and service for humanoid robotics, not manufacturing, with half of its orders coming from outside of China. The company was valued at $2.2 billion.
• Shenzhen-based , a builder of precision tactile sensing technology for robotics, raised $148 million in Series E funding. The 10-year-old company was valued at $1.5 billion.
• London-based , a humanoid robotics company for manufacturing, retail and logistics, raised a $152 million Series A funding led by . The 2-year-old company was valued at $1.4 billion and plans to roll out its wheeled beta version robots to customers in Q4. It has also developed a software brain, KinetIQ, to reason and execute complex tasks alongside humans.
• Full-stack physical AI company emerged from stealth with a $300 million seed funding led by and . The less-than-1-year-old Cambridge, Massachusetts-based company focused on manufacturing and logistics was valued at $1.1 billion.
• , an embodied intelligence company, raised a $147 million seed funding round led by and . The less than 1-year-old Nanjing, China-based company is focused on closed-loop learning, building robotics for manufacturing with the ultimate goal of building a general-purpose robot for the home. The company was valued at $1 billion.
• Dexterous hand robotics company raised a $74 million Series A funding led by . The 1-year-old Hangzhou, China-based company was valued at $1 billion.

AI

• Lithuania-based , a public data web scraping service useful for AI applications and agentic AI, raised its first external financing, a $130 million Series A led by . The company reports $350 million in ARR serving 350,000 tech teams. The 11-year-old company was valued at $3.6 billion.
• Spain-based , a compression technology for AI that improves efficiency and cost, whether on device or in the cloud. It raised a $570 million Series C led by , and . The 7-year-old company was valued at $2.3 billion.
• , creator of synthetic users for consumer research, raised a $200 million Series B led by and . The company raised a $100 million Series A five months earlier. The 1-year-old Palo Alto-based company was valued at $2 billion.
• runs a full-stack platform for companies to train models and agents. It raised a $130 million Series A funding led by . The 2-year-old San Francisco-based company was valued at $1 billion.
• , an enterprise infrastructure management platform for AI, raised a $100 million Series D led by . The 7-year-old San Jose, California-based company was valued at $1 billion.

Multimodal AI

• Beijing-based , a text prompt-to-AI short video startup, raised a $2.8 billion funding round led by , , , , and . The 2-year-old company, a subsidiary of with plans to spin out, was valued at $18 billion.
• , a company that creates 3D visualization from text or image prompts, raised a $400 million Series B funding led by , and . The 5-year-old Sunnyvale, California-based company, used in gaming, 3D printing and design, was valued at $1.5 billion.
• , which provides access to leading models for text, video, image and audio while retaining user privacy, raised a $65 million Series A led by . The service stores communication on a user鈥檚 device. The 2-year-old Wyoming-based company was valued at $1 billion.
• Beijing-based , a multimodal model developer, raised a $222 million Series C led by ,, and . The 3-year-old company, used for film, marketing, and social media content creation, was valued at $1 billion.

Energy

• Munich-based nuclear fusion company raised a $470 million Series B led by , , and . The company has offices in Munich, Zurich and Oxford. The 3-year-old company was valued at $2.7 billion.
• a provider of thermal energy storage for data centers, raised a $550 million Series C funding led by and. The 8-year-old San Jose, California-based company was valued at $2.5 billion.
• , a hydrogen-boron fusion company, raised an undisclosed seed round led by , and . The less-than-8-year-old China-based subsidiary of the was valued at $1.6 billion.

Semiconductor

• Israel-based , a fabless semiconductor company building data processing units and chips for data centers and computing systems, raised a $300 million Series E led by . The 9-year-old company was valued at $2.8 billion. The next generation of will be routing via the company鈥檚 X2 chip, according to VP of Starlink engineering, .
• Shanghai-based developer of a satellite communication baseband chip for 6G communications, raised an undisclosed amount following a $216 million Series C round earlier this year. The 6-year-old company was valued at around $1.5 billion.
• , a chip company that connects smaller chips to make them more efficient, raised a $145 million Series C led by . The 5-year-old Santa Clara, California-based company was valued at $1 billion.

Cryptocurrency

• Singapore-based , a regulated app for buying, trading, and spending cryptocurrencies, raised a $400 million corporate round. Led by , this marks the company鈥檚 first institutional funding. The 10-year-old company was valued at $20 billion.
• , a U.S. stablecoin digital clearing bank for international financial institutions, raised a $180 million Series B led by . The 4-year-old San Francisco-based company was valued at $1 billion.

Defense

• Former Doge employees founded to provide AI-driven cyber capabilities to the U.S. military. Cathedral raised a $160 million Series A led by and . The less-than-1-year-old Washington, D.C.-based company was valued at $1.4 billion.
• London-based , a maritime defense company, raised a $175 million Series B led by . The 6-year-old company was valued at $1 billion.

Marketplace

• , a technology platform for service businesses, raised a $44 million Series D led by . The 10-year-old New York-based company was valued at $1.2 billion. Genius AI operates in the wellness, beauty and health sectors and is approaching a $200 million revenue run rate.

• , a platform for travel advisers, raised a $60 million Series D led by and . The service has 15,000 travel advisers and has booked more than $3 billion in travel over time. The 5-year-old New York-based company was valued at $1 billion.

Data center

• Mumbai-based , a data center service hosting GPUs, one of the largest GPU compute providers in India, raised $150 million in funding. The 7-year-old subsidiary of the was valued at $3.9 billion.

Insurance

• , an insurance platform for some of the largest e-commerce customers, raised $100 million in funding. The 12-year-old New York-based company was valued at $1.9 billion. Its customers include , , , , and , to name a few.

Quantum

• Quantum computing company raised a $300 million Series A led by , and . The less than 1-year-old South Pasadena, California-based company was valued at $1.5 billion.

AI coding

• Autonomous app building startup raised a $130 million Series C led by , and . The 2-year-old Pleasanton, California-based company was valued at $1.5 billion. The company launched a year ago and has enabled non-coders to build applications, with 12 million built on the platform.

Legal

• , an AI legaltech firm that pairs lawyers with agentic AI, raised a $120 million Series C led by . The service is client-oriented, with payments based on outcomes rather than billable hours. The 3-year-old New York-based company was valued at $1.2 billion.

Security

• , an endpoint security firm for the AI era, emerged from stealth, announcing a $100 million Series B led by , , and . In 2025, ahead of launching out of stealth, Glow raised large seed and Series A rounds. The 1-year-old Palo Alto, California-based firm with offices in Tel Aviv was valued at $1.2 billion.

Wearables

• Hong Kong-based smart glass company raised a $150 million Series B led by and . Founded by ex- engineers, the startup is not camera-based but rather a display that beams information visible to the wearer.聽 The 2-year-old company was valued at $1 billion.

Related SA国际传媒 unicorn lists:

• (1,850)
• (644)
• (245)
• (193)
• (117)
• (102)
• (953)
• (546)
• (251)
• (39)
• (491)

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• Global New Unicorn Counts In The First Half Of 2026 Have Already Surpassed 2025鈥檚 Totals

Methodology

The SA国际传媒 Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on SA国际传媒 data. New companies are as they reach the $1 billion valuation mark as part of a funding round.

The unicorn board does not reflect internal company valuations 鈥� such as those set via a 409a process for employee stock options 鈥� as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.

Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to .

Exits analyzed here only include the first time a company exits.

Please note that all funding values are given in U.S. dollars unless otherwise noted. SA国际传媒 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 SA国际传媒 long after the event was announced, foreign currency transactions are converted at the historic spot price.

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This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We鈥檝e included both startups and publicly traded, tech-heavy companies. We鈥檝e also included companies based elsewhere that have a sizable team in the United States, such as , even when it鈥檚 unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as 鈥渦nclear.鈥�

Venture investors, luckily, seem to be more upbeat than they have been in years about the future of fitness and wellness. Startup investment in those categories totaled more than $3.6 billion in the first half of this year, putting 2026 on pace to come in about a third higher than 2025, though notably last year marked the lowest sum for wellness-related startup funding in at least six years.

The recent uptick also puts investment into fitness- and wellness-related startups on pace to top each year since 2022, though deals are concentrating into fewer, larger bets.

Largest fundraisers of H1 2026

This year鈥檚 funding totals have been driven by a handful of outsized deals, like wearable health tracker 鈥檚 $575 million Series G in March.

Other companies that have raised large rounds this year include senior healthcare provider , which raised a $366 million Series F at the beginning of the year, and , which raised a $130 million Series C from investors including in February. Its platform connects patients with professional healthcare advocates who support them through complex medical journeys like cancer, rare-disease management and substance abuse treatment.

Those fundings are markedly different from the hardware plays that received investor attention during the pandemic. For example, connected fitness devices startups and each raised hundreds of millions of dollars during the peak funding years, but haven鈥檛 received new investment in three-plus years.

AI gives devices a second act

That doesn鈥檛 mean investors have entirely given up on hardware. Rather, the more compelling pitch in 2026 appears to be a device that continuously collects health data and uses AI to turn it into personalized guidance to improve overall wellness and fitness.

Along with Whoop鈥檚 Series G, New York-based sleep technology company raised a $50 million Series D in March, while India-based metabolic health wearable maker secured the equivalent of about $44 million in Series C funding in February.

A few entrants are also drawing substantial checks. New Delhi-based raised a sizable $54 million seed round in February for a wearable focused on brain-centered health and performance metrics. The company says its technology tracks cerebral blood flow and uses a proprietary measure called Entropy to quantify users鈥� real-time energy expenditure.

The future of fitness funding and exits

We expect to see continued investor interest in companies that bring AI to bear on wellness-related offerings, including in more specialized areas such as longevity, mental health, sleep and athletic performance.

We may also see more funding for devices that serve as data-collection layers for AI-driven health platforms. At the same time, we don鈥檛 expect investors to broadly return to large, pricey home-gym gadgets or hardware that doesn鈥檛 have a strong recurring software, data or healthcare component.

We could also see more exits in the sector as companies combine their capabilities through M&A deals or private equity roll-ups. And, we would not be surprised to see more established players make strategic buys of smaller companies, as we saw last year with fitness tracking platform 鈥檚 acquisition of running workout planner , or more recently with 鈥檚 purchase of endurance-training platform .

Still, we don鈥檛 foresee a flurry of IPOs from the sector, perhaps with the exception of a few star players. SA国际传媒鈥檚 predictive intelligence tools suggest likely IPO candidates in the fitness and wellness categories include Whoop, rival wearable wellness tracker , mental health platform , and , which operates a network of clinics offering what it bills as AI-driven longevity and preventative health services.

Related SA国际传媒 query:

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The current focus on AI IPOs is largely centered on public market performance. Investors want to know whether these companies justify their valuations and how their shares will trade after listing.

But everybody is watching the wrong metric. The more consequential story begins after the bell rings, when limited partners receive distributions and decide where to deploy that capital next.

At sufficient scale, AI IPOs become a capital formation event for the broader venture ecosystem. If several of the largest AI companies reach the public markets over the next few years, those exits could reshape venture fundraising and further concentrate capital among the industry鈥檚 largest firms.

The real story begins after the IPO

The more meaningful process starts when investors receive distributions from successful exits. Pension funds, university endowments, sovereign wealth funds and family offices rarely leave that capital sitting idle for long. As portfolios are rebalanced, investment committees begin evaluating new commitments across private markets.

Venture has spent several years waiting for meaningful liquidity. Higher private valuations may improve paper returns, but they do not return capital to limited partners. Only successful exits complete that cycle.

鈥檚 $85.7 billion IPO illustrates both the potential and the limits of a single listing. One IPO alone is unlikely to transform venture fundraising. But a sustained wave of listings involving companies such as , , and could steadily return capital to investors and give limited partners fresh resources to recommit.

Liquidity drives the next fundraising cycle

The importance of the next AI IPOs lies less in their individual performance than in their combined effect on venture fundraising.

As capital flows back to limited partners, investment committees gain both the liquidity and the flexibility to make new commitments. How those commitments are distributed will shape the industry鈥檚 next phase.

Recent fundraising trends suggest capital is likely to remain concentrated. According to the , the 10 largest U.S. venture funds captured nearly one-third of all capital raised in 2025, while first-time fund formation in more than a decade. If a new wave of liquidity reaches the market, established managers with proven track records are likely to receive the largest share.

offers a useful illustration. The firm recently raised over $15 billion across five funds, an amount equivalent to more than 18% of all U.S. venture capital dollars raised during 2025. Stronger distributions could leave the industry鈥檚 largest firms in an even better position to raise successor funds.

Capital will not flow evenly

Limited partners typically increase commitments to managers with established track records before expanding relationships with emerging firms. Successful exits reinforce confidence in those managers, making them the natural destination for a disproportionate share of new allocations.

The effects extend beyond fundraising. A $15 billion fund approaches ownership, pricing and portfolio support differently from a $500 million fund. Large funds need meaningful ownership and outcomes capable of returning multibillion-dollar vehicles. They can lead larger rounds, pay higher prices, defend ownership through multiple financings, and support companies for longer.

This is not a liquidity flywheel. It is a concentration flywheel. Successful investments generate distributions. Those distributions help the industry鈥檚 largest firms raise larger successor funds, reinforcing their competitive advantages. Over time, liquidity strengthens fundraising, and fundraising strengthens market position. The market may become larger without becoming broader.

Founders will feel the effects. Large investment platforms can finance companies for longer and compete more aggressively for ownership in the relatively small number of businesses capable of producing returns at their scale. The result could be a more pronounced barbell market: a limited group of companies attracts enormous amounts of capital, while businesses outside the dominant sectors face a more constrained financing environment.

Pay attention to LP liquidity, not just IPO pricing

Public investors will remember this AI IPO cycle by its opening prices. Venture investors may remember it for something else entirely.

It may be the moment capital began concentrating around a handful of firms at a speed the industry has never experienced.

The IPOs themselves will make headlines. The redistribution of power inside venture capital will shape the next decade.

is a general partner at , a VC firm investing in early-stage startups in AI, cybersecurity and digital health, and helping them expand into the U.S. market.

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July ranked as the third-largest funding month of the year, up 10% over June, following on the heels of a record-breaking first half of 2026, when startups raised $515 billion globally.

Fourteen startups raised billion-dollar rounds in July, the highest count in a single month, though not the largest amount raised in such deals, an analysis of SA国际传媒 data shows. The tally includes nine U.S.-based companies, two each from Germany and China, and one company headquartered in Singapore.

The largest startup funding deal last month was a $10 billion investment in , the first external financing for the -founded space exploration company.

, a frontier lab founded by former Chief Scientist , reportedly raised $5 billion from . The next two largest deals were Beijing-based frontier lab 鈥檚 $3.5 billion raise after releasing its latest Kimi K3 model, and raising $2.8 billion for short-video generation.

Two Germany-based companies in defense tech also raised billion-dollar rounds: and . In the U.S., companies that raised billion-dollar-plus rounds spanned the energy, industrial robotics, AI training, security and semiconductor industries.

Funding to AI

A total of $35 billion, or around 53% of global venture funding, went to AI-focused companies in聽 July. Other leading sectors were aerospace, defense and energy.

U.S.-based companies raised a total of $39 billion, or around 59% of global venture capital, last month with roughly half of the capital invested in its AI-focused companies.

Exits

July was also a robust month for startup exits, including via acquisition and public-market debuts.

Venture-backed M&A totaled more than $9 billion in July, with five companies exiting at prices聽 over $1 billion, SA国际传媒 data shows. Notable acquisitions included London-based data center provider 鈥檚 roughly $1.65 billion acquisition of software layer , which was built to manage AI workflows, and in the security sector, AI-native security company 鈥檚 $1 billion acquisition of , a service to manage non-human identities.

Twelve venture-backed companies went public above $1 billion in value in July, including five from China, six U.S.-based companies, and one from Italy. The largest was Chinese chipmaker , which went public at around $85 billion and . Italy-based , an acquirer of software companies including and , went public at a value of $18.5 billion. And last-mile transportation company , founded in 2017, went public at $1.6 billion in value, raising $167 million in the process.

In closing

If the first half of 2026 established that venture has entered a new era of mega-financings, July reinforced that the trend is broadening rather than fading. Record numbers of billion-dollar rounds in both hardware and software, alongside a healthy IPO and M&A market, point to an ecosystem where capital is not only concentrating in category leaders but is also beginning to recycle through exits.

Related SA国际传媒 queries:

Methodology

The data contained in this report comes directly from SA国际传媒, and is based on reported data. Data is as of Aug. 3, 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. SA国际传媒 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 SA国际传媒 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. SA国际传媒 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. SA国际传媒 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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Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital鈥檚 broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm鈥檚 affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with SA国际传媒 News, Docter also discussed how AI is reshaping SaaS and why he doesn鈥檛 believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

SA国际传媒 News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don鈥檛 get at any other VC firm. I鈥檓 using my words carefully because I鈥檓 not saying we鈥檙e better. I鈥檓 just saying we鈥檙e unique.

That unique network is that we have access to network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what鈥檚 going on in the world and understand technology and how it鈥檚 being used. What do Fortune 500 companies want or need? What is asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

classically said, 鈥淚nvest in what you know.鈥� The way I look at it is that we鈥檙e trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that鈥檚 also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there鈥檚 a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn鈥檛 changed from how we鈥檝e always thought about it and how venture capital always thinks about it. First and foremost, you鈥檙e really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It鈥檚 not purely about the technical capability of the founders. There鈥檚 definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder鈥檚 ability to be agile 鈥� to understand when they鈥檙e wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that鈥檚 qualitative. It鈥檚 EQ more than IQ, but a lot of times that determines success. I don鈥檛 think this AI era has changed that. That鈥檚 consistently true.

The answer to the second question is even harder. How do you know if you鈥檙e betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It鈥檚 really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don鈥檛 overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, 鈥淧lay nice but win.鈥� We always try to play nice but win.

It takes a village for these things to work, so it鈥檚 important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the 鈥渟leeping giants鈥� thesis suggests the second wave 鈥� the companies with the foundational architecture in place when a catalyst like generative AI hits 鈥� may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation 鈥� which means it鈥檚 creating a brand-new category of business or software product that doesn鈥檛 exist today and is going to be huge 鈥� or category disruption, meaning there鈥檚 already a very large category that exists and I鈥檓 going to disrupt it with my technology. I鈥檓 doing something much better, faster, cheaper or stronger.

It鈥檚 important to have a sense of whether a company is doing category disruption or category creation. If you鈥檙e doing category creation, being first means you have to educate everybody. It鈥檚 a heavy lift. It鈥檚 a daunting amount of work, capital and effort that goes into explaining something that doesn鈥檛 currently exist and why it鈥檚 going to be needed in the future.

A lot of times, first-mover advantage isn鈥檛 an advantage there. Category creation is often where the second, third or fourth company hasn鈥檛 had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there鈥檚 value in first-mover advantage. You鈥檙e disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There鈥檚 a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It鈥檚 disruptive because it will change how software is built and consumed. Maybe even more importantly, it鈥檚 going to change how it鈥檚 priced. The per-seat pricing model is probably outdated and going to die. It鈥檚 going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don鈥檛 believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They鈥檙e going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They鈥檙e not going to go away.

How they charge and price might be different, but they鈥檙e still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows 1, and .

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they鈥檝e sold to for years and years and have long-standing relationships with. If 鈥� and it鈥檚 a big if 鈥� they understand how to embrace the AI transformation that鈥檚 going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don鈥檛 make the turn. But is that any different from any other technological or industrial revolution? It鈥檚 always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren鈥檛.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: 鈥淲hat is your distribution strategy?鈥� That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it鈥檚 clear that some SaaS companies won’t be able to transform themselves organically. They鈥檙e going to need to undergo an inorganic transformation, meaning they鈥檒l have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you鈥檙e trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year 鈥� including massive liquidity events like , and 鈥� right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I鈥檇 love to say we saw it all coming, but the reality is we can鈥檛 time the market. It just doesn鈥檛 work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, and .

We just try to stay really focused on backing great founders with deeply technical ideas. We鈥檙e investing early and know that sometimes it can take years for the market to fully catch up to what鈥檚 being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You鈥檝e talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Docter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is 鈥淚s their revenue durable?鈥� Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we鈥檙e also seeing is a huge shift away from recurring revenue to something I鈥檓 calling聽 鈥渞e-occuring鈥� revenue. I know that鈥檚 not really a word. What I mean by 鈥渞e-occuring鈥� is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say 鈥渨e got our first deal with in October, and they did a second deal with us in January, and we already did our third deal in March鈥� is very powerful.

Given DTC鈥檚 unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Docter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. 鈥淵ou don鈥檛 get what you don鈥檛 ask for.鈥� I know that鈥檚 an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don鈥檛 be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it鈥檚 management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

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The purported deal, which has been rumored for months, is notable not just for its scale 鈥� it would be one of the largest acquisitions of a technology company in recent years 鈥� but also for its highly unusual nature. Privately held startups typically lack the cash, publicly traded shares and debt capacity to acquire their publicly listed brethren.

Of course, Stripe is not just any privately held company. The fintech startup was, until just a few short years ago, the highest valued startup based in the U.S., before being eclipsed on that metric by AI labs and . In February, the company announced it had inked deals with investors to provide liquidity to current and former employees through a tender offer at a $159 billion valuation, which still ranks it as the fourth most valuable startup in the world.

With substantial private capital 鈥� it has raised some $10.4 billion since inception, 鈥斅燬tripe has long been one of the most acquisitive venture-backed startups. It has made since its 2010 inception, according to SA国际传媒 data. Only three have disclosed prices: stablecoin platform at $1.1 billion (2025), usage-based billing software startup at $1 billion (2026), and Nigerian payments startup at $200 million (2020).

Stripe鈥檚 M&A pace has also accelerated sharply since 2020, SA国际传媒 data shows, with 13 of its 21 acquisitions announced since then.

Its recent strategy appears to be focused on stablecoins and crypto infrastructure 鈥� Bridge, , and 鈥斅燼s well as on billing and money movement through Metronome, payment processing startup and .

If the plan to buy PayPal does go through, it will most certainly make Stripe an even more formidable player in the crowded payments space.

It would also rank as one of the largest acquisitions of a U.S. tech company, public or private, of the past five years, according to SA国际传媒 data, trailing only a handful of larger deals including $61 billion purchase of in 2022 and 鈥檚 acquisition of AI coding platform Cursor and its parent, , for $60 billion last month.

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All told, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year. (It鈥檚 important to note that H2 2025 marked the strongest six-month funding period for fintech startups since the second half of 2022.)

Fintech funding in the first half of 2026 also topped the sector鈥檚 investment totals in 2020 and the pre-pandemic year of 2019, though they remain lower than the peak funding year of 2021 as well as 2018.

Historically, the United States has led the globe when it comes to fintech funding, and the first half of this year was no exception. More than 52% 鈥� $15 billion 鈥� of the global fintech funding in H1 flowed into companies based in the U.S. The United Kingdom was the second-largest recipient of capital, with companies there raising a collective $2.7 billion. India came in third, with a total of $1.9 billion raised, SA国际传媒 data shows.

Deal count drops

Even as dollar volume climbed, deal flow into venture-backed fintech startups fell fairly significantly in H1 2026, SA国际传媒 data shows. The first half of the year saw 1,605 funding deals announced in the sector, a 25.7% decline from the more than 2,161 completed in H1 2025 and down 40% from H1 2024.

Where investors are placing their bets

Active fintech investors who spoke with SA国际传媒 News said they see a split market emerging.

In general, the startup investment market has been cleaved into two extremes, with funding either pouring into brand-new companies or concentrating into a tiny handful of larger, established giants, according to , a partner at (Google Ventures).

The fintech sector is following the same pattern, Sakach told SA国际传媒 News via email, but its biggest players are using their size in an unusual way. 鈥�2026 marks the definitive ‘lab-i-fication’ of the modern corporation,” she noted, with some fintech platforms using their scale and steady profits to fund experimental new divisions.

Because these companies have significant data and distribution advantages, they are becoming magnets for top-tier workers, according to Sakach. For instance, she said, is now competing directly with top AI research labs for engineering talent, while is using its dominant position to build out new products in enterprise billing and blockchain.

For early-stage startups inside the U.S., the focus is shifting away from copying legacy financial services toward creating entirely new categories.

Wealth management is seeing a massive surge, driven by an influx of assets from a younger generation demanding AI tools, Sakach pointed out.

Fintech startups are also targeting massive, hidden corporate headaches.

鈥淎 50% reduction in global chargebacks is a ~$60 billion opportunity when accounting for both the merchant and banking overhead,鈥� she said.

The biggest shift, however, is happening around artificial intelligence and financial services. 鈥淐oding was AI’s first killer use case; financial markets could be the second, given its extraordinarily broad corpus of data,鈥� said Sakach, pointing to new concepts such as automated hedge funds and prediction markets.

, partner at , said the firm鈥檚 investments into the fintech sector have surged this year, as areas such as money movement infrastructure, stablecoins and tracking of real-world assets on the blockchain draw attention.

鈥淲e’ve never been busier: The quality of founders, the size of the markets they’re going after, and the maturity of the technology being built has never been more impressive,鈥� he said.

Those trends showed up among fintech鈥檚 largest fundraisers last quarter, with companies such as New York-based , which is building an agentic decision platform for banks and insurers, and , an African payments infrastructure startup, clinching some of the period鈥檚 largest funding deals. Both raises took place in June, with Taktile raising a $110 million Series C funding round led by and Flutterwave landing a Series E round of an undisclosed amount that valued the company at $3.2 billion.

Risks and opportunities

Even with a wealth of new opportunities in the sector, investors are also wary of the risks introduced by AI and hype around businesses that don鈥檛 have a clear path toward growth or profitability.

Sakach was particularly skeptical of new stablecoin networks that lack a clear way to get users, personal credit card startups with tough profit margins, and traditional banking software.

The problem with selling software to legacy banks is that their slow buying cycles 鈥渆ffectively break the hypervelocity speed needed for AI-level product evolution,鈥� she said. Instead, Sakach believes that AI tools will likely succeed by embedding highly specialized engineering teams directly into specific business units.

The era of the generic digital bank or basic payment app is largely over, in Overdorff鈥檚 view: 鈥淲ithout a real wedge or distribution advantage, it’s hard to build a durable business there.”

The real value of AI right now is its ability to act as the central engine for financial products rather than just a side feature, Overdorff believes. Startups are using the technology to compress complex underwriting, fraud detection and advisory workflows 鈥渢hat used to take teams of analysts weeks into tasks that happen in minutes.鈥�

As a result, traditional industries such as tax and audit are being completely upended, he said.

Traditional financial institutions, which are usually the slowest to adopt new tech, are finally bringing AI into their core operations, though Overdorff cautioned 鈥渢hat shift is opening up as much risk as opportunity.鈥�

He also flagged the cybersecurity risks associated with the rapid adoption of new technologies and AI into the financial system. 鈥淭he compliance and governance layer becomes just as important as the AI itself,鈥� he wrote.

Mega-valuations keep top fintechs private

While the fintech IPO market was robust in 2025, it has been markedly quieter in the U.S. so far this year. Three fintech companies went public in the first half of 2026, and they were all foreign companies opting to list in New York: Brazil鈥檚 and and Japan鈥檚 . That鈥檚 the same number of finance-related startups that went public in the first half of 2025, when , and made their debuts.

Many of the fintech companies expected to list in 2026 have remained private, often at escalating valuations. That includes fintech giants such as Stripe, , Ramp, , and others that have opted for more private financing, secondary sales or simply waiting out the public markets.

For example, in February, payments infrastructure giant Stripe announced it had inked deals with investors to provide liquidity to current and former employees through a tender offer at a $159 billion valuation. That valuation represented an impressive 49% increase from the $106.7 billion Stripe was valued at in September, when it completed .

In early June, expense management startup Ramp announced a $750 million funding round at a $44 billion valuation, just a few months after raising $300 million at a $32 billion valuation.

The H2 outlook

The trend of capital concentration seen in the first half of the year will continue into H2, Overdorff predicted, with 鈥渕ega-rounds for a small set of category leaders, and a tougher fundraising environment for everyone else.鈥�

And while AI adoption will continue to deepen rather than flatten out, the industry will also be watching the stock market closely. The conversation around IPOs is heating up for mature fintech companies, though Overdorff notes that 鈥渢he timing may hinge on how other high-profile tech IPOs perform this year.鈥�

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Last month, of , the corporate venture arm it launched in 2016 and grew to more than $850 million across three funds. The company hired to explore selling portfolio stakes on the secondary market, putting positions in companies such as and in play. The news also arrived weeks after .

Two corporate venture programs shutting down inside six weeks invites speculation that corporations are retreating from venture capital, but in fact the opposite is true.

Measured in dollars, corporate venture has never been stronger. According to , corporate investors participated in 鈥� venture’s strongest funding year since 2021.

, , , , and all led billion-dollar rounds into AI companies last year, per SA国际传媒 data. Nvidia by itself made more than 40 startup investments and appeared in. Meta paid $14.3 billion for its stake in Scale AI. 1听补苍诲 s venture arm backed Anthropic’s.

Amid this strength, though, corporate venture is also quietly splitting in two, and the proof is buried inside the record numbers. Bain attributes the elevated corporate participation , and the billion-dollar rounds trace back to the same short list of names.

Take that handful out of the data and the year looks very different. Venture capital itself went through the same sorting over the past decade, as mega-funds absorbed more and more of the capital while everyone else competed for allocation, and corporate venture is now following the same script. The people with the most at stake are the smaller funds and startups downstream.

And notice that the wind-downs are coming from serious programs. PayPal’s arm ran for a decade and , and Fidelity International manages hundreds of billions of dollars. Size never protected either one, and the dividing line runs through the mandate. For Nvidia, Alphabet, Salesforce and Cisco, startup investing is a core strategy, funded off enormous balance sheets, because their businesses depend on owning a position in the technology cycle. Nvidia backs the companies that build on its chips, and that commitment survives budget season. For most other corporations, venture is one strategic priority among several, competing for capital with the core business itself.

To be clear, there’s nothing wrong with that. When a new chief executive commits to finding , winding down even a well-run program can be the disciplined call, and disciplined capital allocation is what shareholders ask of public companies. Corporate venture has always moved in cycles, and the waves of closures after 2000 and 2008 said far more about parent balance sheets than about the returns on offer. Individual programs are mortal, but the asset class keeps growing.

When I started my career, technology drove roughly 2% of the American economy, and today it drives a double-digit share of GDP and nearly 40% of the stock market.

Who feels it first

For smaller funds and their portfolio companies, the split is already changing the math. ‘s finds corporate funds pursuing fewer, more targeted deals, and the share using the secondary market jumped from 15% in 2024 to 22% in 2025; PayPal’s Jefferies mandate takes that same path at the scale of an entire program.

When a corporate arm winds down mid-life, its portfolio companies lose a strategic backer and a source of follow-on capital at once, the smaller funds that syndicated alongside it lose their anchor for the next round, and a secondary sale replaces a committed partner with a financial buyer.

I spend my days working with early-stage venture funds, and I’m watching this pattern develop in real time: strong companies outside AI, with a departing corporate backer on the cap table, heading into rounds their existing syndicate can’t fill alone.

The lesson for startup management teams and VC fund managers is to plan for corporate capital to come and go. The pro rata rights that funds hold in their best companies become most valuable at exactly these moments, when a strategic investor steps back and ownership in a breakout company becomes available to whoever can fund it.

Smaller funds should line up committed follow-on capacity before their winners come back to market, so a corporate partner’s exit becomes a chance to buy more of a company they already know well. Founders should run the same exercise from the other side of the table and know today which investors on their cap table can carry the next round.

Corporate venture will keep growing because the forces behind it keep growing, and programs will open and close along the way, as they always have. What’s changed is the sorting: permanent capital consolidating at the top of the market, and everyone else learning to plan around that fact. The funds and founders who prepare for it will come out the other side owning more of the companies that matter.

is the founder and managing partner of , a growth-equity firm that co-invests in venture-backed companies by leveraging the unused pro-rata rights of more than 1,000 early-stage VC partners.

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To understand how institutional capital is navigating this gap, SA国际传媒 News recently interviewed , a partner at . Ranum has spent nearly 15 years at the firm, where he focuses on B2B enterprise software, security and industrial infrastructure. Prior to joining Sapphire, he spent 12 years as a product management and strategy executive at .

His recent investments include core infrastructure plays such as and , as well as the industrial AI platform .

In this e-mail interview, Ranum breaks down how the definition of net revenue retention is shifting, why he believes 2026 will see a historic run of major tech IPOs, and where real enterprise demand is materializing on the factory floor.

This interview has been edited for clarity and brevity.

SA国际传媒 News: You鈥檝e been at Sapphire for 15 years. Right now, public market software multiples are at decade lows as Wall Street worries about AI disruption, while private AI valuations are hitting record highs. As a growth investor caught in the middle, how are you valuing companies today? Are traditional growth metrics like net revenue retention still the gold standard, or has the math completely changed?聽

Ranum: The gap between public and private market signals right now is unlike anything I’ve seen. I think it creates a real opportunity for investors who can make sense of it. Public software multiples have come down hard, while private AI valuations are hitting record highs. Those two things can’t both be right indefinitely, but the fundamentals underneath are holding up. Gross margins, free cash flow, and NDR have actually improved. The market is broadly pricing in disruption risk, but the companies that are genuinely building enterprise value are still being built.

What that means for how I evaluate companies is that I’m spending more time on whether something is genuinely embedded in how enterprises work, not just whether the numbers look good today. NRR still matters. It tells you whether customers are finding real value. But it’s a lagging indicator. What tells me more is whether switching away from a product would meaningfully disrupt operations. If the answer is yes, that’s a more durable signal than any retention metric.

The current regulatory environment has essentially frozen large-scale tech M&A, and the IPO market is sluggish. If the traditional exit pathways are bottlenecked, how does that change the way you underwrite a Series B or C bet? Do companies just have to stay private and build to massive scale longer than they used to?聽

Ranum: I鈥檇 push back a bit on the framing that M&A is frozen. Software M&A activity actually picked up meaningfully in 2025, with deal value rising 40% year over year to $334 billion across 678 transactions. We saw that in our own portfolio with over half a dozen acquisitions in the past six months. What鈥檚 changed is the pricing. The valuations are being reset, but the deals are getting done.

On IPOs, I believe 2026 is shaping up to be a historic year, with having gone public, having filed, and reportedly set to file soon. If they follow through, we’re looking at some of the largest IPOs ever over the next several months. That’s a remarkable moment. Below that tier, though, the picture is more nuanced. Companies that meet today’s higher bar will wait for more favorable conditions, likely into 2027 or beyond. That means you have to build accordingly, focusing on margin alongside revenue, so you have real optionality when the time comes. The secondary market also helps, giving companies and their investors more flexibility as they wait.

You used to love investing in what you called 鈥渂oring software,鈥� or tools that quietly automated mundane enterprise tasks. Today, every software company claims to be an AI company. In 2026, does traditional SaaS even exist as a viable investment category anymore, or is a software startup inherently unbackable if it isn鈥檛 AI-native from day one?

Ranum: I don鈥檛 think the narrative is AI vs. SaaS. Instead, it’s AI plus SaaS. The companies that are struggling aren’t struggling because they’re SaaS businesses. They’re struggling because investors are in a 鈥渟how me鈥� era, and they don’t have clear answers yet.

Show me the free cash flow. Show me the path to profitability. Show me how AI is actually helping you win. You can’t get a stock bump anymore just by claiming you’re integrating AI. The market wants evidence of monetization.

The way I think about it is whether a company is building something that fundamentally changes how work gets done, or just layering AI on top of a workflow that a human is still doing. We used to back systems of record and workflow companies where the human was doing all the work. Now we’re in a position where the system itself can come in and actually do some of those tasks. That’s a different category of value entirely, and it changes what we look for. The bar has moved, but the opportunity is very real for the companies that can clear it.

Your core thesis is that the LLM stack is fracturing into distinct, standalone billion-dollar layers, such as orchestration (LangChain) and identity (WorkOS). But we鈥檙e seeing a massive border war. Big model providers like OpenAI are building their own tools, and data giants like are buying up security tools. How do standalone startups protect their turf when giants encroach from both sides?

Ranum: Both fracturing and consolidation are happening simultaneously, and I think that’s actually the right way to think about it. The moat isn’t about being first in a category. It’s about becoming genuinely embedded in how enterprises work. The companies I’m most excited about are the ones capturing orchestrated workflows in which the enterprise’s actual processes run through the product. That makes them very hard to displace, regardless of what the giants are building around them.

Because of your background at SAP, you know how enterprise buyers think. Right now, CFOs are looking at massive AI pilot bills and demanding to see actual ROI. When a startup is pitching an enterprise on a software governance or security tool, how do they defend that line item to a cynical CFO before the enterprise has even fully figured out its core AI strategy?聽

Ranum: What we consistently hear from buyers is that trust has become what actually separates the market. Security, governance, compliance, and auditability aren’t nice-to-haves anymore. They’re what make an AI deployment defensible when the CFO or the board asks hard questions.

And cost predictability is right alongside that. We’re in an era of greater focus on ROI, and enterprises want to know what this will cost them at scale before they commit. The vendors that can answer that question clearly are winning deals over the ones that can’t.

It feels like Silicon Valley is obsessed with the glamour of humanoid robots right now. Meanwhile, Sapphire鈥檚 big bets in this space, like Tractian, focus on practical, unglamorous industrial AI and predictive maintenance. Are humanoid robots an expensive venture capital distraction right now? Where is the actual, contract-signing enterprise demand on the factory floor today?聽

Ranum: The near-term ROI story is in constrained, high-value industrial settings such as packing, picking, inspection, and maintenance. These environments have clear labor economics, manageable deployment risk, and real buying cycles. That’s where the contracts are getting signed today.

Our portfolio company Tractian is a good example of what that looks like in practice. Unplanned downtime costs the world’s 500 largest companies roughly 11% of their revenue annually, which is a massive, measurable problem.

Tractian addresses it directly by combining sensor hardware with AI that detects early warning signs of equipment failure. The value proposition is concrete before you sign the contract, and the platform gets smarter the longer you use it. That’s the kind of embedded, compounding value we look for.

The humanoid era will come, but the gradient approach beats the all-or-nothing bet for near-term value creation. Start with specific, well-defined tasks where the payoff is obvious and work from there. The market is ready for that today.

Heavy industry and manufacturing are notoriously slow to change. A startup can’t just plug a modern AI API into a 30-year-old machine on a factory floor. For founders trying to build in the industrial tech space, is the winning strategy to build entirely new autonomous hardware, or is the bigger venture opportunity in retrofitting the world’s existing infrastructure with smart software?聽

Ranum: I believe the winning strategy is smart software layered on top of existing infrastructure rather than replacing it. Factories aren’t going to rip out 30-year-old machines because a startup has a better alternative. That’s just not how it works. The opportunity is in making those machines intelligent.

That said, the hardware-plus-software combination really does matter. You can’t get the data without the sensors. But the durable value is in the software layer that keeps learning over time. That’s where I鈥檓 focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft 鈥� annoying, but fixable. In robotics and industrial tech, a mistake means a factory line shutting down or a broken multimillion-dollar asset. From a venture perspective, how much harder is it to scale a robotics startup when the cost of product failure is so high in the physical world?聽

Ranum: I’d actually reframe the question. The cost of failure in physical environments is what makes the value proposition defensible. When the downside of getting it wrong is measurable, the upside of getting it right is equally concrete. You can walk into a sales conversation and show a customer exactly what prevention is worth before they sign anything. That’s a different conversation than selling software, where ROI takes quarters to show up.

From a scaling perspective, the key is discipline about where you deploy first.

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