This is a weekly feature that runs down the week鈥檚 Top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

It was another interesting week for AI-related startup funding, with a number of large rounds and quirky companies. The largest financings went to , a developer of AI processors and systems, and AI lab . The quirkiest round, meanwhile, was undoubtedly a $200 million financing for , which wants to breed elite military dogs.

A number of big rounds in areas including biotech, space tech and physical AI rounded out the list.

1. , $1.5B, AI infrastructure: Santa Clara, California-based Tenstorrent, a developer of AI processors and systems, secured $1.5 billion in Series E funding. and led the round, which set a $6.2 billion valuation for the 10-year-old company.

2. , $1B, foundational AI: Flapping Airplanes, an AI lab with a focus on developing models that cost less to train and run, closed on $1 billion in Series A funding led by and . The round comes less than 10 months after the year-old San Francisco startup raised a $180 million seed financing.

3. , $325M, AI for critical infrastructure: ASI, a developer of AI technology for securing and operating critical infrastructure in areas such as aviation, transportation and defense, picked up $325 million in Series C financing. led the round, which set a $3 billion valuation for the Boston-based company.

4. , $250M, space tech: Xona, a startup developing a next-generation version of GPS, raised $250 million in Series D funding led by . The round sets a $2.4 billion valuation for the Burlingame, California-based company.

5. , $237M, biotech: Pathos AI, a startup applying AI to drug development, completed a $237 million Series E financing at a post-money valuation around $3 billion. led the financing for the New York-headquartered company.

6. , $215M, AI, robotics and life sciences: Warp Sciences, a startup applying AI and robotics to help develop new medicines faster, raised a $215M Series A round led by .

7. (tied) , $200M, online business marketplace: Whop, a platform that helps entrepreneurs build and run online businesses, secured $200 million in Series D funding led by and . The round set a $5.1 billion valuation for the New York-based company.

7. (tied) , $200M, biotech: South San Francisco, California-based drug developer聽 Star Therapeutics says it raised $200 million in new financing led by and . The investment follows 鈥檚 July acquisition of , a subsidiary of Star, in a deal valued at up to $2 billion.

7. (tied) , $200M, military dogs: Warhound, a startup that wants to breed working dogs for U.S. military forces and emergency responders, launched from stealth with $200 million in seed funding from a long list of backers including and 1.听 The startup, co-founded by founder , plans to equip the dogs with Anduril technology.

10. , $160M, AI manufacturing software: Arda Industries, a developer of AI-powered software for manufacturing engineering, closed on $160 million in Series A funding at a $1.5 billion valuation, co-led by , and . The San Francisco-based startup also announced a previously undisclosed $72 million seed financing.

Methodology

We tracked the largest announced rounds in the 小蓝视频色情网页版 database that were raised by U.S.-based companies for the period of Oct. 17-23, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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and co-led the round, with participation from insurance industry veterans including , who will join Slingshot鈥檚 board. Rally is also a co-founder of the Minneapolis-based startup, as part of the firm鈥檚 broader approach. So far, it has co-founded roughly eight portfolio companies that have either launched or remain in stealth.

Slingshot, which is led by first-time founder and CEO , focuses on small insurance policies that consumers can buy as part of another purchase, such as coverage when booking a flight. Known as embedded insurance, the model puts coverage in front of customers when they might need it.

The startup鈥檚 software helps businesses offer those policies by connecting them with insurers that back the coverage. It uses AI to process and share data between the two sides, aiming to reduce the administrative work that can make inexpensive policies costly to offer.

Its first customer and design partner is , which serves more than 750,000 customers at an average premium of $25, according to Slingshot. Genteman told 小蓝视频色情网页版 News that Slingshot鈥檚 first program will launch this month.

For now, Slingshot earns revenue as a technology provider working with outside insurers. But the six-person company, which formally began operating July 1, is also pursuing an acquisition that would allow it to back policies itself.

鈥淲e’re not a carrier yet, but we are rapidly closing on our carrier,鈥� Genteman said in an interview.

Making the math work on a $25 policy

One product Slingshot is working on would let parents buy insurance when signing a child up for youth sports, Genteman said. A parent spending $2,000 on a volleyball league, for example, might be offered a $25 policy covering cancellation or an injury during the season.

Each policy includes paperwork and coordination with an insurer, and can bring potential claims. But when policies cost so little, managing them can quickly eat into the money they bring in, noted Genteman.

He believes that as vertical SaaS companies increasingly look beyond traditional subscription fees for growth, embedded insurance is 鈥渁 largely untapped opportunity鈥� to generate incremental revenue from transactions already happening on their platforms while delivering additional value to customers.

鈥淭he challenge is that the insurance and regulatory landscape is incredibly complex, and most SaaS companies don’t have the expertise, industry relationships or understanding of where to begin,鈥� he said.

Slingshot aims to solve this by providing a white-glove service for those vertical SaaS companies, helping them go from an insurance product idea to 鈥渁 fully operational offering.鈥�

He offered a hypothetical example: A program collecting $500,000 in premiums with a 10% margin would have just $50,000 in margin.

鈥淭hat’s not a viable program for the way incumbent carriers have set themselves up operationally,鈥� he said.

Slingshot鈥檚 goal is to replace the spreadsheets, emails and disconnected systems used to manage that work with software that connects policy distributors and insurers directly. The company says its AI automates data-heavy tasks while keeping a record of decisions that the businesses involved can review and check.

It charges a 5% technology fee to administer programs through its platform. Acquiring an insurer would let it provide both the software and the financial backing, according to Genteman.

A customer helped shape the business

The difficulties facing Slingshot鈥檚 first customer helped bring the startup together.

Rally had already helped start Vertical Insure, which uses software platforms to sell insurance to its customers. According to Genteman, that business had opportunities to introduce more products but struggled to find insurers whose technology and processes could keep up.

鈥淲e can design the products our customers need almost overnight, but the industry is highly regulated, and we need delegated authority just to write and distribute what we鈥檝e built,鈥� Vertical Insure co-founder and CEO said in a statement. 鈥淭he antiquated approval process on the carrier-side slows everything down.鈥�

Even after finding an insurer willing to back a product, connecting the two companies鈥� systems could create more delays, Genteman said.

Slingshot was built to address those problems, with Vertical Insure helping shape its product as a design partner. Noland is also a Slingshot co-founder.

The opportunity in vertical SaaS

Genteman sees a broader opening among vertical SaaS companies: businesses that sell software tailored to a particular industry.

As vertical SaaS companies look for ways to grow beyond subscription fees, insurance could give them another avenue to make money from purchases customers already make through their platforms, argues Genteman. They already know their customers and what they鈥檙e buying, putting them in a position to offer coverage that fits the purchase.

A babysitting platform, for example, could offer coverage to help parents pay for replacement childcare if a sitter cancels at the last minute. Software used by pet boarding facilities could offer coverage for unexpected vet bills if a pet is injured during a stay. An equipment rental platform could let customers add protection at checkout against accidental damage or theft.

Those are examples of what could be developed, Genteman said. Slingshot aims to help vertical SaaS companies take ideas like those and turn them into insurance they can sell, providing both the software and help navigating the insurance industry and its regulatory requirements.

鈥淚nsurance becomes another product that can be offered naturally within the existing customer experience,鈥� he said.

For , managing director at Rally Ventures, that opportunity is familiar. Before joining Rally, he ran , where insurance sold through its software became a key revenue source.

鈥淭hat experience gave me firsthand insight into the opportunity for software companies to generate revenue through embedded financial products,鈥� he wrote via email.

Kaufenberg sees similar potential across Rally鈥檚 portfolio, where most companies combine AI products with financial services. He said founders increasingly want insurance partners that can help them introduce coverage quickly.

Rally co-founded Slingshot to bring insurance backing, underwriting, reporting, claims handling and distribution together in one business.

鈥淭o get the carrier, the reinsurance, the underwriting platform, the data flow engine, the reporting capabilities, the claims handling, the embedded distribution engine, and every other part of the offering from a single vendor is unheard of,鈥� Kaufenberg said.

From insurance executive to founder

Before starting Slingshot, Genteman was chief actuarial officer at publicly traded , where he also led data. He joined in 2019 and helped build its actuarial and data team as the company expanded beyond earthquake insurance into other coverage types.

That work led him to explore whether newer technology could reduce the costs of running insurance programs. About a year ago, he began coding and building prototypes in his basement. He started talking with Rally toward the end of 2025 and left Palomar on July 1 to run Slingshot.

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Founded in 2016, Los Angeles-based Fifth Wall has grown into one of the largest investment firms focused on real estate technology, with roughly $3 billion in commitments and capital under management. Its portfolio includes , , and .

Notable exits include , which went public in 2024, and flexible workspace provider , which real estate giant of in January 2025 at a valuation of more than $800 million.

Major real estate and hospitality companies 鈥� including CBRE, , and 鈥� are among its investors, giving the startups it backs a direct connection to potential customers.

Wallace, who serves as CEO and chief investment officer, brings experience on both sides of that relationship. He worked in real estate finance at and before co-founding workforce analytics startup , which was acquired by in 2014, and ridesharing company .

As AI makes certain products easier to replicate, Wallace sees growing potential in startups that use technology to own and operate real estate themselves.

In a recent interview with 小蓝视频色情网页版 News, we discussed that shift, why proptech funding totals may overlook some of the sector鈥檚 biggest investments, what it all means for founders, and the housing problems he believes still need solving.

This interview has been lightly edited for brevity and clarity.

小蓝视频色情网页版 News: Our recent proptech funding analysis showed capital on pace to roughly match or slightly exceed last year, but across fewer deals. What does that tell you about the market?

Brendan Wallace: My sense is that statistic might belie the actual amount of funding flowing in, because what was previously characterized as proptech in the narrowest sense is probably representing a smaller share of where proptech investors are deploying capital.

There are increasingly horizontal solutions that are just as good, if not better, than many of the vertical solutions we鈥檙e seeing. We recently made a very big investment out of our growth fund into , the visualization software company. Most people reflexively think of that as being for movies or rendering scenes. But we saw real estate agents and commercial leasing brokers using it to represent spaces.

For an unbuilt home, you could feed it blueprints, have it build an entire home, put your family inside and walk through it in a full scene. That had surpassed the industry-specific solutions for space visualization that would have been narrowly defined as proptech.

Another category is investment flowing into full-stack businesses: real estate companies themselves. We just invested $135 million into , and I would be surprised if that fell under the definition of proptech.

Those are probably the two categories that might not be captured: horizontal software, with Higgsfield as an example, and businesses that reimagine physical space based on autonomy, AI or robotics. They鈥檙e actually building a physical thing. They鈥檙e building a real estate company, directly or indirectly.

How much is AI driving that shift toward software that serves multiple industries?

Wallace: It鈥檚 a big part of it. Without something profoundly better that you can鈥檛 replicate on your own, there鈥檚 less justification for buying outside software. There still is a justification, to be clear. There鈥檚 just less of one.

No real estate company can build the kind of visualization software Higgsfield has. I don鈥檛 believe they ever will. But a tenant engagement app they might have previously purchased and rolled out to their assets, they can now replicate over a long weekend.

The imperative to buy these third-party solutions has been reduced, and more value has been placed on the best horizontal solutions, whether that鈥檚 a foundation model or a specific use case like sound, audio or visual.

I don鈥檛 know the statistic, but it would be interesting to know how much real estate companies are spending on foundation models versus proptech applications. If I had to guess, we鈥檙e probably close to, or at, an inflection point where it鈥檚 passing it.

No one would characterize as a proptech company, but it might be taking a significant amount of the upstream proptech dollars, and an even larger share downstream once you go through the application layer.

Has AI reset the bar for what makes a venture-scale proptech software company? What does a startup need beyond adding AI to existing workflows?

Wallace: We鈥檙e still figuring that out, but I think it has to be something that can鈥檛 be replicated. Anything where the intellectual property moat is not extremely defensible is very challenged, and I think it will be for some time.

Simple application point solutions are becoming less financeable by venture investors because the capacity of one of the large labs to build that as an interface, or of a company to build it on its own, has caught up so dramatically.

The flip side is that bespoke AI development labs are now building solutions for the real estate industry. We backed one called , which is kind of like a for real estate. Antares works with a number of our strategic limited partners. They go in-house, identify a problem they think can be solved with an AI-based solution, build the intellectual property specifically for the organization, and leave it with them.

It鈥檚 more of a fee-for-development model than an intellectual property licensing model. You鈥檙e seeing that across industries, and you鈥檙e absolutely seeing it in real estate.

In some sectors, founders argue that domain expertise gives specialized AI companies an advantage over broad models. How durable do you think that advantage is?

Wallace: I鈥檓 a little more optimistic about foundation models’ and general intelligence’s capacity to adapt rapidly to specific use cases.

I wouldn鈥檛 argue that specific legal expertise, for example, is not valuable. But I think that moat collapses over time. You might be on a treadmill.

Think about what ChatGPT looked like in 2023 versus what Claude looks like today. At a basic level, it鈥檚 the same thing, but they鈥檙e totally different things. The sophistication, memory, capacity to be personalized, and ability to build off natural language instructions are unlike anything I think anyone could have anticipated in 2023.

What will that look like in 2029? I think what you鈥檙e saying is true today. It鈥檚 less true than it was, and it might become even less true in three years.

Is construction technology still an area of focus for Fifth Wall?

Wallace: It is, but it鈥檚 less of a focus today. When we look at our performance, it has generally favored two things: pure software businesses, whether marketplaces, consumer software or enterprise software, and full-stack businesses.

Anytime you get into physically doing things, the risk profile changes pretty profoundly. I don鈥檛 think it should be surprising that construction tech broadly is probably the biggest graveyard in all of venture. Maybe there鈥檚 a bigger category, but if there is, it鈥檚 not obvious to me.

Why do you think construction tech continues to attract significant funding?

Wallace: Because it鈥檚 still a massive market and a huge problem. If someone can bring greater efficiency to construction, you鈥檙e talking about one of the largest markets on earth. It鈥檚 such a massive prize that the largest funds are going to focus on it.

For founders raising seed or Series A funding, what opportunities would you encourage them to pursue that might not have been fundable a few years ago?

Wallace: You can actually build new real estate companies. That hasn鈥檛 really been conceptualized as being within a venture mandate, but it鈥檚 absolutely something we鈥檇 like to see.

For example, we have a better approach to buying multifamily assets, stripping out costs, and running more of the processes, operations and management with AI. That鈥檚 something I think is eminently fundable today by Fifth Wall and many venture funds. I don鈥檛 think that would have been the case five years ago.

I’d encourage founders to look at building full-stack, capital-R real estate businesses.

The second opportunity is around data. Real estate is characterized by extremely opaque data in a way that鈥檚 different from most large financial markets. Nothing that鈥檚 happened in proptech in the past 10 years has fundamentally changed that.

Where can generative AI provide signal through that opaque data to make capital allocation decisions? Real estate is generally a cash-flowing asset. We don鈥檛 necessarily think of it as comparable to bonds or stocks, but it is, and it should be.

Can you derive signal by looking at more data or different data, or use generative AI to predict what kind of real estate will perform better? The quantitative model that has existed for securities for a very long time now has the potential to affect real estate, because we have computational capacity that wasn鈥檛 previously possible.

Are there opportunities you鈥檙e interested in that have less to do with AI?

Wallace: We鈥檙e intrigued by the intersection of land, real estate and the financial instruments around them. To make most of what we describe as real estate, you usually have to build something on land. Land is an input to almost all real estate, but many real estate owners don鈥檛 want to own it or take on that risk profile.

Is there a way to decouple having to own land from owning and operating a building? That鈥檚 less about AI and more about financial engineering. There are new financial solutions emerging in that category that interest us.

How could AI, robotics and autonomy change the kinds of real estate businesses founders build?

Wallace: I鈥檓 interested in entrepreneurs who can conceptualize real estate through a sociological lens: New catalysts are changing how humans use space compared with the past.

That鈥檚 happened for a long time. E-commerce built the modern industrial real estate industry. Cloud computing, the internet and now AI built the data center industry. Cell towers were born out of mobile phones and smartphone technology. Genome sequencing helped create the lab-space real estate asset class.

I think we鈥檙e on the precipice of three new catalysts that will spawn entirely new real estate asset classes: AI, robotics and autonomy. In many ways, they鈥檙e entangled.

Do you expect fewer dedicated proptech venture firms? What would that mean for founders?

Wallace: I do think you鈥檙e going to see fewer vertical funds, in the narrowest sense of where we started this conversation. Dedicated venture capital funds investing in consumer and enterprise software focused on an industry 鈥斺�� I think you鈥檒l see less of that, and funding will probably continue to go down in that category.

The inverse is venture capital funding real estate businesses themselves. I think that鈥檚 about to explode.

If you were starting a company in this sector today, what problem would you most want to solve?

Wallace: Housing drives everything. Housing costs drive our economy, and the U.S. is grappling with a very real housing affordability crisis. It鈥檚 unclear how to solve it.

There are multiple approaches. One is building homes better, faster and cheaper 鈥� that鈥檚 construction tech. Another is financing homes at a lower cost. How do you bring efficiency to the housing finance market?

That鈥檚 a place where we鈥檝e made a lot of money and invested a lot, but I would argue it鈥檚 still unsolved. Getting a mortgage is still painful, archaic, slow and too expensive.

Related to that is making regulation more efficient to improve housing affordability. That鈥檚 not something an entrepreneur can solve alone, but there鈥檚 potential to bring efficiency to how cities deal with this problem.

There鈥檚 an intersection of regulatory technology, policy work and consulting. How do we make it so more people can afford homes in the cities they live in? That problem isn鈥檛 going away. Despite everything that鈥檚 happened, housing has gotten more expensive, not less.

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Related reading:

• Sector Snapshot: Proptech Funding Holds Up, But Investors Are Placing Different Bets

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She later became a general partner at , where she led early institutional investments in AI chipmaker , and later invested at . Now she鈥檚 founder and managing partner of , a $52 million fund backing startups that use AI to do work in industries such as construction, industrials and insurance.

In an interview with 小蓝视频色情网页版 News, Venkatachalam discusses why she looks beyond familiar founder profiles, what makes an AI company durable, and how an early investment in Groq shaped her approach.

This interview has been edited for clarity and brevity.

You invested at Khosla Ventures before starting Axiom. What did you take from that experience, and what did you want to do differently?

Venkatachalam: One thing I took was 鈥檚 open view of where great founders can come from. Silicon Valley has gravitated toward a fairly narrow idea of who can build the next great AI company: Someone with a computer science or machine learning background, or experience at . We鈥檙e looking for more nonobvious founders, particularly in nonobvious industries.

The other thing was how we think about risk. Instead of asking a company every conceivable diligence question, we focus on the risks that matter for its next set of milestones. Can this team do what it says it will do? And if it can, could the result be massive?

That means accepting that many bets won鈥檛 work out while aiming for the outliers. I think that鈥檚 what my investors are backing me to do: identify categories of the future, rather than participate in the categories everyone already recognizes.

At Axiom, the biggest difference is that we鈥檝e built the firm around people who are actively working with AI. If you aren鈥檛 building, productizing, pricing or taking AI to market regularly, it鈥檚 very hard to keep up. Our team includes people doing exactly that in their other roles. They help keep our investment thinking current, and founders want to work with them because they鈥檝e encountered many of the same challenges.

We also use AI throughout the firm. We鈥檝e built what we call the Axiom Brain to help us make sense of market trends, identify interesting people and companies, and move faster on diligence and other work. For me, the value is the ability to act quickly.

You mentioned that some of those AI practitioners have other jobs. How does their role at Axiom work?

Venkatachalam: They have dedicated time to Axiom and work with us part time. They also receive carry in the fund. They鈥檙e partners in the work, rather than people whose names appear on an adviser list.

Their other jobs are central to the model. Some of the best angel investors are people who are still operating and building. I don鈥檛 need these people full time. In fact, they would be less valuable to Axiom if they left the work that keeps them close to the market.

Axiom says it invests in 鈥淎I for the real world.鈥� What does that mean when you鈥檙e evaluating a startup?

Venkatachalam: Our view is that AI should benefit a much broader population than the early adopters who are already using it. We look at industries underserved by technology, where AI can produce an outcome rather than simply provide another software tool.

That may lead us to construction, industrials or insurance. Some of those companies involve hardware, sensors or robotics; others are entirely software-based. What connects them is that they鈥檙e doing work that matters to customers in real-world industries.

We generally don鈥檛 invest in products that look like conventional enterprise software tools. We want to see AI delivering a result.

You鈥檝e described a shift from software people use to digital workers that perform jobs. Are customers actually paying for AI from labor budgets?

Venkatachalam: Yes, and that鈥檚 one of our investment criteria. Even when a portfolio company is at an alpha or design-partner stage, we do diligence to understand whether customers are willing to buy it that way. We鈥檙e often seeing contract values in the hundreds of thousands of dollars, rather than the much smaller contracts you might expect for a midmarket software tool.

We鈥檝e seen that buying behavior play out across the majority of the portfolio companies we鈥檝e invested in.

AI products are becoming faster to build and easier to imitate. What makes one durable enough to become a large company?

Venkatachalam: If you鈥檙e doing important work inside a customer鈥檚 business, and that work is worth a lot of money, you become difficult to replace. You鈥檙e handling what we call the last mile of the job.

In industrial settings, for example, delivering an outcome means integrating deeply with the systems customers use. You have to understand their data, train on it, learn the workflows that matter, and stand behind the result. That takes more than putting an interface on top of a model.

Those relationships and capabilities can become difficult for another startup to replicate. They also involve work that the large AI model companies may have little interest in doing themselves.

Before Axiom, you backed Groq when AI inference was far from an obvious investment category. What led you to it?

Venkatachalam: I came from both hardware and software. At one point, I became interested in why was building its own networking switches when it could buy them from existing suppliers. As I looked into that, I learned it was building its own chips, too.

That led me to , who had been involved in that work and had left to start Groq. I began learning why large technology companies were building chips to train models. Then Jonathan made the case that the much larger future market would be inference.

I鈥檒l be honest: In 2016, I barely understood inference. But if you believed these models would spread, it made sense that people would build on top of them and need the infrastructure to support that. That insight drove my investment.

How did that experience shape what you look for now?

Venkatachalam: It taught me the value of being a little early and having some patience. You don鈥檛 have to be wildly contrarian, but you do have to see the opportunity before it becomes obvious to everyone else.

In a way, our thesis hasn鈥檛 changed. We鈥檙e still asking what will be built on top of AI infrastructure and models. We want to invest while the answer is emerging, before there鈥檚 a consensus.

What happens when one of those early bets doesn鈥檛 work out?

Venkatachalam: We plan for it. We have a $52 million fund and expect to make 35 investments. We fully expect about half of them to fail, whether that means a company shuts down or simply never reaches the growth trajectory we鈥檙e looking for.

The model depends on finding an exceptional outcome. We need one outstanding investment to return the fund. If you invest early enough and the company becomes very large, that can offset many bets that didn鈥檛 work.

That willingness to accept losses is part of making investments before an opportunity is obvious. It鈥檚 built into how we approach the fund.

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A decade ago, Espahbodi co-founded aerospace and defense startup accelerator and moved back to the U.S. to expand it. On the advice of friends at , he opened an office in El Segundo, California, near , just as more alumni of that company were leaving to launch hard-tech startups of their own and next-generation defense startups including were emerging.

Espahbodi eventually sold his stake in Starburst and launched , which invests in companies spanning industrial infrastructure, manufacturing, energy and water desalination. The firm has backed 14 companies since making its first investment in January 2023.

He also advises federal agencies on working with nontraditional, venture-backed companies. In an interview with 小蓝视频色情网页版 News, he discusses how AI is changing hardware economics, why software investors are rushing into industrial technology, and what he believes many of them misunderstand about the sector.

This interview has been edited for length and clarity.

小蓝视频色情网页版 News: What led you to leave Starburst and launch Generational Partners?

Espahbodi: About four years ago, I noticed that my friends from SpaceX were leaving the space vertical and moving horizontally across physical industries. I reached an inflection point: I didn鈥檛 want to remain locked into the space sector. I wanted to follow my friends.

I sold my equity in the accelerator, and part of the investment team left with me to start Generational Partners. For the past four years, we鈥檝e invested in what you might call the SpaceX-mafia and hard-tech sectors 鈥� anything involving industrial infrastructure, manufacturing, energy or water desalination.

We made our first investment in January 2023, in a North Dakota-based drone company. It was a trial by fire and an opportunity to prove the thesis. We鈥檝e invested in 14 companies since then.

You were already investing in physical, safety-critical industries before the generative AI boom. Has AI materially changed where you invest, or has it mainly reinforced your existing thesis?

Espahbodi: I tend to arrive earlier than others. I embraced the idea that hardware does not have to be capital-intensive. People often confuse hard tech with deep tech, but nomenclature aside, you don鈥檛 need to invest in science to win in these categories.

AI has dramatically changed that narrative and encouraged more people to get on board. I鈥檓 not looking to invest in science. I don鈥檛 necessarily see opportunities in quantum computing, nuclear fusion or other technologies being spun out of laboratories.

People who worked at companies such as SpaceX, and laid their companies鈥� foundations digitally. AI has significantly improved that augmentation and performance, enabling these companies to tackle legacy industries more aggressively and, more importantly, with new business models.

Another major component of the AI question is that frontier labs have become more expensive and capital-intensive than traditional hardware companies. The success of frontier AI labs, combined with the SpaceX IPO becoming an enormous wealth-creation event, creates a new environment. It raises questions about what is truly capital-intensive, what makes a product or its intellectual property defensible, and where companies are reengineering products around different business models.

Hardware has historically been capital-intensive, slower to commercialize and difficult to scale. Under what conditions does its technical defensibility compensate for those challenges?

Espahbodi: Fundamentally, it comes down to the business model. I look for creative software talent combined with commoditized hardware, significant customer demand and a new business model.

One of our portfolio companies was founded by the team that built the factory for user terminals. When you buy a retail Starlink antenna, these people built and scaled the assembly line that produced it at high volume.

While deploying those terminals globally to provide internet access, they observed that poverty often stemmed from a lack of access to clean water. They asked whether they could replicate the proliferated satellite-and-user-terminal architecture for edge water desalination.

Rather than investing in multibillion-dollar, nation-state infrastructure like that used by Gulf countries, they wanted to mass-produce every component in a vertically integrated stack. Their goal was to produce a cooler-sized device that could clean water at the point of need.

used a digital, software-based approach to build the bill of materials needed for mass manufacturing. AI is part of its business and operations, but the company鈥檚 real innovation was inverting the infrastructure model and scaling it.

I helped Vital Lyfe win its first customers within the and . Those organizations can use its devices in the field rather than shipping pallets of bottled water by air freight. That created a signal for overseas partnerships and nonprofit humanitarian-aid applications. It showed that there could be a different way to provide clean water.

Those are the kinds of unique business models that excite me.

What other companies founded by SpaceX alumni demonstrate how hardware businesses can overcome the traditional challenges of the sector? What can these founders build today that would have been difficult five years ago?

Espahbodi: Another example is the team SpaceX recruited to build the autonomous drone ships that catch boosters in the middle of the ocean. The team included former Coast Guard personnel and oil-and-gas technicians.

At SpaceX, they had the freedom to use software and AI tools to automate station-keeping 鈥� the ability of those drone ships to position and navigate themselves and reach the right location.

That team spun out and brought in many former colleagues to change commercial maritime shipping. They retrofit legacy boats operating in harbors and waterways and move supply-chain goods.

They brought a digital-first foundation to automating the controls on tugboats and barges. That had never existed before because the communications link to those ships didn鈥檛 exist. Starlink changed the concept of operations. The company can use its software expertise to change how physical devices operate aboard these boats and allow their sensors to send signals anywhere in the world.

That makes it possible to retrofit and overhaul how legacy shipping vessels navigate harbors and waterways in the U.S. It鈥檚 another example of SpaceX alumni applying the playbook and technologies they learned at SpaceX to a much broader commercial industry.

You鈥檝e said AI is eroding traditional software moats. What evidence are you seeing that investors are responding by moving into hardware and industrial technology?

Espahbodi: I meet many software investors who feel they鈥檙e missing out on hardware but don鈥檛 necessarily understand it. I鈥檝e met beauty investors who now say they鈥檙e defense-tech investors.

Los Angeles is a hotbed of firms that historically invested in software, media or consumer packaged goods. But people forget that Southern California, particularly El Segundo, is the aerospace capital of the world and has the largest concentration of mechanical-engineering talent.

Across the region 鈥� from China Lake to San Diego 鈥� technicians, builders and vocational talent are intersecting with the democratization of software and access to AI tools. Many local VCs have never taken advantage of the hardware talent located around them, so they鈥檙e being thrown for a loop.

Ironically, Bay Area VCs have been among those leaning most heavily into this. But it鈥檚 happening everywhere. I鈥檓 in Washington, D.C., now, and one of the first investors in , the hypersonic missile company, was in Virginia 鈥� before and others became involved.

Los Angeles VCs in particular know there is a talent war underway and that many people are leaving established companies to launch new businesses in these categories. But they struggle to underwrite those deals. They don鈥檛 know how to distinguish a strong opportunity from fear of missing out or something merely cosmetic.

So investors鈥� lack of experience in the space isn鈥檛 deterring them from writing checks or competing for deals?

Espahbodi: You have to ask why. The answer is their limited partners.

Sophisticated allocators, such as endowments, foundations and pension funds, along with more FOMO-driven family offices and high-net-worth investors, are watching this wave of SpaceX, and Anduril alumni create new companies and raise extraordinary rounds.

Many of those companies are no longer raising solely to pursue intellectual property. They鈥檙e building war chests to acquire other companies. The lines between private equity and venture capital are blurring. VC-backed companies are doing private equity-style buyouts, while venture deals are bringing in private equity checks.

That leaves LPs pushing for more. The success of the frontier AI labs has also perpetuated a fear of a 鈥淪aaS apocalypse,鈥� which I don鈥檛 think is real 鈥� although I sometimes question 鈥檚 1聽stock price for fun.

It creates what venture does best: tourists and FOMO. LPs ask why their managers aren鈥檛 investing in the same companies and how they can participate, raise more money and show that they aren鈥檛 missing out. That鈥檚 how I鈥檝e seen investors unfamiliar with these sectors enter the market.

Some of the largest Silicon Valley firms … missed this dynamism wave. Now they鈥檙e leaning in hard, sometimes at ridiculous valuations for companies that have yet to produce anything.

If more venture funding continues to flow into defense, aerospace and industrial technology, what prevents hardware from developing the same problems software experienced, including too many competing companies?

Espahbodi: Bring it on 鈥� hard and fast, and as much as possible.

Venture as a category exists because it was always about hardware. I would argue that the SaaS era, from the dot-com boom until now, was a blip compared with what venture was originally intended to underwrite.

I would move away from the hardware-vs.-software distinction and ask who is reframing the business model. Is there a way to reengineer a combination of software and hardware to unlock customer value? That鈥檚 the more important question.

How important is geography for these startups? Does locating near a major government customer help a company win contracts, and how do startups navigate procurement if they aren鈥檛 based near Washington, D.C.?

Espahbodi: It鈥檚 a common misconception that Washington is where the money is. The Los Angeles Air Force Base houses , which is another way of saying it holds 鈥檚 wallet. El Segundo makes the purchasing decisions for the fastest-growing portion of the military budget.

Washington is a place of considerable activity that needs to be influenced. Venture has never had this degree of influence on an administration and its executive orders. We鈥檙e also seeing portfolio companies backed by influential investors win government contracts worth as much as $1 billion at a time. That鈥檚 extraordinary.

Geographically, companies need to be where the talent is as much as where the customers are. Government customers should signal what matters, but companies shouldn鈥檛 organize themselves entirely around the government.

My catchphrase is that I want everyone to be commercially focused but mission-aware. I don鈥檛 want them to be mission-focused on the government. I want government to signal what it cares about while companies remain commercially focused.

The talent war for this convergence of hardware and digital technology is centered in Southern California. If you aren鈥檛 building and recruiting there, you鈥檙e falling behind. I like that the Bay Area is trying to attract more hardware talent and capitalize on the automotive and humanoid-robotics markets.

But I think the talent base for the factory of the future starts in Southern California and can then be used as a model for expansion into other places, as companies such as Anduril have done in Ohio and Louisiana.

We invested in a company founded by people from SpaceX and . They immediately moved to Austin to build a smart factory for raw-material processing. They wanted to automate the process at its source.

The largest concentration of cotton farming is around Lubbock in the Texas Panhandle. The company is building automated factories from the ground up to mill cotton into yarn and then complete the digital, vertically integrated stack by producing textiles at prices that beat outsourcing to China, Vietnam and other countries.

It sounds crazy, but the founder is determined to do it. If you can prove the model in textiles, you can apply it to copper. If you can do it with copper, you can do it in pharmaceuticals. From there, it could go in any direction.

Do startups located near Space Systems Command have an advantage?

Espahbodi: Not for that reason alone. The advantage is that they鈥檙e part of the ecosystem and geography. They鈥檙e spending time in the same bars and restaurants, and their children attend the same schools. They鈥檙e witnessing the same velocity.

Space Force itself is facing greater demand than ever to protect assets in space. Whatever happens with funding for individual programs, it remains the fastest-growing portion of the Pentagon budget.

I don鈥檛 think startups should locate there solely to be close to the customer. They should be there for the talent they need to build.

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U.S. venture-backed technology聽1 companies have secured nearly $90 billion in domestic public offerings this year, per 小蓝视频色情网页版 data. That鈥檚 already the second-highest annual tally on record, and we鈥檝e still got a few months to go.

However, virtually all the money went to two companies. alone accounted for 83% of the $90 billion raised this year, while AI infrastructure company scooped up another 6%. A potential offering from , meanwhile, could be even bigger.

The remaining field is comparatively modest. Just 21 other venture-backed technology companies went public this year in sizable or offerings 2, per 小蓝视频色情网页版 data. Collectively, their offerings, which include traditional IPOs and SPAC deals, pulled in less than $10 billion.

This small cohort is intriguing for what it excludes as well as what it includes. Enterprise software, long a staple industry among venture-backed IPOs, was essentially a no-show this year. Energy, defense and space tech, by contrast, were well-represented. We also saw smaller offerings from other sectors, including medical devices and consumer-facing startups.

Here are some of the key findings in more detail:

Energy powers the most IPOs: About a quarter of this year鈥檚 tech startup offerings hail from the energy sector. The largest of these was from geothermal energy provider . Several nuclear power-focused startups also made their debuts, including and , developers of small modular nuclear reactors, as well as , focused on advanced nuclear fuel.

A dash of quantum, defense, aerospace, devices and consumer: Beyond energy, quantum computing company delivered one of the year鈥檚 larger debuts, as did equipment rental platform . Defense tech and aerospace were also strong performers, with offerings from satellite intelligence provider and spacecraft developer . And on the consumer front, e-bike and scooter platform finally made its market entrance, albeit at a valuation below its one-time .

An IPO SaaS-pocalipse: But what about SaaS? Mostly MIA. The paucity of enterprise software offerings this year isn鈥檛 entirely surprising given the impact of AI on the sector. VCs are pouring capital into a newer generation of AI-first platforms in legal tech, accounting and other enterprise software sectors. Existing SaaS unicorns are also moving fast to incorporate more AI in their offerings.

One end result is there are an awful lot of SaaS unicorns and former unicorns that have concluded this year is not the time to pursue an IPO.

Winner-takes-almost-all

Another end result is that investment returns are looking more concentrated than ever.

Of course, winning big or not at all is far from a new thing in the startup world. Tech venture returns have always been propped up largely by a few enormous wins, with the remainder of portfolio companies producing either losses or smaller profitable exits. But lately, the winner-take-almost-all-the-IPO-proceeds tilt is more pronounced than ever.

The pipeline of tech companies that have filed for future IPOs doesn鈥檛 offer much consolation that this pattern will change. Giant potential market debuts from Anthropic and still dominate IPO chatter. Enterprise SaaS offerings do not.

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Startups across sales, marketing and customer management have raised $7.5 billion so far this year, according to 小蓝视频色情网页版 data. The largest rounds span everything from advertising and customer data to sales software, e-commerce and customer support 鈥� reflecting just how many companies are still trying to build a better way to market and sell.

The broad trend: Investors are making far fewer bets on sales and marketing startups than immediately before and after the COVID-19 pandemic, but they鈥檙e still writing checks into the space.

Unsurprisingly, AI-focused companies are capturing a much larger share of funding than during the prior peak, with most sales, marketing and CRM investment going to companies in 小蓝视频色情网页版 AI-related categories.

The numbers: So far in 2026, startups in sales, marketing and CRM have raised $7.5 billion globally across 830 funding rounds, 小蓝视频色情网页版 data shows. At the current pace, funding could finish near the $9.3 billion raised in both 2023 and 2024, although potentially below last year鈥檚 $11.1 billion. Deal volume, meanwhile, is on track to fall for a fourth consecutive year 鈥� pointing to a market where investors are putting more money into fewer companies.

Funding in recent years remains far below past levels. In 2022, for example, funding in the sector topped $27 billion, and in 2021, it totaled nearly $41 billion.

Notable deals

The year鈥檚 largest funding recipient so far was, which raised more than $1 billion in a June Series E from , , and . The San Francisco-based marketing measurement company, whose products now include AI agents that analyze marketing data and automate tasks, was valued at $2.7 billion.

Restaurant financing and rewards platform announced $450 million in new capital in February. The Austin-based company did not identify a lead investor or disclose a valuation.

In January, AI-native customer service company raised a $350 million Series D led by . The Berlin-based company develops AI agents that handle customer conversations by phone and other channels. The financing tripled its valuation to $3 billion.

Meanwhile, , an online marketplace for digital products, communities and courses, received a $200 million strategic investment from in February. The deal valued the New York-based company at $1.6 billion.

Another larger deal went to Dubai-based property listings platform , which announced a $170 million equity investment in January. The company uses AI in products including home valuations and tools that help real estate agents improve and prioritize listings. led the deal, with participation from another UAE sovereign wealth fund and existing investor . The company did not disclose a valuation.

On Sept. 9,聽 AI-powered sales automation startup announced it had raised a $115 million Series D at a $7.1 billion valuation. This was more than double the $3.1 billion valuation it achieved when it raised a $100 million Series C in August 2025. Wellington led the latest round, with participation from , , 鈥檚 a16z Perennial wealth management arm, , and others. The company says the raise followed 4x revenue growth in 2025. It also told 小蓝视频色情网页版 News that it’s on track to hit $200 million in ARR this quarter, and $240 million by the end of the fiscal year.

Exits

The sector has produced one notable public offering, but most exits are coming through acquisitions as larger companies buy specialized sales and marketing products to add to their existing platforms, 小蓝视频色情网页版 data shows.

, a Redwood City, California-based mobile advertising and app-marketing company, began trading on the in June. It initially sold 19 million shares at $23 each, raising $437 million. The IPO valued Liftoff at $3.83 billion, based on the outstanding shares disclosed in its IPO prospectus.

There have been a number of M&A deals this year in the sector, too, though in most cases, the acquisition price was not disclosed.

The largest known deal was Dutch payments giant acquisition of , a Berlin-based loyalty and promotions platform, in July for about $880 million. Talon had previously raised over $120 million in venture funding.

Other startup M&A deals in the marketing and sales arena in 2026 include:

• In July, acquired Seattle-based sales intelligence startup to add information about prospective buyers to its sales products.
• In June, agreed to acquire , whose software helps companies identify and contact people visiting their websites.
• Sales platform acquired , which helps sales teams identify prospective customers based on product use and other signals, in March.
• acquired the Estonian startup , whose software connects sales and marketing data, in August.
• acquired India-based marketing intelligence startup in September through a team and technology deal.

Funding is down from peak years, but it鈥檚 clear investors haven鈥檛 lost interest in sales and marketing startups. However, they are putting more money into fewer of them. Companies that help businesses find customers, increase sales, or retain existing business are still landing big checks and attracting buyers. But with acquisitions far more common than IPOs, a public-market exit remains much harder to come by.

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The highest-valued new entrants were China-based humanoid robotics business , valued at more than $6.3 billion; San Jose, California-based photonics company , valued at $5.5 billion; and Palo Alto, California-based AI model platform , and San Francisco鈥檚 semiconductor manufacturing startup , both valued at $5 billion.

AI software featured prominently across model training, assistants, agentic and enterprise workflow automation, coding and voice transcription.

Semiconductors was the second-largest sector, with five new unicorns. Robotics and financial services each added three, while data centers, security and energy each added two.

The U.S. accounted for 16 of August鈥檚 new unicorns. China followed with four. South Korea, India, Singapore, the United Arab Emirates, Switzerland, Germany and Turkey each added one. Nigeria and Indonesia also each added one new unicorn 鈥� for both, their first new unicorn of the year.

Nine companies exited the Unicorn Board in August, per 小蓝视频色情网页版 data: Three that went public 鈥� the most notable being 鈥� and six via acquisition, including , and .

New unicorns in August

Here are August鈥檚 new unicorn companies:

AI and software

• , a Palo Alto, California-based platform for training, fine-tuning and deploying custom AI models based on proprietary data, announced $1.1 billion in funding led by and . The less-than-1-year-old company, founded by former co-founder , was valued.
• San Francisco-based , an AI assistant that executes personal tasks, raised a $250 million Series B led by and . The 1-year-old company was valued at $2.5 billion.
• Shanghai-based , a builder of agents for digital and physical environments, raised a $220 million seed round led by and . The less-than-1-year-old company was valued at $2 billion. Its founder, , a researcher, left earlier this year.
• San Francisco-based , which builds AI-powered voice-writing and meeting-transcription tools, raised a $280 million Series B led by , who also led its Series A in 2025. The 5-year-old company was valued at $2 billion.
• San Francisco-based , which provides AI-powered code review and change-management tools, raised a $143 million Series C at a $1.5 billion valuation. and co-led the round. The 3-year-old company said it would commit more than $10 million to keep its tools free for open-source projects over the next year.
• San Francisco-based , which deploys AI agents across calls, email, documents and enterprise systems, raised a $150 million Series C at a $1.2 billion post-money valuation. and led the round. The company is 4 years old, started in logistics and has expanded to insurance, energy, telecommunications and airlines among others and counts 150 enterprise customers.
• Turkey-based , a developer of consumer mobile applications, raised a $50 million Series A led by . The 4-year-old company was valued at $1.25 billion. Its apps include AI chatbot Nova, diagnosing plants with PlantApp, and art generator DaVinci.

Semiconductors

• , a San Jose, California-based developer of photonic interconnects for AI computing infrastructure, raised a $700 million Series C at a $5.5 billion valuation. , , , and led the round. The 2-year-old company is already deployed within data centers.
• raised $400 million in funding led by hedge fund . The 1-year-old company was valued at $5 billion. The San Francisco-based company creates tooling for semiconductor manufacturing and was founded by researchers.
• South Korea-based , which develops edge AI processors for on-device inference, raised about $29 million in the first tranche of its Series D funding from existing investors. The 8-year-old company targeting robotics and electronics was valued at about $2.2 billion.
• Shanghai-based , an AI chip startup for inference, raised a Series A led by local state capital investors and . The 4-year-old company was valued at about $1.5 billion with plans to ship its product in Q4 2026.
• Santa Clara, California-based , which develops low-power silicon and software for AI data centers and physical AI, raised a $110 million Series A led by . The 4-year old company was valued at more than $1 billion.

Robotics

• China-based , which is building the general-purpose IRON humanoid robot, raised more than $900 million in its first outside financing at a post-money valuation exceeding $6.3 billion. led the round, with participation from and support from and Alibaba Group. The company, a subsidiary of public smart electric vehicle company , is 10 years old.
• Singapore-based , which develops robots to operate in real-world environments, raised about $669 million in funding. The 2-year-old company was valued at about $3.3 billion and is set to deploy robots in a Dairy Queen in Shanghai to handle the entire 55-step process of taking orders, preparing the food and handing it to a customer.
• Zurich-based , which develops autonomous technology for heavy construction machinery, raised a $200 million Series A led by . The 4-year-old company was valued at $1 billion and works across multiple construction brands.

Financial services

• Bengaluru-based , a financial-services company spanning payment, lending and insurance, raised $100 million in funding led by . The 7-year-old company was valued at $1.3 billion.
• Berlin-based , a finance AI platform for European mid-sized businesses to聽 manage spend, card issuing and expenses, raised a $40 million Series C led by and . The 7-year-old company was valued at around $1.15 billion. The company says it has 5,000 businesses that use the service to give finance teams control.
• Palo Alto, California-based , an AI-native enterprise resource planning platform for accounting, raised a $100 million Series C led by . The 4-year-old company was valued at $1 billion.

Aerospace and defense

• Los Angeles-based , a manufacturer of autonomous military drones and counter-drone systems, raised a $250 million Series C at a $2.5 billion post-money valuation. and the co-led the round. The company is 3 years old. Neros has contracts with the U.S. military as well as half a dozen allied countries.
• Mountain View, California-based , which builds and operates satellite constellations for national security, civil and commercial customers, raised a $250 million Series C led by . The 5-year-old company was valued at $1.5 billion.

Data centers

• Palo Alto, California-based , a vertically integrated AI infrastructure platform, raised a $300 million Series A led by , , and . The less-than-1-year-old company was valued at $2.4 billion. Alongside the equity, Volta secured $5 billion in debt to fund data center buildouts.
• , a full-stack AI infrastructure and neocloud platform, received led by Doha-based broadband provider , which holds a 49% stake. Jakarta-based Zankore is less than 1-year-old and is valued at $1.6 billion. The platform is targeting 1 gigawatt of AI computing capacity.

Security

• San Francisco-based , an AI-native security company that provides autonomous penetration testing, raised a $250 million Series E led by and . The 7-year-old company was valued at $2 billion and is used by 7,000 organizations including defense, Fortune 10, banks and healthcare companies among others.
• Palo Alto, California-based , which provides security for AI agents and third-party applications, raised an $85 million Series D led by . The 9-year-old company was valued at $1.1 billion.

Energy

• China-based , a nuclear fusion company developing small modular reactors, raised about $179 million in seed funding. The 1-year-old company was valued at about $1.5 billion.
• Washington, D.C.-based , which develops software that adjusts AI data-center workloads based on power-grid demands, raised a $150 million Series A at a $1 billion valuation. and co-led the round. The 2-year-old company says the round brings total funding to more than $220 million.

Transportation

• Nigeria-based , a vehicle financing and autonomous fleet management infrastructure, raised a $250 million Series C led by , and . The 7-year-old company was valued at $2.1 billion. It operates a fleet of 42,000 vehicles 鈥� both human-driven and autonomous 鈥� across 29 cities, with annual recurring revenue of $420 million.听

Critical minerals

• Houston-based , which builds mines and refineries using its MarianaOS software platform, raised a $310 million Series B led by . The 2-year-old company was valued at $1.5 billion.

Web3

• Dubai-based , an AI-enabled stablecoin neobanking platform for cross-border payments and tokenized assets, raised a $68 million Series C led by Tokyo-based at a $1 billion valuation. The 7-year-old company says it processes more than $40 billion in annualized transaction volume.

Related 小蓝视频色情网页版 unicorn lists:

• (1,862)
• (658)
• (276)
• (195)
• (119)
• (102)
• (961)
• (547)
• (254)
• (39)
• (489)

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• Global Venture Funding Jumps 122% In August As Streak Of Billion-Dollar Deals Continues
• Which Investors Have Backed The Most 2026 Unicorns?
• 40 Companies Joined The Unicorn Board In July, The Highest Count In 4 Years

Methodology

The 小蓝视频色情网页版 Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 小蓝视频色情网页版 data. New companies are as they reach the $1 billion valuation mark as part of a funding round.听

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.听

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

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

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.

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As a partner at Menlo Park, California-based venture firm Norwest, Jacobsohn focuses on enterprise software, drawing on his background in finance, sales and business development. His 15 active portfolio companies range from pre-revenue startups to businesses generating more than $300 million in revenue. Much of his portfolio falls within finance and HR software, although he also invests in supply chain and construction technology 鈥� often in companies building finance applications for those industries.

The common thread, he explained, is a focus on next-generation business applications taking on entrenched providers that have struggled to keep up. Jacobsohn has found particularly fertile ground in finance, where companies already have software budgets and many categories remain dominated by aging systems.

Norwest, founded in 1961, manages $15.5 billion and is investing out of its 17th fund, a $3 billion vehicle raised in 2024. Over time, the global venture and growth equity firm has backed more than 700 companies in sectors spanning enterprise, consumer and healthcare.

In an interview with 小蓝视频色情网页版 News, Jacobsohn discusses where he still sees openings in the crowded market for finance software, how far companies should trust AI with accounting work, why HR startups may be better off attacking the secondary products of large platforms, and why he tests a CEO’s sales ability before investing.

The interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: The office of the CFO is an area where you’ve invested fairly extensively. Why is there still so much room for startups when finance software is already such a crowded market? Where is the opportunity right now?

Jacobsohn: I’m focused a lot on companies that are disrupting legacy players, and there are a lot of legacy players in the office of the CFO. We had more than 500 companies on our Office of the CFO market map, and probably three-quarters of those are legacy players.

What’s interesting about finance is that the CFO approves all software purchases across the organization, but CFOs also buy software for themselves. There’s actually one less layer of approval when they’re buying their own software, so it is a little easier to replace it when they’re the direct buyer.

I’ve found a lot of opportunities in both finance software that sells to every industry and software focused on specific industries. I’ve invested in a lot of horizontal applications, and so far the vertical solutions have been in construction and manufacturing. We’ve also invested in the healthcare space, but that’s not my area of focus. I’m also looking at companies in transportation and logistics.

Are there specific finance workflows that still strike you as surprisingly manual and therefore more ripe for disruption?

Jacobsohn: I actually think most workflows have been automated, but some are being automated by legacy solutions. Some could still be on-premise. Some could be companies making the transition from on-premise to the cloud that are still very legacy. You might even call them SaaS 1.0, because a company can be considered legacy and be only five to 10 years old now that a lot of the new generation is AI-native.

Every company wants to buy AI-native products these days. Some legacy companies have done a better job of reinventing themselves, and others are having more difficulty. Since most everything has been automated by someone, I’m focused on new-generation disruptors of legacy solutions.

What are some of the areas you think are ripe for disruption?

Jacobsohn: I have a portfolio company in some of these categories, and not in others.

One area where I do not have a company is ERP. I think there’s a potential opportunity to disrupt and Those companies have been around for a very long time. I’m seeing more disruption downmarket, and some of these companies will eventually move upmarket.

I think sales tax is another category with some ancient legacy players where there’s an opportunity to disrupt them. Treasury management also has some very old legacy players. Another area I’ve invested in is procurement.

Finance is particularly sensitive when it comes to accuracy and audits. Is that affecting how much work companies will actually hand over to AI agents, especially in accounting?

Jacobsohn: We think about this a lot. Finance people are risk-averse, and they need consistent answers. There’s some concern that there could be errors with AI, and there are.

It’s important to infuse AI into your finance products, but you have to be careful about what you’re giving AI to do. You don’t want AI doing calculations because it is not good at math. There are certain workflows it can handle where it doesn’t produce precise numbers. But when you need precision, accuracy and calculations, you can’t rely on AI for that.

In Norwest鈥檚 recent , you mentioned that categories including payroll, benefits and workforce management can be difficult to disrupt because of the time and expense associated with switching. If a startup wants to take business from Workday or ADP, how can it make switching more enticing?

Jacobsohn: I think it would be very hard to disrupt the core products of Workday, , SAP, and Dayforce. But it’s easier to disrupt some of their secondary products, where the category isn’t their core business. Those companies have really good distribution. Often, the best distribution wins, not necessarily the best product.

Workforce management is a category I’ve invested in through . UKG has a product in the space, but it started as an on-premise company and moved to the cloud. We’ve been a cloud-native AI player, and we’ve done well against it in the market.

Another company I invested in that complements these players is , which is in the benefits space. What’s interesting to me is that I worked at WageWorks, a legacy player in the space. Elevate is disrupting my old employer. Benefits isn’t the core business of the suite players I mentioned, but it’s a big enough market where a specialist can do well.

That’s how I look at it: What are some big markets where suite players aren’t putting much effort behind the product because they can only focus on so many things at once?

Is AI making it easier or harder to build a durable software company? Features and products can be built faster, but they can also be copied faster.

Jacobsohn: I do think it’s making it easier to build companies. We’re going from products that store data and automate some workflows to really smart solutions that understand, predict and execute work for you. It’s changing employees’ jobs. Employees can focus on higher-value work and automate some of their tasks with agents that can work really quickly.

As for whether anyone can vibe-code something, I think if you’re building a simple horizontal workflow for small businesses that isn’t very complex, it could be easy to build the product yourself, or it could lead to a lot of competition.

If you’re building something complex for the midmarket or enterprise, something that needs deep domain expertise or something vertical in nature, any of those areas would be really hard for a lot of people to build internally or for too many startups to compete in. Those solutions would also be really hard to maintain. I’m not seeing much competition from people wanting to build internally at my portfolio companies that are focused upmarket, where you need deep domain expertise.

The IPO market has improved, but it certainly isn’t where it was. How does the current exit environment affect what you’re willing to fund today, if at all?

Jacobsohn: It doesn’t impact our interest in funding. Our primary entry point is seed and Series A. I’ve done some Series B and C deals, so we can be opportunistic at the later stage.

We’re focused on backing entrepreneurs with deep domain expertise who are going after big markets with legacy players ripe for disruption, and we don’t worry about the exit environment. At some point, the IPO market will open up more, and maybe that will help us in the future. But more companies get acquired than go public.

I do want to invest in a company that, if it executes well, someday has the option to go public. But I’m realistic that most companies get acquired before that can happen.

How do you feel about an acquisition as an outcome?

Jacobsohn: You have to support your entrepreneurs and what’s in their company’s best interest. M&A can be a very good outcome, especially since we come in so early. If a company is acquired for less than $1 billion, it still could be a great outcome for us and the company.

The challenge is entering late, at a valuation above $1 billion. Not many companies will acquire another company for billions of dollars. We like to come in early so that if a company sells for less than $1 billion, which is where most buyers have budgets, it can be a really good outcome.

Is there a fundamental belief you have about funding or building startups that you think other investors might disagree with?

Jacobsohn: Something that’s different about me from most VCs is that I come from a sales background, and I think the CEOs I back need to be good at sales.

Just about every CEO I back comes from a product and engineering background, but that’s not enough. You need to be good at selling. You need to sell to customers, partners, investors and employees. Before I invest, I’ll go on a lot of sales calls I set up with the CEO to see how good they are at selling.

To me, that’s a big way of assessing the potential of a company.

Have you ever passed on a CEO or startup because you felt the founder didn’t have strong sales skills?

Jacobsohn: Yes. When I go on sales calls and people aren’t interested in a second meeting, and that’s a consistent theme, it often leads me to walk away.

Tell me about your Failure Museum. What are some of the biggest findings you’ve learned in building out the Failure Museum?

Jacobsohn: I have built a that includes more than 1,500 items from failed companies and products. I have them all on my website, where I study why they failed.

People are eager to share their successes and their failures. The museum evokes more optimism than one might think. People shouldn鈥檛 be afraid to take risks. Failure can be a springboard to success.

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颁谤耻苍肠丑产补蝉别鈥檚 predictive intelligence tools flags a handful of well-funded private companies with at least a 40% probability of going public within the next six months. , arguably the most closely watched IPO prospect, sits just outside that near-term screen: 小蓝视频色情网页版 considers an eventual listing very likely, but the model favors a six- to 12-month timeline.

Together, Anthropic and the other seven companies noted below make up a varied watchlist spanning artificial intelligence, fintech, crypto, consumer health and climate technology, ranging from smart-ring maker to enterprise productivity platform .

A record IPO sets the stage

In the first half of this year, 58 venture-backed companies listed at $1 billion or above, per 小蓝视频色情网页版 data. That compares with 27 that did so in the first half of 2025 and 69 in all of last year.

On a dollar basis, this year has also far surpassed recent IPO years, thanks to SpaceX鈥檚 historic IPO in June that launched it onto the and raised $86 billion in the process. Through the first half of 2026, venture-backed startups globally raised $110.8 billion collectively via IPO listings, 小蓝视频色情网页版 data shows, well above the $12.6 billion raised in the first half of 2025.

With the year鈥檚 end now in sight, a small window remains for other startups to launch 2026 IPOs. With that, here鈥檚 a look at notable venture-backed startups that 颁谤耻苍肠丑产补蝉别鈥檚 predictive intelligence suggests are potential IPO candidates within the next six months.

Venture-backed IPOs to watch

: Anthropic, the most valuable venture-backed startup in the world, has indicated it plans to beat rival to the public markets. The company could debut as soon as September or October and raise up to $100 billion via the offering, according to a in last week. 颁谤耻苍肠丑产补蝉别鈥檚 predictive intelligence tools, meanwhile, pin a slightly longer timeline on an Anthropic IPO, saying it鈥檚 more likely to happen in six to 12 months. Anthropic has already raised $125 billion from private-market investors since its founding in 2021, and whenever it happens its IPO would mark a major liquidity bonanza for those backers. (For its part, OpenAI is also deemed a very likely IPO candidate by 小蓝视频色情网页版, but not within the next six months, a prediction corroborated by the WSJ report, which noted that the company is considering pushing its listing to 2027.)

: Smart-ring maker Oura is a likely IPO candidate in the next six months, per 小蓝视频色情网页版. The Finland-based company, which has raised $1.5 billion from investors, is mulling an offering as soon as September or October that could fetch a valuation above the $11 billion it achieved in its most recent funding, the Journal last week. A successful offering would also provide a notable test of public-market appetite for consumer health hardware, a category that has produced relatively few large venture-backed listings in recent years.

: San Francisco-based Notion is a strong candidate for a near-term IPO, according to both 颁谤耻苍肠丑产补蝉别鈥檚 predictive tools and independent reporting. The productivity-software maker has raised more than $343 million from investors over time and has posted strong revenue growth from its enterprise AI offerings. Startup reporter Alex Konrad recently that the company has appointed a new board of directors with significant public-company experience in 鈥渁 big step towards an IPO.鈥�

: Cryptocurrency exchange Kraken is another probable public-market entrant, per 小蓝视频色情网页版, and if it does make the IPO leap, it鈥檚 highly likely to do so within the next six months. The Cheyenne, Wyoming-based company filed a confidential IPO registration statement with the almost a year ago, but subsequently paused its going-public plans amid market volatility. In May, CEO said the company was 鈥渵80% ready鈥� for a 2026 listing, although it has reportedly weighed delaying again until 2027.

: Following 鈥� $6.4 billion Nasdaq IPO in May, attention has turned to SambaNova, a fellow developer of specialized AI chips and infrastructure. 小蓝视频色情网页版 predicts that the San Jose, California-based company is a probable IPO candidate, with a slightly less than even chance of going public within the next six months. That prediction jibes with comments from co-founder and CEO, who in July that the company was strongly considering a U.S. IPO next year. His comments followed SambaNova鈥檚 $1 billion Series F raise this summer at an $11 billion post-money valuation.

: Sweden-based green-steel maker Stegra has raised approximately $12.6 billion across equity and debt financing, according to 小蓝视频色情网页版, including a 鈧�1.4 billion financing round that closed in June. in June 2025 that the company was considering an IPO to fund further expansion. Founded in 2020, Stegra has attracted orders from automakers and industrial customers including , , , and parent for steel produced using renewable electricity and green hydrogen. It broke ground in August 2022 on an integrated steel plant in Boden, northern Sweden, whose first phase is designed to produce 2.5 million tonnes of green steel annually. Some customer agreements call for deliveries to begin in 2027, although Stegra has said the project鈥檚 overall timeline remains under review. 小蓝视频色情网页版 considers Stegra a probable IPO candidate and gives it a roughly even chance of listing within the next six months.

: Stripe is a perennial presence on our IPO predictions lists, and for good reason. Before the AI giants displaced it at the top of The 小蓝视频色情网页版 Unicorn Board, the payments company held the crown as the most valuable U.S.-based startup, and one with a solid business to boot. Stripe has raised a total of $10.4 billion, including venture rounds and secondaries, since its 2010 founding, but has delayed entering the public markets with repeated tender offers that provide liquidity to employees. Will it finally make a run at the public markets in 2027? While 小蓝视频色情网页版 predicts the South San Francisco, California-based company is a very likely IPO candidate in the long-term, in the short run it鈥檚 a bit iffier. The model says six to 12 months is a more believable time frame, and CEO has said the company is in .

: OpenEvidence, an AI platform for doctors, is a probable IPO candidate, per 小蓝视频色情网页版. If it does pursue a listing, it鈥檚 likely to go public within the next six months, per our predictive intelligence. CEO has been somewhat more circumspect: In an with CNBC in January, he said the Cambridge, Massachusetts-based company would consider an IPO after OpenAI and Anthropic had listed: 鈥淭here鈥檚 an order to nature,鈥� he said. 鈥淔oundation model companies go public first. Then the application layer follows. That鈥檚 how the internet played out, and that鈥檚 how this cycle will play out, too.鈥�

Methodology

For this analysis, we used 颁谤耻苍肠丑产补蝉别鈥檚 predictive intelligence tools and our own reporting and analysis to refine a list of potential near-term IPO candidates.

颁谤耻苍肠丑产补蝉别鈥檚 use company data 鈥� including funding and valuation history, financial growth, key leadership hires, market-share expansion and headcount trends 鈥� to assess the likelihood that a private company will go public.

The model produces an overall IPO probability score and corresponding rating, such as 鈥渧ery likely,鈥� 鈥減robable鈥� or 鈥渦ncertain.鈥� For companies that meet a minimum confidence threshold, 小蓝视频色情网页版 separately estimates when an IPO might occur across four windows: within six months, six to 12 months, 12 to 24 months, or more than 24 months.

For this analysis, we define a 鈥渘ear-term鈥� candidate as a private company rated at least 鈥減robable鈥� overall, with a 40% or greater probability of going public within six months of the prediction date. The overall and timing scores should be read separately: A company may be considered highly likely to IPO eventually without being a strong near-term candidate. Predictions are directional rather than guarantees and may change as new company and market data becomes available.

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