U.S. venture-backed technology听1 companies have secured nearly $90 billion in domestic public offerings this year, per SA国际传媒 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 SA国际传媒 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.

Related SA国际传媒 query:

Illustration:

Startups across sales, marketing and customer management have raised $7.5 billion so far this year, according to SA国际传媒 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 SA国际传媒 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, SA国际传媒 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,听 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 SA国际传媒 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, SA国际传媒 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.

Related SA国际传媒 query:

Related reading:

• Exclusive: Schematic Raises $6.5M To Help Companies Update Their Pricing Faster And Easier In The AI Era
• Exclusive: Winn.ai Raises $18M Series A For AI-Powered Real-Time Sales Assistant
• AI-Powered Sales Automation Startup Clay More Than Doubles Valuation To $3.1B

Illustration:

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 SA国际传媒 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听 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 SA国际传媒 unicorn lists:

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

Related reading:

• 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 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.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. 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.

Illustration:

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 SA国际传媒 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.

SA国际传媒 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.

Related SA国际传媒 queries:

Illustration:

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: SA国际传媒 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 SA国际传媒 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, SA国际传媒 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 SA国际传媒, 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 SA国际传媒. 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 SA国际传媒, 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. SA国际传媒 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 SA国际传媒, 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. SA国际传媒 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 SA国际传媒 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 SA国际传媒 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 SA国际传媒. 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, SA国际传媒 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.

Related SA国际传媒 query:

Related reading:

• IPOs Are Holding Up In 2026, But SaaS Debuts Aren鈥檛 Happening

Illustration:

The Incline Village, Nevada-based company is also acquiring Austin-based agentic AI startup as it looks to automate more of the labor-intensive work involved in investigating financial crime.

led the investment, which includes both primary capital and a secondary tender offer for employees. , , and others also participated. Socure did not disclose the terms of its acquisition of Fravity.

With the latest funding, Socure has raised over $742 million in disclosed funding since its 2012 inception. It was previously valued at $4.5 billion at the time of its Series E round in 2021. The company did not break down how much of its raise was primary and secondary capital.

Rapid growth as fraud surges

The transactions come as Socure says it is seeing both rapid growth in its own business and a sharp rise in increasingly sophisticated fraud. The company is refreshingly open about its financials, telling SA国际传媒 News that it ended the second quarter with $364 million in annual recurring revenue, up 63% from a year earlier, and added 95 customers during the quarter, including , , and . It also claims to be growing 鈥減rofitably.鈥�

Socure uses AI and machine learning to help banks, fintechs and government agencies verify identities so they can 鈥渁pprove real customers instantly while stopping fraud.鈥�

It now has more than 3,000 enterprise customers. They include 19 of the 20 largest U.S. banks, more than 600 fintech companies, major sportsbook and prediction-market operators, and 160 public-sector organizations. Specifically, some of those customers include , , , , and . The company鈥檚 revenue model mixes usage- and transaction-based SaaS.

AI creates both an opportunity and a problem

Socure co-founder and CEO said AI is creating both an opportunity and a problem for the business. For example, Socure saw an 8,000% increase in AI-driven fraud across its network last year, according to the company, as generative AI and other tools make it easier to create convincing fake identities and automate attacks.

At the same time, AI could help address one of the more costly parts of fraud prevention: investigating the large number of cases and alerts that automated systems flag for human review.

That is where Fravity comes in.

Automating fraud investigations

Fravity has built an AI-native platform that uses agents to automate fraud, risk and compliance investigations. Its technology will be incorporated into Socure’s RiskOS platform as RiskOS_Agents, initially focusing on watchlist screening and monitoring and know-your-business checks.

Socure and Fravity already share several enterprise customers that use the two products together, according to Socure. Across its existing deployments, Fravity has reduced cost per case by 80%, sped up case resolution fivefold and cut false positives by as much as 70%, the companies say.

The acquisition puts Socure more directly into what identity intelligence company estimates is a $71.1 billion financial crime investigation market. The problem is particularly acute at banks, where 53% spend at least an hour reviewing each alert, and 37% manually review more than 40% of alerts, according to Liminal.

As AI increases the volume and sophistication of fraud, Ayers argues that the identity layer 鈥� determining whether people and increasingly AI agents are who or what they claim to be 鈥� is becoming more critical to doing business online.

“I believe there are two types of companies that matter in the AI-driven global economy: those that are AI-native, and those that fight the consequences of AI acceleration,” he said in a statement.

Expanding beyond financial services

The investment follows a period of expansion for Socure beyond its financial services roots. In May, the company won a five-year, $163 million federal contract to provide identity-proofing technology for Login.gov. It is also pushing further internationally.

Socure had more than 550 employees as of March 2026, more than 100 more than it had about a year ago, according to Ayers.

Related SA国际传媒 query:

• 听

Illustration:

By conventional Silicon Valley standards, had some of the credentials one might expect of a startup founder: , experience as a VC on Sand Hill Road, and time working inside a fast-growing venture-backed startup.

One thing he decidedly lacked was a technical background.

鈥淚’ve never written a line of code in my life,鈥� Levtov told SA国际传媒 News in an interview. Years later, after building , a 12-year-old San Francisco-based supply chain software company that he says now works with 35 federal agencies and generates double-digit millions of dollars in annual recurring revenue, that remains true: 鈥淎nd I still haven’t written a single line of code.鈥�

Over the years, Levtov raised $42 million in funding for Craft. His experience has given him a close-up view of both the disadvantages non-technical founders face, and the reasons Silicon Valley鈥檚 preference for technical founders may be too simplistic.

An unlikely route into tech

Levtov鈥檚 own route into technology was anything but direct. His family emigrated from the Soviet Union to England when he was a toddler, and with two musician parents, he began playing cello at age four. He later attended a specialist music school in London, studied at the Royal College of Music, and participated in a Columbia- exchange while earning an English literature degree from .

By graduation, Levtov had decided to keep music as a hobby and pursue business instead. He joined , later attended Stanford Business School, and eventually landed at , an ad-tech startup that grew from about 10 employees to roughly 200 during his time there.

鈥淚 was just totally bitten by the bug,鈥� he recalls. 鈥淎nd I said, 鈥楾his is what I want to do with my life. I want to build a company one day. Somehow, entrepreneurship is for me.鈥欌��

Before becoming a founder, though, Levtov spent time on the other side of the table as a venture capitalist at . He later left venture for an operating role at video service provider , and after moving back to Europe, eventually worked at helping Silicon Valley startups including , , and establish distribution partnerships.

His eventual startup grew out of an unsuccessful attempt to build an enterprise social network.

As part of that project, Levtov鈥檚 team created company profiles by collecting information from corporate websites, job pages, management pages and other sources.

Those profiles began showing up prominently in searches, convincing him there might be a business there.

The disadvantage of not being technical

But unlike a technical founder, he could not simply build the product himself.

鈥淢y first coder was literally a $20 an hour Odesk or person,鈥� he said.

That dependence slowed everything down.

鈥淔or the non-technical founder, it’s just fundamentally a much longer time at the very beginning to get to something because a technical founder basically codes their idea on nights and weekends,鈥� he said.

Instead, Levtov had to hunt down developers, explain his vision, and try to determine whether the result would match it. Once he鈥檇 done those things, he then had to find the capital to pay for it.

Still, the business gained traction.

Its company profiles eventually appeared in 100 million search results per month and drew about 2.25 million visitors organically, according to Levtov.

鈥業 guess that means not me鈥�

When Levtov began raising venture funding in London in 2015 and 2016, he ran into another challenge familiar to non-technical founders: Investors preferred founders who could build the product themselves.

鈥淚 decidedly remember this clarity with which I found venture funds whose websites I go to and research. And what did they say? 鈥榃e support technical founders in doing this and that.鈥� And it really was just this moment [of realizing], 鈥榦h I guess that means not me, right?鈥� 鈥� he said.

Even so, Levtov does not describe himself as having been shut out of venture capital. He had Stanford and Venrock on his r茅sum茅 and eventually secured funding from in the U.K., and later after moving back to Silicon Valley.

And he believes the preference for technical founders has some logic behind it.

鈥淭hey’ve got a direct line between the business concept and the code in which it’s executed,鈥� Levtov said.

His own experience showed him how costly that gap could be. He said there were times he hired the wrong technical person and did not have enough expertise to recognize the problem quickly.

鈥淭hat is a real disadvantage: This inevitable disconnect, this gap between the non-technical person’s knowledge and, you know, the bare metal, as it were, or the most intrinsic innards of the software code by which this business product is going to live and breathe,鈥� Levtov said.

He believes those mistakes slowed the company鈥檚 growth.

Finding the business inside the product

But the company鈥檚 eventual breakthrough also illustrated the potential advantage of approaching technology from the business side.

Someone at contacted the company and pointed out that its data could help track changes across a sprawling supply chain. The system could pick up signals such as changes in hiring, executive departures and new product offerings.

Lockheed became its first enterprise customer. Then, in 2020, the reached out about using the product to monitor 300,000 companies in the defense industrial base. The company closed a five-year, $6.5 million deal 94 days later, according to Levtov.

鈥淲e figured out that our company is actually a supply chain company, and we haven’t looked back since then,鈥� he said.

Notably, those customers were not software developers asking for better developer tools. They were, noted Levtov, business users with business problems.

And this is where he believes his own background helped. A non-technical founder may not be able to evaluate code or engineering talent in a way that a technical founder can, he pointed out. But they may be stronger in areas such as understanding customers, managing people, fundraising and building relationships.

AI is further complicating that debate, since software can increasingly be built without traditional coding expertise. But Levtov stops short of arguing that technical founders no longer matter.

鈥淚t really just takes both. It takes both sides,鈥� he said. 鈥淚 think if you can have a technical founder and a non-technical founder, you’re probably in the ideal spot.鈥�

Technical founders may have an edge at the earliest stages, Levtov said. As companies scale, the balance can shift toward skills like hiring, selling, positioning and dealmaking.

At different points in a company鈥檚 life, he said, 鈥渋t’s really about the tech right now,鈥� while at others, 鈥渋t’s all about the dealmaking, or all about the positioning, or the marketing.鈥�

Related SA国际传媒 query:

Related reading:

• 鈥楴obody Wanted to Give A Former Principal Money鈥�: How An Educator Built An Edtech AI Startup With $63M From VCs
• How A Teenage Carpenter Became The Founder Of AI Construction Startup Trunk Tools
• From Humanities To AI: How Ali Hussain Built Fintech Tabs Into A $400M Startup

Illustration:

It鈥檚 been a rapid progression to get here. In the past two years, venture investors have poured more than $7 billion into legal and legal tech startups, most with an AI focus. Funding to the space hit a record level last year, with $4.6 billion invested, per SA国际传媒 data. So far this year, legal tech startups have pulled in more than $2.2 billion.

Top fundraisers

The biggest chunk of funding in recent quarters has gone to startups familiar to followers of the space.

, a provider of AI tools for legal professionals, is the sector鈥檚 top fundraiser with $1.2 billion in investment to date. The 4-year-old, San Francisco-based company is reportedly now another $500 million at a $15.5 billion valuation.

, an AI platform built for lawyers, is also in the midst of a massive scale-up. The Stockholm-based startup raised $600 million in Series D funding this year, securing a valuation of $5.5 billion, tripling over a six-month period.

, a 2008 vintage provider of legal practice management software that has pivoted heavily into AI, has also been attracting growth funding. While it didn鈥檛 secure a round this year, the Vancouver company closed on $1.4 billion in equity financing in 2024 and 2025.

For 2026, meanwhile, at least 12 legal tech-focused startups have secured rounds of $50 million or more. We’ve put together a list below.

Notably, there鈥檚 still quite a bit of activity at the early stage. Out of the 12 largest rounds this year, eight were Series A or Series B financings. Seed-stage dealmaking is also busy, with more than 50 legal- and legal-tech seed rounds of $1 million or more this year, per SA国际传媒 data.

Exits

Legal tech startups are also selling to acquirers at a steady clip.

Legora has been particularly acquisitive of late, snapping up at least five companies this year, all of which raised seed or venture funding. Harvey is also a serial buyer, acquiring at least three companies in 2026. Neither company has disclosed purchase prices.

Among publicly traded acquirers, , a Dutch legal and healthcare software provider, has made at least two sizable legal tech startup acquisitions since last year. It paid $500 million for , a provider of legal spend management tools, and $105 million for , an AI workspace for legal professionals.

We haven鈥檛 seen venture-backed legal tech companies go public lately, but the biggest names seem to be signaling the possibility. Harvey, for instance, it added over $100 million in ARR in the first quarter of this year, indicating it has the revenue and growth trajectory of a strong IPO candidate.

With high investment comes high expectations

Robust investment in legal tech comes amid high expectations for AI-delivered efficiencies among legal professionals.

A of professionals in the space this year found that 80% of respondents believe AI will have a high or transformational impact on their work within the next five years.

Early benefits look promising too, with more than half of respondents attesting that their organizations are already seeing a return on investment from investing in AI. Top use cases include document review, legal research, summarizing documents, and drafting briefs or memos.

One of the highest-impact areas for AI ahead is saving time, with tools that automate repetitive tasks. Generally speaking, that鈥檚 a welcome offering, although legal professionals do widely anticipate it could disrupt the hourly billing model.

Overall, the storyline looks similar to what we see in other industries where AI is shouldering more tasks. AI isn鈥檛 expected to replace lawyers and legal support staff. However, it could free people to spend more time on valuable tasks only a human can do, enable employers to run with a smaller staff, or both.

Related SA国际传媒 query:

Related reading:

• Legal Tech Investment Hits All-Time High With Filevine Funding
• Swedish Legal Tech Startup Legora Lands Another $50M In Nvidia-Led Series D Extension

Illustration:

has been operating in that market since 2013. The New York-based company operates a marketplace for shares of privately held companies, giving employees and other shareholders a way to sell stock before a company goes public or is acquired.

announced plans to acquire EquityZen in October 2025 and completed the deal in January 2026, bringing the company under the investment bank鈥檚 umbrella.

, who co-founded EquityZen and serves as its chief strategy officer, has had a front-row seat to the secondary market’s evolution. SA国际传媒 News spoke with Haslett about what secondary-market pricing says about today鈥檚 most sought-after startups, why AI companies are commanding premiums while many older startups trade at discounts, what the IPO market looks like beyond its biggest names, and why investors are taking a closer look at hard tech.

The following conversation has been edited for length and clarity.

SA国际传媒 News: The second quarter was one of the strongest venture-backed IPO quarters since 2021, but drove much of that activity. If you remove SpaceX, how open is the IPO market for the typical late-stage startup?

Phil Haslett: Generally, I鈥檇 say it鈥檚 better than it was three or six months ago. If you were a private late-stage technology company, you probably were going to wait until after SpaceX anyway, so that hurdle is gone.

Tech markets are also doing well. The stock market is at an all-time high, and there鈥檚 been a strong recovery in tech stocks overall. I assume that we鈥檙e gearing up for a busier summer than usual.

Another thing to consider is IPO performance beyond SpaceX. Some have had initial enthusiasm followed by a slowdown. has come down a bit. So companies may see it as a good time to go public, while post-IPO performance has been, in a word, 鈥渕eh.鈥�

But within AI, I think we鈥檝e seen that there鈥檚 opportunity up and down the production curve 鈥� from energy for data centers, to the technology inside them, to orchestration of compute, to efficient spending on training and inference. There are a lot of interesting companies along that spectrum, and I think that bodes well for companies in the space that want to go public.

A few companies entered your Top 20, including , , and . Does that reflect a durable shift away from traditional software, or are investors chasing a small group of scarce, high-profile hard-tech companies?

Haslett: I think it reflects a thematic shift. The companies entering that list generally fall into AI infrastructure, space tech and robotics.

If those are industries we think will have generational growth opportunities, the logical conclusion is that each sector will have winners. SpaceX gets people thinking about opportunities in space and space tech, and by extension defense tech.

The same applies to AI infrastructure. If the market is that big, and we鈥檝e seen companies go public over the last year or so, it stands to reason investors will be interested in other companies in that space. I think that鈥檚 more important than simply chasing scarce supply.

These businesses tend to be more capital intensive and may take longer to reach predictable revenue than a traditional SaaS company. How are secondary investors underwriting them?

Haslett: If a company needs more capital, investors have to decide whether the overall opportunity is big enough to justify waiting longer and having the company raise more.

If you have to build a factory or get regulatory approval, that can delay the company鈥檚 ability to increase its valuation or reach an exit. Investors discount that into what they鈥檙e willing to pay.

Secondary investors are making the same calculus as primary venture and growth investors, so you鈥檇 imagine much of that is already baked into headline valuations from primary raises.

What鈥檚 changed is that capital-intensive companies now have more financing options. Five or six years ago, a battery company or new chip manufacturer might have had little choice but to raise equity. In 2026, more credit and asset-based financing options are available.

That matters because if one of these companies underperforms or has a distressed asset sale, creditors and lenders get paid first. Secondary investors have to factor that in, too.

EquityZen says the average transaction occurred at a 38% discount to the last funding round, while many AI transactions traded at premiums. What does that say about how bifurcated the private market has become?

Haslett: I don鈥檛 know if it鈥檚 a mispricing. There are essentially two vintages of private companies right now.

Some companies weren鈥檛 built AI-first and have had to adapt. Many raised during the go-go years of 2021, at very high valuations, and may not have raised since. They鈥檝e had to rethink their strategies, which can slow growth and execution. That gets reflected in the discount.

Then there鈥檚 a new wave of companies, from 2023 and beyond, that were built with an AI-first mentality. They started from a clean slate, may operate more efficiently, and have a cleaner story for the market.

Some of those companies are raising rounds in quick succession at higher valuations. Secondary investors may pay a premium because they believe the company鈥檚 trajectory is clear and the next valuation increase could happen quickly.

is an example from the 2021 cohort. It raised at roughly a $10 billion-plus valuation and just sold for substantially less. It鈥檚 still a good business, but when investors compare 20% growth with newer companies going from zero to hundreds of millions in revenue in just a few years, you can understand why their appetite changes.

We may see more companies from that era sell for less than where they raised in 2021.

Over the past few years, many private companies have conducted secondaries because they weren鈥檛 ready to go public. When should founders consider establishing a company-approved secondary program?

Haslett: Historically, companies started thinking about liquidity programs after they鈥檇 been around five, six, or seven years, largely to reward employees for their patience and provide liquidity to early investors.

Now we鈥檙e seeing younger companies engage in controlled liquidity and tender offers.

One reason is talent retention. There are only so many engineers and data scientists, and companies need to compete for them. Secondary liquidity has become more normalized.

More solutions are available than before. Morgan Stanley, for example, has significantly grown its tender-offer activity as investor interest and available tools have expanded.

There鈥檚 also more investor appetite. Investors are increasingly willing to gain ownership through tender offers or secondary transactions. Five years ago, that was far less common.

Right now, it鈥檚 a very founder- and employee-friendly environment, and investors are willing to support secondary liquidity because they want access. If markets turn, that pendulum could shift back.

For investors considering private-company shares, what does a secondary-market price tell them compared with the valuation at the company鈥檚 last fundraise?

Haslett: I think it gives them the true price.

A primary valuation is a point-in-time measure of what investors were willing to pay, and those investors generally received preferred stock with additional rights and liquidation preferences.

The secondary market is more telling of what you could actually get in your pocket now. For companies that embrace secondary liquidity, those prices help employees, former employees and early investors understand what their shares are actually worth.

How does EquityZen calculate popularity and distinguish durable investor demand from curiosity or hype?

Haslett: Our platform allows investors, typically retail accredited investors, to tell us what they鈥檙e interested in. They can browse companies, review our analysis, and indicate which companies they would invest in, if shares became available, and at what size.

That gives us a real-time metric of what our user base wants to invest in and how much. It helps guide where we spend our time bringing opportunities to clients.

The last thing we want is to work with a shareholder when we can鈥檛 find a buyer, or with a buyer when we can鈥檛 find shares for sale.

What does the recent consolidation in the secondary market tell you about how the market is evolving?

Haslett: There was a lot of attention toward the end of 2025 around consolidation in the secondary-market space. went to , and EquityZen went to Morgan Stanley.

To me, that reflects market growth, increasing adoption of secondary liquidity, and the fact that the biggest financial institutions are paying attention. I don鈥檛 expect that to change.

Your data showed that some software companies began trading at premiums again in the second quarter. What separates those gaining investor confidence from those still trading at deep discounts?

Haslett: Execution. Leadership and execution.

It鈥檚 about a company鈥檚 ability to take a legacy SaaS business and turn it into something AI-enabled across the business. Are you using AI tools to improve internal tasks? Are you building AI into your product for clients?

Companies that can combine the stickiness and customer loyalty they鈥檝e already built with their domain expertise and AI are going to do just fine. The ones that are slower to adopt are going to get pummeled.

Six months ago, there was concern that when a company like announced a cybersecurity or legal tool, companies in those sectors would immediately lose value. I think some of that was a knee-jerk reaction.

Customers already using your software have some patience, but they also expect you to keep improving the product and give them a reason not to switch. The companies that are slow to react, or too proud to react, are the ones I think will get hit hardest.

, and 1听are examples of software that is deeply ingrained in large enterprises. If companies can keep their products working well and keep adapting them, they still have a shot at being successful standalone businesses. It comes down to management execution.

Related SA国际传媒 query:

Illustration:

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

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

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

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

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

AI-Sprinkle vs. AI-native

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

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

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

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

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

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

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

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

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

Evolutionary vs. revolutionary

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

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

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

Productivity skyrocketed only when the manufacturing plant itself was redesigned, distributing small electric motors throughout the factory in a horizontal layout, delivering what a single steam engine never could. What interests me most about this story is why it took decades before the factories were redesigned. Why couldn鈥檛 those organizations make the revolutionary leap more quickly? That is where the Burgelman/Grove case study is so helpful.

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

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

After a long pause, head in hand I imagine, Grove looked up at Moore and said, “If we got kicked out and the board brought in a new CEO, what do you think he would do?” And Moore said without hesitation, 鈥淗e would get us out of memories.鈥� Grove replied, in effect, why shouldn鈥檛 you and I take a walk around the building just now, and come back in the door, and do it ourselves?

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

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

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

Management innovation and private equity

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

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

There are some 10,000 privately held software companies in the U.S. today, depending on exactly how you count. Leaders of those businesses know, explicitly or perhaps just through gut feel of the shifting sands, that doing the same thing in the same way in the age of AI is a losing strategy. You won鈥檛 lose all at once. You will be slowly starved as competitors move at 3x your pace around you. Certainly, your prospects to be a leader will close.

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

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

Are those the moves you are making today?

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

Related reading:

• SaaS Is Dead. Long Live SaaS! AI And The End Of The Rationing Of Knowledge Work
• Don鈥檛 Just Talk About AI. Measure Business Outputs. Here鈥檚 How.
• The Rise Of The AI Executive

Illustration: