Ten years ago, a seed-stage startup needed a product, a team and a pitch deck to raise capital. Today, that’s just the start. Technology and strategy have become inseparable, each fueling the other, and the rules that once defined success have quietly shifted under everyone’s feet.

Last month, my firm reviewed more than 2,500 inbound applications. Here are the key shifts we’re seeing in the startup ecosystem at the seed stage.

Broadening capital strategy

Equity is a powerful tool for building high-growth companies. But it’s no longer the only option. Non-dilutive growth capital is increasingly playing a strategic role for companies with revenue visibility and clear ROI channels.

For example, we recently financed a company with $1 million in growth capital it needed immediately to expand its team and infrastructure. Raising that through equity alone would have likely taken months, with significant time and execution cost along the way.

We’re now writing financing checks like this on a near-weekly basis.

Distribution focused

Leading with a “better” product isn’t enough to propel growth. The breakout companies are investing in building stronger distribution systems — aka what founders refer to as “traction.” Distribution is a critical moat for early-stage startups. Rapid scaling is no longer achieved by launching new products; it’s through distribution loops.

Distribution is something startups can now architect intentionally with social platforms, marketplaces and other ecosystems. One of the most common founder mistakes we see is delaying the distribution strategy until after the product launch. At that stage, the architecture is harder to retrofit. Strong startups design distribution before they scale their product.

For example, some of the fastest-growing startups now design their products around existing ecosystems from day one — building apps that tap into merchant marketplaces, AI tools distributed through or Teams integrations, or fintech products embedded directly into banking and payroll workflows. In many cases, the distribution channel becomes more valuable than the underlying product itself.

One of the most common mistakes we see is founders postponing distribution strategy until after the product is built. By then, the architecture is far harder to retrofit. The strongest startups design distribution into the company before they scale the product itself.

Learning over speed

“Move fast” is often dolled out as the best startup advice. Operating in a fast-paced environment remains a strategic asset, but it is not enough to maintain a competitive advantage.

Everyone is fast. It’s no longer a unique attribute. Instead, learning velocity is becoming the defining advantage in early-stage startups. How quickly can you reduce uncertainty? Competitive edge is achieved not by executing blindly, but by closing knowledge gaps faster than everyone else. Execution without learning equals wasted motion.

The founder focus advantage

Last year, my team reviewed close to 25,000 applications for our investment funds and bespoke accelerators. The ones that stand out are the companies doing the fewest things exceptionally well. The most-fundable companies can describe their business in one tight sentence. They can also defend exactly what they are not doing.

Disciplined constraint is one of the highest-leverage traits in venture-backed companies. When we review applications, this pattern consistently stands out.

When a company is focused, the residuals compound: stronger early retention, faster iteration cycles, cleaner capital deployment. In a capital-selective market, focus compounds faster than ambition.

Based on tens of thousands of applicants, close to 82% of the ones that stayed in business a year later had a strong go-to-market foundation in their deck. GTM is built on agility and learning fast.

AI as infrastructure, not experimentation

There’s no lack of interest in AI. But there is an implementation problem. We’ve seen companies struggle when AI is approached as experimentation rather than architecture. Rather than bolting tools onto already fragmented stacks and workflows, designing intelligent systems should be mapped from the ground up.

More than 78% of the founders applying to today are leveraging AI in at least one way in their startup.

The most successful playbook combines execution with operational clarity and emphasizes infrastructure over experimentation. We’ve seen successful implementations that center around two practical paths. The first is validation, with rapid prototypes and identifying market signal opportunities before investing in a full build. The second is system, designing and integrating custom AI agents directly into operating workflows for revenue-generating companies facing operational complexity. Both are required to move AI agents from concept to capability. A disciplined system design often matters more than flashy tooling.

Marketing is the moat

Marketing execution is one of the largest performance gaps we see across early-stage startups. Startups lose when they don’t distribute fast enough once there is something worth selling. Marketing is the propeller for the distribution engine.

Most startups fail at marketing because it is a business function that becomes a founder hustle with support from one junior hire. But breakout growth requires process, cadence and accountability. That’s not achievable without an experienced team and clear plan.

One of the biggest mistakes founders make is treating marketing as something that starts after launch. Founders must create unique strategies, test them and then analyze what works and what doesn’t. From there, they must keep iterating and creating to unlock the most product-market fit and traction.

If we see classic strategies in a pitch deck, it is an auto-reject. And beyond being unique, your strategies must have been tested by your team.

The key is simple: Test ideas early, measure what actually works, refine aggressively and scale the strategies that compound over time.

, a serial technology entrepreneur since age 12, is now a value-driven venture capitalist with a track record of backing more than 1,000 startups across the globe. He is a co-founder and general partner at , one of the world’s most active venture capital firms. Before becoming involved in venture investing, he built and scaled into the world’s largest debate-focused social network and edtech platform, reaching millions of users and serving students across more than 500 school districts, colleges and universities.

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The New York-based company publicly launched Thursday with an undisclosed seed round led by São Paulo-based venture firm and a debt financing partnership with ’s Customer Value Fund. Since its January inception, Skalar has committed to finance more than $125 million in sales and marketing spending across seven technology companies over the next 12 months.

Financing tied to customer revenue

Skalar’s model is fairly straightforward, though somewhat unusual. The company provides startups with capital to fund sales and marketing initiatives. The startups then pay it back out of the revenue generated by the customers acquired with that capital.

If those customers generate less revenue than expected, Skalar says it absorbs the shortfall rather than requiring the company to repay the full original amount.

Skalar’s current deals generally call for it to collect about 1.1x the amount provided.

For example, if a company spends $10 to acquire a customer and expects that customer to pay $1 per month for 30 months, Skalar provides the initial $10 and collects the first $11 that customer generates. Once Skalar reaches that repayment limit, the company can keep the remaining revenue.

But if the customer cancels after eight months, Skalar collects only $8 and writes off the balance, according to co-founder and CEO .

“We only get repaid as they get repaid,” Cárdenas told ���ϳԹ� News.

Notably, the startup doesn’t have to pay the capital back by a certain date. Instead, repayment is tied to revenue from the customers acquired with the financing, rather than a fixed schedule. For example, a company that recoups its acquisition costs in one month repays the loan in one month, while one that takes 12 months repays it over one year. So while the obligation remains contractual, Skalar operates under the premise that a flexible timeline reduces the risk of a cash crunch.

How it differs from other financing

Skalar’s structure differs from both venture debt and existing forms of revenue-based financing, according to Cárdenas.

offers startups flexible funding without equity dilution, but with higher interest and risk. Skalar’s founders contend that paying back that debt can force startups to cut sales and marketing spending or hold onto cash when new growth opportunities emerge.

The model also differs from revenue-based financing, which typically advances money to companies based on signed contracts or revenue they are already generating, the founders said. Instead, Skalar finances a potential new revenue source before it exists and accepts some of the risk that it may never fully materialize.

Taking on that risk means that Skalar has to closely examine a company’s operations. It analyzes detailed transaction data to determine how much the company spends to acquire customers, how long those customers stay, and how much revenue they generate over time. It also means the company is very selective about who it chooses to finance. Skalar’s system continually updates company assessments as new information comes in, according to co-founder and COO Daniel Castrillón.

“We have become experts in understanding these types of risks and when they are sufficiently predictable and sufficiently profitable to be underwritable,” he said.

The risks for founders

The arrangement is not without risk for startups, concedes Cárdenas. Skalar sets minimum revenue targets for the companies it finances. If results fall below those targets, it can require faster repayment. It can also stop providing additional capital under certain circumstances, which could leave a company without funding it had expected to receive.

Its terms are based on estimates involving customer revenue, profit margins, currency fluctuations and which sales can be attributed to a particular marketing investment. If those estimates prove wrong, or if the cost of acquiring customers rises, the startup may receive less benefit from the arrangement than expected, Cárdenas said.

Importantly, Skalar’s agreements do not give it the right to seize a company’s assets in the event of a default, Cárdenas said, and they do not require borrowers to maintain specific financial benchmarks or cash balances.

Still, founders must weigh the possibility of accelerated repayment or interrupted funding when deciding whether the financing fits their plans.

“Our structure is fundamentally different because it absorbs most of the downside risk … and we are unlikely to walk away unscathed if something bad happens. This incentivizes us to always be mindful of not encumbering the companies we work with with credit risk, as this ultimately increases risk for us,” Cárdenas told ���ϳԹ� News.

A narrow initial customer base

Skalar is targeting technology companies that spend between $100,000 and $3 million per month acquiring customers and have a consistent record of earning more from those customers than they spend to acquire them. It also considers whether a company has enough cash to remain in business long enough for that customer revenue to arrive.

Its first seven customers include four or five Latin American companies, Cárdenas said, as well as businesses in the United States. Skalar initially plans to work with no more than 15 companies per year.

The company declined to disclose the size of its seed round, which closed during the first quarter. Cárdenas described it as a large seed round by Latin America’s standards. and several angel investors with relevant industry experience also participated.

is providing the debt capital Skalar will use to finance its customers’ sales and marketing spending. The size of that partnership was also not disclosed.

The General Catalyst connection

Skalar grew out of Cárdenas’ work as an entrepreneur-in-residence at Monashees, where he helped introduce several of the firm’s portfolio companies to General Catalyst’s Customer Value Fund model.

General Catalyst pioneered a similar approach but increasingly focused on larger financing deals, Cárdenas said. That created an opportunity to serve smaller companies, including startups in Latin America.

“The best companies are thoughtful about matching their sources and uses of capital: equity for transformative but unstructured product and R&D bets, low-cost, duration-matched capital for predictable investments like customer acquisition,” , partner at the Customer Value Fund, said in a statement. “Most technology companies in Latin America have never had the choice, and Sebastián came to us with that gap in mind. As an investor in the region, he saw the CVF model transform a handful of companies in his own portfolio, and he pitched us on closing the capital gap together.”

Still, Skalar is not restricted to financing businesses with no connection to either General Catalyst or Monashees. Monashees general partner said his firm does not have access to the confidential operating data that startups provide to Skalar as it evaluates their businesses.

For Monashees, the model addresses the long-standing shortage of growth financing in Latin America. Bolognesi told ���ϳԹ� News that his firm, the largest venture firm in Brazil, has watched companies with strong customer performance struggle to secure enough money to pursue their growth opportunities, particularly as equity investment in the region rose and fell.

“We’ve seen capital flow into and out of the growth stage, leaving some excellent companies struggling to raise the equity they need to keep growing,” he said. “Skalar fills that gap by giving promising companies access to capital while they build the track record investors want to see.”

A market beyond venture-backed startups

Skalar is initially focused strictly on financing customer acquisition. Its founders eventually envision offering similar products for other business expenses that produce sufficiently predictable returns.

Cárdenas also sees a longer-term opportunity beyond the relatively small group of companies able to attract institutional venture capital. Businesses that have trouble raising venture capital because of their location, industry or growth rate may still qualify for Skalar financing based on their financial performance.

“Venture capital solved the problem of funding the top 1% of tech businesses,” he said. “But 99% of tech businesses — out of which I’d say probably more than half could be underwritten by our product — just don’t have access to capital today, and ours is a product that fundamentally changes that.”

In the long run, Skalar is betting that its approach can bring growth financing to a much larger group of companies. For startups that can raise venture capital, it also offers a way to fund predictable growth without giving up more ownership.

Related reading:

• The VC Firm That Helped Build Latin America’s Startup Scene Is Crossing Into Silicon Valley

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Always-busy was the most active backer of U.S.-based startups by deal count, while led or co-led the most rounds of $5 million or more, ���ϳԹ� data shows.

Chip giant , meanwhile, sharply accelerated its dealmaking, ranking among the most active and highest-spending investors for the month. The chip giant participated in nine disclosed rounds of at least $5 million — marking its busiest month for investing since at least the beginning of 2025 — and led or co-led financings collectively valued at $1.3 billion.

The flurry of activity came as global venture funding reached $42 billion in August, up 122% year over year, with seven companies raising billion-dollar-plus rounds last month.

Below, we rank August’s most active startup investors across several categories, including lead backers, prolific venture dealmakers, highest spenders and seed investors.

Active lead investors

San Francisco-based General Catalyst ranked as the most active lead investor in rounds of $5 million or more, leading or co-leading five such deals. Its largest was the $1.1 billion Series A for, which provides custom AI fine-tuning for businesses. General Catalyst also led or co-led a $116 million Series E for, along with three seed rounds ranging from $10 million to $25 million, ���ϳԹ� data shows.

, and tied for second, with four lead or co-lead deals each.

The scale of those rounds varied considerably. The four deals that Andreessen led or co-led totaled more than $1.15 billion, driven by an $800 million Series C for defense tech company and a $300 million Series A for AI infrastructure startup .

Sequoia’s four led or co-led deals totaled $1.3 billion, including a $1 billion Series B for nuclear energy startup .

Busiest venture investors

When we widen the ranking to include both lead and non-lead participation in rounds of $5 million or more, Y Combinator once again takes the top spot.

The accelerator participated in at least 18 such deals in August, per ���ϳԹ� data. As we’ve noted in previous rankings, Y Combinator commonly invests as a non-lead backer in follow-on rounds for companies that previously went through its program.

Andreessen Horowitz ranked second with 13 deals, followed by General Catalyst with 10. and Nvidia tied for fourth with nine each.

Nvidia’s rise in the investor rankings is particularly notable. The Santa Clara, California-based chip giant participated in only four U.S. rounds of $5 million or more in July and one in August 2025. Seven of its nine qualifying investments last month went to companies categorized as AI-focused in ���ϳԹ�, including River AI, , , and .

The August burst extends a notable increase in Nvidia’s venture dealmaking pace this year. ���ϳԹ� data shows that by mid-August, it had participated in a record 59 known startup funding rounds in 2026, already surpassing its 53 investments in all of 2025. It had also led or co-led at least 11 private-company financings this year, underscoring its growing role as both a technology supplier to and financial backer of the AI startup ecosystem.

and Sequoia were next in our August rankings, each with seven U.S. startup investments of $5 million or more. RA Capital’s portfolio reflected its life sciences focus, with August deals including , , , and .

Highest-spending investors

The rankings change again when we look at lead investors associated with the highest aggregate deal values.

For August, was the apparent spendiest lead investor, thanks to its role leading ’ $5 billion deal. The round, the month’s largest, valued the data and AI company at $190 billion.

and followed, each leading or co-leading rounds with an aggregate value of $2.37 billion, as both were listed as lead investors in defense manufacturing startup’s $1.37 billion Series D and home battery provider’s $1 billion Series D.

Nvidia and Sequoia came next, each with $1.3 billion in led or co-led rounds. Nvidia’s total came from Poolside’s $1 billion financing and Volta’s $300 million Series A, while Sequoia led or co-led four rounds, topped by the Valar Atomics financing.

General Catalyst and Andreessen also crossed the $1 billion mark, with approximately $1.26 billion and $1.15 billion, respectively, in aggregate led round value.

As always, this is an approximation of spending rather than a tally of capital actually contributed. Investors rarely disclose how much each participant put into a round, although lead investors generally contribute a substantial share.

Seed dealmakers

At seed, Y Combinator was again the most prolific investor, backing at least 12 U.S.-headquartered companies in August.

ranked second with eight seed investments, all announced as part of the same August cohort. and followed with six seed deals each, while 1��recorded five. (It’s important to note that seed rankings are especially subject to change, since smaller financings often take longer to be reported and added to the ���ϳԹ� dataset.)

Big checks, familiar names

August’s rankings tell a now-familiar story: A relatively small group of large venture firms continues to dominate by deal count, while a handful of megadeals determines who tops the spending ranks. But Nvidia’s acceleration this year also illustrates how corporate investors — particularly those with a direct stake in the AI ecosystem — are becoming increasingly prominent alongside traditional venture firms.

Related reading:

• Global Venture Funding Jumps 122% In August As Streak Of Billion-Dollar Deals Continues
• No Summer Doldrums For Active Startup Investors In July

Methodology

This analysis covers reported investments in U.S.-headquartered companies and is based on ���ϳԹ� data pulled Sept. 10, 2026. Rankings for active venture and lead investors include rounds of $5 million or more. Seed rankings include angel, pre-seed, seed and equity crowdfunding rounds.

Funding data is subject to reporting lags, which are typically most pronounced at the seed stage.

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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’s 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’s 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 — for both, their first new unicorn of the year.

Nine companies exited the ���ϳԹ� in August, per ���ϳԹ� data: Three that went public — the most notable being — and six via acquisition, including , and .

New unicorns in August

Here are August’s 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�� 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 — both human-driven and autonomous — across 29 cities, with annual recurring revenue of $420 million.��

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 ���ϳԹ� 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 ���ϳԹ� In July, The Highest Count In 4 Years

Methodology

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

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

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. ���ϳԹ� 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 ���ϳԹ� 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 — 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 ���ϳԹ� 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.

���ϳԹ� 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’s 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’t be afraid to take risks. Failure can be a springboard to success.

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����ܲԳ����������’s 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: ���ϳԹ� 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 ���ϳԹ� 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’s 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, ���ϳԹ� data shows, well above the $12.6 billion raised in the first half of 2025.

With the year’s end now in sight, a small window remains for other startups to launch 2026 IPOs. With that, here’s a look at notable venture-backed startups that ����ܲԳ����������’s 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. ����ܲԳ����������’s predictive intelligence tools, meanwhile, pin a slightly longer timeline on an Anthropic IPO, saying it’s 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 ���ϳԹ�, 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 ���ϳԹ�. 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 ����ܲԳ����������’s 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 “a big step towards an IPO.”

: Cryptocurrency exchange Kraken is another probable public-market entrant, per ���ϳԹ�, and if it does make the IPO leap, it’s 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 “~80% ready” for a 2026 listing, although it has reportedly weighed delaying again until 2027.

: Following ’ $6.4 billion Nasdaq IPO in May, attention has turned to SambaNova, a fellow developer of specialized AI chips and infrastructure. ���ϳԹ� 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’s $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 ���ϳԹ�, including a €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’s overall timeline remains under review. ���ϳԹ� 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 ���ϳԹ� ���ϳԹ�, 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 ���ϳԹ� predicts the South San Francisco, California-based company is a very likely IPO candidate in the long-term, in the short run it’s 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 ���ϳԹ�. If it does pursue a listing, it’s 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: “There’s an order to nature,” he said. “Foundation model companies go public first. Then the application layer follows. That’s how the internet played out, and that’s how this cycle will play out, too.”

Methodology

For this analysis, we used ����ܲԳ����������’s predictive intelligence tools and our own reporting and analysis to refine a list of potential near-term IPO candidates.

����ܲԳ����������’s use company data — including funding and valuation history, financial growth, key leadership hires, market-share expansion and headcount trends — to assess the likelihood that a private company will go public.

The model produces an overall IPO probability score and corresponding rating, such as “very likely,” “probable” or “uncertain.” For companies that meet a minimum confidence threshold, ���ϳԹ� 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 “near-term” candidate as a private company rated at least “probable” 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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A16z led an $87 million equity investment, with participation from , , , and . And General Catalyst’s Customer Value Fund committed $113 million in debt to fund ��é������’s growth.

Founded in 2020 by and , Miami-based ��é������ has now raised a total of nearly $300 million. The company did not reveal its valuation after its Series C round, saying only it had “increased threefold” since its Series B, a $75 million round led by QED Investors in 2025.

������� says it has processed more than $8 billion in transactions to date and grew revenue more than 2.5x in the past year. It connects people in the U.S. with families across 11 Latin American markets including Mexico, Brazil, Costa Rica, Honduras and Peru.

“I experienced this problem personally,” Godoy said in a statement. “When I came to the U.S., even getting a small loan was harder than it should have been. Traditional financial institutions often start with the product they want you to use. We want to start with the person. You tell ��é������ what you need, in your own words, and we help you figure out the rest.”

, general partner of , said in a statement that he believes ��é������ represents “what the future of financial services can look like for millions of Latinos in the United States.”

“��é������ has packaged two frontier technologies, AI and blockchain networks, into something simple and consumer-friendly: a better way to send and receive money,” he added.

The company plans to use its new capital to expand its offerings and enter new markets across Latin America.

Overall, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year, per ���ϳԹ� data.

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This story was updated to clarify the company’s valuation.

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 ���ϳԹ� 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 “profitably.”

Socure uses AI and machine learning to help banks, fintechs and government agencies verify identities so they can “approve real customers instantly while stopping fraud.”

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’s 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 — determining whether people and increasingly AI agents are who or what they claim to be — 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.

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So far this year, more than 500 seed- or venture-backed private companies across the globe have sold to other private, venture-backed companies, per ���ϳԹ� data. The most prolific acquirers include many of the most famous and valuable unicorns, including , and .

Overall, the pace of dealmaking in 2026 looks relatively flat��1Reported deal counts are down slightly this year from the comparable period, but are likely to even out more over time as some acquisitions, particularly smaller deals, are added to the dataset weeks or months after they close.2 compared to last year. That’s not entirely surprising given that overall market conditions haven’t changed dramatically. The number of tech startup IPOs remains below normal. Hot venture-backed AI companies are still sustaining unheard-of valuations. And the rise of megarounds means favored startup acquirers are flush with cash.

Startups buying startups in recent years

In total, at least 440 funded startups sold to other startups in the first half of this year. The second half is shaping up to be a bit slower, meanwhile, with fewer than 100 deals so far.

For a more expansive chronological view, below we charted startup M&A deal counts by half-year beginning in 2021.

The pace of M&A dealmaking peaked about four years ago and fell afterward, in tandem with a broader dip in startup investment. But activity has picked up over the past couple of years with the rise in AI investment.

Startups that buy a lot of other startups

A few startups have proven particularly acquisitive.

The standout in this category is probably OpenAI, which has acquired eight startups this year, most of them seed- or early-stage companies. To date, the generative AI giant has bought at least 19 companies, per ���ϳԹ� data.

Anthropic has also been a busy buyer. It’s snapped up at least five startups so far this year, including the $400 million purchase of AI biotech startup .

In the fintech space, meanwhile, has been on an M&A spree. The crypto transactions platform acquired five funded startups focused on cryptocurrency or blockchain between April and July.

Others with multiple funded startup M&A deals this year include AI infrastructure unicorn , security provider , and the legal tech startups and .

No big slowdown in sight

While prediction can be a fool’s game, there’s not much in the immediate set of indicators pointing to a slowdown in startups’ appetite for acquisition. Amid fierce competition for an edge in the AI race, well-funded startups commonly find it’s simply faster to buy another company than try to build out certain technologies themselves.

Same goes for talent. Through acquihire transactions, startups can bring on board not just top-tier individuals but experienced teams with a track record of building impressive things together.

Concentration of capital is another factor driving M&A deals. While overall startup funding has risen this year, it’s increasingly spread across a smaller pool of companies. That leaves one large cohort of startups struggling to raise funding while another has plentiful capital for acquisitions.

Go-to-market expenses also factor into M&A considerations. A startup might produce a compelling offering in-house but find it costly to bring it to market. The process may look more feasible under the wing of a larger, more mature startup.

Bottom line: Given the high number of willing sellers and well-funded buyers, expect the startup-to-startup acquisitions to continue.

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spent much of his childhood in his family’s St. Paul, Minnesota, convenience store, where he packed cigarette cartons and watched his father run a business seven days a week, 365 days a year.

Hussain was the son of a first-generation immigrant who arrived from Karachi, Pakistan, and worked his way from employment at a to owning his own corner store. That experience instilled in Hussain a work ethic that stuck with him. But his father made a clear trade-off with him in high school.

“My dad didn’t want me to necessarily come back to the store,” Hussain recalls. “He’s like, ‘Look, like this is what I did and built. Go use school as a mechanism to leave.’ ”

Ultimately, Hussain went on to form , a New York-based AI startup that automates parts of finance and accounting. Founded in 2023, the company has raised around $90 million, employs about 180 people, and was last valued at $400 million, according to Hussain.

But unlike many tech startup founders, Hussain didn’t study computer science in college. Instead, he earned a humanities degree at , won a Marshall Scholarship to , left academia abruptly to work at , and spent six years learning the operational ropes at early-stage startup before launching Tabs in 2023.

From St. Paul to Oxford

Hussain leveraged a scholarship from the to attend Cornell, where he fell in love with comparative politics and history. Fixated on academia, he graduated and immediately headed to Oxford to pursue a Ph.D. Two months in, reality hit.

“I realized this is a terrible idea,” Hussain admits. “I grew up … way too scrappy packing the cooler to survive through a postdoc and potentially a very structured 10-year career, which seemed very hard and long and not in my control.”

Deciding to reset his trajectory at 23, Hussaini took a chance on management consulting at BCG in the Midwest. Though it provided an intensive crash course in business operations, spreadsheet modeling and corporate processes, the structured corporate hierarchy lacked the agency he had seen in his father’s store.

By 2015, he decided to embed himself directly into tech, taking a massive pay cut to join Latch — then a 10-person seed-stage startup — as its first operations hire.

“Had I tried to do this directly out of Oxford or out of BCG, I think [it] would have been impossible,” Hussain told ���ϳԹ� News in an interview. “One of the things that often keeps many non-traditional founders out is … the ability to access capital, but also understand the playbook of how to build, how to design around a real problem, and build a team.”

Over six years at Latch, as the company grew to tens of millions in revenue, Hussain picked up a few lessons about building venture-backed companies. He learned to pursue large markets, to surround himself with people whose strengths complement his own, and to build for major shifts in technology.

Humanities vision meets deep tech

In 2023, Hussain applied those principles to start Tabs, an AI platform that automates revenue recognition, billing and collections. From the beginning, the founder knew he had to leverage his strengths. He also knew his weaknesses. Hussain recognized that he brought commercial vision and operational execution, not the ability to write code, to the table. So he partnered with a deeply technical co-founder, , to balance his own background.

“I came from the humanities,” Hussain noted. “Tabs is a deeply technical and complex problem to solve, and so having someone who could augment my vision … was a very important part.”

Investors took notice. Early relationships and the operational credibility Hussain built during his “apprentice” years paid off. Tabs quickly raised a $4 million pre-seed round co-led by and . Since then, the startup has grown to roughly 180 employees, raised about $92 million in total capital, reached a $400 million valuation in its last round, and maintained triple- to quadruple-year-over-year revenue growth.

To Hussain, non-traditional backgrounds in tech are a strategic advantage that fosters the resilience required to survive early-stage uncertainty.

“I think a lot of non-traditional folks … have to embrace a ton of volatility, even ahead of being a founder, to make the sacrifices to learn,” Hussain said. “Sometimes it’s just the non-traditional background that allows you to embrace non-traditional ways of learning that ultimately get you into entrepreneurship.”

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