We spoke with to talk through the firm’s evolution and how it is meeting this AI moment. She is a partner at the firm who relocated to Silicon Valley from Mexico City.

When the firm was founded in 2005, Brazil didn鈥檛 yet have a startup ecosystem 鈥斅爐here was no network of founders, no LPs backing VC firms based there, and no follow-on investors. That has changed drastically in the couple of decades since, with the country emerging as LatAm鈥檚 startup powerhouse and a place where U.S. investors and tech giants are increasingly courting business.

Brazil is an exceptionally digitally savvy market. It is the for and the third-largest . Its real-time payment rail , created by the , was rolled out in 2020 and is used by more than 90% of adults in the country.

Latin America has also become an important market for U.S. AI labs and technology companies. Monashees recently announced a partnership with called the in which the two companies co-invest up to $2 million in AI-native and deep tech pre-seed or seed-stage startups in Brazil. Its first summit is planned for later this year in San Francisco, where it will gather Latin American startup founders building businesses with AI.

Monashees makes around eight to 10 new investments per year and is finalizing the deployment of its $370 million fund into roughly 35 companies.

The firm opened an office in Mexico City in 2022 and in September 2025 set up an office in San Francisco.

The interview has been edited for brevity and clarity.

Gen茅 Teare: To set this up, tell me about Monashees.

Fabiola 蚕耻颈苍锄补帽辞蝉: Monashees is the pioneer of venture capital in Latin America. It started 20 years ago, in 2005, with the premise that what had happened in Silicon Valley with tech could also happen in LatAm 鈥� that many of the structural problems could be solved with tech.

and , Monashees鈥� co-founders, were crazy enough to believe this could happen, so that鈥檚 when they started Monashees. Just to give you a little context, back in the day there was nothing. It really took time for this flywheel to get started, because if you don鈥檛 have funding, you don鈥檛 have great talent.

Finally, after five years, they managed to crack that. In 2010, you started to have the first wave of tech companies in the region, and Monashees started positioning Brazil on the global tech map.

As a second phase, the team realized that what was happening in the Brazilian ecosystem was also starting to happen in other countries in the region. Great teams were starting to build great companies. That鈥檚 when Monashees decided to expand across Latin America and back these teams. That鈥檚 when we led 鈥檚 seed round, one of the flagship companies of Latin America.

The third wave, which is what we鈥檙e focused on right now, is Global LatAm: backing Latin American founders who are building global businesses, regardless of whether they are building in Latin America or globally.

That has also been the rationale for opening an office in San Francisco. In the context of AI, you have many Latin American founders starting businesses from here because you have to be close to the labs and the talent.

We鈥檙e early-stage investors. We invest at pre-seed, seed and Series A. Seed and Series A are our sweet spot, and we are lead investors. We鈥檙e also generalists. We鈥檙e not sector-specific, we鈥檙e mostly sector-agnostic.

I see this trend when I talk to a lot of European VCs with earlier-stage investors, establishing a U.S. presence. It seems fairly recent, and it seems to be driven by this AI wave. Do you think it鈥檚 the VCs coming here and the founders following, or are the founders coming first and the VCs realizing they need more of a presence here?

蚕耻颈苍锄补帽辞蝉: I think initially it was mostly founders. Now, it鈥檚 a little bit of both; they鈥檙e feeding each other.

The reason we started the office in San Francisco is that the pace at which AI evolves is unseen, even compared with other technology paradigms in the past. If you鈥檙e not here, it鈥檚 very difficult to keep pace and stay up to speed with where the AI frontier is going. You even have a gap with Wall Street, so imagine the gap with Latin America.

If you want to build an AI-native company as a Latin American founder, part of that is coming to San Francisco and Silicon Valley. San Francisco is now the magnet for all of this. It鈥檚 highly dense and concentrated. You have to be here to absorb the tools and understand what other people are doing.

I also think it鈥檚 super important because founders realize that Silicon Valley is the champions league. In Latin America, you do have great talent, but you don鈥檛 know what great looks like if you鈥檝e never worked here.

The reason we opened an office here is to bridge that gap: to help founders be here, see what is happening at the frontier, and understand what the best companies are doing so they can replicate that back in LatAm.

The talent in the region is now sophisticated enough. It has been a 20-year process to get to a point where you have great, ambitious founders who believe they can build global businesses.

You already have success stories like from or from . Founders realize they can build globally.

In the context of AI, many of these global companies have to be based here because you have access to AI talent, but also to funding. Being close to all the Silicon Valley funds is also crucial for them.

I do think several founders are coming here to build, but that also creates some issues. One important thing to note is that many of these founders are building for the Latin American market, where your revenue is in Brazilian reais or Mexican pesos. In terms of headcount, you need to be very careful that you don鈥檛 have a U.S. cost basis when your revenue is in Brazilian reais or Mexican pesos.

These very early-stage startups also cannot compete with the big labs here that are paying a lot of money for talent. Right now in San Francisco, finding AI talent is really difficult and it鈥檚 very expensive.

I think it’s more about coming here, learning and bringing back the best practices. That鈥檚 where the real arbitrage opportunity comes from. You have amazing talent in LatAm, and you can teach them. Now, in the context of AI, you have much better ways to do that and you can operate with a smaller headcount.

That鈥檚 the rationale for founders coming here and for us being here as a bridge. We help our portfolio companies stay close to AI innovation, but we also get access to Latin American founders who are building from the U.S.

We hired a researcher for the Monashees team. Andr茅s [Campero] has a Ph.D. from in AI. He鈥檚 one of the disciples of , who is a very renowned researcher. The rationale for having Andr茅s, who is Mexican, on the team is to help us connect with the research diaspora here in San Francisco. It鈥檚 very different to talk about business than it is to talk to researchers.

This is important because most researchers from Latin America don鈥檛 stay in Latin America. They come to the U.S. and work at the different universities here. It鈥檚 important for us to be connected to where most of the innovation is happening. Andr茅s also helps us identify the best companies emerging in the region from a technology standpoint.

Of those, how many are coming to the U.S. at the earlier stages? What proportion do you expect to come here?

蚕耻颈苍锄补帽辞蝉: Some of the companies we鈥檙e seeing start in LatAm and then expand to the U.S.

We have a portfolio company called . It鈥檚 AI-native, and it develops preventive-maintenance software. The company started in Brazil.

Its customers were global businesses, and those customers started pulling the company into the U.S. Its product was much better than what was available here. The company is now headquartered in Atlanta, so you could say it鈥檚 a U.S. company now.

Most of its revenue comes from the U.S. I think examples like that 鈥� companies born in LatAm that expand globally 鈥� will tend to be around 30% of the portfolio.

Companies we invest in from the U.S., where most of the revenue will be U.S.-based, will probably be around 20%, because we continue to be a LatAm-focused fund. But we鈥檙e also going to see more LatAm-born companies coming here.

You mentioned the focus on Latin America, and talent is obviously very difficult to find here in the U.S. right now. For the companies that are based here, do you see them setting up offices in LatAm to attract talent? Are most of them using a hybrid model, or are some completely U.S.-based?

蚕耻颈苍锄补帽辞蝉: It depends on the stage they鈥檙e at. Later-stage companies 鈥� think Series C or Series D 鈥� tend to have most of their technology teams in Brazil, Argentina or elsewhere in LatAm.

Another example is . It鈥檚 headquartered in Salt Lake City, but most of its technology team is in Brazil, in a smaller city called Jo茫o Pessoa.

The company is building very sophisticated AI infrastructure. It was able to do that because it was very good at hiring a senior team that could teach and transfer knowledge to the local team.

We鈥檙e seeing more of that. You start with senior people, senior researchers or senior data scientists in the U.S., while much of the junior team is in LatAm.

Now, with AI, you can have fewer junior people. But you also have talent in Latin America that is strong enough to act as the senior engineers.

What are the standout companies in the Monashees portfolio that you would highlight?

蚕耻颈苍锄补帽辞蝉: I鈥檝e shared a couple. One is Tractian, the preventive-maintenance software company. The company has been growing. It鈥檚 a success story for us because it started in Brazil, and it has proprietary technology. It combines software and hardware, and it owns the patents for its hardware.

Today, it is really conquering the U.S. market. It鈥檚 a perfect example of an AI-native company born in Brazil, where the AI lab lives in Brazil, but the company is competing at the global level.

We also have Music.AI, which is in a fun industry. If you鈥檙e an amateur musician, its platform allows you to play whatever song you want and play with the instruments in the background. You can play the drums, the flute or whatever you want while having the other instruments behind you. The company has both a B2B and a B2C business. It has more than 50 million users or downloads and is growing very fast. It won iPad App of the Year two years ago. It鈥檚 another success story. It is based in Salt Lake City, but has its technology team in Brazil.

We recently invested in a company called . It鈥檚 an accelerator, so it鈥檚 similar in some ways to what we鈥檙e doing with Google. Shiva is trying to capture this new wave of entrepreneurs who might not have pursued entrepreneurship if Shiva and AI didn鈥檛 exist. , the founder, is a second-time founder. He was one of the early co-founders of one of our portfolio companies, which later went public. You can think of Shiva as the of Latin America in the sense that it is very community-driven. It is also trying to capture these solopreneurs: companies started by just one person that can go global from day one and have revenue from day one.

I think it鈥檚 a super-interesting and very different investment. It speaks to how we鈥檙e always trying to keep pace with how the ecosystem is going to evolve, because this ecosystem is also likely to be disrupted by AI.

I can also tell you about some of the companies in the portfolio that are focused specifically on Latin America.

We have a company called . It鈥檚 an HR platform. It鈥檚 very specific to the Brazilian ecosystem because regulation requires employers to provide certain benefits to employees. Flash managed to build a technology product around that, and now the company is expanding into a full HR platform. It鈥檚 one of the flagships of Fund IX. It鈥檚 growing very fast, and it has become a flagship company in Latin America. Fintech is one of the largest and most important markets in Latin America.

Another company was actually the first investment I made at Monashees. It鈥檚 a payment-orchestration platform called . The company is at the Series B stage. We invested at seed back in the day. It gives an e-commerce company a single integration through which it can manage all of its payment methods. If you鈥檙e a multinational company 鈥� think about 鈥� and you want to enter Brazil, Colombia and Peru, you have to deal with so many payment methods. With Yuno, you have just a single integration. In the context of AI, Yuno has developed a very strong agentic platform that helps with fraud and conversion. Fraud in LatAm is a big issue, and the platform helps companies manage fraud and increase conversion across these marketplaces.

Related SA国际传媒 queries:

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.

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Illustration:

In this bifurcated funding environment, we also see a wide range in valuations, as well as select companies that raised multiple rounds at a significant valuation increase in the space of months.

Among this cohort, robotics and AI neolabs were the leading sectors for new unicorns. Other industries that stand out were in financial services, healthcare and biotech as well as AI infrastructure, AI deployment and devtools, defense, semiconductor and aerospace.

H1 new unicorns have added roughly $440 billion in value to the board 鈥� 5% of the board’s current value. These companies have raised $80 billion over time, representing 5% of funding raised by still-private, unicorn-valued companies.

The most valuable new unicorn this year is China-based open-source model developer , which was valued at $50 billion in its first external financing. Seychelles-based crypto exchange , valued at $25 billion, is the second most valuable company to join in the first half of this year.

San Francisco-based , majority owned by and valued at $14 billion when it raised $4 billion from private equity, is in the third spot.

From this cohort, four companies were valued as decacorns in H1, and a further five were valued above $5 billion, as of early August 2026.

Based on trends for 2025 companies, we expect valuations for this cohort to climb significantly in the next year. For the 193 new unicorns that joined in 2025, 12 were decacorns and another 18 were valued above $5 billion. Nine of those decacorns for this cohort became $10 billion-plus-valued companies in 2026.

US leads, China picks up

The U.S. leads with 110 companies 鈥� 56% of new unicorns in H1. China was in second place with 38 companies, a significant surge from 10 new unicorns in 2025. The U.K. was the third-largest market with 13 companies joining.

By continent, North America accounts for 115 new unicorns, Asia with 50 and Europe with 27. Latin America, Oceania and Africa each count for one.

Fast raises

In the current frenzied funding environment, 19 of H1鈥檚 new unicorns raised fast follow-on rounds, often in six months or less, and doubled on an earlier valuation to reach at least $2 billion or more.

Notable among the fast fundraisers are semiconductor startup , which doubled its prior valuation to $10 billion from $5 billion just six months earlier; defense tech unicorn , whose valuation vaulted to $7.9 billion, up from its prior $1.6 billion valuation seven months earlier; and , building nuclear energy reactors for AI, was valued at $6 billion, up from $2 billion four months earlier.

AI momentum

Trillions in value were added to The SA国际传媒 Unicorn Board in the first half of the year, including from some of the largest-ever venture funding deals. The first six months of 2026 also notched the largest venture-backed exit of all time: 鈥檚 IPO.

Taken together with the rapid follow-on raises at ever-larger valuations some of those companies have achieved, it鈥檚 clear that the momentum around the fastest-growing companies has picked up significantly in this AI cycle.

Related SA国际传媒 unicorn lists:

• (195)
• (1,839)
• (643)
• (232)
• (192)
• (117)
• (102)
• (947)
• (542)
• (250)
• (39)
• (489)

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Methodology

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

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

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. SA国际传媒 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to SA国际传媒 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Illustration:

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

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

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

The real story begins after the IPO

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

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

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

Liquidity drives the next fundraising cycle

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

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

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

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

Capital will not flow evenly

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

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

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

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

Pay attention to LP liquidity, not just IPO pricing

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

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

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

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

Related SA国际传媒 query:

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

Startups raised funding rounds with a lot of zeroes at the end this week. Three companies 鈥� , and 鈥� secured financings of $1 billion or more. Additionally, a robust lineup of companies in sectors including AI, e-commerce, cybersecurity, biotech and even mining also announced sizable new rounds.

1. , $1.37B, manufacturing: Hadrian, a developer of highly automated factories, raised $1.37 billion in Series D funding led by , , , , and . The financing sets a $7.87 billion valuation for the 6-year-old, Torrance, California-based company.

2. (tied) , $1B, energy storage: Austin-based Base Power, a developer of residential battery energy storage systems, secured $1 billion in Series D financing at a $13 billion post-money valuation. , , and led the financing, which coincided with the launch of the company鈥檚 Base Core home battery.

2. (tied) , $1B, nuclear power: Valar Atomics, a developer of technology and infrastructure to deliver nuclear energy, closed on $1 billion in Series B funding led by . The El Segundo, California-based company also secured a $200 million credit facility led by and .

4. , $700M, AI connectivity: Lumilens, developer of a connectivity platform for AI infrastructure, emerged from stealth and announced more than $700 million in new funding. , , , and led the financing for the San Jose, California-based startup.

5. , $545M, live shopping: Live shopping marketplace Whatnot bagged $545 million in Series G funding. The round reportedly a $20 billion valuation for the Los Angeles-based company, with , and as lead investors.

6. , $310M, critical minerals: Mariana Minerals, a software-focused developer of projects for supplying critical minerals, picked up $310 million in Series B financing led by . The 4-year-old company engineers, builds and operates mines and refineries using its software platform.

7. , $300M, AI infrastructure: Volta, a developer of AI cloud infrastructure, from stealth and said it raised a Series A at a $2.4 billion valuation, led by , , and .

8. , $250M, cybersecurity: San Francisco-based cybersecurity provider Horizon3, announced a $250 million Series E. and led the round, which set a valuation of more than $2 billion, triple the value set for its Series D last year.

9. , $188M, biotech: Watertown, Massachusetts-based drug discovery startup LifeMine Therapeutics secured $188 million in Series E funding led by . The funding will go toward clinical development of its lead program and advance its pipeline of transplantation and immunology therapies.

10. , $150M, agentic AI: HappyRobot, developer of an agentic AI platform geared for enterprises in sectors including logistics, financial services, utilities and manufacturing, raised $150 million in Series C funding led by and .

Methodology

We tracked the largest announced rounds in the SA国际传媒 database that were raised by U.S.-based companies for the period of Aug. 1-7. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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Among lead investors, topped the ranks last month, while was by far the busiest backer by deal count. The highest-spending investors for the period, meanwhile, appear to be and .

For more detail, below we ranked active investors for July by several metrics. These include most prolific venture dealmakers, most active lead backers, biggest spenders and highest-volume seed investors.

Active lead investors

We鈥檒l start with active lead investors for the month, which, as usual these days, featured a heavily AI-centric lineup of deals.

Khosla Ventures ranked as the most active lead investor in rounds of $5 million or more, with eight deals in July. The largest were a $300 million Series A for quantum computing startup and a $120 million Series C for AI-enabled legal tech provider .

took the No. 2 slot, with six lead deals, followed by , with five. Below, we charted the top eight lead investors for the month by deal count.

Busiest venture investors

The ranks looked quite different when we widened the category to include both lead and non-lead investments in rounds of $5 million or more.

By this metric, repeat frontrunner Y Combinator once again took first place, participating in at least 19 such rounds. The storied accelerator typically takes a non-lead stake in follow-on rounds for startups it incubated.

Insight Partners and Andreessen Horowitz were next on the list, with 10 deals each, followed by Khosla and , with nine each. For a bigger-picture view, below we ranked the top 18 busiest venture investors for July.

Highest spending investors

When we focus on investors who led the most expensive assortment of startup financings last month, the lineup shifts once again.

For July, Coatue ranked as the apparent highest-spending聽1 lead investor, backing a $10 billion financing for 鈥� rocket company, . (It should be noted though, that Blue Origin, founded in 2000, is probably too old to be considered a startup, although it is still a private company.)

Nvidia also stepped up, backing a $5 billion financing for foundational AI startup . Index Ventures and Andreessen Horowitz ranked high as well, each leading or co-leading rounds collectively valued above $2 billion.

Below, we rank 18 of the highest-spending lead investors for the month.

Seed dealmakers

Seed dealmakers were a bit more challenging to rank for July, in part because there鈥檚 often a time delay before smaller deals enter the dataset. One thing that is apparent is that Y Combinator was the most prolific investor at this stage, while other 鈥渦sual suspects,鈥� like and , also ranked high.

Related reading:

• A Record 14 Billion-Dollar Rounds In July Pushed Venture’s Historic Run Higher

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To dig into this trend, SA国际传媒 News analyzed a dataset of repeat founders who have gone through YC鈥檚 cohorts. That analysis revealed some very interesting insights. The dataset, shared with us directly from YC, consisted of 454 repeat founders through the program as well as 935 founder-company records spanning 2005 through 2026.

What we found is that repeat participation to date has mostly been a two-chapter journey: 428 founders (94%) went through YC exactly twice, while only 25 appeared three times. and co-founder was the sole four-time founder.

Other highlights: Founders typically returned to YC five years after their previous appearance, with an average gap of 5.1 years. However, the data reveals two distinct themes. Nearly 30% of return participations occurred within two years 鈥� including 38 in the same calendar year 鈥� while 61 returns happened after a decade or more. Some founders jump straight into their next venture, while others take years off to build experience before coming back around.

Repeat founder numbers peak in the most recent data, hitting 65 in 2025. But that doesn’t automatically mean people are returning at higher rates. In recent years, YC cohorts have grown significantly, and the 2025-26 numbers include newer batch formats alongside potentially incomplete data.

It’s also clear that returning to YC isn’t always a solo journey.

Several complete founding teams returned together for subsequent companies, including those behind , and , as well as and .

A trend YC partners are watching closely

, a general partner at the San Francisco-based accelerator who worked the spring 2026 batch, has enjoyed a front-row seat to the shift. In that cohort, he said he had 鈥渁 bunch of repeat, second-time founders鈥� he’d worked with before 鈥� several during their previous YC company.

鈥淚t definitely feels like more of a trend now,鈥� Epstein said. Still, he’s careful not to overstate the novelty.

鈥淚t’s not a new thing. But the alumni base of past YC founders continues to grow,鈥� he said in an interview with SA国际传媒 News, and that naturally translates into more people eligible to come back.

Epstein has worked with more than 1,000 startups at YC. Before that, he was a startup entrepreneur himself, co-founding (YC W10), a marketplace for graphic design assets that he sold to in 2014 before spinning it back out as an independent company in 2017.

Ask him what separates second-time founders from first-timers, and he points to experience using the program itself.

鈥淭hey know exactly how to get the most out of the advice, network and resources available to them,鈥� he said. 鈥淗aving been through the startup grind, they get really good at focusing on the signal that matters and cutting out the noise.鈥�

That experience also helps them avoid a specific, costly mistake.

鈥淭he biggest mistake I see second-time founders avoid is overhiring or overspending pre-product-market fit,鈥� Epstein said. 鈥淭he biggest regret of all the successful first-time founders I know is that they hired too many people, moved way slower and didn’t like working at their own companies anymore.鈥�

Leaner teams, powered by AI

That instinct toward leanness shows up in another pattern: Many repeat founders are choosing to start solo the second time around.

鈥淪ome of them (repeat participants) are solo founders, but they’re not building alone,鈥� Epstein said. 鈥淭hey already have networks of people they can bring in as founding employees. This helps them move faster, and feels more fun and less lonely.鈥�

He compares this shift to how cloud computing eliminated the need for startups to raise large sums just to pay for servers.

鈥淚t wouldn’t surprise me if 10-15 years from now you look back at all the money startups had to raise to hire people and realize that’s not a requirement,鈥� he said.

AI is accelerating that shift, and Epstein sees it pulling former company builders, including himself and YC CEO , back into hands-on product work.

鈥淚t’s so easy to get back into it and start building again. And it’s incredibly exciting,鈥� he said. That mix of hard-won product sense and new tooling, he believes, is changing what one person can build alone.

鈥淭hey actually become the people that can produce at 10x or 100x what a traditional engineer would be able to build,鈥� he said.

As an example, Epstein pointed to , a founder he first worked with on in 2020 who’s now building an AI tool that helps founders manage their projects and automate tasks.

Even so, Epstein believes founders keep coming back for the same core reasons: personalized advice from partners, a community of ambitious peers, access to top investors and alumni, and the urgency of the batch environment.

鈥淭he pressure cooker environment of the batch, which pushes them to move even faster, and distribution to thousands of companies within the network,鈥� he said. 鈥淚t’s extremely hard to replicate those things on your own.鈥�

From Opkit to Sazabi

One of the repeat founders Epstein has worked with is , whom YC has now backed twice.

Callaway’s path to Silicon Valley began almost by accident. As a college sophomore, he skipped a lined-up investment banking internship after reading about a software bootcamp in San Francisco 鈥� a decision he calls 鈥減robably the single most important鈥� of his life.

From then on, his goal was clear: 鈥淚 wanted to do my own venture-backed tech startup, and I wanted to do a YC venture-backed tech startup.鈥�

After gaining experience at and fintech , he founded his first company, , in YC’s fully-remote summer 2021 batch. Opkit was a healthcare-fintech startup building insurance verification and revenue-cycle-management software.

鈥淚t was, in retrospect, not the right thing for me to be working on, but a really fun and interesting and rewarding first venture,鈥� he said in an interview. Opkit was later acquired by .

That experience shaped his second company, , a name chosen deliberately in contrast to Opkit.

鈥淥pkit wasn’t very personal to me. It was more of an MBA case study approach to starting a business,鈥� he said. 鈥淲ith Sazabi, it needs to really be in alignment with who I am and my passions and interests.鈥�

Sazabi, an AI-native observability platform competing with incumbents like , draws directly on work Callaway has done throughout his career 鈥� a return, in his words, to 鈥渨hat I know best.鈥� He sees it as part of a common pattern: First-time founders often avoid building in the field they know best, then return to it with their second company.

Callaway hadn’t originally planned to go through YC again, and the reconnection happened almost by chance through an email that looped in his former partner on Opkit, Epstein. Once Callaway decided to return, he was more strategic about timing, even deferring his batch to build out more of the product first.

鈥淚 wanted to use YC as a go-to-market acceleration event,鈥� he said, something he likely wouldn’t have known to do without having gone through the program before.

The founder was back at YC in person for the first time this spring. He described the second-time experience as something entirely new: 鈥淚t was really something special.鈥�

This time around, Callaway also noticed a more experienced cohort than his own first batch, along with new concerns specific to the AI era. 鈥淭here’s a lot of anxiety around what the durable moat is in an AI world when lines of code are effectively free,鈥� he said.

On fundraising, he drew a pointed comparison to 2021. 鈥淪pring 2026 felt similar to fall 2021,鈥� he said, 鈥渂ut unlike 2021, where interest rates and ZIRP drove a lot of that energy, in 2026 it’s driven by AI and by real material gains.鈥�

The company鈥檚 thesis is resonating with investors. In late June, Sazabi announced an $8 million seed round led by , and Y Combinator, with participation from and more than 60 angels from companies including , and .

鈥淎I has changed how software gets written. Now it is changing how software gets operated,鈥� Callaway said. 鈥淪azabi is rebuilding observability from first principles for a world where agents are part of every engineering team.鈥�

Overall, as AI continues to lower technical barriers and YC’s alumni pool keeps growing, second-time founders like Callaway are becoming an increasingly visible part of the accelerator’s lineup.

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

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

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

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

In November 2022, was doing something rare for a longtime educator: taking time off. Having launched his career as a teacher in Atlanta Public Schools, Khan became an assistant principal before founding his own public high school in Denver. After a year spent coaching principals at the district office, he decided to take a 鈥減ersonal sabbatical.鈥�

Then, ChatGPT came out.

Khan began tinkering with the new technology, fascinated by its potential.

鈥淚 actually went out to my old school building, the one that I founded, and started using it with teachers,鈥� Khan recalls. He ran workshops and asked the educators to use the tool in as many scenarios as possible.

The responses were varied. Most teachers barely touched it. A few tried, but felt doing the work manually was faster. However, some had a lightbulb moment.

鈥淭here were one or two teachers who told me, 鈥楾his has completely revolutionized the way I teach,鈥� 鈥� Khan said in an interview with SA国际传媒 News.

Seeing that divide sparked something in him.

鈥淚 thought this technology could impact every teacher, not just teachers who are really enthusiastic about using new technologies,鈥� Khan said. 鈥淪o the task then was, 鈥楬ow can we take all the power of this new technology and make it really accessible to teachers?鈥� 鈥�

Building the 鈥榲ertical AI鈥� for K-12

That experiment set the groundwork for , a platform designed as an all-in-one AI operating system for K-12 educators and students. For teachers, the tool acts as a daily assistant. It performs tasks like building rubrics, differentiating assignments for varied learning levels, and generating practice worksheets and reading materials.

The platform also helps educators offer monitored AI experiences directly to students. Those experiences range from algebra tutors to writing assistants customized with state exam rubrics that deliver tailored feedback to help students revise their essays.

鈥淵ou can think of MagicSchool as the vertical AI solution for K-12 schools,鈥� Khan said. “Enterprises are adopting generative AI in other fields 鈥� Like in law, there鈥檚 and that are vertical AI for legal firms. We鈥檙e kind of that, but for K-12 schools.鈥�

Today, the company鈥檚 primary customers are school districts that want to provide a safe, governed environment for generative AI that aligns with data privacy rules and local curriculum priorities. MagicSchool now partners with large school systems, including Denver Public Schools, and Florida鈥檚 Broward County Schools and Hillsborough County Schools, as well as private institutions.

“One in five children in America go to a school that is in partnership with MagicSchool,” Khan noted. Additionally, roughly 8 million educators worldwide have signed up for the platform, he said.

The uphill battle to raise capital

Despite the platform鈥檚 rapid adoption, Khan’s path to raising capital for MagicSchool was a challenge. In the beginning, he worked with hourly contractors and lacked a formal business model.

鈥淚 had no real business plan,鈥� Khan said. “The most successful tech companies from my perspective as a consumer were the ones that just got a lot of users, and that was my goal 鈥� I was like 鈥榣et’s just get a lot of people using this, and we’ll figure it out from there.鈥� 鈥�

Once MagicSchool鈥檚 user base neared 1 million, Khan began pitching venture capitalists. However, when compared to standard Silicon Valley profiles, his background as an educator initially proved to be a hurdle rather than a selling point.

鈥淣obody wanted to give a former principal money,” Khan said, recalling 鈥渜uite literally hundreds of meetings鈥� before securing an institutional investor.

鈥淚 think that investors are taught to pattern match,鈥� he noted. 鈥淭hey’re saying, ‘Hey, well, did you go to ? Are you a tech person? Did you work at ? ‘… I have none of those things on my resume.鈥�

Even edtech-focused investors were hesitant, leaving Khan frustrated as he watched other founders secure millions based purely on tech-heavy resumes.

鈥淚 remember seeing other edtech companies right around our size raise seed rounds 鈥� with no product, no sales, no nothing,鈥澛� he recalled. 鈥� I would think, 鈥楶eople know what our product is. Millions of teachers know what our product is, and nobody’s heard of that one.鈥� 鈥�

To overcome the skepticism, Khan relied strictly on impressive growth metrics, convincing investors during every fundraising stage that the business鈥� 鈥渢raction was undeniable.鈥�

The strategy paid off. Following early angel investor checks, MagicSchool went on to raise a $2.4 million seed round led by Colorado-based . To date, the company has raised nearly $63 million in total funding, driven by strong financial growth, including 3x year-over-year revenue growth at the end of last year, according to Khan. The startup鈥檚 other backers include , , and.

Expertise as the next wave of innovation

Although Khan no longer manages the high school he founded, he stays connected to the classroom through district visits. The school remains a top-performing public school in Denver under a former founding team member, he noted.

鈥淥f course, I miss that,鈥� Khan admits. 鈥淭here’s nothing that can replace the relationship you build over a long period of time with students.鈥�

Yet, his deep-rooted experience in education ultimately became MagicSchool’s greatest asset 鈥� a trend Khan sees taking hold across the broader AI landscape as domain experts step up to build industry-specific tools.

鈥淚 think that what we’ve learned over time is that the model is no longer the differentiator,鈥� Khan says. 鈥淗ow you contextualize the model with the real problems that people have in the work that they’re doing, and specific expertise, is the thing that’s going to unlock the next wave of innovation and impact for generative AI.鈥�

Related SA国际传媒 query:

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It is difficult to define the extent to which a specific company should morph itself into an “AI native” company. Does this add value for everyone?

AI does not automatically increase exit value. In some cases, it can reduce differentiation, compress margins, complicate diligence and make a company more difficult to acquire. Like pricing, customer service or go-to-market strategy, AI requires a careful balancing act between speed and defensibility, innovation and complexity, short-term productivity and long-term strategic value.

Let鈥檚 jump into three ways AI strategy can impact exit value.

Build an AI architecture that acquirers can trust

Many startups are rapidly adding AI copilots, model integrations, orchestration layers, prompt libraries, vector databases and third-party AI tools across the organization. This may accelerate product development and help teams ship faster. However, from the perspective of an acquirer, it can also create a more complicated architecture.

During due diligence, buyers care about how AI is being used. Which models are embedded in the product? Which vendors are critical to delivery? Where does customer data flow? How are outputs monitored? What happens if pricing changes, APIs break or regulation shifts?

A startup may see AI adoption as innovation. A buyer may see it as integration complexity, vendor dependency, compliance exposure and security risk.

This is especially important for strategic acquirers that need to integrate the target into a larger platform. If AI makes the product easier to scale, automate, secure and maintain, it can support valuation. If it creates a fragile layer of external dependencies, unclear data flows and difficult-to-audit decision-making, it may reduce confidence and lower the price a buyer is willing to pay.

Invest in proprietary data

Even one year ago, adding AI functionality to a product could create excitement by itself. Today, many AI features are becoming easy to replicate. Summarization, search, chat interfaces, recommendations, content generation and workflow assistance are increasingly available through the same underlying models and infrastructure. This matters for exits.

A strategic acquirer rarely pays a premium simply because a startup integrated the latest model. They pay for what they cannot easily build themselves: proprietary datasets, unique customer workflows, strong distribution, deep vertical adoption or network effects that improve with scale.

Founders should therefore ask a simple question: Is our AI strategy creating a defensible asset, or are we just adding features that competitors can copy within weeks or months?

Revisit your buyer map as AI redraws strategic boundaries

Historically, many companies built their exit strategy around a familiar buyer map. A cybersecurity startup might sell to a larger cybersecurity vendor. A vertical SaaS company might sell to a competitor in the same industry. A workflow automation company might sell to a productivity platform. AI is changing those boundaries.

As AI expands what platforms can do, strategic buyers are moving into adjacent markets they previously ignored. An infrastructure company may acquire an identity platform because AI agents need secure access controls. An ERP vendor may acquire workflow automation because AI is moving closer to business process execution. A data platform may acquire a vertical application because domain-specific data is becoming more valuable.

This means CEOs should revisit their buyer map every six to 12 months. The most logical acquirer today may not be the same one that would have been logical even one year ago.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to SA国际传媒 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

Related SA国际传媒 query:

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