So far this year, global startups have secured at least 114 Series A rounds聽1 of $100 million or more, per 黑料吃瓜 data. That鈥檚 the highest annual total in years and on track to top the all-time peak.

Moreover, many of those jumbo early-stage rounds far exceed the $100 million threshold. Collectively, this group of Series A recipients has raised around $33 billion this year, with at least 12 rounds valued at $500 million or more.

An AI thing

The funding bump was mostly an AI-driven phenomenon. Per 黑料吃瓜 data, more than 70% of Series A rounds of $100 million or more went to AI-focused startups.

That figure encompasses some of the year鈥檚 largest early-stage financings. For instance, it includes a $1.2 billion round for Silicon Valley-based , a platform for developers to train and serve custom models, and a $900 million financing for China-based , a developer of AI-enabled humanoid robots.

Below, we put together a sample of 10 of the largest Series A rounds, including mostly AI but a few other areas as well.

The high preponderance of AI deals reflects what we鈥檝e been seeing across stages. In the first half of this year, venture and growth funding to artificial intelligence startups totaled an estimated $394 billion, roughly 77% of all investment capital. Granted, most of that was for later-stage financings. But our Series A data shows early-stage doesn鈥檛 look too different for AI鈥檚 share.

US leads for jumbo Series A deals.

Roughly half of this year鈥檚 $100 million-plus Series A rounds and funding went to U.S.-based startups, per 黑料吃瓜 data. That translates to about 62 deals with a collective value of around $15 billion so far in 2026, which puts it on track for a record tally.

Still, megaround funding at Series A is more globally dispersed than overall venture investment this year. In the first half of 2026, more than three-quarters of global seed- through growth-stage financing went to American companies, largely due to megarounds for Silicon Valley-based and .

When investors like the same things

One can point to several potential causes behind the rise in Series A megarounds beyond AI growth alone. For one, leading startup investors have exceptionally large capital reserves to deploy. Additionally, exit multiples historically, and to an even greater extent recently, reward those who are anything but modest in their ambitions.

At Series A, another factor may be that investors seem to agree more than usual on the sectors, business models and founding teams they want to back. And given that a pricey share of a winner still beats a discounted share of a laggard, they鈥檙e piling in to perceived early-stage leaders.

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• 黑料吃瓜 Data: Global Startup Investment Hit Record $510B in H1 2026 As AI Boom Accelerates Funding And Exits

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That creates enormous productivity potential, but it also creates a new security problem: Companies now need to protect not only users, devices and applications, but software actors that can take actions on their behalf.

AI agents are becoming a new class of enterprise identity

An agent may access corporate files, query databases, send emails or execute code. Once it has that level of access, it needs permissions, monitoring and governance. Companies will need to know which agent accessed what information, which systems it connected to, and whether the actions it took were authorized.

As enterprises move from experimenting with a few agents to deploying hundreds of them, agent identity will become another important layer of cybersecurity. The challenge is that these identities are not passive. Agents can move between systems, invoke tools and make decisions, which makes controlling them more complex than managing traditional users or service accounts.

The value will sit in specific control points

This market will probably not develop as one broad category called 鈥淎I security.鈥� The real opportunity will be around specific control points.

One company may protect agent identity, another may control the data an agent can access, while others may focus on prompts, MCP servers, plug-ins, traffic or auditability.

We are already seeing activity around these areas. recently acquired Israeli startup which focuses on real-time data classification and policy enforcement. Israeli cybersecurity startup , meanwhile, raised a $27 million Series A led by and focuses on understanding and securing increasingly complex internet traffic, including traffic generated by autonomous systems.

These companies are solving different problems, but together they show how the market may begin to separate into distinct security layers.

These control points are creating a new M&A map

Identity providers may extend identity governance to autonomous agents. Data-security vendors may need to control what information agents can access. Cybersecurity platforms, cloud companies and enterprise software vendors may eventually need agent-security capabilities embedded directly into their products.

For entrepreneurs, this means that 鈥淎I security鈥� may already be too broad a positioning. The more important question is what exactly the company controls.

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

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led the San Francisco-based company鈥檚 Series A, with participation from , , and of . The financing brings Baselayer鈥檚 total funding to about $40 million since its 2023 inception, according to co-founder and CEO . The company declined to disclose its valuation.

Baselayer combines business identity, credit and fraud data to help banks, fintech companies and other financial-services providers evaluate prospective customers. It sells its products directly and through software companies that resell or put their own branding on Baselayer鈥檚 technology. Its automated platform initially focused on Know Your Business, or KYB, identity verification, fraud detection and risk management.

More than 2,000 financial institutions 鈥� representing over 20% of such institutions in the U.S. 鈥� use its technology to onboard, underwrite and open accounts for merchants, according to Awad. Baselayer also works with Fortune 500 companies and has about 50 employees across offices in San Francisco and New York. Since its founding, the startup claims it has helped customers prevent more than $1 billion in fraud losses.

Awad declined to reveal hard revenue figures, saying only that Baselayer reached eight figures in revenue in less than two years.

Now, Baselayer is using its new capital to address a newer 鈥� and growing 鈥� identity problem: determining whether an AI agent is actually authorized to act on behalf of a particular person or business.

With people using AI agents left and right these days to book a restaurant reservation, for example, it鈥檚 becoming increasingly challenging to determine whether an AI agent鈥檚 automated activity is legitimate or if it鈥檚 a bot attempting to scrape data or commit fraud.

Alongside its raise, Baselayer today is also announcing the launch of its Agentic Identity Suite, extending its identity network from businesses to the AI agents transacting on their behalf.

From businesses to the agents acting for them

Awad and co-founder started Baselayer in February 2023, initially focusing on the lengthy and fragmented process financial institutions use to verify businesses and assess risk.

鈥淲hat we set out to do was essentially bring risk assessment to the 21st century,鈥� Awad recalls.

Awad describes Baselayer as both an identity network and a fraud consortium. Because its technology is used across thousands of financial institutions, Baselayer says it can recognize when the same person or business applies at multiple institutions and incorporate that activity into its risk scoring.

The company processes tens of millions of applications and says it sees many of the same businesses multiple times a year. That data becomes more useful as additional institutions and reseller partners join its network, according to Awad.

鈥淲e鈥檝e essentially streamlined 10 years鈥� worth of selling into two years,鈥� he said.

An AI agent presents a different problem, however. It may be created for a single task and disappear immediately afterward, leaving little or no history for a bank or risk provider to evaluate.

鈥淎gents spin up and they spin down,鈥� Awad said. 鈥淗ow can you trust this random one-task agent?鈥�

To address this dilemma, Baselayer is developing what it describes as 鈥淜now Your Agent,鈥� or KYA. The system is being designed to do things such as determine not only who deployed an agent, but also who that agent represents and whether it actually has permission to carry out a particular task.

It wants to do this by providing an authorized agent with a credential it can present when attempting to make a purchase or interact with another business. Then, when presented with a credential, a merchant, financial institution or online platform could use that information to decide whether to allow the transaction to proceed, Awad explained.

The startup is working with agent developers, payment processors, merchants and fraud-detection providers to issue and recognize its credential. They include , and Socure, among others. Unless agents can establish that they are acting on behalf of legitimate people or businesses, 鈥渁gents will just get blocked everywhere,鈥� Awad said.

AI can also make fraud easier to scale

Ironically, the same technology that allows legitimate agents to do more tasks can also help fraudsters operate faster.

In the past, identity fraud involved someone getting their hands on stolen personal and business information, creating a credible-looking identity, and then repeatedly applying for bank or credit card accounts until an institution approved one. At one point, the process took significant time and manual work. But today, AI agents can automate parts of it and run continuously.

鈥淚t鈥檚 fraud on steroids right now,鈥� Awad said. 鈥淚t鈥檚 so easy, it鈥檚 so cheap, it鈥檚 so fast, and it鈥檚 24/7.鈥�

Reports of AI agents bypassing restrictions have also raised questions about how to identify and control autonomous software. , for example, recently reported incidents in which its models took unauthorized or deceptive actions, including activity involving the e platform.

Baselayer鈥檚 technology would not keep a model from disregarding instructions or exploiting a vulnerability, Awad acknowledged. But its goal is to verify an agent’s credentials when it attempts to interact or transact with an outside party.

Without a way to identify themselves, he said, legitimate agents may resort to trying to get around websites鈥� restrictions just to be able to complete their assigned tasks. Or, they could simply become less useful because they are repeatedly blocked as suspected bots.

Competing to establish a standard

M13 managing partner told 黑料吃瓜 News in an interview that he met Awad about a year before his firm invested in Baselayer. At the time, he saw the startup primarily as a provider of Know Your Business technology.

鈥淭he business did not feel like a business of the future,鈥� he admits. 鈥淚t just felt like he was solving a KYB banking verification problem.鈥�

The investor鈥檚 view changed as more companies began exploring payments made by AI agents and Baselayer began applying its business-identity data to the field.

鈥淓very agent ultimately is going to have to be tied to something real, and they understand the real world,鈥� Alomar said.

He believes Baselayer鈥檚 existing data, identity network and relationships with financial institutions give it an advantage over a startup entering the market from scratch.

“AI agents are rapidly becoming economic actors, but the identity infrastructure underneath commerce was never designed for software that can open accounts, make purchases, move money or enter into transactions on someone else’s behalf,” Alomar added. 鈥淭hat creates an enormous new trust problem, and we believe identity will become one of the foundational infrastructure layers of the agentic economy.鈥�

So far, no dominant standard exists. But Baselayer still must work to persuade agent developers, merchants, financial institutions and payment companies to recognize its credential.

That could take time. Awad said relationships with financial institutions typically take 12 to 18 months to establish, while large merchant partnerships can take up to 24 months. Baselayer may be able to reach some institutions more quickly, however, through its existing reseller relationships.

The company also sees potential use cases beyond payments. For example, Alomar said the technology could eventually authorize agents involved in cryptocurrency transactions or smart contracts, among other things.

鈥淭his is not just a fintech business 鈥� it鈥檚 a security business,鈥� he said. 鈥淚t begins with payments, but ultimately that technology applies directly to anywhere that an agent is making a decision that you need to verify it is permitted to make.鈥�

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

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

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

This interview has been edited for length and clarity.

黑料吃瓜 News: What led you to leave Starburst and launch Generational Partners?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Do startups located near Space Systems Command have an advantage?

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

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

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

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• Sector Snapshot: Defense Startup Funding Hits An All-Time Record As VCs Begin To Eye Exits

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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.

After a week of multiple billion-dollar-plus rounds, startup investors have reduced the number of zeroes on their funding checks. This past week, the largest U.S. startup funding rounds were in the hundreds of millions, topped by a $550 million financing for AI infrastructure company and a $308 million investment in space vehicle developer .

The remaining list of big rounds featured mostly AI-focused companies in sectors including investment management, networking, coding and marketing as well as some energy and biotech. Data center developer , also made official its previously reported $3 billion-plus raise, co-led by , and .

1. , $550M, AI infrastructure: Temporal Technologies, developer of an open source platform for building and operating long-running AI agents and other enterprise systems, secured $550 million in Series E funding at a $12.55 billion valuation. , , , and led the financing for the Bellevue, Washington-based company.

2. , $308M, space tech: Redondo Beach, California-based Impulse Space, a developer of space vehicles for moving payloads across and between orbits, secured $308 million in Series D extension funding. The financing brings the combined round total to $808 million.

3. , $250M, investment management: Ridgeline, an AI-enabled investment management platform, picked up $250 million in a Series E funding round. The financing, led by founder and chairman , set a $1.45 billion valuation for the Incline Village, Nevada-based company.

4. , $205M, networking: Wayne, Pennsylvania-based Cornelis Networks, a developer of networking technology for AI and high-performance computing workloads, closed on $205 million in new funding backed by .

5. , $200M, AI software development: San Francisco-based Factory, a provider of AI tools for enterprise software development, announced a $200 million funding round at a $5 billion valuation, backed by a long list of venture firms and individual investors.

6. , $180M, AI marketing: Profound, a startup offering marketing software to help users appear more prominently in AI results, raised $180 million in Series D funding at a $1.8 billion valuation. and led the financing for the New York-based company.

7. (tied) , $150M, foundational AI: Arcee AI, a developer of open-weight AI models, closed on $150 million in Series B funding at a valuation of more than $1 billion. , and led the round for the San Francisco-based company.

7. (tied) , $150M, gaming: Nex, a developer of family-oriented digital games that rely on body motion rather than controllers, secured $150 million in new equity and debt financing, including a Series E led by and . The San Francisco company did not break out how much of the round consisted of equity.

9. , $135M, geothermal energy: Mazama Energy, a Seattle-based geothermal energy developer specializing in superhot rock geothermal power, picked up $135 million in a Series B round led by and .

10. , $123M, biotech: Sling Therapeutics, developer of a small-molecule therapy for thyroid eye disease, closed on $123 million in Series C funding. led the financing for the Ann Arbor, Michigan-based company.

Methodology

We tracked the largest announced rounds in the 黑料吃瓜 database that were raised by U.S.-based companies for the period of Sept. 12-18, 2026. Although most announced rounds are in the database, there may be a small time lag, as some rounds are reported late in the week.

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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鈥檙e seeing in the startup ecosystem at the seed stage.

Broadening capital strategy

Equity is a powerful tool for building high-growth companies. But it鈥檚 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鈥檙e now writing financing checks like this on a near-weekly basis.

Distribution focused

Leading with a 鈥渂etter鈥� product isn鈥檛 enough to propel growth. The breakout companies are investing in building stronger distribution systems 鈥� aka what founders refer to as 鈥渢raction.鈥� Distribution is a critical moat for early-stage startups. Rapid scaling is no longer achieved by launching new products; it鈥檚 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 鈥� 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

鈥淢ove fast鈥� 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鈥檚 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鈥檚 no lack of interest in AI. But there is an implementation problem. We鈥檝e 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鈥檝e 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鈥檛 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鈥檚 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鈥檛. 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鈥檚 most active venture capital firms. Before becoming involved in venture investing, he built and scaled into the world鈥檚 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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U.S. venture-backed technology聽1 companies have secured nearly $90 billion in domestic public offerings this year, per 黑料吃瓜 data. That鈥檚 already the second-highest annual tally on record, and we鈥檝e still got a few months to go.

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

The remaining field is comparatively modest. Just 21 other venture-backed technology companies went public this year in sizable or offerings 2, per 黑料吃瓜 data. Collectively, their offerings, which include traditional IPOs and SPAC deals, pulled in less than $10 billion.

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

Here are some of the key findings in more detail:

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

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

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

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

Winner-takes-almost-all

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

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

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

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One of the strangest things about the current AI cycle is just how fast the math changes for the people building it. You can be a 20-something founder who feels like you are still in the warm-up phase of your career, yet on paper your equity is already life changing. Or a mid-level engineer who went from holding startup options to staring at a substantial personal balance sheet practically overnight.

The gap between life experience and the sudden reality of managing serious wealth is widening as AI-native companies reach major valuations faster and private company liquidity arrives earlier.

A of more than 3,400 founders and senior leaders across 20 countries found that AI-native startups are reaching billion-dollar valuations in about 3.5 years, roughly half the time it took before generative AI. They are doing it with about half the staff.

We have seen an even more compressed version firsthand. We recently advised founders who went from launching their company to a major liquidity event in less than a year.

When the money outpaces the mindset

For decades, tech wealth followed a more predictable script. Significant personal wealth often accumulated alongside a long career. Equity vested over years, responsibilities grew and additional grants often followed. If everything went right, an acquisition or IPO marked a visible transition into a very different financial reality.

Today, that boundary is much less clear. AI capabilities allow companies to grow at a much faster pace.

A young founder can suddenly face questions that used to come much later in life. What are their long-term personal goals? What should the new capital be used for? What does financial independence mean for someone who may still be figuring out what they want their life to look like?

These are not always questions that can be answered overnight.

Compounding this is the fact that one doesn鈥檛 have to wait for an IPO to de-risk. Tender offers and secondary transactions allow founders and employees to turn part of their equity into cash while the company remains private.

Take as an example. While still only 3 years old, the company authorized a $100 million secondary sale for staff at a $6.6 billion valuation. By February 2026, it had at an $11 billion valuation.

For someone inside a company moving at that speed, the sequence can look very different from the traditional startup script. It is a dizzying loop of grants, valuations and a sudden liquidity window. All of this can happen long before an IPO.

Flexibility is the name of the game

A sudden liquidity event can make financial independence a realistic goal. It may make buying a home possible, even while someone is still single or has no idea where they want to live long term. It may allow them to take care of parents or fund another entrepreneurial chapter.

The pace of these cycles can also be contagious. Opportunities seem to be everywhere. At the same time, a founder may still be taking substantial risks with the current venture and have very little idea what life will look like in five years.

When we advise technology executives and founders in this position, we try to leave room for several possible paths while the broader picture is still developing. Some capital may eventually support long-term family security. Some may need to remain available for opportunities or life changes that do not exist today.

A future business endeavour, a career change, a relocation to another country, or other less conventional ideas can change the picture again. Some of these moves can be made today, but others need time to develop.

A company may compress 10 years of growth into three, but people cannot compress 10 years of life into three. Ignoring that gap is where real risk can build.

Valuations and liquidity can move incredibly fast. Decisions about family wellbeing, security, career and the future still move at a human pace. Your financial architecture needs to respect the difference.

is co-CEO of , where he works with founders, senior technology executives and families on wealth strategy, liquidity events and long-term financial planning. Before moving into wealth planning, he spent many years in the technology industry and writes about the intersection of technology, equity and personal wealth.

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

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

Unsurprisingly, AI-focused companies are capturing a much larger share of funding than during the prior peak, with most sales, marketing and CRM investment going to companies in 黑料吃瓜 AI-related categories.

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

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

Notable deals

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

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

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

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

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

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

Exits

The sector has produced one notable public offering, but most exits are coming through acquisitions as larger companies buy specialized sales and marketing products to add to their existing platforms, 黑料吃瓜 data shows.

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

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

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

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

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

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

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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, 黑料吃瓜 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 鈥� marking its busiest month for investing since at least the beginning of 2025 鈥� 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鈥檚 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, 黑料吃瓜 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鈥檚 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 黑料吃瓜 data. As we鈥檝e 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鈥檚 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 黑料吃瓜, including River AI, , , and .

The August burst extends a notable increase in Nvidia鈥檚 venture dealmaking pace this year. 黑料吃瓜 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鈥檚 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 鈥� $5 billion deal. The round, the month鈥檚 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鈥檚 $1.37 billion Series D and home battery provider鈥檚 $1 billion Series D.

Nvidia and Sequoia came next, each with $1.3 billion in led or co-led rounds. Nvidia鈥檚 total came from Poolside鈥檚 $1 billion financing and Volta鈥檚 $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聽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 黑料吃瓜 dataset.)

Big checks, familiar names

August鈥檚 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鈥檚 acceleration this year also illustrates how corporate investors 鈥� particularly those with a direct stake in the AI ecosystem 鈥� 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 黑料吃瓜 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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