So far this year, global startups have secured at least 114 Series A rounds听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听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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The New York-based company publicly launched Thursday with an undisclosed seed round led by S茫o Paulo-based venture firm and a debt financing partnership with 鈥檚 Customer Value Fund. Since its January inception, Skalar has committed to finance more than $125 million in sales and marketing spending across seven technology companies over the next 12 months.

Financing tied to customer revenue

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

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

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

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

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

鈥淲e only get repaid as they get repaid,鈥� C谩rdenas told 黑料吃瓜 News.

Notably, the startup doesn鈥檛 have to pay the capital back by a certain date. Instead, repayment is tied to revenue from the customers acquired with the financing, rather than a fixed schedule. For example, a company that recoups its acquisition costs in one month repays the loan in one month, while one that takes 12 months repays it over one year. So while the obligation remains contractual, Skalar operates under the premise that a flexible timeline reduces the risk of a cash crunch.

How it differs from other financing

Skalar鈥檚 structure differs from both venture debt and existing forms of revenue-based financing, according to C谩rdenas.

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

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

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

鈥淲e have become experts in understanding these types of risks and when they are sufficiently predictable and sufficiently profitable to be underwritable,鈥� he said.

The risks for founders

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

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

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

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

鈥淥ur structure is fundamentally different because it absorbs most of the downside risk 鈥� and we are unlikely to walk away unscathed if something bad happens. This incentivizes us to always be mindful of not encumbering the companies we work with with credit risk, as this ultimately increases risk for us,鈥� C谩rdenas told 黑料吃瓜 News.

A narrow initial customer base

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

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

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

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

The General Catalyst connection

Skalar grew out of C谩rdenas鈥� work as an entrepreneur-in-residence at Monashees, where he helped introduce several of the firm鈥檚 portfolio companies to General Catalyst鈥檚 Customer Value Fund model.

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

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

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

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

鈥淲e鈥檝e seen capital flow into and out of the growth stage, leaving some excellent companies struggling to raise the equity they need to keep growing,鈥� he said. 鈥淪kalar fills that gap by giving promising companies access to capital while they build the track record investors want to see.鈥�

A market beyond venture-backed startups

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

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

鈥淰enture capital solved the problem of funding the top 1% of tech businesses,鈥� he said. 鈥淏ut 99% of tech businesses 鈥� out of which I鈥檇 say probably more than half could be underwritten by our product 鈥� just don鈥檛 have access to capital today, and ours is a product that fundamentally changes that.鈥�

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

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

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From putting nuclear reactors on barges to grading how well AI models can control robots, this month鈥檚 crop of interesting startup deals takes AI and other emerging technologies well beyond the conventional software stack.

Other companies that caught our eye are applying automation to the decidedly old-school worlds of building-material procurement, commercial property maintenance and farm recordkeeping. Let鈥檚 take a closer look.

$50M to put nuclear power at sea

Nuclear power plants are famously difficult and time-consuming to build. thinks putting them on barges could offer another way.

The Long Beach, California-based startup raised what it says was an oversubscribed $50 million seed round led by , just two months after emerging from stealth with $10 million in pre-seed funding. The of investors in the deal included , , , and others.

Bluecore is developing compact, water-cooled small modular reactors designed to operate aboard floating barges. Rather than spending years constructing a new power plant and the accompanying infrastructure on land, the idea is to manufacture the systems and move them to where electricity is needed.

鈥淥ur focus is simple. Create and deliver zero-emission energy as safely and quickly as possible,鈥� CEO and founder wrote in a social media . 鈥淥ver 3 billion people live within an hour of water. We want to power them all.鈥�

Its first target is the Port of Long Beach, with other ports and power-hungry AI data centers among the potential customers. The company says it鈥檚 working with the and as it pursues certification.

Bluecore鈥檚 raise comes amid a broader nuclear funding boom. Nuclear fission startups alone pulled in roughly $2 billion in venture funding in 2025, per 黑料吃瓜 data, and investors have continued writing enormous checks this year. More broadly, cleantech-, EV- and sustainability-focused startups raised about $15 billion in the first half of 2026, with second-quarter funding reaching its highest quarterly level since 2024.

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$31M to bring AI to the building-materials business

Ordering cement and steel might not sound like an obvious AI use case, but Saudi Arabia-based sees a lot of room for improvement.

The Riyadh startup last week said it had secured $31 million in new capital, consisting of a $13 million Series B equity round co-led by , 鈥檚 venture arm, and , plus an $18 million growth-debt commitment from under a previously announced facility. The company has now raised more than $83 million, .

BRKZ operates a marketplace that connects construction companies with suppliers of building materials, while also handling sourcing, logistics and financing. More interestingly, it says it has amassed some 38 million structured data points that power an AI pricing engine trained on roughly 40,000 requests for quotes.

The company says 84% to 89% of its predicted prices come within 5% of the eventual transaction price. Another AI agent reads photos of cement delivery notes sent through , matches them to orders and verifies deliveries 鈥� with roughly three-quarters processed without human intervention.

BRKZ is riding a in Saudi Arabia even as startup investors remain selective about construction and property technology more broadly. Global proptech startups raised about $6.5 billion via roughly 640 deals in the first half 2026, 黑料吃瓜 data shows. That’s on pace to top last year鈥檚 dollar figures, even as deal count has dipped this year. Similar to other startup sectors, investors in proptech are increasingly directing capital toward companies using AI and automation to cut costs and streamline operations.

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$24M for robots to take care of commercial properties

Robots are already assembling cars and moving packages around warehouses. wants them to also mow lawns and sweep parking lots 鈥斕齛nd do security patrols of those properties while they鈥檙e at it.

The Santa Clara, California-based autonomous robotics startup last week said it has raised a $24 million Series A led by to scale its fleet of robots designed to handle outdoor property maintenance for commercial real estate owners and operators.

Its machines combine autonomous navigation with attachments that allow them to perform jobs like sweeping, debris removal and landscaping tasks 鈥� all while conducting 鈥渟oft security鈥� 鈥� across large campuses and commercial properties.

Rather than trying to build a general-purpose humanoid robot, Viabot is applying autonomy to repetitive jobs that property owners already pay people and contractors to perform. The startup, which operates on a 鈥渞obot as a service model,鈥� sees an opportunity to fill a labor shortage for what鈥檚 often considered 鈥渄irty, dull and dangerous鈥� outdoor work, , founding partner at , told 黑料吃瓜 News in 2021, when the company raised earlier funding.

Startup investors are pouring money into those sorts of real-world AI applications. Global venture funding to physical AI companies 鈥� including robotics, autonomous vehicles, aerospace, drones, industrial automation and sensors 鈥� reached $47.4 billion across 521 deals in the first half of 2026, 黑料吃瓜 data shows. That’s nearly 4x the $12 billion invested in the second half of 2025 and almost 80% above the year-ago period.

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$10M to give AI robots an independent report card

As frontier AI models move from controlling software to controlling robots and machines in the real world, wants the public to have an independent way to understand what they can actually do.

The 3-month-old San Francisco startup announced last week that it raised a $10 million seed round led by , with participation from , , and others.

Robocurve says it wants to act as an independent third-party auditor for physical AI, testing how well frontier models can control real robots and publicly reporting the results. The company says its own research has already found that general-purpose large language models can outperform specialized robotics vision-language-action models on some simple tasks.

Importantly, Robocurve isn’t positioning itself as a conventional robotics benchmarking startup. It is incorporated as a Public Benefit Corporation with a legal duty to independently evaluate the robotics capabilities of frontier AI systems and report those findings to the public.

The company says AI labs don’t dictate its research agenda, evaluation methodology or published results, and it plans to work with governments, policymakers and civil society as robot capabilities advance.

Academia is a big part of that model, too. Rather than developing every benchmark itself, Robocurve funds academic teams and supplies them with robot hardware to create open-source benchmarks. More than 200 institutions 鈥� including researchers from 19 of the world’s top 20 universities, according to the company 鈥� have signed up for its benchmarking program. Robocurve is offering a combined $500,000 in funding plus free robotic arms to participating academic groups.

Its funding is timely given the massive influx of capital pouring into robotics and physical AI. Within the broader physical AI sector, robotics startups alone raised more than $21 billion globally in the first half of 2026, 黑料吃瓜 data shows, already eclipsing the nearly $16 billion raised in all of 2025 and even the $15.3 billion invested during the venture market’s 2021 peak.

As ever more powerful models move from screens into machines capable of manipulating the physical world, figuring out what those models can 鈥� and can’t 鈥� safely do becomes a more consequential problem.

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$5M to let farmers talk instead of type

A lot of agricultural software has one basic problem: Farmers don鈥檛 spend their days sitting at desks.

With that in mind, this month raised a $5 million pre-seed round led by for a voice-first AI platform designed specifically for farmers, agronomists and other agricultural workers.

Instead of asking someone working in a vineyard or almond orchard to stop and fill out a form or type notes into a computer, Tellia enables them to leave a voice note, send a message, or even submit a photo to log records, generate reports and set reminders. It says its AI then turns that unstructured information into records associated with the correct field, crop and crew.

For example, for a livestock farmer that might mean a voice prompt like: 鈥淭ellia, the vet just checked Herd 3. All clear, next health check due in 6 weeks, log that.鈥�

Or a vineyard manager might ask: 鈥淭ellia, based on this year’s Brix and pH logs, what’s the projected alcohol level for the Cabernet lot?鈥� and receive an instant answer based on previously collected data.

San Francisco- and Paris-based Tellia was founded last year and says its technology is already deployed across 1 million acres, including at and wineries in the U.S., as well as agricultural organizations in Europe.

and also participated in its latest funding.

Its raise comes amid a much tougher environment for agtech startups overall. Venture investment in agriculture and farming remains in a correction from its 2021 peak, when startups in the space raised $10.5 billion across more than 1,400 deals, 黑料吃瓜 data shows. That makes companies applying increasingly cheap and accessible AI to specific, everyday farming problems an interesting corner to watch. Voice AI, in particular, has emerged as one of the hot spots in artificial intelligence funding in recent years.

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Related reading:

• Sector Snapshot: Robotics Startups On Fire As Venture Funding Surges To Record Numbers In 2026
• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Agtech Startups Face A Drier Funding Climate
• Why Big Investors Are All Ears For Voice AI Startups

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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�

U.S. venture-backed technology听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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