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�

鈥淚 learned that you can course-correct if you focus on the early life of the baby. If they’re missing certain beneficial bacteria, it can really cause a lot of issues for the infant,鈥� Sew Hoy recalls. 鈥淚 was reading all this literature and asked my doctors about it, and none of my doctors knew what I was talking about.鈥�

A week after her son鈥檚 birth in 2020, she incorporated Austin-based . Today, the company announced it has raised $33 million in a Series B funding round led by , it told 兔子先生传媒 News exclusively.

The financing brings Tiny Health鈥檚 total funding to $46 million, following a $4.5 million seed round in 2021 and an $8.5 million Series A in late 2023. Existing investors , and participated in the latest round, along with new investors including and . B Capital鈥檚 will join the company鈥檚 board.

Demand for Tiny Health鈥檚 at-home microbiome tests is growing. The startup鈥檚 overall revenue has more than doubled each year, according to Sew Hoy. Its B2B arm, Powered by Tiny, which sells tests through healthcare providers and other companies, grew fourfold last year. Sew Hoy did not disclose revenue figures or the company鈥檚 valuation, though she said the valuation was 鈥渕uch higher鈥� than in the Series A.

From baby gut tests to adult testing

Tiny Health spent two years developing its platform before launching an at-home baby gut test in 2022. Healthcare was new territory for Sew Hoy, whose previous startup, digital coupon company , was sold to in 2013. Before raising a seed round, she put $50,000 of her own money into an initial study and spent about a year collecting stool samples and building the platform.

The baby test gave Tiny Health a focused entry point. In Sew Hoy鈥檚 view, parents of children with conditions such as eczema wanted more information as to the cause, while clinicians had few practical tools to apply emerging microbiome research. The company also offered pregnancy, vaginal microbiome and adult tests, but initially focused on infant testing. Adults have since become its larger testing segment, according to Sew Hoy.

So, how does it work? Customers collect a small stool sample with a swab. After sending in the swab, they receive a report on the microbes detected, along with explanations and suggested next steps. Tiny Health also sells a subscription called Tiny Plus that includes a baseline test and a retest.

Sew Hoy believes one of Tiny Health鈥檚 biggest differentiators is that its tests use shotgun metagenomic sequencing to analyze microbial DNA. The method provides a broader view of the organisms in a sample than tests that look for a limited set of microbes, she said. Chief science officer , who joined Tiny Health in 2021, leads its scientific work. The company鈥檚 other leaders include co-founder , who joined Sew Hoy two years after she started Tiny Health and leads its technology work, and chief medical officer , an integrative pediatrician who previously advised the company.

The reports examine what the detected microbes may be doing and suggest steps such as dietary changes, with links to supporting research.

鈥淲e are a wellness test,鈥� Sew Hoy said. 鈥淲e鈥檙e not a diagnostic 鈥� It鈥檚 very actionable, very evidence-backed.鈥�

Insurance does not cover the tests, which can identify microbes in a sample, but cannot establish what caused a person鈥檚 symptoms.

Other startups are taking different approaches to digestive health. Austin-based makes a device that attaches to a toilet and tracks stool patterns and hydration. Tiny Health analyzes microbial DNA from a collected sample. Throne recently raised $10 million in a Series A round led by .

Beyond direct-to-consumer testing

Tiny Health鈥檚 fastest-growing business is Powered by Tiny, which sells testing through healthcare providers and companies that incorporate it into their own services. Partners include , and 鈥檚 Executive Health and Longevity Programs, according to Tiny Health. It also works with supplement makers and academic partners that want to measure the microbiome in clinical studies.

Customers began taking their direct-to-consumer results to doctors, according to Sew Hoy. Some clinicians then approached Tiny Health for help interpreting the reports. Today, the startup serves more than 6,000 practitioners and offers training on using its tests in patient care.

Its enterprise business supplies testing to other health and wellness companies, including through technology integrations. As mentioned earlier, that B2B arm is growing fourfold. Expanding both, along with clinician training, is a priority for the new funding.

, senior principal at B Capital, told 兔子先生传媒 News that his firm sees an opportunity to make microbiome research more useful to consumers as interest in preventive health grows.

鈥淲e’re in the middle of a real shift in how people engage with their own health. Consumers are paying out of pocket for wearables, blood panels, and full-body scans because they want to understand what’s happening inside their bodies before something goes wrong,鈥� he wrote via email. 鈥淭he gut microbiome is one of the most important and least understood pieces of that picture 鈥� Tiny Health turns that science into something people can actually act on, and we believe the company that does this well will define the category.鈥�

Whitehead also cited Tiny Health鈥檚 standing with clinicians the firm consulted during its diligence.

鈥淭iny Health was consistently the test they rated highest,鈥� he said.

Another attractive feature, Whitehead added, is the company鈥檚 data from families tested beginning in infancy.

Putting its data to work

Tiny Health has collected nearly 200,000 microbiome samples over time, according to Sew Hoy. To make use of those samples, the startup is developing a tool called TinyAI that will draw on its data and research reviewed by its scientific staff to help people understand their results. The company also uses health information customers provide, with identifying details removed, to inform that work.

Long-term, Tiny Health aims to identify patterns that could help spot health risks before a condition develops. has associated gut microbiome imbalances with more than 100 diseases.

鈥淲e want to be able to predict disease before it happens and intervene before it happens,鈥� Sew Hoy said in an interview with 兔子先生传媒 News.

Indeed, one pressing question is whether microbiome-guided interventions can improve health outcomes. Tiny Health says it has published four scientific papers, including a study involving infants, and plans to support further research involving adults.

Presently, the company has about 40 employees and plans to hire 30 more. It will use the new capital to expand its work with healthcare providers and other companies, and to fund research into whether changes in the microbiome can improve health outcomes. It plans to devote $5 million to a program that will give researchers and clinical partners access to its testing and data.

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She later became a general partner at , where she led early institutional investments in AI chipmaker , and later invested at . Now she鈥檚 founder and managing partner of , a $52 million fund backing startups that use AI to do work in industries such as construction, industrials and insurance.

In an interview with 兔子先生传媒 News, Venkatachalam discusses why she looks beyond familiar founder profiles, what makes an AI company durable, and how an early investment in Groq shaped her approach.

This interview has been edited for clarity and brevity.

You invested at Khosla Ventures before starting Axiom. What did you take from that experience, and what did you want to do differently?

Venkatachalam: One thing I took was 鈥檚 open view of where great founders can come from. Silicon Valley has gravitated toward a fairly narrow idea of who can build the next great AI company: Someone with a computer science or machine learning background, or experience at . We鈥檙e looking for more nonobvious founders, particularly in nonobvious industries.

The other thing was how we think about risk. Instead of asking a company every conceivable diligence question, we focus on the risks that matter for its next set of milestones. Can this team do what it says it will do? And if it can, could the result be massive?

That means accepting that many bets won鈥檛 work out while aiming for the outliers. I think that鈥檚 what my investors are backing me to do: identify categories of the future, rather than participate in the categories everyone already recognizes.

At Axiom, the biggest difference is that we鈥檝e built the firm around people who are actively working with AI. If you aren鈥檛 building, productizing, pricing or taking AI to market regularly, it鈥檚 very hard to keep up. Our team includes people doing exactly that in their other roles. They help keep our investment thinking current, and founders want to work with them because they鈥檝e encountered many of the same challenges.

We also use AI throughout the firm. We鈥檝e built what we call the Axiom Brain to help us make sense of market trends, identify interesting people and companies, and move faster on diligence and other work. For me, the value is the ability to act quickly.

You mentioned that some of those AI practitioners have other jobs. How does their role at Axiom work?

Venkatachalam: They have dedicated time to Axiom and work with us part time. They also receive carry in the fund. They鈥檙e partners in the work, rather than people whose names appear on an adviser list.

Their other jobs are central to the model. Some of the best angel investors are people who are still operating and building. I don鈥檛 need these people full time. In fact, they would be less valuable to Axiom if they left the work that keeps them close to the market.

Axiom says it invests in 鈥淎I for the real world.鈥� What does that mean when you鈥檙e evaluating a startup?

Venkatachalam: Our view is that AI should benefit a much broader population than the early adopters who are already using it. We look at industries underserved by technology, where AI can produce an outcome rather than simply provide another software tool.

That may lead us to construction, industrials or insurance. Some of those companies involve hardware, sensors or robotics; others are entirely software-based. What connects them is that they鈥檙e doing work that matters to customers in real-world industries.

We generally don鈥檛 invest in products that look like conventional enterprise software tools. We want to see AI delivering a result.

You鈥檝e described a shift from software people use to digital workers that perform jobs. Are customers actually paying for AI from labor budgets?

Venkatachalam: Yes, and that鈥檚 one of our investment criteria. Even when a portfolio company is at an alpha or design-partner stage, we do diligence to understand whether customers are willing to buy it that way. We鈥檙e often seeing contract values in the hundreds of thousands of dollars, rather than the much smaller contracts you might expect for a midmarket software tool.

We鈥檝e seen that buying behavior play out across the majority of the portfolio companies we鈥檝e invested in.

AI products are becoming faster to build and easier to imitate. What makes one durable enough to become a large company?

Venkatachalam: If you鈥檙e doing important work inside a customer鈥檚 business, and that work is worth a lot of money, you become difficult to replace. You鈥檙e handling what we call the last mile of the job.

In industrial settings, for example, delivering an outcome means integrating deeply with the systems customers use. You have to understand their data, train on it, learn the workflows that matter, and stand behind the result. That takes more than putting an interface on top of a model.

Those relationships and capabilities can become difficult for another startup to replicate. They also involve work that the large AI model companies may have little interest in doing themselves.

Before Axiom, you backed Groq when AI inference was far from an obvious investment category. What led you to it?

Venkatachalam: I came from both hardware and software. At one point, I became interested in why was building its own networking switches when it could buy them from existing suppliers. As I looked into that, I learned it was building its own chips, too.

That led me to , who had been involved in that work and had left to start Groq. I began learning why large technology companies were building chips to train models. Then Jonathan made the case that the much larger future market would be inference.

I鈥檒l be honest: In 2016, I barely understood inference. But if you believed these models would spread, it made sense that people would build on top of them and need the infrastructure to support that. That insight drove my investment.

How did that experience shape what you look for now?

Venkatachalam: It taught me the value of being a little early and having some patience. You don鈥檛 have to be wildly contrarian, but you do have to see the opportunity before it becomes obvious to everyone else.

In a way, our thesis hasn鈥檛 changed. We鈥檙e still asking what will be built on top of AI infrastructure and models. We want to invest while the answer is emerging, before there鈥檚 a consensus.

What happens when one of those early bets doesn鈥檛 work out?

Venkatachalam: We plan for it. We have a $52 million fund and expect to make 35 investments. We fully expect about half of them to fail, whether that means a company shuts down or simply never reaches the growth trajectory we鈥檙e looking for.

The model depends on finding an exceptional outcome. We need one outstanding investment to return the fund. If you invest early enough and the company becomes very large, that can offset many bets that didn鈥檛 work.

That willingness to accept losses is part of making investments before an opportunity is obvious. It鈥檚 built into how we approach the fund.

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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.

This week delivered a bountiful supply of big startup funding rounds, led by two $400 million financings for cybersecurity unicorns and . Rounding out the week were large financings for startups across hot sectors, including foundational AI, drug discovery, neurotech, and even rainmaking.

1. , $400M, cybersecurity: Island, a developer of tools for secure enterprise digital operations, raised $400 million in Series F financing. led the round, which set a $6.4 billion valuation for the Dallas-based company, more than double its 2024 valuation.

2. , $400M, cybersecurity: Cyera, a provider of enterprise data security tools that govern both humans and AI agents, picked up $400 million in Series G extension funding led by and backed by . The round brings total funding for New York-based Cyera to $2.7 billion, .

3. , $350M, foundational AI: San Francisco-based Snorkel AI, provider of tools for frontier labs and AI teams to develop specialized training data and environments, secured $350 million in Series E funding. and led the round, valuing the 7-year-old company at $3.2 billion, and it now has more than $375 million in annual recurring revenue.

4. , $311M, biotech: Enveda, an AI-enabled drug discovery startup, closed on $311 million in Series E financing led by and joined by a long list of new and existing investors. The round brings the Boulder, Colorado-based company’s total capital raised since inception to more than $845 million.

5. , $250M, neurotech: New York-based Precision Neuroscience, a startup focused on brain-computer interface technology, secured $250 million in Series D funding with and the as lead investors.

6. , $200M, wireless communications: Hubble Network, operator of a satellite network that extends connectivity to Bluetooth devices, pulled in $200 million in Series C funding. led the financing, which set a $1.6 billion valuation for the Seattle-based company.

7. , $155M, pharmacy benefits: New York-based Rightway, a provider of pharmacy benefits and care navigation services, raised $155 million in Series E funding led by . The startup will use the financing in part to expand its AI capabilities.

8. (tied) , $100M, cloud seeding: El Segundo, California-based Rainmaker, a startup focused on atmospheric research and producing new freshwater through cloud seeding, picked up $100 million in Series B funding. The round includes investments from , , , and .

8. (tied) , $100M, tax compliance: San Francisco-based Numeral, an AI-powered sales tax compliance platform, closed on a $100 million Series C round led by .

8. (tied) , $100M, AI data: San Francisco-based Micro1, a provider of AI training data to major AI labs, has reportedly raised a fresh funding round of more than $100 million at a valuation of $4 billion, according to a citing people familiar with the deal.

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of Sept. 19-25, 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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From January through August U.S. tech layoffs reached at least 94,046, up 16.8% from 80,486 in the same period of 2025. Interestingly, and unsurprisingly, many of the cuts came as tech companies redirected spending toward AI and restructured operations to reduce costs.

The year started off on a busy note on the layoff front. After job cuts dropped sharply in December 2025 to 5,151, they surged in January to over 20,000. May was particularly brutal. The month drove the year-to-date increase, recording 31,513 layoffs 鈥� including 鈥檚 8,000-job reduction 鈥� the highest monthly count since March 2023, when layoffs reached 36,602.

Recent months indicate a slowdown. Layoffs fell each month after May, reaching 2,347 in August. Overall, June-August 2026 layoffs totaled 19,331, down 16.2% year over year. The decline suggests recent easing, though it is too early to establish a lasting reversal.

Artificial intelligence has become a much more common explanation for layoff decisions, noted , founder of AI was cited in 33% of tech layoff events this year, up from just 1% in 2024. His tracker attributes 92,913 layoffs globally, or 72% of this year鈥檚 total, to AI.

鈥淭here鈥檚 been little evidence that AI is actually replacing the work of the human employees let go,鈥� Lee said of this year鈥檚 largest AI-attributed layoffs. He believes established tech companies are spending heavily on AI and cutting costs elsewhere, hoping to increase productivity with smaller workforces.

Companies cutting

This year, we鈥檝e seen a number of Big Tech and publicly traded companies, as well as startups, make deep cuts.

But interestingly, as with last year, public tech companies have dominated layoff headlines in 2026 so far, led by and Meta.

鈥淏ig companies [have] made up about 87% of everyone laid off in 2026, which is similar to last year, when they made up 85%,鈥� Lee said.

Amazon accounted for 17,388 cuts this year so far through August. Those included a 16,000-worker RIF announcement in January and several smaller subsequent rounds. Meta was next with 10,400 layoffs, including an 8,000-job reduction carried out in May that represented 10% of its workforce.

and recorded the next-largest totals, letting go of 4,800 and 4,760 employees, respectively. , and each recorded 4,000 layoffs, followed by with 3,000, with 2,900 and with 2,600. Notably, the Top 10 list spans a variety of sectors, including cloud computing, social media, payments and enterprise technology.

We should also note that according to reports, 鈥檚 workforce fell by about 21,000 employees in its fiscal year ended May 31, 2026, but the worker count and exact timing for each of聽 those reported cuts was unclear, so we did not include that total in our tracker.

Among privately held companies in the tracker, recorded the largest disclosed total at 1,000, followed by HR software provider with 950 and with 500. Those figures were substantially smaller than the largest public-company reductions, although undisclosed layoff counts limit comparisons between the two groups.

And in early September, reportedly laid off 3,300 workers, or 10% of its workforce.

An AI focus

, of , says AI is affecting jobs in two ways. Some work, including coding, can now be done with fewer people. 鈥淭here are jobs that are literally being replaced by artificial intelligence,鈥� he told 兔子先生传媒 News.

But companies are also changing their priorities. They鈥檙e putting more money into AI and cutting teams working on other parts of the business. 鈥淭hey鈥檙e letting people go from one area of their organization while they might even be hiring in an area that is focused on AI,鈥� Challenger said. That鈥檚 why a company may lay people off and advertise new jobs at the same time.

Tech has announced more job cuts than any other industry this year, Challenger said. Across the U.S. economy, layoffs are down somewhat from last year, though that comparison is skewed by the large number of federal job cuts in 2025. When compared with the period just after the pandemic, when employers struggled to find workers, layoffs remain elevated.

Few companies outside tech have blamed job cuts on AI so far, Challenger said.

It鈥檚 not all negative though, in his view. There’s potential upside for programmers, he said. If AI makes software less expensive to build, companies in other industries might embark on projects they couldn鈥檛 afford before. That could mean new jobs outside tech, though it鈥檚 too early to know whether those jobs will make up for the ones being cut.

Also, it appears that some companies might be regretting their layoff decisions. Amazon is reaching out to eligible former employees about open roles across the company, including in its cloud-computing and AI businesses, according to a聽 report.

Methodology

Layoffs figures are from The 兔子先生传媒 Tech Layoffs Tracker, where we record reported job cuts at U.S. tech employers. The tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence 鈥� both privately and publicly traded 鈥� and is updated at least bi-weekly. Layoff and workforce figures are best estimates based on reporting. Actual layoff figures are likely much higher than reported as many companies do not disclose the number of jobs cut when announcing layoffs. For more about our methodology for tracking layoffs, refer to the tracker鈥檚 methodology section.

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• The 兔子先生传媒 Tech Layoffs Tracker

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So Shariff offered to build an AI agent for a couple of properties. The first, called Alfred, answered guest questions and helped with check-ins and check-outs. Within 90 days, he said, online reviews improved at both properties. He then began deploying it at more hotels, before deciding that guest messaging was only one part of the opportunity.

That work led him to team up with to start , the San Francisco startup they founded in July 2025. The company is now emerging from stealth with $6.7 million in seed funding to build agents that handle reservations, guest requests, staff coordination and other hotel tasks. led the raise, with participation from . The round marks Dextr鈥檚 first institutional financing.

From guest requests to bookings

After early guest management deployments, Dextr turned to incoming calls with a voice reservations agent called Daisy. If a caller has a question about a reservation or something they can’t find on a booking site, answering it could lead to a booking, Shariff found. As hotels began using Daisy, he realized that the phone calls were 鈥渓eading to revenue.鈥�

In fact, today, one large hotel that Dextr works with handles $100,000 to $300,000 a month in bookings through the voice agent, according to Shariff.

Dextr has since expanded into agents for group bookings, staff management and other work. Its goal is to connect those agents so information gathered in one part of a hotel can prompt action in another. For example, if a guest declines housekeeping, a guest management agent could pass that information to an operations agent so that staff schedules could be adjusted accordingly.

Another use case could involve a property operating without an overnight front desk employee. A guest arriving late could call the hotel, have Daisy verify their reservation and receive instructions for entering their room. Daisy and Alfred, Dextr鈥檚 guest management agent, would work together to handle the arrival.

For some properties, using agents to handle late check-ins could remove the need to staff an overnight front desk shift. Shariff estimated that such a shift could cost a property from $70,000 to $80,000 a year in a market such as California.

鈥淚t was very hard to hire for these shifts to begin with,鈥� he said.

Building ROI

Shariff says combining agents is just one of three things that set Dextr apart from other hotel AI tools. An agent that answers calls or messages can handle a particular task, he said, but connecting it with other agents opens up additional uses for a property.

鈥淚t’s important that we do multiple use cases to see the higher ROI,鈥� he told 兔子先生传媒 News in an interview.

The second distinction, according to Shariff, is Dextr鈥檚 approach to getting the agents running. Putting them to work takes more than installing software, he said. Every hotel differs from another in how it manages reservations, assigns staff, and handles guest requests. So Dextr sends engineers to work directly with customers, identify where agents could be useful and then connect them to existing systems. Those engineers stay involved as the property puts the agents to use, he said, and work to find applications that have the potential to deliver a return on investment.

鈥淓very property, every operation is different in this space,鈥� he said. Dextr integrates with property management systems including Oracle Hospitality, OPERA Cloud, , and .

The third is how employees use the agents. They can direct them by text or voice through a companion called Doss, without having to learn a series of steps in a new software system. Shariff said frequent turnover makes it difficult for hotels to train staff on complicated tools and retain that knowledge when employees leave. With Doss, 鈥測ou can call it, you can talk to it, you can text it,鈥� he said.

Hotels and more

Dextr says its agents now handle more than 1 million interactions a month across hundreds of hotels. The startup鈥檚 customers range from a 21-room property to one with 550 rooms. They include independent hotels and properties operating under the , , and brands, as well as reservation centers, vacation rentals and outdoor hospitality businesses.

The startup works directly with the owners and operators of those branded properties, Shariff said, and is beginning its first direct relationship with a hotel brand that he declined to name. It also works with hotel and property management companies, as well as reservation centers, vacation rentals and outdoor hospitality venues such as campgrounds, RV parks, lodges and golf courses.

The company charges a subscription based on the property and the agents it uses, with usage-based fees for some agents. Shariff declined to disclose revenue, profitability or the round鈥檚 valuation. After spending much of last year on pilot projects, he said, Dextr began growing more quickly this spring. He said annual recurring revenue has been growing about 20% to 30% month over month, though he did not disclose hard figures.

Dextr plans to use its new funding to hire more engineers who work directly with customers, expand its software integrations, and develop additional agents. Its team has grown from three people a year ago to 21.

, an AI partner at Elevation Capital, told 兔子先生传媒 News that Dextr鈥檚 early traction got the firm鈥檚 attention. He pointed to customers using the agents to generate bookings and upsells alongside reducing operating costs.

鈥淲hat stood out immediately was Sajid and Scott’s execution. Dextr scaled to [hundreds of] contracted properties and meaningful ARR without raising a single dollar,鈥� he wrote via email. 鈥淚n a space where most AI companies are still searching for product-market fit, Dextr had customers, zero churn, and a clear ROI story. That combination of founder quality and early traction is exactly what we look for at Elevation.鈥�

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So far this year, companies in the space have raised around $2 billion in seed- through growth-stage funding. That’s already ahead of the 2025 full-year total, driven in large part by big rounds for companies at the intersection of AI and gaming.

Lead fundraisers

A few particularly large rounds contributed an outsized share to this year鈥檚 funding tally.

Sunnyvale, California-based , a developer of foundation models for AI-powered 3D generation, was the largest fundraiser, pulling in $400 million in a July Series B at a $1.5 billion valuation. While not a pure-play gaming company, Meshy highlights gaming as a core use case for its 3D AI agent.

, developer of a platform for training AI models, was another investor favorite, pulling in $300 million. Like Meshy, Israel-based Decart is not an AI pure-play; it鈥檚 also known for a video simulation technology pitched as compelling for game development.

We also saw plenty of pure gaming startups in the largest-round list, including motion-based family game developer , which closed on $150 million last week, and Turkish mobile gaming company , which picked up $70 million in May. Below, we highlight nine standout funding recipients this year.

Investors are also scaling up

Gaming-focused venture investors are also attracting fresh capital. One of the larger raises closed last month, with San Francisco-based picking up $250 million for its fourth flagship fund. The closing follows a string of successful investments, including a lead seed stake in viral puzzle game maker , which later exited at a $5 billion valuation.

Makers Fund has continued to invest actively, taking part in at least 10 known rounds this year, per 兔子先生传媒 data. This includes financings for high-profile startups such as , the sports betting platform that recently drew attention for a controversial ad campaign.

A few months earlier, announced a fresh raise of its own, securing $100 million for a fund focused on indie games. The firm鈥檚 Special Opportunities Fund intends to provide financing in exchange for a share of a game鈥檚 revenue, in what it hopes will be a more appealing investment structure for indie studios.

The beginning of an up cycle?

Overall, while gaming startup investment remains far below the peaks seen a few years ago, the substantial year-over-year gains we鈥檙e seeing look encouraging. The direction of funding charts certainly points to an upturn still early in the making.

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So far this year, global startups have secured at least 114 Series A rounds聽1 of $100 million or more, per 兔子先生传媒 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 兔子先生传媒 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 兔子先生传媒 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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• 兔子先生传媒 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 兔子先生传媒 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 兔子先生传媒 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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