The Series H round, led by and , brings the Costa Mesa, California-based company鈥檚 total raised to date to $11.4 billion, per SA国际传媒. The funding comes amid record venture investment into startups developing defense, wartime and national security technologies and a administration push to modernize the U.S. military.

Just through mid-May, defense-related startups 鈥� defined by SA国际传媒 as the industries of military, national security and law enforcement 鈥� have raised nearly $13.6 billion this year, per SA国际传媒 . That puts them on track to more than double the already record-breaking total of $8.8 billion raised in 2025, when Anduril was by far the sector’s largest venture capital recipient.

鈥淲hen we founded Anduril in 2017, defense was not a category that attracted significant venture investment,鈥� company CEO and co-founder said in a . 鈥淭hat has changed meaningfully over the last several years. Investors have increasingly recognized the scale of the technological and industrial challenges facing the United States and its allies. They are also observing an environment in which the most agile, adaptive, and ambitious companies are the ones most capable of solving these challenges.

In March, Anduril signed a $20 billion, 10-year contract with the to supply software and weapons. It also announced that it was part of a group of companies building the $185 billion missile defense system for the U.S. government.

After Anduril, several other defense-tech startups, all based in the U.S., have received sizable investments this year:

• : In March, San Diego-based Shield AI secured $2 billion in fresh funding led by and. The startup develops AI pilots and autonomous aircraft systems for military applications and has raised more than $3.5 billion overall, per SA国际传媒 data.
• : Austin, Texas-based Saronic said in March that it has raised $1.75 billion in a Series D led by. The startup builds unmanned surface vessels for naval and defense use and has now brought in nearly $2.6 billion in total funding.听
• : Centennial, Colorado-based True Anomaly said last month that it has raised $600 million led by and as investors continue pouring capital into space-security infrastructure. The company develops spacecraft and orbital defense systems and has raised more than $1 billion to date, per SA国际传媒.
• : Commercial space company Sierra Space said in March that it had raised $550 million in funding led by . The startup develops commercial space stations, satellite systems and the reusable Dream Chaser spaceplane for cargo and defense-related missions. The company, based in Louisville, Colorado, has now raised roughly $2.2 billion overall, according to SA国际传媒.

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As Ayoub describes it, while the medical profession was sold to him as a lucrative and stable career, the reality of modern healthcare hit home during a gap between his fellowship and his full-time role at . Living in New York City and unable to afford rent, he attempted to pick up extra shifts at a local urgent care. Despite a clear workforce shortage and his own need for income, he was told he couldn’t start for 90 to 120 days.

The culprit? A fragmented, manual credentialing process that acts as the industry’s primary bottleneck.

鈥淭he bottleneck is not the number of doctors, but the fragmented infrastructure connecting them to where they are needed,鈥� said Ayoub, who also serves as an assistant professor of neurosurgery at the Donald & Barbara Zucker School of Medicine. 鈥淢ost people assume the issue in healthcare staffing is a lack of doctors, but what we鈥檝e seen is something different.听

There鈥檚 a large, underutilized workforce that simply can鈥檛 move between systems efficiently,

So in early 2025, Ayoub and began pondering a solution. Their initial ideas eventually turned into , a startup with an AI-powered platform designed to serve as an “automated Dropbox” for physicians.听

Today, the New York City-based startup is announcing it has raised $2.2 million in a pre-seed round led by , SA国际传媒 News reports exclusively. also participated in the round.

AI-driven healthcare takes off

AI-related healthcare has seen a significant rise in venture funding globally, SA国际传媒 shows. Investors put an estimated $14.9 billion into seed- through growth-stage funding to companies in AI-powered health tech categories in 2025, per SA国际传媒 data. That鈥檚 up significantly compared to the $8.6 billion raised in all of 2024.

Many of the recently funded healthcare startups are AI-centric, and, like Saile, are focused on streamlining dated processes.听

In the current system, every time a doctor wants to work at a new facility 鈥� whether it be a hospital, a surgery center, or a telemedicine platform 鈥� he or she must manually resubmit a CV, licenses, and board certifications via email. In applying to an urgent care facility, Ayoub realized that while staffing agencies act as gatekeepers, the underlying infrastructure was broken.听

There was no centralized way for a doctor to maintain a compliant status and share it instantly across different job verticals.

Saile aims to solve that problem by storing and tracking all a doctor鈥檚 credentials in one place and providing alerts before documents expire so that a physician can always be compliant. It goes one step further by providing access to a shift marketplace. In a nutshell, the startup serves as a portable credential passport for physicians to be identified and assigned patients at various hospitals.听

The company鈥檚 five modular AI agents automate what currently takes months of manual coordination across recruiting, onboarding, credentialing, staffing and compliance.听

By combining credentialing and staffing into a single infrastructure layer, Ayoub says Saile has shortened the onboarding timeline by roughly 45 days, from about 90 to 120 days, and reduced administrative tasks for healthcare facilities by an estimated 40%.

鈥淥ther solutions either focus on one piece of the problem or offer staffing tied to a single job type,鈥� Ayoub said in an interview. 鈥淪aile owns the entire journey鈥nd facilities get direct access to a pre-vetted pool of local and regional physicians without juggling multiple vendors or paying for the friction in between.鈥�

Investing in the 鈥榠nfrastructure layer鈥�

What began as a bootstrapped project fueled by word-of-mouth in a tight-knit clinician community has quickly gained momentum. The app has grown to nearly 5,000 active user physicians nationwide. Operating with a lean core team of four, the company plans to use its new capital to expand its AI agent infrastructure, grow its marketplace capabilities, and deepen integrations with facility credentialing systems.

Saile has four core revenue streams, with a primary focus on a per-seat SaaS model for facilities. The approach is to offer facilities access to the pool of physicians, and then charge on a per-seat usage basis for the workflow and credentialing infrastructure that supports it.

, founder and partner at Matchstick Ventures, said his firm was drawn to the founder market fit it saw in Saile.

鈥淢arc had felt the pain of this problem and actually had built this more or less for himself out the gate,鈥� Brosher said in an interview with SA国际传媒 News. 鈥淲e love those combos where founders aren鈥檛 just randomly seeking out a solution to make a buck. This was very much a personal thing for him in the problem that he was solving.鈥�

The firm also saw a 鈥渂ig鈥� market opportunity in offering an 鈥渁ll-in-one鈥� solution for doctors looking to pick up side jobs.

鈥淧eople have tried to go after this a few different ways. They’ve either gone after credentialing, or they are a staffing agency,鈥� Brosher added. 鈥淎nd when we look at this market, we feel like there needs to be disruption here鈥ltimately, Saile is building the infrastructure layer beneath staffing. We feel like having that all-in-one infrastructure layer is actually where the real value is to be had.鈥�

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But while the fundamental need for food security remains high, startup investors have become increasingly risk-averse, driven by structural and economic pressures, SA国际传媒 data shows.

And that aversion is reflected in the number of venture deals being conducted in the sectors so far in 2026.

The broad trend: Venture funding in agriculture and farming is still undergoing a market correction following the hyper-funding era of 2021, when startups in the space raised $10.5 billion across 1,419 deals, a review of SA国际传媒 data shows. Many agtech startups that raised massive rounds during that 2021 peak have struggled to show real-world traction.

At the same time, as with most other industries, AI is having an impact. AI in agriculture has moved from predictive to agentic. The focus is no longer strictly on collecting data, but on autonomous orchestration 鈥� closing the loop between digital insight and physical action on the farm.

The numbers: So far this year through May 7, agtech startups have raised $1.4 billion, on track to be down slightly or on par with the $4.4 billion raised last year and the $4.6 billion raised in 2024. It鈥檚 also well below the peak $10.5 billion raised by agriculture-related startups in 2021 and the $10.3 billion that went to such companies in 2022.听

Deal count looks to be pacing down more significantly, with the 187 deals done year to date tracking much slower than the 784 rounds in 2025 and the 1,038 in 2024.That signals not only less overall funding but also larger round sizes for fewer companies.

Standout deals

The largest round in 2026 so far was raised by , a New Zealand startup that develops a smart collar for cattle, which enables virtual fencing and real-time monitoring. In late March, Halter raised funding led by at a valuation of just over $2 billion.

Boston-based , a weather technology company offering real-time forecasting services to predict and respond to climate-related threats, closed on at a $1 billion valuation in February. Private equity firm and co-led that financing.

贵谤补苍肠别鈥檚 , which has built an amphibious water bomber aircraft for aerial wildfire suppression, raised a $135.2 million Series A round led by in March.

And , a UK-based producer of gene-edited crops, raised $105 million in a Series C round of funding co-led by and in March.

Interestingly, Indian startups have raised three of the 11 largest deals in the sector so far in 2026: , and .

Exits

The agtech exit landscape over the past year has been characterized by strategic consolidation rather than high-profile IPOs. As venture capital interest remains disciplined, established players 鈥� both within the agricultural sector and from broader tech 鈥� are acquiring startups to bolster their AI and automation capabilities.

The third quarter of 2025 saw three notable acquisitions in the space:

• picked up (founded in 1982, the company is manufacturer of driverless orchard sprayer for agriculture applications).
• announced it would buy , which aims to offer high-volume crop production units made from upcycled shipping containers to support farming in any climate, 听补苍诲
• said it would acquire (formerly Agritask), a crop supply intelligence company.

In the first half of 2026, the trend continued.听

In January, announced it would acquire biological insect control group . Interestingly, the company said it plans to.

Also in January, cannabis industry enterprise resource planning (ERP) platform announced it had acquired competitor , an 8-year-old cultivation management software startup.

(FBN), , and have long been viewed as IPO candidates but it remains to be seen when (or if) they鈥檒l take the plunge.

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The energy sector necessary for AI compute also garnered significant funding this year.听

All told, roughly half of European venture funding in 2026 to date has been in AI-related companies, SA国际传媒 data shows.

The uptick in artificial intelligence investment has coincided with an overall gain in startup funding in the region the last past quarters. Funding was up a third year over year in Q4 and Q1, reaching more than $17 billion each quarter.听

AI talent hubs

One area where Europe is seeing momentum is with frontier labs.

Employees from 鈥� the original AI lab established in London in 2010 and acquired by Google in 2014 鈥斕齢ave spawned two new labs in London: and . And , previously Meta AI鈥檚 lead, formed in Paris. Just this year, the three companies have altogether raised $2.6 billion.听

Last year, German-based AI lab raised hundreds of million in funding. One of the earlier model companies from Europe, , founded in 2023, has raised $4 billion in total.听

Europe is also home to one of the early diffusion model companies, . from Heidelberg, Germany, recently merged with Canada-based in April for sovereign and commercial AI deployments, valuing the merged entity at $20 billion, creating a transatlantic competitor to U.S. model companies.听

The recent spate of new AI lab formation and renewed momentum on the funding front could be a driver for talent hubs to concentrate in Europe. Still, although foundation labs in Europe have raised more than , that represents a tiny percentage of the amount raised by frontier model companies in the U.S.

AI-native

In the European report, found 81% early-stage companies, largely pre Series A, are AI-native 鈥� up from 50% a year ago. Leading by company count this year were 12 companies in dev tools and infrastructure and 11 companies in industrials and robotics.听

The advantages of building in Europe are 鈥渁ccess to strong engineers in the very beginning 鈥� having people that want to build and be part of a founding business, and access to good quality talent that you can retain,鈥� said , a principal at Notion Capital who co-wrote the report.听

He also noted that in earlier vintages, the trend was to 鈥渂uild a company, expand to the U.S. at some point around the Series B. Now, from the start, founders tend to think globally from day one.鈥�

The single most dramatic change, however, is how much leaner teams are ahead of the Series A, he said.

US bound

Despite the more recent pickup, European funding growth has lagged behind the U.S. since 2024.听

The leading San Francisco-based model companies 鈥� and 鈥� have raised $254 billion since 2023 and recentered the Bay Area post-pandemic as the place to be for ambitious founders.听

鈥淭he companies that start in the UK, France, Germany, and the Nordics, then come to Silicon Valley to grow,鈥� said , managing partner at , speaking on current market trends.听

鈥淵ou can build an amazing business anywhere in the world now. The barrier to building greatness has shrunk,鈥� said McLoughlin, who himself relocated to听 San Francisco from the UK in 2010. 鈥淏ut, the chances of building a generational company are so much higher, if you come to the Bay Area.鈥�

UK-founded incubator (EF) relocated to the U.S. in 2024. EF sources founders听 from leading universities around the globe to start companies but incorporates each business it funded in the U.S.听

鈥淭he Bay Area program is not just about proximity to capital,鈥� said , CEO and co-founder on announcing EF鈥檚 recent fund raise. 鈥淚t changes the ambition gradient. Founders move faster, think bigger and compete on a global stage from day one.鈥�

鈥淚’m seeing more than ever, companies that started in these emerging markets, and then going to the U.S. very early in their journey 鈥� not to sell themselves, but to sell to customers,” said , general partner at global investment firm . The firm invests on a global basis day zero at pre-seed, with its Elevate fund investing at later stages.听

鈥淭he time to copy a business is a month or two months, as opposed to years,鈥� said Abdel-Nour,听 鈥淵ou have an incentive to go and capture these big markets before your U.S. competition has really reached escape velocity,” he said.听

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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.

Another week, another infusion of big AI rounds. For this past week, the largest fundraiser by a long shot was , a developer of AI customer experience tools that picked up $950 million. Other big rounds went to companies in sectors including satellite development, biotech, and, yes, more vertical AI and AI infrastructure.

1. , $950M, customer experience AI: Sierra, a provider of AI-driven tools for customer experience management, raised $950 million in fresh funding at a $15 billion valuation. and led the financing for the three-year-old, San Francisco-based company.

2.听, $455M, space tech: Astranis, a developer of advanced satellites for high orbits, secured $450 million in equity and debt investment. The financing included a $300 million Series E equity round led by and and up to $155 million in credit through .听

3.听, $250M, biotech: Natick, Massachusetts-based Anagram Therapeutics, a developer of a pill for people living with exocrine pancreatic insufficiency due to cystic fibrosis, pancreatic cancer and related disorders, closed on $250 million in new funding from .听

4.听, $200M, AI software development: Blitzy, developer of an autonomous software development platform, picked up $200 million in fresh funding at a $1.4 billion valuation. Northzone led the financing for the Cambridge, Massachusetts-based company.听听

5. , $160M, insurance: Corgi Insurance, provider of an AI-native insurance platform for startups, secured $160 million in Series B funding. led the financing, which set a $1.3 billion valuation for the San Francisco-based company.听

6. , $140M, renewable energy: Portland, Oregon-based Panthalassa, which aims to perform AI inference computing at sea using power generated from ocean waves, raised $140 million in a Series B financing led by .

7. , $125M, insurance: Reserv, a provider of third-party administrator services to the insurance industry, closed on $125 million in a Series C funding round led by . Launched in 2022, New York-based Reserv has raised over $200 million in known funding to date, per SA国际传媒 data.

8.听, $107M, AI infrastructure: DeepInfra, a cloud platform for high-throughput AI inference, landed $107 million in Series B funding. and led the financing for the four-year-old, Palo Alto, California-based company.

9. , $60M, vertical AI: San Jose, California-based Tessera Labs, developer of an AI platform for enterprise ERP systems and data, secured $60 million in a funding round led by .听

10. , $56M, gaming: Astrocade, developer of an AI platform for creating, building and playing games, announced $56 million in new funding. The funding for the Los Altos, California-based company includes a Series B led by and a Series A led by , Astrocade said.听

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In that role at least, we鈥檙e all pretty familiar with the current state-of-the-art in advertising, marketing and customer experience management technology. And we can all attest it鈥檚 quite skilled at separating us from our disposable income.

Still, startups and their backers see myriad paths to do better. With that in mind, they鈥檝e deployed billions of dollars in recent quarters to a heavily AI-driven cohort of companies largely focused on improving efficiency by deploying agentic tools to their chosen niches.

The broad trend: We鈥檙e not back to boom times. Venture funding to sales, marketing and CRM categories actually hit its cyclical peak in 2021 and 2022. Investment has slowed down considerably since, with annual funding hovering around the $8 billion mark in the past three years, SA国际传媒 data shows.

Not surprisingly, however, AI-focused companies are scooping up a much larger share of funding than during the prior peak, with a majority of sales, marketing and CRM-focused investment going to companies in SA国际传媒 AI-related categories.

The numbers: So far in 2026, companies in sales, marketing and CRM categories have pulled in around $3.7 billion globally in seed- through growth-stage funding, per SA国际传媒 data.听

That puts the space on track to come in roughly flat with or a bit up from the prior three years, and still far below boom-era levels, when sales and marketing investment topped $20 billion.

Standout deals

The past few weeks have been particularly busy on the funding front, with multiple big rounds.

In the customer experience area, AI unicorn pulled in a $950 million megaround this week led by and The financing set a $15 billion valuation for the San Francisco-based company, which offers AI-driven customer experience tools to companies.

Another recent large funding recipient was , developer of an agentic marketing platform, which closed last week on a $150 million Series D financing led by and , valuing the company at $2.75 billion. The San Francisco startup offers AI agents that carry out audience research, generate brand content and conduct digital marketing campaigns.

, developer of an agentic customer experience platform for enterprises operating at scale in what it describes as high-stakes, regulated environments, picked up another sizable financing. Last week, the San Mateo, California-based company closed on $110 million in new funding led by .

Also last month, , developer of agentic AI tools for go-to-market teams, secured its Series B. The New York startup raised $45 million in a round co-led by and .

One of the year鈥檚 biggest rounds for the marketing and CRM space, meanwhile, came earlier this year. , developer of an AI agent management platform for enterprise customer service, locked up $350 million in a January Series D. General Catalyst led the financing, which brought the听 Berlin-based startup鈥檚 valuation to $3 billion.

Exits and outlook

Big startup M&A deals are also happening, albeit in small quantities.

Just two weeks ago, payment platform announced that it will acquire , a loyalty and incentives platform for merchants, for around $880 million. Berlin-based Talon had previously raised over $120 million in venture funding.

Another large transaction, announced last summer, was 鈥� purchase of Dusseldorf-based , a conversational AI platform for customer support, in a deal valuing the latter at around $955 million.

As for IPOs, activity has been more muted. One exception was , a platform for brands to launch TV commercials, which made its debut a year ago and is currently trading well below its post-IPO price.

It鈥檚 not surprising to see IPO activity muted. The enterprise software IPO market has been muted for months now, driven by investor worries over AI impact on SaaS business models. Startups focused on vertical AI since inception, meanwhile, are a more youthful cohort, mostly not yet prime for public markets.听

Give them time, however, and it鈥檚 likely these AI-driven upstarts will come up with plenty of ingenious techniques to separate us from more of our spending money. And when they do, expect exits to follow.

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led the financing, which included participation from (Google鈥檚 early-stage AI fund), , , , and several angel investors. The raise brings Fazeshift鈥檚 total raised to $22 million since its 2023 inception.听

The San Francisco company was founded by a team with an unconventional pedigree: (CEO), a former consultant and mechanical engineer and (CTO), an -trained nuclear submarine officer.听

The two met at , but their lightbulb moment came while running a previous startup, , where they found themselves color-coding spreadsheets to track payments for just 10 customers and realized that the tools they were using failed to solve the basic problem of ensuring money actually hits the bank.

They realized that while there are more than a million accounts receivable (AR) clerks in the U.S. alone, many of them spend their time bouncing between systems such as , CRMs like , bank portals, and email threads because these systems do not natively talk to each other.听

Unlike accounts payable, which a company can standardize internally, Leksana contends, accounts receivable is a “snowflake” problem that remains one of the least automated functions in finance. Every customer has a unique set of requirements; for instance, a large retailer might demand that an invoice be submitted through a specific proprietary portal with Part A and Part B attached as PDFs.听

Fazeshift claims that it can automate more than 90% of manual AR tasks 鈥� from invoicing and collections to payment matching and reconciliation 鈥� by operating on top of existing systems and executing workflows across them. It essentially sits on top of a company鈥檚 current stack as a 鈥渂rain.鈥�

Competitors, according to Leksana, are generally focused on automating tasks, while Fazeshift is working on building what she described as an 鈥渋ntelligent control layer鈥� that helps companies 鈥渃ollect faster, more predictably and with less effort, and that is continuously improving through proprietary payer behavior data.鈥�

鈥淲hat sets us apart is our ability to handle complex workflows that other tools fail to solve 鈥� especially in industries like wholesale, construction, staffing, and HVAC, where AR processes are highly fragmented and manual,鈥� Leksana told SA国际传媒 News in an interview.

An OS for the finance organization

After launching at the start of the Summer 2024 Y Combinator cohort, Fazeshift has seen its revenue grow 12x in a single year, attracting dozens of enterprise customers, including eight unicorns and its first public company, according to Leksana.

Customers include , , , and , as well as one of the largest independent wholesale distributors in the Southeast, the world鈥檚 top e-commerce aggregator, and a leader in music publishing, per Leksana.听

Looking ahead, Leksana believes that Fazeshift has the potential to expand beyond accounts receivables. The goal is for Fazeshift to become the primary operating system for the entire finance organization.

鈥淥ur long-term vision is to expand into a broader CFO suite,鈥� she said, 鈥渂uilding toward a future of autonomous finance where core operational work is executed by AI and human teams can focus on agent management, strategic work, and governance.鈥�

Broken workflows for 鈥榗ritical functions鈥�

, partner at F-Prime Capital, said her firm was impressed by Fazeshift鈥檚 efforts to meet the needs of companies still running AR mostly on spreadsheets and email.

鈥淵ou鈥檇 be surprised how many Fortune 500 companies only started adopting software a few years ago and still have dozens, if not hundreds, of AR clerks on staff,鈥� she wrote via email. 鈥淭hat gap between how critical the function is and how broken the workflows remain is exactly the kind of opportunity we look for.鈥�

Wu also believes the market is at an inflection point where AI is moving from co-pilot to co-worker, and human teams are shifting from doing the work to reviewing and managing AI agents.

鈥淔azeshift is bringing us closer to an autonomous future for finance,鈥� she said. The founders had 鈥渓ived the pain of broken AR workflows firsthand at their last company and set out to build the platform they wished they鈥檇 had. When you meet founders like that, you move fast.鈥�

Fintech startups, particularly those that apply AI to traditionally manual or burdensome processes, have benefited from increased investment in recent quarters. Global funding to VC-backed financial technology startups totaled $53.8 billion in 2025, per SA国际传媒 . That鈥檚 a more than 29% increase from 2024鈥檚 total of $41.6 billion raised.

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With that in mind, its venture arm, , is backing startups that build the financial and technical infrastructure for a more autonomous economy.听

SA国际传媒 News recently conducted an email interview with , managing director of , about the firm鈥檚 investment thesis, the kinds of startups it aims to back, and how it works with founders to build and scale projects in the American Express ecosystem.听

Amex Ventures鈥� recent investments reflect its focus on an autonomous future. In April, it led a round into business identity infrastructure platform and also wrote a check into , an agentic marketing platform that raised a $43 million Series B. It also backed Candex, a startup that uses AI and aims to help large companies pay small, one-time, or irregular vendors without the administrative headache or risk that comes with onboarding them.听

Since joining Amex 15 years ago, Tsang has been responsible for identifying and executing strategic investments in early- to growth-stage startups that fit the American Express thesis. He also leads the firm鈥檚 consumer services investment vertical, focusing on the future of membership, and oversees the firm鈥檚 global portfolio management operations.

Before joining American Express, he was part of the diversified industrials investment banking group at , where he focused on M&A and corporate finance transactions.

The interview has been edited for brevity and clarity.

SA国际传媒 News: In the 鈥�2025 GenAI era,鈥� we focused on tools that inform decisions. Now that we鈥檙e in the ‘2026 Agentic era,’ where agents execute transactions, how has the bar shifted for founders pitching Amex Ventures? Are you prioritizing the AI model or the trust/identity layer that will allow an agent to use a Platinum card autonomously?

Tsang: The bar for Amex Ventures investment has shifted toward agentic commerce systems that can navigate complexity and help facilitate end-to-end workflows for customers, with appropriate user authorization and controls. The most compelling founders operate companies that can handle much more of the full commerce journey, not just surfacing options, but incorporating context, executing user decisions, and ultimately completing tasks, with personalization increasingly becoming a key differentiator in how those experiences are delivered and retained.

Agentic commerce systems are sometimes framed too narrowly as just the final execution step, but the real opportunity 鈥� and where we are seeing the most innovation 鈥� is in systems that can understand preferences, constraints, and intent, and then orchestrate a complete experience that properly reflects them.听

Over time, this could evolve to support more comprehensive experiences, such as planning entire trips rather than just recommending a flight, or managing a series of related actions instead of a single step. AI also has the potential to help scale the kind of high-touch, tailored experiences that were once reserved for a small set of customers to a much broader base.

From our perspective, we are less focused on any single layer, whether that is the underlying model or the trust and identity infrastructure, and more focused on how these components come together to deliver a seamless and high-quality customer experience.听

Trust and security are foundational, especially in financial services. Ultimately, we are looking for founders who are building with the ambition and technical depth to address real-world complexity, while delivering meaningful outcomes for customers with a focus on security and compliance.听

Many founders struggle with the 鈥淐VC paradox鈥� 鈥� getting a pilot with Amex is a huge win, but scaling it globally can take years. What is the most effective way a founder can leverage your firm to move from a localized experiment to a core membership benefit?

We see the most successful commercial partnerships follow a crawl, walk, run model 鈥� starting with a focused pilot, learning quickly, and then building toward broader integration and scale.

Our investment model creates shared incentives to build commercial partnerships that benefit both American Express and our portfolio companies, and we help advance these relationships over time. We look for founders who continue to iterate and innovate so that the scope of these partnerships can expand in a meaningful and sustainable way.

For example, we recently invested in the startup , an agentic marketing platform that helps enterprises understand, influence, and measure how brands are represented across AI-powered channels. At American Express, we are utilizing Bluefish鈥檚 technology to support our agentic search efforts, including our Answer Engine Optimization (AEO) strategy.

For travel and dining startups, customer acquisition cost (CAC) is skyrocketing due to influencer-driven marketing. How much does a startup鈥檚 ability to plug into the Amex 鈥榗losed-loop鈥� ecosystem factor into your valuation, versus their organic growth?

When we evaluate companies, we start with the fundamentals. We are looking for businesses that can stand on their own, with strong products, clear value propositions, and sustainable growth models. That is core to any investment decision we make.

At the same time, one of the advantages of an investment from Amex Ventures is the potential to partner with American Express and engage with our ecosystem, which we see as a meaningful opportunity for many companies. When there is a clear path to creating mutual value through a commercial partnership, it can strengthen our conviction to invest.

That said, we do not invest simply because a company is likely to have a commercial relationship with American Express. To invest, we must believe in the underlying business on its own merits.听 The potential for an American Express partnership represents additional upside and an opportunity to accelerate the company’s growth together.

With tech M&A volumes up significantly this year, are you managing the current portfolio with an eye toward strategic buyouts by Amex鈥檚 parent company, or are you pushing for independent IPO-ready unit economics given the partially reopened window?

We are not managing the portfolio with a specific exit outcome in mind. Our focus is on investing in companies that can build strong, durable businesses and on creating strategic relationships with our portfolio companies that drive value on both sides.

In many cases, that means working closely with our portfolio companies to explore potential commercial relationships with American Express, which is core to our role as a corporate venture investor. In fact, nearly two-thirds of our portfolio companies have had a commercial relationship with American Express.

From there, a company鈥檚 path, whether toward an IPO or M&A, is ultimately driven by the founder and the broader investor group. Our role is not to steer that outcome, but to support the company in building long-term value and maintaining the flexibility to pursue the right exit opportunity.

When you look at the current 鈥渟ervice-as-software鈥� startups, do you see them as long-term standalone companies, or are we looking at a massive consolidation cycle where travel/dining tech eventually gets absorbed back into the major financial rails?

There are a number of ways this could play out, and it will likely vary by industry. In some sectors, particularly those with high levels of specialization or unique requirements such as regulation, there is a clearer path for companies to build durable, standalone businesses that deliver meaningful value over the long term.

At the same time, we are starting to see early signals that consolidation could emerge in certain areas, particularly at more horizontal layers of the AI stack, where capabilities can extend naturally across multiple use cases. It is still early, though, and the boundaries of how far that consolidation will go are being defined.

In lifestyle categories like travel and dining, I believe the outcome will be a mix. For example, in travel, there is already a range of booking options available to consumers. While there has been some consolidation amongst these companies, many have continued to find ways to differentiate and add value.听

We expect a similar dynamic to continue, with some companies scaling independently while others partner more deeply or join broader platforms over time.

Related SA国际传媒 query:

• 听

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Starting a company has never cost less. A founder with the right AI tools can ship a working product in a weekend, stand up a website in an afternoon, and fill out an accelerator application before lunch. But that speed hasn鈥檛 made it easier to get funded.听

Fewer seed-funded startups are graduating to Series A than just a few years ago, and startup funding has been in a downturn so far in 2026. Investors are concentrating capital in fewer, stronger bets. The question is what “stronger” means now.

Every generation of technology resets what investors should expect from founders. Twenty years ago, a founder who wasn鈥檛 internet-native was at a structural disadvantage. Forty years ago, it was computer literacy. Today, AI-native fluency is the baseline 鈥� the ability to build, test, and iterate using AI copilots, APIs, and low-code tools at a speed that would have required a full engineering team just a few years ago.听

Founders who haven鈥檛 embraced these tools in their daily operations aren鈥檛 even at the table. They鈥檙e new-aged dinosaurs. Technical expertise still matters, but when everyone can build, thanks to AI, it no longer differentiates. And that forces a harder question for investors: If the product isn鈥檛 the moat, what is?

Finding the fit

The answer is founder-market fit. Investors are shifting their attention from what a team can build to whether the founder has domain expertise that predates the startup, has done real customer discovery, and can articulate a path to market that competitors can鈥檛 easily replicate.

AI can help a founder build anything, but it鈥檚 what customers have a need for that tells them what鈥檚 worth building. That judgment is steeped in industry knowledge, customer relationships, and a clear-eyed view of what people will actually pay for. That is the scarce resource these days.

That鈥檚 not to say AI can鈥檛 help build a company the right way. It has implications for how early teams should be composed., the average seed-stage company last year had just over six employees, down from more than 10 in 2021.听

With teams that lean, every hire has to pull disproportionate weight. The highest-leverage early additions are a product-minded builder who can ship fast with AI tools, someone who owns the customer relationship and drives early revenue, and someone who can position the product and generate demand. A bench of engineers no longer tops the list.

The investor鈥檚 harder job

Knowing what to look for is one thing. Finding it is another, because AI has made it easier to fake the signals investors rely on.听

There鈥檚 an entrepreneurial equivalent to the that so many people are talking about. Instead of a tidal wave of empty marketing copy about 鈥渆ver-evolving landscapes,鈥� there鈥檚 startup slop that creates a serious evaluation problem for investors. Dealflow volume has become a vanity metric. There鈥檚 a surge of submissions that are pure noise, especially from software startups that can fabricate credibility in a single afternoon.

Deep tech is harder to fake. Building a therapeutics company still requires real science, real key opinion leaders, and real partnerships. The same is true for hardware and advanced manufacturing. There鈥檚 an actual moat in those sectors, which may help explain why investor interest in deep tech has been growing steadily.听

Investors can weed out the startup slop by asking more specific questions. For instance, our accelerator, AlphaLab, is based in Pittsburgh, and we always ask founders why this city is the right place for them to grow their businesses. You can sense how genuine someone is based on their answer. Same goes for asking about the customer discovery process. Even more telling is why someone started their company in the first place, whether the answer reflects real conviction or a market opportunity they read about.听

AI can鈥檛 manufacture what investors are really looking for. The signals that matter most at the early stage are coachability, hustle, and genuine conviction. There are details in an application that suggest someone has actually lived the problem they鈥檙e solving. Investors don鈥檛 want to write a check to someone who has vibe-coded a company they aren鈥檛 passionate about, and the tells are easier to spot than founders think.听

However, AI has reallocated where founders should spend their energy. Because it can help with some of the technical aspects of creating a company, founders should devote more effort to refining their strategy through higher-order skills like judgment, creativity, storytelling, and relationship-building. Speed of communication has become a revealing signal. There is no longer any excuse for taking four days to respond to an email, skipping a weekly investor update, or failing to follow up after a meeting. AI has eliminated the friction in all of those tasks. A founder who is still slow is telling investors something about how they鈥檒l run a company, and investors are paying attention to those soft interactions more than ever.

While the cost of building companies has dropped, the burden of earning investment has risen. And for investors, the evaluation itself has gotten harder, with more noise, more polish, and fewer of the old signals to rely on. The founders worth funding will stand out the same way they always have: by knowing something the rest of the market doesn鈥檛.

听brings 20 years of experience in venture capital, accelerator leadership and strategic operations to his role as director of accelerator programs at听听in Pittsburgh. He oversees听, AlphaLab Gear, AlphaLab Health and Robotics Factory Accelerate, programs that support early-stage startups with mentorship, resources and capital. His leadership has helped create a connected AlphaLab ecosystem that empowers founders across industries and stages of growth. Earlier in his career, Tainter held roles at听听补苍诲听听where he led cross-functional initiatives and evaluated early-stage investments. He also teaches at听, where his work focuses on funding entrepreneurial ventures.

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Two newly founded AI labs, both based in London and both with researchers from , raised large rounds out of the gate and made their Unicorn Board debuts. The two companies, and , both raised large initial fundings out of the gate, though take very different approaches to training AI.听 They were joined by another new unicorn in the foundation AI sector: , an open-source model company from China with on-device smaller models.听

Six companies working on humanoid robotics 鈥斕齠ive from China and one from Japan 鈥� also received billion-dollar-plus valuations last month. Quite a few of these companies are building models for robotic intelligence using simulated data.听

The financial services, defense, developer tools, energy and healthcare sectors each added two or three new unicorns in April.听

Of the 28 companies, 12 are U.S.-based and eight are from China. The UK counted two new unicorns last month, while Germany, Spain, Switzerland, India and Japan each added one.听

April鈥檚 new unicorns

Here are April鈥檚 new unicorn companies. Of the 28 companies, 26 are AI-related.听

Foundational AI听

• , a London-based AI lab using reinforcement learning rather than human-generated data, raised a $1.1 billion seed round led by and . The less than 1-year-old company was founded by of AlphaGo and . It was valued at $5.1 billion in its first funding.听
• London-based , a new AI intelligence lab with the goal of continuous learning improvement, raised a $500 million Series A led by and . Founded by DeepMind researchers and 鈥檚 1 previous AI lead, the less than 1-year-old company was valued at $4.5 billion.听
• Beijing-based , an on-device foundation model developer, raised funding led by and . Its open source MiniCPM is deployed in automotives, smartphones, PCs and home devices. The 3-year-old company was valued at $1 billion.听

Robotics听

• Shanghai-based is a robotics AI company building a foundational model as well as hardware. It uses simulated training to create a model for grasping and spatial awareness. The 1-year-old company raised a Series A round and was valued at $2 billion.
• Shanghai-based humanoid robotics company raised a $513 million seed round led by and HSG. The 1-year-old company was valued at $1.9 billion.听
• Beijing-based , a hardware and software developer of models for robotics using simulated data, raised a $220 million Series B. The 3-year-old company was valued at $1.5 billion.听
• Shenzhen-based , a builder of humanoid and quadruped robots, raised a $200 million Series B led by and . The 2-year-old company robots will be deployed for traffic, security and retail. It was valued at $1.5 billion.听
• Shenzhen-based , a commercial robotics company for delivery and commercial cleaning, raised a $146 million funding led by and . The 10-year-old company was valued at $1.5 billion.听
• Tokyo-based , a humanoid robotics company to address public safety and urban maintenance, raised a Series A led round. The 1-year-old company co-founded by was valued at $1 billion.

Financial services听

• , which automates research for investment banks, raised a $160 million Series D led by . The 4-year-old New York-based company was valued at $2 billion.
• Bangalore-based , a consumer and small business lending service, raised a $220 million Series E led by , , and . The 8-year-old company was valued at $1.5 billion.听
• , a banking and expense management service targeting small businesses and solopreneurs, raised a $100 million Series C led by , and . The 5-year-old San Francisco-based company, founded by college dropouts at the time, was valued at $1.4 billion.听

Defense听

• Space defense company raised a $600 million Series D led by and . The company has built software for space operations and an autonomous orbital vehicle called Jackal. The 4-year-old, Colorado-based company was valued at $2.2 billion.听
• Defense aviation company raised a $200 million Series C led by Khosla Ventures. The 7-year-old El Segundo, California-based builder of autonomous aircraft was valued at $1 billion.听

Developer tools听

• , a web search provider for AI agents used by and , raised a $100 million Series B led by Sequoia Capital. The 2-year-old Palo Alto, California-based company was valued at $2 billion.听
• , an agentic software coding tool for enterprises, raised a $150 million Series C led by . The 3-year-old San Francisco-based company was valued at $1.5 billion.听

Energy听

• , developer of small nuclear reactors to provide direct power for AI data centers, raised a $340 million Series B funding. The 2-year-old El Segundo, California-based company was valued at $2 billion.听
• , a long duration energy storage battery provider, raised a $58 million Series C led by . The 12-year-old Bayern, Germany-based company that supports energy needs for grids, data centers and industry, was valued at $1.2 billion.听

Health care听

• Shanghai-based , a developer of a model for healthcare that includes computer vision and large language models, raised a $73 million Series A round. The 12-year-old company has built an assistant for doctors for screening, diagnosis and patient care, and was valued at $1 billion.听
• Switzerland-based , a developer of a peptide product to address enamel repair without needing surgery, raised a private equity funding led by . The 6-year-old company was valued at $1 billion.听

Data platform

• has built a semantic layer between data and agents necessary to interpret data and provide guardrails for AI. The 4-year-old San Francisco-based company raised a $120 million Series C led by and was valued at $1.5 billion.听

Manufacturing

• Shanghai-based , a collaboration tool to make factories more efficient, raised a $146 million Series D funding. The 10-year-old Shanghai-based company was valued at $1.3 billion.

Agentic AI

• , which builds agents trained on company data, raised a $80 million funding led by . The 1-year-old San Francisco-based company was valued at $1.3 billion.听

Aerospace听

• Madrid-based , which is building data from satellites tracking changes in the earth for various commercial needs, raised a $130 million Series B led by . The 6-year-old company was valued at $1 billion.听

Marketing & sales听

• , a provider of booking and customer service for the services industry using AI, has raised a Series B funding led by and . The 4-year-old New York-based company was valued at $1 billion. The company has raised $125 million in funding from seed through its Series B.听

Biotechnology听

• , an AI biotechnology infrastructure platform speeding up drug discovery, raised a $40 million Series E. The 8-year-old Waltham, Massachusetts-based company was valued at $1 billion.听

Waste management听

• converts unused food products into energy. It raised a Series C funding led by strategic partner . The 19-year-old Concord, Massachusetts-based company was valued at $1 billion.听

Related SA国际传媒 unicorn lists:听

• (1,756)
• (611)
• (128)
• (187)
• (118)
• (102)
• (896)
• (516)
• (239)
• (38)
• (477)

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• The Rising Investors Behind The New Unicorn Class听
• While OpenAI Shattered Records, Robotics and Semiconductor Startups Quietly Added The Most New Unicorns In February听

Methodology

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

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

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

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

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

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