His investment mandate primarily focuses on early-stage technology companies from seed to Series B that align with or impact the global telecommunications, network infrastructure and enterprise software sectors.

Under his leadership, AT&T Ventures targets investments in software, hardware and infrastructure sectors where AT&T’s network scale and internal engineering resources provide a distinct commercial or technical diligence advantage. Portfolio companies include enterprise and deep-tech firms such as , , , , and .

Prior to his current 12-year stint directing AT&T Ventures, Taneja spent more than two decades working across corporate development, venture lending and investment banking. He previously managed M&A and strategic investment activities for during ownership.

Taneja also served as a director at , where he focused on growth-capital debt and equity investments in mid- to late-stage technology businesses, as well as holding corporate finance and investment banking roles at and .

In an email interview with 91��Ʒ News, Taneja shares why he believes that while AI has drastically lowered the barrier to building software, it has also shifted the definition of seed-stage technical risk.

The new dynamics, in his view, gives AT&T Ventures an opportunity to differentiate itself by offering immediate, real-world technical validation and network integration rather than just capital.

The interview has been edited for brevity and clarity.

91��Ʒ News: If startups are building fully functioning apps by the seed round using AI, what does that mean for the traditional definition of technical risk? Is tech risk dead at seed, or has it just evolved into something else?

Vikram Taneja: The old definition of technical risk was “can they build it?” Although not entirely absent at the seed stage, I’d say it is becoming less relevant given the dramatically lower barrier to building software with AI tools.

But what replaced it is actually harder to answer: “Is the tech defensible?” Not just “does it work?” but “does it compound?”

Data moats, proprietary training sets, network effects built into the architecture — that’s the new measure of durability.

In prior cycles, technical complexity alone created some natural protection. As a result, the technical risk conversation has shifted to focus on how a company defends itself over the next three to four years, especially as frontier labs move down the stack into application layers and start targeting entire verticals.

Similarly, the distribution question shows up much earlier. “How can you get this to market?” is increasingly asked at the seed stage rather than later in the cycle.

We’re also seeing increased competition for investors to secure larger stakes at seed that they would have previously pursued at the A round. This is driving investors to be more thorough at the seed stage, and founders have to be prepared to meet higher expectations across the board.

When anyone can use AI tools to spin up a working app in a weekend, product execution happens fast, but moats can be incredibly shallow. At the seed stage, how are you separating a truly defensible platform from a beautifully executed wrapper?

Taneja: In early 2025, we saw a wave of AI wrapper companies built on top of frontier models like ‘s GPT, ’s Claude or LLaMA, and a lot of capital flowed into them. What’s changed is that frontier LLMs have now clearly started to take more of a platform approach — moving into the application layers and beginning to pick off the low-hanging fruit.

This is why defensibility becomes critical in AI investing. No platforms are totally defensible, but on some level, you have to ask that question now at the seed stage.

We’re looking for platforms using proprietary data that can’t be replicated by AI, companies that have embedded deep domain expertise — areas where general-purpose AI still lacks industry context — into their workflows, or highly specialized ecosystems or niche markets that provide another layer of insulation in categories that are too targeted for frontier labs to pursue directly.

Are you seeing a change in the actual headcount or makeup of seed teams? If AI handles the heavy lifting of the initial code, are these founders spending their seed capital on engineers, or are they shifting resources immediately to distribution and go-to-market?

Taneja: There is still an engineering focus in the early stage, as there should be, but we are increasingly seeing product, sales, or partnership roles becoming sought after earlier than in the past. And the reason is, as you stated, that it’s easier to build a working prototype, or even a production-ready application, so the focus very quickly turns to establishing trials with customers or exploring distribution paths to dial in the product features.

For strategic investors like AT&T Ventures, where we often do proof-of-concepts with potential portfolio companies, this is very exciting. We get a chance to work with companies earlier in their formation, can get real technical validation much earlier than otherwise, and can similarly try to find a path to collaborate more quickly.

AT&T Ventures has traditionally played heavily in the Seed to Series B space. If institutional VCs are rushing to seed to grab larger stakes because the tech is mature, how does that change the competitive landscape for CVCs? Are you finding yourself competing directly with traditional multistage funds earlier than before?

Taneja: The makeup of seed rounds has definitely changed. Multi-stage funds used to show up at Series A or B when there was enough traction to underwrite. Now they’re at seed because, as we discussed, the companies are mature enough, and they are trying to find winners earlier in the cycle. So yes, we’re in the same rooms as before.

But I’d push back on the idea that we’re competing directly.

A Tier 1 financial VC’s seed check and an AT&T Ventures seed check are different instruments. They are offering capital, brand, guidance and pattern recognition from backing hundreds of companies.

We’re offering something a financial VC structurally does not: our network teams working with your product in a production environment, oftentimes before we even write the check, for example. That’s free diligence running in both directions. We’re validating the company, but it’s also receiving a real-world signal from one of the world’s largest network operators.

For a seed-stage company that’s already solved the building problem and now needs distribution, that’s tangible value and complementary to what financial VC firms are providing. So that competitive pressure has actually sharpened our value proposition. It forces us to bring more than just capital to the table.

Historically, corporate partners want to see enterprise readiness, security compliance and scalability — things a seed startup rarely has. If a seed startup has a fully functioning product but is still a two-person team, can an enterprise like AT&T actually run a pilot with them, or does the corporate integration timeline become a bottleneck?

Taneja: It starts with strategic rationale. That has always been the entry point for us at AT&T Ventures, and that hasn’t changed. If that is in place, then it doesn’t always require full enterprise readiness to start a pilot. It can be a structured trial or a highly targeted engagement, depending on the company’s stage.

We have a number of ongoing proof of concepts with portfolio companies across areas such as AI-RAN, connected infrastructure and computer vision.

The key is clarity upfront — clarity on what the objective of the engagement is and how we measure success. Once that is clear, even early-stage companies can be integrated into a learning or testing environment without unnecessary delay. The goal is to make the AT&T relationship feel like an accelerant to further adoption.

If seed is the new Series A in terms of product maturity, are you seeing Series A pricing bleed into the seed round? How are you disciplined about valuations when the product looks like a Series A, but the company infrastructure is still very early?

Taneja: Seed pricing indeed looks different than maybe four or five years ago. We’re routinely seeing seed deals priced in the low- to mid-single-digit-million range at about $20 million to $25 million post-money. This is pretty much where Series A deals were a few years ago. But it’s not necessarily unjustified — the makeup and traction of seed-stage companies are much further along than predecessor vintages as we’ve discussed.

We stay disciplined by being explicit about what we’re actually underwriting. We’re not just underwriting the financial return on this round — we’re underwriting the strategic value of the relationship over a five- to 10-year horizon.

Does this company make AT&T’s network more intelligent? Does it open up a new customer segment? Does it validate a thesis we’re building around? Are there commercial opportunities beyond our initial thesis? When you frame it that way, it gives us a longer horizon to work with and provides multiple levers to pull.

And honestly, that’s where our engineering and product teams play a key role. They help us decipher whether the product that looks like a Series A is actually built like one, or whether it’s a great demo sitting on a foundation that hasn’t been stress-tested. That technical read bolsters our conviction when making investments.

A functional AI app at the seed stage still requires massive infrastructure. When you evaluate these early-stage companies, how much does their underlying architecture and how they handle data processing or edge computing factor into your decision?

Taneja: Architecture is a key part of our diligence process. The way we think about it really depends on the ultimate use case. Is it for internal use — i.e., a tool that AT&T will be working with in our environments — or is it something we’d be distributing or incorporating into some form of product offering?

If the former, all aspects of the architecture will be reviewed, and this is most likely to occur throughout trials and proof of concepts as we develop a technical understanding of the application or product. If it’s the latter, then we’re likely most interested in understanding how this product architecture scales over time and what it means from a cost, latency and infrastructure perspective. We love to see companies embracing edge-related technologies, but that doesn’t preclude us from working on applications that use traditional data processing methods.

You’ve spoken before about your interest in “physical AI” and robotics (like Apptronik). The software lifecycle is easily compressed by generative AI, but hardware and physical deployment take time. Does this “seed is the new Series A” trend apply to pure-play software strictly, or are you seeing AI accelerate physical tech and IoT at the early stage too?

Taneja: Physical AI is a sector we’ve been looking at quite a bit, particularly because inference and decisioning in autonomous systems, robotics and connected devices create a very different type of demand profile on networks.

The software layer is clearly accelerating — things like perception, control systems and decisioning are moving faster because of AI (the rounds show it!). That will ultimately help pave the way for the adoption of physical AI. However, the physical deployment cycle still takes time, so you don’t see quite the same level of time compression there.

What is interesting for us at AT&T is the intersection — how intelligence is moving closer to the edge and how that changes the way networks need to be architected to handle those workloads.

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• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo

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

Startup investors were in a spendy mood this week, backing more than a dozen rounds in the multiple hundreds of millions. Of those, the biggest one went to spend-management platform , which closed on $750 million, followed by three $500 million rounds for companies in the AI and space tech sectors.

1.��, $750M, finance software: Spend-management software provider Ramp secured $750 million in a financing led by , �����Ի� . The round set a $44 billion valuation for the 7-year-old, New York-based company.

2. (tied) , $500M, space tech: Redondo Beach, California-based Impulse Space, a developer of spacecraft and propulsion systems for transport, moving and orbital repositioning in space, raised $500 million in Series D funding. and led the financing which brings total investment to date to more than $1 billion.��

2. (tied) , $500M, AI developer tools: Supabase, provider of an open source platform for developers and AI app builders, closed on $500 million in fresh funding. led the financing, which set a $10.5 billion valuation for the 6-year-old, San Francisco-based company.

2. (tied) , $500M, foundational AI: New York-based Flourish, a startup working on artificial intelligence models inspired by the human brain, raised $500 million in initial funding. Backers include , �����Ի� .

5. , $465M, fusion energy: Helion, a startup with a mission to build the world’s first fusion power plant, picked up $465 million in Series G funding led by at a $15.5 billion post-money valuation. The round brings total reported funding for the Everett, Washington-based company to at least $1.5 billion, per .��

6. , $435M, longevity medicines: NewLimit, a developer of medicines designed to restore youthful function in old cells through epigenetic reprogramming, closed on $435 million in Series C funding. led the financing for the South San Francisco, California-based company, which was co-founded by CEO .

7. (tied) , $400M, AI for music: Suno, a provider of AI tools for making music, raised $400 million in Series D funding led by . The round set a $5.4 billion valuation for the company, which is currently facing lawsuits from multiple music labels for training its AI on copyrighted materials.

7. (tied) , $400M, robotics: Generalist AI, a startup focused on using AI to enable robots to do complex tasks, picked up $400 million in new funding led by . The financing reportedly set a $2 billion valuation for the 2-year-old, San Mateo, California-based company.

9. , $350M, AI enterprise software: AlphaSense, an AI-enabled market intelligence and workflow orchestration platform, closed on $350 million in a new funding round led by , , , �����Ի� . The round set a $7.5 billion valuation for the New York-based company.

10. , $300M, defense tech: Defense tech startup Mach Industries raised $300 million in Series C funding at a $1.8 billion valuation. and led the financing for the 3-year-old, Huntington Beach, California-based company.

Methodology

We tracked the largest announced rounds in the 91��Ʒ database that were raised by U.S.-based companies for the period of May 30-June 5. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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AI and software continue to draw the biggest share of startup investment, but most of the interesting companies that caught our eye in the past month were working on problems in the physical world, often far from the glow of a laptop screen.��

They include a defense-tech startup that aims to bring manufacturing closer to the frontlines, a company working to recycle valuable raw materials from defunct solar panels at industrial scale, and a startup that wants to produce cell-based milk for the dairy supply chain. Let’s take a look.

$82M to build near the battlefield

A decade ago, defense tech was considered a niche and sometimes controversial corner of venture capital, with few startup investors daring to place bets on companies working with the military.��

How times have changed. Already this year, $13.6 billion in venture investment has gone into companies in 91��Ʒ’s military, national security and law enforcement categories — more than 1.5x last year’s annual total.��

is one of the latest defense startups to get some of that funding, with an approach that aims to bring manufacturing closer to the battlefield. The San Diego-based startup last month announced an $82 million Series B led by .��

Firestorm builds expeditionary manufacturing systems and modular drones for military use. Its containerized “xCell” manufacturing platforms are designed to produce drones, replacement parts and other systems closer to the battlefield, a concept gaining traction as militaries rethink supply chains and logistics in contested regions such as the Indo-Pacific.

Existing and new investors including, , , , and others also joined its latest funding round, which brings Firestorm’s total funding to nearly $150 million, .

“The ability to produce, adapt, and sustain systems at speed and scale will define outcomes in future conflict,” , founder and chief investment officer at Washington Harbour Partners, said in a statement. “We’re excited to lead Firestorm’s Series B and back a company building a new model for manufacturing that replaces centralized supply chains with deployable, containerized units that can operate at the edge.”

The raise lands amid a broader surge in investor appetite for military tech, not just from defense-industry investors but also some of Silicon Valley’s biggest venture names. Sector heavyweight recently raised another $5 billion at a staggering $61 billion valuation in an – and -led round, underscoring just how mainstream venture-backed defense startups have become.

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$60M for a legal tech operating system

Legal tech has been one of the fastest-growing startup sectors in recent years, at least when measured by funding to the area, with venture investors pouring a record $4 billion-plus into the industry last year. That growth, of course, has been driven by AI’s rapid automation of many aspects of the notoriously paperwork-heavy industry.

Adding to this year’s tally is , a startup that says it’s building the operating system and brand for AI-native law firms. The startup said last month that it raised $60 million in Series A funding at a $750 million valuation from big-name investors. led the round and , and participated.

Manifest OS says it takes a different tack than most legal tech startups. Rather than sell software to traditional law firms that operate under a billable hour model, the company only caters to AI-native firms that charge clients based on outcomes.

“Companies want fee transparency, predictability, and speed,” , a Manifest investor and former general counsel for ��1, and , said in a statement. “Lawyers want to focus on delivering results, not justifying billable hours. Manifest OS’s model and use of advanced technology align those interests in a way the traditional system simply doesn’t.”

Along with AI software that helps attorneys with tasks like client communications, legal research, document drafting and billing, Manifest OS also offers a centralized back office to handle client intake, business development, paralegal work and other administrative tasks. That, according to the firm, frees attorneys up to focus on more complex legal work.

One important caveat: All firms that use its platform operate under the Manifest Law name. According to the startup, that results in a consistent brand presence, pricing, response time and service quality to clients. Its is a business immigration law firm.

The startup says it has already served 150-plus corporate clients, including large tech companies, since launching 18 months earlier. It has hired more than 100 attorneys to date, it said, less than 1% of those that applied.

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$23M for industrial solar panel recycling

French cleantech startup said last month that it has secured €20 million (about $23 million) in Series B and grant funding to tackle a growing problem: industrial-scale solar panel recycling.��

By 2050, tens of millions of tons of solar panels are expected to become defunct, according to ROSI. The company’s technology recovers high-purity raw materials including silver, silicon, copper, aluminum and glass from those panels so that they can be recycled into new products.��

ROSI said the new funding will be used to build its first large-scale recycling plant in Spain. The site will be able to process 10,000 tonnes per year.��

The funding was led by , , and Spanish family office . Zurich-based corporate advisory firm , which specializes in deep tech, acted as strategic financial adviser and investor. Other investors included unnamed Swiss and Polish family offices.

“Our ambition is to build a European-scale industrial platform for circular management and the production of strategic raw materials, transforming end-of-life solar panels into a reliable source of high-purity materials for the European industries of tomorrow,” ROSI President and co-founder said in a statement.

The investment comes as cleantech funding has seen tepid investor enthusiasm in recent years. Overall funding to startups in 91��Ʒ’s cleantech-, electric vehicle- and sustainability-related categories fell to a five-year low in 2025. Still, some areas — including solar and recycling — have continued to see larger rounds.

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$2.3M for a cell-based milk supplier��

Venture investment in food and beverage startups has fallen precipitously in recent years, from more than $22 billion in the peak year of 2021 to . Companies working on cell-based alternatives to traditional sources of protein such as meat and dairy products, in particular, have largely fallen out of favor with startup investors, 91��Ʒ data shows.

That makes Montreal-based ’s recent $3.2 million CAD (roughly $2.3 million) seed round all the more interesting. The company, previously named BetterMilk, says it produces “complete milk” — with proteins, fats and sugars — from mammary cells in a bioreactor, without employing any cows.

Its recent round was led by , with participation from , , and existing investors including , and .

Rather than make a direct-to-consumer play, as many food and beverage startups have done, Opalia is positioning itself as a supplier in the food industry. The company recently inked a two-year deal with dairy supplier and a paid pilot with an unnamed “Canadian division of a leading global dairy group.”

“We see Opalia as a foundational player in the next era of dairy,” , managing partner at Nadarra Venture, said in a statement. “What sets them apart is a combination of highly credible, differentiated science and a clear, executable path to scale within existing dairy infrastructure, addressing the economics required to compete globally. Today, global demand for dairy is outpacing supply, and the traditional system is under increasing pressure from climate and resource constraints, making innovation no longer optional.”

Opalia plans to make its commercial debut in 2028 and said it’s currently working through the regulatory process in North America.

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$16M to automate the factory playbook

Mountain View, California-based last month announced a $16 million seed funding round��to speed up what it calls one of manufacturing’s most stubborn bottlenecks: turning digital product designs into actual production plans.

The startup’s platform, dubbed AutoAssembler, plugs into existing CAD and PLM systems and uses AI to automate process planning, the painstaking engineering work required to determine how parts fit together, in what order they should be assembled, and how products can realistically be built at scale. C-Infinity says workflows that once took weeks can now be completed in minutes.

Its seed round was led by with participation from and

C-Infinity’s pitch taps into a broader trend gaining traction across industrial tech: software that doesn’t just analyze operations, but actively participates in physical production decisions. That kind of investment in physical AI — real-world applications of artificial intelligence, including in factories and on construction sites — has taken off this year.��All told, startups working on physical AI have already hauled in more than $37 billion in venture funding globally in 2026, , shattering the full-year records of $21 billion set in both 2025 and 2021.

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

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.��

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.��

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.��

Fazeshift claims that it can automate more than 90% of manual AR tasks — from invoicing and collections to payment matching and reconciliation — by operating on top of existing systems and executing workflows across them. It essentially sits on top of a company’s current stack as a “brain.”

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

“What sets us apart is our ability to handle complex workflows that other tools fail to solve – especially in industries like wholesale, construction, staffing, and HVAC, where AR processes are highly fragmented and manual,” Leksana told 91��Ʒ 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’s top e-commerce aggregator, and a leader in music publishing, per Leksana.��

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.

“Our long-term vision is to expand into a broader CFO suite,” she said, “building 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.”

Broken workflows for ‘critical functions’

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

“You’d 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,” she wrote via email. “That gap between how critical the function is and how broken the workflows remain is exactly the kind of opportunity we look for.”

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.

“Fazeshift is bringing us closer to an autonomous future for finance,” she said. The founders had “lived the pain of broken AR workflows firsthand at their last company and set out to build the platform they wished they’d had. When you meet founders like that, you move fast.”

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 91��Ʒ . That’s a more than 29% increase from 2024’s total of $41.6 billion raised.

Related reading:

• Fintech Startups Globally Raise More Money In Far Fewer Deals In Q1 2026

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• ��

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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 91��Ʒ debuts. The two companies, and , both raised large initial fundings out of the gate, though take very different approaches to training AI.�� They were joined by another new unicorn in the foundation AI sector: , an open-source model company from China with on-device smaller models.��

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

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

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.��

April’s new unicorns

Here are April’s new unicorn companies. Of the 28 companies, 26 are AI-related.��

Foundational AI��

• , 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.��
• 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 ’s 1 previous AI lead, the less than 1-year-old company was valued at $4.5 billion.��
• 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.��

Robotics��

• 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.��
• 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.��
• 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.��
• 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.��
• 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��

• , 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.��
• , 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.��

Defense��

• 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.��
• 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.��

Developer tools��

• , 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.��
• , 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.��

Energy��

• , 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.��
• , 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.��

Health care��

• 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.��
• 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.��

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.��

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.��

Aerospace��

• 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.��

Marketing & sales��

• , 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.��

Biotechnology��

• , 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.��

Waste management��

• 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.��

Related 91��Ʒ unicorn lists:��

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

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Methodology

The 91��Ʒ 91��Ʒ is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 91��Ʒ data. New companies are as they reach the $1 billion valuation mark as part of a funding round.��

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

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

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. 91��Ʒ 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 91��Ʒ long after the event was announced, foreign currency transactions are converted at the historic spot price.

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Cambridge, Massachusetts-based Blitzy has now raised more than $204.4 million. led its latest financing. New investors including , and also participated, as did existing backers such as and . The company said it also received strategic investments from , and .��

Blitzy claims that its platform autonomously completes months of software development, including automated testing and quality validation. It further claims to increase engineering velocity by 5x “for some of the world’s largest enterprises.”��

While Blitzy did not reveal hard revenue figures, it said that its technology has been adopted by “dozens” of Global 2000 enterprises across 10 industries, including State Street and QAD. The company says it was built on the premise that frontier models alone would not solve enterprise software development.

“We believed that delivering production-ready code for the enterprise would come from fusing hyperscaled agent orchestration and a system that deeply understands the legacy codebases it is working within,” , co-founder and CEO of Blitzy, said in a press release.

Elliott, who also previously founded and is a former Army Ranger, started Blitzy with alum in 2023. Pardeshi holds more than 27 patents related to neural networks, image generation and AI-driven interface translation.

Big money for AI software development

Several other companies in the AI software development space have raised large rounds over the past year. They include:

• , which sells the popular AI coding assistant Cursor. The company has raised $3.4 billion and was most recently valued at over $29 billion. The company has since with that gives the -led space exploration company the right to buy Anysphere for $60 billion later this year.
• , a cloud-based development platform, has raised more than $870 million, including a March that valued the company at $9 billion.��
• , a Swedish AI vibe coding startup, has raised more than $550 million, including a December $330 million Series B financing at a $6.6 billion valuation.

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

The pace of large-scale dealmaking picked up some this week, led by ’s disclosure that it raised another $10 billion to add to its record-setting megaround announced last month. Other big financings went to startups and growth-stage companies in sectors including defense tech, enterprise AI, autonomy and even laundry.

1. , $10B, foundational AI: OpenAI $10 billion in additional funding for its record-setting megaround announced in late February, reportedly bringing the total fundraise to the San Francisco-based company to over $120 billion. Backers in this latest financing include , , , and .

2. , $2B, defense tech: San Diego-based defense tech unicorn Shield AI said it secured $2 billion at a $12.7 billion valuation. The round consists of $1.5 billion in Series G funding led by and along with $500 million in preferred equity financing backed by . Part of the proceeds will help pay for the planned acquisition of , a defense software company whose technology is used to train pilots and test advanced aircraft and autonomous systems.

3. , $350M, transportation safety: Cambridge Mobile Telematics, a telematics and AI company focused on enabling safer mobility, picked up $350 million in a new financing�� led by and . Founded in 2010, the Cambridge, Massachusetts-based company has raised over $850 million to date, per 91��Ʒ .

4. (tied) , $200M, legal tech: Harvey, the fast-growing provider of AI-enabled tools for law firms and in-house legal teams, closed on $200 million in fresh financing at an $11 billion valuation. and led the round, which brings total funding to 4-year-old San Francisco-based Harvey to around $1.2 billion.

4. (tied) , $200M, healthcare: eMed, a provider of GLP-1 programs for employers that counts as chief wellness officer and backer, said it raised $200 million in new funding. led the round, which set a $2 billion plus valuation for the Miami-based company.

6. , $170M, satellite tech: Xona secured a $170 million Series C round led by . The funds will go to scaling satellite production for a planned constellation of next-generation navigation satellites. Founded in 2019, Burlingame, California-based Xona has raised over $320 million to date.

7. , $140M, laundry tech: Cents, a provider of software and payments technology for the laundry industry, secured $140 million in Series C funding led by . The New York-based company said the round represents “the largest single software investment in the laundry vertical to date.”

8. , $125M, AI health tools: Palo Alto, California-based Qualified Health, developer of an enterprise AI platform for health systems, locked up $125 million in Series B financing led by .

9. (tied) , $110M, data observability: Dash0, an agentic observability platform, announced it closed on $110 million in Series B funding led by . Founded in 2023, the New York-based company has raised over $154 million to date.

9 (tied) , $110M, drones: Huntsville, Alabama-based Performance Drone Works, a startup that designs, engineers and manufactures drones for defense and law enforcement, secured over $110 million in Series B funding led by .

Methodology

We tracked the largest announced rounds in the 91��Ʒ database that were raised by U.S.-based companies for the period of March 21-27. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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One of their largest customers had asked for a specific new feature which would help their workflow, the kind of request that was clearly valuable to the customer but not necessarily important enough to jump it to the top of the roadmap.

As usually happens in enterprise software, the request needed to move through business and product discussions, design work, engineering prioritization, testing cycles and security reviews before anyone could even commit to a timeline.

The customer understood that. But after waiting for some time, it raised a different possibility: Rather than continue waiting for the vendor to ship the feature, it was considering using AI coding tools internally to build something themselves that would solve the problem well enough.

That single comment reflects a broader shift that SaaS companies are only beginning to fully absorb as their stock prices take a hit, precisely because of this sentiment.

For years, a feature request was a request to the vendor. It entered the backlog, competed with other priorities, and if the customer was important enough or the use case broad enough, eventually it would make its way into the product.

That logic is now starting to weaken. If customers can increasingly generate narrow workflows, lightweight internal tools or customized interfaces on their own, the role of the traditional feature begins to change. And once that happens, it is worth asking a deeper question: Will features even exist in the way the software industry has historically understood them?

Features were once the product

For decades, SaaS companies built value through predefined functionality. A roadmap was essentially a sequence of decisions about which features to build, which customer pain points to prioritize, and how quickly the product team could turn demand into software.

In many categories, feature depth and feature velocity became the core of competitive differentiation. The company that could ship faster, cover more use cases, and respond more effectively to customer requests often had the advantage.

That model made sense in a world where software creation was expensive, slow and highly constrained by engineering capacity. A feature had weight because it represented significant investment. It required planning, development, quality assurance, release management and support. Customers understood that process because there was no real alternative. If they needed something badly enough, they could ask for it, pay for customization, or wait.

AI-assisted development begins to change that equation. When internal teams can describe a workflow and generate a usable version of it in days rather than quarters, the meaning of a feature starts to erode —��not because functionality is no longer important, but because it no longer has to arrive in the same packaged form.

In some cases, customers may not need the vendor to build every layer of functionality for them. They may only need enough access, flexibility and context to shape part of it themselves.

Functionality may become something dynamic

The real question may not be whether AI will help SaaS companies build features faster, although it clearly will. The more important question is whether the concept of a feature as a fixed unit of product development starts to fade.

For many years, teams gathered requests, translated them into product requirements, scheduled them into roadmaps, and released them as standardized functionality for a broad user base. That process may increasingly look inefficient in a world where software can be generated more dynamically.

In an AI-native environment, the customer may not ask for a feature in the traditional sense at all. They may simply describe the workflow they need, the output they want, the approvals required, the data sources involved, and the rules that should govern the process. The platform could then generate that capability inside the product environment rather than waiting for a formal release cycle. In that scenario, functionality becomes more fluid.

That would represent a meaningful shift in how enterprise software is defined. The feature would no longer be the product’s smallest strategic building block. Instead, the platform would provide an environment in which functionality can be created, modified and governed with greater flexibility.

This matters because it changes where value sits. If the workflow can be generated on demand, then the defensibility does not lie in the isolated feature itself. Rather, it lies in the system that makes that generation possible in a secure, reliable and scalable way.

The platform becomes the real moat

This is also why AI is unlikely to simply make serious SaaS platforms irrelevant. Even when a workflow can be generated quickly, it still needs to operate inside a much larger enterprise reality. It must connect to structured data, respect access controls, interact with existing systems, produce auditable outputs, comply with security policies, and function with a level of reliability that internal experiments rarely match on their own. These are not minor details. In many enterprise environments, they are the actual product.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 91��Ʒ News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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The New York-based startup says its new valuation is $2 billion, double it achieved when raising its $125 million Series D round in December 2023.

In total, Vestwell says it has raised $660 million in capital since its 2016 inception.

Also participating in the latest round were , , , , and .

Vestwell is growing “profitably,” according to CEO , who said the company’s annual recurring revenue is now more than $200 million. The platform has more than 2 million active savers and works with more than 500,000 businesses. In total, Vestwell has over $50 billion in assets administered across workplace, institutional and government channels.

The company grew nearly 50% year over year, Schumm said, and is operating with “strong unit economics and improving margins.”

Vestwell’s revenue model is dependent on its customers and their preferred structure, according to Schumm. Typically, it’s a monthly fee per employer and/or a monthly fee per employee.

The company works with financial institutions, payroll and HR platforms to distribute or integrate its white-labeled savings products to employers and employees nationwide. Those partners include , , , , , , , , , �����Ի� .

Overall funding to wealth management startups totaled $1.9 billion in 2025, per 91��Ʒ , roughly the same amount as in 2024. That’s down from about $3.8 billion raised by such startups in the peak funding year of 2021.

Connecting the dots

Schumm founded Vestwell with the goal of addressing the problem of “fragmented” savings.

“[There were] separate systems for retirement, emergency, education, disability and other savings programs. Each had its own rules, vendors and barriers to participation,” he said. “Vestwell solves that problem by connecting these programs into one interoperable platform.”

Describing the company as an enterprise fintech platform, he said Vestwell makes it easier for employees and employers “to save, manage and grow their money, no matter the size of the company.”

It supports a range of savings vehicles, including retirement: 401(k), 403(b) and IRA savings programs; education such as 529 savings plans; emergency savings accounts; and ABLE accounts for people with disabilities. Its offering is accessible across more than 20 languages.

Presently, Vestwell has 500 employees.

Expansion plans

The company plans to use its new capital to expand its distribution. For example, it is working to embed savings more deeply into payroll, benefits platforms, financial institutions and government-led public programs.

It’s also continuing to invest in AI-native capabilities with the goal of having them personalize guidance, automate administration and surface “actionable” insights for users and their employers.

Before Vestwell, Schumm co-founded wealth management startup , which was by in 2017 .

, principal at Blue Owl, describes Vestwell as “a standout company.”

“Vestwell is taking a holistic approach to savings, making it far more durable than just a recordkeeping platform,” he wrote via email. “It has created the infrastructure layer that connects payroll providers, financial advisors, enterprises and state programs into a unified savings ecosystem.”

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Clarification: This story has changed since its original publication to confirm the company’s current valuation.

The news was big in the fintech world with Brex claiming the pairing would represent “the largest bank-fintech deal in history.” ( had planned to buy in 2020 for $5.3 billion until that deal fell apart a year later due to regulatory concerns.)

In a joint statement, Capital One founder, chairman and CEO said it’s always been a goal of the bank “to build a payments company at the frontier of the technology revolution.”

“Acquiring Brex accelerates this journey, especially in the business payments marketplace,” he said. “Brex invented the integrated combination of corporate credit cards, spend management software and banking together in a single platform. They have taken the rarest of journeys for a fintech, building a vertically integrated platform from the bottom of the tech stack to the top.”

While $5 billion is no small sum, it is less than half the that San Francisco-based Brex was valued at in October 2021. In total, the company has raised $1.7 billion in equity and debt since its 2017 inception — with about $1.2 billion of that being venture funding.

Early investors such as , which led Brex’s in 2017, are likely quite pleased with the outcome. Investors who wrote checks at its later stages are likely less so.

Other early backers include , and 1��.

The company has 1,100 employees, according to a Brex spokesperson who also told 91��Ʒ News that its business is growing 40% year over year and is profitable. Customers include , , , , and , among others.

Close friends Franceschi and , who co-founded Brex, started working together when they founded another company, Brazilian payment processing startup , in 2012 at the wee age of 16. That company ended up getting acquired by Stone Pagamentos for “tens of millions of dollars” — before the two had even gone to college.

A change of plans

Brex began its life as a buzzy startup that served mostly other startups. But in June 2022 — three months after announcing it would make a into software and enterprise — Brex confirmed that it was apparently it started to serve: small to medium-sized businesses.

The abrupt news didn’t sit well with many of the SMBs it served.

Over time, Brex began to seemingly fall behind its largest rival, , when it came to fundraising and revenue generation. Ramp as of last November was valued at $32 billion, having raised a total of $2.3 billion in equity.

By joining Capital One, Brex says it will accelerate Capital One’s presence in corporate cards and spend management, complementing its existing leadership in SMB banking.

Capital One’s purchase of Brex is slated to close midyear.

Fintech M&A expected to pick up

On the heels of a strong year for venture funding to fintech startups, sources who spoke with 91��Ʒ News said they expect exits — both M&A deals and IPOs — in the sector to gain steam in 2026.

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• Fintech Funding Jumped 27% In 2025 With Fewer Deals But Bigger Checks
• Fintech Forecast: Momentum Builds With Big Deals, IPO-Ready Companies And More AI

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