Frontier labs and hyperscalers promise world-shifting innovation. And most deliver it. But, as we鈥檙e seeing with the policy and the evolving and security , they operate without stability.

That鈥檚 deeply concerning because technology organizations that build their entire AI operations and business on top of Anthropic, OpenAI and other paid models need to be able to depend on their reliability.

Meanwhile, open-source organizations like and offer cost-free models with similar quality. The difference in price is stark. And the gaps in utility, safety and accessibility that kept the enterprise away are closing fast.

This evolving dynamic leaves CTOs, CIOs and engineering leaders with a question: How can we keep reliability up and costs down when it鈥檚 impossible to predict whether hyperscalers will drop or raise prices of their next models?

The answer isn鈥檛 clear-cut 鈥� yet. But it鈥檚 never been clearer that engineering leaders need systems that allow their teams to quickly swap models and shift how AI agents work with people and access real data and tools. Building the right foundational layer keeps organizations nimble enough to evolve alongside the industry without cutting corners by chasing the latest trends.

Tokens cost more than time and money

Engineering leaders at Big Tech companies and within enterprises learned the hard way that building toward their organization鈥檚 long-term stability is a much better plan than chasing trends like 鈥渢okenmaxxing,鈥� which results in unsustainable spend and team burnout.

While a fair amount of damage to company accounts and executive reputations has been done, the pendulum is already swinging back from tokenmaxxing to more sober approaches. At the same time, companies like that publicly went all-in on team-wide AI use are reinvesting in engineering team culture.

The goal: boosting morale while removing competition from token use.

Instead of jumping on the next hype train and creating the inevitable bottleneck, organizations should invest in modernizing their infrastructure to empower teams to sustainably iterate on and experiment with AI tools at scale.

The future of enterprise AI empowers people and agents to work seamlessly together. What this looks like:

• Accepting that agents have most of the same capabilities as people, with the added value of being able to test against real infrastructure with access to 鈥渞eal鈥� data swiftly and at scale.
• Ensuring agents have the same guardrails as teams, including making sure credentials and permissions are only granted when needed; under the right circumstances and with full visibility into actions taken when things go wrong.
• Building systems that are able to swap in the latest AI models and frameworks to take advantage of new advancements without losing the custom work done in-house.
• Making sure their companies aren鈥檛 locked into a single provider long-term in order to reduce risk from outages, expensive contracts or dated products.

Models change. Update your architecture

Building resilience starts with accepting that models and how we use them will change. Engineering leaders need to embrace that it will sometimes make sense to go with the latest hyperscaler model. Other times, it will make sense to bring in open-source models with novel harnesses that run at no cost but change how people collaborate with them.

Meanwhile, agents shouldn’t be limited to toy problems or synthetic environments. They need the ability to test against real infrastructure, interact with realistic datasets, and participate meaningfully in real business workflows.

The winning approach: Level the playing field between agents and engineers.

Give agents access to the same environments people use and mandate that they operate under the same guardrails teams follow. Permissions should be granted only when necessary. Credentials should be tightly controlled. Every action should be observable and auditable. When something goes wrong, accountability should follow with clear visibility into what happened and why.

Hold both parties to the highest standards. Build resilience with your team.

There鈥檚 strength in flexibility

The days of custom workflows, automation and operational knowledge being trapped behind a single vendor relationship are over. We鈥檙e entering an AI agent-plus-engineer era that demands building systems and teams around flexibility, elasticity and adaptability.

In other words, it鈥檚 time to eliminate long-term lock-in for good.

Organizations that preserve the flexibility to adopt new models, integrate emerging tools, and respond to changing market conditions without rebuilding everything from scratch build resilience with every model release. It鈥檚 the way of the future. Engineering leaders should adopt this approach today.

is the CEO and co-founder of , which aims to bring enterprise quality infrastructure to autonomous agents and engineers. He was previously an early engineering leader at , and .

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To understand how institutional capital is navigating this gap, 小蓝视频色情网页版 News recently interviewed , a partner at . Ranum has spent nearly 15 years at the firm, where he focuses on B2B enterprise software, security and industrial infrastructure. Prior to joining Sapphire, he spent 12 years as a product management and strategy executive at .

His recent investments include core infrastructure plays such as and , as well as the industrial AI platform .

In this e-mail interview, Ranum breaks down how the definition of net revenue retention is shifting, why he believes 2026 will see a historic run of major tech IPOs, and where real enterprise demand is materializing on the factory floor.

This interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: You鈥檝e been at Sapphire for 15 years. Right now, public market software multiples are at decade lows as Wall Street worries about AI disruption, while private AI valuations are hitting record highs. As a growth investor caught in the middle, how are you valuing companies today? Are traditional growth metrics like net revenue retention still the gold standard, or has the math completely changed?听

Ranum: The gap between public and private market signals right now is unlike anything I’ve seen. I think it creates a real opportunity for investors who can make sense of it. Public software multiples have come down hard, while private AI valuations are hitting record highs. Those two things can’t both be right indefinitely, but the fundamentals underneath are holding up. Gross margins, free cash flow, and NDR have actually improved. The market is broadly pricing in disruption risk, but the companies that are genuinely building enterprise value are still being built.

What that means for how I evaluate companies is that I’m spending more time on whether something is genuinely embedded in how enterprises work, not just whether the numbers look good today. NRR still matters. It tells you whether customers are finding real value. But it’s a lagging indicator. What tells me more is whether switching away from a product would meaningfully disrupt operations. If the answer is yes, that’s a more durable signal than any retention metric.

The current regulatory environment has essentially frozen large-scale tech M&A, and the IPO market is sluggish. If the traditional exit pathways are bottlenecked, how does that change the way you underwrite a Series B or C bet? Do companies just have to stay private and build to massive scale longer than they used to?听

Ranum: I鈥檇 push back a bit on the framing that M&A is frozen. Software M&A activity actually picked up meaningfully in 2025, with deal value rising 40% year over year to $334 billion across 678 transactions. We saw that in our own portfolio with over half a dozen acquisitions in the past six months. What鈥檚 changed is the pricing. The valuations are being reset, but the deals are getting done.

On IPOs, I believe 2026 is shaping up to be a historic year, with having gone public, having filed, and reportedly set to file soon. If they follow through, we’re looking at some of the largest IPOs ever over the next several months. That’s a remarkable moment. Below that tier, though, the picture is more nuanced. Companies that meet today’s higher bar will wait for more favorable conditions, likely into 2027 or beyond. That means you have to build accordingly, focusing on margin alongside revenue, so you have real optionality when the time comes. The secondary market also helps, giving companies and their investors more flexibility as they wait.

You used to love investing in what you called 鈥渂oring software,鈥� or tools that quietly automated mundane enterprise tasks. Today, every software company claims to be an AI company. In 2026, does traditional SaaS even exist as a viable investment category anymore, or is a software startup inherently unbackable if it isn鈥檛 AI-native from day one?

Ranum: I don鈥檛 think the narrative is AI vs. SaaS. Instead, it’s AI plus SaaS. The companies that are struggling aren’t struggling because they’re SaaS businesses. They’re struggling because investors are in a 鈥渟how me鈥� era, and they don’t have clear answers yet.

Show me the free cash flow. Show me the path to profitability. Show me how AI is actually helping you win. You can’t get a stock bump anymore just by claiming you’re integrating AI. The market wants evidence of monetization.

The way I think about it is whether a company is building something that fundamentally changes how work gets done, or just layering AI on top of a workflow that a human is still doing. We used to back systems of record and workflow companies where the human was doing all the work. Now we’re in a position where the system itself can come in and actually do some of those tasks. That’s a different category of value entirely, and it changes what we look for. The bar has moved, but the opportunity is very real for the companies that can clear it.

Your core thesis is that the LLM stack is fracturing into distinct, standalone billion-dollar layers, such as orchestration (LangChain) and identity (WorkOS). But we鈥檙e seeing a massive border war. Big model providers like OpenAI are building their own tools, and data giants like are buying up security tools. How do standalone startups protect their turf when giants encroach from both sides?

Ranum: Both fracturing and consolidation are happening simultaneously, and I think that’s actually the right way to think about it. The moat isn’t about being first in a category. It’s about becoming genuinely embedded in how enterprises work. The companies I’m most excited about are the ones capturing orchestrated workflows in which the enterprise’s actual processes run through the product. That makes them very hard to displace, regardless of what the giants are building around them.

Because of your background at SAP, you know how enterprise buyers think. Right now, CFOs are looking at massive AI pilot bills and demanding to see actual ROI. When a startup is pitching an enterprise on a software governance or security tool, how do they defend that line item to a cynical CFO before the enterprise has even fully figured out its core AI strategy?听

Ranum: What we consistently hear from buyers is that trust has become what actually separates the market. Security, governance, compliance, and auditability aren’t nice-to-haves anymore. They’re what make an AI deployment defensible when the CFO or the board asks hard questions.

And cost predictability is right alongside that. We’re in an era of greater focus on ROI, and enterprises want to know what this will cost them at scale before they commit. The vendors that can answer that question clearly are winning deals over the ones that can’t.

It feels like Silicon Valley is obsessed with the glamour of humanoid robots right now. Meanwhile, Sapphire鈥檚 big bets in this space, like Tractian, focus on practical, unglamorous industrial AI and predictive maintenance. Are humanoid robots an expensive venture capital distraction right now? Where is the actual, contract-signing enterprise demand on the factory floor today?听

Ranum: The near-term ROI story is in constrained, high-value industrial settings such as packing, picking, inspection, and maintenance. These environments have clear labor economics, manageable deployment risk, and real buying cycles. That’s where the contracts are getting signed today.

Our portfolio company Tractian is a good example of what that looks like in practice. Unplanned downtime costs the world’s 500 largest companies roughly 11% of their revenue annually, which is a massive, measurable problem.

Tractian addresses it directly by combining sensor hardware with AI that detects early warning signs of equipment failure. The value proposition is concrete before you sign the contract, and the platform gets smarter the longer you use it. That’s the kind of embedded, compounding value we look for.

The humanoid era will come, but the gradient approach beats the all-or-nothing bet for near-term value creation. Start with specific, well-defined tasks where the payoff is obvious and work from there. The market is ready for that today.

Heavy industry and manufacturing are notoriously slow to change. A startup can’t just plug a modern AI API into a 30-year-old machine on a factory floor. For founders trying to build in the industrial tech space, is the winning strategy to build entirely new autonomous hardware, or is the bigger venture opportunity in retrofitting the world’s existing infrastructure with smart software?听

Ranum: I believe the winning strategy is smart software layered on top of existing infrastructure rather than replacing it. Factories aren’t going to rip out 30-year-old machines because a startup has a better alternative. That’s just not how it works. The opportunity is in making those machines intelligent.

That said, the hardware-plus-software combination really does matter. You can’t get the data without the sensors. But the durable value is in the software layer that keeps learning over time. That’s where I鈥檓 focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft 鈥� annoying, but fixable. In robotics and industrial tech, a mistake means a factory line shutting down or a broken multimillion-dollar asset. From a venture perspective, how much harder is it to scale a robotics startup when the cost of product failure is so high in the physical world?听

Ranum: I’d actually reframe the question. The cost of failure in physical environments is what makes the value proposition defensible. When the downside of getting it wrong is measurable, the upside of getting it right is equally concrete. You can walk into a sales conversation and show a customer exactly what prevention is worth before they sign anything. That’s a different conversation than selling software, where ROI takes quarters to show up.

From a scaling perspective, the key is discipline about where you deploy first.

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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.

U.S. startups announced sizable funding rounds at a steady clip during a truncated holiday week, with energy and AI leading the way.

Houston-based energy startup secured the biggest round, a $1.75 billion strategic financing, followed by , a developer of infrastructure for companies running open source AI models, and , a provider of compliance tools for enterprises.

Other big rounds were for companies focused on therapeutics, homebuilding, and even lacrosse.

1. , $1.75B, energy: Houston-based Joulent, a provider of energy infrastructure focused on the demands of artificial intelligence and other compute-intensive industries, raised $1.75 billion in a strategic investment backed by through its arm.

2. , $800M, AI infrastructure: Together AI, developer of an infrastructure layer for companies running open source AI models, secured $800 million in Series C financing. led the round, which set an $8.3 billion post-money valuation for the San Francisco-based startup.

3. , $180M, compliance: LeapXpert, a provider of tools for tracking enterprise communications for compliance needs, closed on $180 million in growth financing. led the financing for the New York-based company.

4. , $135M, AI software development: Redwood City, California-based 8090 Solutions, developer of a platform for building enterprise software with coordinated AI agents under human-led oversight,听 picked up $135 million in a round led by 1. The company, founded in 2024, counts prominent startup investor as co-founder and CEO.

5. , $126M, biotech: Boston-based Beeline Medicines, a startup focused on precision therapies for autoimmune and inflammatory diseases, secured $126 million in Series A extension funding backed by , and . The financing follows a previously disclosed $300 million Series A.

6. (tied) , $100 million, professional sports: The Premier Lacrosse League, a men’s professional lacrosse league in North America, closed a $100 million Series E financing round led by and . New York-based PLL said the deal represents the largest capital raise in the history of professional lacrosse.

6. (tied) , $100M, video-based AI: Twelve Labs, a San Francisco-based startup developing AI systems trained on video archives, raised $100 million in a Series B round co-led by and .

8. , $95M, AI for homebuilding: Higharc, a developer of AI-enabled tools for designing homes and managing workflows around homebuilding, picked up $95 million in Series C funding. led the financing for the Durham, North Carolina-based company.

9. , $85M, biotech: Cambridge, Massachusetts-based Flare Therapeutics, a startup targeting transcription factors to develop treatments for cancer and other ailments, raised $85 million in Series C funding led by and .

10. , $65M, AI privacy: Venice, developer of a platform enabling private, surveillance-free access to a wide array of AI models, secured $65 million in Series A funding led by . The round set a $1 billion valuation for the 2-year-old Sheridan, Wyoming-based startup.

Methodology

We tracked the largest announced rounds in the 小蓝视频色情网页版 database that were raised by U.S.-based companies for the period of June 27-July 2. 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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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 小蓝视频色情网页版 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.

小蓝视频色情网页版 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 鈥渃an they build it?鈥� Although not entirely absent at the seed stage, I鈥檇 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: 鈥淚s the tech defensible?鈥� Not just 鈥渄oes it work?鈥� but 鈥渄oes it compound?鈥�

Data moats, proprietary training sets, network effects built into the architecture 鈥� 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. 鈥淗ow can you get this to market?鈥� is increasingly asked at the seed stage rather than later in the cycle.

We鈥檙e 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, 鈥檚 Claude or LLaMA, and a lot of capital flowed into them. What鈥檚 changed is that frontier LLMs have now clearly started to take more of a platform approach 鈥� 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鈥檙e looking for platforms using proprietary data that can鈥檛 be replicated by AI, companies that have embedded deep domain expertise 鈥� areas where general-purpose AI still lacks industry context 鈥� 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鈥檚 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鈥檚 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鈥檚 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 鈥� 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鈥檛 changed. If that is in place, then it doesn鈥檛 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 鈥� 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鈥檙e 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鈥檚 not necessarily unjustified 鈥� the makeup and traction of seed-stage companies are much further along than predecessor vintages as we鈥檝e discussed.

We stay disciplined by being explicit about what we’re actually underwriting. We’re not just underwriting the financial return on this round 鈥� 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 鈥� i.e., a tool that AT&T will be working with in our environments 鈥� or is it something we鈥檇 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鈥檚 the latter, then we鈥檙e 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鈥檛 preclude us from working on applications that use traditional data processing methods.

You鈥檝e spoken before about your interest in 鈥減hysical AI鈥� and robotics (like Apptronik). The software lifecycle is easily compressed by generative AI, but hardware and physical deployment take time. Does this 鈥渟eed is the new Series A鈥� 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鈥檝e 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 鈥� 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鈥檛 see quite the same level of time compression there.

What is interesting for us at AT&T is the intersection 鈥� 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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Related reading:

• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• 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鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 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.听, $750M, finance software: Spend-management software provider Ramp secured $750 million in a financing led by , 听and . 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.听

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 , 听and .

5. , $465M, fusion energy: Helion, a startup with a mission to build the world鈥檚 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 .听

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 , , , 听and . 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 小蓝视频色情网页版 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.

Illustration:

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.听

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鈥檚 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.听

How times have changed. Already this year, $13.6 billion in venture investment has gone into companies in 小蓝视频色情网页版鈥檚 military, national security and law enforcement categories 鈥� more than 1.5x last year鈥檚 annual total.听

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 .听

Firestorm builds expeditionary manufacturing systems and modular drones for military use. Its containerized 鈥渪Cell鈥� 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鈥檚 total funding to nearly $150 million, .

“The ability to produce, adapt, and sustain systems at speed and scale will define outcomes in future conflict,鈥� , founder and chief investment officer at Washington Harbour Partners, said in a statement. 鈥淲e’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鈥檚 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鈥檚 rapid automation of many aspects of the notoriously paperwork-heavy industry.

Adding to this year’s tally is , a startup that says it鈥檚 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.

鈥淐ompanies want fee transparency, predictability, and speed,鈥� , a Manifest investor and former general counsel for 听1, and , said in a statement. 鈥淟awyers want to focus on delivering results, not justifying billable hours. Manifest OS鈥檚 model and use of advanced technology align those interests in a way the traditional system simply doesn鈥檛.鈥�

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 鈧�20 million (about $23 million) in Series B and grant funding to tackle a growing problem: industrial-scale solar panel recycling.听

By 2050, tens of millions of tons of solar panels are expected to become defunct, according to ROSI. The company鈥檚 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.听

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.听

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.

鈥淥ur 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,鈥� 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 小蓝视频色情网页版鈥檚 cleantech-, electric vehicle- and sustainability-related categories fell to a five-year low in 2025. Still, some areas 鈥� including solar and recycling 鈥� have continued to see larger rounds.

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$2.3M for a cell-based milk supplier听

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, 小蓝视频色情网页版 data shows.

That makes Montreal-based 鈥檚 recent $3.2 million CAD (roughly $2.3 million) seed round all the more interesting. The company, previously named BetterMilk, says it produces 鈥渃omplete milk鈥� 鈥� with proteins, fats and sugars 鈥� 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 鈥淐anadian division of a leading global dairy group.鈥�

鈥淲e see Opalia as a foundational player in the next era of dairy,鈥� , managing partner at Nadarra Venture, said in a statement. 鈥淲hat 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.鈥�

Opalia plans to make its commercial debut in 2028 and said it鈥檚 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听to speed up what it calls one of manufacturing鈥檚 most stubborn bottlenecks: turning digital product designs into actual production plans.

The startup鈥檚 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鈥檛 just analyze operations, but actively participates in physical production decisions. That kind of investment in physical AI 鈥� real-world applications of artificial intelligence, including in factories and on construction sites 鈥� has taken off this year.听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 小蓝视频色情网页版 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 小蓝视频色情网页版 . That鈥檚 a more than 29% increase from 2024鈥檚 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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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 小蓝视频色情网页版 unicorn lists:听

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

Related reading:

• March Mints 11 New Unicorns, As $200B Is Added Through Up Rounds And Board Posts Strong Exits听
• Billion-Dollar AI Rounds Push April To Third-Highest Startup Funding Month In A Year
• This Is A Momentous Year For Early-Stage Unicorns听
• 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 小蓝视频色情网页版 Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 小蓝视频色情网页版 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. 小蓝视频色情网页版 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 小蓝视频色情网页版 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 .听

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 鈥渇or some of the world鈥檚 largest enterprises.鈥澨�

While Blitzy did not reveal hard revenue figures, it said that its technology has been adopted by 鈥渄ozens鈥� 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.

鈥淲e 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,鈥� , 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.听
• , 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鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

The pace of large-scale dealmaking picked up some this week, led by 鈥檚 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听 led by and . Founded in 2010, the Cambridge, Massachusetts-based company has raised over $850 million to date, per 小蓝视频色情网页版 .

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 鈥渢he largest single software investment in the laundry vertical to date.鈥�

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 小蓝视频色情网页版 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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