Ten of those companies were AI labs, collectively valued at $65 billion. The most well-known was Beijing-based open source model developer 鈥� at $50 billion, the highest valued new unicorn to join the Unicorn Board this year.

The new unicorn frontier labs are focused on new architectures in AI model development in robotics, physics and self-learning, as well as on open source development, and in the case of one India-based startup, sovereign AI.

Other leading sectors with multiple companies were in robotics and AI infrastructure, with four companies in each.

Of the new unicorns, 16 are U.S-based, while eight are from China. Two new unicorns joined the board from India, Germany and the United Kingdom and one each from Netherlands, Belgium, Canada and Saudi Arabia.

Big exits remove a trillion

Despite the influx of newcomers, the total value of The 小蓝视频色情网页版 Unicorn Board dropped by more than $1 trillion in June as , its most valuable company, went public.

Other notable exits from the board last month were , the maker of AI coding tool Cursor, which was acquired by SpaceX for $60 billion after last being valued at $29.3 billion. , an AI infrastructure company that operates as a layer on top of GPUs, was acquired by , and customer experience agent was purchased by 1, both for well above their last private valuations.

New unicorns in June

Here are June’s new unicorn companies:

AI labs

• Hangzhou-based raised a $7.4 billion Series A, its first external financing, in a deal led by CEO . The 2-year-old company was valued at $50 billion and is said to be planning to list in as early as Q2 2027.
• is building a new AI architecture based on neuroscience called Cortex AI that promises lower power use. It raised a $500 million Series A from , , and . The less than 1-year-old New York-based company was valued at $2.5 billion.
• London-based , an AI for physical product design in aerospace, defense, energy, automotive and semiconductors, raised a $300 million Series C led by . The 6-year-old company was valued at $2.4 billion.
• , a model developer for robotics trained on gaming videos from its sister company , raised a $320 million Series A led by . The 1-year-old New York-based company was valued at $2.3 billion.
• , an embodied robotics intelligence company, raised a $400 million Series B led by . The 2-year-old San Mateo, California-based company with researchers from and was valued at $2 billion.
• Shanghai-based , a聽 robotics intelligence company, raised a $220 million seed round led by and . The less than 1-year-old company founded by an researcher was valued at $2 billion.
• , a builder of world models to simulate the real world impacting robotics, science, healthcare and defense, raised a $310 million Series B led by . The 2-year-old Menlo Park, California-based company was valued at $1.5 billion.
• Bengaluru-based , an Indian sovereign AI developer, raised a $234 million Series B first close led by . The 3-year-old company was valued at $1.5 billion.
• , an AI lab seeking to automate AI research for scientific use cases, raised a $200 million seed funding led by and . The less than 1-year-old San Francisco-based company was valued at $1 billion.
• Hangzhou-based , a 3D model developer used in gaming, entertainment and product design, raised a $200 million Series A led by . The 3-year-old company was valued at $1 billion.

Robotics

• Germany-based , a physical AI company building intelligent machines to to work alongside humans, raised a $1.4 billion Series C led by stablecoin issuer among other strategic and growth investors. The 7-year-old company, with $1 billion in its order pipeline and strategic deployments, was said to be valued at $7 billion.
• Shenzhen-based , a builder of humanoid robots, raised a $148 million Series B led by . The 3-year-old company was valued at $1.5 billion.
• Guangdong-based , a humanoid robotics company, raised a $147 million Series B. The 5-year-old company, which projects 1,000 shipments in 2026, was valued at $1.5 billion.
• , a builder of industrial arm robotics for manufacturing that said its technology learns through demonstration, raised a $200 million Series C led by and . The 9-year-old New York-based company was valued at $1 billion.

AI infrastructure

• , which pivoted from crypto mining to data center build out for AI, raised a $400 million funding led by , and . The 2-year-old Coral Gables, Florida-based company was valued at $2.4 billion. The company has filed for a direct listing on .
• Las Vegas-based , a cloud operator that offers customer AMD chips, raised a $350 million Series B led by and . The 2-year-old company was valued at $1.6 billion.
• Beijing-based , an inference solution offering customers API access to hundreds of models, raised a $296 million Series B. The 2-year-old company was valued at $1.2 billion.
• , an AI developer cloud to train, fine-tune and deploy AI, raised a $100 million Series A led by . The 4-year-old New Jersey-based company valued at $1 billion has 1 million developers using the platform.

Defense

• , a precision weapons company enabling existing weaponry to defend against unmanned drones, raised a $200 million Series B led by . The 4-year-old Austin-based company was valued at $2.2 billion.
• , a manufacturer of unmanned aerospace and defense systems, raised a $300 million Series C led by and . The 3-year-old Huntington Beach, California-based company was valued at $1.8 billion.
• , a cyber intelligence company building products for the U.S. military, raised a $100 million Series B led by , and . The 1-year-old Arlington, Virginia-based company was valued at $1 billion.

Proptech

• Montreal-based , a mortgage financing platform, raised a $217 million Series E round. The 8-year-old company was valued at $1.1 billion.
• India-based ,聽 a property brokerage that also owns a mortgage marketplace, a property management platform, and a home interior brand raised a $95 million private equity and debt financing led by . The 13-year-old company was valued at $1 billion.

Data analytics

• Belgium-based , an intelligence platform for global physical trade, raised a $1 billion secondary market funding led by . The 12-year-old company was valued at $3.7 billion.

Biotechnology

• , a biotech company focused on reverse cellular aging, raised a $435 million Series C led by . The 4-year-old San Francisco-based company with plans for clinical trials next year for human liver cells, was valued at $3.1 billion.

Materials

• Cambridge, U.K.-based聽 , building a network of labs using AI for new material discovery, raised a $450 million funding led by and . The 2-year-old company was valued at $2.6 billion.

Cryptocurrency

• , a blockchain and smart contract solution for global financial institutions, raised a $355 million Series F led by . The 12-year-old New York-based company was valued at $2 billion.

Image generation

• Beijing-based , a video generation company, raised a $300 million Series B led by , and . The 3-year-old company was valued at $2 billion and says it has built a creator community of more than 30 million users. As of May 2026 the company has $300 million in annual recurring revenue.

Financial services

• Saudi Arabia-based , a mobile banking company, raised a $400 million Series A. The 6-year-old company was valued at $1.6 billion.

Semiconductor

• Rotterdam-based , a 3D metrology inspection tool for semiconductor manufacturing, raised a $380 million Series D led by . The 10-year-old company was valued at $1.6 billion.

Aerospace

• Beijing-based , a space infrastructure and satellite company, raised a $207 million Series D. The 10-year-old company was valued at $1.5 billion.

E-commerce

• , an e-commerce provider that supports customer interactions post purchase, raised an $81 million Series B led by . The 4-year-old Utah-based company supporting 4,100 brands and 1,750 merchants was valued at $1.3 billion.

AI healthcare

• , an AI agent built for a patient’s healthcare journey and used by healthcare providers, raised a $120 million Series C led by . The 3-year-old San Francisco-based company was valued at $1.2 billion.

Transportation

• Munich-based , a car subscription platform operating in Germany and partnering with 25 brands, raised a $113 million Series D led by . The 7-year-old company was valued at $1.1 billion.

Related 小蓝视频色情网页版 unicorn lists:

• (1,822)
• (637)
• (213)
• (190)
• (118)
• (102)
• (935)
• (539)
• (248)
• (39)
• (488)

Related reading:

• 小蓝视频色情网页版 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• AI Services And Robotics Lead Diverse Crop Of 29 New May Unicorns As SpaceX, Anthropic And OpenAI Line Up Blockbuster Exits

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.

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.

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. 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.

Illustration:

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.

It was not a holiday week on the funding front, as a raft of largely AI-focused companies closed big rounds. The largest of these was a $1.5 billion financing to enterprise AI startup and a Series D for meal and delivery provider . The week also included some big financings for enterprise tech, food delivery, drones and construction automation.

1. , $1.5B, enterprise AI tools: Fireworks AI, a developer of tools for enterprises to turn 鈥済eneral-purpose models into specialized intelligence trained on their own data,鈥� raised $1.505 billion in Series D funding. , and led the financing, which set a $17.5 billion valuation for the San Mateo, California-based company.

2. , $650M, meals and delivery: Wonder, an operator of kitchens and meal delivery services, closed on $650 million in Series D funding at a $9 billion pre-money valuation. Capital will go in part toward expanding operations for the New York-based company, which currently has 140 locations.

3. , $400M, life sciences AI: AI drug discovery startup Chai Discovery secured $400 million in Series C funding at a $3.8 billion valuation. led the financing, investing alongside , , and others.

4. , $300M, robots: Cambridge, Massachusetts-based Walden Robotics, a startup building general-purpose robots for work in manufacturing and logistics, launched out of stealth with $300 million in funding. and led the round, which values the company at $1.1 billion.

5. , $125M, drones: Seattle-based Brinc, a developer of drones for use in public safety and emergency operations, raised $125 million in fresh funding. led the financing, with participation from , and founder and CEO .

6. (tied) , $100M, construction automation: Austin-based TerraFirma, a developer of AI-enabled software and autonomous robotics technology for the construction industry, landed $100 million in new funding, bringing total investment to date to $115 million.

6. (tied) , $100M, enterprise AI: Spectro Cloud, a provider of AI infrastructure management software, said it raised more than $100 million in a Series D round led by . The financing brings total capital raised by San Jose-based Spectro Cloud to $260 million.

8. , $80M, defense tech: Singularity, a startup focused on developing air defense technology, emerged from stealth with $80 million in Series A funding. and 1聽led the financing, which set a $400 million valuation for the Los Angeles-based company.

9. (tied) , $70M, fintech: San-Francisco-based fintech startup Flex, a private banking platform for high-net-worth business owners, raised $70 million in a Series B1 financing led by . The round follows the company’s $60 million Series B in December.

9. (tied) , $70M, AI and policy: State Affairs, an AI platform for policy and regulation, secured $70 million in Series A funding led by Khosla Ventures and .

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 July 11-17. 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:

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?聽

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?聽

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?聽

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?聽

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?聽

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?聽

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.

Related 小蓝视频色情网页版 queries:

Illustration:

Within the region, U.K. startups gained significant share in Q2, raising more than $10 billion. That marked the third-largest funding quarter for the U.K. on record, and came in at less than $500 million below its peak quarter in 2021.

European startup M&A activity also picked up in Q1 and continued that momentum in Q2, even as public-market exits stayed subdued.

Table of contents

• Large rounds drive gains
• H1 2026 up 50%
• UK momentum builds
• By stage
• Looking ahead
• Methodology
• Glossary of funding terms

Large rounds drive gains

Four companies raised venture fundings of a billion dollars or more last quarter, accounting for 25% of all startup investment in the region in Q2, 小蓝视频色情网页版 data shows.

Those billion-dollar-plus rounds were raised by an AI-centric group: -owned AI drug developer , which was spun out of ; green steel production manufacturer ; , which is developing robots for home and industrial applications; and , an AI lab founded by former DeepMind researchers.

However, most of the growth in funding year over year and quarter over quarter was driven by rounds of $100 million and over. The majority of funding 鈥� 65% 鈥斅爓ent to a group of 42 companies that raised rounds of $100 million-plus. Sectors that stood out for these companies include聽 biotech, quantum, financial services, AI labs, aerospace, semiconductor, robotics and energy.

H1 2026 up 50%

Funding to Europe-based startups in H1 was up 50% year over year to total $42 billion, 小蓝视频色情网页版 data shows. Still, the region鈥檚 startup investment for the first half of the year remained well below the 2021 H1 peak, when VC funding in Europe totaled $60 billion.

It鈥檚 also drastically lower than the $392 billion raised in North America鈥檚 record-setting H1, with that region鈥檚 funding up 158% year over year.

Europe鈥檚 funding deal count subsided last quarter, but mostly at the seed stage. Late-stage rounds were up a bit, while early-stage deals dipped slightly year over year. (It鈥檚 worth noting, seed stage rounds are often added to the 小蓝视频色情网页版 data set after the close of the quarter, so those numbers will increase over time.)

UK momentum builds

The United Kingdom widened its venture-funding lead last quarter, as startups based in the country raised $10.4 billion 鈥� not far from the peak in 2021 at $10.8 billion.

The region鈥檚 No. 2 startup market, Germany, trailed with $3.2 billion raised by its startups in Q2, and France followed in third place with $2.4 billion. Sweden was Europe鈥檚 fourth-largest startup market last quarter, with its companies raising $2 billion.

小蓝视频色情网页版 data shows funding to Europe鈥檚 AI-focused companies reached more than $10 billion in Q2 鈥� the largest quarterly amount so far 鈥� but slightly below the Q1 percentage, when those companies raised more than half of the region鈥檚 startup investment.

By stage

Europe鈥檚 late-stage funding totaled $12.1 billion in Q2, up 90% year over year. Large Series C and D rounds were raised by Germany-based robotics developer Neura Robotics; Netherlands-based , which makes inspection tools for semiconductor manufacturing; U.K.-based quantum computing startup ; and Germany-based satellite launcher .

Early-stage funding reached $8.6 billion across 250-plus Europe-based startups last quarter, 小蓝视频色情网页版 data shows. Large Series A and Series B rounds were raised by London-based Isomorphic Labs, London-based AI self-learning lab , Germany-based fusion energy company , London-based semiconductor developer , and London-based quantum processor provider .

European seed funding totaled $3.2 billion last quarter, with a billion dollars of that raised by just one company: Ineffable Intelligence.

Other large seed rounds were raised by , a London-based AI lab for science; Italy-based autonomous driving technology producer ; and Stockholm-based defense tech company .

M&A increase

While IPO activity for European startups was muted, M&A showed strong momentum following increased activity in Q1. A total of 154 Europe-based, venture-backed companies were acquired for a cumulative $11.5 billion or more in Q2, 小蓝视频色情网页版 data shows. That includes three companies acquired for more than $1 billion each in biotech, industrial AI and micromobility.

Looking ahead

European startup investment has now steadily increased since the fourth quarter of 2024, with increased momentum in the just-ended quarter, driven by larger rounds of $100 million and over. The region鈥檚 startup ecosystem shows particular strength in deep tech and financial services as well as the formation of new AI labs, and M&A activity has fueled liquidity for the next batch of startups.

Now the question remains: Will it be enough to keep Europe competitive with the frontrunners, the U.S. and China?

Related 小蓝视频色情网页版 queries:

Related reading:

• 小蓝视频色情网页版 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• North American Startup Funding Shattered Records In First Half Of 2026, Driven By AI

Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of July 6, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

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.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. 小蓝视频色情网页版 also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. 小蓝视频色情网页版 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the 鈥淪eries [Letter]鈥� naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a 鈥渧enture鈥� round. (So basically, any round from the previously defined stages.)

The firm says entrepreneurs starting their second or third companies have created more than $2 trillion in enterprise value across Europe, and expects the coming wave of AI exits to produce another generation of experienced founders.

鈥淭here鈥檚 beginning to be this super cycle of repeat founders in Europe,鈥� co-founder and managing partner said in an interview with 小蓝视频色情网页版 News. He recently relocated from San Francisco back to London, where the firm has also opened a new flagship office.

Repeat founders bring not just experience, but connections and the ability to hire quickly to the table, according to Murphy.

From its new fund, Tapestry plans to invest in a similar number of companies as it did with prior funds: Around 30 companies at pre-seed or seed.

Prior fund check sizes were around $1 million but checks from the new fund will trend larger, from around $1 million to $3 million, according to the firm.

The team seeks out previous founders even before they have decided what’s next. 鈥淟et鈥檚 spend time together before you start your new company. Let鈥檚 ideate, let鈥檚 brainstorm,鈥� said Murphy. 鈥淲e鈥檙e not taking anything for that 鈥� we鈥檙e not an incubator, we鈥檙e not an accelerator.鈥�

The firm鈥檚 earlier bets include smartphone and earbud developer and AI customer service startup , which was recently acquired by 1聽for $3.6 billion.

Other investments over the years include drone delivery startup and , which works to automate manufacturing. It also has a renewed focus around AI security with investments in , and .

New investors in this fund are sovereign investor , alongside pension fund and fund of fund . Notably, , CFO at , is also an investor in the fund.

鈥淚 think encouraging a vibrant boutique seed environment for funding is very important for encouraging creative new people to start interesting, different and weird businesses,鈥� said Murphy.

Related 小蓝视频色情网页版 query:

Illustration:

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

This week, most of the largest U.S. startup funding rounds centered around the sector one would suspect: artificial intelligence. This was true for the week鈥檚 largest venture financing, a $1.5 billion Series F for AI inference technology provider , as well as a majority of rounds in the Top 10. Beyond that, the next-biggest area for startup funding was biotech.

1. , $1.5B, AI inference technology: Baseten, a provider of systems software to run AI applications workloads, raised $1.5 billion in Series F funding, its fourth fundraise in 18 months. , , , and co-led the round, which set a $13 billion valuation for the San Francisco-based company.

2. , $1B, digital marketing: AppsFlyer, a San Francisco-based provider of data analytics with digital marketing as a core use case, reportedly secured more than $1 billion in a Series E funding round at a post-money valuation of $2.7 billion. Backers reportedly include , , and .

3. , $650M, AI inference technology: San Francisco-based Groq closed on $650 million in new funding led by and that it says will be used to scale its AI inference cloud technology and infrastructure. The investment comes just over six months after an acquihire-type transaction in which hired away its founder and key team members and licensed its technology.

4. , $330M, ophthalmic therapies: Ollin Biosciences, a developer of therapies for vision-threatening diseases, picked up $330 million in Series B funding. and led the financing for the Austin-based company.

5. , $320M, foundational AI: General Intuition, developer of a foundational AI model based on gameplay, secured $320 million in Series A funding at a $2.3 billion valuation. led the financing for the New York-based company, while backers including and participated.

6. , $250M, government software: Peregrine Technologies, provider of a platform used by public safety agencies and other government entities, secured $250 million in Series D financing. , , , , and led the financing, which set a $6.8 billion valuation for the San Francisco-based company.

7. (tied) , $200M, risk intelligence: Palo Alto, California-based Quantifind, developer of a risk intelligence platform for financial crime detection and national security operations, closed on $200 million in growth financing led by .

7. (tied) , $200M, foundational AI: San Francisco-based Mirendil, a frontier lab building systems that excel at AI R&D, says it raised a seed round of $200 million led by and . The startup also counts as a backer.

9. (tied) , $190M, AI infrastructure: AI networking infrastructure startup Upscale AI raised $190 million in Series A extension funding, bringing total financing to $500 million. led the round, which set a $2 billion valuation for the Santa Clara, California-based company.

9. (tied) , $190M, biotech: San Francisco-based Osanni Bio, a therapeutics platform focused on ophthalmic therapies and other treatments, secured $190 million in Series B funding led by .

Large non-US deals:

The week also brought some large European rounds:

, $569M, defense tech: Berlin-based defense tech startup Stark reportedly raised $569 million in a financing led by and .

, $546M, insurance: Paris-based health insurance startup Alan secured $460 million in new investment in primary and secondary equity 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 June 18-26. 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:

Globally, robotics startups have so far raised $18.8 billion in 2026, compared to $15 billion in the full year of 2025. The figure also handily surpasses the $14.1 billion raised in the peak venture funding year of 2021, and we still have more than six months of fundraising left.

The impressive rise in funding reflects a marked shift in perception among venture investors about the robotics sector, which was traditionally considered an expensive, asset-heavy hardware gamble. In particular, investors appear to be drawn to startups working on embodied AI, or artificial intelligence with a physical body that interacts with the real world in real time.

Noteworthy recent rounds

The surge in funding is driven by a number of robotics-focused startups raising considerable capital from investors this year. Also, interestingly, two of the five largest raises in 2026 to date have been by Austin-based companies.

Topping the list of largest deals in 2026 so far is Austin-based , a defense tech startup focused on autonomous sea vessels. In March, the 4-year-old company raised $1.75 billion in Series D funding, bringing its total funding to around $2.6 billion. led the round, which set Saronic鈥檚 valuation at $9.25 billion 鈥� more than double its Series C level in 2025.

Earlier this month, Germany鈥檚 , a developer of AI infrastructure for robots to learn, collaborate and operate across real-world environments, said it secured up to $1.4 billion in Series C funding. led that raise.

In January, , a robotics company building an 鈥渙mni-bodied鈥� brain to operate any robot for any task, announced that it had raised $1.4 billion, tripling its valuation to over $14 billion. That financing came just over seven months after Skild raised at a $4.5 billion valuation. led the startup鈥檚 latest round, which included participation from , 鈥檚 venture capital arm.

On June 15, Beijing-based , which creates water robots and intelligent unmanned equipment, raised $1 billion in a massive Series A round led by .

And in February, AI-powered robotics company raised $520 million in an extension of its $415 million Series A raise in February 2025, bringing the total round to over $935 million. Existing backers , , and joined new investors, including and manufacturing giant in participating in the extension.

Interestingly, spinout has already raised two rounds in 2026. In March, the Palo Alto, California-based startup closed on a $500 million Series A round, co-led by and . Then in May, it raised another $400 million in a financing led by . The company is developing an AI-enabled industrial robotics platform focused on automating industrial and manufacturing tasks at scale.

Exits

While mergers and acquisitions have been relatively robust with several strategic buyouts, the robotics IPO landscape is a bit quieter, particularly in the U.S.

In China, however, a number of robotics companies have recently gone public. The of , targeting a $3 billion to $7 billion valuation, was considered a milestone for the industry. In March, the company filed for an to list on the , and its IPO was widely expected to spur other startups in the space to pursue their own public-market debuts.

, a startup based in China鈥檚 Shandong province that makes lightweight industrial robots, in May listed on the , raising about $86 million. And it did not disappoint. Robotphoenix closed its first full day of trading at HK$53.75 ($6.86 U.S.), up nearly 80%, though shares have dipped to the HK$37 range more recently.

On the M&A front, a number of Big Tech and automotive giants have been aggressively acquiring embodied AI and humanoid talent to anchor their physical automation strategies.

In February, AI-powered supply chain provider acquired , an Austin-based maker of autonomous forklifts and lift trucks.

Skild AI in April that it had picked up the robotics arm of in an effort to deploy its technology to warehouses.

And in May, tech giant entered the humanoid robotics field directly by acquiring San Diego-based . The team was absorbed into Meta’s Superintelligence Labs unit to accelerate training of its foundational physical AI model.

Related 小蓝视频色情网页版 queries:

Related reading:

• 5 Startup Sectors Seeing Big Funding Growth
• The Year Of Humanoid Robots
• Embodied AI Fuels Record Robotics Funding In China As IPO Momentum Builds
• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo
• Robotics Startup Skild AI Lands $1.4B, Tripling Valuation To $14B In Just 7 Months

Illustration:

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.

Related 小蓝视频色情网页版 queries:

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

Illustration:

“Silicon Valley has done very well with software, but while software was eating the world, they forgot about silicon,” Barrett told 小蓝视频色情网页版 News in an interview.

The firm recently closed a $475 million fund focused on investing in deep-tech startups at seed and Series A. In the decade-plus since its founding, it has built its investment thesis around the idea that breakthroughs in science and engineering 鈥� not just software 鈥� would create the next generation of valuable companies.

With demand surging for compute, semiconductors and energy, Barrett argues the rest of the industry is now catching up. “We’ve been at it for more than a decade,” he said. “In recent years, as AI is eating software, people are scrambling back to recognize that the energy, semiconductors and infrastructure they operate on all need capital too. We’ve been operating in that regime for a very long time.”

Barrett is originally from Australia and came to Silicon Valley in the 1980s. He’s been coding for 50 years, he said, after developing an early and deep respect for science and engineering as the child of two engineers. His childhood was steeped in punch cards, draftsmen and drawings of control systems and machinery, he said.

鈥淪cience lets you follow breadcrumbs from prehistoric plumage to semiconductors. One principle can be applied somewhere orthogonal and create extraordinary value,鈥� Barrett said in a lengthy interview with 小蓝视频色情网页版 News.

Barrett went on to found video game developer , joined to build the entertainment browser acquired by , and was subsequently CTO at prior to co-founding Playground Global in 2015.

Playground Global operates a lab in the former Palo Alto Research Building in Palo Alto, California. The location hosts 350 people, including those working at its portfolio companies and others with adjacencies working from the lab.

On a recent visit to the warehouse, I saw various models of robots, materials for aerospace construction, and a model of building powerful lasers to increase the speed of semiconductor manufacturing. The quantum computing startup , a Playground portfolio company, moved in when it had three employees and moved out when it reached 90.

The firm has four general partners. Along with Barrett, they are , the former CEO of and who architected CPUs at Intel that helped computing take off at scale, and who joined the Playground team last year as a general partner specializing in semiconductors; , who has made many investments in biotech, including ; and co-founder , who led the investment in .

What follows are highlights from a wide-ranging interview with Barrett that covered topics including sovereign technology, the need to invest in companies that operate on the physical plane, and why he believes putting data centers in space is stupid.

This interview has been lightly edited for clarity.

Gen茅 Teare: What is the thesis for Playground Global?

Peter Barrett: It is about reducing new results in science and engineering into commercial and societal value. That means operating at the boundary between computation and the physical world. We are very interested in new capabilities of computation driving civilization forward, and that inevitably means operating in the same physical plane that we live in.

We’re seeing in our data a huge amount of funding going into space, semiconductors and robotics. It seems as if the whole venture industry has pivoted to this much broader array of companies. Do you see that as a good thing?

Barrett: We lost a lot when people weren’t investing in things that strike us as important. It is good that there is capital chasing the things we care about and that have real consequence.

You can鈥檛 spin up a deep-tech practice overnight. You still need domain expertise. You still need to understand why investing in nuclear reactors is good, and why data centers in space are preposterous.

Silicon Valley hasn’t been very efficient with much of the capital it’s deployed over the past decade or so. But I do think it’s good that people recognize that software may be eating the world, but you can’t eat software. We have to operate in the physical layer.

Do you think Silicon Valley gets more efficient?

Barrett: We need to do the work. You develop the instincts and the platform to deploy capital efficiently into these places.

It’s important that people recognize there’s this unprecedented funnel of technical change. AI is an early indicator of it, but we have technologies like quantum. We know how to produce computation using things beyond transistors and semiconductors.

We’re scratching the surface in terms of AI models. We’re right at the beginning of an explosion and renaissance in materials science driven by things like quantum computing.

Now would be the time 鈥� and candidly, I feel the imperative 鈥� that anywhere there is science and capital, it needs to be turned into value, especially in liberal democracies, because the despots are doing a pretty good job of it. It’s incumbent on us to stay ahead.

We’re in the DOS age of AI. We’re scratching the surface, both in terms of the models we make and the hardware we run them on.

Now would be the time for people to write checks into things that are sensible and valuable. We spent a lot of time on NFTs. How are we doing with cancer? How are we doing with our most difficult challenges in terms of healing and feeding the world?

There are lots of new degrees of freedom that could take capital and turn it into value.

Do you think deep tech fits the venture thesis, despite the long time horizons and the amount of capital it requires?

Barrett: The long time horizons certainly exist. If you’re building PsiQuantum, we’re building million-qubit quantum machines. That takes billions of dollars and a decadal effort.

The corollary is that we’ve had hardware exits in two years. The timelines for hardware aren’t necessarily that different from software.

Therapeutics naturally take a longer time, because of clinical trials. But we’ve also seen exits there. One of our companies tested half a million drugs in a single animal and created a new corpus of AI input for building models to create therapeutics. That’s not a decadal effort 鈥� that’s a handful of years before exit.

We try to craft a portfolio that’s a mix of tactical and strategic. Some of these companies get to hundreds of millions in revenue within a few years. Others, like PsiQuantum or , may take a decade to reach full entitlement. That’s part of portfolio construction.

The biology company you mentioned 鈥斅爓hat’s its name?

Barrett: . It did the largest pharma deal of its kind last year with . The deal could be worth $2 billion on the back end.

It’s a unique mechanism to create giant AI training sets by using physical systems 鈥� using animals and in vivo testing to create that dataset. It affords the ChatGPT and biology moment, where you can have large enough training sets to build big models.

You describe the firm as investing somewhere between improbable and impossible. Are there companies that really fit that thesis when you first met them?

Barrett: When we first met PsiQuantum, they were talking about building a machine which was 10,000x the state of the art. Using then-current technologies, it would have been the size of the Sierra Nevadas.

They required exponential improvements in both hardware and software, and they’ve achieved both. It’s the size of a warehouse, not a laptop.

The work we’re doing in biology, materials, quantum algorithms and superconducting logic 鈥� which will replace transistors and semiconductors 鈥� all of these things sound like science fiction, but they’re much closer to improbable. In many cases they’re entirely practical before we invest; they just seem improbable to those unfamiliar with the domain.

There are things that are not impossible but are still really dumb 鈥� data centers in space, small modular reactors (SMRs), or fusion. The physics may work, but the economics don’t, or the timelines don’t align.

I’m disappointed we haven’t invested in anything that turned out to be more impossible than we thought. None of our portfolio companies failed because the technology didn’t work.

We’ve had capitalization failures. We flew hydrogen planes. We’ve built things that were thought to be virtually impossible that turned out to be straightforward. They may have missed their market or may have been unable to raise the capital to continue.

I want to do something where the technology doesn’t work, and we鈥檝e yet to do one of those.

Is there a company you missed out on where it looked impossible and you wish you’d invested?

Barrett: I wish I hadn’t taken ‘s word for it when was a non-profit.

We haven鈥檛 missed many. As the roadmap developed, we wish we had been earlier in a couple of categories that are really interesting. But overall, we haven’t missed too many.

In which sectors or companies have you invested where the time horizons have shortened due to AI?

Barrett: Adding Pat Gelsinger to the team reflects an interest in scaling semiconductors along various dimensions, including energy efficiency and how power is delivered.

We do everything from nuclear reactors all the way through to transmission, energy conversion outside the data center, inside the data center, under the chip, what kinds of chips you鈥檙e running, what models run on top of those chips, what architectures those chips are made from, and what materials those chips are made from.

At every layer of the infrastructure 鈥� optical interconnects, memory systems 鈥� we have a best-in-class company at every point. We built the first AI accelerator a decade ago, and we鈥檝e broadened that to encompass the entire ecosystem, from the creation of electrons to how they expend themselves doing useful software work.

There are bubbly aspects of the current AI moment, but the bubble is being modulated to some degree by the unavailability of energy.

We鈥檙e in the DOS age of AI. LLMs are embarrassingly incompetent compared to what comes next, but we believe in the durability and growth of AI, and are making investments in model architectures and the ways AIs are trained. We see demand for compute, energy and infrastructure continuing to grow.

We have technologies that can reduce general-purpose compute workloads by 100x to 1,000x over state of the art. We believe we know how to make the energy and deliver it. We know how to connect these systems.

So quixotic pursuits like putting data centers in space are unnecessary.

Talking privately to hyperscalers and Fortune 50 companies, they all say there is way more demand for AI in its future incarnation than exists today. It鈥檚 incumbent on us to figure out how to do it 100x, 1,000x or 10,000x more efficiently, because that demand turns into GDP growth and better solutions to our hardest problems.

What are the companies in energy and semiconductors that you are betting on?

Barrett: One example is the wild superconducting logic company . We can make things that are post-semiconductor and post-transistor, with devices that switch five orders of magnitude more efficiently than transistors.

They operate at cryogenic temperatures, but quantum computers do that, and our extreme ultraviolet lithography system does that. The future of computation is cryogenic. Even after you pay to make it cold, you鈥檙e still 100x to 1,000x more energy-efficient on compute.

This technology has been around since last century, but it鈥檚 mainly been used for secure signals intelligence and radar applications. We鈥檙e generalizing it for compute.

Another example is . People talk about SMRs, which are a physics solution to a financial problem, or fusion, which is still decades away. Alva instead uprates the existing nuclear fleet to get hundreds of megawatts out of each unit by replacing 1970s steam generators with a 2020 steam generator.

We can deliver power in a handful of years. No new fuel, no new regulatory path, and a business model that makes sense for operators. We can put gigawatts onto the grid without moving a fence line of an existing reactor and without upgrades to the electricity grid.

We know how to make AI training wildly more efficient. We know how to train different kinds of AI models that we鈥檝e been unable to train.

The last supercomputer at uses something unlike a CPU or GPU to run existing software. We鈥檝e been running software the same way for 70 years, but there are other ways, with dataflow architectures. We have a company doing that 鈥� [].

The degrees of freedom from materials, systems, code and models have never been greater. We鈥檙e exploring all of them. But most require rolling your sleeves up in the physical world.

LLMs feel like brute-forcing something 鈥� like a drunk looking for keys under the streetlight. We鈥檙e pushing more and more into that, and I think that鈥檚 a dead end. We know other ways of moving forward.

Are you seeing new model companies, separate from LLMs, that are going to solve things?

Barrett: Our brains are not LLMs. They鈥檙e not transformers. Transformers are effective, but they are one of a long line of soon-to-be-extinct models that get replaced by something that works better.

That millionfold gap between our brains and GPUs is an architectural gap. Meat is much worse at computation than hardware can be, so biology shouldn鈥檛 be better.

Physics allows a million times a million more efficiency, and we should start chipping away at that.

Intelligence is useful and can be pressed into service against basic things like photosynthesis. Plants were invented by accident of evolution 3 billion years ago. They鈥檙e pretty, but not efficient. They shouldn鈥檛 be green; they should be black. We know how to make photosynthesis twice as efficient, and probably 5x more efficient.

We鈥檙e not stuck with the physical constraints of our technology or of nature. Nature is beautiful, but cobbled together by a process that we can have agency over.

All the materials that operate our civilization are discovered, not designed, because we can鈥檛 design things we can鈥檛 simulate. Our best computers cannot simulate the quantum nature of nature. That鈥檚 about to change.

We鈥檙e stumbling around in the dark, relying on serendipity and the occasional magical material. Whereas we can construct any number of materials with magical properties that are currently hidden from us by our inability to simulate the quantum mechanical processes that animate chemistry.

We are right on that threshold of unlocking all of these dimensions. And at the same time, we鈥檙e putting money into NFTs, the metaverse and other things that will come and go, without anybody ever caring.

Are you talking about the mix of quantum with biology and model-focused companies?

Barrett: Quantum allows us to directly design materials, directly explore the method of action of drugs, and directly design drugs.

AI has a role to play in biology and understanding structures we can measure. We think there are quantum wet labs where we can measure the performance of small-molecule drugs against models of nature and then verify in nature.

We don鈥檛 know how many things that animate our industry actually work. We don鈥檛 know how Tylenol works. We don鈥檛 know how the Type II superconductors we鈥檙e building fusion reactors out of work. We know that if you take iron and nitrogen and arrange them in a certain way, they produce magnets stronger than rare earth magnets, but we don鈥檛 know why.

There are mysterious things we鈥檝e stumbled across that hint at an Aladdin鈥檚 cave locked behind a wall of computation. That wall is coming down.

Which sectors do you think are going to take a lot longer to come to fruition?

Barrett: Civilization will operate on fusion eventually, but right now the only reactor that works using gravimetric confinement is the sun. I think that鈥檚 a long way off.

Data centers in space are stupid. You can鈥檛 operate a gigawatt data center in a thermos. We have terrestrial answers to those questions that we should pursue.

I鈥檝e always been a detractor of self-driving cars, which are starting to work. Now we need an economic model that makes them sensible and doesn鈥檛 drown our cities. The problem with transportation in cities is not the degree of autonomy. If we cared about traffic deaths, we鈥檇 worry about roundabouts.

There鈥檚 also nonsense with NFTs and the metaverse which have sopped up enormous amounts of capital. Small amounts of capital using these tools against our most difficult diseases would yield results. Small modular reactors are an unwarranted innovation.

There are lots of things that, at first blush, seem good and valuable, but there are far better solutions that are simpler and more imminent. We need to be practical about where the money goes.

There was a company that just joined the Unicorn Board, valued over $1 billion this past month, doing orbital data centers. Are you saying this whole category doesn鈥檛 make sense?

Barrett: To his credit, will show you a picture of what a 100-kilowatt data center looks like, and it鈥檚 bigger than Starship. A 100-kilowatt is a small rack from that is human-sized.

The arguments are that there are a lot of renewables in space. But there are a lot of renewables on the ground too. North Western Australia has solar and wind that are 70% naturally firm, and on the ground, so you can build things on it.

Put a data center in North Western Australia, which we are doing. We have a renewable site 35x the size of Manhattan.

Energy generation and compute in space is a nonstarter because space is not cold. You鈥檙e building things in a thermos and need to get rid of heat. A single human-sized rack is 100 kilowatts, which is about the size of the International Space Station鈥檚 radiators and solar panels.

Starship has yet to actually put anything in orbit. It鈥檚 made some fireworks, which are pretty, and it鈥檚 a beautiful thing. is an amazing company because of Falcon 9 and Starlink. But data centers and power generation in space makes no sense.

We know how to build arbitrary amounts of energy generation on the ground with very safe, very large nuclear reactors. We鈥檝e been doing it for decades.

For all the talent and genius rattling around the Valley, we do spend money on silly things.

Do you think now is the most exciting time to be investing, or have some of those investments already been made and are going to come to fruition?

Barrett: We鈥檝e already made investments in things on a really steep trajectory.

Snowcap will take a decade before we鈥檙e building GPUs with that technology, but we鈥檒l have commercial product from them next year. We鈥檙e getting better at early, undeniable signals.

PsiQuantum is a long journey, but some things just take that amount of time.

X-Lite seems like a ridiculously long journey, although we鈥檙e building the prototype facility now, and it received the first money from the new CHIPS Act.

Some hardware companies making silicon or systems are getting significant revenue in a handful of years.

There鈥檚 a sleeper in Fund I. Its first trick was to make MRI machines 100,000x more sensitive, and they鈥檙e shipping those. In the background they鈥檝e also been developing that core physics to build a new quantum computing modality. So we actually have two quantum computing companies in Fund I.

Even though that鈥檚 a 10-year-old company, there are about to be two companies, one of which will be a unicorn virtually overnight.

There are wild things bubbling under the surface that people are going to wonder where they came from.

Companies like 鈥� the only co-packaged optics on TSMC 鈥� we鈥檝e been working on that for a long time. Now people are waking up to silicon photonics and co-packaged optics.

There are also stealth companies that are indistinguishable from magic. Some of those will come out of stealth this summer.

Is there anything we haven鈥檛 chatted about that you think is worth noting?

Barrett: It鈥檚 a sobering note, but globally there is a need and desire for sovereign capability in tech 鈥� in Western Europe, Australia, Canada and elsewhere.

There are extraordinary pools of capital, pension funds and Australia鈥檚 superannuation fund. Given the things we can invest in, globally the West needs to do a better job translating that capital into societal and economic value.

The safety and durability of liberal democracies depends on creating wealth and staying ahead.

We see a resurgent desire to do that in Europe and Australia. Around those pools of capital, there鈥檚 ambition. We need to drive that ecosystem globally, not just in the U.S.

The pace of innovation in Ukraine, driven by need, is indicative of changes that can be made in parts of the world less friendly to the tenets we hold dear in liberal democracies.

We can鈥檛 operate under the assumption that everybody clever lives in Palo Alto or that we can only invest in things we can drive to. We need to deploy capital globally, and we do. We鈥檙e going to do more of that.

Do you feel encouraged by the amount of infrastructure build-out that鈥檚 going to happen over the next few years? It feels like it will create a boom in all sorts of technologies because the drive for efficiency will become much stronger.

Barrett: LLMs are not the end. We鈥檒l run LLMs on these data centers initially, but we鈥檒l run their descendants and other more useful things on these machines and on quantum machines.

It鈥檚 going to be hard to overbuild because computation is incredibly useful. There鈥檚 no upper bound. We鈥檙e not in a Malthusian zero-sum game for resources.

We know how to make everything more productive. We know how to grow GDP arbitrarily large. But we need food, energy and medicine there, and we need to normalize the distribution of wealth.

There is unbounded abundance we can unlock if we spend capital on the right things. We know how to do much more of that than people suspect.

The fact that sensible people are considering data centers in space indicates they鈥檙e not paying attention to the things we already have in hand that can move the needle.

We do need compute in space. We need AIs in space, sensing in space, and Starlink is great. But we need to use technologies that make sense, not try to make skyscrapers out of toothpicks.

小蓝视频色情网页版 queries:

Related reading:

• Sector Snapshot: Quantum Computing Startup Investment Slows In 2026 While Public Markets Hold Strong
• Sector Snapshot: Semiconductor Startup Funding Still Running Hot

Illustration:

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.

Big fundraising deals did not take a pause for summer this week. In the U.S., the largest financings went to enterprise software company and blockchain technology provider . The largest deals of the week, however, were for European companies, with Germany鈥檚 pulling in $1.4 billion and Finnish space tech company landing $520 million.

1. , $400M, enterprise software: NinjaOne, provider of an IT operations and endpoint management platform, raised over $400 million in Series C extension funding at a $12.3 billion valuation. The Austin-based company said it grew revenue over 70% in 2025 and posted a profit in the first quarter of this year.

2. , $355M, blockchain technology: Digital Asset, a provider of blockchain technology geared for financial institutions, secured $355 million in a later-stage financing led by 鈥檚 crypto fund, . Founded in 2014, the New York-based company has raised at least $847 million in known funding to date, per .

3. , $350M, AI cloud infrastructure: Las Vegas-based TensorWave, an AMD AI cloud technology provider for training and inference workloads, closed on $350 million in Series B funding. and led the financing.

4. , $300M, biotech: Beren Therapeutics, a developer of therapeutics for conditions characterized by defective cholesterol trafficking, raised $300 million in equity and debt funding. The financing for the Thousand Oaks, California-based company includes $165 million in debt funding from as well as $135 million in equity investment.

5. , $200M, robotics: Standard Bots, a manufacturer of AI-native industrial robots, picked up $200 million in Series C funding. and were lead investors in the round, which set a $1 billion valuation for the New York-based company.

6. , $125M, genetic medicines: SonoThera, developer of an ultrasound-mediated genetic medicine platform, secured $125 million in Series B funding. led the financing for the San Francisco-based company.

7. (tied) , $100M, medical devices: Tempe, Arizona-based GT Medical Technologies, developer of a form of radiation therapy called GammaTile that is used at the time of brain tumor removal surgery, picked up $100 million in Series E funding led by .

7. (tied) (aka Genspark), $100M, agentic AI: MainFunc, the company behind Genspark, a developer of agentic AI tools for the workplace, reportedly $100 million in Series B extension funding at a $2.6 billion valuation. Investors reportedly included , and South Korea’s .

9. , $99.5M, biotech: Cambridge, Massachusetts-based City Therapeutics, a developer of RNA interference (RNAi)-based medicines, closed on $99.5 million in Series B funding from backers including new investors and .

10. , $85M, tools for the deaf and hearing-impaired: Rylo, developer of an app for hearing-impaired people, raised $85 million in growth funding from , and existing investors.

Outside the US

, $1.4B, robotics: Germany鈥檚 Neura Robotics, a developer of AI infrastructure for robots to learn, collaborate and operate across real-world environments, says it secured up to $1.4 billion in Series C funding.

, $520M, space tech: Helsinki-based Iceye, operator of a satellite constellation for monitoring conditions on Earth, raised $520 million in a Series F funding round led by , at a valuation of over $12 billion.

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 6-12. 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: