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 poured capital into a varied lineup of large rounds this week, targeting sectors including physical AI, biotech, cybersecurity, AI infrastructure and fintech. By far the largest financing of the week was a $1.7 billion round for founder 鈥檚 physical AI startup, , followed by sizable investments for 3D AI model developer and battery technology company .

1. , $1.7B, physical AI: Atoms, the physical AI startup founded by founder , raised $1.7 billion in a funding round led by . Kalanick touted the Los Angeles-based company鈥檚 vision as 鈥渁bout the coming industrial revolution where large industrial economic sectors get completely digitized.鈥�

2. , $400M, AI for 3D: Silicon Valley-based Meshy AI, a startup developing foundation models for AI-powered 3D generation, closed on $400 million in Series B funding at a $1.5 billion valuation. Lead backers include , and , per 小蓝视频色情网页版 data.

3. , $300M, battery technology: Battery technology company Sila secured $300 million in a new round led by and . The Alameda, California, company will use the funding to expand its silicon anode plant in Moses Lake, Washington.

4. , $300M, inference technology: Etched, a co-designer of chips, racks, software and manufacturing methods for use in frontier models, picked up $300 million in Series C funding. led the round, which set a $10 billion pre-money valuation for the San Jose, California-based company.

5. , $180M, fintech: Augustus, a startup aimed at providing financial institutions around the world direct access to dollar accounts, secured $180 million in Series B funding. led the round, which set a $1 billion valuation for the San Francisco-based company.

6. , $160M, defense tech: Cathedral, a startup aimed at expanding U.S. military cyber capabilities, reportedly $160 million with backing from Sequoia Capital and Andreessen Horowitz. The Washington, D.C.-based startup was reportedly founded by a 鈥媡eam of former DOGE employees.

7. , $130M, biotech: Crystalys Therapeutics, a biotech developing therapies for people living with gout, closed an oversubscribed $130 million Series B round. led the financing for the San Diego-based company.

8. , $120M, healthcare software: San Francisco-based Candid Health, developer of a revenue cycle management platform for the healthcare industry, landed $120 million in Series D funding led by .

9. , $100M, cybersecurity: Glow, a Palo Alto, California-based AI-powered endpoint security startup, launched from stealth and announced it has raised $180 million to date, of which, per 小蓝视频色情网页版, $100 million comes from its newest financing. Lead backers include Sequoia Capital, , , and .

10. , $75M, cybersecurity: Boston-based Neo Security, a startup working on an agentic software control platform for enterprises, picked up $100 million in a new round led by and Andreessen Horowitz.

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 18-24. 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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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.

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Last month, of , the corporate venture arm it launched in 2016 and grew to more than $850 million across three funds. The company hired to explore selling portfolio stakes on the secondary market, putting positions in companies such as and in play. The news also arrived weeks after .

Two corporate venture programs shutting down inside six weeks invites speculation that corporations are retreating from venture capital, but in fact the opposite is true.

Measured in dollars, corporate venture has never been stronger. According to , corporate investors participated in 鈥� venture’s strongest funding year since 2021.

, , , , and all led billion-dollar rounds into AI companies last year, per 小蓝视频色情网页版 data. Nvidia by itself made more than 40 startup investments and appeared in. Meta paid $14.3 billion for its stake in Scale AI. 1听补苍诲 s venture arm backed Anthropic’s.

Amid this strength, though, corporate venture is also quietly splitting in two, and the proof is buried inside the record numbers. Bain attributes the elevated corporate participation , and the billion-dollar rounds trace back to the same short list of names.

Take that handful out of the data and the year looks very different. Venture capital itself went through the same sorting over the past decade, as mega-funds absorbed more and more of the capital while everyone else competed for allocation, and corporate venture is now following the same script. The people with the most at stake are the smaller funds and startups downstream.

And notice that the wind-downs are coming from serious programs. PayPal’s arm ran for a decade and , and Fidelity International manages hundreds of billions of dollars. Size never protected either one, and the dividing line runs through the mandate. For Nvidia, Alphabet, Salesforce and Cisco, startup investing is a core strategy, funded off enormous balance sheets, because their businesses depend on owning a position in the technology cycle. Nvidia backs the companies that build on its chips, and that commitment survives budget season. For most other corporations, venture is one strategic priority among several, competing for capital with the core business itself.

To be clear, there’s nothing wrong with that. When a new chief executive commits to finding , winding down even a well-run program can be the disciplined call, and disciplined capital allocation is what shareholders ask of public companies. Corporate venture has always moved in cycles, and the waves of closures after 2000 and 2008 said far more about parent balance sheets than about the returns on offer. Individual programs are mortal, but the asset class keeps growing.

When I started my career, technology drove roughly 2% of the American economy, and today it drives a double-digit share of GDP and nearly 40% of the stock market.

Who feels it first

For smaller funds and their portfolio companies, the split is already changing the math. ‘s finds corporate funds pursuing fewer, more targeted deals, and the share using the secondary market jumped from 15% in 2024 to 22% in 2025; PayPal’s Jefferies mandate takes that same path at the scale of an entire program.

When a corporate arm winds down mid-life, its portfolio companies lose a strategic backer and a source of follow-on capital at once, the smaller funds that syndicated alongside it lose their anchor for the next round, and a secondary sale replaces a committed partner with a financial buyer.

I spend my days working with early-stage venture funds, and I’m watching this pattern develop in real time: strong companies outside AI, with a departing corporate backer on the cap table, heading into rounds their existing syndicate can’t fill alone.

The lesson for startup management teams and VC fund managers is to plan for corporate capital to come and go. The pro rata rights that funds hold in their best companies become most valuable at exactly these moments, when a strategic investor steps back and ownership in a breakout company becomes available to whoever can fund it.

Smaller funds should line up committed follow-on capacity before their winners come back to market, so a corporate partner’s exit becomes a chance to buy more of a company they already know well. Founders should run the same exercise from the other side of the table and know today which investors on their cap table can carry the next round.

Corporate venture will keep growing because the forces behind it keep growing, and programs will open and close along the way, as they always have. What’s changed is the sorting: permanent capital consolidating at the top of the market, and everyone else learning to plan around that fact. The funds and founders who prepare for it will come out the other side owning more of the companies that matter.

is the founder and managing partner of , a growth-equity firm that co-invests in venture-backed companies by leveraging the unused pro-rata rights of more than 1,000 early-stage VC partners.

Related reading:

• Long Live The AI Tech Bubble
• 6 Charts That Show The Big AI Funding Trends Of 2025

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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.)

First, San Diego-based announced its of a Palo Alto, California-based software startup focused on making it easier for developers to run AI models across different types of computer chips.

At the same time, reports emerged that chip startup is finalizing an $800 million funding round led by , valuing the company at $10 billion. Together, the two deals underscore a growing reality in tech: As hardware remains scarce and expensive, the software layers that connect these chips are becoming just as valuable as the silicon itself.

Watching these shifts unfold firsthand is , a managing partner at who led early investments and holds board seats at both Modular and SambaNova.

Munichiello brings a pragmatic operational background to tech investing, having served as a captain and paratrooper in the U.S. military before transitioning to the private sector. He later worked as an early executive at , helping scale the warehouse automation company through its $775 million acquisition by .

With a background in mathematics and computer science from and an MBA from , Munichiello has spent his venture career focused on core software infrastructure, developer tools and data systems, including early backing of companies such as , and .

In this interview, he discusses the mechanics behind the Qualcomm-Modular deal, the practical realities of managing hardware scarcity, and what the current wave of consolidation means for the future of independent startups.

This interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: The acquisition of Modular by Qualcomm highlights a massive push to decouple AI software from hardware fragmentation. Does this signal that the ultimate value in the AI stack is permanently shifting away from proprietary hardware architectures and toward developer-friendly software layers that can run across any compute environment?

Munichiello: The types of hardware required for AI in the future are becoming heterogeneous. Originally, it looked like it was just GPUs from , and then also GPUs from and other players. But now, the direction hardware is going is toward “disaggregated inference,” which basically means splitting apart the different compute used for different parts of answering a question when engaging with a model.

It increasingly looks like there will be three types of chips used in disaggregated inference: an AI-specific chip, a CPU and a GPU.

For a player like Qualcomm, all three of those components are present, so they need a software layer that sits across them. Everywhere else, Nvidia included, they usually sell alongside CPUs and accelerators, and there hasn鈥檛 really been a software solution that works across all of those.

When did you first start investing in this wave of AI infrastructure and semiconductors?

Munichiello: We鈥檝e been investing in AI since 2016, starting as early as a company called , which was first company, sold to , and became part of the Siri team. After that, we invested in , co-founded by , which was later sold to and became an important part of its stack. HPE actually went on to be the compute partner for and worked very closely with as well.

We also got excited about semiconductors early, long before this current wave, when we led the Series A for SambaNova. I first met that company when it was just three people and a slide deck. We led that round in December 2017 鈥� after led the seed investment 鈥� and I鈥檝e sat on the board since. That initial investment was $15 million at a $480 million valuation.

It seems like a lot of legacy chip giants and major cloud providers are aggressively buying up infrastructure startups. What does this consolidation mean for early-stage founders? Are we entering an era where standalone startups need to plan for an early acquisition, or is there still a path to an independent IPO?

Munichiello: There is definitely a path to an independent IPO. showed that trajectory beautifully, and I’m really happy for and that team. There is absolutely a trajectory to build big, standalone businesses because the demand for compute is completely off the charts. We can’t make semiconductors fast enough, nor can .

Everyone is trying to find extra capacity by making everything more efficient. Technology often emerges with a big boom in mass demand and high prices, and then we figure out how to make it cheaper. We are in that efficiency step right now. Demand for inference is everywhere, from medicine and law to coding, customer support and finance.

We are trying to squeeze every last bit of value out of chips. Squeezing that value comes from using multiple types of chips: using cheaper CPUs when we can, GPUs when we need them, and the most expensive chips only for the most complicated parts of the process.

We are also evaluating software across the stack to ensure every aspect of these queries is as efficient as possible. It鈥檚 not surprising that there are a lot of acquirers. The universe of buyers has expanded from just semiconductor companies buying other semiconductor companies to software companies, hyperscalers and model companies buying chip companies, too. Amazon has Trainium and Inferentia; has Maia; has the TPU, and every big tech company wants to be able to say it has a chip.

How does the rise of open-source models shift this dynamic?

Munichiello: The universe of potential buyers expands even larger when open-source models become prolific. In the Qualcomm announcement, they talked a lot about their enthusiasm for open source 鈥� not just keeping Modular open-source, but for models to be open-sourced. When that happens, instead of enterprise companies paying hundreds of millions of dollars to model providers to do inference, the companies themselves will own their models and run them on their own hardware.

So you firmly believe that IPOs are not totally off the table for early-stage tech and hardware companies?

Munichiello: Not at all. Look at , which is highly hardware-intensive. I think we will see many IPOs here in the next six months. I know of at least 15 or 20 companies that are planning to go public, so it is going to be a very busy period.

In a market where valuations are multiplying rapidly based on technical metrics like chip throughput, how are you able as an investor to separate real, sustainable product-market traction from early hype?

Munichiello: There are a lot of AI companies getting valuations that are disconnected from the business outcomes they are driving. True traction comes down to quarter-over-quarter execution, hitting sales demands and actually fielding physical systems for customers.

A company becomes highly attractive to investors when it delivers a massive volume of technology into production environments 鈥� like data centers for major enterprise brands and devices we use every day.

That, combined with incoming demand from “Neo-Clouds” (new data centers built specifically for inference), shows real traction. These players are looking for any chips they can get their hands on, and the concept of disaggregated inference 鈥� combining three different chip types to lower the total cost of ownership 鈥� is highly compelling. It also alters the competitive landscape; it shows that the market isn’t just a runaway race for one dominant player, but an opportunity for CPU providers to catch up as well.

GV has a track record of backing foundational tech long before the generative AI hype cycle. How has your framework adapted now that AI infrastructure capital requirements have skyrocketed? When a startup needs hundreds of millions just to compete at the frontier, how do you maintain a focus on the team and relationship without getting bogged down by the sheer scale of capital?

Munichiello: It has always been complicated to start from scratch and build a meaningful, generational company. We are not in the business of momentum investing. We don’t invest in something just because we think it will be marked up by other investors over time. We look for fundamental technologies and consequential businesses that can stand on their own.

When we met Modular, it was just Tim and Chris with an idea, and we convinced them to take our $23 million investment. At the time, we were nervous about valuing the company at more than $80 million or $90 million, and it ended up getting valued at $155 million in that first round.

We took 15% of the company right off the bat in a round that felt way out over its skis for that moment in the world. But they hired an amazing team of compiler engineers, started growing and built in a space that became the most strategic in all of AI.

We value different companies based on their specific markets. Some are incredibly capital-intensive and require billions of dollars, meaning we can’t do it alone. As an investor, we must bring our network and a syndicate of other investors who can write hundreds of millions of dollars in checks.

Software companies can move a bit faster, make more mistakes and pivot. In hardware, if you tape out a chip and it doesn’t work, you are set back for years and have to raise significantly more money. It鈥檚 much more binary when it comes to the physical world. A hundred million dollars goes a lot further in software because you can always optimize your token usage or engineering to shift directions, which is incredibly hard to do in robotics or hardware.

This acquisition represents a massive return on your initial investment. What does this success say about your broader investment philosophy?

Munichiello: It鈥檚 a fantastic outcome 鈥� a 27x return on our initial investment and roughly 10x on our total dollars invested. But we aren’t a firm that just leads a Series A and then steps back. We look to write massive checks and co-lead later rounds, especially when things get difficult.

It is inevitable that every company will hit a wall at some point 鈥� whether due to macroeconomic factors, team dynamics or customer challenges. We call these “crucible moments,” and they are what make companies truly interesting. In an internal email I sent to our team, I talked about loving curveballs. We are used to things going sideways, and that’s when we really step up and help our companies. We like to find these incredibly hard problems, back exceptional people with the character and grit to survive those moments, and help them build standalone businesses.

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• Sector Snapshot: Quantum Computing Startup Investment Slows In 2026 While Public Markets Hold Strong
• Silicon Is Back: Playground Global鈥檚 Decade-Long Bet On Hardware, Energy And Deep Tech Looks Prescient
• Why Ex-Meta CTO Mike Schroepfer Says It’s A Great Time To Build A Hard Tech Company: 鈥業nfrastructure Is The Moat鈥�
• Sector Snapshot: Semiconductor Startup Funding Still Running Hot
• Cursor Deal Puts US On Track For Record Startup M&A Year

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That鈥檚 the trend we鈥檙e seeing for the second quarter of 2026. This period has brought us both the largest venture-backed exit of all time, with , and a bevy of other comparatively tinier but still sizable startup exits through acquisition or IPO.

Overall, we鈥檙e still well behind the prior high in terms of the number of big exits, as you can see charted below. The IPO and SPAC boom of five years ago will be hard to match for exit count.

聽Bigger numbers

But while the Q2 exit deal counts may be still below peak, the actual returns are not.

For that, of course, we can thank SpaceX, which earlier this month shattered records with a historical debut that culminated in a staggering $2.1 trillion first-day market cap. Its long-awaited offering raised some $75 billion and served as an enormous liquidity event for founder .

Compared to that, every other Q2 startup exit looks pretty paltry. But by any other comparative metric, these other big exits were also very impressive.

SpaceX鈥檚 $60 billion acquisition of AI coding platform a few days after its IPO, for instance, was the priciest purchase of a private, venture-backed startup ever.

As for IPOs, made a splashy entry in May with an offering that raised at least $5.55 billion. Shares are down from the first-day closing price, but the company still maintains a sizable market cap around $38 billion.

Earlier this month, quantum computing company also had a big debut on , raising $1.7 billion and securing an initial market cap of $15.6 billion. Shares are still up sharply from the initial price.

For a broader view of big deals, below we put together a list of all the Q2 venture-backed private company exits valued at $1 billion or more.

Trend: fewer deals but larger ones

Even though the number of big deals picked up in Q2, the more noteworthy trend is the size of exits rather than the quantity. Size will likely still be the standout feature in coming months, with both and filing confidentially for IPOs that could test the trillion-dollar mark.

At the same time, however, the pace of exits in the billion-dollar-plus club, which in any prior cycle were considered considerable, is showing no signs of slowing.

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• The 小蓝视频色情网页版 Billion-Dollar Exits Board
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founded after departing as CTO in 2022. The firm invests in companies rebuilding the physical economy. As Schroepfer and his partners at the firm see it, surging demand for AI, power and industrial capacity is creating a once-in-a-generation opportunity to rebuild the physical economy 鈥� from energy infrastructure and advanced manufacturing to materials and robotics. And as AI makes software cheaper and easier to create, the competitive advantage increasingly shifts to the hardware, energy systems and supply chains that underpin it all.

Key to starting the fund was Schroepfer’s experience building out the infrastructure to support Meta鈥檚 business. 鈥淚 could see the trends coming. We’re going to need all the compute,鈥� he said. 鈥淚 don’t know where we’re going to get the power, so it’s going to create this massive supply-demand crunch.鈥�

Gigascale raised its first institutional fund this month, a $250 million investment vehicle. The firm has already made more than to date.

Schroepfer鈥檚 partners at the firm are , previously an investor at climate-focused investor , and , previously at .

Before raising the fund, the firm made 22 investments funded by Schroepfer鈥檚 family office in order to prove the model. At the time, the broad perception was you could not make money investing in the hardware layer.

鈥楴ot software with higher capex鈥�

Gigascale invests at pre-seed through Series A with some later-stage investments. Its check size is anywhere from $1 million to $10 million.

Hardware businesses are not the same as software businesses. 鈥淚t’s not a software business with higher capex,鈥� said Schroepfer. 鈥淭he failure modes are very different. The way you plan and test and iterate, and what you understand is very different.鈥�

In our conversation, we spoke about an array of topics, including energy as a major investment focus, his learnings from running Meta, why now is a great time to build a hard-tech company and what excites him about the IPO.

Gen茅 Teare: What is Gigascale’s thesis?

Mike Schroepfer: It’s really simple. We are backing companies that are rebuilding the physical economy. This is how things are powered, built, moved, manufactured and how people are fed.

The belief is there is a confluence of technological changes that are bringing new products and new companies to market that are better, faster, cheaper than what’s out there. This is the biggest part of the economy.

Another way to say it is that we think the future is atoms, not bits, and it’s a really exciting time to be building these companies.

What did you see that made you decide to set up the fund in 2023?

Schroepfer: A lot of the tech trends I have been part of 鈥� from the web transition when I worked on Firefox, to the early web infrastructure at , to the mobile transition in the early 2010s, to founding the Facebook AI Research Lab in 2013, well before ChatGPT 鈥� were looking at the very shallow part of exponential curves. These technological changes did not seem that prevalent yet, but they were on this massive upswing.

I saw the same set of curves in solar cells, batteries and electrolyzers. They were all going through massive exponential cost downs, and at the same time a massive increase in demand. We had electric vehicles showing up, onshoring and manufacturing, and this was pre-data centers. I knew compute demand was going to grow. Where are we going to get the electrons to fuel all of this? It’s going to create an immense supply chain crunch.

Demand and supply were converging at the same time to create massive tailwinds. It just felt like this opportunity to rebuild the entire physical infrastructure in a way that our kids are happy about. Meaning, the new solution wins because it is cheaper, better and faster.

The other co-benefit it brings along with it is that because it is simple and cheaper, it is also less polluting, so it doesn’t hurt humans. I can build a solar farm way faster than I can build a gas power plant. I can live next to a solar farm and get zero pollution. I do not want to live next to a gas plant.

What I understand about the firm is that you are very focused on energy specifically. Is that a misunderstanding?

Schroepfer: It is probably the single biggest area that we invest in. A large chunk of our portfolio is energy. It is a $2 trillion market and it is the place where I think all the disruption is happening. But we also invest in industry, including materials from neodymium to copper, production and recycling, to a lot of AI in the physical world. That includes everything from how I use AI to make my house more efficient with , to how I build power-efficient AI inference chips with .

Then there is the built environment, in terms of buildings, and a little bit in food. We do a little bit in everything, but if you look at our portfolio, the two biggest hunks are really energy and AI in the physical world.

When do you think Silicon Valley woke up to the focus on the physical world?

Schroepfer: In the broad consensus, it happened recently 鈥� in the last six to 12 months. There were some folks who were looking at it early, but I think the broad consensus has just happened recently.

The other thing that I saw is, if AI is going to make software nearly free to write, then I think software businesses might be challenged, and the moat moves to the hardware. The game becomes: How do I get the infrastructure built to have a better AI? That is mostly an infrastructure hardware problem, less of a software coding problem, and that is going to filter through a lot of businesses.

When I started, frankly, three years ago, I had many people 鈥� I am thinking of someone sitting in my office 鈥� saying, 鈥渄on’t do this.鈥� All the money is in software. You can’t make money in hardware.

It doesn’t hurt that , , , and are now household names of companies that have had massive valuation runs because they are such a core part of the physical economy. I used to use Nvidia as my example, but now I can use SpaceX. Talk about a company in the biggest market that is running away from the competition. It’s a really hard company to compete with.

How should we understand the energy needs in the U.S.?

Schroepfer: We’ve been at relatively flat demand over the past 20 years or so, meaning each year that goes by, we don’t need much more power, close to 0%. We are now growing at at least a few percent a year.

Something has gone from almost no growth to relatively high growth. You’ve got hundreds of gigawatts of data centers planned to be built over the next five years alone. That doesn’t count EV charging stations and electrification of homes and factories. It’s a massive supply-demand imbalance right now, and building power takes a long time. If you’ve got to build a power line, if you’ve got to permit a gas power plant, these things take years, not months. It has created massive demand, but everyone wants compute yesterday.

Meta has used tents instead of buildings for their servers because cutting out the time erecting steel for the building gets them compute faster. Everyone is thinking about how to get power faster and how to get compute faster because, again, it’s a competitive advantage when infrastructure is the moat.

Which technologies are you focused on in the shorter term, and then the longer term?

Schroepfer: We have companies deploying things now. In the power crunch, one of the big problems is that the demand for power swings much more widely than it used to. It used to be fairly steady. Now you have big training runs, you have solar that comes on and comes off, and you need a shock absorber to dampen the power or deal with three or four days of clouds or no wind, if you’re depending on renewables.

is a company that has a new kind of battery that lasts for four days. You charge it up, and it’s there for 100 hours. In any event where a power plant is offline or the sun is not shining, Form Energy is there. Utilities think of this instead of building a gas power plant. There are these gas power plants called peakers, which you only turn on when you really need them. They sit there all the time, and then you fire them up in these intervals. Instead of doing that, which is very expensive, you have this Form Energy battery: zero emissions, much cheaper to operate, and built from the ground up for utilities using a totally different technology. They are going to be deploying batteries this year, as an example.

Going in a different direction, the entire supply chain for how we get electrons to a building. I’m going to build a new data center, and I have to hook it up to the grid to get electrons there. There is all this equipment in the middle called power transformers, these big green boxes or big metal boxes. It’s literally 1930s technology. We haven’t changed much since then. They are back-ordered for years now because they are these exquisite hardware machines.

There is a new company, , that said, 鈥渨ait a second, we’ve been shipping this new generation of technology called solid-state power electronics in electric vehicles 鈥� the Model 3, Model Y, and more 鈥� for millions of units a year, with very fast ramps. We’re taking that same technology and putting it on the grid.鈥� We’re replacing this 1930s technology with 2020s technology. It’s more efficient, it’s a third the size and, most importantly, they’re going to start shipping lots of units next year. They’re building their factory right now. In 2027, they’ll be shipping lots of these Heron Link units.

A little bit further out, we have a company called that said, 鈥渨e’ve got about 10 terawatts, which is an immense amount of power, in the Southern Ocean in waves sloshing around with nothing else going on down there. If we can harness that, it is an untapped resource.鈥�

They’re building autonomous buoys that float in the ocean. They bob up and down and turn that wave motion into electricity. Then they use that to power, on the buoy, a compute node to do AI inference and use to send the bits back. They’re kind of exporting electrons via tokens in the Southern Ocean.

They’ve been testing off the coast of Portland, and they’re going to deploy their first units next year. People have talked about data centers in space. My big pitch for this company is that it’s 100x cheaper to put a ton of capacity in the open ocean than it is to put it into space. If you think data centers in space are a good idea, you might want to look at the ocean.

Then you can think about , a company in El Segundo, California. They are building a compact, next-generation microreactor, or nuclear reactor. You can think of it as something you put on a truck or on an airplane, and it can run and power something for five years straight. Instead of, in a remote region in Alaska or on a Pacific island, doing what they do now, which is shipping diesel fuel there to run a diesel generator 24/7, you install one of these boxes, and it produces power for five years before it needs refueling. Most importantly, again, you would not want to sit next to a diesel generator while it’s operating. It has very toxic emissions. This thing has no emissions. It’s good for humans, and it’s actually going to be cost competitive with those things. Those are some examples of things we’re doing in the power sector that I think are really affecting the future.

Is there an unlock in this industry that has made development cheaper and faster at this moment in time?

Schroepfer: The analog I’d use is from computing. We used to build mainframes, these big building-sized computers. Then we had minicomputers that were still really big. This is the motherboard for the first server we designed at Meta that we deployed in 2011, called Freedom. It was a Type 1 server. It was the web server.

I installed millions of these, maybe tens of millions. I don’t even know how many. They’re all the same, every single one of them. They go in a pizza-box-size thing that goes into a rack in a building. That building comes in four units. Each of those is the same. That building is next to another building, which is exactly the same. We build four of those on a site. They all look the same. I did that in 17 places around the world. They all look the same.

The technique we use to make things cheap is mass manufacturing. Everything in your life that has gone down in price or improved in price-performance is mass manufactured: your iPhone, the servers and data centers. They’re all the same. They’re mass manufactured.

The world is full of custom, bespoke stuff that’s wickedly expensive.

In the power grid, for example, all of the stuff I talked about, you custom order it. I want a transformer. I do engineering design. I send it off to someone. Four years later, a truck shows up with the crane and all the rest of it. That’s inherently expensive and gets more expensive every year. Everything that is custom gets more expensive every year, so I think the biggest thing we’re seeing is this move to things that are mass manufactured.

Solar panels are mass manufactured. Batteries, the things that go in your phone or in your electric vehicle, are 99% cheaper than they were 20 years ago. That’s because we manufacture them at a massive scale. Every time you double the size of manufacturing, you get a 10% to 20% reduction in cost, and there are so many other problems like that.

In this case, the power electronics, the transformer, are all special-purpose. Heron Power is going to make the same box for a data center, for an EV car charger, and for a solar farm. It’s the same box. No changes. That’s how we’re going to get a cost curve down for these things. That is the most exciting trend underneath this: the idea that generalization and mass manufacturing of things allows you, year over year, to reduce costs.

When you’re competing in the power industry, fossil fuel costs have been basically stagnant. They go up and down a little bit, but if you average them over 50 years they are not on a cost-down curve. It doesn’t get cheaper to get oil out of the ground. My competition is flat, and I’m getting 10% to 20% cheaper every year. That’s a great business to be in. That’s the big trend behind all of this. We saw it first in solar and in batteries, but it enables a whole bunch of other things in other industries, like power electronics and more.

Are we at this time very dependent on China for mass manufacturing?

Schroepfer: A lot is coming from China, but I visit a factory a week in the United States that is getting spooled up with robotics, with really smart founders from and SpaceX. It turns out that when you start in 2026, you can build a much more efficient, much faster factory. You can use modern technologies.

Right now, China has the industrial base, and we’ve let it go. But I think we have a shot at rebuilding it in the United States, and I see brilliant founder after brilliant founder running at this problem inside the United States every day and every week.

It’s one of the reasons I started this firm, too. I think we have a shot to rebuild that industrial base in a next-generation set of technology. Just like regions around the world that didn’t have landlines went straight to cellphones, we’re going to go straight to fully automated robotic factories with 3D printing, laser milling and the latest technology set. It is not going to be a cut-and-paste of what happened in China, but a next-generation set of technologies that allow the U.S. to be self-sufficient in what we’re doing.

We’ve seen new techniques. As an example, rare earths were something no one ever talked about. Neodymium is this rare earth material that is key to making a magnet. Who cares about magnets? Well, magnets are in every electric motor in anything. Anything that has an electric motor, you care about magnets. Almost all the neodymium is made in China, and it is made in this very polluting, dangerous process. You do not want to visit one of these factories with fluorinated gases 鈥� it’s awful.

We’ve got a company making neodymium in Alameda, California. That is not an easy place to permit polluting things, which is fine for them because their process doesn’t pollute at all. It’s very simple. It’s two reactors. I walked around the facility. You don’t need any protective gear. Because it’s so simple, they are cost-competitive with Chinese imports.

To their customers who are saying 鈥淚’m trying to make magnets,鈥� they’re saying 鈥済reat, I will sell you neodymium. I have it. It’s cost-competitive.鈥�

Everyone is excited, but the thing we’re whispering in the background is, it’s also not polluting. This is how we’re going to win. It’s not a cut-and-paste of that technology over here, but saying, 鈥淗ow do we approach this in a way that’s simpler and cheaper, and then likely cleaner as well?鈥�

We’re doing the same thing in copper. We’ve got a whole bunch of bets in different kinds of materials where I think we can do it better in the U.S. We’ve got a company, , in South Carolina that’s doing this for copper recycling. We’re doing it in cement manufacturing. There is a whole variety of opportunities. I don’t have enough time to meet all these entrepreneurs.

We talked a lot about some of the companies in the energy sector. What are the other areas of investments that you’ve made that you’re excited about?

Schroepfer: I mentioned this a bit, but worth going a little deeper on is applications of AI to the physical world. I talked about one: Fractile, which is building a next-generation AI inference chip that’s much more power efficient.

Another example is a company called , which is using AI to put a simple piece of hardware on a power line, on both sides of a power line, to detect if there is a fault in that power line that might be causing a fire. The idea is that if you detect that fault sooner, you can prevent the fire before it’s a problem instead of waiting for it to happen and then having to respond. Using AI plus hardware to figure these things out is another example of that.

We have another company called that’s using AI to help with the nuts and bolts of how people make transactions to build energy projects. There is a lot of due diligence work and other things that need to happen. You can build, very much like for legal or for doctors, these vertical AI companies. This is a vertical AI company for energy developers. There is a lot to happen there.

Then is doing industrial automation with robots, using next-generation models to train robots to be more effective in factory environments, back to my point of how we are going to do this in the U.S. with advanced robotics. I think AI for the physical world is a big area.

I talked a bit about materials: neodymium, copper. We have a company called that’s making clean chemicals. Those would be the big areas I would highlight.

I know there are a lot of investors that you partner with or work with that are similarly focused in this area.

Schroepfer: The thing that’s been most interesting is that there is a set of folks who have been doing hard tech or climate for a while, and they are great partners of ours, from to to to many others. But what’s been interesting to me is the generalist firms coming in. A very common co-investor for us is , , or . We’re seeing them come in large amounts, because they’ve seen the economic opportunity here.

What did you learn from spending 14 years at Meta?

Schroepfer: I learned a few things. When I joined in 2008, the company had fewer than 100 million users, was not profitable, and had about a 150-person engineering team. We relied on outside parties to do all the hardware work. We were leasing data center space.

Over the next 14 years, we grew dramatically in users and profitability and in the size of the team. But we also moved into the physical world. As I showed you the server, we built our first data center in 2011. I built 10 million-plus square feet of data centers in 17 places all over the world. We then moved to consumer hardware, so we built the smart glasses, the Oculus Quest VR headset, and the Portal. Then we moved into AI research with the Facebook AI Research Lab in 2013.

That shift into the physical world brought a lot of really humbling lessons. There were a lot of times where stuff just went wrong. At the very first data center, I remember touring it under construction, and we had wood blocks on the loading dock because they had graded the loading dock wrong, so the trucks couldn’t back up and unload properly.

It’s this new, awesome, state-of-the-art data center with a free-air cooling system, and we got wrong the thing that every in the country has five of. It’s a million small challenges.

This is the thing I bring to the founders that I see: having learned how to build stuff in the physical world builds an appreciation for the risks and scale, and for how you need to emphasize speed and learning rate.

People learn the wrong lesson. They think hardware means spending a lot of time designing on paper. Wrong lesson. You have to get out there because you don’t know which part is going to blow. You have to get out there and learn as fast as you can and as cheaply as you can, so that when you’re in mass production, you’re not learning things, you’re just repeating.

That lesson, from data centers to consumer hardware, matters. When we build consumer hardware, you spend 18 months building this exquisite pair of glasses or this exquisite headset, but before you sell it, you have to do this drop-test thing, where you literally say, what happens when someone takes it out of the box at home and drops it on the ground? If it breaks, they return it, and we eat the cost. So you sit there and drop this thing with high-speed cameras over and over again to make sure it will survive a drop from head height. You don’t think of these things when you’re designing it. You have to make sure someone can drop it and it’s fine, or spill some wine on it and it’s fine.

Those problems in the real world, plus the challenges of building an executive team and scaling an organization, are the fun part of my job: working with our founders and having their back when things are tough, when they need to recruit someone, or when they’re running into a challenge in the real world, because I’ve seen it. I’ve seen it all.

What’s your reaction to the SpaceX IPO?

Schroepfer: I’m honestly pretty excited about it, because we have a lot of SpaceXers in our portfolio. I have a lot of friends who are alumni or work at SpaceX. Having more people in the world with the financial resources to work on audacious engineering projects is going to be really good.

I think it’s also a lesson in building and hardware. How many companies can land rockets the way SpaceX can? They’ve been doing this for a decade, so they have a very large technical moat in terms of what they’re able to deploy in the world. Starlink is another example. Everyone is racing to catch up. If you’ve ever used Starlink on an airplane, you don’t ever want to be on an airplane without Starlink. It’s hard to describe other companies that have such a singular product as SpaceX. I think it’s exciting that the markets are rewarding that. I can’t wait to see what SpaceX alumni do next.

I imagine there’s going to be a lot of company formation coming out of that IPO.

Schroepfer: It’s going to be an exciting five years. I met you after I started my first company in 2000 and sold it off. We looked at starting another company, and then I worked at and Facebook, so I’ve been through a couple cycles of this. I think it is the most exciting time to start a company, in terms of the capital available, the AI tools available to you, and the physical tools to build things quickly in the physical world. It’s the bet I made: I think this is the most interesting time to be building new companies. That’s the smaller version of why I did this. I think this is the time. This is the thing to be doing.

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

This week was not an exceptionally busy one for large funding deals, though we saw sizable rounds in a lively mix of sectors ranging from AI to fintech to quantum computing and cybersecurity. The biggest raise was for AI world-model developer, which secured a $310 million Series B. Venture investors also put money into AI infrastructure and AI models for biotech.

1. , $310M, artificial intelligence: Menlo Park, California-based Odyssey raised $310 million at a $1.45 billion valuation in a Series B round led by . Other investors included ,,,, and . Odyssey develops AI world models that create multimodal simulations of real-world environments. The startup has now raised $337 million in funding to date, .

2. , $140M, fintech: New York-based Chronograph secured a $140 million private equity round led by . The company provides portfolio monitoring, reporting and diligence software for private capital investors, an increasingly important market as private assets continue to grow. The new raise, which it describes as growth capital, brings its total funding to date to $160 million, according to .

3. (tied) , $100M, AI infrastructure: Boulder, Colorado-based Hydra Host raised a massive $100 million Series A led by . A of other investors joined, including ,, , and . The company operates a bare-metal GPU platform that connects customers to distributed AI computing infrastructure. With the latest investment, it has raised just under $119 million to date.

3. (tied) , $100M, cybersecurity: Startups that promise to protect companies in the AI era are also raising massive sums right out of the gate. This week, Santa Clara, California-based Ent.AI emerged from stealth and said it has raised $100 million in seed funding led by. Other investors included,, 1,, and. The company, founded by former executives and members of the Security Copilot team, offers an AI-powered workspace security platform that it says can analyze user and AI-agent behavior in real time to proactively prevent cyber threats.

3. (tied) , $100M, cybersecurity, defense: Arlington, Virginia-based Twenty Technologies secured a $100 million Series B at a $1 billion valuation. The round was led by, with participation from, and. The company develops AI-enabled cyber warfare systems for the U.S. military and intelligence community, helping automate and accelerate offensive cyber operations at scale. Founded by former cyber operators and defense technologists, Twenty Technologies has now raised $138 million to date,. It鈥檚 part of a growing wave of venture-backed startups building software for military and national security purposes.

3. (tied) , $100M, quantum computing: Berkeley, California-based Atom Computing raised a $100 million Series C led by that brings its total private investment to date to just over $191 million, . and also backed its latest round. Along with the venture money, Atom also received a $100 million Letter of Intent from the under the CHIPS and Science Act that gives the startup additional public backing in exchange for a minority government stake. The company develops neutral-atom quantum computers, one of several competing architectures seeking to commercialize quantum computing. It is one of several quantum startups to receive sizable funding deals this year, following a record-breaking venture investment year for the sector in 2025.

7. , $65M, biotechnology: Watertown, Massachusetts-based Triveni Bio raised a $65 million Series C co-led by and. Additional participation came from. The company develops antibody-based therapeutics for immunological and inflammatory diseases. It has now raised $272 million total from investors, .

8. (tied) , $52M, semiconductor infrastructure: Menlo Park, California-based AttoTude secured a $52 million Series C led by. Other investors included ,,,, 2, and. The startup develops high-speed interconnect technology for AI and hyperscale data centers and has raised $142 million to date, according to . It comes amid robust funding for semiconductor startups this year.

8. (tied) , $52M, digital media: Beverly Hills, California-based Richard Roths Media raised a $52 million venture round led by . The company says it delivered AI-driven marketing and advertising services for 鈥渉igh trust鈥� industries such as banking, law and healthcare. The investment appears to be its first outside capital, per 小蓝视频色情网页版.

10. (tied) , $50M, artificial intelligence: San Francisco-based Bland AI raised a $50 million Series C led by . The of other investors includes , , founder , and others. The company develops AI-powered voice agents that automate inbound and outbound phone conversations for enterprises, a category that has seen growing adoption as businesses look to replace traditional call-center workflows. It has raised $106 million to date, according to .

10. (tied) , $50M, fintech: Brooklyn-based Interchecks secured a $50 million Series C led by,, and. The company operates a payments platform that allows businesses to manage deposits and payouts through a single API, reflecting continued investor interest in infrastructure that simplifies financial operations. It has now raised just under $79 million to date.

10. (tied) , $50M, artificial intelligence, biotechnology: Menlo Park, California-based Radical Numerics emerged from stealth and said it has raised a $50 million seed round led by, with participation from , and . The startup is developing AI models designed to simulate and predict biological systems, with the goal of accelerating drug discovery and advancing precision medicine.

Large non-US deals:

• The largest startup deal outside of the U.S. this week was very large indeed, and also very unusual. , the Chinese AI chatbot startup that briefly roiled public AI-related stocks in early 2025, reportedly took its first outside financing, worth roughly $7.4 billion. The Series A deal, however, comes with a lot of atypical caveats, notably that investors in the deal didn鈥檛 actually receive a stake in DeepSeek, but rather in an LLC controlled by founder , per . Those investors also reportedly face a five-year lockup and receive no voting rights.

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 13-18. 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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“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.

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Related reading:

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

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Checking in five months later, the space continues to sizzle from a startup funding standpoint, even though public markets have pulled back from the space in recent days. So far in 2026, investors have poured around $10.7 billion into seed through pre-IPO rounds for companies in 小蓝视频色情网页版鈥檚 semiconductor category. That puts funding on track to eclipse last year鈥檚 levels.

Noteworthy recent rounds

Beyond , which went public last month after securing a $1 billion February pre-IPO round, a number of semiconductor-focused startups are raising considerable investor capital this year. Using 小蓝视频色情网页版 data, we put together a list of the 10 largest venture funding recipients.

One of the three largest fundraisers after Cerebras is , a developer of chips customized for the large model needs of AI labs. The Silicon Valley startup raised a $500 million Series B in February led by and .听

Another moving up the ranks is , which also secured $500 million in a March Series E financing led by . The San Jose, California-based company is an AI infrastructure startup focused on optics technology, with strategic backers including and .听

, a startup working on chips for AI superintelligence, reportedly also secured $500 million in new funding early this year. led the financing, which was said to set a $5 billion valuation for the Silicon Valley-based company.

The Cerebras factor

For now, the market fate of AI chip and infrastructure developer weighs heavily over the semiconductor startup space.

The Silicon Valley company鈥檚 massive IPO last month raised over $5 billion and saw shares soar in first-day trading. Since then, 11-year-old Cerebras has been heading lower, with shares down about a third from the initial closing price.听

Still, it鈥檚 far from a slacker. With a recent market cap around $50 billion, paired with rapidly rising revenues, Cerebras is finding plenty of investor support for its pitch that it is building 鈥渢he fastest AI infrastructure in the world.鈥澛犅�

High AI valuations and enthusiasm give sector a boost

Broadly, semiconductor startups are benefiting from the more widespread investor enthusiasm around the growth of AI and their continued support for the massive infrastructure outlays it requires.

That鈥檚 visible in the public markets as well, with semiconductor indices trading near all-time highs, a pullback in recent days notwithstanding. Chip designer Nvidia, meanwhile, remains the world鈥檚 most valuable public company.

The semiconductor industry is also young enough that most of today鈥檚 industry behemoths began as venture-backed startups. And given the rich history of innovative upstarts unseating leading players in this space, no one is doubting the chances of that storyline repeating.

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