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

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

Noteworthy recent rounds

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

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

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

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

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

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

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

Exits

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

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

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

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

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

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

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

Related 91精品 queries:

Related reading:

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

Illustration:

Since its launch almost two decades ago in 2007, the firm 鈥� which had an initial fund of just $3 million 鈥斕� has invested in around 550 companies. With this latest fund, its assets under management have reached $1 billion.听

91精品 News spoke with , the firm鈥檚 managing partner who joined Seedcamp in 2010 and , who rejoined the firm in 2022 to head up the select fund and establish a New York presence.听

Since fund 2, it has invested in 100 companies per fund. 鈥淲hat we鈥檝e learned is that you need a community to support each other,鈥� said Espinal. The tipping point for the firm was 70 companies where it became clear that founders were helping one another, becoming customers, and teams starting new companies.

鈥淲e realized early on that the best thing a founder can get is access to another founder who just went through that experience 鈥� not necessarily a founder who is successful 10 years down the road and is a great figurehead, but someone just a little bit ahead. That鈥檚 effectively our secret sauce,鈥� said Espinal.听

Seedcamp also invests in software and vertical AI, but is careful about what is compelling and unique. 鈥淲e鈥檙e trying to monitor so we鈥檙e not one of eight bets in one area that鈥檚 been overinvested within the AI vertical space, and making sure that you鈥檙e not betting on number 100 in a space that鈥檚 hypercompetitive,鈥� Espinal said.听

Seedcamp plans to invest in 35 new companies per year, totaling 100 to 120 for the new fund. It invests up to $1.3 million in its initial check, and will lead roughly 70% of those deals with a 5% to 10% ownership target.听

The firm reserves 40% for follow-on seed and Series A rounds. Its select fund will invest in portfolio companies from Series B onward.

鈥淏uilding is so much easier and faster now,鈥� Howe said. 鈥淪ignals of product-market fit are there earlier. The founder DNA is still the same, but the ability to see it in action earlier is there with the AI lift.鈥�

New York presence

Howe, who heads up the New York office, noted that European companies are heading to the U.S. earlier. 鈥淗istorically, maybe we鈥檇 see a company raise a round and stay in Europe, dominate their local market, raise a few more rounds, and then come to the U.S.鈥� she said. 鈥淣ow we鈥檙e seeing them come right from the get-go.鈥�

From fund 3, its 2014 vintage fund, the firm’s return is 13x distributions to paid-in capital, with Revolut, UiPath and seed investments from that fund.

Related 91精品 queries:

• 听

Illustration:

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

This week 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 91精品.

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 91精品 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.

Illustration:

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

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

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

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

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

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

The interview has been edited for brevity and clarity.

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

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

But what replaced it is actually harder to answer: 鈥淚s the tech defensible?鈥� Not just 鈥渄oes it work?鈥� but 鈥渄oes it compound?鈥�

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

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

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

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

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

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

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

We鈥檙e looking for platforms using proprietary data that can鈥檛 be replicated by AI, companies that have embedded deep domain expertise 鈥� areas where general-purpose AI still lacks industry context 鈥� into their workflows, or highly specialized ecosystems or niche markets that provide another layer of insulation in categories that are too targeted for frontier labs to pursue directly.

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

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

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

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

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

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

A Tier 1 financial VC鈥檚 seed check and an AT&T Ventures seed check are different instruments. They are offering capital, brand, guidance and pattern recognition from backing hundreds of companies.

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

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

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

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

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

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

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

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

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

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

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

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

Taneja: Architecture is a key part of our diligence process. The way we think about it really depends on the ultimate use case. Is it for internal use 鈥� i.e., a tool that AT&T will be working with in our environments 鈥� or is it something we鈥檇 be distributing or incorporating into some form of product offering?

If the former, all aspects of the architecture will be reviewed, and this is most likely to occur throughout trials and proof of concepts as we develop a technical understanding of the application or product. If it鈥檚 the latter, then we鈥檙e likely most interested in understanding how this product architecture scales over time and what it means from a cost, latency and infrastructure perspective. We love to see companies embracing edge-related technologies, but that doesn鈥檛 preclude us from working on applications that use traditional data processing methods.

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

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

The software layer is clearly accelerating 鈥� things like perception, control systems and decisioning are moving faster because of AI (the rounds show it!). That will ultimately help pave the way for the adoption of physical AI. However, the physical deployment cycle still takes time, so you don鈥檛 see quite the same level of time compression there.

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

Related 91精品 queries:

Related reading:

• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo

Illustration:

Six months later, a new tech came along that shook the market. While only a few customers left and the financials remained strong, the stock went down, fast.

This is the danger boards must confront. Disruption rarely announces itself during a crisis. It often appears when the business still looks strong.

For boards, AI, and eventually quantum computing, and other technologies, should not be treated as another technology trend. These technologies can reshape pricing, customer expectations, cybersecurity, product development, talent needs and the company鈥檚 business model itself.

Under this fast pace, evolving tech world, company boards should consider the following three points.

Measure the cost of inaction, not just the cost of adoption

Most boards ask: 鈥淗ow much will this AI initiative cost?鈥� That鈥檚 the easy question.

The harder question is: 鈥淲hat will it cost if we are late?鈥�

If a competitor uses AI to reduce costs, accelerate delivery, improve personalization or launch faster products, the cost of delay may be far greater than the investment required. The company may lose pricing power, customer loyalty and market relevance before the damage fully appears in the financials. Every major technology discussion should include a 鈥渃ost of inaction鈥� analysis.

What happens if the company is 12, 18 or 24 months behind? Which margins come under pressure? Which customers become vulnerable? What market image will I have that will impact future clients? Which parts of the product become commoditized?

Challenge the business while it still looks successful

Boards often become more aggressive only when performance weakens. By then, options are limited. The real test is whether the board can challenge management when revenue is growing, customers are renewing and the strategy still appears to work. Success may blur your vision as to what can go wrong.

Boards should regularly ask: Which part of our business would be most vulnerable if AI (or the next big tech change) made it cheaper, faster or easier to deliver? Which revenue stream depends on friction? Which product feature could become free? Which customer process could be automated by someone else?

These questions may feel uncomfortable when things are going well. That is precisely when they matter most.

Build the company that would disrupt your current company

Instead of asking only how to defend the current model, boards should ask management to design the competitor they would fear most.

What would that competitor do differently? How would it price? What teams would it build? What technologies would it use? Which costs would it eliminate? Would it bypass traditional distribution channels?

This exercise forces the company to think offensively. It pushes management to consider bold changes before they become urgent.

For AI, the impact is already visible across software, services, analytics, support, marketing and operations. For quantum, the timeline may be longer, but the strategic implications could be significant in cybersecurity, finance, pharma, logistics and materials science.

Boards do not need to chase every trend. But when technology changes how work is done at the core, when it changes cost structures, speed of development, brand reputation and distribution channels, it becomes a board-level issue.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 91精品 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

Illustration:

This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We鈥檝e included both startups and publicly traded, tech-heavy companies. We鈥檝e also included companies based elsewhere that have a sizable team in the United States, such as , even when it鈥檚 unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as 鈥渦nclear.鈥�

Only around $942 million 鈥� or just 0.32% of total U.S. venture funding 鈥� went to startups with a Black founder or co-founder last year, per 91精品 . That鈥檚 one of the lowest shares in years, and down more than two-thirds from just three years prior.

This year has started off on a slightly rosier note, with $643 million raised by U.S.-based startups with a Black founder or co-founder as of May 20. The majority of that was raised in the first quarter, marking the most raised in a single quarter since Q2 2022, when $653 million was raised by Black founders or co-founders.

The consistently low numbers have led some Black founders to turn to investing in an effort to help level the playing field. 91精品 News talked with two such founders to hear more about their experiences in raising capital and what they鈥檝e learned from investing.

Clarence Bethea

founded , an extended warranty startup, in 2014. He went on to raise nearly $30 million in venture capital before the startup was ultimately acquired by in 2024.

The process of raising capital for a St. Paul, Minnesota-based startup as a Black founder was arduous, he recalls, describing it as being especially 鈥渧ery hard in the beginning.鈥�

鈥淚 believe that raising money for anyone is very difficult. When you add in race, gender, and proximity, it becomes even more difficult,” he told 91精品 News in an email interview. 鈥�… I often tell founders, raising that first million will be your hardest. Do I believe that race played a factor [in making it harder to raise capital]? Yes! Because it plays a factor in every part of my life.鈥�

It didn鈥檛 take long for Bethea to come to a distinct realization: The system was never designed with everyone in mind.

鈥淭his system wasn鈥檛 built for me, and I knew that from day one,鈥� he reflects. Yet, rather than allowing that structural reality to become a barrier, he shifted his focus toward mastery.

鈥淢y focus quickly became about learning and understanding the game of venture capital,鈥� he said. 鈥淚 didn鈥檛 want the fact that it wasn鈥檛 for me to get in the way of being a part of it.”

Bethea later made the leap into venture capital itself. In 2023, he joined , one of Upsie鈥檚 backers, as an investor and entrepreneur-in-residence. The move, he said, was motivated partly 鈥渂y the people,鈥� and wanting to be in an environment where he was “encouraged to learn deeply about the industry and how to look at deals.”

But it was also driven by a deeper mission to alter the very dynamics he faced on the other side of the table.

鈥淚 wanted to be a voice for founders who either looked like me, weren鈥檛 in-network and didn鈥檛 match the normal 鈥榩edigree鈥� of a founder,鈥� he said.

Stepping into the investor’s shoes provided Bethea with a dual perspective, he said, both validating his instincts as an entrepreneur and revealing new dimensions of the fundraising puzzle.

Becoming a VC 鈥渃onfirmed some things that I knew were true as a founder, but it also opened my eyes to ways founders can improve their chances,鈥� Bethea said.

From his vantage point as an investor, he routinely witnessed what he described as the same avoidable mistakes being made by talented teams. That realization prompted him to move on from his role at True Ventures earlier this year and became the catalyst for his current venture, 鈥溾��

Bethea describes the initiative as an 鈥渁lways-on鈥� educational platform, course and live-programming series designed to give early-stage entrepreneurs clear, unfiltered insight into the real mechanics of company building and venture fundraising.

Built on 鈥渓ived experience,鈥� the platform equips founders with more than 75 high-level videos and 90 workbook pages in an effort to demystify how venture decisions are actually made, what makes a pitch fundable, and how to approach fundraising strategically. The impact is already tangible, according to Bethea, as it鈥檚 helped two founders raise millions so far using its frameworks.

Ultimately, his time in the venture capital trenches has left him looking toward the future with a striking amount of hope.

鈥淚’m more optimistic than ever before,” he said, pointing to technological shifts as a potential massive equalizer for underrepresented builders.

鈥淎I brings down the walls of building an MVP, talking to customers, and starting to gain traction,鈥� he said. 鈥淭hat鈥檚 really exciting for founders who don’t fit the normal founder stereotype. But we have to get better at the game of venture.鈥�

Cortney Woodruff

Over the years, has founded and raised venture capital for two startups: , an online platform that provides software services to personal trainers, and , an online learning platform that provides online courses taught by notable, Black innovators that was co-founded by actor .

Those experiences led him to conclude that while building a company is universally grueling, the playing field is far from level. Reflecting on his early days as an entrepreneur, he notes that “raising venture capital is hard for almost everyone, especially first-time founders,” given that investors must make highly risky decisions with limited information. Yet, he simultaneously observed a stark disparity in how different founders are evaluated.

鈥淚 often felt young minority founders were expected to arrive as finished products,” Woodruff told 91精品 News in an email interview. “There seemed to be less patience, less coaching, less developmental support. I watched founders receive years of benefit-of-the-doubt capital while learning on the job. Many minority founders are expected to prove everything upfront.鈥�

This friction became undeniable during pitches for his first company, Trainersvalut. Despite walking into meetings with customers and real revenue traction, Woodruff recalls that he and his team often left 鈥渇eeling like we were still being evaluated as an idea rather than a business.鈥�

He came to that determination after a number of confusing rejections. While founders would naturally assume they are competing on product, execution and traction, Woodruff eventually concluded that it鈥檚 usually more related to familiarity.

鈥淢any investors are looking for patterns they鈥檝e seen before,鈥� he said. 鈥淚f your background, network, school, or story doesn鈥檛 fit those patterns, you often have to produce significantly more evidence before receiving the same conviction.

鈥淭hat realization changed how I viewed entrepreneurship and venture capital,鈥� Woodruff added.

Driven by a desire to learn more about how decisions were made from the other side of the table, Woodruff began angel investing. The move pulled back the curtain on the industry’s inner workings, confirming just how deeply venture capital relies on pattern recognition to signal success.

鈥淲hat surprised me was how much venture capital is driven by pattern recognition,鈥� he said. 鈥淚nvestors are trying to identify signals that increase the probability of success. The challenge is that those signals are often informed by prior successes, which can unintentionally narrow the range of founders and ideas that receive attention.鈥�

Sitting in the investor’s chair also reframed his perspective on institutional bias. As a founder, it is easy to view every rejection as personal or discriminatory, but underwriting deals revealed to him just how difficult these choices are. Today, Woodruff views the industry’s shortcomings in diversity through a systemic lens rather than an individual one.

鈥淭he people who talk about bias often underestimate the role of networks, while the people who talk about networks often underestimate the role of bias,鈥� he said. “Most investors are not waking up trying to exclude people. However, they are often sourcing opportunities from familiar circles, relying on familiar signals, and backing founders who feel familiar to them. Over time, those patterns compound.鈥�

This concentration of networks helps explain why venture capital continually underinvests in Black founders. Because VC is fundamentally relationship-driven 鈥� reliant on referrals, universities and existing investor circles 鈥� homogeneous networks naturally yield homogeneous deal flow.

鈥淚 don鈥檛 think the issue is simply that investors don鈥檛 want to fund Black founders,鈥� Woodruff said. 鈥淚 think many investors never encounter a sufficiently diverse set of founders in the first place.鈥�

In his view, the resulting disparity isn’t always about who eventually gets a check, but who is given the grace to stumble and iterate. Throughout his years in the ecosystem, Woodruff said he has routinely watched founders with stronger traction receive less enthusiasm than those with stronger narratives.

鈥淭he difference is often not who gets funded eventually. The difference is who receives patience, coaching, introductions, and the opportunity to grow into the founder investors believe they can become,鈥� he said.

Now, Woodruff uses his position to bridge that gap, treating mentorship and network access as critical forms of capital. He focuses on guiding founders through an unfamiliar system, helping them avoid missteps, and opening doors to rooms they otherwise wouldn’t enter.

When looking toward the industry’s future, his outlook is balanced by both optimism and pragmatism. Woodruff is heartened that conversations around representation are more visible than ever and that technology has drastically lowered the barrier to entry for small teams building meaningful businesses. Yet, he recognizes that “systems change slowly. Networks evolve slowly. Institutions evolve slowly.”

Ultimately, he rejects the premise that venture capital can be fundamentally reengineered for fairness.

“I don鈥檛 think venture capital was designed to be equitable. It was designed to generate returns,” Woodruff said. Instead, he believes the real paradigm shift will come from diversifying the perspectives of those who write the checks.

“If every investment committee has similar backgrounds, similar networks, and similar reference points, they will naturally gravitate toward similar founders and similar ideas. I don鈥檛 believe the economics of venture capital need to change as much as the pattern recognition does,鈥� he said. 鈥淭he most successful investors in the future may be the ones who can recognize extraordinary opportunities in places others have been trained to overlook.鈥�

Related 91精品 query:

Related reading:

• 91精品 Data: Venture Dollars For Black Startup Founders Stay Scarce Despite AI Funding Boom
• Share Of Startup Funding For Black Founders Hits Multiyear Low
• Funding To Black Startup Founders Quadrupled In Past Year, But Remains Elusive

Illustration:

I sold my AI research company while I was qualifying as a lawyer in the U.K. I built with researchers from , , and who believed in the mission enough to trust a 21-year-old law student to lead the ship.

, when acquired us, it was the first time in its 170-year history it bought a company pre-revenue. Thomson Reuters acquired us for the science.

Getting there was painful, though. Our published papers put the model among the best in the world at legal reasoning, and we trained it for a fraction of what the large labs were spending. We had been a quieter version of 鈥渢he story,鈥� developing very capable models using novel algorithms with huge capital efficiency.

None of that counted for much in the rooms I walked into. Investors always asked about the product and the traction. U.K. investors passed, and I ended up raising most of our funding in the United States.

I back founders now, and the things I weigh have stayed consistent. As a founder, I was told again and again that science meant little until it was bolted onto a product. That test was wrong then and I believe it is fatal now.

Backing founders in the foundational layer

In the first quarter of 2026, foundational AI startups raised around $178 billion. The market is realizing that foundational AI is where the long-term value sits, and this is the year we may see the exits and IPOs that prove the bet right.

However, the capital and the conviction have also pooled around a few names that were already incumbent. , and took roughly 97% of it, and every other foundational AI company in the world shared what was left.

For a deep-tech founder starting out now, that might push them toward a tempting but dangerous read of the market: that the race is over, and that the sensible move would be to build on top of one of these giants.

I鈥檓 looking for founders who move the other way.

Most application-layer companies, built on a model they do not own, adapt to the pricing and access decided for them by the firms upstream, and compete in categories that the same firm can absorb whenever it chooses.

The more durable place to build is the layer underneath. The cost, speed, reliability, interpretability and safety of AI systems remain unsolved and genuine scientific challenges, and they decide what everything that sits above them can do.

A real advance in training efficiency, model architecture and inference cost is the work that will still matter in five years, long after most of today鈥檚 wrappers have been priced out or absorbed.

Asking the right questions

So the questions I ask AI founders are:

• Is your technical team, scientist-for-scientist, equal to or better than the team at DeepMind?
• Does the problem sit at the level of the model and the system, or is it one more thing stacked on someone else鈥檚?
• Will the 鈥減roduct鈥� get harder to live without over the next five years, or is it looking to reach for early revenue like every other startup?

Some of the companies that ended up mattering most in the AI era are those that survived this line of thought. DeepMind and OpenAI began as research efforts with no obvious product, and both would have looked uncomfortable to a conventional early-stage software investor. Their importance is obvious in hindsight, but the foundational problem-solvers tend to look unfundable right up until it looks inevitable.

Do not build to look fundable this quarter. Build something that the whole stack will depend on in the future. Hire the best team you can find to do it, and solve the hard, foundational problem while it is still unfashionable.

The deep-tech capital market is slow, and it will keep chasing familiar names for a while yet. The work still comes first, and the founders who dare to do it early are the ones the market eventually has to come and find.

The future lies in deep tech, not in the surface wrappers that pass for most products nowadays.

was the founder and CEO of , which was acquired by in 2024. He is an angel investor and senior director at Thomson Reuters Labs.

Illustration:

The acquisition represents an enormous return on investment for Cursor鈥檚 backers. Since its founding just four years ago, parent company raised $3.4 billion from investors including , repeat backer , and and was most recently valued at roughly $30 billion in November, per 91精品.

The acquisition gives SpaceX, which raised $75 billion in its IPO last week, a foothold into the enterprise software development market, where AI-assisted coding has taken off and led large companies to significantly pare back their reliance on human engineers. Cursor said in November last year that it had crossed $1 billion in annualized revenue.

Hawthorne, California-based SpaceX has in recent years expanded beyond space exploration to become something of an umbrella company for CEO 鈥檚 numerous other interests and ambitions, as the company acquired the social media platform (formerly Twitter) and the AI company . SpaceX shares jumped around 16% on Tuesday following the Cursor announcement.

This year has proven robust for M&A activity involving venture-backed startups, 91精品 data shows. Through June 16, at least 1,177 such deals altogether valued at $182.7 billion have been announced. That compares with 1,132 deals valued at $106.7 billion in the same period last year.

Related 91精品 queries:

Related reading:

• SpaceX Shares Close Up 19% After Largest IPO Of All Time

Illustration:

“Silicon Valley has done very well with software, but while software was eating the world, they forgot about silicon,” Barrett told 91精品 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 91精品 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 鈥斕齱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 91精品, 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.

91精品 queries:

Related reading:

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

Illustration: