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

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

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

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

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

Tokens cost more than time and money

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

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

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

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

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

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

Models change. Update your architecture

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

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

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

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

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

There鈥檚 strength in flexibility

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

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

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

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

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While financings of a few million haven鈥檛 gone away, today most venture capital actually goes to rounds of a billion dollars or more. Moreover, it looks like a rising trend.

So far this year, 60% of global startup funding across stages听1 听鈥� around $320 billion 鈥� went to rounds of $1 billion or more, per 小蓝视频色情网页版 data. Such rounds were instrumental in pushing global funding for the first half of the year to record levels.

The U.S. funding tallies are even more tilted to megadeals this year, with 73% of funding going to billion-dollar-plus rounds. Of the $290 billion invested in these deals, just two rounds for AI leaders and account for more than half the total.

As you can see, the notion of billion-dollar-plus rounds accounted for a minority of funding before this year. The lone exception was the first quarter of 2025, when OpenAI closed a $40 billion financing.

Not just bigger deals, more of them too

Giant rounds aren鈥檛 just getting more ginormous. They鈥檙e happening with greater frequency too.

So far this year, U.S. startups have closed 23 known rounds of $1 billion or more, per 小蓝视频色情网页版 data. That puts 2026 already on par with 2025, a record-setting year, and we鈥檝e still got about five months left.

Not surprisingly, these megarounds are generally later-stage rounds or corporate financings. Only two of this year鈥檚 billion-dollar-plus rounds 鈥� and 鈥� were seed or early-stage rounds, per 小蓝视频色情网页版 data.

Lessons from the first crop of billion-plus financings

In the history of startups, meanwhile, the billion-dollar-plus venture funding round is a fairly contemporary phenomenon.

The first American example, per 小蓝视频色情网页版 data, was 鈥檚 $1.2 billion Series D, in 2014. Over the next three years, a handful of others pulled in 10-figure rounds as well, including , , , , , , , and .

Most of those companies went on to go public and reach valuations that well-exceeded levels set for prior megarounds. SpaceX ($1.6 trillion recent market cap), Uber ($148 billion) and Airbnb ($87 billion) were the standout success stories.

Two of the megafund recipients 鈥� Argo AI and WeWork 鈥� did not fare so well, while a third, cancer diagnostics provider Grail, has been up and down. Fanatics, meanwhile, remained private and is still thriving.

If these early billion-plus fundings taught investors anything, it was that pouring unusually large sums into well-regarded unicorns can be quite lucrative but is far from a sure bet.

Uncharted territory

In the current funding cycle, it鈥檚 not enough to ask whether billion-dollar rounds have potential for high returns. With Anthropic and OpenAI, the question now applies to rounds in the tens of billions or even over $100 billion. As both have already filed confidentially to go public, it may not take us long to find out.

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• 小蓝视频色情网页版 Data: Q2 Brought The Most Billion-Dollar Startup Exits Since 2021
• The $100M+ Round Is Now Just Your Typical Late-Stage Financing

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Looking broader into the entire market, I can see how this makes a lot of sense. Companies are becoming increasingly uneasy about where critical infrastructure and sensitive data live. AI fraud is getting better. Voice cloning is becoming more convincing. Vibe coding is allowing less experienced developers to build faster, but not always more securely. Quantum computing is still over the horizon, but serious companies are already thinking about what it may mean for encryption and long-term data protection.

For the past decade, cloud migration was treated as the obvious strategy. It gave companies speed, scale and lower upfront costs. Startups could launch without buying servers. Enterprises could modernize without rebuilding their own infrastructure.

That logic still holds. But we are witnessing an interesting shift where progress is happening so fast, security cannot keep up, thus creating an uneasy feeling causing decision-makers to revert back to older, and perhaps safer perceived strategies.

Here are three trends that I believe are pushing on-prem back into the limelight.

AI fraud is changing the security conversation

In many cases, cloud providers are more secure than what a company could build internally. The issue is that thanks to AI, attackers are becoming more sophisticated, and quick. AI makes phishing more polished, fake invoices more believable, and voice impersonation harder to detect. A call that sounds like the CFO or CEO asking for a payment approval is no longer far-fetched.

That changes how companies think about exposure. The attack surface is not only servers. It is identity systems, SaaS tools, APIs, employee workflows, permissions, contractors and support portals.

For sensitive systems such as communications, payments, identity and customer data, control becomes more valuable. On-prem does not guarantee security. But it can reduce dependency on outside platforms and give companies clearer ownership over the systems they cannot afford to compromise.

Enterprise AI may favor private infrastructure

Cloud AI APIs are excellent for testing. A company can launch a pilot quickly without buying GPUs, managing models, or hiring a large infrastructure team.

But enterprise AI is moving into production. That changes both the economics and the risk.

The most useful enterprise AI applications require proprietary data: contracts, source code, customer records, financial reports, support tickets, security logs, medical files and internal communications. This is the data that gives AI business value. It is also the data companies are most careful with.

For these use cases, on-prem or private AI infrastructure becomes more attractive. The model can run closer to the data. Access can be controlled more tightly. Retention, compliance and audit requirements become easier to manage.

There is also a cost angle. Token pricing is convenient in a pilot, but expensive at scale. When thousands of employees or customers use AI every day, paying per query can become a serious recurring cost. For stable, high-volume workloads, owning or controlling the infrastructure may be cheaper than renting every interaction forever.

Quantum risk is making long-term data protection more strategic

Quantum computing is not breaking enterprise encryption today. But the risk is already part of serious security planning.

The concern is that the minute quantum becomes commercial, all encrypted data sitting in the cloud will be transparent. No existing encryption will hold against a quantum computer. That matters most for companies holding long-life sensitive data: banks, healthcare providers, telecom companies, governments, defense-related organizations and infrastructure providers.

Regardless of whether or not on-prem is the best solution for all this, it is perceived as such. Hence, I believe it will drive higher demand for the legacy on-prem strategy. This early shift is also an opportunity, but that鈥檚 for another article.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 小蓝视频色情网页版 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .听

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I have spent more than 40 years in financial services, and I have learned to be skeptical of people heralding the word “revolution.” Branchless banking was going to end the branch. The blockchain was going to disintermediate the whole system. Big Tech was going to extinguish the bank altogether.

However, consider the most meaningful waves of financial services innovation this generation. The information-based strategy we pioneered at turned data into the engine of a consumer bank, reimagining it from the inside.

Then the internet dissolved the branch as the unit of distribution, putting the bank on a screen. Then digitization moved that screen into the customer’s pocket, unlocking all banking services with a mere touch stroke. Then the cloud collapsed the cost of computing and let a handful of engineers do what once took a data center and an army. AI will be bigger than all of these waves.

The change is here

AI will become the operating system which global finance runs on, rewriting the value chain end to end until the industry that emerges looks nothing like the one it replaced.

You can already see it happening, one layer at a time. Wealth management is being rebuilt around tools like 1, which turns the messy reality of client conversations into structured, actionable intelligence.

Investment banking is being rewired by the likes of and , compressing analytical work that once consumed floors of junior bankers. Filing taxes is being reenvisioned by companies like . AI neobanks like and are automating more pieces of the consumer banking relationship. The call center is being reimagined by companies such as and , resolving the complex, regulated queries that first-generation chatbots could never touch.

The plumbing of the financial system itself is under siege. is building the clearing bank for the AI age, and and are becoming the back-office stack that businesses run on, folding cards, expenses, procurement and accounting into a single semi-autonomous system. Companies like and are rebuilding risk and compliance, identity verification, AML and KYC, for an AI world where the counterparty on a transaction may not be a person at all.

Further out sits the largest prize, an agentic commerce layer where software transacts on our behalf or quietly arbitrages idle deposits away from inert institutions. Layer by layer, the financial system will be systematically uprooted by AI, each piece first made faster and cheaper, then reinvented from the ground up.

Zero marginal costs

The first thing AI does is brutal and simple. It takes the marginal cost to underwrite a loan, clear a compliance review, serve a customer at 2 a.m., and drive it toward zero.

We have spent decades treating those functions as fixed operating costs. Capital One’s insurgency 30 years ago proved that a one-size-fits-all model breaks the moment marginal economics lets you price and serve customers individually. AI applies that logic to the whole stack ruthlessly and simultaneously, remaking business models and org charts all at once.

The second order effect is that AI unlocks products that could not exist before. At Capital One we sought to deliver the right product to the right customer at the right price at the right time, which was always something of an exaggeration, because all we really had was direct mail and statistical inference.

Now it can genuinely be done: products tailored to a customer’s specific needs, credit that moves with daily cash flows, insurance priced to the individual rather than the actuarial average. The frontier of the buildable has moved further in three years than in the prior 20 and founders catching this wave are turning that capability loose. AI is a kind of alchemy, turning lead into gold. We are watching it happen across our own portfolio, expanding the frontier of what鈥檚 possible for many companies.

Upstart fintechs have historically had the most to gain with rising technological waves. Fintech鈥檚 nimbleness, compressed decision timelines, and sheer force of will give them a commanding head start in adopting and implementing AI.

But incumbent financial institutions shouldn鈥檛 be discounted. They sit on the richest proprietary datasets in the economy, decades of transactions, balances, defaults and recoveries that no fintech can buy. If data is the fuel of the AI age, the big banks and insurance companies own the refineries.

And yet I have spent a career watching these institutions confuse consumer loyalty with inertia and watching the advantage that should have been decisive die quietly in committee. Earned-wage access, buy now, paylater, C2C remittances, digital brokerage: whole categories the incumbents never bothered to enter, and where fintechs now sit firmly in command.

That ceded ground has helped mint fintech centicorns like , , and 2. Owning customer data and being capable and willing to act on it are different things, and most of it sits trapped in legacy cores, inside organizations built to protect and defend the existing model, not break it.

Every link in the value chain

The hardest thing for an incumbent is summoning the will to pivot or self-cannibalize. Those treating this as an existential mandate, rebuilding their technology and their talent around AI, will stand alongside leading fintechs in remaking the future of finance over the coming decade. The rest will come to understand what has changed only as they watch their market share erode and the sector consolidate.

If there is one thing I have learned in 40 years, it is that technology rarely rewards whoever owns the asset; it rewards whoever is willing to rebuild around it. AI will rewire every link in the value chain, from the way consumers transact to the way money moves and businesses run, and what emerges on the other side of this technological tidal wave will only vaguely resemble the system we know today.

I have watched four waves reshape this industry, and no word I used for them feels strong enough for this one. The question that matters now is who will summon the conviction to dismantle what works today to build what wins tomorrow.

is the co-founder and managing partner of , a fintech venture capital platform focused on disruptive, high-growth financial services companies. QED has made numerous unicorn investments, including , , , , , and . Morris is also the chairman of and , serves on the boards of , and , and is a board observer for and . Prior to QED, he co-founded in 1994. Under his leadership as president and chief operating officer, Capital One pioneered an information-based strategy that transformed the consumer lending industry. He holds an MBA with distinction from London Business School, where he is also a Fellow.

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In 2026, conversations about Black founders and venture capital have focused on access to funding. But as AI reshapes startup economics, the bigger challenge is no longer simply getting a first check, it’s raising enough capital at the seed stage to successfully reach Series A.

AI has fundamentally lowered the cost of building software companies. Founders can launch products faster, automate operations and accomplish with five employees what once required teams of 30. Yet while AI has reduced the cost of building a startup, it has not reduced the cost of scaling one. Companies still need resources to acquire customers, hire experienced talent, invest in go-to-market strategies, and generate the revenue and growth metrics institutional investors expect before leading a Series A round.

For Black founders, who continue to receive a disproportionately small share of venture capital, the inability to secure fully funded seed rounds has become one of the greatest barriers to building venture-scale companies.

AI is making seed capital more valuable, not less

One of the biggest misconceptions about AI is that startups simply need less money. In reality, AI has shifted when capital matters most. Because startups can now build products more efficiently, investors are increasingly rewarding founders who demonstrate real traction instead of polished ideas. Seed funding is no longer financing an experiment, it is financing proof.

That means founders need enough capital to move beyond building a product and toward building a business. Today’s Series A investors are looking for recurring revenue, customer retention, capital efficiency and repeatable growth. Those milestones require time, execution and sufficient capital.

The startups that reach them are increasingly those that raised enough capital early to stay focused on customers instead of constantly fundraising.

The numbers tell a stark story

The challenge is particularly acute for Black entrepreneurs. According to 小蓝视频色情网页版 data, U.S. startups with a Black founder or co-founder received just $942 million in venture funding in 2025, only 0.32% of all venture capital invested in the nation. That represents one of the lowest funding shares in years and a dramatic decline from 2021, when Black founders raised $5.2 billion during the post-George Floyd investment surge.

While 2026 has shown encouraging signs, with Black-founded startups raising approximately $643 million by late May, the strongest quarter since mid-2022, the improvement was driven largely by a handful of unusually large financings, including a $350 million AI round. Across the broader ecosystem, Black founders remain significantly underrepresented in venture funding.

The issue isn’t simply that too little capital is available. It’s that many Black founders raise partial seed rounds that leave them without enough operating flexibility to achieve the milestones required for institutional Series A financing.

The real gap is between seed and Series A

Historically, venture capital rewarded bold ideas and rapid expansion. Today’s market rewards disciplined execution. Investors expect startups to demonstrate product-market fit, meaningful revenue growth, and efficient operations before committing Series A capital. That has made the journey between seed and Series A longer and more demanding.

Black founders who raise only enough money to survive often find themselves trapped in a cycle of continuous fundraising. Instead of focusing on customers, product development and hiring, they spend valuable months chasing additional capital just to extend their runway.

In an AI-driven market where product cycles move faster than ever, that lost time can determine whether a startup becomes a category leader or gets left behind.

Oversubscribed seed rounds are a competitive advantage

This is why oversubscribed seed rounds are taking on new importance for Black founders. Traditionally, oversubscription was viewed primarily as a signal of investor demand. Today, it is becoming a strategic advantage.

Additional capital gives Black founders flexibility to weather slower fundraising markets, invest aggressively when opportunities emerge, and continue executing without returning to investors every few months. It also allows founders to pursue growth intentionally rather than reactively.

Capital efficiency remains important, but efficiency is most valuable when paired with enough capital to execute.

The AI economy requires longer vision

The venture industry often celebrates AI for making entrepreneurship more accessible. In many ways, that’s true. The barriers to launching a company have never been lower. But lowering the cost of starting a company does not eliminate the capital required to build an enduring one.

Closing the Series A funding gap is therefore not simply about increasing investment in Black founders. It’s about ensuring founders have enough money to reach the milestones that unlock future institutional capital. That鈥檚 how you create more Black unicorns.

For Black founders, the conversation should no longer focus solely on access to capital. It should focus on whether they have enough capital to compete. In the AI economy, the Black-led companies that endure won’t simply be those that build the fastest, they will be the ones with the resources to keep building long enough to win.

and are the co-founders of (Black Ops VC), an early-stage venture capital firm. Norman is a managing partner at Black Ops VC. He is also the CEO of , an AI-powered market research platform used by industry giants such as and that鈥檚 designed for the media and entertainment spaces to gather audience feedback on video content, and a partner at , an accelerator that provides intense programming, resources and capital to overlooked founders.

Along with serving as general partner at Black Ops VC, Green is the founder and CEO of , an AI-powered CRM and inventory management platform specifically designed for art galleries, dealers, auction houses and collectors.听

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Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital鈥檚 broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm鈥檚 affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with 小蓝视频色情网页版 News, Docter also discussed how AI is reshaping SaaS and why he doesn鈥檛 believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

小蓝视频色情网页版 News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don鈥檛 get at any other VC firm. I鈥檓 using my words carefully because I鈥檓 not saying we鈥檙e better. I鈥檓 just saying we鈥檙e unique.

That unique network is that we have access to network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what鈥檚 going on in the world and understand technology and how it鈥檚 being used. What do Fortune 500 companies want or need? What is asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

classically said, 鈥淚nvest in what you know.鈥� The way I look at it is that we鈥檙e trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that鈥檚 also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there鈥檚 a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn鈥檛 changed from how we鈥檝e always thought about it and how venture capital always thinks about it. First and foremost, you鈥檙e really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It鈥檚 not purely about the technical capability of the founders. There鈥檚 definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder鈥檚 ability to be agile 鈥� to understand when they鈥檙e wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that鈥檚 qualitative. It鈥檚 EQ more than IQ, but a lot of times that determines success. I don鈥檛 think this AI era has changed that. That鈥檚 consistently true.

The answer to the second question is even harder. How do you know if you鈥檙e betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It鈥檚 really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don鈥檛 overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, 鈥淧lay nice but win.鈥� We always try to play nice but win.

It takes a village for these things to work, so it鈥檚 important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the 鈥渟leeping giants鈥� thesis suggests the second wave 鈥� the companies with the foundational architecture in place when a catalyst like generative AI hits 鈥� may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation 鈥� which means it鈥檚 creating a brand-new category of business or software product that doesn鈥檛 exist today and is going to be huge 鈥� or category disruption, meaning there鈥檚 already a very large category that exists and I鈥檓 going to disrupt it with my technology. I鈥檓 doing something much better, faster, cheaper or stronger.

It鈥檚 important to have a sense of whether a company is doing category disruption or category creation. If you鈥檙e doing category creation, being first means you have to educate everybody. It鈥檚 a heavy lift. It鈥檚 a daunting amount of work, capital and effort that goes into explaining something that doesn鈥檛 currently exist and why it鈥檚 going to be needed in the future.

A lot of times, first-mover advantage isn鈥檛 an advantage there. Category creation is often where the second, third or fourth company hasn鈥檛 had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there鈥檚 value in first-mover advantage. You鈥檙e disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There鈥檚 a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It鈥檚 disruptive because it will change how software is built and consumed. Maybe even more importantly, it鈥檚 going to change how it鈥檚 priced. The per-seat pricing model is probably outdated and going to die. It鈥檚 going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don鈥檛 believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They鈥檙e going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They鈥檙e not going to go away.

How they charge and price might be different, but they鈥檙e still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows 1, and .

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they鈥檝e sold to for years and years and have long-standing relationships with. If 鈥� and it鈥檚 a big if 鈥� they understand how to embrace the AI transformation that鈥檚 going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don鈥檛 make the turn. But is that any different from any other technological or industrial revolution? It鈥檚 always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren鈥檛.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: 鈥淲hat is your distribution strategy?鈥� That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it鈥檚 clear that some SaaS companies won’t be able to transform themselves organically. They鈥檙e going to need to undergo an inorganic transformation, meaning they鈥檒l have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you鈥檙e trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year 鈥� including massive liquidity events like , and 鈥� right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I鈥檇 love to say we saw it all coming, but the reality is we can鈥檛 time the market. It just doesn鈥檛 work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, and .

We just try to stay really focused on backing great founders with deeply technical ideas. We鈥檙e investing early and know that sometimes it can take years for the market to fully catch up to what鈥檚 being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You鈥檝e talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Docter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is 鈥淚s their revenue durable?鈥� Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we鈥檙e also seeing is a huge shift away from recurring revenue to something I鈥檓 calling听 鈥渞e-occuring鈥� revenue. I know that鈥檚 not really a word. What I mean by 鈥渞e-occuring鈥� is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say 鈥渨e got our first deal with in October, and they did a second deal with us in January, and we already did our third deal in March鈥� is very powerful.

Given DTC鈥檚 unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Docter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. 鈥淵ou don鈥檛 get what you don鈥檛 ask for.鈥� I know that鈥檚 an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don鈥檛 be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it鈥檚 management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

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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.

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

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

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

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

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

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

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

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

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

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

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

Methodology

We tracked the largest announced rounds in the 小蓝视频色情网页版 database that were raised by U.S.-based companies for the period of July 11-17. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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For this matchup, 鈥溌ooooooooool!鈥� is really the only acceptable way to announce a new score. And if you can make that a good 21 seconds in one breath, all the better.

Here at 小蓝视频色情网页版 News, meanwhile, we鈥檝e been prepping for the final in a less vocally demanding but much more data-intense manner. Since both contenders are far more famous for soccer than accomplishments in the startup realm, we figured a small step to rectify that was in order.

To do this, we put together a snapshot of recent startup funding tallies and trends for both Spain and Argentina. As you鈥檒l see, neither accounts for a particularly large share of global or even regional investment. Both however, have an intriguing pipeline of recently funded companies.

Argentina

We鈥檒l start with our second World Cup-related profile of Argentina. After it won the final in 2022, we wrote a venture funding-themed story calling the country鈥檚 startup scene 鈥渟mall, scrappy and sometimes very successful.鈥�

Four years later, that description still holds. Argentinian startups typically pull in a few hundred million dollars in venture funding annually. Investment is, however, lower than for Brazil and Mexico, the two most populous Latin American nations, which commonly lead in funding.

Argentina鈥檚 startup ecosystem has also delivered some big hits over the years. The most famous Argentine-founded internet company 鈥� online marketplace 听 鈥� commands a market cap around $94 billion on . (It鈥檚 currently headquartered in Uruguay but traces its roots to a Buenos Aires garage.)

More recently, Buenos Aires-based fintech has been making waves in the regional startup scene. It鈥檚 raised $1.1 billion in known funding to date, including a $195 million March financing.

Others that have raised good-sized rounds this year are also in the fintech space, including:

• , a payments infrastructure startup, closed on a $55 million Series C round co-led by and .
• , a provider of payments and collections infrastructure, secured $27 million in Series B funding in February.

So far, 2026 is shaping up as a strong year for funding, with investment already ahead of last year鈥檚 total. Funding tends to fluctuate quite a bit from year to year as the presence or absence of a single large round or two can heavily skew the totals.

Spain

Oddsmakers say Spain is the favorite going into the final. However, it鈥檚 well known that often the underdog also prevails. That was the lesson from Spain鈥檚 2:0 defeat of favorite France this week.

But while it may have prevailed over France in soccer, Spain continues to lag in venture funding. So far in 2026, Spanish startups have raised less than $2 billion in funding across stages, which is roughly one-third France鈥檚 total for the same period.

While not large, Spain鈥檚 funded startup pipeline is not lacking in pizazz. Take this year鈥檚 largest funding recipient 鈥� 鈥� which closed a $206 million Series C in March. Its anything-but-modest mission is to be a 鈥済lobal space transportation service provider to support cargo and human spaceflight missions to the Moon and Mars.鈥�

Other standouts among the bigger rounds this year include:

• , an AI-enabled HR and payroll platform, scooped up $150 million in Series D funding at a $2.5 billion valuation in June. To date, the Barcelona-based company has raised over $350 million in equity funding.
• 听, a Madrid startup focused on infrastructure for near space, space tourism and aerospace data, closed on $140 million in Series D funding in May.
• , a Madrid-based developer of AI tools for analyzing geospatial data, picked up $130 million in Series B funding in April.

Overall funding to Spanish startups is also trending higher, with 2026 on track for a year-over-year gain. For the past few years, annual Spanish startup funding has ranged between $1.8 billion and $2.8 billion, as charted below.

Rooting for the underdog

While both Spain and Argentina have a long track record of soccer success, a case could be made that both are underdogs in the startup space. It鈥檚 a familiar situation for secondary hubs in the current AI-driven investment cycle. Capital has been concentrating even more heavily in Silicon Valley and other leading venture hubs.

Given all the follow-on effects a successful startup can have on its region, it鈥檇 be encouraging to see investors spreading their bets more broadly across a wider geography. Spain and Argentina have already proven they have what it takes to prevail in one very competitive arena. Given the capital and opportunity, there鈥檚 no reason to doubt their abilities in the venture-backed startup game either.

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Everywhere you look, venture headlines imply that seed rounds have meaningfully changed shape.

raised $1 billion for a company that didn’t exist a week earlier. launched with $6.2 billion out the gate. hit $475 million two months after founding.

It’s easy to read those headlines and conclude the venture model has been rewritten, that AI is a once-in-a-generation opportunity requiring once-in-a-generation capital.

We disagree. And so does the data.

The biotech parallel

At , we鈥檝e built deep domain expertise in biotech, the sector with the longest history of mega first rounds in venture.

Biotech mega-seeds are common because the science requires it, you can’t run a Phase 1 trial on $3 million, but the return profile is often humbling. Large first rounds in biotech have produced a handful of strong outcomes for first-check investors 鈥� and a very long tail of modest ones. Our experience with this trend in biotech motivated us to compile a dataset and pressure-test our intuition more broadly.

We pulled every publicly available $100 million-plus first round we could find over the last 15 years (roughly 200 deals) and found that only 20% had recorded exits. Of those, only a few delivered what we’d call a venture-like return: 10x MOIC or better for the first-round investor. In other words, approximately 1% of companies that publicly raised $100 million or more in their first financing round generated returns that justify the asset class. Capital intensity, as it turns out, actually worked against venture outcomes.

That distribution will improve with a few well-placed AI outcomes this year. and alone will essentially double the number of outlier returns in this data set when they exit. But even there, the return math is nuanced for first round investors. According to reports, first-round investors are looking at 30-40x returns at OpenAI鈥檚 projected IPO valuations.

That’s a fantastic outcome, but it’s also a fraction of what early institutional investors made on the generational outcomes of prior eras.

and each turned roughly $12.5 million of their checks into around $4 billion, driving reported returns somewhere north of 300x. reportedly turned a roughly $500,000 investment in into $2.5 billion 鈥� nearly 5,000x.

These are exponentially larger outcomes. Why? The difference wasn’t a byproduct of company quality but of entry price. Those historical investors got in at a price that left room for the upside to actually compound.

The mega round is real, but not replacing the market

The number of $50 million-plus seed rounds has exploded since 2018. But traditionally sized first rounds are also growing. The headline-grabbing rounds are a small fraction of what’s actually getting funded, and an even smaller fraction of what will return venture-scale capital.

Moreover, the companies people now hold up as AI winners started small, only further reinforcing this point.

‘s first round was less than $10 million. ‘ was $2 million. ‘s was $11 million. ‘s was $25 million. Even at the frontier-model layer, ‘s first round was $5 million. Today, every one of those companies is valued north of $5 billion and generating hundreds of millions in revenue.

Cursor at less than $10 million is the more representative data point. Project Prometheus at $6.2 billion is the exception.

Capital intensity is not a moat

Raising a massive first round doesn’t inherently make a company more likely to generate venture size returns for its investors. Sometimes it’s a necessary cost of doing business, but the venture math is unforgiving.

High entry prices leave less room for the upside to accrue, regardless of the underlying opportunity. The playbook that has worked across every prior technology wave is to buy meaningful ownership in capital-efficient companies at prices that leave room for the upside.

That playbook doesn’t make for dramatic headlines in 2025. But it’s what the historical data, from Google to Uber to Cursor, consistently vindicates.

A few of today’s mega-seeded AI companies will absolutely deliver 10x-plus MOICs, just as a few winners have in every era. But the data鈥檚 been consistent for 15 years, and building a portfolio around the exceptions, rather than the pattern, is a bet with a long losing track record.

is a principal at , where she draws on a decade of infrastructure and technology investing experience as well as a systems engineering background to support exceptional entrepreneurs building the next generation of frontier technology companies. Prior, McDonald was an investor at , where she focused on growth-stage climate tech companies. She began her career in‘ power and utilities group and then at , where she developed deep expertise across energy, infrastructure and project finance.

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Related reading:

• The Largest Recent Seed Rounds Are All For AI Companies
• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• Data: The Seed Funding Boom Is Concentrating Capital In The San Francisco Bay Area
• Seed Funding Is Bigger Than Ever 鈥� And Harder To Get

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