Among lead investors, topped the ranks last month, while was by far the busiest backer by deal count. The highest-spending investors for the period, meanwhile, appear to be and .

For more detail, below we ranked active investors for July by several metrics. These include most prolific venture dealmakers, most active lead backers, biggest spenders and highest-volume seed investors.

Active lead investors

We鈥檒l start with active lead investors for the month, which, as usual these days, featured a heavily AI-centric lineup of deals.

Khosla Ventures ranked as the most active lead investor in rounds of $5 million or more, with eight deals in July. The largest were a $300 million Series A for quantum computing startup and a $120 million Series C for AI-enabled legal tech provider .

took the No. 2 slot, with six lead deals, followed by , with five. Below, we charted the top eight lead investors for the month by deal count.

Busiest venture investors

The ranks looked quite different when we widened the category to include both lead and non-lead investments in rounds of $5 million or more.

By this metric, repeat frontrunner Y Combinator once again took first place, participating in at least 19 such rounds. The storied accelerator typically takes a non-lead stake in follow-on rounds for startups it incubated.

Insight Partners and Andreessen Horowitz were next on the list, with 10 deals each, followed by Khosla and , with nine each. For a bigger-picture view, below we ranked the top 18 busiest venture investors for July.

Highest spending investors

When we focus on investors who led the most expensive assortment of startup financings last month, the lineup shifts once again.

For July, Coatue ranked as the apparent highest-spending听1 lead investor, backing a $10 billion financing for 鈥� rocket company, . (It should be noted though, that Blue Origin, founded in 2000, is probably too old to be considered a startup, although it is still a private company.)

Nvidia also stepped up, backing a $5 billion financing for foundational AI startup . Index Ventures and Andreessen Horowitz ranked high as well, each leading or co-leading rounds collectively valued above $2 billion.

Below, we rank 18 of the highest-spending lead investors for the month.

Seed dealmakers

Seed dealmakers were a bit more challenging to rank for July, in part because there鈥檚 often a time delay before smaller deals enter the dataset. One thing that is apparent is that Y Combinator was the most prolific investor at this stage, while other 鈥渦sual suspects,鈥� like and , also ranked high.

Related reading:

• A Record 14 Billion-Dollar Rounds In July Pushed Venture’s Historic Run Higher

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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�

July ranked as the third-largest funding month of the year, up 10% over June, following on the heels of a record-breaking first half of 2026, when startups raised $515 billion globally.

Fourteen startups raised billion-dollar rounds in July, the highest count in a single month, though not the largest amount raised in such deals, an analysis of SA国际传媒 data shows. The tally includes nine U.S.-based companies, two each from Germany and China, and one company headquartered in Singapore.

The largest startup funding deal last month was a $10 billion investment in , the first external financing for the -founded space exploration company.

, a frontier lab founded by former Chief Scientist , reportedly raised $5 billion from . The next two largest deals were Beijing-based frontier lab 鈥檚 $3.5 billion raise after releasing its latest Kimi K3 model, and raising $2.8 billion for short-video generation.

Two Germany-based companies in defense tech also raised billion-dollar rounds: and . In the U.S., companies that raised billion-dollar-plus rounds spanned the energy, industrial robotics, AI training, security and semiconductor industries.

Funding to AI

A total of $35 billion, or around 53% of global venture funding, went to AI-focused companies in听 July. Other leading sectors were aerospace, defense and energy.

U.S.-based companies raised a total of $39 billion, or around 59% of global venture capital, last month with roughly half of the capital invested in its AI-focused companies.

Exits

July was also a robust month for startup exits, including via acquisition and public-market debuts.

Venture-backed M&A totaled more than $9 billion in July, with five companies exiting at prices听 over $1 billion, SA国际传媒 data shows. Notable acquisitions included London-based data center provider 鈥檚 roughly $1.65 billion acquisition of software layer , which was built to manage AI workflows, and in the security sector, AI-native security company 鈥檚 $1 billion acquisition of , a service to manage non-human identities.

Twelve venture-backed companies went public above $1 billion in value in July, including five from China, six U.S.-based companies, and one from Italy. The largest was Chinese chipmaker , which went public at around $85 billion and . Italy-based , an acquirer of software companies including and , went public at a value of $18.5 billion. And last-mile transportation company , founded in 2017, went public at $1.6 billion in value, raising $167 million in the process.

In closing

If the first half of 2026 established that venture has entered a new era of mega-financings, July reinforced that the trend is broadening rather than fading. Record numbers of billion-dollar rounds in both hardware and software, alongside a healthy IPO and M&A market, point to an ecosystem where capital is not only concentrating in category leaders but is also beginning to recycle through exits.

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Methodology

The data contained in this report comes directly from SA国际传媒, and is based on reported data. Data is as of Aug. 3, 2026.

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. SA国际传媒 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to SA国际传媒 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. SA国际传媒 also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. SA国际传媒 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

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

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

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Menlo Ventures XVII will invest primarily in seed and Series A companies, while Menlo Inflection IV will provide growth capital to startups at Series B and beyond. The new funds will target companies throughout the AI market, from foundational models and infrastructure to enterprise, healthcare and consumer applications.

The new capital gives the Silicon Valley firm more flexibility to back companies from their earliest days through later funding rounds that can require hundreds of millions of dollars. It also shows how important AI has become to a firm previously known for investments in companies including , and .

In recent years, has become the most prominent company in Menlo鈥檚 AI portfolio. The firm first invested in the AI model developer in 2023 and has added to its investment in later rounds. Menlo鈥檚 other AI investments include app-building platform , music-generation startup , AI model marketplace , voice productivity company , AI infrastructure companies and , robotics startup , and AI research company .

, a partner at Menlo since 2015, has played a central role in developing that strategy. He invests across AI infrastructure, developer tools and AI-native software and has led Menlo鈥檚 investments in companies including Anthropic, Lovable, OpenRouter, AI-powered software delivery platform , code security startup and legaltech startup .听

Before joining Menlo, Murphy spent 15 years as a general partner at Kleiner Perkins, where he was an observer at Google from the firm鈥檚 initial investment through its IPO, helped launch the $200 million iFund with Apple and worked on investments including DocuSign, AppDynamics, Upstart and Shazam. Earlier in his career, he held operating roles at Netboost and Sun Microsystems.

SA国际传媒 News spoke with Murphy about why AI is pushing Menlo toward larger and more concentrated investments, what the firm has learned from its relationship with Anthropic, and where he sees the next opportunities 鈥� as well as potential bottlenecks 鈥� across the AI market.

The interview has been edited for brevity and clarity.

SA国际传媒 News: Inflection IV puts Menlo in competition with some of the biggest late-stage investors in the world. How do you keep the firm鈥檚 close, founder-focused approach when you鈥檙e writing much larger checks?

Murphy: AI companies need more capital than previous generations of software companies. They鈥檙e staying private for longer, and the winners are quicker to break from the pack.
For us, a larger fund gives us the ability to partner with founders from company formation through hypergrowth. Through our venture fund, we invest in seed and Series A companies, but the inflection fund gives us the scale and flexibility to back the clear winners as they emerge.听

This was our strategy with Anthropic, Suno, Wispr, OpenRouter and Lovable.听

You鈥檝e recently invested $100 million in companies including Lovable and Suno. Is that level of concentration becoming a bigger part of Menlo鈥檚 strategy, or is it reserved for a small number of standout AI companies?

The Anthropic investment is an example of us doubling down when we had incredible conviction. Remember, we first invested in the [Series] C round, which gave us a chance to get close to the team, see how well they were executing, and understand where they were going.

When we led the [Series] D round, it was still the largest investment the firm had ever made. We learned from that experience and success, and it’s become a standard part of our approach now. Also and importantly, the market has changed.听

There’s a gold rush around later-stage AI, and the companies that break out are growing at rates we鈥檝e never seen before, at scale. These companies need capital to sustain that growth and, frankly, have earned higher private valuations given the growth rate.听

We’re changing how we invest, but overall we鈥檙e pursuing more of a barbell right now. On the later end, we’re much more aggressive, stage- and capital-wise, for the right companies.听

That said, the bar is still very high. Many AI categories are overfunded, and there is a huge amount of speculation. The winners of this era separate quickly, and we believe they will compound at unprecedented rates.听

Your relationship with and Anthropic gave Menlo an early view into where the AI market was heading. What are you seeing now that you think other investors may still be missing?

I don鈥檛 know that it’s counterintuitive, but I’d say we are moving from Phase 1 to Phase 2 of the market and are seeing an entirely different set of opportunities and challenges.听

In Phase 1, developers just picked a model to start building AI. In Phase 2, we are seeing companies get to scale using AI and looking to optimize their spend and infra choices. A whole host of companies are seeing tailwinds alongside Claude and Claude Code, such as OpenRouter, Fireworks, Modal and .听

It will be a multi-model world. One size won鈥檛 fit all, and we鈥檝e been active in that area as well, including more vertical models such as for life sciences and for robotics.听

The Anthology Fund has helped you spot promising AI companies early. As the application layer matures, what specific bottlenecks are you seeing founders run into when building enterprise-grade defensibility on top of frontier models?

The Anthology Fund has been an incredible source of deal flow and has given us a broad aperture around what areas of AI are disproportionately taking off. It’s been a great program for getting closer to a broad set of application and infrastructure companies and building relationships before deciding where to lean in.听

I wouldn鈥檛 say it’s been the key factor in identifying bottlenecks across the AI ecosystem. For sure it is part of it, but from the broad set of portfolio companies and new companies we meet, the No. 1 bottleneck has been how to take all the new code that has been written and get it into production faster, safely, and securely.听

This has created a big tailwind for companies helping with software delivery, like Harness with application and code security, like Semgrep; and code review and testing like .听

Additionally, the rise of custom models based on open-source/open-weight models has created a number of bottlenecks as companies look for compute, training, sandboxes, and more. Both development and runtime resources have become essential to accommodate this next wave, and companies like Modal and Fireworks are addressing that with their offerings and the compute capacity they鈥檝e been able to aggregate across various compute providers, including Nebius and CoreWeave.

Valuations across the AI market have risen dramatically. Which parts of the market do you think are most likely to produce strong, sustainable businesses: infrastructure,听model tools or industry-specific applications?

We鈥檝e been active across models, infrastructure, and applications. All are showing tremendous potential and tailwinds right now. At the moment, infrastructure is seeing a disproportionate spike in opportunities as enterprises and AI-native companies embrace a multi-model approach and scramble to keep up with the compute and infrastructure management needs that it requires. Coding tools are now mainstream and putting tremendous pressure on organizational processes to release software faster and more efficiently, which is leading to tailwinds for companies like Harness and Gimlet.

It’s fair to say the majority of companies are optimizing for market share right now rather than gross margin, but there are many opportunities for margin improvement over time, and this is a rare land-grab moment.

You鈥檝e backed new AI research labs before they even have a product, including . At that stage, what convinces you that a team has something truly different, and that it can compete with much larger technology companies?

As I mentioned, we believe in a multi-model world, where one size won鈥檛 fit all needs and use cases. We have an explicit strategy to gain early exposure to some of the most compelling AI research teams with distinctive techniques or capabilities, even at a very early stage. Many of these companies are raising $100 million-plus rounds, and while we occasionally lead in this category, we prefer to write smaller checks initially. This helps us build broader exposure across the category and talent pool, and then double down once we see one really taking off.

Frankly, there are too many right now, and all claim some differentiated technique or team. Of the roughly 60 model companies, we believe we鈥檝e invested in more than five of the best and expect to lean into one or two of them as they ramp.

Related SA国际传媒 queries:

Related reading:

• Anthropic Backer Menlo Ventures Raises $3B In New Funds To Back AI Startups Across Stages听

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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.

Another week, another bevy of big rounds. For this past week, the largest round was a reported $5 billion -backed financing for foundational AI unicorn , followed by a $1 billion investment in . Other sizable rounds went to companies in sectors including energy storage, health testing, nuclear power, fintech, and, yes, AI.

1.听, $5B, foundational AI: AI lab Safe Superintelligence announced a long-term with Nvidia to rapidly accelerate its growth. The deal included a $5 billion investment from the chip giant with an eye toward boosting compute resources for Safe Superintelligence, a Silicon Valley startup founded by co-founder .听

2.听, $1 billion, fusion energy: Commonwealth Fusion Systems, a startup working on fusion energy technology and developing a grid-scale fusion power plant, raised $1 billion in fresh funding from unspecified investors. The round brings the total invested to date for the Massachusetts-based company to $4 billion.

3.听, $550M, thermal energy storage: Antora Energy, a company that provides energy through thermal batteries to data centers, announced that it has closed on $550 million in a Series C funding round. and co-led the financing for the nine-year-old, San Jose-based company.

4.听, $450M, health testing: Austin-based Function, a provider of lab testing, imaging, and personal health information that markets to consumers, secured $450 million in growth financing from .听

5.听, $370M, nuclear energy: Antares, a nuclear fission energy company that develops compact microreactors for defense and space applications, raised $370 million in Series C equity funding. and led the financing for the three-year-old company, which raised $100 million in debt funding alongside the equity investment.听

6.听, $200M, AI simulation: Simile, a developer of AI tools for running simulations, said it picked up over $200 million in fresh funding at a $2 billion post-money valuation. led the round, which comes just five months after the Palo Alto-based company launched its product.

7.听, $190M, cybersecurity: Cybersecurity provider ThreatLocker closed on $190 million in Series F funding to hone its platform and expand internationally. led the round for the Orlando-based company.

8.听, $170M, fintech: New York-based CAIS, an alternative investment platform for independent financial advisors, secured $170 million in Series D financing. led the round, which set a valuation for the company of more than $2 billion.

9.听, $160M, fintech: PEX, an AI-enabled provider of prepaid and charge cards for businesses, along with tools to track finances, raised $160 in equity and debt funding, with as lead investor.

10.听, $145M, AI infrastructure: Eliyan, a developer of connectivity technology for AI infrastructure, completed its Series C with a total of $145 million at a $1 billion valuation. led the financing for the five-year-old Santa Clara, California-based company.

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and co-led the financing, which included participation from (/), , 鈥� , (chairman of ), and others. With the latest round, San Jose, California-based Antora has raised $770 million since its 2017 inception, per SA国际传媒. It raised $150 million in a Series B round in February of 2024.

The company did not reveal its valuation.

With so much data center demand amid the artificial intelligence explosion, Antora says it will use its new capital to speed up deployment of 鈥渓arge-scale鈥� projects across the country 鈥渢o meet surging energy demand.鈥�

It recently deployed what it describes as one of the world鈥檚 largest battery storage projects, a in South Dakota. The company says its tech is differentiated in that thermal batteries store low-cost electricity as heat in insulated blocks of solid carbon and 鈥渄eliver it around the clock as heat or power.鈥�

Antora says that the same factory-built modules can serve a chemical plant, a food producer, a steelmaker, a data center, or the grid, without supply- constrained critical minerals or multi-year construction timelines. As a result, it can provide 鈥渃heap, clean energy that鈥檚 fast to deploy.鈥�

The company also claims that its San Jose, California factory ranks among the country鈥檚 largest battery gigafactories.听

鈥淔rom factories to data centers, energy is the bottleneck to industrial growth,鈥� said , co-founder and CEO of Antora, in a release. 鈥淎ntora has shown we can help break that bottleneck鈥攄elivering energy fast, at massive scale, with American innovation.听

Despite surging energy demand from AI data centers, cleantech venture investment has been relatively modest in recent years, and Antora鈥檚 raise marks one of the sector鈥檚 largest this year.听

SA国际传媒 shows investors put more than $15 billion into seed- through growth-stage rounds for companies in SA国际传媒鈥檚 cleantech, EV and sustainability-focused categories in the first half of 2026. That puts this year鈥檚 funding on pace to slightly exceed the 2025 tally, though that was the lowest total in several years and well below the highs reached in 2021 and 2022.

Related SA国际传媒 query:

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• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows听

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has spent nearly two centuries adapting to successive industrial revolutions 鈥� evolving from a 19th century steel and heavy machinery company into a global leader in energy management and automation. Now, through its 1 billion Euro venture fund, , the company is betting that the next transformation will be driven by AI’s collision with the physical world, from data centers and power grids to robotics and industrial automation.

For , who joined SE Ventures in 2022 after leading corporate investments at , AI’s biggest opportunities extend well beyond software. As demand for compute strains energy infrastructure and accelerates reindustrialization, the firm is backing startups building the technologies that underpin the AI economy 鈥� investing in everything from data center infrastructure and grid resilience to robotics and industrial AI.

SA国际传媒 News spoke with Chaturvedy about where those opportunities are emerging, why energy has become AI’s defining constraint, and how industrial technology is being reshaped by the AI era. 鈥淲e were set up with the intent to figure out where the market is headed,鈥� he said.

The significance of the energy and industrial sectors has grown with AI, and that has led even traditionally tech-focused venture investors to rush into the space. 鈥淭oday, the scarce resource in this entire space is the capacity to build 鈥� building, real estate, energy, power and electrification gear,鈥� Chaturvedy said.

SE Ventures , and has notched 12 exits including its most recent, , a 3D metal printing technology acquired by Tokyo-based electronic manufacturer

The firm will often take board seats or board positions and work to bring value to its portfolio companies.

Around 80% of the startups in its portfolio have some level of commercial relationship with a business unit of Schneider Electric. Most often that鈥檚 as a partner servicing Schneider’s customers, which is the holy grail, according to Chaturvedy. Sometimes it鈥檚 as a vendor, although that remains a smaller set of use cases.

In our conversation, we spoke about power scarcity, the electrical grid, workforce training, reindustrialization and notable portfolio companies.

The interview has been edited for length and clarity.

Gen茅 Teare: Which sectors or investments are you focused on? Where there is a lot of drive or interest because of what is happening in AI?

Chaturvedy: Three things come to mind, especially in terms of the areas we invest in versus the broader construct of the market.

First, AI is getting embedded, and you need to train models, whether open source or proprietary. Model training has upleveled to inference so you need AI infrastructure. is a great example of that.

Five years out, when this CapEx cycle starts to come down and new data centers are perhaps not getting created, data center efficiency will become a hot topic. We are also investors today in a company called , which focuses on that problem. That will come three, five or seven years out. It is going to come. It is not a problem today because we are on the upswing of the CapEx cycle.

Together AI and Hammerhead AI are very interested in partnering with Schneider Electric, because Schneider Electric is a leading electrification player in the data center space. It makes a lot of gear and equipment that go into these data centers. Today, the scarce resource in this entire space is capacity to build: buildings, real estate, energy, power and electrification gear.

The other market impacted by the emergence and growth of AI is the interplay with the grid. There are more demands on the grid beyond the electrification of vehicles, and it is 100x or 1,000x bigger than what we saw with vehicles needing to get charged from inside houses. The grid could not keep up with that capacity in the past, and it certainly cannot keep up with these demands today.

More project developers are coming in and setting up renewables or other types of capacity, but again, the interplay is still with the grid. Anything that helps with grid resilience is clearly an area for us to invest in.

The third thing is the transformative impact of AI on the world of industrials. That is where we are quite excited. Robotics is one clear area where a general-purpose model can allow the same robotics hardware to do multiple different tasks that were not possible in the past, because cognition and inference were not possible at the edge before the advent of large language models.

Companies like in our portfolio 鈥斕齱hich is one of the most exciting companies at the intersection of robotics and AI 鈥� are market-leading indicators of where this world is headed.

There is also an element of using AI to deliver better use cases in the field. Companies like in our portfolio essentially capture warranty data, analyze it and feed results back to design engineers in big corporations. There are a lot of OEMs and hardware companies looking for select use cases where AI can actually be very transformative.

That is what customers are looking for: How can AI be transformative for my business? Whichever startup is working with me in that transformation journey is the startup that will move from POC to adoption overnight. That is essentially the world of successful startups.

Overlaying on top of this is a confluence that we see and watch from our vantage point. When you think about the energy efficiency that needs to happen in these industrial worlds, energy technologies and industrial technologies have to collaborate and deliver those use cases while being energy efficient. That was not the case in the past. Energy was cheaper and more readily available.

Now industrial is taking off. There is more AI adoption. The workforce is getting older, and there is no way to overnight train a workforce in America, so you have to rely on AI. You are going to consume more and more AI for industrial use cases, which was never a business imperative in the past.

This is where the worlds of enterprise and industrial are colliding very quickly in the world of AI.

Increasingly, what I hear is that the bottleneck for AI at this point is energy. Are you seeing some short-term solutions that help with this? What about longer-term technologies?

Chaturvedy: It’s very clear that from a short-term basis 鈥� and this isn’t quite an energy-related solution 鈥� it’s more about tokens. If you think about the unit economics of an AI data center, it’s the tokens. To generate a token, it costs electricity. To train your model, or infer from a model, you need a lot of tokens. The bigger the model, the bigger the data set, and the more complex the use cases, the more tokens.

Ultimately, it’s a battle of producing tokens cheaply and also consuming fewer tokens through the models that exist today. That’s where optimization is happening, but that’s more in the enterprise space: How can I write clever versions of software that allow me to do essentially that?

The longer-term solution is going to be about 鈥� actually, maybe there is a middle layer also 鈥� beyond the tokens: When I’m running my data center, can I push inference to a different point in time so I’m not consuming peak electricity rates? Can I manage my HVAC better? You need cooling systems to cool your data center environment, and there are techniques that work really well there. Schneider has also bought some assets in the past.

Then the longer-horizon cycle is really about creating new generation capacity, largely through renewables, hopefully. That’s where I think the whole renewable story, at least in the U.S., becomes very interesting going forward. Related to renewables is storage, which we haven’t touched upon, but BESS 鈥� battery energy storage systems 鈥� is another space that we look at very closely.

There is a huge discussion in Europe and in America around reindustrialization. How do you see that playing out, given the sectors you’re focused on, industrialization and energy?

Chaturvedy: I don’t think America or Europe really have a choice other than to reindustrialize, given the geopolitical situation and a variety of other factors that I’m sure you fully track as well.

We know that technologically, the U.S. has a competitive advantage. We produce great software engineers, we move fast, and innovation is the lifeblood of U.S. society. There is a lot of innovation happening here, whether it’s robotics, newer models, setting up data centers, energy generation, and so on. That’s where the U.S. is going to lead as we think about reindustrialization: training the workforce, doing things more automatically, with the holy grail being AI startups that result in lights-out manufacturing facilities.

That becomes more of a possibility now. We are never going to be able, in my opinion, in the next three to five years, to replace an aging workforce and expect them to be trained to the same level that a technician with 30 or 50 years of experience was at. But it is now possible that every blue-collar worker with AI in their hands as an assistant becomes a knowledge worker.

Earlier, we used to think about knowledge workers as IT people or white-collar jobs. I think that’s changing. Everybody will be a knowledge worker. AI will be such an equalizer in that sense. There will be different use cases in different environments, but that doesn’t change the business reality. Everybody becomes a knowledge worker.

The second thing to note is that not every job will come back. There is a reality of inflation, cost of living, and the quality of living in America that people are used to, whether it’s base pay, hazardous environments, number of shifts or what have you. As a society, we’ve made certain choices. We’ll be smart about how we leverage more AI and more robotics to get what we want, and not try to emulate other manufacturing-heavy geographies.

But as an economy, as more AI comes, we will move to a different level in terms of what constitutes the GDP of America and the goods and services underneath.

How long do you think that takes to play out?

Chaturvedy: There are certain industries where it’s already happening, data centers being at the forefront. Mainly because the need is very urgent, and there are significant dollars at play today in the data center space, where people are willing to spend the money. In a capitalistic society, everybody is going to chase money. The data center happens to be that today.

But in the next three to 10听 years, depending on the CapEx refresh cycle of different industries, we will see more greenfield projects emerge that are natively robotics-oriented and natively industrial automation-oriented, because AI has already caught up.

Right off the bat, every new factory that gets online in the next seven to 10 years will have a basic level of productivity that is way higher than a new factory set up 30, 20 or 15 years ago. The ROI from that factory would be so strong that you would have to expand more capacity there. And by the way, capacity would also be more scalable.

On reimagining or thinking through the data center stack: is there anything you want to say about that as we close out?

Chaturvedy: We specifically invest in AI for energy and industry, looking across the full stack from data infrastructure to training and inference, through to AI agents solving real-world use cases. We also consider the enabling layers around that stack, like multi-cloud, multi-LLM, cybersecurity, and data governance.

Ultimately, every industry is going to build its own version of this stack, and we believe the most compelling companies will be the ones who drive tangible outcomes in enterprise and industrial environments.

Related SA国际传媒 queries:

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

Startup investors 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 SA国际传媒 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 SA国际传媒, $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 SA国际传媒 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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Notably, the quarter also marked the busiest one for General Catalyst since 2021 in terms of investing in rounds of $5 million or above. The firm鈥檚 next-busiest fintech investing quarter in rounds of that size was the fourth quarter of 2025, when it participated in 10 raises of $5 million or above.

Overall, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year. (It鈥檚 important to note that H2 2025 marked the strongest six-month funding period for fintech startups since the second half of 2022.)

Over the past year, startup accelerator Y Combinator has routinely ranked as the most active investor in the fintech space. And overall, it was still the most active investor in the second quarter of this year, participating in 41 deals.

But this time, it ranked behind General Catalyst in terms of backing fintech rounds in the $5 million or more category. General Catalyst participated in 12 of those deals, while YC and each invested in 11.

In overall fintech dealmaking, General Catalyst still ranked far behind YC鈥檚 41, with 13 deals. participated in 12, Index Ventures in 11, and in 10.

Top lead investors at $100M or more

For megarounds 鈥� those deals of $100 million or more 鈥� we once again saw private equity firms topping the list of lead or co-lead investors. , , , and topped that list, according to SA国际传媒 data.

The largest rounds in Q2 were raised by a geographically diverse bunch of fintech startups. They include:

• Expense management startup was the fintech sector鈥檚 largest recipient of capital in the second quarter, raising a massive $750 million Series F round in June co-led by Ontario Teachers鈥� Pension Plan, Iconiq Capital and GIC that valued the company at over $50 billion post-money.
• , a London-based cross-border payments and foreign-exchange fintech majority-owned by , was a close second 鈥� landing $748 million in a private equity financing led by Centerbridge Partners in April.
• Also in April, Indian consumer lending startup raised $220 million in a Series E round co-led by , and that valued it at more than $1.5 billion.
• Paris-based insurtech landed a $545 million Series G led by Prosus that valued it at $6.2 billion.

Top fintech investors at seed

When it comes to investing in seed rounds, unsurprisingly, Y Combinator again topped the list 鈥� by far, with 33 fintech deals. Next up was with seven investments at the seed stage, and then with six.

The investor base shifted when we looked at who led or co-led post-seed rounds in the second quarter. General Catalyst topped that list, with five deals. , , , Index Ventures, and all tied with three investments each.

Related SA国际传媒 query:

Related reading:

• Fintech Funding Surges 23% In H1 2026 As Investors Concentrate Their Bets On AI And Financial Infrastructure

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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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