But startup investors haven鈥檛 abandoned the sector, either, 小蓝视频色情网页版 data shows. Instead, they鈥檙e being more selective about their bets and putting more money into companies using AI and other technology to make construction, property operations and real estate transactions faster and less expensive.

That shift shows up in both the year鈥檚 largest funding rounds and biggest acquisitions 鈥� and, notably, much of the biggest funding activity is happening outside the U.S.

The broad trend: Even before the pandemic-fueled funding peaks, proptech startups received more than double the venture funding in 2019 than in more recent years. While investors haven鈥檛 given up on proptech, funding to startups in the space remains down as interest rates hover in the .

In case you forgot, during the COVID-19 pandemic, home buyers and owners had access to 15-year mortgage interest rates as low as 2.5%. Those historically low interest rates fueled investor interest in the space, especially in the U.S.

Today, venture investors are backing startups working in areas such as AI-driven construction, property operations, underwriting and transaction infrastructure with demonstrable ROI. At the same time, more generic real estate software and later-stage companies without exceptional growth face significant funding challenges, our data shows.

And interestingly, four of the five largest deals in 2026 to date took place outside the United States.

The numbers: So far in 2026, global real estate-related startups have pulled in about $8.7 billion in seed- through growth-stage financing, per 小蓝视频色情网页版 . That compares to $24 billion in 2019, the second-highest year on record after the 2021 venture funding spike. It also compares to $12.3 billion raised in 2025. It appears that with four months left in the year, proptech funding is on pace to roughly match or slightly exceed 2025 levels.

Deal count is also down fairly significantly, with 794 deals so far this year. For context, the space saw more than 2,400 deals in 2019. Last year, the sector notched 1,446 transactions. The lower deal count signals both potentially decreased investor interest in the space and larger round sizes.

Noteworthy deals

The three largest deals in the proptech space so far took place in Europe, and two of those top deals involved companies doing work with steel.

Stockholm-based , a green steel startup, landed the largest haul in a private equity deal led by , also of Sweden. In June, the 6-year-old company raised about $1.6 billion in a transaction that made Wallenberg its majority owner.

In August, of Madrid raised $695 million in a venture round led by another Madrid-based company, , for its own green steel plant. The 3-year-old startup raised the money at a $3.1 billion valuation.

And in January, Amsterdam-based , a cloud-native hospitality management system, closed a $300 million Series D funding round at a $2.5 billion valuation. London鈥檚 led the financing for the 14-year-old company.

The only U.S. company to crack the top five when it comes to the largest deals was San Francisco-based autonomous construction tech startup , which raised $270 million in a Series B funding round in February. The financing, co-led by and , brought Bedrock鈥檚 total funding to over $350 million and valued the company at $1.75 billion.

Montreal-based AI-powered digital mortgage startup rounds out the list with a $216 million Series E raised in June at a $1.47 billion valuation.

Exits

There have been some meaningful proptech exits in 2026, although the activity is much stronger in M&A than in IPOs.

The only known significant initial public offering in the space was conducted in January by Columbia, Missouri-based , a construction-equipment rental company with a jobsite technology platform. EquipmentShare raised about $747 million in primary proceeds by pricing 30.5 million shares at $24.50. Including shares sold by existing holders, the offering totaled approximately $859 million.

Real estate-related startup M&A, however, has been robust in 2026 so far, with several of the largest transactions involving brokerage consolidation. Overall, the broad acquisition trend is centered around incumbents buying data, workflow ownership and distribution so they can build credible AI products more quickly.

The largest deal in the proptech space was $3.6 billion cash purchase of , which operated an AI-powered equipment maintenance and asset management platform, announced in May. (MaintainX had seen its valuation jump to $2.5 billion in 2025 after a $150 million Series D raise.)

There were several other large acquisitions.

• In January, completed its acquisition of in an all-stock $1.6 billion transaction that made it 鈥渢he world鈥檚 largest brokerage,鈥� according to .
• Construction tech giant announced in July that it was acquiring , a provider of aerial and ground-based reality-capture software for construction and other industries, for $845 million in cash. In a smaller deal, Procore also picked up construction AI-agent platform .
• Commercial real estate giant in August completed its $800 million cash purchase of , a housing-market data and technology provider for the homebuilding industry.
• And also in August, officially completed its $880 million acquisition of , forming a new parent entity named the Real REMAX Group.

The AI effect

AI is starting to move from the testing stage into everyday use across real estate and construction, according to a from and titled 鈥淧roptech鈥檚 Impact on Real Estate Innovation and Transformation.鈥�

The report says companies are using it to cut costs, make better decisions, and handle routine work more efficiently. Meanwhile, proptech is expanding beyond property-management software into areas such as construction, energy, infrastructure and climate technology.

Overall, proptech funding remains far below its pandemic-era highs, but the types of companies attracting money are evolving. Investors and buyers tend to favor businesses that can show they save customers time or money, particularly in construction, building operations and real estate finance. As such, the proptech sector increasingly includes companies that look quite different from those funded in years past.

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• Sector Snapshot: Real Estate Tech Funding Sees Slight Rebound, But Still Far Lower Than Peak Years
• Steel Startups Forge Ahead With More Funding
• From Batteries To Building Materials, Green Manufacturing Startups Are Raising Billions

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This month鈥檚 installment of this column is all AI, though applications for the technology range widely, from two startups that apply AI to trash or recycling, to another that promises to help people breathe and sleep better, to a company that says its AI can help architects and builders spot commercial projects before they鈥檙e even announced. Let鈥檚 jump in.

$27M to help recycling plants see what鈥檚 in trash

For decades, the recycling industry has relied on sampling and educated guesses to understand what moves through its facilities. But wants every discarded bottle, carton and wrapper to become data.

The London-based startup said last month that it has raised a 拢20.3 million ($27 million) Series B led by technology investor . The company installs AI-powered camera systems above conveyor belts in recycling plants, then uses computer vision to identify materials, products and brands in real time. Greyparrot says that data helps operators recover more valuable materials, improve sorting efficiency and comply with increasingly strict recycling regulations.

Its systems are now deployed in more than 20 countries and have analyzed more than 1 trillion waste objects, per the company. It counts large waste-processing companies such as and among its customers.

The data gathered at plants also feeds Greyparrot鈥檚 Deepnest platform, which it says consumer brands including , and use to understand what happens to their packaging after consumers throw it away, helping to inform redesigns and comply with Extended Producer Responsibility rules in places such as Canada and the EU.

The fresh funding will help expand the company’s footprint across North America and Europe and support its goal of preventing more than 1 million tons of waste by 2030.

The raise reflects growing investor interest in applying AI in the physical world rather than behind computer screens. Companies in the physical AI sector raised nearly $47.3 billion in the first half of 2026, 小蓝视频色情网页版 data shows, up nearly 80% year over year, as startups increasingly apply artificial intelligence to settings such as factories, recycling plants and other 3D environments.

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$17M to help robots navigate where GPS can’t

For a recently funded robotics company, the next frontier for physical AI is underground: in mines, tunnels and other places where GPS doesn’t work.

Australian startup said last month that it secured $17 million in new funding. That includes a $10 million equity round backed by , , , and as well as a $7 million venture debt facility from the country鈥檚 National Reconstruction Fund Corp. The company plans to scale manufacturing and expand its AI autonomy and cloud mapping platforms.

Emesent is best known for Hovermap, a LiDAR scanning payload that mounts to drones, vehicles or backpacks to create detailed 3D maps of mines, industrial sites and other hazardous environments. But increasingly, the company’s focus is software. Its Cortex AI platform enables robots to navigate autonomously in environments without GPS, while its Aura cloud platform processes and analyzes the resulting spatial data.

The company says its technology is already deployed at more than 200 mine sites worldwide and that it is expanding into the defense, critical infrastructure and construction sectors.

Its raise is another example of increased interest and investment in physical AI. As industries grapple with labor shortages and increasingly dangerous operating environments, startups that combine robotics, computer vision and autonomy are attracting fresh capital to automate work that’s difficult, dirty or unsafe for humans.

Robotics investment funding overall has been on a tear in recent quarters. Startups in the category raised $15 billion globally in 2025 鈥� an annual record that has already been eclipsed partway through 2026 鈥斕±妒悠瞪橥嘲� data shows.

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$12.25M for AI that treats congestion with sound

We鈥檝e covered AI that can do dirty work like help sort through trash or navigate underground mines. What about AI to help people breathe?

San Francisco-based medtech startup recently raised an oversubscribed $12.25 million Series A led by . The company develops FDA-cleared, noninvasive devices that it says use AI and acoustic resonance therapy to treat congestion and improve sleep without drugs.

The company said its flagship Sonu band personalizes sound waves based on a user’s facial anatomy to open nasal passages, while its newer Spatial Sleep device aims to help users fall asleep faster and stay asleep longer.

The raise comes as investors continue to back AI-powered medical devices that combine software with regulated hardware. Companies that intersect 小蓝视频色情网页版鈥檚 AI and medical devices industries raised more than $629 million in the first half of this year, our data shows, up about 33% year over year.

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$3.85M to turn unrecyclable trash into fuel

Trash and recycling emerged as an unexpected theme in this month鈥檚 column. While Greyparrot helps companies better understand what鈥檚 in landfills and recycling plants, another recently funded company, , says it鈥檚 working to turn unrecyclable garbage into industrial fuel.

The Las Vegas-based company last month announced a $3.85 million round co-led by and to commercialize technology that converts hard-to-recycle plastics and other waste into industrial fuel. The startup says its engineered fuel can replace coal in cement, steel and other heavy industries without requiring factories to modify existing equipment. The new funding will help it build its first commercial U.S. biofuel facility outside Las Vegas.

Global venture investment into cleantech-related startups has been steady but not record-breaking in recent years, 小蓝视频色情网页版 data shows. Around $15 billion went into rounds for companies in 小蓝视频色情网页版鈥檚 cleantech-, EV- and sustainability-focused categories in the first half of 2026, putting this year鈥檚 funding on track to slightly exceed the 2025 tally, which was the lowest in several years.

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$3.5M to predict construction projects before they’re announced

The biggest construction opportunities often surface months before the first request for proposals. promises to use AI to spot them first.

The New York-based startup last month raised $3.5 million in seed funding from 鈥檚 accelerator, , and others to build an AI platform for architecture, engineering and construction firms. The startup promises to give such companies an edge over their competitors by helping them discover projects earlier and identify the best path to winning them. Instead of searching public bid databases, Cascade says its tech can analyze signals such as bond filings, property transactions, capital budgets and meeting minutes to identify projects while they’re still taking shape.

Overall funding to real estate-related startups has trended higher in recent quarters, and the sector emerged as a bright spot for seed funding in the first half of 2026, an analysis of 小蓝视频色情网页版 data shows. Other seed-funded real estate startups this year have spanned areas ranging from streamlining planning and building processes to real estate investing to reducing power consumption in buildings.

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Correction: The article was updated to reflect Forge Industries’ correct headquarters location.

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• VCs Pour Billions Into Physical AI As The Next Wave Of AI Investing Takes Shape
• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows

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Before founding , an AI startup for the construction industry, spent more than a decade working in construction. She grew up in a low-income household in Austria, where her father was a carpenter. When the family needed extra money he would take his children to job sites, and Buchner began working as a carpenter as a teenager.

鈥淚鈥檓 a blue-collar worker by background,鈥� she explained.

Buchner never really left the industry. She worked her way up and eventually became a general contractor, managing large construction projects in Europe. But her career took a turn after a worker died on one of the sites she was running.

Experiencing the fatality changed the way she thought about her work.

鈥淥nce you realize that people die under your management, you just look at life differently,鈥� Buchner said.

She decided to build a health and safety app for construction workers. That company was separate from (and before) Trunk Tools, but the project pulled Buchner into software and eventually led her to pursue a Ph.D. focused on data science in construction and early artificial intelligence.

While doing that research, she realized something that would later become central to Trunk Tools: Construction companies produce huge amounts of information, but much of it is fragmented across different systems and buried in documents, making it difficult to analyze.

鈥淭he data was there,鈥� Buchner said, but it was 鈥渟o messy and so unstructured鈥� that traditional data analytics could only go so far.

Turning construction data into an AI business

Buchner saw an opportunity to make that information more useful. She also decided she wanted to build the company in the U.S. rather than Europe.

In 2019, she moved to California to attend . She founded Trunk Tools in 2021 while she was still in business school, graduating in 2022. The early years were messy, as they often are for startups, and Buchner said the company did not really find its first meaningful product and market until 2023.

By then, advances in large language models were beginning to make it more practical to work with large quantities of unstructured information.

That was particularly relevant in construction. Buchner estimates that an average construction site can involve 3 million to 4 million pages of documentation spread across numerous systems. Trunk Tools integrates with those systems and creates a layer on top that can analyze the information and use it to carry out various tasks.

The company sells primarily to general contractors and subcontractors, and has also begun working with some project owners.

Trunk Tools鈥� AI agents can review contracts and construction drawings, flag inconsistencies and help with specifications, submittals and bidding, and pass that information among different workflows. For example, one agent can uncover an issue in a drawing and give that information to another agent to figure out next steps and how any changes might affect other parts of a project.

Such an ability to connect information across a project is important because construction projects are unusually complex, Buchner said.

鈥淔or a human brain, it鈥檚 impossible to calculate the second- and third-order effects of changes because of how complex and connected it is,鈥� she said.

In one case, Buchner said, a project owner requested a change that a construction company might otherwise have simply begun implementing. Trunk Tools calculated that the change would add nearly $4 million to a roughly $100 million project. Once the contractor presented the cost to the owner, the owner decided not to proceed.

In another instance, a worker got chemicals in their eye, and someone nearby used a Trunk Tools agent to ask what to do. The system returned step-by-step instructions for responding to the exposure.

鈥淲ith stuff like that, you have to act fast,鈥� Buchner said, calling it a particularly meaningful example for the company.

From early product to rapid growth

New York-based Trunk Tools now has more than 100 employees. Buchner is a solo founder and said her Ph.D. gave her enough of a technical background that she did not feel she needed to seek out a technical co-founder. She did, however, hire experienced AI engineers early and has since built out a larger technical leadership team.

The startup has also expanded the number of products it offers. Buchner said Trunk Tools had two live AI agents about a year ago and now has more than 10. The pace of development has become fast enough, she said, that the company sometimes struggles to train its own sales and customer-success teams on new products as quickly as they are being released.

The company has raised about $70 million in funding from investors such as , and . Revenue grew fourfold last year and is on track to grow roughly 3.5x this year, according to Buchner. Its most recent funding round was a $40 million Series B in 2025 that valued Trunk Tools at $325 million.

An unconventional founder advantage

Fundraising was more difficult at the very beginning, Buchner shared, in part because she did not fit the mold investors often associate with startup founders.

鈥淚 think it was hard very early on because I might not look like the traditional tech founder,鈥� she said.

But after Trunk Tools gained traction, Buchner believes her industry background became an advantage. She had spent years doing the work her customers do and could answer detailed questions about construction in investor meetings.

鈥淚 walk into a room full of investors, and I know that 鈥� I鈥檓 definitely the smartest person in the room when it comes to my industry,鈥� she said.

Bringing AI to a tech-wary industry

Historically, the construction sector has been slower than others such as finance and legal services to adopt new software or technologies. One reason for this, Buchner believes, is that there is often a long distance between the person buying a product and the person expected to use it.

Executives may make purchasing decisions while sitting in an office. Meanwhile, the actual users are often out in the field working on job sites and under constant pressure to keep projects moving. Contractors also typically operate on relatively thin margins. So if a bad decision is made around purchasing technology, the potential cost is greater.

Still, Buchner believes that AI is being adopted faster than other technologies she has seen enter the construction industry.

Over time, Trunk Tools has also learned that simply selling the software is not always enough. The company now provides training and change-management services for customers trying to roll out AI across large organizations.

It鈥檚 another area where having an industry background makes a difference, in Buchner鈥檚 view. Rather than sticking to a model of what a conventional software business is supposed to look like, she said, she is more focused on what construction companies actually need.

鈥淚鈥檓 just thinking about solving a problem,鈥� she said.

Looking back, Buchner said she always expected to start a business someday. For years, she assumed it would be her own construction company.

Instead, the carpenter-turned-contractor ended up building a software company for the industry she had already spent much of her career working in.

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That was the intent of our latest 小蓝视频色情网页版 News data dive into seed-stage trends. For this installment, we focused on mid-sized rounds of between $5 million and $10 million, analyzing around 800 global seed financings that closed this year.听

Why this range? In a startup investment climate characterized by the ascendance of megarounds, the idea was to focus on rounds more representative of the classic seed deal: a risky bet on unproven founders, technologies or business models.

Using this methodology we identified multiple popular investment themes and zeroed in on five. The first 鈥� cybersecurity 鈥� we tackled in a separate piece. Here we delve into the other four: proptech, cancer therapeutics, space tech and robotics.

No. 1: Proptech

Real estate is the world鈥檚 most valuable asset class, providing startups a huge and varied addressable market. By one estimate a few years ago, real estate accounted for a staggering two-thirds of global net worth.

Given the size of the space, actual venture investment tied to real estate and construction looks comparatively meager. Last year, per 小蓝视频色情网页版 analysis, proptech startup investment totaled just over $10 billion, far below peaks hit several years ago.

Seed investors seem to believe there鈥檚 a good case for startup driven growth ahead. In particular, they鈥檙e funding a lot of rounds in the $5 million to $10 million range for companies looking to add efficiencies to the planning and building process, streamline rental operations, reduce building power consumption, and more.

To illustrate, below we put together a sample set of 15 companies that closed seed rounds in our target range this year:

A few standouts include , an AI-powered home management system, , a developer of software to support real estate decarbonization, and , an AI-enabled construction supply chain platform.听

No. 2: Cancer treatments

Startup founders don鈥檛 need persuasive superpowers to convince investors that cancer is a sufficiently serious area to address. Today, it鈥檚 that 39% of Americans will be diagnosed with cancer at some point in their lives. Cancer also ranks as the second leading , behind heart disease.听

Seed-stage companies aren鈥檛 expected to bring down numbers in the near term, but as they progress, it鈥檚 increasingly plausible. That鈥檚 the apparent mindset for investors at this stage, who鈥檝e backed a good-sized number of rounds in the $5 million to $10 million range this year for developers of cancer therapeutics and diagnostics, charted below:

Three California startups secured $10 million, the largest financing in our sample set. They include: , which is working on AI-driven discovery of undetected cancer targets, , a developer of targeted therapies for solid tumors, and , which is focused on cancer diagnostics.

No. 3: Space and satellite tech

This year鈥檚 most attention-getting event in space tech finance was obviously the IPO of sector pioneer . But while that debut may have dominated headlines, quite a few smaller, earlier, lower-profile deals were also getting done.

Per 小蓝视频色情网页版 data, space tech was a popular area for seed financings in the $5 million to $10 million range. To illustrate, below we put together a sample set of nine such companies that raised rounds this year:

The largest fundraiser in our target range was , which is focused on developing reusable satellites. Next was , focused, as its name implies, on in-space propulsion systems, followed by , developer of an ML-native operations platform for satellite fleets.

No. 4: Robotics

Robotics is a perennial favorite in our seed-funding data dives, including the last one, focused on AI. This time, the sector made the ranking again, thanks to a bevy of intriguing seed-stage companies that met our parameters.

Turns out, you can jumpstart some highly ambitious ventures on a $5 million to $10 million seed round. To illustrate, below we aggregated a sample of 18 funded this year:

Robotics was also the most geographically dispersed sector in our lineup, with startups hailing from Asia, North America, Europe and Australia. A few that stood out include , a developer of what it calls 鈥渋ntimacy robots,鈥� , a maker of autonomous underwater robots, and , focused on robots for greenhouse harvesting.

Big picture: Midsized seed rounds for outsized ambitions

Overall, seed funding trends reviewed above may tell us more about the kinds of companies investors are willing to bet on than about the sectors attracting interest, which are already well-established.

Clearly, startup investors still believe that small, modestly funded teams with grand missions remain a worthwhile and viable wager. That鈥檚 particularly encouraging these days, when the venture and seed financings we most commonly hear about tend to be the largest ones.

That鈥檚 not to diss large rounds. Startups that are led by prominent serial entrepreneurs or have established traction hold obvious appeal, even at pricier terms. But for those of us who enjoy rooting for the underdog, it鈥檚 encouraging to see lower-profile companies with outsized ambitions are still in the game.

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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.

小蓝视频色情网页版 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.

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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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Related reading:

• 小蓝视频色情网页版 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• 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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Ten of those companies were AI labs, collectively valued at $65 billion. The most well-known was Beijing-based open source model developer 鈥� at $50 billion, the highest valued new unicorn to join the Unicorn Board this year.

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

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

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

Big exits remove a trillion

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

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

New unicorns in June

Here are June’s new unicorn companies:

AI labs

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

Robotics

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

AI infrastructure

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

Defense

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

Proptech

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

Data analytics

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

Biotechnology

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

Materials

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

Cryptocurrency

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

Image generation

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

Financial services

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

Semiconductor

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

Aerospace

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

E-commerce

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

AI healthcare

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

Transportation

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

Related 小蓝视频色情网页版 unicorn lists:

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

Related reading:

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

Methodology

The 小蓝视频色情网页版 Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 小蓝视频色情网页版 data. New companies are as they reach the $1 billion valuation mark as part of a funding round.

The unicorn board does not reflect internal company valuations 鈥� such as those set via a 409a process for employee stock options 鈥� as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter.

Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to .

Exits analyzed here only include the first time a company exits.

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

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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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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.

U.S. startups announced sizable funding rounds at a steady clip during a truncated holiday week, with energy and AI leading the way.

Houston-based energy startup secured the biggest round, a $1.75 billion strategic financing, followed by , a developer of infrastructure for companies running open source AI models, and , a provider of compliance tools for enterprises.

Other big rounds were for companies focused on therapeutics, homebuilding, and even lacrosse.

1. , $1.75B, energy: Houston-based Joulent, a provider of energy infrastructure focused on the demands of artificial intelligence and other compute-intensive industries, raised $1.75 billion in a strategic investment backed by through its arm.

2. , $800M, AI infrastructure: Together AI, developer of an infrastructure layer for companies running open source AI models, secured $800 million in Series C financing. led the round, which set an $8.3 billion post-money valuation for the San Francisco-based startup.

3. , $180M, compliance: LeapXpert, a provider of tools for tracking enterprise communications for compliance needs, closed on $180 million in growth financing. led the financing for the New York-based company.

4. , $135M, AI software development: Redwood City, California-based 8090 Solutions, developer of a platform for building enterprise software with coordinated AI agents under human-led oversight,听 picked up $135 million in a round led by 1. The company, founded in 2024, counts prominent startup investor as co-founder and CEO.

5. , $126M, biotech: Boston-based Beeline Medicines, a startup focused on precision therapies for autoimmune and inflammatory diseases, secured $126 million in Series A extension funding backed by , and . The financing follows a previously disclosed $300 million Series A.

6. (tied) , $100 million, professional sports: The Premier Lacrosse League, a men’s professional lacrosse league in North America, closed a $100 million Series E financing round led by and . New York-based PLL said the deal represents the largest capital raise in the history of professional lacrosse.

6. (tied) , $100M, video-based AI: Twelve Labs, a San Francisco-based startup developing AI systems trained on video archives, raised $100 million in a Series B round co-led by and .

8. , $95M, AI for homebuilding: Higharc, a developer of AI-enabled tools for designing homes and managing workflows around homebuilding, picked up $95 million in Series C funding. led the financing for the Durham, North Carolina-based company.

9. , $85M, biotech: Cambridge, Massachusetts-based Flare Therapeutics, a startup targeting transcription factors to develop treatments for cancer and other ailments, raised $85 million in Series C funding led by and .

10. , $65M, AI privacy: Venice, developer of a platform enabling private, surveillance-free access to a wide array of AI models, secured $65 million in Series A funding led by . The round set a $1 billion valuation for the 2-year-old Sheridan, Wyoming-based startup.

Methodology

We tracked the largest announced rounds in the 小蓝视频色情网页版 database that were raised by U.S.-based companies for the period of June 27-July 2. 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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Existing backer (KIP) led the financing, which is described as a bridge round. (KBIC) also participated. The raise brings Santa Fe Springs, California-based Xpanner鈥檚 total funding to $38 million since its 2020 inception.

Xpanner turns construction equipment that customers already own into automated assets 鈥渨ithout replacing a single machine,鈥� according to , the company鈥檚 co-founder and CEO.听

鈥淭here鈥檚 no upfront investment, no rip-and-replace,鈥� Lee added. 鈥淟ike a smartphone gaining new capabilities through app updates, customers expand their automation through simple software updates.鈥�

The benefits for its customers include significant cost savings and shorter project durations, according to Lee.

Originally founded in South Korea, Xpanner moved its headquarters to the U.S. in 2023. Today, , , and are among its customers.听

Notably, all of Xpanner鈥檚 co-founders have deep industry experience. Lee spent two decades in executive positions at and , driving unmanned construction projects and corporate venture initiatives in the heavy equipment world. CFO spent over 12 years working in heavy equipment at Bobcat, followed by eight years in venture capital at Korea鈥檚 largest commercial bank. CTO led robotics and automation at for 20 years, becoming the first in the industry to commercialize semi-automation features for construction machinery.

Growth and a path to profitability

Xpanner is refreshingly transparent about its financials. The company grew revenue from $3 million in 2023 to $7 million in 2024 to $21 million in 2025, according to Park. It saw $8 million in revenue and $1 million in EBIT (earnings before interest and taxes) in the first quarter of 2026.

The startup is targeting $60 million in ARR by year鈥檚 end.

Impressively, the company says it maintains a gross margin above 80%, thanks mostly to its subscription-based AaaS model. It achieved monthly break-even in 2025, and Park said Xpanner is on track for full-year profitability this year.

鈥淥nce hardware is deployed, incremental subscription and service revenue flows at near-zero marginal cost,鈥� he said.听

The company plans to use its new capital in part to strengthen its development capabilities by advancing its next-generation physical AI hardware and software platform, deepening its core component engineering, and expanding its data and AI infrastructure.听

Some of its customers are still on a perpetual modular model, which includes the one-time purchase of its X1 Kit hardware paired with its software. Looking ahead, Xpanner expects to be fully on its subscription model by the end of the year.听

The company is also actively expanding into adjacent verticals, including battery energy storage systems (BESS) and AI data center construction.

鈥楽trong gross margins, near-zero churn鈥�

, managing director at KIP, told 小蓝视频色情网页版 News via email that his firm was impressed by Xpanner鈥檚 commercial traction and unit economics.

鈥淪trong gross margins, near-zero churn, and rapid account expansion are signals that the value proposition is real and not pilot-driven,鈥� he said.

director at KBIC, believes that most construction automation companies hit a scalability wall because they automate entire machines end-to-end. However, he said that since Xpanner’s task-specific approach scales through software rather than hardware redesign, the company 鈥渃an expand wallet share inside accounts without proportional cost.鈥�

鈥淭hat’s a software-economics business operating in a hardware-dominated market, and it’s rare,鈥� he wrote via e-mail.

Physical AI funding smashes records

Xpanner sits at the intersection of two sectors that have received strong interest from investors in recent years.

Startups working on physical AI 鈥� real-world applications of artificial intelligence, including technologies such as automated hardware and robotics 鈥� have already hauled in more than $37 billion in venture funding globally this year, , shattering the full-year record of $21 billion set in both 2025 and 2021.

At the same time, venture investment in real estate and property-related startups rebounded last year, largely driven by funding to AI-centric companies.

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