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 兔子先生传媒 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, 兔子先生传媒 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.

Related 兔子先生传媒 queries:

Methodology

The data contained in this report comes directly from 兔子先生传媒, 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. 兔子先生传媒 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 兔子先生传媒 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. 兔子先生传媒 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. 兔子先生传媒 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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That was the intent of our latest 兔子先生传媒 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 兔子先生传媒 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 兔子先生传媒 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.

Related 兔子先生传媒 lists:听

Related reading:

• AI Seed Investors Flock To Cybersecurity听
• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically听
• AI Seed Trends: More Multimedia, Backend Automation, Agentic Security, And Yes, Robots

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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.

兔子先生传媒 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.

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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 兔子先生传媒 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 兔子先生传媒 兔子先生传媒 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 兔子先生传媒 unicorn lists:

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

Related reading:

• 兔子先生传媒 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 兔子先生传媒 兔子先生传媒 is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on 兔子先生传媒 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. 兔子先生传媒 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 兔子先生传媒 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 兔子先生传媒 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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To understand how institutional capital is navigating this gap, 兔子先生传媒 News recently interviewed , a partner at . Ranum has spent nearly 15 years at the firm, where he focuses on B2B enterprise software, security and industrial infrastructure. Prior to joining Sapphire, he spent 12 years as a product management and strategy executive at .

His recent investments include core infrastructure plays such as and , as well as the industrial AI platform .

In this e-mail interview, Ranum breaks down how the definition of net revenue retention is shifting, why he believes 2026 will see a historic run of major tech IPOs, and where real enterprise demand is materializing on the factory floor.

This interview has been edited for clarity and brevity.

兔子先生传媒 News: You鈥檝e been at Sapphire for 15 years. Right now, public market software multiples are at decade lows as Wall Street worries about AI disruption, while private AI valuations are hitting record highs. As a growth investor caught in the middle, how are you valuing companies today? Are traditional growth metrics like net revenue retention still the gold standard, or has the math completely changed?听

Ranum: The gap between public and private market signals right now is unlike anything I’ve seen. I think it creates a real opportunity for investors who can make sense of it. Public software multiples have come down hard, while private AI valuations are hitting record highs. Those two things can’t both be right indefinitely, but the fundamentals underneath are holding up. Gross margins, free cash flow, and NDR have actually improved. The market is broadly pricing in disruption risk, but the companies that are genuinely building enterprise value are still being built.

What that means for how I evaluate companies is that I’m spending more time on whether something is genuinely embedded in how enterprises work, not just whether the numbers look good today. NRR still matters. It tells you whether customers are finding real value. But it’s a lagging indicator. What tells me more is whether switching away from a product would meaningfully disrupt operations. If the answer is yes, that’s a more durable signal than any retention metric.

The current regulatory environment has essentially frozen large-scale tech M&A, and the IPO market is sluggish. If the traditional exit pathways are bottlenecked, how does that change the way you underwrite a Series B or C bet? Do companies just have to stay private and build to massive scale longer than they used to?听

Ranum: I鈥檇 push back a bit on the framing that M&A is frozen. Software M&A activity actually picked up meaningfully in 2025, with deal value rising 40% year over year to $334 billion across 678 transactions. We saw that in our own portfolio with over half a dozen acquisitions in the past six months. What鈥檚 changed is the pricing. The valuations are being reset, but the deals are getting done.

On IPOs, I believe 2026 is shaping up to be a historic year, with having gone public, having filed, and reportedly set to file soon. If they follow through, we’re looking at some of the largest IPOs ever over the next several months. That’s a remarkable moment. Below that tier, though, the picture is more nuanced. Companies that meet today’s higher bar will wait for more favorable conditions, likely into 2027 or beyond. That means you have to build accordingly, focusing on margin alongside revenue, so you have real optionality when the time comes. The secondary market also helps, giving companies and their investors more flexibility as they wait.

You used to love investing in what you called 鈥渂oring software,鈥� or tools that quietly automated mundane enterprise tasks. Today, every software company claims to be an AI company. In 2026, does traditional SaaS even exist as a viable investment category anymore, or is a software startup inherently unbackable if it isn鈥檛 AI-native from day one?

Ranum: I don鈥檛 think the narrative is AI vs. SaaS. Instead, it’s AI plus SaaS. The companies that are struggling aren’t struggling because they’re SaaS businesses. They’re struggling because investors are in a 鈥渟how me鈥� era, and they don’t have clear answers yet.

Show me the free cash flow. Show me the path to profitability. Show me how AI is actually helping you win. You can’t get a stock bump anymore just by claiming you’re integrating AI. The market wants evidence of monetization.

The way I think about it is whether a company is building something that fundamentally changes how work gets done, or just layering AI on top of a workflow that a human is still doing. We used to back systems of record and workflow companies where the human was doing all the work. Now we’re in a position where the system itself can come in and actually do some of those tasks. That’s a different category of value entirely, and it changes what we look for. The bar has moved, but the opportunity is very real for the companies that can clear it.

Your core thesis is that the LLM stack is fracturing into distinct, standalone billion-dollar layers, such as orchestration (LangChain) and identity (WorkOS). But we鈥檙e seeing a massive border war. Big model providers like OpenAI are building their own tools, and data giants like are buying up security tools. How do standalone startups protect their turf when giants encroach from both sides?

Ranum: Both fracturing and consolidation are happening simultaneously, and I think that’s actually the right way to think about it. The moat isn’t about being first in a category. It’s about becoming genuinely embedded in how enterprises work. The companies I’m most excited about are the ones capturing orchestrated workflows in which the enterprise’s actual processes run through the product. That makes them very hard to displace, regardless of what the giants are building around them.

Because of your background at SAP, you know how enterprise buyers think. Right now, CFOs are looking at massive AI pilot bills and demanding to see actual ROI. When a startup is pitching an enterprise on a software governance or security tool, how do they defend that line item to a cynical CFO before the enterprise has even fully figured out its core AI strategy?听

Ranum: What we consistently hear from buyers is that trust has become what actually separates the market. Security, governance, compliance, and auditability aren’t nice-to-haves anymore. They’re what make an AI deployment defensible when the CFO or the board asks hard questions.

And cost predictability is right alongside that. We’re in an era of greater focus on ROI, and enterprises want to know what this will cost them at scale before they commit. The vendors that can answer that question clearly are winning deals over the ones that can’t.

It feels like Silicon Valley is obsessed with the glamour of humanoid robots right now. Meanwhile, Sapphire鈥檚 big bets in this space, like Tractian, focus on practical, unglamorous industrial AI and predictive maintenance. Are humanoid robots an expensive venture capital distraction right now? Where is the actual, contract-signing enterprise demand on the factory floor today?听

Ranum: The near-term ROI story is in constrained, high-value industrial settings such as packing, picking, inspection, and maintenance. These environments have clear labor economics, manageable deployment risk, and real buying cycles. That’s where the contracts are getting signed today.

Our portfolio company Tractian is a good example of what that looks like in practice. Unplanned downtime costs the world’s 500 largest companies roughly 11% of their revenue annually, which is a massive, measurable problem.

Tractian addresses it directly by combining sensor hardware with AI that detects early warning signs of equipment failure. The value proposition is concrete before you sign the contract, and the platform gets smarter the longer you use it. That’s the kind of embedded, compounding value we look for.

The humanoid era will come, but the gradient approach beats the all-or-nothing bet for near-term value creation. Start with specific, well-defined tasks where the payoff is obvious and work from there. The market is ready for that today.

Heavy industry and manufacturing are notoriously slow to change. A startup can’t just plug a modern AI API into a 30-year-old machine on a factory floor. For founders trying to build in the industrial tech space, is the winning strategy to build entirely new autonomous hardware, or is the bigger venture opportunity in retrofitting the world’s existing infrastructure with smart software?听

Ranum: I believe the winning strategy is smart software layered on top of existing infrastructure rather than replacing it. Factories aren’t going to rip out 30-year-old machines because a startup has a better alternative. That’s just not how it works. The opportunity is in making those machines intelligent.

That said, the hardware-plus-software combination really does matter. You can’t get the data without the sensors. But the durable value is in the software layer that keeps learning over time. That’s where I鈥檓 focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft 鈥� annoying, but fixable. In robotics and industrial tech, a mistake means a factory line shutting down or a broken multimillion-dollar asset. From a venture perspective, how much harder is it to scale a robotics startup when the cost of product failure is so high in the physical world?听

Ranum: I’d actually reframe the question. The cost of failure in physical environments is what makes the value proposition defensible. When the downside of getting it wrong is measurable, the upside of getting it right is equally concrete. You can walk into a sales conversation and show a customer exactly what prevention is worth before they sign anything. That’s a different conversation than selling software, where ROI takes quarters to show up.

From a scaling perspective, the key is discipline about where you deploy first.

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Within the region, U.K. startups gained significant share in Q2, raising more than $10 billion. That marked the third-largest funding quarter for the U.K. on record, and came in at less than $500 million below its peak quarter in 2021.

European startup M&A activity also picked up in Q1 and continued that momentum in Q2, even as public-market exits stayed subdued.

Table of contents

• Large rounds drive gains
• H1 2026 up 50%
• UK momentum builds
• By stage
• Looking ahead
• Methodology
• Glossary of funding terms

Large rounds drive gains

Four companies raised venture fundings of a billion dollars or more last quarter, accounting for 25% of all startup investment in the region in Q2, 兔子先生传媒 data shows.

Those billion-dollar-plus rounds were raised by an AI-centric group: -owned AI drug developer , which was spun out of ; green steel production manufacturer ; , which is developing robots for home and industrial applications; and , an AI lab founded by former DeepMind researchers.

However, most of the growth in funding year over year and quarter over quarter was driven by rounds of $100 million and over. The majority of funding 鈥� 65% 鈥斕齱ent to a group of 42 companies that raised rounds of $100 million-plus. Sectors that stood out for these companies include听 biotech, quantum, financial services, AI labs, aerospace, semiconductor, robotics and energy.

H1 2026 up 50%

Funding to Europe-based startups in H1 was up 50% year over year to total $42 billion, 兔子先生传媒 data shows. Still, the region鈥檚 startup investment for the first half of the year remained well below the 2021 H1 peak, when VC funding in Europe totaled $60 billion.

It鈥檚 also drastically lower than the $392 billion raised in North America鈥檚 record-setting H1, with that region鈥檚 funding up 158% year over year.

Europe鈥檚 funding deal count subsided last quarter, but mostly at the seed stage. Late-stage rounds were up a bit, while early-stage deals dipped slightly year over year. (It鈥檚 worth noting, seed stage rounds are often added to the 兔子先生传媒 data set after the close of the quarter, so those numbers will increase over time.)

UK momentum builds

The United Kingdom widened its venture-funding lead last quarter, as startups based in the country raised $10.4 billion 鈥� not far from the peak in 2021 at $10.8 billion.

The region鈥檚 No. 2 startup market, Germany, trailed with $3.2 billion raised by its startups in Q2, and France followed in third place with $2.4 billion. Sweden was Europe鈥檚 fourth-largest startup market last quarter, with its companies raising $2 billion.

兔子先生传媒 data shows funding to Europe鈥檚 AI-focused companies reached more than $10 billion in Q2 鈥� the largest quarterly amount so far 鈥� but slightly below the Q1 percentage, when those companies raised more than half of the region鈥檚 startup investment.

By stage

Europe鈥檚 late-stage funding totaled $12.1 billion in Q2, up 90% year over year. Large Series C and D rounds were raised by Germany-based robotics developer Neura Robotics; Netherlands-based , which makes inspection tools for semiconductor manufacturing; U.K.-based quantum computing startup ; and Germany-based satellite launcher .

Early-stage funding reached $8.6 billion across 250-plus Europe-based startups last quarter, 兔子先生传媒 data shows. Large Series A and Series B rounds were raised by London-based Isomorphic Labs, London-based AI self-learning lab , Germany-based fusion energy company , London-based semiconductor developer , and London-based quantum processor provider .

European seed funding totaled $3.2 billion last quarter, with a billion dollars of that raised by just one company: Ineffable Intelligence.

Other large seed rounds were raised by , a London-based AI lab for science; Italy-based autonomous driving technology producer ; and Stockholm-based defense tech company .

M&A increase

While IPO activity for European startups was muted, M&A showed strong momentum following increased activity in Q1. A total of 154 Europe-based, venture-backed companies were acquired for a cumulative $11.5 billion or more in Q2, 兔子先生传媒 data shows. That includes three companies acquired for more than $1 billion each in biotech, industrial AI and micromobility.

Looking ahead

European startup investment has now steadily increased since the fourth quarter of 2024, with increased momentum in the just-ended quarter, driven by larger rounds of $100 million and over. The region鈥檚 startup ecosystem shows particular strength in deep tech and financial services as well as the formation of new AI labs, and M&A activity has fueled liquidity for the next batch of startups.

Now the question remains: Will it be enough to keep Europe competitive with the frontrunners, the U.S. and China?

Related 兔子先生传媒 queries:

Related reading:

• 兔子先生传媒 Data: Global Startup Investment Hit Record $510B In H1 2026 As AI Boom Accelerates Funding And Exits
• North American Startup Funding Shattered Records In First Half Of 2026, Driven By AI

Methodology

The data contained in this report comes directly from 兔子先生传媒, and is based on reported data. Data is as of July 6, 2026.

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

Please note that all funding values are given in U.S. dollars unless otherwise noted. 兔子先生传媒 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 兔子先生传媒 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. 兔子先生传媒 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. 兔子先生传媒 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.)

The firm says entrepreneurs starting their second or third companies have created more than $2 trillion in enterprise value across Europe, and expects the coming wave of AI exits to produce another generation of experienced founders.

鈥淭here鈥檚 beginning to be this super cycle of repeat founders in Europe,鈥� co-founder and managing partner said in an interview with 兔子先生传媒 News. He recently relocated from San Francisco back to London, where the firm has also opened a new flagship office.

Repeat founders bring not just experience, but connections and the ability to hire quickly to the table, according to Murphy.

From its new fund, Tapestry plans to invest in a similar number of companies as it did with prior funds: Around 30 companies at pre-seed or seed.

Prior fund check sizes were around $1 million but checks from the new fund will trend larger, from around $1 million to $3 million, according to the firm.

The team seeks out previous founders even before they have decided what’s next. 鈥淟et鈥檚 spend time together before you start your new company. Let鈥檚 ideate, let鈥檚 brainstorm,鈥� said Murphy. 鈥淲e鈥檙e not taking anything for that 鈥� we鈥檙e not an incubator, we鈥檙e not an accelerator.鈥�

The firm鈥檚 earlier bets include smartphone and earbud developer and AI customer service startup , which was recently acquired by 1听for $3.6 billion.

Other investments over the years include drone delivery startup and , which works to automate manufacturing. It also has a renewed focus around AI security with investments in , and .

New investors in this fund are sovereign investor , alongside pension fund and fund of fund . Notably, , CFO at , is also an investor in the fund.

鈥淚 think encouraging a vibrant boutique seed environment for funding is very important for encouraging creative new people to start interesting, different and weird businesses,鈥� said Murphy.

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

This week, most of the largest U.S. startup funding rounds centered around the sector one would suspect: artificial intelligence. This was true for the week鈥檚 largest venture financing, a $1.5 billion Series F for AI inference technology provider , as well as a majority of rounds in the Top 10. Beyond that, the next-biggest area for startup funding was biotech.

1. , $1.5B, AI inference technology: Baseten, a provider of systems software to run AI applications workloads, raised $1.5 billion in Series F funding, its fourth fundraise in 18 months. , , , and co-led the round, which set a $13 billion valuation for the San Francisco-based company.

2. , $1B, digital marketing: AppsFlyer, a San Francisco-based provider of data analytics with digital marketing as a core use case, reportedly secured more than $1 billion in a Series E funding round at a post-money valuation of $2.7 billion. Backers reportedly include , , and .

3. , $650M, AI inference technology: San Francisco-based Groq closed on $650 million in new funding led by and that it says will be used to scale its AI inference cloud technology and infrastructure. The investment comes just over six months after an acquihire-type transaction in which hired away its founder and key team members and licensed its technology.

4. , $330M, ophthalmic therapies: Ollin Biosciences, a developer of therapies for vision-threatening diseases, picked up $330 million in Series B funding. and led the financing for the Austin-based company.

5. , $320M, foundational AI: General Intuition, developer of a foundational AI model based on gameplay, secured $320 million in Series A funding at a $2.3 billion valuation. led the financing for the New York-based company, while backers including and participated.

6. , $250M, government software: Peregrine Technologies, provider of a platform used by public safety agencies and other government entities, secured $250 million in Series D financing. , , , , and led the financing, which set a $6.8 billion valuation for the San Francisco-based company.

7. (tied) , $200M, risk intelligence: Palo Alto, California-based Quantifind, developer of a risk intelligence platform for financial crime detection and national security operations, closed on $200 million in growth financing led by .

7. (tied) , $200M, foundational AI: San Francisco-based Mirendil, a frontier lab building systems that excel at AI R&D, says it raised a seed round of $200 million led by and . The startup also counts as a backer.

9. (tied) , $190M, AI infrastructure: AI networking infrastructure startup Upscale AI raised $190 million in Series A extension funding, bringing total financing to $500 million. led the round, which set a $2 billion valuation for the Santa Clara, California-based company.

9. (tied) , $190M, biotech: San Francisco-based Osanni Bio, a therapeutics platform focused on ophthalmic therapies and other treatments, secured $190 million in Series B funding led by .

Large non-US deals:

The week also brought some large European rounds:

, $569M, defense tech: Berlin-based defense tech startup Stark reportedly raised $569 million in a financing led by and .

, $546M, insurance: Paris-based health insurance startup Alan secured $460 million in new investment in primary and secondary equity led by .

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of June 18-26. 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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Globally, robotics startups have so far raised $18.8 billion in 2026, compared to $15 billion in the full year of 2025. The figure also handily surpasses the $14.1 billion raised in the peak venture funding year of 2021, and we still have more than six months of fundraising left.

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

Noteworthy recent rounds

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

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

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

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

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

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

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

Exits

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

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

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

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

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

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

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

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Related reading:

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

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