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America is building walls around drones and robots. China’s scale may just walk around them

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China robotics scale

A summer of restrictions

Washington spent July and August drawing fresh lines around foreign robotics. New tariffs on imported drones and their components are set to land in September, with another wave of component duties following in 2027. Both moves cite national-security concerns. The FCC’s Covered List — launched in 2021 to target telecom and surveillance gear from Huawei, ZTE and Hikvision — has since expanded to cover foreign-made drones and, most recently, advanced robotic devices.

Here’s the tension: the restrictions arrive at a moment when Chinese manufacturers dominate both drones and humanoid robots, often at price points Western rivals can’t touch.

The scale gap nobody can sanction away

Robotics isn’t semiconductors. There’s no single choke-point technology that one country can simply switch off, notes Ankur Saxena, an investment director at TDK Ventures. That distinction matters.

The numbers tell the story. Global humanoid robot shipments hit 22,000 units in the first half of this year, with Chinese manufacturers accounting for the vast majority, according to Counterpoint. The world’s five largest humanoid makers by shipments — AgiBot, Unitree, Galbot, UBTECH and Leju Robotics — are all Chinese. Together they shipped 86% of the global total.

That lead compounds. Lower prices mean more robots deployed. More deployments generate real-world data. That data improves the technology. Better tech drives costs down further. Saxena calls it a self-reinforcing loop that U.S. companies, operating at far smaller scale, simply can’t enter.

Chinese firms are also pulling the tech stack in-house. Unitree is developing more components internally. Automakers like XPeng are leaning on their chip and vehicle-manufacturing experience as they pivot into robotics.

Saxena sums up the divide bluntly: “The United States leads in frontier AI, software and semiconductor innovation. China leads in manufacturing scale, supply-chain depth and cost.”

His warning cuts to the core of the policy debate: “You cannot sanction your way around a cost curve. You can only out-build it, and America has yet to begin making the decade-long investment that will require.”

Where does China go next?

The likely answer: everywhere else. Even locked out of the U.S. market, Chinese robotics firms still hold a vast domestic base and room to expand into regions hungry for affordable automation.

Soumen Mandal, a principal analyst at Counterpoint, sees Chinese companies already targeting price-sensitive markets with acute labor shortages across Europe, Southeast Asia, Latin America and the Middle East. He expects humanoids to follow the playbook Chinese EV makers perfected: build scale at home, expand overseas, then set up local production.

The drone market offers a preview of this fragmented future. Bentzion Levinson, founder and CEO of Virginia-based Heven AeroTech, describes an industry splitting into two ecosystems: a U.S.-led market built around NDAA-compliant systems, and a China-led market focused on low-cost, high-volume production.

Western makers shouldn’t bother chasing the low-end consumer drone segment, Levinson argues — cost advantages there are simply too steep. Instead, U.S. and allied firms should compete in long-range autonomous systems for defense and critical infrastructure, where security requirements carry more weight than price tags.

The next battleground: power and payloads

Levinson sees the competitive frontier shifting from the drones themselves to what powers them and what they carry. “The next battleground is over who owns the next-gen energy and payload architecture,” he says, pointing to battery constraints as a particular pressure point.

Agility Robotics welcomed the FCC’s July decision, arguing it could address security concerns around foreign-made robots before they become as deeply embedded in U.S. markets as drones did. The company points to its Digit humanoid, designed and assembled stateside, while calling for continued access to the tools and technologies needed to advance robotics research.

A more regional robotics market

“The alternative to China isn’t a purely domestic U.S. supply chain; it’s a diversified allied one,” Saxena says.

That opens doors elsewhere in Asia. Japan brings decades of industrial robotics and precision manufacturing experience. South Korea has strengths in electronics, batteries and autos. Taiwan remains a semiconductor heavyweight. But none can simply replace China, given how deeply Chinese components remain embedded across the global robotics supply chain.

Asian manufacturers could carve out a middle ground between low-cost Chinese robots and pricier U.S. offerings. Hyundai, which owns Boston Dynamics, and Toyota are among the automakers investing heavily in robotics, drawing on their vehicle and autonomous-systems expertise.

Yang Fang of Beagle Technology, a California agtech startup converting conventional farm equipment into autonomous machines, expects robotics to become more regional as companies design for local labor needs and working conditions. Chinese firms may focus on products suited to China and nearby markets; U.S. companies will likely build for industries across North America.

The likely outcome isn’t two neatly separated U.S.- and China-led industries. It’s something messier: Chinese companies competing on cost and scale across much of the world, U.S. and allied manufacturers gaining ground where security matters most, and Japan, Taiwan and South Korea fighting to hold the middle.

The restrictions may protect parts of the American market. They don’t address China’s global manufacturing scale. And as the humanoid robot market expands and US drone import rules take effect, the real competition may simply move elsewhere.

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XDOF, three months out of stealth, is already closing in on a $1.2B Series B

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XDOF Series B

From stealth to unicorn talk in record time

Three months. That’s how long XDOF has been out in the open. And already, the robotics data startup is in late-stage conversations to raise a Series B at a valuation hovering around $1.2 billion, according to multiple sources familiar with the negotiations. The round would be led by 8VC.

Not bad for a company that didn’t even exist publicly until June.

XDOF was co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO). Their origin story traces back to a research project called GELLO — a low-cost teleoperation system that lets a human operator control a robotic arm from a distance. The goal? Generate training data for robots. That work produced an influential paper in robotics and, eventually, a company.

The startup’s Series A, a $70 million round announced in June, drew participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. At the time, XDOF wasn’t planning to raise again so soon. But the market had other ideas.

Why investors are knocking on XDOF’s door

The reason for the sudden interest? Growth. Real, measurable growth.

XDOF’s annualized revenue is approaching $50 million, sources say. That kind of traction, so soon after a Series A, tends to make venture capitalists sit up and take notice. It also tends to make them pick up the phone.

“They weren’t out raising,” one person familiar with the situation told TechCrunch. “The VCs came to them.”

Terms aren’t final, and the total capital being raised remains unclear. TechCrunch couldn’t confirm whether the $1.2 billion valuation includes the new funding or sits on top of it. Both XDOF and 8VC declined to comment.

The Scale AI for physical robots

XDOF’s pitch is straightforward: it builds the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies would rather not build themselves. Think of it as an outsourced data-supply chain for the robotics industry.

Investors describe XDOF as the Scale AI or Mercor of physical robotics — a nod to the data-labeling giants that powered the AI boom. The comparison makes sense. Large language models trained on the entire internet. Physical robots? They don’t have that luxury. There’s no massive, ready-made dataset of real-world robot interactions sitting online. That scarcity makes data collection the critical bottleneck on the road to general-purpose machines.

Wu felt that bottleneck firsthand as a PhD student. His research on how robots learn from large datasets kept hitting the same wall: “large-scale data to work with” simply didn’t exist, he told TechCrunch in June.

Building the ABC dataset

XDOF is tackling that problem head-on. The startup is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled. The dataset is called ABC.

Collecting that data requires a hybrid approach. XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks. Think folding clothes. Flattening boxes. The mundane, physical chores that robots still struggle to master.

The company plans to hire and train teams of data collectors around the world. Two main roles are emerging:

  • Teleoperators who steer robots remotely to demonstrate tasks
  • Egocentric operators who wear body sensors to capture natural movement data

Early traction and the competitive landscape

XDOF has already signed up 20 customers, including several frontier AI labs, according to previous statements to TechCrunch. That customer base, combined with the revenue trajectory, helps explain the valuation chatter.

But XDOF isn’t alone in this niche. Other startups chasing real-world data for robot training include Mecka AI. And the human-data platforms that started with LLMs — like Scale AI and Micro1 — are expanding beyond text and images into physical domains.

The race to build the data infrastructure for physical AI is heating up. Whoever wins it will effectively control the fuel supply for the next generation of robots. That’s a position worth paying up for.

Whether the $1.2 billion valuation holds remains to be seen. Deals at this stage can shift. But the fact that XDOF is even in this conversation — three months after emerging from stealth — says something about the demand for what it’s building.

For more on how data is shaping the future of AI, check out AI data labeling trends and robotics funding rounds in 2024.

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New York City Pulls AI From Younger Classrooms—Here’s Why It Matters

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NYC AI ban

New York City Just Hit Pause on AI in Classrooms

New York City Public Schools is drawing a hard line: no generative AI for students from 2-K through eighth grade during the 2026-2027 school year. That’s over half a million kids walking into classrooms next week without access to AI-powered tools.

The district says it will remove software with student-facing AI features and block AI companion chatbots. High schoolers? They’re exempt. This isn’t a blanket ban—it’s a targeted move to decide when kids should actually start using the technology.

Mayor Zohran Mamdani put it bluntly: “The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary. We do not see it that way.”

Why NYC Is Worried About AI for Younger Students

The fear isn’t just that a kid will ask ChatGPT to write an essay. City officials want younger students to build core skills—critical thinking, creativity, communication—without leaning on AI as a crutch. They’re also pushing for stronger human connections in classrooms, not another screen.

Schools Chancellor Kamar Samuels said the city refuses to assume that innovation automatically equals more technology in front of students. It’s a deliberate slowdown, and it follows last year’s bell-to-bell cellphone ban that already limits device use during school hours.

What Happens When Kids Reach High School?

AI doesn’t vanish once students hit ninth grade. Instead, the district plans to roll out AI literacy classes twice a year. The goal? Teach teenagers how to think critically about the technology before they become dependent on it.

That’s a different approach from just saying no. It’s about timing—letting younger minds develop without AI, then giving older students the tools to question it.

The National Battle Over AI in Education

NYC’s decision sits at the center of a much bigger fight. The White House has pushed educators to embrace AI responsibly. Some teachers already use it to craft lesson plans, give feedback, or break down tough subjects. But not everyone’s on board.

The Department of Health and Human Services recently gathered childhood experts to talk about excessive screen time. Officials have also called for tougher safeguards around social media and AI. The message? Kids are spending too much time in front of screens, and AI might make it worse.

A Bold Experiment With Zero AI

Here’s what makes NYC’s move so interesting: instead of asking how much AI younger students should use, the largest school district in the country is starting with none. For one academic year, they’re testing whether classrooms are better off with AI kept outside the door.

That’s a radical stance, and it’s not without critics. Some educators argue AI can personalize learning or help struggling students catch up. But NYC is betting that a year without AI will reveal what kids actually need—not what tech companies think they need.

What This Means for Parents and Teachers

If you’re a parent in NYC, expect changes. AI-based apps may disappear from your child’s school day. Teachers will need to plan lessons without generative AI tools. And students in grades 2-K through 8 will rely more on traditional methods—paper, pencils, and human interaction.

For teachers elsewhere, this could be a signal. NYC is the biggest district to take this stance, and its findings could shape policies nationwide. The next year will be watched closely by educators, policymakers, and tech giants alike.

What Happens Next?

The district will spend the year studying how generative AI affects students before deciding what comes next. That research could lead to a permanent ban, a partial rollout, or something entirely different.

For now, NYC is making a statement: childhood shouldn’t be an AI beta test. Whether that’s the right call or a step backward, we’ll know more in 2027. Until then, the debate over AI in schools just got a lot more interesting.

If you’re curious about how AI is shaping other areas, check out our take on AI in education trends or classroom technology policies.

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Meta’s Muse Spark 1.3 takes on GPT-5.6 and Claude — but can it really win?

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Muse Spark 1.3

Meta just fired a serious shot in the AI arms race

The company quietly unleashed Muse Spark 1.3, its most advanced AI model to date, and it’s aiming straight at the top dogs. Developers can already access and pay for the update, which Meta says represents a massive leap forward in performance.

It won’t stay confined to the developer sandbox for long. Over the coming weeks, the model will roll out across Instagram, Facebook, and the Meta AI assistant — putting it in front of billions of everyday users.

What makes Muse Spark 1.3 actually different?

Meta’s Chief AI Officer, Alexandr Wang, didn’t mince words. He called this “the biggest jump so far on model performance,” pointing to serious gains in two areas: coding and agentic tasks — the kind where the AI acts on your behalf rather than just answering questions.

But here’s the catch. Wang told Bloomberg that Muse Spark 1.3 is competitive with Anthropic’s Claude Fable 5.1 and even beats OpenAI’s GPT-5.6 Sol at coding. That’s a bold claim, especially with OpenAI’s upcoming Astra model lurking in the wings.

Take those comparisons with a grain of salt, though. Benchmarks can be gamed, and a model that crushes one test might stumble on a totally different task. Real-world performance is what actually matters.

Efficiency gains under the hood

Wang broke down a few upgrades that set Muse Spark 1.3 apart:

  • 25% fewer tokens needed to complete the same job — meaning lower costs and faster responses
  • Multi-workflow handling — it can juggle several tasks at once instead of forcing separate sessions
  • Better context retention across long, complicated instructions
  • Self-awareness of limits — the model now pauses to ask for clarification before taking any irreversible action

That last point is quietly important. AI that knows when it doesn’t know is a big step toward trustworthiness, especially for agentic use cases.

Pricing stays flat, adoption explodes

Here’s something developers will appreciate: Meta isn’t raising prices. Muse Spark 1.3 costs the same as its predecessor, Muse Spark 1.2. That’s a smart move when rivals are hiking rates.

Wang told Bloomberg that adoption on Meta’s developer platform has been strong — some users are burning through trillions of tokens every week. Those numbers suggest real usage, not just hype.

What about open-source fans? Meta hasn’t decided whether it will release the model’s weights — the blueprint that lets outside developers build on top of it. The older Muse Spark 1.2 weights are still headed for release, but the new model’s future remains unclear.

The bigger picture: Meta’s spending spree continues

Meta is still pouring billions into AI infrastructure, and Muse Spark 1.3 is the clearest signal yet that the company believes it’s closing the gap with OpenAI and Anthropic. Whether that’s true or just corporate bravado will play out in the benchmarks and real-world deployments over the coming months.

For now, the model is available to developers, and the app rollout is imminent. If you’re building on Meta AI tools, this update is worth a serious look. And if you’re just a curious user, you’ll likely meet Muse Spark 1.3 in your Instagram feed sooner than you think.

One thing’s certain: the AI race just got a lot more interesting.

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