Robotaxis: The Economics Still Don’t Work — But the US and China Push Ahead Anyway
- Emin Askerov
- 7 days ago
- 4 min read
A few weeks ago, I posted a clip from Total Recall where Arnold Schwarzenegger tears the robotic taxi driver out of his seat and takes over manually. For all the futuristic imagery, that scene captured something intimately familiar: you don’t trust a robot.
This week, I read two Economist articles back-to-back—one on the US robotaxi sector, one on the Chinese one. And after going through both, one conclusion is impossible to ignore: robotaxi economics still don’t work. Not in the US. Not in China.
But both countries push ahead anyway—just for very different reasons.
The US, where unit economics are a tragedy, but TAM slides look magnificent.
Start with the US. Here, the unit economics are a tragedy, but the TAM slide looks magnificent. The Economist article on the US essentially boils down to three problems. First, the cars are too expensive. A full US-spec robotaxi stack (lidar–radar–cameras–compute) is a six-figure machine, however you turn it. And these vehicles depreciate fast.
Second, the operating costs are comically high. Robotaxis require large teams to monitor fleets, endless software updates, high-bandwidth connections, map maintenance, remote “intervention” staff, and high insurance premiums. Robotaxi companies do replace drivers - with 300 software engineers and 24/7 remote support.
Third, regulation keeps the fleets tiny. Robotaxis are allowed to roam only in designated parts of towns, if they are allowed at all. Small fleets won’t let you have economies of scale, which means costs are still high and startups are still unprofitable.
Yet the US keeps pushing. Why? Because America still believes every mobility problem can be solved by a startup, a valuation model, and maybe a positive EBITDA slide. The tech optimism is deeply baked into the culture. After all, this is the country that turned coworking into a high-conviction asset class.
China, where unit economics still don’t work but the system is trying to brute-force them into working.
Now let’s move over 10,000 km west of San Francisco. In China, the robotaxi economics also don’t work, but the system is built to brute-force it until it does. The second Economist article is even more interesting, because despite the hype around China “pulling ahead,” the underlying economics are still shaky - China’s robotaxis lose money too.
Even though Chinese robotaxis have one zero fewer on the price tag, per-kilometre costs are still higher than ride-hailing with human drivers. China has lower manufacturing costs and has deployed many more robotaxis, but it's still not enough to be profitable.
So why does China look like it’s pulling ahead? Because China’s system solves the problem the opposite way around: if the cost curve doesn’t work, build the scale first and push the cost curve down later. This is the same logic behind EVs, solar, and batteries. Economics today matter less than strategic positioning tomorrow.
Despite both countries losing money on robotaxis today, the probability of getting to profitability is much higher in China. First, the hardware is cheaper, and it is getting cheaper. China produces lidar, sensors, compute hardware, and EVs domestically at far lower cost. A US robotaxi is a science experiment on wheels. A Chinese robotaxi is an industrial product rolling off an existing EV supply chain. When your bill of materials is cheaper, your break-even point suddenly looks less like a fever dream.
Second, Chinese cities welcome robotaxis and actively redesign infrastructure to support them, with the curious exception of Beijing, Shanghai and some other large cities. In the US every city requires separate approvals, hearings, pilot phases, and local negotiations. In China municipalities compete to be early adopters.
Third, scale is a policy decision, not a market outcome. China can mandate large fleets, provide regulatory clarity and subsidise deployments. If robotaxis need 30–50k units on the road to hit favourable economics, China can make that happen. The US cannot—not without a decade of hearings and lawsuits.
So why push so hard if the economics are broken? Because in both countries, non-economic drivers dominate. In the US founders need the “inevitable autonomous future” narrative to stay alive. Investors need one last big technological frontier. Companies like Tesla and Waymo need autonomy to anchor long-term valuations. In China robotaxis are part of the national industrial strategy. They support domestic supply chains for EVs, computing, and sensors. They help cities hit local digitalisation and emissions targets. The government wants to define global standards for autonomous mobility.
The incentives are political, strategic, and reputational—not financial.
A long drive to profitability
Robotaxis today resemble green hydrogen was five years ago: lots of headlines, small deployments, great PowerPoints, and a cost curve that stubbornly refuses to bend.
But while the US treats robotaxis like a startup race, China treats them like infrastructure. And that difference will define the next decade. The US will probably lead in core autonomy algorithms and chips. China will lead in deployment volume, cost reduction, and profitability. The first positive unit economics will appear in China, not in Silicon Valley, because ultimately, this is an industrial game.
And in industrial games, China tends to play the long, patient, scale-first strategy—while the US plays the venture-funded, demo-first strategy.
Robotaxis don’t make money today. Unlike hydrogen, there are no physics telling us that they won’t make money tomorrow. With enough scale and cheaper computing power, they will. What will it take? Two things. First, a ton of innovation aimed at improving the efficiency of algorithms and energy use. The hardware has to get cheaper, and it will. When cell-to-chassis finally comes about, that will be one sign. Second, it will need scale, and if anyone can force them to make money tomorrow, it’s the country that already forced EVs, solar panels, and battery factories onto workable cost curves.
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