How AI Companies Are Simulating the Robot Takeover
One of the promises underwriting the AI boom is that software will be able to use computers on our behalf. At the low end, this looks like the “deep research” tools AI firms have already released that search the web and synthesize information on your behalf, attempting to automate the task of using Google. More theoretically, it might mean models trained to use productivity software in a work context, which may then be used to attempt to automate increasingly complicated jobs. In between, you’ve got the stuff that companies like Google and OpenAI keep touting in demos: AI that can book flights for you, AI that can get you a reservation, AI that can comparison-shop for you. As a category of “agentic” behavior, this sort of stuff is both ambitious and conceptually funny — the great big intelligence in the sky is being deployed, first, to click-farm the entire economy.
So far, the results of such an approach have been mixed: Computer-use agents and self-clicking AI browsers make for great demos but struggle to offer obvious utility to most people. One might argue for a different path to broadly useful and flexible AI agents, one that doesn’t take a long detour through web interfaces designed for human cursors and thumbs.
Or, as many in the AI industry would have it, you might argue instead that this is an obvious first step and that these tools don’t yet work well only because they haven’t been adequately trained and because they don’t have enough access to the types of sites they’re intended to use. That’s how you get, as reported by Cade Metz at the New York Times, AI start-ups making training-ready replicas of websites, including Gmail, Airbnb, and United Airlines:
These new shadow sites are a significant part of the tech industry’s efforts to transform today’s chatbots into A.I. agents, which are systems designed to book travel, schedule meetings, build bar charts and complete other computing tasks. In the coming years, many companies believe, A.I. agents will become increasingly sophisticated and could replace some white-collar workers.
“We want to build training environments that capture entire jobs that people do,” said Robert Farlow, whose start-up, Plato, is among those recreating popular websites and other software applications.
The pitch for these companies is basically: We’ll build replicas of popular websites so your AI can train on them and your agentic tools can attempt to use them with no limitations. Sending your half-baked AI agent out to try to book concert tickets will look, to the company selling them, a lot like spam, scraping, or sniping. Better, the thinking goes, to do most of this practice in simulation, where the airline might not even know you’re planning to automate its ticketing interface.
There’s a lot of similar stuff going on in AI, from “enterprise simulation,” as Salesforce calls it — in which companies can use “virtual environments that mirror the noise, accents, crosstalk, and complexity of real-world operations” to train, for example, customer-service agents — to virtual environments where robotics companies can collect data from thousands of scenarios in gamelike environments that can then be used to train, for example, humanoid machines. Right now, on trade-show floors around the world, you’ve got companies touting robots that have been “trained in simulation,” often using the same software from Nvidia:
Say hi to Dex! Our first mobile humanoid robot built for real-world industrial work.
— Richtech Robotics (@RichtechRobots) October 28, 2025
Built for the factory floor, powered by NVIDIA AI, trained in simulation, and ready for the real world.
Catch a first look of Dex at #GTCDC25 Booth 368, where he’ll show off his dexterity by… pic.twitter.com/cU7wiHvgCh
Like the software agents training on airline and e-commerce sites, there’s an interesting tension at the core of humanoid-robot efforts: Does it make sense to automate tasks, or groups of like tasks, or to try to automate the entire person who typically does those tasks? But the singularity-in-a-sandbox parallel is illuminating in another way, too. Setting aside the question of how well such broad simulations map to the real world, the demand for such a thing is obvious. Running a bunch of tests with robot prototypes is prohibitively time-consuming and expensive, and doing the same thing in something akin to a video-game engine might get you to a productive feedback loop much more quickly. The reason you need a simulated Airbnb interface, though, is different: It’s not that training a robot to use a website is particularly time or resource intensive. It’s that Airbnb probably doesn’t want you to build what you’re building at all.
AI-training replicas are an early expression of a crucial question about the near future of AI agents, at least as companies like OpenAI have been talking about them: How will the internet, and the world in general, react to the presence of a bunch of freelance, mercenary machines? (Or, to put a finer point on it, to the replacement of human customers with bots transacting on their behalf?) Companies like Plato and AGI exist because one of the more obvious ways for a company to train a computer-use agent, aside from monitoring users’ screens or ingesting years of screen-captured videos — to have it try a whole bunch to see what works — is interpreted as antagonistic or threatening by the companies whose interfaces they’re training on. Amazon is already suing an automated-browser company for “unauthorized access and trespass” to its site, referring to a shopping agent as a sort of “intruder.” This is understandable but also revealing: To Amazon, customers are worth a lot more than the products they buy, particularly as audiences for advertising. Replica sites promise to give AI companies a means to get at least part of the way to a working product without interacting with companies like Amazon at all (although, as the Times notes, these companies are already getting sued too). Much in the way that the appeal of using ChatGPT instead of conventional search is partly because it just doesn’t have many ads (yet), a chatbot shopping routine might be appealing because it lets you spend less time in Amazon’s intentionally disorienting, ad-laden, hypermonetized interface.
AI companies don’t spend a lot of time dwelling, at least publicly, on what might happen after their agents-in-progress get deployed en masse. This is partly because they don’t know. But the most obvious paths forward are all pretty bumpy. At one extreme, you have a situation in which every other company in the world has a severe immune response to what you’re doing and sees AI agents as a generalized attempt to take over their relationship with their customers. At another, they happily accept that AI companies can provide them with a bunch of business, albeit under different terms than they’re used to, and try to work something out.
Anything short of the latter scenario leaves AI firms with a fight. They could compromise, working out partnerships with, say, airlines, which, among other things, might mean that ChatGPT doesn’t have to pretend to be a person using their websites but which would let airlines keep more control over how their tickets are sold through chatbots. But the rise of replica sites suggests that AI companies don’t particularly want to ask for permission or partnership and that they know their approaches will be treated with suspicion. (Also, they’re all building chatbot booking and shopping interfaces of their own.) They’d rather force the issue — see if these features work, see if users want them — then approach would-be partners with more leverage.
They’re also a consequence of the AI industry’s all-or-nothing ethos at the moment. Gradually incorporating a bunch of commerce partners into popular new chat interfaces is a plausible and familiar business plan, but at this stage of AI narrative cycle — and at this level of investment — plausible and familiar won’t cut it. Partnering with Airbnb and getting commission on bookings probably doesn’t add up to a trillion-dollar start-up valuation. Standing between your captive users and the entirety of the online economy, on the other hand, might. So that’s what they’re going to try, first in simulation, then in real life.