When I look at why I expect the world to change a lot in the next few years, and why other people expect slower changes, I think a big component is disagreement on the extent to which AI will affect non-computer work. Sure, programming has sped up massively with Claude Code etc, and models like Astra seem posed to make similar changes to work with spreadsheets and other common business tools, but what about work that doesn’t include computers at all?
The classic picture of AIs doing things in the world is robots, but I think a more realistic picture of the near future is computers telling people what to do. Leaning into the way the world has become very scifi, we could call this “teleoperating” people. Many things that are hard for robots are very easy for people, there are strong economic reasons that push towards teleoperation, and this bypasses many legal and social limitations on what AI can do. We should expect this to lead to large and rapid changes in the physical world.
One of the most widespread examples today is driving. I put my destination into the GPS, and it tells me what to do. I handle the low-level physical motions and responding to the local circumstances; the GPS has a broader view of the world and handles the strategy.
When I think about why this happened much earlier than the huge amount of “teleoperation” I expect to see soon, a few factors. Driving is a major human activity, so it was worth making navigation software at a time when AI wasn’t very good yet, even though this meant a ton of human hours going into building the system. It was also a place where the strategic component was a very strong fit for automation. You can memorize the map with enough work, but even then you won’t have real-time street-by-street traffic information. AI solved this problem so well we don’t even call it “AI” anymore. On the other hand, driving is a realtime control problem in an unconstrained environment where people die if you screw up and you can’t even always safely stop. This makes it hard to automate, but also would make it impractical for an AI to guide non-drivers through the process. The only reason Uber etc have been able to commodify driving as they have is that so many people already know how to drive.
Thinking about where else we might see this, most AI use today looks a lot like management. You figure out what you want it to do, and describe in detail. It asks you some questions up front and others while it works. After some churning you get some a work product to assess. Maybe there’s more back-and-forth, or maybe it’s good as is. You set strategy and give context; the AI handles the implementation. Today’s AI is normally only applied to the implementation to the extent that the task can happen fully within the computer. In cases when the AI can’t physically, legally, or intellectually do something, the most efficient path to completing the task will often be the for the AI to handle strategy while delegating to a human to fill these gaps.
To illustrate what this delegation pattern can look like, let’s look at how I recently got my Whistle Synth app into the Mac App Store.
At a high level, I set the strategy: “Can you walk me through the process of getting this into the Mac App Store?” But everything after that was either handled by the AI or delegated back to me. It handled included figuring out what tasks needed to be done, modifying the implementation to be compatible with the App Store restrictions, building the app, and giving me instructions. And then it delegated to me to record a demo video involving whistling (physical), register as a Mac Developer (legal), and clean up its App Store description (intellectual).
This was mostly pure instruction-following on my part: I was being teleoperated. Here’s one example:
Claude Code:
Open the Profiles list https://developer.apple.com/account/resources/profiles/list — sign in with the account for [team].
Click the blue + next to “Profiles”. You land on “Register a New Provisioning Profile”, a page of radio buttons grouped into Development and Distribution sections.
Under Distribution, select “Mac App Store Connect”. Not “Developer ID” — that’s for distributing outside the store. Click Continue.
...
This was relatively mindless work for me. Just like being navigated through a city I don’t expect to return to, I didn’t bother trying to learn how this worked. I was loosely paying attention to make sure I wasn’t doing anything dumb, but for future more capable systems I expect people to stop attending even that little.
Once it finished walking me through submission I had to wait a few days for review. It was accepted in the first round with no reviewer comments. This is a pretty big deal: App Store rules are notoriously complex, the reviewers very picky, and as a first-time amateur Mac developer there’s no way I would have gotten this all right on the first attempt pre-AI.
Even though this was an almost entirely within-computers case, the important thing here is the pattern: by following AI instructions I did something that would have taken me a ton of work to learn how to do alone.
Note that in this case I was both doing the high level strategy (”put this in the app store”) and filling in gaps for the AI (clicking a blue plus in App Store Connect). As “teleoperation” becomes more common I expect some of this, as people automate away parts of their jobs. Other times I expect it will look like, for example, a highly AI-pilled startup founder directing AIs that direct employees. A lot like gig workers “below the API“ today. I expect early iterations of these jobs to be frustrating, with the AI not delegating well. Then, as AIs get sufficiently good at directing and anticipating, they’ll be pretty mindless, for better or worse, as you stop needing to think for yourself at all.
What sort of jobs might switch to being teleoperation? The top candidates are any where the physical motions are relatively straightforward, timing is not critical, and people today are paid a lot for their knowledge and judgement. If you had an expert looking over your shoulder and telling you what to do, I expect most of you could do most of the work of an electrician. In fact, that’s the bulk of how electricians learn their trade: through apprenticeship. Same goes for mechanics, healthcare technicians, inspectors, etc: they combine physical and intellectual components, where it’s the knowledge that keeps a random person off the street from being able to do the job. People wearing glasses with built-in cameras, connected to today’s strongest AIs could already do a lot with a bit of scaffolding.
To have a large impact, teleoperated workers wouldn’t need to be able to do 100% of an existing job category. As long as the parts that can and can’t be done this way can be easily separated, 90% could be done by teleoperated novices, while some of the former professionals spend their time on the remaining 10%. When I think about how these other jobs are likely to go, I expect we start with ones without regulatory barriers: HVAC techs (typically unlicensed) before electricians (licensed) before surgeons (licensed + heavily regulated + realtime + high stakes). [1]
So, teleoperation is probably very economically productive. Is it a good thing? I think mostly no, for several reasons. The big one is that I expect it to speed up the rate at which AI advances turn into additional AI advances. This shortens the time our society has to figure out what to do about these massive changes, and increases the risk that immature technology is rolled out widely. Rushed deployment is more likely to lead to disaster, and there are many ways this could go extremely wrong. And by “extremely wrong” I mean “AI kills everyone wrong”. Creating minds smarter than ourselves is the most consequential thing humanity has ever done or will ever do, and we have to get it right.
Which is why I’m heartened to see a lot of support, including from the CEOs of Anthropic and OpenAI, for managing the pace at which these systems become increasingly capable. But even if we held constant at the capabilities of models publicly available today (let alone trained but not yet released) I think widespread teleoperation is still very likely. I expect this to be a massive disruption, one very difficult to integrate into our existing societal system.
The first issue is just that I expect these to be unpleasant jobs with low negotiating power. Since there are many tasks that almost anyone could do if expertly advised, and the employer can easily filter out the people who can’t or won’t, there’s very little to keep wages or working conditions up. Then add in competition from laid-off knowledge workers, and I expect unprecedented unemployment.
So even if we can avoid the large risks of losing control of the future, falling into AI-enabled authoritarianism, facilitating bioattacks, etc, how we handle a world in which most people can’t find work that pays them enough to live on will be an serious challenge. I expect this will require very large scale redistribution. [2] I’m not sure this happens by default, but I think it’s achievable with significant effort. And as a very small fraction of spending in a vastly larger economy it would be a much easier sell.
[1] For a future post:
$ echo “[redacted]” | sha512sum
7820a2ecae1fcab8d7a29fe4f98f56b96c403cdfb9a6833fad0198070e118233490a078c9230de0c6ed5cf1c6cf557fef493aeb33118e75b401deabbf3a1aae4 -[2] Looking at what there is already, the US does less than most rich countries, but even here we have medicaid, EITC, CTC, WIC, SNAP, SSI, TANF, Section 8, LIHEAP. We spend maybe 3-5% of GDP on means-tested programs. Then ~7-10% of GDP goes to things like universal public education and medicare which aren’t directed specifically at the poor but are still effectively redistributive. Internationally there’s been some of this, but much less; until recently the US was spending maybe 0.04% of GDP on the kind of foreign aid (ex: PEPFAR) that is really about helping the world’s poorest, and then the private sector (Gates etc) adding maybe 0.1% of GDP.
I feel like I’m being teleoperated every time I cook a meal these days. I’m a mediocre cook but a good instruction follower. So I’m happy to have an LLM guide my hands through the process.