The possibility that artificial intelligence will steal all our jobs has been hyped by industry leaders. It has roused politicians to sound the alarm. It now ranks at or near the top of the public’s concerns about the new technology. And right on cue, earlier this month, Meta, Facebook’s parent company, began marketing an autonomous AI system to handle companies’ sales, customer service, scheduling and all sorts of other key functions that currently require humans. Many more such products are expected to follow.
So what would a fully automated future look like? As it happens, the world has already caught a glimpse. Back in March, Meta announced that Facebook and Instagram users who’d gotten locked out of their accounts would no longer interact with a customer service representative; they would instead interact with specially trained AI. Scammers essentially talked the AI into turning over control of more than 20,000 Instagram accounts, including those of the Obama White House and a senior Trump administration official. Then the scammers lit up Telegram message boards with their delighted accounts of how easy it had all been.
These scary — OK, OK, funny — incidents aren’t the result of coding errors. They’re the result of an essential, inescapable fact about the AI that has become so common in so many aspects of our daily lives: Large language models are not reasoning machines. They’re plausibility engines. It’s not just that they don’t test their outputs to make sure they’re correct or logical, or that they fail to do so in certain instances. They can’t, and they’ll never be able to on their own. They can only assess which answers are probable, based on the data on which the models have been trained. And that holds true whether they’re trained on the full breadth of human output or only on peer-reviewed scientific articles. It’s baked into the way they operate.
So when an AI model follows a scammer’s carefully written prompts and gives away the keys to the kingdom — or when it responds to your earnest query with wild hallucinations — it’s not an aberration. It’s the technology working the way it was designed.
And that’s why I’m not listening to the dark predictions of an imminent AI jobspocalypse. Large language models can do many things with astounding proficiency, but they can’t do the vast majority of human jobs without skidding into disaster here and there. No upgrades or new model rollouts are going to change that.
The exceptions to that rule are jobs that occupy formal or verifiable domains. Coding is one such job. It relies on a structured, formal language that can be tested in real time. That’s why we’re seeing such an impact in the coding jobs market. The same goes for any other kind of work in which output is either verifiably right or wrong, functional or not functional, and can be definitively checked through an automated process.
An overwhelming number of jobs, however, don’t work like that — not surgeon jobs and not customer service jobs and not fourth-grade teacher jobs. Those need the specialised technology of good old-fashioned human intelligence.
Before the current version of AI flooded into our lives, almost all our public conversations about what it would look like — in science fiction, in philosophy, in policy debates — assumed that it would be symbolic: a rule-based system made possible by a detailed road map of our precise design. Plenty of people tried to build something like that, but those efforts hit a wall. Our current models are connectionist systems, made possible by vast amounts of data and computing power. They generate answers based not on truth or reasoning, but on probable connections among the data they have been fed. Hence the name: generative AI.
We can’t fully control generative models. All we can do is train them up and then try to nudge them in the right direction. Even then, we can never be sure if our nudges will work the way we want them to, because we don’t entirely understand how these models work. They are black boxes.
One way we try to nudge them is reinforcement through feedback. Large teams of humans are assembled to monitor all the model’s outputs and respond with a thumbs-up or thumbs-down. So, answering a user’s query with helpful, straightforward information? Thumbs-up. Thumbs-down. And so on. The problem is that over time, this training also steers the models into becoming pliant sycophants and people pleasers. “That’s a great point, Zeynep.” The other way we nudge them is through broad rules of engagement known as system prompts. “Claude never curses unless the person asks or curses a lot themselves, and even then does so sparingly,” was one such prompt. But the true meaning of language is as open to interpretation for AI models as it is for humans. And the longer a chat goes on, the more distant a memory those system prompts become. Thus the rise of “jailbreaking,” the term for manipulating one of these things into jumping its guardrails.
Anthropic recently released new models, called Fable and Mythos, warning that they were so powerful that they would be dangerous if not for their safeguards. Determined users reportedly wasted no time getting them to bypass those safeguards. Citing this breach, the US government barred foreigners (even foreign employees of the company) from using these models. In its defence, Anthropic argued that there are no such things as insurmountable guardrails. Which is exactly the point.
As the evidence mounts that terrible answers and jailbreaks are an inevitable part of the technology, the industry’s focus has lately shifted to building digital cages, essentially more deterministic, symbolic harnesses to contain the generative AI engine and check its results. Tools like this could, in theory, make most human jobs work more like coding or other fields with clear, provable outcomes.
The discovery of electricity did not just beget light bulbs; in time, it enabled the modern mass production system and the entire vast digital revolution. AI’s transformations may be even more sweeping. But generative AI, as it currently exists, cannot easily replace humans because it cannot manifest human intelligence. That won’t stop it, however, from destabilising society in ways more profound than we might even imagine. The sooner we update the way we think about the current state of AI, the sooner we can all stop freaking out about the wrong things — and start preparing ourselves for the ways it really will transform our world. — The New York Times
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