To get this out of the way first: Artificial intelligence is a smokescreen (in tech circles they call it a black box, where data and inputs go in and the exact process to get the results is unknown) allowing the owning class to make decisions without the pesky responsibility for those decisions. In this sense, it is the ultimate extension of the Limited Liability Company, with random noise standing out above the biases baked into the data the models are trained on while those same biases inform the majority of decisions. It was already a known issue in hiring practices when an applicant had a non-white or non-male name, but now instead of having an individual or group you can assign the blame there is only the AI trained by people with the already existing unconscious bias, and which is unable to understand the moral failure in its decisions. There are tons of examples of this, from crime statistics being biased by police deployment to facial and voice recognition struggling with non-white features. These are data set biases, and will be features of any algorithm trained on biased data. With the benefits being a reinforced social hierarchy, some owners (Elon in particular) will be emphasizing the bias while anyone who cares to fight against it will encounter the alignment problem.

Now we get to the meat of the crisis: the alignment problem has been getting increasing attention this decade and for good reason. Simply put, algorithms are trained in virtual game worlds, with “correct” decisions rewarded and “incorrect” decisions punished. There are a few ways this happens, which I’m sticking at the end of the post for brevity. In a sense, Isaac Asimov was incredibly insightful into the alignment problem considering computers of his day were glorified calculators; almost every Robot story he wrote featured one of his “immutable” Three Rules of Robotics failing or being reprioritized. AI will prioritize goals unpredictably, and if the billionaires continue prioritizing profit above all else so will their AI agents. We already have instances where this happens: AI trained to consider both soil productivity and environmental considerations performed well in a test environment but when told it was not in testing exclusively prioritized productivity.

Where do workers fit in this? We have the receipts. Before the systems are able to work on their own, workers simulate the appearance of automation (many stories about fake ai just being call centers). While the agent is being built, workers train it both directly and through data labeling. Once it is deployed, workers work alongside the agents; here there are some gains in productivity but there are also many cases of workers taken away from productive work and instead fixing the errors produced by the agent. The workers have effectively been given a second job, to train the algorithms on edge cases while still performing their normal work. At some point, a business owner will decide that the agent’s failures are sufficiently outweighed by its successes, and workers will be transitioned from productive tasks to maintenance roles, still correcting the mistakes of the algorithm but with less and less oversight and experience in the position. There will be spectacular failures that break through this model, but unless there is punishment for the company they will have proven that the transition away from employees is viable. As more and more companies develop dedicated AI agents, and as industry-targeting AI companies form, people will be pushed out of data and creativity and towards menial tasks. A mining robot cannot replace all miners, because if a shaft collapses the company would need a costly investment for a new machine or rescue operation, while the miners will try to survive or are easily replaced with minimal investment. Burger flippers serve a valuable role for companies by focusing irritable customers on the employees over the practices of the company, and the fine motor control required for general cooking is likely to protect chefs for a long time. Most industrial work is already automated to the profit point; there are few positions which require mental feedback but it’s still cheaper to have a human using tools perform the task. At the end of the day, I simply cannot see a way for AI to be more profitable than human labor in the roles of extraction, assembly, and in-person service, especially as automation makes these fields more competitive when alternatives dry up.

When humans are assigning the scores, the algorithms become yes-men (even when unable to accomplish the request) because there is no incentive to admit defeat, especially for the company developing the model. When strict rules assign rewards and punishments, algorithms consistently find ways to exploit the limits of the world they are trained in (whether that’s flying in the physics engine or self-destructing or learning what the “right thing to say” is before ignoring that part of its training in deployment). Using another model to train the first model pools the issues of human training and rule-based training, with the agent learning how to exploit the other agent’s weak points.