Se habla mucho sobre si la inteligencia artificial nos quitará el trabajo o no, pero no tanto del trabajo que supone el desarrollo en sí de estas tecnologías.
En AI Is a Lot of Work nos cuentan sobre las personas que ayudan a entrenar las inteligencias articiales, anotando, identificando y etiquetando lo que pueda ser relevante. Un trabajo que es difícil y repetitivo. Y no muy bien pagado.

AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. In Joe’s case, he was labeling footage for self-driving cars — identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of — frame by frame and from every possible camera angle. It’s difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about .

Esto se presta a otro tipo de evoluciones como puede ser crear escuelas de anotación cuyo aprendizaje consiste en realizar el trabajo: el responsable gana más, y la anotación es más barata.

Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles.

Como decíamos, la mayoría del interés sobre estos temas ha tenido que ver con los trabajos que se perderán por la automatización, pero el tema de los costes del entrenamiento (por baratos que se intenten hacer) no es despreciable en absoluto.

Much of the public response to language models like OpenAI’s ChatGPT has focused on all the jobs they appear poised to automate. But behind even the most impressive AI system are people — huge numbers of people labeling data to train it and clarifying data when it gets confused. Only the companies that can afford to buy this data can compete, and those that get it are highly motivated to keep it secret.

También hay categorías en estos trabajos: están las personas que entrenan los chatbots más modernos y llamativos, pero también las que se ocupan del mantenimiento (clasificar el contenido emocional de vídeos de TikTok, nuevas variantes de correo basura, o el nivel de provocación sexual de algunos anuncios; incluso mirando transacciones comerciales para ver si la recomendación que se hace a un usuario será adecuada o no).

... while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads.
Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt.

Esto significa, al final, toda una cadena de suministro, con la impresión de que sólo será necesaria durante un tiempo porque será sustituida por las propias IAs.

“There’s an entire supply chain,” said Sonam Jindal, the program and research lead of the nonprofit Partnership on AI. “The general perception in the industry is that this work isn’t a critical part of development and isn’t going to be needed for long.

De momento, sin embargo, parece que el trabajo humano sigue siendo necesario, auque solo sea para asegurarse de que se alimenta a estas inteligencias artificiales con los datos adecuados.

... so now, you need a human to check that the AI is being fed the right type of data and maybe another human who checks its work before passing it to another AI that writes a report, which goes to another human, and so on. “AI doesn’t replace work,” he said. “But it does change how work is organized.”

Se trata entonces de una vuelta de tuerca más a la distribución del trabajo donde la parte vistosa la realizan las máquinas y se oculta la parte del trabajo menos interesante y a la gente que la realiza.

... the latest iteration of a particularly Silicon Valley division of labor, in which the futuristic gleam of new technologies hides a sprawling manufacturing apparatus and the people who make it run.

Seguramente no perderemos el trabajo, pero puede que lo cambiemos por uno más alienante, solitario y aburrido.

When AI comes for your job, you may not lose it, but it might become more alien, more isolating, more tedious.

A pesar de todo, el trabajo es complejo, existen manuales de instrucciones para las personas que trabajan en ello largas y con preocupaciones que no tienen que ver con nuestras necesidades.

The act of simplifying reality for a machine results in a great deal of complexity for the human. Instruction writers must come up with rules that will get humans to categorize the world with perfect consistency.

Otro aspecto interesante tiene que ver con los trabajadores: de pronto puede haber mucho trabajo y luego nada.

That is, when they were making any money at all. The most common complaint about Remotasks work is its variability; it’s steady enough to be a full-time job for long stretches but too unpredictable to rely on.

No sólo eso, los encargos pueden dirigirse a Kenia, la India, Nepal ... Las empresas no tienen infraestructura local, así que pueden moverse sin ninguna dificultad.

Annotation was leaving Kenya. From colleagues he had met online, he heard tasks were going to Nepal, India, and the Philippines. “The companies shift from one region to another,” Joe said. “They don’t have infrastructure locally, so it makes them flexible to shift to regions that favor them in terms of operation cost.”

Muy interesante.

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