What I Learned About AI in 2023 - by Eryk Salvaggio

For some reason, I’m haunted by fear that I am a pessimist. In my daily life, I try to remember that cynicism isn’t realism, and that optimism doesn’t necessarily make us into suckers. Despite this, when it comes to AI, I can’t shake the feeling that every advancement is part of a long pattern of repetition, where each new iteration erases any learning from previous failures. In technology, failure doesn’t exist: it just gets replaced by whatever will fail next. My optimism is what draws me into the practice of writing about AI the way I do. In many ways, I would like to see AI improve. But improvement, to me, means more just, more ecological: more attuned to solving meaningful problems, rather than creating new ones. The mad burst of activity we’ve seen in 2023 around AI has steered us in that direction. The technologies that came to define AI this year seem, in fact, hopelessly disconnected from problem solving, in spite of the siren song of “potential” that emerges from the companie

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