When the machines win, what is gained and what is lost?

Yesterday, I finished watching DeepMind’s AlphaGo documentary. One of the things that stuck out to me about that documentary was how sad both Lee Sedol and the Korean people overall were that AlphaGo was able to win 4-1. The outpouring of emotion when Lee won game 4 was immense, in contrast to the silent melancholy that filled the press room after every other game.

There is something somber about seeing technology improve at something that we see as having a uniquely human element to it. Take the Luddites, for example. They were actively against labor automation technology to the point of physically destroying the machines. I don’t want to assume that I know all of the reasons that the Luddites did such a thing, but surely one of the reasons was that machines do not find meaning in the labor that they perform¹.

And yet, the world today is arguably a better place in large part due to the automation of physical labor that machines have provided for us. Physical labor still exists, but we have machines that are able to assist us and make it easier – not entirely replace us. There may yet be a future where all physical labor is automated, but even with the incredible recent advancements of bipedal robots and drone swarms, it seems unlikely to me that full automation of all labor will happen anytime soon.

Take chess as another, more recent example. Chess has become incredibly popular since superhuman engines like Stockfish have came online², and neural network based engines have only improved on minimax algorithms like Stockfish. There are no humans that are able to beat these engines, but that does not mean that chess is a game that is not worth playing. On the contrary – people use these engines to optimize their own human play, and find new ways to enjoy the game.

I see AlphaGo and its successors similarly. Sure, it automates a process for us. But it can also be used as a tool for transcending and improving the human element of these tasks. The physical laborer can still find meaning in woodworking, whether they’re cutting the trees down from scratch or buying pre-cut lumber from a mill. And learning chess is now more scalable than ever – people can learn from chess engines where their mistakes were without having to get a human expert to analyze their games.

On the face of it, I can understand the despair that the Koreans had when seeing cold, lifeless AlphaGo defeat the greatest human player of their time. But in retrospect, Go has shared a similar fate to chess. Go surged in popularity since the AlphaGo/Lee Sedol tournament. The end of the documentary mentioned that there was a global shortage of Go boards after that competition. The game has more players than ever, especially in the West. Like chess, we can now use Go engines built with the same architecture AlphaGo to improve our own games. I know that personally, if it weren’t for AlphaGo, I might not have decided to try Go for myself. I am still a novice, but I find something beautiful and very enjoyable about the game.

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The AlphaGo/Lee Sedol tournament was back in 2016 – nearly a decade ago. And I’ve painted a very rosy picture of AI optimism with this story. But can we still paint such a picture with AI in 2025?

I fear not.

The modern internet is filled with swarms of bots run by LLMs, who are attempting to enrage people to increase platform engagement and to sell more ads. AI is used to optimize which ads you might click on, and to optimize engagement in vicious activities such as social media, porn, and gambling. They’re used to more easily find exploitative bugs in critical software and cause cyberattacks. They’ve caused a massive build out in data centers due to the insatiable demand of not only training smarter models, but also for fast inference for customers. This causes tension in communities where these data centers are built, and legitimate concerns are raised by those communities about the build out of this infrastructure³.

That is not to say that contemporary AI technologies are not useful and that we need to revert to Luddism. I use LLMs like Claude multiple times a day for brainstorming and code generation. But the technology has diffused in such a way that it has made certain aspects of everyday life substantially worse. People are incentivized to release slop on the internet without any care for quality or correctness. It’s bullshit⁴ not in statements, but in output – there is no regard to the veracity, nor the quality of the output, because it is a profitable means of production as there is near-zero marginal cost.

This is not a commentary on the abilities of contemporary AI, or any moral or ethical concerns regarding the technology. It is a commentary on the negative cultural milieu we find ourselves in as these technologies become larger parts of our lives.

With AlphaGo, there was the naive depression of cold, calculating machines performing better than a human at something seen as uniquely human, followed by the hopeful realization that we can use these machines to transcend our limitations while maintaining our humanity. This is not present in contemporary AI. Our moment seems to be one of a hopeless anxiety as we see the unbelievable unchecked power of corporations being wielded to further push people into vicious cycles. Instead of transcending the human element of the things that provide us meaning, we transcend how much garbage we can consume while our meaning is stripped in the process. This is antithetical to the good life.

I want to go back to the world of hope that we had in 2016. It was easy to see how the naive fear of the machine could be combated with a positive, transcendental vision of the future. But in our current moment, the power dynamics at play deny me from seeing such a future.