The video is now available (thank you, SXSW London). Below is a quick look at my top five. Let me know if you would have picked different ones! 1. Generative AI is now so good it’s scary. Maybe you think that’s obvious. But I am constantly having to check my assumptions about how fast this technology is progressing—and it’s my job to keep up. A few months ago, my colleague—and your regular Algorithm writer—James O’Donnell shared 10 music tracks with the MIT Technology Review editorial team and challenged us to pick which ones had been produced using generative AI and which had been made by people. Pretty much everybody did worse than chance.What’s happening with music is happening across media, from code to robotics to protein synthesis to video. Just look at what people are doing with new video-generation tools like Google DeepMind’s Veo 3. And this technology is being put into everything.My point here? Whether you think AI is the best thing to happen to us or the worst, do not underestimate it. It’s good, and it’s getting better.
2. Hallucination is a feature, not a bug. Let’s not forget the fails. When AI makes up stuff, we call it hallucination. Think of customer service bots offering nonexistent refunds, lawyers submitting briefs filled with nonexistent cases, or RFK Jr.’s government department publishing a report that cites nonexistent academic papers. You’ll hear a lot of talk that makes hallucination sound like it’s a problem we need to fix. The more accurate way to think about hallucination is that this is exactly what generative AI does—what it’s meant to do—all the time. Generative models are trained to make things up.What’s remarkable is not that they make up nonsense, but that the nonsense they make up so often matches reality. Why does this matter? First, we need to be aware of what this technology can and can’t do. But also: Don’t hold out for a future version that doesn’t hallucinate. 3. AI is power hungry and getting hungrier. You’ve probably heard that AI is power hungry. But a lot of that reputation comes from the amount of electricity it takes to train these giant models, though giant models only get trained every so often.What’s changed is that these models are now being used by hundreds of millions of people every day. And while using a model takes far less energy than training one, the energy costs ramp up massively with those kinds of user numbers. ChatGPT, for example, has 400 million weekly users. That makes it the fifth-most-visited website in the world, just after Instagram and ahead of X. Other chatbots are catching up. So it’s no surprise that tech companies are racing to build new data centers in the desert and revamp power grids.The truth is we’ve been in the dark about exactly how much energy it takes to fuel this boom because none of the major companies building this technology have shared much information about it. That’s starting to change, however. Several of my colleagues spent months working with researchers to crunch the numbers for some open source versions of this tech. (Do check out what they found.)
