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.)
4. Nobody knows exactly how large language models work.
Sure, we know how to build them. We know how to make them work really well—see no. 1 on this list.
But how they do what they do is still an unsolved mystery. It’s like these things have arrived from outer space and scientists are poking and prodding them from the outside to figure out what they really are.
It’s incredible to think that never before has a mass-market technology used by billions of people been so little understood.
Why does that matter? Well, until we understand them better we won’t know exactly what they can and can’t do. We won’t know how to control their behavior. We won’t fully understand hallucinations.
5. AGI doesn’t mean anything.
Not long ago, talk of AGI was fringe, and mainstream researchers were embarrassed to bring it up. But as AI has got better and far more lucrative, serious people are happy to insist they’re about to create it. Whatever it is.
AGI—or artificial general intelligence—has come to mean something like: AI that can match the performance of humans on a wide range of cognitive tasks.
But what does that mean? How do we measure performance? Which humans? How wide a range of tasks? And performance on cognitive tasks is just another way of saying intelligence—so the definition is circular anyway.
Essentially, when people refer to AGI they now tend to just mean AI, but better than what we have today.
There’s this absolute faith in the progress of AI. It’s gotten better in the past, so it will continue to get better. But there is zero evidence that this will actually play out.
So where does that leave us? We are building machines that are getting very good at mimicking some of the things people do, but the technology still has serious flaws. And we’re only just figuring out how it actually works.
Here’s how I think about AI: We have built machines with humanlike behavior, but we haven’t shrugged off the habit of imagining a humanlike mind behind them. This leads to exaggerated assumptions about what AI can do and plays into the wider culture wars between techno-optimists and techno-skeptics.
It’s right to be amazed by this technology. It’s also right to be skeptical of many of the things said about it. It’s still very early days, and it’s all up for grabs.
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