Faster/Slower: Where AI Is Moving Ahead of Expectations and Where its Lagging
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The AI Daily Brief: Artificial Intelligence News and Analysis
17 min
1 speaker
2 chapters
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Today on the AI Daily Brief, a fun game called Faster and Slower where we see what's moving more quickly in AI than expected and what's moving a little bit less quickly than expected. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends. Welcome back to another AI Daily Brief. As you know, I am traveling this week, so things are a little bit different. No video for one, some slightly different topics for another, but I think you're going to have fun with this one, or at least I hope you will. One of the things that's absolutely happening right now, and I think everyone who's paying close attention feels like it, is that we are in a punctuated equilibrium moment. For those of you who aren't familiar with that term, it comes from Stephen Jay Gould and was a term that was used to describe and really change how we thought about evolution.
For a long time, we thought about evolution as a steady, gradual incline, all at kind of the same pace and up into the right curve at the same angle the whole time. In point of fact, what it actually looks like when you dig into the fossil record is long periods of dormancy followed by massive explosionary periods of change followed by periods of dormancy followed by periods of massive change in this sort of interesting step function that goes up and gets us to the same spot, but happens in a very different and much messier way than we thought. Technology evolution feels a bit like that as well, where sometimes, yes, there's just general increases, but you have these periods where it feels like you're kind of on a low burn, and then other times where it feels like everything is shifting all at once.
Now, of course, when you dig underneath, perhaps part of what the difference was, was that things were bubbling and brewing during those theoretically quiet times. But whatever it is, I think that it's safe to say that a good chunk of 2024 felt like one of those low periods. So much of the time was spent trying to catch up to GPT-4, and then everything got there. And we just kind of sat there for a while. That was until the end of the year when it started to feel like things were picking up again with the launch of reasoning models, the emergence of more capable agents, and a number of other trends that have all contributed to the sense that I think people have now that we are in another punctuated equilibrium moment.
So with that as background, let's talk about a few things that are moving faster and slower. And what we're going to do is go through three sets of lists. We're going to talk through first the quick list that I came up with off the top of my head. Then second, we're going to look at what the deep research tools from Grok, OpenAI, and Perplexity thought. And then we're going to look at one list curated from the web, which I thought was particularly interesting and had some different details than I had put in mine. All right, so starting with my list, and I'm going to bounce between faster and slower because as you'll see, sometimes they're a both and. So just to really level set, let's talk about capabilities.
I kind of gave this away a little bit in the intro, but I think that for most of 2024, it felt like capabilities, and by that I obviously mean the specific capabilities of the underlying models and the state of the art, was a little bit slower than people expected. It felt like there was this blistering race across 2023, but then we stagnated for most of 2024 at GPT-4 level, roughly speaking. That seemed really weird, and in fact, some people wondered if this was just OpenAI slow-walking it because it made more sense strategically than to get out as far ahead as it seemed like they were probably going to. Obviously, now that has started to shift a little bit, and it feels like there has been a major capabilities increase.
Part of that has to do with the switch to a new approach to scaling that isn't strictly based on the amount of compute and data thrown in pre-training, but is based on new strategies like test time compute. And in fact, that leads me to my next slower. It's very clear that the pre-training scaling model has slowed down in terms of its efficacy.
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