The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
Faster/Slower: Where AI Is Moving Ahead of Expectations and Where its Lagging
Thu, 27 Feb 2025
To many, it feels like a moment of significant acceleration. We're getting a new model every few days, major advances in agents, and generally there's a sense of things getting faster. And yet, not everything is moving in lock-step. NLW breaks down what in AI is moving faster than expected, vs what's moving slower.Brought to you by:KPMG – Go to www.kpmg.us/ai to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - https://vanta.com/nlwThe Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown
Chapter 1: What is the main topic of this episode?
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.
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Chapter 2: Why is AI experiencing a punctuated equilibrium?
Flipping over again to the faster side, both China and open source have been moving, I think, much more quickly than people would have anticipated. With Llama 3 last year, open source moved very close to the state of the art.
And really, ever since the beginning of this, open source has been, I think, out-competing the closed source models in ways that people outside of the open source movement, at least, might not have expected. Now, how related that is to the fact that China is clearly not nearly as far behind as we would have thought is an open question. Obviously, U.S.
presidential administrations have been very aggressive with China when it comes to access to advanced AI chips. And yet still, the big shocking event of the last couple of months is DeepSeek, a model which, while not necessarily beating out the state of the art from companies like OpenAI, was close enough and good enough that it has absolutely changed the competitive landscape.
You'll know if you're a regular listener that there are a lot of geopolitical implications of China being as hot on the U.S. 's heels as they are. And that, I think, is going to be a big factor in how things play out over the next year. Finally, let's talk about agents.
Once again, I think agent capabilities up until about the last couple of months felt to many like they were moving slower than people might have anticipated. And in some areas, I still think that that's true even to today. For example, I think agentic computer use is a bit behind where people thought it would have been.
And I think that historically, there's been so much concentration around general purpose agents, right? Like people's personal agent assistants, that the fact that that use case hasn't really come to fruition has been surprising for some. Now, I never thought that that was where agents were going to go, so that doesn't surprise me as much.
But I also think that we are now officially heading into the faster category as agent capabilities, particularly in specific verticals and in specific functions, start to come online. Basically, we needed to make a shift from thinking about agents as general purpose to specific purpose, and now things are really starting to accelerate.
Alongside that, agent adoption is poised to absolutely explode as well. Something that you can probably tell if you listen regularly is that agent adoption has completely sucked all of the oxygen out of the room when it comes to every other type of AI discussion in corporate boardrooms right now. And I think that that is going to do nothing but accelerate.
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