Why I don’t think AGI is right around the corner

**Dwarkesh Patel** (0:00)
Okay, this is a narration of a blog post I wrote on June 3rd, 2025, titled, Why I Don't Think AGI is Right Around the Corner. Quote, things take longer to happen than you think they will, and then they happen faster than you thought they could. Rootager Dornbusch. I've had a lot of discussions on my podcast where we haggle out our timelines to AGI. Some guests think it's 20 years away, others, two years. Here's where my thoughts lie as of June 2025 Continual learning.
Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the Internet. I disagree. I think that the LLMs of today are magical, but the reason that the Fortune 500 aren't using them to transform their workflows isn't because the management is too stodgy. Rather, I think it's genuinely hard to get normal human-like labor out of LLMs. And this has to do with some fundamental capabilities that these models lack. I like to think that I'm AI-forward here at the Dwarkesh Podcast, and I've probably spent on the order of 100 hours trying to build these little LLM tools for my post-production setup. The experience of trying to get these LLMs to be useful has extended my timelines. I'll try to get them to rewrite auto-generated transcripts for readability the way a human would, or I'll get them to identify clips from the transcript to tweet out. Sometimes I'll get them to co-write an essay with me passage by passage. Now, these are simple, self-contained, short-horizon, language in, language out tasks. The kinds of assignments that should be dead center in the LLMs repertoire. And these models are five out of 10 at these tasks. Don't get me wrong, that is impressive. But the fundamental problem is that LLMs don't get better over time the way a human would. This lack of continual learning is a huge, huge problem. The LLM baseline at many tasks might be higher than the average human's. But there's no way to give a model high-level feedback. You're stuck with the abilities you get out of the box. You can keep messing around with the system prompt. But in practice, this does not produce anywhere close to the kind of learning and improvement that human employees actually experience on the job. The reason that humans are so valuable and useful is not mainly their raw intelligence. It's their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task. How do you teach a kid to play a saxophone? Well, you have to try to blow into one and listen to how it sounds and then adjust. Now, imagine if teaching saxophone worked this way instead. A student takes one attempt, and the moment they make a mistake, you send them away and you write detailed instructions about what went wrong. Now, the next student reads your notes and tries to play Charlie Parker Cold. When they fail, you refine your instructions for the next student. This just wouldn't work. No matter how well honed your prompt is, no kid is just going to learn how to play saxophone from reading your instructions. But this is the only modality that we as users have to teach LLMs anything. Yes, there's RL fine-tuning, but it's just not a deliberate adaptive process the way human learning is. My editors have gotten extremely good, and they wouldn't have gone that way if we had to build bespoke RL environments for every different subtask involved in their work. They've just noticed a lot of small things themselves and thought hard about what resonates with the audience, what kind of content excites me, and how they can improve their day-to-day workflows. Now, it's possible to imagine some ways in which a smarter model could build a dedicated RL loop for itself, which just feels super organic from the outside. I get some high-level feedback, and the model comes up with a bunch of verifiable practice problems to RL on, maybe even a whole environment in which to rehearse the skills that thinks it's lacking. But this just sounds really hard, and I don't know how well these techniques will generalize to different kinds of tasks and feedback. Eventually, the models will be able to learn on the job in the subtle organic way that humans can. However, it's just hard for me to see how that could happen within the next few years, given that there's no obvious way to slot in online continuous learning into the kinds of models these LLMs are. Now, LLMs actually do get kind of smart in the middle of a session. For example, sometimes I'll co-write an essay with an LLM. I'll give it an outline, and I'll ask it to draft an essay passage by passage. All that suggestions up to paragraph four will be bad. And so I'll just rewrite the whole paragraph from scratch and tell it, hey, your shit sucked, this is what I wrote instead. And at that point, it can actually start giving good suggestions for the next paragraph. But this whole subtle understanding of my preferences and style is lost by the end of the session. Maybe the easiest solution to this looks like a long rolling context window like cloud code has which compacts the session memory into a summary every 30 minutes. I just think that titrating all this rich tacit experience into a text summary will be brittle in domains outside of software engineering, which is very text based. Again, think about the example of trying to teach somebody how to play the saxophone using a long text summary of your learnings. Even cloud code will often reverse a hard-earned optimization that we engineered together before I hit slash compact, because the explanation for why it was made didn't make it into the summary. This is why I disagree with something that Sholto and Trenton said on my podcast, and this quote is from Trenton.