I’ve had a lot of discussions on my podcast where we haggle out timelines to AGI. Some guests think it’s 20 years away - others 2 years. Here’s an audio version of where my thoughts stand as of June 2025. If you want to read the original post, you can check it out here. Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Full Episode
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. Rudiger 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 Thorcash 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 LLM's repertoire. And these models are 5 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 just 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 her 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.
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