In this interview, the speakers discuss the rapid evolution of generative AI, noting a shift from sentence‑completion copilots to more autonomous AI agents that can handle larger portions of work. They compare the current excitement to the crypto boom, anticipate next‑generation models like Meta’s GPT‑5 with longer context windows, and highlight projected productivity gains—50‑75% for copilots versus up to 250% for agents, citing examples such as Klarna’s reduction of two‑thirds of its customer‑support staff and the automation of security tasks. The conversation also explores the societal impact of automation, using robotic surgery as an analogy to illustrate a training gap where senior experts are replaced by machines, potentially leaving junior professionals without hands‑on mentorship. Technically, they describe emerging hybrid workflows where agents summarize tasks, generate queries to external databases, and pass results through classifiers to decide if human attention is needed. The dialogue ends with a brief query about trends in non‑generative AI, setting the stage for further discussion.
Generative AI has obviously been the big story of the last few years and it seems to be changing pretty fast. So what trends are you seeing at the moment?
I think you're right. I mean it feels like crypto did maybe two or three years ago where every morning you'd wake up and there'd be a new paper and the world would have changed. And I definitely feel that way. I think we're all waiting with bated breath for the next generation of the models, the ones that Facebook Meta has promised that GPT5 which seem like they'll be able to work on much longer duration tasks. So until we know what that looks like. But in the meantime, I think there's definitely been a shift, I would say last year to this year from preferring co pilots which complete your sentences to agents which will fully automate work or at least some fraction of the rope work.
Okay, no, that's very interesting because yeah, there's been a big refrain of us year or two like AI is not going to take your job, it's going to augment it. But agents sort of promise to just like automate things and completely take away that human from the loop.
So.
Yeah, tell me more about that.
Yeah, for sure. So I mean I think what we've seen some of the productivity stats on copilots which are about 50 to 75%, that's Microsoft and service. Now the other way around, Microsoft is 75. If the LLM agents are anything like mechanical robots, the promise is to increase productivity by about 250%. So one robot would take the place to two and a half humans, which is what's happened on automotive manufacturing lines. We're still really early there. I would say we're starting to see the very first applications of this and security automation or customer support, where Klarna cut two thirds of their customer support staff and we can debate the relative effectiveness of the bots, but they did it and they saved a lot of money doing it. And so I think what we're seeing across the industry, people are really excited about this trend and the, the nature of like entry level tasks will change. There was a really interesting Twitter thread about this where they were talking about, okay, so where robots have taken off in the last 10 years is robotic surgery, particularly like urology or areas in the body where there's just a very difficult way, a very difficult space to navigate. And as a result robots, you know, they can move in ways that humans can't because their fingers, so to speak, are much narrower. There's now this training gap where the, the senior surgeons are no longer teaching the junior surgeons side by side, the junior surgeons are now just watching the robot operate. And so as a result, there'll be this generation of surgeons or security analysts or customer support reps who are experts in the domain. And then the younger ones don't receive the benefit of the training. And so that's, that's an interesting dynamic that we're sort of paying attention to. But in terms of like the technology itself, we're looking for tasks where there's a lot of summarization, maybe some initial customization. Some of these agents are actually starting to write their own queries to hit external databases to enrich and then produce a summary output. And then that goes to a classifier that says is this worth a human's attention or not?
So on the other side of AI, so what about non generative AI? Are you seeing any changes there?
I think what? Well, it's not moving as fast. It's not to say that it's not important. We're starting to see combinations of generative AI and class, we'll call them classical methods. One reason for this is that the generative AI mechanisms tend to be chaotic and non deterministic. If I ask how do I reset my password period? And I ask the question how do I reset my password space period, that's a different question that will elicit a different answer from a generative AI model. And if you start to chain these inputs and outputs across generative AI steps, you might have error that explodes. And so one of the, and this is very early on, but we're starting to see companies take the output of a generative AI step and then apply a classical machine learning method to it to classify it or just to kind of constrain the error and then pass it on to the next step. Pass it on to the next step. We've met a company recently where there's three or four different kinds of machine learning that are being used all in one system. Generative AI is really good at two different things. It's really good at generating ideas. So four to five great titles for this blog post that I wrote right now. And then deterministic methods as a classical methods are the ones that are really good at picking. And so I think you'll see this nice marriage between the two where most generative AI applications will not be entirely genai, they won't just be the wrappers. And as we get to the next step, you'll see we call them constellation of models and so there's just to kind of talk about it in one level more detail. You know, input from the user comes in. There's a classifier that could be a classical classifier or Genai classifier that says, what kind of query is this? Have I seen this before? And then given that output, it passes it either to a small language gen AI model that knows what it's talking about, or if it's like a completely new query, it goes to something like a ChatGPT4. Is it going to handle a whole universe? Or if you go to a classical.
