Ep 881: Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? (Replay)

Jordan Wilson (0:01)

I am the only full-time employee at Everyday AI. Sure, I have a few contractors helping out, but I'm the only full-time human responsible for creating this daily podcast that reaches millions of people a year. Others are kind of always shocked to hear that, as most of the other top 25 tech podcasts have large teams to produce the same amount that we produce. But do you know what I do all day? I just manage agents. I mean, literally right now, I have Codex running with at least 50 sub-agents active. I have Codex controlling Cloud Code, and I have scheduled agents running in Google Gemini, Cloud Cowork, Copilot, and others. Maybe I'm a little overly productive from a single human-to-output perspective, but it's about to get worse or maybe better depending on your perspective. That's because as soon as today or Friday at the latest, we'll have a new OpenAI model powered by Cerebras that runs up to 20 times faster than their current models at the same capabilities. That got me thinking, what the heck is the future of work going to be like? I mean, I'm already agent-pilled in producing more content and hopefully value than much larger teams with many more humans. So what happens when all of my agents are 20 times faster? I'm not sure yet, but I wanted to kind of opine about that out loud on today's show and explore this weird conversions that we're in now. Because not only are agents producing economically valuable work end to end at a higher quality bar than expert humans, but now we are entering a new phase where those AI agents might be working 20 times faster. I don't know if anyone truly understands what this convergence means for the future of work, but let's unpack it and see what we might come up with anyways on today's episode of Everyday AI. So here is the big picture. What the heck is going to happen when millions of professionals, like me and you, business leaders that are on the cutting edge of AI, what's going to happen when we just become managers of faster agents and they become more powerful? Right now, AI is fast and parallel enough that one person can direct dozens of agents a day. That's pretty much all I do all day. But OpenAI, as well we are seeing, maybe by the time you're listening to this, it will already be released. But at least as of now, the rumors are saying that we're going to be getting a new OpenAI model powered by Cerebras. That's essentially this really fast ship. Essentially, let's say you put a very smart agent out there and it thinks, and normally it takes five minutes and you get a great output. Well, what happens when that five minutes becomes 30 seconds? Normally, you go, I don't know, manage other agents or do other tasks during that time that you're waiting. What happens when things are just instantly done? The upside here is real, with workers reporting more autonomy and more outputs. But the risk is maybe a workday spent managing machines, while human trust and mentorship quietly just thins out. On today's show, here's what you're going to learn. You're going to know why employees are increasingly asking agents before managers, specialists, or awkward conversations. You're going to understand how faster inference turns solo work into supervising parallel machine teams. You're going to know how winning companies can empower human judgment, while still automating most of the manual knowledge work. And you're going to leave the show with the three human handoffs to protect before your workflow quietly deletes them. All right. Welcome to Everyday AI. If you're new here, my name is Jordan Wilson. We do this every day. It's for you. It's your daily, unedited, unscripted live stream podcast and free daily newsletter, helping business leaders like you and me keep up with drinking from the fire hose of new AI updates. I turn that fire hose down. I serve you nice glass of water and say, here, this is what matters today. Go take this information and share it with your team and become really smart in AI. That's what we do here. Hopefully, the podcast is helpful. If you didn't know, we recap it all in our daily newsletter. Make sure that you go check out our newsletter for that and all of the other AI news. All right.

When did this start happening?

I'm not sure.

As someone that follows this every single day, this kind of convergence now that is maybe going to start today for people on the edge of the edge. But I think the rest of the enterprise will start to feel with this in maybe six to nine months. But we are now going to be at this convergence very quickly, where we've had these extremely capable agents. Now for the most part, running on desktop. So whether you're using Codex, ChatGPT Work, Cloud Desktop, something else, right? But I'd say those and maybe Cursor are the big players, at least when it comes to people, number of people using them. I don't think Google is there just yet with, you know, anti-gravity or their Gemini desktop. Microsoft CEO Satya Nadella did mention again yesterday, their super app coming out. So I do know that most of our, many of our listeners out there are using Microsoft Windows and Copilot. So you'll be living in this world soon too if you haven't already, maybe with, you know, Copilot, Cowork. But we're at this place now where we have these agents that if you know what you're doing, they will literally on-demand output the same level, the same asset, the same deliverable as expert humans, right? We see this in important benchmarks like GDPVAL that, you know, a human and a large language model get the same inputs and then they create outputs. They're judged blindly by subject matter experts. And you know, we're at the point now where AI models win or tie about 90% of the time. So AI models or AI agents can actually do the same work that humans do and create these assets or artifacts or deliverables, which is cool and all, right? But you know, I'd say the downside? No, it's not even technically a downside, right? I always tell people, be a little patient. But you have to, number one, know what you're doing. Number two, you have to understand kind of agent guardrails and context engineering, which those are kind of basics, table stakes now. But number three is a little bit of time, right? And that little bit of time is now what could be changing today. Whereas before, you know, I have agents that work for many hours. But I'd say my average agent task maybe lasts about five minutes. So for the most part, all day, I'm just flipping between agents, right? And one of the big things I like doing is doing that on ChatGPT voice modes.