The AI Daily Brief: Artificial Intelligence News and Analysis
Why Agents Still Need Humans
24 May 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is the next wave of human-agent collaboration?
Today on the AI Daily Brief, the next wave of human agent collaboration. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Robots and Pencils, Zen Coder, Assembly, and Super Intelligent. To get an ad-free version of the show, go to patreon.com slash ai-dailybrief, or you can subscribe on Apple Podcasts.
If you want to learn more about sponsoring the show or really know anything else about the show, you can go to ai-dailybrief.ai. One thing I would point you to is the newsletter, which is back. If you're ever wondering where you can find the links to all of the articles and quotes and tweets and things that I reference, the newsletter is going to be your best bet for that.
Now today, we are doing a long read slash big think episode, and we're getting at a theme that's at the core of AI operations this year. Obviously, 2026 has been all about agents actually becoming real. And they became real because of a combination of the model advancements at the end of last year, as well as the greater focus on harnesses, i.e.
the interfaces through which we interact with agents. Through the combination from January till now, the way that we use AI is no longer sit there, prompt it, wait for an answer, and go off and do the rest of our work. Instead, increasingly, it is about spinning up or managing agents that go out and produce things on our behalf.
Agents that can use code to build things or solve problems, even when we're not coders ourselves. And of course, the implications of this have been massive. Business models are shifting as companies are no longer able to subsidize the biggest power users of AI who can consume hundreds of millions or even billions of tokens themselves individually in a single month.
Indeed, more broadly, we are starting to live inside a world of token shortage, where the total amount of AI that would be consumed if it could is higher than the amount of AI that is available thanks to constraints of compute. Throughout the last few weeks, we've been talking about some of these big implications.
But one that we haven't mentioned for a little while now is what it means for the patterns in how we work. At the beginning of the year, as open-claw excitement raged, it was all about Mac minis and even for some, Mac studios, running 24-7 agents, doing everything you could possibly imagine. Not only automating your existing world of work, but uncovering new things that were never possible before.
And what was interesting in all of this is that both the promise and the fear of AI... the promise of AI that would reduce how long it took to do your work so you could go enjoy more leisure time, and the fear of AI that would negate your value as a worker, were both very far away from the lived reality of the most advanced users.
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Chapter 2: How are agents changing the way we work in 2026?
Dan writes, there is a paradox at the heart of AI. At every, we've automated everything we can. We use Codex and Cloud Code across coding, writing, design, customer service, and more. We alpha test all of the new models from OpenAI, Anthropic, and Google before they come out. We are riding the exponential boom in model intelligence and automation as far and as fast as possible.
And yet it seems like for us, there's more human work to do than ever. We're a team of almost 30 people, and we haven't fired all of our employees in favor of agents. We haven't ditched SaaS products in favor of vibe-coded apps. We still hire humans to do customer service with a lot of agent assistance, and we still hire human writers and editors and engineers.
Our work does look completely different than it used to, though. We don't write code by hand anymore. If you at mention someone in our Slack, it's a toss-up whether you're talking to a human or an agent. Managers are committing code like ICs, and engineers are talking directly to customers. For the last several weeks, AI has responded to 95% of my work emails.
In short, the future looks weird, but also familiar. The familiarity is surprising because one thing CEOs, knowledge workers, and investors seem to agree on is that AI is a threat to jobs, the economy, safety, and human meaning. Anthropic CEO Dario Amadei warns that AI could wipe out up to half of all entry-level white-collar jobs.
Meta just laid off 8,000 people and is installing software on U.S. employees' computers to capture mouse movements, clicks, and keystrokes for a higher-quality source of AI training data on advanced knowledge work. Even Citadel's Ken Griffin seems shaken, saying recently, these are not mid-tier white-collar jobs.
These are extraordinarily high-skilled jobs being, I'm going to pick a word, automated by agendic AI. Dan then points out that all the benchmarks seem to validate this set of capabilities. It seems, he continues, that we are on the cusp of an AI smarter than any human, with the autonomy to work for almost a full day at a time. And yet, the paradox remains.
If you talk to anyone in the AI industry or to early adopters outside of it, you'll hear the same thing we've noticed internally. There's more work to do than ever. The big question, within the industry and without, is is this just a temporary state of affairs? Will the next model drop be the one to replace everyone?
We watch the benchmarks and sweat, wondering if there's a tipping point around the corner where all of the jobs go away. There's no tipping point coming where things flip and the jobs are gone. The new reality is the opposite. The more we automate, the more expert human work there is to do. Here's why.
AI commoditizes the residue of human expertise, whatever can be made explicit enough to train on. That collapses the value of default model output and creates demand for what's different. And demand for what's different is demand for human experts, even as we approach artificial general intelligence. Moving down, Dan discusses the two modes of working with agents.
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Chapter 3: What is the concept of the infinite backlog in AI?
And what he lands on and what the human sandwich implies is that agents need humans in order for the work to work. Now, interestingly, I noticed a couple weeks ago that Every had also shifted their philosophy of agents in a pretty dramatic way. Initially, around the first blush of open-claw excitement, Every had basically every employee spin up their own AI agent who was a replica of themselves.
The problem they found very quickly was what happened when everyone had their own agent. In another essay reflecting on their first set of experiments, they wrote, every time an agent broke, the person it belonged to had to fix it for themselves. Even with a stable harness, agents require maintenance to perform. This was great for someone who likes tinkering.
The maintenance and back and forth are part of the appeal. For every tinkerer, however, there are a lot of people who want the benefits of an agent without the obligation of having to manage and mend it.
What they discovered, they wrote, is that rather than agents as extensions of their creators, a more successful model is agents as co-workers who reliably perform parts of many different people's jobs. This, among other things, takes the maintenance burden off of the individual. They continue, Imagine a shared analytics agent.
Everyone on the team uses it for metrics-based work, and when its capabilities need to expand, one person updates the agent's skills and the whole team benefits. In the personal agent version of the same scenario, that same update has to happen across 10 different agents. Team-based agents also solve a continuity problem.
A personal agent's value is tied to whomever trained it and disappears if that employee leaves. A team agent with defined capabilities retains company context and knowledge, acting more like a project manager, sales lead, or chief of staff than a private assistant. Now, this maintenance was part of the reason, going back to the essay we started on, why agents were creating more work for humans.
But Dan points out that there is a second reason as well. Continuing with the after automation essay, Dan writes, if you look at AI's exponential trajectory over the last few years and think about how its architecture works and where its powers come from, you'll see clear feedback loops that create more human work. AI makes yesterday's human competence cheap.
Language models are trained on the visible residue of human competence. Code, prose, images, support tickets, product specs, and more. They take all of it, the exhaust of successfully completed tasks, and package it in a form that's available to anyone cheaply.
The net effect is that skills that used to be rare, coding a pull request, making a YouTube thumbnail, writing a newsletter, are now broadly available to almost anyone. Cheap competence gets rapidly adopted. When the cost goes down for something previously rare, supply suddenly goes way up. At Every, we see this all the time.
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Chapter 4: How does Every experiment with AI agents in the workplace?
I'm currently running the Codex app on two devices, my MacBook and my Mac Mini. My laptop isn't reliably connected to Wi-Fi enough, so I keep a Mac Mini on my desk that is always connected. When I kick off new threads from my phone, because remember, a full-featured codex is now in the ChatGPT app, Nick continues, I start them on the Mac Mini. When I'm working from my desk, I run them there too.
The cool part is that I've added my MacBook and Mac Mini as connected devices to each other. That means I can start and resume threads from either device. So if I'm in a meeting but want to continue a thread on my laptop that was started on my Mac Mini, I can do that. What this means, I have an always-on codex that is accessible from my phone with its own dev environment.
All threads are always accessible from any of the three devices, and I can run heartbeat threads that stay on 24-7. It's a little makeshift today, but the shape of it feels very real to me. Codex is no longer tied to whichever computer happens to be open in front of me, it starts to feel like something I can stay connected to across whatever device I'm using.
Okay, so zooming out again, we've got these early experiments in autonomy with OpenClaw that maybe concluded that the managerial burden of that autonomy wasn't the best fit for that particular harness. Meanwhile, these work operating systems in Codex and Cloud Code feel a little bit closer to the right way to manage the correct level of autonomy for these agents as they currently exist.
And with advances of the UX in these harnesses, specifically features that make them more accessible from different devices and on the go, they become less and less reliant on any given device, and more nimble and semi-synchronous.
Now, if you listened to my episode from earlier this week about how to get the most out of Codex, the OpenAI author that inspired the piece, Jason Liu, was nominally giving nine tips for how he gets the most out of Codex,
But when you take a step back, they pretty much all come back to how to better parallel process and live in a state of semi-synchronicity with your agents instead of being stuck in some turn-based paradigm.
In other words, we don't want the purely turn-based paradigm of assisted AI where you give a prompt, you sit around waiting for its response, you review the output, and then you give the next prompt. But we also need more ability to manage the mega autonomy of something like OpenClaw that's mostly just using heartbeats to run itself with you checking in via telegram.
A lot of what people are then experimenting with now is how to use harnesses for some middle space where there's less latency between the instructions and guidance that you need to give the AI and the way that agents can go do that work. So two experiments to consider, one on a personal level, one on an organization level.
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