Why Agents Still Need Humans

**Nathaniel Whittemore** (0:01)
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.
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 in 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. 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 OpenClaw 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. In fact, instead of finishing your workday at 3 p.m., the more common challenge was people having to force themselves to go to bed at 3 a.m. Tearing themselves away from the next thing they could accomplish, which was always just sitting there waiting for them.
Now, I discuss this phenomenon in my episode, Why Agents Make Every Job a Startup. In that episode, I introduced the concept of the infinite backlog, and argued that simply put, agents make it feel like for the first time, we have beaten the end boss of time. Even in the assisted AI paradigm, there was a reasonable end to your work, because you, as the user of AI, simply couldn't do anymore. But agents aren't you doing a thing, agents don't get tired, they don't have to stop. The only reason that an agent isn't working is if you haven't given it something to do, which means that it no longer feels like there is an actual end, there was always just work that you didn't give the agent. And as it turns out, the amount of work to be done is not actually bounded, there is always something next. This is what I referred to as the infinite backlog.
What's amazing about agents is that they can do more of the infinite backlog than was ever possible before. A single person can go deeper into that infinite backlog than was literally ever possible, which is why it feels so incredible to build things with agents that you had never dreamed of. At the same time, agents make it feel like you should be able to do the entire infinite backlog, and that anything your agents are not doing is because you haven't given them the tools to do it.
This is a very particular and new type of overwhelm that was not on most people's radars when it came to the implications of AI for work.
Now, one of the most interesting companies at the forefront of experimentation with AI is Every. Every is part publication, part product company, part consultancy, and really walks the walk when it comes to experimenting with how to run an AI-native company.