The AI Daily Brief: Artificial Intelligence News and Analysis
Does Work Still Matter in the Age of AI?
11 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
Today, we are discussing one of the most unknowable but much thought about questions in and around AI, which is, of course, how it will change our jobs and the work that we all do.
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Now, this is a weekend episode, which means, of course, a long read slash big think episode. And as I mentioned last week, we are still working our way through the spate of big think essays that ended last year and began this year.
For today's show, we're actually going to string excerpts of about five together, with the first being from Dwarkesh Patel and Philip Trammell called Capital in the 22nd Century. Now, this is an extremely long form and dense essay, and there has been a ton of debate around it.
It's brought up questions of redistribution and wealth policy and tax policy, but that's sort of not exactly the line that I'm going to thread. In fact, we're going to focus on the parts that pick up and set the story for this post from Ben Thompson at Stratechery called AI and the Human Condition. So let's read the first excerpt from Capital in the 22nd Century.
Dworkesh and Philip write, In his 2013 Capital in the 21st Century, the socialist economist Thomas Piketty argued that, absent strong redistribution, economic inequality tends to increase indefinitely through the generations, at least until shocks like large wars or prodigal sons reset the clock.
This is because the rich tend to save more than the poor and because they can get higher returns on their investments. As many noted at the time, this is probably an incorrect account of the past. Labor and capital complement each other. Wealthy people can keep accumulating capital, but hammers grow less valuable when there aren't enough hands to use all of them.
And hands grow more valuable when hammers are plentiful. Capital accumulation thus lowers interest rates, aka income per unit of capital, and raises wages, income per unit of labor. This effect has tended to be strong enough that, though inequality may have grown for other reasons, inequality from capital accumulation alone has been self-correcting.
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Chapter 2: How does AI change the nature of work and expertise?
He writes, Part of the thinking is that once AI can create AI, it can rapidly accelerate the development of robotics as well, until robots are making robots each generation more capable than the last, until everything humans do today, both in the digital but also the physical world, can be done better by AI.
This is the world where capital drives all value and labor none, in stark contrast to the approximately 33% share of GDP that has traditionally gone to capital, with 66% share of GDP going to labor. After all, you don't pay robots for marginal labor.
You build them once, check that they build themselves for materials they harvested, not just here on Earth but across the galaxy, and do everything at zero marginal cost, a rate at which no human can compete. From there, however, Ben gets into his skepticism.
I get the logic of the argument, he writes, but I, perhaps once again over-optimistically, am skeptical about this being a problem, particularly one that needs to be addressed right here, right now, before the AI takeoff occurs, especially given the acute need for more capital investment at this moment in time.
The world Patel and Trammell envision sounds like it would be pretty incredible for everyone. If AI can do everything, then it follows that everyone can have everything, from food and clothing to every service you can imagine. Remember, the AI is so good that there are zero jobs for humans, which implies that all of the jobs can be done by robots for everyone.
Does it matter if you don't personally own the robots if every material desire is already met? Second, on the flip side, this world also sounds implausible. It seems odd that AI would acquire such fantastic capabilities and yet still be controlled by humans and governed by property laws as commonly understood in 2025.
Third, it's worth noting that we have seen dramatic shifts in labor in human history. Consider both agricultural revolutions. In the pre-Neolithic era, 0% of humans worked in agriculture. Fast forward to 1810 and 81% of the US population worked in agriculture. Then came the second agriculture revolution, such that 200 years later, only 1% of the US population works in agriculture.
It's the decline that is particularly interesting to me. Humans were replaced by machines, even as food became abundant and dramatically cheaper. No one is measuring their purchases based on how much food cost in 1700, just as they won't measure their future purchases on the cost of material goods in a pre-robotics world. That's because humans didn't sit on their hands.
Rather, entirely new kinds of work were created, which were valued dramatically higher. Much of this was in factories, and then over the last century, there was a rise of office work.
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Chapter 3: What are the implications of AI on economic inequality?
Even if AI does all of the jobs, humans will still want humans, creating an economy for labor precisely because it is labor. You can't make the case for the potential that jealousy ought to drive authoritarian capital controls while completely dismissing the possibility that the prospect of desirability gives everyone jobs to do, even if we can't possibly imagine what those jobs might be.
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Chapter 4: How do AI and automation affect job creation?
The good, he writes, though, is that software engineers are more valuable than before. Tech-lead traits are in more demand, being more product-minded to be a baseline at startups, and being a solid software engineer and not just a coder will be more sought after than before. The ugly, he writes, is uncomfortable outcomes.
More code generated will lead to more problems, weak software engineering practices start to hurt sooner, and perhaps a tougher work-life balance for devs. And certain roles, he estimates, are going to change in pretty fundamental ways. The one that he's interested in is product management versus software engineering.
Product managers can now generate software easier, he writes, needing fewer engineers to realize their goals, but software engineers also need less product management. Both professions are set to overlap with one another more than before.
Now what's interesting and valuable is not just that people are starting to have the conversation about how these roles will change, they're starting to try to lean into it and create new blueprints that people can actually put into practice. Google senior AI product manager Shobham Sabu wrote a piece on X called The Modern AI PM in the Age of Agents.
It is an exploration of exactly this, how the role of product manager is changing. And I think as you listen to parts of this, you'll find that there is probably a lot here that's not just relevant for product managers. He writes, the job of a PM used to be translation. You talk to customers, synthesize their problems, wrote specs and handed them to engineers.
You were the bridge between what people need and what gets built. The value was in that translation layer. That layer is compressing. When agents can take a well-formed problem and produce working code, the PM's job shifts. You're no longer translating for engineers. You're forming intent clearly enough that agents can act on it directly. The spec is becoming the product.
He continues, The cycle took weeks. Now, they write a clear problem statement with constraints, point an agent at it, and review working code in an hour. The time between, I don't know what we should build and here it is, collapsed. But the work of knowing what to build didn't get easier. It got more important. You don't need to write the code yourself.
You need to know what you want clearly enough that an agent can build it. The spec and the prototype are becoming the same thing. You just describe what you want, watch it take shape, course correct, and iterate. The bottleneck isn't implementation anymore, and the speed of shipping is only accelerating.
I've been at Google for around three to four months now, and it feels like we've shipped years worth of AI progress. Every big and small AI company is shipping at this pace thanks to AI coding agents. The cycle times that used to define product development from quarterly planning, monthly sprints to weekly releases are compressing into something closer to continuous deployment of ideas.
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Chapter 5: What role does creativity play in an AI-driven future?
He writes, "...we're all becoming gamers. We're quickly moving towards a world where, with AI, we'll all be able to craft tools to help us better play the game of life. For those who grew up playing video games, you understand what I mean. It should help you turn ideas into real things, instantly get unstuck on hard problems, and operate beyond what one person could normally do alone."
Nowhere is this more true than in AI development platforms like Replit. At scale, these platforms will make life start to feel like you're progressing through a game. Each new challenge is a level, and AI is how you craft a way forward. For centuries, humans have built tools to get ahead, sometimes individually, sometimes together.
But as economies matured, most of us stopped building tools and started relying on the ones already available to work faster, live better, and scale what we were doing. Software took this trend to its extreme. Most people don't use software that's designed for them.
They use general-purpose tools built for the median user, tools that improve generic workflows but rarely map cleanly onto the specific problems any one person is actually trying to solve. That trade-off made sense, as generalized software could scale to help more people and generate more revenue.
For the user, though, it created a paradigm where a specific tool to solve a specific problem was hard to find. So you either had to patch a bunch of consumer software together, annoying, learn to code, time-consuming, or could convince someone else to do it for you, often expensive. With Replit, that paradigm has been shattered.
Now building software is easy, and it almost feels like you're playing a game, trying to craft the perfect tool to beat the level that's been stumping you for weeks. A useful analogy here is Minecraft. Minecraft doesn't give you a finished solution or a prescribed path. It gives you a world, a set of primitives, and fast feedback. If you need a tool, you build it.
If the tool isn't right, you can try another way. You don't wait for a perfect object to exist. You craft what you need from what's available. Replit increasingly feels like that kind of environment for software. Reed concludes, In a few years, we'll shift from thinking, what can I buy to help me, to what can I build to help me?
Work and life will feel like progressing through levels, where each new challenge is met not by waiting for the right software to exist, but by creating it. The real change isn't that everyone becomes a programmer. It's that everyone gains the ability to shape their environment, extend their capabilities, and move forward under their own control.
The real change is that everyone becomes a gamer, building for the most important game they'll play. I don't know ultimately what the future of AI is. I don't know how it's ultimately going to change software engineering jobs to say nothing of the rest of knowledge work.
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