The AI Daily Brief: Artificial Intelligence News and Analysis - Why Agency Could Be The Most Important Attribute in the AI Economy

Episode Date: April 27, 2025

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Today on the AI Daily Brief, why agency is the most important attribute for the next decade. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends, happy weekend. It being the weekend, we are back with another Long Reads episode. And this one got a lot of discussion going on AI Twitter this week. It's called Agency as Eating the World. It was written by Gianzegado, a founding data scientist and engineer at Replit. Now, Replit is, of course, a vibe coding platform.

And so, Gian is living right in the middle of these transitions. Today, we are going to once again turn this over to an AI version of myself from 11 Labs to read John's piece, and then we'll come back and discuss it with the non-AI me. In 2023, Sam Altman famously said, there'll soon be a one-person billion dollar company. Two years later, we're watching his prediction unfold. And not simply because of AI, but because of the individuals who are wielding it. A new breed of companies is emerging, lean, unconventional, and wildly successful.

They generate hundreds of millions of dollars yet have no sales teams, no marketing departments, no formal HR, not even vertically specialized engineers. They're led by a handful of people doing the work of hundreds, leveraging machines to scale their impact. For years, we feared automation would replace humans. But as AI reshapes the economy, it's becoming clear that far from replacing human ingenuity, AI has amplified it. The critical dividing line in our economy is no longer simply education or specialization, but rather agency itself, the raw determination to make things happen without

waiting for permission. Agency. The word agency is thrown around a lot these days. It's not always clear what it means. Many people in tech today, myself included, are working to create digital systems capable of interacting with and adapting to the external environment to achieve specific outcomes. We call these programs agents. A legal agent might autonomously scan court documents and compile a legal strategy. A financial agent might continuously monitor market conditions and dynamically adjust pricing or investment strategies. For the past three years, I've been part of the core team behind Replit, a coding agent

that writes, runs, and deploy software programs autonomously. Despite its status as standard industry nomenclature, I've grown to dislike the term agent. I find it inaccurate. when building agents were giving programs everything except agency. The appeal of these tools lies in their combination of capability, maneuverability, and predictable behavior. They execute complex tasks while remaining fundamentally responsive to instructions. When some time ago, a few companies started offering more independent coding models, customers didn't like them. The most successful AI

products today are reactive, not proactive, and don't exhibit genuine independence, primarily because that's what customers want, programs that listen when told what to do. High agency people don't. True agency is an unruly psychological trait. It's the willingness to act without explicit validation, instruction, or even permission. It's the meme, you can just do things knowing that you can poke life and something will pop out the other side. It's a venture capitalist with no academic background founding the most important AI lab in history, a gaming entrepreneur creating a $30 billion military company, a fintech CEO birthing the private space industry out of thin air. True agency involves defiance, improvisation, instinct, and often irrationality.

You don't need to be in tech to have high agency, yet you'll find most of these people there, as they naturally gravitate toward low-structure, high-impact environments, namely startups. While driven individuals capable of making things happen have always existed, their bandwidth has historically been limited. It's impossible to scale impact entirely alone. We live in a complex world. To accomplish anything significant, you still need to specialize at some point, which takes time. High agency people aiming to achieve their goals still need to hire specialists or spend years selecting and mastering a discipline. When I founded my previous company a decade ago, it took me nine months to go from idea to working prototype. Nine months spent essentially learning the basics

required to build a digital product. And yet, this effort still didn't put me anywhere near the proficiency of a professional software developer, designer, or marketer. Becoming an expert takes time. Specialization is a necessary precondition for success, making it a form of local monopoly. We live in a homeostatic equilibrium. Even the most intelligent and driven people can't possibly perform every job simultaneously. Stable ecological systems sustain themselves long term precisely because the apex predator can't be everywhere at once. This dynamic explains why being more skilled and educated confers a persistent advantage and why on average our society favors credentials over outcomes.

The optimal strategy is typically to follow the lead, stay in your lane, do your homework, and achieve top marks. To get a job, keep that job, specialize, and climb the ladder. There's a reason college tuition has outpaced inflation multiple times over. People hope it will help them develop specialized skills, creating their own form of vertical monopoly within the workforce. We don't live in a world that's kind of generalists. Well, until AI, that is.

A phase shift. We're now facing a rupture, a phase transition. AI has eroded the value of specialization because, for many tasks, achieving the outcome of several years of experience now takes a $20 chat GPT subscription. If a decade ago, it took me nine months to gain enough experience to ship a single prototype,

now it takes just one week to build a state-of-the-art platform ready to be shipped. A project once only achievable by a full team of professionals. Skeptics will push back against this techno-optimism, arguing that AI is sloppy, intrinsically probabilistic, and prone to mistakes. Build using AI, the story goes, and things will sure enough eventually break, you need an expert to know when to trust the machine. I understand, but disagree. While it's true that specialization hasn't become irrelevant, it no longer matters indiscriminately and uniformly. I expect a bi-modal-shaped distribution of AI deployment, depending on how comfortable we are as a society and with risk. In industries where untrained people equipped with imperfect AI models could make costly

mistakes, we are going to see demand for specialized human accountability. This will include sectors such as defense, healthcare, space exploration, biological research, and AI advancement itself, all domains where the variance of prediction models is higher than the acceptable risk threshold. Wherever mistakes can kill and AI can't prove to be virtually all-knowing, we can expect regulation to enforce natural barriers and the need to hire experts. It's similar to why we continue to require human pilots despite having the technological capacity for autonomous flight. Sometimes we just want the ability to point a finger. However, for most jobs, this is not true. Wherever we are okay with trying again after getting a bad AI generation, we will see market disruption.

Data science, marketing, financial modeling, education, graphic design, counseling, and architecture will all experience an influx of non-specialized high-agency individuals. Sure, machines will keep making mistakes, but their rate of improvement has been astronomical and will only continue to delay the point at which generalists feel the need to hire experts. Three years ago, AI could simply auto-complete small code snippets. Two years ago, it started fixing broken programs. Last year, it began creating new projects from scratch. It now can autonomously understand large projects created by thousands of human developers

and be used even by non-professionals. The game has shifted, and the winning strategy with it. It's no longer about understanding specialized details. It's about grasping the high-level global picture. It's less about knowing how to patch a system and more about knowing that it needs to be patched. It's more about architecture and less about implementation. precisely where generalists thrive. For these individuals, boundaries between professions are beginning to blur and overlap. I've begun to see product managers developing business financial

models, designers writing commercial ads, barbershops building custom booking systems, and restaurant owners creating advanced pricing tools. Even domains seemingly far from tech, like agriculture, are beginning to see this impact, with farmers building crop tracking systems. These people always had it in them to do these things. The key difference is that it no longer takes years to learn how. If you extend this argument to the limit, you end up with individuals running entire companies by themselves. The share of solo founder startups has almost doubled in the last few years, and the first examples of businesses with a handful of employees generating hundreds of millions of dollars in revenue have emerged. Henri She, who spent nearly a decade growing super.com into a

150 million ARR business is now tracking his progress towards Altman's one-person billion-dollar company goal on a public leaderboard. He reports an average of $2.8 million in revenue per employee, coincidentally the same as Apple, the most valuable publicly traded U.S. company of the past two decades. Companies like Mid Journey, with its 40 employees and $500 million of yearly revenues, represent a structural shift, not an anomaly. These companies are now full of high-agency individuals carrying the work of several teams and are easily competing with much larger companies. This is the unraveling of credentialism.

Having an edge in the market is no longer about knowing how to do something very specific very well. It's about being biased toward making it happen. A New World. My entire world model has collapsed into a single bit. Agency or no agency. The transition will take time and it will be far from painless.

Institutions built around credentials won't go gently into the good night. Middle management will fight to keep headcount, because it will take time to shift away from the idea that more people working on a problem, signals a more important problem. Schools and colleges will take a while to adapt their teaching methods and content. Only bottom-up market competition will force change. Structure isn't always bad. One-person companies come with high chaos potential and cascading errors can be hard to contain at scale. A solo operator lacks the redundancy of a team.

When the AI fumbles, there's no safety net, and small. mistakes like mishandled tax compliance can snowball into audits, bug fixes, social media rants, and refund chaos. Yet these entrepreneurs will still prove to be mighty competitive forces that big firms can't ignore. The good news is that having high agency is an internal state of mind, and it can be absorbed. It means freeing oneself from artificial constraints and defying them. It's Morpheus challenging Neo in the Matrix. Do you think that's air you're breathing now? the limitations we've accepted as natural, degrees, credentials, specialized skills, years of experience are no longer the barriers we believe they were to making things happen.

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A couple different points of conversation. I think it's a great piece. It's very thought-provoking, and I'm really excited that Gian has shared it and gotten us all to chatter. Now, agency is a safe bet for the term of the moment when it comes to how people are thinking about the transformation we're in on a very high and very broad level. You've probably heard people like why Combinators Gary Tan talk about agency as the most important skill. And in many ways, this is sort of becoming conventional wisdom in Silicon Valley, although that's a very high and that's far from the case outside of it. There are actually a number of different points that Gian is making here.

One of them is about the erosion of the value of specialization. I think Gian's division of the world into effectively risk profiles where the consequence of errors on the one hand are extremely high or the consequence of errors are, on the other hand, fairly low, kind of makes sense and I think is fairly likely to come to fruition. In fact, we're even seeing this sort of division in the way that companies are experimenting with agents right now. there are certain parts of their business where they simply can't abide the current fail rate or

hallucination rate or underperformance rate or however you want to determine it of agents because it's so mission critical. On the other hand, there are areas where the consequence of those problems is simply less pertinent. It is in those consequence light areas that companies are getting their reps with agents now with the knowledge that capabilities continue to trend up. The other part of this argument, however, is about how much more individual people can do. or in other words, how much farther their own agency gets them in a world where they can also hire and deploy AI. The point that I wanted to make here is actually less about startups, which are Gian's focus, and more I wanted to connect the dots back to the Microsoft Work Trend Index that

we talked about earlier in the week that basically predicted that the end state of agents in the office is human orchestrators and agent operators, basically that humans were going to do the planning and that agents were going to do the execution. That's a different way of saying that the key skill sets and attributes of people in the workforce in the future is going to be around planning and coordination of agent armies or swarms

or whichever term becomes popular. And this is why I think the current crop of upskilling and AI education platforms is so fundamentally off. They're still operating on a model where the skills that matter are things like prompting co-pilots and assistant tools,

when the reality is that everyone has to turn themselves into agent managers or to use Microsoft's term an agent boss. I think that the more that we recognize and prioritize the sort of agency that Giann is talking about here, the more quickly we'll be able to reprioritize how we think about training, skills, personal development, and more as we head into this new agenic era. Anyways, one more big thanks to Gian for writing this piece, and thanks to you guys, as always, for listening. Until next time, peace.