There’s been a lot of debate online around what the future of skilled work looks like in a world of AI agents automating much of the tasks that we do today.
With all the examples we’ve seen most recently of AI coding agents being able to handle longer and more complex tasks, this has created a sort of existential dread in some around what the role of humans and existing skills will be in a world where AI can do almost anything.
Even Boris Cherny, the creator of Claude Code, noted that 100% of his contributed code was written by Claude in the past 30 days in late December. And we’re seeing countless examples of engineers that are running up to dozens of agents in parallel, all handling different parts of a project, swiftly jumping between the work of each one, doing the equivalent of weeks of engineering work in hours or a couple of days.
If AI can do all the coding for us, this begs the question, is there even a role for skilled engineers in the future? And more broadly, is there a role for anyone with deep domain expertise -whether it’s legal, marketing, architecture, or medical fields- in a future where AI can instantly do so much of what today’s jobs look like?
The answer, emphatically, is yes. In fact, I’d argue, there has never been a time in history where it’s been better to be a highly skilled in a particular field.
When you assess what an engineer does today, the process is far more complex than just writing code. You’re taking in requirements from the customer (or from a product manager, etc.), testing your code, integrating it with a broader project, ensuring that it ships properly, fixing future bugs and defining new features, handling issues as they arise, coming up with the next set of features to build based on customer problems, upgrading various libraries and packages for security issues and new dependencies, and dozens of other individual tasks that deliver real, working software. AI increasingly can do any single one of these tasks individually, but job of an engineer is putting all the work together, successfully.
And those that understand how those tasks all come together are in the best position to deliver the most value, and get the greatest upside, with AI.
This is why many of the best engineers you know are having the time of their life right now with AI coding. Because instead of having to spend a uniform amount of time on both the parts of coding that are value-add and differentiating from the stuff that’s rote, you can deploy tasks to agents that offload to the drudgery parts, freeing up more time for the work that is fun and delivers the real value.
History doesn’t repeat itself, but it often rhymes
Now, you may be wondering, this must fundamentally change the nature of work in a way that leaves less to be done by people, ultimately rendering skills useless, right?
In fact, there’s already plenty of historical precedent for this evolution of work. If you look at the processes of any high-skill job starting from the pre-PC era to the AI era, nearly every single job would look fundamentally different today than it did just a few decades ago.
There's a scene in the 1982 movie The Verdict where Paul Newman and Jack Warden's characters are combing through volumes of legal books for hours while doing research for an upcoming trial. You can just feel from watching the scene how long and tedious this process once was. Just a few years later, a lawyer would have pulled up a computer and retrieved the same data nearly instantly.
Just as Sam Altman said a subsistence farmer might look at today’s jobs as way too easy and trivial, the lawyer from 1982 might imagine how easy law has become and devoid of critical thinking or research. And yet, the nature of the legal profession has only become more complex and sophisticated over time with a growing range of regulatory challenges, legal risks companies face, and so on. The foundational skills of the legal profession have been largely unchanged, and counterintuitively, despite many efficiency gains over time, the ABA pegs active attorneys having gone from roughly 400,000 in 1975 to roughly 1,375,000 in 2025.
Or take that modern engineering job, right before the moment AI coding went mainstream. Many things have gotten simpler in the past few decades of coding: there are open source libraries you just pull down instantly for almost every problem some other engineer has once dealt with in the wild, and you no longer have to think about managing infrastructure, sharding databases, handling identity services, doing billing, or an array of tasks that every engineer had to do themselves just a couple decades back. And yet, we now have more software than ever before to manage and more complexity in our systems than ever before; and as a result, no less of a need for experts to help with all this work.
What we know from history is that the jobs simply morph over time. We make one thing easier through automation or abundance, and as a result our expectations begin to grow on what we can now do in that space, and the skills become just as important. The expectations of what an engineer must deliver on today has just grown in scope, the demands of a lawyer’s advisory capabilities just increase, and the presentation and market knowledge you expect from your banker or consultant has gone up tenfold.
Skills as leverage
Max Levchin said it best just this week: “just as today we think of codegen AI as a 10x productivity multiplier for CS degrees (or equivalent), in a year or two we will be thinking of CS degrees as 10x productivity multipliers over codegen AI.”
You can replace “CS” and “codegen” for any skill and see the same effect.
The real leverage from AI comes from your depth of knowledge in the field you’re bringing automation to. One of the most important foundations for getting ahead with AI agents is being able to understand what tasks to give them, how to frame those tasks, how to know when those tasks aren’t being executed successfully, and then figuring out what to do *after* the AI has delivered the task.
Critically, being able to hold a full mental model in your head of what the agent is doing, where it should go next, what “good” should look like is the critical judgment necessary to produce real value with agents vs. just use them to take shots in the dark. Without this underlying knowledge you’re going to have a very hard time producing ongoing value at any reasonable scale or frequency.
The expert CG animator will know the right ways to get the AI to produce the best scenes, and then stitch them together the best to get emotion out of the audience; the expert lawyer knows when advice for a case would be impractical or when the AI should be overridden due to subtle nuance about a situation; the expert engineer knows when an agent has backed itself into an architectural dead-end that won’t scale for future features; and the expert marketer knows the message that will resonate with their core audience best.
By today's standards, all of these jobs will look fundamentally different in the future. We'll barely recognize the things that the experts are typing into their machines to do their work, but all of their underlying knowledge and skills of what they are doing and why will be as necessary as ever.
And instead of people and companies simply using AI agents to automate the work that we already do manually, jobs will simply be expanded over time to expect higher, or new types of output. We’ll demand far more from our software, we’ll expect more medical breakthroughs, require better expertise from our healthcare providers, and bar will just rise in essentially every other domain out there.
As a minor example of this dynamic, at Box, we now have solutions engineers (the deeply technical counterparts to our sales team) build fully custom, working demos of Box’s APIs embedded into our customer’s software. Instead of a static, generic demo, they can now bring the full platform to life in a way that would have been cost prohibitive to demonstrate for every client before. But now it’s a quick task. As this trend propagates across the market, customers will soon expect similarly customized experiences when they’re pitched by software companies. Just at the same moment when the rote task of pitches or architectures gets automated by AI, a new set of more creative and elaborate tasks become required as a part of a solution engineer’s job.
And here’s the amazing thing with AI. Just as it makes the experts far more capable than before, it also makes it so anyone can learn to become an expert much faster. Dylan Field frames this as AI lowering the floor and raising the ceiling. In his case, it means anyone who’s motivated can now learn how to do amazing design if they want.
The tools that every young person now has available to them is unprecedented. Anyone, if they so choose, has a complete tutor available to them in every skill on the planet. When I was growing up and getting into building websites, it was only by sheer coincidence that my neighbor (and ultimately a Box cofounder) happened to be a programmer that could help multiply my skills; but now everyone has that neighbor available to them 24/7.
Right now, the next Zuckerberg is getting into coding for the first time because of AI. The next George Lucas is someone developing AI worlds that have never been seen before, and producing a level of entertainment that would not have been possible even a couple of years ago without tens of millions of dollars. The same will be true in life sciences, finance, and every other technical field out there.
This is the greatest time in history if you’re curious, resourceful, and are fundamentally want to learn and pick up new skills. Skills don’t go away in the 21st century with AI. The leverage you get from them have only gone up.