I sat down with Aaron Levie on the O’Reilly AI Codecon two weeks in the past. Aaron cofounded Field in 2005, and 20 years later, his firm manages content material for about two-thirds of the Fortune 500. Aaron is among the few CEOs of an incumbent enterprise software program firm pondering deeply in public about what AI means for the complete enterprise stack. There are lots of people who’re constructing firms from the bottom up with AI, others who’re dragging their toes adapting present enterprises to it, after which there’s Aaron. He sits in a sort of Goldilocks zone, enthusiastic however not uncritical, partaking within the onerous work of adapting AI to the enterprise and the enterprise to AI.
The engineering demand paradox
I began out by asking about one thing from Lenny’s Publication that Aaron had retweeted. Regardless of all of the doom rhetoric, TrueUp information reveals software program engineering job postings are at a three-year excessive. Product supervisor jobs are manner up. AI jobs as a complete are manner up.
The precise information could also be extra equivocal than the TrueUp report suggests. The trustworthy learn of the literature as of spring 2026 (Brynjolfsson et al., Humlum and Vestergaard, BLS Software program Builders, BLS Pc Programmers) is that one thing actual is going on to entry-level software program work, that it’s taking place sooner than most earlier know-how transitions, that it has completely different results relying on which job code you have a look at, and that it’s not but clear whether or not the online impact on complete software program employment will probably be detrimental, impartial, or ultimately optimistic. Nonetheless, the TrueUp report was a set off for the dialogue that adopted.
Aaron famous that engineers have traditionally been concentrated at tech firms as a result of the price of a software program challenge was too excessive to justify wherever else. But when brokers make an engineer two to 10 instances extra productive, all of the software program initiatives that have been by no means economically viable out of the blue grow to be viable. Demand doesn’t shrink. It diffuses throughout the complete financial system. In his tweet, he referred to as it “Jevons paradox taking place in actual time.” In our dialog, he stated:
“What’s going to occur is the complete world goes to be taking a look at all of the potential software program that they construct. They usually’re going to begin to say, Oh, I can lastly justify going out and doing any such challenge the place I couldn’t earlier than.”
Engineers empowered by AI brokers received’t simply construct software program for IT groups. The entire addressable position of the engineer expands from the know-how division to each operate within the enterprise. They’ll be wiring up automation for advertising and marketing, authorized, accounting, and each different company operate.
He’s completely proper. Go searching in any respect the crappy workflows, the crappy processes, the unbelievable overhead of issues that must be easy. You assume firms ought to lay off their builders to cut back prices when there’s a lot shitty software program on the market? Actually? There’s a lot that must be improved. He had an important line: “Silicon Valley is spooked by its personal know-how.”
Over to me: The rhetoric from the labs about job destruction is actively counterproductive. I used to be speaking not too long ago with somebody in healthcare who described a hospital system making an attempt to fill a large gap from decreased Medicare funding. They see AI as a strategy to acquire effectivity of their again workplace to allow them to release extra sources for affected person care. And naturally the union is combating it as a result of they’ve been instructed AI is a monster that’s going to take their jobs. In case you inform a special story, one about making the system higher and serving extra individuals extra affordably, that’s one thing individuals can get behind. We have now to alter the narrative.
Context, not connectivity, is the actual drawback
I additionally requested Aaron whether or not protocols like MCP are making context moveable sufficient to erode aggressive moats. He agreed that the business has broadly converged on openness and interoperability (with some toll cubicles to work by means of). However getting your programs to speak to one another doesn’t resolve the tougher drawback of getting your information structured in order that brokers can really discover the proper info on the proper second.
“If it’s in 50 completely different programs and it’s not organized in a manner that brokers can readily make the most of, what you’re going to be is on the mercy of how effectively that agent finds precisely the context that it must do its work. And also you’re sort of simply rolling the cube each time you do a workflow.”
He predicts a decade of infrastructure modernization forward, which sounds about proper. At O’Reilly, I preserve working into this myself. I’ll see a job that’s excellent for an agent and shortly uncover that the information I want is scattered throughout 4 programs and I’ve to leap by means of hoops to determine who is aware of the place the information is and the way to get entry. A buddy working a big (however comparatively new) enterprise that’s turbocharging productiveness and repair supply with brokers instructed me not too long ago {that a} huge a part of his group’s success was attainable as a result of they’d spent lots of time getting their information infrastructure so as from the beginning.
IMO, lots of the tales you hear about OpenClaw and different harbingers of the agent future could be deceptive in an enterprise context. They’re doing greenfield setups, largely working client apps with well-defined interfaces, and even then, it takes weeks to arrange correctly. Now think about agentic frameworks for firms with 1000’s of workers, tons of of legacy apps, and deep wells of proprietary information. A decade of infrastructure modernization is beneficiant. With out assist, many enterprises could have problem making the transition.
Engineering the trade-offs
I introduced up Phillip Carter’s “two computer systems” framing, that we’re now programming a deterministic pc and a probabilistic pc on the identical time. Expertise are a bridge, as a result of they’ve each context for the LLM which might work probabilistically and instruments which might be constructed with deterministic code. Each programs coexist and work in parallel.
Aaron referred to as the boundary between the 2 computer systems “the trillion-dollar query.” When does a course of cross the brink the place it must be locked into repeatable, deterministic code? When ought to it keep adaptive? Mortgage processing must work the identical manner each time. Worker HR queries could be probabilistic. And the irony, as Aaron identified, is that making these trade-offs appropriately requires deep technical understanding. AI makes the sector extra technical, not much less.
I added that typically this judgment is a person expertise query, typically a value query. You are able to do one thing with an LLM, however it is perhaps lots cheaper with canned code. At different instances, though the LLM prices extra, the flexibleness of a liquid person interface is much better.
That is additionally a locus of creativity. What you convey out of AI is what you convey to it. Steve Jobs wasn’t a coder, however he knew the way to get essentially the most out of coders. He would have gone nuts with AI brokers, as a result of he was the essence of style and judgment and setting the bar.
The place startups win
I requested Aaron concerning the dangers to present enterprises from greenfield AI startups that may simply transfer sooner, reinventing what the incumbents do with an AI native resolution, with out all the luggage. He replied:
“If there’s already a considerable quantity of the information for that exact workflow in an present system, and the incumbent is agile sufficient and responsive sufficient, then they’re in a very good place to construct both the options or to monetize that set of labor that’s going to be completed….What brokers are actually good at is automating the unstructured areas of labor, the messy, collaborative human-based components of labor, the tax course of, the authorized evaluate course of, the audit and danger evaluation strategy of your entire contracts and unstructured information. And so in these areas, there’s no incumbent. The one incumbent is probably going skilled companies corporations. In order that’s the place I’d favor startups.”
Software program startups like Harvey are already taking companies domains and constructing brokers for them. But it surely’s not simply software program startups. Aaron additionally sees plenty of alternative for AI-native regulation corporations, accounting corporations, and advert businesses that may throw away legacy workflow, begin from scratch, and ship two to 5 instances the output at decrease value could have an enormous benefit.
I did push again with a degree I believe is underappreciated: Current enterprises face an actual danger that the group will attempt to stuff AI into present workflows quite than asking what the AI-native workflow could be. Persons are hooked up to their jobs, their roles, the org chart. We have now to wrestle with that truthfully if we’re going to actually reinvent what we do.
People get context totally free
Considered one of Aaron’s factors about brokers is that people carry an infinite quantity of ambient context that brokers lack. You understand what constructing you’re in and who else works there and what they do. You understand the assembly that simply occurred the place a group modified course on a method that hasn’t been written down but. You might have 20 years of collected area information. All of that’s free context that we’ve by no means needed to formalize. As he put it, “We’ve by no means constructed our enterprise processes in a mannequin the place we assume that there’s a brand new person in that workflow that appeared one second in the past and in underneath 5 seconds, they should get the entire info attainable to do this job.”
He instructed that a technique to think about brokers is as new workers who’re specialists however arrive with zero context and must be totally briefed. And the context must be exact, not simply complete. Give an agent an excessive amount of context and it will get confused. Give it too little and it rolls the cube. SKILLS.md and AGENTS.md information are makes an attempt to offer precisely the surgical context an agent wants for a particular course of.
However 99% of data work doesn’t have an AGENTS.md file, he famous. The info is in all places. The context is in all places. So in an present enterprise, it’s important to reengineer workflows from the bottom as much as ship the proper info to brokers on the proper second.
Aaron summed up Field’s strategic pivot in a single sentence: swap the phrase “content material” for “context” and the remainder of the technique stays the identical. Enterprise context lives in contracts, analysis supplies, monetary paperwork. That’s all enterprise content material however it isn’t at all times simply accessible as context. The evolution is making brokers first-class residents alongside individuals as customers of that content material. This very a lot maps to what we’re desirous about at O’Reilly too.