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 without doubt one of the few CEOs of an incumbent enterprise software program firm considering deeply in public about what AI means for the complete enterprise stack. There are lots of people who’re constructing corporations from the bottom up with AI, others who’re dragging their ft adapting current enterprises to it, after which there’s Aaron. He sits in a type of Goldilocks zone, enthusiastic however not uncritical, participating within the exhausting 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 E-newsletter that Aaron had retweeted. Regardless of all of the doom rhetoric, TrueUp knowledge exhibits software program engineering job postings are at a three-year excessive. Product supervisor jobs are manner up. AI jobs as an entire are manner up.
The precise knowledge 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 occurring to entry-level software program work, that it’s occurring quicker than most earlier expertise transitions, that it has completely different results relying on which job code you take a look at, and that it isn’t but clear whether or not the online impact on whole software program employment might be detrimental, impartial, or ultimately constructive. Nonetheless, the TrueUp report was a set off for the dialogue that adopted.
Aaron famous that engineers have traditionally been concentrated at tech corporations as a result of the price of a software program undertaking was too excessive to justify wherever else. But when brokers make an engineer two to 10 occasions extra productive, all of the software program initiatives that had been by no means economically viable immediately turn into viable. Demand doesn’t shrink. It diffuses throughout the complete financial system. In his tweet, he known as it “Jevons paradox occurring in actual time.” In our dialog, he stated:
“What’s going to occur is the complete world goes to be 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 one of these undertaking the place I couldn’t earlier than.”
Engineers empowered by AI brokers received’t simply construct software program for IT groups. The full addressable function of the engineer expands from the expertise 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 corporations ought to lay off their builders to scale back prices when there’s a lot shitty software program on the market? Actually? There’s a lot that must be improved. He had a terrific line: “Silicon Valley is spooked by its personal expertise.”
Over to me: The rhetoric from the labs about job destruction is actively counterproductive. I used to be speaking lately with somebody in healthcare who described a hospital system making an attempt to fill an enormous gap from diminished Medicare funding. They see AI as a approach to acquire effectivity of their again workplace to allow them to unlock extra assets for affected person care. And naturally the union is preventing it as a result of they’ve been advised AI is a monster that’s going to take their jobs. In the event you inform a unique 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 vary the narrative.
Context, not connectivity, is the actual downside
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 way of). However getting your methods to speak to one another doesn’t clear up the more durable downside of getting your knowledge structured in order that brokers can truly discover the proper info on the proper second.
“If it’s in 50 completely different methods 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 nicely that agent finds precisely the context that it must do its work. And also you’re type 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 process that’s good for an agent and shortly uncover that the information I would like is scattered throughout 4 methods and I’ve to leap by way of hoops to determine who is aware of the place the information is and tips on how to get entry. A good friend working a big (however comparatively new) enterprise that’s turbocharging productiveness and repair supply with brokers advised me lately {that a} massive a part of his crew’s success was doable as a result of they’d spent plenty of time getting their knowledge infrastructure so as from the beginning.
IMO, plenty of the tales you hear about OpenClaw and different harbingers of the agent future may 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 corporations with hundreds of staff, a whole lot of legacy apps, and deep wells of proprietary knowledge. 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 laptop and a probabilistic laptop 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 are constructed with deterministic code. Each methods coexist and work in parallel.
Aaron known as the boundary between the 2 computer systems “the trillion-dollar query.” When does a course of cross the brink the place it ought to be locked into repeatable, deterministic code? When ought to it keep adaptive? Mortgage processing must work the identical manner each time. Worker HR queries may be probabilistic. And the irony, as Aaron identified, is that making these trade-offs accurately requires deep technical understanding. AI makes the sector extra technical, not much less.
I added that generally this judgment is a person expertise query, generally a price query. You are able to do one thing with an LLM, however it may be rather a lot cheaper with canned code. At different occasions, though the LLM prices extra, the flexibleness of a liquid person interface is much better.
That is additionally a locus of creativity. What you deliver out of AI is what you deliver to it. Steve Jobs wasn’t a coder, however he knew tips on how to get probably 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 in regards to the dangers to current enterprises from greenfield AI startups that may simply transfer quicker, 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 current system, and the incumbent is agile sufficient and responsive sufficient, then they’re in an excellent 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 elements of labor, the tax course of, the authorized evaluate course of, the audit and danger evaluation means of all your contracts and unstructured knowledge. And so in these areas, there’s no incumbent. The one incumbent is probably going skilled providers corporations. In order that’s the place I might favor startups.”
Software program startups like Harvey are already taking providers domains and constructing brokers for them. However it’s not simply software program startups. Aaron additionally sees plenty of alternative for AI-native regulation corporations, accounting corporations, and advert companies that may throw away legacy workflow, begin from scratch, and ship two to 5 occasions the output at decrease value could have an enormous benefit.
I did push again with some extent I believe is underappreciated: Current enterprises face an actual danger that the group will attempt to stuff AI into current workflows slightly than asking what the AI-native workflow can be. Individuals are connected to their jobs, their roles, the org chart. We have now to wrestle with that truthfully if we’re going to really reinvent what we do.
People get context without spending a dime
One in all Aaron’s factors about brokers is that people carry an infinite quantity of ambient context that brokers lack. what constructing you’re in and who else works there and what they do. the assembly that simply occurred the place a crew modified course on a technique that hasn’t been written down but. You’ve 20 years of collected area data. 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 doable to try this process.”
He advised that a method to consider brokers is as new staff who’re consultants however arrive with zero context and must be absolutely briefed. And the context needs to 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 selected course of.
However 99% of data work doesn’t have an AGENTS.md file, he famous. The info is in every single place. The context is in every single place. So in an current 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 obtainable 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 occupied with at O’Reilly too.
