There's a gap opening up between what a San Francisco startup looks like in 2026 and what a JP Morgan looks like in 2026, and it's going to get worse before it gets better. Box CEO Aaron Levie had a conversation with Martin Casado and Steven Sinofsky on The a16z Show that named the three forces pulling the tech industry apart from the rest of the economy — and the biggest mistake most executives are making about the shape of the next five years.
The diffusion gap
Silicon Valley assumes that because startups can now automate a marketing team with one person and Claude Code, every enterprise is a year away from the same leverage. That's wrong, and it's wrong for a reason that doesn't get discussed often: algorithmic thinking is rare.
Sinofsky's framing: walk into a marketing team of 50 people at a global brand. Ask each one to draw a flowchart of their own job. Maybe one person can do it. The others know how to do the job — they don't know how to describe it as a system. That's the bottleneck for AI adoption inside large companies. It's not GPU access, it's not budget, it's not even resistance to change. It's that most people in most jobs were never trained to think in terms of process, branches, and feedback loops. The training they got was apprenticeship — watch someone, copy them, get good. That doesn't translate into directing an agent.
The viral Anthropic example (one growth marketer replacing five or ten people via Claude Code) works because that person was already a systems thinker. Put the same tool in front of most marketing teams and they freeze.
The optimistic version of this, which Levie believes: the abstraction layer just moves up. Sinofsky's cousin joined a bank right before spreadsheets arrived. She couldn't use Excel, so she supervised a room of interns who could. Two years later she was the spreadsheet person, and the interns were doing something higher. That's the pattern. Today's "rocket scientist coordinating 42 agents" is temporary. In a year or two there's just a skill-level domain agent — "marketing-ish" — and the average marketer can ask it for things.
Fine for the ten-year outcome. Ugly for the two-year transition. And the gap in capability between a startup using agents from day one and a 200,000-person bank trying to retrofit them is widening, not narrowing.
Build for agents, not for humans
But there's a subtle mistake in the discourse that Martin Casado called out and it's worth internalizing: you are not "marketing to agents." Agents don't care about your documentation, your positioning, your interface polish. The one thing agents are genuinely great at is finding the right backend for the job. They pick on cost, durability, semantics, the actual properties of the system.
This is a gift and a threat. The gift: merit actually wins. If your product is technically the best for the job, the agent will find it and use it, and you don't need to buy Gartner quadrants or run bigger trade-show booths. The threat: if your product is mediocre but has dominated because of brand or sales, the agent will skip you. "Which database should I use for this?" is a question the agent is going to answer from first principles now. Gartner is going to matter less.
(Casado's dry caveat: Silicon Valley will ruin the meritocracy quickly once the incumbents figure out how to pay-to-influence agent selection. Give it two years.)
The enterprise control problem is brutal
Every big tech company is confronting the same scenario right now: 5,000 employees, each one running Claude Code with access to the Box CLI or its internal equivalent. That's potentially 10,000 write operations per hour against the shared system of record — with agents creating nested directories without limit, conflicting with each other, racing each other on file moves, and accidentally leaking M&A data room contents because a prompt injection slipped in from a shared document.
The intuition that "treat the agent like a human" doesn't work cleanly. Humans have a right to privacy. Agents don't. You can log in as the agent and audit its entire output; you can't do that with an employee. But if you can log in as the agent, the agent can't really operate as a separate identity at all — any agent it talks to could be routed back to you. So the mental model collapses.
Sinofsky drew the parallel that fits best: the open source era. For years, engineers debated in conference rooms how much open source code could be pulled into Windows or Office, what the licensing constraints were, how to manage the security posture. None of that debate happened in public. It took a decade to build the norms. The same debate is happening now — except it's happening on podcasts and X in real time, and everyone expects the end state to arrive in six months.
It won't. The enterprise lockdown is coming first. Startups will pull further ahead in that window.
The engineering compute budget — the most consequential line item of the next two years
Levie's final warning deserves its own highlighted paragraph. R&D spend for a typical tech company is 14–30% of revenue. If tokens are 2× your engineering team cost, that's your entire EPS eaten. If they're 3%, you're fine. The delta between those two numbers is being debated today with effectively zero data.
CFOs are going to be forced to pick a number. Wall Street will hold them to it. Some will be spectacularly wrong. Some will get fired. Most of the economics people are using to model this right now are off by an order of magnitude — in the same way the PC market was underestimated (nobody predicted a thousandfold increase in MIPS-per-desk, or that software would sell separately from hardware), and the cloud was underestimated (nobody predicted that giving every engineer elastic compute would lead to a thousandfold increase in consumption, not a lateral migration of 60,000 servers).
The IBM analogy closes the loop. For years IBM was selling more MIPS for fewer dollars every year. They were pricing mainframes on MIPS anyway, and didn't notice they were on a decreasing curve — making MIPS faster than they could charge for them. Today's AI pricing is on the same trajectory. The companies pricing by token are going to be the ones pointing at their own decreasing curve in three years.
Three things to actually do about all this
1. If you run a SaaS business: stop treating your API like a compliance afterthought. The agent is your new main user. Your monetization model, your identity system, your rate limits — all of it gets redesigned around agent volume. The companies that get this right become infrastructure. The ones that don't become line items.
2. If you run an enterprise: resist the instinct to build a new governance layer for agents. Use the identity systems you already have — give the agent its own Gmail, its own phone number, its own RBAC role, its own payment method. Treat it like a separate identity with tight scopes. Adding a fresh policy plane on top of your existing mess just slows everything down.
3. If you're doing financial planning: assume your compute budget assumption is off by at least 10×. In both directions. Plan scenarios, not point estimates. CFOs who commit now to a precise number will be the ones getting fired later.
The line that closed the conversation: "They thought the Dakotas would be covered in vacuum-tube warehouses to fight World War II. Then someone invented the transistor." AI is in its vacuum-tube-warehouse phase. Act accordingly.
Source: Box CEO Aaron Levie on the AI Adoption Gap — The a16z Show
