When production becomes cheap, another cost quickly begins to dominate: coordination. Most organizations run on structures that evolved when work itself moved slowly, when waiting two days for someone else’s input did not feel catastrophic because the ratio of delay to labor remained small, almost negligible, a rounding error in the larger timeline of any meaningful project.

But now imagine you’re a PM who builds a working feature in an hour using agents. The code sits in a branch waiting for review. But the engineer responsible for the system can only look at it next week. So, your original speed advantage disappears completely into the queue, swallowed by the processes that were supposed to ensure quality but now feel like the primary constraint on throughput.

Naturally, the cleanest way to remove these costs is to remove the handoff itself. You speak to the customer, understand the problem, build the solution with agents, ship it, and remain responsible for what happens afterward — owning that thread end-to-end for perpetuity.

However, the organization’s knowledge has to exist somewhere outside people’s heads for this to work at all.

In most companies it lives in personal notepads, scattered documents, and the lossy ledger of human recall, which means context moves slowly and loses fidelity with every handoff, every retelling. Even when you have detailed documentation and artifacts, no one reads them.

But now, no one has to! The agent can do it.

And a better way to share artifacts and work on shared artifacts has already existed in software development for decades! It’s called Git.

Code lives in systems where every change is recorded, versioned, and visible to anyone who needs it. Only now, with agents, you can now treat even non-technical organizational knowledge in the same way, with customer conversations, messaging experiments, product decisions, skills, and insights all becoming entries in a shared, structured system that both humans and agents can read, query, and build upon without waiting for permission or scheduling a meeting.

This is the deeper structural primitive nobody’s talking about. The leverage agents actually unlock remains structurally inaccessible until your non-technical knowledge runs on version-controlled, agent-readable substrates too.

Say a salesperson logs a fresh objection into the shared repo. Agents downstream reading from that same shared repository absorb it, remix the phrasing, and pipe it into campaign copy without anyone needing to have a meeting about it.

Or for instance, if ten SDRs speak to prospects, all using a different tone and verbiage, agents can enforce consistent messaging and tone across them. Each person then benefits from the skills, discoveries, and individual expertise of people in their team.

Over time the company begins to behave like an intelligent hive mind that compounds, where each person works not merely with their own judgment but with the accumulated discoveries of everyone else, where authority sits close to the work, and where iteration speeds up because the entire cycle from insight to action stays compact, tight, and unburdened by organizational friction.

Many companies will adopt AI tools. That step alone changes little.AI raises your individual output dramatically, sure. But only some companies will be able to translate those gains to their bottomline, because only some will rebuild themselves around shared, agent-readable systems where you can leverage everyone else’s skills and context. Today, if an individual contributor performs a task manually using their own judgment, the benefit stays local. The business gains the output but not the capability. But when that same skill is expressed through agents, others can reuse it. Each person works with the accumulated skills of the entire team.

So, the real inflection comes from a foundational rewiring of the communication and org structure that made sense in a world before agents. Because the 1000x leverage that agents can offer is promptly pilfered away by traditional bottlenecks. You cannot realize the real throughput possible with agents without it. I think the best talent will naturally prefer AI-native teams due to a vastly higher multiple on individual leverage and attributable impact, further compounding a structural moat that no amount of mere tool adoption alone will be able to cross.