AI Makes Coordination More Important, Not Less
The faster teams can generate work, the more deliberately they have to coordinate it
With ChatGPT, Claude, and the AI ecosystem, our optionality has expanded at what feels like exponential pace. It’s made the effort, focus, and cost to pursue a multitude of ideas less expensive.
Cheaper options create a second problem: someone still has to decide which options deserve attention. A team can generate more plans, branches, and implementation paths than it could before. And while that in itself can be genuinely useful, attention has not gotten cheaper just because generation has.
For as long as software development has been a profession, the friction of building things limited how many ideas a team could reasonably pursue. You could never chase every half-formed idea because there was literally not enough capacity to do it all. That constraint was frustrating, but it forced teams to be more deliberate about their bets.
AI-enabled development has fewer of those old constraints. To be fair, you still shouldn’t chase every half-formed idea, but you can feasibly do more than you used to.
Optionality Adds to Sunk Cost
I like optionality. I believe lot of good product work depends on preserving options until you know enough to choose well. The problem is that AI can make options cheap to create and weirdly expensive to abandon.
More plans beget more outputs and WIP where, but the work starts to feel more real because there are artifacts around it, not because of any actual commitments made. Once an option has activity surrounding it, it starts to feel like valuable momentum, which is psychologically expensive to abandon.
That is how cheap options create sunk cost. Not because the team spend weeks building the wrong thing, but because the option accumulated enough visible progress to become awkward to kill.
What we should remember is that progress is not the number of artifacts around an idea. Progress is the reduction of uncertainty in a direction that matters. A generated option only helps if it improves judgment. Otherwise it is just another thing the team now has to explain, review, preserve, or politely unwind.
Coordination Turns Options Into Progress
Coordination gets treated like overhead because we often picture it as meetings, status updates, and people asking for alignment. That is the boring version of collaboration.
The useful version of coordination is much more basic: it’s how a group preserves shared judgment while work changes hands.
The important questions are not complicated. What are we trying to accomplish? re we still doing work that helps accomplish it? What are we choosing not to do? What did we learn that the next person should not have to rediscover?
You might think that AI removes the need for these types of questions but I’ve found it actually makes them show up earlier and more often.
Recently in my org a team was sharing out their adoption of an AI SDLC workflow. They had seen increasing throughput and lower volatility, but the most interesting part was that faster work created more need for communication, not less.
When their work started moving faster, the team had to spend more intentional time deliberately reviewing what was in progress. They added dedicated standup time every day for reframing on what was in flight, who was focused on what, and what the priorities were.
The team also had to keep re-asking why the work mattered at the moment it started. AI can help generate a plan, but it cannot know whether the team is solving the right problem unless someone ensures it has the appropriate context to solve it well.
The takeaway? Faster AI work created more communication.
That is the communication burden AI exposes. More work can start. More options can become plausible. More artifacts can appear before anyone has had the normal human conversations that used to happen slowly through implementation. So the team has to create new habits for anchoring work and sharing context to prevent weak context from spreading.
This is where teams can get uncomfortable. Faster building makes it easier to see how much ambiguity people were absorbing all along.
The Team Memory Problem Gets Bigger
The second-order effect is one that impacts context. A lot of teams are about to discover that their real asset is not their prompt library. It’s their shared memory.
How do we make decisions here? What patterns have we learned to avoid? Which abstractions are local to this codebase? What does this team mean when it says “simple”? Where did we already try this and regret it?
These things usually live in people. They live in the senior engineer who remembers the migration, the designer who knows why the flow is weird, the PM who can navigate the conflicting incentives or a stakeholder group, or in the tech lead who knows which “temporary” workaround worked best last time.
AI makes that arrangement more fragile. If the entity that generates work is not a person, and it’s pumping out code or artifacts at an unsustainable pace, you will quickly experience a memory bottlenecks. Technically, teams can move quickly but they do so without the benefit of information that will keep them from shipping AI slopcannon.
So the question becomes simple: what needs to be remembered by the team, and what needs to be remembered by the workflow?
Not everything needs to become a rule. But some things need to be made durable: operating principles, architectural constraints, examples of good work, known traps, decision records, review checkpoints, and the small bits of opinionated craft that keep a team from becoming a collection of independent prompt operators.
Independence Is Not Autonomy
AI lets individuals do more without waiting. Waiting is expensive and permission culture is exhausting. Teams shouldn’t need a meeting every time someone wants to explore an idea. Less waiting is good.
But independence is not the same as autonomy. Autonomy requires shared context. It works when people can make local decisions that still add up to a coherent whole. Without that shared context, individual speed becomes organizational drag. Everyone is moving, but the work starts pulling in slightly different directions.
This is why coordination matters more when using AI in multi-player mode, not less. The goal is not to slow everyone down. The goal is to keep the work from fragmenting as it speeds up.
A team using AI well should probably have fewer planned meetings and more asynchronous check-ins with visible decision trails. Less “go generate something” and more more early inspection of intent.
The Simpler Principle
The principle I keep coming back to is this:
when creation gets cheaper, coherence gets more valuable.
AI can draft faster, but the team still has to know what good means. AI can make options cheap, but the team still has to pay attention.
In my eyes, Simply generating the most output or executing the fastest is not a winning strategy. Teams that build better need to find approaches to select, connect, remember, and decide.
That’s real coordination. And it’s becoming more important, not less.