Latency imposes a Tax on Velocity

The cost of slow loops

Your team identifies a user problem, designs a solution, ships it, and waits. Feedback on whether it worked arrives on a schedule determined by your release cadence and your measurement tooling. If that schedule is weekly, each experiment costs a week. A team operating this way might run four or five meaningful product iterations per month. A team with a shorter loop might run twenty. Over a year, that gap compounds into a fundamentally different product.

This is what latency looks like in a product team - not the milliseconds of a database query, but the hours and days between action and feedback. Like compound interest working against you, latency doesn't just cost you the time of each delay. It degrades the quality of every decision made inside the loop.

What latency is

In a team that primarily produces software, velocity can be improved in two ways: increasing the amount of work that happens at once (concurrency) or reducing the time between action and feedback (latency). Scaling with concurrency is not free. Building products is an iterative process. The work done today depends on yesterday's decisions, and the knowledge accumulated shapes what comes next. Agents help, but they quickly hit the ceiling of what concurrency can do. The bigger opportunity is reducing latency.

Successful iteration depends on a tight feedback loop. Knowing what works, what is practical, and what feels right is easier when you can see the effects of your changes immediately. This is as true for an engineer testing a hypothesis as it is for an agent searching for a solution. Both follow the same essential loop: observe, orient, decide, act - the OODA loop.

Most product development workflows try to minimize latency by default, by integrating front-end, back-end, APIs, databases, and infrastructure into a cohesive toolset. But teams often split along lines of end user, feature, or technology. When a team is split across a horizontal boundary, like between a platform team and a product team, feedback loops suffer because work must be coordinated across organizations. This is where the tax becomes most visible.

Fast Loop

3+ iterations per day

Slow Loop

< 1 iteration per day

How the tax compounds

A two-second delay in a feedback loop is tolerable. Fifteen minutes is disruptive. Two to three hours means at most three or four meaningful iterations per day. A weekly release cycle means you're collecting fifty learning cycles per year where you could be collecting hundreds. Each iteration that doesn't happen is a decision deferred. A wrong assumption left unchallenged. A compounding debt on your understanding of your own product.

Any workflow that introduces friction exhibits the same pattern. The dependency graph turns latency into a bottleneck. Any change that cannot be immediately validated must pass through that bottleneck to receive feedback. Products ship more slowly. Teams lose momentum. Agents make more guesses and explore worse solution spaces.

These loops are nested. A large loop is your product release and validation cycle. Inside it are smaller loops: code review, CI, staging environments. Inside those are smaller loops still: unit tests, type checking, local development servers. Each inner loop that becomes slow degrades everything that depends on it. The smallest loops should be the fastest. When they're not, the tax accumulates upward through every layer.

The friction isn't purely technical, either. Approval processes, required reviews, and onboarding dependencies all impose latency, and can be harder to fix. They have an implied order of operations and require human-in-the-loop coordination. You can't parallelize a mandatory sign-off.

The problem isn't that organizations scale or that teams specialize. The problem is when the fastest feedback loops are split across boundaries that introduce latency. When inner loops are slow, iteration collapses. Learning slows even if delivery continues to look healthy.

Agents amplify the problem

An agent working inside a slow loop will keep iterating, burning time and context, inside a broken process. A human pauses after a few failed attempts and questions the approach. An agent keeps going until it runs out of attempts.

Fast feedback loops let agents converge on good solutions. Slow loops turn them into expensive guessing machines. An agent with a fast inner loop, like a test suite that runs in seconds or a preview environment that deploys in moments, can iterate its way to a correct answer. An agent waiting ten minutes for a CI pipeline gets three attempts per hour. The tax is the same; the agent just has less judgment about when to stop paying it.

Investing in loop speed is not just a developer experience improvement. It's what determines whether your AI tooling actually works.

Where to start

Start by identifying your bottlenecks. Most teams have never experienced a fast feedback loop, so they don't know what's possible. These problems are not unique to a single team and are not limited to engineering. Bottlenecks often live in the spaces between teams, in the interfaces and processes used to coordinate work.

For any area of product, engineering, or design work:

1. Identify the critical feedback loops in your workflows
2. Measure the actual time and friction of each loop
3. Eliminate or automate the systems that impose the most latency
4. Iterate on the improvements themselves. Go back to step 1.

The heuristic is simple: a slower feedback loop means lower-quality decisions made inside it. The compounding works in both directions. Reduce latency and you don't just move faster. You learn faster. And learning faster is the only durable advantage available to a team that builds products.