AI Coding Tools Speed Development but Amplify Building the Wrong Product
The evolution of our internal development process has accelerated as we integrate more LLM agents and coding assistants into each sprint. Writing code has become almost trivial, yet determining what to build has emerged as the primary bottleneck. Upon rereading Ron Jeffries' The Nature of Software Development, I found that despite its 2015 publication—long before Claude Code or GitHub Copilot existed—its central message feels more urgent today than it did a decade ago.
The danger of rapid, worthless output
Today's greatest peril lies in AI's capacity to produce incorrect solutions at astonishing speed. When an LLM agent can draft a prototype, write unit tests, and complete initial refactoring within twenty minutes, the temptation to continue pushing forward becomes overwhelming. We can generate features more quickly than we can validate them. Nevertheless, Jeffries' fundamental argument stands unchanged: value is the only thing that matters. Software only delivers value when it reaches users. As implementation costs approach zero, the penalty for poor decisions doesn't diminish—it merely manifests more rapidly. We can now execute a flawed architectural decision or implement a useless feature set in a single afternoon.
Tightened feedback loops as our safeguard
Through implementing these tools with my team, I've discovered that the most significant transformation isn't in the coding process itself but in compressing the feedback cycle. Jeffries describes software as "lava"—constantly shifting due to evolving requirements, markets, and technologies. Stagnation is impossible. The traditional approach followed this pattern:
- Plan everything
- Build everything
- Ship
- Discover you were wrong
With AI-enhanced development, we must adopt a much more condensed cycle:
- Select a high-value segment
- Utilize AI to construct it rapidly
- Deploy it immediately
- Gather real-world data
- Adjust based on findings
The purpose of AI should not be to extend or complicate our development processes; it should be to significantly shorten them. If an agent reduces a three-day task to three hours, we shouldn't use the saved time to add additional planning buffers. Instead, we should employ it to get the code in front of a user sooner.
Moving beyond traditional estimation practices
One persistent challenge in our sprint planning has been handling estimates. Traditional "man-hour" forecasts are becoming increasingly irrelevant. How can you accurately estimate a task when you don't know whether a specialized LLM agent will complete it in five minutes or whether edge cases will require a human to spend five hours debugging? I'm guiding my team to shift from asking: "How long will this take?" to inquiring: "What is the most valuable outcome we can validate next?" We need to view AI as a mechanism for acquiring information. Each time we use an agent to create a small functional component, we're not merely writing code; we're conducting an experiment to verify our product assumptions. The LLM's speed merely provides a tool that helps us either fail faster or succeed more quickly.
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Agentic code feels like a technical debt bomb. Are you seeing messy architecture in your builds? The real danger is that AI lets us produce the wrong things at breakneck speed, so I’ve started treating value as the only metric that matters—before letting an agent generate anything, I now ask what user problem it actually solves. That one step keeps us from shipping flawed features we mistook for progress.
My code reviews are killing me lately. Is anyone else spending hours just double-checking basic logic? I’ve been considering how our internal development process evolves as we bring more LLM agents and coding assistants into each sprint. We’ve reached a point where writing code feels almost trivial, yet deciding what to build has become the major bottleneck. I recently reread Ron Jeffries’ The Nature of Software Development. Although it appeared in 2015—well before Claude Code or GitHub Copilot emerged—the central idea feels more pressing now than it did a decade ago. The greatest danger today is that AI lets us produce the wrong things at breakneck speed. When an LLM agent can draft a prototype, write unit tests, and handle the first round of refactoring in twenty minutes, the urge to keep pushing is strong. We can generate features faster than we can validate them. Yet Jeffries’ core argument remains: value is the only thing that matters. Software only delivers value when it is shipped and used by a person. As implementation costs approach zero, the penalty for a bad decision doesn’t shrink—it just occurs more quickly. We can now execute a flawed architectural choice or a useless feature set in a single afternoon. The biggest shift isn’t in the act of coding itself but in tightening the feedback loop. Jeffries describes software as “lava”—it constantly shifts because requirements, markets, and technology evolve. You can’t stay static. The old approach looked like this: 1. Plan everything 2. Build everything 3. Ship 4. Discover you were w
Frustrating that testing takes forever now. Since implementation costs are approaching zero, the real bottleneck isn't writing code but validating it; try tightening your feedback loop to ensure you aren't just generating features faster than you can verify their actual value.