How Our Engineering Team Compressed Two Weeks Into One Day With AI
TL;DR: Our 6-person startup compressed feature development from two weeks to one day with three changes: (1) Lighter specs—build first, discuss after (2) AI reviews first, humans focus on architecture (3) Preview environments accelerate iteration.
“You should be able to open the PR by tomorrow, right?”
When I said this to a colleague last week, I suddenly realized something: for the same feature, two years ago I would have said “this will probably take two weeks.”
This isn’t just about better tools. Our entire team’s workflow—how we write specs, how we divide work, how we review code—has fundamentally changed.
We’re a 6-person startup that spent the past two years transforming from a “traditional development process” to an “AI-native team.” The core changes were three things: lighter specs, AI-first reviews, and preview environments for faster iteration.
The Biggest Change: Lower Iteration Costs
Some background: most of our engineers have backend and cloud infrastructure backgrounds—they’re not traditional frontend developers. Getting them to write frontend code used to work, but it was painful.
That’s different now.
With AI Agent assistance, they can iterate quickly—build the UI, debug, then have a coding agent verify code quality against best practices. Frontend pages that used to take a week or two of fumbling around can now have a reviewable version in one day.
This “lower iteration cost” has deeper ripple effects than I initially expected.
How We Operate Now
Spec-Light, Explore-First
Our PM doesn’t always write comprehensive specs. Once we have directional alignment, we let engineers build an initial version to explore.
This sounds loose, but here’s the logic: one of AI Agent’s greatest strengths is rapidly exploring different approaches. Since modification costs are now lower, we don’t need to spend as much time debating “is this approach good or not”—just build it and see.
Exploration has boundaries:
- Time: 1-2 days to produce a “demo-able version”
- Scope: Cover one critical happy path, skip edge cases
Once those conditions are met, we discuss using actual screens rather than continuing to argue over documents.
AI Research → AI Implementation
Another huge time saver is research.
Previously, integrating a new third-party API meant engineers had to read documentation, test requests, figure out response formats, and only then start writing integration code.
Now we have the agent do research first. In practice, we toss in official documentation links and have it summarize: key endpoints, common error codes, two or three typical request/response examples. Then we feed that research output directly to a coding agent for integration.
The “human reads documentation” step has been dramatically compressed.
AI Code Review → Human Code Review
Our review process now works like this: code goes through an AI review first to catch bugs and check for best practice violations. AI review results go back to the coding agent for fixes, so by the time a human engineer reviews, code quality is already at least 80%.
Specifically, we treat AI review as pre-review: have AI find bugs, style issues, and missed edge cases, then send those comments back to the agent for fixes. Human reviewers only look at the final version, focusing on “will this design hold up long-term.”
What We Learned
Senior Engineers Were Slower to Adopt
This is somewhat counterintuitive. Our team is all senior engineers—they should pick up new tools quickly. But initially, they didn’t trust AI coding agents’ capabilities and weren’t actively exploring their boundaries.
The turning point was my persistent demos in weekly meetings showing features I’d built with one-shot or few-shot coding agents, then encouraging everyone to try.
This has now become natural culture. People naturally share “I built this feature with Cursor’s agent in no time,” and we’re increasingly clear on which tasks are suited for AI.
The Senior-Junior Gap Widens
Here’s another observation: in an AI-assisted environment, the gap between seniors and juniors doesn’t shrink—it grows.
The reason is seniors are better at judging “when to stop letting the agent keep trying.” After two or three failed attempts, seniors know this task is faster for a human. Juniors might keep wrestling with the agent.
Additionally, seniors are better at catching AI hallucinations. AI sometimes confidently gives wrong answers, and without sufficient experience, it’s hard to notice.
AI Improves Exploration Speed, Not Quality
This is important: the biggest change from AI coding agents is exploration speed, not code quality.
AI-generated code isn’t necessarily better than human-written code—sometimes it’s worse. But it lets you try different directions at lower cost, discovering what works and what doesn’t faster.
This means human review remains critical. AI makes you fast, humans make you right.
One-Week Action List for Tech Leads
If you want to start after reading this, here’s my recommended sequence:
Subscribe to coding agent tools for the whole team. Cursor, Claude Code, Codex—pick one. Don’t make engineers pay themselves; this is a company expense.
Pick a low-risk feature and deliberately try the spec-light, explore-first approach. Agree with PM: 1-2 days for a demo version, discuss through implementation rather than long specs first.
Make AI review a required step; have human review focus on architecture and business logic. Get engineers used to: fix AI comments first, then humans look.
Schedule for this quarter: build preview environments. This will truly unlock your iteration speed.
In Closing
Looking back at these two years, the biggest shift wasn’t which tools we used, but how our perception of “reasonable timelines” changed.
When we used to say “this will take two weeks,” the underlying assumption was: engineers need to read specs, do research, write code, debug, self-review, then open a PR. This whole process takes time.
That assumption no longer holds. Many steps can have AI run first, with humans confirming and adjusting after. Overall cycle time has dramatically shortened.
“You should be able to open the PR by tomorrow, right?”
When you can say this naturally, and it’s actually a reasonable expectation, you know your team has changed.