AI is Coding at Warp Speed, but Your Delivery is Still Stuck in Traffic
- Gueri Segura
- 20 minutes ago
- 3 min read
By Gueri Segura, Founder/CEO at Tenmás
Engineering teams are pumping out code faster than ever thanks to LLMs, but more code isn’t the same as faster shipping. Individual implementation is up. The systems meant to review, test, and deploy that code are the ones redlining.
Takeaways:
Review saturation is the primary blocker: AI turns PRs into a firehose that 2–3 senior engineers (the usual suspects) can’t keep up with.
Testing debt drives rework spikes: Faster development exposes weak testing discipline, leading to flaky pipelines and manual QA constraints.
AI competence is now table stakes: Skill with Copilot or Claude isn’t a differentiator; leaders still hire for judgment and ownership.
Tenmás angle: We built AI Pods (engineers + an AI system) to solve the 2026 reality: the bottleneck is review/QA/context, not typing. The pod brings guardrails, test standards, and reviewer load reduction so output turns into shipping—not backlog.
The Review Saturation Trap
AI increases the volume of pull requests. Review capacity stays flat. In most teams, the same few senior engineers still carry responsibility for “everything that matters.”
Reality check: speeding up developers upstream can make you slower overall if your seniors are drowning in notifications. Waiting for review—not writing code—becomes the dominant cost.
What helps: reduce reviewer load, not just developer effort.
Narrow what truly requires senior review vs. what doesn’t
Enforce standards before humans touch the PR (linting, CI checks, automated analysis)
Use AI to summarize diffs and explain intent so reviewers spend less time “figuring it out”
Goal: not faster PRs—cheaper reviews.
Faster Coding, Flakier Tests
When code ships faster, tests often lag. You get a “busy” team—lots of commits, lots of movement—but less predictability because QA becomes the constraint.
Translation: you’re running faster toward a cliff. If test enforcement doesn’t shift earlier in the flow, AI-assisted speed just creates more rework after merge.
What helps:
Treat coverage as a gate, not a suggestion
Generate tests earlier (and automatically), then enforce standards via CI
Keep pipelines fast and reliable so “testing” doesn’t become a second review queue
The Context Loss Tax
AI-generated code often looks correct but lacks obvious intent. It can introduce unfamiliar patterns that increase cognitive load: reviewers aren’t just checking correctness—they’re reverse-engineering “why.”
The catch: over time, this turns into architecture drift. Local optimizations stack up. The system gets harder to understand, change, and onboard into.
What helps:
Explicit conventions for structure + intent (PR templates, code patterns, ADRs)
Guardrails that encode “how we do things here” (lint rules, scaffolds, test standards)
Make architectural decisions visible and enforced—not tribal
Example
A developer uses Cursor to finish a complex feature in two hours—work that used to take two days. Great. Then the PR sits in a senior engineer’s queue for three days because review volume tripled, and the AI-generated logic is just “weird” enough to require extra scrutiny.
Net gain in delivery time? Zero.
Closing
So what? If you don’t fix review and testing throughput, AI isn’t making you faster—it’s turning your workflow into a PR factory.
Are you measuring success by how much code your team writes, or by how quickly that code reaches the customer?
Tenmás helps engineering leaders navigate these bottlenecks by placing vetted senior talent who prioritize judgment and architectural integrity over raw code volume
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If you want to discuss a specific scenario—your company, your stack, or your budget—you can reach out via the Tenmás website or book a quick call. We’re happy to help you evaluate whether Tenmás is the right fit to help you scale your delivery capacity.