AI allows us to write code faster. But that doesn’t necessarily mean we are delivering software faster.

This is one of the most interesting contradictions emerging in software development.

According to GitLab’s 2026 AI Accountability Report, conducted by The Harris Poll among 1,528 developers and technology decision-makers across six countries:

• 78% say developers are writing and committing code faster since adopting AI tools.

• 79% believe individual developer productivity has improved.

• However, 85% agree that the bottleneck has shifted from writing code to reviewing and validating it.

• Additionally, 82% believe AI-generated code could create a new form of technical debt that their organizations are not yet prepared to manage.

GitLab calls this the “AI Paradox”: individual productivity increases, but the overall software delivery process does not accelerate at the same pace.

Research from DORA, Google Cloud’s software delivery performance research program, points in a similar direction.

Its findings show that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability.

This does not mean that AI always harms software development. These results show an association, not a universal rule or a causal relationship that applies to every team.

The problem is not using AI.

The problem begins when an organization accelerates code generation while leaving the rest of its development process unchanged:

• Slow manual reviews. • Limited automated testing. • Ambiguous requirements. • Oversized pull requests. • Lack of architectural criteria. • Poor traceability. • Unclear ownership of AI-generated code.

If we double the speed of one stage without improving the next ones, we simply move the bottleneck somewhere else.

Manav Khurana, Chief Product and Marketing Officer at GitLab, summarized it clearly:

“Speed without control is a liability, not an advantage.”

As a full-stack developer, I believe AI can genuinely accelerate tasks such as creating components, writing documentation, exploring solutions, refactoring code, or generating an initial set of tests.

But producing more code should never be the main measure of success.

The result should be measured in software that:

• Solves a real need correctly. • Remains maintainable over time. • Includes testing and security controls. • Can be understood by other developers. • Reaches production without multiplying incidents.

AI does not eliminate the need for architecture, testing, and technical judgment. On the contrary, the faster we can generate code, the more important these elements become.

The next competitive advantage will not be generating more code with AI. It will be verifying it, governing it, and turning it into reliable software.

Has AI actually reduced delivery time for your team, or has it simply increased the amount of code waiting to be reviewed?

And remember: good software starts with good decisions. See you in the next one.

The distinction between writing code faster and delivering software faster is exactly where many teams get stuck. Which non-coding bottleneck do you think AI-assisted teams underestimate most?

Mimir — the AI execution layer for software

Build story

I kept hitting the same wall with AI agents: they can tell you what to do, but the moment you let one actually touch a real system — payments, orders, refunds — you're one hallucinated call away from a disaster. The demo is easy. The safety is the whole product. So I built Mimir around that problem instead of around the demo.

The core move is a single conversion: every API endpoint becomes one safe, callable operator. GET /orders becomes get_orders(). You point Mimir at an OpenAPI spec — or just paste the docs — and the model reads it, normalizes it, and commits it as a set of typed, validated tools an agent can run.

Three decisions shaped the build:

Boundaries live in the runtime, not the prompt. Permissions, approval gates, and destructive-write flags are enforced in code. You can't jailbreak a policy that was never a sentence. The agent is able to act, unable to overreach.

The model never sees the key. Credentials are vaulted and injected at execution time. The LLM operates the system without ever holding the secret to it.

Every action is signed and logged. Runs are stamped to an operator and written to an audit trail — so "the AI did it" is always answerable with who, what, and when.

The part I'm most proud of: I engineered it to run on a local 7B model, not a frontier API. That forced real discipline — small-model-first, code-enforces-not-prompts, eval-driven, structured output over brittle parsing, and a rule I made non-negotiable: the AI understands the docs and normalizes them; code only validates against known types. No hard-coding one company's naming conventions, because the next API won't share them. It's a deliberately-engineered system, not a wrapper around a chat box — and it's executed real refunds end-to-end.

Stack: Next.js 16 · React 19 · Tailwind v4 · TypeScript on the front; FastAPI · Postgres · LiteLLM → Ollama on the back. The interface is a custom design system I call "Operations Ledger" — carbon, bone, and one acid accent; Archivo Black; flat brutalist. It's meant to feel like an instrument panel, not a landing page.

Software you can operate by intent — safely. #SoftwareArchitecture #SystemDesign #FullStack #SaaS #AIEngineering #DistributedSystems #React #PostgreSQL #NodeJS #Python #ProductEngineering #WebDevelopment