When AI Writes the Code, Who Makes It Trustworthy?
When AI Writes the Code, Who Makes It Trustworthy?
AI-generated code is fast. Teams build features in hours that used to take weeks. The productivity is real, and it's changing how software gets made.
The problem isn't the code. It's what happens when no one can explain it.
When compliance asks why a system handles data a certain way, and the team has no answer.
When a security review surfaces an assumption no one remembers making.
When someone needs to extend the logic but can't understand the decisions behind it.
AI solved speed. It didn't solve accountability. And that gap between what code does and whether anyone can defend it is where risk lives now.
This is about what comes after AI writes the code. What systems, practices, and ownership structures actually make that code trustworthy.
What separates teams that move fast from teams that move fast and can stand behind what they build.
AI wrote the code. Now what?
Code Is No Longer the Hard Part
AI changed software by making code abundant. A working implementation can appear in minutes. The code compiles, tests pass, the diff looks reasonable. McKinsey's 2024 study of 300 enterprise engineering teams found developers using Copilot committed 35% more code per sprint.
The creation constraint is completely gone.
What disappeared in that acceleration wasn't correctness. It was the understanding that used to accumulate naturally as teams built things line by line.
Teams feel this most during review. Not because the code is wrong. Because no one can answer "what happens if this fails?"
Everyone stares at the diff trying to reverse-engineer decisions that were never consciously made.
The bottleneck wasn't the code. It was developers asking "why did it do it this way?" three times per function and getting no answer.
Velocity moved forward. Understanding didn't keep pace.
Why "Code Janitors" Exist at All
Someone has to take responsibility for what the system actually does, not what it was intended to do.
Someone has to connect behavior back to business purpose and decide whether this code can be trusted six months from now.
That gap is why the "code janitor" role exists. Not to clean up messy code, but to own what the team can and cannot defend.
Traditional QA was built around human-authored systems where intent lived close to implementation. AI-assisted workflows break that assumption. Intent can be inferred from output without ever being chosen by the team.
Shopify made this explicit when leadership told teams to assume AI would be used by default, then paired that mandate with a hard line: humans remain accountable for outcomes. The message wasn't "let AI run free." It was "speed is allowed, abdication is not."
What changed in practice?
Teams started requiring that every PR answer two questions in the description: "What does this do when it fails?" and "Why this approach instead of alternatives?"
Not because Shopify distrusts AI. Because they learned that speed without answerability creates risk the business can't carry.
What Quality Means After AI
Quality used to center on correctness and coverage. In AI-assisted workflows, quality has to include whether the code is understandable enough to defend, extend, and secure.
AI-generated code often looks polished. It follows conventions, includes validation, reads cleanly. But it can embed unsafe defaults or incomplete assumptions that only surface under production conditions.
Veracode analyzed 50,000 AI-generated code samples in 2024 and found 23% implemented authentication with hard-coded session timeouts set to "never" or no timeout validation at all. The code compiled. Tests passed. The security model was broken by default. Functionally correct, operationally dangerous, invisible to standard review.
The shift is where judgment now has to live.
Mature teams focus on how code behaves in context, not just whether it runs. They ask whether the logic aligns with business intent.
They care whether a system can be explained to an auditor, a customer, or a future teammate without guesswork.
That's the new quality bar. Not "does it work?" but "can we defend this?"
Trust Is Built Around the System
Trust never comes from the tool. It comes from the system wrapped around it.
Recent vulnerabilities in AI-powered IDE extensions exposed how assistants with deep repository access could unintentionally create data exposure risks.
The failure wasn't bad code. It was a system that assumed trust without sufficient boundaries.
The same pattern shows up inside teams. When verification becomes optional, risk compounds silently.
When review gets rushed because output feels routine, assumptions slip through. When no one feels explicit ownership, trust becomes performative.
The teams that avoid this make responsibility visible. One infrastructure team put a simple rule in place: every AI-generated function over 50 lines requires inline documentation explaining not what the code does, but why this approach was chosen and what the alternatives were. If the team can't answer that, the code doesn't merge.
Another team created a rotating "accountability owner" role—one engineer per sprint whose job is asking "can we explain this in six months?" before anything ships. Not a bottleneck. A filter.
These aren't flashy practices. They're deliberate systems that turn fast output into something the team can stand behind.
The answer isn't to slow down or reject AI.
AI solved creation. Code is abundant now. Backlogs move faster. Features get built in hours instead of weeks.
What AI didn't solve is accountability. Who can explain this code in six months? Who can defend it to compliance? Who owns the decisions the AI made without anyone choosing them?
Teams that succeed treat these questions as requirements, not afterthoughts.
They build review practices that prioritize understanding over approval. They create roles where someone's job is asking "can we defend this?" before code goes to production.
They accept that generating code is cheap and building trust around it is not.
The standard hasn't changed. If you put code in production, you need to be able to explain it. AI just made that harder to meet.
What separates effective teams now isn't speed. It's whether they've built the systems and practices that let them stand behind what they build.
That means treating speed and accountability as equally important.
AI writing code is the baseline. The work that matters is what happens after. The questions that get asked.
The gaps that get closed. The understanding that gets built back in.
We believe that business is built on transparency and trust.
We believe that good software is built the same way.
When AI Writes the Code, Who Makes It Trustworthy?
When AI Writes the Code, Who Makes It Trustworthy?
AI-generated code is fast. Teams build features in hours that used to take weeks. The productivity is real, and it's changing how software gets made.
The problem isn't the code. It's what happens when no one can explain it.
When compliance asks why a system handles data a certain way, and the team has no answer.
When a security review surfaces an assumption no one remembers making.
When someone needs to extend the logic but can't understand the decisions behind it.
AI solved speed. It didn't solve accountability. And that gap between what code does and whether anyone can defend it is where risk lives now.
This is about what comes after AI writes the code. What systems, practices, and ownership structures actually make that code trustworthy.
What separates teams that move fast from teams that move fast and can stand behind what they build.
AI wrote the code. Now what?
Code Is No Longer the Hard Part
AI changed software by making code abundant. A working implementation can appear in minutes. The code compiles, tests pass, the diff looks reasonable. McKinsey's 2024 study of 300 enterprise engineering teams found developers using Copilot committed 35% more code per sprint.
The creation constraint is completely gone.
What disappeared in that acceleration wasn't correctness. It was the understanding that used to accumulate naturally as teams built things line by line.
Teams feel this most during review. Not because the code is wrong. Because no one can answer "what happens if this fails?"
Everyone stares at the diff trying to reverse-engineer decisions that were never consciously made.
The bottleneck wasn't the code. It was developers asking "why did it do it this way?" three times per function and getting no answer.
Velocity moved forward. Understanding didn't keep pace.
Why "Code Janitors" Exist at All
Someone has to take responsibility for what the system actually does, not what it was intended to do.
Someone has to connect behavior back to business purpose and decide whether this code can be trusted six months from now.
That gap is why the "code janitor" role exists. Not to clean up messy code, but to own what the team can and cannot defend.
Traditional QA was built around human-authored systems where intent lived close to implementation. AI-assisted workflows break that assumption. Intent can be inferred from output without ever being chosen by the team.
Shopify made this explicit when leadership told teams to assume AI would be used by default, then paired that mandate with a hard line: humans remain accountable for outcomes. The message wasn't "let AI run free." It was "speed is allowed, abdication is not."
What changed in practice?
Teams started requiring that every PR answer two questions in the description: "What does this do when it fails?" and "Why this approach instead of alternatives?"
Not because Shopify distrusts AI. Because they learned that speed without answerability creates risk the business can't carry.
What Quality Means After AI
Quality used to center on correctness and coverage. In AI-assisted workflows, quality has to include whether the code is understandable enough to defend, extend, and secure.
AI-generated code often looks polished. It follows conventions, includes validation, reads cleanly. But it can embed unsafe defaults or incomplete assumptions that only surface under production conditions.
Veracode analyzed 50,000 AI-generated code samples in 2024 and found 23% implemented authentication with hard-coded session timeouts set to "never" or no timeout validation at all. The code compiled. Tests passed. The security model was broken by default. Functionally correct, operationally dangerous, invisible to standard review.
The shift is where judgment now has to live.
Mature teams focus on how code behaves in context, not just whether it runs. They ask whether the logic aligns with business intent.
They care whether a system can be explained to an auditor, a customer, or a future teammate without guesswork.
That's the new quality bar. Not "does it work?" but "can we defend this?"
Trust Is Built Around the System
Trust never comes from the tool. It comes from the system wrapped around it.
Recent vulnerabilities in AI-powered IDE extensions exposed how assistants with deep repository access could unintentionally create data exposure risks.
The failure wasn't bad code. It was a system that assumed trust without sufficient boundaries.
The same pattern shows up inside teams. When verification becomes optional, risk compounds silently.
When review gets rushed because output feels routine, assumptions slip through. When no one feels explicit ownership, trust becomes performative.
The teams that avoid this make responsibility visible. One infrastructure team put a simple rule in place: every AI-generated function over 50 lines requires inline documentation explaining not what the code does, but why this approach was chosen and what the alternatives were. If the team can't answer that, the code doesn't merge.
Another team created a rotating "accountability owner" role—one engineer per sprint whose job is asking "can we explain this in six months?" before anything ships. Not a bottleneck. A filter.
These aren't flashy practices. They're deliberate systems that turn fast output into something the team can stand behind.
The answer isn't to slow down or reject AI.
AI solved creation. Code is abundant now. Backlogs move faster. Features get built in hours instead of weeks.
What AI didn't solve is accountability. Who can explain this code in six months? Who can defend it to compliance? Who owns the decisions the AI made without anyone choosing them?
Teams that succeed treat these questions as requirements, not afterthoughts.
They build review practices that prioritize understanding over approval. They create roles where someone's job is asking "can we defend this?" before code goes to production.
They accept that generating code is cheap and building trust around it is not.
The standard hasn't changed. If you put code in production, you need to be able to explain it. AI just made that harder to meet.
What separates effective teams now isn't speed. It's whether they've built the systems and practices that let them stand behind what they build.
That means treating speed and accountability as equally important.
AI writing code is the baseline. The work that matters is what happens after. The questions that get asked.
The gaps that get closed. The understanding that gets built back in.
We believe that business is built on transparency and trust.
We believe that good software is built the same way.
Our superpower is custom software development that gets it done.
