Your Developers Use AI Every Day — Are They Getting Better or Worse?

On May 13, 2026, 404 Media published a story that rattled a few uncomfortable cages. Developers at Meta, Google, Microsoft, and other major tech companies reported that their heavy reliance on AI coding tools — Copilot, Claude, Cursor — was eroding their ability to think through problems independently. One engineer described pushing through code "unaudited" just to keep pace with workload. Others said they had stopped reaching for the skills they spent years building because the AI handles it now.

The headline did the rounds: Software Developers Say AI Is Rotting Their Brains.

It is easy to dismiss this as tech-world hand-wringing. But if you are a CTO evaluating outsourced development teams right now, the story deserves more than a quick skim. Because if this is happening at companies with rigorous engineering cultures and internal code review, it is absolutely happening at some outsourcing vendors — and the consequences land on your project, not theirs.

What Is Actually Happening

AI coding tools are genuinely useful. They autocomplete boilerplate, suggest implementations, catch common errors, and compress what used to take hours into minutes. Nobody disputes this.

The problem is the dependency they create when used without discipline. A developer who routinely accepts AI-generated code without understanding it stops exercising the mental muscles that catch edge cases, architect for scale, and recognize when something technically works but is fundamentally wrong. The skill atrophy is real — it mirrors the cognitive studies on GPS navigation. Give people turn-by-turn directions for long enough, and they lose the spatial reasoning to navigate without it.

In software, this matters more than in most fields. Code that works today can fail catastrophically at scale. Security vulnerabilities are often invisible to the untrained eye and obvious to someone with deep pattern recognition. Architecture decisions made early compound into either a maintainable system or a debt spiral. These require judgment, not just code generation.

When developers outsource their thinking to AI tools, they stop building the judgment. They get faster. They get shallower.

AI-Assisted vs. AI-Dependent — A Line Worth Drawing

There is a version of AI-augmented development that is excellent. A senior engineer using Copilot to accelerate routine implementation, while still architecting the system, reviewing output, and catching the places where the AI confidently produces something wrong — that is a productivity multiplier. The engineer's judgment is in the loop.

Then there is AI-dependent delivery: accepting whatever the model produces, shipping fast, reviewing nothing because the deadline is today and the client is waiting and the AI seemed confident. That is not senior engineering delivered quickly. It is mediocrity with a fast turnaround.

The difference is invisible in proposals. Both vendors will describe themselves as "AI-powered" or "AI-enhanced." Neither will volunteer that one team treats AI output as a draft to scrutinize and the other treats it as a deliverable.

This is the distinction you need to probe.

What to Look For When Evaluating an Agency's AI Workflow

The question is not "do you use AI tools?" — every serious team does, and any agency that claims otherwise is either lying or dangerously behind. The question is: how do you govern them?

Here are the questions worth asking in any vendor evaluation:

What is your code review process for AI-generated code? A mature team will describe review practices that specifically account for AI output — they know where models tend to hallucinate, where they produce plausible-but-wrong implementations, and they check those areas explicitly. A team without this process will give you a vague answer about "quality standards."

Who is responsible for catching what the AI misses? There should be a clear answer: a senior engineer, a lead, a review gate. If the answer is "we trust the tools," that is your signal.

Can you show me an example of a time the AI was wrong and how your team caught it? This is the most revealing question. Teams that actively engage their critical thinking can tell you specific stories. Teams that defer to the AI will struggle to recall clear examples.

What does your architecture process look like, and does AI play a role there? Architecture decisions — how systems are structured, how data flows, how services communicate — should be made by humans with domain knowledge. If AI is driving architecture choices on your project, you have a problem.

What is your onboarding process for new team members? Agencies that maintain real engineering culture have structured ways of teaching juniors to think through problems, not just prompt through them. This question reveals whether they are growing engineers or farming AI operators.

How TMNSolutions Approaches This

We use AI tools. We use them regularly, and we are not apologetic about it. Copilot, Claude, and similar tools make our developers faster at the implementation layer — and that speed benefits clients directly in both cost and timeline.

What we do not do is treat AI output as finished work.

Every piece of AI-generated code at TMNSolutions goes through the same review a human-written implementation would. Senior engineers on each project have explicit responsibility for the architectural decisions — AI does not make those calls. When we take on a new project, we spend time understanding the business context before writing a line of code, because that context is what turns correct code into appropriate code.

We have also been deliberate about not creating AI dependency on our team. Junior developers are expected to understand the code they ship — not just the code they prompted for. We run internal sessions on what AI tools get wrong, specifically, so engineers know where to apply skepticism.

The goal is engineers who are made faster by AI without being made shallower by it. That balance requires active management. It does not happen by default.

Practical Questions for Your Next Vendor Evaluation

If you are currently evaluating outsourced development partners — or auditing an existing relationship — here is a short list of practical tests:

1. Ask for a code walkthrough. Have the agency's developer explain a recent implementation in detail. If they struggle to explain why decisions were made, that is a signal the code was generated without full comprehension.

2. Request a post-mortem on a past issue. How does the team diagnose and resolve problems? Deep debugging requires the kind of independent thinking that atrophies fastest under AI dependency.

3. Look at their team structure. Is there a clear seniority ladder? A one-tier "everyone is an AI developer" model is a red flag. Experience matters for judgment, and judgment is not something AI tools distribute evenly.

4. Review the code directly. If you have a technical person on your side, have them look at actual deliverables. AI-generated code has recognizable patterns — over-commented, overly generic, occasionally hallucinated method names. A good reviewer can spot the difference between code a developer wrote and code a developer accepted.

5. Ask about their AI governance policy. Not whether one exists (anyone can write a policy), but what it contains — specific rules about what AI can and cannot do without human review.

The Real Risk Is Not That Agencies Use AI

The real risk is that they use it without judgment — and charge you as if they used it with judgment.

The speed gains of AI tools have made it easier to bid lower and deliver faster, which on the surface looks like a better deal for the client. But speed without quality is technical debt with a delivery receipt. You accept it today, pay for it over the next twelve months in bugs, rework, and systems that do not scale.

The agencies that deserve your business are the ones who have figured out how to use AI to move faster without thinking less. That distinction is not automatic, and it is not obvious from a proposal deck.

It requires questions. Ask them.