Your AI Tools Aren't Making You More Productive

But they could be, if you take of the fundamentals first

It seems today everyone is talking about AI and betting hard on it delivering significant productivity gains. I've heard so many versions: it's making engineers 10x more productive, 100x more productive; it'll replace entire software development teams; it's revolutionizing content managers, product managers, project managers—you name it.

There are even companies drinking the Kool-Aid in one gulp, declaring themselves "AI First" without ever defining what that actually means.

Here's what I suspect is actually happening: your AI tools aren't making you more productive. They're making you busier.

I think teams are likely spending more time on training, technical debt cleanup, and integration overhead than they're saving on code generation. Here are the key areas where I see problems arising:

• The training tax seems inevitable. Senior developers likely need weeks to learn effective prompting. Junior developers risk becoming dependent on AI suggestions without understanding underlying patterns. Code review processes probably become more complex when you're debugging both human logic and AI-generated assumptions.

• Then there's the data problem. Teams often assume their codebase is clean enough for AI to understand. Based on my experience with legacy systems, I suspect it's not. Teams probably spend months cleaning up documentation and refactoring code so AI tools can provide useful suggestions. That's not productivity—that's paying off technical debt you should have addressed years ago.

• The security implications worry me most. Based on my experience in regulated industries, adding AI tools means adding new attack vectors. Your threat model expands to include prompt injection, data leakage through AI APIs, and the challenge of auditing AI-generated code for compliance.

I suspect we will see many teams trade immediate coding speed for long-term maintenance complexity; and even worse they might not realize they are doing this trade.

Do I think there is no value or productivity gains to get from AI? No, I think the productivity gains can be real—just not for most teams yet. The companies actually seeing value from AI aren't the ones with the flashiest implementations. They're the ones who did the boring foundational work first.

The fundamentals that actually matter:

• Clean data - AI can't fix messy, inconsistent data

• Clear documentation - If your team can't understand your codebase, AI won't magically fix that

• System understanding - Teams need to be able to validate AI output, not just accept it

• Quality processes - If your documentation is scattered or outdated, AI tools will just hallucinate more confidently

The productivity gains are real, but they come after you solve the integration problems no one wants to talk about.

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What's your experience with AI tool adoption? Are you seeing the promised productivity gains, or spending more time on the integration overhead?