The Hard Truth About AI and Developer Productivity: What Our July Webinar Actually Taught Us

Last month, Jeff Keyes and I got on a webinar to talk about something every executive is asking: "How do we use AI to make our developers more productive without completely screwing up our software?"

The response blew us away. Clearly, we hit a nerve. So let me give you the straight talk on what we learned, because this stuff matters too much to sugarcoat.

The Revolution Already Happened (While You Were Planning for It)

Here's the thing nobody wants to admit: AI in software development isn't some future trend we need to prepare for. It's here. Right now. 97% of developers are already using it, and 81% of companies are throwing money at AI development.

But here's what's keeping me awake at night—almost everyone says they want to adopt AI, but actually doing it effectively? That's where things fall apart.

This is the internet all over again. Twenty-five years ago, companies that figured out the web early got huge advantages. Same thing's happening now. The question isn't whether AI will change everything—it's whether you'll be ready when it does.

Speed Doesn't Equal Success (But Everyone Acts Like It Does)

I keep hearing the same thing from executives: "Our developers should be cranking out twice as much code now that we have AI tools."

Stop. Just stop.

More code is not better code. It's like thinking a faster car automatically makes you a better driver. Sure, you can go faster, but if you're crashing into things because you can't handle the speed, you're not getting anywhere—you're just creating expensive problems.

What you really need is the three-legged stool of great software: speed, quality, and focus. All three, working together. That's when AI actually transforms your business instead of just making a mess faster.

The Baseline Problem (It's Bigger Than You Think)

During our webinar, I kept coming back to one word: baseline. You can't measure improvement if you don't know where you started. Sounds obvious, right?

Yet I have conversations all the time where someone tells me, "We're delivering twice as fast with AI!" My response? "Show me. Prove it. Where's that number coming from?"

One success story isn't data. One developer's experience isn't a trend. If you want real productivity gains from AI, you need to measure your entire team's performance today, implement your AI strategy, then measure again.

Without baselines, you're just guessing. And guessing is expensive.

You Still Need to Know How to Code

Here's something that can't be said enough: AI tools don't make you a software expert. They make experts more powerful.

These tools are impressive, but they're not perfect. They make mistakes—"hallucinations," if you want to be polite about it. If you don't understand how software actually works, you could be introducing serious problems into your codebase without even knowing it.

This is especially scary for junior developers. Someone without solid fundamentals using AI-generated code is like someone who doesn't know how to drive getting behind the wheel of a Formula 1 car. It might look impressive, but it's probably going to end badly.

The developers who are crushing it with AI aren't just using the tools—they're using them expertly because they already know what they're doing.

Pick One Thing and Get Really Good at It

One question we got was about avoiding "tool sprawl" while still innovating. My answer? Pick one framework and master it.

DORA metrics, SPACE framework, flow metrics—I don't care which one you choose. Pick something, establish that baseline we talked about, and consistently measure against it. These frameworks give you a foundation for real conversations about how software development is actually performing.

At Allstacks, we've seen what happens when teams have access to real engineering intelligence data. They stop guessing about what's wrong and start fixing actual problems. They guide their own improvement instead of having "productivity initiatives" forced on them from above.

It's Still a Team Sport

Software development has always been collaborative, but AI is making this even more obvious. No piece of software comes from one person anymore. Code gets reviewed, tested, performance-tested, and deployed by different people.

This is why measuring individuals based on code metrics is not just wrong—it's destructive. Focus on team-level metrics: team velocity, team health, value delivered as a unit. The more you embrace the team aspect, the better your AI adoption will be.

What You Should Do Right Now

Based on our discussion and all the questions afterward, here's what engineering leaders need to do:

Get your baselines today. You can't improve what you don't measure. You can't measure improvement without knowing where you started. This isn't optional.

Train people, don't just buy tools. AI tools aren't magic. Your teams need to understand prompt engineering, risk awareness, and fundamental programming principles to use them effectively.

Write the business case first. Before implementing any AI tool, explain what specific problem you're solving and how you'll measure success. If you can't do this, you're not ready.

Experiment with intention. Try one tool at a time. Get good at it. Measure the results. Then decide whether to expand or try something else. No tool sprawl.

Use data, not feelings. Self-reported status updates and "how do you feel" surveys aren't enough. You need objective metrics from your actual development pipeline.

The Real Talk

The companies that win this AI revolution won't be the ones with the most tools or the highest velocity. They'll be the ones that thoughtfully integrate AI while maintaining focus on quality, predictability, and business outcomes.

AI is here to make good developers great, not to replace expertise with automation. The leaders who understand this—who invest in both the tools and the skills to use them effectively—those are the people who'll create lasting competitive advantages.

This revolution is happening whether you're ready or not. The question isn't whether to embrace AI in software development. It's how to do it in a way that actually moves your business forward instead of just making you feel busy.

Ready to get serious about measuring your AI impact? Let's talk. Because assumptions are expensive, and data is power.