Engineering Leadership Videos

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AI can write code but accountability hasn’t moved. When you check code in you’re still signing your name to it whether you typed every line or orchestrated an agent that did. Ownership doesn’t disappear just because execution got faster.

AI can generate functions suggest refactors and scaffolded whole systems. That’s delegation. And delegation only works if judgment stays in the loop. You’re responsible for deciding what to hand off reviewing logic validating edge cases and making sure output aligns with real requirements.

The job is not changing from coder to prompt engineer it’s shifting from implementer to orchestrator. You define intent scope and constraints then guide AI through execution. Without clarity speed becomes confusion.

In production work AI does not free you from discipline it demands more of it. Testing documentation version control and code review aren’t overhead or optional. They’re safeguards.

If something fails in production it is not AI’s bug. It is your process that allowed it through. Learning to scale precision not just speed is the real professional shift.

Let AI draft debug and refactor but never let it decide what good means. Judgment still belongs to you.

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Automation does something sneaky. It takes away the practice. When the system can automate drafts, code, research plans, people stop doing it normally and then become supervisors, which sounds like promotion or nightmare, depending on what you like about your job.

But here’s the thing. The system runs fine most of the time. And because it runs fine most of the time, you’re least prepared for when it doesn’t. And that’s not an AI problem, or even an automation problem really.

Your teams using AI to crank out client recommendations, right, week after week, you get polished decks. They’re clean, you can turn them out quickly. But one day the model misses something, security compliance, actual business reality, and now you have to catch it. But you haven’t been building that muscle because the system made it feel unnecessary.

Are you gonna catch it or is your process gonna let it through? Again, the differentiator isn’t just who can generate, everyone can generate. It’s who keeps judgment sharp, verification habits, escalation paths, decision logs, and failure drills for the weird cases.

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I was talking to my brother the other day about how he uses AI, and one of the things that he does is ask it to build the fastest, most efficient plan possible, for whatever task he’s trying to accomplish, reduce the manual steps, cut the friction, get to the result quicker, which is really smart and makes perfect sense.

But as a counterpoint, AI is already making the work faster, 10faster50faster, sometimes10times faster. And so before you go looking for even more speed, maybe slow down for a second, and make sure you actually understand the problem. Look at your options. Build a mental model, train your judgment. See if there are different ways to approach this problem. Make sure you’ve got your car pointed in the right direction, before you hit the nitrous. Cause once AI’s in the loop, speed amplifies everything. If you’re headed the right way, that’s amazing. But if you’re headed the wrong way, and regardless, you still need structure and auditing and feedback loops, a way to catch mistakes as early as possible. Because speed doesn’t just amplify progress. It amplifies errors too.

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One of the weird things I’ve noticed about AI is that it sometimes creates work faster than it automates it, and I’ve run into this three times recently.

The first was a website performance project. We wanted to reduce cumulative layout shift, which is when you load a web page, the pieces and images move around. That’s called layout shift. So the way to fix that in a CMS-driven website is to specify the correct dimensions for the images. And a few years ago, that would have meant a lot of manual testing and manual review. Instead, I generated the code changes with AI. I built a crawler with AI, I generated screenshots of the entire site, and then I compared the before and after results with AI, and then fixed the outliers, again with AI.

The second was an SEO project. We wanted every URL on the site to use the correct canonical format, and it required updates to both code and content. So again, instead of reviewing pages manually, we crawled the entire site, generated a report of every issue, fed that report back into AI, found every place URLs were rendered, and then fixed them systematically.

And I see the same thing happening in marketing. What used to be one campaign is now becoming several: checking accounts for young adults and retirees and small businesses, and for different geographies.

I realized these are all the same story. AI makes generation really cheap, and when generation becomes cheap, you do more of it. And that creates more code to test, more content to review, more campaigns to validate, more experiments to measure.

AI is automating a lot of tasks in jobs right now, but it is also creating more work to do.