It was bound to happen.
At some point, everyone in tech was going to have to choose a side — and not in the way people think. Not AI vs no AI, and not technical vs non-technical. The real divide is much simpler: are you using AI to multiply your thinking, or are you using it to avoid thinking altogether?
That distinction matters more than most people realize, because AI has fundamentally changed the value equation in our industry. The act of building, coding, and shipping still matters, but the actual production of code is becoming less rare by the day.
What used to separate people technically is now being compressed by tools, and once execution becomes easier, the market starts rewarding something else at a much higher level: judgment, structure, discernment, and the ability to think clearly through complexity.
That’s a big reason why I no longer rush to call myself a software engineer. These days, architect feels more honest.
AI has commoditized execution and thinking is now the new premium.
The prestige has changed
There was a time when being called a software engineer carried a certain weight, and rightfully so. You had to know how to think through systems, structure logic, understand constraints, debug edge cases, and ship something that actually worked. That still matters, but AI has changed how we execute.
Today, the ability to produce code is no longer the premium it once was. As tools make implementation faster and more accessible, the market naturally starts placing more value on the people who can think beyond the output itself.
The real differentiator is no longer just who can build. It’s who can take ambiguity, translate it into structure, and make sound decisions across systems, people, and business needs. That’s the layer that sits above code, and it’s also the layer that becomes more valuable as execution gets easier. At a certain point, that’s no longer just engineering. That’s architecture.
Choose a side
I do think it’s time to choose a side, but not in some dramatic “adapt or die” kind of way. I mean it in the most practical and competitive sense possible. You have to decide whether you want to keep working in a way that feels familiar, or whether you want to become materially more effective.
If you’re still approaching your work like it’s 2021, you’re already behind.
I’ve had a front row seat to how senior engineers, technical partners, and product teams are using AI in real workflows, and the difference is not subtle. The people using it well are not just moving a little faster. They are reducing friction across their entire process.
They test more ideas, get unstuck quicker, spend less time grinding through repetitive tasks, and spend more time making decisions that actually move the work forward. That advantage compounds quickly, and over time it becomes impossible to ignore.
Leverage is the real game
I recently heard someone say that if you’re paying a 250,000 engineer and by the end of the year they’ve barely used any tokens, that should probably concern you.
Now, obviously, token count alone is not the KPI. Blindly throwing prompts at AI doesn’t make someone competent. But the point underneath that statement is absolutely right: your best people should be actively looking for leverage. They should be asking how to reduce time-to-clarity, eliminate repeat work, pressure test more ideas, and move faster without sacrificing quality.
That’s where AI becomes powerful — not as a replacement for expertise, but as a multiplier for organized thinking. AI does not replace thought, it just amplifies it.
How you thing is the real advantage
This is the part a lot of people are still getting wrong. The real advantage of AI is not that it can generate code fast. It’s that it can accelerate someone who already knows what they’re doing. In the hands of someone with weak thinking, AI just produces faster confusion. In the hands of someone with structured thinking, it becomes an incredibly effective force multiplier.
That person can frame better prompts, pressure test assumptions, identify weak spots earlier, and arrive at stronger decisions faster. Their edge is not just technical ability. It’s clarity. It’s the ability to organize complexity, isolate what matters, and move from ambiguity to direction without wasting unnecessary cycles. That is where the premium is moving, and that is also why clear thinking is becoming one of the most valuable technical assets a person can have.
Engineer A vs Engineer B
Give two engineers the same problem, say...building a shipment exception workflow for customers missing customs documents.
Engineer A opens AI and says, “Build me a shipment exception workflow missing customs documents”
The output will probably look decent on the surface: a few forms, a few statuses, maybe a notification or two. But it’s shallow. It doesn’t ask what happens if the wrong document gets uploaded twice, who owns the exception after 24 hours, whether operations needs a review queue, whether the process changes by country, or how this affects invoicing, storage, and customer communication.
Engineer A built a feature. Hooray.
Engineer B also uses AI, but first they structure the problem. They map the states, identify the actors, think through failure points, define the business rules, and understand the operational impact before they ever start generating. Then they use AI to accelerate execution.
Same tool. Completely different result.
Not because one person had better prompts, but because one person had better thinking. That’s the difference between someone who can produce output and someone who can produce clarity.
Your thinking is our IP
One part of this conversation still shocks me.
I recently had a client suggest, in so many words, that my thinking was their IP. I remember sitting there trying to process how absurd that sounded, because what exactly do people think they’re paying for?
You’re not just paying for code, screens, tickets, or deliverables. You’re paying for judgment and experience. You’re paying for someone to see what you don’t see, think through consequences before they happen, reduce expensive mistakes, and create structure where there was previously confusion.
That is the work. It has always been the work. AI has just made it much harder for people to confuse output with value.
I'm not anti-engineer
None of this is an argument against engineering. If anything, it’s an argument for its evolution.
The job is not disappearing, but it is changing. The people who will become most valuable in this next era will not be the ones who reject AI in defense of some older idea of “real engineering,” nor the ones who blindly outsource their thinking to it. The people who will stand out are the ones who can think deeply, move quickly, and use AI without becoming dependent on it.
That combination is becoming increasingly valuable because it reflects something bigger than technical ability alone. It reflects maturity, judgment, and range. In a world where more people can generate output, the real premium will continue to shift toward the people who can direct, shape, and elevate it.
So yes, it’s architect for me
I still respect the title engineer, and I always will. But for where I am, how I think, and how I work, architect feels more honest. Because the code was never the whole job. The real work was always in the thinking, the structure, the tradeoffs and judgements behind the thing.
It's nowimpossible to ignore all of this with AI being front and center.
So no, I’m not abandoning engineering. I’m simply calling the game what it is now. And if I’m being honest, if AI is your sidekick and your mind is still the main engine, you probably were never just an engineer anyway.
So yeah, it’s architect for me.