The Commodification Trap

Let me tell you about something that happened a few years ago. Someone who actually worked at Warner Bros. posted this story on Reddit about what went down at HBO Max, and it stuck with me because it perfectly captures how companies think about us (enginners).

So picture this: HBO Max had this core streaming platform built in Elixir. If you don’t know Elixir, it’s this niche functional language that can handle insane scale with a tiny team. The system was beautiful—fast, reliable, elegant. The kind of codebase engineers dream about.

Then new executives showed up with their spreadsheets.

They looked at the budget and saw a problem. Elixir engineers are expensive. They’re specialists. Hard to find, hard to replace. So these executives made a decision: rewrite everything in JavaScript.

Now, this wasn’t because JavaScript was somehow better for the task. It wasn’t because the Elixir system couldn’t handle the load. The Elixir system was handling millions of concurrent requests just fine. The reason was simpler and uglier: JavaScript developers are everywhere. You can fire them on Monday and hire replacements by Friday.

The plan was transparent. Treat engineering as a commodity. Get rid of the expensive specialists. Replace them with cheap, interchangeable cogs.

What happened next? Exactly what you’d expect. The migration teams tried to rebuild the platform in JavaScript. They failed. They tried again. They failed again. Meanwhile, that “outdated” Elixir system kept running perfectly, handling traffic that the new architecture couldn’t manage.

Eventually, most of the engineering team just quit. And here’s the thing—they didn’t leave because of the technology. They left because they’d been told, in the most explicit possible way, that their expertise didn’t matter. That years of learning specialized skills were worthless because some executive wanted to optimize a budget line.

Why This Keeps Happening

Look, I need to be honest with you about how companies actually see us. In boardrooms, we’re not craftspeople who solve complex problems. We’re cost centers. Line items. Resources to be optimized.

Software engineers are expensive. Really expensive. When you’re looking at a company’s operating budget, payroll is usually the biggest number, and engineering payroll is usually the biggest chunk of that. So naturally, the people signing the checks start wondering: how do we get this number closer to zero?

The dream is 100% efficiency. Maximum output per dollar. And in that dream, humans are the problem. Humans need salaries. Humans get tired. Humans want vacations and health insurance and stock options.

Machines? Machines are cheap. They don’t complain. They don’t unionize. They don’t ask for raises.

This thinking isn’t new. What’s new is that AI has given it this dangerous veneer of inevitability. Suddenly it’s not just cost-cutting—it’s “the future.” It’s “disruption.” It’s progress, and if you question it, you’re a Luddite standing in the way of innovation.

The Sales Pitch

Walk into any investor meeting at an AI company right now and here’s what they’re selling: “Replace your entire engineering department. Zero payroll. Pure profit.”

Close your eyes and imagine it. All those expensive engineers, gone. All that salary money, converted directly to shareholder value. No more standups. No more technical debt discussions. No more “we need two sprints to refactor this.” Just pure, beautiful automation generating code 24/7 without bathroom breaks or equity packages.

It sounds magical, doesn’t it? Revolutionary. Like the future we were promised in science fiction.

It also sounds exactly like a scam.

You know that aunt everyone has? The one who falls for every get-rich-quick scheme? “Invest a thousand pesos with me, and in a month you’ll have ten thousand!” When something sounds too good to be true, it’s usually because it is.

This isn’t innovation. This is a sales pitch dressed up as destiny. And the people buying it aren’t stupid—they’re just incentivized to believe. When you’re an investor looking at a spreadsheet, “fire everyone and keep the money” looks like a great strategy. Until it isn’t.

The Story of John Henry

There’s this old American folk tale about John Henry. He was a steel-driving man, working on the railroad, hammering spikes into tracks. Then the company brought in a steam drill—a machine that could do his job without getting tired.

John Henry decided to prove that humans were better. He challenged the steam drill to a race. All day long, he hammered as fast as he could. And he won. He drove more spikes than the machine.

Then he collapsed and died.

We tell this story like it’s about the triumph of the human spirit. Like John Henry proved that people are stronger than machines. But that’s not the point at all.

The point is that John Henry died, and the railroad got built anyway. The machine didn’t care that it lost the race. It didn’t need sleep or food or a paycheck. It just kept working until every human steel-driver was obsolete.

Trying to outwork the machine is a losing strategy. It always has been.

But Here’s What Actually Happens

I’m not going to sit here and tell you to panic. Really, I’m not.

Think about every technological revolution in history. The Industrial Revolution was supposed to destroy jobs, as well the computer revolution, as the The internet were supposed to destroy jobs. And yeah, some jobs disappeared. Some industries transformed.

But we’re still here. We figured it out. We adapted. We always adapt.

What You Should Actually Do

So here’s my honest advice.

Don’t fall for the false prophets. Not the ones selling magic beans to investors, and not the ones claiming coding is completely dead.

Maybe AI will replace some of what we do. Maybe it’ll replace a lot. But here’s what history teaches us: when the day comes that your job fundamentally changes, humanity figures it out. We always have. Not by fighting the steam drill like John Henry, but by finding what comes next.

So yeah, engineers are expensive. Companies will keep trying to treat us like commodities. Some of them will succeed for a while, until they realize that expertise actually matters and you can’t optimize your way to quality.

Keep building. Keep learning. We’ll figure it out together.