Replacing People Was the Mistake

Before I go any further, I owe you an apology. This is a heavy way to start a first blog post in 2026. I would much rather open the year with hope, momentum, and possibility than with frustration and loss. But pretending this didn’t happen, or softening it into something more comfortable, doesn’t help anyone. If we are going to move forward, we have to be honest about where we are.

I am still unsure what to call what happened in 2025. It was not a recession in the traditional sense, nor was it simply a market correction. It was a displacement driven by a belief. A belief that AI would optimize everything, reduce costs, and save millions. On paper, the math looked compelling. In practice, the damage landed unevenly.

The most significant impact showed up at both ends of the tech worker spectrum. The most experienced were pushed out, labeled as too expensive, too slow, or too resistant to change. The least experienced were never hired in the first place, blocked from gaining the very experience the industry now claims it still needs. The middle narrowed. The tech ladder broke.

What makes this moment different from past cycles is not just the technology, but the assumption behind it. The idea was not that AI would assist people, but that it could replace them wholesale. That belief ignored something fundamental about how systems actually work. Experience is not optional overhead. It is what keeps systems stable when the unexpected happens. Growth does not come from tools alone. It comes from people learning, failing, and improving over time.

By removing entry points, we are starving the future. By discarding senior talent, we are erasing institutional memory. AI trained on yesterday’s data cannot replace judgment forged through years of real-world consequences. Data goes stale—context matters. And without humans in the loop, optimization becomes brittle instead of resilient.

There is also a fundamental economic truth that seems to have been forgotten. An economy only works if people can participate in it. If large portions of the workforce are underemployed, unemployed, or perpetually “almost ready,” demand collapses. Efficiency without income is not progress. It is a dead end.

So how do we fix this mess?

We stop treating AI as a replacement strategy and start treating it as an amplification tool. AI should remove toil, not erase careers. That means deliberately designing roles that keep humans accountable, creative, and in control.

We rebuild the ladder. Entry-level roles, apprenticeships, and mentored pathways are not charity; they are infrastructure. If we do not invest in learning now, there will be no expertise later.

We keep experience in the system. Senior practitioners should not be viewed as cost centers but as stabilizers, teachers, and sense-makers—their value compounds when paired with emerging talent and modern tools.

And finally, we broaden the definition of success. Optimization cannot be the only metric. Resilience, sustainability, and human well-being have to matter again. Systems that appear efficient but often collapse under the strain of poor decisions. Do not be that company.

Now, another apology. I promise future posts won’t all start this heavy. But if 2026 is going to be better than 2025, it will be because we chose to correct course, not because we ignored the warning signs.

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