Sometimes the biggest improvement isn’t optimizing the process. It’s eliminating the thing everyone assumed was permanent.

In late 1996, a temp agency placed me at CampusMCI, a college-focused ISP division of MCI Communications, as a call center support operator. I’d spent two years translating print to web for a gaming magazine I co-founded.

On my first day, I learned that each shift started with a photocopied hot list of known problems. I sat down at a VT terminal and started taking calls.

On my first break, I asked the floor supervisor, “Have you considered doing this paperless? You’d save paper and have something you can edit in real time.” She said they hadn’t. I asked if I could demonstrate something on my workstation.

She said: “Sure, go ahead.”

And that’s how the daily floor notices went digital.

It proved popular enough that a spare PC was allocated to host the pages. I started building call scripts for Tier-1 issues: winsock.dll failures, PPP authentication, modem negotiation. Step-by-step visual guides with branching logic that showed the CSR exactly what the caller was seeing… on an Intranet webpage in 1996.

Before the guides, certain calls could only go to a few people on the floor who had the settings panels memorized. After the guides, anyone could take them.

Requests for improvements started coming in. I moved the tools onto OS/2 Warp, using REXX, Java applets, and IBM’s native web server. That stack was deliberate: the applications could migrate to AIX, giving enterprise IT a straightforward code handoff if they wanted to formally adopt the system. I soon had a team assisting me in my work.

I added a shared support mailbox so CSRs could answer emails without stepping on each other. Account lookup tools. Then a call disposition form where CSRs clicked a few buttons after each call, feeding a Java applet that gave floor management real-time visibility.

Suddenly three calls about a modem not answering in Georgia weren’t just three calls. Together, they signaled that someone needed to contact the onsite team before the problem spread.

Those efficiencies helped protect our location through successive call center closures. At least three times while I was there, equipment from closed locations was consolidated into ours. That continued after I left.

I went from temp to the end of the Tier-3 escalation chain leading a team of five in about eighteen months, and it started with a question on my first coffee break.

The instinct in any operation is to optimize what’s already there. Better photocopies. Faster distribution. Neater binders. But sometimes the thing you’re optimizing around is the thing that needs to go. And the person who sees it might be the one who just walked in the door, because they haven’t learned to accept it yet.

I don’t think there is any question about it. Your AI cost crisis can only be attributable to human error.

Pay for intelligence when you need intelligence. If you’re doing the same task twice, have the model write a program to do the task for both of you.

I put $30 on the Anthropic API three months ago. I burned $2.70 in the first few days figuring out my approach. Twenty cents since. Less than $3 total, and most of that was tuition.

That twenty cents built a text analysis engine. Sentence tokenizer, statistical metrics, entropy calculations, composite scoring for a website I built. The model wrote the code, I reviewed it, I deployed it as PHP on a Linux box I already own. The electricity cost is a rounding error on my power bill.

I keep reading about companies spending six figures a month on AI tokens and I’m doing the blinking eye meme. Putting every customer ticket, every document summary, every code review through a model, thousands of times an hour, 24/7, paying per token every time, seems crazy when the model could build you a deterministic tool instead.

Deterministic work belongs in traditional code.

But the integration pattern the industry has settled on is “put the model in the hot path.” Keep the AI in the loop on every request, forever. Perpetual token cost for work that stopped being an AI problem the moment someone understood the requirements. Your output changes when a new model version ships. Your pipeline breaks not because your code changed but because theirs did.

The model can write its own replacement for most of these use cases. Ask it to classify tickets? It can write you a classifier. Ask it to score text against a rubric? It can write the scoring engine. One build session. One deployment. Done. Stable output you control.

Companies pay the model to do the same work over and over because that’s how the tooling is sold. The SDKs make it easy to call the API. The tutorials show you how to put the model in your pipeline. The pricing page shows per-token costs that look cheap until you multiply by volume. Nobody in that funnel is suggesting you use the model to build something and then turn it off.

This is the SaaS treadmill applied to AI. Recurring revenue for the provider. Recurring cost for the customer. For work that could be a one-time build.

AI APIs are the right call when you need reasoning on novel inputs. Creative work, ambiguous classification, anything where the rules can’t be fully specified in advance. Pay for intelligence when you need intelligence. But if you’re sending the same shaped request a thousand times a day and getting predictable outputs, you don’t have an AI problem. You have an engineering problem. And the AI is the best engineer available to solve it for you once, without ever touching API billing.

Twenty cents. The result runs on its own.

AI is the latest advancement in automation. The job losses, the industry shifts, the civil upheaval. None of this is new folks, but that also doesn’t make it fun or exciting to be the one replaced. I’ve been on both sides of this, trust me I feel your pain.

Humans like to make work easier and more efficient. Are there still people working in some of these industries? Sure but not at the scale of their peak when these jobs would have been a career choice. Let’s just go back say 150 years…

The steam-powered drill replaced human miners (and yes, John Henry beat it once, and it killed him)

The gas-powered tractor replaced significant human and animal labor

The moving assembly line and subsequent robotics replaced the skilled factory worker

Various waves of agricultural harvesting automations have reduced the use of manual field labor (from cotton to strawberries)

(Automatic) Computers replaced human “Computers” wiping out an entire career staffed primarily by women

ATMs have replaced bank tellers

Automatic telephone switchboards eliminated an entire career path

Spreadsheets replaced formal Bookkeepers and Accounting Clerks

Desktop publishing wiped out the prepress industry

Online hotel and travel reservation booking has replaced the travel agent

And for the average person on LinkedIn, this is probably more personal than previous waves of automation because it reaches into knowledge work, creative work, and professional identity in ways people didn’t expect. For the last 40 years we told people those careers were safe.

And unless you’ve been raging against tractors, ATMs, spreadsheets, online booking, desktop publishing, industrial robotics, and every other labor-saving tool with the same energy, then maybe this isn’t really a principled objection. That’s not consolation to you or those who came before you though, is it?

Do I have an answer? No. But sci-fi writers have been proposing them for decades: universal basic income, radical restructuring of how we think about work and value, decoupling survival from employment. The ideas aren’t new. We just refuse to take them seriously until the crisis is personal. And even then, we’d rather fight about whether the automation is fair than talk about what comes after it.