The following article originally appeared on Medium and is being republished here with the author’s permission.

Don’t get me wrong, I’m up all night using these tools.

But I also sense we’re heading for an expensive hangover. The other day, a colleague told me about a new proposal to route a million documents a day through a system that identifies and removes Social Security numbers.

I joked that this was going to be a “million-dollar regular expression.”

Run the math on the “naïve” implementation with full GPT-5 and it’s eye-watering: A million messages a day at ~50K characters each works out to around 12.5 billion tokens daily, or $15,000 a day at current pricing. That’s nearly $6 million a year to check for Social Security Numbers. Even if you migrate to GPT-5 Nano, you still spend about $230,000 a year.

That’s a success. You “saved” $5.77 million a year…

How about running this code for a million documents a day? How much would this cost:

import re; s = re.sub(r”bd{3}[- ]?d{2}[- ]?d{4}b”, “[REDACTED]”, s)

A plain old EC2 instance could handle this… A single EC2 instance—something like an m1.small at 30 bucks a month—could churn through the same workload with a regex and cost you a few hundred dollars a year.

Which means that in practice, companies will be calling people like me in a year saying, “We’re burning a million dollars to do something that should cost a fraction of that—can you fix it?”

From $15,000/day to $0.96/day—I do think we’re about to see a lot of companies realize that a thinking model connected to an MCP server is way more expensive than just paying someone to write a bash script. Starting now, you’ll be able to make a career out of un-LLM-ifying applications.