Claude Code Token Limits: What Google Ads Taught Me About AI Pricing

The Gold Rush Nobody Talks About

The recent changes to Claude Code’s token limits have a lot of developers and marketers paying attention.

Including me.

I used to buy clicks for $0.05.

It was 2003. Google Ads — AdWords back then — was barely a toddler. The inventory was wide open. Drop $50 into a campaign and own the top of the page for almost anything. The money came back multiplied and nobody was asking hard questions yet.

Then more advertisers showed up.

Competition drove costs up, Google got smarter about what it rewarded, and the easy money dried up. The people who panicked switched platforms, chased the next cheap thing, or just quit. The people who won slowed down and actually studied how the system worked. Built with the platform instead of against it.

That’s the whole game.

AI is in that same early window right now. And Claude Code’s token limits are just the first sign the curve is coming.

(Stick with me here — I know “token limits” sounds like nerd stuff. But the lesson underneath has nothing to do with Claude specifically. I’ve been teaching the same thing about Google Ads for 13 years. If you’re using AI for any part of your marketing, this is worth 5 minutes.)

The tokens are still cheap for what you get. Most people are still figuring out what to build. And the ones getting the most out of it aren’t the ones spending the most — they’re the ones who understand how the system is actually designed.

That window won’t stay open.

Why Claude Code Token Limits Were Predictable

Every scarce resource follows the same curve.

Google has a finite number of top search positions. No matter what you spend, there are only a few slots and everyone wants them. Claude has finite compute. The infrastructure is expensive, capacity is constrained, and as more people pile in, that gets priced accordingly.

The Claude Code pricing changes aren’t a surprise. They’re just the curve arriving on schedule.

The shift forces a choice.

You can spray-and-pray — throw budget or tokens at a problem and hope something sticks. Or you can figure out what the platform is actually trying to do.

Google’s mission is to connect searchers with exactly what they want, fast. Claude’s mission is to be a reliable, steerable system that works within defined constraints. When your work aligns with that mission, the platform rewards you. When it doesn’t, you pay more for less.

Most people treat these platforms like vending machines. Money in, output out. They wonder why costs keep rising and results stay flat. They’re fighting the platform instead of working with it.

Constraints Are the Point

Quality Score wasn’t a punishment.

It was Google telling advertisers that relevance matters more than budget. A $50/day campaign with the right keyword, the right ad, and the right landing page beat a $500/day campaign running broad match on everything. Efficiency beat budget, every time, once the system started keeping score.

Token economics is that same forcing function.

And honestly? I needed the reminder.

I came across Nate B. Jones’s stupid button framework on his Substack — check it out here — basically a gut-check on whether what’s in your AI setup is actually doing something or just adding noise. When I ran it on my own setup, what I found was humbling in the best way. I was burning through way more conversation turns than I needed to. I had skills running in the background that were eating tokens before the real work even started. Stuff I hadn’t noticed because things were still working fine.

Reminded me of the first time I pulled a real account audit for a client. They’d been spending $800/month on clicks that had never converted. The campaigns technically “worked.” Nobody had just looked.

That’s what happens when compute is still cheap and nothing’s obviously broken.

Tight doesn’t mean sparse — it means intentional. Right system prompt, right tools scoped to the task, structured inputs that tell the model what you actually need. Get those three things aligned and your cost drops. Get them wrong and you’re just burning tokens and wondering why.

That’s the Quality Score equivalent for AI.

What to Do With This

“If Claude gets expensive, I’ll just switch to GPT.”

Fair. But this is the direction all of them are heading. Every model provider is building on expensive infrastructure, and every one of them will eventually face the same compute crunch. They stay affordable by having smarter users — not by handing out free tokens forever.

The skill that saves you isn’t tied to one platform. It travels with you.

Here’s where to start.

Audit your system prompt — if it’s long, cut anything that doesn’t change the output. Scope your tools to what the task actually needs. Measure what you’re getting back per dollar spent — the same way you’d track cost per conversion in an ad account. (The Unit Economics Calculator is a good place to start if you’ve never laid your numbers out.)

You don’t have to overhaul everything today.

Just start treating token spend the way you’d treat ad spend.

(If you’re not sure where the waste is in your broader marketing setup — not just your AI stack, all of it — that’s exactly what a Red Line Review is built to find. Flat $495, and most people walk away knowing where a full quarter of budget went.)

The people who learned AdWords when it was still cheap to experiment built something that held up for years. The people who waited had to learn under pressure with no margin for error.

The window is still open. But it closes the same way.

Learn it now, while you can still afford the tuition.

Have a good one,

Corey