Augmenting developers with AI is going to cost real money. A lot more than companies expect.

Your developers will soon need AI augmentation the way they need IDEs, laptops, and health insurance. And that cost will land somewhere around $1,000 per month per developer.

I know that sounds like a lot, but stay with me. I’ll walk through why I’m convinced this is where things are heading — and why the economics actually make perfect sense.

The best models today — Claude 4.5 Opus, Gemini 3 Pro — don’t just help you write code faster. They change what you expect from your tools entirely. Once you’ve experienced what a true developer-coauthor feels like, one that can take entire features from concept to implementation, you don’t go back. You don’t go back to the budget models. You don’t go back to doing things the slow way. You just use it for everything. Architecture, implementation, debugging, troubleshooting, refactoring — all of it. And when you’re running frontier models all day, every day, the costs add up fast.

The Trend: AI Dev Tools Are Getting More Capable, More Central — And More Expensive

Not long ago, teams were seriously debating the economics of GitHub Copilot. “How do we prove the ROI on $15/month per developer?” People held meetings about that. Actual meetings. And to be fair, in a company with 2,000+ developers, even a $15 line item suddenly feels like a real budget conversation.

Fast forward to today, that whole era looks almost adorable.

Because if you’re a serious developer trying to build real software with tools like Claude 4.5, Gemini 3 Pro, or Cursor Ultra, you’re going to use them all day, every day. Not for occasional help or the odd code snippet — for virtually everything. Architecture discussions, implementation, debugging, refactoring, testing. The AI becomes your constant collaborator, and that’s what drives the usage through the roof.

Every minute you’re coding without these tools is a minute you’re burning salary on work that could’ve been done 3–5x faster.

What I’m Paying Right Now: $800/Month and Rising

I started where everyone else did, the $20/month Cursor plan. Month after month, the bill kept creeping upward. Not because I was prompting 10x more, but because Cursor kept adjusting their usage limits and overage thresholds. Of course I’ve always used the most capable leading models, which are also the most expensive. Anything less defeats the purpose. If you’re spending half your time correcting the model or spelling out every little thing, it’s not really helping. The whole point is multiplying your time.

So my Cursor costs gradually went from $20 to $160 over several months. Then they introduced the $200 Ultra tier, marketed as the plan for power users who need unlimited access. For me, it covers about 8 days of usage. After that, the meter runs hard.

Cursor is well funded, but they’re not Google or Amazon. I don’t think their price increases came from greed. I think they simply couldn’t afford their heaviest users anymore — thousands of developers like me burning tens of thousands of tokens per day on frontier models.

On top of Cursor, I also use Claude Code with a Claude Max subscription. Anthropic still subsidizes usage heavily, so you get far more headroom before hitting limits. But I can tell you flat out, they’re losing money on me. That won’t last forever either. These companies will eventually stop hemorrhaging cash for power users.

Put it all together, and my spend today is at least $800/mo and rising. When the subsidies finally end, the true cost will surface. And the true cost isn’t $20/mo. It’s closer to $1,000/mo. Am I thrilled about that? No. But I’m not going back to the old way, and the value is undeniable.

Why $1K/mo Actually Makes Sense (The Economics)

Let’s break down why this isn’t insane.

Developer costs vary wildly — maybe $150K at a startup, $400K+ at a top tech company. If we assume $200K/year, that’s roughly $17K per month. If AI makes that developer even 20% more productive, that’s $3,400 in recaptured value per month — already 3x the cost of the AI tooling. But the real lift isn’t 20%. For developers who know how to use these tools well, AI accelerates real work by 3–5x. At that multiplier, $1K/month is a bargain.

Here’s the catch: those gains don’t come from just any AI. They come from the frontier models — Claude 4.5 Opus, Gemini 3 Pro, whatever’s newest and most capable. The cheaper models can help, but they’re not the ones delivering 3–5x productivity. You get what you pay for. And what you pay for the best models is what adds up to $1K/month.

And if $1K/month sounds like a lot, consider this – the providers haven’t fully figured out how much value they’re leaving on the table yet. When they do, $1K might look like the good old days.

What This Means for Companies

A few predictions:

1. Budgeting will shift from “developer cost” to “developer + model cost”

Companies currently think “we pay the developer, tools are cheap.” That’s going to flip. The developer and the model become a unit, and the model isn’t cheap anymore.

2. Companies with thousands of devs are going to get sticker shock

If you have 5,000 developers and this lands at $1K/mo per developer, that’s $60 million a year just for AI. Nobody is mentally prepared for that number yet. And the path to get there won’t be clean. Companies will start with basic plans, find them inadequate, upgrade heavy users to premium tiers, watch them blow through the limits anyway, and then face the overage question. The right answer is to pay it — cutting off a developer’s AI access costs you far more in lost productivity — but that’s a hard conversation to have when you’re setting next year’s budget. The direction is clear; the organizational willingness to follow it will likely lag behind.

3. Hiring expectations will shift

If two developers cost the same but one delivers 4x more output because they know how to work with AI, who gets hired? AI augmented developers will become the expectation. Non-augmented developers will start to look like a liability.

What This Means for Developers (Both Senior and Entry-Level)

1. Senior developers who know how to orchestrate AI become dramatically more valuable

They’re not just writing code anymore. They’re the ones who own the critical decisions — architecture and data model choices, API contracts and interface design, system design patterns, testing strategy — even when AI helps think them through. They bring the context, the accountability, and the intuition to recognize when something’s off and push back before it goes further.

2. Developers who resist AI are taking a real risk

Teams who use AI will outship teams who don’t so dramatically that companies simply won’t tolerate non-augmented developers. It will be like refusing to use version control in 2025. If you’re not augmented, you’re slow. If you’re slow, you’re expensive. If you’re expensive, you’re at risk.

3. Entry level developers face a harder path

The grunt work that used to be training ground for juniors — boilerplate, scaffolding, skeleton code — is gone. AI handles all of it. The problem is that junior level work is automatable in pretty much every specialty. What differentiates developers isn’t what they know how to do, it’s the judgment to know *why* certain approaches work and others don’t. That kind of intuition used to develop slowly through years of exposure.

What juniors actually need is to develop senior-level judgment faster. That means understanding the why behind practices, not just executing tasks. It probably means using AI to accelerate their own learning — getting more exposure to real problems, understanding system trade offs, developing intuition through volume. But the path is less clear than it used to be.

If I had to give advice, it would be to focus less on coding proficiency and more on architectural thinking. Learn to build things *with* AI, not just how to code. If you’re trying to break in, build something real — an actual application, soup to nuts — and force yourself to make real decisions. If you’re already working, seek out the infrastructure and system design work. Volunteer for the parts that involve trade offs. Should this be async? Message queue or scheduled job? Do I need a caching layer? That’s where the learning happens, and that’s what will separate you from the pack.

And treat AI like another senior developer on the team — one you can ask anything, anytime. If you don’t have a mentor showing you how to work this way, AI can partially fill that role. But you have to actually use it that way. Not just for code generation, but as a thinking partner for debugging, architecture questions, trade off analysis. The juniors who figure this out will compress years of learning into months.

4. The career ladder changes

The old progression was about coding proficiency — you wrote more code, handled more complexity, eventually led teams. The new progression is about judgment and AI fluency. How quickly can you make good architectural decisions? How well can you direct AI toward the right solution? How reliably can you spot when something’s off? That’s what separates levels now.

The Bottom Line

We are heading toward an era where AI becomes a required part of the developer cost structure, and the real cost will be around $1K/month per developer. Not because the AI is expensive, but because the developer’s time is expensive — and the AI multiplies that time.

This isn’t where most companies are today. But it’s where things are going. The models are getting dramatically better, and with that comes both higher costs and higher expectations. The developers who learn to work this way will outpace the ones who don’t. The companies that budget for it will outship the ones that don’t.

If your team isn’t already thinking about how to adopt AI first development practices, now is the time to start. Not because the future is certain, but because the direction is obvious — and the gap between early adopters and laggards is only going to widen.

The costs I’m describing aren’t hypothetical. They’re what happens when you actually use AI to its potential. Most developers aren’t there yet, but they will be. And when they get there, their companies will face the same economics. The future is already here — it’s just not evenly distributed.