The AI Subsidy Trap
Why Waiting for the "Real" Price of AI Is the Most Expensive Decision You'll Make
Everyone believes AI is currently underpriced. The VCs are burning cash, subsidizing your ChatGPT subscription while they race for market dominance. Smart money says wait and let the dust settle, let the real prices emerge, then make your move.
This is exactly backwards.
The most expensive decision you can make right now is waiting for AI prices to "normalize." Not because prices won't eventually rise (they might), but because you're misunderstanding what you're actually paying for. You're not buying computation time. You're buying compound advantage in a world that's about to split into two groups: those who learned while learning was cheap, and everyone else.
The Subsidy Illusion
Here's what most people see: VCs pour money into AI companies → Companies offer below-cost pricing → VCs eventually want returns → Prices must rise → Better to wait.
Here's what's actually happening: VCs are creating a market, not subsidizing a product.
The difference matters. When Amazon sold books at a loss in the 1990s, were they subsidizing reading? Or were they training millions of people that buying things online was safe, convenient, and obvious? The "subsidy" wasn't about the books. It was about rewiring human behavior.
Today's AI pricing follows the same pattern, but with a crucial twist. Amazon was teaching you to click "buy." AI companies are teaching you to think differently.
What’s actually being subsidized
Think about what's really being subsidized right now:
Your learning curve. Every prompt you write, every workflow you rebuild, every assumption you challenge—you're developing a new form of literacy. The companies aren't just eating the compute costs. They're paying for your education.
The ecosystem buildout. Thousands of developers are building tools, integrations, and specialized applications because the user base exists. It's strategic market creation that you will benefit from.
The data flywheel. More users means more interactions, which means better models, which means more value. The "subsidy" is actually an investment in a compound loop that makes the product more valuable over time (to you AND to the company).
This creates a weird new dynamic: The VCs aren't subsidizing your AI usage. They're subsidizing your transformation into someone who thinks with AI.
The Convergence Point
Here's where the math gets interesting.
Two curves are racing toward each other:
Curve 1: The VC Funding Curve - Yes, this will eventually flatten. Companies will need sustainable unit economics. The free money party ends.
Curve 2: The Efficiency Curve - Computational costs are plummeting. Model efficiency is skyrocketing. What cost $100 to compute last year costs $10 today.
Most people fixate on Curve 1 and miss Curve 2 entirely. They're so worried about the subsidy ending that they don't notice the underlying costs are collapsing even faster.
Consider what's happening right now:
Algorithmic breakthroughs are making models 10x more efficient
Specialized hardware is dropping inference costs by orders of magnitude
Open source alternatives are creating permanent price pressure
By the time the "subsidies" end, the actual cost of AI might be lower than today's subsidized price.
The Widening Competency Gap
But let's say I'm wrong. Let's say prices do rise significantly. You're still making the wrong calculation.
The real cost isn't in the subscription fee. It's in the competency gap.
Today: Early adopters are learning to prompt, building AI-integrated workflows, developing an intuition for what AI can and can't do. This knowledge compounds daily.
Tomorrow: Late adopters will pay the "real" price AND face a massive learning curve AND compete against people with years of experience AND try to integrate AI into workflows designed without it.
The price differential between early and late adoption isn't 2x or 5x. It's exponential.
Here's what makes this even more powerful: The real game isn't even about AI capabilities.
It's about becoming the kind of organization that integrates new capabilities faster than others. Every prompt you write today isn't just solving today's problem—it's building the muscle memory to adopt tomorrow's breakthrough in hours instead of months.
The early adopters aren't just using current AI. They're building a permanent advantage in adaptation speed. When GPT-5 launches, when the next breakthrough arrives, when the paradigm shifts again they'll be able to integrate it in days while others spend months figuring out basics.
Each new tool you master makes the next one easier. Each workflow you build makes future workflows obvious. Each mental model you develop compounds into faster pattern recognition.
You're not paying for AI. You're paying for compound learning acceleration.
The Learning Window
This creates a Learning Window. It’s a brief period where the cost of experimentation is artificially low while the value of learning is extraordinarily high.
Windows like this are rare. We saw one with the early internet. Another with mobile apps. Miss the window, and you don't just pay more later. You pay more to learn less while competing against those who learned more while paying less.
These windows only look obvious in retrospect.
Right now, you can:
Experiment with cutting-edge models for the price of a Netflix subscription
Build AI capabilities while your competitors debate ROI
Develop institutional knowledge while the tools are still evolving
Shape your workflows around AI from the ground up
Or you can wait for the "real" prices.
The Hidden Price of Waiting
When people say they're waiting for AI prices to stabilize, they're really saying they're waiting for AI to become a commodity. But commodities are defined by their lack of competitive advantage.
Think of it like learning a language. You can wait until Duolingo is free, but meanwhile, native speakers are having conversations you can't even comprehend. By the time you start learning, they're writing poetry while you're still conjugating verbs.
The strategic value of AI isn't in using it. It's in learning to think with it. And that learning is happening right now, at artificially subsidized rates.
Consider two companies:
Company A starts using AI today. They fumble, experiment, build custom workflows, develop prompt libraries, train their team. They're paying the "subsidized" rate.
Company B waits for prices to "normalize." In two years, they start fresh. They're paying the "real" rate.
Who's actually paying more? Company B is. Not just in dollars, but in competitive position, in team capability, in missed opportunities. They saved on subscription fees and lost on everything that matters.
The gap isn't just about what they know. It's about how fast they can learn what comes next.
Today’s Adoption Arbitrage
There's a hidden arbitrage in every new technology:
Phase 1: Technology is "expensive" but learning is cheap (few competitors)
High perceived cost creates artificial scarcity of adopters
Low competition for expertise means rapid skill development
Mistakes are private and recoverable
Phase 2: Technology becomes "affordable" but learning is expensive (many competitors)
Lower cost creates flood of new users
High competition for expertise means slower differentiation
Mistakes are public and costly
Phase 3: Technology is commoditized but advantage has crystallized
Cost becomes irrelevant because value is assumed
Early adopters have compound advantage baked in
Late adopters compete on price in a race to the bottom
We're in Phase 1. Most people are waiting for Phase 2. The winners are building for Phase 3.
This is the arbitrage: You're trading today's dollars for tomorrow's compound advantage. The market is mispricing the learning opportunity because it's focused on the subscription cost.
Capturing the Arbitrage
So what should you actually do?
Start using AI for real work immediately. Not demos. Not toys. Real projects with real stakes.
Build workflows that assume AI will only get better and cheaper. Design for capabilities that don't exist yet.
Develop an organizational culture of AI experimentation. Make prompting a core skill like Excel or email.
Most importantly: Stop thinking about AI as an expense to minimize. Start thinking about it as compound leverage to maximize.
The window is open now, but not forever
The VC "subsidy" narrative is a distraction. It's causing smart people to make a catastrophically bad calculation by optimizing for saving hundreds of dollars while missing millions in compound advantage.
The real subsidy isn't in the pricing. It's in the permission to learn while learning is cheap, to experiment while experimentation is encouraged, to build competency while competency is rare.
That window is open right now. It won't stay open forever.
But when it closes, it won't be because prices went up.
It'll be because the advantage shifted permanently to those who acted while everyone else was waiting for the "real" price.
An analogous example is pursuing an MBA versus building your own business. In the former example you pay to learn; in the latter, you are paid to learn.