If you’re building on top of AI APIs today, you’re enjoying artificially low prices. The current state of AI pricing feels “too good to be true” because it is. Behind every API call that costs you fractions of a cent lies a complex economic reality: venture capital is subsidizing your AI habit, and like all subsidies, this one will eventually end.
For developers and founders building AI-powered products, this creates a dangerous foundation. Your unit economics might work beautifully today, but they’re built on sand. When the subsidy era ends (and it will) the companies that survive will be the ones that saw it coming.
The numbers are staggering. AI infrastructure companies have collectively raised tens of billions in venture capital. According to Crunchbase, OpenAI alone has raised over $78 billion and Anthropic has raised more than $33 billion. Google and Microsoft are pouring similar amounts into their AI divisions. This isn’t money being spent carefully—it’s being deployed strategically to capture market share at any cost.
The gap between what it costs to run these models and what companies charge for API access is substantial. Training runs for frontier models cost hundreds of millions of dollars. The GPU clusters required for inference at scale represent billions in infrastructure. Energy costs alone are astronomical—a single ChatGPT query uses roughly 10x the energy of a Google search.
Yet somehow, you can access GPT-4 for pennies per thousand tokens. Claude Sonnet for even less. Google offers Gemini with generous free tiers. These prices don’t just ignore the capital costs—they often don’t even cover the marginal cost of compute.
Why? Because right now, we’re in a land grab. Every major AI company is prioritizing ecosystem lock-in over profitability. They want developers building on their platforms. They want users getting comfortable with their tools. They want to be the default choice before someone figures out how to monetize this properly. The model is familiar: subsidize heavily, capture the market, then figure out how to make money.
We’ve seen this movie before. Uber spent years subsidizing rides to below the actual cost of providing them, burning through billions in VC money. WeWork convinced people that office space economics didn’t apply to them. In the early days of cloud computing, AWS and others underpriced services to drive adoption, then steadily increased prices as customers became dependent.
The AI correction will follow a similar pattern, but potentially more severe. Several triggers will force the shift:
VC patience runs out. Investors have been remarkably tolerant so far, but even in AI, you can’t lose money forever. As companies mature beyond Series B and C, the pressure to show a path to profitability intensifies.
Market consolidation. Right now we have aggressive competition keeping prices low. As the market matures and consolidates—and it will—pricing power will shift to the remaining players.
Regulatory and energy constraints. The energy consumption of AI is already drawing regulatory attention. Carbon costs, grid constraints, and environmental regulations could force prices up regardless of what companies want.
Industry analysts estimate that current API pricing may need to increase 3-10x to reach sustainable economics. Some models might need to go even higher. A comprehensive inference request that costs you $0.01 today might cost $0.05 or $0.10 tomorrow. For high-volume applications, that’s not a line-item adjustment—it’s an existential threat.
When will this happen? The honest answer is we don’t know. But the signs are already visible. Companies are starting to differentiate pricing more aggressively. Free tiers are shrinking. Usage limits are tightening. The shift won’t be a single event, it will be a gradual squeeze that accelerates over the next 2-4 years.
Let’s get concrete about what this means for your product. If you’re building something AI-intensive, your margin structure probably looks comfortable right now. But run the numbers with a 5x cost increase and see what happens.
The consumer app death zone. Building a consumer AI app with a $10/month subscription? You might have decent margins today. But if your AI costs go from $2 per user per month to $10, you’re underwater. You can’t 5x your price without losing most of your users. This isn’t hypothetical, this is the reality facing thousands of AI apps currently in the market.
High-volume automation gets crushed. If you’re processing documents, analyzing support tickets, or running automated workflows at scale, you’re especially vulnerable. These use cases often involve hundreds or thousands of API calls per user. A modest per-call price increase multiplies into a massive total cost impact.
The margin squeeze cascades. Here’s the really painful part: you can’t just pass the costs along. Your customers chose your product at a certain price point. If you need to double or triple prices to maintain margins, you’ll churn users. But if you don’t raise prices, your unit economics collapse. You’re stuck.
Some businesses have natural protection. Enterprise software with high switching costs and deep integration can often pass through price increases. Products where AI is a feature rather than the core value proposition have cushion. B2B tools with complex implementations and high customer lifetime value can absorb cost shocks better.
But if you’re building a product where AI is the primary value and you’re selling to price-sensitive customers? You need to be very worried.
You have time to build defensibility. Here’s how:
Design for efficiency from day one. Every token matters. Optimize your prompts ruthlessly. Implement aggressive caching strategies. Use smaller, cheaper models wherever possible and reserve the expensive ones for where they truly add value. Companies that treat AI tokens like they’re already expensive will have a massive advantage when they actually become expensive.
Build margin cushion into your pricing now. Don’t price based on current AI costs. Price based on what you think costs might be in two years, then work backward. Yes, this might make you less competitive today. But you’ll be the one still standing tomorrow. If you can’t build a viable business with 5x current AI costs, you don’t have a viable business—you have a subsidized science project.
Embrace multi-provider strategies. Don’t let your product become too dependent on a single model or provider. Design your architecture so you can swap between providers or models based on price and performance. The companies that can switch from GPT-4 to Claude to Gemini to Llama without rewriting their entire application will have negotiating power.
Know when to consider alternatives. Self-hosting open source models isn’t right for everyone, but it deserves serious consideration for high-volume use cases. Running Llama or Mistral on your own infrastructure has higher upfront costs but can dramatically lower unit economics at scale. For some applications, this transition from API calls to self-hosted inference might be essential for survival.
The counterintuitive truth: efficiency will become a core competitive advantage. Right now, many teams don’t optimize because tokens are cheap. But the teams building efficiency into their DNA today will be the ones that can profitably serve customers at price points competitors can’t match tomorrow.
Before you panic, understand that this transition won’t be overnight, and it’s not all negative.
Price increases will be gradual. No AI provider can afford to 5x prices overnight—they’d lose their entire customer base. Expect steady increases over years, not months. You’ll have time to adjust, optimize, and adapt.
Open source is improving incredibly fast. Llama 3, Mistral, and other open models are closing the gap with proprietary offerings. As they continue improving, they create a price ceiling—if OpenAI or Anthropic prices get too high, more users will switch to self-hosted alternatives. This competitive pressure will moderate the worst-case scenarios.
Not all AI will get expensive at the same rate. Commodity inference will remain relatively affordable as competition and efficiency improve. It’s the cutting-edge frontier models that will command premium prices. Smart product design can work around this by using expensive models sparingly.
Perhaps most importantly, the price correction will force discipline. Right now, it’s too easy to build AI features nobody really needs or wants. When those features start costing real money, teams will focus on where AI creates genuine value. The market will mature. The products that survive will be the ones solving real problems, not just impressive demos.
The fundamental question for anyone building on AI today is simple: does your business model work if your AI costs quintuple?
If the answer is yes, you’re building something durable. If the answer is no, you need to change something—your pricing, your architecture, your value proposition, or possibly your entire business model.
Don’t let subsidy-era pricing lull you into complacency. The companies that treat current AI prices as temporary will build differently than those who assume they’re permanent. They’ll optimize more aggressively. They’ll price with more margin. They’ll design for provider flexibility. They’ll use AI more strategically rather than sprinkle it everywhere.
When the VC-subsidized era ends—and history suggests it will—those companies will still be standing. The ones that assumed cheap AI was forever will be scrambling to survive or quietly shutting down.
The correction is coming. The only question is whether you’ll be ready for it.
The time to stress test your unit economics is today, not when your AI provider announces a 3x price increase with 90 days notice. Run the numbers. Be honest about your margins. Build for the world that’s coming, not the one that exists today.
The true cost of AI isn’t what you’re paying now. It’s what you’ll be paying when the subsidies run out.
