Prepare for the AI token rug pull

06 Jun 2026 06:37 19,176 views

AI tokens are being sold below cost, and most businesses have no idea what their real AI usage will cost once subsidies end. A new AI token price index aims to change that—and it’s a warning sign to rethink how and where you run your AI workloads.

AI feels cheap right now. You can plug into powerful models from OpenAI, Anthropic, Google, and others for fractions of a cent per request and build entire products on top of their APIs. But under the surface, those prices are being propped up by massive investor subsidies—and there’s a growing risk that many businesses are walking straight into an AI "rug pull" on token pricing.

What exactly is an AI token?

To understand the coming price shock, you first need to understand what you’re actually paying for: tokens.

When you send text to an AI model, it doesn’t read words the way humans do. Instead, it breaks language into small pieces called tokens. These pieces are:

Smaller than most words, but bigger than single letters
Often whole common words like "the" or "and"
Or chunks of longer words, like turning "tokenization" into "token" + "ization"

On average, one token is roughly three-quarters of an English word. That’s why model pricing is usually listed as “$X per million tokens” instead of “per word” or “per request.”

From subscriptions to pay-per-token

In the early days of consumer AI chatbots, you mostly paid for a subscription—one flat monthly fee for access. Behind the scenes, though, the real cost has always been driven by tokens: how much text you send in, and how much the model sends back out.

Now the industry is shifting toward pure usage-based pricing. Instead of paying for a plan and hoping it covers your usage, more and more products are charging directly by the token. Different providers set their own prices:

OpenAI has one set of token rates
Anthropic has another
Google and others price by model, capability, and context window size

If you’re building AI-powered products or internal tools, your “token bill” is quickly becoming one of your most important variable costs. But until recently, there was no simple way to see what a token should actually cost across the market.

Introducing the AI token price index

In traditional markets, we rely on benchmarks to understand price and value. The S&P 500 tracks major US stocks. Brent crude is the reference price for a barrel of oil. Even the Big Mac Index helps compare purchasing power across countries.

AI has been missing an equivalent benchmark for token prices. That’s what the AI token price index aims to provide.

The index aggregates token pricing across major commercial AI models and produces a single reference number—similar in spirit to how Brent crude tells you what a barrel of oil is worth on a given day. It looks at:

Input tokens (what you send to the model)
Output tokens (what the model generates)
Cached tokens (reused context, where supported)
Specialized or “reasoning” tokens for more advanced models

The result is an objective, market-wide view of where AI token prices sit right now—and where they’re heading.

Why an AI token index matters

An AI token price index isn’t just a curiosity. It’s a practical tool that’s going to matter a lot for anyone running serious AI workloads.

1. Budgeting for real AI costs

If you’re forecasting AI spend for the next few years, you need more than a guess. You need a defensible reference point that finance and leadership can understand and trust.

An index gives you a baseline to answer questions like:

What would our costs look like if we doubled usage?
What if token prices move back toward true market rates?
How sensitive is our business model to a 2x or 3x increase in token costs?

2. Negotiating with vendors

As AI spend grows, renewals and enterprise contracts will get tougher. Without a benchmark, you’re negotiating blind. With an index, you can see whether a provider is pricing above or below the broader market and push back accordingly.

3. Setting AI strategy at the board level

AI is quickly becoming a board-level topic, and boards love clear, comparable metrics. An AI token price index turns a messy, fast-moving pricing landscape into a single, trackable number that can be discussed in strategy sessions and risk reviews.

The uncomfortable truth: today’s token prices are not real

Here’s the catch: the prices you see today are not what it actually costs to run these models. They’re heavily subsidized.

We already know that:

OpenAI reportedly lost around $5 billion last year
Anthropic is burning investor capital at a similar pace

That means every token you consume is likely being sold below cost. The difference is being made up by investors who are betting that, once the dust settles and weaker players are gone, the survivors will be able to raise prices and lock in customers.

This isn’t a sustainable business model. It’s a customer acquisition strategy.

How the AI token rug pull could play out

The risk isn’t that AI will disappear. The risk is that the economics will snap back to reality much faster than most businesses are prepared for.

Several triggers could cause this:

Funding rounds slow down or dry up
One or more major players go bust or pull back
Regulation, energy prices, or geopolitical events (for example in Europe or the Middle East) push infrastructure and compute costs higher

When that happens, the subsidies end. Providers will have to charge closer to the true cost of compute, energy, and infrastructure. And businesses that have built entire workflows on top of cheap, cloud-hosted AI will wake up to find their token bill has doubled—or worse—with very little recourse.

The AI token price index will make this shift visible. Instead of price hikes being anecdotal, we’ll have a market-wide record showing that the direction of travel is, as of now, unambiguously up.

Why routing and geography suddenly matter

Most companies don’t think much about where their AI traffic is physically going. Queries are often routed through big cloud regions like Frankfurt or Virginia by default.

But as energy markets tighten and regional regulations evolve, the cost of running large AI workloads in certain data center hubs could rise significantly. If your entire AI stack depends on a handful of cloud regions you don’t control, you’re exposed to cost shocks that have nothing to do with your own efficiency or usage.

This is one reason why smarter infrastructure patterns—like efficient tool calling, better context management, and reduced token waste—are becoming critical. For example, approaches that dramatically cut token usage for AI agents, such as those discussed in smarter MCP patterns for AI agents, can meaningfully reduce your exposure.

The oldest play in the platform economy

If this all sounds familiar, it’s because we’ve seen similar playbooks before in tech:

Subsidize prices to attract as many users and developers as possible
Make it easy (and cheap) to build on the platform
Once you’re deeply integrated and switching is painful, raise prices

In other words: subsidize to capture, capture to monetize, and then monetize until customers can’t afford to leave. The AI token price index will make the monetization phase much easier to see in real time.

Who survives the AI token rug pull?

If token prices are likely to rise, the obvious question is: what can you do about it?

One strong view is that the most resilient businesses will be those that reduce their dependence on remote, heavily subsidized cloud models and move more AI workloads onto hardware they control.

That can mean:

Running smaller, optimized models on your own servers
Using on-device AI where possible (laptops, phones, edge devices)
Leaning into efficient open-source models that you can self-host and tune

The rapid progress of open-source models—with huge context windows and strong performance—makes this increasingly realistic. For example, models like DeepSeek V4, covered in our breakdown of DeepSeek V4’s 1M-token context, show how powerful non-proprietary options are becoming.

This doesn’t mean abandoning cloud AI entirely. But it does mean treating it as a strategic dependency, not an unchangeable utility. Hybrid strategies—mixing local models, open-source, and selective use of premium APIs—will likely be the safest path.

How to prepare your AI stack now

If you’re serious about AI, it’s worth acting before prices move, not after. Some practical steps:

Measure your token usage. Know exactly how many tokens you’re burning per feature, per user, and per workflow.
Model price shocks. Run scenarios where token prices 2x–5x and see which products or features become unprofitable.
Reduce waste. Trim unnecessary context, cache aggressively where possible, and optimize prompts and agent patterns.
Experiment with local and open-source models. Start building the muscle now so you’re not scrambling later.
Track the index. Use an AI token price index as a regular input into budgeting, vendor negotiations, and board discussions.

Final thoughts

AI isn’t going away, and neither are tokens. But the era of ultra-cheap, investor-subsidized AI usage will eventually end. When it does, the businesses that survive the AI token rug pull will be the ones that: