Reasoning models, a new class of AI systems that think step-by-step before answering, consume far more tokens than traditional AI because they generate thousands of hidden internal reasoning tokens for each question. This architectural shift is reshaping the economics of AI inference, the ongoing cost of running models after they're trained, and forcing major companies like Microsoft, Walmart, and Uber to rethink how they deploy these powerful systems.

What Are Tokens and Why Do Reasoning Models Cost More?

To understand the emerging cost challenge, it helps to know what a token is. A token is the basic unit of data that AI models process. When you type a message to an AI system, the text gets broken into small chunks. A token is typically a word, part of a word, or a punctuation mark. The word "understanding" might split into "under" plus "standing," while common words like "the" count as a single token. As a rough rule, one token equals about three-quarters of an English word, so 1,000 tokens represents roughly 750 words.

Traditional AI models read your question as a sequence of tokens and generate a response by predicting one token at a time. But reasoning models work differently. They don't just answer your queries. Instead, they think step by step before responding, generating thousands of hidden tokens of internal reasoning for a single question. Each processed token requires GPU compute, electricity, and data center capacity.

How Are Enterprises Managing Soaring Inference Costs?

The explosion in token consumption has caught the attention of enterprise leaders. Large companies have begun rationing access to AI tokens to cut costs and are instructing staff to use AI tools more efficiently. This shift reflects a broader concern: soaring capital expenditure on AI infrastructure has yet to boost productivity or profits in measurable ways.

The pricing model for AI has fundamentally changed. When AI platforms first launched, they offered flat monthly subscriptions. Now, as usage has exploded, platforms have moved to token-based or usage-based pricing. Heavy usage can easily outpace the subscription fee you pay. API access to AI models is billed per million tokens, making tokens the fundamental economic unit of the AI industry.

Steps to Optimize AI Token Usage in Your Organization

• Monitor Token Consumption: Track how many tokens your team uses daily and weekly to identify patterns and unexpected spikes in usage that could indicate inefficient workflows or unnecessary use of expensive reasoning models.
• Set Usage Policies: Establish clear guidelines for when employees should use AI tools and which models to prioritize, similar to how companies manage cloud computing budgets and restrict access to premium services.
• Evaluate Model Efficiency: Test whether simpler, faster models can handle routine tasks before defaulting to advanced reasoning models that consume far more tokens per query and drive up monthly bills.
• Implement Cost Alerts: Configure automated notifications when token spending approaches budget thresholds, allowing teams to adjust usage before costs spiral out of control.

Why Are Token Prices Falling Even as Demand Explodes?

Despite rising consumption, token prices have been declining rapidly due to intense competition among AI developers and massive improvements in infrastructure efficiency. This has triggered a fierce price war in the AI market. When GPT-3 launched nearly six years ago, a certain capability level cost $60 per million tokens. By March 2026, multiple models exceeded that same benchmark at just $0.06 per million tokens. Enterprise spending data shows the average cost per million tokens across major providers fell from roughly $10 to $2.50 in a single year.

This price collapse follows a pattern economists call the Jevons paradox: cheaper tokens unlock new use cases, which in turn leads to consumption of far more tokens. A recent study by Epoch AI, an AI research institute, reported prices declining between nine times and 900 times per year across different benchmarks, with a median decline of 50 times per year. The pace of price declines is still accelerating.

"This effectively enables the compute supply to expand, as more tokens can be monetised on the same fleet of xPUs," noted Ross Sandler.

Ross Sandler, AI Analyst at Barclays

Are AI Labs Finally Becoming Profitable?

The changing economics of tokens are reshaping profitability at AI labs themselves. When selling inference workloads, such as Anthropic tokens through AWS Bedrock, Amazon earns both traditional infrastructure fees and a revenue share with Anthropic, which significantly boosts margins. This type of API workload has taken off in recent months as agentic AI, which automates multi-step tasks, has gained adoption.

Anthropic, one of the leading AI labs, is now on track to turn profitable. The company's adjusted operating margin is expected to reach positive 5% in the April-June quarter, up from negative 13% in the previous quarter. A year ago, Anthropic didn't even expect to turn a profit until 2028. If an AI lab like Anthropic is now making 70% inference margin on current-generation models, it implies that previous-generation models were running at around negative 20% margins.

The shift toward profitability at AI labs suggests that the industry is moving past the phase where every query loses money. However, the challenge for enterprises remains: reasoning models consume vastly more tokens than traditional systems, making them expensive to deploy at scale. As these models become more capable, organizations must decide whether the improved reasoning justifies the higher inference costs, or whether they should reserve reasoning models for high-value tasks and use faster, cheaper models for routine queries.