The cost of artificial intelligence

The Foundation of Modern AI

Artificial intelligence can be broadly defined as the ability of a computer system to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, reasoning, and generating content.

Unlike traditional software, which follows predefined rules, modern AI systems learn patterns from vast quantities of data and can apply those patterns to situations they have never explicitly encountered before. This ability to generalize is what differentiates AI from conventional automation.

The technological breakthrough that accelerated today's AI revolution was the introduction of the Transformer architecture in the landmark 2017 paper Attention Is All You Need, published by researchers at Google. This architecture became the foundation for modern Large Language Models (LLMs), which power widely used AI platforms such as ChatGPT, Claude, and Gemini.

How AI Generates Cost

At a high level, a Large Language Model processes information by converting text into smaller units known as tokens. Tokens may represent words, parts of words, punctuation marks, or other language elements.

When a user submits a prompt, the model performs an enormous number of mathematical calculations across billions of parameters to determine the most likely next token. This process is repeated continuously until a complete response is generated.

Every token processed requires computational resources. Those computations consume:

• High-performance GPU processing power
• Electricity
• Data centre infrastructure
• Networking resources
• Hardware depreciation and replacement costs

The cumulative effect of these requirements forms the fundamental cost structure of modern AI systems.

Why AI Is Typically Priced Per Token

Because computational effort scales with the amount of information processed, most leading AI providers (including OpenAI, Google, and Anthropic) price their models according to token consumption.

The more text a model reads, processes, or generates, the greater the underlying computational cost. This creates a consumption-based pricing model similar to cloud computing, where customers pay according to usage rather than a fixed monthly operating cost.

The Business Challenge of Predictable Pricing

While token-based pricing accurately reflects infrastructure consumption, it introduces a challenge for organizations building commercial products on top of AI platforms.

Businesses require predictable pricing structures. Customers generally prefer fixed subscription fees or clearly defined service tiers rather than variable charges that fluctuate according to usage patterns.

However, AI consumption is inherently difficult to predict. User behaviour varies, workloads fluctuate, and the complexity of requests can differ significantly from one customer to another. As a result, organizations delivering AI-powered solutions must carefully balance two competing realities:

• The variable cost structure of underlying AI platforms.
• The market demand for stable and predictable pricing.

Why FinOps Matters in the Age of AI

This is where Financial Operations (FinOps) becomes increasingly important. FinOps provides the governance, monitoring, forecasting, and optimization disciplines necessary to manage consumption-based technology costs. Originally developed to address cloud computing expenditure, FinOps principles are now becoming equally relevant for AI investments.

Organizations that successfully adopt FinOps practices gain the ability to:

• Monitor AI consumption in real time
• Forecast future usage trends
• Optimize model selection and infrastructure costs
• Package AI-enabled services into commercially viable offerings
• Maintain profitability while delivering predictable customer pricing

These capabilities enable technology providers such as Deis Technologies to deliver AI-powered solutions with predictable commercial models while effectively managing the variable costs associated with underlying AI platforms.

Looking Ahead

Artificial intelligence is driving more than an operational transformation; it is reshaping how organizations think about technology economics. Unlike traditional software, where costs are largely fixed after deployment, AI introduces a dynamic consumption model in which every interaction carries a measurable computational cost. Understanding that cost structure is becoming a strategic capability for both technology providers and enterprise adopters.

As AI adoption accelerates, organizations that combine technological innovation with disciplined financial management will be best positioned to unlock sustainable value from this transformative technology.