In the world of technical economy, marketing promises rarely align with long-term financial realities. Over the past few years, major companies have marketed generative artificial intelligence as a magic wand that will reduce operating costs for companies to their lowest levels. But as this technology matures and moves from the "experimentation" phase to "practical application," the contours of AI economics are beginning to reveal a completely different reality: artificial intelligence costs are rising sharply, and the coming technical breakthrough will not be in the user's favor.

Through an analytical reading of today's technology market, we explore how business models in the artificial intelligence sector are shifting from a "user acquisition" strategy to the "profit realization" phase, and what this means for the future of companies and the job market.

The End of the "Subsidy" Era and the Shift Toward Usage-Based Pricing

The massive data centers that train giant language models were built with huge investment capital that were not originally designed to bear the costs of operation (Inference) on this massive scale. Today, companies like "OpenAI" and "Google" realize that training is one thing, and running models to serve millions of users daily is something completely different.

To offset these costs, we're witnessing a radical shift in pricing strategies. For example, "Microsoft" abandoned the fixed subscription model (Seat-based) for its development tool (GitHub Copilot) in favor of a pay-per-use model, realizing that fixed subscriptions become economically losing when the customer consumes $5,000 worth of computing resources against a subscription that doesn't exceed $200. In parallel, "OpenAI" raised token prices with the release of its (GPT-5.5) model, and Google followed suit with its (Gemini Flash 3.5) models that came at 3 to 6 times higher costs compared to previous versions.

Infrastructure Evolves .. But for "Profit Margins"

On the supply side, chip makers are racing to reduce operating costs. "Nvidia's" recent acquisition deal of startup company (Groq) for $20 billion, and moves by "AMD" and "Intel," all aim to re-engineer AI accelerators to reduce "token cost."

But according to corporate economics rules, a decrease in production costs in a market lacking real price competition (due to current losses for developers) does not lead to lower prices for consumers, but translates directly into an increase in profit margins (Profit Margins). The less expensive systems that will spread by mid-2027 will help tech giants (Hyperscalers) exit the loss loop, while consumers will continue to pay the same high prices; because the market has simply proven its ability to bear them.

The Illusion of "Cheap Labor" and "Equivalent Employee Cost" Pricing ($/FTE)

One of the biggest current economic paradoxes is the wave of employee layoffs driven by reliance on artificial intelligence; where "Meta" laid off 10% of its workforce, "Cisco" laid off 4,000 employees, and even the New Zealand government plans to lay off 9,000 government employees based on this technology.

Executives who believed artificial intelligence would provide a human substitute for "mere pennies" are facing a financial shock today. Tools built on top of these models (Agent Harnesses) consume massive resources much faster than ordinary chatbots. The future direction of AI companies will not be to price their services based on computing costs, but based on what is known as "equivalent employee cost" ($/FTE). If an employee costs the company $40 per hour, the AI provider will price its service at $30 per hour; keeping prices high and seizing the cost difference in its favor, instead of providing the technology at its cheap actual price.

Market Dynamics and the Future

Free competition is the classical remedy for rising prices, but in the artificial intelligence market, all major developers are operating with negative profit margins currently. This reality gives absolute advantage to giant companies that have other profitable divisions that cover AI research losses.

Economic history teaches us that any technology bubble begins with fierce competition, but ends with "mergers and acquisitions" (Consolidation) when the bubble bursts and markets stabilize. Artificial intelligence will undoubtedly change the shape of business, but the bill for this change will be steep, and the largest financial beneficiary will not be companies that adopt the technology to cut costs, but those that sell the pickaxe in the new digital gold rush mine.