The era of unlimited AI subscriptions for the price of two cups of coffee is rapidly coming to an end. As lazy chatbot interactions give way to autonomous agentic sessions, the underlying business model for providers is undergoing a fundamental shift. According to an analysis by The Decoder, current flat-rate tariffs are becoming economically unsustainable. Humans are slow; we pause, reflect, and type at a limited speed. An autonomous agent, however, knows no fatigue. A single agentic session can last for hours, continuously calling tools, rewriting code, and verifying intermediate results in an infinite loop that burns through tokens at a scale developers are no longer willing to subsidize.
An industry that has poured hundreds of billions of dollars into data centers and silicon is predictably pivoting toward usage-based billing. Market heavyweights are already restructuring their price lists. Starting June 1, 2026, GitHub Copilot will transition to a system of "GitHub AI Credits"—a calculated move targeting chat and agentic features, even if basic code autocompletion has been mercifully spared for now. Anthropic harbors no illusions either: company representatives note that tools like Claude Code transform the neural network from a mere advisor into a full-fledged "digital employee" whose appetite for resources is incomparable to that of a standard user.
The Primary Risks of the New Model
For executives, this shift contains a dangerous metric trap. There is a significant risk of mistaking token consumption for a measure of value. While providers slice token costs across axes of speed and specialization, a low price tag per million units may simply be cheap bait.
Measuring ROI based on activity rather than specific business outcomes encourages algorithmic inefficiency. In the world of agentic systems, "lots of work" often signifies poor prompt optimization rather than a genuine productivity breakthrough.
Transitioning to credit-based payment systems is becoming inevitable for the survival of AI companies. Traditional SaaS subscriptions cannot withstand the workload generated by autonomous agents. Businesses must revise their AI implementation KPIs, shifting focus from generation volume to final results.