From a data and technology perspective, AI should be affordable for basic use cases, but not necessarily free without limits.

Historically, transformative technologies become valuable when they are widely accessible. The more people use AI for learning, research, productivity, and problem-solving, the more innovation it generates across industries. Free or low-cost access helps students, startups, researchers, and small businesses participate in the AI economy rather than being excluded from it.

However, AI is fundamentally different from traditional software. Every interaction consumes computing resources, infrastructure, and energy. Unlike a static application, AI systems incur ongoing operational costs that increase with usage. As models become more advanced, the cost of maintaining quality, reliability, and security also rises.

A sustainable approach is a tiered model: basic AI capabilities should remain accessible and affordable, while advanced reasoning, higher usage limits, specialized models, and enterprise-grade features can justify premium pricing. This ensures broad adoption while allowing providers to continue investing in innovation and infrastructure.

The real question may not be whether AI should be free, but how we can balance accessibility, sustainability, and continued technological progress.