The question hovering over AI investment: when does commercial viability arrive? The latest numbers tell a story more complex than either AI optimists or skeptics acknowledge.
Commercial viability requires three conditions: costs that businesses can absorb, value that exceeds those costs, and reliability that enables production deployment. Progress on each dimension is uneven.
Inference costs have plummeted—down 90%+ from 2023 levels. This dramatic decline opens use cases that were economically impossible 18 months ago. The learning curve effect is working: scale drives cost reduction drives adoption drives scale.
But training costs remain astronomical. Foundation model development is consolidating to players who can spend billions. This bifurcation—cheap inference, expensive training—shapes competitive dynamics.
The Value Question
Value demonstration remains the bottleneck. Many AI deployments generate activity without generating measurable business impact. The companies reaching commercial viability share a pattern: they identified specific, measurable workflows where AI creates undeniable value, then expanded from that beachhead.
Generic “AI transformation” fails. Specific, measurable applications succeed. The MVP approach applies: find one workflow where AI creates 10x value, prove it, then expand.
The Timeline
Commercial viability isn’t a single threshold—it’s a rolling frontier that reaches different use cases at different times. Some applications are commercially viable today. Others remain years away. Strategic clarity requires mapping which specific applications cross the viability threshold when.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
