The Real Cost of Using AI in 2026
The article examines the hidden and escalating costs of deploying AI systems by 2026, arguing that infrastructure, energy, and maintenance expenses will significantly outweigh the initial model costs, challenging the assumption that AI will become cheap and ubiquitous.
Background
- The article warns that running large AI models (like GPT-4, Gemini, or Llama 3) at scale will become dramatically more expensive in 2026 as "inference" demand grows — inference is the process of a model actually generating an answer, not just being trained.
- It argues that current pricing (a few cents per API call) is unsustainably low, subsidized by venture capital and investor hype. Once the market matures and subsidies disappear, the real cost of AI inference could be 10–100x higher.
- Key concepts: "inference" (using a model to answer queries), "API pricing" (pay-per-use access offered by companies like OpenAI and Anthropic), and "APUs/WSEs" (specialized AI chips from Groq, Cerebras, etc. vs. standard Nvidia GPUs).
- The piece contrasts "cheap AI" today — enabled by overprovisioned cloud capacity and loss-leading prices — versus the harder economics of running inference reliably for millions of users on dedicated hardware.