The AI Inference Cost Collapse and What It Unlocks
Inference costs for frontier models have dropped 99% in two years. This isn't just an economic story — it's a product design story.
In January 2023, running GPT-4 cost approximately $0.06 per 1,000 tokens. By early 2025, models with comparable capability cost less than $0.001 per 1,000 tokens. That’s not a linear improvement — it’s a collapse.
Why This Is Happening
Three forces are compounding: hardware improvements (H100s yielding to B200s with dramatically better inference throughput), architectural efficiencies (quantization, speculative decoding, MoE routing), and intense competition among providers. Google, Anthropic, Groq, Together AI, and dozens of others are racing to offer the best price/performance ratio.
What Gets Unlocked
When inference is expensive, you optimize ruthlessly — short prompts, minimal context, single-shot wherever possible. When inference costs collapse, a new product design space opens:
Per-keystroke assistance: AI that responds to every line you type. Real-time contextual assistance across any workflow becomes economically viable.
Background intelligence: Applications that run inference continuously — monitoring, summarizing, flagging — without prohibitive cost.
Redundant verification: Running the same query through multiple model configurations to check consistency, previously too expensive for production.
The Catch
Cheap inference doesn’t solve the context window problem or eliminate hallucinations. Use the cost savings to run more evaluations, not fewer. The best teams are using cheaper inference to build better testing pipelines.
