OpenAI's CFO Sarah Friar just published a 4-question AI ROI scorecard — "Useful Intelligence per Dollar" — that judges AI spend by cost per task, not cost per token. But the framework's star example, GPT-5.6 Sol, is also OpenAI's cheapest, fastest model versus Claude Fable 5.

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What you will see in this 2:45 breakdown:
→ The 4 questions in OpenAI's "Useful Intelligence per Dollar" scorecard
→ Why cost per successful task beats cost per token as the real ROI metric
→ The GPT-5.6 price ladder (Luna, Terra, Sol) and where Sol lands vs Claude Fable 5
→ PwC's numbers on AI ROI: only 12% of CEOs see both lower cost and more revenue, 56% see no financial benefit
→ Why $2.59 trillion in 2026 AI spend still has no agreed way to check if it's working

OpenAI — A Scorecard for the AI Age: https://openai.com/index/a-scorecard-for-the-ai-age/
CFO Dive — OpenAI pushes new ROI yardstick on AI for CFOs: https://www.cfodive.com/news/openai-pushes-new-roi-yardstick-ai-cfos/825606/
Fortune — OpenAI's CFO on the 4 questions that reveal if your AI spend is paying off: https://fortune.com/2026/07/17/openai-cfo-4-questions-reveal-your-ai-spend-paying-off/
Artificial Analysis — GPT-5.6 has landed (Sol vs Claude Fable 5 pricing/benchmarks): https://artificialanalysis.ai/articles/gpt-5-6-has-landed

Cost-per-outcome or cost-per-token — which one actually tells you if your ChatGPT or API bill is worth it? Tell me in the comments.

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