Each of these appears in briefings, presentations, and trade press. Three fail as repeated, while the Gallup figure holds with its context. We used one of them ourselves until we traced it, which is why this page exists: a claim nobody can source is a claim nobody should make, and that has to apply to us first.
| The claim | Why it does not hold | What can be said instead |
|---|---|---|
| Inference is 80 to 90% of AI energy | No primary source exists for it. It is absent from the International Energy Agency's Energy and AI report and circulates through trade press citing itself. | Google measured one April week in each of 2019 to 2021: about three-fifths inference to two-fifths training for its own machine-learning energy. The CPU share of inference was estimated from profiling and may be high. See training and inference. |
| Seventy-one percent of Americans oppose data centers locally | Gallup reported 71% opposed and 48% strongly opposed in a telephone survey of 1,000 US adults, fielded March 2 to 18, 2026, with a margin of ±4 points. | In that March 2026 Gallup poll, 71% opposed a data center being built in their area, with a margin of ±4 points. |
| A rack draws 150 kW | No primary source for 150 kW as a general rack figure was found. NVIDIA's power-management pilot sets envelopes of 375 kW for three GB200 NVL72 racks and 405 kW for three GB300 NVL72 racks. | About 125 kW and 135 kW a rack, dividing NVIDIA's three-rack pilot envelopes. These are published power budgets without measured-draw reporting. |
| 95% of generative AI pilots produce no profit | A preliminary, non-peer-reviewed report hosted by a law firm, based on 52 interviews, 153 survey responses, and more than 300 public initiatives reviewed from January to June 2025. | The report's headline says 95% of organizations were getting zero return, and says 5% of integrated AI pilots were extracting millions in value. That does not establish that 95% of all generative AI pilots fail. See predictive and generative AI. |
| AI will use 12% of US electricity by 2030 | Lawrence Berkeley National Laboratory's June 2026 report covers all US data centers, with AI as one driver inside the total. | LBNL's modeled scenarios put all US data centers at 9.5% to 15.3% of US electricity in 2030, with an 11.8% reference case. |
Why the pilot figure misleads
The definition does most of the work. A coding assistant that quietly saves engineering hours across a company is real value, and a six-month payback test scores it as a failure. Generative returns are harder to attribute, which makes them a measurement problem. The figure treats that problem as proof of failure.
The test that produced this page
Ask of any number what it was measured on, and by whom. Where the answer is a survey of intentions, a press release, or a compilation of other people's compilations, the number describes a mood. Where nobody can name a primary source at all, there is no number.
The test is worth more than the list. Apply it to every AI figure that reaches a decision, including the ones in vendor material.
Frequently asked questions
- Is it true that 95% of AI pilots fail?
- Not as repeated. The figure comes from a preliminary, non-peer-reviewed report hosted by a law firm, based on 52 interviews, 153 survey responses, and more than 300 public initiatives reviewed from January to June 2025. Its headline says 95% of organizations were getting zero return, which does not establish that 95% of all generative AI pilots fail.
- Does the Gallup figure on local data centers hold up?
- Yes, when its context travels with it. Gallup reported 71% opposed and 48% strongly opposed to a data center being built in their area, in a telephone survey of 1,000 US adults fielded March 2 to 18, 2026, with a margin of ±4 points.
- What can be said instead of a 150 kW rack?
- No primary source for 150 kW as a general rack figure was found. NVIDIA's power-management pilot sets envelopes of 375 kW for three GB200 NVL72 racks and 405 kW for three GB300 NVL72 racks, which divide to about 125 kW and 135 kW a rack. These are published power budgets without measured-draw reporting.
- What does the Lawrence Berkeley report say about electricity use in 2030?
- Lawrence Berkeley National Laboratory's June 2026 report covers all US data centers, with AI as one driver inside the total. Its modeled scenarios put all US data centers at 9.5% to 15.3% of US electricity in 2030, with an 11.8% reference case.
- How can you check an AI number before repeating it?
- Ask what it was measured on, and by whom. Where the answer is a survey of intentions, a press release, or a compilation of other people's compilations, the number describes a mood. Where nobody can name a primary source at all, there is no number. Apply the test to every AI figure that reaches a decision, including those in vendor material.
Keep going
What is the difference between training and inference?
Does all AI need a hyperscale data center?
Keep the full table beside your next board pack
Free practical kitAI numbers to stop using. Seven figures, including AI's share of US electricity in 2030, a bottle of water per prompt, and a shared 120 kW rack figure. Each row gives what the primary source says, the best current figure with its date, and a sentence to use instead. Includes a worksheet for checking any number before you repeat it. Six pages, PDF.
A practical next step
Take the readiness assessment to ground the board paper in your own numbers.
For regulated work
See the platform that records governed AI activity for your own evidence.
Sources
- International Energy Agency, Energy and AI, April 2025.
- Patterson, Gonzalez, Hölzle, et al., The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink, IEEE Computer, 2022. Google's machine-learning energy split, measured for one April week in each of 2019, 2020, and 2021, with the CPU inference share estimated from profiling.
- Gallup, Americans Oppose AI Data Centers in Their Area, published May 13, 2026, fielded March 2 to 18, 2026. n=1,000, ±4 points. Gallup reports 23% somewhat opposed and 48% strongly opposed, totaling 71%.
- NVIDIA Dynamic Power Software 0.8 pilot runbook: three-rack power envelopes of 375 kW for GB200 NVL72 and 405 kW for GB300 NVL72, read October 8, 2026.
- MIT Project NANDA, The GenAI Divide: State of AI in Business 2025. Preliminary, non-peer-reviewed findings based on more than 300 public initiatives, 52 interviews, and surveys of 153 senior leaders.
- Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, June 2026. Modeled 2030 reference case and scenario range for all US data centers.