“OpenAI CEO Sam Altman said that for every 38,000 ChatGPT queries, that is the same amount of water used in the production of a single almond in California" Accompanying post caption: "Sam Altman says producing one almond in California uses about as much water as 38,000 ChatGPT queries... The numbers are surprisingly close: an estimated…”
Plain restatementTwo distinct claims are embedded here. (A) Attribution claim: Sam Altman made this statement in a recorded interview. (B) Substantive claim: The underlying water comparison is roughly correct, with per-query ChatGPT water use near 0.32 mL and per-almond water use near 12.3 L.
Sam Altman did say this. On the September 1, 2026 Sources Podcast with Alex Heath, the OpenAI CEO said that 38,000 ChatGPT queries use about as much water as producing one California almond. The quote is accurate. The math behind it is not. Altman himself immediately added that he was unsure of the number and it might be wrong, a caveat the viral post leaves out. The 38,000 figure only works if you pair OpenAI's own unverified low-end estimate for water per query with the highest-end estimate for almonds, which counts rainfall and pollution offsets rather than actual irrigation. PolitiFact rated the claim Mostly False and found the real figure is closer to 1,000 to 10,000 queries per almond. What remains genuinely unknown is ChatGPT's actual water use, because OpenAI has never published the methodology behind its number, and independent estimates range from about 0.6 to 17 milliliters per prompt. The remarks were made while California was considering whether to require data centers to disclose their water use.
[drifted from the evidence:] OpenAI CEO Sam Altman said that for every 38,000 ChatGPT queries, that is the same amount of water used in the production of a [drifted from the evidence:] single almond in California" Accompanying post caption: "Sam Altman [drifted from the evidence:] says producing one almond in [drifted from the evidence:] California uses about as much water [drifted from the evidence:] as 38,000 ChatGPT [drifted from the evidence:] queries... The numbers are surprisingly close: an estimated 0.32 mL [drifted from the evidence:] of water [drifted from the evidence:] per ChatGPT query versus roughly 12.3 [drifted from the evidence:] liters for one almond.
[added by the neutral restatement:] Two distinct claims are embedded here. (A) [added by the neutral restatement:] Attribution claim: Sam Altman [added by the neutral restatement:] made this statement in [added by the neutral restatement:] a recorded interview. (B) Substantive claim: The underlying water [added by the neutral restatement:] comparison is roughly correct, with per-query ChatGPT [added by the neutral restatement:] water use near 0.32 mL [added by the neutral restatement:] and per-almond water [added by the neutral restatement:] use near 12.3 [added by the neutral restatement:] L.
Red-tinted words in the claim drifted from the evidence. Green-tinted words are what a neutral restatement needs.
The trace / claim to source
- Altman did make this statement, in these words, on the Sept 1, 2026 Sources Podcast with Alex Heath. The quote is verbatim and correctly attributed.
- The video credit to @alexheath is correct.
- The 0.32 mL per query figure is real in the sense that Altman published it himself in June 2025.
- The ~12 L per almond figure is real and traces to a legitimate peer-reviewed study.
- The internal arithmetic is consistent: 38,000 × 0.32 mL = 12.16 L, matching the ~12 L almond figure.
- The post correctly notes that water use varies by model, data center, and cooling system, and that the almond figure is a broader water footprint.
- **Omitted qualifier (the speaker's own hedge).** Altman said the number might be wrong. The post presents it as a confident assertion in a headline card. The most important caveat came from Altman himself and was dropped.
- **Marketing as evidence.** The caption's line "The numbers are surprisingly close" presents an unverified, self-reported, self-serving corporate estimate as if it were independently validated. The two numbers are close only because one of them was reverse-engineered from the other.
- **Circular reasoning presented as convergence.** 38,000 × 0.32 mL happens to equal ~12 L. That is not two independent measurements agreeing. It is one input producing one output.
- **Apples-to-oranges water accounting.** Comparing a full agricultural water footprint (rainfall plus irrigation plus pollution dilution) against a narrow operational data center figure is not a like-for-like comparison. The post's caveat that the almond figure is "a broader water footprint" gestures at this but does not explain that it invalidates the comparison.
- **Exaggeration by source selection.** Altman used a low-end AI estimate against a high-end almond estimate. PolitiFact's range of 1,000 to 10,000 queries per almond means the real ratio is roughly 4x to 38x smaller than claimed.
- **Omitted context: unverifiable source.** OpenAI has published no methodology. The post presents the figure without noting that no independent party can check it.
- **Omitted context: motive and timing.** The remarks were made amid an active California policy fight over mandatory data center water disclosure. The post presents them as a neutral observation.
- **Water type omission.** Almond irrigation water is generally not treated municipal drinking water. Data center cooling water often is. The post's framing invites a comparison that the underlying resource types do not support.
- The full podcast transcript was not retrieved directly. The quote is confirmed by multiple independent outlets including one that stated it reviewed a transcript, but the primary recording was not accessed in this investigation.
- The true per-query water consumption of ChatGPT cannot be established. OpenAI has not published sufficient data, and independent estimates span roughly 0.6 mL to 17 mL depending on model, prompt length, and whether electricity generation is counted.
- Whether Altman's 0.32 mL includes water used for electricity generation is unknown.
- The "12.3 liters" figure in the Instagram caption is slightly different from the 12 L in Fulton et al. The precise source of the .3 decimal was not located.
- Shaolei Ren's peak-demand research is a preprint under review and has not completed peer review.
- Almond water use has reportedly declined since the 2004 to 2015 study period. The current per-almond figure may be lower than 12 L. ---
**On attribution:** Altman did say this. Multiple independent outlets, including PolitiFact and Tom's Hardware (which stated it reviewed a transcript), report the identical wording from the Sept 1, 2026 episode of the Sources Podcast with Alex Heath. The quote in the post is verbatim. **One critical omission:** PolitiFact reports Altman prefaced the figure by saying he was not sure of the exact number, and that it "might be wrong, but it's close." The Instagram post does not include this hedge. **On the substance:** The comparison does not survive scrutiny. - **The ChatGPT side.** Altman's 0.32 mL figure comes from his own 2025 blog post. PolitiFact reports that Altman's post is the only source it found for that number, and that the post gave no explanation of what the measurement included. OpenAI has not published data allowing verification. - **Independent per-query estimates are far higher.** A Sept 1, 2026 study estimating total water consumption including electricity generation found ChatGPT's 4o model used between 0.6 and 17 mL per prompt depending on input length. EcoLogits estimates a GPT-5.5 email at 6.11 mL. That is roughly 2x to 50x Altman's figure. - **The almond side.** The 12 L figure is real and peer-reviewed, from Fulton et al. (2019), which found California almonds averaged "10,240 liters per kilogram kernels (or, 12 liters per almond kernel)." But this is a full *water footprint*, including rainfall (green water) and pollution-dilution volume (grey water). PolitiFact puts the *direct* fresh water consumption closer to 6 L per almond. A separate USGS-affiliated estimate cited in coverage puts it near 3.56 L. - **The resulting arithmetic.** PolitiFact concludes you would need roughly 1,000 to 10,000 conversational AI prompts to equal the 6 L of fresh water used to grow one California almond. Even using Altman's own favorable 0.32 mL figure against the 3.56 L almond estimate yields about 11,000 queries, not 38,000. - **Verifiability.** CalMatters quotes UC Riverside's Shaolei Ren saying "The information we have is so limited," and reports that far too many variables (location, outside temperature, cooling system, prompt length, reasoning load, output length) prevent reduction to a single statistic. ---
Complete reasoning
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Ask this case
Answers come only from the case file above; nothing is added.
Did Sam Altman actually say this?
Yes. On the September 1, 2026 Sources Podcast with Alex Heath, Altman said that 38,000 ChatGPT queries use about as much water as producing one California almond. The quote is verbatim and correctly attributed.
So is the comparison accurate?
No. PolitiFact rated it Mostly False. It only works by pairing OpenAI's own unverified low-end water estimate per query with the highest-end almond water estimate, which includes rainfall and pollution dilution rather than actual irrigation.
What did the viral post leave out?
It left out that Altman himself said he wasn't sure the number was right and that it 'might be wrong, but it's close.' It also presented his self-reported 0.32 mL figure as if it were independently validated, when the math is essentially circular: 38,000 times 0.32 mL equals about 12 liters by construction, not by two independent measurements agreeing.
What is the real ratio of queries to almonds?
PolitiFact estimates it takes roughly 1,000 to 10,000 ChatGPT queries to equal the water used for one almond, meaning the true ratio is likely 4 to 38 times smaller than the claimed 38,000.
Do we actually know how much water a ChatGPT query uses?
No. OpenAI has not published the methodology behind its 0.32 mL figure, and independent estimates range from about 0.6 to 17 milliliters per prompt depending on the model, prompt length, and whether electricity generation is counted.