Many users criticized the Atlantic article on generative AI for distorting data and citations, expressing shock at its publication and accusing the author of ongoing bias.
Based on 6 visible X reactions from 6 accounts; directional sample.
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@BlackHC We have seen an exponential increase in intelligence per cost This has been a basic consistent principle since the start with GPT-3.5 The author claims to be a "tech investigator", it's like letting a flat earther write about space exploration https://epoch.ai/data-insights/llm-inference-price-trends
12:51 PM · Jul 16, 2026@BlackHC @GaryMarcus Sorry I'll be more direct This article is blatantly distorting its own data and citations and @TheAtlantic should be ashamed of its fact-checking standards
1:45 AM · Jul 16, 2026@BlackHC @GaryMarcus @TheAtlantic And this isn't an one off, this author has been distorting data on the subject for long
3:03 AM · Jul 16, 2026@anko_979 @GaryMarcus @TheAtlantic Agreed. Def shocked that The Atlantic published it as is
1:47 AM · Jul 16, 2026The Atlantic @TheAtlantic says generative AI is "an engineering disaster." I had Claude fact-check all 23 checkable claims against primary sources: 7 check out · 7 need context · 9 don't hold Verdict: the economics hold up. The computer science doesn't (I checked it too) 🧵
8:44 AM · Jul 16, 2026The argument rests on a chart from @EpochAIResearch that serving more tokens costs more But its x-axis is tokens per second PER REQUEST! Costs rise here because serving each user faster forces small batches. More volume at the same speed makes tokens cheaper. That's not it!
8:44 AM · Jul 16, 2026@anko_979 @GaryMarcus @TheAtlantic Agreed. Def shocked that The Atlantic published it as is
1:47 AM · Jul 16, 2026The Atlantic @TheAtlantic says generative AI is "an engineering disaster." I had Claude fact-check all 23 checkable claims against primary sources: 7 check out · 7 need context · 9 don't hold Verdict: the economics hold up. The computer science doesn't (I checked it too) 🧵
8:44 AM · Jul 16, 2026The argument rests on a chart from @EpochAIResearch that serving more tokens costs more But its x-axis is tokens per second PER REQUEST! Costs rise here because serving each user faster forces small batches. More volume at the same speed makes tokens cheaper. That's not it!
8:44 AM · Jul 16, 2026The fact-check was researched & written by Claude Fable 5, an Anthropic model, and Anthropic is named in the article. My prompt is published verbatim in the report. Every claim links a primary source. Don't trust this, check it: Report: https://claude.ai/public/artifacts/0a5910fb-73f5-4edb-bfe2-f2d47d303ad6
8:44 AM · Jul 16, 2026"175 billion parameters in 2020 to more than 1 trillion today." The frontier hit ~1.8T in March 2023 (GPT-4) and then then *shrank*. GPT-4o ~200B, Claude 3.5 Sonnet ~400B (Epoch estimates). That reversal is the efficiency work the article says never happened. It goes unmentioned.
8:44 AM · Jul 16, 2026@boazbaraktcs @_aidan_clark_ I had Fable look into the article as a whole by itself and fact-check it/find all the flaws... it's really bad. I missed some myself because there are so many https://claude.ai/public/artifacts/0a5910fb-73f5-4edb-bfe2-f2d47d303ad6
8:27 AM · Jul 16, 2026Better still: the paper that chart is from opens by noting inference revenue is growing 3×/yr while models get *smaller and cheaper* than in 2023 The article cites this source but then argues against it and claims the opposite
8:44 AM · Jul 16, 2026@GaryMarcus Turned the criticisms into a thread + report:
8:59 AM · Jul 16, 2026Many users criticized the Atlantic article on generative AI for distorting data and citations, expressing shock at its publication and accusing the author of ongoing bias.
Based on 6 visible X reactions from 6 accounts; directional sample.
Ask a question below.
Published answers will appear here.
The fact-check was researched & written by Claude Fable 5, an Anthropic model, and Anthropic is named in the article. My prompt is published verbatim in the report. Every claim links a primary source. Don't trust this, check it: Report: https://claude.ai/public/artifacts/0a5910fb-73f5-4edb-bfe2-f2d47d303ad6
8:44 AM · Jul 16, 2026"175 billion parameters in 2020 to more than 1 trillion today." The frontier hit ~1.8T in March 2023 (GPT-4) and then then *shrank*. GPT-4o ~200B, Claude 3.5 Sonnet ~400B (Epoch estimates). That reversal is the efficiency work the article says never happened. It goes unmentioned.
8:44 AM · Jul 16, 2026@boazbaraktcs @_aidan_clark_ I had Fable look into the article as a whole by itself and fact-check it/find all the flaws... it's really bad. I missed some myself because there are so many https://claude.ai/public/artifacts/0a5910fb-73f5-4edb-bfe2-f2d47d303ad6
8:27 AM · Jul 16, 2026Better still: the paper that chart is from opens by noting inference revenue is growing 3×/yr while models get *smaller and cheaper* than in 2023 The article cites this source but then argues against it and claims the opposite
8:44 AM · Jul 16, 2026@GaryMarcus Turned the criticisms into a thread + report:
8:59 AM · Jul 16, 2026