GoKawiilEssay argues no equivalent 'commons' exists for posttraining LLM data
A new essay examines how large language model pretraining relied on an enormous, freely available body of text—Wikipedia, Common Crawl, ArXiv, Stack Exchange, Project Gutenberg and open-source code—that had accumulated over decades. The author notes that Common Crawl alone holds roughly 10 petabytes of archived web data, and that models like GPT-3 and the original Llama were built largely from this material. The piece then argues that no comparable public resource exists for the posttraining stage of building AI models.
GoKawiil's interpretation of the reporting above, not reported fact.
The argument suggests that as pretraining data becomes more fully exploited, the industry's next bottleneck may be acquiring the curated, high-quality data needed for posttraining steps like instruction tuning and alignment. This could push AI labs toward proprietary data deals, synthetic data generation, or paid human labeling rather than relying on freely available public archives, according to the essay's framing. If true, this dynamic could further concentrate advanced AI capability among well-funded companies able to afford or produce such data at scale.
- Pretraining benefited from decades of freely accumulated public text, including Wikipedia, Common Crawl, ArXiv and open-source code.
- Common Crawl alone has archived about 10 petabytes of web data since 2007, underpinning models like GPT-3 and Llama.
- The essay contends no equivalent open 'commons' exists for posttraining data, implying a different sourcing challenge for future AI development.
Source: openathena.ai, 2026-11-03
Published there as: “There is no posttraining data commons”
Read the original report → The summary and analysis above are GoKawiil's own, written from reporting by the source above. Facts and quotes belong to the original publisher.