A new project wants to build open, shared AI training data that anyone can inspect

Key points

• OpenWALDO, launched by CentOS and Rocky Linux founder Gregory Kurtzer, is a shared, openly licensed dataset intended as a public foundation for training AI models.
• The project is funded by CIQ, Kurtzer's AI infrastructure company, and currently holds 167.3 billion reference tokens drawn from government records, academic papers and public-domain literature.
• Frontier AI models typically train on tens of trillions of tokens, meaning OpenWALDO's current dataset is a small fraction of what large models need.
• CIQ argues that hidden training data puts businesses at legal and security risk, because no one can verify what copyrighted or restricted material may be baked into a model.
• No company has publicly confirmed training a model on OpenWALDO yet; CIQ did not respond when asked.

Most AI models are built on secrets. The data used to teach them what to say, how to reason and what to avoid is almost never disclosed to the people who buy and use those models. A new project called OpenWALDO, first reported by The Register AI, wants to change that.

OpenWALDO stands for Open Weights, Artifacts, Licenses, Data, Origins. The name is a mouthful, but the idea is straightforward: build a shared, publicly inspectable set of training data, the raw text and information that AI models learn from, so that any company or researcher can use it as a verified starting point.

Why does secret training data matter to ordinary businesses?

Hidden training data is a liability, not just a philosophical problem. If a model learned from copyrighted books, scraped user conversations, or outputs from rival AI systems, the businesses that build products on top of that model could face legal exposure without knowing it.

CIQ, the AI infrastructure firm behind OpenWALDO, put it plainly: "There is often no way to know what data trained a given model, under what license, or with what consent."

For a hospital buying an AI tool, a law firm using an AI assistant, or a retailer building a customer chatbot, that opacity is a real risk. You cannot audit what you cannot see.

There is also a waste problem. Every AI lab training its own model in secret repeats work others have already done. A single, shared, clean dataset, the OpenWALDO team argues, would let companies skip duplicated effort and add their own private data on top of a verified baseline.

How does OpenWALDO compare to what big AI labs use?

The gap is significant right now. See the numbers:

A token is roughly three-quarters of an English word, so 167 billion tokens is a large library, but still a fraction of what models like GPT-4 or Meta's Llama series consumed during training.

Kurtzer draws a direct parallel to Linux, the open-source operating system that now powers most of the internet's servers. Critics once called open-source code insecure and untrustworthy. It won anyway, because anyone could inspect, fix and improve it.

"Linux didn't win by being certified safe," Kurtzer said. "It won by being inspectable, forkable, and community validated."

Whether OpenWALDO follows that path or fades quietly is genuinely uncertain. The open-source AI training space is crowded with good intentions and thin adoption.

The honest takeaway: if your business depends on an AI tool, ask your vendor what data trained it and whether they can show you the licence for that data. You probably will not get a full answer today. But asking puts you ahead of most buyers, and projects like OpenWALDO exist precisely because that question deserves one.