Clean Knowledge In. Trusted Answers Out.

Here’s the reality in 2025/2026: there isn’t one universal “where does AI gets its facts from?” because most frontier labs don’t fully disclose their training mixes anymore. But we can anchor this in what’s publicly documented and what regulators/researchers keep pointing at.

You know what people get wrong about AI?

They think it “looks things up” like a clever librarian. Most of the time it doesn’t. Most of the time it’s answering from whatever got baked into it during training, which is basically: a massive chunk of the public internet, plus books and reference stuff.

And when I say “internet”, I don’t mean a neat stack of peer-reviewed papers.

I mean web crawls. The big one everyone circles back to is Common Crawl, which is essentially a firehose archive of the open web that loads of models have relied on in one filtered form or another.

Then you’ve got curated bits like Wikipedia and books mixed in. And yes, there’s a Reddit angle, but it’s not quite the “trained on Reddit comments” thing people repeat. In one of the clearest published examples (GPT-3), Reddit was mainly used as a quality filter for which web pages to include (pages that were linked and upvoted), not a giant diet of comment threads.

Now roll forward to 2025/2026 and it gets even messier, because scraping everything is legally radioactive. So there’s a proper land-grab happening for licensed datasets and private archives, and a lot more synthetic data getting used too.

So when you ask an AI for business strategy or career advice, here’s what you’re really doing:

You’re asking a model to remix patterns it’s seen across:

• web crawl content (some brilliant, some garbage)

• curated references

• books

• whatever got licensed

• and increasingly, model-generated data

Which is why you can get answers that sound unbelievably confident… and are still wrong.

Because popularity isn’t truth. Repetition isn’t expertise. And “sounds smart” isn’t the same as “is correct”.

If you want to use AI without letting it quietly borrow confidence from the internet, do this:

• Ask where it’s getting the claim from (primary vs secondary, and what assumptions it’s making).

• Force two sides of the argument (mainstream view vs critics, and where the disagreement really is).

• Make it use recent sources when recency matters.

• Add context back in (industry, geography, regulation, risk appetite).

And here’s the part most companies are about to learn the hard way:

In enterprise, the most dangerous source isn’t Reddit or Wikipedia. It’s your own internal knowledge base.

Because if your SharePoint/Confluence/ServiceNow is full of duplicates, contradictions, stale SOPs, and “final_v7_REALLY_FINAL” documents… then plugging AI into that just gives you confident nonsense with a corporate logo on it.

That’s exactly why we built index AI.

We help organisations keep their actual source-of-truth clean:

• Scan finds duplicates, contradictions, ROT, broken links, and drift

• Solve fixes it with governance (approvals, evidence, audit trails) instead of rogue auto-edits

• and we keep it from decaying again, because knowledge always decays

AI is only as trustworthy as what it’s allowed to learn from.

So instead of asking “can AI answer questions?”, the better question is:

“Are we feeding it anything worth believing?”