Blog

AI rarely fails because the model is “dumb”, it fails because the knowledge it’s pulling from is messy. If you’ve ever had an AI answer at work that sounded confident but made you think “hang on… is that actually true?”, you’ve hit the real issue: shaky inputs create shaky outputs. In this short video I explain why, in 2025/2026, enterprise AI is only as trustworthy as the knowledge base behind it, and why duplicates, contradictions and outdated docs quietly turn into errors,

AI Slop Is Everywhere. The Fix Is Not “Better AI”, It’s Better Provenance. You know what’s changed in the last year or two? It’s not just that AI can make content, it’s that it can make plausible  content at industrial scale, and the incentives online reward whatever gets clicks and shares. That might be harmless entertainment, but it becomes a serious issue the moment it influences business decisions, customer conversations, internal comms, or even what teams treat as “true”

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 trai