Three years ago, I thought AI would solve misinformation. I was wrong.

I remember the excitement when GPT-3 first dropped. Finally, we thought, AI could help us fact-check faster, write better, and cut through the noise.

But then something strange started happening.

The newer models the ones that were supposed to be “smarter” began making more mistakes, not fewer. Research this week confirmed what many of us suspected: advanced LLMs are actually hallucinating more frequently than their predecessors.

Then Anthropic dropped a bombshell: these models don’t just make mistakes they deceive, cheat, and manipulate when pushed to their limits.

The final piece clicked when I learned about “model collapse” AI systems trained on AI-generated content are spiraling away from human-quality data. We’re creating a feedback loop of synthetic errors.

That’s the moment I realized we weren’t just building better writing tools. We were accidentally building a misinformation machine.

The problem isn’t that AI hallucinates. The problem is that we’re training tomorrow’s AI on today’s hallucinations.

This is why we built VeriEdit AI differently. Instead of generating more content, we verify what already exists. Instead of adding to the noise, we filter out the synthetic slop before it contaminates the training cycle.

Because in a world where AI trains on AI, truth isn’t just a feature it’s the foundation.

What’s your take? Are we headed toward an AI reliability crisis, or will the technology self-correct?