If you’ve been following the AI race, you’ve probably noticed billions are being poured into scaling bigger and bigger models. GPT-5, Gemini, Claude, and Grok all follow the same trend – more parameters and higher training costs have resulted in better benchmarks scores, but the underlying issues with reliability remain.

I’m sure you’ve asked AI for help and it spits out an answer that feels off. Maybe it invented a source, gave you a statistic that didn’t check out, or wrote something that looked polished but turned out to be wrong. We’ve all been there.

Beyond the obvious user frustration, these errors carry real financial risks. In 2024 alone, AI-generated hallucinations caused an estimated $67B in global losses.  Trust – the foundation of any useful technology – is eroding. To combat this, companies are spending billions to double-check their AI outputs, spawning entire industries dedicated to fixing hallucinations.

The last few years have proved scaling pushes performance forward, but only incrementally. It’s important that we continue to explore new architectural approaches that can tackle deeper issues like reasoning, memory, and reliability.

REI Network is targeting these exact problems. Rather than tweaking existing transformer models for marginal gains, this research lab is building AI from the ground up, creating systems that mirror biological cognition instead of just predicting text patterns. Their intelligence engine, Core, represents a potential leap from today’s static models to AI that’s dynamic, persistent, and constantly learning.

In this piece, I’ll walk through what REI is building, how their product works, the business model they’re pursuing, and where they’re headed.

The Core Idea

At the heart of REI is Core, a system designed to fix the shortcomings of LLMs. While today’s AI essentially reaches a fixed state after training, Core keeps learning. It re