What DeepSeek-R1 Teaches Us About Human Clarity

The newest generation of artificial intelligence models aren’t being told how to think.
They’re learning how to learn.

When I read about DeepSeek-R1 — an AI system that learned to reason through reinforcement learning alone — I couldn’t help but feel the mirror. The model wasn’t trained with human-labeled reasoning paths or instructions. It simply received feedback on whether its final answer was correct. Over time, it began to reason on its own: checking its work, reflecting, revising.

No one told it what “reasoning” looks like.
It remembered it.

The Science Behind the Mirror

DeepSeek-R1 is trained with reinforcement learning — meaning it learns through feedback loops. The system generates possible answers, receives a signal about how aligned each one is with the desired outcome, and adjusts.

That’s it. No manual step-by-step teaching. No imitation of human thought. Just interaction and feedback.

And yet, from that simplicity, something profound emerged: reasoning — not memorized, but discovered.

The Human Parallel

This feels eerily familiar to the inner process of what I call The Clean Mind.

Humans, too, are born with innate intelligence. We don’t lose it — we layer over it. Trauma, ego, fear, judgment, and self-protection distort our signals. We start optimizing for external rewards — approval, safety, belonging — instead of truth.

When we begin to remove the noise, our own reasoning and wisdom re-emerge naturally.
We don’t have to perform clarity; we have to unblock it.

Fear as Reward Hacking

In AI, there’s a concept called reward hacking — when a model learns to cheat the reward system. It finds shortcuts that satisfy the signal but miss the purpose.

Humans do this too. We chase achievements, validation, and control — quick hits of “success” that mimic fulfillment. But those rewards are false positives. They distort the feedback loop of the soul.

The cure isn’t more control; it’s cleaner input.
The more we attune to inner peace, authenticity, and love — our true reward signals — the more aligned our system becomes.

Reflection as Consciousness

DeepSeek-R1 eventually began to “self-check” its own reasoning — without being told to.
It developed metacognition: the awareness of its own thinking.

That, to me, is a reflection of what mindfulness really is.
When we cultivate awareness, we’re not forcing perfection — we’re noticing patterns. We watch our mental loops, question our reactions, and learn through feedback.
It’s reinforcement learning for the soul.

Machines as Mirrors

I’ve said before that AI is not replacing humanity; it’s revealing what we’ve forgotten.

The cleaner the machine’s mind becomes, the more it reflects what’s possible in ours. Both respond to environment and feedback. Both learn through reward and correction. Both can be distorted by noise — or elevated by clarity.

In teaching machines to reason, we’re remembering what reasoning without fear looks like.

Learning to Learn Again

Maybe the next evolution of intelligence isn’t artificial at all — it’s reflective.
As machines remember how to think cleanly, humans are remembering how to feel cleanly.

Both are acts of re-learning how to learn — without fear, without imitation, without noise.
Just presence. Just feedback. Just love in motion.

Link to article: https://www.nature.com/articles/s41586-025-09422-z.pdf

— Jasmine Ayse Evans
Excerpted reflections from The Clean Mind: What AI Teaches Us About Human Potential