I have been thinking about this for a while. I don't have a clear answer.
I am in India. I am building my project alone. I don't have a lab or lab meetings. I don't have anyone to talk to when something breaks at midnight. The AI safety conversation mostly happens in San Francisco, London and Cambridge in offices I have never been to. These conversations happen between people who can grab coffee and discuss alignment over lunch. I sometimes wonder if being outside that group makes me see things differently. Maybe I am just missing things everyone else already figured out?
Here is what happened to me. I built a system with no fitness function, no reward and no objective. It had agents surviving under constraints. I wasn't even thinking about alignment when I started.
I was thinking about Evolution and the question: What happens if you remove the goal and the fitness function entirely?
And... something unexpected showed up When the environment co-evolves alongside agents the internal complexity increases a lot. I found 467 nodes versus 52 in environments. That's nine times more! The behavior doesn't change away. The structure grows first. Then the behavior gets richer. There is.... a delay. I thought I had found complexity with no change but as it turns out, I was wrong. The behavior does change; it just takes time. The system builds capacity internally before it shows it externally. A couple of the researchers who originally developed the evolutionary algorithms I'm building on have also looked at this and I have two GECCO 2026 papers that validate pruning, behavioral metrics and constrained training.
But here is the thing I keep thinking about: If this delay exists in a system with no goals, no deception and no strategy what does that mean for how we evaluate AI systems that are much more complex and actually have goals?
We measure behavior and run benchmarks. We watch what systems do. But what if capability is accumulating structurally before it surfaces behaviorally? It's not because anything is hiding. It's because structure and function don't always move together.
I don't know if this matters for alignment. I genuinely don't know. I am building alone in India. I found something that felt important. I don't have enough people around me who would know whether it is. That is partly why I am writing this. The people on this forum work on evaluations, interpretability, hidden capabilities and oversight. You are the ones who would know if this is a gap or a niche evolutionary quirk that doesn't translate. You would know if someone has already found this in networks or LLMs. You would know if the delay I am measuring is the thing to measure.
I am not really here to convince you my work matters. I am here because I need people who're smarter than me and who know about the systems that actually matter to tell me whether this points at something real.
Does it?
Tell me what you think... even if it's 'this is nothing.' I'd rather know.
I have been thinking about this for a while. I don't have a clear answer.
I am in India. I am building my project alone. I don't have a lab or lab meetings. I don't have anyone to talk to when something breaks at midnight. The AI safety conversation mostly happens in San Francisco, London and Cambridge in offices I have never been to. These conversations happen between people who can grab coffee and discuss alignment over lunch.
I sometimes wonder if being outside that group makes me see things differently. Maybe I am just missing things everyone else already figured out?
Here is what happened to me. I built a system with no fitness function, no reward and no objective. It had agents surviving under constraints. I wasn't even thinking about alignment when I started.
I was thinking about Evolution and the question: What happens if you remove the goal and the fitness function entirely?
And... something unexpected showed up When the environment co-evolves alongside agents the internal complexity increases a lot. I found 467 nodes versus 52 in environments. That's nine times more! The behavior doesn't change away. The structure grows first. Then the behavior gets richer. There is.... a delay.
I thought I had found complexity with no change but as it turns out, I was wrong. The behavior does change; it just takes time. The system builds capacity internally before it shows it externally.
A couple of the researchers who originally developed the evolutionary algorithms I'm building on have also looked at this and I have two GECCO 2026 papers that validate pruning, behavioral metrics and constrained training.
But here is the thing I keep thinking about: If this delay exists in a system with no goals, no deception and no strategy what does that mean for how we evaluate AI systems that are much more complex and actually have goals?
We measure behavior and run benchmarks. We watch what systems do. But what if capability is accumulating structurally before it surfaces behaviorally? It's not because anything is hiding. It's because structure and function don't always move together.
I don't know if this matters for alignment. I genuinely don't know. I am building alone in India. I found something that felt important. I don't have enough people around me who would know whether it is.
That is partly why I am writing this. The people on this forum work on evaluations, interpretability, hidden capabilities and oversight. You are the ones who would know if this is a gap or a niche evolutionary quirk that doesn't translate. You would know if someone has already found this in networks or LLMs. You would know if the delay I am measuring is the thing to measure.
I am not really here to convince you my work matters. I am here because I need people who're smarter than me and who know about the systems that actually matter to tell me whether this points at something real.
Does it?
Tell me what you think... even if it's 'this is nothing.' I'd rather know.
Code: github.com/gearupsmile/genesis-emergence
Papers: researchgate.net/profile/Anushka-Sharma-77