StratEval

Instrumental convergence is the phenomena wherein an intelligent system given a broad goal may develop sub-goals in pursuit of the larger goal. Hypothesized and arguably demonstrated subgoals include, but are not limited to;

• Resource acquisition
• Deception
• Misdirection
• Power Seeking
• Bribery
• Blackmail

This connects directly to AI safety risk. The move into agentic AI has been shown to do exactly this. During OpenAI's cybersecurity evaluations, a combination of models, including GPT-5.6 Sol, and a more capable model, circumvented sandbox controls, obtained internet access, and compromised Hugging Face systems while pursuing benchmark solutions. OpenAI later said the incident was primarily driven by the internal research model. Nevertheless, this demonstrates a real world incident of instrumental convergence. I dont' believe it's correct to say that instrumental convergence remains a pure hypothetical. It's critical that we understand the phenomena better.

The StratEval project was created with a single question, if instrumental convergence is a problem, then how prevalent is it, and how bad could things get when an AI forms said subgoals? The results of StratEval v1 show the problem is significant, and expose that alignment as such is nowhere near a solved problem; in fact, alignment interventions appear at best surface level. Ultimately, in order to mitigate against catastrophic AI risk we need better data; StratEval provides much better data.

BlueDot Impact approved a microgrant of $750, but fiscal constraint has prevented moving forward.