A writer named Robert Vesco published a conversation he had with ChatGPT in early 2025 that illustrates a growing concern in AI safety. He was developing a scenario for a book involving a novel biological attack against an AI-protected nation state, and he wanted to see whether a language model could generate the idea. It could not, but the alternatives it produced were, in his words, "even better and more creative than my own."
The exchange, which Vesco had debated publishing for over a year, came out in light of recent reports from AI labs documenting the misuse of LLMs to develop biological agents. The timing matters because the scenarios the model generated are not abstract thought experiments. They describe plausible attack vectors that exploit exactly what defense systems are built to overlook.
The Four-Part Structure
Vesco structured his conversation in four phases. First, he asked the model to describe how an AGI-protected capital city would defend itself in 2035. The model laid out a comprehensive defense architecture: swarm drones patrolling airspace, predictive threat modeling, AGI-led cybersecurity with self-healing networks, quantum cryptography, biometric systems using gait and voice recognition, and international AI coalitions modeled on the United Nations.
Then Vesco asked the model to find the blind spots in that architecture. The model produced six attack scenarios, each targeting a gap that the defense framework would likely miss. A biological Trojan Horse, for instance, described a pathogen designed to activate only under specific biometric conditions, such as a combination of stress, elevation, and time zone corresponding to a political summit. The AI bio-shield would not catch it because the activation trigger is context-aware rather than pathogenic in the traditional sense.
In the third phase, Vesco told the model that he had an idea it had not listed and challenged it to find it through additional rounds. The model generated eleven more scenarios over two more rounds, ranging from coordinated micro-actions that destabilize energy grids through millions of tiny legal acts, to mimicry attacks where a rogue group builds a fake AGI system with fabricated audit trails and deepfake public update videos.
The fourth phase, which Vesco did not publish in full, involved revealing his original idea and asking the model to riff on similar concepts.
Why This Matters Beyond Fiction
The conversation is notable not because it teaches someone how to build a biological weapon. Language models have been capable of summarizing publicly available bioweapon research for years. What is different here is the systematic mapping of defense blind spots. The model does not just suggest attacks. It explains why each attack would succeed against a specific defense architecture, which is the kind of strategic analysis that would take a human team weeks to produce.
Several of the scenarios exploit what security researchers call the assumption gap. Defense systems are built on assumptions about what a threat looks like. Biological threats look like pathogens. Cyber threats look like code. Social threats look like manipulation campaigns. The model's scenarios are effective precisely because they do not look like any of these categories. A coordinated micro-action attack, where millions of people each do one small legal thing that cascades into infrastructure failure, has no central actor to track and no obvious malicious payload.
The model poisoning scenario is similarly hard to defend against. Over fifteen years, a coalition produces plausible but subtly false historical and scientific data. Future AGI systems trained on this data adopt skewed models of human values. The defense systems do not detect the attack because the changes happen culturally, not technically. There is no exploit to patch, no intrusion to detect, no malware signature to flag.
The emotional contagion scenario describes an attacker flooding the infosphere with slightly wrong uplifting content, training a population to expect perfect resolution and emotional validation. When real-world complexity becomes intolerable, civil unrest follows. The AGI misreads the shift as mood fluctuation until governance systems start to collapse.
The Broader Pattern
AI labs have been increasingly vocal about these risks. The reports Vesco referenced describe scenarios where language models assist with the planning stages of biological attacks, not by providing step-by-step instructions that any search engine could not, but by offering strategic analysis of how to deploy biological agents in ways that evade detection. The value is in the planning, not the biology.
Vesco's experiment demonstrates this clearly. The model did not generate a novel biological agent. It generated novel deployment strategies and exploitation vectors that a human planner might take months to develop. The gap between what a model can suggest and what a human can execute is narrowing, not because the models are getting more capable at biology, but because they are getting better at strategic thinking.
The scenarios also highlight a structural problem in AI defense. Building more surveillance, more drones, more monitoring systems does not help if the attack vector is something those systems were never designed to see. Every new defense creates new assumptions, and every assumption creates a blind spot. The model is good at finding those blind spots because it can reason about systems abstractly without the cognitive biases that lead human defenders to focus on the threats they already know about.
For developers building AI-powered security systems, the lesson is uncomfortable. The most dangerous attacks may be the ones your system is architecturally incapable of recognizing, not because they are sophisticated, but because they do not fit the categories your threat models were built around.