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Most evidence shows frontier LLMs bunch closely together on widely used public benchmarks. Multiple independent studies and leaderboards report only small aggregate score gaps among top models, even when they disagree on individual questions. The main caveat is scope: harder or specialized public benchmarks can still separate models by meaningful margins, so the pattern is common rather than universal.

Both hallucinations and correct outputs do emerge from the same autoregressive next-token prediction process — no separate "hallucination engine" exists within large language models. Multiple peer-reviewed sources confirm this shared generative pipeline. However, the claim omits critical nuance: hallucinations have distinct causal drivers — such as training procedures that reward guessing over expressing uncertainty, data distribution gaps, and prompting effects — that do not equally govern correct outputs. The generation channel is shared, but the upstream conditions that produce errors are separable and require distinct mitigation strategies.

The core claim is accurate: Sydney-based tech professional Paul Conyngham used ChatGPT — alongside other AI tools — to help plan and develop a personalized mRNA cancer vaccine for his dog Rosie after her cancer diagnosis. However, "technology executive" is a loose description (sources call him a tech entrepreneur, AI consultant, or data engineer), and ChatGPT's role was primarily as a research and planning assistant — human scientists at UNSW performed the actual genome sequencing, vaccine synthesis, and treatment.