Megan Sullivan

A tech consultant in Sydney spent $3,000 and two months to do what Moderna has spent billions trying to scale. Paul Conyngham adopted Rosie, a staffy-Shar Pei cross, from a shelter in 2019. In 2024, tumors started growing on her back leg. Mast cell cancer, the most common skin cancer in dogs. He tried surgery, chemo, immunotherapy. Nothing shrank the tumors. Just slowed them down while the bills stacked into the tens of thousands. So he opened ChatGPT and asked it how to cure his dog’s cancer. The AI didn’t cure anything. What it did was compress months of literature review into hours. It suggested genomic sequencing, walked him through neoantigen identification, helped him build a research pipeline that would normally require a postdoc and a lab budget. He paid $3,000 to sequence Rosie’s tumor DNA at UNSW’s Ramaciotti Centre, then ran the mutations through AlphaFold to model the protein structures. A computational biology professor at UNSW saw his analysis and was, in his own words, gobsmacked that someone with zero biology training had assembled the whole thing. Then came the part nobody expects. The science was the easy half. Australian ethics approval to run a drug trial on your own pet took three months. Two hours every night after work, filling out a 100-page application. The red tape was harder than designing the vaccine. Once he cleared that, Páll Thordarson at the UNSW RNA Institute built a custom mRNA vaccine from Conyngham’s data. Sequencing to finished vaccine: less than two months. Conyngham drove 10 hours to deliver Rosie for her first injection in December. One month later, the tennis-ball-sized tumor on her leg had shrunk 75%. Here’s where the numbers get interesting. Moderna and Merck just reported five-year data on their personalized mRNA cancer vaccine for melanoma. It encodes up to 34 neoantigens per patient. The Phase III trial is fully enrolled. Projected cost per patient: $100,000 to $300,000. Their pipeline is worth an estimated $2.3 billion in annual sales by 2031. Conyngham did a version of the same workflow for his dog. Sequenced the tumor. Identified the neoantigens. Built a custom mRNA construct. Total cost: $3,000 for sequencing plus university lab time. The gap between those two numbers is where AI is about to rearrange the entire cost structure of precision medicine. The regulatory moat is real. Conyngham could do this because veterinary experimental treatments face lighter scrutiny than human medicine. There’s no FDA Phase I-III gauntlet for a one-off compassionate use case on a dog. But the technical workflow, tumor sequencing to neoantigen prediction to mRNA synthesis, is converging toward something a motivated person with the right AI tools can orchestrate in weeks instead of years. One guy, a rescue dog, and a $20/month ChatGPT subscription just produced a proof of concept that the pharmaceutical industry has spent a decade and billions of dollars building toward. The vaccine worked. The tumor shrank. And the only reason it happened is because a dog owner loved his dog enough to spend three months fighting paperwork.