Grammy-nominated musician Al O. Black discusses his unexpected journey into biotech, where he founded Pep Do ID to develop new therapies for diseases like pancreatic cancer. He shares insights into the challenges of funding in biotech, the innovative drug discovery platform developed by his co-founder, and his perspective on how AI is reshaping both scientific research and the economics of the creative industries.
Summarized by Podsumo
Grammy-nominated artist Al O. Black pivoted from music to biotech, founding Pep Do ID to tackle diseases like pancreatic cancer, driven by a desire for greater impact beyond philanthropy.
Al O. Black is currently bootstrapping his biotech venture with personal funds and seeking non-dilutive government funding, strategically waiting for peer-reviewed scientific papers before approaching venture capitalists.
His co-founder, Dr. Golnica Udugamisoria, developed a unique "AI before AI" drug discovery platform that physically produces hundreds of thousands of molecules and identifies those binding to biomarkers, significantly reducing R&D time and cost by years.
AI's impact on the music industry is dual: it offers leverage for indie artists (ideation, rapid prototyping) but also makes them replaceable, raising complex questions about copyright, compensation for training data, and the ultimate economic beneficiaries (likely record labels).
Al O. Black personally uses AI tools like Suna for rapid song prototyping, valuing the journey of creation over just the destination, and believes humanity's purpose is found in engaging that journey.
"I just enjoy trying to make as much impact as possible. The music is one way that I do it, but with biotech, my goal was to use my influence and my income to make a market difference in how we approach creating new therapies for disease, immune dysregulation, and for oncology."
"The hardest part about biotech ultimately is competing against other really smart scientists and projects to get a funding and convincing the investors."
"I think anyone who has created anything that goes into a model should be compensated for their work being used to train the model."