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12 posts
Tylenol is one of the most thoroughly mapped drugs in human biology. Watch the safe and toxic pathways branch, saturate, and fail — and see why AI drug discovery needs to see all of it at once.
Today's devices were built for the classical era — search, files, manual tasks. The intelligence era needs new hardware and OSes built for agents.
AI drug discovery isn't waiting on better models — it's waiting on biology to catch up. The 'table of context' for a single molecule, and what it means for timelines.
AI drug discovery's biggest gap isn't models — it's pathway infrastructure. Six investments biology needs before AI can deliver on its promise.
AMD was the cost-effective alternative for a generation of tech kids. Now NVIDIA leads AI and Intel is courting Musk. Five strategic moves AMD needs to make.
The next AI winners won't be the broadest chatbots — they'll be the products that deeply understand specific professions. Why domain knowledge is becoming the moat.
AI didn't just speed up coding — it changed how we plan, build, and deliver software. The shift from ticket-driven to system-driven engineering.
When your SDK, design system, and brand guide become machine-readable curriculum, AI coding assistants stop building into a void — and the SDLC's compression gaps disappear.
Graphics cards turned game worlds into clarity. The same engine could turn drug discovery into a fluid, real-time visual workflow. The Scientific Visual OS thesis.
Agentic AI and microservices share the same DNA — modular, scalable, autonomous. Why the pairing is the right architecture for AI in pharmaceutical R&D.
Three strategies to bridge AI/ML expertise and scientific domain knowledge in drug discovery — collaboration hubs, shared data platforms, and AI-first experimentation.
AI drug discovery models often miss the bigger picture: the human body is an interconnected operating system. Pairing AI with systems biology is the next leap.