We were told AI would cure disease in years. That’s a fallacy.
Today, over 90% of clinical drug candidates still fail. It costs more than $2 billion and over a decade to bring a single drug to market. And now the industry faces a brutal paradox, which is the promise of exponential progress stuck inside a system governed by diminishing returns.
This is pharma’s version of Eroom’s Law, i.e. the inverse of Moore’s Law, where the return on drug development declines as science advances. This disconnect has created the “valley of death”: the chasm between discovery and clinical validation, where risk is high, capital is scarce, and most ideas go to die.
Pharma’s response? Avoid early risk. Wait for efficacy signals. Let small biotechs take the first leap. The result: a broken innovation model, a biotech echo chamber, and too few new answers for patients.
The Myths Holding Back Progress
Myth #1: Pharma knows how to develop drugs better.
Not true. Drug research and development remains probabilistic, messy, and context-specific. Big Pharma excels at scaling, not always at discovering. There’s no secret playbook locked inside corporate vaults.
Myth #2: AI can develop drugs better.
Also false, or, at best, wildly overstated. Today’s AI has helped with discovery, but not with development. It can suggest new molecules or predict binding, but it can’t yet navigate the complex, high-dimensional path to safe, effective therapies.
The current wave of hype assumes that AI can inform drug development just by reading scientific papers. But as AI pioneer Yann LeCun has argued: true intelligence doesn’t come from reading alone – it comes from interacting with the world and learning from experience.
Why Drug Development Needs Physical AI
Drug development is deeply physical. It spans chemistry, immunology, toxicology, pharmacokinetics, clinical design, and real-world outcomes. Optimizing this requires more than clever language models, it demands systems that can sense biology in motion.
Pharma has tried to bolt AI onto existing pipelines. But without coherent, standardized, longitudinal, multi-modal biological data, AI cannot reason. Instead of a world model for biology, we get piecemeal tools trained on static PDFs or siloed snapshots.
If a car breaks down, would you read the logbook or look under the hood?
Why Pharma Can’t Do This Alone
Even if large pharma wanted to build this, they would hit three walls:
1. Siloed data: Fragmented across departments, trials, and vendors.
2. No longitudinal integration: Incompatible formats across the preclinical-to-clinical arc.
3. Lack of engineering culture: Building physical AI requires sequencing platforms, Machine Learning pipelines, cloud infrastructure, and rapid iteration: things pharma doesn’t do natively.
Too often, valuable samples (e.g., blood, tissue) are collected but never turned into usable data. Or they’re analyzed and then locked inside PDFs, inaccessible to any AI.
The Way Out: Collaboration Over Control
Pharma brings capital, regulatory experience, and clinical access. Techbio startups bring the engineering muscle, velocity, and learning culture.
This is not a build-vs-buy decision. It’s a rethink-the-whole-system decision. The only viable path forward is partnership, where both sides commit to generating structured, machine-readable biological data, tied to outcomes.
A Call to Action: Let’s Get Serious
We can escape Eroom’s Law and cross “the valley of death.” But only if we engineer our way out instead of relying on commercial strategies.
To build a real-world model of human biology, we must:
- Capture biological and clinical data across the full development arc
- Make that data multi-modal, longitudinal, and interoperable
- Train models on biology itself, not just literature
- Design collaborations that align incentives around learning, not just licensing
This is how we stop hallucinating and start reasoning. This is how we bring intelligence to biology. This is how we give patients better health outcomes and cure diseases.
About Noam Solomon
Noam Solomon is the co-founder and CEO of Immunai, a biotech startup using single-cell genomics and machine learning to discover and develop novel therapeutics that reprogram the immune system. Noam, who began his studies at Tel Aviv University at the age of 14 and earned his bachelor’s degree in computer science by 19, continued his academic journey earning two PhDs at MIT and Harvard. While at MIT, Noam met his co-founder, Luis Voloch, and launched Immunai in 2018. Under Noam’s leadership, Immunai has partnered with top pharmaceutical companies and academic institutions to improve clinical trial success rates and therapy effectiveness.
