Back Of The Envelope.

Comparison 2020 vs 2026

Hawkish fed, and any other commentary from any other central bank is expected to be hawkish considering global inflation from the war - resulting in emerging markets and metals selling and cash or t-bills positions.

The $10 Billion "Efficiency Dividend": How AI is Quietly Re-Engineering the Cost of Human Life The recent turbulence in Global IT Services has many asking if the "AI hype" has finally met its reality check. While the markets focus on the shift in software outsourcing, the most profound impact of AI isn't happening in a code editor—it’s happening in a laboratory. AI's ability to compress decades of research into months is moving us from a world of "Discovery by Chance" to "Discovery by Computation." 1. Beyond the IT Crash: A New Operating System for Science The current volatility in IT stocks isn't just about reduced headcounts; it’s a signal of a structural shift. AI is no longer just "automating tasks"; it is becoming the infrastructure for complex problem-solving. While traditional IT firms grapple with this evolution, the Life Sciences sector is using it to break the "Eroom’s Law" (the observation that drug R&D becomes slower and more expensive over time). 2. The $10 Billion Question: Precision R&D at Scale Consider the "Big Three"—Novo Nordisk, Eli Lilly, and AstraZeneca. Between them, billions are spent annually to find the "needle in the haystack": the precise molecular modification that turns a protein into a life-saving therapy like Liraglutide or Semaglutide. Based on industry R&D spending of nearly $25B–$30B collectively for these giants, even a conservative 30% efficiency gain via AI-driven Monte Carlo simulations and "Digital Twin" trials represents a staggering $10 Billion in saved capital - for the research of only one therapy - Imagine the scale of savings if this becomes a common market practice. What does this mean for the patient?If we factor this $10B "Efficiency Dividend" into the cost of therapy: The Math: Aggressive market-sizing suggests upwards of 100 million patients globally could eventually require GLP-1 therapies for diabetes and obesity. The Save: A $10 Billion R&D saving, amortized over a decade-long patent life for 100M patients, implies a potential $100 reduction in the "R&D tax" per patient. The Reality: In markets like India, where the difference between a ₹10,000 and ₹5,000 monthly treatment cost determines accessibility for millions, AI-driven R&D isn't just a corporate margin booster—it’s a public health necessity. 3. The "Discovery Moat" is Shifting The "Discovery" of Liraglutide involved finding the exact attachment point for a fatty-acid tail on a 31-amino-acid chain. Historically, this was a multi-year "wet-lab" trial-and-error process. Today, AI models can predict these interactions in seconds, drastically lowering the "barrier to entry" for complex biologics. As the cost of thinking (R&D) drops due to AI, the value of doing (high-purity manufacturing) rises. This is where the global supply chain is being redrawn. If discovery and trials can happen anywhere at digestible costs, and manufacturing capabilities exist in India.... An Open Question for the Future: If AI can reduce the cost of a single major drug trial by 50%, what does this mean for countries like India, which have historically been "Low R&D" but "High Execution" hubs? Could the combination of India’s massive patient data pools and AI-driven "Dry-Labs" allow us to leapfrog the traditional $2 Billion-per-drug R&D model? Are we standing at the edge of an era where India doesn't just manufacture the world's medicine, but discovers it at a fraction of the global cost? OR for a populous country like India, it takes away any moat India may have with human clinical trials? I’d love to hear your thoughts: Is the next "Blockbuster" drug more likely to come from a traditional lab in Basel, or a GPU cluster in Bengaluru?