The democratization of product development via AI will lead to a flood of new products—an estimated 600 to 800 million by the end of 2026. However, the prediction is that a staggering 90-95% of these will fail, highlighting the intense competition and need for disciplined execution.

Large pharmaceutical companies face losing up to 50% of their revenues by 2030 due to the largest patent expiration wave in history. To survive, they will be forced to acquire innovation from the biotechnology sector, fueling a sustained M&A cycle for years to come.

Martin Shkreli argues that the primary bottleneck in drug development isn't finding new molecules, but the immense inefficiency caused by poor communication, irrational decision-making, and misaligned incentives across numerous human departments. He believes AI's greatest contribution will be optimizing this complex organizational process rather than just improving discovery.

The traditional pharma leadership model focused on minimizing risk through tight, linear control is no longer competitive. The future requires a shift to agile coordination, allowing leaders to reallocate priorities quickly in a data-driven, connected way.

Despite scientific breakthroughs and better technology, the cost per approved drug has steadily increased over the last 60 years. This phenomenon, the reverse of Moore's Law, is called Eroom's Law and highlights a fundamental productivity problem in the biopharma industry, with costs approaching $1B+ per successful drug.

The pandemic acted as an unavoidable wake-up call, compelling the slow-moving pharmaceutical industry to rapidly adopt digital engagement models and embrace a more agile, customer-focused commercial approach, achieving in one year what would have taken ten.

The increasing volume of new therapies requires pharma companies to stop treating each launch as a unique event. Instead, they must develop a scalable, repeatable, and excellent launch capability to handle the future pipeline efficiently and consistently.

The pharmaceutical industry risks repeating Kodak's failure of inventing but ignoring a disruptive technology. For Kodak, it was digital photography; for pharma, it's AI. The industry possesses vast amounts of data (the new 'film'), but the real danger lies in failing to embrace the AI-driven intelligence layer that can interpret and act on it.

While AI-driven drug discovery is the ultimate goal, Titus argues its most practical value is in improving business efficiency. This includes automating tasks like literature reviews, paper drafting, and procurement, freeing up scientists' time for high-value work like experimental design and interpretation.