Despite pervasive AI marketing at JPM—on billboards, in presentations, and even on Uber apps—the industry has yet to see a fully AI-designed drug reach approval. This gap highlights a technology hype cycle where branding and perceived necessity are currently outpacing proven, real-world outcomes in drug discovery.

While pharmaceutical companies plan to build their own siloed AI chatbots, physicians and patients are already adopting public tools like ChatGPT for clinical communication. This creates a risk of developing redundant solutions that ignore established user behavior.

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.

People overestimate AI's 'out-of-the-box' capability. Successful AI products require extensive work on data pipelines, context tuning, and continuous model training based on output. It's not a plug-and-play solution that magically produces correct responses.

Despite hype in areas like self-driving cars and medical diagnosis, AI has not replaced expert human judgment. Its most successful application is as a powerful assistant that augments human experts, who still make the final, critical decisions. This is a key distinction for scoping AI products.

Since AI can deliver results instantly, customers may perceive the output as low-effort and thus low-quality. To combat this, shift the focus from the speed of delivery to the immense effort, experience, and investment required to build the underlying AI system in the first place.

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.

Marketers often approach AI with inflated expectations, wanting a perfectly finished product. The correct mindset is to view AI as a tool to overcome the "zero to one" hurdle. It's a powerful assistant for creating a solid first draft or getting 50% of the way there, which a human then refines.

AI is seen not as a replacement but as a tool to handle repetitive tasks like checking abbreviations, style guides, and grammar. This automation allows human editors to focus on higher-value work: shaping the narrative, ensuring audience comprehension, and partnering on strategic messaging.