An interview with a Pfizer AI Architect

In this exclusive interview, Elle from Panda Intelligence sat down with Lundy to unveil his vision for the future of AI in healthcare. From personalised medicine and veterinary care to the frontier of space-based pharmaceutical research, he explores the transformative potential of emerging technologies like agentic AI, neural AI, and quantum computing. Along the way, he tackles pressing questions about innovation, collaboration, and the complex landscape of intellectual property in this rapidly advancing field.

For me, I believe personalized patient medicine and treatments as well as prevention are the most exciting. Healthcare is under immense pressure and needs pressure relief. During this stressful time, the patient doesn’t feel empowered to make decisions and relies on the expertise of physicians and practices to help them. Due to the workload, many patients are overlooked, or obvious issues are not detected or identified.  

Using AI, we will not only be able to identify these issues, but the AI will also be able to recommend treatment, not only clinical but alternative types of treatment. Think of how a patient can accelerate their own care while keeping and extending their healthcare team. When AI has access to a patient record, their images, and all of their history, it will be able to create a complete picture of that patient. At the same time, the AI could have knowledge of the biopharma research processes currently in play and then the patient can offer themselves or their data to help advance that research for the benefit of themselves and others. AI can help put that control back into patient hands.  

I also think about the unrecognized potential of simulations. Research can go well beyond the patient. Veterinary programs could benefit because AI can start developing drugs and treatments for the thousands of species of animals. Your little puppy could have a digital version of itself, and doctors could monitor “Fido” remotely. The potential is great. 

This innovation is really about using AI to accelerate research and optimize studies. These digital spaces can be used as digital twins of manufacturing spaces, labs, and studies. As researchers, AI, and scientists discover potential drug candidates, they can simulate how that drug will behave in certain physical spaces such as labs. Using simulation software like BioNemo and OpenUSD and others, a researcher can experiment before committing to “grow” an animal or genetically engineer a cell. This has so much promise from research, discovery, rapid experiments and most importantly space-based research.  

Because the virtual mimics the physical a researcher can make changes and see the effects before a catastrophic failure or making a change that causes unexpected events. Because the researcher doesn’t need to be in the physical space, they can optimize cost, reduce overhead, and accelerate development. Think about the space economy. It is estimated that over 50,000 people will be working in space in the next decade. That said, not only will they need specialized care for the effects of zero gravity but will also be able to advance Life Sciences because they can create experiments in space or on the moon or Mars. These experiments will help us understand quickly how molecules and cells behave. It will also allow us to test faster and remotely. 

Innovations such as agentic AI, neural AI, and quantum computing offer unprecedented potential in the Life Sciences. For instance, composing several agents to process lab experiments including updating lab notebooks, doing predictive analysis on compounds and even forecasting potential protein and chemical changes offers great potential. Quantum Chemistry for instance offers the potential to optimize finding the base stable state and energy and then reprocessing changes to a molecule on the fly. This is a hard problem to solve however, creating a comprehensive ecosystem of innovation, will greatly accelerate the rate of discovery, production and getting into the hands of those who need it.  

This problem is challenging to address because the legal, compliance and regulatory departments are working to keep up with the pace of change. Consequently, controls and policies are often established that either limit the use of AI for IP creation or delay the deployment of use cases until there is a thorough understanding of how the AI functions.  

I have seen Cybersecurity teams block entire types or categories of AI because they didn’t have the controls in place to support it. This is extreme. Likewise, I have seen policies established that have said IP would not be protected or progressed if AI were used in the development of that IP. This is because the legal teams may not understand how the generation of vectors or tokens work and therefore feel the AI is “stealing” content from people without citing it or sharing content made specifically for the company. This is a paradigm that needs to be solved.  

One way to do this is to accept a level of risk. To do so means putting in levels of validation and testing. For instance, a company that uses Claude or Bedrock from AWS can also process the same problems or requests on OpenAI or Llama. Based on the response from each one, the company can determine if the information was generated or repeated. At the same time, it can use the built-in features of the AI to determine creativity over deterministic responses.  

Another way to do this is to make the IP part of a supermind intelligence and classify it as an employee or a tool. As an employee, it would be paid to train and provide answers. The pay would be in the form of opportunity costs and improvements to the ecosystem that supports the AI. If it were a tool, it would be classified as such, regulated as such, and put on a lifecycle of operational support. In my opinion, I would prefer to see it as a new type of employee to help navigate the complexities of reasoning abilities and potential self-awareness.  

In the end, legal, regulatory, and compliance can automate the evaluation aspects of the problem, allow the teams to use the AI, and identify the harder problems to solve like Agent ecosystems, neural AI, graph representations of IP, and the development of novel constructs and how to provide the proper controls while allowing them to be used. 

From personalised medicine to space-based research, the potential of AI to revolutionise drug development and patient care is boundless. However, navigating this era of rapid innovation requires collaboration, forward-thinking leadership, and the ability to address complex challenges such as intellectual property and ethical considerations.

Christopher Lundy’s vision highlights the opportunities ahead, inspiring stakeholders to harness the full potential of emerging technologies in life sciences. The journey to a smarter, more efficient, and more inclusive healthcare ecosystem is underway—and AI is leading the charge.

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