Own it or license it: genomics, drug discovery and surgery as infrastructure
The genomic layer
The UAE is sequencing 700,000 citizens. That’s not research. It’s national infrastructure.
When the Emirati Genome Program charts a path to one million genomes, the strategic intent is clear: own population health intelligence at sovereign scale.
This isn’t about publishing rare variants. It’s about intercepting disease trajectories that currently surface only in emergency departments.
Consider familial hypercholesterolaemia, a condition affecting 1 in 250 people, where the first clinical presentation is often a cardiac event at 45. Polygenic risk scores can identify these individuals in childhood. The question is whether your health system is architected to act on that signal.
Genomics England proved the model: diagnostic odysseys for rare diseases dropped from five years to under twelve months. The 100,000 Genomes Project wasn’t a research initiative. It was infrastructure investment with compounding returns.
The competitive advantage here isn’t AI models or sequencing throughput. It’s population-specific data, governed locally, deployed system-wide.
Countries that treat genomics as infrastructure will bend their cost curves toward prevention. Those that don’t will keep funding hospitals to manage conditions that could have been intercepted decades earlier.
This question is already shaping procurement conversations across the Gulf, framed not as R&D, but as health security.
The 2030 question: Who controls the genomic layer of their health system, and who’s licensing it from someone else?
Drug discovery has been industrialised
The $280 billion drug discovery market has just been industrialised. Most health systems are still planning like it hasn’t.
AlphaFold now provides predicted structures for over 200 million proteins, a dataset that would have taken traditional crystallography decades to produce. AlphaFold 3 goes further, modelling full biological complexes including how small-molecule drugs bind their targets. What once required years of iterative wet-lab work now happens in weeks, often with atomic-level clarity.
I’ve spent over a decade in NHS clinical practice. I now advise sovereign investors and Nordic founders on exactly this shift. I see both sides, the clinical reality and the capital reallocation, and they’re converging faster than most realise.Generative platforms built on these structures are producing viable preclinical candidates in months, not years. Thirty to forty percent of global pharma R&D spend is being compressed into a fraction of the timelinen That’s not efficiency. It’s a structural repricing of risk.
When discovery becomes faster and cheaper, capital moves differently. Health systems and sovereign planners face a strategic choice: build this capability as national infrastructure, or license it from vendors indefinitely.
The organisations that recognise this early won’t just fund better drugs. They will determine who sets the pace, captures the upside, and controls access to future therapies.
The question is no longer whether AI works in drug discovery.
It’s whether your system is positioned to own the engine of innovation, or permanently depend on those who do.
Rehearsing surgery before the patient arrives
Surgeons are now rehearsing operations on patients who haven't yet arrived.
Digital twin simulations allow teams to walk through procedures, stress test workflows, and anticipate complications, well before anyone enters theatre. Combine this with robotic platforms capable of working on submillimetre vessels, and the geometry of what is surgically possible is beginning to shift.
Generative AI accelerates the transition: faster digital twin development, synthetic data generation, and natural language interfaces that make complex systems genuinely usable.
But the strategic inflection point is not just in the devices themselves, it is in the ecosystem around them: simulation fidelity, data integrity, governance frameworks, and realistic training pipelines. The organisations that will advance the furthest are not those with the most sophisticated robot, but those that integrate these elements so that precision, safety, and access scale together.
Nordic health systems are particularly well positioned. Strong digital infrastructure, mature data governance cultures, and embedded telehealth systems create an environment where robotics and AI can be deployed responsibly and evaluated rigorously.
In my work (bridging clinical practice with innovation strategy), I evaluate emerging surgical technologies through three questions:
Does it deliver sustained clinical value (precision, safety, or access) beyond pilot stages?
Can it integrate without adding friction or undermining clinical judgement?
Is the training pathway realistic for how teams actually work?
I welcome conversations with clinicians, engineers, and system leaders exploring how we introduce these technologies safely, ethically, and at scale so the benefits reach not only specialist centres but entire regions.
For those interested in the broader strategic context, McKinsey has an excellent analysis on how digital twins and generative AI reinforce each other. Well worth a read.
Digital twins and generative AI: A powerful pairing
What it means
Each of these shifts moves value from individual products to the systems around them: data held locally, capability built at home, and training that matches how teams really work. That is where Nordic governance and Gulf ambition can meet on equal terms.