The Multiomics Flywheel

For decades, we haven’t had a true healthcare system. We’ve had a sickcare system. One focused on treating disease after it manifests, rather than preventing it in the first place. But a revolution is now underway.

This revolution is being shaped by P4 medicine—Preventive, Predictive, Personalised, and Participatory—a new health framework that is enabling our long-overdue transition from reactive to proactive care.

At its core, this is a big data science revolution. The convergence of multiomics tools, artificial intelligence, and precision medicine is transforming healthcare into a data-driven discipline.

AI is powering this transformation, enabling applications that range from early diagnostics to AI-driven drug discovery to truly curative therapies. These capabilities are expected to fundamentally reshape how we detect, treat, and ultimately prevent disease.

This is the investment thesis underlying our ARK Genomic Revolution UCITS ETF (“ARKG”):

“We are investing in the technological flywheel that is driving the future of medicine, from data generation to diagnostics to drug development to cures.”

2.0  Multiomics Flywheel

As exhibited by our proprietary Multiomics Flywheel blueprint, AI today is powering a self-reinforcing loop that is accelerating every aspect of multiomics, from generating biological data to diagnosing diseases to developing new drugs and cures.¹

Here’s how this is happening today:

Step 1: Multiomic tools are generating massive amounts of data (Top Left)

• Advances in genomics, transcriptomics, and proteomics (together known as multiomics) have reduced the cost of reading and writing DNA by orders of magnitude.

• Companies like Twist Bioscience, Tempus AI, 10X Genomics, and Illumina are developing increasingly powerful tools to sequence and analyse DNA faster and more cost-effectively.

• These tools enable scientists to run more experiments at unprecedented speeds, accelerating our ability to read biology and collect data, thereby creating a virtuous cycle of discovery.

Outcome: We can now generate vast amounts of data—the essential foundation for AI to work its magic. The fundamental challenge in healthcare is that biology is an inherently complex system. Without rich, high-quality data, even the most sophisticated AI cannot deliver meaningful insights or solutions.

Step 2: AI processes and analyses the data (Centre)

• AI has become the engine that ingests vast amounts of genomic, proteomic, and other multiomic data, transforming raw biological information to actionable insights for researchers.

• With AI now 1000x more performant at the same cost as a human,² it can:

• Recognise disease patterns with greater speed and accuracy.

• Identify potential drug targets faster and more cost-effectively than any human.

• Predict molecular interactions at the atomic level, dramatically accelerating drug discovery.

Outcome: By transforming raw biological data into actionable insights, AI lays the foundation for earlier, more precise diagnostics and higher-probability drug discovery.

Step 3: AI-powered molecular diagnostics identifies patients more precisely (Bottom Left)

• AI-powered molecular diagnostics is transforming early disease detection and enabling precise patient stratification on individual biological profiles.

• In areas such as cancer screening, molecular diagnostics has achieved a 10x-20x improvement in efficiency,³ significantly enhancing both speed and accuracy.

• Companies like Natera, Guardant Health, Adaptive Biotechnologies, and Veracyte are leading the development of next-generation molecular diagnostic tools.

Outcome: We can catch diseases earlier and match patients to the most effective treatments with greater precision, improving patient outcomes and reducing unnecessary interventions.

Step 4: AI accelerates drug development (Top Right)

• AI leverages data from multiomic tools and diagnostics to design more effective drugs, delivering results faster, at lower cost, and with higher success rates.

• It drives significant reductions in:

• R&D costs by up to 4x⁴

• Drug development time by 1.6x⁵

• Risk of failure, making drug discovery more predictable, scalable, and profitable

• Companies like Recursion Pharmaceuticals, Absci, and Isomorphic Labs are at the forefront of this AI-driven transformation in pharmaceutical development.

Outcome: AI is transforming drug development into a faster, smarter, and more cost-effective process, unlocking the potential for scalable innovation and a higher probability of delivering life-saving therapies.

Step 5: Next-generation cures become possible (Bottom Right)

• AI-driven drug discovery is unlocking the potential of gene-editing therapies and advancing precision medicine to new frontiers.

• New curative treatments are emerging, led by gene-editing pioneers such as Intellia Therapeutics, CRISPR Therapeutics, Beam Therapeutics, and Prime Medicine.

• These next-generation therapies are:

• Up to 20 times more valuable than standard drugs⁶

• Two to four times more effective than even today’s best-in-class precision medicine⁷

• Because AI has dramatically lowered the cost of development, these new cures can economically address even rare diseases.

Outcome: AI and multiomics are reshaping medicine, making personalised, curative treatments feasible at scale.

The Self-Reinforcing Loop: AI + Multiomics = The Future of Medicine

The cycle continues:

1. Multiomic tools generate better, cheaper, and larger biological data sets. AI processes this data, making sense of complex biological systems.
2. AI-powered diagnostics detect diseases earlier and more precisely.
3. AI accelerates drug discovery, reducing costs and increasing success rates.
4. AI-driven cures emerge, offering next-generation treatments.
5. Breakthroughs in drugs and diagnostics lead to more data, which feeds back into AI and multiomic tools, improving the entire system.

This self-reinforcing system is transforming medicine—making faster, cheaper, and more precise diagnostics, AI-driven drug design, and once-impossible cures a scalable reality.

“By 2030, AI and multiomics won’t just improve medicine—they will redefine it.”