The Flywheel Effect: How AI is Reshaping Genetics-Based Healthcare

Amazon, OpenAI, Google, Anthropic and NVIDIA are referenced to describe developments in artificial intelligence and life sciences. These companies are not held by the VanEck Genomics and Healthcare Innovators UCITS ETF at the time of writing. References to specific companies do not constitute investment advice or a solicitation to buy or sell any security.

Perhaps the most consequential shift in life sciences recently is not a single drug approval, a clinical trial readout, or a regulatory milestone. It is something more structural: artificial intelligence is no longer simply another software tool available to researchers. It is beginning to emerge as part of the research infrastructure through which genetic medicine is designed, validated and advanced.

AI Is Becoming Research Infrastructure

Within less than three months - between April 14 and June 30, 2026 - four of the world's largest and most well-capitalised AI developers launched dedicated life sciences platforms: Amazon, OpenAI, Google and Anthropic. Amazon Bio Discovery arrived on April 14, 2026¹, an AI-powered cloud platform unifying biological data ingestion, AI model selection and laboratory workflow integration in a single environment. Two days later, OpenAI launched GPT-Rosalind², a frontier reasoning model built specifically for biology, drug discovery and medical research. Google followed on May 20, 2026 with Gemini for Science³, a broader scientific research environment whose life sciences capabilities build on tools including AlphaFold, AlphaGenome and AlphaMissense. And on June 30, 2026, Anthropic launched Claude Science⁴, a dedicated AI workbench designed to help researchers analyse genetic, cellular, protein and chemical data using more than 60 scientific databases and NVIDIA’s BioNeMo Agent Toolkit.

NVIDIA itself had moved earlier still. BioNeMo - first launched in June 2023⁵ and substantially expanded in 2025 and 2026 - occupies a distinct and arguably more durable competitive position than any of its application-layer rivals.

What makes this moment structurally different from previous waves of technology adoption in life sciences is not the sophistication of any individual platform. It is the simultaneity. These companies, with access to substantial capital, computing infrastructure and research talent, have independently concluded that life sciences represents a major strategic frontier for AI. That consensus is not coincidental. It reflects the convergence of three preconditions that have now been met simultaneously: biological datasets large enough to train meaningful models, AI architectures powerful enough to extract signal from those datasets, and a clinical and regulatory environment in which AI is increasingly being used across the drug development lifecycle, including in submissions supporting regulatory decision-making⁶. The competitive dynamics between these platforms will be significant and are still being determined.

Our Genomics ETF’s Exposure to the Cycle

The investment opportunity created by the AI platform race of 2026 extends well beyond identifying which single technology company will emerge as the dominant life sciences AI winner. That question - Google versus OpenAI or Anthropic versus Amazon - is genuinely open and may remain so for years. The more structurally interesting observation for investors is that every platform competing in this race draws on the same underlying ecosystem, and the VanEck Genomics and Healthcare Innovators UCITS ETF (Ticker: CURE) is built to capture multiple parts of that ecosystem, while simultaneously benefiting from the AI capabilities those platforms deploy.

The ETF tracks the MVIS® Global Future Healthcare ESG Index, which covers a broader range of healthcare innovation themes, including companies that derive revenues from genetics-based therapies, technology platforms that enable them, and related laboratory equipment and services⁷. That three-layer construction maps onto crucial parts of the broader infrastructure supporting AI-enabled life sciences, and onto areas where AI may deliver measurable commercial benefits.

Notably, the relationship between AI and genetics-based healthcare is not a linear value chain. It is a self-reinforcing cycle.

Source: VanEck internal data.

1. The cycle begins with sequencing. As the cost of reading DNA continues to fall⁸, driven by advances such as Illumina’s instrument improvements⁹, 10x Genomics’ single-cell technologies¹⁰ and Oxford Nanopore’s long-read capabilities¹¹, more biological data is generated across more patients, tissue types and disease contexts. That expanding dataset is the raw material on which AI platforms are trained. Claude Science, GPT-Rosalind, Gemini for Science and BioNeMo are building their life sciences capabilities on exactly this expanding biological dataset, including sequencing output, clinical records and molecular profiles.
2. Better-trained AI models then improve the diagnostic layer. Companies like Natera, Guardant Health, QIAGEN and Adaptive Biotechnologies are using AI-enabled analytics and sequencing data to extract more clinically meaningful signals from biological information, improving early cancer detection¹², treatment selection¹³, and patient stratification¹⁴.
3. These diagnostic insights feed directly into therapy development: knowing which patients carry a given alteration, and in some cases how abundant it is and in which tissue it appears, can help inform the design of specific gene-based treatments or personalized vaccines. From there, AI might accelerate every stage of the pipeline - from target identification and molecule design through to clinical trial optimisation and regulatory submission. For example, Moderna says its Scientific Intelligence Engine combines data, AI and machine learning, automation and robotics to accelerate discovery across its mRNA platform¹⁵, while Alnylam’s collaboration with Inceptive uses generative AI to speed up the discovery of new RNA-based therapies¹⁶.
4. And then the cycle closes. Each therapy that reaches patients generates new clinical data: treatment response, resistance mechanisms, biomarker dynamics, long-term outcomes. That data flows back into the sequencing and diagnostic layer, training better models, improving future therapies, and justifying investment in the next generation of sequencing instruments. In that way, the cycle reinforces itself, and the flywheel accelerates with each revolution.

Eventually, what the VanEck Genomics ETF provides, in aggregate, is not a bet on any single AI platform, any single therapy modality, or any single clinical outcome. Instead, it offers diversified exposure across the value chain, where AI both depends on these companies to function and may also create commercial benefits for them in the process - a two-directional relationship that may allow the portfolio to participate in value creation across different parts of the cycle.

The Scale of the Opportunity

Based on current third-party estimates¹⁷, the market intersection of AI and genetics-based healthcare could expand significantly, although these are projections and actual growth may differ materially. The global AI in genomics market was valued at $1.2 billion in 2025 and is projected to reach $18.8 billion by 2033, growing at an estimated compound annual growth rate (CAGR) of 40.3%.

Source: grandviewresearch.com/industry-analysis/ai-genomics-market-report. Investing is subject to risk, including the possible loss of principal

That growth rate reflects the same structural forces the flywheel describes: expanding genomic datasets, accelerating AI model development, and rising demand for precision therapeutics. However, future growth will depend on factors including clinical success, regulation, data availability, adoption by healthcare providers and the ability of companies to convert scientific advances into commercially viable products.

More broadly, the global precision medicine market was valued at $116.6 billion in 2025 and is projected to reach $405.1 billion by 2033 at a CAGR of 17.0%¹⁸ according to third-party estimates. Together, these figures may suggest that AI is not simply accelerating an existing market - it is creating a distinct and faster-growing layer on top of it. Nevertheless, these projections are not guaranteed and may not materialize as anticipated, as external factors could materially influence the outcome.

At the same time, the regulatory environment is evolving in ways that may support this trajectory. Historically, one of the biggest obstacles to bringing gene therapies to patients has been the regulatory burden: developers were required to repeat costly foundational studies from scratch for each new product, even when the underlying science was well established. The FDA moved decisively to address this in 2026, issuing a connected series of guidances¹⁹ designed to streamline the development of gene-editing and RNA-based therapies. These guidances allow developers to build on existing scientific knowledge, use data across related programmes and avoid unnecessary repeat testing without compromising safety standards. Separately, the FDA’s draft guidance on artificial intelligence²⁰ provides a risk-based credibility assessment framework for AI-generated information or data intended to support regulatory decision-making on safety, effectiveness or quality for drugs and biological products, reinforcing a clearer path for AI-enabled development across the life sciences lifecycle.

Looking Ahead

Artificial intelligence is unlikely to transform genetics-based healthcare overnight, and not every AI initiative will translate into commercial success. The history of technology adoption in life sciences is littered with tools that accelerated research without proportionally accelerating approvals - and the clinical, regulatory and manufacturing bottlenecks that have always defined this sector do not disappear because the discovery phase gets faster. Investors should be careful not to conflate research productivity gains with near-term revenue.

That said, the direction remains uncertain, although several structural conditions that could support further development continue to strengthen. Sequencing costs continue to fall. Biological datasets continue to expand. The regulatory environment is evolving. And four of the world's largest AI developers have committed dedicated platforms and significant capital to the life sciences domain within a three-month window - a convergence that reflects growing interest in the potential applications of AI across life sciences.