Report Details
Market Research Report · Forecast 2026–2035
Global Artificial Intelligence in Genomics Market Size, Share, Trends & Forecast to 2035
The Global Artificial Intelligence in Genomics Market was valued at approximately USD 1.49 billion in 2025 and is anticipated to reach around USD 34.33 billion by 2035, registering a CAGR of approximately 36.85% during 2025–2035. Genomic foundation models, GPU-accelerated sequencing analysis, generative AI and precision medicine are driving this growth.
Last updated: · Published by Global Data Route Analytics Research Team · Base year: 2025 · Forecast: 2026–2035
What is the AI in genomics market size?
The global artificial intelligence in genomics market size was USD 1.49 billion in 2025 and is projected to reach USD 34.33 billion by 2035, growing at a CAGR of 36.85%. Growth is driven by AI-powered variant interpretation, genomic foundation models, drug discovery and precision medicine. Key companies include Microsoft, NVIDIA, IBM, Alphabet, Illumina, Thermo Fisher Scientific and QIAGEN.
Table of Contents
- Report Overview
- Report Scope
- Market Size & Forecast
- How AI Transforms Genomics
- Drivers, Restraints, Opportunities, Challenges
- Technology Milestones
- SWOT Analysis
- PESTEL Analysis
- Porter’s Five Forces
- Segmentation Analysis
- Regional Analysis
- Competitive Landscape
- Company Profiles
- Recent Developments
- Key Players List
- FAQs
Global Artificial Intelligence in Genomics Market – Report Overview
Global Artificial Intelligence in Genomics Market was valued at approximately USD 1.49 billion in 2025 and is anticipated to reach around USD 34.33 billion by 2035, registering a CAGR of approximately 36.85% during 2025–2035.
The study takes 2025 as the base year for estimating market segments, and the forecast period is 2026 to 2035. The report analyzes and forecasts each market segment in terms of value (USD Million).
The AI in genomics market covers the use of artificial intelligence, machine learning, deep learning, natural language processing and related computational technologies to analyze, interpret and manage genomic and biological data. AI-based genomics solutions can process the large, complex datasets generated by DNA sequencing, genomic profiling, transcriptomics and other molecular techniques to find patterns, relationships and clinically or biologically relevant signals. These technologies are increasingly used in drug discovery, precision medicine, genetic disease research, oncology, biomarker identification, population genomics and agricultural genomics.
Faster Analysis
AI speeds up variant identification and pattern recognition across very large sequencing datasets.
Personalized Care
Combining genomic, clinical and molecular data helps tailor diagnosis and treatment.
Drug Discovery
AI helps prioritize disease genes and therapeutic targets, and map biological pathways.
Foundation Models
DNA and RNA foundation models, multimodal AI and HPC are opening new frontiers in biology.
The market is evolving through advances in foundation models, deep learning, multimodal analysis, cloud computing, high-performance computing and increasingly sophisticated genomic databases. AI tools are being built into next-generation sequencing workflows, clinical genomics platforms, bioinformatics pipelines and drug-discovery systems. At the same time, attention to model interpretability, data privacy, genomic data security, validation, regulatory requirements and bias management is shaping how these solutions are developed and adopted.
Key Takeaways
- The market is set to grow about 23-fold, from USD 1.49 billion (2025) to USD 34.33 billion (2035).
- Software (variant interpretation, bioinformatics platforms, clinical decision support) is the core value pool, while GPU and HPC hardware power large-scale analysis.
- Generative AI and genomic foundation models such as Evo 2, CodonFM and IBM’s BMFM-DNA are the defining technology shift.
- North America leads adoption, while Asia-Pacific is the fastest-growing region.
AI in Genomics Market Report Scope
| Report Attribute | Details |
| Market Size (2025) | USD 1.49 Billion |
| Market Size (2035) | USD 34.33 Billion |
| CAGR (2025–2035) | 36.85% |
| Base Year | 2025 |
| Forecast Period | 2026–2035 |
| Units | Value (USD Million) |
| Segments Covered | Component, AI Technology, Genomic Functionality, Application, Genomic Data Type, Deployment Model, Region |
| Regions Covered | North America, Europe, Asia-Pacific, Middle East & Africa, Latin America (23 countries) |
| Companies Profiled | 100 companies, including Microsoft, NVIDIA, IBM, Alphabet, Illumina, Thermo Fisher Scientific, QIAGEN, SOPHiA GENETICS, Tempus AI, DNAnexus |
| Analysis Included | Market sizing, DROC, SWOT, PESTEL, Porter’s Five Forces, regulatory scenario, investment scenario, company ranking & market share, company profiles |
AI in Genomics Market Size & Forecast (2025–2035)
Compounding at 36.85% a year, market value roughly doubles every 2.3 years. Growth speeds up in the second half of the forecast as genomic foundation models, clinical-grade AI interpretation and AI-as-a-Service platforms reach scale.
Figure 1: Global AI in Genomics Market Size, 2025–2035 (USD Billion). Intermediate years are interpolated.
How AI Transforms the Genomics Workflow
Figure 2: AI-enabled genomics value chain, from raw sequence to clinical and research insight
AI in Genomics Market Dynamics
Drivers
- Falling sequencing costs producing massive genomic datasets
- Rising demand for precision medicine and companion diagnostics
- Pharma adoption of AI for target identification and biomarker discovery
- GPU, cloud and HPC advances making large-scale analysis affordable
Restraints
- Genomic data privacy, consent and security concerns
- High cost of AI infrastructure and skilled bioinformaticians
- Limited interpretability (the “black box” problem) of deep-learning models
- Fragmented, non-standardized genomic and clinical data
Opportunities
- Genomic and RNA foundation models for variant-effect prediction
- Rare-disease diagnosis and reanalysis of unsolved cases
- AI-as-a-Service and cloud genomics platforms for smaller labs
- Agricultural and livestock genomics for food security
Challenges
- Clinical validation and regulatory approval of AI tools
- Bias from under-representation of diverse populations in datasets
- Cross-border rules on genomic data transfer
- Integrating AI into established clinical workflows
AI in Genomics Technology Milestones
Human Genome Project completed
Deep-learning variant calling (DeepVariant) open-sourced
SOPHiA GENETICS–Microsoft multimodal partnership
Evo 2 genomic foundation model on NVIDIA BioNeMo · IBM BMFM-DNA released
NVIDIA CodonFM RNA model · Sheba–Mount Sinai genomic FM collaboration
NVIDIA Israel AI group joins genomic foundation model work
Figure 3: Key milestones in the evolution of AI-driven genomics
SWOT Analysis of the AI in Genomics Market
PESTEL Analysis of the AI in Genomics Market
Genomics
Market
Figure 4: PESTEL framework
1. Political Landscape
- National genome programs and precision-medicine initiatives are funding large-scale sequencing.
- Government AI strategies treat health and life sciences as priority sectors.
2. Economic Landscape
- Strong venture and pharma investment in AI-driven drug discovery.
- Lower sequencing and cloud-compute costs are widening access.
3. Social Landscape
- Growing public acceptance of genetic testing and personalized medicine.
- Concerns about genetic privacy and discrimination shape adoption.
4. Technological Landscape
- Genomic foundation models, multimodal AI and GPU acceleration are advancing quickly.
- Long-read and single-cell sequencing are producing richer data.
5. Legal Landscape
- GDPR, HIPAA and genomic-data laws govern consent, storage and sharing.
- Regulators are defining frameworks for AI-based diagnostics.
6. Environmental Landscape
- Energy use of large-scale AI training and data centers is under scrutiny.
- AI-driven crop and livestock genomics supports climate-resilient agriculture.
Porter’s Five Forces Analysis
GPU and cloud infrastructure is concentrated among a few providers, such as NVIDIA and the hyperscalers.
Pharma companies and large health systems negotiate hard; smaller labs depend on platforms.
Conventional bioinformatics pipelines remain an alternative but are falling behind on scale.
Open-source models lower barriers, but data access and clinical validation remain hurdles.
Big tech, sequencing leaders and 90+ AI-native start-ups compete intensely.
AI in Genomics Market Segmentation Analysis
The report segments the global AI in genomics market by Component, AI Technology, Genomic Functionality, Application, Genomic Data Type, Deployment Model and Region.
AI in Genomics Market: Regional Analysis
Figure 5: Indicative regional share, 2025
North America
The largest market, home to NVIDIA, Microsoft, IBM, Illumina and most AI-genomics start-ups, with strong NIH and pharma investment.
United StatesCanadaMexicoEurope
Driven by Genomics England, the European Health Data Space and a strong AI-biotech scene (SOPHiA GENETICS, Lifebit, Congenica).
United KingdomGermanyFranceSpainItalyRest of EuropeAsia-Pacific
The fastest-growing region, supported by BGI and MGI, national genome programs in China, India and Japan, and rising pharma R&D.
ChinaIndiaJapanAustraliaSouth KoreaRest of Asia PacificMiddle East & Africa
Population-genome programs in Saudi Arabia, the UAE and Qatar, plus Israeli AI genomics research.
Saudi ArabiaSouth AfricaRest of MEALatin America
An emerging market led by Brazil’s genomic medicine initiatives and research institutes.
BrazilArgentinaRest of Latin AmericaAI in Genomics Market Competitive Landscape
The market is highly fragmented. Big technology providers supply compute, cloud and foundation models; sequencing leaders embed AI in instruments and pipelines; and a long tail of AI-native start-ups focus on interpretation, diagnostics and drug discovery. Players compete through partnerships, model launches, acquisitions and platform expansion.
| Company | Compute & Cloud | Foundation Models | Sequencing | Clinical Interpretation | Drug Discovery |
| Microsoft | ●●● | ●● | – | ●● | ●● |
| NVIDIA | ●●● | ●●● | ● | ● | ●● |
| IBM | ●● | ●●● | – | ● | ●● |
| Alphabet | ●●● | ●●● | – | ● | ●● |
| Illumina | ● | ●● | ●●● | ●●● | ● |
| Thermo Fisher | ● | ● | ●●● | ●● | ●● |
| QIAGEN | ● | ● | ●● | ●●● | ● |
| SOPHiA GENETICS | ● | ● | – | ●●● | ● |
| Tempus AI | ● | ●● | ● | ●●● | ●● |
| DNAnexus | ●● | ● | – | ●● | ● |
| Deep Genomics | – | ●● | – | ● | ●●● |
| BenevolentAI | – | ●● | – | – | ●●● |
Figure 6: Qualitative capability matrix (●●● strong · ●● moderate · ● emerging · – limited)
Company Profiles: Key Players in the AI in Genomics Market
1. Microsoft Corporation
Revenue (USD Million)
Microsoft takes part in the AI in genomics market through Microsoft Genomics, medical, health & genomics research and Azure cloud infrastructure that supports partner-developed AI applications. Microsoft Research’s Evidence Aggregator (EvAgg) uses generative AI to extract and synthesize scientific evidence on human genes, variants and clinical features for rare-disease investigation. In an evaluation it cut analyst review time by 34%. Microsoft has also introduced Quine, an AI research system designed for the complexity of biology.
Recent Developments
- September 2025: Published research on AI-assisted rare disease diagnosis with Drexel University and the Broad Institute, featuring a generative AI assistant for genetic professionals.
- May 2024: SOPHiA GENETICS, Microsoft and NVIDIA collaborated on a whole-genome sequencing application combining SOPHiA DDM on Azure with NVIDIA Parabricks.
- Partnered with 1910 Genetics to enable AI-driven drug discovery, as part of its broader AI healthcare solutions.
- November 2022: SOPHiA GENETICS signed a multiyear strategic partnership with Microsoft to accelerate multimodal health data analysis on Azure.
2. NVIDIA Corporation
Revenue (USD Million)
NVIDIA supplies the GPUs, accelerated computing and AI software behind much of modern genomics analysis. Its Parabricks suite accelerates sequencing analysis with deep-learning variant callers (DeepVariant, DeepSomatic). Its genomics-specific tools include NVScoreVariants (developed with the Broad Institute), NIM for Evo 2 for genomic foundation-model deployment, and CodonFM Encodon for codon-level RNA analysis. More is available through NVIDIA’s healthcare & life sciences platform, genomics deep-learning resources, life sciences and biology resources and its article on how AI is transforming genomics.
Recent Developments
- September 2026: NVIDIA’s Israeli Applied AI group joined genomic foundation model development with Sheba Medical Center and Mount Sinai.
- March 2026: Roche announcement on advancing AI-driven healthcare and life-science innovation.
- November 2025: Sheba Medical Center and Mount Sinai launched a three-year collaboration with NVIDIA to build an LLM-based Genomic Foundation Model.
- October 2025: Introduced CodonFM, an open RNA foundation model for predicting the effects of synonymous and missense variants.
- February 2025: Made the Evo 2 model available through BioNeMo, including an NVIDIA NIM microservice.
- Partnerships with Illumina on AI-based genomics, QIAGEN and Microsoft for healthcare and life sciences (March 2024).
3. International Business Machines Corporation (IBM)
Revenue (USD Million)
IBM’s AI genomics work centers on IBM Research and open-source model development. Its BMFM-DNA family, comprising reference-genome (BMFM-DNA-REF) and variant-aware (BMFM-DNA-SNP) DNA foundation models released under Apache 2.0 in June 2025, generates sequence representations for genomic prediction tasks such as promoter detection. IBM also runs AI for single-cell research, studies agentic AI systems that harness multi-omics foundation models, and offers enterprise AI solutions.
Recent Developments
- December 2025: IBM-affiliated researchers presented a NeurIPS workshop paper using biomedical foundation models and patient SNPs to predict patient-specific drug response.
- June 2025: Released the open-source BMFM-DNA-REF and BMFM-DNA-SNP foundation models.
- IBM Think analysis: how IBM and DeepMind AI models are pushing DNA research toward a “GPT era”.
- Precision medicine with IBM and Amazon Omics, plus wider industry coverage via Innovations Report.
AI in Genomics Industry Recent Developments
Nov 2022
SOPHiA GENETICS–Microsoft multiyear strategic partnership on Azure
Mar 2024
Microsoft and NVIDIA collaborate on healthcare and life sciences AI
May 2024
SOPHiA GENETICS, Microsoft and NVIDIA build a whole-genome sequencing application
Feb 2025
Evo 2 genomic foundation model launched on NVIDIA BioNeMo
Jun 2025
IBM releases the BMFM-DNA open-source DNA foundation models
Sep 2025
Microsoft Research publishes its generative AI rare-disease assistant
Oct–Nov 2025
NVIDIA CodonFM launch · Sheba–Mount Sinai–NVIDIA Genomic Foundation Model
Dec 2025
IBM NeurIPS paper on patient-specific drug-response prediction
Sep 2026
NVIDIA Israel AI group expands genomic foundation model work
Key Players in the Global AI in Genomics Market (100 Companies)
The report profiles 100 companies across big tech, sequencing and diagnostics, AI-native genomics and AI drug discovery. The top 20 are highlighted.
AI in Genomics Market FAQs
What is the size of the global AI in genomics market?
The global artificial intelligence in genomics market was valued at approximately USD 1.49 billion in 2025 and is anticipated to reach around USD 34.33 billion by 2035.
What is the CAGR of the AI in genomics market?
The market is projected to grow at a CAGR of approximately 36.85% during 2025–2035, making it one of the fastest-growing segments of healthcare AI.
What is AI in genomics?
AI in genomics is the use of machine learning, deep learning, NLP and generative AI to analyze, interpret and manage genomic and biological data, from DNA sequencing and variant calling to drug discovery and precision medicine.
Who are the key players in the AI in genomics market?
Key players include Microsoft, NVIDIA, IBM, Alphabet, Illumina, Thermo Fisher Scientific, QIAGEN, SOPHiA GENETICS, Deep Genomics, Tempus AI, DNAnexus and BenevolentAI, among the 100 companies profiled in the report.
Which region leads the AI in genomics market?
North America leads the market, supported by major technology companies, research institutes and pharma investment, while Asia-Pacific is the fastest-growing region.
What are genomic foundation models?
Genomic foundation models are large AI models trained on DNA or RNA sequences, such as Evo 2, NVIDIA CodonFM and IBM BMFM-DNA. They can predict the effects of mutations, interpret genetic variation and support drug discovery.
What are the main applications of AI in genomics?
Main applications include drug discovery and development, precision medicine, diagnostics (including cancer and rare diseases), disease prediction and risk assessment, agricultural and animal genomics, and research and discovery.
What are the challenges facing AI in genomics?
Key challenges include genomic data privacy and security, model interpretability, clinical validation, regulatory approval, dataset bias and the high cost of AI infrastructure.
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Note: Segment and regional shares in the charts are indicative estimates for illustration; detailed values are available in the full report. Company revenues are consolidated company-wide figures and do not represent AI-in-genomics revenue.
Report Structure
Global Artificial Intelligence in Genomics Market Report – Table of Contents
Report at a Glance
Segmentation Overview
AI in Genomics Market Segmentation and Key Players
The global artificial intelligence in genomics market is analyzed by component, AI technology, genomic functionality, application, genomic data type, deployment model, region and country, along with 100 profiled companies.
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1 Component2 AI Technology3 Genomic Functionality4 Application5 Genomic Data Type6 Deployment Model7 Region & Country8 Key PlayersDownload Sample Request Form
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Frequently Asked Questions
What is driving the growth of the AI in genomics market?
Growth is driven by falling sequencing costs that produce massive genomic datasets, rising demand for precision medicine, pharmaceutical adoption of AI for target and biomarker discovery, and advances in GPU, cloud and high-performance computing.
Which component segment leads the AI in genomics market?
Software leads the market, including genomic data analysis, variant interpretation, clinical decision support and bioinformatics platforms. Hardware such as GPUs and AI accelerators, and services such as AI model development, support its adoption.
How is generative AI used in genomics?
Generative AI and genomic foundation models such as Evo 2 and CodonFM are used to interpret DNA and RNA sequences, predict the effects of mutations, design genetic sequences and summarize scientific evidence on genes and variants for faster diagnosis.
How does AI help in rare disease diagnosis?
AI tools can scan scientific literature and genomic data to find evidence linking genes and variants to disease. For example, Microsoft Research's Evidence Aggregator reduced analyst review time by 34% in rare-disease case evaluations.
What is the base year and forecast period of the AI in genomics market report?
The report uses 2025 as the base year and covers the forecast period from 2026 to 2035, with market values presented in USD million.