Will bioinformatics scientists be replaced by AI?
Updated
Bioinformatics scientists score 57 of 100 for AI exposure. The largest share of their task time, 77%, is work AI speeds up while they stay in charge. AI can take over 15% outright, and 8% stays with people.
- 15% AI can do it
- 77% AI helps
- 8% Needs a person
High exposure
Higher than 87% of the 923 occupations we scored.
Task by task
We graded 20 tasks, listed here from most to least exposed. AI can do 3 of them today, speeds up 15 and leaves 2 to people.
| Task | AI exposure |
|---|---|
| Prepare summary statistics of information regarding human genomes. Summary statistics over genome data are routine scripted computation. | 70% AI can do it |
| Compile data for use in activities, such as gene expression profiling, genome annotation, or structural bioinformatics. Scripted compilation of expression and annotation data is largely automatable. | 62% AI can do it |
| Manipulate publicly accessible, commercial, or proprietary genomic, proteomic, or post-genomic databases. AI writes database queries and retrieval scripts; person checks results. | 62% AI can do it |
| Develop new software applications or customize existing applications to meet specific scientific project needs. AI coding assistants write and adapt most application code; scientist directs design. | 58% AI helps |
| Test new and updated bioinformatics tools and software. AI generates test cases and runs checks; tester judges usability and results. | 58% AI helps |
| Create or modify web-based bioinformatics tools. AI generates web tool code; developer handles architecture and deployment. | 55% AI helps |
| Communicate research results through conference presentations, scientific publications, or project reports. AI drafts text, slides and summaries; researcher is responsible for findings and framing. | 50% AI helps |
| Develop data models and databases. AI helps draft schemas and queries; designer makes structural choices. | 50% AI helps |
| Design and apply bioinformatics algorithms including unsupervised and supervised machine learning, dynamic programming, or graphic algorithms. AI writes algorithm code and suggests methods; expert selects and validates. | 50% AI helps |
| Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination. AI writes statistical tools and code; scientist validates biological meaning. | 50% AI helps |
Show the other 10 tasks
| Task | AI exposure |
|---|---|
| Improve user interfaces to bioinformatics software and databases. AI produces interface code and mockups; user testing remains human. | 50% AI helps |
| Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes. Pipelines and AI speed analysis, but interpretation needs expert review. | 45% AI helps |
| Keep abreast of new biochemistries, instrumentation, or software by reading scientific literature and attending professional conferences. AI summarizes literature quickly; attending conferences remains human. | 45% AI helps |
| Create novel computational approaches and analytical tools as required by research goals. AI speeds prototyping, but novel method design stays research-driven. | 35% AI helps |
| Recommend new systems and processes to improve operations. AI can suggest process ideas; recommendations need organizational judgement. | 30% AI helps |
| Collaborate with software developers in the development and modification of commercial bioinformatics software. Collaboration is mainly human; AI helps with code review and drafts. | 30% AI helps |
| Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies. Live consultation needs human judgement; AI only helps prepare. | 25% AI helps |
| Instruct others in the selection and use of bioinformatics tools. AI can draft training materials; hands-on instruction stays human. | 25% AI helps |
| Confer with departments, such as marketing, business development, or operations, to coordinate product development or improvement. Cross-department coordination relies on live meetings and relationships. | 18% Needs a person |
| Direct the work of technicians and information technology staff applying bioinformatics tools or applications in areas such as proteomics, transcriptomics, metabolomics, or clinical bioinformatics. Managing technicians and IT staff is interpersonal and accountable work. | 15% Needs a person |
How bioinformatics scientists compare
All occupations (923)
Life, physical and social science (60)
Pay and outlook in the US
- Median pay
- $98,920 a year, May 2025
- People employed
- 13,962 in the US, May 2025
- Projected change in jobs
- +4.7% from 2024 to 2034
Skills to build
- Write and verify SQL queries against genomic databases
- Build genome analysis pipelines in Python and R
- Run and check summary statistics on human genome data in R
- Develop and maintain C++ or Java tools for proteomics software
- Consult with researchers on computational strategy and study design
- Administer UNIX and Linux servers for bioinformatics workloads
Safer roles nearby
5 related occupations with lower scores.
Questions about bioinformatics scientists and AI
Will AI replace bioinformatics scientists?
Partly. About 5 in 10 working hours go to tasks AI can already do, mostly summary statistics on human genomes and compiling expression and annotation data. Consulting researchers, directing technicians and recommending systems stay with people because they need live judgement and accountability.
Which bioinformatics tasks is AI taking over?
Summary statistics on human genomes carry an exposure score of 70, the highest in this job, and AI can script them as routine computation. Compiling data for gene expression profiling and genome annotation scores 62 and is largely automatable. Manipulating genomic and proteomic databases also scores 62, since AI writes the queries and the scientist checks the results.
Which bioinformatics tasks stay human?
Directing technicians and IT staff scores 15, the lowest exposure in this job, because managing people is interpersonal and accountable work. Cross-department coordination scores 18 and relies on live meetings and relationships. Consulting with researchers scores 25 because live consultation needs human judgement.
How can bioinformatics scientists stay valuable?
Take responsibility for the checking and consulting work around AI output. Verify AI-written SQL and Python retrieval scripts against source databases, and review generated C++ or Java code before release. Lead consultations with researchers on computational strategy, and sign off on which pipeline results go into a study.
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