Will AI replace bioinformatics?
Updated
Bioinformatics technicians score 66 of 100 for AI exposure. The largest share of their task time, 64%, is work AI speeds up while they stay in charge. AI can take over 26% outright, and 10% stays with people.
- 26% AI can do it
- 64% AI helps
- 10% Needs a person
High exposure
Higher than 97% of the 923 occupations we scored.
Task by task
We graded 19 tasks, listed here from most to least exposed. AI can do 4 of them today, speeds up 13 and leaves 2 to people.
| Task | AI exposure |
|---|---|
| Write computer programs or scripts to be used in querying databases. Code assistants write SQL and query scripts reliably with review. | 78% AI can do it |
| Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases. AI agents can query and retrieve records from standard APIs. | 70% AI can do it |
| Create data management or error-checking procedures and user manuals. AI drafts SOPs, error checks and manuals; staff review them. | 70% AI can do it |
| Document all database changes, modifications, or problems. AI drafts change logs from commits; person confirms completeness. | 68% AI can do it |
| Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques. AI writes analysis code; scientist checks results and interpretation. | 62% AI helps |
| Design or implement web-based tools for querying large-scale biological databases. AI drafts web tool code; humans test, secure and deploy. | 60% AI helps |
| Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation. AI can generate database and app code; staff validate outputs. | 58% AI helps |
| Package bioinformatics data for submission to public repositories. AI formats metadata and checks files; submitter confirms accuracy. | 58% AI helps |
| Extend existing software programs, web-based interactive tools, or database queries as sequence management and analysis needs evolve. AI drafts code changes; engineers still take responsibility for design decisions. | 55% AI helps |
| Participate in the preparation of reports or scientific publications. AI drafts sections and edits text; authors take responsibility for claims. | 55% AI helps |
Show the other 9 tasks
| Task | AI exposure |
|---|---|
| Develop or apply data mining and machine learning algorithms. AI writes ML pipeline code; experts choose and validate models. | 55% AI helps |
| Test new or updated software or tools and provide feedback to developers. AI can write tests and summarize bugs; testers judge usability. | 55% AI helps |
| Conduct quality analyses of data inputs and resulting analyses or predictions. AI can script checks; judging data quality needs expert review. | 50% AI helps |
| Perform routine system administrative functions, such as troubleshooting, back-ups, or upgrades. AI helps read logs; backups and upgrades need system access. | 40% AI helps |
| Maintain awareness of new and emerging computational methods and technologies. AI summarizes new papers, but staying current needs human reading. | 35% AI helps |
| Monitor database performance and perform any necessary maintenance, upgrades, or repairs. Monitoring tools help; live repairs and upgrades need engineers. | 35% AI helps |
| Train bioinformatics staff or researchers in the use of databases. Training materials are easy to draft; live teaching stays human. | 30% AI helps |
| Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities. Requirements talks with researchers and IT need human judgement. | 15% Needs a person |
| Confer with database users about project timelines and changes. Timeline and scope talks depend on live human negotiation. | 15% Needs a person |
How bioinformatics technicians compare
All occupations (923)
Computer and mathematical (36)
Pay and outlook in the US
- Median pay
- $81,490 a year, May 2025
- People employed
- 3,720 in the US, May 2025
- Projected change in jobs
- +7.1% from 2024 to 2034
Skills to build
- Write and review SQL queries in Microsoft SQL Server
- Validate analysis scripts written in Python and Perl
- Administer Linux servers for backups, upgrades and log review
- Confer with researchers to set data requirements and timelines
- Train researchers on database use in live sessions
- Review AI-drafted SOPs and error-check procedures before release
Safer roles nearby
5 related occupations with lower scores.
Questions about bioinformatics technicians and AI
Will bioinformatics be replaced by AI?
Partly. About 5 in 10 working hours go to tasks AI can already do, mostly writing query scripts and retrieving records from genomic and mutation databases. Requirements talks with researchers, timeline negotiations and live system repairs stay with people, along with checking results before a scientist relies on them.
Which tasks will AI take over for bioinformatics technicians?
Writing SQL and query scripts is the most exposed task, scoring 78, because code assistants produce working queries that a person only needs to review. Retrieving records from structural, mutation and genomic databases scores 70, since AI agents can call standard APIs and pull the same fields every time. Drafting SOPs, error checks and change logs follows the same pattern, with staff reviewing the draft.
Which tasks stay human for bioinformatics technicians?
Requirements talks and timeline negotiations score lowest, at 15 each. Conferring with researchers, clinicians or IT staff on data needs depends on live judgement about what a scientist actually needs. Negotiating project timelines and scope changes with database users also depends on live conversation. Live teaching stays human too, though AI can draft the training materials.
How can bioinformatics technicians stay valuable?
Take responsibility for the review step on AI output, check every SQL query and Python script against the database schema before it runs. Keep hands-on Linux administration for backups, upgrades and log troubleshooting, since those tasks need system access. Learn to validate Perl and Python analysis results against known gene annotations so the scientist can trust the interpretation.
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