Will AI replace Bioinformatics Scientists?
AI can do 98% of the work Bioinformatics Scientists do at least twice as fast. People already use it for 9 of their 20 tasks, while 10 more are ready but barely used so far. 1 still needs a person.
AI is already doing this9 · 45% of time
People already use AI for these tasks in real work, based on Anthropic's analysis of how Claude is used.
- Develop new software applications or customize existing applications to meet specific scientific project needs.Mostly automated13% of time
- Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.Mostly assisted9% of time
- Design and apply bioinformatics algorithms including unsupervised and supervised machine learning, dynamic programming, or graphic algorithms.Mostly automated6% of time
- Manipulate publicly accessible, commercial, or proprietary genomic, proteomic, or post-genomic databases.Mostly automated6% of time
- Develop data models and databases.Mostly automated5% of time
- Test new and updated bioinformatics tools and software.Mostly automated2% of time
- Instruct others in the selection and use of bioinformatics tools.Mostly assisted2% of time
- Recommend new systems and processes to improve operations.Mostly automated1% of time
- Create or modify web-based bioinformatics tools.Mostly automated1% of time
AI could do this next10 · 52% of time
AI can speed these up by at least half, but real use has not caught up yet. These are next in line.
- Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.15% of time
- Communicate research results through conference presentations, scientific publications, or project reports.9% of time
- Create novel computational approaches and analytical tools as required by research goals.7% of time
- Keep abreast of new biochemistries, instrumentation, or software by reading scientific literature and attending professional conferences.6% of time
- Compile data for use in activities, such as gene expression profiling, genome annotation, or structural bioinformatics.6% of time
- Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination.3% of time
- Prepare summary statistics of information regarding human genomes.2% of time
- Confer with departments, such as marketing, business development, or operations, to coordinate product development or improvement.1% of time
- Collaborate with software developers in the development and modification of commercial bioinformatics software.1% of time
- Improve user interfaces to bioinformatics software and databases.1% of time
Still needs a person1 · 2% of time
No real AI use, no proven AI speedup and no robot that can do it yet. This is the part of the job to build on.
- Direct the work of technicians and information technology staff applying bioinformatics tools or applications in areas such as proteomics, transcriptomics, metabolomics, or clinical bioinformatics.2% of time
Your week is not the average
Describe what you actually do and get a personal report: your own tasks, what AI can already do, what stays yours and a 90 day plan.
What this means
AI can already do almost all of this work at least twice as fast. People in this job already use it heavily. Expect the role to shift toward deciding what to do, checking AI's work and owning the result.
What to do next
- Do the 9 tasks AI is already handling with AI yourself. Your peers already are, and it is quickly becoming the baseline.
- Get ahead on the 10 tasks AI could do next. These move as tools improve, so learning them early pays off.
- Build on the 1 task that stay human. This is where your judgment, skill and relationships matter most.
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