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AI Job Checker

Biological Scientists All Other

Science

AI Impact Likelihood

AI impact likelihood: 63% - High Risk
63/100
High Risk

The SOC 19-1029.00 'Biological Scientists, All Other' category spans four meaningfully distinct sub-occupations — Bioinformatics Scientists (19-1029.01), Molecular and Cellular Biologists (19-1029.02), Geneticists (19-1029.03), and Biologists (19-1029.04) — with dramatically different AI exposure profiles. The bioinformatics sub-specialty is in acute danger: its defining tasks (large-scale sequence analysis, algorithm development, database pipeline construction, protein structure prediction) are being directly and comprehensively automated by AI systems that outperform expert humans on benchmark tasks. Bioinformatics Scientists represent the clearest case of PhD-level scientific work being automated, not augmented, within a 2-5 year horizon. Molecular and cellular biologists occupy a more complex threat landscape. While wet lab execution remains temporarily protected by the physical requirements of bench work, AI is rapidly automating the interpretive, planning, and writing layers of their work. AI-assisted grant writing, automated literature synthesis, AI-designed experimental protocols, and robotic liquid handling systems are collectively eroding the time-intensive components that justified their compensation.

AI foundation models trained on biological sequence data (AlphaFold 3, ESM3, Evo) have already automated the highest-value, highest-paid core tasks of bioinformatics and computational molecular biology, compressing what once required PhD-level expertise into API calls — and this displacement is spreading upstream into hypothesis generation and experimental design faster than the field acknowledges.

The Verdict

Changes First

Computational and bioinformatics tasks — genomic data analysis, statistical modeling, literature synthesis, and algorithmic pipeline development — are already being displaced by AI foundation models (AlphaFold, ESM2, Evo) and automated analysis platforms at an accelerating rate.

Stays Human

Hands-on wet laboratory experimentation, field specimen collection, interdisciplinary experimental design requiring physical intuition, and supervisory judgment over junior researchers and technicians remain the most defensible human territory for now.

Next Move

Biological scientists must urgently pivot toward AI-augmented experimental roles — learning to direct, validate, and interpret AI-generated hypotheses and genomic models rather than performing the computational steps themselves; those who remain purely computational are facing direct substitution within 2-3 years.

Most Exposed Tasks

TaskWeightAI LikelihoodContribution
Large-scale biological data analysis (genomics, proteomics, transcriptomics)20%85%17
Scientific literature review and hypothesis generation12%78%9.4
Scientific writing (manuscripts, grant proposals, technical reports)12%68%8.2

Contribution = weight × automation likelihood. Full task breakdown in the Essential report.

Key Risk Factors

Biological AI Foundation Models Automating Core Expertise

#1

AlphaFold 3 (DeepMind, 2024) extended protein structure prediction to nucleic acids, small molecules, and complexes, achieving structural prediction accuracy competitive with cryo-EM for many targets. ESM3 (EvolutionaryScale) is a 98-billion-parameter model that reasons over protein sequence, structure, and function simultaneously, enabling generative protein design from text prompts. Evo (Arc Institute, 2024) is a foundation model trained on 2.7 million prokaryotic and phage genomes that can perform zero-shot functional prediction, generative sequence design, and variant effect prediction across entire genomic sequences — tasks that previously required teams of computational biologists and months of work now execute in minutes via API.

Robotic Laboratory Automation Displacing Bench Scientists

#2

The lab automation market reached $6.4B in 2023 and is projected to double by 2028. Recursion Pharmaceuticals operates one of the world's largest automated biology labs, running millions of experiments per week with minimal human bench scientists — their platform executes cell biology assays at a scale impossible for human researchers. Strateos offers cloud-accessible robotic labs where external scientists can submit protocols and receive results without touching a pipette. Emerald Cloud Lab and Transcriptic provide similar services. Integrated AI-robotic systems now close the loop: Bayesian optimization algorithms (as used in Pasteur.AI and similar platforms) design the next experiment based on current results, execute it robotically, and iterate — entirely removing the human from the bench experimental cycle.

Full analysis with experiments and mitigations available in the Essential report.

Recommended Course

AI for Medicine Specialization

Coursera

Builds critical AI model evaluation and oversight skills specific to biological and medical AI systems, positioning you as the expert who validates and governs what AlphaFold and ESM3 produce rather than being replaced by them.

+7 more recommendations in the full report.

Frequently Asked Questions

Will AI replace Biological Scientists All Other?

Biological Scientists face a 63/100 AI replacement risk — classified as high risk but not certain displacement. Risk is highly uneven across four sub-occupations and tasks. Supervision (18% automation), wet lab work (32%), and fieldwork (28%) face lower risk, while large-scale biological data analysis (85%), statistical analysis (82%), and literature review (78%) face near-term disruption within 1-2 years. Adaptation toward exploratory, human-centered research is critical.

Which biological science tasks face the highest AI automation risk?

Three task categories face urgent disruption: large-scale biological data analysis (genomics, proteomics, transcriptomics) at 85% automation likelihood (1-2 years); statistical analysis and bioinformatics algorithm design at 82% likelihood (1-2 years); and scientific literature review and hypothesis generation at 78% likelihood (1-2 years). These disproportionately affect Bioinformatics Scientists (19-1029.01) and Molecular Biologists (19-1029.02) but are less critical for field-based specialists.

Which biological science tasks are most resistant to AI automation?

Supervision of technicians, postdocs, and junior scientists carries only 18% automation risk (7+ years), making it most protected. Fieldwork, specimen collection, and in-situ organism observation carry 28% risk (5-9 years). Wet laboratory experimentation — cell culture, DNA extraction, electrophoresis, cloning, sequencing — faces 32% automation (5-8 years). These hands-on, context-dependent activities remain difficult for current robotic systems despite the lab automation market reaching $6.4B in 2023.

How is AI already transforming biological science work?

Multiple AI systems are reshaping the field now. AlphaFold 3 (DeepMind, 2024) predicts protein structures plus nucleic acids, small molecules, and complexes—automating core structural prediction work. Laboratory automation is projected to double from $6.4B (2023) to $12.8B by 2028, with firms like Recursion Pharmaceuticals operating large-scale automated labs. Over 60% of researchers (Nature 2024 survey) use AI for writing, especially methods sections. AI drug discovery platforms raised over $5 billion (2022-2024) specifically to replace human pharmaceutical biology functions.

What strategies can biological scientists use to adapt to AI disruption?

Focus expertise on lower-automation-risk tasks: deepen wet lab skills (32% risk), field observation and ecology (28% risk), and supervisory capabilities (18% risk). Transition from competing with AI systems to augmenting them—use AlphaFold 3 and AI writing assistants strategically. Consider specializing in field-based Biologist roles (19-1029.04) or Geneticist positions (19-1029.03) emphasizing novel variant discovery. Build grant-writing and mentorship expertise, which remain difficult to automate. Bioinformatics Scientists should focus on interpreting AI-generated predictions rather than generating predictions themselves.

Which biological scientist sub-occupations face the most AI risk?

Bioinformatics Scientists (19-1029.01) face highest risk because core work—data analysis (85% automation) and algorithm design (82% automation)—directly aligns with current AI capabilities. Molecular and Cellular Biologists (19-1029.02) face substantial risk from automation of literature review (78%), statistical analysis (82%), and scientific writing (68%). Geneticists (19-1029.03) and field-focused Biologists (19-1029.04) have lower exposure by emphasizing variant discovery, field observation, and hands-on experimental work resistant to near-term automation.

What is the timeline for AI automation of biological science work?

Near-term disruption (1-2 years): large-scale data analysis (85%), statistical analysis (82%), and literature review (78%) will be significantly automated. Medium-term (2-4 years): protocol design and optimization faces 55% automation likelihood. Longer-term (5-9 years): wet lab work (32% automation, 5-8 years) and fieldwork (28% automation, 5-9 years). Most protected: supervision (18% automation, 7+ years). Scientists should prioritize skill development in longer-timeline, lower-risk tasks immediately.

Go deeper

Essential Report

Diagnosis

Understand exactly where your risk is and what to do about it in 30 days.

  • +Full task exposure table with AI Can Do / Still Human analysis
  • +All risk factors with experiments and mitigations
  • +Current job mitigations — skill gaps, leverage moves, portfolio projects
  • +1 adjacent role comparison
  • +Full course recommendations with quick-start picks
  • +30-day action plan (week-by-week)
  • +Watchlist signals with severity and timeline

Complete Report

Strategy

Design your next 90 days and your option set. Not more pages — more clarity.

  • +2x2 Automation Map — every task plotted by automation risk vs. differentiation
  • +Strategic cards — best leverage move and biggest trap
  • +3 adjacent roles with task deltas and bridge skills
  • +Learning roadmap — 6-month course sequence tied to risk factors
  • +90-day action plan with monthly milestones
  • +Personalise Your Assessment — 4 dimensions, 72 combinations
  • +If-this-then-that playbooks for career-critical moments

Unlock your full analysis

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Essential Report

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Full task breakdown + 1 adjacent role

  • Task-by-task score breakdown
  • Risk factors with timelines
  • Skill gaps + leverage moves
  • Courses + 30-day action plan
  • Watch signals
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Complete Report

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Deep analysis + 3 adjacent roles + strategy

  • Everything in Essential
  • Automation map (likelihood vs. differentiation)
  • Deep evidence per task & risk factor
  • 3 adjacent roles with bridge skills
  • If-this-then-that playbooks
  • 3-month learning roadmap
  • Interactive personalisation matrix

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