Share and intensity of work current AI systems can materially affect.
SOC 15-1221
Computer and Information Research Scientists AI displacement risk
AI assists the research workflow — literature review, experiment code, paper drafts — but this is the field creating the technology. Problem formulation, novel method design, and judging whether a result is real keep research scientists scarce and augmentation-led.
Likely potential for exposed tasks to move to software after workflow integration.
The 'AI builds AI' framing is real for routine experimentation, yet the binding constraint in research is taste: which problem matters, which result is trustworthy. That judgment is exactly what the occupation sells.
Distribution
Where Computer and Information Research Scientists sits across 620 tracked roles
Displacement pressure 22 — higher than 29% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
15 O*NET task statements matched to SOC 15-1221. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $140,300 (May 2025, US national). The latest BLS row matched SOC 15-1221.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Computer and Information Research Scientists
SOC 15-1221 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 22/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Computer and Information Research Scientists
The current evidence import matched 15 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks15
SOC15-1221
- Core task / ID 14623
Analyze problems to develop solutions involving computer hardware and software.
- Core task / ID 14626
Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.
- Core task / ID 14624
Assign or schedule tasks to meet work priorities and goals.
- Core task / ID 14628
Meet with managers, vendors, and others to solicit cooperation and resolve problems.
- Core task / ID 14633
Design computers and the software that runs them.
- Core task / ID 14629
Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Formulate problems and models
Exposure 40, automation 16%, augmentation 72%.
O*NET evidence: Conduct logical analyses of business, scientific, engineering, and other technical prob... (ID 14629)
Design experiments and new technology
Exposure 42, automation 18%, augmentation 74%.
O*NET evidence: Apply theoretical expertise and innovation to create or apply new technology, such as a... (ID 14626)
Write and publish research
Exposure 68, automation 36%, augmentation 78%.
Consult on computing needs and systems
Exposure 34, automation 12%, augmentation 58%.
O*NET evidence: Consult with users, management, vendors, and technicians to determine computing needs a... (ID 14627)
TaskExposureAutomationAugmentation
Formulate problems and models4016%72%
Design experiments and new technology4218%74%
Write and publish research6836%78%
Consult on computing needs and systems3412%58%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowAI Research Lead
Training horizon: 3-6 months. Skill overlap 76. Wage preservation signal 112.
- Own a research agenda
- Mentor research teams
- Evaluate AI-generated experiments
adjacent role
LowApplied Research Engineer
Training horizon: 3-6 months. Skill overlap 68. Wage preservation signal 100.
- Move methods toward production
- Build evaluation harnesses
- Bridge research and product teams
Comparison guides
Compare the next move before you commit
3-6 months
Computer and Information Research Scientists to AI Research Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Computer and Information Research Scientists into AI Research Lead.
3-6 months
Computer and Information Research Scientists to Applied Research Engineer
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Computer and Information Research Scientists into Applied Research Engineer.
What the AI risk score means for Computer and Information Research Scientists
The displacement pressure score for Computer and Information Research Scientists is 22. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Write and publish research carries 36% automation pressure, while Write and publish research carries 78% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $140,300 (May 2025, US national). Employment context: The occupation that designs AI itself. Typical education: Master's or doctoral degree typical.
Wage vulnerability is 16, while transition feasibility is 72. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Among the strongest demand profiles tracked
- Frontier judgment is the scarcity
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Computer and Information Research Scientists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Research design
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Mathematical modeling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Scientific writing
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
AI-assisted experimentation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to AI Research Lead, such as own a research agenda.
- By 90 days, compare internal openings and external postings for AI Research Lead or Applied Research Engineer and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Computer and Information Research Scientists
Will AI replace Computer and Information Research Scientists?
AI assists the research workflow — literature review, experiment code, paper drafts — but this is the field creating the technology. Problem formulation, novel method design, and judging whether a result is real keep research scientists scarce and augmentation-led. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Computer and Information Research Scientists work are most exposed to AI?
Write and publish research and Design experiments and new technology show the strongest automation pressure in this model. Write and publish research and Design experiments and new technology are better treated as AI-augmented work.
What should Computer and Information Research Scientists learn next?
Start with Research design, Mathematical modeling, Scientific writing. The most practical adjacent paths in this model are AI Research Lead and Applied Research Engineer.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureMcKinsey Global Institute / 26 July 2023Generative AI and the Future of Work in AmericaOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics