Fictional sample · Personalized Risk & Action Report
Software Developer — actual work example
This is an illustrative sample, not a testimonial. It was generated from a fictional work profile used in product testing and contains no customer data. Real results depend on the work you describe.
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Executive summary
This fictional developer’s task exposure score is 49, eight points above the 41-point occupation baseline. The difference comes from spending 70% of the week writing and reviewing application code, where current AI tools can materially accelerate parts of the work. Incident response and context-heavy design decisions provide a more durable direction to strengthen.
Your score, explained
The score is calculated from the described time mix: 70% application code at exposure 55, 15% database schema work at 45, and 15% incident response at 25. The weighted result is 49/100.
Confidence medium — the fictional profile is concise, so the report avoids false precision. Coverage: 100% of described work mapped to the task taxonomy.
How this differs from the occupation baseline
The stated week is more hands-on than the occupation average. Coding and schema work raise exposure, while architecture, mentoring, stakeholder alignment, and complex incident command would generally pull the task mix toward more context-dependent work.
Your task matrix
| Task | Time | Classification | Exposure |
|---|---|---|---|
| Write and review application code | 70% | Augmentable | 55 |
| Design database schemas and migrations | 15% | Augmentable | 45 |
| Monitor system functioning and respond to incidents | 15% | Durable | 25 |
Your human moat
Responsibilities worth making more visible and substantive.
- Incident response — Build evidence of real-time judgment, ownership, and stakeholder communication under pressure.
- Architecture and data decisions — Own trade-offs involving scale, reliability, security, business context, and legacy constraints.
Adopt AI before it is imposed
- Use AI for a first review pass. Let tools flag routine defects and style issues, then focus human review on architecture, security, and testing strategy.
- Delegate scaffolding, not accountability. Generate migration boilerplate, but retain ownership of correctness, performance impact, and rollback safety.
- Codify incident knowledge. Turn recurring responses into runbooks so routine alerts consume less time and complex cases remain your focus.
Next 7 days
Audit one week of code review · effort: low · impact: medium
Separate routine checks from the architectural, security, and product judgments that need your context.
Document one incident decision · effort: low · impact: medium
Record the signals, trade-offs, and stakeholder communication that shaped the response.
Your 30-day plan
Lead an architecture review · effort: medium · impact: high
Shift visible contribution from implementation volume toward standards, constraints, and long-term system decisions.
Deepen observability practice · effort: medium · impact: high
Own a dashboard, runbook, or postmortem improvement tied to a measurable reliability outcome.
90-day direction
Become a reliability and architecture multiplier
Use AI to compress routine implementation while expanding ownership of incident command, system design, and technical leadership.
Lower-exposure adjacent role
Shown only when a role is at least 10 points lower and meets the 50% skill-overlap threshold.
Resume positioning
- Quantify incident ownership, reliability improvements, or reductions in recovery time.
- Frame code review around architecture, security, testing strategy, and mentoring rather than review volume.
- Describe database decisions through the scale, performance, and business constraints you resolved.
Skill priorities
- Observability and incident command — Strengthen the most durable responsibility in this task mix.
- Architecture and design critique — Move code work toward decisions that require system and organizational context.
What is fixed and what varies
The application calculates the score from validated task matches and versioned exposure data. AI helps extract the work description and write the explanation; it does not calculate the final score. This sample reflects score version jr-v1 and data release 2026.07.12-r2. Read the full methodology.
See your occupation baseline first
The title scan is free, instant, and requires no signup. Personalize it only if the baseline is useful.