US Job Openings (JOLTS)
6.542M
-46.1% vs Mar 2022 peak
Dec 2025 reading, down 5.592M from peak
Market Snapshot
Labor Demand Down, Equity Pricing Up
S&P 500 Index
6,836
+50.9% vs Mar 31 2022
As of Feb 13, 2026 close
Labor-Market YoY
-12.9%
Dec 2024 to Dec 2025
7.508M to 6.542M openings
Equity-Market YoY
+16.4%
Dec 31 2024 to Dec 31 2025
S&P 500: 5,881.63 to 6,845.50
Research Framing
Why This Regime Matters for Career Risk
In prior playbooks, professionals could treat a strong stock market as a broad proxy for labor demand. Current data breaks that shortcut. Brainedge therefore isolates personal adaptability factors and applies market-aware weights instead of using a single macro sentiment signal.
Signal 1: Hiring demand has normalized sharply from the 2022 peak.
Signal 2: Equity indices remain near cycle highs and can mask role-level weakness.
Decision rule: prioritize transferable execution capacity over title momentum.
Divergence Gauge
Direction-of-change comparison
Job openings since Mar 2022-46.1%
S&P 500 since Mar 2022+50.9%
This is modeled as an uncommon late-cycle divergence regime. The engine increases the penalty for low market-awareness and low skill-transfer signals when this pattern persists.
Evidence Table
Observed Regime Shift
| Metric | Point A | Point B | Delta | Interpretation |
|---|---|---|---|---|
| Job openings (JOLTS level) | Mar 2022: 12.134M | Dec 2025: 6.542M | -46.1% | Labor demand has cooled materially from the post-pandemic peak. |
| S&P 500 | Mar 31 2022: 4,530.41 | Feb 13 2026: 6,836.17 | +50.9% | Risk assets repriced upward despite weaker hiring demand. |
| YoY cross-check | Dec 2024 to Dec 2025 | Jobs -12.9%, S&P +16.4% | Opposite signs | A clear late-cycle divergence signal in labor vs equity data. |
Modeling Stack
Evaluation Pipeline
Structured Inputs
Likert + role context
Normalization
1-5 to 0-1 scale
Dimension Mapping
8-factor vector assembly
Market Weighting
Regime-aware weights
Benchmarking
Peer and role comparisons
Report Rendering
Versioned, reproducible output
Mathematical Core
Deterministic Score Construction
Brainedge uses a reproducible weighted linear model. Macro conditions can update weights, but equal inputs under the same engine version always produce equal outputs.
Normalization
N(x) = (x - 1) / (k - 1)
Converts each Likert response to a 0-1 scale for cross-dimension comparability.
Dimension Aggregation
D_i = mean(N(x_j))
Each axis is the average of mapped normalized items for that construct.
Final Score
S = 100 x SUM(D_i x w_i)
Weights are governance-controlled and reviewed under market-regime criteria.
8-Axis Resilience Surface
Sample ProfileAI Integration
How often you already use AI to shorten cycle time, reduce execution drag, and increase output quality.
Focus and Stability
Your ability to sustain deep work and recover cognitive bandwidth under uncertainty.
Market Awareness
How quickly you detect demand shifts and adapt your position before salary pressure appears.
Integrity Controls
Guardrails and Limitations
Version Locking
Every generated report includes engine version metadata. Historical outputs are not silently re-scored.
Input Hashing
Inputs are hashed for reproducibility checks and privacy hygiene. Identical inputs lead to identical scoring outputs.
Boundary Conditions
This system estimates career adaptability, not guaranteed compensation outcomes. Macro shocks can temporarily dominate personal fundamentals.
FAQ and Citations
Common Questions
Is this divergence really unprecedented?↓
We treat it as an uncommon regime, not a permanent law. The key point is practical: labor-demand cooling and equity repricing can happen at the same time, so career decisions should not rely on stock-market headlines alone.
How often are inputs and weights updated?↓
Market benchmarks are refreshed daily, and weight reviews are performed on a scheduled model-governance cycle. Historical reports remain pinned to their original engine version for auditability.
Does Brainedge use generative AI for the final score?↓
No. The final score is deterministic and formula-driven. Language models are used only for report wording, not for changing your numeric score.
How should I use this page?↓
Read this as a methods appendix: assumptions, equations, limits, and data lineage. Then run your assessment and track changes over time with the same framework.
Run the model on your own profile.
Get a role-specific resilience baseline, identify your weak dimensions, and prioritize the next 30 days with measurable actions.
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