⏳ Age of AI and the Remaining Useful Life (RUL) of Roles e.g. Managers, Coders, and Factory Workers

1. Why Talk About RUL for Work?

In engineering and reliability science, Remaining Useful Life (RUL) tells us how long a component will function before it fails.

In this thought exercise we are extending that logic to roles in the workforce.

• A component part wears out due to stress, load, or obsolescence.

• A role becomes obsolete when organizational structure, automation, or skill mismatches accumulate---amongst other variables /parameters of course.

👉 Key point: In our application of the concept of RUL to a role we would not measure an individual employee’s performance. It measures the viability of the role definition itself — “How long does this role remain relevant before structural change, AI, or new org design make it redundant?”

At Amazon Web Services (AWS), for example, entire layers of middle management saw their role RUL collapse to zero almost overnight. Not because people failed, but because the structure shifted.

📌 What is λ(t)?

In survival analysis and reliability engineering, λ(t) is called the hazard function (or failure rate).

• Definition:

In words: the instantaneous risk that the “thing” fails at time t, given that it has survived until time t.

• Intuition: Imagine you’re looking at a role (like “middle manager”). If it’s still viable today, λ(t) tells you the instantaneous risk that it becomes obsolete tomorrow.

• Units: hazard is expressed as “per unit time” (e.g., per month, per year).

📌 Why do we use it in our RUL model?

Because RUL is not about a fixed countdown clock — it’s about how risk changes over time.

• For middle managers at AWS: hazard λ(t) spiked when the org flattened in 2025.

• For coders: hazard rises as Copilot adoption accelerates between 2026–2028.

• For warehouse workers: hazard stays moderate for a while, then spikes when humanoids arrive ~2032.

Once you know λ(t), you can calculate:

• The survival curve:

which gives the probability the role is still viable at time t

• The Remaining Useful Life (RUL):

the expected time left before obsolescence, aggregated over the survival curve.

2. About Our Assumptions

Before diving into results, it’s important to highlight what drives the numbers.

• Baseline Hazard (λ₀): We set this at ~ln2/24 months → a neutral role has a median “half-life” of ~2 years. That anchors many scenarios near the 2-year mark unless offset by protective factors.

• Sectoral Variance: In reality, horizons differ by industry. Highly codifiable roles (coders) compress faster, while embodied labor (warehouse workers) stretches slightly due to physical automation challenges.

• Scenario Sensitivity: The model is not deterministic. Adjusting λ₀ or slowing adoption shifts RUL significantly. We show ~2-year outputs because we wanted to pressure-test rapid disruption assumptions.

3. The RUL Equation

Adapted first-principle hazard models from survival analysis:

Where:

• SM(t) = structural mismatch (pyramid vs. diamond vs. flat)

• RO(t)* = automation risk dampened by AI fluency (AIF)

• RC, EX, DT, STF = relationship capital, executive leverage, domain translation, sales-transition fit

• PERF, LV, IMO = performance gap, learning velocity, internal mobility

• ψtenure(t) = tenure/mobility dynamics (job-hopping vs. hugging)

• M(t) = temporary mitigation (e.g., labor shortages)

• J = tipping jump once automation crosses a threshold

Remaining useful life equation:

Right now, our model is a hand-crafted hazard function:

• We define the features ourselves (structural mismatch SM, automation exposure RO*, AI fluency AIF, etc.).

• We set the form of the hazard (exponential of a weighted sum of risk factors).

• The weights are currently assumed, not learned.

So:

• Mathematically: what we’re doing now is equivalent to a shallow network (linear combination → exponential).

• With training data: you could replace hand-weights with parameters learned via maximum likelihood (survival regression) or via a deep model (CNN/LSTM for time-series RUL prediction).

👉 This makes the model interpretable today, while leaving a clear path to trainable, data-driven versions in the future.

Why keep performance in the model at all?

RUL is about the role:

• RUL answers: “How long does this role, as defined today, remain relevant?”

• It’s systemic: shaped by org structure, automation horizon, and task mix.

• Example: “Middle manager in a flattening org has 17 months RUL.” That’s a role-wide statement, not about any specific manager

Performance is about the person:

• Performance answers: “How well is this individual delivering in the role they currently occupy?”

• It’s person-specific: a high performer might delay their personal exit (redeployment, retention) even if the role itself is decaying.

• In the equation, PERF enters as a risk term, but it has smaller weight than structural and automation terms.

How we reconcile the two

• Role RUL sets the ceiling. If the role collapses, even top performers can’t keep it alive (e.g., AWS middle managers eliminated by design).

• Performance shifts the curve within that ceiling. A strong performer can extend survival probability a bit longer (redeployed, retained, moved into adjacent work).

• Think of performance as local resilience, while RUL is systemic lifespan.

Our RUL model is about roles, not people. That said, we include performance as a modifier: strong performers may extend their personal runway (through redeployment or retention), but they cannot fundamentally change the systemic lifespan of a role once structure and automation forces drive obsolescence

4. Case 1: AWS Middle Managers — Flattened Overnight

• Baseline flattening manager: RUL ≈ 17 months

• High AI fluency & leadership: RUL ≈ 25 months

• Redeployed into expert roles: RUL ≈ 30 months

• No flattening scenario: RUL ≈ 18 months

📉 Insight: Structural mismatch was decisive. Survivors still saw compressed RULs. Only AI fluency or redeployment extended the runway.

5. Case 2: Full-Stack Coders — The Copilot Horizon

• Assumptions: Copilots/agents automate boilerplate coding by 2026–2028.

• Outputs:

📉 Insight: Survival isn’t about code volume. It’s about AI teaming, career trajectory, and soft skill growth.

6. Case 3: Warehouse Workers — The Humanoid Horizon

• Assumptions: Robotics already in play; humanoids scale by 2032–2035.

• Outputs:

📉 Insight: Obsolescence isn’t “if” but “when.” Upskilling into robotics maintenance is the only sustainable hedge.

🚀 Contrarian Case: Product Managers — An Extended RUL

Not all roles face decline. For some, automation extends RUL.

Why Product Managers may thrive:

1. AI raises leverage, not replacement — PMs define the why and what, not the how.

2. Prototyping acceleration — faster AI builds = more cycles, more PM impact.

3. Leadership and orchestration — visionary PMs amplified in flatter orgs.

⚖️ The flip side: “Ticket-pusher” PMs shrink. But motivated, visionary PMs who ideate and lead see their RUL extend.

🌍 Workforce Impact: What “2-Year RUL” Really Means

Our coder model shows a 2-year RUL under agentic coding. That’s not a prediction of mass layoffs.

• RUL measures role viability, not headcount.

• It’s a half-life, not a cliff.

• For a large Systems Integrator (SI):

• Hazard is uneven: juniors without AI skills are exposed; survivors are those who adapt.

👉 In short: a “2-year coder RUL” signals structural change in how coding creates value, not collapse.

7. What These Cases Teach Us

• Managers → collapse when the org flattens, unless redeployed.

• Coders → expire quickly without AI teaming, but thrive if they adapt.

• Factory workers → face the humanoid horizon, unless they reskill.

• Product Managers → may see extended RUL, as ideation and leadership grow in value.

Across all, RUL doesn’t drift down gradually — it often collapses or extends sharply once org design or automation adoption hits a tipping point.

8. Closing Thought

RUL for roles is not a performance review metric. It’s a forward-looking survival curve.

The real determinants of workforce longevity are:

• Structural fit (pyramid, diamond, flat)

• Automation horizon (copilots, humanoids)

• AI fluency (replace vs. augment)

• Redeployment options (paths into new roles)

• Vision and leadership (for roles like PMs)