" Eliminating those at the bottom of growth is, in essence, eliminating cognitive inertia and adaptive inertia."
In this AI-driven era of upheaval, the first principle of enterprise survival has shifted from 'efficiency competition' to 'evolution-speed competition.' Therefore, the foundation of the traditional 'rank-and-yank' system
01 Rank-and-Yank: Your Post Is Phased Out by the 'Age' Before You Are
As year-end approaches, many enterprises begin their reviews, and 'rank-and-yank' becomes an exceptionally sensitive term. Some worry whether they will be classified as 'bottom of the ranks'
The most dangerous illusion in the workplace is none other than 'qualified performance = job security.' Many treat static results like short-term sales and output as an 'iron rice bowl,'
In the AI age, this kind of job elimination will only be faster. A few days ago, news of Canon's Zhongshan factory closing stirred widespread sentiment, marking the fall of an era
Under Jensen Huang, NVIDIA does not run traditional performance rankings, yet it has built a mechanism of 'individual + organization' co-evolution: chip-design roles must use AI for simulation
These two outcomes precisely burst the core truth: relying on static performance to secure a post is self-deception; abandoning performance rankings does not mean abandoning elimination; not doing rank-and-yank does not mean
02 The First Principle of Job Elimination: Speed Iteration of Growth and Evolution
Market competition in the AI age is essentially a contest between 'job iteration speed' and 'organizational evolution speed.' For enterprises, failing to build an 'individual +
The first principle of job elimination is the speed of growth and evolution. Not doing rank-and-yank does not mean lingering on; the post itself will be eliminated faster; only when the individual evolves together with the organization
03 Why Has Co-Evolution Become the Life-and-Death Line of the AI Age?
(1) Technology-paradigm disruption accelerates; job elimination is crueler than individual elimination
In the industrial age, the life cycle of a core technology could last decades, enough for personal experience to accumulate into a deep moat. But in the AI age, the cycle of technological disruption
This is not like an automation machine replacing a worker at some workstation, but more like the internal-combustion engine replacing the horse, or the smartphone replacing the feature phone. It does not eliminate 'the bottom individual,' but
(2) Organizations face the risk of 'systemic cognitive lag'; not evolving means collective elimination
When the pace of change in the external technological environment and market demand consistently outstrips the organization's internal speed of learning and adaptation, 'cognitive debt' arises: this is not one or two employees
The endgame of this systemic risk is not some tactical mistake or fierce competition, but the entire traditional manufacturing model and capability system it carries being swept away in the wave of intelligence
(3) The definition of individual value is refreshed; evolution speed becomes core competitiveness
When the 'post' itself becomes transient and unstable, the core yardstick for measuring individual value has long shifted from 'what you currently possess (stock knowledge / skills)'
For the organization, protecting an 'expert' with excellent current performance but stagnant learning may be riskier than investing in an 'explorer' who has temporarily failed but has a steep growth curve
(4) No evolution mechanism + no active evolution = dual elimination of post and individual
For enterprises, if they fail to build an 'individual + organization' co-evolution mechanism, only stare at the performance of existing posts, and do not lay out new posts or guide employees to evolve
For individuals, if they fail to actively evolve with the organization, relying only on old skills to scrape by on existing-post performance, even if short-term qualified, they will be unemployed once the post is eliminated. This is A
04 From Static Performance to Co-Evolution, Escape the Rank-and-Yank Trap
(1) Old model: staring at static performance, trapped in the 'job-elimination trap'
The old model's assessment is simple: it only looks at short-term data like sales and output, eliminating only those 'who miss performance targets,' while keeping mostly those 'who brush up performance with old experience,
But this 'safety' is only lingering on; once the post is eliminated by AI, these 'performance-qualified' people can only be passively unemployed. The organization also falls into 'defending
(2) New model: staring at co-evolution, adapting to or even creating new posts
The core of the new model's assessment is the 'co-evolution speed of individual and organization': not only adapting to existing-post iteration, but also coping with future post elimination, and even creating together
Even if short-term performance lags, as long as the evolution speed is fast enough, one can quickly adapt to new posts and create new value. The organization then forms a 'co-evolution → create new posts → market leadership
05 How to Turn Co-Evolution into Concrete Action?
(1) Organizational-culture dimension: convey 'co-evolution = resisting post elimination,' breaking the lingering-on misconception
Clarify the consensus: repeatedly emphasize in internal meetings and employee communications that 'not doing rank-and-yank does not mean safety; the post itself will be eliminated faster; only by evolving together with the organization
Set benchmarks: internally publicize real cases of 'evolving with the organization and jumping out of post elimination,' such as a technical-role employee joining a new AI project, moving from 'traditional chip
(2) Management-system dimension: build a 'co-evolution' mechanism, bringing everyone along to jump out of the post trap
Quantify evolution metrics: customize actionable evolution metrics by post type, avoiding 'one-size-fits-all': 1 the AI-empowerment ratio of existing posts (technical roles ≥60%,
Supporting empowerment mechanisms: provide employees with dual support of 'existing-post upgrade + new-post exploration': 1 post-specific AI tools and training resources (such as
(3) Individual-effort dimension: actively evolve with the organization, accumulate transferable capabilities
Use AI to expand post awareness: spend 30 minutes each week using AI to break down one new industry-post case and competitor dynamics — a marketing role analyzes competitor
Use AI to optimize work + accumulate transferable capabilities: each month use AI to rebuild two core work flows, handing repetitive labor to AI (such as a sales role
Use AI to check the evolution gap: each quarter use AI to assess your gap with 'the organization's evolution direction' and 'new industry-post demands,' such as using AI
06 Recognize the Truth of Post Elimination, Master the Law of Co-Evolution
In the AI age, the cruelest truth is not 'rank-and-yank,' but 'the post itself will be eliminated faster': not doing rank-and-yank does not mean lingering on; it only postpones
The year-end review should not only stare at past performance data, but should see clearly the survival law of the AI age: Jensen Huang's 'no rank-and-yank' is not indulging laziness
In 2026, the real iron rice bowl is not a 'performance-qualified post,' but 'the ability to evolve with the organization and adapt to or even create new posts'; the real
