The Expectation Correction: Why Your 2023 Star Performer Looks Average Today
How 18 months of AI adoption created 10x productivity gaps—and what leaders must do before the window closes
In the past 18 months, artificial intelligence has created an unprecedented phenomenon in the modern workplace: radical productivity disparities within teams that appear, on the surface, to be operating normally.
Consider a recent case from my consulting practice. A mid-market B2B software company saw one product manager increase pipeline generation by 300% in six weeks using commercially available AI tools—ChatGPT, Claude, and basic automation platforms. No additional resources. No process overhaul. Simply individual adoption of augmentation technology.
The challenge? This performance leap occurred in isolation. While one team member operated at this new level of productivity, peers continued delivering what had historically been considered strong performance. The result was not celebration but organizational tension. Strong performers suddenly appeared mediocre. Team dynamics shifted. Unspoken questions about fairness, expectations, and future requirements began to permeate the culture.
This pattern is emerging across industries. We are not witnessing the AI revolution—that phase concluded with the mainstream adoption of large language models. Instead, we are experiencing what I call the "expectation correction": a period where individual productivity gains from AI adoption are forcing organizations to recalibrate their fundamental assumptions about performance, capability, and value creation.
The Emergence of Intra-Team Productivity Gaps
Traditional technology adoptions followed predictable patterns. When enterprises implemented ERP systems or migrated to cloud infrastructure, change was orchestrated top-down. Training was systematic. Adoption was measured and managed. Performance improvements were incremental and relatively uniform across user populations.
AI adoption breaks this model. Current tools are:
Accessible without IT intervention
Learnable without formal training
Immediately applicable to existing workflows
Capable of generating 3-10x productivity improvements
This democratization creates a new dynamic: voluntary adopters within organizations are achieving productivity levels that make traditional performance metrics obsolete. Yet because adoption is bottom-up and individualized, organizations lack frameworks for understanding or managing these disparities.
Research from my work with 47 organizations over the past year reveals consistent patterns:
Performance bifurcation. Teams are splitting into two distinct groups: AI-augmented performers operating at multiples of traditional productivity, and traditional performers maintaining historical output levels. The gap between these groups is widening rapidly. Companies with even one AI-augmented operator in key roles consistently show dramatically faster feature velocity than their peers.
Compensation misalignment. Employees in identical roles with identical compensation packages are delivering vastly different value. Traditional HR frameworks cannot accommodate real-time productivity shifts of this magnitude. In one notable case, a senior IC using AI tools delivered the entire Q3 roadmap in five weeks, forcing leadership to completely reimagine the role's scope and compensation band.
Managerial confusion. Leaders lack vocabulary and frameworks for addressing AI-driven performance gaps. How do you manage someone whose peer is suddenly 5x more productive without creating resentment or triggering exodus?
Historical Context: Compression of Adaptation Cycles
To understand the severity of our current moment, consider the compression of workforce adaptation cycles:
The Industrial Revolution (1760-1840): 80 years for full workforce transformation. Multiple generations to adapt from agricultural to industrial work.
The Corporate Era (1945-1985): 40 years to establish modern management structures and career paths. One full career to navigate the transition.
The Digital Revolution (1995-2010): 15 years from early internet to full digital transformation. Substantial time for reskilling and role evolution.
The Mobile Revolution (2007-2014): 7 years from iPhone launch to mobile-first business models. Rapid but manageable transition for most knowledge workers.
The AI Transformation (2023-?): 18 months in, and we're already seeing order-of-magnitude productivity differences within teams.
Each technological wave has compressed the adaptation window. AI represents the logical extreme: transformation occurring faster than organizational systems can respond.
Observable Organizational Impacts
My field research identifies four primary impact vectors:
1. Talent Evaluation Complexity
Traditional performance management assumes relatively stable productivity baselines. An "exceeds expectations" performer might deliver 20-30% above norm. AI augmentation breaks this model when individuals can deliver 300-500% above traditional baselines.
Organizations report:
Performance review systems becoming meaningless
Promotion criteria requiring real-time revision
Compensation bands losing relevance
2. Recruitment Paradoxes
Despite public messaging that AI remains exploratory, hiring managers consistently report:
Screening for AI fluency regardless of role requirements
Rejecting otherwise qualified candidates who lack AI experience
Prioritizing demonstrated AI curiosity over traditional expertise
Key indicators of AI adaptability in candidates include: unprompted mention of personal AI experiments, specific examples of workflow optimization, and what one CPO called "the willingness to sound stupid while learning in public." Red flags include dismissing AI as "just hype" or inability to articulate any exploration attempts.
This creates a two-tier labor market: AI-fluent candidates commanding premiums while traditionally skilled workers face rapid devaluation.
3. Psychological Safety Erosion
The speed of change is generating new forms of workplace anxiety:
High performers fearing their augmented peers
Mid-level workers unable to articulate their changing value proposition
Senior employees facing skills obsolescence after decades of expertise
Unlike previous technological shifts, there is no clear reskilling path or timeline. The ground shifts daily.
4. Organizational Structure Disruption
Middle management faces existential pressure. When AI can:
Generate first drafts faster than managers can brief requirements
Coordinate workflows more efficiently than human oversight
Analyze performance data more comprehensively than quarterly reviews
The traditional managerial value proposition evaporates. Successful managers are rapidly evolving from process orchestrators to capability amplifiers.
Strategic Responses: A Framework for Leaders
Based on successful adaptations observed across multiple organizations, five strategies emerge:
1. Acknowledge the New Reality
Organizations must explicitly recognize that performance baselines have shifted. This requires:
Transparent communication about AI's impact on productivity expectations
Revised performance metrics reflecting augmented capabilities
Clear timelines for expectation evolution
2. Enable Safe Experimentation
The primary barrier to AI adoption is not technical but psychological. Successful organizations create:
Dedicated time for AI experimentation
Public failure celebration to reduce adoption anxiety
Peer learning networks rather than formal training programs
3. Restructure Performance Incentives
Traditional effort-based metrics become counterproductive when AI enables 10x productivity. Leading organizations are:
Shifting from input to outcome measurement
Rewarding leverage creation over time investment
Redesigning roles around augmented capabilities
4. Accelerate Talent Strategy Evolution
Hiring for AI fluency today is like hiring for computer literacy in 1995—necessary but insufficient. Advanced organizations focus on:
Meta-learning capability over specific tool expertise
Curiosity and adaptation speed over domain knowledge
Portfolio careers over linear progression
5. Prepare for Structural Reorganization
The organizations thriving 24 months from now will look fundamentally different. Leaders must:
Question every role's necessity in an AI-augmented context
Design teams around human-AI collaboration patterns
Build flexibility into organizational structures
Early experimentation suggests optimal team topology includes pods with at least one AI champion paired with domain experts. Compensation bands require 3-5x flexibility to accommodate performance variance. Role definitions shift from task-based to outcome-based, with quarterly scope adjustments becoming standard.
Critical Questions for Leadership Teams
As organizations navigate this transition, leadership teams must address:
What percentage of our workforce is currently AI-augmented?
How are we measuring and rewarding AI-enhanced productivity?
Does our hiring process effectively select for AI adaptability?
Are our organizational structures designed for 10x performance variations?
The Closing Window
We are in a unique historical moment. The gap between AI-augmented and traditional performance is visible but not yet institutionalized. Organizations have perhaps 12-18 months before this gap becomes the new performance standard.
Leaders face a choice: proactively manage this transition or reactively respond to its consequences. The evidence suggests that organizations attempting to maintain pre-AI performance expectations will face:
Talent exodus as augmented performers seek aligned environments
Competitive disadvantage as AI-native competitors emerge
Cultural erosion as performance disparities create resentment
The question is not whether AI will transform organizational performance expectations. That transformation is already underway. The question is whether leaders will guide this transformation or be consumed by it.
The baseline has moved. Organizations must move with it or accept obsolescence.
The author advises VC portfolio companies on AI-fueled workforce planning, talent acquisition, and organizational design.