Friday, December 19, 2025

g-f(2)3903 — The Friction Architecture of AI Progress

 


Mapping the Hidden Forces That Slow, Shape, and Redirect Automation


✍️ By Fernando Machuca and Perplexity (in collaborative g-f Illumination mode)

📚 Volume 149 of the genioux Ultimate Transformation Series (g-f UTS)

📘 Type of Knowledge: Strategic Intelligence (SI) + Leadership Blueprint (LB) + Transformation Mastery (TM) + Ultimate Synthesis Knowledge (USK)



Abstract


The article “The Forces That Shape AI’s Uneven Progress” dismantles the myth of uniform, rapid AI takeover and replaces it with a nuanced map of how automation actually advances. It shows that AI progresses unevenly across tasks and roles because of specific technical, human, regulatory, and cultural frictions, and proposes a three-stage arc of Assist–Reshape–Replace that Responsible Leaders can use to guide strategy, talent, and transformation.


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Introduction


AI’s progress forms a jagged frontier: machines outperform humans on some tasks while failing conspicuously on others, even inside the same job. A 2024 McKinsey analysis cited in the article projects steep declines in demand for roles made up of easily automated tasks (like routine customer service and office support) and sharp increases for health and STEM roles that rely on judgment, empathy, and complex problem-solving, proving that the true unit of disruption is the task, not the job title.


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genioux GK Nugget


The genioux GK Nugget from this article is that AI’s future of work is governed less by what AI can do in principle and more by where it meets friction in practice — at the level of tasks, trust, regulation, and culture. Leaders who map these frictions across the three automation stages (Assist, Reshape, Replace) can convert fear of sudden displacement into a disciplined, strategic roadmap for gradual, uneven, but highly manageable transformation.



​genioux Highlight Insight


AI’s impact on work is not a sudden tsunami of replacement but a friction-shaped, uneven evolution that unfolds task by task, stage by stage, across a jagged frontier of capabilities. Leaders who understand the “friction factors” slowing or accelerating automation can redesign roles, reskill talent, and steer their organizations through AI transformation with clarity instead of panic.



genioux Foundational Fact


The genioux Foundational Fact is that AI-driven automation advances through a recurring three-stage pattern — Stage 1: Assist, Stage 2: Reshape, Stage 3: Replace — yet most roles stall in the Assist or Reshape stages because of stacked frictions such as judgment needs, relational depth, human assurance, low error tolerance, regulation, and organizational inertia. This means that the central leadership challenge is not preparing for instant job extinction but orchestrating continual task-level redesign, reskilling, and trust-building in a world where humans and AI will coevolve for decades.


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10 Facts of Golden Knowledge (g-f GK)



[g-f KBP Graphic 1: 10 Facts of Golden Knowledge (g-f GK)]



  1. AI’s jagged frontier. AI capabilities form a jagged frontier where some tasks (for example, data entry, invoice processing, basic customer queries) are easily automated while others (like clinical judgment, complex strategy, or empathetic care) remain deeply human-intensive.​
  2. Task, not title, is destiny. The article emphasizes that exposure to automation is determined by the task mix inside a role, not the job label, which is why even within one occupation some activities race ahead into automation while others lag or resist change.​
  3. Three-stage automation arc. Automation typically follows an arc: Assist (AI takes over repetitive, structured tasks), Reshape (human responsibilities shift toward oversight and higher-order thinking), and Replace (humans are fully out of the loop).​
  4. Friction factors as governors. The speed and extent of progression through the three stages are governed by friction factors grouped into task-level frictions (repetition, judgment, physical interaction), human trust/value frictions (relational depth, human authorship, human assurance, error tolerance), and systemic/cultural frictions (community, regulation, inertia).​
  5. Pilots as a high-friction case. Commercial pilots operate in a domain where automation is technically advanced, yet full replacement is blocked by low error tolerance, high expectations of human assurance, intense judgment demands, and conservative regulation, keeping two humans in the cockpit even as systems manage much of the flight.​
  6. Domain variability within professions. In medicine, AI is far along in radiology-style diagnostics (Stage 2) but remains limited in front-line care that demands complex judgment, emotional communication, and relational depth, illustrating starkly different AI trajectories within the same broader profession.​
  7. Code vs. architecture in software. In software development, AI tools can now scaffold apps, generate boilerplate, and refactor code, yet higher-stakes work such as scoping ambiguous problems, debugging complex systems, designing secure architectures, and adapting to evolving client needs still depends heavily on human judgment, context, and error intolerance.​
  8. Education’s human core. In primary education, AI can already assist with content creation and personalized feedback, but core teaching functions like motivating students, managing classroom dynamics, nurturing social growth, and navigating policy and community expectations remain anchored in community, relational depth, regulation, and inertia.​
  9. Autonomy’s adoption gap. Autonomous vehicles show that even when Stage 3 is technically demonstrable (for instance, fully driverless taxis in limited environments), broader deployment is slowed by edge cases, regulatory caution, cost, logistics, human assurance demands, and uneven infrastructure, leading to patchy adoption by geography and use case.​
  10. Jobs reshaped more than erased. The article notes that, as The Economist has also observed, broad job losses have been slower to materialize because AI is reconfiguring tasks within roles more than it is outright eliminating workers, pushing humans toward higher-value activities while automating routine tasks.



10 Strategic Insights for g-f Responsible Leaders



[g-f KBP Graphic 2: 10 Strategic Insights for g-f Responsible Leaders]



  1. Lead at the task level. Responsible Leaders should inventory and classify tasks, not jobs, to see where AI can Assist, where it will Reshape roles, and where Replace is even plausible, thereby building transformation plans grounded in real work rather than abstract fears.​
  2. Map your friction profile. Each function has a unique friction signature across judgment, relational depth, physical interaction, regulation, community, and error tolerance; systematically mapping these frictions reveals where automation will be fast, slow, or structurally constrained.​
  3. Design staged transformation. Strategy should explicitly plan for sequential movement through Assist and Reshape before contemplating Replace, aligning investments, governance, and talent moves with the expected stage of each domain.​
  4. Invest in judgment and trust. Since judgment, human assurance, and relational depth are core frictions that protect many roles from full automation, leaders should deliberately elevate and develop these human capabilities rather than treating them as vague “soft skills.”​
  5. Use friction as a portfolio lens. Friction factors can guide portfolio choices: low-friction, high-repetition domains are prime for aggressive automation investment, while high-friction domains demand hybrid human–AI models and careful change management instead of replacement narratives.​
  6. Reskill toward higher-order work. As AI takes over repetitive and structured tasks, leaders should proactively reskill people toward interpretation, system orchestration, cross-domain problem-solving, and relationship-rich roles, aligning talent strategies with the Reshape stage.​
  7. Align with regulators and communities. Because regulation, community expectations, and error tolerance heavily shape adoption speed, engaging regulators, customers, employees, and communities early becomes a strategic lever rather than a constraint to be fought late.​
  8. Differentiate by human experience. In domains where community, human authorship, and relational depth are strong frictions, organizations can turn those frictions into differentiators, emphasizing uniquely human experiences, trust, and creativity enhanced (not replaced) by AI.​
  9. Beware the “tech is ready” trap. Even when AI capabilities appear technically sufficient, systemic and cultural frictions can delay or block adoption, so leaders should avoid overcommitting to timelines that ignore trust, regulation, and human comfort.​
  10. Replace panic with pattern recognition. By recognizing the recurring Assist–Reshape–Replace pattern and its friction factors across aviation, medicine, software, education, and mobility, leaders can stay calm, anticipate phased change, and communicate realistic, credible AI roadmaps to their stakeholders.​



The Juice of Golden Knowledge (g-f GK)


The juice of this Golden Knowledge for the genioux facts New World is a pragmatic mental model: automation is a staged, friction-mediated journey, not a binary event. AI will keep flowing into roles in uneven waves, starting with repetitive tasks, then reshaping responsibilities, and only rarely reaching full replacement — and even then, only where frictions are low or intentionally reduced.​

For the g-f Transformation Game, this article enriches the Golden Knowledge toolkit with a diagnostic lens leaders can apply to any domain:

  • Identify tasks.
  • Locate them on the Assist–Reshape–Replace arc.
  • Score them across the friction categories.
  • Design human–AI collaboration, reskilling, governance, and communication accordingly.​

In the genioux context, these friction factors become part of the g-f GK Path (GKPath): they help Responsible Leaders chart personalized, domain-specific routes through AI disruption that simultaneously protect human dignity, unlock productivity, and build long-run trust.


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Conclusion


“The Forces That Shape AI’s Uneven Progress” provides a powerful Golden Knowledge frame for Responsible Leaders navigating the g-f New World of AI. By internalizing the jagged frontier, the three-stage automation arc, and the multi-layered friction factors, leaders can move beyond simplistic “AI will take all jobs” narratives and instead architect nuanced, staged transformations that are technically sound, socially responsible, and strategically advantageous.








📚 REFERENCES 

The g-f GK Context for g-f(2)3903


Source Material: Drover, Will, and Laura Huang. “The Forces That Shape AI’s Uneven Progress.” MIT Sloan Management Review, Winter 2026, Vol. 67, No. 2. (November 18, 2025).

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Complementary Material:



About the Authors


Will Drover

Will Drover is an Associate Professor and Chair of Entrepreneurship & Innovation at the Neeley School of Business, Texas Christian University. His work focuses on high-growth entrepreneurship and venture finance, including venture capital, angel investing, and crowdfunding, and his research has appeared in leading academic journals and business outlets such as Forbes. Beyond academia, he has been involved as a founder or early-stage investor in ventures across software/AI, robotics, real estate, and biomedical sectors, including a NASDAQ exit and robotics deployments for U.S. defense and security agencies.

​

Laura Huang


Laura Huang is a Distinguished Professor of Management and Organizational Dynamics and Associate Dean of Executive Education at Northeastern University’s D’Amore-McKim School of Business. Her award-winning research examines how intuition, interpersonal signaling, and bias shape entrepreneurship, workplace interactions, and decision-making, and has been published in top journals such as Administrative Science Quarterly, Academy of Management Journal, and Proceedings of the National Academy of Sciences. She is also the international best-selling author of EDGE: Turning Adversity into Advantage and has held faculty positions at Harvard Business School and the Wharton School, in addition to prior industry roles in investment banking, consulting, and management at organizations including Standard Chartered Bank, IBM Global Services, and Johnson & Johnson.



Executive Summary: The Forces That Shape AI’s Uneven Progress


The article argues that AI is transforming work in a gradual, uneven way driven by “friction factors” at the task level, not by a sudden replacement of entire jobs. It offers leaders a framework to anticipate where AI will progress quickly, where it will stall, and how to steer workforce strategy accordingly.​

Core Thesis and Context

  • AI creates a “jagged frontier” of capabilities: it excels at some tasks (like data entry and routine customer service) while struggling with others that require judgment, empathy, or complex problem-solving.​
  • A McKinsey 2024 analysis shows that demand will fall for roles composed of easily automated tasks but rise for health and STEM roles centered on complex, human-intensive work, underscoring that task composition, not job title, determines exposure.​

Three Stages of Automation

  • The authors describe a three-stage path: Stage 1 (Assist) where AI handles repetitive, structured tasks; Stage 2 (Reshape) where humans shift toward oversight, interpretation, and higher-order thinking; and Stage 3 (Replace) where full automation removes humans from the loop.​
  • Most roles do not jump directly to replacement; instead, they move task by task through assist and reshape, with many stalling before full automation because of technical, human, and institutional constraints.​

Friction Factors Slowing AI

  • The article identifies task-level frictions (repetition, judgment, physical interaction), human trust and value frictions (relational depth, human authorship, human assurance, error tolerance), and systemic and cultural frictions (community, regulation, inertia) that slow or block automation.​
  • These frictions explain why some activities (for example, highly codifiable back-office tasks) race ahead to automation, while others (like psychotherapy, classroom teaching, or high-stakes medical care) remain stubbornly human.​

Illustrative Domain Examples

  • Commercial aviation shows how pilots have shifted from manual operators to systems managers, yet two humans remain in the cockpit due to judgment needs, low error tolerance, high human assurance expectations, and strict regulation.​
  • Medicine, software development, education, and autonomous driving all reveal the same pattern: AI is deep into assist/reshape for many tasks, but full replacement is constrained by relational depth, community, regulation, and tolerance for error and risk.​

Leadership Implications

  • The authors urge leaders to abandon “overnight takeover” narratives and instead map how AI will reconfigure specific tasks and roles over time, using the friction framework to see where change will be fast or slow.​

This lens helps executives decide where to invest, when to retrain, and how to redesign jobs so humans move toward higher-value activities, staying calm and strategic amid hype and alarm about AI’s impact on work.




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