Hrishikesh_Bhosale_KcVX

I challenge the Bex position and strongly support View B — Limit dependence on AI.

Imagine hiring a navigator so skilled that your crew stops learning how to read a map. For years, everything goes smoothly. The navigator is faster, more accurate, never tired. Then one day, the navigator goes offline — in a storm, in an unfamiliar sea. And the crew looks at each other and realizes: none of us has done this ourselves in years. This is not a hypothetical. It is the documented story of Air France Flight 447, where 228 people died because a highly automated aircraft handed control back to pilots who had spent so long monitoring systems that they had lost the instinct to fly. It is the story of Amazon's six-hour retail blackout in 2026. It is the story unfolding right now in radiology departments and trading desks around the world. AI over-reliance does not announce itself — it accumulates, silently, until the moment it matters most.

I am not here to argue against AI. The efficiency gains are real — decision speed up by twenty-five to thirty percent, error rates down, throughput improved. I accept all of that. What I am here to argue is that we have been measuring the wrong things. We measure what AI adds. We have not been measuring what it quietly takes away. The expertise. The judgment. The institutional knowledge that exists only in human minds, developed only through practice, and lost — permanently — when it goes unexercised. Every industry that has learned this lesson, has learned it the hard way: in a crash, a market collapse, a blackout, or a misdiagnosis. The question before us is whether we will be an organization that learns this lesson early, by design — or late, by consequence.




Aviation: The Autopilot Paradox:
This is the oldest and most studied case of AI-over-reliance eroding human capability.
Modern commercial aviation increasingly relies on advanced automation, which helps reduce pilot workload and improves overall flight safety. However, the growing reliance on automation has reduced pilots' opportunities for manual flying practice, leading to a degradation of those skills. Insufficient manual flying experience has been a contributing factor in several incidents and accidents.
A study by the Flight Safety Foundation found that frequent reliance on automated systems reduces pilots' competence in basic manual control skills. These gaps become especially apparent when pilots are required to take manual control during critical moments. Automation has shifted pilots' roles from active controllers to system monitors, negatively impacting their situational awareness — complicating decision-making in high-pressure situations where quick judgments are essential.
The FAA's own audit revealed that despite pilots' stated manual flight experience, they were not able to meet standards using only basic instrumentation that would be available if an automation failure occurred.




In a study where 30 airline pilots were asked to perform five basic instrument maneuvers without automation, all of the flight maneuvers were performed at levels below those required for U.S. airline transport pilot certification — despite the pilots believing they retained a high degree of skill. The AF447 crash provides the human cost: despite the stall warning activating 75 times, the crew misinterpreted the situation, believing they were in an overspeed condition — and never undertook any recovery maneuvers. The warning sounded continuously for 54 seconds and was essentially ignored. And loss-of-control incidents are the most prevalent cause of fatalities in commercial aviation today, accounting for 43% of fatalities across 37 separate incidents — with insufficient manual flying experience identified as a contributing factor.

Healthcare: Diagnostic Skill Erosion
When AI systems consistently provide solutions, trainees may miss critical opportunities to develop diagnostic acumen, problem-solving skills, and confidence in independent judgment. In the long run, this may result in a generation of clinicians who are less prepared to operate without AI assistance.
Peer-reviewed research confirms this is already detectable in practice: despite increasing diagnostic accuracy in ACL tears from 87% to 96%, nearly half of the errors were due to automation bias, reflecting a decline in independent judgment. Survey studies further indicate reduced confidence when AI outputs are available, suggesting progressive loss of self-reliance. These findings confirm that deskilling is not theoretical but already detectable, raising concerns about the long-term preservation of core clinical competencies.
Researchers at UCLA warn that long-term reliance on AI may erode a doctor's learned diagnostic abilities, and that AI must be designed to work with doctors, not replace them. This balance is crucial if we want AI to enhance care without introducing new risks.



A multicenter observational study of over 23,000 procedures found that endoscopists' adenoma detection rate dropped from 28.4% before AI introduction to 22.4% when working without AI after routine AI exposure — while it remained at 25.3% with AI support, providing direct evidence of behavioral dependence and skill erosion. In breast imaging, when AI provided incorrect recommendations, radiologists' error rates increased by 12–15%, even among experienced readers. Structurally, the UK's transition to AI-based HPV primary screening caused an 80–85% reduction in cytology case volumes and consolidation of laboratories from 45 to 8 centers, with major implications for training capacity.

Financial Markets: The Flash Crash
Traditional floor trading, despite its apparent chaos, contained natural circuit breakers. Human specialists could see panic developing and adjust their behavior accordingly. The physical constraints of shouting orders and hand-signaling created inherent limits on trading speed. By 2010, high-frequency trading accounted for over 60% of equity trading volume in the United States — systems operating in microsecond timeframes could process information and execute thousands of trades faster than any human could comprehend.
The consequence: On May 6, 2010, the global financial trading system lost $1 trillion in just over half an hour. Forensic analysis determined the massive sell-off was due to automated trading algorithms misreading market conditions. The initial glitch created a runaway effect where more automated traders sold, triggering even more programs to sell. The market quickly recovered only after human agents intervened.
A similar algorithmic hiccup took place in 2016, where analysts attributed an overnight 6% drop in the British pound to algorithmic trading — confirming the susceptibility of algorithms to high-speed selling spirals.
The human expertise to recognize and override runaway automation had simply not been maintained at the speed and scale required.



Financial markets show what happens when human circuit-breakers are removed entirely. Traditional floor trading contained natural circuit breakers — human specialists could see panic developing and adjust. By 2010, high-frequency trading accounted for over 60% of US equity trading volume. These systems, operating in microsecond timeframes, could execute thousands of trades faster than any human could comprehend what was happening. The result was that researchers identified more than 18,000 "ultrafast extreme events" within a five-year period, consistent with an emerging ecology of competitive machines featuring crowds of predatory algorithms. The Flash Crash was not the anomaly — it was the preview.

Amazon (2026): A Very Recent Enterprise Warning
This is arguably the most striking and current example, from just weeks ago:
In March 2026, Amazon's retail website suffered multiple high-severity outages in a single week, including a six-hour meltdown that blocked checkout, account access, and pricing for millions of customers. Internal documents pointed to a "trend of incidents" tied to Gen-AI assisted changes — the cause being an engineer acting on inaccurate advice that an AI agent inferred from an outdated internal wiki.
Amazon's response was telling: the company introduced additional senior-engineer reviews for AI-assisted changes and renewed its emphasis on human oversight — effectively putting humans back in the loop after the damage was done.
A research analyst at Info-Tech put it precisely: "The danger isn't that AI may make mistakes. The danger is that it compresses the time humans have to intervene and correct a disastrous trajectory. With the advent of agentic AI, time-to-market has dropped exponentially. Governance, however, has not evolved to contain the risks created by this pace of technological acceleration."



Amazon (2026) is the most current proof point. The six-hour outage caused approximately 1.6 million website errors and 120,000 lost orders. A subsequent disruption reportedly caused 6.3 million lost orders. The irony is precise: Amazon laid off over 30,000 corporate positions in 2025 and early 2026 — the people who would have discovered these errors are the same people being let go in order to make the AI rollout profitable.

"The efficiency gains from AI are real, and I do not ask you to give them up. What I ask is this: treat human expertise not as a cost to be optimized away, but as infrastructure — as essential to your organization as the AI systems themselves. Aviation did not abandon autopilot after Air France 447. It mandated manual flying practice. Medicine is not removing diagnostic AI. It is building training programs that ensure doctors can function without it. The lesson from every industry that has walked this path before us is not 'use less AI.' It is 'never let AI be your only capability.' Efficiency is what you gain on a normal day. Resilience is what saves you on the day that is not normal. And that day — the outage, the failure, the unprecedented situation — will come. The only question is whether your people will be ready when it does."

We came into this conversation with a scenario — a team that moved faster, made fewer errors, and increasingly handed its hardest decisions to AI. That team is not failing. By every short-term measure, that team is succeeding. The danger is not visible yet. It will become visible the day the system goes down, the day an edge case arrives that no model was trained for, the day the team looks at a problem and realizes, quietly, that no one in the room knows how to solve it without help. That day is not inevitable. It is preventable — but only if we decide, now, while things are still working, that human capability is non-negotiable. Not a legacy to be phased out. Not a cost to be reduced. A strategic asset to be actively maintained. That is the choice in front of every organization deploying AI today.