Transcript for Can Machines Take Over All Our Jobs? by Merlin AI
0:00 – In the early decades of the 21st
0:02 – century, humanity is witnessing the rise
0:04 – of one of the most powerful and
0:05 – disruptive forces in history, artificial
0:08 – intelligence. As machine learning
0:10 – systems become more advanced, capable of
0:13 – processing immense volumes of data,
0:15 – making predictions, and automating
0:16 – complex tasks, they begin to reshape not
0:19 – only industries and institutions, but
0:21 – the very economic logic that has
0:22 – underpinned modern society for more than
0:24 – two centuries.
0:26 – Capitalism, the dominant global economic
0:29 – model since the industrial revolution,
0:31 – is built on a set of foundational
0:32 – principles, private ownership of
0:34 – capital, market competition, wage labor,
0:36 – and the pursuit of profit. Its success
0:39 – has been profound. It has lifted
0:42 – billions out of poverty, driven
0:44 – innovation, and transformed standards of
0:46 – living around the world. But it has also
0:48 – created structural inequalities,
0:50 – recurring crises, and deep social
0:52 – divisions.
0:54 – Today, capitalism is confronted by a
0:56 – challenge it was never designed to face,
0:58 – intelligent
0:59 – automation. In the traditional
1:01 – capitalist framework, labor is not only
1:03 – a cost of production. It is also the
1:06 – source of income that enables
1:07 – consumption. Workers earn wages. Wages
1:10 – fund spending, and spending drives
1:12 – demand. But when intelligent machines
1:15 – begin to displace human workers on a
1:17 – large scale, this cycle begins to
1:19 – unravel. According to a study by Goldman
1:22 – Sachs, artificial intelligence could
1:24 – automate the equivalent of 300 million
1:26 – full-time jobs globally. While new jobs
1:29 – may be created, they will often require
1:31 – skills or access to education that
1:33 – displaced workers do not possess. The
1:36 – result is a widening gap between those
1:38 – who benefit from technological change
1:40 – and those who are excluded from it. At
1:42 – the same time, AI does not participate
1:45 – in the economy as humans do. It does not
1:48 – consume, pay taxes, vote, or require
1:51 – shelter. It produces value without
1:53 – generating demand. As more companies
1:56 – turn to AIdriven systems for efficiency
1:58 – and profitability, the link between
2:00 – human labor and economic growth begins
2:02 – to dissolve. This has profound
2:06 – implications. Capitalism assumes that
2:08 – productive labor is the foundation of
2:10 – wealth creation and that competition
2:11 – ensures the fair distribution of
2:13 – resources through the market. But in an
2:16 – AIdriven economy, capital becomes
2:18 – increasingly concentrated in the hands
2:20 – of those who control algorithms, data,
2:22 – and computational
2:23 – infrastructure. The owners of these
2:25 – tools reap enormous profits while large
2:28 – portions of the population face
2:29 – declining job prospects, stagnant wages,
2:32 – and economic
2:33 – procarity. This trend is not
2:35 – speculative. It is already underway.
2:39 – Companies like Amazon, Google, and
2:41 – Microsoft are investing billions in AI
2:43 – infrastructure. not to enhance human
2:45 – labor, but to replace it. In sectors
2:48 – from logistics to finance, the most
2:50 – profitable business model is not one
2:52 – that empowers workers, but one that
2:54 – eliminates them. As machines outperform
2:57 – humans in cognitive tasks once thought
2:59 – uniquely human, legal research, medical
3:01 – diagnostics, customer service, even
3:04 – creative writing, the economic value of
3:06 – the average worker
3:07 – declines. This is not a temporary
3:10 – dislocation. It is a structural shift.
3:14 – Artificial intelligence is not simply
3:15 – another wave of automation like the
3:17 – steam engine or the assembly line. It is
3:20 – qualitatively different. It does not
3:23 – merely augment labor. It competes with
3:25 – it across both manual and intellectual
3:27 – domains. In doing so, it breaks the
3:30 – historical compromise that made
3:31 – capitalism sustainable. The idea that
3:34 – economic growth would benefit both
3:35 – capital and labor. As this compromise
3:38 – erodess, new forms of inequality emerge.
3:42 – Wealth is no longer tied to effort or
3:44 – merit but to technological ownership.
3:46 – Some economists argue that we are
3:48 – entering a new phase of technudalism
3:51 – where value is extracted not through
3:52 – traditional markets but through digital
3:54 – monopolies and algorithmic control. In
3:57 – this environment, traditional policy
3:59 – tools such as taxation, welfare, and
4:02 – education are often insufficient to
4:03 – restore balance. Even universal basic
4:07 – income, once considered radical, is now
4:09 – being debated seriously as a potential
4:11 – response to mass technological
4:13 – unemployment. Yet, these proposals treat
4:16 – the symptoms, not the causes of systemic
4:18 – transformation.
4:19 – The deeper question is whether
4:21 – capitalism itself as an economic system
4:23 – built on labor, competition, and
4:25 – scarcity can survive in a world where
4:27 – labor is obsolete. Competition is
4:29 – replaced by algorithmic optimization and
4:32 – scarcity is redefined by digital
4:34 – abundance. Some futurists envision a
4:37 – post-c capitalist society enabled by AI,
4:40 – a world of decentralized autonomous
4:42 – organizations, resource sharing
4:43 – networks, and data commons. Others warn
4:46 – of a darker scenario, rising
4:48 – authoritarianism, mass surveillance, and
4:50 – the entrenchment of corporate
4:53 – technocracies. One thing is clear,
4:55 – artificial intelligence is not merely a
4:57 – tool within
4:58 – capitalism. It is a force that may
5:00 – ultimately transcend it. As we move
5:03 – further into the 2020s, we must ask not
5:06 – only what AI can do for our economies,
5:08 – but what kind of economies we need in a
5:10 – world shaped by AI. The very premise of
5:13 – modern economic life that labor creates
5:15 – value and that this value is distributed
5:17 – through wages becomes increasingly
5:19 – fragile in an age when machines not
5:21 – humans produce the value yet cannot
5:23 – themselves consume or demand goods and
5:26 – services. This dynamic introduces a
5:29 – paradox. The more efficiently machines
5:32 – produce, the less need there is for
5:33 – human workers. Yet the fewer workers who
5:36 – are paid wages, the less aggregate
5:38 – demand exists to absorb production. This
5:41 – is what classical Keynesian economists
5:43 – referred to as the underconumption
5:45 – problem and in the AI era it threatens
5:47 – to become permanent rather than
5:49 – cyclical. John Maynard Kanes writing in
5:52 – the early 20th century warned that
5:54 – technological unemployment might one day
5:56 – outpace the economy’s ability to
5:57 – reabsorb displaced workers. At the time
6:00 – he was largely dismissed. The postwar
6:04 – boom and successive waves of innovation
6:06 – created millions of new jobs in new
6:08 – industries. But the AI revolution is not
6:11 – simply creating new sectors. It is
6:13 – recursively improving itself, learning
6:15 – from data, and becoming increasingly
6:17 – generalized in its
6:18 – capabilities. Whereas past industrial
6:21 – revolutions replaced muscle, the current
6:23 – revolution replaces
6:24 – cognition. Tasks that were once
6:26 – considered uniquely human, such as
6:28 – language comprehension, decision-making,
6:31 – pattern recognition, and even empathy,
6:33 – are now being mimicked and in some cases
6:36 – surpassed by machine intelligence.
6:38 – This calls into question not only the
6:40 – role of labor, but the very definition
6:42 – of human economic utility. When large
6:45 – language models are capable of
6:46 – generating text indistinguishable from
6:48 – that written by experts, and when AI
6:50 – systems can interpret complex medical
6:52 – images more accurately than
6:54 – radiologists, entire professional
6:56 – sectors face redundancy. Unlike previous
6:59 – disruptions, this one affects not only
7:01 – bluecollar workers but also white-collar
7:03 – professionals including accountants,
7:05 – analysts, journalists, and even
7:07 – educators. In this context, the
7:10 – traditional notion of a meritocratic
7:12 – market where education and skill
7:13 – acquisition lead to upward mobility
7:15 – becomes increasingly tenuous. If a
7:18 – machine can outperform a highly educated
7:20 – worker at a fraction of the cost, then
7:22 – education ceases to be a reliable path
7:24 – to economic security.
7:26 – This undermines one of the ideological
7:29 – pillars of capitalism, the belief in the
7:31 – fairness and accessibility of
7:33 – opportunity. The economic consequences
7:35 – are significant. As labor’s share of
7:38 – income declines, so too does consumer
7:40 – purchasing power. Meanwhile, capital
7:43 – owners, those who control AI
7:45 – infrastructure, proprietary data sets,
7:47 – and computing resources, see their
7:49 – profits rise. This dynamic accelerates
7:52 – wealth concentration, erodess the middle
7:55 – class, and increases economic
7:57 – polarization. A 2022 report by the OECD
8:01 – warned that without intervention, the
8:03 – rise of automation could result in a
8:04 – dual economy where a small elite
8:06 – controls vast technological assets while
8:09 – a growing majority experiences economic
8:12 – marginalization. This is not merely an
8:14 – issue of fairness. It is an issue of
8:17 – systemic stability. Capitalism relies on
8:20 – mass
8:21 – participation. When large segments of
8:23 – the population can no longer engage
8:25 – meaningfully in the economy, whether as
8:27 – producers or consumers, the system
8:29 – begins to fracture. History offers ample
8:33 – precedent. The economic crisis of the
8:35 – 1930s, the stagflation of the 1970s, and
8:39 – the financial collapse of 2008 each
8:41 – revealed how fragile the capitalist
8:42 – system can be when confronted with deep
8:44 – structural imbalances.
8:46 – What makes the current transition more
8:48 – perilous is the speed and scope of
8:50 – change. In the past, industrial
8:53 – transformation unfolded over decades.
8:56 – Today, AIdriven disruption can reshape
8:58 – entire industries in a matter of months.
9:01 – Consider the case of automated trading
9:04 – algorithms. Once introduced, these
9:06 – systems rapidly took over global
9:08 – financial markets, executing trades in
9:10 – milliseconds and making decisions that
9:12 – human traders could not match. While
9:15 – efficient, this shift also contributed
9:17 – to market instability as seen in the
9:19 – 2010 flash crash when automated trading
9:22 – systems triggered a rapid and severe
9:23 – market drop within minutes. Similar
9:26 – dynamics are now appearing in other
9:28 – sectors. In journalism, AI generated
9:31 – articles are replacing human writers. In
9:34 – customer service, conversational bots
9:36 – are answering millions of inquiries per
9:38 – day. In law, document review and
9:41 – contract analysis are increasingly
9:43 – performed by
9:44 – algorithms. These changes reduce costs
9:47 – for firms, but they also eliminate jobs
9:49 – and reduce the flow of wages into the
9:51 – economy. Moreover, as AI systems become
9:54 – more integrated into corporate
9:56 – decision-making, the logic of short-term
9:58 – efficiency increasingly overrides
10:00 – longerterm considerations such as worker
10:02 – well-being, community stability, or
10:04 – environmental
10:05 – sustainability. Algorithms do not
10:07 – possess moral intuition.
10:09 – They optimize for measurable outcomes
10:11 – often profit without regard for social
10:14 – context. This can lead to decisions that
10:17 – while rational from a computational
10:19 – perspective are ethically problematic
10:21 – and socially corrosive. At the
10:23 – macroeconomic level, this creates a
10:25 – feedback loop. As labor is displaced and
10:28 – wages stagnate, aggregate demand
10:31 – weakens. To sustain consumption,
10:33 – households turn to credit. Debt rises.
10:37 – Financial institutions respond with more
10:39 – complex instruments to manage risk.
10:42 – Eventually, the system becomes unstable.
10:45 – When defaults rise or asset bubbles
10:47 – burst, the resulting crises require
10:50 – massive public intervention, bailouts,
10:52 – stimulus packages, and emergency
10:54 – monetary policy. Each time, the state
10:57 – steps in to preserve a system that
10:58 – increasingly fails to distribute its
11:00 – benefits
11:01 – equitably. But what happens when even
11:03 – the state itself becomes automated? With
11:06 – the rise of algorithmic governance,
11:08 – predictive policing, and AI assisted
11:10 – bureaucracy, public institutions are
11:12 – also becoming more reliant on machine
11:15 – intelligence. This raises concerns about
11:17 – transparency, accountability, and
11:19 – democratic
11:20 – legitimacy. If decisions about resource
11:23 – allocation, social benefits, or legal
11:25 – judgments are made by opaque algorithms,
11:27 – who is responsible when outcomes are
11:29 – unjust?
11:31 – Some thinkers such as economist Mariana
11:33 – Mazicado have argued for a reimagining
11:35 – of the role of the state not as a
11:37 – passive regulator of market outcomes but
11:39 – as an active shaper of innovation and
11:41 – equitable growth. Others such as Uvil
11:44 – Noah Harrari have warned that the
11:46 – combination of AI and data monopolies
11:48 – could lead to the rise of digital
11:50 – dictatorship systems of power so
11:52 – efficient and omnipresent that they
11:54 – render traditional political agency
11:56 – obsolete. These are not hypothetical
11:58 – threats. They are plausible trajectories
12:01 – in a world where control over data and
12:03 – algorithms becomes the primary source of
12:05 – economic and political power. And yet
12:08 – for all its disruption, AI does not
12:10 – necessarily signal the end of economic
12:12 – life. What it does challenge is the
12:15 – assumption that capitalism in its
12:17 – current form is the best or only
12:18 – framework for organizing that life. As
12:21 – technological capability expands, so too
12:24 – must our imagination.
12:26 – If you found this exploration
12:28 – insightful, make sure to subscribe to I
12:30 – Finance Mastermind for more in-depth
12:32 – analysis on how artificial intelligence
12:34 – is transforming finance, economics, and
12:36 – our world. Your support helps us
12:39 – continue creating thoughtful
12:40 – research-driven content that matters.
12:43 – Don’t forget to like, share, and leave a
12:45 – comment with your perspective. This
12:48 – video is intended for educational
12:50 – andformational purposes only. It does
12:53 – not constitute financial advice or
12:54 – professional economic guidance. Always
12:57 – do your own research and consult with a
12:59 – qualified adviser before making any
13:01 – economic or investment decision.
Can Machines Take Over All Our Jobs? The Mainstream Awakening
When the Financial Press Discovers the Discontinuity
The YouTube channel “I Finance Mastermind” recently released “Can Machines Take Over All Our Jobs?”—a mainstream financial analysis that inadvertently validates the core arguments of the Discontinuity Thesis. What makes this video significant isn’t its originality, but its source: when establishment financial media begins echoing arguments about capitalism’s structural incompatibility with AI, the ideas have moved from fringe theory to economic reality.
The video represents a crucial inflection point—the moment when mainstream economic thinking begins to grapple with the systematic implications of artificial intelligence rather than treating technological unemployment as a manageable policy challenge.
The Accidental Validation
The Consumption Circuit Breakdown
The video’s central insight directly validates the Discontinuity Thesis’s P2 (Insufficient Re-inflation) argument:
“Workers earn wages. Wages fund spending, and spending drives demand. But when intelligent machines begin to displace human workers on a large scale, this cycle begins to unravel.”
This is the wage-demand circuit that the Discontinuity Thesis identifies as capitalism’s fundamental vulnerability. The video recognizes that AI creates an unprecedented paradox: “The more efficiently machines produce, the less need there is for human workers. Yet the fewer workers who are paid wages, the less aggregate demand exists to absorb production.”
This is P2 formalized: when AI eliminates human income, it simultaneously eliminates the customer base necessary for economic circulation.
The Structural Shift Recognition
The video correctly identifies that AI represents qualitative rather than quantitative change:
“Artificial intelligence is not simply another wave of automation like the steam engine or the assembly line. It is qualitatively different. It does not merely augment labor. It competes with it across both manual and intellectual domains.”
This anticipates the Discontinuity Thesis’s core argument: AI automates cognition itself, not just specific cognitive tasks. Unlike previous technological revolutions that displaced particular types of work, AI challenges human economic utility across all domains.
The Cognitive Obsolescence Problem
The analysis recognizes that professional and creative work face systematic displacement:
“When large language models are capable of generating text indistinguishable from that written by experts, and when AI systems can interpret complex medical images more accurately than radiologists, entire professional sectors face redundancy.”
This validates the Verification Divide analysis: when AI can generate professional-quality output faster than humans can verify it, the entire knowledge economy collapses into a tiny elite of verifiers and a vast mass of obsolete creators.
What the Video Gets Right
The Speed Problem
The video correctly identifies acceleration as a crucial factor:
“What makes the current transition more perilous is the speed and scope of change. In the past, industrial transformation unfolded over decades. Today, AI-driven disruption can reshape entire industries in a matter of months.”
This validates the Discontinuity Thesis’s emphasis on exponential rather than linear change. Competitive pressures create adoption cascades that compress transformation timelines from decades to years.
The Concentration Dynamics
The analysis recognizes wealth concentration as inevitable rather than incidental:
“Capital becomes increasingly concentrated in the hands of those who control algorithms, data, and computational infrastructure. The owners of these tools reap enormous profits while large portions of the population face declining job prospects.”
This anticipates the elite capture dynamic where AI ownership becomes the primary determinant of economic outcomes, creating unprecedented inequality between technology controllers and everyone else.
The Policy Inadequacy
The video acknowledges that traditional policy tools are insufficient:
“Traditional policy tools such as taxation, welfare, and education are often insufficient to restore balance. Even universal basic income… treat the symptoms, not the causes of systemic transformation.”
This validates the Discontinuity Thesis’s argument that incremental policy responses cannot address systematic economic transformation. When the fundamental basis of economic circulation is automated away, redistributive policies become mathematically inadequate.
The System-Level Question
Most importantly, the video reaches the core question that the Discontinuity Thesis addresses:
“The deeper question is whether capitalism itself… can survive in a world where labor is obsolete, competition is replaced by algorithmic optimization, and scarcity is redefined by digital abundance.”
This is the fundamental discontinuity: when machines can think, the economic system built on human cognitive work becomes obsolete.
What the Video Misses
While the analysis correctly identifies the problems, it lacks the systematic framework necessary to understand their inevitability and political implications.
The Mathematical Inevitability
The video treats AI displacement as a policy challenge rather than a mathematical inevitability. It suggests that “intervention” might address the problem without recognizing that:
- P1 (Unit Cost Dominance) makes AI adoption inevitable through competitive pressure
- P2 (Insufficient Re-inflation) makes job replacement mathematically impossible
- The Multiplayer Prisoner’s Dilemma prevents coordination between competing actors
The Political Analysis Gap
The video discusses potential responses but ignores why those responses are politically impossible:
- Democratic Capture: Existing institutions are controlled by interests that profit from AI deployment
- The Scapegoat Cycle: Political systems systematically redirect economic anxiety toward visible minorities rather than invisible technological causes
- Coordination Impossibility: Market competition prevents collective action even when actors understand the systematic risks
The Psychological Dimension
The video focuses on macroeconomic dynamics while ignoring the individual psychological impact of cognitive obsolescence. It doesn’t address:
- Cognitive Obsolescence Syndrome: The psychological devastation experienced by individuals whose intellectual capabilities become economically worthless
- Educational Betrayal: The trauma faced by people who invested years developing skills that become obsolete
- Identity Collapse: The existential crisis when human cognitive value disappears
The Global Implications
The analysis remains focused on developed economies while missing the global displacement dynamics:
- Skills Arbitrage Elimination: AI destroys the economic foundation supporting 3+ billion people in developing countries
- Migration Pressure: Technological displacement creates unprecedented migration flows that no border system can contain
- Technological Colonialism: AI represents a new form of economic imperialism that eliminates developing world comparative advantages
The Mainstream Lag Problem
The video’s emergence in 2024 illustrates the information processing lag between technological reality and institutional understanding.
The Recognition Timeline
- 2014: CGP Grey identifies the systematic nature of technological unemployment
- 2022: ChatGPT demonstrates human-level cognitive capabilities
- 2023: Corporate AI adoption accelerates across knowledge work sectors
- 2024: Mainstream financial media begins acknowledging systematic implications
- 2025: Displacement effects become undeniable in employment statistics
The lag represents the time required for institutional thinking to process information that challenges institutional existence.
The Analytical Limitations
Mainstream economic analysis is constrained by several factors:
Institutional Bias: Financial media cannot fully acknowledge the obsolescence of financial institutions Audience Limitations: Content must remain accessible to audiences invested in current economic systems Solution Pressure: Analysis must conclude with actionable recommendations even when no actions can address systematic problems
The Validation Function
Despite its limitations, mainstream acknowledgment serves crucial validation functions:
Credibility Transfer: Ideas gain legitimacy when adopted by establishment sources Audience Expansion: Reaches populations who dismiss “fringe” technological analysis Political Preparation: Creates intellectual foundation for policy discussions when crisis becomes undeniable
The Implicit Framework
While the video lacks explicit theoretical structure, it implicitly operates from assumptions that align with Discontinuity Thesis analysis:
Economic Determinism
The video treats technological unemployment as systematic rather than cyclical, acknowledging that “this is not a temporary dislocation. It is a structural shift.”
Competitive Inevitability
The analysis recognizes that individual companies cannot avoid AI adoption: “Companies like Amazon, Google, and Microsoft are investing billions in AI infrastructure not to enhance human labor, but to replace it.”
System-Level Transformation
The video acknowledges that incremental responses are inadequate: “These proposals treat the symptoms, not the causes of systemic transformation.”
The Missing Pieces
To complete its analysis, the video would need to address:
The Verification Trap
How AI transforms knowledge work from creation (expensive) to verification (cheap), concentrating economic value in a tiny elite while rendering most cognitive workers obsolete.
The Rent-Seeking Amplification
How decades of wealth extraction created the pre-existing conditions that make AI displacement instantly catastrophic rather than gradually manageable.
The Political Impossibility
Why democratic institutions are structurally incapable of addressing technological unemployment through coordination, regulation, or redistribution.
The Psychological Reality
How cognitive obsolescence creates unprecedented individual psychological distress that cannot be addressed through economic policy alone.
The 2028 Convergence
The video’s emergence in 2024 positions it within the Discontinuity Thesis’s prediction timeline:
The Recognition Phase (2024-2026)
Mainstream economic analysis begins acknowledging systematic implications of AI displacement, though without adequate theoretical frameworks for understanding political and psychological dimensions.
The Crisis Phase (2026-2028)
Scapegoating intensifies as economic displacement becomes visible, while actual causes remain politically unaddressable.
The Clarity Phase (2028+)
When border controls fail to stop migration, deportations fail to restore employment, and trade wars fail to prevent displacement, the connection between AI and economic instability becomes undeniable.
The video represents early recognition that will be validated by subsequent events.
The Educational Function
Despite its limitations, the video serves important educational functions:
Intellectual Preparation
Introduces mainstream audiences to concepts necessary for understanding systematic economic transformation without requiring radical theoretical frameworks.
Vocabulary Development
Establishes terminology (“technological unemployment,” “underconsumption problem,” “digital dictatorship”) that will become essential for future political discussions.
Analytical Foundation
Provides logical stepping stones between current economic understanding and recognition of systematic obsolescence.
Credibility Building
Demonstrates that technological unemployment concerns are legitimate economic analysis rather than speculative futurism.
The Trajectory Implications
The video’s existence suggests several important trajectory implications:
Accelerating Recognition
Mainstream economic analysis is beginning to process systematic implications faster than previous technological transitions, potentially compressing the recognition timeline.
Institutional Preparation
Financial institutions are beginning intellectual preparation for managing economic transitions that they cannot prevent or control.
Political Groundwork
Early mainstream acknowledgment creates foundation for policy discussions when crisis management becomes necessary.
Intellectual Evolution
Economic thinking is beginning to evolve beyond assumption of permanent human economic necessity.
Conclusion: The Bridge Analysis
“Can Machines Take Over All Our Jobs?” represents bridge analysis between conventional economic thinking and recognition of systematic technological displacement. It validates core Discontinuity Thesis arguments while remaining constrained by institutional limitations that prevent complete analytical development.
The video’s significance lies not in its analytical completeness, but in its institutional source. When mainstream financial media acknowledges that capitalism may be structurally incompatible with artificial intelligence, the ideas have moved from theoretical possibility to practical consideration.
What the video demonstrates: The economic establishment is beginning to recognize systematic challenges to existing economic organization.
What the video cannot demonstrate: The political impossibility of addressing those challenges through existing institutional mechanisms.
The bridge function: Prepares mainstream audiences for more radical analytical frameworks while maintaining institutional credibility.
The Discontinuity Thesis provides the systematic analysis that the video’s insights require. Together, they represent the intellectual evolution necessary for understanding economic transformation that current political systems cannot manage.
The video asks the right question: Can capitalism survive when machines can think?
The Discontinuity Thesis provides the answer: No, and here’s the mathematical proof of why not.
The convergence of mainstream recognition with systematic analysis suggests that 2024 may represent the inflection point where technological unemployment transitions from theoretical concern to practical planning requirement.
We are witnessing the moment when economic reality begins to outpace institutional capacity to deny it.