The Age of Autonomous Intelligence - Part 1: The First Revolution That Thinks For Itself
Technological revolutions have always reshaped economies, labour markets, and societies. Historically, each wave of innovation, mechanization, electrification, digitization has ultimately created more opportunity than it destroyed, even if the transition was disruptive. Generative artificial intelligence (GenAI) challenges that assumption.
This article series from our Fundamental Equities and Asset Allocation teams examines why the current AI moment may differ fundamentally from prior technological revolutions, what that difference means for labour and markets, and why the policy and investment implications deserve far more debate than they have received so far.
In Part I, we explore why GenAI represents a structural break from past technological change. Later in the series we will discuss why the impact of the current technological resolution may take much longer than many expect as well as the considerations around governance and sustainability.
From Adam Smith to Artificial Intelligence
On March 9, 2026, we will mark a quarter-millennium of An Inquiry into the Nature and Causes of the Wealth of Nations, the secular scripture that codified the mechanics of the modern economy. Author Adam Smith’s core ideas1 - specialization of labour leading to “universal opulence,” and the “Invisible Hand” of self-interest driving efficient capital allocation - have proven to be a potent combustible for leaders and intellectuals across time and space.
They have served as a dialectical target for Karl Marx, an ideological North Star for Ronald Reagan, and a foundational blueprint for a values-based society for leaders such as Prime Minister Mark Carney. Smith’s ideas have withstood the test of time, even if he had little visibility into the dramatic changes that were to come. His framework was fundamentally pre-industrial; he had not witnessed the application of mass labour operating powered machines at scale.
The longevity of the book stems from Smith being the first to identify the triad of modern prosperity: the accumulation of capital, the productivity of labour, and the organizational framework that connects them.
That framework brings us to today, and to the need for an intense—but cordial—dialogue around the moment we are living in.
The Scale of the Current Inflection
The debate centres on what happens if GenAI proves to be what technologists believe it is.
We would summarize those beliefs as the end of labour as an economic necessity. One way that the AI future has been described is where the marginal cost of intelligence, and therefore the marginal cost of labour, trends towards zero.
In our view, the defining feature of this moment is not speed, scale, or even intelligence. It is substitution.
For the first time, technology is no longer merely augmenting human labour. Technology is no longer temporarily displacing labour only to create new and better employment opportunities for subsequent generations. If the technologists are right, AI is going to replace us. Full stop.
That distinction matters.
Early Innings of AI and Automation – Amazon
Robots on the rise while employee counts have stalled
Source: Company Reports. As of Dec 31, 2025.
From Digitizing the Physical to Replacing the Human
Over the last two decades, we believed we were living through a digital revolution. We weren’t. Instead, we experienced a period of hybrid stasis—an equilibrium in which the world remained inherently physical, simply wrapped in a digital veneer.
We moved from paper files to PDFs, from physical meeting rooms to Zoom, from rolodexes to CRMs. But the underlying logic of labour remained unchanged. Digital tools enabled the process; they did not replace the operator. Software coordinated labour, data optimized it, and platforms scaled it, but humans remained at the centre.
GenAI breaks that stasis. It does not merely facilitate work; it does the work.
Large language models (LLMs) and agentic systems now execute tasks that previously required judgment, synthesis, and experience. They are collapsing the marginal cost of cognition toward zero. In the previous era, technology removed friction. In this era, technology removes the need for the human mind. The boundary between “tool” and “worker” is eroding.
LLMs Are Our Gutenberg Moment
Early trends in ChatGPT usage confirm a revolution is underway
Note: LLMs = Large Language Models. Public representations by Google and OpenAI representatives and TDAM estimates. Source: "How People Use ChatGPT" OpenAI, Duke University, Harvard University, National Bureau of Economic Research. As of Sep 15, 2025.
Why the Historical “Base Effect” Is Broken
The standard response to concerns about technological displacement is familiar and reassuring: every technological revolution disrupts labour, but society adapts. The Industrial Revolution destroyed artisanal labour, yet living standards rose. Mass manufacturing automated production, yet employment expanded. Sure, there were frictions but, in the end, we were generally better off.
A plane climbs as long as thrust is applied and the airframe generates lift, but this relationship does not persist at all altitudes; what holds true between 1,000 and 10,000 metres does not apply indefinitely.
Those past revolutions occurred in a world of low bases.
Literacy – In the 19th century, we had a vast, untapped reservoir of human potential. Upskilling a subsistence farmer to a factory worker was a massive arbitrage on human capital.
Consumption – People had unmet basic needs like clothes, tools, and shelter. There was immense headroom for growth.
Labour Supply – We automated into an expanding labour force fueled by rural-to-urban migration.
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Entering the Convergence Era
A golden era of innovation is upon us
Note: For illustrative purposes only. Source: TD Asset Management Inc.
We now live in a fully financialized, fully consumerized, highly educated society. We are at the ceiling. We have a highly specialized workforce where the "next rung" on the ladder is not obvious. You cannot simply "educate" and “re-train” your way out of displacement when the machine performs cognitive tasks better than a PhD. We recall the wisdom of Dario Amadei, CEO of Anthropic, who recently cautioned that AI could wipe out half of all white collar entry jobs and spike unemployment to 20%2. According to Dario, AI companies and governments need to stop sugarcoating what's coming.
The last point I'd like to make here is that the West is now "fully consumerized." Demand is now manufactured, not organic. It is mathematically harder to create entirely new categories of necessary labour when basic human needs are already over-serviced. Past technological revolutions created abundance and absorbed labour. This one risks creating abundance without absorption.
The Polanyi Moment
We are approaching what can be described as a "Polanyi Moment."
Karl Polanyi, in The Great Transformation, argued that market economies become destructive when they "disembedded" from social relations—when labour, land, and money are treated purely as commodities without regard for their social function.
We are testing this thesis in real-time. If GenAI treats human cognition as a commodity with a price approaching zero, the "market mechanism" will fail to distribute purchasing power effectively. The consensus view assumes the market will naturally find a new equilibrium where everyone is employed as a "prompt engineer" or "empathy worker." This is wishful thinking.
If an algorithm can perform the work of a junior analyst, a paralegal, or a radiologist for pennies, the friction isn't just "transitional unemployment", it is structural obsolescence. The labour share of GDP has been declining for decades; this technology is the accelerant that could push it off a cliff.
Conclusion: Efficiency at Any Cost?
Efficiency is not a neutral good, and history is clear on this. The relentless optimization of global supply chains—off-shoring, just-in-time production, single-source dependencies—delivered lower costs, until it didn’t. The Covid-19 Pandemic exposed how fragile “maximum efficiency” really was: brittle systems, hollowed-out domestic capacity, and societies unprepared for shocks.
The lesson is not to reject efficiency, but to recognize its limits. An AI-driven world that optimizes relentless speed, cost, and substitution risks repeating the same mistake at a deeper level, this time with labour and social cohesion. Displaced workers, weakened institutions, and eroded trust are not externalities; they are systemic risks. Before we pursue AI efficiency at any cost, we need to study where resilience, redundancy, and human participation are worth preserving, even if they look inefficient on a spreadsheet.
This does not mean catastrophe is inevitable. It does mean that outcomes are no longer pre-ordained. We need open debate about the role of the state, the responsibilities of capital, and the objectives of technological progress itself. Not as ideology. As risk management.
Part II of our series will explore the stubborn gap between innovation and impact,
why productivity miracles rarely arrive overnight, and how markets tend to price success well before it shows up in the data.
¹ An Inquiry into the Nature and Causes of the Wealth of Nations, Adam Smith, 1776.
² Behind the Curtain: A white-collar bloodbath, Axios, Interview with Dario Amadei, May 28, 2025.
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