The Diamond Lesson
For more than a century, diamonds represented one of the world’s most successful stores of value.
Their value was never derived from utility alone. Diamonds were valuable because they were scarce. Their worth rested on a simple assumption: nature-controlled supply. The rarity of diamonds was determined by geology, not industry.
Then technology changed the equation.
Lab-grown diamonds possess the same physical, chemical, and optical properties as mined diamonds. To the consumer, they are virtually indistinguishable. To a scientist, they are diamonds.
What changed was not the product. What changed was scarcity!
A gemstone that once required millions — if not billions — of years of geological processes can now be manufactured in weeks. Production is increasingly governed not by nature, but by industrial capacity. The consequences have been severe.
Lab-grown diamond prices have collapsed as manufacturing scales and production efficiencies improve. Natural diamond prices have also come under sustained pressure as consumers increasingly question the premium attached solely to geological origin. Inventories have accumulated, trading activity has slowed, and the industry’s traditional pricing power has weakened.
Perhaps the clearest manifestation of this disruption is De Beers itself.
For decades, De Beers shaped and controlled the global diamond market. It perfected one of the most successful scarcity narratives in commercial history. Yet today De Beers is grappling with structural disruption. De Beers’ parent, Anglo American, has long sought to divest the business as part of a broader restructuring.
What was once considered one of the world’s most desirable commodity and luxury assets companies has struggled to command the valuations that would once have been considered obvious.
The problem is not that diamonds became less relevant to the bride; the problem is that they became less scarce.
Technology did not only attack the diamond’s brilliance, clarity, hardness or beauty. Technology attacked rarity itself.
The Abundance Engine
The diamond industry offers a glimpse into a much larger phenomenon that markets are only beginning to understand. For centuries, economics has been built upon scarcity.
Scarcity creates value; Scarcity creates rents; Scarcity creates stores of value; Scarcity creates geopolitical power.
Technology, however, is fundamentally an ‘abundance engine’.
Every major technological revolution has followed the same pattern. It has taken something scarce and made it abundant.
The printing press made knowledge abundant; the internet made information abundant; industrialisation made manufactured goods abundant; artificial intelligence is making intelligence abundant.
From Cognition to Production
The first wave of AI focused on cognition — understanding language, generating content and augmenting human intelligence. The next wave is increasingly focused on automating production. Between these two waves lies perhaps the most consequential development of all: AI-driven innovation.
Artificial intelligence is no longer merely processing existing knowledge; alongside intelligent humans, it is increasingly participating in the creation of new knowledge through cycles of recursive learning, distillation, experimentation and optimisation — at a scale, speed and level of iteration previously unimaginable. AI systems are now being deployed to discover new molecules, engineer novel materials, optimise manufacturing processes, redesign supply chains, accelerate scientific research and reduce the cost of converting ideas into physical outcomes.
In doing so, AI is beginning to challenge the foundations of supply-side economics. Indeed, this evolution appears to be a natural extension of AI’s current trajectory. Once intelligence becomes scalable, continuously available and increasingly autonomous, the bottleneck shifts from thinking to production.
Historically, scarcity emerged because discovery, optimisation and execution were constrained by the availability of intelligence itself. Scientific breakthroughs were slow, experimentation was expensive, and production systems were limited by human decision-making and organisational complexity.
As Eureka moments become industrialised, many of these constraints begin to weaken. The result is a world in which the production of goods, materials, energy and even scientific breakthroughs may become dramatically cheaper, faster and more scalable than previously imagined.
The implications extend beyond supply. Economic systems have long assumed that demand adapts gradually while supply remains constrained. AI may invert that relationship. If production capacity expands faster than demand can absorb output, markets may increasingly confront abundance rather than scarcity. Such a shift would not merely alter production economics; it would challenge traditional assumptions regarding pricing, capital allocation, competitive advantage, and value itself.
For centuries, economics has largely been concerned with the allocation of scarce resources.
The intelligence economy may increasingly require economic systems capable of managing abundance.
Abundance Risk
Across the world, billions of dollars are now being invested into artificial intelligence systems focused on biology, chemistry, materials science, advanced manufacturing and energy. AI is increasingly being used to discover new materials, identify substitutes for scarce inputs, optimise chemical compounds, accelerate pharmaceutical development and improve industrial production processes.
Historically, commodity markets have been governed by geology; increasingly, they may be governed by computation.
This raises an uncomfortable question.
What happens when artificial intelligence discovers materials that outperform scarce resources? What happens when advanced composites replace critical metals? What happens when synthetic biology creates alternatives to natural inputs? What happens when scarcity itself becomes programmable?
Traditional finance is used to understanding and hedging risk; the coming decades may require investors to understand a new category — Abundance Risk.
Abundance Risk emerges when technology destroys scarcity faster than markets can reprice it.
The consequences can be profound because many assets derive their value not from utility, but from assumptions regarding future scarcity.
The moment technology challenges those assumptions, value can dissipate with surprising speed. The implications of abundance risk may ultimately extend beyond commodities and into the nature of capital itself.
Capital in the Intelligence Economy
For centuries, economic systems have been organised around the allocation of scarce resources. Capital emerged as the mechanism through which societies determined who could access those scarce resources and in what quantities. Money, in many respects, is society’s most important scarcity-allocation technology.
This is why recent comments by technology leaders such as Elon Musk are particularly noteworthy. Musk has repeatedly suggested that in a sufficiently advanced AI-driven future, the availability of goods and services may matter more than the availability of money itself. While such observations are often viewed through the lens of technological optimism, they point towards a deeper economic question: what happens when intelligence, innovation and production capacity become increasingly abundant?
Historically, capital was scarce because value creation was constrained. Knowledge was scarce. Expertise was scarce. Discovery was scarce. Coordination was scarce. Production capacity was scarce.
Artificial intelligence directly challenges each of these constraints. If intelligence was the ultimate scarce resource, artificial intelligence represents humanity’s first serious attempt to industrialise intelligence itself.
As intelligence becomes abundant, autonomous systems increasingly participate in research, engineering, design, optimisation and execution. The result is not merely greater productivity; it is the expansion of humanity’s productive frontier itself. The cost of discovery falls. The cost of innovation falls. The cost of optimisation falls. The cost of production falls.
As these costs decline, abundance ceases to be an outcome and increasingly becomes a system characteristic.
The consequences extend beyond technology and production. They strike at the foundations of economics itself. If economic systems have historically been designed to allocate scarce capital, scarce labour and scarce resources, then an intelligence economy characterised by increasingly abundant intelligence, increasingly autonomous production and progressively lower marginal costs may require entirely new approaches to value creation, capital allocation and risk pricing.
For centuries, finance has specialised in allocating scarcity. The intelligence economy may require economic and financial systems capable of pricing abundance.
Conclusion
That transition may prove as consequential to economics and finance as the Industrial Revolution was to manufacturing. The lesson from diamonds is therefore larger than commodities.
Technology does not merely create new products; at its most disruptive, technology changes what society considers scarce.
As artificial intelligence moves from cognition to innovation and increasingly into production, it is beginning to challenge not merely individual industries, but some of the foundational assumptions upon which modern economic and financial systems were built.
The diamond industry’s mistake was assuming scarcity was permanent. The intelligence economy may reveal that scarcity itself is a variable — and that intelligence was the scarce resource behind every other scarcity.
This article was originally published as an opinion column on CNBC TV18 on 28 July 2026. The views expressed in the column are personal.