A seismic shift: What comes with AI will not be business as usual

Artificial intelligence has crossed a threshold. What was, until recently, a largely academic pursuit or a backend enhancement to enterprise software is now reshaping how businesses operate, how professionals work, and how entire sectors think about productivity. 

From code generation to customer interaction, AI systems are being integrated not as tools, but as active agents in decision-making and output creation.

This marks more than a leap in computing. It signals the arrival of a new economic architecture. 

The last time we saw something comparable was with the arrival of the internet, and before that, with the advent of electrification and industrial machinery. 

Each of those moments brought not just technological change, but far-reaching transformations in labour, capital, and social organisation.

The signs are clear. We are at the onset of another such shift. Predicting whether AI will “take over” and cause existential risk may be secondary to the real issue. 

The more pressing issue is the kind of economic and social transformation this new wave of intelligence will bring. And how do we identify the early contours of that change? 

Societal implications of AI

Technological revolutions do not simply replace tools; they reorder systems. 

The Industrial Revolution did go beyond making production faster. It redefined the concept of work, upended agrarian economies, and concentrated labour into cities. Electrification enabled new forms of organisation, from assembly lines to 24-hour operations. 

The rise of the internet reshaped global commerce and altered how information moves.

What these shifts had in common was not just the adoption of a new technology, but the downstream consequences such as productivity gains that favoured capital over labour, the emergence of new classes of winners and losers, and a period of institutional strain as society adjusted to new realities.

AI sits in this lineage. The economic impact of AI is no longer hypothetical. 

It is already showing up in company reports, venture capital flows, and studies on productivity. 

In sectors like software development, legal services, and customer operations, AI is accelerating output without a proportional increase in headcount. The first wave of automation targeted routine and physical work. This one is shifting the economics of high-skill labour.

A key shift lies in the marginal cost of intelligence. Tasks that once required human reasoning – such as summarising reports, drafting legal memos and writing code – are now being handled at scale, instantly, and at minimal cost. 

This alters the input structure of entire business functions. The substitution of labour by software is not new, but the substitution of judgement, language, and even strategy marks a new phase. 

Moreover, this trend appears to be intensifying, with tech optimists like OpenAI CEO Sam Altman predicting that the cost of “intelligence” will approach zero.

Investment behaviour is adjusting accordingly. 

Capital is flowing into AI-native startups with remarkably lean teams, challenging the headcount-heavy growth model that defined the last generation of tech firms. 

Incumbents, meanwhile, are racing to retrofit AI into existing workflows to stay relevant as the basis of competition shifts.

At this stage, it is clear that AI is not a narrow productivity tool. It is beginning to change how value is created, who captures it, and at what scale.

Every major technology wave reconfigures labour, but AI is doing so in unfamiliar ways. Unlike past tools that primarily displaced repetitive or manual work, AI’s reach extends into “intelligence” territory and areas once thought insulated by education or specialisation.

The early indications suggest a split. Some roles may get obsolete, while others are becoming more productive through augmentation. 

This aligns with Nvidia CEO Jensen Huang’s position that “some jobs will be lost, some jobs will be created; but every job will be affected.”

Customer support teams using AI can handle more volume with fewer staff. Lawyers and analysts can complete tasks in hours that once took days. 

But this productivity is not evenly distributed. Workers who understand how to direct AI effectively are seeing their leverage increase; those who do not risk being sidelined.

This leads to a more polarised labour market. The demand may grow for AI-fluent professionals who combine domain expertise with the ability to integrate and guide machine intelligence. 

Meanwhile, mid-tier roles that rely heavily on process and repetition are under pressure. The result is not mass unemployment, but a reshuffling of how compensation is allocated.