Ozan Ahmet CetinArtificial 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.
The challenge for institutions is more than retraining.
Traditional educational cycles are too slow to respond. On-the-job learning, bootcamps, and private credentialing platforms are moving faster, but at the risk of deepening inequality between those with access to rapid skill acquisition and those without.
Institutional and social reconfiguration
Technological shifts always put institutions under strain. AI is doing so at a speed and scale that challenges the responsiveness of education systems, regulatory frameworks, and social safety nets.
The result is a growing mismatch between how fast capabilities are evolving and how slowly structures adapt.
Education is the most visible pressure point. Traditional models with four-year degrees, standardised curricula and slow accreditation are increasingly misaligned with the needs of an AI-infused economy.
Skills are becoming obsolete faster than institutions can teach them. The credibility of alternative credentials is rising, but without a clear standard, the labour market becomes harder to navigate for both employers and workers.
Income distribution is another fault line. AI tends to amplify the output of those already well-positioned, such as entrepreneurs, technical talent, and knowledge workers in high-growth industries.
Meanwhile, it compresses the value of generalised labour. Without intervention, this dynamic could accelerate inequality not just within countries, but between urban and rural regions, and between companies able to invest in AI and those left behind.
Such expectations have sparked interest in ideas such as a universal basic income.
This imbalance is further reinforced by the material requirements of AI itself. Unlike earlier software revolutions that required little more than bandwidth and talent, this wave favours capital-rich environments.
The infrastructure required for AI, such as high-performance computing clusters, access to proprietary datasets, and reliable energy at scale, tilts the playing field toward already-dominant firms and countries.
This concentration may limit the democratising potential of the technology unless counterweights emerge.
What to watch next
The pace of AI adoption is accelerating, but the second-order effects on policy, corporate behaviour, and social structure are only beginning to surface. Several fault lines will determine the trajectory from here.
In terms of policy, governments are still finding their footing. Regulatory focus has been concentrated on safety and misinformation, but another pressing frontier may be economic regarding how to update labour protections and public investment strategies for a world where intelligence scales independently of headcount.
It is crucial to watch for early signals in workforce policy, education funding, and whether AI infrastructure is treated as a public good or left entirely to the private sector.
Regarding public sentiment, AI’s reception has been shaped by curiosity and cautious optimism so far.
But that can turn quickly. If the technology is seen to disproportionately benefit elites, or if disruptions accumulate without a clear upside for the broader population, we can expect political and cultural resistance.
The question is no longer whether AI will change the economy. That threshold has been crossed.
The more relevant issue is how broad and uneven that change will be and whether institutions, firms, and individuals are prepared to adjust at the required pace.
Every major technological shift brings a period of uncertainty. But it also brings agency.
There is nothing predetermined about the outcomes of this transition.
Choices made now on education, infrastructure, labour policy, and corporate governance will shape whether AI serves to widen divides or to raise the floor.
(This is the second of a four-part series on how AI is changing the world. Next: Regulating the machine)
