AI by 2040: What the World Will Really Look Like in the Next 15 Years
- Stéphane Guy
- 2 hours ago
- 10 min read
By 2040, artificial intelligence will no longer be just another technology. It will be the invisible infrastructure of nearly everything: how we heal, how we learn, how we work, how we govern, how we move. This isn't prophecy, it's the cautious consensus of hundreds of studies published over the past five years by institutions as rigorous as the World Economic Forum, McKinsey Global Institute, and MIT Technology Review.
By 2040, AI is expected to have fundamentally restructured labor markets (up to 30% of current tasks automated, according to McKinsey), revolutionized preventive and personalized medicine, transformed educational systems, and reshuffled geopolitical power dynamics. The outcome won't be utopia or dystopia, it will be something more complex, more unequal, and more decisive than anything we can comfortably imagine today.
Fifteen years is both very short and dizzying. In 2010, the iPhone 4 had barely launched, ChatGPT didn't exist, and nobody had heard the phrase "large language model." What 2040 holds for us, we can barely glimpse. But the signals are there.
In short
Work is transforming, not disappearing: the WEF projects a net creation of 78 million jobs by 2030; the real challenge isn't quantity, it's the massive reskilling this transition demands.
The medicine of 2040 will be predictive before it is curative: AlphaFold (2024 Nobel Prize in Chemistry) has opened a new era in drug discovery, and diagnostic algorithms are advancing at a pace that surprises even veteran researchers.
Education faces a fundamental reinvention: AI-powered personalized tutors are no longer science fiction, they are entering classrooms worldwide, raising hard questions about the role of the teacher.
The geopolitics of AI is already a cold war: the US-China competition over chips, data, and foundation models is redrawing global alliances.
The question of AGI remains open: the most serious experts are radically divided on whether artificial general intelligence will arrive by 2040, and that disagreement itself is precisely what should concern us.
Work in 2040: Neither Apocalypse Nor Status Quo
Let's start with the topic generating the most polarized debate, one that deserves treatment without either of its two familiar caricatures.
On one side: those predicting the end of human work. On the other: those repeating that "it's always worked out fine" since the Industrial Revolution. Neither position is being entirely honest.
According to the Future of Jobs Report 2025 from the World Economic Forum, 170 million new roles are expected to be created by 2030, while 92 million will be displaced, a net positive balance of 78 million jobs.* This looks reassuring on the surface. Behind that arithmetic, however, lie very concrete realities: the jobs being created are not the jobs being lost, not geographically, socially, or in terms of the competencies they require.
McKinsey, in its landmark report on the economic potential of generative AI (June 2023), estimates that generative AI alone could contribute between $2.6 and $4.4 trillion to the global economy annually.* The same report notes that generative AI could enable labor productivity growth of 0.1 to 0.6% per year through 2040. However, these projections vary significantly depending on technology adoption speed and scope. Some observers compare this shift to the invention of electricity; others see an inflated bubble driven by enthusiasm far exceeding actual capability.
What this means concretely: repetitive, codifiable, data-intensive tasks, drafting standardized reports, processing datasets, conducting initial reviews of legal or medical files, will be heavily augmented or replaced. High-order intellectual competencies (creativity, complex project leadership, nuanced negotiation) and socio-emotional skills will command an increasing premium.
PwC's 2025 Global AI Jobs Barometer, built from analysis of nearly one billion job postings across six continents, quantifies the emerging divide: workers in the same role who demonstrate AI competency command a 56% wage premium over peers who don't. That gap jumped from 25% the previous year.*
The real stakes of 2040 may be here: not the extinction of a professional category, but the emergence of an unprecedented fracture between those who know how to work with AI and those who don't. For a sector-by-sector breakdown of which roles are most at risk, our analysis of the jobs AI will kill, transform, or create maps the terrain in detail, as does our broader examination of AI and the future of work, which covers the key challenges and real-world impacts.
Medicine in 2040: When the Algorithm Becomes the First Line of Sight
There are domains where AI doesn't merely optimize, it changes the nature of what's possible. Medicine is the most striking case.
Consider AlphaFold, the Google DeepMind program that predicts the three-dimensional structure of virtually all known proteins. Its creators, David Baker, Demis Hassabis, and John M. Jumper, received the Nobel Prize in Chemistry in 2024.* This is not a minor detail: it means that the world's most rigorous scientific institution recognized that an AI accomplished in a few years what thousands of researchers had failed to achieve over decades. The field of drug discovery will never look the same.
In radiology, dermatology, and ophthalmology, today's algorithms already detect anomalies invisible to the human eye: melanomas, diabetic retinopathy, intracranial hemorrhages. Aidoc's AI system, for instance, reduced emergency turnaround time for intracranial hemorrhage cases by 36.6% at the University of Rochester Medical Center, and has been shown to nearly halve time-to-treatment for large vessel occlusion strokes.*
By 2040, what seems probable is a two-speed medicine, but in the reverse of what's often feared. Countries that have invested heavily in anonymized, interoperable health data infrastructure will be able to offer citizens personalized early diagnostics at marginal cost. Those that have lagged, for regulatory, political, or technological reasons, will accumulate a deficit that compounds over time and proves difficult to close.
The ethical dimension is far from trivial. While AI is unlikely to replace radiologists outright, a radiologist using AI can be substantially more productive, and the line between assistance and dependency becomes harder to draw. Who is liable when an AI-assisted diagnosis goes wrong? Does the framework of medical responsibility shift?
For a closer look at how AI is already reshaping healthcare for underserved and underrepresented populations, our analysis of AI and medical research for disability documents significant recent progress.
Education: The End of the Lecture, Finally?
This debate is roughly as old as television, personal computers, and tablets. At every technology wave, someone predicted school would be radically transformed. Every time, the institution absorbed the shock without fundamentally changing, for better or worse, depending on your perspective.
With AI, something feels different. Not because the technology is more impressive, though it is, but because it attacks a structural constraint at the core of modern education: the impossibility of large-scale personalization. A teacher with thirty students cannot adapt the pace to each one. An AI tutor can.
The first systems of this kind already exist and are scaling fast. Khan Academy's Khanmigo, an AI-powered tutor and teaching assistant, scaled from 68,000 users in the 2023-24 school year to over 700,000 in 2024-25, now deployed across a very large number of school districts in the United States and expanding globally into countries including India, Brazil, and the Philippines.* AI grading and personalization tools are simultaneously being piloted in schools across the UK, Scandinavia, and parts of Southeast Asia. These are no longer prototypes, they are early infrastructure.
By 2040, this type of tool and approach is likely to be as embedded in learning environments as textbooks are today.
The real question is not "will AI replace teachers?", it won't, at least not as figures of human accompaniment and judgment. The real question is: what will still justify, in 2040, thirty children learning the same material at the same pace in the same room? This is a challenge AI is posing with a bluntness that most educational institutions haven't yet directly confronted. The speed at which curriculum reform, union negotiations, regulatory frameworks, and public investment actually move rarely matches the pace at which technology reshapes the underlying activity.
Smart Cities, Mobility, Environment: The Promises and the Price Tag
Smart cities have generated glossy promises for over two decades. By 2040, some of those promises will have been kept. Others will have accumulated a substantial energy debt.
On the delivered-promise side: real-time traffic flow optimization, predictive management of water and energy networks, early detection of infrastructure failures. Singapore, Amsterdam, and Helsinki are already running these systems at scale.* By 2040, the question won't be whether these technologies work, it will be who can afford to deploy them, and who owns the data they generate.
Autonomous mobility, meanwhile, is progressing more slowly than the 2015-era forecasts promised, but it is progressing. Fully autonomous vehicles operating freely in urban centers will probably not be the norm by 2040; autonomous fleets on highways and geofenced zones likely will be.
Then there's the environmental reckoning. AI's energy and water consumption is already a documented concern. If model efficiency improves but usage multiplies, net gains could easily cancel out. This is the Jevons Paradox applied to AI: the more efficient a technology becomes, the more its total resource consumption grows as applications proliferate. That rebound effect remains the blind spot in most optimistic projections. For a full accounting, our investigation into the environmental cost of AI worldwide breaks down the numbers.
The Geopolitics of AI: A New Cold War, and Not Just Metaphorically
By 2040, whoever controls the most powerful foundation models controls a form of leverage without precedent in history. That statement is not a metaphor.
The US-China competition over advanced semiconductors, training data, and AI talent is already structurally decisive. American export restrictions on Nvidia chips to China, and Beijing's massive investment in its national AI champions, DeepSeek being the most visible recent illustration, are shaping a technological bipolarity whose effects will cascade into the regulatory choices of every other government on the planet, including those in the European Union.
Europe is playing a different hand. The EU AI Act, the world's first comprehensive regulatory framework for artificial intelligence, which entered into force in 2024, represents a calculated bet: regulate early, set the global standard, protect digital sovereignty. The risk is real: if European regulation constrains domestic innovation while less scrupulous actors operate without equivalent constraints, the sought-after sovereignty could quietly become disguised technological dependency.
The stakes become concrete when you zoom in on public health. If European hospitals massively adopt medical imaging and diagnostic software developed by American tech giants, a single geopolitical decision or trade dispute could lead those companies to cut off access. Radiologists would overnight find themselves without tools that have become indispensable to patient triage, with no immediately deployable local alternative.
What 2040 will reveal is whether democracies managed to build AI governance frameworks that protect fundamental rights without sacrificing competitiveness. That answer is still being written.
AGI in 2040: Myth, Risk, or Horizon?
The question everyone asks behind closed doors, and few address directly: is artificial general intelligence, an AI capable of reasoning autonomously across any domain, like (and potentially better than) humans, actually plausible by 2040?
The most credentialed experts disagree sharply. Turing Award winner Yann LeCun, former Chief AI Scientist at Meta, has consistently argued that we remain far from AGI, that current architectures have fundamental limitations that simple scaling will not overcome, and that large language models represent a "dead end" on the road to human-level intelligence. : “There are still a lot of scientific and technological challenges ahead, and it’s very likely that there’s going to be yet another AI revolution over the next three to five years because of the limitations of current systems,”*
Others, including leading researchers at OpenAI, Google DeepMind, and Anthropic, consider a form of emergent AGI plausible within ten to twenty years.
This disagreement among serious people is not trivial. It means we are playing a game whose rules and stakes we cannot yet clearly define. Our analyses of the technological singularity and AI and transhumanism examine precisely these scenarios in depth: their most vertiginous implications, their actual probabilities, and the safeguards some researchers are racing to build before the window narrows.
What is certain: even without AGI in the strong sense, the AI systems of 2040 will be of a power that we struggle to envision from where we stand in 2025. The question isn't so much "will we be surpassed?" as "will we have had enough time to build institutions equal to the challenge?"
What 2040 Says About Us, Right Now
There's something slightly futile about predicting 2040 with precision. MIT researchers in 2010 didn't anticipate large language models. The futurists of 1985 didn't predict the Soviet Union's collapse, or the internet.
What current trends show, consistently and convergently, is that 2040 will belong to those who adapted their institutions, not just their technologies. Countries that trained their workforces at scale for new competencies. Healthcare systems that built robust and ethical data infrastructure. Democracies that found ways to regulate AI without suffocating research.
And, perhaps more fundamentally: societies that decided what they actually wanted from this technology, rather than simply absorbing what it imposes on them.
Because the AI of 2040 is already us. Our investment decisions, our regulatory choices, our research priorities today are its building blocks. That's an uncomfortable responsibility to carry, but it's the right way to read what's coming.
FAQ
Will AI replace most jobs by 2040?
Not in the sense of mass extinction, but yes, in the sense of deep transformation. The WEF estimates a net positive balance of 78 million jobs created by 2030. The real challenge isn't quantity; it's reskilling at scale. The jobs being created will require very different competencies from those disappearing.
Which sector will be most transformed by AI by 2040?
Medicine is probably where the changes will be most spectacular: predictive diagnostics, accelerated drug discovery, AI-assisted surgery. Finance, law, and education follow closely.
Will AGI (Artificial General Intelligence) exist by 2040?
The scientific community is deeply divided. Some leading experts consider it plausible by 2040; others argue that current architectures have fundamental limitations pushing the horizon well beyond that timeframe. This disagreement among top-tier researchers is itself a significant signal worth taking seriously.
Which countries will lead the AI revolution by 2040?
The US and China hold structural advantages. The UK, Canada, and several Nordic countries are close behind. Europe occupies a difficult position: strong on regulation, weaker in terms of industrial AI champions. The Global South risks being largely excluded from where the benefits actually land. Our analysis of AI and the digital divide maps this inequality landscape in detail.
Will AI worsen inequality by 2040?
This is one of the most seriously documented risks. AI concentrates its productivity gains where data and infrastructure are already strong. Without active redistribution and digital inclusion policies, it mechanically tends to widen gaps, between countries and within societies. Our piece on AI, automation, and societal risk addresses this directly.
How should I personally prepare for 2040?
Build and refine competencies that AI cannot easily replicate: contextual judgment, genuine creativity, human relationship management, and the capacity to work alongside AI systems rather than against them. Curiosity may be the most underrated competency of all, and if you're wondering whether today's heavy reliance on AI tools is already reshaping how we think, our examination of whether AI is making us cognitively lazier is worth reading before 2040 arrives.
Will AI be "conscious" by 2040?
Consciousness is as much a philosophical question as a scientific one, and among the least understood in the entire AI debate. No serious consensus exists on what it would even mean for an artificial system to be conscious. What we can say: the systems of 2040 will be capable of simulating behaviors we will easily interpret as signs of consciousness. Our collective challenge will be resisting the urge to conflate simulation with reality, and to avoid projecting human qualities onto machines. Our deep-dive into whether AI can actually feel emotions lays out the science, the limits, and the ethical stakes.
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