There’s a great deal of debate about how generative AI like ChatGPT will mature and impact society, employment, inequality, and even ethics and rights. But we can take an educated guess at where AI will exert the most influence in industries in the short to medium term. For example, we know that to tackle growing healthcare costs and inadequacies, we can look to AI for better diagnostics – reducing misdiagnosis, poorly aligned treatments and outcomes – and for improving the administrative and operational efficiency of the system. But how will these different stages of AI’s development affect society overall?

Macro-economic and social conditions

The 2020s are the decade of early AI and climate adaptation. The first real debates on the broader ethics and implications of AI are already occurring with Biden’s administration discussing regulation, with a broad call for a “pause” in AI development and broader regulatory moves in the EU and China. On climate, with extreme weather events increasing and increased movement of eco-refugees, the debate on effective climate response will also intensify while markets focus on the immense wealth creation opportunities that technology like AI promotes.

From an AI perspective, this phase of development means early adopters will argue that their AI-assisted advice is better than that of a lone human. AI-powered doctors, lawyers, wealth managers, architects and consultants are all arguing that they can provide you with a better service because they incorporate AI in their business. This is the era of those with machines versus those without.

The 2030s will be the decade of Agency, where humans individually and at the corporation level increasingly use AI to execute elements of our day-to-day existence or, in the case of businesses, day-to-day operations. Smart assistants will be integrated into our homes, vehicles and devices, and companies will employ a whole range of algorithms, not only for back office and operational elements, but to automate the supply chain itself, from autonomous transportation systems to smart contracts and tokenization of the world.

We will have had the 20s to train us to realize that the best advice and the best organizations are those based on AI. Thus, the 30s will see us increasingly trust AI to execute in domains where previously we relied on humans but now prefer the accuracy and efficiency of AI – where we give agency to our AIs to act on our behalf.

The 2040s get a bit fuzzier, but it seems likely that we will start to look to technology to mitigate the effects of climate change at a planet-wide scale. We’ll address these challenges on a city-wide or nation-state level based on automation with AI’s ability to target major systemic improvements such as energy, water and waste management, supply chain automation, autonomous transportation, food production and more – systems that we barely understand today, which AI could transform much more efficiently in respect to resource utilization and public costs. We’ll also be looking at a much more cooperative worldwide approach when it comes to climate mitigation. Efficiencies of scale will be essential, but given what a massive issue the eco-refugee crisis has become globally, mitigation will need to include immigration and the need to coordinate a global response.

The 2050s will be the era of smart economies: massive economic growth powered by AIs separating smart versus traditional economies in much the same way we previously talked about the developed versus developing world.

The four stages of AI dependency

If we assume this broad state of progression of society, and of course there is some variability possible, this does show us a reasonably clear state of development of AI with respect to how it fits in the progression of the 21st century.

Alignment phase

As AI starts to impact society around us, we are forced to deal with something that has only really been academic until now – the existential threat that AI presents to humanity at large. Not only in consideration as to what motivations a super-powerful AI might present, but more mundane considerations such as what ethics AIs should demonstrate, what rules should go with AIs living and working with us and to what extent agency is permitted and when. Not quite the three laws of robotics created by Azimov but analogous to that.

For now, the biggest issue we have is AI that replicates many of the biases and idiosyncrasies that we ourselves demonstrate. Still, self-driving cars and the so-called “trolley problem,” are good illustrations of how we have a lot of in-built regulatory requirements for the safe deployment of AI.

While some industry-based regulators in the financial services and medical sectors have already had to deal with the early implications of high-frequency trading algorithms and robotic surgeons, for example, we’re increasingly going to need broad national and global regulation when it comes to the widespread use of robotics and AI.

Advice phase

Over the next three to five years, expect every service industry to start to advertise that their humans are augmented by next-generation AI. From doctors who promise to diagnose your condition via AI to lawyers, accountants and financial advisors who claim their AI-assisted status not only guarantees a better result but promises to be cheaper and faster at getting results too.

This phase of AI is the early start of reshaping labor markets around technology-based automation. The early phase of technology disruption is in arenas where human advisors are increasingly differentiated by how augmented their advice is, where it’s not man versus machine, but where battle lines are drawn according to whether or not you use AI in the delivery of your service.

This also optimizes day-to-day service engagements so you will deal with a personalized and optimized service experience that uses AI to solve most problems with highly optimized efficiency. You will only be passed off to a human in edge-cases where AI models are incomplete; a design premise that means service organizations will begin to view humans as a design failure in systems where AIs become increasingly capable.

Where this dramatically will improve our day-to-day life is in arenas like medicine, where the combined knowledge of the global medical community can be collectively applied in real-time for optimal diagnosis and treatment. It’s like having a whole crowd of the world’s best researchers and PhDs working on your problem every time and at the cost of a local family doctor; algorithms that know your needs instinctively and tailor solution sets to your unique circumstances every time; a hyper personalized era powered by broad access to data, behaviors and preferences; and highly agile business platforms that use such responsiveness as an underpinning to garnering consumer loyalty and subscription.

Agency phase

Once we’ve learned to trust AIs in various parts of our lives, our preference will increasingly be for services run by AI. And we’re well on our way. In the name of efficiency, we’ve already accepted AI-incorporated services like ride sharing, dating apps, translation apps, GPS, restaurant and grocery delivery services or the ubiquitous Amazon or Taobao package.

Just as we’ve allowed automated services to schedule and deliver goods, we’ll start to use our AI assistants to book our travel and vacations, medical services, personalize tuition, business optimization and dietary and fitness considerations. Daily, we’ll give more of our life over to algorithms in the name of cost effectiveness, efficiency and convenience.

To cope with this behavioral shift, similar to how companies were forced to create online stores and then apps to engage their customers, more and more companies will need to create integration with AI agents. They’ll need to be more adaptive and responsive to rapidly changing customer requirements, resulting in large scale automation of operations and commerce applications, thus producing an expanding boom of automated companies attracting large scale engagement, replacing traditional service organizations and brands.

In this era, we expect to measure the world in terms of a lower carbon footprint, more sustainable production methods enabled through better automation and cheaper, faster outcomes across marketplaces. Increasingly, this will lead us to look at the economy as something that can be re-engineered to be much more resource efficient – the ultimate application of productivity.

Autonomous phase

At this level, we’re ready to replace entire systems of governance and public sector infrastructure with autonomous capabilities. At the same time, we’ll be moving to make infrastructure resilient against higher global temperatures, increased flooding and sea-level rise along with more extreme weather events as well as general infrastructure improvements to support both automation and better energy and resource management.

We can already hear talk of national 2030 vision plans where smart cities will compete for new residents and corporations, where countries court digital nomads and technical talent to emigrate and contribute to the growing dependence on tech and automation. This is the rise of smart economies, smart marketplaces, digital currencies and increasingly integrated physical and virtual worlds.

The markets will have to argue that corporations and emerging technologies are the solution to climate change and technology displacement to continue to survive and stay relevant. Older, more traditional corporations will struggle to maintain their relevance in a rapidly changing world. Consumers late to the AI party will also struggle to remain off-grid in a world where digital identity, accessibility and automation are expected of citizens and corporations alike.

A technology revolution unlike any other

All of these changes seem inevitable. Humanity not only craves progress of this sort, but the entire way we measure the success of our markets and economies will push us to use more and more of these capabilities to compete, especially as the U.S. and China square up over AI flavors and their technological capability.

Unlike other technology revolutions such as the Internet, AI has the ability to impact all of our systems, industries and components of our societies simultaneously. In previous eras of great technological leaps like the industrial revolution or the emergence of commercial internet, we could argue regulation and implications over many years, even decades. However, the very nature of AI to accelerate change means we’re going to have to make decisions on regulation, safety and governance in a much shorter timeframe, or else we’re likely to face increasingly significant events that illustrate our broad lack of control over AI.

Our biggest change may be living with the implications of an alternate intelligence integrated into human society, one that doesn’t necessarily think the way we do but is undeniably critical to our own ability to advance and progress.

Brett King is an author, world-renowned futurist and media personality. He hosts the world’s number one fintech radio show and podcast, Breaking Banks, which has 6.5 million listeners, and is the founder of the mobile startup, Moven.