The Coming Wave: AI, Power, and Our Future

by Mustafa Suleyman

Almost every foundational technology ever invented, from pickaxes to plows, pottery to photography, phones to planes, and everything in between, follows a single, seemingly immutable law: it gets cheaper and easier to use, and ultimately it proliferates, far and wide.

The coming wave is defined by two core technologies: artificial intelligence (AI) and synthetic biology. Together they will usher in a new dawn for humanity, creating wealth and surplus unlike anything ever seen. And yet their rapid proliferation also threatens to empower a diverse array of bad actors to unleash disruption, instability, and even catastrophe on an unimaginable scale. This wave creates an immense challenge that will define the twenty-first century: our future both depends on these technologies and is imperiled by them.

AI has been climbing the ladder of cognitive abilities for decades, and it now looks set to reach human-level performance across a very wide range of tasks within the next three years. That is a big claim, but if I’m even close to right, the implications are truly profound.

Is it even possible to step away from developing new technologies and introduce a series of moratoriums? Unlikely. With their enormous geostrategic and commercial value, it’s difficult to see how nation-states or corporations will be persuaded to unilaterally give up the transformative powers unleashed by these breakthroughs. Moreover, attempting to ban development of new technologies is itself a risk: technologically stagnant societies are historically unstable and prone to collapse. Eventually, they lose the capacity to solve problems, to progress.

This is the core dilemma: that, sooner or later, a powerful generation of technology leads humanity toward either catastrophic or dystopian outcomes. I believe this is the great meta-problem of the twenty-first century. This book outlines exactly why this terrible bind is becoming inevitable and explores how we might confront it.

This widespread emotional reaction I was observing is something I have come to call the pessimism-aversion trap: the misguided analysis that arises when you are overwhelmed by a fear of confronting potentially dark realities, and the resulting tendency to look the other way.

Pessimism aversion is an emotional response, an ingrained gut refusal to accept the possibility of seriously destabilizing outcomes.

How do we keep a grip on the most valuable technologies ever invented as they get cheaper and spread faster than any in history?

The various technologies I’m speaking of share four key features that explain why this isn’t business as usual: they are inherently general and therefore omni-use, they hyper-evolve, they have asymmetric impacts, and, in some respects, they are increasingly autonomous.

These waves of technology and innovation are at the center of this book. More important, they are at the center of human history. Understand these complex, chaotic, and accumulating waves, and the challenge of containment becomes clear. Understand their history and we can start to sketch their future. So, what is a wave? Put simply, a wave is a set of technologies coming together around the same time, powered by one or several new general-purpose technologies with profound societal implications. By “general-purpose technologies,” I mean those that enable seismic advances in what human beings can do. Society unfolds in concert with these leaps. We see it over and over; a new piece of technology, like the internal combustion engine, proliferates and transforms everything around it.

We are not just the creators of our tools. We are, down to the biological, the anatomical level, a product of them.

The more tools you have, the more you can do and the more you can imagine new tools and processes beyond them.

For the futurist Alvin Toffler, the information technology revolution was a “third wave” in human society following the Agricultural and Industrial revolutions. Joseph Schumpeter saw waves as explosions of innovation igniting new businesses in bursts of “creative destruction.” The great philosopher of technology Lewis Mumford believed the “machine age” was actually more like a thousand-year unfolding of three major successive waves. More recently the economist Carlota Perez has talked about “techno-economic paradigms” rapidly shifting amid technological revolutions. Moments of booming disruption and wild speculation regear economies. Suddenly everything relies on railways, cars, or microprocessors. Eventually, the technology matures, becoming embedded and widely available.

General-purpose technologies become waves when they diffuse widely. Without an epic and near-uncontrolled global diffusion, it’s not a wave; it’s a historical curiosity.

Proliferation is catalyzed by two forces: demand and the resulting cost decreases, each of which drives technology to become even better and cheaper. The long and intricate dialogue of science and technology produces a chain of insights, breakthroughs, and tools that build and reinforce over time, productive recombinations that drive the future. As you get more and cheaper technology, it enables new and cheaper technologies downstream.

Technology’s unavoidable challenge is that its makers quickly lose control over the path their inventions take once introduced to the world.

The more powerful a technology, the more ingrained it is in every facet of life and society. Thus, technology’s problems have a tendency to escalate in parallel with its capabilities, and so the need for containment grows more acute over time.

Inventions cannot be uninvented or blocked indefinitely, knowledge unlearned or stopped from spreading.

Technology is an eternally dangling carrot, constantly promising more, better, easier, cheaper. Our appetite for invention is insatiable. The seeming inevitability of waves comes not from the absence of resistance but from demand overwhelming it. People have often said no, desired contained technology for a plethora of reasons. It’s just never been enough. It’s not that the containment problem hasn’t been recognized in history; it’s just that it has never been solved.

Technology causes problems, and always has. And yet none of that seems to matter. It might take time, but the pattern is unmistakable: proliferating, cheaper, and more efficient technologies, wave upon wave of them. As long as a technology is useful, desirable, affordable, accessible, and unsurpassed, it survives and spreads and those features compound. While technology doesn’t tell us when, or how, or whether to walk through the doors it opens, sooner or later we do seem to walk through them.

For most of history, the challenge of technology lay in creating and unleashing its power. That has now flipped: the challenge of technology today is about containing its unleashed power, ensuring it continues to serve us and our planet.

The more technologies there are, the more they can in turn become components of other new technologies so that, in the words of the economist W. Brian Arthur, “the overall collection of technologies bootstraps itself upward from the few to the many and from the simple to the complex.” Technology is hence like a language or chemistry: not a set of independent entities and practices, but a commingling set of parts to combine and recombine.

The engineer and futurist Ray Kurzweil talks about the “law of accelerating returns,” feedback loops where advances in technology further increase the pace of development.

Deep learning uses neural networks loosely modeled on those of the human brain. In simple terms, these systems “learn” when their networks are “trained” on large amounts of data. In the case of AlexNet, the training data consisted of images. Each red, green, or blue pixel is given a value, and the resulting array of numbers is fed into the network as an input. Within the network, “neurons” link to other neurons by a series of weighted connections, each of which roughly corresponds to the strength of the relationship between inputs. Each layer in the neural network feeds its input down to the next layer, creating increasingly abstract representations. A technique called backpropagation then adjusts the weights to improve the neural network; when an error is spotted, adjustments propagate back through the network to help correct it in the future. Keep doing this, modifying the weights again and again, and you gradually improve the performance of the neural network so that eventually it’s able to go all the way from taking in single pixels to learning the existence of lines, edges, shapes, and then ultimately entire objects in scenes. This, in a nutshell, is deep learning. And this remarkable technique, long derided in the field, cracked computer vision and took the AI world by storm.

LLMs take advantage of the fact that language data comes in a sequential order. Each unit of information is in some way related to data earlier in a series. The model reads very large numbers of sentences, learns an abstract representation of the information contained within them, and then, based on this, generates a prediction about what should come next. The challenge lies in designing an algorithm that “knows where to look” for signals in a given sentence. What are the key words, the most salient elements of a sentence, and how do they relate to one another? In AI this notion is commonly referred to as “attention.” When a large language model ingests a sentence, it constructs what can be thought of as an “attention map.” It first organizes commonly occurring groups of letters or punctuation into “tokens,” something like syllables, but really just chunks of frequently occurring letters making it easier for the model to process the information. It’s worth noting that humans do this with words of course, but the model doesn’t use our vocabulary. Instead, it creates a new vocabulary of common tokens that helps it spot patterns across billions and billions of documents. In the attention map, every token bears some relationship to every token before it, and for a given input sentence the strength of this relationship describes something about the importance of that token in the sentence. In effect, the LLM learns which words to pay attention to.

Based on this, it then suggests which tokens should come next in the sequence, what output logically follows the input. In other words, it autocompletes what might come next. These systems are called transformers.

We are only beginning to scratch at the profound impact large language models are about to have. If DQN and AlphaGo were the early signs of something lapping at the shore, ChatGPT and LLMs are the first signs of the wave beginning to crash around us. In 1996, thirty-six million people used the internet; this year it will be well over five billion. That’s the kind of trajectory we should expect for these tools, only much faster. Over the next few years, I believe, AI will become as ubiquitous as the internet itself: just as available, and yet even more consequential.

In the words of John McCarthy, who coined the term “artificial intelligence”: “As soon as it works, no one calls it AI anymore.” AI is—as those of us building it like to joke—“what computers can’t do.” Once they can, it’s just software.

I think of this as “artificial capable intelligence” (ACI), the point at which AI can achieve complex goals and tasks with minimal oversight. AI and AGI are both parts of the everyday discussion, but we need a concept encapsulating a middle layer in which the Modern Turing Test is achieved but before systems display runaway “superintelligence.” ACI is shorthand for this point.

General-purpose technologies are accelerants. Invention sparks invention. Waves lay the ground for further scientific and technological experimentation, nudging open the doors of possibility. This in turn yields new tools and techniques, new areas of research—new domains of technology itself. Companies form in and around them, attracting investment, pushing the new technologies out into small and big niches alike, further adapting them for a thousand different purposes. Waves are so huge and historic precisely because of this protean complexity, this tendency to mushroom and spill over.

AIs are products of bits and code, existing within simulations and servers. Robots are their bridge, their interface with the real world. If AI represents the automation of information, robotics is the automation of the material, the physical instantiations of AI, a step change in what it is possible to do. Mastery of bits comes full circle, directly reconfiguring atoms, rewriting the bounds not just of what can be thought or said or calculated but what can be built in the most tangible physical sense. And yet the remarkable thing about the coming wave is that this kind of blunt atomic manipulation is nothing compared with what’s on the horizon.

The coming wave is, however, characterized by a set of four intrinsic features compounding the problem of containment. First among them is the primary lesson of this section: hugely asymmetric impact. You don’t need to hit like with like, mass with mass; instead, new technologies create previously unthinkable vulnerabilities and pressure points against seemingly dominant powers. Second, they are developing fast, a kind of hyper-evolution, iterating, improving, and branching into new areas at incredible speed. Third, they are often omni-use; that is, they can be used for many different purposes. And fourth, they increasingly have a degree of autonomy beyond any previous technology.

Omni-use technologies like steam or electricity have wider societal effects and spillovers than narrower technologies. If AI is indeed the new electricity, then like electricity it will be an on-demand utility that permeates and powers almost every aspect of daily life, society, the economy: a general-purpose technology embedded everywhere. Containing something like this is always going to be much harder than containing a constrained, single-task technology, stuck in a tiny niche with few dependencies.

The new wave of autonomy heralds a world where constant intervention and oversight are increasingly unnecessary. What’s more, with every interaction we are teaching machines to be successfully autonomous. In this paradigm, there is no need for a human to laboriously define the manner in which a task should take place. Instead, we just specify a high-level goal and rely on a machine to figure out the optimal way of getting there. Keeping humans “in the loop,” as the saying goes, is desirable, but optional. Nobody told AlphaGo that move 37 was a good idea. It discovered this insight largely on its own. It was precisely this feature that struck me so forcibly watching DQN play Breakout. Given some clearly specified objective, systems now exist that can find their own strategies to be effective. AlphaGo and DQN were not in themselves autonomous. But they hint at what a self-improving system might look like. Nobody hand codes GPT-4 to write like Jane Austen, or produce an original haiku, or generate marketing copy for a website selling bicycles. These features are emergent effects of a wider architecture whose outputs are never decided in advance by its designers. This is the first step on the ladder toward greater and greater autonomy. Internal research on GPT-4 concluded that it was “probably” not capable of acting autonomously or self-replicating, but within days of launch users had found ways of getting the system to ask for its own documentation and to write scripts for co...

In the words of Xi Jinping, speaking to the Twentieth CCP Congress in 2022, “to meet strategic needs” the country “must adhere to science and technology as the number-one productive force, talent as the number-one resource, [and] innovation as the number-one driving force.”

Openness is science and technology’s cardinal ideology. What is known must be shared; what is discovered must be published. Science and technology live and breathe on free debate and the open sharing of information, to the extent that openness has itself grown into a powerful (and amazingly beneficial) incentive.

The truth is that the curiosity of academic researchers or the will of motivated governments is insufficient to propel new breakthroughs into the hands of billions of consumers. Science has to be converted into useful and desirable products for it to truly spread far and wide. Put simply: most technology is made to earn money.

The sheer breadth of human wants and needs, and the countless opportunities to profit from them, are integral to the story of technology and will remain so in the future.

Large language models enable you to have a useful conversation with an AI about any topic in fluent, natural language. Within the next couple of years, whatever your job, you will be able to consult an on-demand expert, ask it about your latest ad campaign or product design, quiz it on the specifics of a legal dilemma, isolate the most effective elements of a pitch, solve a thorny logistical question, get a second opinion on a diagnosis, keep probing and testing, getting ever more detailed answers grounded in the very cutting edge of knowledge, delivered with exceptional nuance. All of the world’s knowledge, best practices, precedent, and computational power will be available, tailored to you, to your specific needs and circumstances, instantaneously and effortlessly. It is a leap in cognitive potential at least as great as the introduction of the internet.

The energy scholar Vaclav Smil calls ammonia, cement, plastics, and steel the four pillars of modern civilization: the material base underwriting modern society, each hugely carbon-intensive to produce, with no obvious successors. Without these materials modern life stops, and without fossil fuels the materials stop.

Making history, doing something that matters, helping others, beating others, impressing a prospective partner, impressing a boss, peers, rivals: it’s all in there, all part of the ever-present drive to take risks, explore the edges, go further into the unknown. Build something new. Change the game. Climb the mountain.

The overwhelming majority of these technologies will be used for good. Although I have focused on their risks, it’s important to keep in mind they will improve countless lives on a daily basis.

The philosopher of technology Lewis Mumford talked about the “megamachine,” where social systems combine with technologies to form “a uniform, all-enveloping structure” that is “controlled for the benefit of depersonalized collective organizations.”

The physicist Richard Feynman famously said, “What I cannot create, I do not understand.”

Put all the elements here together and there is an outline of what will meet and match the coming wave. 1. Technical safety: Concrete technical measures to alleviate possible harms and maintain control. 2. Audits: A means of ensuring the transparency and accountability of technology. 3. Choke points: Levers to slow development and buy time for regulators and defensive technologies. 4. Makers: Ensuring responsible developers build appropriate controls into technology from the start. 5. Businesses: Aligning the incentives of the organizations behind technology with its containment. 6. Government: Supporting governments, allowing them to build technology, regulate technology, and implement mitigation measures. 7. Alliances: Creating a system of international cooperation to harmonize laws and programs. 8. Culture: A culture of sharing learning and failures to quickly disseminate means of addressing them. 9. Movements: All of this needs public input at every level, including to put pressure on each component and make it accountable. Step 10 is about coherence, ensuring that each element works in harmony with the others, that containment is a virtuous circle of mutually reinforcing measures and not a gap-filled cacophony of competing programs. In this sense, containment isn’t about this or that specific suggestion but is an emergent phenomenon of their collective interplay, a by-product of societies that learn to manage and mitigate the risks thrown up by Homo technologicus.