AIを通常の技術として捉える

This post is over 15,000 words long—it is a new paper on our vision for the future of AI. We are pleased to announce that an expanded version of these ideas will become our next book together.

The paper is also published in HTML and PDF formats on the Knight First Amendment Institute’s website. We are grateful for the extensive feedback we’ve received on drafts of the paper.

Update (September 2025): We have published a companion to this essay titled A guide to understanding AI as normal technology.

We articulate a vision of artificial intelligence (AI) as normal technology. To view AI as normal is not to understate its impact—even transformative, general-purpose technologies such as electricity and the internet are “normal” in our conception. But it is in contrast to both utopian and dystopian visions of the future of AI which have a common tendency to treat it akin to a separate species, a highly autonomous, potentially superintelligent entity.1

The statement “AI is normal technology” is three things: a description of current AI, a prediction about the foreseeable future of AI, and a prescription about how we should treat it. We view AI as a tool that we can and should remain in control of, and we argue that this goal does not require drastic policy interventions or technical breakthroughs. We do not think that viewing AI as a humanlike intelligence is currently accurate or useful for understanding its societal impacts, nor is it likely to be in our vision of the future.2

The normal technology frame is about the relationship between technology and society. It rejects technological determinism, especially the notion of AI itself as an agent in determining its future. It is guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion. It also emphasizes continuity between the past and the future trajectory of AI in terms of societal impact and the role of institutions in shaping this trajectory.

In Part I, we explain why we think that transformative economic and societal impacts will be slow (on the timescale of decades), making a critical distinction between AI methods, AI applications, and AI adoption, arguing that the three happen at different timescales.

In Part II, we discuss a potential division of labor between humans and AI in a world with advanced AI (but not “superintelligent” AI, which we view as incoherent as usually conceptualized). In this world, control is primarily in the hands of people and organizations; indeed, a greater and greater proportion of what people do in their jobs is AI control.

In Part III, we examine the implications of AI as normal technology for AI risks. We analyze accidents, arms races, misuse, and misalignment, and argue that viewing AI as normal technology leads to fundamentally different conclusions about mitigations compared to viewing AI as being humanlike.

Of course, we cannot be certain of our predictions, but we aim to describe what we view as the median outcome. We have not tried to quantify probabilities, but we have tried to make predictions that can tell us whether or not AI is behaving like normal technology.

In Part IV, we discuss the implications for AI policy. We advocate for reducing uncertainty as a first-rate policy goal and resilience as the overarching approach to catastrophic risks. We argue that drastic interventions premised on the difficulty of controlling superintelligent AI will, in fact, make things much worse if AI turns out to be normal technology— the downsides of which will be likely to mirror those of previous technologies that are deployed in capitalistic societies, such as inequality.3

The world we describe in Part II is one in which AI is far more advanced than it is today. We are not claiming that AI progress—or human progress—will stop at that point. What comes after it? We do not know. Consider this analogy: At the dawn of the first Industrial Revolution, it would have been useful to try to think about what an industrial world would look like and how to prepare for it, but it would have been futile to try to predict electricity or computers. Our exercise here is similar. Since we reject “fast takeoff” scenarios, we do not see it as necessary or useful to envision a world further ahead than we have attempted to. If and when the scenario we describe in Part II materializes, we will be able to better anticipate and prepare for whatever comes next.

A note to readers. This essay has the unusual goal of stating a worldview rather than defending a proposition. The literature on AI superintelligence is copious. We have not tried to give a point-by-point response to potential counter arguments, as that would make the paper several times longer. This paper is merely the initial articulation of our views; we plan to elaborate on them in various follow ups.

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Part I: The Speed of Progress

Figure 1. Like other general-purpose technologies, the impact of AI is materialized not when methods and capabilities improve, but when those improvements are translated into applications and are diffused through productive sectors of the economy.4 There are speed limits at each stage.

Will the progress of AI be gradual, allowing people and institutions to adapt as AI capabilities and adoption increase, or will there be jumps leading to massive disruption, or even a technological singularity? Our approach to this question is to analyze highly consequential tasks separately from less consequential tasks and to begin by analyzing the speed of adoption and diffusion of AI before returning to the speed of innovation and invention.

We use invention to refer to the development of new AI methods—such as large language models—that improve AI’s capabilities to carry out various tasks. Innovation refers to the development of products and applications using AI that consumers and businesses can use. Adoption refers to the decision by an individual (or team or firm) to use a technology, whereas diffusion refers to the broader social process through which the level of adoption increases. For sufficiently disruptive technologies, diffusion might require changes to the structure of firms and organizations, as well as to social norms and laws.

AI Diffusion in Safety-critical Areas Is Slow

In the paper Against Predictive Optimization, we compiled a comprehensive list of about 50 applications of predictive optimization, namely the use of machine learning (ML) to make decisions about individuals by predicting their future behavior or outcomes.5 Most of these applications, such as criminal risk prediction, insurance risk prediction, or child maltreatment prediction, are used to make decisions that have important consequences for people.

While these applications have proliferated, there is a crucial nuance: In most cases, decades-old statistical techniques are used—simple, interpretable models (mostly regression) and relatively small sets of handcrafted features. More complex machine learning methods, such as random forests, are rarely used, and modern methods, such as transformers, are nowhere to be found.

In other words, in this broad set of domains, AI diffusion lags decades behind innovation. A major reason is safety—when models are more complex and less intelligible, it is hard to anticipate all possible deployment conditions in the testing and validation process. A good example is Epic’s sepsis prediction tool which, despite having seemingly high accuracy when internally validated, performed far worse in hospitals, missing two thirds of sepsis cases and overwhelming physicians with false alerts.6

Epic’s sepsis prediction tool failed because of errors that are hard to catch when you have complex models with unconstrained feature sets.7 In particular, one of the features used to train the model was whether a physician had already prescribed antibiotics —to treat sepsis. In other words, during testing and validation, the model was using a feature from the future, relying on a variable that was causally dependent on the outcome. Of course, this feature would not be available during deployment. Interpretability and auditing methods will no doubt improve so that we will get much better at catching these issues, but we are not there yet.

In the case of generative AI, even failures that seem extremely obvious in hindsight were not caught during testing. One example is the early Bing chatbot “Sydney” that went off the rails during extended conversations; the developers evidently did not anticipate that conversations could last for more than a handful of turns.8 Similarly, the Gemini image generator was seemingly never tested on historical figures.9 Fortunately, these were not highly consequential applications.

More empirical work would be helpful for understanding the innovation-diffusion lag in various applications and the reasons for this lag. But, for now, the evidence that we have analyzed in our previous work is consistent with the view that there are already extremely strong safety-related speed limits in highly consequential tasks. These limits are often enforced through regulation, such as the FDA’s supervision of medical devices, as well as newer legislation such as the EU AI Act, which puts strict requirements on high-risk AI.10 In fact, there are (credible) concerns that existing regulation of high-risk AI is so onerous that it may lead to “runaway bureaucracy”.11 Thus, we predict that slow diffusion will continue to be the norm in high-consequence tasks.

At any rate, as and when new areas arise in which AI can be used in highly consequential ways, we can and must regulate them. A good example is the Flash Crash of 2010, in which automated high-frequency trading is thought to have played a part. This led to new curbs on trading, such as circuit breakers.12

Diffusion is Limited by the Speed of Human, Organizational, and Institutional Change

Even outside of safety-critical areas, AI adoption is slower than popular accounts would suggest. For example, a study made headlines due to the finding that, in August 2024, 40% of U.S. adults used generative AI.13 But, because most people used it infrequently, this only translated to 0.5%-3.5% of work hours (and a 0.125-0.875 percentage point increase in labor productivity).

It is not even clear if the speed of diffusion is greater today compared to the past. The aforementioned study reported that generative AI adoption in the U.S. has been faster than personal computer (PC) adoption, with 40% of U.S. adults adopting generative AI within two years of the first mass-market product release compared to 20 % within three years for PCs. But this comparison does not account for differences in the intensity of adoption (the number of hours of use) or the high cost of buying a PC compared to accessing generative AI.14 Depending on how we measure adoption, it is quite possible that the adoption of generative AI has been much slower than PC adoption.

The claim that the speed of technology adoption is not necessarily increasing may seem surprising (or even obviously wrong) given that digital technology can reach billions of devices at once. But it is important to remember that adoption is about software use, not availability. Even if a new AI-based product is instantly released online for anyone to use for free, it takes time to for people to change their workflows and habits to take advantage of the benefits of the new product and to learn to avoid the risks.

Thus, the speed of diffusion is inherently limited by the speed at which not only individuals, but also organizations and institutions, can adapt to technology. This is a trend that we have also seen for past general-purpose technologies: Diffusion occurs over decades, not years.15

As an example, Paul A. David’s analysis of electrification shows that the productivity benefits took decades to fully materialize.16 Electric dynamos were “everywhere but in the productivity statistics” for nearly 40 years after Edison’s first central generating station. 17 This was not just technological inertia; factory owners found that electrification did not bring substantial efficiency gains.

What eventually allowed gains to be realized was redesigning the entire layout of factories around the logic of production lines. In addition to changes to factory architecture, diffusion also required changes to workplace organization and process control, which could only be developed through experimentation across industries. Workers had more autonomy and flexibility as a result of the changes, which also necessitated different hiring and training practices.

The External World Puts a Speed Limit on AI Innovation

It is true that technical advances in AI have been rapid, but the picture is much less clear when we differentiate AI methods from applications.

We conceptualize progress in AI methods as a ladder of generality.18 Each step on this ladder rests on the ones below it and reflects a move toward more general computing capabilities. That is, it reduces the programmer effort needed to get the computer to perform a new task and increases the set of tasks that can be performed with a given amount of programmer (or user) effort; see Figure 2. For example, machine learning increases generality by obviating the need for the programmer to devise logic to solve each new task, only requiring the collection of training examples instead.

It is tempting to conclude that the effort required to develop specific applications will keep decreasing as we build more rungs of the ladder until we reach artificial general intelligence, often conceptualized as an AI system that can do everything out of the box, obviating the need to develop applications altogether.

In some domains, we are indeed seeing this trend of decreasing application development effort. In natural language processing, large language models have made it relatively trivial to develop a language translation application. Or consider games: AlphaZero can learn to play games such as chess better than any human through self-play given little more than a description of the game and enough computing power—a far cry from how game-playing programs used to be developed.

Figure 2: The Ladder of Generality in Computing. For some tasks, higher ladder rungs require less programmer effort to get a computer to perform a new task, and more tasks can be performed with a given amount of programmer (or user) effort.19

However, this has not been the trend in highly consequential, real-world applications that cannot easily be simulated and in which errors are costly. Consider self-driving cars: In many ways, the trajectory of their development is similar to AlphaZero’s self-play—improving the tech allowed them to drive in more realistic conditions, which enabled the collection of better and/or more realistic data, which in turn led to improvements in the tech, completing the feedback loop. But this process took over two decades instead of a few hours in the case of AlphaZero because safety considerations put a limit on the extent to which each iteration of this loop could be scaled up compared to the previous one.20

This “capability-reliability gap” shows up over and over. It has been a major barrier to building useful AI “agents” that can automate real-world tasks.21 To be clear, many tasks for which the use of agents is envisioned, such as booking travel or providing customer service, are far less consequential than driving, but still costly enough that having agents learn from real-world experiences is not straightforward.

Barriers also exist in non-safety-critical applications. In general, much knowledge is tacit in organizations and is not written down, much less in a form that can be learned passively. This means that these developmental feedback loops will have to happen in each sector and, for more complex tasks, may even need to occur separately in different organizations, limiting opportunities for rapid, parallel learning. Other reasons why parallel learning might be limited are privacy concerns: Organizations and individuals might be averse to sharing sensitive data with AI companies, and regulations might limit what kinds of data can be shared with third parties in contexts such as healthcare.

The “bitter lesson” in AI is that general methods that leverage increases in computational power eventually surpass methods that utilize human domain knowledge by a large margin.22 This is a valuable observation about methods, but it is often misinterpreted to encompass application development. In the context of AI-based product development, the bitter lesson has never been even close to true.23 Consider recommender systems on social media: They are powered by (increasingly general) machine learning models, but this has not obviated the need for manual coding of the business logic, the frontend, and other components which, together, can comprise on the order of a million lines of code.

Further limits arise when we need to go beyond AI learning from existing human knowledge.24 Some of our most valuable types of knowledge are scientific and social-scientific, and have allowed the progress of civilization through technology and large-scale social organizations (e.g., governments). What will it take for AI to push the boundaries of such knowledge? It will likely require interactions with, or even experiments on, people or organizations, ranging from drug testing to economic policy. Here, there are hard limits to the speed of knowledge acquisition because of the social costs of experimentation. Societies probably will not (and should not) allow the rapid scaling of experiments for AI development.

Benchmarks Do not Measure Real-World Utility

The methods-application distinction has important implications for how we measure and forecast AI progress. AI benchmarks are useful for measuring progress in methods; unfortunately, they have often been misunderstood as measuring progress in applications, and this confusion has been a driver of much hype about imminent economic transformation.

For example, while GPT-4 reportedly achieved scores in the top 10% of bar exam test takers, this tells us remarkably little about AI’s ability to practice law.25 The bar exam overemphasizes subject-matter knowledge and under-emphasizes real-world skills that are far harder to measure in a standardized, computer-administered format. In other words, it emphasizes precisely what language models are good at—retrieving and applying memorized information.

More broadly, tasks that would lead to the most significant changes to the legal profession are also the hardest ones to evaluate. Evaluation is straightforward for tasks like categorizing legal requests by area of law because there are clear correct answers. But for tasks that involve creativity and judgment, like preparing legal filings, there is no single correct answer, and reasonable people can disagree about strategy. These latter tasks are precisely the ones that, if automated, would have the most profound impact on the profession.26

This observation is in no way limited to law. Another example is the gap between self-contained coding problems at which AI demonstrably excels, and real-world software engineering in which its impact is hard to measure but appears to be modest.27 Even highly regarded coding benchmarks that go beyond toy problems must necessarily igno