AIを通常の技術として理解するためのガイド

When we published AI as Normal Technology, the impact caught us off guard. It quickly became the most influential thing either of us had ever done.1 We took this as a strong signal to spend more of our time thinking and writing about the medium-term future of AI and its impacts, offering grounded analysis of a topic that tends to attract speculation. This is a shift in focus away from writing about the present-day and near-term impacts of AI, which is what the AI Snake Oil project was about.

Reflecting this shift, we have renamed this newsletter. We have already published two follow-up essays to AI as Normal Technology and will publish more regularly as we expand our framework into a book, which we plan to complete in late 2026 for publication in 2027.

Today, we address common points of confusion about AI as Normal Technology, try to make the original essay more approachable, and compare it to AI 2027.

Table of contents

Normal doesn’t mean mundane or predictable

A restatement of our thesis

If disappointment about GPT-5 has nudged you towards AI as normal technology, it’s possible you don’t quite understand the thesis

Why it’s hard to find a “middle ground” between AI as Normal Technology and AI 2027

It is hard to understand one worldview when you’re committed to another

Reaping AI’s benefits will require hard work and painful choices

The surreal debate about the speed of diffusion

Why AI adoption hits different

Concluding thoughts

Normal doesn’t mean mundane or predictable

While the essay talks about what we mean by normal (more on that below), we could have been more explicit about what it doesn’t mean.

Our point is not “nothing to see here, move along”. Indeed, unpredictable societal effects have been a hallmark of powerful technologies ranging from automobiles to social media. This is because they are emergent effects of complex interactions between technology and people. They don’t tend to be predictable based on the logic of the technology alone. That’s why rejecting technological determinism is one of the core premises of the normal technology essay.

In the case of AI, specifically chatbots, we’re already seeing emergent societal effects. The prevalence of AI companions and some of the harmful effects of model sycophancy such as “AI psychosis” have taken most observers by surprise.2 On the other hand, many risks that were widely predicted to be imminent, such as AI being used to manipulate elections, have not materialized.

What the landscape of AI’s social impacts will look like in say 3-5 years — even based on the diffusion of current capabilities, not future capabilities — is anyone’s guess.

The development of technical capabilities is more predictable than AI’s social impacts. Daniel Kokotajlo, one of the authors of AI 2027, was previously famous in the AI safety community for his essay “What 2026 looks like” back in 2021. His predictions about the tech itself proved eerily accurate, but the predictions about social impacts were overall not directionally correct, a point he was gracious enough to concede in a podcast discussion with one of us.

All this makes AI a more serious challenge for institutions and policymakers because they will have to react nimbly to unforeseeable impacts instead of relying on the false comfort of prediction or trying to prevent all harm. Broadly speaking, the policymaking approach that enables such adaptability is called resilience, which is what our essay advocated for. But while we emphasized resilience as the approach to deal with potentially catastrophic risks, we should have been clearer that resilience also has an important role in dealing with more diffuse risks.

Perhaps the reason some readers misunderstood our view of predictability is the word “normal”. Again, our goal is not to trivialize the task of individually and collectively adapting to AI. In an ideal world, a better title would have been simply “AI as Technology”, but we didn’t think that that would effectively communicate that our goal is to provide an alternative to the exceptionalism that characterizes the superintelligence worldview which currently dominates the discourse.

A restatement of our thesis

If we were to extract and simplify the core of our thesis, it would be something like this:

There is a long causal chain between AI capability increases and societal impact. Benefits and risks are realized when AI is deployed, not when it is developed. This gives us (individuals, organizations, institutions, policymakers) many points of leverage for shaping those impacts. So we don’t have to fret as much about the speed of capability development; our efforts should focus more on the deployment stage both from the perspective of realizing AI’s benefits and responding to risks. All this is not just true of today’s AI, but even in the face of hypothetical developments such as self-improvement in AI capabilities. Many of the limits to the power of AI systems are (and should be) external to those systems, so that they cannot be overcome simply by having AI go off and improve its own technical design.

Aspects of this framework may have to be revised eventually, but that lies beyond the horizon bounding what we can meaningfully anticipate or prepare for:

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.

Anyway, to reiterate, the core of the thesis is the underlying causal framework for understanding the relationship between AI and society, not any of the specific impacts that it might or might not have. In our view, if you share this causal understanding, you subscribe to the normal technology thesis. We have found that this framework is indeed widely shared, albeit implicitly.

That makes the thesis almost tautological in many readers’ minds. We are making what we see as — and what those readers should see as — a very weak claim! Not recognizing this causes readers to search for something much more specific that we may have meant by “normal”. But we didn’t. We aren’t classifying technologies as “normal” and “abnormal” and then putting AI into the “normal” bucket. We’re just saying we should treat AI like we treat other powerful general-purpose technologies.

This is not specific to large language models or any particular kind of AI. Incidentally, that’s why the title is “AI as normal technology” and not “AI as a normal technology”. Our views apply to the whole umbrella of technologies that are collectively referred to as AI, and other similar technologies even if they are not referred to as AI.

If our worldview is almost tautological, why bother to state it? Because it is in contrast to the superintelligence worldview. That’s the thing about worldviews: there can be mutually contradictory worldviews that each seem tautological to those who subscribe to them.

If disappointment about GPT-5 has nudged you towards AI as normal technology, it’s possible you don’t quite understand the thesis

It’s notable that there’s been a surge of interest in our essay after the release of GPT-5, and reasonable to surmise that at least some of that is because of people shifting their views a bit after being disappointed by the release.

This is strange! This isn’t the first time this has happened — we previously expressed skepticism of a big narrative shift around scaling that happened based on almost no new information. If a single update to one product shifts people’s views on the trajectory of AI, how reliable is people’s evidence base to begin with?

The reason why the normal technology framework predicts slow timelines is not because capabilities will hit a wall but because impacts will be slow and gradual even if capabilities continue to advance rapidly. So we don’t think disappointment with a new release should make you more sympathetic to viewing AI as normal technology. By the same token, a new breakthrough announced tomorrow shouldn’t cause you to be more skeptical of our views.

The best way to understand GPT-5 is that it’s a particularly good example of AI developers’ shift in emphasis from models to products, which we wrote about a year ago. The automatic model switcher is a big deal for everyday users of ChatGPT. It turns out that hardly anyone was using “thinking” models nearly a year after they were first released, and GPT-5 has bumped up their use dramatically.

In some communications Altman was clear that the emphasis for GPT-5 was usability, not a leap in capabilities, although this message was unfortunately undercut by the constant hype, leading to disappointment.

This broader shift in the industry is actually highly consistent with companies themselves (reluctantly) coming around to acknowledging the possibility that their path to success is to do the hard work of building products and fostering adoption, rather than racing to build AGI or superintelligence and count on it to sweep away any diffusion barriers. Ironically, in this narrative, GPT-5 is an example of a success, not a failure.

In fact, model developers are starting to go beyond developing more useful products (the second stage of our technology development & adoption framework) and working with deployers to ease early adoption pains (the third stage). For example, OpenAI’s Forward Deployed Engineers work with customers such as John Deere, and directly with farmers, on integrating and deploying capabilities such as providing personalized recommendations for pesticide application.

Why it’s hard to find a “middle ground” between AI as Normal Technology and AI 2027

Many people have tried to articulate middle ground positions between AI 2027 and AI as Normal Technology, perhaps viewing these as two ends of a spectrum of views.

This is surprisingly hard to do. Both AI 2027 and AI as Normal Technology are coherent worldviews. They represent very different causal understandings of how technology will impact society. If you try to mix and match, there is a risk that you end up with an internally inconsistent hodgepodge. (By the way, this means that if we end up being wrong, it is more likely that we will be wrong wholesale than slightly wrong.)

Besides, only in the Silicon Valley bubble can AI as Normal Technology be considered a skeptical view! We compare AI to electricity in the second sentence of the essay, and we make clear throughout that we expect it to have profound impacts. Our expectations for AI’s impact on labor seem to be at the more aggressive end of the range of expectations from economists who work on this topic.

In short, if you are looking for a moderate position, we encourage you to read the essay in full. Don’t let the title fool you into thinking we are AI skeptics. Perhaps you will conclude that AI as Normal Technology is already the middle ground you are looking for.

We realize that it can be discomfiting that the two most widely discussed frameworks for thinking about the future of AI are so radically different. (Our essay itself offers much commentary on this state of affairs in Part 4, which is about policy.) We can offer a few comforting thoughts:

We do have many areas of agreement with the AI 2027 authors. We are working on a joint statement outlining those areas. We are grateful to Nicholas Carlini for organizing this effort.

In our view, more important than agreement in beliefs are areas of common ground in policy despite differences in beliefs. Even relatively “easy” policy interventions that different sides can agree on will be a huge challenge in practice. If we can’t achieve these, there is little hope for the much more radical measures favored by those worried about imminent superintelligence.

There have been a few ongoing efforts to identify the cruxes of disagreement and agree on indicators that might help adjudicate between the two worldviews. We have participated in a few of these efforts and look forward to continuing to do so. We are grateful to the Golden Gate Institute for AI’s efforts on this front.

Speaking of developing indicators, we are in the process of expanding the vision for our project HAL, Holistic Agent Leaderboard. Currently it tries to be a better benchmark orchestration system for AI agents, but the new plan is to develop it into an early warning system that helps the AI community identify when AI agents have crossed capability thresholds for transformative real-world impacts in various domains.

We see these capability thresholds as necessary but not always sufficient conditions for impact, and as and when they are reached, they will much more acutely stress our theses about non-technological barriers to both benefits and risks.

Note that HAL is not about prediction; it is about situational awareness of the present. This is a theme of our work. What is remarkable about the AI discourse in general, and us versus AI 2027 in particular, is the wide range of views not just about the future but about the things we can observe, such as the speed of diffusion (more on that below). Unless we as a community get much better at measurement of the present and testing competing causal explanations of progress, the level of energy directed at prediction will be misdirected, because we lack ways of resolving those predictions. For example, we’ve argued that we won’t necessarily know if “AGI” has been built even post facto. To some extent these limitations are intrinsic because of the lack of conceptual precision of ideas like AGI, but at the same time it’s true that we can do a lot better at measurement.

It is hard to understand one worldview when you’re committed to another

We wrote:

AI as normal technology is a worldview that stands in contrast to the worldview of AI as impending superintelligence. Worldviews are constituted by their assumptions, vocabulary, interpretations of evidence, epistemic tools, predictions, and (possibly) values. These factors reinforce each other and form a tight bundle within each worldview.

This makes communication across worldviews hard. For example, one question we often receive from the AI 2027 folks is what we think the world will look like in 2027. Well, pretty much like the world in 2025, we respond. They then push us to consider 2035 or 2045 or whatever year by which the world will be transformed, and they consider it a deficiency of our framework that we don’t provide concrete scenarios.

But this kind of scenario forecasting is only a meaningful activity within their worldview. We are concrete about the things we think we can be concrete about. At the same time, we emphasize the role of human, institutional, and political agency in making radically different futures possible — including AI 2027. Thus, AI as normal technology is as much a prescription as a prediction.

These communication difficulties are important to keep in mind when considering the response by Scott Alexander, one of the AI 2027 authors, to AI as Normal Technology. While we have no doubt that it is a good-faith effort at dialogue and we appreciate his putting in the time, unfortunately we feel that his response mostly talks past us. What he identifies as the cruxes of disagreement are quite different from what we consider the cruxes! For this reason, we won’t give a point-by-point response, since we will probably in turn end up talking past him in turn.

But we would be happy to engage in moderated conversations, a format with which we’ve had good success and have engaged in 8-10 times over the past year. The synchronous nature makes it much easier to understand each other. And the fact that the private conversation will be edited before making it public makes it easier to ask stupid questions as each side searches for understanding of the other’s point of view.

Anyway, here are a couple of important ways in which Alexander’s response talks past us. Recursive Self-Improvement (RSI) is a crux of disagreement for Alexander’s point of view, and he is surprised that it is barely worth a mention for us. In fairness we could have been much more explicit in our essay about what we think about RSI. In short, we don’t think RSI will lead to superintelligence because the external bottlenecks to building and deploying powerful AI systems cannot be overcome by improving their technical design. That is why we don’t discuss it much.3

Although it is not a crux for us, we do explain in the essay why we think the AI community is nowhere close to RSI. More recently we’ve been thinking about the fundamental research challenges that need to be solved, and there are a lot more than we’d realized. And it is worth keeping in mind that the AI community might be particularly bad at finding new paradigms for progress compared to other scientific communities. Again, this is an area where we hope that our project HAL can play a role in measuring progress.

Another topic where Alexander’s response talks past us is on the speed of diffusion, which we comment on briefly below and will address in more detail in a future essay.

The best illustration of the difficulty of discourse across worldviews is Alexander’s discussion of our hypotheses about whether or not superhuman AI abilities are possible in tasks such as prediction or persuasion. After reading his response several times, it is hard for us to figure out where exactly we agree and disagree. We wrote:

We think there are relatively few real-world cognitive tasks in which human limitations are so telling that AI is able to blow past human performance (as AI does in chess). ... Concretely, we propose two such areas: forecasting and persuasion. We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections). We make the same prediction for the task of persuading people to act against their own self-interest.

You can read his full response to this in Section 3B of his essay, but in short it focuses on human biological limits:

Humans gained their abilities through thousands of years of evolution in the African savanna. There was no particular pressure in the savanna for “get exactly the highest Brier score possible in a forecasting contest”, and there is no particular reason to think humans achieved this. Indeed, if the evidence for human evolution for higher intelligence in the past 10,000 years in response to agriculture proves true, humans definitely didn’t reach the cosmic maximum on the African savannah. Why should we think this last, very short round of selection got it exactly right?

But rejecting a biological conception of human abilities is a key point of departure for us, something we take pains to describe in detail in the section “Human Ability Is Not Constrained by Biology”. That’s the problem with discussion across worldviews: If you take a specific statement, ignore the premises and terminological clarifications leading up to it, and interpret it in your worldview, it will seem like your opponent is clueless. Does Alexander think we are suggesting that if a savanna-dweller time traveled to the present, they would be able to predict elections?

He does emphasize that human performance is not fixed, but somehow sees this as a refutation of our thesis (rather than cen