Normal Technology: Powerful AI, but a Tool Nonethless

Thomas Topuz — Fri, 25 Sep 2026 12:15:00 +0000

This is a reaction on the article: https://knightcolumbia.org/content/ai-as-normal-technology

The normal technology thesis is particularly convincing to me since it draws a distinction between technological development and the real-world impact of the technology in terms of applications, use-cases and adoption.

Technological progress does not necessarily imply the economic impact, the latter depends on applications, business cases, integration into the current process and eventually adoption.

However, the process of development and adoption happen on different speeds, AI evolves on the pace of research while adoption happens on the pace of humans.

This leads to the feeling that organizations are always behind, each couple of months brings a new model or a new capability, and there is constant pressure to adapt to the changes, yet adoption happens much slower since organizational change implies the change of processes, responsibilities, incentives, and potentially even cultural shifts.

Adoption is one of the key bottlenecks of AI

As far as I see it now, adoption is the bottleneck rather than capability.

AI models might work really well in the lab conditions, however adoption is the moment when AI meets the real world, which is diverse, uncertain and full of exceptions. Most importantly, AI cannot adopt itself, humans adopt AI.

This is well seen in the field of software development: coding became much faster thanks to AI, however software product development did not speed up as well, the bottleneck shifted from implementation towards making decisions on what should be implemented and what would bring value to the business.

The key question is increasingly becoming not "how do we build this" but "what should we build and why".

That is why I completely agree with the thesis that there is a significant distinction between invention, innovation, adoption and diffusion, better technology doesn't imply useful products.

I also think there might be a bottleneck in the area of invention, since the transformer architecture, most of the developments in frontier AI were made through scaling of the computational power and the amount of data rather than through the discovery of an equally fundamental invention. The fact that some Chinese models became competitive with lower amounts of computational power suggests that more sophisticated research might bring substantial gains.

High-risk applications will adopt AI slowly

I also agree that adoption will be slower in areas where mistakes have severe consequences.

AI cannot be adopted in such domains as healthcare, law, finance and others in the same manner as AI for writing assistants or coding tools. Even if AI becomes much more reliable than today, I think that people will still be cautious.

For instance, an AI judge that is statistically better and less biased than a human judge, would be considered morally wrong by many people to sentence another human being.

Thus, the key problem is not only reliability, but also legitimacy and responsibility.

AI may accumulate power indirectly

There is a certain point, in regard to which I am less convinced of the argument about "normal technology".

I agree that AI does not automatically get any formal authority, but it is capable of accumulating power indirectly.

People already outsource parts of the cognitive work to AI. A person with decision-making authority may increasingly start relying on AI to analyze the situation, to propose a course of action and finally recommend a decision. Formally the person will make a decision, but the real source of this decision will become unclear.

This is already visible in the software development workflows: business analyst uses AI to write a user story, developer uses AI to implement it and QA uses AI to generate test cases. The role of people in this workflow decreases and most of the cognitive work is being outsourced.

The more people outsource their critical thinking, the more power AI will accumulate.

AI is likely to take the jobs of professionals

I am also more pessimistic in regard to job replacement.

In case an AI system becomes much more efficient than a human at performing most of the tasks that define a profession, I don't see strong reasons to suppose that this profession won't be fundamentally transformed.

Humans will probably move to the jobs, where AI is less capable.

One of the capabilities that is particularly important is a taste, an understanding of what exactly another human wants from an output.

A client does not only need a technically correct solution. Clients have certain preferences, certain expectations and sometimes they are not sure what exactly they want. Humans are still better in dealing with this.

In software engineering, I expect increasing polarization, on one side there will be very deep technical roles, on the other side there will be consulting, sales, product management and AI adoption roles, where human skills become more valuable.

Middle layer of the product-oriented full stack developers will probably shrink.

AI is not a new species

On the other hand, I completely reject the idea that AI should be treated as a new species.

From my perspective, AI remains a tool, although a very powerful one. A human being using a tool can usually be more capable than the tool alone.

Similar to electricity or the Internet, AI is a horizontal technology that is capable of transforming many domains. But there is an important difference between AI and other horizontal technologies, AI operates directly via language.

Language is intrinsically human. Being capable of communication in natural language, AI generates anthropomorphism and creates a perception that AI is more threatening than other technologies.

The largest risks still come from humans

Overall, I believe that the largest risks come from the way how people use AI.

AI does not require any intentions in order to create a threat, a powerful tool can be dangerous when used by malicious people.

Discrimination is an interesting example, AI is learning from the historical data, however the historical data contains historical biases. Therefore we want AI to learn from the past, but not to reproduce all of the values from the past.

That is why we expect AI not only to learn patterns, but to incorporate values of the modern society.

AI creates a false impression of competence

Another issue I would like to discuss is the way AI affects the learning process.

In my understanding, knowledge resembles a tree with branches. AI will let you develop the knowledge in a particular area really fast.

However, you will run into problems when you start adding more branches to the knowledge you have just obtained. In case you did not fully assimilate the knowledge of the topic, it may become difficult for you to distinguish right answers from mistakes made by the AI.

In this case, it will create a fake impression of competence in the topic.

Where I disagree the most

It is in the area of recursive self-improvement.

I do not think that the concept of AGI is very useful. But I do think that a transition to the state of AI capable of self-improvement is possible, and this transition will bring a new challenge in the form of control problem.

I am therefore convinced by the idea of AI as normal technology, but with a condition, as long as humans are the drivers of its development and adoption.

The key questions is not only how capable AI becomes. It is also how much thinking, judgment and responsibility humans outsource to AI.

DEV Community: Thomas Topuz