The revenge of "slow AI": why the future of automation is not instant, but reasoned

For years, the main metric by which we judged technology was latency. The faster, more "reactive," and snappy a system was, the better it was considered. Even the first wave of generative AI followed this dogma: the "wow" effect was driven by ChatGPT's ability to spit out text, a summary, or a translation in real-time, almost faster than we could read.

However, anyone who has tried to use these tools for complex business tasks has run into their intrinsic limit: superficiality. Fast models tend to "guess" the most probable answer, not necessarily the most logical one, often stumbling into banal errors or hallucinations when the task requires multiple deductive steps.

Today we are facing a paradigm shift that we could define as "slow AI" or, more technically, the advent of reasoning models.

The AI that learns to stop and think

The big technological news of recent months (driven by models like OpenAI's o1 series and similar) is the introduction of a "chain of thought" before the response.

When we ask these new models a complex question, the system doesn't rush to generate the output. On the contrary, it takes a pause. In those seconds of silence—which can seem like an eternity to a user accustomed to instantaneity—the AI is working behind the scenes: it breaks the problem down into sub-problems, plans the necessary steps to solve them, verifies intermediate results, and, if necessary, goes back and corrects itself.

It's the difference between "shooting out an answer" on instinct and taking a minute to reflect before speaking.

Why companies need "slow" AI

For the business world, this slowdown is actually an acceleration towards real adoption. Speed is a value in first-level customer service or simple information retrieval, but it becomes a risk in almost all other critical processes.

Think of analyzing a legal contract, optimizing a logistics route with multiple constraints, or writing software code for a custom application. In these cases, the company doesn't need an answer in 500 milliseconds that is "probably right." It needs an answer in 30 seconds (or even 5 minutes) that is accurate, verified, and logically sound.

Reliability, in the B2B context, beats speed ten to zero.

Towards a new type of software

This shift will also impact how we design interfaces and workflows. Until today, we have integrated AI in the form of "copilots" or chatbots to converse with in real-time. With reasoning models, we will move towards an "agent" model. We won't sit there watching the cursor blink; we will assign a complex task to the system (e.g., "analyze these 50 suppliers and tell me who has the highest financial risk by cross-referencing balance sheet data"), close the window, and go do something else, letting the AI take the time necessary to "reason" and provide us with a structured report.

Conclusion

At Moko, we are observing this evolution with great interest because it solves one of the main brakes on AI adoption in critical processes: trust.

Accepting that a machine "thinks" for a few extra seconds is not a step back in performance, but a step forward in the quality of the result. We are moving from the era of simple task automation to the era of complex reasoning automation, and this time, it's worth the wait.