Superintelligence: why AI needs human guidance

Superintelligence is a hypothetical AI whose intellectual abilities go beyond those of humans (Source: IBM, 2023). Today’s systems are not that: they are specialized tools for clearly defined tasks, and they need human guidance. Treat them as if they knew everything and you get results that sound like answers and are not.

3x

as often should organizations use small, task-specific models by 2027 compared with general-purpose language models

Gartner, forecast 2025

95%

of GenAI pilots deliver no measurable P&L effect

MIT NANDA, 2025

≥30%

of GenAI projects are abandoned after the proof of concept

Gartner, 2024

Contents

• What superintelligence actually means
• Why today’s AI is a set of specialized tools
• When AI is everywhere: AI Overviews and AI slop
• Where focused AI pays off
• Three questions before you build AI into a product
• Conclusion: task first, then AI
• Frequently asked questions about superintelligence
• Sources

What superintelligence actually means

The term shows up in headlines, corporate strategies and board decks, often as a synonym for “very good AI”. It means something else. IBM distinguishes three levels: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) (Source: IBM, 2023).

• ANI, narrow AI: systems that do specific tasks very well but do not learn new skills on their own and have no deep understanding of the world. Everything we use today belongs here.
• AGI, general AI: a system that solves problems as broadly and flexibly as a human. According to IBM, true AGI has not been developed yet.
• ASI, superintelligence: a hypothetical system whose intelligence exceeds human intelligence (Source: IBM, 2023).

The order matters. Superintelligence presupposes a general AI, and that does not exist. What we have, in IBM’s words, are limited AI systems (Source: IBM, 2023). Whether and when that changes is an open research question, not a planning basis for your 2027 roadmap.

For decision makers the consequence is sober: the superintelligence debate is worth having as a societal question. It does not help with the question of where you deploy AI today. There, what counts is what a tool achieves in a specific task.

Why today’s AI is a set of specialized tools

Modern language models seem general because they produce text on almost any topic. That obscures how they work: they compute plausible continuations from training data. For tasks close to that data, the results are impressive. For tasks with their own context, their own rules and their own quality standards, they become unreliable.

The analysts at Gartner therefore expect a shift: by 2027, organizations are to use small, task-specific AI models three times as often as general-purpose large language models (Source: Gartner, forecast of April 9, 2025). That is a forecast, not a measurement, and it shows the direction: away from one model for everything, toward many tools with a clear job.

For presentations:SVGPNG

In practice: a tool for proposal texts needs different data, different rules and a different review than a tool that sorts support requests. Handle both in the same general chat window and you save the setup, then pay for it later in rework.

When AI is everywhere: AI Overviews and AI slop

From the assumption “AI can do everything” comes a reflex: it has to go into every product, every page, every touchpoint. Most of us know the result from daily life. Bloated summaries, interchangeable texts, features nobody asked for. The term AI slop has caught on for it: mass-produced content of low quality. On top of that comes a growing fatigue among users. We did not find a reliable study with numbers on this, so we describe it as an observation.

The best-known example is web search. When Google rolled out AI Overviews widely in May 2024, screenshots of absurd answers spread quickly. Google itself named the causes: the systems struggled with unusual queries, took satire as fact and drew on ironic forum posts. The advice to keep cheese on pizza with glue comes from that group. Google then narrowed the searches for which the summaries appear (Source: Google, May 30, 2024).

A model that is supposed to answer every question also answers when it would be better off staying quiet.

The case shows the pattern. A general system was built into a huge touchpoint without brief, data basis and review fitting the use case. It was corrected afterward and by narrowing: fewer triggers, fewer sources, more rules. That is exactly the guidance that was missing beforehand.

How AI search affects clicks and error rates is covered with independent data in AI search: better answers or expensive hype?. The numbers are there, this article is about the consequence for your own deployment. Why wrong answers become a top risk for companies is in Inaccuracy becomes the top risk.

Where focused AI pays off

The data on enterprise projects speaks for focus. 95 percent of GenAI pilots deliver no measurable effect in the profit and loss statement (Source: MIT NANDA, 2025). At least 30 percent of GenAI projects are abandoned after the proof of concept (Source: Gartner, 2024). Behind both numbers there is rarely a technology problem. Usually the clearly defined task is missing, the one against which success can be measured. How such projects get stuck is described in The Pilot Graveyard.

Focused AI has typical features:

• A defined task. Summarize documents, pre-sort requests, draft standard texts. Each of these tasks has a start, an end and a recognizable result.
• A known data basis. The tool works on your documents and rules, not on the whole web. That reduces the cases in which it has to guess.
• A human review. Someone decides what moves on. The higher the damage of a wrong answer, the earlier this review applies.

This is the approach we take in AI Operations: language models take over defined steps in processes that people steer and control. It is less spectacular than a do-it-all and usually delivers more usable results.

Three questions before you build AI into a product

Before an AI feature moves into a product, a workflow or a platform, three questions help. If you cannot answer one of them, the feature is not ready.

1. Which concrete task does the feature solve, and how do you know it is solved? “Support users” is not enough. “Sort requests into four categories, with fewer than one correction per ten cases” is a task.
2. What data does it work on, and who reviews the result? Without a defined data basis and a named person who checks, you get exactly the slop your users are tired of.
3. What happens when the answer is wrong? If you cannot describe the failure case, you have not planned for it. Sometimes the honest answer is: here we leave the AI out.

The third question deserves a second look. A feature that is not built costs nothing and annoys nobody. The courage to skip AI at a touchpoint is part of good product work.

Conclusion: task first, then AI

Superintelligence is a thought experiment, not a product. Today’s AI consists of specialized tools, and it delivers value where a person defines the task, sets the data and reviews the result. Build AI in everywhere out of fear of missing out and you get slop, fatigue and rework.

For your next AI initiative that means: pick one task, set one data basis, name one review. Then measure whether it works. How to set that up for your company, we are happy to work out with you in a free diagnostic call.

Frequently asked questions about superintelligence

What is superintelligence?

Superintelligence is a hypothetical AI whose intellectual abilities go beyond those of humans (Source: IBM, 2023). It does not exist so far. Today’s systems are specialized tools for defined tasks.

What is artificial superintelligence?

Artificial superintelligence (ASI) is the same concept under its English name. IBM describes it as a hypothetical system that would build on a general AI (AGI), which has not been developed yet either (Source: IBM, 2023).

How do AGI and superintelligence differ?

An AGI solves problems as broadly and flexibly as a human. A superintelligence would exceed humans at that (Source: IBM, 2023). Both are concepts so far, not available products.

Is ChatGPT a superintelligence?

No. ChatGPT and comparable language models belong to narrow AI: they produce text, images or code for tasks they derive from training data. They are useful tools, but neither general nor superior to humans. Their answers need review.

What is AI slop?

AI slop describes mass-produced AI content of low quality: bloated, interchangeable or inaccurate texts that take up space without helping the reader. It arises mostly where AI is used without a clear brief and without human review.

Why does AI need human guidance?

Because AI systems do not define tasks themselves, do not assess their own data basis and do not reliably detect their own errors. People do that: they set the brief, choose the data and review the result.

Sources

• IBM, 2023: What is artificial superintelligence? (Tim Mucci, Cole Stryker, December 14, 2023)
• Google, 2024: AI Overviews: About last week (Elizabeth Reid, May 30, 2024)
• Gartner, 2025: Gartner Predicts by 2027, Organizations Will Use Small, Task-Specific AI Models Three Times More Than General-Purpose Large Language Models, April 9, 2025
• Gartner: Hype Cycle for Artificial Intelligence, 2024
• MIT NANDA: The State of AI in Business, 2025