ΑΙhub.org
 

How to spot AI hype


by
19 November 2021



share this:
microphone in front of a crowd

Hype itself is not inherently bad, but it sets inflated expectations about the technology, drives unnecessary fears, and detracts from meaningful discussions about today’s technology. We’ve already talked about how to avoid creating hype, but it’s equally important to know how to recognise it.

So how can you spot it and avoid the hype train?

1. Sensational and absolute language

Examples: “AI will completely revolutionise healthcare / construction / agriculture ”, “X is the most important development in AI – ever”.

Words like “best”, “worst”, “ever” “never” can be useful as a shorthand, but lack nuance. If you see phrases like this (especially in a headline) remember that there is likely to be more to the story.

2. There are no limits!

Examples: “AI can now predict the weather with 100% accuracy.”

No one is perfect and neither is AI so any claims that don’t acknowledge its limitations are likely to be hype. Look out for words and phrases such as “may”, “might”, and “could” that indicate that there are limitations without directly specifying them.

3. Emotive language

Examples: “Terrifying future for millions of workers as AI takes over”

Descriptions that evoke strong emotions are attention grabbing and engaging, but are not an accurate reflection of the technology.

4. Anthropomorphism

Example: “Depressed security robot throws itself into fountain”

Describing AI in terms that would normally be used for humans or living things is inaccurate as it ascribes abilities (e.g. having feelings) that it is not capable of.

5. Leaning into biases

Example: “If you think robots will steal your job – you’re completely right”

People are more likely to pay attention to and accept information that aligns with their beliefs. To avoid being drawn in by hype, evaluate claims that align with your values as thoroughly as information that doesn’t.

6. Underlying motivation

Example: “‘AI is the future of housework’ says maker of robot vacuums.”

Think about why the source could be using hype: Are they trying to change your mind, or behavior, or sell you something? Who benefits from the hype?

7. Personalities over science

Examples: “AI Company X is a legacy of the genius of person Y”

While people are ultimately responsible for creating AI, discussions focused on people and personalities (both good and bad) distracts from the science itself.

8. Unrealistic or unrelated images

Examples: Sci-fi film robots, glowing blue brains

It’s true that representing AI visually can be difficult, but images of science fiction robots or fantastical images creates the impression of greater advancement than is realistic.

9. Vague claims

Examples: All of the above.

Headlines are brief by necessity, but the rest of the story should give more information about how the technology works. For example, explanations about the AI’s specific task, the data, algorithm, and hardware used.

10. If in doubt, check the original source

If you spot hype, or are unsure, one of the best things you can do is check an original source, the journal or conference proceedings. If you can’t find any, it is more likely that the story is all hype with no substance.

We hope you find this guide to spotting AI hype helpful, and you can also find all the tips in this PDF.



tags: ,


Joe Daly Engagement Manager for AIhub
Joe Daly Engagement Manager for AIhub

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Deep learning-powered biochip to detect genetic markers

System can detect extremely small amounts of microRNAs, genetic markers linked to diseases such as heart disease.

Half of AI health answers are wrong even though they sound convincing – new study

  12 May 2026
Imagine you have just been diagnosed with early-stage cancer and, before your next appointment, you type a question into an AI chatbot.

Gradient-based planning for world models at longer horizons

  11 May 2026
What were the problems that motivated this project and what was the approach to address them?

It’s tempting to offload your thinking to AI. Cognitive science shows why that’s a bad idea

  08 May 2026
Increased offloading to new tools has raised the fear that people will become overly reliant on AI.

Making AI systems more transparent and trustworthy: an interview with Ximing Wen

  07 May 2026
Find out more about Ximing's work, experience as a research intern, and what inspired her to study AI.

Report on foundation model impacts released

  06 May 2026
Partnership on AI publish a progress report on post-deployment governance practices.

Forthcoming machine learning and AI seminars: May 2026 edition

  05 May 2026
A list of free-to-attend AI-related seminars that are scheduled to take place between 5 May and 30 June 2026.

AI for Science – from cosmology to chemistry

  01 May 2026
How AI is transforming science, from a day conference at the Royal Society



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence