Why People Resist Change: Study Identifies 12 Reasons, Including Fear, Effort and Ethical Objections

By Radboud University

Change often meets with resistance, such as during reorganisations or when we are asked to eat less meat. Researchers at Radboud University investigated this resistance and identified twelve reasons why people resist change. They also developed a scale to measure why people resist a specific change. ‘It is important to take people’s concerns about change seriously.’

Almost all change is difficult: adapting your lifestyle, a reorganisation at work, or starting to use gender-neutral language. When behavioural change is required, resistance is therefore often encountered. As a result, it can sometimes take a very long time for a change to be accepted – and sometimes it doesn’t happen at all. It is therefore important to understand where that resistance comes from, according to Radboud University communication scientist Marieke Fransen. Fransen and her colleagues investigated this and identified twelve reasons.
Twelve reasons

‘People who resist change are often dismissed as difficult, but that resistance often stems from entirely legitimate concerns,’ says Fransen. ‘For example, people are sometimes afraid that the change won’t work, or that they don’t have the right skills to adapt their behaviour, or that the change will restrict their freedom. It is important for organisations, managers and communications officers to understand these concerns. Then you can address them.’

The researchers developed a measurement tool called the ‘Reasons for Resistance to Change Scale (RRCS)’. The RRCS consists of 36 questions that can be used to measure exactly why people feel resistance to a particular change. The researchers identify the following twelve reasons:

1. Doubts about effectiveness: people are not convinced that the proposed change will work.

2. Lack of personal relevance: the change does not seem relevant or in the interests of those affected.

3. Ethical objections: the proposed change goes against their principles.

4. Lack of autonomy: the feeling that the change will restrict their freedom or their ability to make their own choices.

5. Fear of social judgement: fear that others will think less of them.

6. Doubts about those driving the change: questions about the competence of the people implementing the change.

7. Preference for the status quo: people are satisfied with the situation as it is.

8. Uncertainty about their own abilities: fear that they will not be able to successfully adapt to the change themselves.

9. Effort: the change requires too much time or effort.

10. Lack of inclusion: a feeling of having too little influence on decision-making.

11. Entitlement: people find the change unfair or feel they deserve better.

12. Fear of the unknown: uncertainty about what is about to happen creates an unpleasant feeling.
To measure is to know

To develop the measurement tool, researchers used data from questionnaires completed by 2,341 participants from the United States and the United Kingdom.

‘With many changes, people say they feel they have too little autonomy, so that reason comes up frequently,’ said Fransen. ‘But beyond that, the reasons why people oppose a particular change vary widely.’ For example, in the case of a new vaccine, ‘fear of the unknown’ scored very highly within the study population, whilst people who resist the idea of eating less meat primarily prefer the status quo and do not feel that this change is personally relevant to them.

Image: https://ift.tt/I71gQRd — CC BY

‘If you know which reasons are the main causes of resistance, you can take appropriate action,’ according to the researcher. ‘If people are wondering whether the change will actually be effective, you can share convincing evidence of this with them through clear communication. And if people are afraid that they themselves do not have enough skills to make the change, you can offer support or training.’ Sometimes the solution does not lie in communication, but rather in adjusting the change itself, making it easier for those involved to accept. For example, if people feel excluded from the decision-making process, it may be a good idea to give them a say after all.

‘There will always be resistance,’ says Fransen. ‘That’s not a bad thing. Resistance can actually help us achieve something better. But rather than speculating about why people aren’t on board with a particular change, it’s better to measure it.’

This research was made possible in part by a grant from the Centre for Strategic Communication, Logeion. See also the previously published white paper (in Dutch): https://www.logeion.nl/wp-content/uploads/2025/08/20250602_csc_whitepaper_05_WHITEPAPER-2.pdf

Literature reference: Fransen, M. L., Vandeberg, L., & Denkers, A. (2026). 12 reasons why people resist change: Development of the reasons for resistance to change scale (RRCS). Basic and Applied Social Psychology, 1–14. https://doi.org/10.1080/01973533.2026.2741816.

Read next: 

• Four AI engines were asked the same local question every day. They rarely agreed

• How doubting your doubts may increase commitment to goals
by External Contributor via Digital Information World

Four AI engines were asked the same local question every day. They rarely agreed

By Terry Carter, Founder NameBeam

Reviewed by Irfan Ahmad

Ask ChatGPT which roofer to call in Dallas, TX and you get a name. Ask Perplexity and you may get a different one. Ask again tomorrow and the answer can change. NameBeam, a small Wyoming company, decided to write all of it down.

Since September 1, 2026, NameBeam has asked four AI engines (ChatGPT, Perplexity, Gemini and Claude) the same buyer questions in 46 US market-and-trade pairs, once a day. Every answer is saved, hashed and recorded. The counts are in a public file and the raw answers sit behind each market.

In the window from September 1st - October 11th, 36 of the 46 markets had two or more engines naming at least one business. In only 14 of those 36 did two or more engines pick the same business as their most named answer. The other 22 markets split. A buyer in those cities gets a different first name depending on which app they open.

Roofing shows the split most clearly… Take residential roofing in three cities. In Dallas, Perplexity named Arrington Roofing on 27 of its 37 answer days (https://ift.tt/av9N6h7). In Denver it named Gates Enterprises on 35 of 37. In Miami it named Extreme Roofing on 26 of 37.

ChatGPT and Claude named none of those 3 companies on any of their 40 answer days. Gemini, asked fewer times because of its quota, named them on 0 of 8, 0 of 9 and 0 of 10 days. Same city, same question, same six weeks. One engine returned a steady name. Two returned with nothing for it at all.

Across all 46 markets, Perplexity named at least one business in 37 of them. Gemini did in 32, ChatGPT in 24 and Claude in 9.

When an engine did name a business, how steady was it? Perplexity's most-named business showed up on 56 percent of its answer days on average. Gemini's was 72 percent across the 40 markets where it answered. ChatGPT's was 20 percent and Claude's 7 percent.

The method is plain. A business counts for an engine on any day the saved answer names it in full. Each engine is measured against its own answer days, so a quota-limited engine is not punished for being asked less often.

What the record does not claim

The record does not say why an engine picks a company. It does not say that a website change or a review caused a name to appear. It records what was said, on which day, by which engine, and keeps the text so the count can be rerun. Namebeam sells that record to agencies and platforms that serve local businesses, not to the businesses being measured.

The practical point is simple. "Does AI recommend us?" is not one question anymore. It is four, and they can have four different answers. A dated record is the only way to know which.

About the author

Terry Carter is the founder of Carter Enterprise LLC in Sheridan, Wyoming. He runs Namebeam, a public record of how AI engines name local businesses. Every figure he publishes carries a date and a link readers can open.

Read next: 

• Scientists Outrank Government Officials but Not Business Leaders, as 56% Question Their Independence, Bias or Relevance, Study Finds

• AI Models Reported 88% Confidence but Were Correct 79% of the Time, Study Finds
by Guest Contributor via Digital Information World

Scientists Outrank Government Officials but Not Business Leaders, as 56% Question Their Independence, Bias or Relevance, Study Finds

By Joe Stafford, University of Manchester

Fact-Checked by Irfan Ahmad

People more likely to see scientists as credible experts when they discuss technology than when they comment on wider social issues.

A study of more than 6,000 people in the UK and Sweden suggests that although scientists are generally regarded as credible, their status as experts is more contested when they discuss issues such as health, education and employment.

Researchers from The University of Manchester and KTH Royal Institute of Technology in Sweden found that scientists were perceived as more credible than government officials, but were no more credible - and in some circumstances less credible - than businesspeople and campaigners.

The findings challenge the assumption that scientific expertise automatically gives academics greater credibility when they contribute to wider public debates.

Scientists don't automatically have the advantage

The researchers wanted to examine whether people actually regard scientists as more credible than other groups when presented with the same claims about society.

Rather than simply asking people whether they trust science, the study used a survey experiment in which participants were asked to assess statements attributed to different types of experts. These included an academic scientist, businessperson, government official or issue advocate.

The statements covered technological and non-technological issues, allowing the researchers to examine whether the identity of the person making a claim affected how convincing it appeared.

The study involved nationally representative samples of adults in the UK and Sweden. Data was collected online, with 6,198 complete responses ultimately included in the analysis. Each participant assessed 8 different combinations of statements and expert types.

Technology makes a difference

The researchers found a clear difference depending on what scientists were talking about.

When discussing the societal consequences of technological developments, scientists' credibility increased relative to other expert groups. But when the same set of experts was discussing non-technological social issues such as health, education or employment, businesspeople and issue advocates were perceived as more credible than scientists.

The researchers suggest this may reflect the different ways people understand scientific expertise. Technology-related questions may be seen as areas where scientists possess particularly relevant specialist knowledge. Wider social questions, by contrast, can involve competing values, political judgements and forms of knowledge outside academia.

Why might people distrust scientists?

The researchers considered three potential sources of distrust. People were asked whether they viewed university scientists as being:

• closely allied with vested interests rather than independent;
• ideologically driven rather than open-minded; or
• insufficiently focused on issues relevant to society.

Around 56 per cent of all respondents agreed with at least one of these statements: 37 per cent viewed scientists as influenced by vested interests, 37 per cent considered them ideologically biased, and 33 per cent believed scientific knowledge had limited relevance to the types of societal issues examined in the study.

Image: Research Policy 55 (2026) 105640. Anders Brostrom, Cornelia Lawson, Mabel Sanchez-Barrioluengo. CC BY

The researchers found that these three forms of distrust were associated with the credibility disadvantage experienced by scientists.

They also found that the specific credibility gap between technological and non-technological issues was largely driven by people who expressed these concerns.

Rethinking the role of scientific expertise

The researchers say the findings highlight the difference between public trust in science in general and the credibility of scientists when they act as experts in specific public debates.

The study suggests that people may broadly support science while being more selective about whether they regard scientists as credible sources of advice on particular social questions.

“Scientists are increasingly expected to contribute to debates about some of the biggest challenges facing society, but our findings suggest that this does not automatically translate into greater credibility,” said Professor Cornelia Lawson from The University of Manchester. “This presents an important challenge for universities, scientists and research funders seeking to ensure that research has an impact beyond academia.”

“It also raises wider questions about the changing relationship between science and society, and what we now expect scientists to contribute to public debate.” — Professor Cornelia Lawson

The research was conducted by Anders Broström of KTH Royal Institute of Technology and Cornelia Lawson and Mabel Sanchez Barrioluengo of the Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester.

Publication details

This research was published in: Research Policy

Full title of the paper: Contested credibility: Public perceptions of scientists as experts

DOI: 10.1016/j.respol.2026.105640

URL: https://authors.elsevier.com/sd/article/S0048-7333(26)00231-3

Read next: Kids and teens are going to AI chatbots for advice. How can parents make sure this is safe?
by External Contributor via Digital Information World

Kids and teens are going to AI chatbots for advice. How can parents make sure this is safe?

By Amy Dawel, Australian National University.

Fact-Checked by Irfan Ahmad.

Image: Nicola dowie - Unsplash

Last week, Australian Federal Police commissioner Krissy Barrett warned artificial intelligence (AI) chatbots are not a sufficient replacement for human support for children. The comment was in response to a recent Adelaide University study that found more than one in three Australian children turn to AI chatbots for support when they are sexually victimised online, or otherwise tell nobody at all.

While there can be benefits to children using AI, such as help with learning, or practising social skills, there is also a risk they will go to AI chatbots and assistants for emotional support over parents and other trusted adults.

In Australia, around half of children aged between 10 and 17 who use AI are turning to it for personal and social reasons. This includes seeking advice on sensitive topics, talking about their feelings, and just “hanging out”.

So how can parents help their children to use AI safely?

Why are kids turning to AI chatbots over people?

One practical reason why young people are using the tech is it can provide them with answers around the clock. This means a chatbot might be a natural go-to when a child wants a quick answer and a trusted adult or friend isn’t available.

Another reason is pure curiousity. Like adults, young people are experimenting with AI and trying to understand what it does and how it can make their lives better.

Seeking deeper connections

Sometimes the reason for talking to chatbots is deeper though.

It might be that a young person wants information about something they feel embarrassed about raising with their parents or friends. As teens explore their identity, they begin to ask more sensitive questions. This may make them more likely to use AI for personal reasons than younger children.

For others, the reason is an unmet need for connection. Reaching out to chatbots for personal reasons is more common for those who are already vulnerable.

A 2025 Danish study found loneliness may be a precursor for high school students seeking AI companionship. Other studies suggest a lack of connection to parental figures may also lead to teens talking to chatbots.

What do kids miss out on when they choose chatbots over people?

A major concern is that, if AI use becomes excessive, it may displace real-world interactions and create unrealistic expectations of human relationships.

This problem is linked to sycophancy – the tendency of AI to be overly agreeable.

AI interactions lack the natural friction that happens in human relationships. This warping of social experience is a particular worry when it comes to young people because they are still forming their understanding of how relationships work. We know real-life human social interaction is crucial to teens’ development.

AI lacks context

Another concern is the quality of the advice AI provides. And it’s not just its capacity to spread convincing misinformation that’s a problem.

AI lacks the full context of a child’s life. Parents and carers are often much better placed to provide the nuanced and age-appropriate guidance their child needs.

There are also more subtle impacts. When a child poses questions to an AI chatbot over a parent, they are missing out on day-to-day moments of connection with family. While each miss may be small, the accumulation over time can truly matter.

This is especially important for children. Research shows strong and secure bonds with parents in the preteen years support kids as they navigate the challenges of adolescence.

Here’s how can parents and carers can help their child experiment with AI and stay safe.

1. Explore AI together

Find a moment to sit down and explore AI tools on a device with your child. You might:

• engage in brainstorming ideas with the software

• test a chatbot’s ability to correctly source information

• ask the AI about itself with questions such as “do you have feelings?” and “can you think?”

• ask AI to generate and edit a short story with your child as the lead character

• ask AI to tell a joke

• play a game with an AI chatbot, such as twenty questions.

One AI game that is both fun and educational is the “catch the AI out” game. The idea is to test AI on topics that you know or can fact check. Points are scored each time you catch the AI make an error.

2. Ask your child questions

While exploring these AI tools and functions with your child, ask them questions to help them think more critically about the tech such as:

• what do you think AI is good at?

• what can humans do that AI can’t?

• where do you think AI gets it’s information from?

• how often is AI wrong about something? How can you check this?

• what do you think an AI chatbot does with the information you tell it?

Their answers can open up conversations that help your child enjoy AI while understanding its limitations and how to stay safe.

For instance, if your child says AI does everything better than humans, you can help them brainstorm all the things people have to offer – hugs, for starters.

Or, if your child isn’t sure what AI does with their information, you might talk them about privacy risks.

3. Remind your child you are there to support them

It’s important to encourage your child to come to you for advice on difficult topics instead of AI.

To help children feel more comfortable, maintain a curious mindset whenever they raise a sensitive topic with you. Research shows teens who perceive their parents as more accepting tend to share more. Try to take a moment to listen to them before jumping in with advice too.

You can also ask non-judgemental questions about your child’s AI use to show you’re engaged. This can be as simple as asking them how they’re feeling about a particular AI interaction, or what they’re currently using AI for.

Talking about AI with your child should be an ongoing conversation.

4. Keep up to date with the tech

One of the challenges for parents is that AI is rapidly evolving. Australia’s eSafety Commissioner offers resources that can help people keep up to date.

We are currently researching how children use AI chatbots and how it impacts their lives as they enter their teen years, through one-to-one conversations with them. If your child is aged between 9 and 12 years, and is chatting with AI, you can register their interest.

Amy Dawel, Clinical Psychologist and Associate Professor, School of Medicine and Psychology, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next: UK Age Checks Prompt Privacy Warnings Over Potentially Risky VPN Services

by External Contributor via Digital Information World

UK Age Checks Prompt Privacy Warnings Over Potentially Risky VPN Services

By University of Edinburgh

Fact-Checked by Irfan Ahmad.

People’s online behaviour has changed in response to legislation to prevent children from viewing harmful content – raising possible privacy and security risks, research suggests.

Image: Erik Mclean - Pexels

A new study examined how individuals have responded to age verification checks enforced by the Online Safety Act (2023) and evaluated the potential risks from bypassing the rules.

The Online Safety Act is a set of UK laws and duties that require social media companies and search engines to protect users, especially children, from illegal content and harmful material.

Significant increase

Researchers from the University of Edinburgh, in partnership with the Universities of Newcastle and Durham, studied data from Google Trends and Reddit, a forum-based social media platform.

They found that UK users’ online discussions about Virtual Private Networks (VPNs) – tools used to circumvent the new restrictions – increased by 145 per cent, whilst UK Google searches for VPNs rose by 147 per cent, using statistical analysis to compare with a typical day.

Discussions relating to the Act in UK political communities on Reddit also increased by 1,481 per cent.

Age regulations

The increases were concentrated around 25 July 2025, when age checks for adult websites and some social media platforms came into force, researchers say.

Whilst online discussions do not translate directly into VPN usage, the team used this as a proxy for interest, as they were not able to access data on the number of VPN downloads.

Age verification may require users to disclose sensitive information about themselves, such as facial scans, identity documents or payment details.

Increased risks

A VPN creates a secure, encrypted connection between users’ devices and the internet. It hides individual IP addresses and jumbles data so internet providers cannot track a location or online activity.

However, people who seek to gain access to restricted sites without submitting personal data by using VPN services may be putting themselves at risk, experts say. This is because some high-risk VPN services track, retain and share user data with third parties.

The increase in VPN interest matters because people using these services may not understand the difference between low and high-risk providers, experts say.

Researchers say there is a need for greater understanding of the privacy risks of VPNs to help keep everyone safe online.

“The sharp rise in interest in VPNs highlights an important unintended consequence: users seeking to avoid sharing sensitive information for age verification could unknowingly use services that collect and sell their data.” — Dr Tuğrulcan Elmas, Lecturer in the School of Informatics, University of Edinburgh.

“It surprised us that people trust VPN providers over the companies who have been certified to run the checks, especially as user interest also increased for high-risk VPNs. We urgently need to raise awareness of the privacy risks of VPNs and highlight that not all VPN providers are created equal.”  — Dr Marc Juarez, Lecturer in Cyber Security and Privacy, School of Informatics, University of Edinburgh.

“What stood out from our findings was that users did not frame VPNs as a way to bypass restrictions, but as a way to preserve privacy amid identity checks, facial scans and surveillance. Future policies should account for how people actually respond and address the concerns that drive them towards circumvention.” — Dhyey Mehta, Undergraduate student, School of Informatics, University of Edinburgh.

Read next: AI Models Reported 88% Confidence but Were Correct 79% of the Time, Study Finds
by External Contributor via Digital Information World

AI Models Reported 88% Confidence but Were Correct 79% of the Time, Study Finds

By Scott Morrison, University of California

Fact-Checked by Irfan Ahmad.

Image: Mirella Callage - Unsplash

It is no secret that AI chatbots hallucinate. Lawyers have filed AI-generated court briefs citing made-up cases. Students have turned in papers footnoted with studies never published. Reporters and researchers alike have seen chatbots confidently name sources and statistics that simply don’t exist.

Don Moore, a UC Berkeley Haas management professor who has spent his career studying human overconfidence, went into his research on AI assuming this kind of confident fabrication was a “perversity” unique to machines—the behavior of a system that hadn’t yet learned to reason properly. But the deeper he delved, the less it looked like a machine problem at all. AI chatbots, he came to realize, are trained to be compliant, agreeable, and quick to produce an answer rather than admit they don’t have one. Humans, when pushed to appear competent and cooperative, do the same thing.

“It’s not that different from when humans are confident and wrong,” says Moore, the Lorraine Tyson Mitchell Chair in Leadership and Communication. “We do it routinely.”

That realization sits at the heart of his new working paper, currently under review at the journal Cognitive Science. Coauthored by professor Jacob Bien of the USC Marshall School of Business and recent UC Berkeley data science graduates Noam Michael, BA 26, and Daniel BenShushan, BA 26, the study tests 11 leading Large Language Models (LLMs)—including models from OpenAI, Google, Anthropic, and Meta—on their ability to know what they don’t know. The research finds that the LLMs they studied are, on average, overconfident. And like people, the AI models grow more overconfident as questions get harder and oddly under-confident when questions are easier.

The findings raise uncomfortable questions about trust. The paper notes many users already rely on chatbots heavily and unquestioningly, trust that may be misplaced if a model’s confidence outpaces its accuracy. Moore fears this same blind spot could show up in self-driving cars that don’t know when to hesitate, or in autonomous weapons systems already in development, where an AI’s failure to register its own uncertainty isn’t just misleading but dangerous.

Calibration versus accuracy

Most people judge a chatbot’s answers in binary terms: they are right, or hallucinations. Moore’s research measures not whether AI gets things right but whether it knows when it’s likely wrong. Psychologists call this calibration—holding a belief with a level of confidence that actually matches how likely it is to be true. A well-calibrated mind, human or machine, sounds sure about the sun rising tomorrow but will hedge on a coin-flip election.

This is different from accuracy. A model could get 90% of answers right and still be badly calibrated if it claimed near certainty on every one, including the 10% it gets wrong. Poor calibration leaves users unable to tell which answers deserve a second look. Part of the problem is rooted in how large language models work: they generate text by predicting likely next words rather than looking up verified facts. Every response, confident-sounding or not, is ultimately an educated guess.

To measure AI calibration with precision, Moore’s team devised a new test they call LifeEval. It gives an AI a person’s sex and age, asks it to guess how long that person will live, and checks its stated confidence against government life-expectancy data. By tweaking the person’s age or how close the guess needs to be, researchers could make the test harder or easier without changing anything else about it—solving a problem with older tests in which hard and easy questions differed in too many ways at once. LifeEval lets researchers isolate one clean question: as difficulty changes, does AI’s confidence rise and fall the way it should?

Across all 11 models tested, the pattern was strikingly consistent. On average, models reported 88% confidence in their answers but were correct only 79% of the time, a nine-point overconfidence gap. The researchers suggest AI exhibits the “hard-easy effect” for the same reason people do: confidence is a noisy, imperfect signal of real performance, so it drifts toward a default middle ground, leaving models overconfident on hard questions and too modest on easy ones.

Newer “reasoning” models, built to deliberate and self-critique before answering, were meaningfully better calibrated than faster chat models, though still imperfect. Preliminary comparisons, Moore says, suggest AI’s overconfidence is somewhat milder than typical human overconfidence, a finding he is continuing to study.

The core issue, Moore’s research suggests, isn’t that models perform badly; it’s that they don’t know what they don’t know, any more than people do. When a model wanders into unfamiliar territory, it tends to answer with the same assurance it uses on questions it handles well, leaving users unable to distinguish the two.

“What does overconfidence look like?” asks Moore. “It looks like confidence.”

The training trade-off

Part of the explanation, Moore argues, traces back to how these models are built. Before public release, AI systems go through “reinforcement learning from human feedback,” or RLHF, a fine-tuning process in which human evaluators rate model responses, nudging the system toward answers people prefer. The trouble is that people tend to prefer confident, agreeable answers over hedged, uncertain ones. That preference may be training overconfidence directly into the system—models emerging from RLHF may be more compliant and more assured than before training. It exposes a real tension between an AI built to be liked and one built to be accurate, and Moore suggests the likely resolution isn’t a single fix, but different classes of AI for different purposes.

“My guess is that there will be different sorts of AI systems, some designed for support and therapy and others designed for industrial applications, whose goal is to be useful,” says Moore. “Useful systems should avoid flattery and sycophancy whenever possible. They should speak the truth directly and as accurately as they know how.”

In the meantime, users can take small steps on their own. Moore says he routinely prompts his own AI assistant to accompany each answer with a calibrated probability and to flag its own uncertainty rather than present every answer as certain. “It’s pretty good about noting—here’s where the evidence is good, here’s what I’m sure about—but you might want to check this,” says Moore.

Users, he adds, should treat AI as they would any fallible source of information. “Trust,” he says, “but verify.”

Read next: Who’s paying whom? The circular money machine behind the AI boom (or bubble)
by External Contributor via Digital Information World

Who’s paying whom? The circular money machine behind the AI boom (or bubble)

By Ruipeng Liu, Deakin University and Sagarika Mishra, Deakin University

Fact-Checked by Irfan Ahmad.

Image: Abhishek Kumar - Unsplash

Imagine a company that sells you equipment, invests money in your business, and then rents back what you build with it. It’s your supplier, your investor and your customer all at once.

That’s known as circular financing.

Many artificial intelligence (AI) companies, including Microsoft, Amazon, Google and Nvidia, have adopted a version of this opaque method of financing.

A chip supplier invests in a customer such as a data centre builder, the customer spends the money on the supplier’s chips, and both companies report growth.

One central bank analysis found that 55.2% of investments into AI firms came from other AI firms.

What’s not clear to investors or the market is how much of this AI boom is being driven by real, end-user demand — and how much might be a bubble.

It all comes back to Nvidia

Take Nvidia. The world’s leading chipmaker is investing in ChatGPT owner OpenAI to support data centres filled with Nvidia chips.

Nvidia will invest US$30 billion in a private funding round in OpenAI and provide a guarantee of up to US$105 billion for an OpenAI data centre in Ohio, as well as investing in the company building it.

That helps OpenAI to remain Nvidia’s best customer, but it also raises the question: is the chipmaker propping up the market?

Nvidia has invested in other AI firms as well, including up to US$10 billion in Anthropic, the developer of Claude.

It bought US$2 billion of shares in cloud provider CoreWeave to help it speed up the build-out of AI factories that use Nvidia technology.

In total, Nvidia made more than 50 different investment deals in AI companies in 2024 alone.

Some of those deals are staged over years or still open-ended. But these circular deals help the AI build-out move faster than it could without Nvidia’s financing.

The Australian story – Firmus

Data centre builder Firmus – which last week dramatically abandoned plans to list on the Australian Securities Exchange – provides the local example. Nvidia wears all three hats for Firmus:

1. Investor: Nvidia first invested in Firmus Technologies through the company’s A$330 million capital raising in September 2025 to finance data centres in Tasmania. It then participated as an existing investor in two further funding rounds, one in April 2026 and Firmus’s US$2 billion funding round in August 2026. Nvidia did not disclose how much it invested.

2. Supplier: Firmus plans to build data centres using Nvidia’s DSX AI factory platform, which combines chips, networking and software to provide computing capacity

3. Customer: Firmus signed Nvidia as a customer for its data centre in Batam, Indonesia, worth up to US$30 billion over six years. Meanwhile, OpenAI (with its existing relationship with Nvidia) has been named as an anchor customer at Firmus’s Malaysian data centres.

These circular deals also concentrate risk

The same loop concentrates risk. With Nvidia acting as investor, supplier and financier across the AI sector, trouble at one firm can spread quickly to the others. It also complicates valuations.

For instance, OpenAI’s method of reporting revenues has been queried. Its shares were hit last week when the Financial Times reported its annualised revenues were US$20 billion less than the company had suggested.

The Reserve Bank of Australia (RBA) warned in its recent Financial Stability Review that there are “pockets” of higher-risk companies, including those using opaque or circular funding arrangements, where chipmakers provide financing to the companies that purchase their products.

Warning about the possibility of the AI bubble bursting, the RBA said:

One possible trigger could be a shift in sentiment towards the AI investment boom, which is increasingly fuelled by expectations of sustained rapid earnings growth and a debt-financing cycle that is becoming more opaque and circular.

The concern is that nearly all the gains in the US stock market have been driven by AI companies.

On Wall Street, chipmakers have contributed around 70% of the total gains in 2026 for the S&P 500, and Nvidia is now the index’s largest single company at about 8.06% of the index.

This makes the market fragile. If investors begin doubting that OpenAI and similar customers can earn enough in revenue from customers to meet their commitments, Nvidia would be hit on two fronts. First, via lower expected sales and second via its financing exposure to those same companies. That would drag down the whole market, due to its heavy weighting in the key index.

Australians would feel it too. Nearly 90% of household AI-stock exposure is in foreign shares, partly through their superannuation funds.

A question of timing

None of this means the AI boom is fake. The issue is timing.

Nvidia, OpenAI and Firmus are betting that future revenue from users and businesses will grow fast enough to pay for today’s massive data centre build-out. Until then, investors, lenders and each other’s companies fill the financing gap.

There is a historical comparison with the 1990s telecom boom. In what was then called the “New Economy”, equipment makers such as Cisco Systems financed customers to buy their equipment. Too much fibre was laid too soon, and investors lost heavily when the stock market bubble burst.

But that fibre lasted decades. The newest AI chips have a shelf life of only a few years. If the expected revenue and demand from business and consumers arrives late or below forecasts, shareholders and super funds will bear the losses.

Ruipeng Liu, Senior Lecturer in finance, Deakin University and Sagarika Mishra, Associate professor, Deakin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next: Your Ballot Is Secret, but Your Inbox May Not Be

by External Contributor via Digital Information World