Nvidia CEO Jensen Huang Calls 10-Year AI Extinction Prediction ‘Completely False’

Fact-Checked by Irfan Ahmad.

Nvidia CEO Jensen Huang, in an interview, said the prediction that humanity could be gone within a decade is “completely false,” while saying concerns about AI safety are not wrong.

The interview was conducted by CBS News senior business and technology correspondent Jo Ling Kent on Sunday, Sept. 20, 2026.

Image: Jensen huang stanford by Anderseidesvik, CC BY-SA 4.0.

Huang explained further that the narrative is “dramatic” and gets a lot of attention. He said the people raising the concerns care deeply about making sure AI products are built safely.

Huang said the AI industry is transitioning from developing technology that is becoming capable to technology that has become useful. Companies are moving from laboratories to engineering and products, he said, requiring more research and computing to be dedicated to safety.

As products mature and roll out into society, Huang said more engineering is dedicated to verification, testing, evaluation, benchmarking, standards and reliability. He compared this transition with aviation, saying more people work on airline, aircraft and air-travel safety than on making new types of aircraft.

On regulation, Huang argued for using existing laws and regulations before creating new ones. He pointed to cybersecurity laws that address unauthorized entry into systems and said existing laws also cover damage caused by such activity. He also referred to product-liability laws, saying that if a product causes harm, the company can be held liable under those laws.

He also said AI companies need more secure sandboxes, isolation and containment for experiments, closer monitoring during testing and evaluation, and public sharing of incidents so the industry can learn.

Huang said AI should advance “as fast as we can, but not faster than we should”, and that compromising safety “can’t happen.”

Read next: 

• AI is distorting archaeology and flattening Indigenous Knowledge

• Nearly Half Of Investors Have Used GenAI, 17.7% Of Users Adopted It As A Routine Tool, And 54% Cited Reliability And Accuracy Concerns

• How to know if you can trust an AI’s answer to your question
by AI Analysis via Digital Information World

AI is distorting archaeology and flattening Indigenous Knowledge

By Dr Michelle Richards Professor Michael-Shawn Fletcher and Anthony Romano

If you image search on Google for ‘cultural heritage Australia’, near the top of the results is a photograph of giant carved stone heads rising from a red desert.

They look ancient and real. But the image is a generative artificial intelligence (GenAI) fabrication that never existed, and it is circulating the internet as though it is a real record of Australia’s history.

Image: DIW. CC BY.

While GenAI, the technology behind chatbots and image generators, has the potential to broaden access to knowledge, it can also popularise false histories and strip Indigenous Knowledge of its context.

It’s something that can undermine both scientific expertise and Indigenous authority.

As researchers, we are increasingly concerned about how these systems scrape and reuse Indigenous Cultural and Intellectual Property (ICIP) without consent.

So, how do we combat misinformation while keeping genuine scientific knowledge publicly accessible? And what do we give up when we share that knowledge openly, only to have AI appropriate it?

What is pseudoscience?

Archaeology has long attracted fringe narratives and pseudoscientific claims.

Pseudoscience presents itself as science while skipping the rigorous methods or reliable evidence that make science work.

Real science advances by correcting itself. As evidence accumulates, weaker ideas are discarded and disproven theories drift to the fringe.

Archaeology generates its own fringe whenever fresh discoveries overturn old interpretations. But some ideas persist.

The so-called 'lost continents' of Mu and Lemuria have no scientific basis, but the myth of mysterious superior civilisations sunken in the Pacific continue to circulate today.

But pseudoscience is rarely harmless nonsense. At its worst, it has been used to decide who counts as fully human.

Doctrines like eugenics and Social Darwinism claimed scientific legitimacy while justifying systematic dehumanisation. Much of today's pseudoarchaeology draws on those same intellectual traditions.

Why fringe ideas spread and why it matters

Fringe narratives promise hidden truths, mystery and revelation, while careful archaeological work tends to be slower and more complicated.

Stories about a lost civilisation or ancient aliens will usually beat an evidence-based interpretation for sheer entertainment.

The consequences are not trivial.

Fringe narratives erode public trust in science and expertise, as the relentless flat-earth movement shows.

And pseudoarchaeology has a long record of diminishing Indigenous histories. Some of the most remarkable monuments ever built have been credited to almost anyone but the Indigenous peoples who made them.

The moai of Rapa Nui, the great stone figures carved and hauled into place by Polynesian islanders, have been attributed to ancient aliens and lost civilisations in bestselling books and now in AI-generated 'histories'.

Nan Madol, a city of basalt raised on artificial islets in Micronesia, gets credited to a vanished ‘super race’ rather than the Saudeleur people who engineered it.

In the Kimberley in Western Australia, some say the exquisite Gwion Gwion paintings are the work of a mysterious foreign people, not the Aboriginal ancestors whose descendants still care for them. New Caledonia's petroglyphs attract the same treatment.

Each of these false histories severs living communities from their ancestral heritage and props up racist assumptions about Indigenous people’s capabilities.

What GenAI has to do with it

Most GenAI systems learn from the open web, where misinformation and fringe theories flourish.

Unlike a researcher, these systems do not weigh evidence or test a claim. They predict the next plausible word based on patterns in their training data, which means they can reproduce something untrue with complete confidence.

Accuracy is only half the problem.

GenAI works at scale, compressing vast quantities of information into statistical patterns. Indigenous Knowledge works the other way. It is local, place-based and bound to particular Country and communities.

These are the qualities that give Indigenous Knowledge its strength, which are flattened by large-scale AI systems.

A model can absorb Indigenous knowledge as training data with no consent, no acknowledgement, no compensation and no community control over how it is used next

Our recent study found that AI can often tell scientific and Indigenous consensus apart from well-worn fringe narratives. But that ability relies on training data collected or reproduced without community permission in the first place.

Pushing back

Fighting misinformation means engaging the public, but if strong research is inaccessible, fringe narratives fill the gap.

Those narratives then go on to feed the next generation of AI training data.

Indigenous Knowledges reach well beyond stories and cultural expression into environmental records, landscape knowledge and data built through generations of relationship with Country.

Indigenous Knowledges are not inert or ownerless information waiting to be mined. They are relational, situated and governed by protocols.

Many researchers in Australia and the Pacific have spent decades building trust with Aboriginal, Torres Strait Islander and Pacific Islander communities. These communities are now using AI themselves, for language revitalisation and for research that requires access to cultural archives.

When communities agree to publish, they are choosing to make their knowledge public, on their own terms. What they have not agreed to is a machine stripping it of context and getting it wrong.

There are some practical steps research can take.

Research journals and repositories should require evidence of community permission before publishing Indigenous data and audit existing collections for their ICIP status. Universities and funding agencies should build ICIP into research design, ethics review and peer review.

Above all, Indigenous communities must retain authority over how their Knowledge is interpreted and reused. That means true partnership, not tick-box consultation.

Expertise and understanding

Despite its rapid advance, GenAI has not replaced expertise.

An Elder, Traditional Owner, Knowledge holder or scientist can be questioned, challenged and held accountable.

Their Knowledge grows out of lived experience, long relationships and responsibility. A chatbot offers none of that.

The real experts, including Country itself, are embedded in the lives and Knowledge systems from which understanding actually grows.

This article was first published 21 September 2026 on Pursuit. Read the original article.

Fact-checked by Irfan Ahmad.

Read next:

• Nearly Half Of Investors Have Used GenAI, 17.7% Of Users Adopted It As A Routine Tool, And 54% Cited Reliability And Accuracy Concerns

• How to know if you can trust an AI’s answer to your question

by External Contributor via Digital Information World

How to know if you can trust an AI’s answer to your question

By Leo S. Lo, University of Virginia

I recently typed a simple question into Google search: How much screen time is too much for teenagers? Instead of presenting links, as Google had been doing for many years, it gave me an AI-generated answer. The artificial intelligence agent cited a number, then complicated that reply, noting that quality and balance of time could matter more than the number of hours, and that “too much” time could depend on a teenager’s sleep, exercise, school demands and mood.

I tried another search: Should I take a daily aspirin? This time the AI answer presented me with medical information, warned about risks and offered more tailored guidance if I provided my age and medical history.

These were good replies. What interested me was that they were different kinds of replies.

Debate about AI answers has focused on accuracy: Did the system get the answer right? That matters, but accuracy is only one test. Each kind of answer requires a user to judge something different.

I find it useful to sort AI answers into an “answer typography” of four broad types: factual, interpretive, constructive and strategic. A factual claim can often be checked against a source. An interpretation can be accurate and still reflect choices about which evidence matters. A construction can be well reasoned and still be wrong for the person receiving it. A beautifully written strategic document may not be true. Yet AI presents all four types of answers in much the same fluent, authoritative form; the differences are easy to miss.

I’m university librarian and dean of libraries at the University of Virginia who leads national efforts to develop AI competencies for library professionals, and I consult widely on AI literacy. I first proposed the typography in the Journal of Academic Librarianship.

The four categories are not airtight boxes. A response from an AI agent can reflect several types. That said, I describe each type of answer below, and offer guidance for deciding whether a reply is ready to use or needs more investigation.

Which answer is Google giving you?

A factual answer makes a claim that can, in principle, be checked against evidence. When was the University of Virginia founded? What is the chemical symbol for gold?

To determine whether a factual answer is robust enough for you to use, verify the claim against an appropriate source. If the answer cites a source, follow that link instead of simply treating the answer itself as proof.

An interpretive reply is built on evidence, but there is not a single takeaway. How much screen time is too much for a teenager? Does remote work raise productivity? The answer depends on what evidence is included, what is left out and how disagreement is understood.

Google’s initial answer to my screen-time question indicated that two hours was a limit for teenagers. Then it noted that pediatric guidance puts more weight on the quality and context of screen use than on simple hours. The American Academy of Pediatrics says there is no exact recommended amount for teens and emphasizes the kind of screen use and what activities it might be displacing. A question that looked numerical turned out to require interpretation.

To assess interpretive answers, do more than check facts. Ask yourself what evidence the system emphasized, what it left out and whether another defensible interpretation exists. A useful follow-up question to present to the search engine is: “What is the strongest evidence for a different conclusion?”

Image: DIW. CC BY

Constructive answers are made rather than discovered. Ask AI to draft a cover letter, write a eulogy, suggest a lesson plan or reorganize a paragraph – there is no single correct result.

You can judge the response by considering purpose, audience and voice. A eulogy can be grammatically perfect and still sound nothing like the person delivering it, or it may land flat on family members hearing it. It may not capture the deceased person well, either. Consider these kinds of effects as you read.

Strategic questions ask what to do. Should I take a daily aspirin? Should I buy the house? The answers combine information with judgment about goals, risks, trade-offs and personal circumstances.

My aspirin search shows why context matters. Google warned about risks, told me to consult a medical professional and offered more tailored information if I provided my age, cardiovascular history and risk of bleeding. That caution matches the U.S. Preventive Services Task Force guidance. It says the decision to start low-dose aspirin for prevention of heart attacks and strokes should be individualized and weigh cardiovascular benefit against bleeding risk.

For strategic answers, ask what the system would need to know before its advice could reasonably apply to you individually. Consider the stakes, the alternatives and whether a qualified person should be involved. For the aspirin question, a useful follow-up would be: “What details about my age, medical history or bleeding risk could change this advice? What should I discuss with my doctor before deciding?” The final judgment remains yours because you are the person who has to live with the outcome.

The first question after an answer

My questions began as ordinary Google searches. I did not open a chatbot. The AI-generated responses simply arrived, and links were appended.

The responses were useful. Google added context, acknowledged complications and offered tailored guidance if I supplied additional information. Within each response, though, the type of answer could change. Reporting what a medical guideline says is different from deciding how it applies to a particular person. A fluent response can move between those types of answers without a noticeable change in voice.

As a user, try to recognize what kind of intellectual work the AI agent did for a response you receive. Consider whether the interpretation is persuasive or the advice fits your circumstances.

Before asking whether an AI answer is right, ask a more basic question: What kind of answer is this? The type will tell you what to do next.

Leo S. Lo, Dean, University of Virginia

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

Fact-Checked by Irfan Ahmad.

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• Oversight Board Says Meta's AI Deepfake Policies Are Inadequate

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by External Contributor via Digital Information World

Oversight Board Says Meta's AI Deepfake Policies Are Inadequate

Fact-Checked by Irfan Ahmad

Meta's Oversight Board said Sept. 17 that its policies on AI-manipulated imagery and deepfake labeling are inadequate in two cases involving Facebook posts.

Image: Zulfugar Karimov - Unsplash

In one case, the Board ordered removal of an AI-generated video targeting a young Muslim woman in Europe. A majority found Meta's definition of "unwanted manipulated imagery" too narrow because it does not cover falsifying a private person's words and actions. The Board said, "Meta also defines mass harassment campaigns too narrowly, according to the majority, and the company should adopt new measures to reduce the burden on victims reporting them."

In a separate case, the Board ordered removal of an AI-generated video that falsely portrayed a Scottish Labour councillor making a statement about refugees. The Board found that the video violated Meta's Hateful Conduct policy. A majority also found Meta's rules for labeling deepfakes inadequate and said the video should have received a "High Risk AI" label.

The Board recommended expanding the situations when "high risk" labels can be applied, taking more measures to reduce the spread of deceptive AI content, increasing penalties for accounts that repeatedly share it, and providing more transparency on data about when AI labels are applied.

Editor's Note: This post was mistakenly published under the External Contributor category. It was intended to be published under DIW AI Analysis.

Read next:

• AI Is Producing More Software. Why Isn’t It Being Used?

• “Almost Every Job Has Tasks That AI Can Change”. Economist Erik Brynjolfsson says AI’s transformation of work is just beginning and our institutions aren’t ready for it
by External Contributor via Digital Information World

AI Is Producing More Software. Why Isn’t It Being Used?

By Seb Murray

A Wharton study finds that while AI dramatically speeds up software development, human bottlenecks prevent many of those gains from reaching customers.

Image: Joy Christian - Unsplash

Artificial intelligence coding tools dramatically increase developers’ productivity, but much of those gains are lost before they translate into finished software because human bottlenecks persist later in the production process.

That’s according to a new study written by Leon Musolff, a Wharton professor of business economics and public policy, and MIT researchers Mert Demirer and Liyuan Yang. (Editor’s note: This paper was updated with new data after this article was written.)

When looking at the impact of AI tools on coding activity, the gains grew sharply with each new generation of AI tools. Autocomplete systems that suggest the next line of code increased coding activity by 40%. Adding “sync agents,” which edit code alongside developers in real time, lifted the cumulative increase to 140%; “async agents,” which work autonomously from a prompt, pushed it to 180%.

Yet even the biggest cumulative gain translated into only a 50% increase in software projects, and a 30% increase in software releases. “In software, the binding constraint appears to be shifting from writing code to reviewing, integrating, and ultimately distributing it,” wrote the authors in the paper.

What’s Blocking AI Productivity Gains?

The researchers tracked more than 100,000 developers on GitHub, the world’s biggest software development platform, comparing their productivity before and after they adopted the three successive generations of AI coding tools, from 2022 to 2026. They combined those public GitHub records with Microsoft data on developers’ use of the tools to identify when they first adopted the technology.

The findings suggest that AI can dramatically speed up individual coding tasks, but those gains will not automatically translate into more finished software — unless AI can also automate more of the work involved in reviewing, integrating, and releasing software.

Increasingly powerful AI coding tools have made it possible to generate working software from simple prompts, dramatically lowering the barriers to software development. That has helped fuel the recent “vibe coding” boom, allowing employees with limited programming experience to build applications in minutes. But the research suggests writing code is no longer the main block.

“If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower.” — Leon Musolff

Will AI Coding Tools Improve?

So what lessons should companies draw from the findings? “If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower,” Musolff said.

However, the tools are improving apace. “We studied these tools in a previous paper, and it’s night and day,” said Musolff. “A 30% increase in software releases — there are very few technologies you can invest in today that deliver those kinds of gains.”

The researchers also found that each new generation of tools is tackling a later stage of the software development process, so the gap between gains in coding productivity and gains in finished software could begin to narrow as the AI gets better.

The paper says that if the tech can produce higher-quality code that requires less human review, today’s bottlenecks may prove temporary.

Some tech companies are already trying to tackle that problem by developing AI tools that review machine-written code. But Musolff is unconvinced they can yet match human judgement.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem. The review just isn’t of the same quality,” he said.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem.” — Leon Musolff

Is User Adoption the Next Barrier?

Even once software is released, it still has to find traction with an audience. The research found AI is increasing the number of new software applications, but not user adoption.

The researchers studied the four biggest software marketplaces — Apple App Store, Google Play Store, Chrome Web Store and SourceForge — and found a broad surge in new software applications since mid-2025. But crucially, no increase in overall usage.

On Apple’s App Store, for example, monthly new releases rose from around 30,000 before AI coding agents arrived in early 2025 to roughly 100,000 per month by April 2026. Yet total usage remained flat or declined across the four major app stores.

“It could simply be that it’s much harder to discover new applications when there’s such a flood of them,” said Musolff. “Alternatively, even once you’ve shipped an app, there’s another skill involved: iterating with users.”

In other words: Getting software into users’ hands is only the start.

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©2026 Knowledge at Wharton. Originally published by Wharton School, University of Pennsylvania on September 8, 2026. Republished on Digital Information World with permission.

Fact-Checked by Irfan Ahmad.

Read next: “Almost Every Job Has Tasks That AI Can Change”. Economist Erik Brynjolfsson says AI’s transformation of work is just beginning and our institutions aren’t ready for it
by External Contributor via Digital Information World

FBI Warns of AI-Assisted Scams Impersonating Government Officials as AI Fraud Shows Higher Profitability

The FBI warned Sept. 17 that scammers are impersonating law enforcement and government officials to extort money or personally identifiable information from victims.

Image: Growtika - Unsplash

The agency's Internet Crime Complaint Center (IC3) received nearly 61,000 complaints of law enforcement or government impersonation scams between January 2025 and July 2026, with losses exceeding $1.6 billion.

Scammers primarily use unsolicited phone calls, but also texts and emails. They may spoof phone numbers, email addresses and employee names. The FBI said scammers can accuse victims of crimes, missed jury duty or court dates, or expiring licenses while threatening arrest, fine, prosecution or other consequences.

The FBI said scammers use artificial intelligence (AI) to appear as law enforcement and government officials during video calls, adding the appearance of legitimacy.

The FBI's warning about AI-assisted impersonation comes as Interpol has reported a financial advantage for AI-enabled fraud. The Financial Times reported Sept. 14 that Interpol recently found AI-enabled fraud was 4.5 times more profitable than traditional fraud.

The FBI said government and law enforcement authorities will never contact the public by phone or text to demand payment or request personal or sensitive information. They also "will never request payment via prepaid cards, cryptocurrency, or courier." 

The FBI said, verify callers and their credentials using official contact information, and carefully check URLs before clicking to ensure they match the agency's official domain.

Fact-Checked by Irfan Ahmad.

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• How advertising turns our insecurities into profit — and how you can resist the manipulation
by AI Analysis via Digital Information World

Advertising is coming to AI chatbots — and it could influence the answers you get

By Carlos Diaz Ruiz, Hanken School of Economics; Broderick Turner, Virginia Tech; Erick M. Mas, University of South Florida, and Zeynep Arsel, Concordia University

Image: Solen Feyissa - Unsplash

When Google founders Sergey Brin and Larry Page were still university researchers, they warned that advertising could undermine the trustworthiness of search engines. In their landmark paper presenting Google, they also wrote:

“The goals of the advertising business model do not always correspond to providing quality search to users […] we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumer.”

More than two decades later, a similar question is emerging around artificial intelligence. As companies race to commercialize AI systems to offset the amount of cash they are burning through, advertising is becoming an increasingly attractive source of revenue.

But advertising within AI systems presents a new challenge. Unlike online ads, which generally appear alongside information as banners or sponsored links, AI advertising is being integrated into the conversation itself, potentially changing the answers you receive.

AI’s advertising model

Today, around half of consumers report using AI conversations for search. Indeed, conversations with AI are becoming so common that some even develop dependencies with AI companions. As advertising merges with AI, it could reshape these conversations.

OpenAI has introduced ChatGPT ads designed around the way people interact with AI assistants, while Google is testing sponsored answers within Gemini conversations. Both approaches appear to follow closely how advertising technologies currently work, including real-time bidding and micro-targeting users, which allow advertisers to reach individual users based on behavioural data.

Organizational theorist Henry Chesbrough has argued that technology by itself has no inherent value; that value only arises when it is commercialized through a business model. His insight suggests that emerging technologies can only be examined as commercial systems shaped by financial incentives.

AI companies say advertising will not change answers. Yet advertising technology is difficult to audit because outsiders often lack access to the data, and AdTech companies weaponize complexity to prevent oversight.

Preventing paid influence from shaping AI-generated answers remains largely voluntary and depends on AI companies honouring their promises.

Some AI providers have already raised concerns about the impact of advertising on trust. Perplexity, an AI-powered answer engine, tested sponsored conversations but discontinued the program.

Even with clear labels, users may struggle to distinguish advertising from organically generated information when both appear within an AI conversation.

New risks in the zero-click internet

What happens when advertising no longer directs traffic to a website, but instead becomes part of a monetized AI conversation?

Commercial AI systems are heading towards a “zero-click internet” in which users make purchases within a corporate-controlled AI environment, not independent websites.

This represents a new form of e-commerce where advertising and transactions are integrated directly into AI chats. As a result, valuable user attention and commercial opportunities remain under the control of AI providers.

Our research explores the implications of merging advertising into AI environments. It identifies risks for free speech and free markets. AI-generated answers compress competing perspectives and may make alternative viewpoints less visible. This could create challenges for minority voices and dissenting perspectives.

It also raises questions about whether large corporations could purchase preferential visibility within AI systems. Smaller firms may face pay-to-play barriers, potentially increasing market concentration.

AI and disinformation

Another concern is the amplification of disinformation through AI. The United Nations warns that embedding micro-targeted adverts into AI-generated content could amplify misinformation and polarizing material.

We know that AI answers can be steered by their controlling corporations. One example is SpaceXAI’s Grok chatbot, which generated false “white genocide” claims in response to unrelated prompts and produced other extremist content reportedly due to an unauthorized code change an employee had made.

Merging advertising with AI adds another layer of risk. Existing digital advertising technologies allow messages to be targeted at specific users based on their personal data.

If similar techniques are integrated into AI conversations, misleading or manipulative messages could be delivered privately, without the public scrutiny that accompanies traditional political advertising.

This is one concern associated with “dark money” in AdTech: when third-party entities use digital advertising technologies for illegitimate purposes, such as election interference.

Why current defences fail

Current defences against disinformation were not designed for AI conversations.

Some of the most advanced legislation to counter disinformation, such as the European Union’s Digital Services Act, was designed for social media and search engines, not chatbots. ChatGPT falls under the act’s scope if it works as a search engine. However, the act’s rules do not guarantee the accuracy of each chat.

The principle responses to disinformation, namely fact-checking and media literacy, were developed in an information environment where misleading claims were public and persistent.

Fact-checkers assess claims with public relevance, such as statements from politicians or hoaxes circulating widely on social media. AI conversations, by contrast, are personalized and ephemeral, meaning misleading claims made to individual users may never enter the public information environment where they can be examined.

Imagine a chatbot repeating a fabricated allegation about a candidate in a private conversation with a voter. The practical challenge is that a personalized falsehood may remain invisible to fact-checkers, leaving the voter misled.

Media literacy faces similar limitations. Users are encouraged to examine sources and compare evidence. However, while search engines presented users with ranked sources, AI systems increasingly provide a single generated response: not simply “an” answer, but “the” answer.

For example, a company may flood the web with fabricated reports. The reports can poison or taint the sources an AI system retrieves. When a consumer asks about the product, the AI turns those planted claims into an authoritative answer, complete with citations.

• Also read: How to Identify and Combat Misinformation in the Digital Age

Setting boundaries

Advertising-funded social media is addictive and often rewards conflict. Because controversy attracts attention, platforms can turn it into engagement metrics that advertisers value. Advertisers are also not limited to commercial brands; anyone willing to pay can attempt to increase their reach.

While policymakers are increasingly focused on the governance of digital platforms, there are still few guidelines for sponsored AI conversations.

Policymakers should establish clear boundaries preventing sponsors from shaping the evidence and substance of AI-generated conversations. A label alone is unlikely to be enough.

AI companies promise that advertising revenue will not influence the substance of their answers. However, such promises may come with a “for now” qualification.

Carlos Diaz Ruiz, Associate Professor of Marketing, Hanken School of Economics; Broderick Turner, Assistant Professor of Marketing, Virginia Tech; Erick M. Mas, Assistant Professor of Marketing, Muma College of Business, University of South Florida, and Zeynep Arsel, Professor, Management, University of Bath and Concordia University Chair in Consumption, Markets, and Society, Concordia University

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

Fact-Checked by Irfan Ahmad.

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by External Contributor via Digital Information World