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Across corporate earnings calls, investor presentations and marketing pitches, “artificial intelligence” has become the buzzword of choice. Yet a troubling pattern lies under the hype.
Many claims vastly overstate actual AI sophistication, misleading people about true capabilities, future outcomes and potential harms.
A case in point is the recent 600% share price surge of Allbirds, after the once-trendy sustainable footwear business issued a vague announcement in April 2026 that it would pivot to AI. In the coming months, the company plans to rename itself NewBird AI and give up its status as a public benefit corporation.
As a scholar who studies corporate sustainability, I see parallels between this “AI washing” phenomenon – when companies oversell the benefits of AI while glossing over the risks – and the greenwashing trend in the recent past, when companies claimed to commit to sustainability but didn’t enact fundamental change. Widespread deception was rampant, with businesses spending far more on green marketing than on actual sustainability improvements. And those efforts often backfired on both the companies and the communities they served. Even more worrisome: AI washing’s rapid rise and widespread adoption will likely eclipse the greenwashing trends.
And the motive is exactly the same – sell more widgets.
AI washing is thriving because companies and policymakers ignore four important principles. These shortfalls, in the past, also characterized greenwashing.
First, AI guidelines lack standardization. By 2019, 84 sets of AI ethics principles and guidelines had already been published. By 2023, this number had exploded to more than 200 – a mess of voluntary frameworks from companies, research institutions and public organizations.
[...] In the early 2000s, corporate sustainability faced a similar credibility crisis. Every company measured sustainability differently, making comparisons impossible and greenwashing easy. The breakthrough came only when standardized, industry-specific metrics allowed meaningful benchmarking. Initiatives such as the Global Reporting Initiative and Sustainability Accounting Standards Board established common metrics for measuring environmental impact, social responsibility and quality of governance, known by the shorthand ESG.
In other words “Green” companies had to either prove it or lose it.
[…] When companies must report carbon emissions using the same methodology, for example, or disclose labor conditions using identical categories, investors can compare performance, identify laggards and allocate capital accordingly. This push made comparisons possible and deception harder, although it still wasn’t foolproof. For example, a 2023 United Nations Environment Programme report on the fast-fashion industry found that many companies continue to make “vague and inflated” sustainability claims.
Second, there are no comprehensive frameworks in the U.S. that require businesses to judge how AI affects them in a material way and publicly disclose those impacts. Examples of AI-driven material impacts include whether algorithmic bias shapes business outcomes, or whether decisions on how to use AI systems carry significance for shareholders and the public.
Instead, AI governance remains dominated by the narrow inner circle of companies that build the AI systems, while affected communities rarely have a say in determining which AI impacts are material enough to warrant public attention. For example, Big Tech companies like Google, Microsoft, Apple, NVIDIA and others adhere to their own AI governance guidelines, with relatively little public input.
[…] A third problem is the general lack of third-party verification, making AI washing trivially easy.
Effective disclosure means reporting all material impacts – not just cherry-picked successes.
In practice, AI audits can vary dramatically in rigor, scope and methodology. One auditor might conduct extensive testing across demographic groups, analyze decision-making and validate the quality of training data. Another might simply review documentation and accept company explanations at face value. Given the variety of AI auditing models out there, different auditors may use incompatible methodologies, making results impossible to compare. If companies adopted third-party accreditation systems to assess how they use AI, they would help ensure the accountability that self-reported claims cannot match.
[…] The fourth principle is robust enforcement. Early ESG initiatives relied on reputational pressure and stakeholder goodwill – things that corporations routinely ignored when profits were at stake. When change came, it was because regulations established legal liability and financial penalties.
These consequences changed how corporations assess risk and continue to shape sustainability practices today. Volkswagen’s 2015 ‘Dieselgate’ scandal, for example, cost the company over US$30 billion in fines, settlements and criminal charges after U.S regulators found that the carmaker was cheating emissions tests. BP faced billions in penalties and liabilities for the 2010 Deepwater Horizon disaster, the biggest oil spill in the history of marine oil drilling operations.
The current enforcement gap in AI creates a predictable dynamic. The expected value of AI washing – like potential investment gains, competitive advantage, and market valuation increases – far exceeds the expected cost in terms of penalties and risk of detection. Until enforcement imposes consequences that exceed benefits, AI washing will persist as a rational business strategy rather than a risk to a business’s reputation.
Fortunately, investors are beginning to step up. The Federal Trade Commission, for example, launched Operation AI Comply in 2024, targeting deceptive AI claims, although this effort has been partially scaled back by the current Trump administration.
Without standards and audits, even well-intentioned companies can’t know if their work meets adequate rigor. Without assessments of material impact, some groups of consumers or shareholders will be hurt. And without liability, even thorough auditors won’t be able to identify whether a business’s claims about AI are truthful.
These principles, applied broadly, also help explain why greenwashing persists. For example, the lack of universal reporting standards continues to leave some gaps, with data-quality issues persisting even as reporting frameworks emerged. More fundamentally, political buy-in for ESG has diminished significantly, particularly in the U.S., where over 150 bills were introduced across multiple states by 2023 to disincentivize firms from adopting ESG. Major financial institutions – including JP Morgan, State Street, BlackRock and PIMCO – have retreated from their earlier climate commitments amid political pressure as well as antitrust concerns.
This trend shows that even well designed accountability measures require durable political support to succeed. After all, corporate sustainability took more than 25 years to develop from an initial framework to mandatory standards, and it still remains a work in progress. AI, by contrast, is advancing exponentially in terms of its reach and societal impact. There may not be 25 years to catch up – but at least there are lessons from the recent past.
Adding “AI-powered” is like ‘virtue signalling’, but in this case it’s more ‘tech signalling’. There is no doubt that AI will be ubiquitous. But most of it will be invisible. For example, a clothing company may use AI to get the latest fashion trends but as far as buyers are concerned the clothing company is up to date with the latest trends.
In any case, it’s history repeating itself and the reason is exactly the same – businesses seeing an opportunity to make more of a buck. But at least it makes a difference from seeing rainbow stickers everywhere.
