The rapid rise of artificial intelligence (AI) has sparked a global frenzy, with companies and investors rushing to capitalize on its potential. However, beneath the surface of this excitement lies a growing issue: AI washing, where technologies are misleadingly marketed as AI to attract investment and consumer interest. This practice distorts perceptions, inflates valuations, and risks long-term economic and social consequences. Written for readers of The Brink, this piece uncovers lesser-known facets of the issue and predicts future developments.
AI Washing: Definition and Prevalence
AI washing refers to the practice of branding products or services as "AI-powered" when they rely on basic automation, human labor, or minimal algorithmic processes. This tactic exploits the hype surrounding AI to boost marketability, secure funding, or inflate company valuations. For example, a company might claim its app uses "cutting-edge AI" when it actually depends on manual inputs or simple rule-based systems.
Prevalence: 30% of AI-labeled products in the tech market may involve exaggerated claims, particularly in consumer-facing apps and services.
Some companies have been exposed for using human workers to mimic AI outputs, such as in customer service chatbots or app development platforms. This mirrors historical low-tech deceptions, like ATMs humorously depicted as having people behind the wall handing out cash.
FOMO-Driven Investment
The fear of missing out (FOMO) is a significant driver of AI washing, pushing companies and investors to prioritize speed over substance. CEOs, particularly in emerging markets like India, are under pressure to adopt AI to remain competitive, even when the technology’s practical applications or returns on investment (ROI) are unclear.
Data Point: 69% of Indian CEOs are investing heavily in AI, driven by FOMO, despite limited clarity on revenue generation.
Implication: This rush leads to misallocated resources, with companies diverting funds to superficial AI implementations rather than solving real problems or building sustainable innovation.
Recent Examples of AI Washing
Presto Automation Inc. (2024)
Presto Automation Inc., a restaurant technology company, claimed its AI-assisted speech recognition technology powered seamless, automated customer interactions in drive-thrus. However, in 2024, the U.S. Securities and Exchange Commission (SEC) settled charges against Presto for misleading investors about the extent of AI in its systems. Investigations revealed that the technology heavily relied on human operators to process orders, with AI playing a minimal role. The company’s marketing materials, including investor presentations, overstated the system’s automation capabilities, leading to a civil penalty and reputational damage.
Impact: The case highlighted how AI washing can mislead investors in niche sectors like restaurant tech, where automation promises are particularly appealing. Presto’s stock value dropped significantly after the SEC’s action, underscoring the financial risks of such practices.
Joonko (2024)
Joonko, a now-defunct AI recruitment startup, was accused of AI washing in a high-profile SEC and Department of Justice (DOJ) case against its founder and former CEO. The company claimed its platform used AI to match candidates with jobs efficiently, raising over $27 million from investors. However, the SEC alleged that Joonko’s platform relied heavily on manual processes, with fabricated claims about its customer base, user numbers, and AI capabilities. Raz was charged with securities fraud and wire fraud in June 2024, with the DOJ unsealing a parallel criminal indictment.
Details: The SEC complaint noted that teh CEO misrepresented Joonko’s technology as “AI-driven” while concealing its reliance on non-AI processes. Additionally, forged bank statements and fictitious purchase orders were used to inflate revenue claims, further deceiving investors.
Implication: This case marked a significant escalation in regulatory scrutiny, as it was one of the first AI washing cases to involve both civil and criminal charges, signaling a tougher stance on fraudulent AI claims.
DocGo Inc. (2025)
In a March 2025 securities class action lawsuit, DocGo Inc., a provider of mobile health and medical transportation services, faced allegations of AI washing. The company claimed its “proprietary central AI system” managed logistics for scheduling, patient interactions, and resource allocation. However, plaintiffs argued that these claims were misleading, as the system relied heavily on conventional software and human oversight. The U.S. District Court for the Southern District of New York denied DocGo’s motion to dismiss, citing the materiality of AI-related claims to investors, especially given the CEO’s public statements tying his educational background to the company’s AI capabilities.
Significance: The case illustrates how AI washing can extend beyond tech startups to healthcare and logistics, where investors are increasingly sensitive to AI claims. The court’s ruling emphasized that misstatements about AI can be deemed material, even if tied to seemingly unrelated factors like executive credentials.
Fintech Startup (2025)
A lesser-known fintech startup, was investigated for claiming its loan approval app used “advanced AI algorithms” to assess creditworthiness. It was found that the app primarily used basic rule-based systems and human reviewers, with minimal AI involvement. The company settled, agreeing to revise its marketing claims and pay a fine. The case received limited media coverage but was noted in regulatory circles as part of a broader sweep targeting AI-related deception.
Context: The FTC’s action was part of a five-case initiative in 2025, highlighting how AI washing extends to consumer-facing financial products, where trust in automation is critical.
Consumer Electronics Brand (2024)
A consumer electronics company, cited in a 2024 UK Advertising Standards Authority (ASA) investigation, advertised a photo-editing app as “AI-enhanced” on Instagram. The ASA ruled that the app’s claims exaggerated its capabilities, as the editing features relied on pre-programmed filters rather than machine learning or generative AI. The company was required to retract the ad and clarify its technology’s functionality.
Relevance: This case underscores AI washing in consumer products, where vague terms like “AI-enhanced” mislead users about the sophistication of the technology. The ASA’s swift action reflects growing regulatory sensitivity to such claims in advertising. Economic and Social Consequences
Economic Risks: Overhyped AI projects fail to deliver promised ROI, leading to financial losses. For instance, a $1.5 billion company claiming to simplify app design with AI was revealed to rely on hundreds of human engineers, not advanced algorithms, causing a collapse in trust and valuation.
Challenges in AI Implementation
True AI implementation requires robust data, infrastructure, and expertise, which many companies lack. Generative AI, often at the heart of AI washing claims, is statistically driven and prone to errors, lacking the contextual understanding needed for accuracy.
Data Limitations: InIndia, limited high-quality data for regional languages hinders AI development, despite initiatives like the National Language Translation Mission.
Customization Needs: Generative AI models require extensive fine-tuning for commercial use, which many companies bypass in favor of quick, superficial deployments.
Global Context and India’s Role
India is a key player in the global AI ecosystem, ranking second in AI-related GitHub contributions (19.9% in 2024) but falling behind in private investment ($11.29 billion since 2013) compared to the US ($470.9 billion) and China ($119.3 billion). The country’s focus on AI applications for specific sectors (e.g., healthcare, agriculture) could position it as a leader, but AI washing risks diverting resources from meaningful innovation.
AI washing is also widespread in India due to FOMO.
A company valued at $1.5 billion collapsed due to AI washing.
Assumption: AI washing is purely a marketing issue.
There’s almost no mention of global or regional regulations to curb AI washing. The EU’s AI Act (2024) sets some standards for transparency, but India lacks a comparable framework, which could perpetuate misleading claims.
Long-Term Impact: Overhyped projects may divert funding from genuine R&D, stunting progress in critical areas like healthcare or climate tech.
Consumer Perspective: There’s limited data on how consumers perceive AI-washed products beyond anecdotal distrust. Surveys on user experiences could provide deeper insights.
Predictions for the Future
Increased Scrutiny: By 2026, expect tighter regulations, especially in the EU and US, requiring companies to disclose the extent of AI in their products. India may follow with its own AI governance framework maybe by 2027, driven by the National AI Mission.
Market Correction: High-profile failures of AI-washed companies will lead to a market correction by 2028, with investors prioritizing startups with verifiable AI capabilities. This could reduce India’s AI investment gap.
Focus on Niche AI: India will likely shift toward sector-specific AI applications (e.g., agriculture, healthcare) to avoid the pitfalls of overhyped generative AI. Initiatives can help regional language AI, enhancing accessibility.
Workforce Adaptation: As AI washing exposes inefficiencies, companies will invest in upskilling employees to complement AI, reducing job displacement fears. By 2030, AI-driven productivity could save workers 20-30% of time on mundane tasks.
AI washing is a symptom of a broader tech industry obsession with hype over substance, driven by FOMO and competitive pressures. While India’s AI ecosystem shows promise, the risks of misallocated resources, erodes this trust, and stifled innovation ideas are not working. By prioritizing transparency, regulation, and targeted AI applications, the industry can move beyond superficial claims to deliver meaningful impact.
For The Brink readers, understanding AI washing is crucial to navigating the tech landscape critically, ensuring investments and innovations align with reality rather than rhetoric.
-Chetan Desai (chedesai@gmail.com)
