Is It AI? 6 Cases of ‘AI Washing’ and What They Reveal
In today’s tech-driven economy, artificial intelligence (AI) is more than just a buzzword; it’s a valuation multiplier. Startups are raising millions, even billions, by promising revolutionary AI-driven platforms. But what if many of these platforms aren't powered by AI?
Welcome to the world of AI washing. The practice of exaggerating, faking, or misrepresenting AI capabilities to attract investors, customers and media attention. While real AI systems are reshaping everything from healthcare to logistics, many companies are dressing up traditional software or manual processes as cutting-edge AI. The outcome is a marketplace filled with false promises, leading to confusion, regulatory clampdowns and loss of money by investors.
What Is Real AI - And What Is Not?
Actual AI systems have the capability to adjust, learn through data and make decisions with a minimum amount of human intervention. They often include machine learning algorithms that improve with feedback, natural language processing systems that can follow a conversation context, or computer vision systems that can identify unknown objects without being explicitly programmed.
In comparison, simple automation, rule-based reasoning and spreadsheet macros are not AI, though companies may call them “smart” or “AI-enhanced.” Marketing professor Luca Cian, professor in marketing at the Darden School of Business, states that. “If a system follows pre-programmed if-then logic or performs basic data sorting, I would not call it AI.”
Here are six cases of AI washing:
Builder.ai - Human Coders Powered AI-Powered App Builder
Builder.ai hit the news when its founders said that they could automatically generate completely new apps using AI with little human intervention. The sales pitch was impossible to resist: drag-and-drop ease of use with a powerful no-code AI platform behind it. It appeared that the company was bound to become a success, having reached a valuation of up to 1.5 billion dollars and with huge investors behind it.
But under the hood, something else was calling the shots. AI was not writing the code, but a group of about 700 human coders in India, posing as an automated process, manually constructed apps.
- The investors would get shown shiny AI dashboards, covering up the actual human backend operations.
- Clients also mentioned inconsistency in the delivery time, which was inconsistent with the promised AI speed and scale.
- Whistleblowers produced internal memos warning the executives about compliance and ethics issues.
Analysts noted the overuse of buzzwords like generative AI and hyper-automation in marketing by the company.
This pretence came apart following a sequence of audits and Builder.ai entered bankruptcy protection in 2025. The scandal has led to broader industry debates on how readily companies can mislead investors by claiming to be AI-first when little automation is occurring.
Nate -AI Shopping Assistant- Turned Out to Be People in a Room
Nate was positioned as a slick AI personal assistant that could automate e-commerce. It could select products, check out and even enter payment information on your behalf. Based on this promise, the founder raised $50 million in venture capital, stating that their proprietary machine learning technology would learn user preferences.
Sadly, the technology was not available. The force behind Nate's transactions was the dozens of Filipino workers who were manually filling customer purchases. The AI interface was merely a cover for a human-operated backend. When the fraud was discovered, regulators intervened and the firm soon imploded.
This example demonstrates that even sophisticated investors may find it challenging to distinguish real AI behind the facade of human-powered, slick software products because:
- Investors failed to verify the accuracy of technical representations before they invested massive funds.
- Automation made users believe in it, and they were served with human labour unknowingly.
- Instead of creating AI, the company chose the easy way out by scaling operations through the use of cheap outsourced labor.
- Regulators had to act after the misleading practices were exposed by the media.
The scandal also raised further issues of AI transparency and startup due diligence.
Amazon Go - Just Walk Out
Amazon's futuristic stores with Just Walk Out technology promised to revolutionize shopping. Shoppers could enter the store, grab their products and leave without waiting in line or scanning a bar code. Sensors, cameras and AI would allegedly detect purchases on the fly.
However, in practice, AI was not performing the majority of the tasks. In 2024, one report indicated that as many as 75 percent of all transactions were manually verified by about 1,000 employees in India. The system heavily relied on human resources to verify the purchases of the customers and bill them instead of using an AI-smart machine.
Though Amazon had not officially mentioned that the technology was 100 percent autonomous, its advertising and promotion had significantly hyped the AI innovation. This led people to think that the technology was more automated than it actually was.
Delphia and Global Predictions - SEC Charges of AI Deception
In early 2024, the U.S. Securities and Exchange Commission accused Delphia and Global Predictions of faking AI-based investment strategies advertisements. Both firms used state-of-the-art algorithms and machine learning-based predictive modeling to optimize client portfolios.
In their investigation, the SEC discovered that there was little or no AI in use. Rather, both corporations depended on the standard financial modeling and spreadsheets and, in certain instances, manual discretion. These companies were said to be attaching the label of AI more to raise funds and market themselves, as opposed to differentiating their technology.
The case was a turning point: regulatory agencies started to consider AI exaggeration a marketing misstep and a possible variant of securities fraud.
Koop.ai - Spreadsheet Logic Insurances Sales
Koop.ai provided autonomous vehicle companies and other high-tech companies with AI-enabled insurance. It claims that their system evaluates risks on a real-time basis through machine learning and offers custom policies through automated intelligence.
Technology journalists found out, however, that the startup was still using outdated underwriting spreadsheets and manual data entry. Their AI was no more than commercial software packages. Koop had succeeded in raising millions on the back of its AI boasts, but in the end, investors had been sold a very traditional insurance company.
This example also alludes to how easily the existing business models, in this case, insurance, can be repackaged into the language of AI to create venture capital interest.
Promobot - A Humanoid Robot Hiding a Human Voice Behind the Curtain
Promobot is a Russian robotics company that introduced a humanoid robot in various tech expos, claiming that the robot could understand and respond based on sophisticated AI and emotion detection. Demos showed the robot talking to people in an ordinary way, nodding, smiling and even making jokes.
Not many were aware that the answers were being provided by a human operator who fed in the answers through a hidden microphone and in real time.
This is a typical Wizard of Oz strategy, which was revealed by the robotics experts present during the demos, who observed discrepancies in the robots' response time and voice modulation.
The Promobot situation is not a matter of fraud but rather a theatrical misrepresentation; yet it is a good reminder that AI theater can be convincing, particularly when supported by strong visuals.
Why Companies Fake It
Why do businesses go the AI-washing way? The solution is in incentives.
Investors are particularly bullish on AI, and a premature declaration of a company as AI-first can result in a valuation increase of 30% to 40%. Pitch decks including references to machine learning or generative AI have a higher chance of securing meetings and funding.
In an overly competitive technological landscape, firms are worried that they will be left out without an AI story to tell. Most use what might be termed cosmetic AI, superficial AI capabilities that might not power their product but make it appear contemporary and competitive.
The media's fixation on disruptive AI stories forms a dangerous combination of hype, pressure and lies. Cian states, "When investors are specifically hunting for AI deals, companies feel pressure to reframe existing capabilities through an AI lens, even when the underlying technology hasn't fundamentally changed.”
How to Spot Real vs. Fake AI
Not every company using the term “AI” is being dishonest, but caution is essential. Here are some tips to separate the real from the fake:
- Look for specifics. True AI companies can describe how their algorithms learn or improve over time.
- Ask for outcomes. What results has the AI produced? Are there measurable performance gains or learning improvements?
- Watch the headcount. A company with a large manual operations team behind the scenes may not be as automated as it claims.
- Follow the funding. Companies overselling AI tend to pivot quickly once the money dries up or scrutiny increases.
The Road Ahead: Regulation and Responsibility
AI washing may be profitable in the short term, but its long-term costs are significant. Misleading investors can result in lawsuits. Overpromising capabilities can erode public trust. Fake AI undermines real innovation by creating confusion and hype fatigue.
Regulators are catching on, as the SEC cases show and public awareness is increasing. Ultimately, companies that build true AI systems, invest in transparency and educate their users will stand the test of time.
As consumers and stakeholders, we need to ask better questions, not just “Does it use AI?” but “How does the AI actually work?” In the age of synthetic everything, truth might be the smartest technology.
Post a comment