23 Daily — According to techcrunch.com, Tyler Shultz, a key whistleblower of Theranos, recently warned in an interview with The San Francisco Standard that while the artificial intelligence (AI) industry is rapidly growing, it also carries significant risks of deception and misinformation. He emphasized the necessity for investors to rigorously verify the authenticity of AI products. This warning comes amid renewed attention from the documentary "You Can See Everything," which chronicles Theranos founder Elizabeth Holmes’ final weeks before prison.

Lessons from Theranos on Investment Blind Spots

Tyler Shultz played a pivotal role in exposing Theranos' dangerously inaccurate blood testing devices that were deployed in Walgreens stores, delivering faulty results to patients. Despite its technology never truly working, Theranos reached a peak valuation of around

0 billion. Shultz pointed out that Theranos' funding primarily came from wealthy individual investors, such as the DeVos and Walton families, rather than prominent venture capital firms. The company declined to share crucial scientific data with potential investors, citing trade secret concerns, which deterred many top Silicon Valley investors. After the Theranos scandal surfaced, the investment community was reminded of the importance of due diligence, but Shultz worries that this lesson is being forgotten in the AI sector.

Challenges and Risks in AI Investment

Shultz believes that conducting proper due diligence on AI technologies is extremely challenging. Investors often cannot easily distinguish between truly innovative technology and superficially wrapped products that rely on existing models like ChatGPT. He warns that the complexity and appeal of AI provide significant opportunities for deception, making it critical that the lessons from Theranos are applied to the AI investment boom.

Editor's take: Strict Verification Essential to Avoid Repeating Past Mistakes

The Theranos case illustrates how high valuations can mask significant risks, especially when companies withhold key technological data and evidence. As AI startups attract massive funding waves, entrepreneurs and investors alike must uphold transparency and factual verification, resisting the temptation to invest based solely on hype and marketing. Practical verification measures include requesting actual performance data, involving independent third-party audits, and openly explaining technical details — all fundamental steps to prevent blind investment. For entrepreneurs, focusing on explainability and real-world effectiveness of AI products is crucial for sustainable long-term success.