EDITION OF OCTOBER 30, 2026
Tyler Shultz warns of the difficulty of verifying AI companies’ promises
In an article published on October 30, TechCrunch relays Tyler Shultz’s concerns about due diligence ahead of AI investments. The former Theranos whistleblower believes the difficulty of distinguishing an original innovation from a mere technological wrapper can make deception easier.
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Verifying technological promises is a theme running through this edition: Tyler Shultz warns about the evaluation of AI companies, while Arrowmax presents a screwdriver that, according to its description, can identify the bits needed. On governance, Pope Leo XIV calls for a ban on lethal autonomous weapons.
The difficulty of evaluating artificial intelligence companies could make deception easier, Tyler Shultz believes. In an article published on October 30, TechCrunch reports remarks from the former Theranos whistleblower, gathered by The San Francisco Standard. His warning concerns investors’ ability to verify what truly sets a technology apart before funding its development.
Shultz highlights, in particular, the difficulty of distinguishing an original innovation from an application built around ChatGPT. This is his assessment: it demonstrates neither fraud at any specific company nor a lack of value in applications using an existing model. It does, however, raise a concrete question for investment: what evidence establishes what a company has actually developed and what depends on technologies available to others?
The Theranos precedent sheds light on his reasoning. According to TechCrunch, Shultz helped expose dangerous flaws in his former employer’s blood-testing devices. These devices had been deployed in Walgreens stores and had provided patients with incorrect results. The outlet also reports that the company used third-party equipment for some of its tests, while its own technology did not work as promised.
Theranos often received funding from individual investors, including the DeVos and Walton families, rather than venture capital firms, TechCrunch reports. The company refused to share scientific information or evidence that its machines worked with potential investors, citing the protection of its trade secrets. According to the outlet, this stance had kept many Silicon Valley investors away.
Applied to AI, the lesson can be read as a requirement for verification proportionate to the promises made. A convincing demonstration alone cannot establish the source of a technical capability, its reliability or its economic value. These are questions to examine separately, rather than conclusions to draw from a polished presentation.
The reported remarks constitute a warning, without measuring how frequently deception occurs in the sector. Their scope therefore remains limited: Shultz urges investors not to let the difficulty of evaluating a technology take the place of examining the evidence. The parallel with Theranos serves here as a warning about the approach to funding, without establishing an equivalence between AI companies and that fraud.