Tyler Shultz, known for blowing the whistle on Theranos, cautions that the current surge in AI technology is creating a wide open field for deception. With companies often flying blind on real tech validation, he believes investors are ignoring important lessons from the Theranos disaster. Shultz sees parallels in today’s AI world—massive valuations, lofty promises, and often little proof to back them up.
From Theranos to the AI Era
Shultz started his career at Theranos and helped expose how the company’s blood-testing devices were routinely giving unreliable results—despite being sold in major retail chains. Although the firm was valued at close to $10 billion at its peak, its core technology never performed as promised. That failure of oversight still haunts the startup world.
Today, many AI startups are securing huge investments without delivering consistent results—or sometimes without even revealing how their tools really work. Shultz worries that, without transparency, investors and the public won’t be able to spot the difference between a genuinely new AI innovation and something that’s merely repackaged hype.
Investors, Risk, and the Power of Narrative
Shultz urges investors to go beyond glossy pitches and ask the hard questions: show scientific validation, allow third-party testing, make code or underlying models auditable. But in the AI boom, he’s seeing many investors once again entranced by personality, presentation, and promise rather than substance.
A specific concern is that AI companies can scale deception faster than any previous tech trend. Wrap a business in buzzwords like “deep learning,” “large language models,” or “agentic systems,” and suddenly a lot of guesswork gets masked as groundbreaking work. Shultz warns this opens the door for charlatans to rake in funding while failing to deliver.
He also points to emerging cases like Theranos 2.0—Elizabeth Holmes’ new diagnostics company reportedly raising roughly $20 million—as examples of how a founder’s narrative and branding can overshadow technical reality. According to Shultz, it’s not just about new startups; it’s about how the culture around tech investment has become generous with trust.
Shultz wants today’s stakeholders—investors, scientists, regulators, and the public—to remember that failing to be demanding now could lead to serious harm down the road. He stresses that AI isn’t inherently bad, but that the systems around it—due diligence, accountability, oversight—need to be stronger.
What this means is simple: If investors don’t insist on rigorous evidence, AI could become the next playground for manipulation. It’s a warning grounded in a real past. One to watch closely as the next wave of AI companies seeks to convince us all.
