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AI has a public perception problem. More than half of the general public says they don’t trust it.
In this episode, Piyanka, Shanti, and Ant dig into why that skepticism exists and what builders and buyers can actually do about it.
The conversation covers the gap between demos and production, why confident AI outputs can be more dangerous than uncertain ones, and what real due diligence looks like before signing a contract. There is also a candid look at vibe coding, black-box development, and the security vulnerabilities that tend to show up nine months after you thought the product was done.
The episode ends where the harder questions live: AI bias, explainability, and whether the industry is asking the right things about the training data underneath all of it.
By Piyanka JainAI has a public perception problem. More than half of the general public says they don’t trust it.
In this episode, Piyanka, Shanti, and Ant dig into why that skepticism exists and what builders and buyers can actually do about it.
The conversation covers the gap between demos and production, why confident AI outputs can be more dangerous than uncertain ones, and what real due diligence looks like before signing a contract. There is also a candid look at vibe coding, black-box development, and the security vulnerabilities that tend to show up nine months after you thought the product was done.
The episode ends where the harder questions live: AI bias, explainability, and whether the industry is asking the right things about the training data underneath all of it.