Don't Build Your Own AI (Unless You Have To)

Where this was said

AI Bias Is Still Real: A Personal Demonstration

At 45:00 · chapter starts 44:50

The abstract discussion of AI bias lands with sudden, personal force when Akshaya shares what happened at a Zendesk internal finance leadership event. The team was using ChatGPT to generate personalised action figures from staff photos. When Akshaya uploaded his own picture, the system automatically labelled the figure 'Ankur Patel' — a name he describes, wryly, as 'the stereotypical Indian name in the United States.' ^{[1] — Akshaya Murthy "I put in my picture and said, create an action figure of me and it automatically named my action figure Ankur Patel." 45:00 Play} The incident is both funny and pointed: it demonstrates, in a single data point, that the training data powering leading commercial models remains heavily skewed toward English-language, American cultural norms. Non-English languages and non-Western cultures occupy a far smaller slice of the training corpus, which means cultural nuance, dialectal variation, and naming conventions from outside the dominant training distribution are routinely flattened or stereotyped. Akshaya's assessment is measured but clear: bias is not a solved problem, it is a managed one, and controlling for it at the application layer — through guardrails, domain-specific fine-tuning, and careful output validation — is the current best practice until the underlying training pipelines improve.