Podbit · The MongoDB Podcast
Akshaya Murthy asked ChatGPT to make an action figure from his own photo. It named the figure 'Ankur Patel' — a stereotypical Indian-American name. Bias isn't theoretical. It's baked into the training data and shows up in production.
Where this was said
At 44:48 · chapter starts 43:00
Shane asks whether the architectural considerations amount to 'responsible AI' — and Akshaya's answer is a layered yes, grounded in the NIST Risk Management Framework's four-function cycle: Govern, Map, Measure, and Manage. The key shift from traditional software, he argues, is that AI requires trustworthiness to be embedded at every phase of the model lifecycle, not just at the application layer. Unacceptable risks must be identified and eliminated early; high-risk use cases must be scoped out of the product footprint; bias and privacy protections must be baked in by design. He connects this to EU AI Act compliance, where the stakes for getting it wrong are both legal and reputational. The cautionary example is Google's early AI stumbles — high-profile bias incidents that damaged trust in ways that were slow to repair. His conclusion: any enterprise building an AI product should treat the NIST cycle as a minimum operating standard, not as optional overhead.
Responsible AI isn't a checkbox at the end of the build cycle. The NIST govern-map-measure-manage framework needs to run through every phase: pre-training, training, post-training, delivery, and application. Bias and privacy must be by design, not bolt-ons.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
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