AI can generate a healthcare app before your coffee gets cold. But when real users log in, the dashboard collapses, the APIs fail and patient data you can't trust.

In this episode of AI Thoughtmakers, we sit down with Rakshith, Product Manager at GeekyAnts, to unpack why AI-generated healthcare products consistently fail in production and why the problem almost always starts long before the first line of code is written.

From data standardization and architecture gaps to the dangerous overconfidence AI coding tools create in founders, we explore why healthcare is the hardest industry for AI-generated products to survive in, and what responsible AI product development actually looks like.

Key topics covered:

Why healthcare AI products that look great in demos collapse in production
How visual completeness fools founders and product teams into shipping too early
The data standardization problem AI simply cannot solve on its own
Why architecture, scalability, and security are always the first casualties of moving fast
The product manager's role in an AI-accelerated world — and why prioritization matters more than ever
Why some AI-generated healthcare products should not go live
What "responsible AI" actually means in a high-stakes, data-sensitive industry
How AI is changing engineering velocity — and what will always remain a human decision

Subscribe for more conversations on AI, engineering, product development, and the future of software.

Connect with the Speakers:

LinkedIn - / rakshith-gowda-pm

Connect with Prem:

LinkedIn - / premgoswami