AI Thoughtmakers

About AI Thoughtmakers

AI ThoughtMakers is a leadership-driven podcast featuring conversations with CTOs, founders, engineering leaders, and AI experts discussing real-world AI adoption, scalable engineering, product innovation, and the future of technology.

Publisher

GeekyAnts

Category

news · technology

Language

en

Explicit

No

First episode

22 May 2026

Latest episode

1 Oct 2026

Latest episodes

24 episodes in the feed.

1. 

   1 Oct 2026

   AI Native vs AI Enabled: What's the Real Difference?

   There's a simple way to tell if a product is truly built around AI or just using it as an add-on. Remove the AI. If the product still works fine, it was never really AI native (https://geekyants.com/en-us/ai-powered-product-engineering/native-engineering?utm_source=transistor&utm_medium=podcast) to begin with. In this episode of AI Thoughtmakers, we sit down with Ashita Prasad, Developer Advocate at AWS (https://geekyants.com/aws-consulting-services?utm_source=transistor&utm_medium=podcast), for one of the clearest and most grounded conversations we've had about what it actually means to build applications for the AI era — from how interfaces are changing, to why the information you give AI matters more than which AI you're using. From whether microservices are dying (they're not — they're evolving), to what a software engineer (https://geekyants.com/service/custom-software-development-services?utm_source=transistor&utm_medium=podcast)'s job actually looks like today, to what question every CEO should ask before building anything with AI — this episode will change how you think about what you're building and why. Key topics covered: The simple test that tells you if an application is truly AI native — or just AI enabled Why interfaces aren't disappearing — they're just changing from click-based to intent-based What product designers actually need to be thinking about now, and in five years Why the information you give AI matters more than which AI model you're using What AI should remember about users — and what it should forget Why microservices aren't going anywhere — but they do need to be redesigned around user goals How a software engineer's job has shifted from writing code to judgment and specification Why one product engineer with AI isn't going to outperform a whole team just yet The one question every CEO should ask before they start building The biggest AI hype right now: autonomy without accountability If you're a developer, product designer, tech lead, or founder trying to understand what building for the AI era actually means — this episode is one of the clearest explanations you'll find. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Ashita: LinkedIn -   / ashitaprasad  (https://www.linkedin.com/in/ashitaprasad/) Connect with Prem: LinkedIn -   / (https://www.youtube.com/redirect?event=video_description&redir_token=QUZZTVljSEtfZ0JuMWw1VXhNZks0S25LUGhoM3xBTl9pYzRmYVdlNUlJSUQtSkp1MWVKSEloYW5VanRnTlhQWW1zaXdOaUZUeVF6emx6V1BCcUhVMnBXT3VvRE1FeXl0V2cwQ0NqQ0JfRTFHLVdieG9wbEQyV2ZFOEVZZV9ETElK&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpremgoswami%2F&v=_RHOLzWpaGo)premgoswami  (https://www.linkedin.com/in/premgoswami/)

2. 

   1 Oct 2026

   How AI Is Redefining MVPs: Speed vs. Judgment

   Before AI, a team of four needed 36 to 48 hours to ship one MVP (https://geekyants.com/service/mvp-development-service?utm_source=transistor&utm_medium=podcast)at a hackathon. Now two people can do it in eight hours. But does building faster actually mean building better? In this episode of AI Thoughtmakers, we sit down with Vinayak, who leads Developer Relations at Sevalla, to talk about how AI is redefining the Minimum Viable Product development. We cover what's changed since AI entered the picture, what hasn't, and why the real risk today isn't building too slowly, it's building the wrong thing very quickly. From the shrinking hackathon to one founder's costly lesson in skipping market research, to why "what should we not build" might be the most important question a team can ask, this is the episode for founders, product teams, and engineers rethinking how ideas become products in the AI era. Key topics covered: How AI is reducing MVP development time from days to hours Why the fundamentals of a good MVP, like customer interviews and market research, still matter How non-technical founders can use AI to build and launch products Are teams building MVPs too quickly, and what gets lost when they do A founder's story: shipping fast, gaining 50k+ users, then realizing the market was wrong Why AI hasn't reduced the need for people, just changed what teams look like Building a team of human members and AI agents working side by side Why AI helps engineers at every experience level, not just senior ones How enterprises should use AI (https://geekyants.com/artificial-intelligence-consulting?utm_source=transistor&utm_medium=podcast) for decisions: for gathering data, not for making the call Whether you're a founder deciding what to build next, a product manager weighing speed against validation, or an engineer curious about where judgment still matters most, this episode gives you a clearer way to think about building in the AI era. Subscribe for more conversations on AI, engineering (https://geekyants.com/ai?utm_source=transistor&utm_medium=podcast), product development, and the future of software. Connect with Vinayak: LinkedIn -   / vinayakgavariya    (https://www.linkedin.com/in/vinayakgavariya/) Connect with Prem: LinkedIn -   / (https://www.youtube.com/redirect?event=video_description&redir_token=QUZZTVljRmhsOWNSZEhUckpWMWZ0M3JVRHJUd3xBTl9pYzRkdlVSZVdhejhUY0ppVUY2U1I4dXI1VTlJa1pNV09PUGZ5cFpRMzhNdmFHNkpqSDJVQTIzQUVkLWVaanNQTlljSXEyQ1hXX1oyU29QNDhGLUdjQmprdTZPb0t2djhv&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpremgoswami%2F&v=GWateoqImrs)premgoswami   (https://www.linkedin.com/in/premgoswami/)

3. 

   1 Oct 2026

   What It Really Takes to Make AI Work at Enterprise Scale

   Giving your team access to AI tools doesn't make your company AI native (https://geekyants.com/en-us/ai-powered-product-engineering/native-engineering?utm_source=transistor&utm_medium=podcast). Not even close. Making AI work at enterprise scale requires the right engineering systems, codebase context, workflows, and measurement. In this episode of AI Thoughtmakers, we sit down with Chaitanya Deorukhkar, Principal Engineer at Atlassian — the company behind Jira, Confluence, and Bitbucket — for one of the most honest and grounded conversations about what it actually takes to make AI work inside a large company. Atlassian's codebase has over 500,000 files and millions of lines of code. When an AI agent (https://geekyants.com/ai/ai-agent-development-services?utm_source=transistor&utm_medium=podcast)steps into that environment without the right setup, it doesn't just slow down — it gets completely lost. Chaitanya breaks down exactly what that looks like, why throwing more information at AI doesn't always help, and how Atlassian built an internal tool to actually measure whether their AI setup is working or not. This is not a conversation about hype. It's about the real, unglamorous engineering work that makes AI useful at scale. Key topics covered: • The difference between teams that use AI tools and teams that are truly built around AI • Why the same AI that works great for a small startup completely breaks down in a company with 500,000+ files • The kitchen analogy — why the environment around AI matters just as much as the AI itself • How AI agents get lost in large codebases and waste time looking in the wrong places • Why adding more information to AI doesn't always make it better — and can actually make things worse • How Atlassian built an internal scoring tool to measure how well their AI agents are actually performing • Why every team that owns a piece of the codebase should also be responsible for how well AI understands it • How Jira, Confluence, and Bitbucket now work together so AI agents can see the full picture of what's happening in an organization • Why writing code is no longer the hard part — and what the new bottlenecks actually are If you're an engineer, tech lead, or anyone building AI (https://geekyants.com/en-us/ai?utm_source=transistor&utm_medium=podcast) into a real product at real scale — this episode will save you a lot of trial and error. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Chaitanya: LinkedIn -   / chaitanyadeorukhkar  (https://www.linkedin.com/in/chaitanyadeorukhkar/) Connect with Prem: LinkedIn -   / (https://www.youtube.com/redirect?event=video_description&redir_token=QUZZTVljRjZjUjRreFJ3THRJbmlZdFA1SWdVc3xBTl9pYzRlZndfd3NOemhILXN2YzlWbHEwRXMySVJieGJ4LVVZcUVzWlZWS19tZVhnemJFWXltUUlrS0Q1cF91aHpsRzZULWtrbVk5YzJ1NERiRGxmUWo3aGNaRFZab3BvZmF2&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpremgoswami%2F&v=1MKjZXtkMSg)premgoswami  (https://www.linkedin.com/in/premgoswami/) (https://www.youtube.com/redirect?event=video_description&redir_token=QUZZTVljRjZjUjRreFJ3THRJbmlZdFA1SWdVc3xBTl9pYzRlZndfd3NOemhILXN2YzlWbHEwRXMySVJieGJ4LVVZcUVzWlZWS19tZVhnemJFWXltUUlrS0Q1cF91aHpsRzZULWtrbVk5YzJ1NERiRGxmUWo3aGNaRFZab3BvZmF2&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpremgoswami%2F&v=1MKjZXtkMSg)

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Based on episode analysis; this does not confirm that the show is currently accepting guests.