AI Thoughtmakers

GeekyAnts

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Technology

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.

Oct 1

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 to begin with. In this episode of AI Thoughtmakers, we sit down with Ashita Prasad, Developer Advocate at AWS, 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'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 Connect with Prem: LinkedIn - /premgoswami
Oct 1

How AI Is Redefining MVPs: Speed vs. Judgment

Before AI, a team of four needed 36 to 48 hours to ship one MVP 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 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, product development, and the future of software. Connect with Vinayak: LinkedIn - / vinayakgavariya Connect with Prem: LinkedIn - / premgoswami
Oct 1

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. 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 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 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 Connect with Prem: LinkedIn - / premgoswami
Oct 1

What Engineers Must Own in the AI Era

Token costs are going down. Total AI spend is going up. And most people are only looking at half the picture. In this episode of AI Thoughtmakers, we sit down with Saurabh Srivastav, Senior Software Engineer at GeekyAnts, for a sharp, no-fluff conversation about the real economics of AI — what it actually costs, where founders should and shouldn't be building, and why judgment is about to become the scarcest skill in the entire industry. From why you should never build a generic AI app, to why Friday deploys are a death wish in the AI era, to why context engineering will outlive prompt engineering by a mile — this is the episode for engineers, founders, and leaders who want to understand the AI economy from the inside. Key topics covered: • The real cost of AI — what subscribers pay vs what AI companies actually spend • Why token costs going down doesn't mean total AI spend is going down • Where founders without big capital backing should and should not be building • Why generic AI apps are a trap — and what to build instead • Why judgment will be the scarce skill as implementation becomes easier • One judgment engineer vs five prompt engineers — which one to hire and why • Why context engineering matters more than prompt engineering going forward • Why AI products fail after deployment — the verification problem nobody talks about • Adding AI to an existing product — real innovation or just marketing? • Customer support as the perfect example of AI integration done right • Why deploying AI-powered software on a Friday can be a bad idea Whether you're a founder trying to figure out where to place your bets, an engineer wondering where your value lies, or a hiring manager rethinking what skills actually matter — this episode gives you a clearer picture of where the AI economy is actually heading. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Saurabh: / saurabhsrivastava108 Connect with Prem: /premgoswami
Oct 1

Human Engineering in an AI Native Future |

AI is writing code, fixing bugs, and deploying software. So what does the future of software engineering look like when AI can handle more of the work? In this episode of AI Thoughtmakers, we sit down with Jaspreet, Tech Lead at GeekyAnts and a team of one shipping and owning everything on his project, for a conversation that every engineer needs to hear right now — about what it actually means to be indispensable in an AI-native world, and why the engineers who are thriving aren't the ones who are scared of AI, but the ones who've made it their multiplier. From why coding has always been just 20-30% of the job (AI just made it visible), to why you can outsource your thinking but never your understanding — this is the most grounded, practical take on the future of human engineering you'll find. Key topics covered: • Why there are two kinds of engineers today — those panicking and those shipping faster than ever • The headlines that are misleading engineers — and what Google's "25% AI-written code" actually means • What system-level thinking is — and why it's the only thing that will keep engineers relevant • What you should never delegate to AI — customer calls, understanding, and judgment • Why coding has always been 20-30% of the job — and AI just made that visible • Why universities should stop treating syntax as mastery — and focus on fundamentals instead • The one skill every engineer must master: debugging • Why communication matters more than coding in the AI era • How prompt engineering evolved into context engineering — and why that's actually bigger • How managers need to evolve — from counting tickets to measuring system stability and outcomes If you're an engineer anxious about where things are going, a tech lead building AI-native teams, or a student trying to figure out what skills actually matter — this episode will give you more clarity than any headline. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Jaspreet: LinkedIn - / jaspreets0026 Connect with Prem: LinkedIn - / premgoswami
Aug 19

Evaluating AI-Generated Code Quality: A Guide for Non-Technical Founders

AI can build your MVP before lunch. That doesn't mean it's ready for your users. In this episode of AI Thoughtmakers, we sit down with Vishal, Tech Lead at GeekyAnts, and Roshan, Senior Software Engineer at GeekyAnts, for a conversation every non-technical founder needs to hear about what AI-generated code actually looks like under the hood, why it passes the demo but fails in production, and why the biggest misconception in the founder world right now is thinking you don't need engineers. From exposed API keys hiding in plain sight to why AI will hallucinate its way through your codebase when you're not looking, this is the honest, no-filter guide to evaluating AI-generated code quality before it costs you everything. Key topics covered:• Why AI is powerful for MVP development but risky without an engineering layer• The biggest founder misconception right now and why engineers are still non-negotiable• Should non-technical founders trust AI-generated code? The answer is clear• The most common warning signs in AI-generated code founders miss entirely• What to verify before you deploy — compliance, security, and edge cases• Why AI still can't handle long-term maintainability and code structure• Senior software engineer vs. premium AI coding model — which one would you choose?• Security risks such as exposed API keys, weak authentication, and PII leakage• Can AI-generated code pass a compliance audit? What needs to happen first• Who is responsible when AI-generated code causes a security breach?• How software teams will look 5 years from now — and why engineers aren't going anywhere If you're a non-technical founder building an app with AI, a startup leader evaluating AI-generated software, or an engineer explaining why an AI-built MVP is not ready for production, this episode will help you understand what happens between an impressive demo and reliable production software. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Vishal: / vishalsh2299 Connect with Roshan: / roshan-kumar-ojha Connect with Prem: / premgoswami
Aug 19

Why do traditional software architectures fail with AI?

Your architecture isn't broken. But the assumptions baked into it probably are. In this episode of AI Thoughtmakers, we sit down with Aditya Prakash, Lead DevOps Engineer at GeekyAnts, for a deeply technical and refreshingly honest conversation about why traditional cloud infrastructure, microservices, REST APIs, and auto-scaling strategies struggle to support modern AI applications. From why AI blows up your cloud bill before your CPU alert even fires, to why prompt injection is the new SQL injection, to why the one architecture pattern every team needs right now is an AI gateway, this is the episode your DevOps and platform engineering teams need to hear. Key topics covered: • What traditional software architecture actually means and why AI is breaking its core assumptions • How AI infrastructure differs from traditional cloud infrastructure • Why AI scaling is fundamentally different from CPU and RAM-based auto scaling • How AI sends your cloud bill through the roof before you even realize it • Why service communication and gateway timeouts break when AI enters the picture • AI security best practices: Prompt Injection, PII protection, and AI governance • Why auditing AI applications is harder than anything we've dealt with in traditional systems • The Reverse Information Paradox and AI data privacy concerns • Why every team needs an AI gateway and what it actually does • How platform engineering needs to evolve to support AI infra properly • GPU fundamentals every DevOps engineer should understand If you're an engineering leader, DevOps engineer, or CTO trying to figure out how to make your infrastructure AI-ready without burning your budget or your security posture, this is the conversation that brings it all into focus. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Adithya: LinkedIn - / aditya-2811 Connect with Prem: LinkedIn - / premgoswami
Aug 19

Stop Hiring More Developers | Product Thinking in the AI Era

Does hiring more software developers actually build better products? In this episode of AI Thoughtmakers, we sit down with Rahul, Data Engineer and YouTuber (Start Practicing – 280K subscribers), and Arfin, Cloud Solution Architect at Microsoft, for one of the most honest conversations we've had on the podcast about why the companies hiring more engineers are solving the wrong problem, and why product thinking is the real competitive advantage in the AI era. From why Apple, Airbnb, and Netflix don't just ship features to why AI is now helping companies build the wrong product faster and what it actually means to own your decisions when AI is writing the code, this episode will change how you think about what you're building and why. Key topics covered: Why hiring more developers is a capacity solution to a clarity problem How big teams create communication overhead and still miss deadlines What product thinking actually means and why it isn't just a PM's job Finding clarity from ambiguity: the real skill is deciding what NOT to build What separates Apple, Airbnb, and Netflix from companies that ship and fail Has AI exposed weak product strategies or just helped companies build wrong products faster? Why you should never outsource your decision making to AI Startups vs enterprises: agility vs maturity. Who has better product thinking? If you had a budget for just one hire — product thinker or developer? Why the future belongs to people who are better decision makers Whether you're a founder, product leader, software engineer, or someone building in the AI era, this is the episode that brings clarity back to the conversation. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Rahul: LinkedIn - / rahul-khemka-aa487a10 Connect with Arfin: LinkedIn - / arfin-parween Arfin’s YouTube channel - / @startpracticing Connect with Prem: LinkedIn - / premgoswami

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Creator

GeekyAnts
Years Active

2026
Episodes

24
Rating

Clean
Show Website

AI Thoughtmakers

AI Thoughtmakers

Episodes

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

How AI Is Redefining MVPs: Speed vs. Judgment

What It Really Takes to Make AI Work at Enterprise Scale

What Engineers Must Own in the AI Era

Human Engineering in an AI Native Future |

Evaluating AI-Generated Code Quality: A Guide for Non-Technical Founders

Why do traditional software architectures fail with AI?

Stop Hiring More Developers | Product Thinking in the AI Era

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