AI Thoughtmakers

GeekyAnts

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.

  1. 20h ago

    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

    Stop Hiring More Developers | Product Thinking in the AI Era

  2. 20h ago

    AI Writes the Code. But Who Owns the Bug? | AI Thoughtmakers

    AI is producing more code than ever. So why does it feel like quality is getting worse? In this episode of AI Thoughtmakers, we sit down with Ankit and Joydeep, Tech Leads at GeekyAnts, for a no-filter conversation about what code quality and engineering excellence actually mean in the age of AI and why the rules of the game haven't changed as much as people think. From who owns a $10 million bug when AI wrote the code, to why chasing 100% test coverage is overrated, to what engineering excellence will look like when AI is writing 95% of the code by 2030, this is the conversation senior engineers are having internally but rarely say out loud. Key topics covered: • Why more AI-generated code means more bugs — and why reviews are now more critical than ever • How engineering priorities have shifted from writing code to questioning architecture • The 100% test coverage myth — and what actually guarantees software quality • Why business alignment matters more than advanced tech choices for startups • Who is accountable when AI-generated code ships a bug to production? • Why AI code needs more scrutiny than human-written code — and how to review it right • The right way to do code reviews — stop checking variable names, start checking logic • Why cutting corners is sometimes valid — but only if you document it • What engineering excellence means in 2030 when AI writes 95% of the code • Why the future engineer's most valuable skill is decision-making, not coding If you're an engineer, tech lead, or engineering manager trying to stay ahead of the curve without losing sight of what actually makes software good — this episode is for you. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Ankit: LinkedIn -   / spanion    Connect with Joydeep: LinkedIn -   / joydeep-nath-09b8a0164    Connect with Prem: LinkedIn -   / premgoswami

    AI Writes the Code. But Who Owns the Bug? | AI Thoughtmakers

  3. Jun 26

    How AI Made Small Teams Powerful Enough to Rent

    No one can say traditional hiring will disappear, but there is a clear shift toward pods, contractors, and smaller AI-enabled teams. In this episode of AI ThoughtMakers, Prem sits down with Suresh, Solution Architect at GeekyAnts, to explore one of the biggest shifts happening in technology teams today: the move from traditional full-time engineering teams to expert pods. They discuss: • What an AI-native pod is and how it works • How pods operate differently from traditional outsourcing • Why startups and enterprises are embracing pod-based execution • The evolving role of engineering managers in the age of AI • How AI is changing hiring, team structures, and productivity • When to choose AI-native pods vs. full-time engineering teams • Situations where building an in-house team still makes more sense • How founders and CEOs can evaluate pod performance in the first 90 days • The risks, limitations, and decision-making boundaries of pod-based models • Whether the future of software development is shifting away from traditional hiring Subscribe for more conversations on AI, product engineering, software development, and the future of work. Connect with the Speakers LinkedIn -Suresh Connect with Prem:  LinkedIn -   / premgoswami

    How AI Made Small Teams Powerful Enough to Rent

  4. Jun 25

    Why AI Healthcare products fail | AI thoughtmakers

    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 productionHow visual completeness fools founders and product teams into shipping too earlyThe data standardization problem AI simply cannot solve on its ownWhy architecture, scalability, and security are always the first casualties of moving fastThe product manager's role in an AI-accelerated world — and why prioritization matters more than everWhy some AI-generated healthcare products should not go liveWhat "responsible AI" actually means in a high-stakes, data-sensitive industryHow AI is changing engineering velocity — and what will always remain a human decisionSubscribe 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

    Why AI Healthcare products fail | AI thoughtmakers

  5. Jun 25

    The Missing Backend : Why AI Prototypes Fail in Production

    AI can generate impressive apps in minutes. But why do so many AI-powered prototypes fail when real users start using them? In this episode of AI Thoughtmakers, we sit down with Manuinder Sekhon, Tech Lead I at GeekyAnts, to discuss why many AI-generated applications struggle in production despite looking polished on the surface. From authentication gaps and database bottlenecks to observability, scalability, and backend reliability, we explore the invisible engineering work that separates a demo from a real product. Key topics covered:  • Why AI-generated prototypes often collapse under real user traffic  • The hidden 90% of engineering work founders don't see  • Common backend issues in AI-generated applications  • Why observability and monitoring are essential for production systems  • The risks of shipping AI-built products too early  • How AI coding tools are changing startup expectations  • The difference between validating an idea and launching a product  • What founders should do before building with AI tools Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with the Speakers LinkedIn -   / manuindersekhon   Connect with Prem: LinkedIn -   / premgoswami

    The Missing Backend : Why AI Prototypes Fail in Production

  6. Jun 25

    Observability in AI: From Systems to Decision

    In this episode of AI ThoughtMakers, Aditya Prakash, Lead DevOps Engineer at GeekyAnts, breaks down one of the biggest gaps in modern AI system operations: why traditional monitoring tools fail when non-deterministic AI models enter the picture. Today’s monitoring dashboards can track standard infrastructure metrics in milliseconds. But modern AI systems are not judged by how healthy their CPU looks. They are judged by output quality, behavioral predictability, and correctness. This conversation explores why critical AI operational needs like smart data collection, failure classification, and automated guardrails remain extremely difficult to manage using traditional logs and dashboards. Using real-world engineering challenges, Aditya explains why AI observability succeeds not because it captures massive volumes of data, but because it focuses strictly on actionable signal. The discussion also uncovers the hidden risks and fundamental shifts teams often ignore while scaling AI-powered applications:  • Why traditional "loud" failures are replaced by silent, incorrect outcomes  • The high costs and privacy noise created by blindly logging all prompts and inputs  • How intelligent agents can automate log analysis and eliminate manual debugging  • Why managing behavioral predictability introduces entirely new operational overheads  • The critical role of AI Gateways as a centralized control plane for request tracing  • The difference between monitoring system health and evaluating decision quality  • Why true AI observability requires a continuous evaluation feedback loop If you’re building or scaling AI products today, this episode raises one important question: Are you just monitoring whether your system is up, or are you actually measuring the quality of its decisions? Connect with the speakers Aditya - LinkedinPrem - Linkedin

    Observability in AI: From Systems to Decision

  7. Jun 17

    The AI Fraud War - The Future of AI Fraud Detection

    AI is making fraud smarter. But it’s also making fraud detection faster, sharper, and more intelligent than ever before. In this episode of AI ThoughtMakers, we sit down with Gaurav Porwal, Principal Technical Consultant at GeekyAnts, to explore how AI is reshaping the future of fraud prevention in banking and financial systems. From deepfake-enabled KYC frauds to biometric authentication, liveness detection, AI-powered security systems, and the growing race between fraudsters and financial institutions — this conversation breaks down the real-world challenges enterprises face in the AI era. Key topics covered: • How deepfakes are changing financial fraud • Why traditional KYC systems are no longer enough • AI vs fraudsters: who is winning? • The future of passwords, biometrics, and digital identity • How financial institutions use AI to detect fake identities • Why human verification still matters in AI systems • The role of compliance, security, and trust in AI-powered finance “If attackers are evolving with AI, financial institutions are evolving faster with data.” Watch the full episode to understand where AI security is headed next. Connect with Gaurav Porwal & Prem LinkedIn -   / gaurav-porwal-811488118   LinkedIn -   https://www.linkedin.com/in/premgoswami/

    The AI Fraud War - The Future of AI Fraud Detection

  8. Jun 17

    Beyond AI Prototyping : SSO , Audit Logs , RBAC

    In this episode of AI ThoughtMakers, Sarika Gautam, Principal Technical Consultant, Geekyants, breaks down one of the biggest gaps in modern AI product development: why AI prototyping tools fail when enterprise-grade security and trust enter the picture. Today’s AI tools can generate polished applications in minutes. But enterprise systems are not judged by how good the demo looks. They are judged by security, scalability, auditability, and trust. This conversation explores why critical enterprise features like SSL, RBAC (Role-Based Access Control), and audit logs remain extremely difficult to generate reliably using AI-first prototyping tools. Using real-world examples from platforms like Slack and Okta, Sarika explains why enterprise products succeed not because they are flashy, but because they are secure, reliable, and trusted. The discussion also uncovers the hidden risks teams often ignore while moving fast with AI-generated products: Why SSL and RBAC are far more complex than they appear The overlooked importance of audit logs in enterprise systems Why AI-generated demos create “false completeness” Security risks in AI-assisted product development Technical debt created by fast AI-generated systems The difference between presentation-ready and production-ready products Why enterprise trust cannot be generated with prompts alone If you’re building AI products today, this episode raises one important question: Are you building something that only looks impressive, or something enterprises can actually trust? Connect with Sarika Gautham & Prem LinkedIn -   / sarika-gautam-047749238  LinkedIn -   https://www.linkedin.com/in/premgoswami/

    Beyond AI Prototyping : SSO , Audit Logs , RBAC
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About

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.

Information

  • Creator
    GeekyAnts
  • Years Active
    2026
  • Episodes
    19
  • Rating
    Clean
  • Copyright
    © 2026 GeekyAnts
  • Show Website
    AI Thoughtmakers