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.

AI Thoughtmakers is a news podcast hosted by Unknown Host, with 19 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts AI Thoughtmakers, a news show with 19 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #19

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

  Aug 19, 202628 minS1

  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

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  Episode #18

  Why do traditional software architectures fail with AI?

  Aug 19, 202617 minS1

  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

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  Episode #17

  Stop Hiring More Developers | Product Thinking in the AI Era

  Aug 19, 202632 minS1

  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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  Episode #16

  Why fast-growing companies move away from traditional full-time hiring?

  Aug 19, 202620 minS1

  Full-time hiring takes 3 to 4 months. The market expects results in the same timeframe. Something has to give. In this episode of AI Thoughtmakers, we sit down with Vikas, Full Stack Engineer and Team Lead at GeekyAnts , for a no-jargon conversation about fractional engineering teams — what they actually are, when they make sense, when they don't, and why the fastest growing companies are already using them to ship faster without bloating their headcount. From the house renovation analogy that finally makes fractional hiring click, to the biggest myth founders believe about handing over ownership, this episode cuts through the confusion around fractional hiring, outsourcing, and freelancing — and gives you a clear framework for when to use each. Key topics covered: • Why traditional tech hiring can slow down product development • Fractional engineering teams explained in plain language — no jargon • Fractional vs permanent hiring: speed vs longevity — and how to decide • The biggest myth about fractional teams that's costing companies trust and output • When you should NOT hire a fractional engineering team • How knowledge transfer actually works — and why most companies get it wrong • The real ROI of fractional engineering — beyond simply reducing costs • How AI is changing the fractional model and shrinking the team sizes needed to ship • Why the future of engineering is smaller teams shipping more products Whether you're a founder building a product, trying to move fast without overhiring, an engineering leader plugging gaps in your team, or an enterprise looking to scale specific capabilities quickly — this episode gives you the playbook. Subscribe for more conversations on AI, engineering, product development, and the future of software. Connect with Vikas: LinkedIn - / vikaskumar75 Connect with Prem: LinkedIn - / premgoswami

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  Episode #15

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

  Aug 19, 202619 minS1

  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

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