January has a very particular energy.

The holidays are behind us. The inbox is slowly filling up again. Calendars are waking up. And there's always this short window, just a few quiet days, where it feels like everything could still go in a different direction.

I've been thinking a lot during this pause.

Over the last couple of years, AI and large language models have gone from experiments to expectations. What used to feel optional is now part of daily work, whether someone asked for it or not. And the biggest shift I've personally noticed isn't technical.

It's psychological.

People aren't asking "What can AI do?" anymore.

They're asking "What should we actually build?", "What do we trust?", and "What's worth shipping versus waiting?"

That question shows up everywhere, especially in product teams.

Because as exciting as LLMs are, shipping the wrong AI feature is worse than shipping none at all.

And that's exactly why today's conversation matters.

This episode is not about hype.

It's about judgment, timing, and responsibility in product leadership.

Chapters:

00:00 Introduction to Angshuman Rudra
01:06 The Impact of Large Language Models on Product Management
03:14 Balancing Innovation and User Needs
04:37 Navigating Generative AI in Product Development
06:46 Driving Adoption of New Features
09:34 Challenges and Lessons in Generative AI Products
11:15 Evolving Roles of Product Leaders with AI
12:39 The Future of Multi-Agent Systems
14:36 Translating User Requirements into Product Features
17:31 Finding the Next Big Feature
19:56 Adopting AI in Development Cycles
21:24 Tips for Job Seekers in Tech
23:10 Market Shifts in Marketing Technology
25:01 Exciting Use Cases in Marketing Technology
26:52 Concluding Thoughts and Future Outlook

Episode # 178

Angshuman Rudra is a senior product executive at TapClicks, where he leads a portfolio of data, analytics, and AI products for a market-leading martech platform.

• Website: Angshuman Rudra

What Listeners Will Learn:

• How to evaluate real user demand for AI features (not hype)
• When AI adds value and when it creates unnecessary complexity
• How product leaders should think about LLMs as tools, not magic
• Why many AI features fail after launch
• How to balance innovation with resource constraints
• What "AI adoption" actually looks like inside real companies
• Why multi-agent systems are promising but not ready to be fully autonomous
• How PMs can use AI for research, specs, and design without losing judgment
• What skills will matter most for product leaders over the next 3–5 years

• Angshuman Rudra

In this episode of Open Tech Talks, host Kashif Manzoor sits down with Bobbie Chen, a product manager working at the intersection of fraud prevention, cybersecurity, and AI agent identification in Silicon Valley.

As generative AI and large language models rapidly move from experimentation into real products, organizations are discovering a new reality. The same tools that make building software easier also make abuse, fraud, and attacks easier. Vibe coding, AI agents, and LLM-powered workflows are accelerating innovation, but they are also lowering the barrier for bad actors.

This conversation breaks down why security, identity, and access control matter more than ever in the age of LLMs, especially as AI systems begin to touch authentication, customer data, financial workflows, and enterprise knowledge. Bobbie shares practical insights from real-world security and fraud scenarios, explaining why many AI risks are not entirely new but become more dangerous when speed, automation, and scale increase.

The episode explores how organizations can adopt AI responsibly without bypassing decades of hard-earned security lessons. From bot abuse and credit farming to identity-aware AI systems and OAuth-based access control, this discussion helps listeners understand where AI changes the threat model and where it doesn't.

This is not a hype-driven episode. It is a grounded, experience-backed conversation for professionals who want to build, deploy, and scale AI systems without creating invisible security debt.

Episode # 177

Bobbie is a product manager at Stytch, where he helps organizations like Calendly and Replit fight against fraud and abuse.

• LinkedIn: Bobbie Chen

What Listeners Will Learn:

• How LLMs and AI agents change the economics of fraud and abuse, making attacks cheaper, faster, and more customized
• Why vibe coding is powerful for experimentation, but risky when used without security review in production systems
• The difference between exploring AI ideas and asking users to trust you with sensitive data
• Standard security blind spots in AI-powered apps, especially around authentication, parsing, and edge cases
• Why organizations should not give AI systems blanket access to enterprise data
• How identity-aware AI systems using OAuth and scoped access reduce risk in RAG and enterprise search
• Why are many AI security failures process and organizational problems, not tooling problems
• How fraud patterns like AI credit farming and automated abuse are emerging at scale
• Why security teams must shift from being gatekeepers to continuous partners in AI adoption
• How professionals in security, product, and engineering can stay current as AI threats evolve

• Bobbie Chen
• The two blogs I mentioned:
• Simon Willison: https://simonwillison.net
• Drew Breunig: https://www.dbreunig.com

In this episode of Open Tech Talks, I sit down with Professor Russell Van Brocklin, a New York State Senate-funded researcher, known as "The Dyslexic Professor," to unpack a very different way of thinking about AI, problem-solving, and dyslexia.

Russell's work sits at the intersection of cognitive enhancement and AI integration.

He shows how an "overactive" front part of the dyslexic brain (word analysis and articulation) can be turned into a superpower not just for dyslexic learners, but for professionals and businesses working with AI.

We talk about how his program took dyslexic high-school students who were writing like 12-year-olds and, in one school year, moved them up 7–8 grade levels in writing… at a fraction of the cost of traditional dyslexia programs.

From there, he connects it to AI collaboration: how the same mental models (context → problem → solution) can make anyone dramatically more effective when working with LLMs like ChatGPT.

Episode # 176

Russell Van Brocklen speaking, the Dyslexia Professor, shifting daily reading frustrations into confident academic wins for students facing dyslexia

• Youtube: RussellVan

What Listeners Will Learn:

• How dyslexic thinking becomes a competitive advantage in the age of AI
• Why the dyslexic brain processes information differently, and how that translates into deeper reasoning
• A practical framework for working with AI: context → problem → solution
• How to use "hero, universal theme, and villain" to sharpen thinking and guide AI more effectively
• How to perform word analysis with AI (action words, synonyms, key concepts) to get more focused outputs
• A step-by-step way to compress long AI responses into clear, structured insights
• How to generate business solutions by running context through a "universal theme lens"
• Why AI is exceptional for first drafts and why humans must still lead the final edits
• How dyslexic learners can use deep reading and repetition for breakthroughs in comprehension
• Practical strategies for teachers in the AI era: how to allow AI but still ensure authentic student work
• How non-technical users can collaborate with AI to write books, solve problems, and accelerate learning
• Real stories of professionals and students transforming their work through structured AI thinking

• RussellVan