Zero to One: Product Journeys

Piyush Gupta has been building in AI since 2005, and now leads product for the Enterprise AI Platform at Uniphore, where he works on the models and agentic layers that Fortune 100 companies are deploying. He has been a CPO three times with two exits, and before that founded a mapping company in India that Google tried to acquire, and built a mobile banking product that brought millions of people into the financial system for the first time.

In this episode, we cover:

• Why he thinks about security as an architecture rather than a guardrail, and what a launch blocking spec for an agent actually looks like
• The kill switch question most agentic platforms have not answered: how fast can you pause an autonomous workflow
• Why the model was not the problem in the Hugging Face incident, and why Sam Altman's response is the more worrying part
• The difference between technology and product, and why we are deep in a technology cycle right now
• Building a way to send money by missed call in India, taking a year to reach 100 users, then $10M in payments the year after
• What the World Bank found about EKO's customers that brought him to tears
• Why open weight model adoption is about to accelerate, and how model training becomes part of the product development lifecycle
• Why evals at scale in production is still the unsolved problem blocking enterprise agents

A conversation for PMs and product leaders building AI products who want to understand what actually breaks between proof of concept and production, and how to design for it before you ship.

Talia Schmerling started out doing cancer research before switching to computer science, and her path runs through the Mayor's Office in LA, Mint.com, Disney, Nordstrom, and now as VP and Head of Product and GTM at Swiftly, alongside seven years as an Entrepreneur in Residence at Scopus Ventures.

In this episode, we cover:

• How she talked her way into working with Dan Ariely at Intuit after discovering the company had him on retainer and wasn't using him
• Why prevention beats aspiration - "don't miss a transaction" outperforms "get your full financial picture" - and what loss aversion actually does to behaviour
• The anchoring experiment she coded and shipped herself days before Black Friday, flipping which price customers saw first, and the millions in sales it drove over one weekend
• Why the old PM model of writing specs and handing them off breaks down when you can prototype in days
• Her honest take on PMs using AI: if it's all Claude, she can just ask Claude herself
• Why seed to Series B companies are the most exposed right now, caught between AI native startups and incumbents with data and distribution
• Why she stopped presenting one-year roadmaps and what she does instead

A conversation for PMs and product leaders who want to understand what the role is actually becoming, from behavioural experimentation to building in a market where the foundations shift every six months.

Jaclyn Zhuang grew up in Singapore, spent years consulting across Southeast Asia, and came to American tech with what she calls an outsider's view. Her path runs through Deloitte, EA, Google, Twitch, Facebook, Zoom, Atlassian, and now as a former VP of Product and GM for Eightfold AI.

In this episode, we cover:

• How gaming at Electronic Arts taught her systems thinking - and why multiplayer game launches are one of the most demanding product environments you can work in
• Why she made every career move without a grand strategy, and the principle behind each one: you don't need a plan, you need a good reason
• The leaky bucket moment - leadership pushing hard on acquisition while retention was quietly broken, and why fixing the foundation first was the right call, even when it wasn't the exciting one
• Her metric for evaluating any business: seats used divided by seats sold, and what it reveals before the churn shows up
• Why structuring PM teams around features is a mistake, and what changes when you organize around personas instead
• The AI hesitation she sees outside Silicon Valley - policy vacuums, EU AI Act, workers' councils - and why the real opportunity is closing the adoption gap, not building better models
• Two things she's carried throughout her career: aim for the best given the circumstances, not perfect, and logic only moves people so far

A conversation for PMs and product leaders who want to think more clearly about where to actually focus, from retention and org design to what enterprise AI adoption really looks like on the ground.

Deepak Tiwari came up through engineering and strategy consulting at Accenture before moving into product, and his career has run through some of the most consequential AI and infrastructure builds of the last 15 years, across Google, Lyft, Turing, and Meta. At Turing, he served as CPO and built the training data platform used by OpenAI, Anthropic, DeepMind, and Meta. Before that, he helped take Google Cloud from fewer than $0 to $500M in annual revenue. He's now at Meta, leading product across ranking, relevance, and generative AI systems at Instagram scale.

In this episode, we cover:

• What it took to pitch Google Cloud's CEO on a net-new product from scratch, and what happened when customers started using it in ways the team never anticipated
• How the enterprise transformation playbook that worked at Google Cloud became the blueprint for Turing's 10x growth
• What working directly with the frontier labs taught him about why data is the real moat in AI
• Building Meta's first generative AI ads product with no prior playbook, and reading transformer and diffusion model papers from scratch to understand what was actually possible
• Why the biggest drivers of user experience at Lyft had nothing to do with the app, and everything to do with backend ML
• The three levers that actually improve a model, and why good PMs need to hold all three in view
• Why the manager who barely had time for one-on-ones taught Deepak the most important career lesson

An interesting conversation for PMs and product leaders who want to understand how AI products actually get built, from the infrastructure up.

Rajitha Chaparala has spent 20 years building data and AI products in some of the world's most heavily regulated industries. She founded Intralinks' AI Center of Excellence, scaled the data platform at ZoomInfo through two simultaneous acquisitions delivering eight figures of incremental revenue in year one, and has spent the last four years as VP of Product for Data and AI at Procore, bringing AI into one of the most document-heavy, safety-critical industries in the world.

In this episode, we cover:

• How she built AI for M&A due diligence when she couldn't access actual customer documents, and why crowdsourcing training data on Upwork was the only path forward
• The redaction product that emerged as a happy accident from the same NLP foundation, and what that taught her about building on existing platforms
• The five principles she used to run two massive parallel workstreams during ZoomInfo's acquisition integration, and why clarity of vision matters more than process when you're moving fast
• The build vs buy decision at every company she's been at, and why the answer has been different every time
• Why human-in-the-loop isn't a limitation in construction AI, it's the right design for where accuracy and governance currently stand
• Why she hires for adaptability above everything else, and what breaks when you don't

A conversation for data and AI product leaders who want a grounded view of what it takes to build AI products in industries that don't forgive mistakes.

Firstly, welcome to Season 3, and thank you to all my listeners! Please follow and rate the show so I can keep bringing on incredible Product leaders on the podcast!

Robert Hamilton has been building products since before the Web existed. He founded the world's first SMS search and shopping company in 1999, spent 8 years at Google shipping the Google Mobile App, Voice Search, and Nexus hardware, and helped build the Oculus VR platform before anyone had figured out headset retention. He now helps and coaches PMs and product leaders full-time to accelerate their careers.

In this episode, we cover:

• What it was like building Scan Mobile in 1999, raising $10M, and learning that being early pays the same as being wrong
• How Google approached app distribution before app stores existed, and what that bet taught the team about building for the future
• The real challenge of VR retention and why getting someone to put something on their head is harder than it sounds
• What keeps pulling Robert toward the frontier rather than the established thing
• Why the biggest barrier holding PMs back has nothing to do with skills or AI tools
• His framework for PMs at every stage: increase clarity and drive progress

A conversation for PMs who want to think differently about building, navigating platform shifts, and understanding themselves before they try to understand the technology. You can find out more about Robert's current work here.

How do you actually scale enterprise platforms inside companies like AWS, Google Cloud, and Stripe?

In this episode of Zero to One, I sit down with Bob Krentler, former GM at AWS and Product leader at Stripe and Google Cloud, to break down what it really takes to build, operate, and monetize Enterprise-grade platforms at massive scale.

Bob has led multi-billion dollar product lines, driven Enterprise modernization efforts across the cloud, and built vertical solutions powering some of the world’s most important companies.

We cover:

• The biggest mistakes teams make when modernizing legacy infrastructure
• What AWS taught him about operating at true cloud scale
• The hardest trade-offs in enterprise, including competing with your own partners
• Why every horizontal platform eventually moves vertical, and how Stripe executed it
• What most product leaders get wrong about AI-first product strategy
• How AI is reshaping the role of product, engineering, and decision-making
• Why enterprise requires long-term thinking, not short-term wins

If you’re building for Enterprise, thinking about Platforms, or trying to understand where AI fits into your roadmap, this episode is a masterclass in how the best operators actually think.

What did product look like before product management even existed?

In this episode of Zero to One, I sit down with Joshua Burgin, former Chief Product Officer at Upwind Security and early Amazon employee, to break down how product thinking evolved from the earliest days of Amazon into today’s world of AI, cloud infrastructure, and platform-scale systems.

Joshua has led product and engineering across AWS, VMware, and Zynga, building and scaling products from zero to billions in revenue.

We cover:

• The origins of product management inside Amazon’s first 100 employees

• The biggest unsolved problems in cloud infrastructure and AWS today

• How pricing strategy impacts adoption in B2B SaaS and AI platforms

• What changes when AI makes products non-deterministic

• Why observability, evals, and decision-making frameworks are now core PM skills

• How to balance speed vs stability when scaling product teams

This episode is for product managers, product leaders, and builders working in AI, SaaS, and infrastructure who want to understand what’s actually changing, and what still holds true.

Find more of Joshua’s work: https://www.joshuaburgin.io/

Sriram Iyer has spent the last two decades building and scaling enterprise platforms across Salesforce, Adobe, and Freshworks.

Over that time, he has helped grow products from sub-$1B to multi-billion dollar ARR, working across platform strategy, AI systems, and now full general management with P&L ownership.

In this conversation, we explore the journey behind those platforms and the decisions that shaped them.

Sriram shares how he moved from consulting into product management during the early days of cloud computing at Salesforce, helped build intelligence layers that later evolved into Einstein AI, and later led the growth of Adobe’s Digital Video business from $700M to $2.2B ARR at Adobe.

We also discuss his transition from product leadership into full GM ownership and what changes when you move from building products to building businesses.

Topics we cover include:

• Transitioning from consulting into product leadership

• Building intelligence layers inside Salesforce before AI became mainstream

• The technical and infrastructure challenges behind scaling Adobe’s video platform

• What changes when product leaders take on GM and P&L ownership

• Why AI is a generational technology wave and how enterprise adoption may evolve

• Advice for product managers thinking about long-term careers

A thoughtful conversation on platform thinking, enterprise AI, and the realities of scaling products inside large technology companies.

Product marketing sits at the intersection of product, sales, and growth, but it’s often misunderstood or treated as an afterthought.

In this episode, I’m joined by Michael Rothschild, a product marketing leader who’s spent his career translating deeply technical products into stories that actually land with customers.

We go deep on what product marketing really exists to solve, and where teams tend to get it wrong.

We cover:

• How product marketing differs from traditional marketing, and why the distinction matters

• What it really means to turn complex products into clear, customer-first narratives

• Lessons Michael carried over from teaching into product marketing leadership

• How product marketing should scale from early-stage companies to large enterprises

• Where launches, messaging shifts, and rebrands often break down

• How to position highly technical products for C-suite buyers without losing substance

• What product marketing should measure beyond pipeline and dashboards

• The role product marketing plays in making acquisitions actually work

  
  

If you’ve ever wondered how great products get misunderstood, or how teams can bridge the gap between what they build and how it’s perceived, this episode will be an interesting listen!