ThinkData Podcast

AI is moving faster than almost any technology we’ve seen before. But is governance keeping up?
In this episode of the ThinkData Podcast, I sat down with Rowan Stewart, AI & Data Safety Product Leader at Transcend, and discussed what happens when organisations rush AI products to market without the right data, privacy and governance foundations in place.
Before joining Transcend, Rowan spent around six years at BCG X, helping organisations build and launch new technology. Today, she works with companies including Robinhood, Brex and Groupon as they navigate the increasingly complex world of AI and data governance.
We discussed why so many businesses are still treating AI governance as something to solve later, whether regulation really has to slow innovation, what responsible AI actually looks like in practice, and the consequences when businesses get it wrong.
We also explored one of the biggest challenges facing startups today: how do you continue moving at startup speed while ensuring the AI products you build are trustworthy, compliant and capable of scaling?
In this episode
Rowan’s journey from BCG X to Transcend
Why companies are struggling with AI governance
Why governance becomes harder when it is bolted on later
How startups can move quickly without creating unnecessary risk
What “responsible AI” actually means in practice
The role data foundations play in trustworthy AI
Where responsibility for AI governance should sit inside an organisation
The commercial and reputational consequences of getting it wrong
Whether regulation slows innovation or can actually enable it
How AI governance is likely to evolve as adoption accelerates

AI is transforming every industry, but few are changing as quickly as recruitment.
This week, I'm joined by Arsham Ghahramani, Co-Founder and CEO of Ribbon, an AI-powered hiring platform helping companies interview every applicant through conversational AI.
Before founding Ribbon, Arsham led machine learning teams at Amazon and completed a PhD focused on AI bias and model stress-testing. Since launching Ribbon, the company has grown to more than 500 customers, raised $8 million in funding, and was recently named Fast Company's #1 AI recruiting platform.
In this episode, we discuss:
Building AI that recruiters and candidates can trust
Why AI should improve—not replace—the hiring process
The biggest misconceptions around AI recruitment
Why bias remains one of the hardest problems to solve
Product-market fit and scaling an AI startup
What hiring could look like in five years' time

Whether you're a founder, recruiter, hiring manager, or simply interested in where AI is taking the future of work, this is a conversation you won't want to miss.

What happens when AI can browse, understand, and interact with the web just like a human?
This week on the ThinkData Podcast, I'm joined by Abhishek Das, Co-founder and Co-CEO of Yutori.
Before founding Yutori, Abhishek was a research scientist at Meta, where he worked on AI agents long before the recent explosion in generative AI.
Today, Yutori is building specialised AI models designed specifically for web agents—systems capable of navigating websites, completing tasks and operating autonomously in complex online environments.
In this episode we discuss:
• Why specialised AI models outperform general-purpose LLMs for web automation
• The biggest technical challenges of building reliable AI agents
• Why AI demonstrations often fail in production
• Product-market fit in one of AI's fastest-moving markets
• What founders consistently underestimate when building AI companies
• How AI agents could fundamentally change the way businesses and consumers use the internet
If you're interested in AI infrastructure, startups, product, engineering, or the future of autonomous software, this is an episode you won't want to miss.

Enterprise data has never been more valuable, yet most companies still struggle to answer simple business questions.
This week, I sat down with Ethan Ding, Co-Founder and CEO of TextQL, to discuss why traditional analytics tools weren't built for the age of AI, how autonomous agents are changing enterprise infrastructure, and what happens when AI starts generating thousands of queries where humans once generated dozens.
We discuss:
• Why today's BI tools are reaching their limits
• The biggest technical challenges behind enterprise AI
• How TextQL is rethinking the modern data warehouse
• Why data analysts aren't disappearing—but their jobs are changing
• Finding product-market fit in one of AI's most competitive markets
• What enterprise software looks like over the next five years
If you're building, investing in, or buying AI products, this is a conversation you won't want to miss.

Agentic engineering is quickly becoming one of the most important shifts in software development.
In this episode, I sit down with Scott Breitenother, Co-Founder & CEO of Kilo Code, to discuss why individual coding assistants won't drive the future of software development, but by autonomous AI agents capable of planning, building, testing, and shipping software.
Scott shares the journey from building and selling Brooklyn Data Company to launching Kilo, an open-source agentic engineering platform designed to help developers become dramatically more productive in the AI era.
We discussed product-market fit, engineering adoption, the realities of competing with Cursor, Copilot, and Claude Code, and what engineering leaders should be thinking about as AI fundamentally changes how software teams operate.
If you're a founder, engineering leader, developer, or simply interested in the future of AI-powered software development, this is an episode you won't want to miss.