Ryan chats with Kevin Frazier, director of the AI Innovation and Law program at the University of Texas School of Law, about the legal and social impacts of data centers, the realities of workforce disruption, and regulating AI for child safety using existing consumer protection laws.

Episode notes:

The AI Innovation and Law program at the University of Texas School of Law explores the complex legal and policy questions surrounding artificial intelligence, which Kevin also covers on his own podcast, Scaling Laws.

This episode was recorded at the Ai4 conference. Listen to our other Ai4 conversation with Paypal’s Srini Venkatesan and Zscaler’s Sam Curry. 

Connect with Kevin on LinkedIn and X.

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Ryan chats with Sam Curry, CSO at Zscaler, about where human intelligence sits in the new security landscape with AI, why shifting security protections closer to applications helps limit probes for vulnerabilities, and why building more resilient code infrastructure is the best way to address the vulnerabilities AI does discover.

Episode notes:

Zscaler is a cybersecurity company that provides cloud-based security solutions, zero trust architecture, and TLS inspection to help organizations securely operate in multi-tenant environments.

This episode was recorded at the Ai4 conference. Listen to our other Ai4 conversation with Anaconda’s Greg Jennings and Paypal’s Srini Venkatesan. 

Connect with Sam Curry on LinkedIn or reach out via email at [email protected].

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SPONSORED BY IBM

Ryan welcomes Markus Eisele to the program to talk about why your coding agent should be writing Java. They talk about why the long history of Java both makes for a stable language and a source of training data for AI. On top of that, they discuss how agentic Java excels by working with powerful partners: a massive ecosystem of Java libraries and agentic harnesses. 

Try out Bob, IBM’s coding agent that Markus talked about in the episode. 

Connect with Markus on 

Congrats to Populist badge winner, Alex K, whose answer to ffmpeg usage to encode a video to H264 codec format outshined the accepted answer.

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Episode notes: 

This episode with Paypal’s CTO Srini Venkatesan was recorded at the Ai4 conference. 

Listen to our other Ai4 conversation with Greg Jennings, VP of Engineering for AI Products at Anaconda, about building secure-by-default AI coding agents here. 

Connect with Srini on LinkedIn.

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Ryan chats with Greg Jennings, VP of Engineering for AI Products at Anaconda, about what it takes to build a secure-by-default AI coding agent, why prompts shouldn't be treated as strict security guardrails, and how Anaconda is using strategic acquisitions to secure the AI software supply chain.

Episode notes:

Anaconda is a foundational platform for Python data science and machine learning, providing secure software supply chain governance and enterprise AI infrastructure.

Connect with Greg on LinkedIn and X.

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Ryan sits down with Tim Lindholm, an early contributor to the Java language at Sun Microsystems, to chat about what it was like building one of the most popular programming languages ever at its inception, why it was strategically important for the Java team to create a cross-platform ABI to compete with Windows NT, and how applets were initially just an interesting demo. 

Episode notes: 

Watch the new documentary by Cult.Repo on YouTube to learn more about the early history of Java and the people who built it.

Connect with Tim Lindholm on LinkedIn.

Shoutout to user gabuzo for winning a Populist badge for answering What is the standard method for generating a nonce in Python?.

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Ryan chats with Leo de Moura, Senior Principal Applied Scientist at AWS and the creator of the Lean language, about proving correctness in AI agents with the Lean language, how automated reasoning complements probabilistic AI models, and the use of AI for continuous code optimization.

Episode notes:

Lean is a functional programming language and proof assistant that allows developers to write programs and verify their mathematical correctness within the exact same system.

Connect with Leo on LinkedIn and check out his many badges on Stack Overflow.

Congrats to Populist badge winner Peter Lawrey for winning the badge on their answer to Check two float/double values for exact equality.

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Ryan is joined by Praveen Bodigutla, Principal AI Researcher at LinkedIn, to chat about the four-layer memory system his team built to give LinkedIn's hiring assistant a persistent, personalized state. Praveen explains why his team moved off GraphRAG in favor of a tree-structured memory for faster incremental updates and how they balance retrieval freshness, latency budgets, and access control at LinkedIn's scale.

Connect with Praveen on LinkedIn.

Congrats to Populist badge winner Luka Ganić, who answered SVG image as button in Flutter so well it outscored the accepted answer!

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Andi Gutmans, head of Agentic Data Cloud at Google and co-creator of PHP, joins Leaders of Code to talk about why agentic development feels less like a break from the past and more like the next chapter of the same story. This is part one of a two-part conversation.

In this episode, Eira May and Stack Overflow Director of Platform Engineering Peter O'Connor talk with Andi Gutmans about the throughline between democratizing web development with PHP and democratizing software development with agents today. Andi makes the case that every individual contributor is becoming “a team lead of agents” and walks through how that reshapes code review, interviewing, and the balance of human versus agent judgment. He also explains why he thinks the biggest bottleneck left isn't the models, but the challenge of getting an organization's data into a state where agents can actually reason over it.

The discussion also:

• Covers how Google is changing its own interview process to evaluate how candidates reason with and guide agents, rather than how well they can hand-code a solution alone.
• Explores the "human in the loop, agent in the loop, agent on the loop" framework for deciding where review actually needs to happen — and why that's fundamentally a risk-management question, not a trust-in-AI question.
• Uses the Waymo safety-data-versus-perception gap as an analogy for why organizations (and individuals) sometimes resist agent autonomy even when the numbers favor it.
• Introduces Google's "borderless lakehouse" concept and the shift from human data stewardship to agent-driven ontology building.

Notes:

• Connect with Andi Gutmans on LinkedIn.
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• Connect with Peter O'Connor on LinkedIn.

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