How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off.

Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Angie shares the challenges they faced, the security guardrails they built, and why letting employees choose their own models was critical to adoption.

We also dive into vibe coding (including Angie's experience watching Jack Dorsey vibe code a feature in 2 hours), how non-engineers are building their own tools, and what MCP unlocks when you connect multiple systems together.

Chapters:

00:00 Introduction

02:02 How Block deployed AI agents to 12,000 employees

05:04 Challenges with MCP adoption and security at scale

07:10 Why Block supports multiple AI models (Claude, GPT, Gemini)

08:40 Open source models and local LLM usage

09:58 Measuring velocity gains across the organization

10:49 Vibe coding: Benefits, risks & Jack Dorsey's 2-hour feature build

13:46 Block's contributions to the MCP protocol

14:38 MCP in action: Incident management + GitHub workflow demo

15:52 Addressing MCP criticism and security concerns

18:41 The Agentic AI Foundation announcement (Block, Anthropic, OpenAI, Google, Microsoft)

21:46 AI democratization: Non-engineers building MCP servers

24:11 How to get started with MCP and prompting tips

25:42 Security guardrails for enterprise AI deployment

29:25 Tool annotations and human-in-the-loop controls

30:22 OAuth and authentication in Goose

32:11 Use cases: Engineering, data analysis, fraud detection

35:22 Goose in Slack: Bug detection and PR creation in 5 minutes

38:05 Goose vs Claude Code: Open source, model-agnostic philosophy

38:17 Live Demo: Council of Minds MCP server (9-persona debate)

45:52 What's next for Goose: IDE support, ACP, and the $100K contributor grant

47:57 Where to get started with Goose

Connect with Angie on LinkedIn: https://www.linkedin.com/in/angiejones/

Angie's Website: https://angiejones.tech/

Follow Angie on X: https://x.com/techgirl1908

Goose GitHub: https://github.com/block/goose

Connect with Conor on LinkedIn: https://www.linkedin.com/in/conorbronsdon/

Follow Conor on X: https://x.com/conorbronsdon

Modular: https://www.modular.com/

Presented By: Galileo AI

Download Galileo's Mastering Multi-Agent Systems for free here: https://galileo.ai/mastering-multi-agent-systems

Topics Covered:

- How Block deployed Goose to all 12,000 employees

- Building enterprise security guardrails for AI agents

- Model Context Protocol (MCP) deep dive

- Vibe coding benefits and risks

- The Agentic AI Foundation (Block, Anthropic, OpenAI, Google, Microsoft, AWS)

- MCP sampling and the Council of Minds demo

- OAuth authentication for MCP servers

- Goose vs Claude Code and other AI coding tools

- Non-engineers building AI tools

- Fraud detection with AI agents

- Goose in Slack for real-time bug fixing

Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space.

Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI.

The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping.

The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space.

00:00 Introduction

01:30 Paige's Background & Connection to Modular

02:29 Gemini Integration Across Google Products

03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview

03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing

06:10 Choosing the Right Gemini Model

09:42 Google's Hardware Advantage: TPUs & JAX

10:16 TensorFlow History & Evolution to JAX

11:45 NeurIPS 2025 & Google's Research Culture

14:40 Google Brain to DeepMind: The Merger Story

15:24 Palm II to Gemini: Scaling from 40 People

18:42 Gemma Open Source Models

20:46 Anti-Gravity IDE Deep Dive

23:53 MCP Protocol & Chrome DevTools Integration

26:57 Gemini CLI in Google Colab

28:00 Image Generation & AI Studio Traffic Spikes

28:46 Space Math Academy: Gamified NASA Curriculum

31:31 Vibe Coding: Building with AI Studio & Anti-Gravity

36:02 AI From Bits to Atoms: The Robotics Frontier

36:40 Stanford Puppers: Gemini on Raspberry Pi Robots

38:35 Google's Robotics Trusted Tester Program

40:59 AI in Scientific Research & Automation

42:25 Project Suncatcher: Data Centers in Space

45:00 Sustainable AI Infrastructure

47:14 Non-Dystopian Sci-Fi Futures

47:48 Closing Thoughts & Resources

- Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/

- Follow Paige on X: https://x.com/DynamicWebPaige

- Paige's Website: https://webpaige.dev/

- Google DeepMind: https://deepmind.google/

- AI Studio: https://ai.google.dev

Connect with our host Conor Bronsdon:

- Substack – https://conorbronsdon.substack.com/

- LinkedIn https://www.linkedin.com/in/conorbronsdon/

Presented By: Galileo.ai

Download Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systems

Topics Covered:

- Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities)

- When to use Gemma open-source models

- Anti-Gravity IDE, Jules, and Gemini CLI workflows

- Google's TPU hardware advantage

- History of TensorFlow, JAX, and Google Brain

- Space Math Academy demo (gamified education)

- AI-powered robotics (Stanford Puppers on Raspberry Pi)

- Project Suncatcher (orbital data centers)

You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype.

Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. The companies actually shipping production AI at scale have figured out something different—they've turned evaluations into infrastructure, into IP, into the layer where domain expertise becomes executable governance.

Vikram Chatterji, CEO and Co-founder of Galileo, returns to Chain of Thought to break down eval engineering: what it is, why it's becoming a dedicated discipline, and what it takes to actually make it work. Vikram shares why generic evals are plateauing, how continuous learning loops drive accuracy, and why he predicts "eval engineer" will become as common a role as "prompt engineer" once was.

In this conversation, Conor and Vikram explore:

• Why treating evals as infrastructure—not checkboxes—separates production AI from prototypes
• The plateau problem: why generic LLM-as-a-judge metrics can't break 90% accuracy
• How continuous human feedback loops improve eval precision over time
• The emerging "eval engineer" role and what the job actually looks like
• Why 60-70% of AI engineers' time is already spent on evals
• What multi-agent systems mean for the future of evaluation
• Vikram's framework for baking trust AND control into agentic applications

Plus: Conor shares news about his move to Modular and what it means for Chain of Thought going forward.

Chapters:00:00 – Introduction: Why Evals Are Becoming IP01:37 – What Is Eval Engineering?04:24 – The Eval Engineering Course for Developers05:24 – Generic Evals Are Plateauing08:21 – Continuous Learning and Human Feedback11:01 – Human Feedback Loops and Eval Calibration13:37 – The Emerging Eval Engineer Role16:15 – What Production AI Teams Actually Spend Time On18:52 – Customer Impact and Lessons Learned24:28 – Multi-Agent Systems and the Future of Evals30:27 – MCP, A2A Protocols, and Agent Authentication33:23 – The Eval Engineer Role: Product-Minded + Technical34:53 – Final Thoughts: Trust, Control, and What's Next

Connect with Conor Bronsdon:Substack – https://conorbronsdon.substack.com/LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (Twitter) – https://x.com/ConorBronsdon

Learn more about Eval Engineering:⁠https://galileo.ai/evalengineering⁠

Connect with Vikram Chatterji:LinkedIn – ⁠https://www.linkedin.com/in/vikram-chatterji/⁠

AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation.

Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-check the absurd AI environmental claims you've heard at parties, in articles, and even in bestselling books. Andy recently went viral for discovering what he calls "the single most egregious math mistake" he's ever seen in a book—a data center water usage calculation in Karen Hao's NYT Bestseller, Empire of AI, that was off by a factor of 4,500.

In this conversation, Andy and Conor break down the myths around AI’s water and energy usage and explore:

• The viral Empire of AI error and what it reveals about the broader debate

• Why most AI water usage statistics are misleading or flat-out wrong

• How one ChatGPT prompt represents just 1/150,000th of your daily emissions

• Trade-offs around data center cooling + decision making

• Why "tribal thinking" about AI is distorting environmental activism

• Where AI might actually help the climate through deep learning optimization

If you've ever felt guilty about using AI tools, been cornered at a party about AI's environmental impact, or simply want to understand what the data actually says, this episode, and Andy’s deep dive articles, arm you with the facts.

Chapters:

00:00 – Introduction: The Party Guilt Problem

01:54 – Andy's Background and What Sparked This Work

03:50 – The 4,500x Error in Empire of AI

06:39 – Breaking Down the Math: Liters vs. Cubic Meters

10:39 – The Unintended Consequence: Air Cooling vs. Water Cooling

12:51 – Karen Hao's Response and What's Still Missing

19:08 – Why Environmentalists Should Focus Elsewhere

21:41 – The Danger of Tribal Thinking About AI

25:49 – What Is Effective Altruism (And Why People Attack It)

29:15 – EA, AI Risk, and P(doom)

34:31 – Why Misinformation Hurts Your Own Side

37:39 – Using ChatGPT Is Not Bad for the Environment

42:14 – The Party Rebuttal: Practical Comparisons

45:23 – Water Use Reality: 1/800,000th of Your Daily Footprint

48:27 – The Personal Carbon Footprint Distraction

53:38 – Data Centers: Efficiency vs. Whether to Build

55:13 – AI's Net Climate Impact: The Positive Case

59:34 – Deep Learning, Smart Grids, and Climate Optimization

1:03:45 – Final Thoughts

Key references

IEA Study: AI and climate change - https://www.iea.org/reports/energy-and-ai/ai-and-climate-change#abstract

Nature: https://www.nature.com/articles/s44168-025-00252-3

The Empire of AI Error: https://andymasley.substack.com/p/empire-of-ai-is-wildly-misleading

Using ChatGPT isn’t bad for the environment: https://andymasley.substack.com/p/a-short-summary-of-my-argument-that

https://andymasley.substack.com/p/a-cheat-sheet-for-conversations-about

Connect with Andy Masley:

Substack – https://andymasley.substack.com/

X (Twitter) – https://x.com/AndyMasley

Connect with Conor Bronsdon:

Substack – https://conorbronsdon.substack.com/

LinkedIn – https://www.linkedin.com/in/conorbronsdon/

X (Twitter) – https://x.com/ConorBronsdon

AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era.

Jeetu breaks down the three bottlenecks he sees holding AI back today:

• Infrastructure limits: not enough power, compute, or data center capacity

• A trust deficit: non-deterministic models powering systems that must be predictable

• A widening data gap: human-generated data plateauing while machine data explodes

Jeetu then shares how Cisco is tackling these challenges through secure AI factories, edge inference, open multi-model architectures, and global partnerships with Nvidia, G42, and sovereign cloud providers. Jeetu also explains why he thinks enterprises will soon rely on thousands of specialized models — not just one — and how routing, latency, cost, and security shape this new landscape.

Conor and Jeetu also explore high-performance leadership and team culture, discussing building high-trust teams, embracing constructive tension, staying vigilant in moments of success, and the personal experiences that shaped Jeetu’s approach to innovation and resilience.

If you want a clearer picture of the global AI infrastructure race, how high-level leaders are thinking about the future, and what it all means for enterprises, developers, and the future of work, this conversation is essential.

Chapters:

00:00 – Welcome to Chain of Thought

0:48 - AI and Jobs: Beyond the Hype

6:15 - The Real AI Opportunity: Original Insights

10:00 - Three Critical AI Constraints: Infrastructure, Trust, and Data

16:27 - Cisco's AI Strategy and Platform Approach

19:18 - Edge Computing and Model Innovation

22:06 - Strategic Partnerships: Nvidia, G42, and the Middle East

29:18 - Acquisition Strategy: Platform Over Products

32:03 - Power and Infrastructure Challenges

36:06 - Building Trust Across Global Partnerships

38:03 - US vs. China: The AI Infrastructure Race

40:33 - America's Venture Capital Advantage

42:06 - Acquisition Philosophy: Strategy First

45:45 - Defining Cisco's True North

48:06 - Mission-Driven Innovation Culture

50:15 - Hiring for Hunger, Curiosity, and Clarity

56:27 - The Power of Constructive Conflict

1:00:00 - Career Lessons: Continuous Learning

1:02:24 - The Email Question

1:04:12 - Joe Tucci's Four-Column Exercise

1:08:15 - Building High-Trust Teams

1:10:12 - The Five Dysfunctions Framework

1:12:09 - Leading with Vulnerability

1:16:18 - Closing Thoughts and Where to Connect

Connect with Jeetu Patel:

LinkedIn – https://www.linkedin.com/in/jeetupatel/

X(twitter) – https://x.com/jpatel41

Cisco - https://www.cisco.com/

Connect with ConorBronsdon

Substack – https://conorbronsdon.substack.com/

LinkedIn – https://www.linkedin.com/in/conorbronsdon/

X (twitter) – https://x.com/ConorBronsdon

The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices

Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability.

This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise.

But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more.

If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode.

Connect with ⁨Maxime Labonne⁩ :

LinkedIn – https://www.linkedin.com/in/maxime-labonne/

X (Twitter) – @maximelabonne

About Maxime – https://mlabonne.github.io/blog/about.html

HuggingFace – https://huggingface.co/mlabonne

The LLM Course – https://github.com/mlabonne/llm-course

Liquid AI – https://liquid.ai

Connect with ⁨Conor Bronsdon⁩ :

X (twitter) – @conorbronsdon

Substack – https://conorbronsdon.substack.com/

LinkedIn – https://www.linkedin.com/in/conorbronsdon/

00:00 Intro — Welcome to Chain of Thought

00:27 Guest Intro — Maxime Labonne of Liquid AI

02:21 The Hybrid LLM Architecture Explained

06:30 Why Bigger Models Aren’t Always Better

11:10 Convolution + Transformers: A New Approach to Efficiency

18:00 Running LLMs on Laptops and Wearables

22:20 Post-Training as the Real Moat

25:45 Synthetic Data and Reliability in Model Refinement

32:30 Evaluating AI in the Real World

38:11 Benchmarks vs Functional Evals

43:05 The Future of Edge-Native Intelligence

48:10 Closing Thoughts & Where to Find Maxime Online

Most AI agents are built backwards, starting with models instead of system architecture.

Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you.

Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.

Connect with Aishwarya Srinivasan:

LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/

Instagram: https://www.instagram.com/the.datascience.gal/

Connect with Conor: https://www.linkedin.com/in/conorbronsdon/

00:00 Intro — Welcome to Chain of Thought

00:22 Guest Intro — Ash Srinivasan of Fireworks AI

02:37 The Challenge of Responsible AI

05:44 The Hidden Risks of Reward Hacking

07:22 From Prompt to Context Engineering

10:14 Data Quality and Human Feedback

14:43 Quantifying Trust and Observability

20:27 Evaluation-Driven Development

30:10 Open Source Models vs. Proprietary Systems

34:56 Gaps in the Open-Source AI Stack

38:45 When to Use Different Models

45:36 Governance and Compliance in AI Systems

50:11 The Future of AI Builders

56:00 Closing Thoughts & Follow Ash Online

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This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show.

From The Humans of AI:

Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity.

Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement.

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