Chain of Thought

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)

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 the conversation 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.

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

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

Presented by: Galileo.ai
Download Galileo's Mastering Multi-Agent Systems for free!: 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