What if you could automate your entire work life with a personal AI Chief of Staff? In this episode, Caleb Sima reveals "Pepper," his custom-built AI agent to Ashish that manages emails, schedules meetings, and even hires other AI experts to solve problems for him .

Using Claude Code and a "vibe coding" approach, Caleb built a multi-agent system over a single holiday weekend, without writing a single line of Rust code himself . We discuss how he used this same method to build a black-box testing agent that auto-files bugs on GitHub and even designed the branding for his venture fund, White Rabbit .

We explore why "intelligence is becoming a commodity," and how you can survive by becoming an architect of AI agents rather than just a worker

Questions asked:

(00:00) Introduction(03:20) Meet "Pepper": Caleb's AI Chief of Staff (05:40) How Pepper Dynamically Hires "Expert" Agents (07:30) Pepper Builds its Own Tools (MCP Servers) (11:50) Do You Need to Be a Coder to Do This? (12:50) Using "Claude Superpowers" to Orchestrate Agents (16:50) Automating a Venture Fund: Branding White Rabbit with AI (20:50) Building a "Black Box" Testing Agent in Rust (Without Knowing Rust) (28:50) The Developer Who Went Skiing While AI Did His Job (32:20) The Coming "App Sprawl" Crisis in Enterprise Security (36:00) Security Risks: Managing Shared Memory & Context (41:20) The Future of Work: Is Intelligence Becoming a Commodity? (44:50) Why Plumbers are Safe from AI

This is a forward-looking episode, as Ashish Rajan and Caleb Sima break down the 8 critical predictions shaping the future of AI security in 2026

We explore the impending "Age of Zombies", a crisis where thousands of unmaintainable, "vibe-coded" internal tools begin to rot as employees churn . We also unpack controversial theory about the "circular economy" of token costs, suggesting that major providers are artificially keeping prices high to avoid a race to the bottom .

The conversation dives deep into the shift from individual AI features to centralized AI Platforms , the reality of the Capability Plateau where models are getting "better but not different" , and the hilarious yet concerning story of Anthropic’s Claude not being able to operate a simple office vending machine without resorting to socialism or buying stun guns

Questions asked:

(00:00) Introduction: 2026 Predictions(02:50) Prediction 1: The Capability Plateau (Why models feel the same) (05:30) Consumer vs. Enterprise: Why OpenAI wins consumer, but Anthropic wins code (09:40) Prediction 2: The "Evil Conspiracy" of High AI Costs (12:50) Prediction 3: The Rise of the Centralized AI Platform Team (15:30) The "Free License" Trap: Microsoft Copilot & Enterprise fatigue (20:40) Prediction 4: Hyperscalers Shift from Features to Platforms (AWS Agents) (23:50) Prediction 5: Agent Hype vs. Reality (Netflix & Instagram examples) (27:00) Real-World Use Case: Auto-Fixing 1,000 Vulnerabilities in 2 Days (31:30) Prediction 6: Vibe Coding is Replacing Security Vendors (34:30) Prediction 7: Prompt Injection is Still the #1 Unsolved Threat (43:50) Prediction 8: The "Confused Deputy" Identity Problem (51:30) The "Zombie Tool" Crisis: Why Vibe Coded Tools will Rot (56:00) The Claude Vending Machine Failure: Why Operations are Harder than Code

Is your organization stuck in "read-only" mode with AI agents? You're not alone. In this episode, Dev Rishi (GM of AI at Rubrik, formerly CEO of Predibase) joins Ashish and Caleb to dissect why enterprise AI adoption is stalling at the experimentation phase and how to safely move to production .

Dev reveals the three biggest fears holding IT leaders back: shadow agents, lack of real-time governance, and the inability to "undo" catastrophic mistakes . We dive deep into the concept of "Agent Rewind", a capability to roll back changes made by rogue AI agents, like deleting a production database and why this remediation layer is critical for trust .

The conversation also explores the technical architecture needed for safe autonomous agents, including the debate between MCP (Model Context Protocol) and A2A (Agent to Agent) standards . Dev explains why traditional "anomaly detection" fails for AI and proposes a new model of AI-driven policy enforcement using small language models (SLMs) as judges .

Questions asked:

(00:00) Introduction(02:50) Who is Dev Rishi? From Predibase to Rubrik(04:00) The Shift from Fine-Tuning to Foundation Models (07:20) Enterprise AI Use Cases: Background Checks & Call Centers (11:30) The 4 Phases of AI Adoption: Where are most companies? (13:50) The 3 Biggest Fears of IT Leaders: Shadow Agents, Governance, & Undo (18:20) "Agent Rewind": How to Undo a Rogue Agent's Actions (23:00) Why Agents are Stuck in "Read-Only" Mode (27:40) Why Anomaly Detection Fails for AI Security (30:20) Using AI Judges (SLMs) for Real-Time Policy Enforcement (34:30) LLM Firewalls vs. Bespoke Policy Enforcement (44:00) Identity for Agents: Scoping Permissions & Tools (46:20) MCP vs. A2A: Which Protocol Wins? (48:40) Why A2A is Technically Superior but MCP Might Win