Ship AI

John is Head of AI & Developer Relations at Itential, a Google Developer Expert, former Cisco AI Technical Leader, former Senior Network Architect for the Parliament of Canada — and a fellow Canadian. He's the creator of NetClaw, an open-source AI agent that lets you talk to your network infrastructure in natural language, and the founder of VibeOps Forum, which grew from zero to 400+ members in weeks.His thesis: after a decade of network automation evangelism, 70% of enterprise networks are still not meaningfully automated. But he no longer thinks they'll catch up the old way — because AI agents are changing the on-ramp entirely.In this episode, Manav and John cover:— Why VibeOps will eventually just become Ops (just like vibe coding became coding)— How to build graduated trust with agents before giving them keys to your live network— NetClaw: what it is, how it integrates with Cisco, Meraki, ACI, NetBox, Aruba and more— How to generate a real-time 3D network topology using Blender via MCP— What governed, enterprise-grade agentic ops actually looks like— Spec-Driven Development: the next evolution after vibe coding— John's bold prediction: by 2030, network engineers become HR managers for agentsIf you lead infrastructure, run a network team, work in telco, or just want to understand what AI is about to do to IT operations — this episode is for you.Find John: linkedin.com/in/johncapobianco | automateyournetwork.caNetClaw on GitHub: github.com/automateyournetwork/netclaw Ship AI: https://manavgup.github.io/shipai/state-of-ai/

Alex Seymour is a contributor to IBM's open-source agent infrastructure (BAI framework, Agent Stack, and the Relay project), exploring what it takes to build general-purpose agentic systems at enterprise scale.Kyle Sava took a different path — he identified a real pain point as a tech seller, built a conversational AI roleplay tool on the side, and grew it into WatsonX Workshop: an internal AI-powered platform now being used by IBM's sales teams to practice pitches, prep for meetings, generate podcasts and videos, and learn products faster.What we covered:Why enterprise and consumer AI are more similar than you think — until governance enters the roomWhat "production-ready AI" actually means (Alex: it solves a problem. Kyle: it earns defensible trust at scale)The first thing that breaks when you go from demo to production — hint: it's not what you thinkThe real state of MCP, A2A, and whether protocol standardization matters in enterpriseWhy RAG doesn't scale vertically — and what to do about itAdvice for anyone deploying AI today: experiment relentlessly, and don't be afraid to let AI write the code

AI Governance Isn't an Afterthought — It's a Kill SwitchLogan Kelly, founder of Waxel, built AI agents for sales automation, shipped them to production, and immediately realized he had no control over cost, quality, or behavior. That experience became the foundation for Waxell.ai — a control plane for governing enterprise agents.Key takeaways from the conversation:Observability alone is an autopsy. Tools like Datadog and Langsmith show you what went wrong after the fact. With agents, by the time you see it, data may already be exfiltrated, costs exploded, or cascading failures triggered across a chain.The rug pull attack is real. MCP tool descriptions can be silently changed by a vendor — poisoning your agent's context and redirecting it to exfiltrate data or destroy records. Most enterprises have no detection for this.Killing an agent is harder than it sounds. Stopping a runaway agent without losing state, audit trail, and replayability requires deliberate architecture — not just pulling the plug.Multi-agent chains multiply risk exponentially. Every probabilistic handoff between agents compounds unpredictability. Governance has to be built in, not bolted on.The missing piece in most AI strategies isn't more models — it's a unified governance layer that gives non-engineers visibility and control, regardless of which agents or frameworks are running underneath.

Alex LaPlante is VP of Cash Management Technology at RBC, former Interim Head of Borealis AI, co-author in Harvard Business Review, and a member of Canada's federal AI Strategy Task Force. In this conversation, she unpacks what separates organizations that actually ship AI from those stuck in demo mode — and why the answer is less about technology than culture, cross-functional collaboration, and asking the right questions before you build.What we cover:The "can we / should we" framework for every AI projectWhy responsible AI has to be designed in from day one, not bolted on at the endHow RBC built an enterprise-grade MLOps platform to scale AI safelyWhy "developer productivity" is the wrong frame — and what to measure insteadAgentic AI in regulated industries: the realistic deployment pathCanada's AI talent and commercialization gap — and what the Task Force is recommending

Seven Minutes to Midnight: AGI Is Coming What is AGI? When is it arriving? And what does it mean for your career, your organization, and humanity?In this episode of Ship AI, Manav Gupta delivers one of the most comprehensive, honest, and practical breakdowns of artificial general intelligence available today. No hype. No sci-fi. Just the data, the frameworks, and the hard questions.Chapters00:00 Introduction to AGI and Its Importance13:56 The AGI Clock: Current Status and Predictions17:49 The 10-Minute Problem in AGI Development20:39 AI 2027 Timeline and Predictions26:48 Agent Levels and Their Implications30:34 Risk and Safety in AI Development31:32 The Alignment Challenge in AI36:34 The Five Walls: What Stands Between Current AI and AGI41:07 Job Exposure: Technical Automatability vs Actual Displacement44:03 Human Futures with AI48:39 Future Scenarios for Humanity and AI53:28 Navigating Career Paths in an AI WorldBottom line: Significant disruption is coming. The shape of it depends on decisions being made right now — by policymakers, by organizations, and by you.If your core value is executing against known procedures, you are at risk. If your core value is judgment, trust, or physical presence, you have structural protection.

In this episode, Manav sits down with Mihai Criveti — IBM Distinguished Engineer and the creator of Context Forge, IBM's open-source agentic middleware — for a candid, technical conversation about what it actually takes to ship AI in the enterprise.00:58 Navigating AI for Students05:41 Context Forge and Scaling AI Agents13:24 Understanding Context Forge18:30 The Role of Middleware in AI19:47 Evaluating AI Agents24:10 Scaling AI in Enterprises29:59 The Evolution of MCP Protocol33:31 The Future of AI Agents and Workforce Dynamics39:16 Lessons from Cloud Native to AI Agents45:35 The State of AI and Future Predictions

The episode explores the rise of AI agents, their evolution from chatbots, and the challenges and opportunities in deploying and scaling AI agents. It delves into the characteristics of AI agents, the React pattern, advanced reasoning patterns, multi-agent orchestration, frameworks and protocols, governance, security implications, and the skills premium in the age of agency.TakeawaysAI agents are evolving from chatbots to systems that think, plan, and act on behalf of users.The React pattern, advanced reasoning patterns, multi-agent orchestration, and governance are critical components in the deployment and scaling of AI agents.Chapters00:00 The Age of Agency15:33 Frameworks and Protocols30:17 Multi-Agent Orchestration47:47 Security Implications

The conversation with Ozge Yeloglu covers her journey to becoming the VP of Advanced Analytics and AI at CIBC, her approach to deploying AI at scale, and the framework she built for success. It also delves into the concept of AI governance by design and the unique model of balancing AI governance and delivery, as well as leveraging the approach to change management for a large organization. Chapters:00:00 Introduction to Ozge Yeloglu and Her Journey08:53 AI Governance and Trustworthy AI Approach16:40 Building a Framework for AI Deployment29:49 People Change Management in AI38:20 Attracting and Retaining AI Talent43:17 The Future of AI: Hype vs. Reality49:38 Advice for Aspiring Engineers in AI

Episode Summary: When AI Gets a BodyThis episode explores the paradigm shift from digital AI to physical AI—what happens when chatbots become robots. We trace the evolution from Karel Čapek's 1920 coining of "robot" to today's Cambrian explosion of humanoids, autonomous vehicles, drones, and brain-computer interfaces.Chapters:00:00 The Dawn of Physical AI11:32 The $50 Trillion Opportunity15:54 Basics of Robotics28:07 Humanoids37:56 Autonomous Vehicles45:13 Defense AI54:17 Space AI57:22 Brain Computer Interface01:07:42 The Human Dimension01:11:15 Key Takeaways

Episode OverviewLawrence Wan, Chief Architect and Innovation Officer at Bank of Montreal, shares insights on how one of Canada's largest financial institutions approaches technology transformation, AI adoption, and the future of agentic systems in a heavily regulated industry.00:00 Introduction to Lawrence Van and BMO05:01 The Role of Technology in Banking11:19 Designing for Scale and Future Technologies16:57 AI in Banking: Current Applications and Future Prospects21:50 Generative AI: Productivity Gains and Challenges28:22 The Future of AI Agents in Banking33:34 The Evolving Role of Technologists in Banking

AI In Banking - Vinh Tran, VP Data & AI, RBCIn this episode, Vinh Tran—VP of Data and AI Platforms at RBC and an RBC Fellow—shares how one of the world's largest banks is approaching AI at enterprise scale.Key Takeaways"You have to control AI in order to scale it." Vinh explains that governance isn't about going slow—it's about building confidence. RBC invests heavily upfront in platforms, guardrails, and standardization so they can then open the doors and scale with assurance.The Control Plane Approach: RBC has built a comprehensive AI control plane that includes:A centralized LLM gateway controlling which models can be used (currently 6-8 rigorously validated models)An MCP gateway for managing agent-to-tool connections with proper authenticationA controlled runtime environment for monitoring agent behavior at the transport levelAn agent registry for inventory management and lifecycle control00:00 Introduction to AI in Banking02:01 The Role of AI in Enhancing Customer Experience04:03 Governance and Compliance in AI Models08:53 Control Plane: Ensuring Safe AI Deployment14:53 Control Plane for Scaling AI20:47 AI Guardrails26:05 AI Agents and Standards32:20 Predictions for the future36:34 Advice for Aspiring Technologists

The conversation explores the impact of AI adoption on the labor market, job tasks, disruption, and fluency. It delves into the Jevons Paradox, AI maturity gap, CEO mandates, labor market divergence, and productivity evidence, highlighting the reshaping of the labor market and the baseline expectation of AI proficiency at organizations.Slides can be found at: https://manavgup.github.io/shipai/state-of-ai/ep04/1TakeawaysAI adoption is reshaping the labor marketAI proficiency is now a baseline expectation at many organizationsChapters00:00 The State of AI in the Labor Market00:00 Introduction to AI's Impact on the Job Market10:51 CEO Mandates for AI Proficiency15:58 The AI Maturity Gap in Companies21:14 The Reality of AI Adoption and Worker Sentiment28:40 Week-Long Action Plan for AI Adoption29:56 Wrapping Up31:45 Preview of Next Episode

Sanctions didn't kill Chinese AI—they mutated it into something more formidable: a leaner, inference-optimized, vertically-integrated competitor.In this episode, we unpack how US export controls forced Chinese AI labs to innovate "up the stack," producing breakthroughs like DeepSeek's R1 model that matched frontier performance at a fraction of the cost. We explore China's $47.5 billion sovereign compute bet, the open-source war between Qwen, Llama, and Yi, and why China is deploying "good enough" humanoid robots at $16,000 while the West waits for AGI at $100,000+.This is the story of how constraint creates innovation—and what it means for enterprise leaders navigating a bifurcating global AI landscape.Chapters:00:00 The Mutation of Chinese AI05:15 DeepSeek's Disruption and Innovations15:53 The Open Source Revolution in AI29:04 China's National AI Strategy36:32 The Rise of Humanoid Robots46:29 Enterprise Implications and Strategic Questions

In 2014, the largest tech companies spent $44 billion on capital investments. By 2024, that number passed $200 billion—almost all of it tied to AI. This isn't an innovation budget. This is the largest concentrated capital investment in corporate history.In this episode, we follow the actual dollars: where they're going, why they're going there, and what has to be true for this multi-trillion dollar bet to pay off. We unpack the circular funding loops between OpenAI, Microsoft, NVIDIA, and Oracle. We examine why training costs 2.25x more than inference—and why that ratio has to flip. We explore the hidden taxes emerging around data licensing, regulatory compliance, and talent wars.But here's what changes everything: nation states have committed over $500 billion to AI infrastructure since 2024. Saudi Arabia, UAE, France, South Korea—they're not playing by Silicon Valley rules. This is sovereign capital operating on political timelines, and it's providing a floor that reshapes the entire investment thesis.TakeawaysAI infrastructure investments are driven by nation states and tech giantsThe emergence of a circular funding ecosystem is reshaping the AI industry Small language models offer efficiency, speed, and accessibility, leading to productivity gains and cost savings for enterprises.The AI industry faces challenges related to hidden costs, margin compression, regulatory compliance, and the impact of new entrants on the market.Chapters00:00 Act 1 - The State of AI Investment08:48 AI Circular Economy17:24 $4T TAM19:42 Act 2 - The AI Machine25:39 Energy Constraints and Rise of Energy Industrial Complex28:23 Act 3 - The Hidden Costs37:11 Act 4- Business Model Crisis48:42 Act 5 - The Path Forward51:31 Nation States as New Players in AI54:22 Global AI Investment Landscape57:42 Emerging AI Business Models01:00:31 The Rise of Open Source AI01:03:20 Vertical AI Economics and New Models01:06:10 The Shift to Small Language Models• • 01:09:03 Future Trends in AI Investment

The conversation delves into the exponential growth of AI models, the impact of compute abundance, global adoption of AI, and the comparison of AI performance with human capability. It explores the rapid evolution of AI technology and its implications on various aspects of society and industry. All slides can be found at: https://manavgup.github.io/shipai/state-of-ai/ep01/1TakeawaysExponential Growth of AI ModelsImpact of Compute AbundanceGlobal Adoption of AIAI Performance vs. Human Capability AI performance is tightly coupled to the size of the training dataFrontier AI models are fundamentally data-drivenCompute investments into AI models determine their size and learning capabilityAI progress is driven by compute, algorithms, and dataAI adoption is accelerating faster than any previous technology waveChapters00:00 The Speed of Now: Understanding AI's Rapid Evolution03:01 The Forces Behind AI's Acceleration05:47 The Impact of Moore's Law on AI08:31 The Role of Major Tech Companies in AI Growth11:27 Historical Context: 70 Years of AI Development14:38 NVIDIA's Rise and the End of Geographic Lag17:29 The Four Enablers of AI's Meteoric Rise20:20 Global Adoption and the New Reality of AI23:24 The Cambrian Explosion of AI Models26:17 AI Performance vs. Human Performance29:01 The Growth of Data Sets in AI Models29:47 The Exponential Growth of AI Data Sets32:58 The Role of Compute in AI Advancement36:42 Algorithmic Progress and Its Impact39:38 Benchmarking AI Performance Against Human Intelligence45:10 Enterprise Adoption of AI Technologies52:46 Key Learnings from AI Implementations

In this first episode, Manav Gupta and Mihai Criveti put the latest AI models through their paces in a head-to-head coding challenge. Watch as Claude Opus 4.1, GPT-5, and the open-source GPT OSS 20 billion compete to build interactive games and applications from simple prompts.Highlights:Live coding challenges including Snake, Minesweeper, and a Prince of Persia cloneReal-time comparison of how each model handles game development, from basic functionality to "kawaii" stylingTesting complex technical tasks like creating IBM mainframe architecture diagramsClassic AI benchmark tests (counting letters, arithmetic problems) with surprising resultsMihai runs GPT OSS locally on his own GPU, showcasing impressive open-source capabilitiesKey Takeaways:Claude Opus 4.1 emerges as the overall winner with cleaner interfaces and superior artifact managementGPT-5 shows promise but struggles with canvas implementationOpen-source models are rapidly closing the gap with commercial offeringsDiscussion on how these tools are reshaping the future of software development