Ship AI

About Ship AI

From 0 to Production. Practical tips, tricks, and best practices to make AI useful for production in the real world!

Publisher

Manav Gupta

Category

technology

Language

en

Explicit

No

First episode

10 Aug 2025

Latest episode

28 Jul 2026

Latest episodes

17 episodes in the feed.

1. 

   28 Jul 2026

   Sumeet Vaidya | AI Driven SDLC

   Guest: Sumeet Vaidya, Co-founder & CEO, Crafting (previously Facebook, Uber, Discord) Sumeet joins Manav to argue that the industry is optimizing the wrong half of the software lifecycle. Code generation is nearly free; validation is where the real engineering now lives. What we cover: The model debate is a distraction. Claude Code vs. Codex vs. Cursor matters tactically for an IC picking a tool today, but it's the wrong question for an engineering leader deciding where to invest. The right question is resilience — can your organization absorb the next wave without being locked in? Resilience has two meanings. First, the boring one: if your primary provider goes down, does your engineering org stop? We learned this lesson with cloud providers and auth providers; AI infrastructure deserves the same treatment. Second, the harder one: if engineers increasingly don't touch code, how do you retain a deterministic way to evaluate and resolve failures in a probabilistic system? Harnesses are mostly markdown. Sumeet's sharpest critique: the average "harness" is a pile of markdown files with no real contract, no true guardrails, and no answer for credentials, dependencies, or access. Loop engineering has the same gap — "check if it works" is doing enormous heavy lifting and getting glossed over. Spec-driven development, honestly assessed. It serves the purpose architecture docs used to: forcing the orchestrator to refine the ask. It does not put hard guardrails on the agent. A beautiful spec that passes human scrutiny can still fall apart in execution. Useful, not a silver bullet. The test taxonomy collapses. With the right infrastructure, the distinction between unit test, integration test, and CI/CD stops mattering — a test simply verifies something works, whether that spans one service or a hundred. Manav pushes on a pattern he sees repeatedly: engineers fooling themselves with high unit-test coverage while integration remains the weak point. The compounding math. Even if loops cut regression probability in half, 10x more code shipped means roughly 5x more outages. Production-monitoring agents that detect and patch errors are another band-aid. Every step of that chain gets resolved upstream by real infrastructure investment. Enterprise reality. Crafting deploys into customer clouds — no data leaves. Shared pre-production clusters, sanitized DB snapshots, credential injection via Vault, network interception for shared staging. Named customers include Brex, Fair, and Webflow; larger regulated logos are in flight. What enterprises actually ask about: security, visibility, and — increasingly — cost. Token maxing is blowing through budgets across the board. Productivity, measured soberly. Not 100–200% gains. Double digits. Anyone counting PRs has lost the plot. Sumeet expects a winnowing of the middle layer of engineering, particularly outside Silicon Valley, alongside genuinely new demand — every dental office and clinic plausibly supports one engineer. For new grads: CS isn't dead, it's changing. Someone who has relearned AI tooling four times in six months may be more adaptable than a senior engineer optimized for the old stack. The closing question for your organization: When an agent's change passes every check and is still wrong in production — what's your blast radius, and who's accountable? If you can't answer cleanly, that's your roadmap.

2. 

   21 Apr 2026

   John Capobianco | VibeOps, NetClaw & Network Automation

   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 agents If 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 (http://linkedin.com/in/johncapobianco) | automateyournetwork.ca (http://automateyournetwork.ca) NetClaw on GitHub: github.com/automateyournetwork/netclaw (http://github.com/automateyournetwork/netclaw) Ship AI: https://manavgup.github.io/shipai/state-of-ai/ (https://manavgup.github.io/shipai/state-of-ai/)

3. 

   14 Apr 2026

   Alex Seymour & Kyle Sava | From Demo to Production: What Actually Breaks

   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 room What "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 think The real state of MCP, A2A, and whether protocol standardization matters in enterprise Why RAG doesn't scale vertically — and what to do about it Advice for anyone deploying AI today: experiment relentlessly, and don't be afraid to let AI write the code

4. 

   7 Apr 2026

   Logan Kelly | Governing AI Agents

   AI Governance Isn't an Afterthought — It's a Kill Switch Logan 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.

5. 

   31 Mar 2026

   Responsible AI by Design | Alex LaPlante, RBC

   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 project Why responsible AI has to be designed in from day one, not bolted on at the end How RBC built an enterprise-grade MLOps platform to scale AI safely Why "developer productivity" is the wrong frame — and what to measure instead Agentic AI in regulated industries: the realistic deployment path Canada's AI talent and commercialization gap — and what the Task Force is recommending

6. 

   17 Mar 2026

   Episode 7 - AGI is coming

   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. Chapters 00:00 Introduction to AGI and Its Importance 13:56 The AGI Clock: Current Status and Predictions 17:49 The 10-Minute Problem in AGI Development 20:39 AI 2027 Timeline and Predictions 26:48 Agent Levels and Their Implications 30:34 Risk and Safety in AI Development 31:32 The Alignment Challenge in AI 36:34 The Five Walls: What Stands Between Current AI and AGI 41:07 Job Exposure: Technical Automatability vs Actual Displacement 44:03 Human Futures with AI 48:39 Future Scenarios for Humanity and AI 53:28 Navigating Career Paths in an AI World Bottom 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.

7. 

   10 Mar 2026

   Mihai Criveti, Context Forge

   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 Students 05:41 Context Forge and Scaling AI Agents 13:24 Understanding Context Forge 18:30 The Role of Middleware in AI 19:47 Evaluating AI Agents 24:10 Scaling AI in Enterprises 29:59 The Evolution of MCP Protocol 33:31 The Future of AI Agents and Workforce Dynamics 39:16 Lessons from Cloud Native to AI Agents 45:35 The State of AI and Future Predictions

8. 

   6 Mar 2026

   Episode 6 - AI Agents.

   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. Takeaways AI 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. Chapters 00:00 The Age of Agency 15:33 Frameworks and Protocols 30:17 Multi-Agent Orchestration 47:47 Security Implications

9. 

   24 Feb 2026

   Ozge Yeloglu, VP AI & Analytics, CIBC

   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 Journey 08:53 AI Governance and Trustworthy AI Approach 16:40 Building a Framework for AI Deployment 29:49 People Change Management in AI 38:20 Attracting and Retaining AI Talent 43:17 The Future of AI: Hype vs. Reality 49:38 Advice for Aspiring Engineers in AI

10. 

    17 Feb 2026

    Episode 5 - AI Gets a Body

    Episode Summary: When AI Gets a Body This 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 AI 11:32 The $50 Trillion Opportunity 15:54 Basics of Robotics 28:07 Humanoids 37:56 Autonomous Vehicles 45:13 Defense AI 54:17 Space AI 57:22 Brain Computer Interface 01:07:42 The Human Dimension 01:11:15 Key Takeaways

11. 

    10 Feb 2026

    Lawrence Wan, Chief Architect, BMO

    Episode Overview Lawrence 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 BMO 05:01 The Role of Technology in Banking 11:19 Designing for Scale and Future Technologies 16:57 AI in Banking: Current Applications and Future Prospects 21:50 Generative AI: Productivity Gains and Challenges 28:22 The Future of AI Agents in Banking 33:34 The Evolving Role of Technologists in Banking

12. 

    3 Feb 2026

    AI In Banking - Vinh Tran, RBC

    AI In Banking - Vinh Tran, VP Data & AI, RBC In 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 authentication A controlled runtime environment for monitoring agent behavior at the transport level An agent registry for inventory management and lifecycle control 00:00 Introduction to AI in Banking 02:01 The Role of AI in Enhancing Customer Experience 04:03 Governance and Compliance in AI Models 08:53 Control Plane: Ensuring Safe AI Deployment 14:53 Control Plane for Scaling AI 20:47 AI Guardrails 26:05 AI Agents and Standards 32:20 Predictions for the future 36:34 Advice for Aspiring Technologists

13. 

    2 Feb 2026

    Episode 4 - AI At Work

    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/ep04/1 (https://manavgup.github.io/shipai/state-of-ai/ep04/1) Takeaways AI adoption is reshaping the labor market AI proficiency is now a baseline expectation at many organizations Chapters 00:00 The State of AI in the Labor Market 00:00 Introduction to AI's Impact on the Job Market 10:51 CEO Mandates for AI Proficiency 15:58 The AI Maturity Gap in Companies 21:14 The Reality of AI Adoption and Worker Sentiment 28:40 Week-Long Action Plan for AI Adoption 29:56 Wrapping Up 31:45 Preview of Next Episode

14. 

    27 Jan 2026

    Episode 3 - The Red Silicon Curtain

    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 AI 05:15 DeepSeek's Disruption and Innovations 15:53 The Open Source Revolution in AI 29:04 China's National AI Strategy 36:32 The Rise of Humanoid Robots 46:29 Enterprise Implications and Strategic Questions

15. 

    20 Jan 2026

    Episode 2 - Follow the Money

    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. Takeaways AI infrastructure investments are driven by nation states and tech giants The 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. Chapters 00:00 Act 1 - The State of AI Investment 08:48 AI Circular Economy 17:24 $4T TAM 19:42 Act 2 - The AI Machine 25:39 Energy Constraints and Rise of Energy Industrial Complex 28:23 Act 3 - The Hidden Costs 37:11 Act 4- Business Model Crisis 48:42 Act 5 - The Path Forward 51:31 Nation States as New Players in AI 54:22 Global AI Investment Landscape 57:42 Emerging AI Business Models 01:00:31 The Rise of Open Source AI 01:03:20 Vertical AI Economics and New Models 01:06:10 The Shift to Small Language Models • • 01:09:03 Future Trends in AI Investment

16. 

    13 Jan 2026

    Episode 1 - The Speed of Now

    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/ep01/1 (https://manavgup.github.io/shipai/state-of-ai/ep01/1) Takeaways Exponential Growth of AI Models Impact of Compute Abundance Global Adoption of AI AI Performance vs. Human Capability AI performance is tightly coupled to the size of the training data Frontier AI models are fundamentally data-driven Compute investments into AI models determine their size and learning capability AI progress is driven by compute, algorithms, and data AI adoption is accelerating faster than any previous technology wave Chapters 00:00 The Speed of Now: Understanding AI's Rapid Evolution 03:01 The Forces Behind AI's Acceleration 05:47 The Impact of Moore's Law on AI 08:31 The Role of Major Tech Companies in AI Growth 11:27 Historical Context: 70 Years of AI Development 14:38 NVIDIA's Rise and the End of Geographic Lag 17:29 The Four Enablers of AI's Meteoric Rise 20:20 Global Adoption and the New Reality of AI 23:24 The Cambrian Explosion of AI Models 26:17 AI Performance vs. Human Performance 29:01 The Growth of Data Sets in AI Models 29:47 The Exponential Growth of AI Data Sets 32:58 The Role of Compute in AI Advancement 36:42 Algorithmic Progress and Its Impact 39:38 Benchmarking AI Performance Against Human Intelligence 45:10 Enterprise Adoption of AI Technologies 52:46 Key Learnings from AI Implementations

17. 

    10 Aug 2025

    Episode 1: GPT-5, Opus 4.1, GPT-OSS-2B, and more!

    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 clone Real-time comparison of how each model handles game development, from basic functionality to "kawaii" styling Testing complex technical tasks like creating IBM mainframe architecture diagrams Classic AI benchmark tests (counting letters, arithmetic problems) with surprising results Mihai runs GPT OSS locally on his own GPU, showcasing impressive open-source capabilities Key Takeaways: Claude Opus 4.1 emerges as the overall winner with cleaner interfaces and superior artifact management GPT-5 shows promise but struggles with canvas implementation Open-source models are rapidly closing the gap with commercial offerings Discussion on how these tools are reshaping the future of software development

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