Explore every episode of the podcast Ship AI

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

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

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

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

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

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

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

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