Ship AI

Manav Gupta

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

Episodes

  1. JAN 20

    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 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. Chapters 00:00 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

    1h 11m
  2. JAN 13

    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 Takeaways Exponential 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 wave Chapters 00:00 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

    1 hr

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