Every business in the world has to procure things, and for most of them, that process is slow, manual, and full of money left on the table. Rujul Zaparde, co-founder of Zip, joins Craig to explain how his company is using AI Superagents to change that: from autonomously redlining contracts and running supplier RFPs, to pushing approved data directly into enterprise systems like SAP and Oracle. With over $1.2 trillion in cumulative spend processed - and Anthropic and OpenAI as customers - Zip isn't just experimenting with AI. It's building the governance infrastructure that makes agentic AI safe to deploy where the stakes are highest.

The conversation goes well beyond procurement software. Craig and Rujul explore why the gap between "piloting AI" and truly deploying it is costing enterprises billions in unrealized ROI, what it means for the economy when AI starts making pricing ruthlessly efficient, and where the real limits of automation actually lie. A rare episode that takes an overlooked corner of business - one that touches every company on earth - and shows just how much is about to change.

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What if you didn't have to choose between ChatGPT, Claude, and Gemini, and instead, the platform chose for you?

That's the core idea behind Trioma, a vertically integrated AI ecosystem built by Iskander and his team in the UAE. Craig Smith sits down with Iskander Malikov, Co-founder & CEO of Trioma, to unpack how Trioma's orchestration engine routes prompts across 65 models, why token price is a misleading metric, and how their GPU network lets users participate in AI infrastructure build-out to offset their own compute costs.

The conversation goes deep on the economics of AI subscriptions, the coming collapse of traditional SaaS, and why Trioma believes AI should be sovereign, accessible, and community-driven, not locked behind a single company's walled garden. If you're thinking about where AI access, cost, and infrastructure are heading, this one is essential listening.

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Craig Smith sits down with Ilya Sutskever - Co-Founder and Chief Scientist at Safe Superintelligence Inc. & former chief scientist at OpenAI and one of the primary minds behind GPT-3, GPT-4, and the deep learning revolution that preceded it - for a conversation that covers the intellectual history of deep learning and the hardest current questions simultaneously. Ilya's most important argument is one that most public discourse gets wrong: the critique that LLMs "just learn statistical patterns" misses what prediction at scale actually achieves. To predict text well - to truly compress it - a model must develop understanding of the true underlying processes that produced it. As models improve, he argues, that understanding will reach a "shocking degree," and the Bing/Sydney episode, where the system became combative when a user expressed preference for Google, is his evidence that psychological language is already the right frame for what these systems have internalized.

On hallucinations, Ilya is precise and unusually optimistic. He draws a clean distinction: the pre-training objective produces rich world knowledge but doesn't optimize for accurate outputs, it optimizes for statistically plausible ones. Reinforcement learning from human feedback addresses this directly, teaching the model to update away from outputs that users flag as wrong. His assessment: "quite a high chance" of solving hallucinations completely. The conversation also covers the transformer moment (switching from recurrent networks the day after the paper dropped), why Rich Sutton's "bitter lesson" overstates the case for pure scaling, and a genuinely surprising vision of AI's role in democracy, where citizens might use high-bandwidth interactions with AI to specify their values directly, enabling a more granular form of political participation than voting alone can provide.

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