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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Everyone assumes AI in customer operations means fewer people and lower costs. The data from 700 customer operations leaders is more complicated, and more honest. Dan O'Connell, CEO of Front, joins Craig Smith with findings from a survey of customer support, service, and account management leaders that reveal what's actually happening as enterprises deploy AI at scale, and the central finding is a problem nobody has named yet. For every hour of actual customer resolution, organizations spend roughly three hours on internal coordination: the handoffs, context transfers, escalation management, and back-channel communication that happens invisibly between customer interactions. More than 40% of companies don't measure this at all. AI is making it worse before it makes it better, because more agentic automation generates more coordination events, not fewer, and the companies reporting the highest AI technology satisfaction in the survey are also reporting higher coordination burdens.

The survey also pushes back on a dominant narrative: that AI in customer operations is primarily about cutting headcount. O'Connell's respondents are overwhelmingly focused on improving net revenue retention and customer expansion. The conversation closes with O'Connell's most forward-looking observation: the coordination tax is a temporary problem that AI should eventually solve, but measuring it is the prerequisite that most enterprises are still skipping.

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