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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Ukraine tracks every confirmed drone kill with video evidence, deduplicates the data to prevent double-counting, converts verified strikes into e-points, and delivers newly ordered weapons to frontline units in two weeks instead of twelve months. Andrii Hrytseniuk, CEO of Brave1- Ukraine's government defense innovation agency - joins Craig Smith to explain in precise operational detail how what started as a consumer loyalty program concept became the most consequential military procurement reform in modern warfare. The Army of Drones Bonus Program, running on the Delta command ecosystem with AI-assisted verification and a marketplace built like a military Amazon, has produced outcomes nobody predicted at launch: 35,000 Russian casualties per month confirmed with video evidence, the ability to change military targeting priorities instantly across an entire army by adjusting point values, and the identification of unknown high-performing frontline units whose methods are now being replicated across the force.

The conversation reveals details that rarely surface in coverage of the conflict. Capturing Russian soldiers earns more bonus points than killing them, a deliberate design choice reflecting the preference not to kill when alternatives exist, and drones are actively being used to guide surrendering Russian soldiers to Ukrainian positions. The Delta ecosystem contains the world's largest military AI training dataset, already being used by defense companies to train targeting, interception, and recognition models. And perhaps most significantly: the US and France are now implementing similar marketplace and decentralized procurement approaches modeled directly on what Brave1 demonstrated works at scale. Ukraine is no longer just a recipient of military support. It is the primary laboratory for the future of warfare, and it is actively exporting what it has learned.

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