The Deep View: Conversations

The Deep View

From frontier labs and enterprise platforms to emerging startups reshaping entire industries, The Deep View: Conversations podcast interviews the brightest minds and the most influential leaders in AI.

  1. 3d ago ·  Video

    #64 - What OpenAI is building for a post-prompt future - Tara Seshan, Ty Geri

    AI is transitioning from just answering questions to doing valuable work. The next challenge is making agents more accessible and simple enough that the technical details fade into the background. In this episode of The Deep View Conversations, we sit down with two members of OpenAI's ChatGPT Work team, Tara Seshan and Ty Geri, to dig into ChatGPT Work and what OpenAI is doing to make advanced agent capabilities useful to a lot more people. We also dig into some of the current challenges and how the team is approaching them.  Seshan and Geri explain how scheduled tasks and proactive assistance are changing the way people start their workdays, why AI lets teams move from debating ideas to testing prototypes, and how personalized software can turn one-off needs into purpose-built tools. They also discuss the challenge of token costs and model selection, why "super app" isn't the most useful framing for ChatGPT and Codex, and what it will take for agents to become more persistent, proactive, and connected. The conversation also covers:• How OpenAI is trying to bridge local and cloud workflows• Why Tara and Ty start their days with agents instead of Slack• Building personal apps and tools without traditional software overhead• The tradeoff between model capability, cost, and user control• More persistent agents and proactive personal assistance• Connecting agents to email, calendars, enterprise systems and third-party tools• Privacy, security and administrative controls for agentic work If you’re figuring out where agents fit into your work or what has to improve before you trust them with more of it, then this conversation offers a practical look at how OpenAI is preparing for that transition. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #64 - What OpenAI is building for a post-prompt future - Tara Seshan, Ty Geri

  2. Sep 3 ·  Video

    #63 - Cheap AI raises the cost of bad judgment - Florian Douetteau

    AI makes software easier to create, but the harder and more valuable challenge is controlling what gets built, proving that it works, and managing it over time. In this episode of The Deep View Conversations, we sit down with Florian Douetteau, CEO and co-founder of Dataiku, to explore how large organizations can turn AI agents from impressive demos into safe, maintainable systems that deliver measurable business results. Douetteau explains why enterprise AI models are becoming commoditized, why companies may buy 90% of their agents but build the 10% that differentiates their business, and why the emerging discipline of "agent management" will be essential. He also breaks down the dilemma facing CEOs: move too slowly and competitors may gain a structural cost advantage; move too quickly without control and one major AI failure could create a crisis. Topics covered: • Why the cost of creating with AI is falling toward zero• Where value will accrue as models commoditize• How to balance openness, innovation and enterprise control• Why subject-matter experts must retain ownership of AI agents• Why business problems, not perfect data, should drive data strategy• How enterprises can prioritize transformative AI use cases without stifling experimentation• The three qualities Dataiku now values most when hiring• How leaders can use AI without falling into cognitive laziness If you’re trying to move enterprise AI beyond pilots, govern a growing portfolio of agents or understand where durable value will emerge as AI creation becomes cheaper, this conversation offers a practical framework for building quickly without losing control. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #63 - Cheap AI raises the cost of bad judgment - Florian Douetteau

  3. Aug 30 ·  Video

    #62 - How openness became AMD’s AI strategy - Andrew Dieckmann

    AI's appetite for compute keeps growing, but so does the pressure to deliver more intelligence per watt and per dollar. Can AMD's first rack-scale AI system open up an ecosystem dominated by Nvidia? In this episode of The Deep View Conversations, we sit down with Andrew Dieckmann, AMD's general manager of its data center GPU business, to unpack the company's Helios platform and the rapidly changing economics of AI infrastructure.  Dieckmann explains why frontier AI requires more than just GPUs. It demands tightly engineered racks that combine GPUs, CPUs, networking, software, cooling and serviceability. The conversation examines the tension around AI data centers: hyperscalers still cannot get enough compute, while communities worry about power, water and whether the benefits justify the buildout. Andrew argues that responsible deployment and open ecosystems are essential as these systems become intelligence factories. The conversation then turns to Helios: AMD's performance claims against Nvidia Vera Rubin, pricing and value, the first likely customers, and the Cerebras partnership for high-throughput, low-latency inference. Andrew closes with his advice for leaders navigating AI velocity: reassess priorities more often and use coding agents as force multipliers for scarce engineering talent. Topics covered: • Why AMD is moving from chips to full rack-scale systems• AI demand, data center constraints, and community impact• Open hardware, open software and customer choice• How agentic AI changed infrastructure planning• Helios performance, efficiency, pricing and customers• AMD Helios versus Nvidia Vera Rubin• How AMD and Cerebras split inference workloads This conversation offers a clear look at the technology and economics shaping the infrastructure that will power everyday AI and the breakthroughs to come. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #62 - How openness became AMD’s AI strategy - Andrew Dieckmann

  4. Aug 28 ·  Video

    #61 - The economics pushing AI toward open models - Jeff Morgan

    AI's next power shift isn't gonna happen in a data center.  In this episode of The Deep View Conversations, we sat down with Jeff Morgan, co-founder and CEO of Ollama, to explore why open models are gaining momentum, and why enterprises and developers increasingly want more control over their AI. Morgan explains how Ollama grew from a two-week experiment into software used across 80% of the Fortune 500, how the economics of coding agents are pushing teams toward open models, and why cost, privacy and control are becoming decisive advantages. He also breaks down the hardware shift bringing data-center-class AI workloads to Apple silicon, Nvidia DGX Spark and systems powered by AMD, Intel and Qualcomm. The conversation also covers: • How the team behind Docker Desktop came to build Ollama• Why open models could soon process the majority of enterprise AI tokens• The role of harnesses, tool calling, routing and subagents• How Ollama fits into the open-source AI stack and where its business model comes in• Why new US and European open-model labs are emerging• Why companies may need to own and customize their intelligence layer If you’re interested in open models, coding agents, enterprise AI or the shift from cloud-only AI to powerful local systems, this conversation offers a clear look at where the ecosystem is heading. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #61 - The economics pushing AI toward open models - Jeff Morgan

  5. Aug 23 ·  Video

    #60 - Are foldables the best AI phones now? - Sabrina Ortiz

    For almost a decade, foldable phones have been a product looking for a problem to solve. They may have found their lane. In a special episode of The Deep View Conversations, we make sense of Google's and Samsung's latest hardware and the AI announcements that came with them. But mostly, we talk about the new folding phones, the Pixel 11 Pro Fold and the Z Fold 8.   While folding and flip phones have existed for years, this summer both Google and Samsung upped the ante by launching new experiences that let AI enthusiasts make the most of the added screen real estate for AI workflows.  Topics covered include: The new AI features available on the Pixel 11 phones How Gemini contributes to the AI experience on mobileDoes Google still have the lead in AI hardware?The minimal hardware improvements to the Pixel devicesThe advantages of owning a foldable in the AI era How Samsung's Galaxy Z Fold 8 series comparesThe advantages of the Z Fold 8's "passport" form factorHow Apple's foldable, rumored to launch in September, will compete  If you're trying to understand how AI is changing what you can do with a smartphone, and what your next phone purchase should be if you prioritize AI, you won't want to miss this episode.  Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transistor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #60 - Are foldables the best AI phones now? - Sabrina Ortiz

  6. Aug 16 ·  Video

    #59 - Let's talk about AI bubbles - Nat Rubio-Licht

    AI bubble talk is rearing its head again, but the context is very different from the conversations in late 2025.  In this episode of The Deep View Conversations, we unpack the common arguments about an AI bubble and explain why reality naturally falls somewhere in between the doomsayers and AI absolutists.  We look at AI's "Tinker Bell problem": the boom depends partly on people continuing to believe in AI's potential, even as public skepticism grows. Beneath that belief cushion, enterprise contracts drive most of AI labs' revenue, while strong hyperscaler earnings and compute shortages suggest durable demand is building.  We debunk a viral claim that a $200 Claude subscription costs Anthropic $8,000 to serve. We also look at enterprises' push for more control, efficiency and measurable ROI, including one company's claim that some engineers' token use costs 1.5 times their compensation.  Other topics include:• Training, inference, API pricing and token economics• Real value, snake oil and the hype cycle• Why AI demand outruns compute supply• Why the AI bubble may look more like bubble wrap• Market rotation into energy and materials  If you're trying to separate durable AI demand from hype and understand where a real correction could begin, then this conversation offers a framework for thinking about what may pop, what may deflate and what may keep growing. Keep in mind that this is industry analysis and not investor advice.  Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #59 - Let's talk about AI bubbles - Nat Rubio-Licht

  7. Aug 13 ·  Video

    #58 - Android's big leap is from apps to agents - Sameer Samat

    The smartphone has been built around apps and taps for nearly two decades. Google thinks AI will fundamentally change that. In this episode of The Deep View Conversations, we talked with Sameer Samat, president of Android ecosystem at Google, about what the company means when it says it's transforming Android from an operating system into an intelligence system.  Samat explains why the next generation of computing could shift us from micromanaging our devices to simply telling them what we want to accomplish. We dig into how AI agents could navigate apps and complete multistep tasks and why those agents need to follow us across phones, computers, cars, watches and glasses. And what happens to the app-centric model that has defined smartphones for the past 15 years? We also get into some of the practical ways this is already taking shape. Samat discusses Google’s app automations and Rambler, the new Google Keyboard experience that can turn your voice brain-dumps into polished text. He also explains how Google is thinking about permissions, sandboxing and human oversight as AI agents gain the ability to take action on our behalf. The conversation goes well beyond the phone. We talk about why smart glasses and cars could be especially powerful interfaces for AI agents, what Google learned from the original Google Glass, and why the best AI features may be the ones consumers barely think of as AI. Other topics covered include:• How AI is already changing work inside Google• Why product managers can now build functional prototypes themselves• Samat's favorite overlooked AI tool• His "calendar cleanse" strategy for getting time back If you’re trying to understand where mobile computing goes next, what AI agents will actually look like on phones, and how Google plans to weave intelligence across devices, this conversation offers insights into what the company is building and why. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #58 - Android's big leap is from apps to agents - Sameer Samat

  8. Aug 9 ·  Video

    #57 - Why AI's next era may not belong to LLMs - Zuzanna Stamirowska

    What comes after large language models? In this episode of The Deep View Conversations, we talked with Zuzanna Stamirowska, CEO of Pathway, to explore why her team believes today’s dominant AI architecture has fundamental limits, and what it could take to move beyond them. Pathway is developing Dragon Hatchling, a new architecture designed to give AI native memory, continual learning, and a different approach to reasoning. Stamirowska explains why today’s LLMs can appear to remember without actually internalizing what they learn, why reasoning through language creates its own constraints and costs, and how Pathway is trying to build models that can think in a more abstract way.  The conversation looks at how those architectural changes could affect hallucinations, interpretability, safety, and the enormous compute demands of modern AI. Stamirowska shares how her background in complex systems and game theory shaped Pathway’s approach, why the company made an early bet on challenging the transformer, and how the AI coding revolution has already radically changed the way her own team works. Topics covered:• Why transformers struggle with memory and continual learning• How Pathway’s Dragon Hatchling architecture works• How a different architecture could reduce compute costs• How interpretability could make advanced AI more predictable• Why Pathway’s engineers have largely stopped writing code themselves• How Stamirowska uses Codex, Claude Code, and other AI tools• Why leaders should be ruthless about identifying the critical path If you’re interested in what could come after today’s LLMs, and whether the next big leap in AI will require more than simply scaling transformers, this conversation offers a fascinating look at one of the teams betting on a fundamentally different path. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

    #57 - Why AI's next era may not belong to LLMs - Zuzanna Stamirowska
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From frontier labs and enterprise platforms to emerging startups reshaping entire industries, The Deep View: Conversations podcast interviews the brightest minds and the most influential leaders in AI.

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