The Deep View: Conversations

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

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

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

What happens when powerful AI no longer has to live in the cloud?

In this episode of The Deep View Conversations, we talked with Mark Papermaster, CTO of AMD, about why the next major shift in AI could happen on the device sitting on your desk.

Papermaster explains how computers could soon run sophisticated models and teams of private AI agents locally, offering greater speed, security and control without recurring token costs. He also makes the case that AI will be even more transformative than the smartphone because it will be embedded across nearly every device, industry and aspect of daily life.

The conversation also covers:
Why open ecosystems matter in the AI era
How AI is accelerating science, agriculture and industry
The growing energy demands of AI
How leaders can reinvent workflows with agents
Why local AI could reduce cloud dependence and vendor lock-in
Papermaster’s lessons from four decades in technology
If you’re interested in less lock-in, open ecosystems, and how enterprises can run AI more efficiently and privately, this conversation offers a look at what a more distributed and secure AI future could look like.

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

AI systems almost always have an answer, even when they should say, “I don’t know.”

In this episode of The Deep View Conversations, senior reporter Sabrina Ortiz speaks with Ruchir Puri, chief scientist at IBM Research, about why uncertainty modeling may be AI’s most urgent technical challenge.

Puri explains why today’s models struggle to recognize the limits of their own knowledge, how that failure contributes to hallucinations, and what researchers must solve before AI can become more reliable. He also explores the need for self-improving models, the enormous energy gap between artificial and human intelligence, and why the future of AI depends on doing more with less compute.
The conversation also covers:
• Why Puri predicted in 2020 that AI would transform software development
• How big data, GPUs, and transformer architectures created the current AI boom
• Why intelligence involves more than IQ
• The roles of emotional and relationship intelligence
• Why language models cannot capture the full complexity of the physical world
• How AI could help redesign software, quantum computing, and chip development
• Why Puri prefers "artificial useful intelligence" over AGI
Rather than chasing abstract definitions of general intelligence, Puri argues that the industry should focus on building AI that is useful, efficient, adaptable, and honest about what it does not know.

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