The Deep View: Conversations

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

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 mobile
Does Google still have the lead in AI hardware?
The minimal hardware improvements to the Pixel devices
The advantages of owning a foldable in the AI era 
How Samsung's Galaxy Z Fold 8 series compares
The advantages of the Z Fold 8's "passport" form factor
How 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

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