Eye On A.I.

Training a frontier AI model today requires hundreds of thousands of GPUs, months of compute time, and a budget that only a handful of companies on earth can afford. Steffen Cruz, co-founder and CTO of Macrocosmos, thinks that model is about to break, and he's spending his time building what comes next. His project IOTA, operating within the BitTensor blockchain ecosystem, uses distributed training to split large language models across thousands of devices located around the world, coordinated by blockchain, and powered by surplus cheap energy wherever it exists. After nine months of research, the system can reproduce baseline benchmark performance using what Cruz calls "wonky vegetables" - unreliable, churning, globally distributed compute - and turn it into something indistinguishable from centralized training if you use the right approach.
The conversation with Craig Smith covers the mechanics of how this actually works, why the blockchain's role is far narrower and more practical than most people assume, and why the Mac mini stockpiling trend creates an unexpected supply of distributed compute that can earn passive income when idle. Cruz's target: a 70 billion parameter model by mid-2025, trained at 10-20% of what it would cost through a hyperscaler, and aimed squarely at the legal firms, hospitals, and cash-strapped startups that have been waiting to train their own sovereign models but couldn't afford the price tag.
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Errol Gardner has spent 35 years advising the world's largest organizations through major technology transitions, and his assessment of where enterprise agentic AI actually stands is one of the most grounded you'll hear anywhere. His number: less than 1 out of 10 on a maturity scale. Not because the technology isn't ready, but because deploying agentic AI across an organization doesn't tweak how it works, it requires rebuilding how it works. And that is a fundamentally different kind of challenge than anything the AI hype cycle is currently acknowledging.
In this conversation with Craig Smith, Gardner walks through why cloud adoption still hasn't reached 7 out of 10, what that means for agentic AI timelines, why the single biggest barrier to adoption is human resistance rather than technical limitation, and why governments will ultimately have to step in to manage workforce displacement at scale. He also raises a question that almost nobody is asking: is the value exchange between the technology sector and traditional industries sustainable in the long run? It's a conversation that doesn't just describe where AI is, it explains why the gap between the narrative and the reality has never been wider.
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IBM's VP of Quantum Systems, Oliver Dial, has spent his career building quantum computers from the ground up, and he's unusually direct about what they can and can't do. In this conversation with Craig Smith, Oliver Dial walks through where the field actually stands in 2026: quantum utility was achieved in 2023, quantum advantage is the target for this year, and a fully error-corrected machine capable of tackling the hard problems is on IBM's roadmap for 2029. That last milestone, Dial says, now feels both achievable and terrifying.
The episode is worth your time because Dial doesn't hype. He explains why IBM built a 1,000-qubit computer and then took it apart almost immediately, why Google's quantum advantage claims remain scientifically contested, and how a new error-correcting code IBM developed just reduced the qubit overhead required for fault-tolerant quantum computing by an order of magnitude. For anyone trying to understand what quantum computing will actually mean for their industry, and when, this is the clearest map of the road ahead available right now.
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Kris Lovejoy, Global Strategy Leader at Kyndryl, has spent her career at the intersection of IT infrastructure and security. Right now, she's one of the people enterprises call when they want to move from AI experimentation to real deployment. Her diagnosis is clear: agentic AI is a bullet train sitting on tracks built for 30 miles per hour. The technology is ready. Most organizations aren't, and the gap between a successful pilot and a production system running at scale is far wider than the hype suggests.
In this conversation with Craig Smith, Lovejoy walks through why IT service management is the smartest entry point for agentic adoption, how cost savings of up to 90% in that area can fund broader modernization, and why the security risks in agentic systems are less about sophisticated hackers and more about misconfiguration, bad context, and human error. She closes with a specific prediction: half of traditional IT administration tasks will be handled by AI agents by 2031, and a surprising take on who will actually thrive in the agentic era: not coders, but people trained to ask the right questions.
For anyone making decisions about AI adoption, this is the most practical conversation available right now.
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AI has fundamentally changed the cybersecurity threat landscape, not by inventing new attack types, but by collapsing the timeline. The same tools that make software developers more productive are now being used by attackers to move from vulnerability disclosure to active exploit in a matter of hours. That shift, argues Loris Degioanni, CTO and founder of Sysdig, changes everything about how defense needs to work.
In this episode, Craig Smith talks with Loris Degioanni about why human-centered security is becoming a structural liability, what "headless cloud security" means in practice, and why the coding agent (tools like Claude Code or Codex) may become the new operating system through which all enterprise security workflows run. It's a conversation about architecture, urgency, and what it actually means to fight a tank when you've been trained to use a baseball bat.
If this conversation made you think differently about AI and security, subscribe to Eye on A.I. for weekly conversations with the people building and defending the future.