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Tesla building its own fabs sounds inevitable, until you look at what it actually takes: Supply is tight. Demand is rising. Control matters. But fabs are a different game in terms of cost, talent, and timelines. Patrick Moorhead Daniel Newman debate Tesla's Terafab project for The Flip on Ep. 298 of The Six Five Pod. Control may win the strategy, but execution wins reality. Is this a brilliant move or overreaching?

Tesla building its own fabs sounds inevitable, until you look at what it actually takes: Supply is tight. Demand is rising. Control matters. But fabs are a different game in terms of cost, talent, and timelines. Patrick Moorhead Daniel Newman debate Tesla's Terafab project for The Flip on Ep. 298 of The Six Five Pod. Control may win the strategy, but execution wins reality. Is this a brilliant move or overreaching?

8,763,287 views • 2 months ago

The quantum conversation is no longer hypothetical for security leaders. Data being collected now could be decrypted by attackers later. On Ep. 41 of The Main Scoop, Daniel Newman, Greg Lotko, and Tom Cosenza break down Y2Q and what “harvest now, decrypt later” really means. Quantum has the potential to transform fields like medicine and materials science, but it also threatens the present-day encryption standards. Shor’s algorithm could break RSA encryption. And even symmetric encryption becomes increasingly vulnerable with advances such as Grover’s algorithm.

The quantum conversation is no longer hypothetical for security leaders. Data being collected now could be decrypted by attackers later. On Ep. 41 of The Main Scoop, Daniel Newman, Greg Lotko, and Tom Cosenza break down Y2Q and what “harvest now, decrypt later” really means. Quantum has the potential to transform fields like medicine and materials science, but it also threatens the present-day encryption standards. Shor’s algorithm could break RSA encryption. And even symmetric encryption becomes increasingly vulnerable with advances such as Grover’s algorithm.

7,002,143 views • 2 months ago

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

7,297,468 views • 2 months ago

We keep hearing about the infrastructure boom as an AI pivot. At NVIDIA GTC,we talked with Lenovo’s Vlad Rozanovich and IREN’s Kent Draper about what actually had to be in place before any of this scaled: Access to power. Control of land. The ability to build ahead of demand. By the time AI showed up at this level, the groundwork was already laid. The AI boom just exposed who was actually prepared.

We keep hearing about the infrastructure boom as an AI pivot. At NVIDIA GTC,we talked with Lenovo’s Vlad Rozanovich and IREN’s Kent Draper about what actually had to be in place before any of this scaled: Access to power. Control of land. The ability to build ahead of demand. By the time AI showed up at this level, the groundwork was already laid. The AI boom just exposed who was actually prepared.

5,242,465 views • 2 months ago

Most AI discussions ignore the physical reality: a lot of facilities still can’t support liquid cooling. At NVIDIA GTC, Lenovo’s Jon Alexander explained that across 4,400 global locations, many sites still aren’t ready for liquid cooling. Some environments support megawatts of power, others are limited to air cooling in last-mile telco facilities. That reality is shaping how AI infrastructure actually gets deployed. At the same time, AI is moving so fast that supply chain and time-to-market are becoming competitive advantages. The companies that can build, ship, and deploy infrastructure quickly are the ones that will keep up with demand.

Most AI discussions ignore the physical reality: a lot of facilities still can’t support liquid cooling. At NVIDIA GTC, Lenovo’s Jon Alexander explained that across 4,400 global locations, many sites still aren’t ready for liquid cooling. Some environments support megawatts of power, others are limited to air cooling in last-mile telco facilities. That reality is shaping how AI infrastructure actually gets deployed. At the same time, AI is moving so fast that supply chain and time-to-market are becoming competitive advantages. The companies that can build, ship, and deploy infrastructure quickly are the ones that will keep up with demand.

1,759,958 views • 1 month ago

Speed is the wrong metric for AI in service. Enterprises that optimized for faster response times found that faster wrong answers still produce the same escalation rates. The metric that actually matters is resolution. Melody Brue and Keith Kirkpatrick sit down with Shashi Upadhyay, President of Product, Engineering, and AI at Zendesk, live from Zendesk Relate 2026 to break down what autonomous service systems actually demand at the engineering level.

Speed is the wrong metric for AI in service. Enterprises that optimized for faster response times found that faster wrong answers still produce the same escalation rates. The metric that actually matters is resolution. Melody Brue and Keith Kirkpatrick sit down with Shashi Upadhyay, President of Product, Engineering, and AI at Zendesk, live from Zendesk Relate 2026 to break down what autonomous service systems actually demand at the engineering level.

232,898 views • 13 days ago

AI demand is exploding, but scaling it isn’t just about more compute. At NVIDIA GTC, Samsung Semiconductor US President Paul Cho shares where the real innovation is happening. Samsung’s HBM4 shows that the next phase of AI performance will be driven by memory, integration, and system design. And the companies solving those layers will define what AI can actually do.

AI demand is exploding, but scaling it isn’t just about more compute. At NVIDIA GTC, Samsung Semiconductor US President Paul Cho shares where the real innovation is happening. Samsung’s HBM4 shows that the next phase of AI performance will be driven by memory, integration, and system design. And the companies solving those layers will define what AI can actually do.

1,208,573 views • 2 months ago

Enterprise PCs are entering a new innovation cycle. Olivier Blanchard 💬 sits down with Rob Bruckner, Zach Noskey, and Paul Doczy of Dell Technologies to discuss the new Dell Pro portfolio, accelerated roadmaps, modular design, and what the next generation of enterprise PCs means for IT teams and users.

Enterprise PCs are entering a new innovation cycle. Olivier Blanchard 💬 sits down with Rob Bruckner, Zach Noskey, and Paul Doczy of Dell Technologies to discuss the new Dell Pro portfolio, accelerated roadmaps, modular design, and what the next generation of enterprise PCs means for IT teams and users.

725,498 views • 2 months ago

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

294,803 views • 1 month ago

AI is pushing compute power back to the workstation. At Dell’s CSI Lab in Austin, Ryan Shrout sits down with Dell Technologies leaders Rob Bruckner, Charlie Walker, and Paul Doczy to unpack the new Dell Pro Precision portfolio. As AI development, simulation, and creative workloads scale, workstations are becoming a key bridge between local experimentation and datacenter infrastructure. Where do you see the workstation fitting in the AI stack?

AI is pushing compute power back to the workstation. At Dell’s CSI Lab in Austin, Ryan Shrout sits down with Dell Technologies leaders Rob Bruckner, Charlie Walker, and Paul Doczy to unpack the new Dell Pro Precision portfolio. As AI development, simulation, and creative workloads scale, workstations are becoming a key bridge between local experimentation and datacenter infrastructure. Where do you see the workstation fitting in the AI stack?

347,871 views • 2 months ago

AI at scale is hitting a hard limit with energy. As AI infrastructure expands, power demand, not just compute architecture, is emerging as the gating constraint and pushing frontier builders toward vertically integrated deep tech bets spanning quantum discovery and fusion energy. Lenovo Data Center's Flynn Maloy and Jason Gibson, CEO and Co-founder of Quantum Formatics, discuss their collaboration to meet this challenge. They're betting on a dual-front strategy: leveraging quantum-driven simulation to accelerate material discovery while simultaneously pursuing fusion as the long-term energy unlock. Next-generation infrastructure requires breakthroughs not just in chips, but in the physical systems that power them.

AI at scale is hitting a hard limit with energy. As AI infrastructure expands, power demand, not just compute architecture, is emerging as the gating constraint and pushing frontier builders toward vertically integrated deep tech bets spanning quantum discovery and fusion energy. Lenovo Data Center's Flynn Maloy and Jason Gibson, CEO and Co-founder of Quantum Formatics, discuss their collaboration to meet this challenge. They're betting on a dual-front strategy: leveraging quantum-driven simulation to accelerate material discovery while simultaneously pursuing fusion as the long-term energy unlock. Next-generation infrastructure requires breakthroughs not just in chips, but in the physical systems that power them.

224,069 views • 1 month ago

Agentic workloads generate 20-50x more tokens than chat. That multiplier hits compute, storage, and orchestration simultaneously. Google Cloud's Mark Lohmeyer (who owns all of Compute) and Patrick Moorhead break down how GC is building infrastructure to absorb that pressure across the full AI Hypercomputer stack from Google Cloud Next 2026.

Agentic workloads generate 20-50x more tokens than chat. That multiplier hits compute, storage, and orchestration simultaneously. Google Cloud's Mark Lohmeyer (who owns all of Compute) and Patrick Moorhead break down how GC is building infrastructure to absorb that pressure across the full AI Hypercomputer stack from Google Cloud Next 2026.

162,434 views • 1 month ago

"Your differentiation is going to be how you operate in an AI mode." — Rob Thomas, SVP and CCO, IBM S&P margin growth is concentrated. The enterprise AI divide is real and widening. Patrick Moorhead got the full breakdown at IBM Think 2026.

"Your differentiation is going to be how you operate in an AI mode." — Rob Thomas, SVP and CCO, IBM S&P margin growth is concentrated. The enterprise AI divide is real and widening. Patrick Moorhead got the full breakdown at IBM Think 2026.

94,590 views • 22 days ago

Retail SMBs aren’t struggling with AI access, they’re struggling with where to apply it. Tiffani Bova speaks with Cindy Carlson of Dell Technologies to break down what’s actually working: Inventory visibility. Security at scale. AI that reduces friction without adding complexity.

Retail SMBs aren’t struggling with AI access, they’re struggling with where to apply it. Tiffani Bova speaks with Cindy Carlson of Dell Technologies to break down what’s actually working: Inventory visibility. Security at scale. AI that reduces friction without adding complexity.

123,880 views • 1 month ago

On-brand is the baseline for content, not the goal. Patrick Moorhead and Daniel Newman pressed Adobe's Varun Parmar on how Brand Intelligence predicts creative performance before a campaign activates.

80,619 views • 23 days ago

TSMC controls 90% of advanced AI silicon and moves markets when it skips a supplier's equipment. But ASML is a single point of failure for every advanced node on the planet, and Applied Materials and Lam control nearly every other fab process step. Patrick Moorhead and Daniel Newman make the case for both sides.

TSMC controls 90% of advanced AI silicon and moves markets when it skips a supplier's equipment. But ASML is a single point of failure for every advanced node on the planet, and Applied Materials and Lam control nearly every other fab process step. Patrick Moorhead and Daniel Newman make the case for both sides.

108,634 views • 1 month ago

Most enterprises have yet to solve the consistency problem with generative AI. "Just a slight change in the seed value or the prompt can dramatically change the output." — Varun Parmar, SVP and GM, Adobe GenStudio and Firefly Enterprise Varun Parmar told Patrick Moorhead & Daniel Newman how Adobe is addressing it: creative technologists build the workflow once, validate the output quality, then deploy it across the entire organization. Marketers in every geography get access to personalized campaigns they never had the budget or resources to run before.

Most enterprises have yet to solve the consistency problem with generative AI. "Just a slight change in the seed value or the prompt can dramatically change the output." — Varun Parmar, SVP and GM, Adobe GenStudio and Firefly Enterprise Varun Parmar told Patrick Moorhead & Daniel Newman how Adobe is addressing it: creative technologists build the workflow once, validate the output quality, then deploy it across the entire organization. Marketers in every geography get access to personalized campaigns they never had the budget or resources to run before.

78,020 views • 27 days ago

AI is taking on more of the labor. It is not taking on the accountability. Daniel Newman and Greg Lotko talk with Darren Surch of Interskil Management about why mainframe teams now have to interpret and stand behind AI-driven outputs, and why organizations that stop investing in training are setting themselves up for avoidable risk.

AI is taking on more of the labor. It is not taking on the accountability. Daniel Newman and Greg Lotko talk with Darren Surch of Interskil Management about why mainframe teams now have to interpret and stand behind AI-driven outputs, and why organizations that stop investing in training are setting themselves up for avoidable risk.

94,270 views • 1 month ago

Manufacturers don’t have a data problem. They have an execution problem. David Nicholson and Todd Edmunds of Dell Technologies break down what’s changing: AI is moving from dashboards to decisions. Digital twins are becoming operational. Automation is filling workforce gaps. Find out how SMB manufacturers can operationalize data & build scalable foundations to successfully transition into more autonomous, resilient environments.

Manufacturers don’t have a data problem. They have an execution problem. David Nicholson and Todd Edmunds of Dell Technologies break down what’s changing: AI is moving from dashboards to decisions. Digital twins are becoming operational. Automation is filling workforce gaps. Find out how SMB manufacturers can operationalize data & build scalable foundations to successfully transition into more autonomous, resilient environments.

85,951 views • 1 month ago

Point integrations won’t carry enterprise AI forward. At RSAC, Commvault's Michelle Graff lays out the shift: organizations need a new operating model where ecosystems move beyond integrations to agentic solutions that connect data, security, and infrastructure in real time. Watch the full interview to see how enterprises are redesigning resilience for the AI era:

Point integrations won’t carry enterprise AI forward. At RSAC, Commvault's Michelle Graff lays out the shift: organizations need a new operating model where ecosystems move beyond integrations to agentic solutions that connect data, security, and infrastructure in real time. Watch the full interview to see how enterprises are redesigning resilience for the AI era:

99,352 views • 1 month ago