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Modernization, AI adoption, security, and cost control used to be sequential decisions. Now they are landing on enterprise leaders' desks at the same time. At HPE Discover 2026, David Nicholson and Tom Hollingsworth sat down with Dante Orsini and Justin Giardina of 11:11 Systems to get into why infrastructure...

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88,714 görüntüleme • 2 ay önce •via X (Twitter)

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Distribution used to mean fulfillment. Now Eric Kohl of Ingram Micro USA calls complexity "job security," and the partner ecosystem has grown complicated enough to prove him right. At HPE Discover 2026, David Nicholson and Tom Hollingsworth sat down with Kohl to break down what distribution actually requires across networking, security, and AI convergence, from automated quoting to identity as the new perimeter.
10:38

Distribution used to mean fulfillment. Now Eric Kohl of Ingram Micro USA calls complexity "job security," and the partner ecosystem has grown complicated enough to prove him right. At HPE Discover 2026, David Nicholson and Tom Hollingsworth sat down with Kohl to break down what distribution actually requires across networking, security, and AI convergence, from automated quoting to identity as the new perimeter.

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A 2.7x cost-per-token reduction on the same infrastructure, just from optimizing memory tiering and KV cache offload. That's the kind of number CIOs bring to the board, not a roadmap slide. At HPE Discover 2026, David Nicholson and Matthew Kimball sat down with Justin McGarry of HPE and Justin Christiansen of Intel to break down what's actually driving enterprise AI infrastructure decisions, including a new edge device running 80B parameter models from under an airplane seat.

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Most organizations are solving today's problems with today's tools. Quantum computing, intelligent systems, and next-gen infrastructure are already converging, well before most enterprises have finished their current AI roadmap. At HPE Discover 2026, David Nicholson sat down with Kirk Bresniker of HPE to look past this week's AI headlines toward what's actually shaping enterprise computing by 2030.

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A year is a long time in the AI infrastructure market right now, long enough for enterprise deployments to move from isolated pilots into platforms that have to function end-to-end. Networking, compute, and open ecosystem architecture are converging faster than most organizations have reorganized their strategy to match. At HPE Discover 2026, David Nicholson and Matthew Kimball sat down with Trish Damkroger and Praveen Jain of HPE to break down sovereign AI's growing urgency and where Helios fits into HPE's broader AI strategy.
18:20

A year is a long time in the AI infrastructure market right now, long enough for enterprise deployments to move from isolated pilots into platforms that have to function end-to-end. Networking, compute, and open ecosystem architecture are converging faster than most organizations have reorganized their strategy to match. At HPE Discover 2026, David Nicholson and Matthew Kimball sat down with Trish Damkroger and Praveen Jain of HPE to break down sovereign AI's growing urgency and where Helios fits into HPE's broader AI strategy.

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12:10

Meena Gowdar's answer to "what if you lose cloud access" is blunt: customers want a real fallback plan, not a hope. That's reshaping how Microsoft and HPE build hybrid infrastructure together. At HPE Discover 2026, David Nicholson and Alastair Cooke sat down with Meena Gowdar and Justin Slane of Microsoft, who calls Microsoft's coming FY27 an explosive year for the HPE partnership.

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Sat down with Kari Ann Briski, VP of Generative AI Software for Enterprise @ NVIDIA, to discuss NVIDIA’s Nemotron open models, how training data can be used for building agentic systems, and scaled AI enterprise adoption. Thank you to Kari NVIDIA AI Developer for taking the time!

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This week on Founder2Founder, Aaron Spivak sits down with Karen Quintos, former CMO and Chief Customer Officer at Dell Technologies. Karen has led at the highest levels of Merck, Citibank, and Dell, navigating billion-dollar decisions while staying anchored in faith, humility, and people-first leadership. They talk through what it really means to lead with integrity at scale, why servant leadership outperforms ego, how to build brands from the inside out, and why success doesn’t have to come at the cost of your soul. This episode is a reminder that the strongest leaders are grounded, patient, and deeply human, and that purpose-driven leadership is immensely strategic. Live now wherever you get your podcasts.

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4:05

David Friedberg david friedberg: Anthropic is trying to commoditize everyone's business. Friedberg, a life-sciences CEO and All-In host, sat in on Anthropic's enterprise pitch to biotech: share your proprietary data, get model access. Nearly every company he spoke with reached the same conclusion - sharing the data makes Anthropic's model stronger and their own moat thinner at the same time. David Sacks David Sacks corroborates from the VC side: enterprise clients are "just unhappy," convinced they hand over IP and get generic intelligence back. Bill Gurley Bill Gurley of Benchmark has pushed the same skepticism from another angle: much of the AI enterprise value story is partially fake. Any SaaS product built on a frontier API inherits this trust problem the moment customers decide the model layer commoditizes their domain. Watch churn language, not just ARR. Why two independent sources landing here is close to confirmation: Source: All-In Podcast -

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$NVDA $PLTR Really grateful to have had the chance to speak to Justin Boitano, VP of Enterprise AI at NVIDIA, and Kevin Kawasaki, Head of Business Development at Palantir, about Nvidia and Palantir's latest collaboration. These are two of the most important companies in our lifetime when it comes to artificial intelligence. The infrastructure needed to produce the intelligence and the software needed to operationalize that intelligence are now working together to drive business outcomes, power the datacenter buildout, and make sure AI leads to a healthier and more sustainable economy long term. Spoke with both Justin and Kevin below on the future they see for the partnership!

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“Honestly, I think we’re just a few months away.” @dikshawells sits down with David Minarsch ☴ to unpack how close AI agents really are to mainstream adoption — not just for builders, but for beginners and everyday DeFi users too. After years spent deep in technical papers on agent systems, and now building in the space full-time, David explains why the timeline is compressing so quickly. And why the biggest challenge may no longer be the tech itself, but getting people aligned around how to actually use it

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$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."
1:10

$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."

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