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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...

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The Rollup

32,804 Aufrufe • vor 1 Jahr

@kenzimori sits down with nick, Co-Founder of Allora, to unpack the early conviction that pulled him into crypto — not as a trend, but as a new way to rethink power, coordination, and how systems are built. Before founding Allora, Nick led blockchain development at a major asset manager and insurance firm across North America, during a time when most enterprises were still focused on private chains and consortiums. Instead, he pushed toward public Ethereum — building real enterprise use cases around decentralized insurance and trying to capture what actually made blockchain powerful. From those early experiments to the AI x crypto frontier, this conversation traces how Nick’s view of decentralized systems kept expanding.
4:05

@kenzimori sits down with nick, Co-Founder of Allora, to unpack the early conviction that pulled him into crypto — not as a trend, but as a new way to rethink power, coordination, and how systems are built. Before founding Allora, Nick led blockchain development at a major asset manager and insurance firm across North America, during a time when most enterprises were still focused on private chains and consortiums. Instead, he pushed toward public Ethereum — building real enterprise use cases around decentralized insurance and trying to capture what actually made blockchain powerful. From those early experiments to the AI x crypto frontier, this conversation traces how Nick’s view of decentralized systems kept expanding.

BeaconLayer Podcast

30,776 Aufrufe • vor 4 Monaten

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud
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the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 Aufrufe • vor 1 Monat

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.
0:51

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

Black Panther Capital

30,138 Aufrufe • vor 7 Monaten