I interviewed @bubbleboi about his ratings of AI supply chain bottlenecks. We talked about DRAM, advanced packaging, CPO, HBF, PCBs, power delivery, etc. 0:00 HBM, DRAM, the cartel 7:24 Silicon photonics, CPO, Lumentum lasers 11:35 Advanced packaging: TSMC vs Intel 13:49 HBF: Sandisk/SK monopoly window 17:02 Memory accelerators TurboQuant 22:35 PCBs Unitika 27:27 Power: transition from 48V to 800V 31:32 Outsourced assembly test, fiber coupling 36:23 HBM vs DRAM vs NAND in 2-3 years 42:03 Where will hardware founders come from? 44:10 Alt accelerators: Etched, Taalas, MatX 48:43 Emerging tech: CPO bearishness 49:23 Hyperclouds 49:53 HBF timeline CXL 51:48 Voltage cooling wall 56:30 Rapid fire: Intel, Nvidia, TSMC, Alphabet 1:05:25 ASML, Hynix, Lumentum, Wolfspeed 1:13:25 Building conviction in Intel 1:19:37 Pitching Intel to funds 1:22:39 X accounts, analysts why care about any of this

Everyone is focusing on the soaring memory cost in the Vera Rubin rack. But the real shocker in this Morgan Stanley slide is actually power, because the industry is now talking about moving from roughly 120kW per rack today toward potentially 600kW per rack by the Vera Rubin Ultra generation in 2027, which is an almost unimaginable escalation in power density within an incredibly short period of time. To put this into perspective, many traditional enterprise datacenters historically operated at only a few kilowatts per rack, while even modern hyperscale campuses today often consume only tens of megawatts in total facility power draw. But once you begin deploying hundreds or thousands of 600kW AI racks simultaneously, the math becomes almost absurd because a large-scale Vera Rubin Ultra cluster could eventually consume gigawatts of electricity, effectively rivaling the energy demand of a mid-sized city. And this is where the market still massively underestimates the second-order implications of the AI boom, because the bottleneck is no longer simply semiconductors, GPUs, or memory supply. The bottleneck increasingly becomes electricity itself. The US power grid can barely keep up with current AI infrastructure demand already, while transmission congestion, transformer shortages, substation constraints, cooling limitations, permitting bottlenecks, and aging grid infrastructure are becoming increasingly visible across major datacenter hubs. Importantly, grid infrastructure cannot scale at semiconductor speed. You can accelerate chip production with enough capital expenditure and engineering talent, but building transmission lines, substations, generation capacity, cooling systems, and interconnection approvals often requires many years due to environmental reviews, local opposition, labor shortages, and physical construction constraints. This is precisely why we continue believing the AI buildout is not a two-to-three-year investment cycle, but instead a decade-long industrial transformation that increasingly resembles the buildout of railroads, electricity networks, and telecom infrastructure during previous industrial revolutions. And this is also why energy infrastructure is quietly becoming one of the most important and underappreciated AI trades globally. The winners are no longer just GPU companies. The winners increasingly include utilities like Constellation Energy and Vistra, nuclear-related plays like Oklo and NuScale Power, gas infrastructure companies like Kinder Morgan and Williams Companies, grid and electrical equipment suppliers like GE Vernova, Eaton, Schneider Electric, and Vertiv, as well as transformer, cooling, and datacenter infrastructure providers that now sit directly inside the physical backbone required to support next-generation compute. Hyperscalers themselves are starting to understand this reality. Companies like Microsoft, Amazon, Alphabet, and Meta are no longer simply software companies buying servers. They are increasingly becoming quasi-energy infrastructure companies because securing long-duration power availability is becoming strategically inseparable from securing compute capacity itself. That is why nuclear power is quietly returning to the center of the conversation. Hyperscalers may eventually fund or directly partner on nuclear generation projects out of pure necessity because renewable intermittency alone cannot reliably support ultra-high-density AI clusters operating continuously at scale. In many ways, AI is beginning to collide with physical reality. You cannot run trillion-dollar next-generation compute infrastructure on transmission systems and grid architectures that were largely built decades ago for a completely different industrial era. The semiconductor story may have started the AI race, but energy infrastructure may ultimately determine who wins it.

𝐓𝐡𝐞 𝐑𝐨𝐚𝐝 𝐀𝐡𝐞𝐚𝐝 IREN has been building Layer 1 for eight years. We are deploying Layer 2 at scale right now. And with Mirantis, we have taken the first deliberate step toward Layer 3. The physical infrastructure advantage comes first. Everything else compounds from there. It has become almost cliché to say we are still in the early innings. But it genuinely feels that way. The opportunity feels generational. We have assembled the key inputs to be in a position to compound a genuine time-locked competitive advantage – and right now, the biggest opportunity in the world is simply bringing more of that compute online. These journeys are rarely linear. There will be speed bumps and challenges along the way that we cannot yet see. But we have never been more excited about what lies ahead, and intend to make the most of it.

"The biggest bottleneck right now in AI is no longer just the models or the GPUs." Power, data centers, cooling, interconnect, time to compute. The bull case for $IREN sits on the physical layer.

Building the infrastructure stack for the AI era. @nvidia @danroberts0101 @MichaelDell