YES PLS

Have waited for a while but if you are a relative value trader now is the time to short $NBIS and long $IREN. We may have got the software layer wrong on the past. But we have the runway no one has in the world and now we have all the layers. I expect $IREN to overtake $NBIS in MC by March 2027

$IREN: Financial Model 2.0.0 jiahanliu.github.io/IREN-Com… Summary With more data from IREN's Q1 earnings call on buildout cadence (1), I made significant changes to the 1.2.5 financial model so this will be the first 2.0.0 version. True to the ethos of the original model, the App allows the user to input all relevant variables and expose the App's calculation details in real time so that the user can audit and provide feedback. Charting As a corollary to the guidance on buildout guidance, we are able to implement great suggestions from @StockAnalystPro to chart "share price" vs "year". Additionally, the X and Y Axises can be selected so you can plot for example "share price" vs "MWs active" or "Earnings before Tax, SGA" vs "Market Cap". I only read charts for TA. For FA, I'm always read numbers and never charts so there's probably improvements / suggestions that can be applied to improve the charting. GPU Pricing Improvements Previously I did improvement as a percentage of contract topline. Thanks to @GlobalCollapse for pointing out that's non-intuitive. I removed the odd mechanism completely and provide a flat GPU pricing for different years. For example I have "B300 - 2025 Pricing" which corresponds to both the unit price and hourly rate and "B300 - 2026 Pricing" correspondingly. The default GPU pricing are either direct IREN datapoints or linearly extrapolated as described below. Linear Extrapolation I do mostly linear extrapolation. For example in the case of GPU pricing, IREN always gives GPU pricing included servers, Inter-rack Networking, cabling costs while publicly available figures are per GPU or per rack. To get IREN's all-in pricing for Vera Rubins I calculate = (IREN's All-In GB300 Price in 2025) * (Market Per GPU Vera Rubin in 2026) / (Market Per GPU GB300 in 2025). Dilution $IREN does Convertible Debt for their Datacenter Capex and GPU Backed Debt for their GPUs. Both the datacenter capex and GPU capex are subtracted off revenue before calculating "Earnings before Tax, SG&A". In practice this is equivalent to $IREN using revenue to pay off the convertible debt before it matures. Other Neoclouds like $NBIS also issues convertibles debt since it doesn't immediately count as dilution and can be paid back in cash. CRWV has more corporate debt which has higher interest rate but non-negotiable cash redemption. On top of that I count 10% dilution per year up to 40% in 2029 to account for slippage in buying back convertibles even with the insurance that IREN buys and cost of site works, misc. In 2030+, cashflow should be strong enough so dilution stays at 40%. Stock Price The 2026 ARR supports a stock price of $94.63 while 2027 ARR supports $236.44 even though 2027 having lower PE and more dilution accounted for. This is because IREN has become a priority partnered of Nvidia and will get GPU deliveries that corresponds to their full buildout cadence with buildout guidance of 1210MW by 2027, a big jump from 480MW in 2026. Important to note is that 2027 buildout will start with contracts signed in 2026 so in H2 2026 we may see partial pricing in of 2027 buildout as contracting gives market confidence on the supply side and concrete pricing. Delays IREN's build out cadence is bottleneck by GPU deliveries. Full system test of liquid cooling can only begin once the GPUs arrive so late GPU deliveries means late triaging of liquid cooling bugs. Then stress test networking scripts can only be run for long amounts of time after liquid cooling system is fully validated. As you can see there is only so much theory preparation you can do. In engineering, debugging the real hardware is a process with many sequential locks. Other I'm pretty busy these days so my commentary is incomplete but number savy people like @_Sgr_A_Star, @GyujinAAIG might be able to good commentary on numbers. @_Sgr_A_Star has a great model for very zoomed in tracking of earnings financials that can probably validate or invalidate some elements of my model. I welcome feedback from everyone as that's the point of this open sourced model.

There is no larger long-term strategic move than this — NVIDIA joins forces with $IREN to build the flagship AI factory deployment for the DSX architecture The market will continue to repeatedly reinterpret the deeper intent and long-term objectives behind the partnership between NVIDIA and IREN. On May 7, 2026, IREN’s CEO reposted NVIDIA’s official announcement on X regarding the partnership between the two companies: NVIDIA and IREN Limited today announced a strategic partnership to accelerate the deployment of next-generation AI infrastructure. NVIDIA announcement nvidianews.nvidia.com/news/n… At the same time, IREN also released another announcement on its own website: IREN signs a US$3.4 billion AI cloud services agreement with NVIDIA. IREN announcement iren.gcs-web.com/static-file… The two announcements, each emphasizing different aspects of the cooperation, carry extremely significant implications. First, after careful verification, this is the first time NVIDIA has sought external compute leasing. There are three major turning points in industry development embedded in this move. A reversal of roles: NVIDIA becomes a “major external compute customer” for the first time In the past, NVIDIA’s relationship with infrastructure companies was almost always centered around “selling hardware” or “borrowing hyperscaler data centers for DGX Cloud.” But in this US$3.4 billion agreement with IREN, NVIDIA is, for the first time in its history, leasing third-party compute capacity at large scale and on a long-term basis as a customer, for use by its own AI research teams. This kind of “reverse leasing” is unprecedented for NVIDIA in both scale and nature. The selective external exposure of its most core secrets: this point carries the deepest implications For a long time, NVIDIA has insisted on keeping its most critical R&D work — chip design, driver optimization, and large-model training — inside its self-built supercomputers such as Selene and Eos, creating a closed loop of “building the shovels and mining with them itself.” But this time, outsourcing a 60MW research workload to an external data center is highly significant. It signals that compute-chip R&D is beginning to transition toward external collaboration. The first opening of stack management: introducing Mirantis to manage NVIDIA’s internal R&D clusters Previously, NVIDIA’s internal cluster management was handled entirely by its own engineering teams. But under this agreement, NVIDIA is for the first time allowing third-party management, bringing in Mirantis to participate in cluster orchestration and operations. This also signals a transformation in NVIDIA’s latest compute architecture R&D approach — beginning to “strengthen external collaboration” for lower-level operational work such as server cooling, restarts, and Kubernetes configuration. As the ability of individual GPU chips to increase computing performance gradually approaches physical and engineering limits, the next phase of AI compute advancement is shifting from “single-chip performance competition” to “system-level scalability competition.” This is NVIDIA’s direction of transformation. The primary paths for the next stage of AI compute improvement include: GPU clustering, high-speed interconnects, rack-scale computing, and data-center-level coordination. This requires GPU manufacturers (NVIDIA), data center designers/builders/operators (IREN), and supercluster operating systems (Mirantis) to jointly collaborate on development. What they are developing is precisely the NVIDIA DSX architecture referenced in the NVIDIA-IREN partnership announcement. And IREN’s hyperscale SW site in Texas is becoming the flagship deployment location for NVIDIA’s DSX architecture. This is absolutely not a simple narrative of NVIDIA investing in a company and becoming a shareholder. For the world’s leading company that holds the core secrets of AI compute chip R&D, this is not a trivial matter. From NVIDIA’s perspective, there appear to be many potential partners, such as CoreWeave, Nebius, Oracle, Microsoft Azure, Amazon Web Services, and Crusoe, and NVIDIA has already invested in or partnered with these firms before. But why did it choose IREN for this most important transformation? Because IREN possesses too many things that are uniquely its own: Multiple GW-scale single sites with secured long-term power supply Grid interaction capabilities Vertical integration Ultra-long-term site planning and abundant land supply Green energy Acting as its own design-and-build general contractor Long-term accumulation of data center operational experience Advanced design and technical capabilities Compared with the companies above that NVIDIA has already partnered with, even if IREN temporarily lacked software capabilities, NVIDIA was still willing to wait until IREN acquired a software company before announcing this deep cooperation. Moreover, Mirantis has long been one of the three software companies that have collaborated with NVIDIA for many years. It is highly possible that NVIDIA itself played the role of connector behind IREN’s acquisition. NVIDIA is transforming toward system-level compute scaling and building an AI factory template. In the future, the products it sells may no longer simply be GPU chips, but complete racks, clusters, or even entire AI factories. That inevitably requires standardized data centers in order to guarantee performance, compatibility, scalability, and token efficiency. What NVIDIA needs are facilities with massive long-term secured power supply, land, GW-scale campuses, HPC DNA, rapid construction capability, neutrality, automated scheduling capability, workload routing, GPU virtualization, fault recovery, and cluster operating systems capable of distributed training management. At present, IREN is the only company that possesses all of these elements simultaneously. What they are trying to build is the industrial standard for the next phase of the AI industry. The greatest companies do not merely participate in industries — they define the standards. From this perspective, there is no larger strategic theme than this one. Selling compute capacity to hyperscalers, partnering with Anthropic, or developing new sovereign AI businesses are all important, but none compare with this. The deeper meaning of last week’s announcement will require time for the market to fully interpret and understand. I believe I have already analyzed this trend relatively clearly. This move by NVIDIA and IREN, once executed successfully, could once again widen the gap between the NVIDIA ecosystem and Google just as Google had begun catching up — and it carries major implications for the entire AI industry.

Have waited for a while but if you are a relative value trader now is the time to short $NBIS and long $IREN. We may have got the software layer wrong on the past. But we have the runway no one has in the world and now we have all the layers. I expect $IREN to overtake $NBIS in MC by March 2027

Bringing compute online is the hard part. Mirantis helps us do it better - strengthening how compute is deployed, managed and operated for customers. $IREN

Got it—thanks for the clarification. You’re referring to **1+ GW data center campuses** (secondary users/consumers) on the grid, matched with renewables—not the power plants themselves. Those are still extremely rare at full operational scale. Most hyperscale sites top out at hundreds of MW today. A handful of 1 GW+ AI campuses are in late planning or early build (e.g., select Texas, Europe, and Middle East projects), but energized examples like IREN’s Sweetwater 1 (1.4 GW substation now live) are genuine milestones, especially in a wind/solar-rich ERCOT grid. You’re right that IREN is pushing the frontier here. Congrats again to the team.