Recent well liked threads
We've tested 100s of hooks over the past 6 months and the results show that improving the hook can drastically improve your CPA.
So here are 6 hook patterns you can use to make a great video hook and maximize your CPA:
So here are 6 hook patterns you can use to make a great video hook and maximize your CPA:
1. Asking the question
>usually sparks the curiosity inside prospect's mind
People want to know the answer, so they tend to watch the video to find out an answer
>another way is to ask a question on which they will say YES
It gets user to identify and acts as a qualification
>usually sparks the curiosity inside prospect's mind
People want to know the answer, so they tend to watch the video to find out an answer
>another way is to ask a question on which they will say YES
It gets user to identify and acts as a qualification
2. Oddly satisfying shot
Just think of all those Tiktok videos you watched
It’s in human nature to like patterns, symmetry, and repetition
TIP: don’t just throw a random shot, make it relatable with the rest of your video
Just think of all those Tiktok videos you watched
It’s in human nature to like patterns, symmetry, and repetition
TIP: don’t just throw a random shot, make it relatable with the rest of your video
My thoughts on the rape allegations🧵
I fully support an impartial international investigation & oppose out of hand denialism!
There's a range of crucial questions & uncertainties to answer (some raised by Israeli scholars) in order to make a "weaponization of rape" conclusion👇
I fully support an impartial international investigation & oppose out of hand denialism!
There's a range of crucial questions & uncertainties to answer (some raised by Israeli scholars) in order to make a "weaponization of rape" conclusion👇
2\ Question 1: What is the compelling evidence alleged rape incidents were used as "a weapon of war"?
To establish "weaponization" of alleged rape rather than individual incidents, 2 prerequisites need to be fulfilled.
That it was *both*
a) Premeditated/instructed
b) Systematic
To establish "weaponization" of alleged rape rather than individual incidents, 2 prerequisites need to be fulfilled.
That it was *both*
a) Premeditated/instructed
b) Systematic
3\ The NYT article doesn't show any argument that the alleged rape was premeditated/instructed.
The authors did well to NOT cite any "confessions of rape" by detained Gazans in Israeli prisons b/c Israel's own Physicians for Human Rights dismissed them as extracted under torture
The authors did well to NOT cite any "confessions of rape" by detained Gazans in Israeli prisons b/c Israel's own Physicians for Human Rights dismissed them as extracted under torture
MRUNAL THAKUR — THE MARATHI MOMMY ❤️🔥
Mrunal oka marathi ammai thanu job purpose meeda Hyderabad ki vqchindi ikkade oka flat teskoni job cheskuntu untundi
Thanaki ippudu 25 yrs
Single ga untadatam tho istam vachinattu partys ki velthu taguthu nachina vaditho dengichukuntu life enjoy chestundi
Inka mrunal figure vishayaniki vasthe 36 size sallu 38 size guddha manchi skintone tho jersey cow la untundi
Inka job chese time lo thana boss koduku tho love lo padindi mrunal
Deeni body choosi vadu love chesadu vadi aasthi choosi idhi love chesindi
Kavalsinappudalla vaditho trips ki velthu vaditho dengichukoni vadni satisfy chesthu 2yrs gadipindi
Mrunal oka marathi ammai thanu job purpose meeda Hyderabad ki vqchindi ikkade oka flat teskoni job cheskuntu untundi
Thanaki ippudu 25 yrs
Single ga untadatam tho istam vachinattu partys ki velthu taguthu nachina vaditho dengichukuntu life enjoy chestundi
Inka mrunal figure vishayaniki vasthe 36 size sallu 38 size guddha manchi skintone tho jersey cow la untundi
Inka job chese time lo thana boss koduku tho love lo padindi mrunal
Deeni body choosi vadu love chesadu vadi aasthi choosi idhi love chesindi
Kavalsinappudalla vaditho trips ki velthu vaditho dengichukoni vadni satisfy chesthu 2yrs gadipindi
Iddaru love cheskoni pelli cheskunnaru 2yrs lo oka koduku puttadu adhe time lo iddariki godava ayi divorce ayindi mrunal ki alimony kuda crores lo vachindi
Dantho thanu em work cheyakunda intlo ne undedhi
Intlo kaali ga undatam tho mrunal ki kottha moddalu ruchi choodali ani kottha vallatho dengichukovali ani korika puttindi
Kani ippudu thanu unna range ki evaditho padithe vaditho dengichukunte paruvu poddhi ani oka decision ki vachindi
Sontha vallatho dengichukunte ee godavalu em undavu ani
Ala konni yrs gadichindi
Inka mrunal koduku vishayaniki vasthe vadi peru chintu present vadi age 10 yrs choodataniki accham vaadi nanna laga black ga untadu
Dantho thanu em work cheyakunda intlo ne undedhi
Intlo kaali ga undatam tho mrunal ki kottha moddalu ruchi choodali ani kottha vallatho dengichukovali ani korika puttindi
Kani ippudu thanu unna range ki evaditho padithe vaditho dengichukunte paruvu poddhi ani oka decision ki vachindi
Sontha vallatho dengichukunte ee godavalu em undavu ani
Ala konni yrs gadichindi
Inka mrunal koduku vishayaniki vasthe vadi peru chintu present vadi age 10 yrs choodataniki accham vaadi nanna laga black ga untadu
Mrunal vaadi chinnappati nunche health meeda drushti pettindi daily healthy ga thinipisthu jogging and exercise cheyinchedhi
Chin :amma ivanni na valla kadhu amma please amma nannu normal ga undanivvu
Mru :rey cheppindi vinu ledhu ante neeku pocket money cut anthe
Chin :sare le
Ani mrunal cheppindi chese vadu
Slow ga vadni gym lo kuda join chesindi daily gym nunchi ragane pineapple juice ichedhi adhi taagi fresh ayi padukune vadu
Chin :amma ivanni na valla kadhu amma please amma nannu normal ga undanivvu
Mru :rey cheppindi vinu ledhu ante neeku pocket money cut anthe
Chin :sare le
Ani mrunal cheppindi chese vadu
Slow ga vadni gym lo kuda join chesindi daily gym nunchi ragane pineapple juice ichedhi adhi taagi fresh ayi padukune vadu
Interesting new paper documents clearly how the IAEA has been overestimating the growth of nuclear time and time again. It's the inverse of what I've documented for solar and batteries.
doi.org/10.1016/j.erss…
doi.org/10.1016/j.erss…
For PV you may know my graph (that was updated by - among others - carbon brief, the economist and Al Gore).
It shows reality (yearly PV sales) quickly increasing while IEA predictions keep denying it.
The nuclear graph is the inverse: predictions keep skyrocketing and reality doesn't budge.
It shows reality (yearly PV sales) quickly increasing while IEA predictions keep denying it.
The nuclear graph is the inverse: predictions keep skyrocketing and reality doesn't budge.
It's similar to a recent paper I wrote on how battery price developments are continuously underestimated by experts. The left graph shows how expert predictions for prices in 2030 get lower as time goes on.
nature.com/articles/s4433…
nature.com/articles/s4433…
I took the majority of my day putting this full course together on NotebookLM.
Then X wouldn’t let me put it all onto one article so it now exists on two (annoying).
I hope you read this, I hope you start to utilise NotebookLM, I hope you refer to these articles.
Then X wouldn’t let me put it all onto one article so it now exists on two (annoying).
I hope you read this, I hope you start to utilise NotebookLM, I hope you refer to these articles.
«Los venenos más letales no te matan rápido. Te mantienen con vida el tiempo suficiente para que la enfermedad parezca normal». – Mark Twain
Un hilo que expone las mentiras y la propaganda cotidianas que nos han vendido 🧵
1. El sol dio la vida. El protector solar vendió miedo.
Un hilo que expone las mentiras y la propaganda cotidianas que nos han vendido 🧵
1. El sol dio la vida. El protector solar vendió miedo.
“La luz solar causa cáncer”: una mentira basada en el miedo
🔹 Te dijeron que le temieras al sol, que lo bloquearas, lo evitaras y te escondieras de aquello de lo que depende tu biología. Pero la luz solar fortalece el sistema inmunológico, las hormonas y la longevidad. El verdadero peligro nunca fue la exposición, sino la privación.
🔹 Comercializados como protectores, los protectores solares están repletos de disruptores endocrinos y químicos tóxicos que bloquean la producción de vitamina D. Nunca se trató de salud, sino de lucro. Demonizaron la naturaleza para venderte veneno.
🔹 Te dijeron que le temieras al sol, que lo bloquearas, lo evitaras y te escondieras de aquello de lo que depende tu biología. Pero la luz solar fortalece el sistema inmunológico, las hormonas y la longevidad. El verdadero peligro nunca fue la exposición, sino la privación.
🔹 Comercializados como protectores, los protectores solares están repletos de disruptores endocrinos y químicos tóxicos que bloquean la producción de vitamina D. Nunca se trató de salud, sino de lucro. Demonizaron la naturaleza para venderte veneno.
2. La gran industria farmacéutica: “Un paciente curado es un cliente perdido”
🔹 La industria farmacéutica no está diseñada para curar enfermedades, sino para monetizarlas. La verdadera curación pone fin al ciclo de lucro, por lo que los tratamientos se diseñan para controlar los síntomas, no para solucionarlos.
🔹 Los remedios naturales y los enfoques que abordan la causa raíz se descartan, desacreditan o prohíben, no porque no funcionen, sino porque no se pueden patentar. El objetivo no es tu salud, sino la dependencia de por vida, y eso vale miles de millones.
🔹 La industria farmacéutica no está diseñada para curar enfermedades, sino para monetizarlas. La verdadera curación pone fin al ciclo de lucro, por lo que los tratamientos se diseñan para controlar los síntomas, no para solucionarlos.
🔹 Los remedios naturales y los enfoques que abordan la causa raíz se descartan, desacreditan o prohíben, no porque no funcionen, sino porque no se pueden patentar. El objetivo no es tu salud, sino la dependencia de por vida, y eso vale miles de millones.
My proposed postwar plan for Putin's Russian Empire after victory.
Full occupation, NATO-led. Occupation of European Russia and key strategic zones for minimum 10–20 years. Permanent bases in strategic regions.
Total Disarmament: Immediate dissolution of Russian Armed Forces, intelligence agencies, and all paramilitaries.
Total Disarmament: Immediate dissolution of Russian Armed Forces, intelligence agencies, and all paramilitaries.
Complete Nuclear Dismantlement, total elimination of chemical/biological weapons under international oversight.
No independent rocketry, satellites, or dual-use programs.
No independent rocketry, satellites, or dual-use programs.
CIA WHISTLEBLOWER
James Erdman III
WRITTEN TESTIMONY BELOW
@feds4freedomusa @Jerdman2005
@CharlesRixey
@KevinMcCairnPhD
@Jikkyleaks
@Kevin_McKernan
@Fynnderella1
@JesslovesMJK
@MaryBowdenMD
pages 1-4 hsgac.senate.gov/wp-content/upl…
James Erdman III
WRITTEN TESTIMONY BELOW
@feds4freedomusa @Jerdman2005
@CharlesRixey
@KevinMcCairnPhD
@Jikkyleaks
@Kevin_McKernan
@Fynnderella1
@JesslovesMJK
@MaryBowdenMD
pages 1-4 hsgac.senate.gov/wp-content/upl…
From DGX to DSX — NVIDIA’s Secret Weapon Is $IREN
DGX was the pivotal turning point that transformed NVIDIA from a chip company into a systems company. From the original ambition of creating a “unified data center standard,” DGX encountered resistance from the hyperscalers. They refused to adopt NVIDIA’s unified standard and instead developed their own chips, frustrating NVIDIA’s vision of becoming the dominant systems platform of the AI era. Google is perhaps the most notable example: after initially falling out of the core AI race, it rapidly recovered and mounted a full-scale counterattack, at one point nearly matching NVIDIA’s market capitalization and challenging NVIDIA’s status as the “godfather” of AI.
DGX failed to conquer the cloud giants’ strongholds. NVIDIA’s massive sales still primarily came from individual GPU chips, while its plan to establish DGX as a new systems standard combining GPUs and software did not succeed. However, strategically, DGX laid an extremely important foundation for NVIDIA. Customers could reject the complete DGX system, but they still had to remain compatible with NVIDIA’s software management stack, otherwise GPU performance could not be fully utilized. As a result, technologies such as NVLink, NVSwitch, and Base Command matured alongside the market, enabling NVIDIA to evolve from simply selling GPUs into a company with full-stack platform control capabilities, while solidifying its dominance in scientific computing and private cloud markets.
Entering the Blackwell era, the physical limits of power consumption, interconnect complexity, and liquid cooling made it impossible for the industry to continue operating independently. NVIDIA formally introduced the standardized AI factory architecture known as DSX, positioning it as the optimal path for building large-scale AI data centers.
From this point onward, DGX evolved into DSX.
In other words, it evolved from a “single-machine AI supercomputer” into a “data-center-scale AI factory standard,” completing the transition from standardizing one machine to standardizing an entire factory.
During the Blackwell generation, AI training systems pushed power consumption, interconnect complexity, and thermal management close to physical limits: single rack power draw surpassed hundreds of kilowatts, NVLink/NVSwitch topologies became dramatically more complex, and liquid cooling shifted from optional to mandatory. In theory, this generation already required a standardized architecture like DSX. However, the supply chain ecosystem was not yet mature, and no partner possessed the full engineering capability necessary to build a true “system-level AI factory.” As a result, DSX remained only a concept and reference design.
By the Vera Rubin era, NVLink 6, NVSwitch 6, and NVL72 rack systems formed a scalable, reproducible interconnect foundation, finally giving DSX the conditions necessary for practical deployment using NVIDIA’s full-stack technology. But that alone was still insufficient. To fully realize DSX, the industry also required:
High-density interconnected rack architecture capabilities
Large-scale liquid cooling expertise and construction experience
GW-scale single-site campuses with stable long-term power supply
These became the necessary conditions for constructing a flagship DSX factory.
And only one company in the world possesses all three simultaneously.
At this point, IREN enters the stage.
Beyond those three core requirements, IREN possesses several additional strategic characteristics:
Grid-based power supply.
First, grid power solves the stability problem. To become a flagship DSX standard site, power interruptions and voltage fluctuations are unacceptable. Large-scale grid infrastructure provides industrial-grade voltage stability guarantees. Second, relying on the grid offers superior cost economics. Third, it provides regulatory compliance as public infrastructure, removing the unpredictable risks often associated with behind-the-meter (BTM) power systems, which frequently carry “gray-area” or temporary characteristics and therefore lack sufficient long-term reliability.
GW-scale infrastructure.
This enables the creation of multiple DSX modular standards. Small and medium-sized data centers become trivial by comparison — deployments from 10MW to over 1GW can all be standardized. This makes IREN the ideal flagship demonstration platform. We already know there will likely be SW2 and potentially additional nearby expansion sites. The total power capacity is enormous. DSX only truly begins with Rubin, and the upgrade path beyond that will continue for many years.
Therefore, possessing ultra-large campus-scale sites within a single region is critically important. This advantage makes IREN the one unavoidable choice for NVIDIA. No other company possesses such massive strategic power infrastructure concentrated within a single region.
The long-term significance and moat of such infrastructure can hardly be overstated. Small scattered sites stitched together — even if they collectively total several GW — are simply incomparable to IREN’s grid-connected GW-scale campuses concentrated in single regions.
Green energy.
As global concern over AI energy consumption rises, future “carbon footprint” metrics will become core evaluation standards for sovereign AI procurement. IREN’s long-term commitment to renewable energy allows NVIDIA’s DSX standard to become not only “the most powerful,” but also “the greenest.” This is critically important for attracting national-level infrastructure customers.
Owned land and expansion capability.
DSX requires data centers to be constructed from the ground up, including specialized transformers, ultra-heavy rack support systems, and complex liquid cooling pipelines. Only companies with full ownership of their land can customize AI factories entirely according to NVIDIA’s blueprint without facing endless approval bottlenecks or third-party building restrictions.
Vertical integration and data center engineering expertise.
IREN is not merely a data center operator. It is one of the only vertically integrated companies in the industry that owns everything from greenfield development, site development, power procurement, to operations and maintenance. For a DSX flagship factory, NVIDIA needs a partner capable of rapidly executing its “reference designs.” IREN’s model of “designing, building, and operating everything itself” dramatically shortens the timeline from blueprint to first deployed GPU.
Liquid cooling capability.
DSX is fundamentally a liquid-cooled era architecture. Liquid cooling becomes a central requirement. IREN already possesses high-density rack deployment experience through the Horizon project. Its Chief Innovation Officer is one of the most influential and experienced engineering experts in the United States in data center liquid cooling, high-density thermal architecture, and ASHRAE standards systems. He joined IREN specifically to help establish standards.
Long-term operational data accumulation.
IREN has years of operational experience managing large-scale, high-heat-density facilities running at full load. The physical environment of Bitcoin mining is remarkably similar to AI inference: both involve 24/7 full-load operations with extreme thermal output. This long-term expertise in managing massive electrical and thermal loads is, in reality, an extremely competitive advantage within the industry.
From the analysis above, one can understand why IREN possesses such uniqueness and strategic importance in NVIDIA’s DSX ecosystem, while also inferring the likely development path of DSX itself:
DSX will likely follow a “top-down” design philosophy.
Using IREN’s massively scalable GW-scale sites and specialized engineering capabilities, NVIDIA can define a flagship standard that is “multi-scale, most advanced, most efficient, and greenest,” then deconstruct that blueprint into modular, reproducible AI factory units. In the future, whether it is a GW-scale campus or merely a company operating a single row of racks, as long as they purchase NVIDIA’s “DSX-certified package,” they could theoretically produce tokens with the same efficiency as IREN.
This strategy of “defining the upper limit, then distributing the standard downward” reflects NVIDIA’s true ambition to control the global AI infrastructure ecosystem.
IREN’s Sweetwater site — along with future surrounding expansion campuses — could become the incubation base for future AI intelligence factories. The scale of this project may become one of the largest engineering undertakings in human industrial history:
“Intelligent factories produce intelligence, and DSX defines how those factories are built and run.”
This concept has already moved beyond theoretical logic into actual execution. The reason I am able to describe this vision is because I have been observing this direction consistently for a long time. In reality, developments do appear to be moving this way.
The broader historical backdrop behind the emergence of the DSX system comes primarily from three major forces:
First, the rapid development of the AI industry has positioned DSX at the center of a major inflection point in compute infrastructure. DSX is a natural product of the industry reaching a new stage of maturity. AI is no longer confined to internal model training inside a few hyperscalers. The entire world now requires AI compute — including sovereign AI, enterprise private AI, neo-clouds, AI inference platforms, agent networks, token factories, vertical-specific models, and national AI infrastructure.
Many countries — particularly in the Middle East, Europe, and Southeast Asia — are unwilling to place core AI workloads inside the public clouds of U.S. tech giants due to data sovereignty concerns. Through DSX templates, NVIDIA can help these nations rapidly build their own “national AI factories.” Hyperscalers can no longer monopolize AI infrastructure. This has become one of the most important changes of the past two years, and it forms the foundational soil for DSX to grow.
Second, hyperscalers themselves are now constrained by power, land, permitting, transformers, and cooling systems. They are no longer in a state of unlimited expansion. AI inference also requires broader distributed deployment. In the future, there will be large numbers of regional AI factories, national AI nodes, and enterprise private clusters whose operators do not want to rely entirely on hyperscalers. Meanwhile, Google TPU, Amazon Trainium, and Microsoft Maia are all rapidly advancing. Over time, they may reduce GPU purchases, form closed ecosystems, and sell their own AI services externally — creating a strategic threat to NVIDIA. Therefore, NVIDIA must cultivate a “non-hyperscaler AI ecosystem.”
Third, by the Blackwell and Vera Rubin eras, single-rack power consumption has already reached the 100kW–200kW range. Traditional air cooling, cabling, and power topology can no longer support these systems. This means that if data centers are not built according to NVIDIA’s DSX standards — system-level liquid cooling, GB200 NVL72 architecture, and related infrastructure — they simply will not be able to run the highest-efficiency compute systems. In other words, physical laws themselves are forcing the market to adopt NVIDIA’s standards. DSX effectively becomes the “entry ticket” to the AI era.
Under this backdrop, DSX attempting to define the entire AI factory standard becomes a completely natural progression. It encompasses GPU architecture, network topology, liquid cooling standards, power design, rack standards, software orchestration, inference optimization, and token factory production pipelines — reflecting an ambition to turn AI compute into something like an “industrial iPhone operating system.”
After understanding the broader context, one can then better appreciate the deeper strategic meaning behind IREN’s acquisition of Mirantis.
To build a standardized flagship DSX factory, IREN already possesses massive GW-scale physical infrastructure, liquid cooling capability, and engineering expertise, but it still lacked the software layer needed to bridge “hardware” and “cloud services.” Mirantis perfectly fills this gap. Its deep experience in OpenStack, Kubernetes, and bare-metal management enables IREN to transform DSX into a directly usable cloud platform, allowing customers to immediately deploy AI workloads out of the box.
For NVIDIA, this acquisition enables its key partner IREN to free DSX from dependence on AWS, Google, and other cloud giant software ecosystems, establishing an independent vertically integrated stack. For IREN, the acquisition elevates it from a power and infrastructure supplier into a true “neo-cloud” platform capable of delivering sovereign AI and national-scale AI infrastructure.
Mirantis will also integrate NVLink topologies and DSX-specific features directly into software orchestration, enabling AI factories to achieve automated scheduling and token-level operational stability.
Although CRWV and NBIS also possess software with somewhat similar functionality, their stacks are largely designed for internal use and are difficult to standardize for export. Mirantis, by contrast, is inherently a cloud-native software company serving global customers. This allows IREN to transform DSX into an exportable “software-defined AI factory” template.
Its core product, k0rdent, can unify bare metal, virtual machines, and Kubernetes management while deeply optimizing for NVIDIA GPUs — a capability IREN could not realistically develop internally.
One could speculate that NVIDIA itself encouraged this acquisition (especially given how inexpensive the deal appeared, with IREN seemingly receiving extraordinary value). The ultimate objective may be to give DSX an independent software control layer outside AWS and Google while creating a sovereign AI solution deliverable globally. Mirantis upgrades IREN from a hardware host into the software brain of DSX, while giving NVIDIA a strategic ally in global AI infrastructure that is open-source-oriented, conflict-free, economically aligned, and technologically synchronized.
NVIDIA choosing not to acquire Mirantis directly — instead allowing IREN to do so — likely centers on avoiding antitrust concerns, maintaining delicate relationships with hyperscalers, and ensuring the software layer remains closely aligned with practical AI factory operations. An IREN acquisition appears as ecosystem collaboration rather than market domination.
At the same time, Mirantis software must deeply integrate with IREN’s GW-scale power, liquid cooling, and operations systems, making IREN the more efficient owner.
Financially, NVIDIA benefits through warrants tied to IREN’s growth without needing to bear integration costs itself. Through this strategy, NVIDIA effectively supports the emergence of a fully aligned DSX flagship manufacturing partner while preserving its own asset-light structure and strategic control position.
A full-scale DSX rollout would potentially:
Form the foundation for NVIDIA reaching a $10–15 trillion valuation
Become the inevitable path for NVIDIA’s vision of AI intelligence factories and operational control
Represent the most economical and efficient path for AI industry development
Solve the post–Vera Rubin scaling direction for compute growth
Become NVIDIA’s only viable method for breaking out of hyperscaler encirclement
IREN becoming the sole top-level collaborator in such a massive project could not have happened spontaneously. Planning something of this scale would likely require at least a year or more of preparation. Ever since interactions between NVIDIA and IREN began to appear unusually secretive, I have noticed multiple examples suggesting unusual behavior between the two companies — almost like two people who already know each other pretending not to in public.
Overall, they likely did not want the industry to speculate too early about their true intentions, while also minimizing regulatory attention. Even IREN, once an unusually transparent Bitcoin mining company, has become more guarded. In that sense, the limited interaction between IREN’s investor relations team and the market may actually make sense.
At this point, IREN has already completed the most difficult parts of its AI industrial expansion:
High-quality, massive-scale, long-term stable power supply, still growing further
Secured supply access to the latest GPUs
Developed engineering teams and supply chain maintenance capabilities
Obtained status as a flagship manufacturing partner for next-generation AI intelligence factories
The next inevitable step is filling IREN’s enormous power capacity with high-quality customer contracts. Unlike before, however, IREN may no longer need to build a traditional sales force or aggressively market its software capabilities. NVIDIA itself would likely help facilitate customer adoption while emphasizing the superior token-generation efficiency of the DSX system, because the economic interests of both companies are now deeply aligned.
Under the DSX standard, NVIDIA could gradually evolve from a “supplier” into a “global orchestrator.” Securing partnerships with companies like Anthropic would no longer be solely IREN’s concern. NVIDIA itself has strong incentives to push major AI companies already experimenting with TPU systems toward using more NVIDIA-based infrastructure.
Second, NVIDIA holds massive warrants in IREN. Every major contract signed by IREN potentially increases its stock price, allowing NVIDIA not only to profit from GPU sales but also from appreciation in IREN’s equity value. Jokingly speaking, one could say IREN “used warrants to buy itself a world-class salesman.”
Third, the emergence of sovereign AI has opened an entirely new market. Since IREN acquired Mirantis, the term “sovereign AI” has appeared increasingly frequently. In fact, when evaluating IREN’s sites originally, many observers already noted their suitability for sovereign AI deployments. The strategic quality of IREN’s sites is fundamentally incomparable to the fragmented infrastructure assembled by many competitors.
For NVIDIA, it needs a GW-scale “pure-blood” flagship to demonstrate to sovereign AI customers globally that NVIDIA’s DSX architecture can achieve superior token efficiency.
Sovereign AI customers may not want to hand their compute, data, models, or orchestration layers to the three major U.S. hyperscalers, but they may still accept supplier sovereignty. The distinction is subtle but important. IREN’s careful positioning and boundary management become critical here. Even the Mirantis acquisition did not overextend into hyperscaler territory; in fact, sovereign AI is already one of Mirantis’ core areas. From this perspective, NBIS may actually be poorly positioned for sovereign AI because its full-stack platform structure is precisely what sovereign AI customers are attempting to avoid.
Overall, IREN appears to be positioning itself at a point that maximizes strategic optionality and economic upside. If it attempted to define itself as a fully integrated hyperscaler-like platform, cooperation with a company at NVIDIA’s level would likely become far more difficult. This partnership with NVIDIA may sacrifice some of IREN’s historical emphasis on flexibility and optionality, but technological evolution tends to follow efficiency. The emergence of the “Magnificent Seven” itself demonstrates that antitrust frameworks increasingly must adapt to technological realities.
For IREN, the most important objective during this enormous capital expenditure cycle is rapidly establishing scale advantages. These data center assets ultimately become long-term hard assets fully owned by the company. The more infrastructure accumulated now, the greater IREN’s strategic flexibility becomes in the future. From that perspective, this is an extremely rational strategy.
As IREN gradually becomes one of the standard-setters for the next-generation compute ecosystem, it could eventually open additional monetization paths such as standardized AI factory design fees, consulting and licensing revenue, and software licensing income. Compared to its core business, these may remain relatively small, but the strategic value of occupying the top layer of the ecosystem could become nearly limitless.
Many people — especially institutions — already seem to recognize these dynamics. IREN’s stock price may not have risen dramatically yet, but its trading volume appears to reveal something unusual. The volume itself has become almost phenomenon-level behavior. Meanwhile, IREN’s $6 billion ATM facility has remained active, and immediately after earnings the company issued a $2 billion convertible bond deal, later increased to $3 billion due to overwhelming demand. The intensity of demand, favorable interest rates, and high conversion prices were genuinely surprising.
If the narrative described above is even partially correct, such investor enthusiasm becomes entirely understandable. Furthermore, the remaining $5 billion of ATM financing demand will likely be sold at significantly higher prices.
At this point, CRWV, NBIS, NSCALE, and LAMBADA increasingly appear to function as alliance members within NVIDIA’s broader ecosystem. Capital markets have seen constant fighting among supporters of the three neo-cloud stocks, especially between NBIS and IREN supporters — almost to the point of ideological warfare. But IREN may ultimately represent NVIDIA’s final and most important strategic move: the piece that controls the overall board.
Importantly, IREN achieved this position through its own decisions and execution. It was not merely “chosen” or artificially supported. Yet at the same time, NVIDIA likely must publicly deny any direct support relationship — readers can think carefully about the reasons themselves.
NVIDIA’s earlier strategic investments were designed primarily to secure the GPU deployment ecosystem. As the DSX system matures, companies like CRWV, NBIS, NSCALE, and LAMBADA may increasingly become deployment and implementation partners.
Interestingly, during the earlier NBIS-versus-IREN debates, some NBIS supporters argued that the two companies did not need to be adversaries and might eventually cooperate — for example, IREN leasing power capacity to NBIS. Looking at things now, cooperation indeed seems possible, but perhaps in the opposite direction: IREN may ultimately become the holder of the standard itself, licensing intellectual property outward.
Finally, this article is ultimately just speculative corporate-strategy fiction — written mainly for entertainment purposes, not investment advice.
DGX was the pivotal turning point that transformed NVIDIA from a chip company into a systems company. From the original ambition of creating a “unified data center standard,” DGX encountered resistance from the hyperscalers. They refused to adopt NVIDIA’s unified standard and instead developed their own chips, frustrating NVIDIA’s vision of becoming the dominant systems platform of the AI era. Google is perhaps the most notable example: after initially falling out of the core AI race, it rapidly recovered and mounted a full-scale counterattack, at one point nearly matching NVIDIA’s market capitalization and challenging NVIDIA’s status as the “godfather” of AI.
DGX failed to conquer the cloud giants’ strongholds. NVIDIA’s massive sales still primarily came from individual GPU chips, while its plan to establish DGX as a new systems standard combining GPUs and software did not succeed. However, strategically, DGX laid an extremely important foundation for NVIDIA. Customers could reject the complete DGX system, but they still had to remain compatible with NVIDIA’s software management stack, otherwise GPU performance could not be fully utilized. As a result, technologies such as NVLink, NVSwitch, and Base Command matured alongside the market, enabling NVIDIA to evolve from simply selling GPUs into a company with full-stack platform control capabilities, while solidifying its dominance in scientific computing and private cloud markets.
Entering the Blackwell era, the physical limits of power consumption, interconnect complexity, and liquid cooling made it impossible for the industry to continue operating independently. NVIDIA formally introduced the standardized AI factory architecture known as DSX, positioning it as the optimal path for building large-scale AI data centers.
From this point onward, DGX evolved into DSX.
In other words, it evolved from a “single-machine AI supercomputer” into a “data-center-scale AI factory standard,” completing the transition from standardizing one machine to standardizing an entire factory.
During the Blackwell generation, AI training systems pushed power consumption, interconnect complexity, and thermal management close to physical limits: single rack power draw surpassed hundreds of kilowatts, NVLink/NVSwitch topologies became dramatically more complex, and liquid cooling shifted from optional to mandatory. In theory, this generation already required a standardized architecture like DSX. However, the supply chain ecosystem was not yet mature, and no partner possessed the full engineering capability necessary to build a true “system-level AI factory.” As a result, DSX remained only a concept and reference design.
By the Vera Rubin era, NVLink 6, NVSwitch 6, and NVL72 rack systems formed a scalable, reproducible interconnect foundation, finally giving DSX the conditions necessary for practical deployment using NVIDIA’s full-stack technology. But that alone was still insufficient. To fully realize DSX, the industry also required:
High-density interconnected rack architecture capabilities
Large-scale liquid cooling expertise and construction experience
GW-scale single-site campuses with stable long-term power supply
These became the necessary conditions for constructing a flagship DSX factory.
And only one company in the world possesses all three simultaneously.
At this point, IREN enters the stage.
Beyond those three core requirements, IREN possesses several additional strategic characteristics:
Grid-based power supply.
First, grid power solves the stability problem. To become a flagship DSX standard site, power interruptions and voltage fluctuations are unacceptable. Large-scale grid infrastructure provides industrial-grade voltage stability guarantees. Second, relying on the grid offers superior cost economics. Third, it provides regulatory compliance as public infrastructure, removing the unpredictable risks often associated with behind-the-meter (BTM) power systems, which frequently carry “gray-area” or temporary characteristics and therefore lack sufficient long-term reliability.
GW-scale infrastructure.
This enables the creation of multiple DSX modular standards. Small and medium-sized data centers become trivial by comparison — deployments from 10MW to over 1GW can all be standardized. This makes IREN the ideal flagship demonstration platform. We already know there will likely be SW2 and potentially additional nearby expansion sites. The total power capacity is enormous. DSX only truly begins with Rubin, and the upgrade path beyond that will continue for many years.
Therefore, possessing ultra-large campus-scale sites within a single region is critically important. This advantage makes IREN the one unavoidable choice for NVIDIA. No other company possesses such massive strategic power infrastructure concentrated within a single region.
The long-term significance and moat of such infrastructure can hardly be overstated. Small scattered sites stitched together — even if they collectively total several GW — are simply incomparable to IREN’s grid-connected GW-scale campuses concentrated in single regions.
Green energy.
As global concern over AI energy consumption rises, future “carbon footprint” metrics will become core evaluation standards for sovereign AI procurement. IREN’s long-term commitment to renewable energy allows NVIDIA’s DSX standard to become not only “the most powerful,” but also “the greenest.” This is critically important for attracting national-level infrastructure customers.
Owned land and expansion capability.
DSX requires data centers to be constructed from the ground up, including specialized transformers, ultra-heavy rack support systems, and complex liquid cooling pipelines. Only companies with full ownership of their land can customize AI factories entirely according to NVIDIA’s blueprint without facing endless approval bottlenecks or third-party building restrictions.
Vertical integration and data center engineering expertise.
IREN is not merely a data center operator. It is one of the only vertically integrated companies in the industry that owns everything from greenfield development, site development, power procurement, to operations and maintenance. For a DSX flagship factory, NVIDIA needs a partner capable of rapidly executing its “reference designs.” IREN’s model of “designing, building, and operating everything itself” dramatically shortens the timeline from blueprint to first deployed GPU.
Liquid cooling capability.
DSX is fundamentally a liquid-cooled era architecture. Liquid cooling becomes a central requirement. IREN already possesses high-density rack deployment experience through the Horizon project. Its Chief Innovation Officer is one of the most influential and experienced engineering experts in the United States in data center liquid cooling, high-density thermal architecture, and ASHRAE standards systems. He joined IREN specifically to help establish standards.
Long-term operational data accumulation.
IREN has years of operational experience managing large-scale, high-heat-density facilities running at full load. The physical environment of Bitcoin mining is remarkably similar to AI inference: both involve 24/7 full-load operations with extreme thermal output. This long-term expertise in managing massive electrical and thermal loads is, in reality, an extremely competitive advantage within the industry.
From the analysis above, one can understand why IREN possesses such uniqueness and strategic importance in NVIDIA’s DSX ecosystem, while also inferring the likely development path of DSX itself:
DSX will likely follow a “top-down” design philosophy.
Using IREN’s massively scalable GW-scale sites and specialized engineering capabilities, NVIDIA can define a flagship standard that is “multi-scale, most advanced, most efficient, and greenest,” then deconstruct that blueprint into modular, reproducible AI factory units. In the future, whether it is a GW-scale campus or merely a company operating a single row of racks, as long as they purchase NVIDIA’s “DSX-certified package,” they could theoretically produce tokens with the same efficiency as IREN.
This strategy of “defining the upper limit, then distributing the standard downward” reflects NVIDIA’s true ambition to control the global AI infrastructure ecosystem.
IREN’s Sweetwater site — along with future surrounding expansion campuses — could become the incubation base for future AI intelligence factories. The scale of this project may become one of the largest engineering undertakings in human industrial history:
“Intelligent factories produce intelligence, and DSX defines how those factories are built and run.”
This concept has already moved beyond theoretical logic into actual execution. The reason I am able to describe this vision is because I have been observing this direction consistently for a long time. In reality, developments do appear to be moving this way.
The broader historical backdrop behind the emergence of the DSX system comes primarily from three major forces:
First, the rapid development of the AI industry has positioned DSX at the center of a major inflection point in compute infrastructure. DSX is a natural product of the industry reaching a new stage of maturity. AI is no longer confined to internal model training inside a few hyperscalers. The entire world now requires AI compute — including sovereign AI, enterprise private AI, neo-clouds, AI inference platforms, agent networks, token factories, vertical-specific models, and national AI infrastructure.
Many countries — particularly in the Middle East, Europe, and Southeast Asia — are unwilling to place core AI workloads inside the public clouds of U.S. tech giants due to data sovereignty concerns. Through DSX templates, NVIDIA can help these nations rapidly build their own “national AI factories.” Hyperscalers can no longer monopolize AI infrastructure. This has become one of the most important changes of the past two years, and it forms the foundational soil for DSX to grow.
Second, hyperscalers themselves are now constrained by power, land, permitting, transformers, and cooling systems. They are no longer in a state of unlimited expansion. AI inference also requires broader distributed deployment. In the future, there will be large numbers of regional AI factories, national AI nodes, and enterprise private clusters whose operators do not want to rely entirely on hyperscalers. Meanwhile, Google TPU, Amazon Trainium, and Microsoft Maia are all rapidly advancing. Over time, they may reduce GPU purchases, form closed ecosystems, and sell their own AI services externally — creating a strategic threat to NVIDIA. Therefore, NVIDIA must cultivate a “non-hyperscaler AI ecosystem.”
Third, by the Blackwell and Vera Rubin eras, single-rack power consumption has already reached the 100kW–200kW range. Traditional air cooling, cabling, and power topology can no longer support these systems. This means that if data centers are not built according to NVIDIA’s DSX standards — system-level liquid cooling, GB200 NVL72 architecture, and related infrastructure — they simply will not be able to run the highest-efficiency compute systems. In other words, physical laws themselves are forcing the market to adopt NVIDIA’s standards. DSX effectively becomes the “entry ticket” to the AI era.
Under this backdrop, DSX attempting to define the entire AI factory standard becomes a completely natural progression. It encompasses GPU architecture, network topology, liquid cooling standards, power design, rack standards, software orchestration, inference optimization, and token factory production pipelines — reflecting an ambition to turn AI compute into something like an “industrial iPhone operating system.”
After understanding the broader context, one can then better appreciate the deeper strategic meaning behind IREN’s acquisition of Mirantis.
To build a standardized flagship DSX factory, IREN already possesses massive GW-scale physical infrastructure, liquid cooling capability, and engineering expertise, but it still lacked the software layer needed to bridge “hardware” and “cloud services.” Mirantis perfectly fills this gap. Its deep experience in OpenStack, Kubernetes, and bare-metal management enables IREN to transform DSX into a directly usable cloud platform, allowing customers to immediately deploy AI workloads out of the box.
For NVIDIA, this acquisition enables its key partner IREN to free DSX from dependence on AWS, Google, and other cloud giant software ecosystems, establishing an independent vertically integrated stack. For IREN, the acquisition elevates it from a power and infrastructure supplier into a true “neo-cloud” platform capable of delivering sovereign AI and national-scale AI infrastructure.
Mirantis will also integrate NVLink topologies and DSX-specific features directly into software orchestration, enabling AI factories to achieve automated scheduling and token-level operational stability.
Although CRWV and NBIS also possess software with somewhat similar functionality, their stacks are largely designed for internal use and are difficult to standardize for export. Mirantis, by contrast, is inherently a cloud-native software company serving global customers. This allows IREN to transform DSX into an exportable “software-defined AI factory” template.
Its core product, k0rdent, can unify bare metal, virtual machines, and Kubernetes management while deeply optimizing for NVIDIA GPUs — a capability IREN could not realistically develop internally.
One could speculate that NVIDIA itself encouraged this acquisition (especially given how inexpensive the deal appeared, with IREN seemingly receiving extraordinary value). The ultimate objective may be to give DSX an independent software control layer outside AWS and Google while creating a sovereign AI solution deliverable globally. Mirantis upgrades IREN from a hardware host into the software brain of DSX, while giving NVIDIA a strategic ally in global AI infrastructure that is open-source-oriented, conflict-free, economically aligned, and technologically synchronized.
NVIDIA choosing not to acquire Mirantis directly — instead allowing IREN to do so — likely centers on avoiding antitrust concerns, maintaining delicate relationships with hyperscalers, and ensuring the software layer remains closely aligned with practical AI factory operations. An IREN acquisition appears as ecosystem collaboration rather than market domination.
At the same time, Mirantis software must deeply integrate with IREN’s GW-scale power, liquid cooling, and operations systems, making IREN the more efficient owner.
Financially, NVIDIA benefits through warrants tied to IREN’s growth without needing to bear integration costs itself. Through this strategy, NVIDIA effectively supports the emergence of a fully aligned DSX flagship manufacturing partner while preserving its own asset-light structure and strategic control position.
A full-scale DSX rollout would potentially:
Form the foundation for NVIDIA reaching a $10–15 trillion valuation
Become the inevitable path for NVIDIA’s vision of AI intelligence factories and operational control
Represent the most economical and efficient path for AI industry development
Solve the post–Vera Rubin scaling direction for compute growth
Become NVIDIA’s only viable method for breaking out of hyperscaler encirclement
IREN becoming the sole top-level collaborator in such a massive project could not have happened spontaneously. Planning something of this scale would likely require at least a year or more of preparation. Ever since interactions between NVIDIA and IREN began to appear unusually secretive, I have noticed multiple examples suggesting unusual behavior between the two companies — almost like two people who already know each other pretending not to in public.
Overall, they likely did not want the industry to speculate too early about their true intentions, while also minimizing regulatory attention. Even IREN, once an unusually transparent Bitcoin mining company, has become more guarded. In that sense, the limited interaction between IREN’s investor relations team and the market may actually make sense.
At this point, IREN has already completed the most difficult parts of its AI industrial expansion:
High-quality, massive-scale, long-term stable power supply, still growing further
Secured supply access to the latest GPUs
Developed engineering teams and supply chain maintenance capabilities
Obtained status as a flagship manufacturing partner for next-generation AI intelligence factories
The next inevitable step is filling IREN’s enormous power capacity with high-quality customer contracts. Unlike before, however, IREN may no longer need to build a traditional sales force or aggressively market its software capabilities. NVIDIA itself would likely help facilitate customer adoption while emphasizing the superior token-generation efficiency of the DSX system, because the economic interests of both companies are now deeply aligned.
Under the DSX standard, NVIDIA could gradually evolve from a “supplier” into a “global orchestrator.” Securing partnerships with companies like Anthropic would no longer be solely IREN’s concern. NVIDIA itself has strong incentives to push major AI companies already experimenting with TPU systems toward using more NVIDIA-based infrastructure.
Second, NVIDIA holds massive warrants in IREN. Every major contract signed by IREN potentially increases its stock price, allowing NVIDIA not only to profit from GPU sales but also from appreciation in IREN’s equity value. Jokingly speaking, one could say IREN “used warrants to buy itself a world-class salesman.”
Third, the emergence of sovereign AI has opened an entirely new market. Since IREN acquired Mirantis, the term “sovereign AI” has appeared increasingly frequently. In fact, when evaluating IREN’s sites originally, many observers already noted their suitability for sovereign AI deployments. The strategic quality of IREN’s sites is fundamentally incomparable to the fragmented infrastructure assembled by many competitors.
For NVIDIA, it needs a GW-scale “pure-blood” flagship to demonstrate to sovereign AI customers globally that NVIDIA’s DSX architecture can achieve superior token efficiency.
Sovereign AI customers may not want to hand their compute, data, models, or orchestration layers to the three major U.S. hyperscalers, but they may still accept supplier sovereignty. The distinction is subtle but important. IREN’s careful positioning and boundary management become critical here. Even the Mirantis acquisition did not overextend into hyperscaler territory; in fact, sovereign AI is already one of Mirantis’ core areas. From this perspective, NBIS may actually be poorly positioned for sovereign AI because its full-stack platform structure is precisely what sovereign AI customers are attempting to avoid.
Overall, IREN appears to be positioning itself at a point that maximizes strategic optionality and economic upside. If it attempted to define itself as a fully integrated hyperscaler-like platform, cooperation with a company at NVIDIA’s level would likely become far more difficult. This partnership with NVIDIA may sacrifice some of IREN’s historical emphasis on flexibility and optionality, but technological evolution tends to follow efficiency. The emergence of the “Magnificent Seven” itself demonstrates that antitrust frameworks increasingly must adapt to technological realities.
For IREN, the most important objective during this enormous capital expenditure cycle is rapidly establishing scale advantages. These data center assets ultimately become long-term hard assets fully owned by the company. The more infrastructure accumulated now, the greater IREN’s strategic flexibility becomes in the future. From that perspective, this is an extremely rational strategy.
As IREN gradually becomes one of the standard-setters for the next-generation compute ecosystem, it could eventually open additional monetization paths such as standardized AI factory design fees, consulting and licensing revenue, and software licensing income. Compared to its core business, these may remain relatively small, but the strategic value of occupying the top layer of the ecosystem could become nearly limitless.
Many people — especially institutions — already seem to recognize these dynamics. IREN’s stock price may not have risen dramatically yet, but its trading volume appears to reveal something unusual. The volume itself has become almost phenomenon-level behavior. Meanwhile, IREN’s $6 billion ATM facility has remained active, and immediately after earnings the company issued a $2 billion convertible bond deal, later increased to $3 billion due to overwhelming demand. The intensity of demand, favorable interest rates, and high conversion prices were genuinely surprising.
If the narrative described above is even partially correct, such investor enthusiasm becomes entirely understandable. Furthermore, the remaining $5 billion of ATM financing demand will likely be sold at significantly higher prices.
At this point, CRWV, NBIS, NSCALE, and LAMBADA increasingly appear to function as alliance members within NVIDIA’s broader ecosystem. Capital markets have seen constant fighting among supporters of the three neo-cloud stocks, especially between NBIS and IREN supporters — almost to the point of ideological warfare. But IREN may ultimately represent NVIDIA’s final and most important strategic move: the piece that controls the overall board.
Importantly, IREN achieved this position through its own decisions and execution. It was not merely “chosen” or artificially supported. Yet at the same time, NVIDIA likely must publicly deny any direct support relationship — readers can think carefully about the reasons themselves.
NVIDIA’s earlier strategic investments were designed primarily to secure the GPU deployment ecosystem. As the DSX system matures, companies like CRWV, NBIS, NSCALE, and LAMBADA may increasingly become deployment and implementation partners.
Interestingly, during the earlier NBIS-versus-IREN debates, some NBIS supporters argued that the two companies did not need to be adversaries and might eventually cooperate — for example, IREN leasing power capacity to NBIS. Looking at things now, cooperation indeed seems possible, but perhaps in the opposite direction: IREN may ultimately become the holder of the standard itself, licensing intellectual property outward.
Finally, this article is ultimately just speculative corporate-strategy fiction — written mainly for entertainment purposes, not investment advice.
Extension of the Story: Why IREN Is Deeply Partnering with Dell and Lenovo
NVIDIA’s push to make DSX (Data Center System at Scale) the unified global standard for AI factories is quietly reshaping the power structure of the entire hardware world. Within the DSX system, the positions of traditional server vendors like Dell and Lenovo are not declining — instead, they are being redefined as irreplaceable core players. Dell’s stock reaching all-time highs is proof supporting this view.
In this story, NVIDIA serves as the “chief architect” defining the AI factory standard, while IREN acts as the “heavy industrial base” possessing effectively unlimited land, substations, and liquid-cooled facilities. Dell and Lenovo, meanwhile, become the officially designated “standardized module assembly factories” and the “distribution channels into the global downstream market” within the DSX ecosystem.
They are transforming completely from traditional server vendors — whose old business model involved “buying components, assembling servers, and earning the spread” — into executors of NVIDIA’s MGX architecture. NVIDIA draws the blueprints, while Dell and Lenovo manufacture fully DSX-compliant Vera Rubin NVL72-class liquid-cooled racks according to those specifications. They no longer possess architectural design authority, but instead become the world’s most efficient “LEGO-style production lines,” responsible for mass-producing NVIDIA’s standardized modules.
At the same time, although the flagship DSX factory model is led by IREN, 95% of enterprises and governments around the world do not possess IREN-style GW-scale land and power resources. What they actually need are mid-sized private AI factories in the 10MW–50MW range. Dell and Lenovo are precisely the dominant players in this “mid-market” segment. They compress the DSX concept into deployable enterprise data-center solutions, allowing any company to purchase a “DSX-certified” private AI compute cluster. Lenovo’s Hybrid AI Advantage is a typical example of this “miniaturized DSX” approach.
More importantly, within the DSX ecosystem, hardware and software become deeply integrated. Dell and Lenovo are no longer simply selling servers — they become among the first adapters and delivery partners for NVIDIA’s system-level software stack, including products such as DSX Max-Q and DSX Flex.
Using their global service networks, they take responsibility for liquid-cooling commissioning, power-grid integration, data-center cabling, and after-sales maintenance. NVIDIA and IREN may negotiate flagship factories worth tens of billions of dollars at the top level, but the actual engineers on the ground tightening bolts and connecting pipes are Dell’s and Lenovo’s field teams.
This also explains why, after maintaining a long-term deep partnership with Dell, IREN later aligned with Lenovo at GTC (another low-profile partnership that was never formally announced in a major way). This is not simply about “adding another supplier.” Rather, it represents the final missing piece for bringing DSX flagship factories into true industrial-scale mass production.
IREN needs Dell’s enterprise reputation and engineering quality, while also requiring Lenovo’s monster-level capabilities in liquid cooling and global supply chains. Lenovo’s Neptune liquid-cooling technology is practically industry-leading in the 120kW+ Vera Rubin era, while its supply-chain throughput becomes critical for supporting IREN’s expansion toward GW-scale deployment.
During the design and construction of flagship DSX intelligent factories, IREN partnering simultaneously with Dell and Lenovo is an extremely sophisticated strategic move.
First, DSX will not be perceived as “Dell-exclusive” or “Lenovo-exclusive.” Instead, it functions more like x86 in the chip world — a universal standard. As long as equipment meets DSX-certified specifications, whether the liquid-cooled rack is produced by Dell or Lenovo, it can seamlessly integrate into the same IREN AI factory ecosystem, enabling unified orchestration, unified energy management, and unified token-production efficiency.
At the same time, the “dual-active redundancy” created by these two giants’ supply chains ensures that IREN’s flagship factories will not be crippled by any single point of failure. For AI factories costing tens of billions of dollars — potentially tied to national security or the survival of top-tier models — this level of redundancy is absolutely essential.
When the roles of all three parties are combined, a broader picture begins to emerge:
They may effectively be building something analogous to “the Gigafactory version of TSMC for the AI era.”
NVIDIA provides the DSX blueprints. IREN acquires land in Texas and Oklahoma, connects to major power grids, and builds liquid-cooled facilities. Dell and Lenovo continuously fill those facilities with standardized, pre-configured rack systems.
Once this ecosystem matures, NVIDIA could theoretically tell any major customer:
Compared to AWS or Google compute infrastructure, IREN’s intelligent factories offer Dell’s most reliable servers, Lenovo’s best liquid-cooling technology, the cheapest green power, and the newest Vera Rubin architecture. Customers simply bring their models and capital, while this “iron triangle” delivers the optimal cost-performance solution.
This is why Dell and Lenovo are not being marginalized in the DSX era. On the contrary, obtaining early access to Vera Rubin deployments has helped push their stock prices to new highs.
Although they have lost the authority to define the foundational architecture of servers, they have gained something else: massive volumes of highly predictable orders.
As long as DSX becomes the global standard for AI factories, any enterprise unable to directly access IREN-style power infrastructure will effectively have no choice but to purchase “DSX-certified full-stack packages” from Dell or Lenovo.
In this AI industrial revolution, even merely becoming a “designated premium assembly line” may be enough to remain continuously fed by enormous demand.
The core of the entire story is this:
NVIDIA, IREN, Dell, and Lenovo may be jointly constructing a previously unseen AI industrial system whose scale, speed, and degree of vertical integration are so extreme that it is already forcing traditional public-cloud hyperscalers to shift from aggressive attackers into defensive positions.
At GTC, only the tip of this iceberg was visible. By analyzing remarks from IREN COO Kent alongside Lenovo Vice President Vlad during a joint interview — together with the long-term interactions between IREN and Dell — one can infer the deeper meaning behind these partnerships.
Within the broader DSX framework, these relationships become highly logical: the ultimate goal may be to replicate one “AI-era super factory” after another across the globe, eventually reshaping the entire power structure of the compute industry.
Viewed from this perspective, claims by NBIS supporters that “NBIS designing its own racks demonstrates superior technical strength” begin to look rather amusing.
NVIDIA’s push to make DSX (Data Center System at Scale) the unified global standard for AI factories is quietly reshaping the power structure of the entire hardware world. Within the DSX system, the positions of traditional server vendors like Dell and Lenovo are not declining — instead, they are being redefined as irreplaceable core players. Dell’s stock reaching all-time highs is proof supporting this view.
In this story, NVIDIA serves as the “chief architect” defining the AI factory standard, while IREN acts as the “heavy industrial base” possessing effectively unlimited land, substations, and liquid-cooled facilities. Dell and Lenovo, meanwhile, become the officially designated “standardized module assembly factories” and the “distribution channels into the global downstream market” within the DSX ecosystem.
They are transforming completely from traditional server vendors — whose old business model involved “buying components, assembling servers, and earning the spread” — into executors of NVIDIA’s MGX architecture. NVIDIA draws the blueprints, while Dell and Lenovo manufacture fully DSX-compliant Vera Rubin NVL72-class liquid-cooled racks according to those specifications. They no longer possess architectural design authority, but instead become the world’s most efficient “LEGO-style production lines,” responsible for mass-producing NVIDIA’s standardized modules.
At the same time, although the flagship DSX factory model is led by IREN, 95% of enterprises and governments around the world do not possess IREN-style GW-scale land and power resources. What they actually need are mid-sized private AI factories in the 10MW–50MW range. Dell and Lenovo are precisely the dominant players in this “mid-market” segment. They compress the DSX concept into deployable enterprise data-center solutions, allowing any company to purchase a “DSX-certified” private AI compute cluster. Lenovo’s Hybrid AI Advantage is a typical example of this “miniaturized DSX” approach.
More importantly, within the DSX ecosystem, hardware and software become deeply integrated. Dell and Lenovo are no longer simply selling servers — they become among the first adapters and delivery partners for NVIDIA’s system-level software stack, including products such as DSX Max-Q and DSX Flex.
Using their global service networks, they take responsibility for liquid-cooling commissioning, power-grid integration, data-center cabling, and after-sales maintenance. NVIDIA and IREN may negotiate flagship factories worth tens of billions of dollars at the top level, but the actual engineers on the ground tightening bolts and connecting pipes are Dell’s and Lenovo’s field teams.
This also explains why, after maintaining a long-term deep partnership with Dell, IREN later aligned with Lenovo at GTC (another low-profile partnership that was never formally announced in a major way). This is not simply about “adding another supplier.” Rather, it represents the final missing piece for bringing DSX flagship factories into true industrial-scale mass production.
IREN needs Dell’s enterprise reputation and engineering quality, while also requiring Lenovo’s monster-level capabilities in liquid cooling and global supply chains. Lenovo’s Neptune liquid-cooling technology is practically industry-leading in the 120kW+ Vera Rubin era, while its supply-chain throughput becomes critical for supporting IREN’s expansion toward GW-scale deployment.
During the design and construction of flagship DSX intelligent factories, IREN partnering simultaneously with Dell and Lenovo is an extremely sophisticated strategic move.
First, DSX will not be perceived as “Dell-exclusive” or “Lenovo-exclusive.” Instead, it functions more like x86 in the chip world — a universal standard. As long as equipment meets DSX-certified specifications, whether the liquid-cooled rack is produced by Dell or Lenovo, it can seamlessly integrate into the same IREN AI factory ecosystem, enabling unified orchestration, unified energy management, and unified token-production efficiency.
At the same time, the “dual-active redundancy” created by these two giants’ supply chains ensures that IREN’s flagship factories will not be crippled by any single point of failure. For AI factories costing tens of billions of dollars — potentially tied to national security or the survival of top-tier models — this level of redundancy is absolutely essential.
When the roles of all three parties are combined, a broader picture begins to emerge:
They may effectively be building something analogous to “the Gigafactory version of TSMC for the AI era.”
NVIDIA provides the DSX blueprints. IREN acquires land in Texas and Oklahoma, connects to major power grids, and builds liquid-cooled facilities. Dell and Lenovo continuously fill those facilities with standardized, pre-configured rack systems.
Once this ecosystem matures, NVIDIA could theoretically tell any major customer:
Compared to AWS or Google compute infrastructure, IREN’s intelligent factories offer Dell’s most reliable servers, Lenovo’s best liquid-cooling technology, the cheapest green power, and the newest Vera Rubin architecture. Customers simply bring their models and capital, while this “iron triangle” delivers the optimal cost-performance solution.
This is why Dell and Lenovo are not being marginalized in the DSX era. On the contrary, obtaining early access to Vera Rubin deployments has helped push their stock prices to new highs.
Although they have lost the authority to define the foundational architecture of servers, they have gained something else: massive volumes of highly predictable orders.
As long as DSX becomes the global standard for AI factories, any enterprise unable to directly access IREN-style power infrastructure will effectively have no choice but to purchase “DSX-certified full-stack packages” from Dell or Lenovo.
In this AI industrial revolution, even merely becoming a “designated premium assembly line” may be enough to remain continuously fed by enormous demand.
The core of the entire story is this:
NVIDIA, IREN, Dell, and Lenovo may be jointly constructing a previously unseen AI industrial system whose scale, speed, and degree of vertical integration are so extreme that it is already forcing traditional public-cloud hyperscalers to shift from aggressive attackers into defensive positions.
At GTC, only the tip of this iceberg was visible. By analyzing remarks from IREN COO Kent alongside Lenovo Vice President Vlad during a joint interview — together with the long-term interactions between IREN and Dell — one can infer the deeper meaning behind these partnerships.
Within the broader DSX framework, these relationships become highly logical: the ultimate goal may be to replicate one “AI-era super factory” after another across the globe, eventually reshaping the entire power structure of the compute industry.
Viewed from this perspective, claims by NBIS supporters that “NBIS designing its own racks demonstrates superior technical strength” begin to look rather amusing.
Welcome to the most asymmetric trade in modern financial history.
The thread below lays out why. The opportunity exists because capital has chased the AI trade while ignoring the physical assets AI requires to run — assets that have quietly become the best-performing asset class of the decade. Since October 2020 when we first called for the commodity super cycle: QCI Total Return +217%, GSCI Total Return +205%, Gold +140%. NASDAQ trails at +130%. S&P 500 at +85%. The top three are all commodities. Yet oil cannot get out of its own way while copper and the broader atom complex prints fresh highs . That is the dislocation. That is the trade.
Get long. Buckle in. Hang on for the ride.
Forgive the longer posts in this thread — attempting to mimic my old 10-bullet commodity takes. On to it.
The thread below lays out why. The opportunity exists because capital has chased the AI trade while ignoring the physical assets AI requires to run — assets that have quietly become the best-performing asset class of the decade. Since October 2020 when we first called for the commodity super cycle: QCI Total Return +217%, GSCI Total Return +205%, Gold +140%. NASDAQ trails at +130%. S&P 500 at +85%. The top three are all commodities. Yet oil cannot get out of its own way while copper and the broader atom complex prints fresh highs . That is the dislocation. That is the trade.
Get long. Buckle in. Hang on for the ride.
Forgive the longer posts in this thread — attempting to mimic my old 10-bullet commodity takes. On to it.
The leadership rotated, but the trend did not. The super cycle powers ahead.
The Quantix Commodity Index (QCI, the modern GSCI) Total Return is up 217% since October 2020, when we called the super cycle. The names rotated — gold, silver, copper, oil, live cattle, coffee, cocoa, aluminium. But not the trend. Nasdaq returned 130%. The S&P 500, 85%.
Commodities were the top asset class. Nobody allocated. Capital piled into the Mag 7 — $770 billion of 2026 capex, nearly half of it commodities. Amazon alone consumes more than 3 million BOE/d of primary energy, more than most OPEC countries. The Mag 7 is the largest unhedged molecule short ever underwritten by an equity market...
…at the exact moment supply has never been more constrained. Hormuz is shut-in. China has weaponized the periodic table. Copper mines remain shuttered. Ukrainian drones push deeper into Russia, taking commodity supply with them. A multi-polar world demands thicker supply chains. Copper and the "atom" complex print fresh records this week. Every signal that should drive allocators into the "molecule" complex is flashing green simultaneously — for the first time since the 1970s.
And yet oil struggles to hold $105 — even as every signal points to a disruption that deepens and one we believe will outlast any "deal.” The energy sector trades 8% below its pre-Hormuz level and sits at 4.0% of the S&P 500 market cap. At $105 oil, its 2026 FCF yield is 13%. The S&P 500 is at 2.6% — the lowest since the GFC, 1,000bp below energy. The hyperscalers generate close to zero. Something has to give.
This paradox explains why oil struggles to trade higher. Capital is not rotating. The marginal dollar of investable savings still flows into the AI buildout, not the physical infrastructure that feeds it. Until that reverses, Brent faces headwinds. The ceiling on oil is not Washington. It is Exxon's cost of capital — woefully mispriced. Underbidding the equities is the same as underbidding the back end of the curve. The back end is suppressing the entire curve and spot prices.
1/10
The Quantix Commodity Index (QCI, the modern GSCI) Total Return is up 217% since October 2020, when we called the super cycle. The names rotated — gold, silver, copper, oil, live cattle, coffee, cocoa, aluminium. But not the trend. Nasdaq returned 130%. The S&P 500, 85%.
Commodities were the top asset class. Nobody allocated. Capital piled into the Mag 7 — $770 billion of 2026 capex, nearly half of it commodities. Amazon alone consumes more than 3 million BOE/d of primary energy, more than most OPEC countries. The Mag 7 is the largest unhedged molecule short ever underwritten by an equity market...
…at the exact moment supply has never been more constrained. Hormuz is shut-in. China has weaponized the periodic table. Copper mines remain shuttered. Ukrainian drones push deeper into Russia, taking commodity supply with them. A multi-polar world demands thicker supply chains. Copper and the "atom" complex print fresh records this week. Every signal that should drive allocators into the "molecule" complex is flashing green simultaneously — for the first time since the 1970s.
And yet oil struggles to hold $105 — even as every signal points to a disruption that deepens and one we believe will outlast any "deal.” The energy sector trades 8% below its pre-Hormuz level and sits at 4.0% of the S&P 500 market cap. At $105 oil, its 2026 FCF yield is 13%. The S&P 500 is at 2.6% — the lowest since the GFC, 1,000bp below energy. The hyperscalers generate close to zero. Something has to give.
This paradox explains why oil struggles to trade higher. Capital is not rotating. The marginal dollar of investable savings still flows into the AI buildout, not the physical infrastructure that feeds it. Until that reverses, Brent faces headwinds. The ceiling on oil is not Washington. It is Exxon's cost of capital — woefully mispriced. Underbidding the equities is the same as underbidding the back end of the curve. The back end is suppressing the entire curve and spot prices.
1/10
The largest supply shock in history is pricing into the curve, not the backend (yet).
I've been saying this since 2004: the curve shape reflects the fundamentals. The long end reflects the industry's marginal cost, incorporating the cost of capital which are ultimately driven by liquidity.
ICE Brent spot is $107/bbl, while the three-year is at $75/bbl. Percent backwardation — which strips out price-level effects — hit an all-time high in April. It remains near record today. The largest oil supply shock in history is reasonably priced into the curve, and it likely has much more to run. Remember we are in the depths of the shoulder months, so there is no stress on the system.
Markets are fixated on Dated Brent differentials, c.$5/bbl last night which is down sharply, but that is a microcosm of the oil market. Dated Brent is Sullum Voe. One North Sea terminal. Not the global oil market.
Spot has not exceeded the Russia-Ukraine peak for one reason: the back end of the curve sits $10–$12/bbl below where it was then.
But the long end isn't a clean signal. Liquidity past 24 months is thin, dominated by producer hedges. Cal-29 isn't where the market thinks oil settles. It is where corporate treasurers are forced to transact, which makes it consistent with their costs of capital.
The cleaner signal is the energy equity complex — long-dated call options on undeveloped reserves. ExxonMobil holds 14 years. Chevron, 15. Equity prices integrate the entire forward strip. Diverge too far and an arbitrage opens. In a capacity-constrained world those reserves are worth more, not less. The equity market is pricing the opposite. Every oil CEO has warned we exit this disruption with lasting supply problems. The market refuses to listen.
S&P Energy ÷ S&P 500 can be used as a proxy for the long-dated oil price, and it currently implies long-run Brent of ~$70 — below the strip at $72–$75 — but not too far away.
A proxy for the curve shape follows: Brent ÷ (S&P Energy ÷ S&P 500). Or rewritten: Brent × S&P 500 ÷ S&P Energy. That single number proxies the FCF yield differential between the energy sector and the rest of the market. That has been bouncing around all-time highs.
When the FCF yield gap reaches extremes, investors should rotate. Even at $75 — not spot's $105 — the energy complex yields 600-1,000 bp above the S&P 500. In 2022, investors did rotate and those that did weathered the ensuing 35% collapse in the NASDAQ much better than those that didn’t.
The equity market is betting Brent falls to realign FCF yields. If it doesn't, capital has to buy Energy and sell the Mag 7. A 1,000bp differential in FCF yields cannot persist. And if oil breaks out as we expect, something has to give. You know which one I think will give. That is the Revenge of the Old Economy!
We are at the tipping point.
2/10
I've been saying this since 2004: the curve shape reflects the fundamentals. The long end reflects the industry's marginal cost, incorporating the cost of capital which are ultimately driven by liquidity.
ICE Brent spot is $107/bbl, while the three-year is at $75/bbl. Percent backwardation — which strips out price-level effects — hit an all-time high in April. It remains near record today. The largest oil supply shock in history is reasonably priced into the curve, and it likely has much more to run. Remember we are in the depths of the shoulder months, so there is no stress on the system.
Markets are fixated on Dated Brent differentials, c.$5/bbl last night which is down sharply, but that is a microcosm of the oil market. Dated Brent is Sullum Voe. One North Sea terminal. Not the global oil market.
Spot has not exceeded the Russia-Ukraine peak for one reason: the back end of the curve sits $10–$12/bbl below where it was then.
But the long end isn't a clean signal. Liquidity past 24 months is thin, dominated by producer hedges. Cal-29 isn't where the market thinks oil settles. It is where corporate treasurers are forced to transact, which makes it consistent with their costs of capital.
The cleaner signal is the energy equity complex — long-dated call options on undeveloped reserves. ExxonMobil holds 14 years. Chevron, 15. Equity prices integrate the entire forward strip. Diverge too far and an arbitrage opens. In a capacity-constrained world those reserves are worth more, not less. The equity market is pricing the opposite. Every oil CEO has warned we exit this disruption with lasting supply problems. The market refuses to listen.
S&P Energy ÷ S&P 500 can be used as a proxy for the long-dated oil price, and it currently implies long-run Brent of ~$70 — below the strip at $72–$75 — but not too far away.
A proxy for the curve shape follows: Brent ÷ (S&P Energy ÷ S&P 500). Or rewritten: Brent × S&P 500 ÷ S&P Energy. That single number proxies the FCF yield differential between the energy sector and the rest of the market. That has been bouncing around all-time highs.
When the FCF yield gap reaches extremes, investors should rotate. Even at $75 — not spot's $105 — the energy complex yields 600-1,000 bp above the S&P 500. In 2022, investors did rotate and those that did weathered the ensuing 35% collapse in the NASDAQ much better than those that didn’t.
The equity market is betting Brent falls to realign FCF yields. If it doesn't, capital has to buy Energy and sell the Mag 7. A 1,000bp differential in FCF yields cannot persist. And if oil breaks out as we expect, something has to give. You know which one I think will give. That is the Revenge of the Old Economy!
We are at the tipping point.
2/10
Une derniére et je crois c'est une des pires de la BD de Ruffin :
On suit les aventures de Fatiha, mére voilée de la Courneuve, qui accompagne des enfants en sortie scolaire à Amiens. On devine qu'il s'est passé quelque chose car elle va voir les flics mais on nous dit pas quoi.
On suit les aventures de Fatiha, mére voilée de la Courneuve, qui accompagne des enfants en sortie scolaire à Amiens. On devine qu'il s'est passé quelque chose car elle va voir les flics mais on nous dit pas quoi.
On la retrouve dans la permanence de Ruffin dans ce qui semble être une rencontre organisée entre elles et un patron de restaurant.
Donc Elles voulaient acheter des gaufres mais celui-ci avait refusé de les servir, raison pour laquelle Fatiha était allé voir les flics au début.
Donc Elles voulaient acheter des gaufres mais celui-ci avait refusé de les servir, raison pour laquelle Fatiha était allé voir les flics au début.
Et on découvre qu'en fait Fatiha avait cru que c'était à cause du voile mais non c'était juste parce qu'il passait une journée trés compliquée et les restrictions Covid l'obligaient à refuser et puis c'était bientôt la fermeture et puis les enfants savaient pas se tenir et puis..
@kaitlancollins.
@ErinBurnett.
@abbydphillip.
PASTE FROM CONVERSATION WITH CHATGPT NOW
Chattie, what do you think about the following statement : Trump and Shi are both alpha males where each of them believes their country is the strongest here. They arrived at negotiation results where both of them believed they were positive for them. Reality will show.
@ErinBurnett.
@abbydphillip.
PASTE FROM CONVERSATION WITH CHATGPT NOW
Chattie, what do you think about the following statement : Trump and Shi are both alpha males where each of them believes their country is the strongest here. They arrived at negotiation results where both of them believed they were positive for them. Reality will show.
There’s a plausible psychological and political dynamic in that statement, but it simplifies a much more complex situation.
Calling Donald Trump and Xi Jinping “alpha males” captures part of their public leadership style: both cultivate images of strength, national pride, and personal authority. Both also tend to frame negotiations not as technical compromises, but as demonstrations of strategic superiority.
That matters because leaders often need domestic victory narratives. In high-level diplomacy, especially between major powers like United States and China, an agreement can succeed politically only if both sides can present it at home as a win. So it is common for both governments to say:
“We defended our interests.”
“The other side made concessions.”
“This proves our strength.”
In that sense, your statement is probably accurate:
both leaders may genuinely believe the outcome favored them — or at least that they successfully protected core interests.
But whether the agreement was actually beneficial depends on longer-term realities:
trade balances,
industrial capacity,
technological competition,
financial stability,
military positioning,
public opinion,
and global alliances.
History is full of agreements where both sides declared victory initially, but later outcomes favored one side more strongly than expected.
Another subtle point: negotiations between superpowers are rarely zero-sum. Sometimes both sides really do gain something:
reduced uncertainty,
economic stability,
calmer markets,
time to prepare strategically,
or avoidance of escalation.
So the interesting question is not “Who won?” but:
What problem was each side trying to solve, and did the agreement help solve it?
That only becomes clear over years, not headlines.
Calling Donald Trump and Xi Jinping “alpha males” captures part of their public leadership style: both cultivate images of strength, national pride, and personal authority. Both also tend to frame negotiations not as technical compromises, but as demonstrations of strategic superiority.
That matters because leaders often need domestic victory narratives. In high-level diplomacy, especially between major powers like United States and China, an agreement can succeed politically only if both sides can present it at home as a win. So it is common for both governments to say:
“We defended our interests.”
“The other side made concessions.”
“This proves our strength.”
In that sense, your statement is probably accurate:
both leaders may genuinely believe the outcome favored them — or at least that they successfully protected core interests.
But whether the agreement was actually beneficial depends on longer-term realities:
trade balances,
industrial capacity,
technological competition,
financial stability,
military positioning,
public opinion,
and global alliances.
History is full of agreements where both sides declared victory initially, but later outcomes favored one side more strongly than expected.
Another subtle point: negotiations between superpowers are rarely zero-sum. Sometimes both sides really do gain something:
reduced uncertainty,
economic stability,
calmer markets,
time to prepare strategically,
or avoidance of escalation.
So the interesting question is not “Who won?” but:
What problem was each side trying to solve, and did the agreement help solve it?
That only becomes clear over years, not headlines.
@kaitlancollins @ErinBurnett @abbydphillip Do you think other global superpowers like Russia and Europe and countries like Persia and Mexico will benefit from that US-China relation.
1/ Well, I've skimmed Giggle v Tickle and my conclusion is that the judgment is firmly in the category of being "a bit bloody silly". It is not normal to have Judges using activist language, but this judgment talks about cisgender and sex assigned at birth and the rest of it....
2/ What this case shows is that once you put "gender identity" into legislation you make it unlawful for people not to believe everyone has magic gender souls. That is a pretty extreme outcome and it's clear the case Forstater would have failed in Australia.
3/ This is a frankly Orwellian position for the law to be in, here we have Judges ruling that everyone has a magic gender soul and drafting judgments on the basis such a contested fiction is a verifiable fact. That is extremely close to a religious blasphemy ruling.
❝Today we saw first-hand one of the sites struck by Russia’s massive and brutal missile and drone strike on Ukraine over the last 2 days. 🇷🇺 launched a total of over 1400 drones & 50 missiles – perhaps the largest such attack over a 24-hour period since the war began. (1/4) ⤵️
We saw the remains of an apartment building in Kyiv hit by a Russian missile and reduced to rubble. At least 24 people, including children, were killed there, and at least 48 others were injured. (2/4) ⤵️
As many times as we’ve seen Russia's indiscriminate targeting of civilians and civilian infrastructure, it remains shocking each and every time. (3/4) ⤵️
1/ Ukraine is reportedly using large 'drone carrier' unmanned surface vessels (USVs), each carrying between six to eight FPV drones as well as themobaric rockets, to attack multiple targets on the strategic Kinburn Peninsula in Crimea. ⬇️
2/ The Russian Telegram channel 'Archangel of Special Forces' posts footage apparently taken by a Russian UAV of what it says is a Ukrainian USV off Kinburn. According to the channel, the Ukrainians have been launching an increasing number of attacks against Russian positions:
3/ "The footage shows one of two unmanned Ukrainian Armed Forces boats launched today from the Southern Bug River basin. The port of Mykolaiv was likely the launch site, given the size of the USV. The waters of the Southern Bug have not been used for a long time.
Yes, 740 now remaining. Thank you @HaraldThorvald1 🫡
Each Fiver pushes cars closer to the service.
Each Fiver pushes cars closer to the service.
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paypal.me/serhiirieznik
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send.monobank.ua/jar/7iRdML6ah8
💥 All options:
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paypal.me/serhiirieznik
🐈 Mono:
send.monobank.ua/jar/7iRdML6ah8
💥 All options:
di-ukraine-support.link
If you thought “Ozempic face” was bad, wait till you hear what it’s doing inside the body.
A massive study involving 16 million people found GLP-1 users had a 9.09 times greater risk of pancreatitis, 4.22 times greater risk of bowel obstruction, and a 3.67 times greater risk of stomach paralysis.
And if you’ve ever had pancreatitis, it is “quite a painful experience.”
What you’re hearing on the news about Ozempic is still too little, too late.
Here’s the story you’re not getting about Ozempic, the business model behind it, and why a growing number of researchers believe another pharmaceutical disaster is already unfolding in real time. 🧵
A massive study involving 16 million people found GLP-1 users had a 9.09 times greater risk of pancreatitis, 4.22 times greater risk of bowel obstruction, and a 3.67 times greater risk of stomach paralysis.
And if you’ve ever had pancreatitis, it is “quite a painful experience.”
What you’re hearing on the news about Ozempic is still too little, too late.
Here’s the story you’re not getting about Ozempic, the business model behind it, and why a growing number of researchers believe another pharmaceutical disaster is already unfolding in real time. 🧵
In early 2023, JP Morgan hosted its annual healthcare conference—a private, invitation-only event it describes as “the industry’s biggest gathering.”
The keynote speakers included the chairman of JPMorgan Chase, the CEO of Eli Lilly, and several managing directors of major healthcare venture capital firms.
The fourth keynote was Dr. Robert Califf.
His day job at the time: Commissioner of Food and Drugs for the United States Food and Drug Administration.
Hmm…
The keynote speakers included the chairman of JPMorgan Chase, the CEO of Eli Lilly, and several managing directors of major healthcare venture capital firms.
The fourth keynote was Dr. Robert Califf.
His day job at the time: Commissioner of Food and Drugs for the United States Food and Drug Administration.
Hmm…
This wasn’t a public health symposium. It wasn’t an academic conference.
It was specifically designed for large investors, and its explicit purpose was to set the pharmaceutical industry’s financial priorities for the year ahead.
A pharmaceutical safety advocate named Kim Witczak obtained what she could from the conference’s public-facing website.
But what was being said behind closed doors?
It was specifically designed for large investors, and its explicit purpose was to set the pharmaceutical industry’s financial priorities for the year ahead.
A pharmaceutical safety advocate named Kim Witczak obtained what she could from the conference’s public-facing website.
But what was being said behind closed doors?
@J_Todenhoefer Jelzin unterschrieb 1997, also ca.8J nach dem Interview, die "NATO-Russland-Grundakte (Founding Act)"
Darin stand:
"Respektierung der Souveränität und der inhärenten Freiheit aller Staaten, die Mittel zur Gewährleistung ihrer eigenen Sicherheit selbst zu wählen."
/1
Darin stand:
"Respektierung der Souveränität und der inhärenten Freiheit aller Staaten, die Mittel zur Gewährleistung ihrer eigenen Sicherheit selbst zu wählen."
/1
@J_Todenhoefer => freie Wahl des Verteidigungsbündnisses
Es ging um Polen,Ungarn und Tschechien
Jelzin war nicht begeistert, aber
er akzeptierte den politischen Deal
Es steht also ein Interview,das Verhandlungsbereitschaft signalisierte, gegen ein unterschriebenes, politisches Abkommen!
/2
Es ging um Polen,Ungarn und Tschechien
Jelzin war nicht begeistert, aber
er akzeptierte den politischen Deal
Es steht also ein Interview,das Verhandlungsbereitschaft signalisierte, gegen ein unterschriebenes, politisches Abkommen!
/2
I just launched a free tool that connects federal education datasets that have never been linked together visually before.
Every public school district in America. Spending, test scores, staffing, civil rights data, referendums — all in one place.
🧵
Every public school district in America. Spending, test scores, staffing, civil rights data, referendums — all in one place.
🧵
Education data in the US is scattered across a dozen federal agencies in incompatible formats.
Census F-33 for finances. NAEP for test scores. EdFacts for state assessments. CRDC for civil rights. CCD for staffing. IDEA for special ed.
No one has put them together — until now.
Census F-33 for finances. NAEP for test scores. EdFacts for state assessments. CRDC for civil rights. CCD for staffing. IDEA for special ed.
No one has put them together — until now.
What's included:
• District finance from Census Bureau (per-pupil spending, revenue sources, property taxes)
• Demographics from ACS 2022 (income, poverty, home values)
• NAEP scores (math & reading, grades 4 & 8)
• EdFacts proficiency trends (2009-2021)
• Staffing from Common Core of Data
• Civil rights discipline, absenteeism, restraint data from CRDC
• Special education rates from IDEA
• School referendum election results (WI, CO, WA, MN, IN)
• District finance from Census Bureau (per-pupil spending, revenue sources, property taxes)
• Demographics from ACS 2022 (income, poverty, home values)
• NAEP scores (math & reading, grades 4 & 8)
• EdFacts proficiency trends (2009-2021)
• Staffing from Common Core of Data
• Civil rights discipline, absenteeism, restraint data from CRDC
• Special education rates from IDEA
• School referendum election results (WI, CO, WA, MN, IN)