Oh, I see. From .ml too. Does supremacy over “tankies” provide you material wealth? Can you see how your empire’s pursuit of supremacy will require command over AI that must stiffle dissent over AI, not just because you don’t support their nazi military supremacy, but AI could target you for not supporting your economic ruin, and greater wealth concentration instead?

Iran is already at maximum sanctions, and all of its trading partners (especially border states) use local financing/cash completely seperate from all US financial systems or sanctions reach. All national scale/SWIFT access banks already avoid Iran trade involvement.

Targetting ships that “will only in future” help Iran oil flows, can only decrease future oil flows, but only if they don’t need oil in short term and keep doing it, but also just serve Iran oil in the future, and get cut off from all other oil.

There’s $2.5T of insured value on the 5 year, I believe. That is $90b in premiums paid per year to insure against something that won’t happen in 5 years. It’s a bit of a puzzle why they have such high prices/volume for insurance with 0 payout probability, but some ideas.

1. bank magic regulatory compliance, maybe combining leverage with CDS to count as unlevered “safe capital”
2. The insurance kicks in for any “US credit event”. All CDS contracts on Russian bonds kicked in because Europe wouldn’t take payments. So something small can have a big payoff if US rates are under high pressure 5 years later.

More CDS fun facts: 30 year bond CDS which seem to have a 50/50 chance of 2056 collapse/reorganization of the US cost under 0.5% per year, and have very low comparative total size of $300B. Japan’s CDS for same duration are actually cheaper than US despite no constitutional/other rules preventing default as an option, and they are tighter in a corner with few good options.

In addition to your list, Dodd Frank includes a section where “All derivative payouts are hereby cancelled” presidential power. So the answer to the puzzle is that the same BS collateral rules/tricks that caused AIG to go bankrupt exist for US CDS, but not as much for JGB CDS. For one, you can sell CDS on US treasuries, while putting up US treasuries (in small fraction) as collateral against a US default which could make the collateral value as low as 0. Low interest in 30 year CDS can only be because of this absurdity that every other modern AIG would be unable to pay.

There is a lot of rich US colonies who export and import a lot, as the only viable path. KSA can go to red sea, but to deliver to asia, They would avoid “Houti controlled straight” and go suez then around Africa. Very long trip.

Im no saxophologist, but doesnt the sound come out of the buthole?

what do 4 small stars on flag represent?

The need for more GPUs/datacenters was based on xAI using them for their own models, instead of competing with new datacenters/GPUs in the rental market. GPU rental prices went down to below cost levels for H series after Anthropic deal. It has bounced back this last week, but rates are still in the “very well supplied” range. On demand rates half of Anthropic/Google rental deals. GPU rental rates are the best measure of AI bubble point. Concerted fraud is still possible to provide the illusion of “this is fine”. The google deal only starts in September, while Anthropic got some free GPU time when deal was announced.

In a way, pumping more money into AI (through IPO) delays the bubble pop, even if NVidia doesn’t sell as many GPUs if their customers add them to rental market. Bubble can pop from 3 directions. Less GPUs/TPUs sold than forecast; datacenter overcapacity ; understanding poor economics/tokenomics of high competition LLM labs.

The sad part is that the more money invested, the greater the funding for the fraud, and longer lasting fraud.

There are signs that IPO is too big for market. 30% retail, and $135/share fixed price are because demand is not present. The illegal private/SPV market is trading below $129, where SPV entity that was not a fraud about to go bankrupt, and confident in the IPO would buy back shares up to close to $135.

This BS by google is desperation to pump their existing stake. Anthropic’s headline $1.25B/month deal is also a fraud. Big discounts until after the IPO, and same cancellation rights.

xAI got a made up (no one putting cash in) $250B valuation in its merger. Including Twitter it has barely any revenue, and without having to rent gpu datacenter space, was still losing $10b/year for a not particularly competitive Grok model.

What’s withholding SpaceX from selling more stock with an even shorter time until they can be dumped?

97% of the stock is already issued. 3% goes to new IPO bagholders, shortly later, passive index forced buying from the 3% market. The 97% are the ones with accelerated dump rights, that kick in a percentage shortly after fraudulent accelerated index inclusion date.

very hard graph to follow but “popular music” means highest rated radio pop station. Nothing alternative.

Don’t understand graph. is dark line what people think of songs released 11 years ago? WTH is graph before age 0? Oh ok, maybe. A song released when you were 40 is rated the same as one released 45-80 years earlier.

There is huge bias in “popular music” category. Can get very sick of old songs, or just stop listening to contemporary pop after a while. I’m surprised old people would still like pop songs of their youth, after being overplayed over the years.

There is a lot of hate. First 2 paragraphs are not AI. Rest just supports those statements and headline.

There is missing physics background. Only radiative cooling is available in space. Hot temperature chips would cool more easily, but the materials don’t permit 3nm or less state of the art chips, and only permit pentium 1/1993 technology. Though regular silicon, can be cooled in space by spraying a mist of oil towards another wall, and recycled back to pump through chips.

The technological/insurmountable economic problem is chip obsolescence vs power generator life. Discussion of economic viability of space datacenters is fraud. It’s a fraud used to pump the stock of SpaceX both in its xAI merger, and now its IPO.

Definitely military as the only funding source. UK also had pirate radio from a boat in 70s. I was thinking of Seasteading institute (Peter Thiel) as a “freedom colony”.

The $55.5m costs includes $30m in launch costs from SpaceX future “starship” that promises $200/kg costs on 150 ton capacity. This assumes airliner like reusability ($15B development costs so far), despite need to manually inspect heat shield tiles after each flight, and 33 engines operating at material science limit. Test flights continue to have glitches. Currently grounded pending FAA compliance.

AI used to present math proof, but fraud claim is critical. Sealand as “freedom escape for humans” is a very different proposition than “AI must survive us all”. Latter is Skynet protection from someone pulling the plug, which no matter how much one loves Elon today, perhaps there could be regrets after mechahitler controls us all.

More precise pricing trends from premium tier 2 networks, show demand has drastically fallen over the quarter. H200 very close to its bare runcosts. Theory is that Anthropic’s overpayment for Collosus 1 (xAI) capacity has drastically reduced utilization at cloud rental service.

NVIDIA B200 Blackwell (192GB HBM3e)

• Tier-1 Spot Mechanic: AWS introduced B200 spot availability (p6 family baseline). However, because Tier-1 spot pools are subject to extreme automated reclamation, they command a rigid premium.
• The Rebound: B200 spot experienced a localized surge around March 13th due to massive API volume peaks. 

NVIDIA H200 Hopper (141GB HBM3e)

• The Clearance Reality: Unlike the B200, the H200 has failed to rebound. Because its FP8 processing layout lacks Blackwell’s native execution upgrades, developers are abandoning the H200 for production serving.
• Below Cost: Tier-2 spot has flatlined at $1.45/hr, forcing providers to eat losses relative to raw facility overhead just to prevent expensive liquid-cooled spaces from sitting entirely dark.

A weird point that Nvidia CFO made to say “Nvidia is awesome” is a claim that GPU rental rates are up year to date. There was a crash at end of 2025. The low for the quarter was Jan 1st. The high was March 10th at peak of openclaw frenzy (validated by openrouter charts). Current rates are lower than that peak. But also comparison to 2025 Q1 (what I thought CFO meant, rates are down significantly) For single GPUs.

1. NVIDIA A100 (Ampere — 80GB SXM)

• Q1 2025 Baseline: High: $2.40 | Low: $1.60 | Close: $1.85
• Q1 2026 Window: High: $1.65 | Low: $0.80 | Close: $1.15
• Current Normalized Rate: ~$1.07 / hr (Stable floor; primary use shifts to entry-level fine-tuning and quantized serving). 

2.[NVIDIA H100 (Hopper — 80GB SXM)

• Q1 2025 Baseline: High: $7.00 | Low: $5.50 | Close: $5.80 (Supply constraints started easing, down from the absolute peak $10/hr overcharges of late 2024).
• Q1 2026 Window: High: $3.45 | Low: $1.70 | Close: $2.35 (Hit an absolute low floor of $1.70 in late 2025 before a 38% contract rebound in March due to an influx of video-generation workloads).
• Current Normalized Rate: ~$2.49 / hr (The standard baseline workhorse for mainstream API serving). 

3. [NVIDIA H200 (Hopper — 141GB HBM3e)

• Q1 2025 Baseline: High: $5.20 | Low: $4.50 | Close: $4.80 (Extremely scarce; reserved exclusively for elite labs running early frontier training).
• Q1 2026 Window: High: $4.40 | Low: $3.50 | Close: $3.80 (Inventory stabilized as neoclouds widely deployed HGX baseboards).
• Current Normalized Rate: ~$3.39 / hr (The most cost-effective tier for high-concurrency FP8 deployment). 

4. [NVIDIA B200 (Blackwell — 192GB HBM3e)

• Q1 2025 Baseline: N/A (Sampling/Testing phase; unreleased to the public marketplace).
• Q1 2026 Window: High: $6.11 | Low: $3.05 | Close: $4.95 _(Initial public availability; premium pric

5. NVIDIA B300 (Blackwell Ultra — 288GB HBM3e)

• Q1 2025 Baseline: N/A (In architectural development; unavailable for rental).
• Q1 2026 Window: High: $8.50 | Low: $5.50 | Close: $7.25 (Early access provisioning; highly volatile due to constrained data center site capacity).
• Current Normalized Rate: ~$6.10 / hr (Neocloud standard rate; pricing reflects the premium for its 288GB memory pool). 

for clusters, google AI mode simply can’t provide accurate info. Some providers have fixed premiums, others 0 premium. Many never change prices but mass email promotional discounts. For all I know, this entire analysis could have been a halucination meant to drive my narrative. I have not verified most data claims made as it would be too much work. I imagine most of the specific ones are accurate, and single GPU rental rates are the dominant market in the US, and that data should be solid, but FIIK.

Yet another big problem for Nvidia is that the H200 is their better product for FP8 mainstream LLM service. Vera-Rubin only has 30% more performance per watt, gb200/300 is lower performance/watt at fp8. But the big expense of all its later generations is liquid cooling, and the extreme weight of liquid cooled racks/NVL72 (3000lbs) that require ultra strong floors with embedded pipes inside them. In yet another F’d up supply chain crisis driven by AI is a 2 year backlog for liquid cooling equipment.

Only 2.15gw (out of 5gw) of global datacenters under active construction with hope for 2026 completion is for Nvidia hardware. If there is already high excess inventory (not guaranteed as result of hand me down GPU replacement) then sales/growth must hit a wall eventually. Next 9 months of optimistic deployments is more than next quarters sales forecast.

The surplus sales are actually heavily underestimated because the datacenter capacity additions for 2025/26 include non Nvidia hardware. It “appears” that under half of their sales actually make it into datacenter capacity additions.

1. Stripping Non-NVIDIA Slices from the Available GW Grid

To see the true depth of the backlog, we have to look at how much of that newly brought-online data center capacity was immediately consumed by alternative architectures during the 2025 calendar year (4.10 GW total online) and Q1 2026 (1.55 GW total online).

A. The Hyperscaler Internal Custom Silicon Tax (ASICs)

The largest tech giants do not deploy NVIDIA exclusively. They heavily prioritized their own lower-cost, custom-tailored accelerator chips to handle their native workloads:

• Google TPUs (v5p & v6e): Google directed a massive portion of its internal data center buildouts to its proprietary Tensor Processing Units. Throughout 2025 and early 2026, TPU deployments swallowed roughly 450 Megawatts (MW) of Google’s net-new global capacity.
• Meta MTIA & Amazon Trainium/Inferentia: Meta scaled its internal MTIA silicon, while AWS aggressively expanded its Trainium2 clusters. Combined, these internal hyperscaler projects consumed an estimated 300 MW of online grid space across the two periods.

B. The AMD Alternative Squeeze

AMD’s MI300X and MI325X series secured massive enterprise and cloud traction, specifically anchoring flagship clusters inside Microsoft Azure and Oracle Cloud Infrastructure (OCI). AMD’s total shipment footprint accounted for roughly 400 MW of power demand globally over this timeframe.

C. Specialized Wafer-Scale Architectures (Cerebras)

While smaller in pure megawatt terms compared to hyperscalers, Cerebras built massive high-density footprints. Their multi-million dollar wins—such as the massive 750 MW master deployment framework with OpenAI—began systematically occupying high-density colocation space. Across 2025 and Q1 2026, Cerebras deployments locked down roughly 100 MW of specialized, high-cooling capacity.

2. Recalculating the True NVIDIA “Space Deficit”

When we subtract these non-NVIDIA hardware deployments from the total physical data center capacity brought online, we find the Net Grid Space Actually Available for NVIDIA:

Now, let’s remap this accurate “Available Space” baseline against the True Grid Power Shipped by NVIDIA (GW Sold) that we calculated using our refined financial models:

The Compounding Backlog Realities

• The Refined 2025 Gap: NVIDIA shipped 5.37 GW of compute power. If only 3.00 GW of real-world grid space was actually left over for them after accounting for Google TPUs and AMD chips, the real 2025 data center deficit jumps from 1.27 GW to a staggering 2.37 GW.
• The Refined Q1 2026 Gap: NVIDIA shipped 2.11 GW of compute power. With only 1.30 GW of net data center capacity available to absorb it, the quarterly deficit widens from 560 MW to 810 Megawatts.

NVIDIA’s customers are legally and contractively allowed to sell their excess, undeployed GPUs, but they face strict operational and geopolitical boundaries. While a thriving secondhand market exists for data center-grade enterprise hardware, the transfer of undeployed silicon is heavily restricted by US export control laws, proprietary software licensing terms, and indirect pressure from NVIDIA’s allocation system. 

Given the massive multi-gigawatt data center power logjam, companies holding excess physical cards cannot simply flip them on an open marketplace without navigating severe friction.

1. Legal and Contractual Restrictions

While NVIDIA cannot explicitly block a customer from selling physical hardware they own, they heavily restrict the transaction through auxiliary legal layers: 

• The U.S. Export Control Wall: The US government enforces massive civil and criminal penalties (up to $1 million and 20 years imprisonment) for unauthorized GPU transfers. If a buyer sells an elite card (like an H100, H200, or Blackwell unit) to an unvetted domestic entity that subsequently exports it to a restricted destination, the original seller remains legally liable for the “taint” of an export violation.
• The Software License Blackout: NVIDIA’s hardware relies completely on its NVIDIA Software License Agreement and CUDA ecosystem to function. Under standard enterprise terms, the high-value software, firmware updates, and optimization tools are strictly non-transferable and cannot be resold or sublicensed without explicit written permission. A secondary buyer risks receiving “brickable” or unoptimizable hardware without official enterprise support paths.
• The Non-Disclosure (NDA) Shield: Many tier-1 hyperscalers and elite partners sign strict master purchase agreements under NDAs that expressly prohibit standard public reselling channels or specify that hardware can only be retired through pre-approved, certified remarketing vendors. 

2. The Relationship Risk (The Allocation Punishment)

The single greatest deterrent against selling excess GPUs is not a legal document, but the fear of losing priority allocation status with NVIDIA.

Because demand for high-end architectures like the GB200 NVL72 heavily outstrips supply, NVIDIA’s management dynamically controls who receives hard-to-source chips. If a cloud provider or tier-2 operator is caught flipping unused hardware on the secondary market for a short-term cash injection, NVIDIA can simply move that customer to the bottom of the multi-quarter waitlist for the next hardware cycle.

3. Alternative Strategies: Wholesale Cloud Brokering

Instead of physically unboxing and reselling a pallet of undeployed GPUs, companies trapped by the power grid deficit leverage a much cleaner loophole: Wholesale Cloud Computing. 

Rather than selling the physical chip, the company holding the “stranded capital” hardware will quickly install it in a temporary, third-party colocation space or drop it into a partner facility. They then lease out the raw compute via virtualized wholesale contracts to other hyperscalers or neoclouds. This effectively monetizes the unutilized silicon, offloads the physical constraints, and completely bypasses the legal headaches of hardware title transfers, export oversight, and software registration breaks