All Energy Consulting (All means all. Oil & Gas & Power & Renew & Emissions & BTC - yrs of real experience in all areas). Advising high net worth individuals🤘
Houston, TX
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It would be cool if you showed Temp - also perhaps annotated or mention every now and then where the rocket is relative to what is underneath it...e.g. over Africa etc...
Agree with Barclays - it would seem Project Jupiter will be a limited issues given the progress already made at the site and that there is a solution to the gas pipeline via federal route blog.synmax.com/vulcan-exclu…
zerohedge@zerohedgeUnited States
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Sept 24: Barclays
"[The Oracle Force Majeure] is neutral from a credit perspective, in our view, given the possible capex offsets, and we do not believe ORCL spreads should move in any meaningful way on the headline today."
Sept 25: ORCL CDS blows out +10 to a record 237bps
Perhaps it should but its not because of Project Jupiter blog.synmax.com/vulcan-exclu…
I agree with your sentiment. Common sense suggests that forecasting investments in multiple areas of the power and chip market leading to perfect investment timing is nearly impossible.
This is what we documented in the “Placing the Bust” paper we just wrote. SemiAnalysis is highly optimistic on the chip side. Advanced packaging likely will not resolve itself until they figure out CoPoS, which probably will not happen until the 2028–2030 period. 48005458.fs1.hubspotusercont…
How's domestic production going? Plus all the other generation outside natural gas... Cheniere 2.0 in China?
From a libertarian perspective, your analysis is not necessarily accurate without economic context. At these crack spreads, it would be insanity not to run the refinery to capacity; in fact, running over capacity is highly likely. Let's assume an export restriction occurs and the crack spread drops by half—saving the consumer about $30/bbl, or $0.70/gallon at the pump. The refinery is still highly incentivized to run all out with a 3-2-1 crack spread of over $30/bbl. For more context the last decade was $21/bbl.
The ability of the government to soft-land the spread is more likely the issue, and if you had addressed that, your statements would be more credible. Crude oil exports were restricted for decades. The ability of the domestic market to create value from using its own natural commodity is the real question. Can the economic multiplier grow more domestically, if given the ability to use the commodity, than the premium observed in selling externally? Can the government be trusted to measure that?
If they were explicit that an export restriction kicks in when 3-2-1 crack spreads are greater than $30/bbl (pick a number), then the market would probably go to that figure. As long as the number is high, the refineries will run!
Congress needs to form a committee and then a governance board. They need a Wesley Mouch to head this and get the top scientists to weigh in.
Seems like this supports your thesis
39% of U.S. data-center capacity planned for 2027 — about 14.1 GW — hasn’t cleared land.
If silicon keeps refreshing faster than the grid comes online, what happens to the GPUs waiting for power?
That’s the question behind our forthcoming white paper.
⚡Read the latest Vulcan report for a preview:
f.mtr.cool/po4dezd0f5
#DataCenters #AIInfrastructure #PowerMarketsResearch
Why then do industry leaders do stock buybacks rather than invest in expansion? Is not actually making the hbm more lucrative?
@grok looking back in time for team history plus using evaluating current fantasy points as an analysis of roster strength and comparing it last year's winning team...predict the NFL playoff teams along with who will win each stage to the final Superbowl champion
Please share the prompt...this is really bad? Did you tell it to research the last 10 yrs? Or was the prompt pick teams based on color and amount of letters?
@grok after the gain of 8.33 the remainder deposit in savings of 3% and calculate the effective comparison to the Treasury for the year.
@grok recalculate the return as the playoff starts before a whole year so the annualized return is greater than 8.33%
I absolutely believe in neuroplasticity. This has allowed me to cure my panic attacks with no medication. Discipline is key. Realize a fixed amount of suffering is needed. This supports one of my favorite Ralph Waldo Emerson quotes: “The only person you are destined to become is the person you decide to be.”
Excerpt from my soon to release report Placing the Bust where I use that graph: This dynamic carries a distributional consequence worth naming, because it shapes who captures the AI gains. If token demand keeps accelerating faster than efficiency and power supply can respond, the binding scarcity of energized compute pushes the marginal cost of AI up, not down, at least until the grid catches up years later. Rising compute costs are absorbed very differently across the economy. Hyperscalers and large corporations can sign 15-year power contracts, prepay for advanced-packaging allocation, land-bank substations, and vertically integrate into generation, as Section IV documents. Small and mid-sized businesses can do none of this; they buy AI at retail, on someone else’s terms, and are the first to be priced out when compute is scarce. The likely result is a concentration of AI capability in the largest firms rather than its broad diffusion.
That concentration cuts against the usual case for the technology. The optimistic story for AI is broad productivity gains, new business formation, and employment shifting toward higher-value work. But if scarce, expensive compute is disproportionately available to incumbents, the near-term effect can run the other way: large corporations use AI to automate and consolidate, capturing efficiency gains as margin and headcount reduction, while smaller competitors who might have used cheap, abundant AI to innovate and hire are locked out of the input. Scarcity that favors incumbents tends to reduce competition, slow the diffusion of innovation, and weigh on employment, the opposite of the broadly shared prosperity the buildout is often sold on. This is not a prediction the paper stakes its thesis on, but it is the socioeconomic shadow of the megawatt constraint: when the scarce input is controlled by those who can afford to hoard it, the gains concentrate, and the constraint becomes a moat.
AI compute demand is going parabolic and we need more compute than ever before (Save this).
OpenRouter’s chart shows weekly token usage growing 25 times in one year and doubling in the last month.
That means more demand for GPUs, memory, networking equipment, electricity, cooling systems and data center capacity.
The companies building this infrastructure could become some of the biggest beneficiaries of the AI boom.
CoreWeave and Nebius are two of the clearest examples because they provide rented GPU capacity to AI labs, developers and enterprises that cannot build enough infrastructure themselves.
CoreWeave is benefiting from strong demand from hyperscalers and AI companies that need access to GPUs quickly.
Nebius is also expanding rapidly as customers compete to secure GPU capacity, power, and data-center space.
NVIDIA benefits by selling the GPUs, while Micron and SK Hynix supply the HBM and DRAM that help those systems operate efficiently.
Arista and Broadcom benefit from the networking equipment required to connect thousands of GPUs, while Vertiv and Eaton provide power-management and cooling infrastructure.
The opportunity also extends to data center developers, utilities, construction companies, and equipment manufacturers.
The AI usage will continue to grow as models become cheaper, more capable and increasingly integrated into coding, research, customer service, and business automation.
When AI agents begin running continuously in the background of millions of users, inference demand will grow far beyond today’s chatbot usage.
That would turn compute into a recurring infrastructure expense and make access to GPUs and power even more valuable.
The companies with available GPUs, electricity, and data center capacity could maintain strong pricing power while the rest of the industry races to keep up.
If you enjoyed reading this, make sure to follow @MelvinInvests for more AI infrastructure insights and if you want to see exactly what I'm buying as an analyst at Milk Road Pro, check out the link below.
It's relative too. Let's compare this to other economic drivers such as a strip mall, steel factory, refinery, office building, gas station, power plant, etc... Unless the goal is to keep everything the same and have no economic progress? Hyperscaler data centers are built in building expansion. City developers can have them build out in expansion increments with each step being investigated.
To me, Loudoun County is the perfect example of why data centers aren't as bad as critics claim. If the naysayers were right, Loudoun would be a wasteland full of broke, exhausted residents. The reality is the exact opposite, and it proves there is a right way to handle this kind of development.
Most counties are looking at projects that are a fraction of Loudoun's massive 7 GW capacity. The median proposed size is only around 300 MW. A county doesn't have to jump straight into gigawatt territory; they can easily start with a smaller facility and scale up from there.
Exercising caution shouldn't mean throwing the entire concept out just to appease alarmists. True leadership sometimes requires making decisions that aren't popular right away, much like a parent making kids eat their vegetables. As long as there is proper planning and oversight, data centers can bring massive long-term value to a local community.
Probably the strongest pro-data center case in the US is Loudoun County, VA. The fiscal numbers are real but it is also an outlier case.
Loudoun says data centers sit on ~4% of commercial parcels and generate 38% of General Fund revenue. FY27 budget has the industry closer to ~$1.3B, or ~45% of local taxes.
Loudoun taxes the equipment inside the buildings at $4.15 per $100 of assessed value, with a 60/45/30/15/10/5% depreciation schedule. JLARC, using older FY23 data, found mature Virginia markets ranged from <1% to 31% of local revenue from data centers - Loudoun was the 31%.
There are limits to all the positive externalities (which doesn't mean they are negatives too btw). JLARC estimates a typical 250k sq ft facility supports ~50 full-time workers vs ~1,500 at peak construction, and a typical Dominion-system residential customer could see +$14-37/month of generation and transmission costs by 2040. Loudoun also ended by-right approvals in March 2025 over siting, noise and infrastructure, which I find interesting given it's the pinnacle example.
To be transparent, the clip itself is advocacy. Innovation Council Action is a pro-Trump, pro-AI 501(c)(4); Axios reported plans for $100M+ of political spending in 2026. The speaker is a Loudon resident, though. We need voices on the ground speaking their truth and we should listen to them.
I’d use Loudoun as evidence of how valuable data centers can be when a locality actually captures the tax base. I would not use it as evidence that the next 300 MW project in a different tax code, power market, and greenfield site will look always like Loudoun.
There’s enough real economic upside here that I don’t think the pro-data center case needs exaggeration. The better argument is the factual one: these projects can bring enormous investment and tax revenue, but the benefits depend heavily on tax structure, power costs and where/how they’re built. Here for communities and partners (labs, hypers, sponsors) that do this the right way and quickly - we need more of them!