This media is not supported in your browser
VIEW IN TELEGRAM
Nancy Pelosi's net worth before Congress:$785,000
After Congress:$133,000,000
Truly the greatest (Insider) trader of all time.
After Congress:$133,000,000
Truly the greatest (Insider) trader of all time.
💯1
🇨🇭 Global One SI - Prompt
$MU/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
Micron $MU now has a larger market cap than Meta Platforms $META
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
This media is not supported in your browser
VIEW IN TELEGRAM
🚨🇺🇸 US just named its real red line with Iran...
It's the Strait of Hormuz.
Secretary of State Marco Rubio says he wants a final Iran deal that guarantees freedom of navigation through Hormuz.
He said it on the final leg of a Gulf tour aimed at reassuring regional allies.
This is the tell on what Washington actually cares about.
Keep the oil moving.
The deal isn't only about Iran's program, it's about the waterway.
For the market, that's a stabilizing signal.
The US is anchoring any agreement to an open Hormuz.
Open passage written into a deal means the closure threat fades from a recurring risk to a settled term.
If navigation becomes the sticking point, the whole deal stalls on it.
It's the Strait of Hormuz.
Secretary of State Marco Rubio says he wants a final Iran deal that guarantees freedom of navigation through Hormuz.
He said it on the final leg of a Gulf tour aimed at reassuring regional allies.
This is the tell on what Washington actually cares about.
Keep the oil moving.
The deal isn't only about Iran's program, it's about the waterway.
For the market, that's a stabilizing signal.
The US is anchoring any agreement to an open Hormuz.
Open passage written into a deal means the closure threat fades from a recurring risk to a settled term.
If navigation becomes the sticking point, the whole deal stalls on it.
✍1
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
🚨MASSIVE: The world's biggest miner just tried to merge with a trading giant and failed.
Now it's partnering with one of the biggest names in oil instead.
Bloomberg reports Rio Tinto is in early talks with Vitol over a freight and logistics joint venture.
🎯The goal: cut Rio's shipping costs and sharpen how it manages freight risk.
One area under discussion is Vitol providing risk management tools, including freight derivatives trading.
Notably, the venture would not involve Vitol marketing Rio's iron ore or copper.
This is about logistics, not selling the metal.
The context makes it bigger than it looks.
In February, Rio walked away from a $200 B+ merger with Glencore.
That deal would have turned Rio into a trading house.
It collapsed over value terms.
So this is Plan B.
Bolt a world-class trading brain onto Rio's flows, without becoming Glencore.
Vitol moves massive volumes of oil, gas, and coal, with elite freight and derivatives know-how.
(The caveat: it's early-stage and unconfirmed. Both companies declined to comment, and no deal is signed.)
Now it's partnering with one of the biggest names in oil instead.
Bloomberg reports Rio Tinto is in early talks with Vitol over a freight and logistics joint venture.
🎯The goal: cut Rio's shipping costs and sharpen how it manages freight risk.
One area under discussion is Vitol providing risk management tools, including freight derivatives trading.
Notably, the venture would not involve Vitol marketing Rio's iron ore or copper.
This is about logistics, not selling the metal.
The context makes it bigger than it looks.
In February, Rio walked away from a $200 B+ merger with Glencore.
That deal would have turned Rio into a trading house.
It collapsed over value terms.
So this is Plan B.
Bolt a world-class trading brain onto Rio's flows, without becoming Glencore.
Vitol moves massive volumes of oil, gas, and coal, with elite freight and derivatives know-how.
(The caveat: it's early-stage and unconfirmed. Both companies declined to comment, and no deal is signed.)
A single AI data center swallows 27,000 tonnes of COPPER.
The AI boom is a raw-materials supercycle.
One 1-gigawatt AI data center contains 25 minerals and 179,940 tonnes of material.
Steel dominates by mass at 132,660 tonnes, a staggering 73.8% of the total.
But copper is the one to watch.
27,212 tonnes per facility, 15.1% of the build.
Aluminium follows at 14,320 tonnes, with graphite and nickel filling out the heavy metals.
Then come the transition metals.
Cobalt, silicon, lithium, tin, and lead.
The top 10 alone account for 99.7% of all the mass.
And that's before the Rare earths, gallium, germanium, tantalum, and indium are all in there too.
Now multiply this by the hundreds of data centers being built worldwide.
Every gigawatt of AI compute means tens of thousands of tonnes of copper pulled from the ground.
This is why copper miners are the quiet kingmakers of the AI era.
The model needs metal before it needs intelligence.
The AI boom is a raw-materials supercycle.
One 1-gigawatt AI data center contains 25 minerals and 179,940 tonnes of material.
Steel dominates by mass at 132,660 tonnes, a staggering 73.8% of the total.
But copper is the one to watch.
27,212 tonnes per facility, 15.1% of the build.
Aluminium follows at 14,320 tonnes, with graphite and nickel filling out the heavy metals.
Then come the transition metals.
Cobalt, silicon, lithium, tin, and lead.
The top 10 alone account for 99.7% of all the mass.
And that's before the Rare earths, gallium, germanium, tantalum, and indium are all in there too.
Now multiply this by the hundreds of data centers being built worldwide.
Every gigawatt of AI compute means tens of thousands of tonnes of copper pulled from the ground.
This is why copper miners are the quiet kingmakers of the AI era.
The model needs metal before it needs intelligence.
This media is not supported in your browser
VIEW IN TELEGRAM
"If you really want to make money, found an agentic AI company.
I mean, build an agent to do something. This is the agentic period in AI. Everyone's going to build agents. The agents are all going to compete."
~ Eric Schmidt, Ex Google CEO.
I mean, build an agent to do something. This is the agentic period in AI. Everyone's going to build agents. The agents are all going to compete."
~ Eric Schmidt, Ex Google CEO.
✍1
🇨🇭 Global One SI - Prompt
$IBM just FANTASTIC #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
$IBM debuts world’s first sub-1 nanometer chip.
They just announced a 0.7nm chip technology.
(0.7nm is about 100,000 times thinner than a human hair, only a few atoms across as a technology marker,)
It stops treating smaller chips as a flat-layout problem and starts stacking transistors upward through a new nanostack architecture.
A chip node no longer means every feature has that exact size, but 7 angstroms means IBM is pushing transistor design into a scale where individual atoms start to shape the engineering limits.
Traditional scaling packs more switches side by side, while nanostack vertically staggers nanosheet transistors, letting each layer use different materials for speed or lower power instead of forcing one transistor recipe across the chip.
IBM says the design can fit nearly 100B transistors on a fingernail-sized chip, deliver up to 50% more performance or 70% better energy efficiency than its 2nm node, and shrink SRAM by 40%, which is huge because AI chips constantly wait on memory.
Practically it means future chips that can run bigger AI models, phones, laptops, servers, and cloud systems faster while using much less power, because more compute and memory can fit into the same tiny chip area.
They just announced a 0.7nm chip technology.
(0.7nm is about 100,000 times thinner than a human hair, only a few atoms across as a technology marker,)
It stops treating smaller chips as a flat-layout problem and starts stacking transistors upward through a new nanostack architecture.
A chip node no longer means every feature has that exact size, but 7 angstroms means IBM is pushing transistor design into a scale where individual atoms start to shape the engineering limits.
Traditional scaling packs more switches side by side, while nanostack vertically staggers nanosheet transistors, letting each layer use different materials for speed or lower power instead of forcing one transistor recipe across the chip.
IBM says the design can fit nearly 100B transistors on a fingernail-sized chip, deliver up to 50% more performance or 70% better energy efficiency than its 2nm node, and shrink SRAM by 40%, which is huge because AI chips constantly wait on memory.
Practically it means future chips that can run bigger AI models, phones, laptops, servers, and cloud systems faster while using much less power, because more compute and memory can fit into the same tiny chip area.
Goldman Sachs Research: "Token use by AI agents is expected to multiply 24 times by 2030"
AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.
A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop.
That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.
Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year.
The fight is now between agent productivity and token waste.
Earlier this month, Microsoft began revoking developer access to Claude Code, with plans to move them to its in-house Copilot Command Line Interface tool by June 30. The company has framed this as consolidating teams around its own tools, but the timing at the fiscal year’s end hints it may also be about lowering costs.
AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.
A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop.
That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.
Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year.
The fight is now between agent productivity and token waste.
Earlier this month, Microsoft began revoking developer access to Claude Code, with plans to move them to its in-house Copilot Command Line Interface tool by June 30. The company has framed this as consolidating teams around its own tools, but the timing at the fiscal year’s end hints it may also be about lowering costs.
This media is not supported in your browser
VIEW IN TELEGRAM
Former Goldman Sachs executive Raoul Pal explains how AI is going to eat traditional software/SAAS.
If your product is just software, agentic AI can reproduce it on demand, optimize it, and redeploy it to a better market.
"Agentic AI means it’s like having Fiverr, a website of experts you can ask any question. It’ll go away and do the task.... Agentic AI will build, design the website, code it, register the domain name, figure out the branding, figure out the marketing, figure out the email list, figure out the whole thing.
So then you and I are in competition. You’ve built this incredible new website.
I just go to my AI and say, “Love Steven’s website. Can you just build it better. Boom. 3 minutes.
How can we be entrepreneurs in software? Now there’s this theory going around that AI is going to eat software, and I kind of get it."
If your product is just software, agentic AI can reproduce it on demand, optimize it, and redeploy it to a better market.
"Agentic AI means it’s like having Fiverr, a website of experts you can ask any question. It’ll go away and do the task.... Agentic AI will build, design the website, code it, register the domain name, figure out the branding, figure out the marketing, figure out the email list, figure out the whole thing.
So then you and I are in competition. You’ve built this incredible new website.
I just go to my AI and say, “Love Steven’s website. Can you just build it better. Boom. 3 minutes.
How can we be entrepreneurs in software? Now there’s this theory going around that AI is going to eat software, and I kind of get it."
✍1
This media is not supported in your browser
VIEW IN TELEGRAM
“Dad I don’t think I want to gamble my money away on meme and look at charts all day”
Methode Electronics $MEI - The Overlooked Hidden Champion in Automotive, Cloud & Industrial Electronics
With a market cap of approximately $320 million, well below its $1.0 billion in fiscal 2025 sales, Methode Electronics is a true hidden champion that most investors completely miss.
Founded in 1946, Methode is a leading global supplier of custom-engineered solutions for user interfaces, LED lighting systems, power distribution and sensor applications.
The company serves some of the world’s biggest OEMs across transportation, cloud computing infrastructure, construction equipment and more.
Massive Blue-Chip Customer Portfolio
Methode’s customer list reads like a who’s who of global industry:
Automotive & Commercial Vehicles:
BMW, GM, Ford, Volkswagen, Mercedes-Benz, Toyota, Tesla, Stellantis (FCA), Jaguar Land Rover, Volvo, Scania, Peterbilt, Freightliner, Kenworth, Mack, MAN, Caterpillar and many more.
Aerospace & Defense: Boeing, Lockheed Martin, United Technologies, RTX.
Electronics & Cloud: Cisco, Dell, Hewlett Packard Enterprise, Juniper Networks, Alcatel, Extreme Networks.
Industrial & Medical: Bosch, Schaeffler, Magna, Yamaha, Alstom, plus major hospital systems including Cleveland Clinic, MD Anderson Cancer Center and Northwestern Memorial Hospital.
7,200 employees worldwide with strong engineering depth.
VDesigns, tests and manufactures almost everything in-house, extremely independent from third-party suppliers.
More than 35 locations across North America, Europe, Middle East and Asia.
Diversified revenue: While automotive makes up the majority, $MEI is deeply embedded in high-growth areas like cloud/AI server power & data connections, construction machinery lighting and medical technology.
With a market cap of approximately $320 million, well below its $1.0 billion in fiscal 2025 sales, Methode Electronics is a true hidden champion that most investors completely miss.
Founded in 1946, Methode is a leading global supplier of custom-engineered solutions for user interfaces, LED lighting systems, power distribution and sensor applications.
The company serves some of the world’s biggest OEMs across transportation, cloud computing infrastructure, construction equipment and more.
Massive Blue-Chip Customer Portfolio
Methode’s customer list reads like a who’s who of global industry:
Automotive & Commercial Vehicles:
BMW, GM, Ford, Volkswagen, Mercedes-Benz, Toyota, Tesla, Stellantis (FCA), Jaguar Land Rover, Volvo, Scania, Peterbilt, Freightliner, Kenworth, Mack, MAN, Caterpillar and many more.
Aerospace & Defense: Boeing, Lockheed Martin, United Technologies, RTX.
Electronics & Cloud: Cisco, Dell, Hewlett Packard Enterprise, Juniper Networks, Alcatel, Extreme Networks.
Industrial & Medical: Bosch, Schaeffler, Magna, Yamaha, Alstom, plus major hospital systems including Cleveland Clinic, MD Anderson Cancer Center and Northwestern Memorial Hospital.
7,200 employees worldwide with strong engineering depth.
VDesigns, tests and manufactures almost everything in-house, extremely independent from third-party suppliers.
More than 35 locations across North America, Europe, Middle East and Asia.
Diversified revenue: While automotive makes up the majority, $MEI is deeply embedded in high-growth areas like cloud/AI server power & data connections, construction machinery lighting and medical technology.
🇨🇭 Global One SI - Prompt
$QMCO Every new datacenter, AI model and compute cluster ultimately depends on one thing: Power — and the efficient, tiered data storage infrastructure needed to ingest, manage, and archive massive unstructured datasets without blowing up costs or energy…
$QMCO ($190M) represents a high-potential opportunity in the AI infrastructure landscape because it directly addresses the critical bottleneck of modern data centers: data delivery speed.
As artificial intelligence models scale exponentially, advanced GPUs require massive pipelines of data to operate efficiently, making high-performance storage the literal backbone of the current AI boom.
Quantum Corp solves this "GPU starvation" problem by offering an end-to-end data lifecycle solution.
They combine ultra-fast NVMe flash storage (Myriad) for active, heavy AI model training with highly cost-effective tape systems for massive, long-term archival data retention.
Their modern, software-defined architecture allows enterprises and data centers to scale storage capacity and processing performance independently and dynamically without experiencing any system downtime.
The company's immense commercial potential is heavily validated by its deep strategic integration with the world's most dominant technology leaders.
Most notably, Quantum’s Myriad All-Flash architecture natively integrates with $NVDA GPUDirect Storage (GDS), which bypasses the CPU entirely to pump datasets directly into NVIDIA's high-end GPUs, drastically maximizing chip utilization.
This crucial synergy carries over to $SMCI, where $QMCO provides the high-parallelism storage backends needed to prevent data bottlenecks within Supermicro’s rapidly expanding, expensive GPU server clusters.
Furthermore,$QMCO software deeply integrates with $MSFT Azure, enabling seamless hybrid-cloud pipelines that bridge local enterprise data with Azure's cloud-based AI tools.
On the corporate enterprise front, long-standing relationships with $DELL and $IBM allow $QMCO to weave its robust storage and tape architectures directly into global enterprise infrastructures, helping large organizations efficiently archive, manage, and secure the vast amounts of unstructured data required for next-generation AI workflows.
As artificial intelligence models scale exponentially, advanced GPUs require massive pipelines of data to operate efficiently, making high-performance storage the literal backbone of the current AI boom.
Quantum Corp solves this "GPU starvation" problem by offering an end-to-end data lifecycle solution.
They combine ultra-fast NVMe flash storage (Myriad) for active, heavy AI model training with highly cost-effective tape systems for massive, long-term archival data retention.
Their modern, software-defined architecture allows enterprises and data centers to scale storage capacity and processing performance independently and dynamically without experiencing any system downtime.
The company's immense commercial potential is heavily validated by its deep strategic integration with the world's most dominant technology leaders.
Most notably, Quantum’s Myriad All-Flash architecture natively integrates with $NVDA GPUDirect Storage (GDS), which bypasses the CPU entirely to pump datasets directly into NVIDIA's high-end GPUs, drastically maximizing chip utilization.
This crucial synergy carries over to $SMCI, where $QMCO provides the high-parallelism storage backends needed to prevent data bottlenecks within Supermicro’s rapidly expanding, expensive GPU server clusters.
Furthermore,$QMCO software deeply integrates with $MSFT Azure, enabling seamless hybrid-cloud pipelines that bridge local enterprise data with Azure's cloud-based AI tools.
On the corporate enterprise front, long-standing relationships with $DELL and $IBM allow $QMCO to weave its robust storage and tape architectures directly into global enterprise infrastructures, helping large organizations efficiently archive, manage, and secure the vast amounts of unstructured data required for next-generation AI workflows.