🇨🇭 Global One SI - Prompt
The world's largest data centers tell you what fuels them. China Telecom + China Mobile: both in Hohhot, Inner Mongolia ...China's coal heartland. US: Nevada, Utah, Atlanta, Chicago mainly gas. US AI runs on gas. Chinese AI runs on coal. The map is the…
🚨This AI data center in Utah produces the thermal energy of 23 atomic bombs ─ EVERY DAY!
It spans over 40,000 acres.
It spans over 40,000 acres.
This media is not supported in your browser
VIEW IN TELEGRAM
Muscle-Computer Interface + Robot Hand: Entering the Cyborg Era
This carbon-fiber bionic hand--the PSYONIC Ability Hand doesn’t just give amputees near-natural dexterity. It’s already being used on Apollo, AEON, and other humanoid robots for real tasks, making true human-machine shared use a reality.
Now, the Ability Hand has taken another huge leap: near thought-controlled operation.
Thanks to Phantom Neuro, an Austin-based startup, their Muscle-Computer Interface (MCI) makes it possible.
With a quick 15-minute minimally invasive outpatient procedure, tiny sensors are implanted under the skin into the residual limb muscles.
These sensors wirelessly read muscle electrical signals (EMG) in real time and translate your intention into precise actions--gripping, rotating the wrist, pinching, and even flipping the middle finger.
Even better: you can still wirelessly control devices even when the hand is removed.
Compared to Neuralink’s brain implants that require skull surgery, Phantom Neuro’s muscle interface is lower risk, faster to deploy, and far more practical for most amputees.
Tester Alex (who lost his right arm at age 11) now uses the hand fluently in everyday life, with impressively smooth control.
Human-machine fusion might not start in the brain after all… but in the muscles.
This carbon-fiber bionic hand--the PSYONIC Ability Hand doesn’t just give amputees near-natural dexterity. It’s already being used on Apollo, AEON, and other humanoid robots for real tasks, making true human-machine shared use a reality.
Now, the Ability Hand has taken another huge leap: near thought-controlled operation.
Thanks to Phantom Neuro, an Austin-based startup, their Muscle-Computer Interface (MCI) makes it possible.
With a quick 15-minute minimally invasive outpatient procedure, tiny sensors are implanted under the skin into the residual limb muscles.
These sensors wirelessly read muscle electrical signals (EMG) in real time and translate your intention into precise actions--gripping, rotating the wrist, pinching, and even flipping the middle finger.
Even better: you can still wirelessly control devices even when the hand is removed.
Compared to Neuralink’s brain implants that require skull surgery, Phantom Neuro’s muscle interface is lower risk, faster to deploy, and far more practical for most amputees.
Tester Alex (who lost his right arm at age 11) now uses the hand fluently in everyday life, with impressively smooth control.
Human-machine fusion might not start in the brain after all… but in the muscles.
🇨🇭 Global One SI - Prompt
Photo
Here are the 10 Best GitHub Weather Bots for Polymarket Right Now (Beginner to Advanced)
I went through dozens of repositories and filtered out the ones that are actually usable in 2026
From simple ready-to-run setups to serious multi-model ensembles with Kelly sizing and live execution - here’s what’s worth your time
> Beginner-friendly / quick start
1. MoonsatProtocol/Polymarket-Weather-Bot: TypeScript, Kelly Criterion sizing, scans NWS forecasts, finds mispriced temperature markets, simulation mode + JSON logs. Has a paid advanced version
Repo: github.com/MoonsatProtocol/Polymarket-Weather-Bot
2. MusicBoiyzzz/Polymarket-Weather-Bot: same Kelly + NWS stack, focus on easy setup, ships with a ready-to-run .zip
Repo: github.com/MusicBoiyzzz/Polymarket-Weather-Bot
3. hcharper/polyBot-Weather: designed for regular hardware (Mac Mini, home PC). Gaussian probability model on NOAA data for weather markets. Also covers crypto via Black-Scholes and binary arbitrage. Copy trading built in. Streamlit dashboard
Repo: github.com/hcharper/polyBot-Weather
> Multi-model ensemble (serious edge)
1. alteregoeth-ai/weatherbot: pure Python, three forecast sources: ECMWF, HRRR for US cities, METAR observations. Scans temperature markets, enters when EV is positive. Kelly sizing + simulation mode
Repo: github.com/alteregoeth-ai/weatherbot
2. AadiXD200/polymarket-weather-bot: autonomous quant bot, multi-model ensemble forecasting, Kelly sizing, live CLOB execution. One of the more complete implementations.
Repo: github.com/AadiXD200/polymarket-weather-bot
3. yangyuan-zhen/PolyWeather: monitors 52 cities, uses DEB (Dynamic Error Balancing): blends multiple model forecasts weighted by recent accuracy per city. Generates calibrated probability buckets, scans for mispricing. Web dashboard + Telegram bot
Repo: github.com/yangyuan-zhen/PolyWeather
> GFS ensemble specialists
1. suislanchez/polymarket-kalshi-weather-bot: trades Polymarket and Kalshi simultaneously. Uses 31-member GFS ensemble from Open-Meteo, converts ensemble agreement into probability, enters when edge vs market price exceeds 8%. React dashboard, FastAPI backend, SQLite tracking.
Repo: github.com/suislanchez/polymarket-kalshi-weather-bot
2. kylecwalden/polymarket-sniper-bot: sniper bot with a dedicated weather strategy using GFS 31-member ensemble. Also includes crypto strategies.
Repo: github.com/kylecwalden/polymarket-sniper-bot
> Kalshi-focused (transferable logic)
1. AruneshDev/Automated-Trading-System-Kalshi-Weather-Model - ML-based system (XGBoost) built specifically for weather event contracts on Kalshi. The model architecture is directly transferable to Polymarket weather markets.
Repo: github.com/AruneshDev/Automated-Trading-System-Kalshi-Weather-Model
2. cpratim/Kalshi-Weather-Trading: simple weather trading bot for Kalshi, clean codebase, good reference for understanding the base execution layer
Repo: github.com/cpratim/Kalshi-Weather-Trading
You don’t need to reinvent the wheel. These 10 repositories already solved 80% of the hard problems
Pick one that matches your skill level, study how they structure the code, and build on top of it
The traders making consistent money on weather markets right now aren’t the ones writing everything from scratch - they’re the ones who start with good foundations
I went through dozens of repositories and filtered out the ones that are actually usable in 2026
From simple ready-to-run setups to serious multi-model ensembles with Kelly sizing and live execution - here’s what’s worth your time
> Beginner-friendly / quick start
1. MoonsatProtocol/Polymarket-Weather-Bot: TypeScript, Kelly Criterion sizing, scans NWS forecasts, finds mispriced temperature markets, simulation mode + JSON logs. Has a paid advanced version
Repo: github.com/MoonsatProtocol/Polymarket-Weather-Bot
2. MusicBoiyzzz/Polymarket-Weather-Bot: same Kelly + NWS stack, focus on easy setup, ships with a ready-to-run .zip
Repo: github.com/MusicBoiyzzz/Polymarket-Weather-Bot
3. hcharper/polyBot-Weather: designed for regular hardware (Mac Mini, home PC). Gaussian probability model on NOAA data for weather markets. Also covers crypto via Black-Scholes and binary arbitrage. Copy trading built in. Streamlit dashboard
Repo: github.com/hcharper/polyBot-Weather
> Multi-model ensemble (serious edge)
1. alteregoeth-ai/weatherbot: pure Python, three forecast sources: ECMWF, HRRR for US cities, METAR observations. Scans temperature markets, enters when EV is positive. Kelly sizing + simulation mode
Repo: github.com/alteregoeth-ai/weatherbot
2. AadiXD200/polymarket-weather-bot: autonomous quant bot, multi-model ensemble forecasting, Kelly sizing, live CLOB execution. One of the more complete implementations.
Repo: github.com/AadiXD200/polymarket-weather-bot
3. yangyuan-zhen/PolyWeather: monitors 52 cities, uses DEB (Dynamic Error Balancing): blends multiple model forecasts weighted by recent accuracy per city. Generates calibrated probability buckets, scans for mispricing. Web dashboard + Telegram bot
Repo: github.com/yangyuan-zhen/PolyWeather
> GFS ensemble specialists
1. suislanchez/polymarket-kalshi-weather-bot: trades Polymarket and Kalshi simultaneously. Uses 31-member GFS ensemble from Open-Meteo, converts ensemble agreement into probability, enters when edge vs market price exceeds 8%. React dashboard, FastAPI backend, SQLite tracking.
Repo: github.com/suislanchez/polymarket-kalshi-weather-bot
2. kylecwalden/polymarket-sniper-bot: sniper bot with a dedicated weather strategy using GFS 31-member ensemble. Also includes crypto strategies.
Repo: github.com/kylecwalden/polymarket-sniper-bot
> Kalshi-focused (transferable logic)
1. AruneshDev/Automated-Trading-System-Kalshi-Weather-Model - ML-based system (XGBoost) built specifically for weather event contracts on Kalshi. The model architecture is directly transferable to Polymarket weather markets.
Repo: github.com/AruneshDev/Automated-Trading-System-Kalshi-Weather-Model
2. cpratim/Kalshi-Weather-Trading: simple weather trading bot for Kalshi, clean codebase, good reference for understanding the base execution layer
Repo: github.com/cpratim/Kalshi-Weather-Trading
You don’t need to reinvent the wheel. These 10 repositories already solved 80% of the hard problems
Pick one that matches your skill level, study how they structure the code, and build on top of it
The traders making consistent money on weather markets right now aren’t the ones writing everything from scratch - they’re the ones who start with good foundations
✍1
The final aspects and details of the deal are currently being discussed and will be announced shortly.
Among many other elements of the agreement, the Strait of Hormuz will be opened.
Please open Telegram to view this post
VIEW IN TELEGRAM
🇨🇳China produced 960.8 million tonnes of steel.
⚠️That's more than the next 12 countries combined.
Global steel production in 2025: 1,849.4 Mt
China's share: 960.8 Mt, 52% of the entire planet's output.
The full ranking:
🇨🇳 China — 960.8 Mt
🇮🇳 India 164.9 Mt
🇯🇵 Japan 80.7 Mt
🇺🇸 United States 82 Mt
🇷🇺 Russia 67.8 Mt
🇰🇷 South Korea 61.9 Mt
🇹🇷 Türkiye 38.1 Mt
🇧🇷 Brazil 33.3 Mt
🇩🇪 Germany 34.1 Mt
🇮🇷 Iran 31.8 Mt
🇻🇳 Vietnam 24.7 Mt
🇹🇼 Taiwan 17.1 Mt
Asia is rising fast.
Beyond China, India at 164.9 Mt is solidly 2nd and growing.
Vietnam, Indonesia, Iran despite sanctions.
The center of gravity in industrial production is shifting south and east permanently. 🌍
🇪🇺Europe is shrinking.
The continent that built the industrial revolution is now a rounding error in global steel output.
High energy costs, carbon pricing, and deindustrialisation are showing up directly in these numbers.
Steel is not just a commodity.
It's the physical substrate of military capacity, infrastructure, shipbuilding, and industrial output.
A country that produces 52% of the world's steel has a structural advantage in every hard-power competition from naval construction to pipeline infrastructure to weapons manufacturing.
⚠️That's more than the next 12 countries combined.
Global steel production in 2025: 1,849.4 Mt
China's share: 960.8 Mt, 52% of the entire planet's output.
The full ranking:
🇨🇳 China — 960.8 Mt
🇮🇳 India 164.9 Mt
🇯🇵 Japan 80.7 Mt
🇺🇸 United States 82 Mt
🇷🇺 Russia 67.8 Mt
🇰🇷 South Korea 61.9 Mt
🇹🇷 Türkiye 38.1 Mt
🇧🇷 Brazil 33.3 Mt
🇩🇪 Germany 34.1 Mt
🇮🇷 Iran 31.8 Mt
🇻🇳 Vietnam 24.7 Mt
🇹🇼 Taiwan 17.1 Mt
Asia is rising fast.
Beyond China, India at 164.9 Mt is solidly 2nd and growing.
Vietnam, Indonesia, Iran despite sanctions.
The center of gravity in industrial production is shifting south and east permanently. 🌍
🇪🇺Europe is shrinking.
The continent that built the industrial revolution is now a rounding error in global steel output.
High energy costs, carbon pricing, and deindustrialisation are showing up directly in these numbers.
Steel is not just a commodity.
It's the physical substrate of military capacity, infrastructure, shipbuilding, and industrial output.
A country that produces 52% of the world's steel has a structural advantage in every hard-power competition from naval construction to pipeline infrastructure to weapons manufacturing.
US trade with China collapsed $49 billion in one quarter.
Taiwan surged $39 billion.
The tariff war is redrawing global trade ⚠️
Biggest winners Q1 2026 vs Q1 2025
→ Taiwan: +$39B AI chips
→ Vietnam: +$18B manufacturing shift
→ Mexico: +$16B nearshoring
Biggest losers:
→ China: -$49B
→ Ireland: -$44B pharma normalisation
→ Switzerland: -$36B
→ Canada: -$22B
Taiwan's +$39B is the entire AI supply chain story in one number. TSMC's output has become a macroeconomic variable.
Vietnam and Mexico are where China's factories went.
The trade data now confirms it.
China's -$49B is the largest collapse on the chart and this is before the full tariff escalation cycle completed.
The decoupling isn't a trend. It's a fact.
Taiwan surged $39 billion.
The tariff war is redrawing global trade ⚠️
Biggest winners Q1 2026 vs Q1 2025
→ Taiwan: +$39B AI chips
→ Vietnam: +$18B manufacturing shift
→ Mexico: +$16B nearshoring
Biggest losers:
→ China: -$49B
→ Ireland: -$44B pharma normalisation
→ Switzerland: -$36B
→ Canada: -$22B
Taiwan's +$39B is the entire AI supply chain story in one number. TSMC's output has become a macroeconomic variable.
Vietnam and Mexico are where China's factories went.
The trade data now confirms it.
China's -$49B is the largest collapse on the chart and this is before the full tariff escalation cycle completed.
The decoupling isn't a trend. It's a fact.
ANTHROPIC 🔥: Mythos 1, "claude-mythos-1-preview", is being prepared for a release on Claude Code and Claude Security.
The model became visible for a short amount of time on Claude; besides that, new strings mentioning Mythos have been added.
> Access to the Claude Mythos model in Claude Code and Claude Security.
It still doesn't mean the general public will have access to this exact model, according to Anthropic's earlier communication.
The model became visible for a short amount of time on Claude; besides that, new strings mentioning Mythos have been added.
> Access to the Claude Mythos model in Claude Code and Claude Security.
It still doesn't mean the general public will have access to this exact model, according to Anthropic's earlier communication.
👀1
🇨🇭 Global One SI - Prompt
🔥 AMD’s Ryzen AI Halo PC official updates ⭐ What We Now Know 📅 June 2026 → First Ryzen AI Halo launches • $3,999 at Micro Center only • Ryzen AI Max+ 395 (Strix Halo) • 128 GB unified memory • Up to 200B parameter models locally 🎯 Q3 2026 → Big refresh…
🚀 Acer Veriton GN100 is an amazing alternative to the DGX-Spark and great addition to NVIDIA Spark ecosystem!
🧠 Powered by the NVIDIA GB10 Grace Blackwell Superchip📦 Tiny 5.9" form factor with 128GB unified LPDDR5x + up to 4TB Gen5 SSD
✅ Review Highlights (StorageReview):
Coolest running system in the entire Spark lineup 🔥
Excellent thermal headroom (CPU ~75°C, GPU ~69°C)
Strong GPU Direct Storage performance
Pre-loaded with NVIDIA DGX OS + full AI stack
💰 Pricing Check:
Was $3,800 on sale at Best Buy last weekend
Now $4,100 for the 4TB model
Still a good deal for a compact, high-performance AI workstation?
🧠 Powered by the NVIDIA GB10 Grace Blackwell Superchip📦 Tiny 5.9" form factor with 128GB unified LPDDR5x + up to 4TB Gen5 SSD
✅ Review Highlights (StorageReview):
Coolest running system in the entire Spark lineup 🔥
Excellent thermal headroom (CPU ~75°C, GPU ~69°C)
Strong GPU Direct Storage performance
Pre-loaded with NVIDIA DGX OS + full AI stack
💰 Pricing Check:
Was $3,800 on sale at Best Buy last weekend
Now $4,100 for the 4TB model
Still a good deal for a compact, high-performance AI workstation?
👀1
🚨 Is RAMpocolypse Ending? Chinese Memory Comes to the Rescue ⛑️ Localmaxxers have hope.
🔥 A high-end Intel Arrow Lake system (Core Ultra 9 285K) has been spotted running Kingbank DDR5-8400 memory!
Key Specs:
🏷️ Brand: Kingbank Technology (Chinese DRAM maker)
⚡ Speed: DDR5-8400 CL44-56-56-128
🎯 Platform: MSI PRO B860M-S
📊 Status: ✅ Stable (passing MemTest86)
📌 This is another strong sign that Chinese memory manufacturers are successfully breaking into the mainstream global PC market — even in high-performance builds.
With Samsung, SK Hynix & Micron heavily focused on AI memory (HBM), Chinese brands like Kingbank and CXMT are stepping in to fill the supply gap.
📈 Expect to see a lot more Chinese DRAM in 2026 PC builds!
🔥 A high-end Intel Arrow Lake system (Core Ultra 9 285K) has been spotted running Kingbank DDR5-8400 memory!
Key Specs:
🏷️ Brand: Kingbank Technology (Chinese DRAM maker)
⚡ Speed: DDR5-8400 CL44-56-56-128
🎯 Platform: MSI PRO B860M-S
📊 Status: ✅ Stable (passing MemTest86)
📌 This is another strong sign that Chinese memory manufacturers are successfully breaking into the mainstream global PC market — even in high-performance builds.
With Samsung, SK Hynix & Micron heavily focused on AI memory (HBM), Chinese brands like Kingbank and CXMT are stepping in to fill the supply gap.
📈 Expect to see a lot more Chinese DRAM in 2026 PC builds!
🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
✍1
10 WAYS TO PLAY ROBOTICS IN 2026
1. $TSLA building one of the most vertically integrated humanoid stacks combining Optimus Gen 3 hardware, its in-house AI5 inference chip & same FSD-based AI brain powering its vehicle fleet.
2. $NVDA platform layer for entire robotics industry with Isaac GR00T foundation models, Cosmos world models for synthetic training data & Jetson Thor on-board compute.
3. $PLTR Foundry & Warp Speed run mission control layer for robotic & autonomous fleets by turning sensor data & machine telemetry into a single deployable operating picture.
4. $OUST leading public pure-play in 3D digital lidar where Rev8 native-color sensor line is now shipping to $GOOGL & Volvo Autonomous while being integrated directly into Nvidia Jetson robotics stack.
5. $AVGO supplies custom networking silicon, Tomahawk + Jericho switching ASICs & high-speed connectivity that move data between every robot, sensor & server in autonomy stack.
6. $AVAV, $KTOS, $AVEX & $ONDS are building the UAV drone fleets that feed into the autonomous defense networks.
7. $QCOM provides Snapdragon Ride autonomous platform & on-device AI inference SoCs that power the perception, decision & motor-control loops inside robots, drones & vehicles.
8. $SYNA Astra multimodal edge AI processors run wireless connectivity & sensing inside smart appliances, factory automation & autonomous robotic systems with Core IoT product sales growing 31% YoY.
9. $ISRG, $PRCT, $SYK & $MDT bring surgical robotics that are reshaping operating rooms.
10. $AMZN deploys hundreds of thousands of warehouse robots to move & pick goods while $SYM & $SERV build the autonomous mobile robots that power fulfillment & last-mile retail logistics.
1. $TSLA building one of the most vertically integrated humanoid stacks combining Optimus Gen 3 hardware, its in-house AI5 inference chip & same FSD-based AI brain powering its vehicle fleet.
2. $NVDA platform layer for entire robotics industry with Isaac GR00T foundation models, Cosmos world models for synthetic training data & Jetson Thor on-board compute.
3. $PLTR Foundry & Warp Speed run mission control layer for robotic & autonomous fleets by turning sensor data & machine telemetry into a single deployable operating picture.
4. $OUST leading public pure-play in 3D digital lidar where Rev8 native-color sensor line is now shipping to $GOOGL & Volvo Autonomous while being integrated directly into Nvidia Jetson robotics stack.
5. $AVGO supplies custom networking silicon, Tomahawk + Jericho switching ASICs & high-speed connectivity that move data between every robot, sensor & server in autonomy stack.
6. $AVAV, $KTOS, $AVEX & $ONDS are building the UAV drone fleets that feed into the autonomous defense networks.
7. $QCOM provides Snapdragon Ride autonomous platform & on-device AI inference SoCs that power the perception, decision & motor-control loops inside robots, drones & vehicles.
8. $SYNA Astra multimodal edge AI processors run wireless connectivity & sensing inside smart appliances, factory automation & autonomous robotic systems with Core IoT product sales growing 31% YoY.
9. $ISRG, $PRCT, $SYK & $MDT bring surgical robotics that are reshaping operating rooms.
10. $AMZN deploys hundreds of thousands of warehouse robots to move & pick goods while $SYM & $SERV build the autonomous mobile robots that power fulfillment & last-mile retail logistics.
🚨 THE ENTIRE AI BOOM MIGHT BE BUILT ON FAKE REVENUE.
Latest corporate filings show that OpenAI and Anthropic alone make up over half of the entire $2 trillion future cloud backlog held by Microsoft, Oracle, Google, and Amazon.
This massive pipeline is actually being created through a circular accounting trick called a round trip revenue loop.
But how it works ?
A tech giant gives billions of dollars to an AI startup as an "investment". But hidden in the contract is a strict rule forcing the startup to hand that exact same money straight back to the tech giant to rent their computer servers.
Look at the documented case of Microsoft and OpenAI.
When Microsoft invested $13 billion into OpenAI, it didn't just give them cash; it gave them "cloud credits" to use Microsoft servers. OpenAI used those exact credits to train its AI models, and Microsoft then turned around and recorded that server usage as brand new "cloud revenue" from a customer.
The tech giant is literally paying itself with its own money and calling it a sale.
This is why OpenAI’s annual cloud bill has ballooned to over $60 billion, double its actual revenue of $25 billion, kept alive solely by this recycled funding loop.
Anthropic runs the exact same play, spending $2.66 billion on Amazon Web Services in just nine months, which was basically 100% of all the money it earned at the time.
This manufactured demand triggers a second accounting trick where tech giants book massive paper profits. Every time a startup gets a higher value from a new funding round, the tech giant updates the value of its investment on its books and counts that unearned paper gain as direct profit.
In Q1 2026, Alphabet reported a record $62.6 billion profit, but $28.7 billion nearly half, was just a paper markup on its Anthropic investment. In the same quarter, Amazon reported $30.3 billion in profit, but $16.8 billion of it was just an Anthropic paper gain.
While Amazon reported record profits, its actual free cash flow collapsed 95% to just $1.2 billion because it had to spend $44.2 billion in real cash to build physical data centers.
This has created a massive danger where these giant companies rely heavily on just one or two unstable startups. Microsoft has 49% of its $627 billion future backlog tied to OpenAI, while Oracle has an incredible 54% of its entire $553 billion pipeline relying on OpenAI alone.
This perfectly mirrors the 2001 dot-com crash when Global Crossing and Qwest Communications swapped identical fiber-optic network capacity with each other just to book fake sales.
Qwest had to erase $1.4 billion in fake income, and Global Crossing went completely bankrupt.
The only difference is that the dot-com swaps were illegal, but today's AI loop is fully legal under current accounting rules.
This legal loop inflates tech company stock prices, forcing automatic retirement accounts and index funds to buy even more of these tech stocks. It is a self feeding loop where investments, sales, and stock prices all go up on paper without the AI technology ever making real cash profits.
Latest corporate filings show that OpenAI and Anthropic alone make up over half of the entire $2 trillion future cloud backlog held by Microsoft, Oracle, Google, and Amazon.
This massive pipeline is actually being created through a circular accounting trick called a round trip revenue loop.
But how it works ?
A tech giant gives billions of dollars to an AI startup as an "investment". But hidden in the contract is a strict rule forcing the startup to hand that exact same money straight back to the tech giant to rent their computer servers.
Look at the documented case of Microsoft and OpenAI.
When Microsoft invested $13 billion into OpenAI, it didn't just give them cash; it gave them "cloud credits" to use Microsoft servers. OpenAI used those exact credits to train its AI models, and Microsoft then turned around and recorded that server usage as brand new "cloud revenue" from a customer.
The tech giant is literally paying itself with its own money and calling it a sale.
This is why OpenAI’s annual cloud bill has ballooned to over $60 billion, double its actual revenue of $25 billion, kept alive solely by this recycled funding loop.
Anthropic runs the exact same play, spending $2.66 billion on Amazon Web Services in just nine months, which was basically 100% of all the money it earned at the time.
This manufactured demand triggers a second accounting trick where tech giants book massive paper profits. Every time a startup gets a higher value from a new funding round, the tech giant updates the value of its investment on its books and counts that unearned paper gain as direct profit.
In Q1 2026, Alphabet reported a record $62.6 billion profit, but $28.7 billion nearly half, was just a paper markup on its Anthropic investment. In the same quarter, Amazon reported $30.3 billion in profit, but $16.8 billion of it was just an Anthropic paper gain.
While Amazon reported record profits, its actual free cash flow collapsed 95% to just $1.2 billion because it had to spend $44.2 billion in real cash to build physical data centers.
This has created a massive danger where these giant companies rely heavily on just one or two unstable startups. Microsoft has 49% of its $627 billion future backlog tied to OpenAI, while Oracle has an incredible 54% of its entire $553 billion pipeline relying on OpenAI alone.
This perfectly mirrors the 2001 dot-com crash when Global Crossing and Qwest Communications swapped identical fiber-optic network capacity with each other just to book fake sales.
Qwest had to erase $1.4 billion in fake income, and Global Crossing went completely bankrupt.
The only difference is that the dot-com swaps were illegal, but today's AI loop is fully legal under current accounting rules.
This legal loop inflates tech company stock prices, forcing automatic retirement accounts and index funds to buy even more of these tech stocks. It is a self feeding loop where investments, sales, and stock prices all go up on paper without the AI technology ever making real cash profits.
👀1