🇨🇭 Global One SI - Prompt
THE BOSS OF ALL BOSSES!!!😎🇺🇸🔥🔥🔥
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Iran’s negotiating team every time Trump hits post
🇨🇭 Global One SI - Prompt
Whoever controls energy and compute will be the new Rockefeller.
Controversial Opinion: If you replace all of your employees with AI then you no longer own your company...
Anthropic does.
Anthropic does.
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ANTHROPIC HAS GONE FROM $4 BILLION TO $965 BILLION IN JUST 3 YEARS
Here is every funding round in the company's history:
- 2021: Founded
- 2023: $4.1B valuation (early fundraising)
- March 2025: $61.5B valuation (Series E)
- September 2025: $183B valuation (Series F)
- February 2026: $380B valuation (Series G)
- May 2026: $965B valuation (Series H)
That is a 235x increase in valuation in roughly 3 years from 2023 to today.
For context: Anthropic now has a $47 billion annual revenue run rate. It had $1 billion in run rate revenue at the start of 2025.
The company has filed confidentially for an IPO at a $965 billion valuation. It would be the second largest company to ever go public behind SpaceX.
Here is every funding round in the company's history:
- 2021: Founded
- 2023: $4.1B valuation (early fundraising)
- March 2025: $61.5B valuation (Series E)
- September 2025: $183B valuation (Series F)
- February 2026: $380B valuation (Series G)
- May 2026: $965B valuation (Series H)
That is a 235x increase in valuation in roughly 3 years from 2023 to today.
For context: Anthropic now has a $47 billion annual revenue run rate. It had $1 billion in run rate revenue at the start of 2025.
The company has filed confidentially for an IPO at a $965 billion valuation. It would be the second largest company to ever go public behind SpaceX.
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
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🚨The Iranian delegation just walked out of the Switzerland talks.
The oil market should be paying closer attention than it is.
New footage shows the Iranian team leaving the venue in protest over Trump's threats, and VP JD Vance moving straight into talks with Pakistani PM Shehbaz Sharif right after the exit.
A walkout isn't a pause.
It's a signal that the diplomatic track is fragile.
On Monday I said this peace wasn't stable, and the price action keeps proving it.
As long as the negotiating table stays empty, the risk premium has room to build, and I think crude grinds higher this week.
The oil market should be paying closer attention than it is.
New footage shows the Iranian team leaving the venue in protest over Trump's threats, and VP JD Vance moving straight into talks with Pakistani PM Shehbaz Sharif right after the exit.
A walkout isn't a pause.
It's a signal that the diplomatic track is fragile.
On Monday I said this peace wasn't stable, and the price action keeps proving it.
As long as the negotiating table stays empty, the risk premium has room to build, and I think crude grinds higher this week.
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The real war isn’t political. It’s theological.
Xueqin Jiang: “You have to replace God with another God. You can’t just not have God.”
Money has been the operating system of human civilization for centuries. Not a tool. A deity.
Every structure of power exists to serve the people who control the ledger.
Jiang: “Finance wants to maintain money as the God, and the tech sector wants to promote AI as the God.”
You cannot dismantle a ruling class by attacking their capital. You have to replace their god.
Jiang: “Most people see it as a struggle between the left and the right, but if you go deeper, it becomes one of globalist versus nationalist. And then if you go deeper, it’s also a conflict between finance versus the tech sector.”
Left versus right. The culture wars. The populist uprising. Surface tremors above a fault line most people will never see.
Finance built civilization on scarcity. AI is about to make intelligence infinite. One of those systems survives.
Jiang: “The tech sector is trying to replace finance, but finance is like, ‘screw you, I’ve been in power for like hundreds of years, I’m not going to go away peacefully.’”
No god abdicates willingly. But every time humanity replaced a dying god, we expanded. The printing press broke the Church’s monopoly on knowledge. Democracy broke the monarchy’s monopoly on power.
The god of scarcity does not survive the age of abundance. This is not a collapse. It is the destruction of a prison.
Everything you thought was politics was a succession crisis between gods.
Xueqin Jiang: “You have to replace God with another God. You can’t just not have God.”
Money has been the operating system of human civilization for centuries. Not a tool. A deity.
Every structure of power exists to serve the people who control the ledger.
Jiang: “Finance wants to maintain money as the God, and the tech sector wants to promote AI as the God.”
You cannot dismantle a ruling class by attacking their capital. You have to replace their god.
Jiang: “Most people see it as a struggle between the left and the right, but if you go deeper, it becomes one of globalist versus nationalist. And then if you go deeper, it’s also a conflict between finance versus the tech sector.”
Left versus right. The culture wars. The populist uprising. Surface tremors above a fault line most people will never see.
Finance built civilization on scarcity. AI is about to make intelligence infinite. One of those systems survives.
Jiang: “The tech sector is trying to replace finance, but finance is like, ‘screw you, I’ve been in power for like hundreds of years, I’m not going to go away peacefully.’”
No god abdicates willingly. But every time humanity replaced a dying god, we expanded. The printing press broke the Church’s monopoly on knowledge. Democracy broke the monarchy’s monopoly on power.
The god of scarcity does not survive the age of abundance. This is not a collapse. It is the destruction of a prison.
Everything you thought was politics was a succession crisis between gods.
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🇨🇭 Global One SI - Prompt
Meta can throw billions at AI and still somehow look late. The new model is decent, but the whole thing feels like panic buying talent and hoping scale fixes taste. Do you actually see them catching up?
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Zuckerberg says Meta's Llama race changes in the next 12-18 months: most of the code will be written by AI - not autocomplete, but agents that run tests and improve the model loop.
"We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically."
"Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI."
"And I don't mean autocomplete."
"I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues."
"It writes higher quality code than the average very good person on the team already."
The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.
"We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically."
"Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI."
"And I don't mean autocomplete."
"I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues."
"It writes higher quality code than the average very good person on the team already."
The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.
🇨🇭 Global One SI - Prompt
Who’s Building With Whom in Humanoid Robots? Notable deployments, pilots and AI stacksJune 2026 snapshotNodes scaled by revenue / company scale CENTRAL ROBOT MAKERS ➤ Tesla$94.8B revenueOptimus internal developmentConnected only to Tesla Factories ➤ Figure…
The Humanoid Robot Technology Stack
A visual map of the core technologies shaping humanoid robotics.
At the center:
➤ Enablers
‣ Sensors
‣ Compute
‣ Data
‣ Actuators
‣ Safety
This is not a market adoption chart.
It is a technology stack map.
1/5
➤ EDUCATION
These are the tools that help humanoid robots learn, train, test and improve before real deployment.
‣ Robot Operating System
‣ Simulation Platforms
‣ Vision Models
‣ Data Collection
‣ Teleoperation Tools
‣ Fleet Analytics
‣ Training Pipelines
‣ Motion Planning
‣ Reinforcement Learning
‣ Dataset Management
‣ Behavior Cloning
‣ Safety Testing
2/5
➤ EXPOSURE
These systems connect humanoid robots to shared models, feedback loops and wider research ecosystems.
‣ Cloud Robot Learning
‣ Shared Skill Libraries
‣ Foundation Models
‣ Multi-Robot Learning
‣ Human Feedback
‣ Developer Communities
‣ Benchmark Platforms
‣ Open-Source Models
‣ Research Labs
‣ Knowledge Transfer
3/5
➤ ENVIRONMENT
This is the infrastructure that lets humanoid robots operate outside the lab.
‣ Edge Compute
‣ Navigation Stack
‣ Perception Stack
‣ Battery System
‣ Wireless Connectivity
‣ Mapping Tools
‣ Remote Monitoring
‣ Deployment Dashboards
‣ Robot Apps Marketplace
‣ Maintenance Software
‣ Compliance Tools
4/5
➤ EXPERIENCE
This is where humanoid robots turn training into real-world behavior.
‣ Real World Practice
‣ Task Demonstrations
‣ Factory Pilots
‣ Warehouse Trials
‣ Home Testing
‣ Grasping Practice
‣ Recovery Training
‣ Human-Robot Interaction
‣ Operator Feedback
‣ Scenario Replay
5/5
How to read the map:
➤ Solid circle
Core robot technology
➤ Double outlined circle
Adapted external technology
➤ Circle size
Current importance in humanoid robotics
Humanoid robots are not one technology.
They are a stack: body software data training deployment and feedback loops working together.
A visual map of the core technologies shaping humanoid robotics.
At the center:
➤ Enablers
‣ Sensors
‣ Compute
‣ Data
‣ Actuators
‣ Safety
This is not a market adoption chart.
It is a technology stack map.
1/5
➤ EDUCATION
These are the tools that help humanoid robots learn, train, test and improve before real deployment.
‣ Robot Operating System
‣ Simulation Platforms
‣ Vision Models
‣ Data Collection
‣ Teleoperation Tools
‣ Fleet Analytics
‣ Training Pipelines
‣ Motion Planning
‣ Reinforcement Learning
‣ Dataset Management
‣ Behavior Cloning
‣ Safety Testing
2/5
➤ EXPOSURE
These systems connect humanoid robots to shared models, feedback loops and wider research ecosystems.
‣ Cloud Robot Learning
‣ Shared Skill Libraries
‣ Foundation Models
‣ Multi-Robot Learning
‣ Human Feedback
‣ Developer Communities
‣ Benchmark Platforms
‣ Open-Source Models
‣ Research Labs
‣ Knowledge Transfer
3/5
➤ ENVIRONMENT
This is the infrastructure that lets humanoid robots operate outside the lab.
‣ Edge Compute
‣ Navigation Stack
‣ Perception Stack
‣ Battery System
‣ Wireless Connectivity
‣ Mapping Tools
‣ Remote Monitoring
‣ Deployment Dashboards
‣ Robot Apps Marketplace
‣ Maintenance Software
‣ Compliance Tools
4/5
➤ EXPERIENCE
This is where humanoid robots turn training into real-world behavior.
‣ Real World Practice
‣ Task Demonstrations
‣ Factory Pilots
‣ Warehouse Trials
‣ Home Testing
‣ Grasping Practice
‣ Recovery Training
‣ Human-Robot Interaction
‣ Operator Feedback
‣ Scenario Replay
5/5
How to read the map:
➤ Solid circle
Core robot technology
➤ Double outlined circle
Adapted external technology
➤ Circle size
Current importance in humanoid robotics
Humanoid robots are not one technology.
They are a stack: body software data training deployment and feedback loops working together.
I keep wondering what will matter more for AI progress over the next decade:
quantum computing or fusion reactors?
Quantum could change what we can solve.
Fusion could change how much intelligence we can afford to run.
My gut says energy wins first. Cheap compute needs cheap power.
But if quantum suddenly becomes useful, all bets are off.
Which one do you think matters more?
quantum computing or fusion reactors?
Quantum could change what we can solve.
Fusion could change how much intelligence we can afford to run.
My gut says energy wins first. Cheap compute needs cheap power.
But if quantum suddenly becomes useful, all bets are off.
Which one do you think matters more?
GPT-5.6 rumor watch is getting spicy.
Stuff people are talking about:
• possible release this Thursday
• stronger coding
• better reasoning
• more agentic workflows
• bigger context window
• maybe a cleaner, faster ChatGPT experience
No official confirmation yet, so treat it as rumor smoke.
But if even half of this is real, Thursday could be a very fun day for AI.
Stuff people are talking about:
• possible release this Thursday
• stronger coding
• better reasoning
• more agentic workflows
• bigger context window
• maybe a cleaner, faster ChatGPT experience
No official confirmation yet, so treat it as rumor smoke.
But if even half of this is real, Thursday could be a very fun day for AI.
A viral Europe 2031 scenario warns that Europe could become economically weaker, politically dependent, and strategically exposed if it fails to build its own frontier AI capacity.
- Europe misread DeepSeek R1 as proof that small, clever teams could compete without massive compute, even though the deeper lesson was that reasoning models worked and compute still decided who could scale them.
- Europe announced big AI numbers, including €200B for InvestAI, but much of it was aspirational, spread across years, and far smaller than what US hyperscalers were already spending on data centers.
- Europe lacked enough AI compute, with the report framing the US advantage as 17.3GW of buildout versus 1.4GW in Europe, which meant fewer chips, fewer experiments, weaker models, and slower catch-up.
- Europe moved too slowly on energy, permitting, and data centers, so its Gigafactories were delayed while American firms were already building giant facilities and signing massive compute deals.
- Europe’s strongest AI firms could not raise capital at frontier scale, so companies like Mistral were compared against US labs raising sums that made European rounds look structurally insufficient.
- Europe lost talent because top researchers and founders could get larger compute budgets, higher pay, faster teams, and more serious AI ambition in Silicon Valley than in Brussels, Paris, or Berlin.
- Europe’s own institutions often blocked staff from using the best frontier tools for data-protection reasons, which meant policymakers were regulating systems they barely used in daily work.
- Europe’s companies adopted AI more slowly because of fragmented rules, cautious management, sector restrictions, labor protections, and internal policies that pushed workers toward weaker European tools.
- Europe focused on sovereignty mandates before it had strong sovereign suppliers, so “buy European” policies risked forcing public agencies and companies onto weaker systems.
- Europe underestimated inference access as a strategic chokepoint, because even if US models were available commercially, Washington could later ration the compute needed to run them.
- Europe had leverage in parts of the semiconductor chain, especially through ASML, but the scenario argues it failed to turn that leverage into a serious bargaining position before AI dependence hardened.
- Europe misread DeepSeek R1 as proof that small, clever teams could compete without massive compute, even though the deeper lesson was that reasoning models worked and compute still decided who could scale them.
- Europe announced big AI numbers, including €200B for InvestAI, but much of it was aspirational, spread across years, and far smaller than what US hyperscalers were already spending on data centers.
- Europe lacked enough AI compute, with the report framing the US advantage as 17.3GW of buildout versus 1.4GW in Europe, which meant fewer chips, fewer experiments, weaker models, and slower catch-up.
- Europe moved too slowly on energy, permitting, and data centers, so its Gigafactories were delayed while American firms were already building giant facilities and signing massive compute deals.
- Europe’s strongest AI firms could not raise capital at frontier scale, so companies like Mistral were compared against US labs raising sums that made European rounds look structurally insufficient.
- Europe lost talent because top researchers and founders could get larger compute budgets, higher pay, faster teams, and more serious AI ambition in Silicon Valley than in Brussels, Paris, or Berlin.
- Europe’s own institutions often blocked staff from using the best frontier tools for data-protection reasons, which meant policymakers were regulating systems they barely used in daily work.
- Europe’s companies adopted AI more slowly because of fragmented rules, cautious management, sector restrictions, labor protections, and internal policies that pushed workers toward weaker European tools.
- Europe focused on sovereignty mandates before it had strong sovereign suppliers, so “buy European” policies risked forcing public agencies and companies onto weaker systems.
- Europe underestimated inference access as a strategic chokepoint, because even if US models were available commercially, Washington could later ration the compute needed to run them.
- Europe had leverage in parts of the semiconductor chain, especially through ASML, but the scenario argues it failed to turn that leverage into a serious bargaining position before AI dependence hardened.
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🚨 BOMBSHELL! Anthropic CEO Dario Amodei completely exposes Silicon Valley elites.
He reveals tech billionaires are secretly begging the government to seize AI technology.
This is why China is our guardrail to make sure everyone can access cheap massive intelligience.
He reveals tech billionaires are secretly begging the government to seize AI technology.
This is why China is our guardrail to make sure everyone can access cheap massive intelligience.
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
U.S. and Iran say they made "encouraging progress" in Switzerland and agreed on a roadmap to reach a final deal within 60 days, according to a joint statement released by Qatar and Pakistan.
Key statement points:
- High-level U.S.-Iran talks concluded in Switzerland.
- Working groups formed on nuclear issues, sanctions and dispute resolution.
- Direct communication line established to avoid incidents in the Strait of Hormuz.
- De-confliction cell created to support the Lebanon ceasefire.
- Technical talks to continue this week in Burgenstock, Switzerland.
Iran's FM Araghchi said mediated talks involving Pakistan and Qatar delivered “major progress” on the Lebanon conflict, including waived oil and petrochemical export restrictions, lifting of the blockade, partial release of frozen assets and a reconstruction plan launched for Iran.
He cautioned that the first “real test” will be the operational rollout of the Lebanon deconfliction cell.
Key statement points:
- High-level U.S.-Iran talks concluded in Switzerland.
- Working groups formed on nuclear issues, sanctions and dispute resolution.
- Direct communication line established to avoid incidents in the Strait of Hormuz.
- De-confliction cell created to support the Lebanon ceasefire.
- Technical talks to continue this week in Burgenstock, Switzerland.
Iran's FM Araghchi said mediated talks involving Pakistan and Qatar delivered “major progress” on the Lebanon conflict, including waived oil and petrochemical export restrictions, lifting of the blockade, partial release of frozen assets and a reconstruction plan launched for Iran.
He cautioned that the first “real test” will be the operational rollout of the Lebanon deconfliction cell.
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