🔥 US macroeconomic data:
▪️ Unemployment rate (May)
FACT: 4.3%
FORECAST: 4.3%
PREDICTION.: 4.3%
▪️ Change in the number of employed in the non-agricultural sector (May)
FACT: 172K
FORECAST: 85K
PREDICTION.: 179K
▪️ Average hourly wage (compared to the same period last year) (y/y) (May)
FACT: 3.4%
FORECAST: 3.4%
PREDICTION.: 3.6%
▪️ Average hourly wage (m/m) (May)
FACT: 0.3%
FORECAST: 0.3%
PREDICTION.: 0.2%
▪️ Unemployment rate (May)
FACT: 4.3%
FORECAST: 4.3%
PREDICTION.: 4.3%
▪️ Change in the number of employed in the non-agricultural sector (May)
FACT: 172K
FORECAST: 85K
PREDICTION.: 179K
▪️ Average hourly wage (compared to the same period last year) (y/y) (May)
FACT: 3.4%
FORECAST: 3.4%
PREDICTION.: 3.6%
▪️ Average hourly wage (m/m) (May)
FACT: 0.3%
FORECAST: 0.3%
PREDICTION.: 0.2%
Goldman Sachs just dropped the most precise map of where $7.6 trillion is going over the next five years and it tells you exactly which companies are standing in the middle of an unavoidable flood of capital (Save this).
The numbers are worth understanding precisely before talking about who benefits.
Goldman's baseline projects $765 billion in AI capital expenditure in 2026 alone, growing to $1.6 trillion annually by 2031.
Over the full 2026 to 2031 period, cumulative spend breaks down to $5.1 trillion in compute, $2.1 trillion in data centers, and $358 billion in power.
Nvidia is assumed to command 75% of all compute spend throughout the period, using the Rubin VR200 chip at $80,500 per GPU as the baseline.
The data center specification charts reveal how dramatically physical requirements are escalating.
A standard cloud data center runs 5–15 kW per rack while a transitional Blackwell era AI data center runs 130–200 kW per rack.
The AI factory of the future, running Rubin and Feynman silicon operates at 500+ kW per rack, at greater than 1 gigawatt scale, with liquid cooling only.
Traditional hyperscale data centers cost roughly $10 million per megawatt to build while the next generation AI data centers are being discussed at $15 to $20 million per megawatt.
Goldman identifies silicon useful life as the single biggest swing factor in the entire model.
At a 3-year replacement cycle, cumulative compute depreciation hits $3.99 trillion and at 7 years, it drops to $2.23 trillion, a $1.76 trillion difference on one assumption alone.
Power is only $358 billion of the total, but Goldman is explicit, it is the only component that can prevent the other 95% of the stack from deploying.
Amazon's Andy Jassy put it, "Our single biggest constraint is power." Connecting large data centers to the grid takes years.
Now here are the companies standing directly in the path of each layer of this capital.
Nvidia is still the most concentrated bet on the compute layer.
At 75% of $5.1 trillion in compute spend over six years, that is $3.8 trillion in cumulative revenue flowing through one company's products.
The 75% gross margin on data center GPUs is the reason every hyperscaler is trying to build custom silicon to escape it while simultaneously continuing to buy Nvidia because nothing else performs at the same level.
Vertiv is the direct infrastructure play on the data center upgrade cycle.
Every rack going from 40 kW to 500+ kW needs liquid cooling systems, power distribution, and thermal management infrastructure that simply did not exist at prior density levels.
Vertiv just deepened its liquid cooling capabilities through a strategic acquisition and was named a key partner on Hut 8's large AI-focused Texas data center campus.
The liquid cooling market is growing from $5.5 billion today to $15.75 billion by 2030, and Vertiv is the dominant provider in that market.
Vistra is the power thesis in its most direct form.
The $358 billion power segment is the critical path for the entire $7.6 trillion, and Vistra has spent the last 18 months locking up that critical path through long-term nuclear power purchase agreements.
Vistra secured a 20 year agreement with Meta for over 2,600 MW of nuclear energy, plus a separate deal with AWS from its Comanche nuclear facility.
Goldman Sachs and Jefferies both upgraded the stock after the Meta deal was announced.
The architecture of this trade is simple.
Goldman's model is not a prediction of whether AI spending happens but rather a model of the minimum physical capital required to deploy infrastructure that has already been contracted, already been announced, and is already under construction.
The compute layer requires the chips, data center layer requires cooling and power infrastructure and the power layer requires nuclear at scale on multi-decade contracts.
All three layers are being funded simultaneously, and all three have identifiable public companies sitting directly in the path of the capital.
The numbers are worth understanding precisely before talking about who benefits.
Goldman's baseline projects $765 billion in AI capital expenditure in 2026 alone, growing to $1.6 trillion annually by 2031.
Over the full 2026 to 2031 period, cumulative spend breaks down to $5.1 trillion in compute, $2.1 trillion in data centers, and $358 billion in power.
Nvidia is assumed to command 75% of all compute spend throughout the period, using the Rubin VR200 chip at $80,500 per GPU as the baseline.
The data center specification charts reveal how dramatically physical requirements are escalating.
A standard cloud data center runs 5–15 kW per rack while a transitional Blackwell era AI data center runs 130–200 kW per rack.
The AI factory of the future, running Rubin and Feynman silicon operates at 500+ kW per rack, at greater than 1 gigawatt scale, with liquid cooling only.
Traditional hyperscale data centers cost roughly $10 million per megawatt to build while the next generation AI data centers are being discussed at $15 to $20 million per megawatt.
Goldman identifies silicon useful life as the single biggest swing factor in the entire model.
At a 3-year replacement cycle, cumulative compute depreciation hits $3.99 trillion and at 7 years, it drops to $2.23 trillion, a $1.76 trillion difference on one assumption alone.
Power is only $358 billion of the total, but Goldman is explicit, it is the only component that can prevent the other 95% of the stack from deploying.
Amazon's Andy Jassy put it, "Our single biggest constraint is power." Connecting large data centers to the grid takes years.
Now here are the companies standing directly in the path of each layer of this capital.
Nvidia is still the most concentrated bet on the compute layer.
At 75% of $5.1 trillion in compute spend over six years, that is $3.8 trillion in cumulative revenue flowing through one company's products.
The 75% gross margin on data center GPUs is the reason every hyperscaler is trying to build custom silicon to escape it while simultaneously continuing to buy Nvidia because nothing else performs at the same level.
Vertiv is the direct infrastructure play on the data center upgrade cycle.
Every rack going from 40 kW to 500+ kW needs liquid cooling systems, power distribution, and thermal management infrastructure that simply did not exist at prior density levels.
Vertiv just deepened its liquid cooling capabilities through a strategic acquisition and was named a key partner on Hut 8's large AI-focused Texas data center campus.
The liquid cooling market is growing from $5.5 billion today to $15.75 billion by 2030, and Vertiv is the dominant provider in that market.
Vistra is the power thesis in its most direct form.
The $358 billion power segment is the critical path for the entire $7.6 trillion, and Vistra has spent the last 18 months locking up that critical path through long-term nuclear power purchase agreements.
Vistra secured a 20 year agreement with Meta for over 2,600 MW of nuclear energy, plus a separate deal with AWS from its Comanche nuclear facility.
Goldman Sachs and Jefferies both upgraded the stock after the Meta deal was announced.
The architecture of this trade is simple.
Goldman's model is not a prediction of whether AI spending happens but rather a model of the minimum physical capital required to deploy infrastructure that has already been contracted, already been announced, and is already under construction.
The compute layer requires the chips, data center layer requires cooling and power infrastructure and the power layer requires nuclear at scale on multi-decade contracts.
All three layers are being funded simultaneously, and all three have identifiable public companies sitting directly in the path of the capital.
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The Deeper the Mine, the Harder the Economics. ⛏️🌍
The world’s deepest mines reach almost 4 km below the surface.
Most are in South Africa, where gold and uranium have been extracted at extreme depths for decades.
But depth changes everything.
🔹 More heat
🔹 More pressure
🔹 More ventilation
🔹 More safety risk
🔹 More power demand
🔹 Higher costs
This is the hidden side of mining.
The easy ore gets mined first.
What comes next is deeper, harder, hotter, and more expensive.
The world’s deepest mines reach almost 4 km below the surface.
Most are in South Africa, where gold and uranium have been extracted at extreme depths for decades.
But depth changes everything.
🔹 More heat
🔹 More pressure
🔹 More ventilation
🔹 More safety risk
🔹 More power demand
🔹 Higher costs
This is the hidden side of mining.
The easy ore gets mined first.
What comes next is deeper, harder, hotter, and more expensive.
Here are some of the companies that capture every layer of the $7.6 trillion in Capex (Save this).
$NVDA: Nvidia captures 75% of every compute dollar spent at 75% gross margins, making it the most monopolistic position in enterprise technology history.
$TSM: Every Nvidia chip, every custom hyperscaler ASIC, and every ARM based server CPU gets manufactured at TSMC, which raised its 2026 capex guidance to $52–56 billion just to keep up with AI demand.
$ASML: ASML is the only company on earth making the EUV lithography machines that TSMC needs to produce 2nm chips, raised its 2026 sales forecast to €36–40 billion, and has zero competitors and zero substitutes.
$ARM: ARM collects royalties on every chip in the stack, Nvidia Vera, Amazon Graviton, Google Axion, and Microsoft Cobalt are all ARM architecture.
$VRT: Vertiv dominates liquid cooling and power distribution, and the rack density upgrade from 40 kW to 500+ kW physically cannot happen without their systems, with the liquid cooling market growing from $5.5 billion to $15.75 billion by 2030.
$ETN: Eaton provides the electrical infrastructure, switchgear, and UPS systems that every gigawatt scale AI factory requires before a single GPU ever gets turned on.
$EQIX: Equinix is the world's largest data center REIT with 260+ facilities across 72 metros, and as hyperscalers require neutral interconnection points globally, Equinix owns the real estate they cannot build around.
$FIX: Comfort Systems saw technology-related work hit 45% of total revenues in 2025, with its backlog doubling year over year to a record $12 billion as data center construction accelerates.
$PWR: Quanta Services entered 2026 with a record backlog of $48.5 billion, with management stating that utilities are "being asked to double in size", Quanta builds the transmission lines and data center electrical infrastructure that makes the entire buildout possible.
$VST: Vistra locked in a 20-year nuclear power purchase agreement with Meta for over 2,600 MW, secured a separate deal with AWS, and received upgrades from both Goldman Sachs and Jefferies following the announcements.
$CEG: Constellation Energy owns the largest nuclear fleet in the United States and signed the landmark deal to restart Three Mile Island exclusively for Microsoft, with hyperscalers now paying 3–4x spot electricity prices just to secure 20-year nuclear contracts.
$GEV: GE Vernova supplies the gas turbines, grid equipment, and electrification infrastructure that every new power plant connecting to the grid requires, with its backlog now growing faster than the company can execute.
$NVDA: Nvidia captures 75% of every compute dollar spent at 75% gross margins, making it the most monopolistic position in enterprise technology history.
$TSM: Every Nvidia chip, every custom hyperscaler ASIC, and every ARM based server CPU gets manufactured at TSMC, which raised its 2026 capex guidance to $52–56 billion just to keep up with AI demand.
$ASML: ASML is the only company on earth making the EUV lithography machines that TSMC needs to produce 2nm chips, raised its 2026 sales forecast to €36–40 billion, and has zero competitors and zero substitutes.
$ARM: ARM collects royalties on every chip in the stack, Nvidia Vera, Amazon Graviton, Google Axion, and Microsoft Cobalt are all ARM architecture.
$VRT: Vertiv dominates liquid cooling and power distribution, and the rack density upgrade from 40 kW to 500+ kW physically cannot happen without their systems, with the liquid cooling market growing from $5.5 billion to $15.75 billion by 2030.
$ETN: Eaton provides the electrical infrastructure, switchgear, and UPS systems that every gigawatt scale AI factory requires before a single GPU ever gets turned on.
$EQIX: Equinix is the world's largest data center REIT with 260+ facilities across 72 metros, and as hyperscalers require neutral interconnection points globally, Equinix owns the real estate they cannot build around.
$FIX: Comfort Systems saw technology-related work hit 45% of total revenues in 2025, with its backlog doubling year over year to a record $12 billion as data center construction accelerates.
$PWR: Quanta Services entered 2026 with a record backlog of $48.5 billion, with management stating that utilities are "being asked to double in size", Quanta builds the transmission lines and data center electrical infrastructure that makes the entire buildout possible.
$VST: Vistra locked in a 20-year nuclear power purchase agreement with Meta for over 2,600 MW, secured a separate deal with AWS, and received upgrades from both Goldman Sachs and Jefferies following the announcements.
$CEG: Constellation Energy owns the largest nuclear fleet in the United States and signed the landmark deal to restart Three Mile Island exclusively for Microsoft, with hyperscalers now paying 3–4x spot electricity prices just to secure 20-year nuclear contracts.
$GEV: GE Vernova supplies the gas turbines, grid equipment, and electrification infrastructure that every new power plant connecting to the grid requires, with its backlog now growing faster than the company can execute.
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🇨🇭 Global One SI - Prompt
$BB The EPIC breakout seems inevitable. For weeks: No fear. No distribution. No meaningful selling. Just relentless accumulation. Now price is pressing directly into the next major resistance zone near $12. The interesting part? Most traders are still…
$BB
The most interesting thing here isn’t today’s pullback.
It’s what happened BEFORE it.
✅ Massive breakout above $6.95
✅ Volume exploded to multi-month highs
✅ OBV continues trending higher
✅ TTM Squeeze momentum remains strong
✅ Higher highs and higher lows remain intact
After a move from the low $3s to nearly $11, some profit-taking is completely normal.
What matters now is whether buyers defend the breakout zone.
As long as $8.00-$8.20 holds, the broader trend remains firmly in the bulls’ control.
The bigger picture hasn’t changed:
Cybersecurity.
IoT.
Secure communications.
Government contracts.
And now the chart finally reflects it.
A healthy pullback after a 200%+ move isn’t a breakdown.
It’s how strong trends reset.
Watching closely for the next leg👀
$BB #BlackBerry #Stocks #Trading
The most interesting thing here isn’t today’s pullback.
It’s what happened BEFORE it.
✅ Massive breakout above $6.95
✅ Volume exploded to multi-month highs
✅ OBV continues trending higher
✅ TTM Squeeze momentum remains strong
✅ Higher highs and higher lows remain intact
After a move from the low $3s to nearly $11, some profit-taking is completely normal.
What matters now is whether buyers defend the breakout zone.
As long as $8.00-$8.20 holds, the broader trend remains firmly in the bulls’ control.
The bigger picture hasn’t changed:
Cybersecurity.
IoT.
Secure communications.
Government contracts.
And now the chart finally reflects it.
A healthy pullback after a 200%+ move isn’t a breakdown.
It’s how strong trends reset.
Watching closely for the next leg👀
$BB #BlackBerry #Stocks #Trading
$SOAR
The most bullish thing here isn't the 20% move.
It's what happened AFTER the move.
No panic selling.
No major breakdown.
No heavy distribution.
Just tight consolidation near the highs while volume remains elevated.
Breakout → Compression → Expansion.
Watching closely. 👀
#SOAR #Stocks #Trading #Momentum
The most bullish thing here isn't the 20% move.
It's what happened AFTER the move.
No panic selling.
No major breakdown.
No heavy distribution.
Just tight consolidation near the highs while volume remains elevated.
Breakout → Compression → Expansion.
Watching closely. 👀
#SOAR #Stocks #Trading #Momentum
$DGNX
Most traders still don't understand this story.
This isn't just ESG software.
It's infrastructure for an increasingly regulated digital economy.
As reporting requirements expand globally, companies need:
• compliance automation
• governance tracking
• supply-chain transparency
• sustainability reporting
The market is obsessed with AI.
But AI still needs trusted data, reporting systems and compliance layers.
That's where names like $DGNX become interesting.
Technically, the stock appears to be stabilizing after a major run.
Volume remains elevated.
The next expansion phase could surprise people if the broader narrative gains traction.
Most traders still don't understand this story.
This isn't just ESG software.
It's infrastructure for an increasingly regulated digital economy.
As reporting requirements expand globally, companies need:
• compliance automation
• governance tracking
• supply-chain transparency
• sustainability reporting
The market is obsessed with AI.
But AI still needs trusted data, reporting systems and compliance layers.
That's where names like $DGNX become interesting.
Technically, the stock appears to be stabilizing after a major run.
Volume remains elevated.
The next expansion phase could surprise people if the broader narrative gains traction.
🇨🇭 Global One SI - Prompt
$SPY $800 is coming, IS COMING
BREAKING 🚨
According to FinTwit, CNBC, YouTube gurus and the guy who predicted the last 47 crashes...
The market is crashing again.
Meanwhile $SPY:
✔ Still above key support
✔ Still inside a rising channel
✔ Still making higher highs and higher lows
✔ Pulling back after a monster rally
Apparently every healthy pullback is now the end of civilization.
I'll continue watching price. 😎
According to FinTwit, CNBC, YouTube gurus and the guy who predicted the last 47 crashes...
The market is crashing again.
Meanwhile $SPY:
✔ Still above key support
✔ Still inside a rising channel
✔ Still making higher highs and higher lows
✔ Pulling back after a monster rally
Apparently every healthy pullback is now the end of civilization.
I'll continue watching price. 😎
🇨🇭 Global One SI - Prompt
Elon Musk just confirmed SpaceXAI’s Colossus 2 is currently training 7 different AI models IN PARALLEL • Imagine V2 • 2 variants of 1T • 2 variants of 1.5T • 6T • 10T (Yes, 10 TRILLION parameters) Colossus 2 is the world’s first 1-Gigawatt…
Colossus the World’s Largest Coherent Supercomputer built faster and cheaper than industry benchmarks
Key highlights:
- 1st Gigawatt Scale Training Cluster
- 1st Gigawatt Scale Megapack Battery Installation
- 1st to deploy GB200s & GB300s at significant scale
- 1GW Nameplate Compute Draw
Elon and the SpaceXAI moving at a speed that rest of the industry can only dream of..... scaling AI compute at a speed and efficiency the industry has never seen before
Key highlights:
- 1st Gigawatt Scale Training Cluster
- 1st Gigawatt Scale Megapack Battery Installation
- 1st to deploy GB200s & GB300s at significant scale
- 1GW Nameplate Compute Draw
Elon and the SpaceXAI moving at a speed that rest of the industry can only dream of..... scaling AI compute at a speed and efficiency the industry has never seen before
🇨🇭 Global One SI - Prompt
Google $GOOGL invested $900M into SpaceX back in 2015 as part of a $1B funding round with Fidelity, securing ~7.5% ownership SpaceX was valued at ~$10B-$12B when Google invested SpaceX is now going public at a valuation of ~$1.75 Trillion Google doesnt…
SpaceX just signed a massive compute deal with Google
On June 5, 2026, SpaceX entered into a Cloud Service Agreement to provide Google with access to approximately 110,000 NVIDIA GPUs along with CPUs, memory, and related infrastructure
Google will pay SpaceX $920 million per month from October 2026 through June 2029
This is a massive multi-year commitment
SpaceX is rapidly expanding into large-scale AI compute infrastructure
On June 5, 2026, SpaceX entered into a Cloud Service Agreement to provide Google with access to approximately 110,000 NVIDIA GPUs along with CPUs, memory, and related infrastructure
Google will pay SpaceX $920 million per month from October 2026 through June 2029
This is a massive multi-year commitment
SpaceX is rapidly expanding into large-scale AI compute infrastructure
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🇨🇭 Global One SI - Prompt
SpaceX just signed a massive compute deal with Google On June 5, 2026, SpaceX entered into a Cloud Service Agreement to provide Google with access to approximately 110,000 NVIDIA GPUs along with CPUs, memory, and related infrastructure Google will pay SpaceX…
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This is WILD!
One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this).
That is $11 billion per year and up to $30 billion over the life of the contract.
This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029.
Two of the most consequential AI labs in the world combined committed value over $70 billion.
The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic?
Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline.
That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost.
The answer is that the compute itself was always the product.
Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race.
xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days.
That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck.
The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined.
Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first.
The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026.
Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue.
That changes the entire valuation conversation of SpaceX completely!
One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this).
That is $11 billion per year and up to $30 billion over the life of the contract.
This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029.
Two of the most consequential AI labs in the world combined committed value over $70 billion.
The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic?
Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline.
That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost.
The answer is that the compute itself was always the product.
Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race.
xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days.
That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck.
The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined.
Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first.
The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026.
Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue.
That changes the entire valuation conversation of SpaceX completely!
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🚨 TRUMP IN TALKS TO NATIONALIZE AI LABS
>“american public essentially becomes a partner with the ai companies”
>openai. anthropic. spacex
>“i have spoken to ALL of them”
>“they’re all coming to the white house next week”
>reporter: “bernie sanders proposed 50% public-private partnership”
>trump: “we’re not that far apart”
ITS HAPPENING
>“american public essentially becomes a partner with the ai companies”
>openai. anthropic. spacex
>“i have spoken to ALL of them”
>“they’re all coming to the white house next week”
>reporter: “bernie sanders proposed 50% public-private partnership”
>trump: “we’re not that far apart”
ITS HAPPENING
👀1
🇺🇸🇨🇳🇮🇷 The Financial Times reports Anthropic has embedded roughly 6 engineers inside the NSA to deploy its Mythos AI model for offensive cyber operations, targeting networks in China and Iran.
Mythos was withheld from public release specifically because of its ability to autonomously discover and exploit vulnerabilities. The NSA is now using exactly that capability.
Meanwhile Anthropic is simultaneously suing the Pentagon after the DoD labeled the company a supply chain risk (the first time a US company has received that designation) for refusing to allow its Claude models to be used for mass surveillance and autonomous weapons.
Mythos was withheld from public release specifically because of its ability to autonomously discover and exploit vulnerabilities. The NSA is now using exactly that capability.
Meanwhile Anthropic is simultaneously suing the Pentagon after the DoD labeled the company a supply chain risk (the first time a US company has received that designation) for refusing to allow its Claude models to be used for mass surveillance and autonomous weapons.
Google and Anthropic paying SpaceX $2.17 Billion per month for compute is literally just the tip of the iceberg.
Deals like this are going to explode once SpaceX AI satellites scale up.
Morgan Stanley projects SpaceX revenue hitting $3.4 Trillion by 2040. Revenue is going to be nuts.
I don’t see how SpaceX doesn’t become the most valuable and important company in the world in the next couple of years. They’re the passageway to cheaper, more sustainable AI compute for all companies.
Deals like this are going to explode once SpaceX AI satellites scale up.
Morgan Stanley projects SpaceX revenue hitting $3.4 Trillion by 2040. Revenue is going to be nuts.
I don’t see how SpaceX doesn’t become the most valuable and important company in the world in the next couple of years. They’re the passageway to cheaper, more sustainable AI compute for all companies.
🇨🇭 Global One SI - Prompt
Google and Anthropic paying SpaceX $2.17 Billion per month for compute is literally just the tip of the iceberg. Deals like this are going to explode once SpaceX AI satellites scale up. Morgan Stanley projects SpaceX revenue hitting $3.4 Trillion by 2040.…
Anthropic previously committed to paying SpaceX $1.25B per month for GPU compute.
With the new Google cloud deal that was disclosed today, adding $920mil monthly, the two AI labs (Google + Anthropic) together are now collectively paying SpaceX $2.17B per month, a huge $26 billion annualized revenue run rate.
To note, Alphabet also has made a huge gain from backing SpaceX. Google invested about $900M in SpaceX in Jan-2015, for roughly 7%, when SpaceX’s valuation was around $12 B.
And SpaceX now targets a $1.75T IPO valuation. A reported 6.11% Google stake at 12-25 would be worth about $107B at $1.75T, while a diluted 5% stake would be worth about $87.5B .
Against a roughly $900M entry cost, that implies around 97x to 119x on paper, before taxes, lockups, dilution, or any discount investors apply after trading starts.
The business shift is also important: Alphabet first backed SpaceX in 2015, partly for satellite internet, but the upside now includes Starlink, launch dominance, and newly filed AI compute contracts.
With the new Google cloud deal that was disclosed today, adding $920mil monthly, the two AI labs (Google + Anthropic) together are now collectively paying SpaceX $2.17B per month, a huge $26 billion annualized revenue run rate.
To note, Alphabet also has made a huge gain from backing SpaceX. Google invested about $900M in SpaceX in Jan-2015, for roughly 7%, when SpaceX’s valuation was around $12 B.
And SpaceX now targets a $1.75T IPO valuation. A reported 6.11% Google stake at 12-25 would be worth about $107B at $1.75T, while a diluted 5% stake would be worth about $87.5B .
Against a roughly $900M entry cost, that implies around 97x to 119x on paper, before taxes, lockups, dilution, or any discount investors apply after trading starts.
The business shift is also important: Alphabet first backed SpaceX in 2015, partly for satellite internet, but the upside now includes Starlink, launch dominance, and newly filed AI compute contracts.
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