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Truman by Truflation is an AI harness for real-time economic data: helping you see where the economy is heading before the official releases, and turns that view into trading, hedging strategies, reports and real-time signals. x.com/i/article/2113…
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Stefan Rust retweeted
Diversity is not our strength.
A clip 👇 from the Strictly Education episode where I explain the concept of the title of my book - Diversity is not our strength.
It is out next Thursday - November 12th!
You can pre-order it on Amazon:
amzn.eu/d/00go9Uff
A clip 👇 from the Strictly Education episode where I explain the concept of the title of my book - Diversity is not our strength.
It is out next Thursday - November 12th!
You can pre-order it on Amazon:
amzn.eu/d/00go9Uff
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Stefan Rust retweeted
🚨 NOMURA: THE MARKET MAY BE MISREADING THE ENTIRE AI DATA CENTER BOOM.
Big Tech is spending hundreds of billions on AI infrastructure. Free cash flow is under pressure. Investors are worried the AI boom isn't profitable.
But Nomura argues something very different:
AI dy highly profitable. The cash flows simply haven't caught up with the investment cycle.
Here's the breakdown 🧵👇
1/ The profitability numbers tell a different story.
According to Nomura's November 2 report:
• AWS revenue: $46B, +39% YoY
• AWS operating margin: 38.7%
• Google Cloud revenue: $30.7B, +103% YoY
• Google Cloud operating margin: 36.2%
• Azure growth: +48%
These are not the numbers of an industry struggling to generate operating profits.
2/ The real issue is timing, not necessarily profitability.
Big Tech pays for data centers, GPUs, networking and memory upfront.
Revenue and cash generation arrive over subsequent years.
That creates a classic free-cash-flow J-curve:
CAPEX accelerates → FCF deteriorates → Infrastructure generates revenue → Investment growth moderates → FCF recovers.
3/ Nomura expects operating income to surge before free cash flow.
Its illustrative forecasts suggest operating-income growth could peak around 2029, while FCF growth accelerates later.
That distinction matters enormously for investors valuing AI infrastructure businesses.
4/ Why can't hyperscalers simply stop spending?
Because AI increasingly threatens their existing businesses.
Search, advertising, shopping, software and cloud services are being reshaped by AI agents.
Companies that underinvest today risk surrendering customers, data and market share tomorrow.
5/ Demand may not be the primary constraint. Capacity is.
Nomura cites enormous contracted cloud backlogs:
• Google Cloud: $560.8B
• AWS: ~$496B
• Microsoft commercial RPO: $677B
These figures have different reporting definitions, but collectively illustrate substantial contracted demand.
6/ Big Tech has financing alternatives to cutting CAPEX.
Nomura identifies four paths:
① Accelerate investment
② Freeze CAPEX
③ Use joint ventures and operating leases
④ Monetize contracted future cash flows
Its preferred expectation is continued investment supported by increasingly sophisticated financing structures.
7/ Oracle offers a real-world example.
Nomura highlights Oracle's combination of long-term leases, project financing, customer prepayments and customer-provided hardware.
The broader message: hyperscalers can restructure who finances infrastructure without necessarily reducing how much infrastructure gets built.
8/ This distinction is critical for semiconductors and memory.
Negative hyperscaler FCF does not automatically imply collapsing demand for GPUs, DRAM, HBM or other data-center components.
If spending continues through financing structures, semiconductor demand can remain resilient even while hyperscalers optimize their balance sheets.
9/ What could invalidate the thesis?
A genuine compute oversupply.
Watch:
• Cloud revenue growth
• GPU rental pricing
• Contracted backlog growth
• Data-center utilization
• Memory supply commitments
• Hyperscaler investment guidance
These are more useful indicators than negative FCF alone.
10/ The investment takeaway:
The key question is not simply:
"Why is Big Tech burning so much cash?"
It is:
"Will the infrastructure being built today generate enough durable cash flow to justify its cost of capital?"
Nomura is optimistic.
But the answer ultimately depends on pricing, utilization, investment discipline and the durability of AI demand.
The AI infrastructure cycle may be a cash-flow timing problem rather than a profitability crisis.
$NVDA $MU $GOOGL $MSFT $AMZN $META $ORCL
Big Tech is spending hundreds of billions on AI infrastructure. Free cash flow is under pressure. Investors are worried the AI boom isn't profitable.
But Nomura argues something very different:
AI dy highly profitable. The cash flows simply haven't caught up with the investment cycle.
Here's the breakdown 🧵👇
1/ The profitability numbers tell a different story.
According to Nomura's November 2 report:
• AWS revenue: $46B, +39% YoY
• AWS operating margin: 38.7%
• Google Cloud revenue: $30.7B, +103% YoY
• Google Cloud operating margin: 36.2%
• Azure growth: +48%
These are not the numbers of an industry struggling to generate operating profits.
2/ The real issue is timing, not necessarily profitability.
Big Tech pays for data centers, GPUs, networking and memory upfront.
Revenue and cash generation arrive over subsequent years.
That creates a classic free-cash-flow J-curve:
CAPEX accelerates → FCF deteriorates → Infrastructure generates revenue → Investment growth moderates → FCF recovers.
3/ Nomura expects operating income to surge before free cash flow.
Its illustrative forecasts suggest operating-income growth could peak around 2029, while FCF growth accelerates later.
That distinction matters enormously for investors valuing AI infrastructure businesses.
4/ Why can't hyperscalers simply stop spending?
Because AI increasingly threatens their existing businesses.
Search, advertising, shopping, software and cloud services are being reshaped by AI agents.
Companies that underinvest today risk surrendering customers, data and market share tomorrow.
5/ Demand may not be the primary constraint. Capacity is.
Nomura cites enormous contracted cloud backlogs:
• Google Cloud: $560.8B
• AWS: ~$496B
• Microsoft commercial RPO: $677B
These figures have different reporting definitions, but collectively illustrate substantial contracted demand.
6/ Big Tech has financing alternatives to cutting CAPEX.
Nomura identifies four paths:
① Accelerate investment
② Freeze CAPEX
③ Use joint ventures and operating leases
④ Monetize contracted future cash flows
Its preferred expectation is continued investment supported by increasingly sophisticated financing structures.
7/ Oracle offers a real-world example.
Nomura highlights Oracle's combination of long-term leases, project financing, customer prepayments and customer-provided hardware.
The broader message: hyperscalers can restructure who finances infrastructure without necessarily reducing how much infrastructure gets built.
8/ This distinction is critical for semiconductors and memory.
Negative hyperscaler FCF does not automatically imply collapsing demand for GPUs, DRAM, HBM or other data-center components.
If spending continues through financing structures, semiconductor demand can remain resilient even while hyperscalers optimize their balance sheets.
9/ What could invalidate the thesis?
A genuine compute oversupply.
Watch:
• Cloud revenue growth
• GPU rental pricing
• Contracted backlog growth
• Data-center utilization
• Memory supply commitments
• Hyperscaler investment guidance
These are more useful indicators than negative FCF alone.
10/ The investment takeaway:
The key question is not simply:
"Why is Big Tech burning so much cash?"
It is:
"Will the infrastructure being built today generate enough durable cash flow to justify its cost of capital?"
Nomura is optimistic.
But the answer ultimately depends on pricing, utilization, investment discipline and the durability of AI demand.
The AI infrastructure cycle may be a cash-flow timing problem rather than a profitability crisis.
$NVDA $MU $GOOGL $MSFT $AMZN $META $ORCL
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Will 2027 reflect the 2000-2003 era post dotcom era and see a capital rotation moving from tech and AI to crypto, metals, energy and commodities seeking opportunities after taking profit from SpaceX and Anthropic gain. wdyt?
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the cabal is looking after each other. It's musical chairs. France PM -> ECB president -> WEF.
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Inflation isn't drifting higher. It's climbing stairs.
Truflation US CPI is up 99 bp since August (2.27% to 3.26%), and 78 bp of it came in four days. Each step resets the floor.
Don't wait for the 10 Nov CPI print. Count the steps. 👇 x.com/i/article/2115…
Truflation US CPI is up 99 bp since August (2.27% to 3.26%), and 78 bp of it came in four days. Each step resets the floor.
Don't wait for the 10 Nov CPI print. Count the steps. 👇 x.com/i/article/2115…
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and if you're just into the outlook. you get the quick summary T-1 @truflation spot on again. Sign up here truflation.com/t-1
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this is a good interview.
Revolut founder Nik Storonsky says one of his investors tried forcing him to fire his best person:
“He was forcing me to fire my best person, because he was too hardcore.”
“Of course I didn’t fire him. I said no.”
“Pretty early on I realized that quite a few VCs, they are not hey can be actually dangerous for the company if they have enough power. So every single investment round, we just remove VCs.”
“Now I don't have any VCs on the board. I think that's the best. You always need to have operators rather than VCs on the board, people who actually build business and who know how to do things.”
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Mobile phones Prices down 92% in 27 years. Adoption went from 20 to 113 per 100 people.
LLM tokens Prics down ~10x a year. Google’s monthly token volume went from 9.7T to 3.2 quadrillion in 2 years.
Same curve, 10x faster clock, and tokens have no one-per-person ceiling.
LLM tokens Prics down ~10x a year. Google’s monthly token volume went from 9.7T to 3.2 quadrillion in 2 years.
Same curve, 10x faster clock, and tokens have no one-per-person ceiling.
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the data is telling up prices for compute is still going up! Economics interprets this as demand > supply!
In this week’s Reference Price newsletter we flagged an interesting “generational slope” in on-demand GPU rental rates. Over the past week the increases were ordered by chip generation: B300 +8.6% → B200 +4.7% → H200 +4.2% → H100 +2.5% → A100 +2.0%.
To the extent there is a pears concentrated at the newest chips. Long-context inference is increasingly memory-bound, which favors the chips with the most high-bandwidth memory — and that memory remains tight. The question is whether rents are starting to separate by capability rather than rising uniformly. Worth watching closely to see if the slope persists.
Over the weekend @drwconvexity and @GavinSBaker discussed the “crunch spread” (relative moves in GPU rental rates vs. usage-weighted token prices). @deedydas also noted that the most advanced GPUs continue to deliver rising token throughput efficiency, which raises a related question: how much of the rental premium is justified by throughput gains versus pure scarcity of the newest hardware?
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and AI will become an integral part of household spending very much like utilities, internet, communications #CPI
"It's early days" (drink)... AI adoption appears to be in early stages (Fidelity)
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Truman by @truflation can simplify this. Or try the salary calculator truflation.com/salary-calcula…
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