Jensen Huang just told you where the bottleneck is.
Not in one investment. In a pattern.
In March alone, $NVDA committed $2 billion each to $LITE and $COHR, both photonics companies.
Then $2 billion to $MRVL. Then $2 billion to $SNPS. A stake in Nokia. A stake in OpenAI. A stake in Intel.
That is over $10 billion deployed in a single month. All in the infrastructure layer of AI.
All in the components that connect GPUs to each other, to memory, and to the world.
Here is what that pattern tells you.
The GPU is not the bottleneck. Nvidia is building millions of them.
The bottleneck is the optical interconnect. The laser components. The silicon photonics that let GPUs in a data center talk to each other at the speed and scale required for modern AI training runs.
$LITR makes the lasers that go inside data center transceivers. $COHR makes the optical networking products that connect those transceivers across rack-scale systems. $MRVL makes the custom silicon that manages how those signals get routed and processed.
This is the physical plumbing of the AI factory, and Nvidia just spent $8 billion telling you that it is not optional.
Now look at $AAOI.
Applied Optoelectronics designs and manufactures its own Indium Phosphide lasers in-house. It has guided for $1 billion in revenue in 2026, a 119% increase in a single year.
It has already received a greater than $200 million volume order for its 1.6T transceivers.
And it just filed to receive a new technical presentation at an upcoming industry event.
Nvidia has not made a direct investment in $AAOI. That is the gap.
But the company is building exactly what the Nvidia investment pattern says the world needs:
Vertically integrated laser manufacturing. High-speed transceiver products for hyperscaler data centers. And a 2026 revenue ramp that puts it squarely in the photonics buildout.
The risk is real: the expanded ATM equity program ($500M) creates dilution pressure, and an insider sold 29,000 shares in mid-March. Every bull case has a catch.
But if the thesis is right, and $8 billion from Nvidia in a single month says it probably is, then $AAOI is one of the few names with the product, the scale, and the manufacturing integration to matter.
The Nvidia pattern matters.
-BP
This is not financial advice. Please do your own research before investing.
Not in one investment. In a pattern.
In March alone, $NVDA committed $2 billion each to $LITE and $COHR, both photonics companies.
Then $2 billion to $MRVL. Then $2 billion to $SNPS. A stake in Nokia. A stake in OpenAI. A stake in Intel.
That is over $10 billion deployed in a single month. All in the infrastructure layer of AI.
All in the components that connect GPUs to each other, to memory, and to the world.
Here is what that pattern tells you.
The GPU is not the bottleneck. Nvidia is building millions of them.
The bottleneck is the optical interconnect. The laser components. The silicon photonics that let GPUs in a data center talk to each other at the speed and scale required for modern AI training runs.
$LITR makes the lasers that go inside data center transceivers. $COHR makes the optical networking products that connect those transceivers across rack-scale systems. $MRVL makes the custom silicon that manages how those signals get routed and processed.
This is the physical plumbing of the AI factory, and Nvidia just spent $8 billion telling you that it is not optional.
Now look at $AAOI.
Applied Optoelectronics designs and manufactures its own Indium Phosphide lasers in-house. It has guided for $1 billion in revenue in 2026, a 119% increase in a single year.
It has already received a greater than $200 million volume order for its 1.6T transceivers.
And it just filed to receive a new technical presentation at an upcoming industry event.
Nvidia has not made a direct investment in $AAOI. That is the gap.
But the company is building exactly what the Nvidia investment pattern says the world needs:
Vertically integrated laser manufacturing. High-speed transceiver products for hyperscaler data centers. And a 2026 revenue ramp that puts it squarely in the photonics buildout.
The risk is real: the expanded ATM equity program ($500M) creates dilution pressure, and an insider sold 29,000 shares in mid-March. Every bull case has a catch.
But if the thesis is right, and $8 billion from Nvidia in a single month says it probably is, then $AAOI is one of the few names with the product, the scale, and the manufacturing integration to matter.
The Nvidia pattern matters.
-BP
This is not financial advice. Please do your own research before investing.
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RT KaizenInvestor
I have written a full article on the AI chip supply chain.
The supply chain is structured into 4 different phases with 13 layers:
1. Raw Materials: $SHECY, $SUOPY, GlobalWafers, $WAF.DE, $SHWDF, $AXTI, $IQE
2. Manufacturing Equipment: $ASML, $ASM.AS, $AMAT, $LRCX, $KLAC
3. EDA & Core Intellectual Property: $SNPS, $CDNS, $ARM, $RMBS
4. Chip Design: $NVDA, $AMD, $INTC, $QCOM
5. Foundries: $TSM, Samsung Semiconductor, $SMIC
6. Memory and HBM: SK Hynix, Samsung Electronics, $MU
7. Packaging and OSAT: ASE Technology, $AMKR, JCET Group
8. Server and Rack Integration: $SMCI, $DELL, $HPE, Foxconn
9. Networking Silicon: $AVGO, $MRVL, $CSCO, $ANET
10. Photonics and Optical Components: Ayar Labs, $ALAB, $CRDO, $COHR, $LITE
11. Power, Thermal management and Grid: $VRT, $MOD, $NVT, $SU.PA, $IREN, $CIFR
12. Hyperscalers: $AMZN, $GOOGL, $MSFT, $META
13. AI Storage, platforms and Data: VAST data, Weka, NetAPP, $PLTR, Blue Yonder, $KXSCF
The article covers it all.
KaizenInvestor: http://x.com/i/article/2029218092270628864
I have written a full article on the AI chip supply chain.
The supply chain is structured into 4 different phases with 13 layers:
1. Raw Materials: $SHECY, $SUOPY, GlobalWafers, $WAF.DE, $SHWDF, $AXTI, $IQE
2. Manufacturing Equipment: $ASML, $ASM.AS, $AMAT, $LRCX, $KLAC
3. EDA & Core Intellectual Property: $SNPS, $CDNS, $ARM, $RMBS
4. Chip Design: $NVDA, $AMD, $INTC, $QCOM
5. Foundries: $TSM, Samsung Semiconductor, $SMIC
6. Memory and HBM: SK Hynix, Samsung Electronics, $MU
7. Packaging and OSAT: ASE Technology, $AMKR, JCET Group
8. Server and Rack Integration: $SMCI, $DELL, $HPE, Foxconn
9. Networking Silicon: $AVGO, $MRVL, $CSCO, $ANET
10. Photonics and Optical Components: Ayar Labs, $ALAB, $CRDO, $COHR, $LITE
11. Power, Thermal management and Grid: $VRT, $MOD, $NVT, $SU.PA, $IREN, $CIFR
12. Hyperscalers: $AMZN, $GOOGL, $MSFT, $META
13. AI Storage, platforms and Data: VAST data, Weka, NetAPP, $PLTR, Blue Yonder, $KXSCF
The article covers it all.
KaizenInvestor: http://x.com/i/article/2029218092270628864
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ALTIMETER CAPITAL'S Q4:
Bought new positions in:
- $CRWV 3,213,230 shares
- $SHOP 570,210 shares
- $BE 260,730 shares
Sold completely:
- $ARM 1,098,139 shares
- $BABA 969,100 shares
- $CART 3,324,951 shares
- $SNPS 258,280 shares
Added to:
- $NVDA 454,875 shares
- $MSFT 115,260 shares
- $AMZN 48,186 shares
- $TSM 150,550 shares
- $GOOGL 206,158 shares
- $MELI 30,639 shares
Bought new positions in:
- $CRWV 3,213,230 shares
- $SHOP 570,210 shares
- $BE 260,730 shares
Sold completely:
- $ARM 1,098,139 shares
- $BABA 969,100 shares
- $CART 3,324,951 shares
- $SNPS 258,280 shares
Added to:
- $NVDA 454,875 shares
- $MSFT 115,260 shares
- $AMZN 48,186 shares
- $TSM 150,550 shares
- $GOOGL 206,158 shares
- $MELI 30,639 shares
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THE HBM sector could grow from $4B in 2023 to $130B by 2033 according to Bloomberg.
These companies are set to benefit:
The Big 3:
$MU | Micron
$SSNLF | Samsung
$HXSCL | SK Hynix
Ecosystem:
$ASML | ASML
$TSM | TSMC
$INTC | Intel
$AMD | Advanced Micro Devices
$NVDA | Nvidia
Advanced Equipment Suppliers
$BESI | BE Semiconductor
Hanmi Semiconductor
Disco Corp
Packaging:
$AMKR | Amkor
ASE Technology Holding
JCET Group
Design and IP
$RMBS | Rambus
$CDNS | Cadence Design Systems
$SNPS | Synopsys
Others:
$MRVL | Marvell
Fujitsu
Powertech Technology
$AMAT | Applied Materials
$KLIC | Kulicke and Soffa
These companies are set to benefit:
The Big 3:
$MU | Micron
$SSNLF | Samsung
$HXSCL | SK Hynix
Ecosystem:
$ASML | ASML
$TSM | TSMC
$INTC | Intel
$AMD | Advanced Micro Devices
$NVDA | Nvidia
Advanced Equipment Suppliers
$BESI | BE Semiconductor
Hanmi Semiconductor
Disco Corp
Packaging:
$AMKR | Amkor
ASE Technology Holding
JCET Group
Design and IP
$RMBS | Rambus
$CDNS | Cadence Design Systems
$SNPS | Synopsys
Others:
$MRVL | Marvell
Fujitsu
Powertech Technology
$AMAT | Applied Materials
$KLIC | Kulicke and Soffa
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HBM Supply Chain Overview
$MU - Micron
$HXSCL - SK Hynix
$SSNLF - Samsung
$TSM - TSMC
$NVDA - Nvidia
$AMD - Advanced Micro Devices
$SNPS - Synopsys
$BESI - BE Semiconductor Industries
$AMAT - Applied Materials
$AVGO - Broadcom
$MRVL - Marvell
$DD - DuPont
$MSFT - Microsoft
$GOOGL - Alphabet
$META - META
$LCRX - Lam Research
$MU - Micron
$HXSCL - SK Hynix
$SSNLF - Samsung
$TSM - TSMC
$NVDA - Nvidia
$AMD - Advanced Micro Devices
$SNPS - Synopsys
$BESI - BE Semiconductor Industries
$AMAT - Applied Materials
$AVGO - Broadcom
$MRVL - Marvell
$DD - DuPont
$MSFT - Microsoft
$GOOGL - Alphabet
$META - META
$LCRX - Lam Research
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RT TacticzHazel
HBM Supply Chain Overview
$MU - Micron
$HXSCL - SK Hynix
$SSNLF - Samsung
$TSM - TSMC
$NVDA - Nvidia
$AMD - Advanced Micro Devices
$SNPS - Synopsys
$BESI - BE Semiconductor Industries
$AMAT - Applied Materials
$AVGO - Broadcom
$MRVL - Marvell
$DD - DuPont
$MSFT - Microsoft
$GOOGL - Alphabet
$META - META
$LCRX - Lam Research
HBM Supply Chain Overview
$MU - Micron
$HXSCL - SK Hynix
$SSNLF - Samsung
$TSM - TSMC
$NVDA - Nvidia
$AMD - Advanced Micro Devices
$SNPS - Synopsys
$BESI - BE Semiconductor Industries
$AMAT - Applied Materials
$AVGO - Broadcom
$MRVL - Marvell
$DD - DuPont
$MSFT - Microsoft
$GOOGL - Alphabet
$META - META
$LCRX - Lam Research
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