<p>Bros, <span class="security-tag" type="security-tag" counter_id="ST/US/TSLA" name="Tesla, Inc." trend="0" language="en">$Tesla(TSLA.US)</span> is still falling so much today! I get trapped every time I enter. Coming to pick people up. Try a short position, let's go!</p>

🚀📊 AI chips are not a single-point opportunity, but an entire capital transmission chain.

If you're still using "which company won this quarter" to understand AI, you're already behind.
What's really happening is a multi-year torrent of capital flowing through a highly coupled, inseparable chip ecosystem.

When you change your perspective, the question is no longer "who's rising fast," but:
Which links will repeatedly gain pricing power by becoming bottlenecks?

At the very upstream, it determines the physical limits and yield boundaries.
Design tools and IP are not just software; they are tickets to advanced processes.
$Arm(ARM.US) $Cadence Design(CDNS.US) $Synopsys(SNPS.US)
The more advanced the node, the lower the substitutability, the higher the AI intensity, and the stronger the lock-in effect.

The same goes for the equipment layer.
$ASML(ASML.US) $Applied Materials(AMAT.US) $Lam Research(LRCX.US) $KLA(KLAC.US) $Teradyne(TER.US)
As long as capacity expands, this layer hardly needs to judge "whose chips sell best," because everyone must pass this gate first.

Entering the midstream, capital density begins to rise sharply, and it's also where market attention is most focused.

Manufacturing and Advanced Packaging
$Taiwan Semiconductor(TSM.US) $Intel(INTC.US) $GlobalFoundries(GFS.US) $Amkor Tech(AMKR.US)
AI is shifting value from "pure logic processes" towards packaging, heterogeneous integration, and yield control.
Advanced packaging is no longer a supporting role but a core variable determining system performance.

Compute Layer
$NVIDIA(NVDA.US) $AMD(AMD.US) $Broadcom(AVGO.US) $Marvell Tech(MRVL.US)
This layer directly interfaces with model demand. As long as the expansion speed of training and inference continues to outpace supply, pricing power won't easily disappear.

Interconnect is being severely underestimated.
$Credo Tech(CRDO.US) $Astera Labs(ALAB.US) $Coherent Corp.(COHR.US) $Ciena(CIEN.US) $Amphenol(APH.US) $Lumentum(LITE.US)
When cluster scale expands, the real bottleneck often isn't compute power, but how data moves, how power consumption is managed, and how latency is reduced.

Storage and Memory
$Micron Tech(MU.US) $Western Digital(WDC.US) $Sandisk(SNDK.US) $Seagate Tech(STX.US)
AI is a typical memory-bound workload.
Bandwidth, hierarchy, and HBM are transforming from "cost items" into "throughput amplifiers."

Power and Analog
$Texas Instruments(TXN.US) $Analog Devices(ADI.US) $STMicroelectronics NV(STM.US) $ON Semiconductor(ON.US) $Microchip Tech(MCHP.US) $Monolithic Power(MPWR.US)
As single-rack power keeps increasing, the efficiency of every watt is amplified system-wide. This layer imposes an implicit tax on all deployments.

The most downstream is where capital expenditure truly converts into revenue.
$Super Micro Computer(SMCI.US) $Dell Tech(DELL.US) $Hewlett Packard Enterprise(HPE.US)
They turn cloud providers' budgets into physical systems that can be delivered, racked, and powered on.

Putting these layers together, you'll discover a key fact:
AI was never a "single wave of market activity," but a long-term construction happening synchronously across layers, each with its own rhythm.

If you only focus on the most prominent names, you easily miss two things:
How upstream builds long-term durability;
Where new scale constraints are forming in the midstream.

📬 I will continue to deconstruct the true pricing logic of AI, semiconductors, and compute infrastructure from the perspective of capital flows and structural bottlenecks.
Subscribe to see clearly which links truly hold leverage before the market catches up with the surface narrative.

#AI #Semiconductors #AIChips #ChipEcosystem #DataCenters #CloudComputing #CapitalExpenditure