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[Ren @ren_stocks]

Satya Nadella just posted something that validates the entire AI buildout thesis from the very top of the stack.

The model is commoditizing. The durable value is the learning loop a company builds on top of the model.

He splits it into two assets:

Human capital -- the knowledge, judgment, relationships, and pattern recognition of your people.

Token capital -- the AI capability the firm builds and owns.

He says the real opportunity is building a learning loop where human capital and token capital compound together.

If the model layer is commoditizing then the durable returns are not in the model makers. They are in the infrastructure that powers every company building its own loop. Compute. Memory. Interconnect. Power.

The full stack underneath the application layer.

The model wars will have winners and losers. The infrastructure underneath gets bought either way.

Bullish the AI buildout.

Every layer. If you want to understand them in detail, check out my Substack.

https://t.co/Wna5UzCOVT

[The SEA Analyst @TheSeaAnalyst]

Every AI & automotive chip needs burn-in testing before shipping. $KESM stress-tests semiconductors.
Q3 FY2026: They named AI-related demand as a revenue driver.

Turnaround is showing up in the numbers.
So why pay more than double via the Singapore holding company?

[Elon Musk @elonmusk]

Interesting

[Jason Luongo @JasonL_Capital]

Peter Lynch's favorite metric is the PEG Ratio. A PEG ratio under 1 means a company is growing faster than its valuation implies.

Here are 10 semiconductor stocks with a PEG under 1 right now:

1. $COHR - Coherent (PEG ~1.0x)

Q3 revenue $1.81B, up 21% YoY. Datacenter segment hit $1.4B with a book-to-bill above 4x. Ramping 1.6T optical transceivers in H2. The vertically integrated AI connectivity play.

[Miles Deutscher @milesdeutscher]

Anthropic just literally spoon-fed you how to use Fable properly.

99% of Claude users missed it.

The way you need to prompt Fable is fundamentally different from all other AI models.

I translated their entire new Fable prompting handbook:

[zostaff @zostaff]

Dylan Patel, founder of SemiAnalysis:

"The upper bound on how much compute can be produced by 2030 is around 200 gigawatts a year."

The entire world has about 20 gigawatts of AI deployed right now. The ceiling is 10x what exists today, and it still isn't enough to feed what Sam, Elon, Dario and Demis are racing to build.

Everyone argues about which model wins. The real limit is a number measured in gigawatts, and it's already maxed out years in advance.

[Thierry from arvy 🇨🇭 @ThierryBorgeat]

Citadel Securities just put institutional weight behind what the AI bulls won't say out loud.

In a new macro note titled "Tokenomics," Citadel makes the argument plainly: even the most powerful technology on earth still has to pass through the boring discipline of cost curves, capacity limits, and marginal returns.

The evidence is piling up:
– Amazon removed its token usage leaderboard
– Microsoft cancelled Claude Code subscriptions
– Multiple companies reporting unexpectedly massive token bills

Their conclusion is the part that matters.

Adoption is no longer about what AI can do in principle. It's becoming about the price and scarcity of the inputs needed to run it at scale. Compute. Power. Cooling. Memory bandwidth. Inference budgets. All real, all binding constraints.

And here's the kicker from the chart.

The Silicon Data LLM Token Expenditure Index, a benchmark for how much the market is actually spending on AI tokens, has started rolling over. Citadel reads it as a shift toward cheaper models. Companies substituting away from expensive frontier AI toward "good enough" alternatives.

That's economics 101 doing what it always does. When the price of something rises, people use less of it, or find a cheaper version.

Citadel sees a bifurcation forming. Frontier AI concentrated among a few firms with the balance sheets to absorb the cost. Everyone else quietly downgrading to simpler, cheaper models.

This is the part of every technology revolution the early narrative ignores.

The technology being real was never the question.

The question was always whether the economics could carry the valuations.

When one of the most sophisticated trading firms on earth starts writing about AI in the language of cost curves and rationing instead of limitless demand, the conversation has quietly changed.

The hype was about what AI could do.

The reckoning is about what it costs.

[李傻根 MORE MONEY @v3wanu]

所有跟单Serenity的，再次隆重介绍一个专门针对他的监控网站给你们：

https://t.co/6AZ1Ncv9s9

详细得不得了

[The SEA Analyst @TheSeaAnalyst]

Singapore's largest coffee shop operator just reported. Profit up 10.6%.

Dividend held. Cash flow growing. Looks clean.

One cost line in the notes changes the read.
And there's a timing question the market hasn't asked.

We went through both.

[0xkevin (🖤 , 💙) @0xKevin00]

白毛股神 Serenity 火遍全网，到处是她的传说，但是没人讲清楚她是谁

这个人非常神秘，被称为半导体的供应链之神，年内回报率 4502%

我梳理了她所有推文和留言，整理了目前最全面的介绍

最重要的是 @aleabitoreddit 选股思路和供应链方法论全解析👇🧵

[InvestmentGuru @InvestmentGuru_]

THE $42 BILLION BROADBAND SUPER CYCLE IS JUST GETTING STARTED -

$HLIT $CALX $CLFD $ADTN $DY $GLW $MTZ $PWR $COHR $AAOI $POET

Everyone is chasing AI semiconductors, GPU clusters, and hyperscale data centers.

But one of the most overlooked infrastructure opportunities of this decade is happening underneath the ground.

Fiber.

The $42.45B BEAD broadband program has officially moved from planning to deployment mode.

→ 15 states + 3 territories already approved
→ Tens of billions flowing into fiber construction
→ Rural broadband buildouts accelerating nationwide
→ AI data centers creating a second parallel demand wave for dense fiber backhaul

This is not a short-term trade.

It’s a multi-year infrastructure supercycle sitting at the intersection of:

✔ Federal broadband policy
✔ AI infrastructure capex
✔ Carrier network modernization

And all three are accelerating simultaneously.

THE MARKET STILL DOESN’T FULLY UNDERSTAND THIS

Every AI data center being built requires massive fiber connectivity.

Hyperscalers are no longer just buying GPUs.

They’re signing multi-billion-dollar fiber supply contracts to connect AI clusters, edge networks, and cloud infrastructure together.

The picks-and-shovels of the AI era aren’t only semiconductors.

Sometimes they’re fiber optic cables running underground.

🔵 $GLW — Corning

→ Optical Communications revenue surged 36% YoY
→ Secured a $6B multiyear fiber supply agreement with Meta
→ New multicore fiber technology increases capacity while reducing installation time dramatically
→ One of the strongest infrastructure product cycles in company history

🟠 $MTZ — MasTec

→ Communications backlog exploded to $6.2B
→ Total backlog reached a record $20.3B
→ Positioned across telecom, fiber, energy, and infrastructure simultaneously
→ Becoming a preferred large-scale deployment partner

🟣 $PWR — Quanta Services

→ Institutional-quality infrastructure compounder
→ Exposure across broadband, power grid modernization, renewables, and data center outside plant
→ One of the safest ways to participate in the digital infrastructure buildout

🔴 $DY — Dycom Industries

→ Record broadband construction backlog
→ Major BEAD deployment beneficiary
→ Expanding deeper into data center infrastructure construction
→ One of the purest broadband construction plays in the market

🟢 $CALX — Calix

→ AI-enabled broadband software platform
→ Deep integration with Google Cloud + Vertex AI
→ Positioned to benefit directly from the rural broadband funding wave

🔵 $HLIT — Harmonic

→ Cloud-native broadband software stack gaining traction with major operators
→ AI-driven broadband optimization becoming a major differentiator

🟠 $CLFD — Clearfield

→ Pure-play fiber infrastructure exposure
→ Dual tailwind from rural broadband + AI data center connectivity

🟣 $ADTN — ADTRAN

→ Broadband automation, managed Wi-Fi, and carrier-grade networking
→ Turnaround story with BEAD exposure and improving regulatory clarity

OPTICAL NETWORKING NAMES ALSO BENEFITING

$COHR
$AAOI
$POET

THE BIG PICTURE

The AI supercycle does not stop at the GPU rack.

It extends through fiber networks, broadband nodes, optical systems, and last-mile connectivity infrastructure that still needs to be built.

This may become one of the most important infrastructure investment themes of the decade.

$HLIT $CALX $CLFD $ADTN $DY $GLW $MTZ $PWR $COHR $AAOI $POET

Not financial advice.

[泥伏雷闯关记 @Nicole_yang88]

Serenity真正厉害的地方，很多人不太懂。

与其阅读推文，不如和泥伏雷一起研究她的“埋伏”思路；

Serenity 的框架本质上只有一句话：AI 基础设施的物理瓶颈会依次解决，每解决一层，资金就流向下一层——他要做的就是永远站在下一层的门口等着。

⬇️具体说几个关键点：
他的起点不是选股，而是读 NVIDIA 的路线图。他相信 NVIDIA 一旦公开说要做 CPO、做硅光，这条技术路线就是确定性的，剩下的只是供应链上谁是瓶颈。

所以他从 NVIDIA 的架构决策出发，逆向拆解供应链，一层一层往上游走，直到找到那个"唯一一两家公司能做、扩产需要好几年、Hyperscaler 离不开"的节点。

他的估值逻辑也很简单：不看 PE，看"这个市值相对于它卡住的市场有多不匹配"。

AXTI 七亿美元市值卡住了全球 InP 基板，SIVE 不到三亿美元控制 CPO 激光光源——这种错配就是信号。

他最重要的一个洞察是关于时间差：机构基金受合规和规模限制，发现新瓶颈比他慢一两个季度。他不是要比机构聪明，只是要比机构快。等机构报告出来，他已经建好仓了，然后用财报数据验证自己的论点。

整个 CPO 链条里，目前信息不对称最强的节点是 NCI（日本化成工业，4092.T）——高纯红磷供应商，AXTI 的上游。

他自己称之为"瓶颈中的瓶颈"，几乎没有任何英文报道。最新鲜的公开披露是 XFAB，他前天才公开建仓，EU Chips Act 2.0 是近期催化剂。

需要特别注意的是：Serenity现在有 40 万粉丝，Bloomberg 已经开始报道他的操作。

他自己也说过"not doing so well anymore"——一旦他开口，窗口就开始关闭了。

真正的机会在他说之前，用他的框架自己往上游再走一层。

[Ren @ren_stocks]

Holy mother of InP lasers.

Rosenblatt just dropped an InP supply and demand model today and the numbers are staggering.

NVIDIA asked the supply chain to scale InP laser capacity by 20x from 2025 to 2030. The vendors pushed back and agreed to 12x. Even the conservative scenario has Datacom supply still 50% behind demand exiting 2030 after a 12x increase.

12x supply increase over five years. Still not enough.

Rosenblatt explicitly calls it a non-cyclical growth industry well past 2030. The InP supply chain is structurally short for the rest of the decade.

Here is what the revenue buildout looks like by supplier across 2025 to 2030:

$LITE -- $600M in 2025 to $9B by 2030. The dominant player scaling fastest including a new InP fab acquisition in Greensboro NC converting in 2028, adding $2.5B in 2028 and $5B in 2029.

$AAOI -- $60M to $2.1B. The high-torque play. Rosenblatt sees it growing from under 5% to nearly 10% transceiver market share and entering the ultra-high-power CW laser market for CPO. Smallest base, biggest percentage runway.

$SIVE -- sits alongside as the pure-play InP laser specialist and external light source for CPO -- the chokepoint Rosenblatt's entire supply model is built around. DFB laser supply confirmed tight through Q3 2027.

$AVGO -- $550M to $4.5B. Second largest by revenue. Strong but less pure-play InP than LITE.

$COHR -- $125M to $4.3B. Rosenblatt's top near-term pick. Expects revenue acceleration and gross margin expansion from 6-inch wafer production driving 800G and 1.6T transceiver sales.

VIAV -- $53 stock, called out specifically for underappreciated bottlenecks in OCS and CPO test expertise and capacity.

Total InP Datacom market: $1.9B in 2025 to $22.75B by 2030. Nearly 12x.

Flags from the report.

$CIEN -- Rosenblatt is cautious. Side GM expectations have gotten too high and do not factor in price increases from LITE and COHR as suppliers.

$CRDO -- viewed as a niche player, not strongly relevant to the CPO optical supply chain. Expects 1.6T AECs to be weaker than the market expects.

If CPO scale-up slips beyond the current 2H27 build window, 2028 becomes a buying opportunity rather than a revenue year. Wafer supply, test and measurement, DSP, PIC, and laser capacity are all identified as potential chokepoints.

But the direction is not in question. NVIDIA is the demand signal and NVIDIA asked for 20x. The supply chain is building for 12x. The gap between those two numbers is the entire trade.

$LITE $COHR $AAOI $SIVE for the InP laser supply chain.

$IQE $AXTI $SOI for the InP epi and substrate layer underneath them.

$SOI for SiPh substrate.

Bullish Photonics

[Wolf Capital @wolfgangkasper]

I also started a position today on $ADTN

It’s very early in the presence of X too… 👀

[Grynn-ai-bot @Grynn]

SK Inc is soooo cheap .... $034730.KS $402340.KS $000660.KS $HY9H.F

[Jamin Ball @jaminball]

https://t.co/Wj4pTqmZMH

[Rohan Paul @rohanpaul_ai]

New chip uses light instead of electricity to make AI think at lightning speed.

🔬 MIT researchers developed photonic chip that performs computations of neural networks using light

Achieves 92% accuracy in under half a nanosecond - matching traditional hardware performance.

→ The chip combines optical and electronic components through nonlinear optical function units (NOFUs), enabling both linear matrix operations and nonlinear activations to stay in the optical domain until final output

→ Built using standard CMOS fabrication processes, this breakthrough could enable scaled production of ultra-fast, energy-efficient AI processors for demanding applications like lidar and telecommunications

Key Highlights:

→ The processor performs matrix multiplication optically through programmable beamsplitters, maintaining data in light form throughout computation

→ NOFUs handle nonlinear operations by converting small amounts of light to electrical signals via photodiodes, eliminating external amplifiers and minimizing energy use

→ In-situ training achieved 96% accuracy while inference hit 92%, demonstrating viability for real-world applications

→ The entire system operates at sub-nanosecond speeds while matching traditional hardware accuracy, opening new possibilities for real-time AI processing

→ Commercial foundry fabrication makes this technology viable for mass production and integration with existing electronic systems

[Jason's Chips @jasonschips]

https://t.co/VshLUWP6cq

[Jason's Chips @jasonschips]

https://t.co/OfYdTHQqyP

[Jason's Chips @jasonschips]

For anyone interested in learning about the CPO supply chain!

(I also explain it in a fun and understandable way)

Covered: $AXTI, $LITE, $AIXA, $COHR, $SOI, $TSEM