Joined February 2018
Why Anthropic losing token share at $META and $MSFT is bullish for the AI infra trade Meta and Microsoft have reduced their internal employee use of Claude. Microsoft cut projected internal Anthropic spending by >1/3, while Claude Code users at Meta fell from ~60,000 to 30,000. The immediate revenue impact may be contained, but the strategic signal is more concerning: Anthropic’s customers increasingly have both the incentive and capability to replace Claude. This matters even more if you look at the customer structure at Anthropic showing that 2 customers generated ~1/4 of Anthropic’s revenue (as of end 2025). Those 2 are probably Cursor and GitHub Copilot. If that’s the case then we have early evidence that customers that have their own models start becoming less dependent on Anthropic while at the same time both Cursor (Grok) and GitHub Copilot (MAI-Code) have internal model options to replace Anthropic’s Claude. I think this is more bullish than bearish for the AI infra buildout as it diversifies the customer base of compute and I dont think frontier AI will be disrupted anytime soon (look at most recent OpenAI ARR growth). However, I recognize that Anthropic has $518B worth of chips and compute orders, of which $413,7 are take or pay, so alot of the AI infra money depends on them. My view is that they need to get closer to the customer and own distribution, otherwise replacement risk is real.
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TAIWAN'S NEW STATE CPO GRANTS GO TO ALIGNMENT AND TEST. THE CALL ITSELF SAYS TESTING CAN EAT 20–30% OF A MODULE'S COST The chart, compiled by the council's semiconductor expert group, places $LITE and $COHR in the laser slot, $TER and $KEYS under test equipment, $AMKR and $GFS in CPO packaging, and $GLW under glass substrates. H/T @carrioresearch for the awesome chart
$TER $LITE $COHR $KEYS $AMKR WOW! TAIWAN'S NEW STATE CPO GRANTS GO TO ALIGNMENT AND TEST. THE CALL ITSELF SAYS TESTING CAN EAT 20–30% OF A MODULE'S COST TAIPEI, Oct. 6 — Taiwan's National Science and Technology Council has opened calls for two three-year university research programs, one on silicon photonics and co-packaged optics (CPO), the other on power conversion for next-generation AI data centers. The council's engineering department sent the calls out in a letter dated Oct. 2, universities began forwarding them on Oct. 6, and campus deadlines fall on Dec. 31. Funded projects would run from Aug. 1, 2027 to July 31, 2030. The photonics call is aimed at the packaging and assembly level, and its documents state plainly that chip design and wafer manufacturing are out of scope. The suggested target specs: - Fiber-to-chip alignment accuracy of ±0.5 µm, coupling loss under 1 dB - A 16-channel fiber array aligned in under 90 seconds on a demo line, under 60 seconds as a stretch goal - CPO packaging cycle under 120 seconds per module, yield above 85% - 800G to 1.6T module testing in under 60 seconds per module, 4 to 8 channels in parallel, more than 85% automated - CPO first-pass yield above 80% The documents say testing may account for 20% to 30% of module cost as demand for 800G and 1.6T data center modules rises, and that fiber-array coupling with external lasers, thermally stable packaging and testability are among the biggest obstacles to industrialization. They put CPO's power saving at roughly 60% versus pluggable optics in rack-to-rack (scale-out) links, and roughly 80% if it moves inside the rack (scale-up). They also describe 2026 as the year silicon photonics and CPO move from qualification into volume ramp, citing $NVDA's newsroom. Each team must sign a domestic company before applying and show its milestones at that partner's site or at the partner's customer. Funding is capped at NT$8 million a year per project, about US$252,000, and the program generally won't pay for large equipment. The power call is capped at NT$7 million a year and is designed for a DC bus above NVIDIA's 800 V. It assumes buses will rise to 1,200 V or 1,500 V and sets these targets: - A solid-state transformer taking 34 kV or 13.2 kV medium-voltage AC down to 1,200 V or 1,500 V DC, demonstrated above 12 kW, at 97% peak efficiency - A 1,200 V or 1,500 V to 54 V converter at 12 kW and 98% peak efficiency - A 54 V to 1.8 V stage delivering 100 A with a power-density goal of 1,000 W per cubic inch - A DC solid-state circuit breaker that opens in under 25 microseconds DIGITIMES reported the two programs on Oct. 6. I put the photonics call's supply-chain chart next to the U.S. optical and test names we follow. The chart, compiled by the council's semiconductor expert group, places $LITE and $COHR in the laser slot, $TER and $KEYS under test equipment, $AMKR and $GFS in CPO packaging, and $GLW under glass substrates. $AAOI and $FORM are not on it, and the probe-card slot goes to Taiwan's MPI (6223 TT). The chart says its list is not exhaustive. The grant won't pay for testers, so that equipment has to come from partners and existing labs, and on the chart's test-equipment row the only U.S. names are Teradyne and Keysight.
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$MRVL Marvell CEO Matt Murphy sees a massive $400B TAM by 2030 🚀 The company expects revenue to reach $70B–90B by FY31. Shares: +9% 📈
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CPU boom is full on. Here is our most recent CPU forecast revision. We don’t have nearly enough compute. 🚀
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WFE winners: Teradyne, LAM, Advantest, ASML Source: @clausaasholm
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If you understand why HBM gets tested one layer at a time before it's stacked, you already understand co-packaged optics test. $SKHY's HBM4 puts 16 DRAM layers on a base die for 48GB. One bad layer and the whole stack is scrap, so every die gets tested on the wafer before stacking. Memory calls that known good die. $FORM sells the probe cards for this step, and management said on its last call that HBM was about two-thirds of its DRAM revenue. A co-packaged switch has the same shape. Optica reported 18 silicon photonics engines around the switch chip in each $NVDA Quantum-X Photonics package. If one engine turns out bad after bonding, the whole package is at risk, so every photonic chip and every engine has to be tested before it goes on. Same fix, this time called known good optical engine. The difference is the light. Probing a DRAM die is electrical. Probing a photonic chip means lining a fiber up to it, and TrendForce says a full test still takes more than 100 seconds per chip, mostly because that alignment is done by hand. The full piece covers who sells equipment into each stage of optical test.
16 optical engines per co-packaged switch at 99% each: only 85% of packages come out whole, so optical test is moving down to the wafer. Who gets paid at each step, and what to check in $AEHR's next report.
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Goldman shows 83bps of downside to the 10yr yield NTM Brent retreating by 25% to $77
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PEG ratio for Chip stocks. Over 1 = Overvalued , Under 1= Undervalued $MU 0.1x $AMD 0.7x $CRDO 0.5x $NVDA 0.5x
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Micron profitability HBM vs non-HBM - Driven by pricing strength in conventional memory, Micron's Mobile & Client GM outperformed Cloud Memory (HBM) for the 3rd consecutive quarter. 79% vs 74% in Q2, 87% vs 83% in Q3, and 90% vs 83% in Q4 FY26. - Higher prices and a richer mix boosted Mobile & Client revenue by 14% q/q despite lower bit shipments. - HBM contracts have lagged the pricing cycle. Micron’s 2026 HBM prices were negotiated in 2025, and higher HBM mix held Cloud Memory GM flat at 83% in Q4. - 2027 should change the economics. Micron claims to have secured substantially higher HBM prices that kickoff at the start of CY27, likely helping it narrow the profitability gap with conventional DRAM.
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SAMSUNG'S REPORTED 3X HBM ASK FOR 2027 BREAKS INTO TWO STEPS: 🚨ABOUT 1.4X FOR MOVING FROM HBM3E TO HBM4, THEN ABOUT 2.2X ON THE SAME HBM4 STACK SEOUL, Oct. 5 — Samsung Electronics is seeking 2027 prices for its next-generation high-bandwidth memory that are more than three times the price of its current HBM3E, DigiTimes reported on Monday, citing industry sources. The figures reported in Korea are estimates and proposals, not disclosed contract prices. — Samsung's reported 2027 HBM4 ask: in the mid-$4 range per gigabit. — Current HBM3E: about $1.50 per gigabit. — A 12-high, 36GB HBM4 stack: about $600 this year and about $1,300 next year, according to industry estimates cited by Seoul Economic Daily. — Micron ($MU), Sept. 30 earnings call: the vast majority of its calendar 2027 HBM supply is already under agreement, at prices significantly higher than 2026. I put Samsung's per-gigabit ask next to the per-stack estimate. A 36GB stack holds 288 gigabits, so $4.50 a gigabit comes to $1,296 a stack, which lines up with the separate $1,300 estimate. At $600, this year's HBM4 already costs about $2.08 a gigabit, roughly 1.4 times HBM3E. The other 2.2 times or so is HBM4 itself getting more expensive from 2026 to 2027. Micron has said its 2027 HBM is priced significantly higher than 2026 without giving a figure. Samsung's reported ask puts a number on it: the same HBM4 stack at more than twice this year's price.
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Everyone says the market is expensive Meanwhile, the S&P 500 PEG ratio is near its 30 year low:
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Agentic AI may be the more underappreciated $AMD story with server units accelerating ~40% in 2026 and 2027 before compounding ~25% through 2030 while ASPs keep rising. Agents still need CPUs to run tools, orchestrate workflows and host environments they act in which could take AMD server CPU revenue toward ~$60B by 2030. If AMD can hold ~50% share then CPUs become a second major AI growth engine alongside GPUs with custom $ARM chips from the hyperscalers as main thing to watch.
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At the stock level, AI infrastructure stocks are expected to drive more than half of S&P 500 EPS growth in Q3. The top 10 contributing stocks are expected to account for over two-thirds of aggregate S&P 500 earnings growth this quarter, with Micron (MU) and Nvidia (NVDA) together accounting for more than 1/3 of index growth. Micron’s results last week were a strong start to the quarter, with the company reporting year/year earnings growth of 1,003%, beating consensus EPS estimates and providing strong forward guidance.
Goldman: The Q3 2026 earnings season will kick off during the week of October 12. Consensus expects S&P 500 EPS growth of 27% year/year in Q3. The consensus estimate for Q3 growth is the highest heading into any reporting season since 2021. However, consensus estimates imply a deceleration in Q3 from the 33% growth rate realized in Q2, even excluding the "other income" generated from appreciating equity investment stakes last quarter. Neither recent macroeconomic data nor signals relating to the AI investment boom point to a slowdown in Q3, setting the stage for another quarter of above-consensus results.
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🇺🇸 Sentiment The S&P 500 may be near a record high, but US retail investors aren't exactly celebrating, with the AAII bull-bear spread still negative. Contrarian investors couldn't ask for a more favorable situation 👉 isabelnet.com/blog/ h/t @dailychartbook #AAII $spx #spx
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What tech stock bubble?
🤖 Made with AI
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S&P 500 sector valuations... GS
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Shocking stat of the day: Each of the top 4 S&P 500 companies is now larger than the entire Russell 2000 Index, which has a total market cap of $3.5 trillion. Nvidia, $NVDA, alone, with a $5.7 trillion market cap, exceeds the small-cap index by $2.2 trillion. At the same time, Apple, $AAPL, with its $4.9 trillion market cap, is worth $1.4 trillion more than the Russell 2000. Alphabet, $GOOGL, and Microsoft, $MSFT, are valued at $4.2 trillion and $3.8 trillion, respectively, also surpassing the combined market cap of the Russell. These 4 big tech stocks are now collectively valued at a massive $18.6 trillion, accounting for 26% of the S&P 500’s total market cap. Meanwhile, the Russell 2000 represents ~7% of the total US equity market, despite containing nearly 2,000 companies. Big tech has never been bigger.
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$QCOM might be one of the most overlooked picks-and-shovels plays in personal AI. Meta just showed off Muse across its AI glasses and new VR hardware — and Qualcomm’s Snapdragon technology sits underneath much of Meta’s XR ecosystem. It doesn’t stop there. Samsung + Google’s new intelligent eyewear is powered by Snapdragon AR1 Gen 1, and Qualcomm is also working with OpenAI as it develops its upcoming consumer hardware. That gives $QCOM exposure to multiple competing AI-device ecosystems without needing to pick which brand wins. Meta. Google. Samsung. OpenAI. If personal AI moves from phones into glasses, wearables and dedicated devices, Qualcomm could quietly be supplying the picks and shovels behind a lot of it. $QCOM is becoming one of my favorite under-the-radar personal AI plays. #QCOM #AI #SmartGlasses #Snapdragon #Stocks #Investing
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Mo na retweeted
The $SPX bottom-up EPS estimate for Q3 2026 increased by 1.4% during the quarter, which is above the 5-year average of -2.2% and the 10-year average of -2.5%. #earnings, #earningsinsight, bit.ly/4hAqrup
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