Afterule

• 내 손안의 애널리스트
• 국내외 주식과 비트코인을 다룹니다

[NoLimit @NoLimitGains]

Every time $WMT has dropped 20% or more it’s been a great buying opportunity.

Nothing I’m seeing tells me this time is any different.

[Afterule @Afterule]

@elonmusk @grok what does this mean?

[AskLivermore @asklivermore]

Top stocks at their 200-weekly moving average:

1. Oracle
2. Netflix
3. Procter & Gamble
4. Home Depot
5. McDonalds
6. T-Mobile
7, Disney
8. Intuitive Surgical
9. Qualcomm
10. Lowes
11. S&P Global
12. Progressive
13. Mondelez
14. Cigna
15. Colgate
16. Air Products & Chemicals
17. Ferrari
18. NXP Semiconductors
19. Carrier
20. IDEXX Labs
21. D.R. Horton
22. Kroger
23. Wynn Resorts
24. Restaurant Brands Internatonal
25. T Rowe Price
26. Super Micro Computer
27. BJ's Wholesale
28. Norwegian Cruise Lines
29. Wyndham Hotels
30. Stryker
31. Medtronic
32. Occidental Petroleum
33. Halliburton
34. Microchip Technology
35. Yum Brands
36. PayPal
37. AIG
38. The Hershey Company
39. Keurig Dr. Pepper
40. Las Vegas Sands

Buy great companies when they're giving you a discount.

[Afterule @Afterule]

시장도 안 좋은데, 증권사 어플 설치만 하셔도 5천원 보내드립니다. 계좌까지 만들어주시면 1만원 드려요!

- 이상한 거 아니고 대형증권사 어플입니다.
- 한다고 잘못되는 거 없고 직원이라 실적 좀 올려보려고 합니다 ..ㅠㅠ
- 주식 거래수수료 0.01%, 예치금 이자율도 0.1%라서 나름 업계 최고치입니다.
- 설치 이후에는 의무적으로 해야할 건 없고 방치하시거나 지우셔도 무방합니다!

좋아요, 댓글, DM으로 연락주시면 답변 드리겠습니다!

1) 어플 설치
https://t.co/lsndDZF3Z2

2) 계좌 개설
https://t.co/qvIwl5Slwb

[TheValueist @TheValueist]

$GOOGL $AMZN Battle for the Bench: Google TPU vs. AWS Trainium.

Google TPUs and AWS Trainium/Inferentia are best understood as vertically integrated AI infrastructure platforms rather than standalone accelerator products. Google is further along technically and institutionally: TPUs have been deployed in Google data centers since 2015, have been shaped around Google’s own model, compiler, networking, and data-center architecture, and now underpin Gemini, Search, Photos, Maps, and Google Cloud AI services. AWS is later in accelerator maturity but has a stronger cloud-operating abstraction layer through EC2, Nitro, Bedrock, and Neuron, which allows AWS to hide more of the silicon transition from customers and monetize capacity quickly once deployed. Google’s advantage is system-level performance and workload co-design; AWS’s advantage is commercial packaging, price-per-token positioning, and the ability to use Bedrock APIs and EC2 instance types to make custom silicon feel less like a developer migration.

The central investment conclusion is that Google has the higher-quality custom AI silicon stack, while AWS has the more explicit near-term monetization wedge via Anthropic and OpenAI commitments. Google’s TPU roadmap is more advanced in scale-up networking and system co-design, with TPU v7 Ironwood at 9,216 chips per pod, TPU 8t scaling to 9,600 chips per pod, and Virgo connecting 134,000 TPU 8t chips across data centers at 47 Pbps. AWS Trainium3 is narrower as an accelerator architecture but commercially powerful: it is positioned as a 3nm, 2.52 PFLOPS FP8 chip with 144 GB HBM3e, 4.9 TB/s memory bandwidth, 4.4x performance, 3.9x memory bandwidth, and 4x energy efficiency versus Trainium2.

The biggest portfolio-management implication is that the highest-confidence public equity beneficiary remains Broadcom, not Alphabet or Amazon directly, because the TPU externalization cycle, Meta MTIA, OpenAI ASICs, and AI networking create a more directly monetizable revenue line. JPMorgan estimates Broadcom’s FY27 AI backlog at >$150B, including ~6.5M Google TPU units in Broadcom’s FY27 order book, and expects Broadcom AI revenue near $60B in FY27, up ~3x Y/Y. However, Broadcom’s upside is paired with execution risk in advanced packaging, potential hyperscaler insourcing, and dependence on TSMC/CoWoS/HBM. MediaTek has materially higher convexity through Google TPU v8/v9 exposure, but its outcomes are more binary, with credible sell-side estimates ranging from roughly $5B-$18B in 2027 TPU-related revenue and a much wider 2028 dispersion depending on whether Humufish reaches volume production.

[Boring_Business @BoringBiz_]

Jensen Huang's advice to students is to learn how to suffer

If I was in my early 20s, I would listen to this over and over again until it really sticks

Too many people are chasing success, but not enough people are willing to sacrifice their comfort to have it

[Vaelis @Vaelis_X]

Been spending a lot of time digging into the TGV industry lately

The deeper I go, the more one company keeps standing out: It appears to sit in a uniquely important position within the ecosystem, with technology that may be extremely difficult to replicate and even harder to replace

If my research is correct, it may have something very close to a monopoly on a critical bottleneck in the TGV supply chain

I’m gonna post the stock next week 🇹🇼👀

[Afterule @Afterule]

데이비드 V. 고켈러(샌디스크 CEO)
(HBF 코멘트) 저희는 작년 2월 회사를 출범하며 이 기술을 처음 공표했을 때부터 아주 큰 기대를 걸고 있었습니다. 저희는 오랫동안 AI가 추론 단계에 진입하는 순간 낸드가 핵심적인 기술로 부각될 것이라 믿어왔습니다.

추론 수요를 대규모로 확장하기 위해서는 메모리 아키텍처의 근본적인 변화가 필요하다는 사실을 굳이 저희에게 설득하실 필요가 없습니다. 그것이 바로 HBF 기술이 지향하는 핵심이기 때문입니다. 그렇다고 HBF가 기존 기업용 SSD 시장을 완전히 대체한다거나, 디램을 전면 대체한다는 뜻은 결코 아닙니다.

핵심은 추론 시장이 확장됨에 따라 이 영역에 엄청난 혁신의 기회가 열리고 있다는 점입니다. 새로운 아이디어를 가진 이들에게, 현재 AI 분야에서 일어나고 있는 이 거대한 스케일의 확장은 혁신을 향한 '거대한 청신호'와 같습니다. 새로운 아이디어가 있다면 언제든 가져오라는 것이죠.

전 세계가 이 경이로운 기술을 어떻게 확장할지 해답을 찾기 위해 고군분투하고 있습니다. 아까 말씀드렸듯이, 경제성(비용 구조)만 제대로 맞춰준다면 기술을 완전히 마찰 없는 방식으로 순식간에 확장할 수 있습니다.

적절한 경제성만 확보된다면 이 위대한 기술이 얼마나 빠른 속도로 대중에게 보급될 수 있는지 지켜보는 것은 정말 놀라운 경험입니다. 그런 관점에서 HBF는 추론 단계에 엄청난 밀도를 공급하기 위한 최적의 전략입니다. 아시다시피 추론은 본질적으로 '읽기 중심'의 활동이며, 결정론적인 읽기 기반(Deterministic read-based)의 성격을 띠기 때문입니다. 따라서 저희는 이 기술에 매우 고무되어 있습니다.

이제 막 시작하는 단계입니다. 현재 낸드 다이를 개발 중이며 올해 말까지 확보할 예정입니다. 그리고 내년 중에는 최종 시스템 제품을 선보일 계획입니다. 그 위에 탑재될 컨트롤러도 직접 개발하고 있습니다. 아직 해야 할 작업이 많습니다. 현재 고객들과 이 기술을 그들의 아키텍처에 어떻게 통합할 수 있을지 긴밀히 논의하고 있습니다. 왜냐하면 이 제품은 단순히 기존 부품을 빼고 우리 걸 끼워 넣는 플러그 앤 플레이(Plug-and-play) 방식이 아니기 때문입니다.

이것은 철저한 '시스템 플레이(System play)'입니다. 따라서 고객이 새로 구축하는 시스템에 이 기술을 채택하도록 유도해야 하며, 현재 그 과정을 밟아나가고 있습니다. 앞으로 진행 상황에 맞춰 계속 업데이트해 드리겠습니다.

* HBF는 HBM과 마찬가지로 고객사와 협력이 매우 중요한 프로젝트. 27년 공개 타임라인 유지. 컨트롤러는 직접 개발한다고 언급

[Dwarkesh Patel @dwarkesh_sp]

New blackboard lecture w @reinerpope

How do chips actually work – starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do.

0:00:00 – Building a multiply-accumulate from logic gates
0:16:20 – Muxes and the cost of data movement
0:25:59 – How systolic arrays work
0:39:00 – Clock cycles and pipeline registers
0:51:40 – FPGAs vs ASICs
1:03:14 – Cache vs scratchpad
1:07:16 – Why CPU cores are much bigger than GPU cores
1:11:49 – Brains vs chips
1:15:22 – A GPU is just a bunch of tiny TPUs

Look up Dwarkesh Podcast on YouTube/Spotify/etc to watch. Enjoy!

[Naval @naval]

Co-founder is marriage without the sex.

[Killa @KillaXBT]

I've learned from a lot of experience to tell you exactly what's going to happen next.

The same people who flipped bullish at $80K are already going silent.

You're going to see a flood of tweets saying Bitcoin is dead.

You're going to see boomers come out of the woodwork bragging about stock market returns.

You're going to see FUD after FUD after FUD, to the point where you start questioning Bitcoin itself.

You're going to hear people say quantum computing will kill Bitcoin. Then some new narrative will show up and supposedly kill Bitcoin too.

Most importantly, you're going to watch people continuously lower their targets. They'll say they're waiting to buy, but somehow their entry keeps moving lower... and lower... and lower.

Same story. Every single cycle.

So what do I recommend?

Ignore the noise.
Ignore the FUD.
Ignore the news.

Most of it is designed to influence your emotions if you don't already understand how the game works.

Gradually accumulate spot $BTC within the current range. DCA your entries. Trust the plan.

Do not deviate, no matter what number is flashing on the screen.

Mentally accept that the money you've invested is already gone. $0.

Don't try to time the market like some genius. Trust me, you are not him, and neither am I.

All you have to do is buy, DCA, forget about it, and hibernate for the next 2–3 years.

And when you're all millionaires, I accept donations in my DMs.

[Brandon Carl @brandonjcarl]

Documenting the headwinds I now see for AI.

It won't seem like it, but I love AI and am long-term positive. But when "math doesn't math" I take note.

1. The core thesis for foundation model lab investment has been high upfront investment made worthwhile by significant long-term profits.

2. These are capital intensive businesses and the compute commitments are very high relative to revenue and require strong growth over long time periods. The "leverage" (commitments versus revenue) is extremely high.

3. The fundamentals are not as positive as they previously were:
• Input costs are higher (commodities, chips, power)
• Interest rates are higher
• Competition is more intense
• Scaling Laws are now problematic: exponential costs/power cannot continue

4. Forecasting compute spend is challenging and high risk due to (a) revenue uncertainty and (b) algorithm uncertainty

5. Revenue growth appears to be slowing. The technology is valuable, but ROI is proving to be more expensive and take longer than anticipated.

6. The future is likely "different models for different use cases" with the lower end of the market being highly competitive.

7. Core use cases such as agentic software engineering are likely to need approaches beyond next-token prediction. They are Σ₂ᴾ complexity problems requiring multi-objective optimization and likely a combination of Transformers and other methods.

8. Current forecasts in memory makers are built largely on quadratic attention. That will not persist: we are already seeing work from DeepSeek, Minimax and Nvidia that can cut RAM needs by 80% or more.

9. This means semiconductor valuations are substantially overinflated and will go through the traditional glut versus shortage cycle.

10. For foundation model providers: lower costs with competitive differentiation is good. However, lower costs with a lack of differentiation would mean lower revenues. This makes it harder to (a) service commitments and (b) pay back investors.

11. Leverage is substantially higher than in previous cycles, evidenced by leveraged ETFs, call option activity and margin loans. Korea is particularly susceptible.

12. 0DTE options create a profile that has stronger parallels to portfolio insurance and 1987 than any other point I can remember.

13. The combination of exponential increases in call activity coupled with the ties of semiconductors to structured products means there is a non-trivial systemic risk to the financial system.

14. Implied earnings growth rates are inconsistent with other periods in history.

15. Macroeconomically we cannot and should not fund exponential cost increases. History has shown us repeatedly that there are better ways (see Quick Sort and Simplex).

16. Significant supply is hitting the market via IPOs.

––

Taken together: costs and competition are increasing while revenue growth is likely slowing. Valuations are fragile and prone to technology disruptions that are already here. Systemic financial market risk is extremely high.

[MacroScope @MacroScope17]

Quick BTC update. In the ten years I’ve been here, this is by far the most subdued sentiment I’ve seen. It’s understandable, but the extent is striking -- especially in light of a favorable policy climate in DC, continued sovereign and corporate accumulation, etc.

While overleveraged traders are getting blown out on dips, a sovereign wealth fund like Abu Dhabi’s Mubadala is happy to sit on the bid and take it in.

For traders and investors looking to take advantage of this type of environment, the key is to watch for when price starts to diverge positively from depressed sentiment. For assets in long-term uptrends, that's when huge percentage gains can happen. This was certainly true for eventual winners like AMZN in the years after the tech bust a quarter-century ago; sentiment was terrible, but price gradually started to diverge upwards, quietly regaining levels that were previously lost and reflecting the long-term underlying growth story.

I posted about this in 2023 -- when BTC was 25k, and sentiment was also subdued.

[Afterule @Afterule]

현재 미국 주식시장은 S&P500 내 상위 종목의 시가총액 비중이 매우 높아져, 명목상 500개 종목 지수임에도 실질 분산효과는 약 42개 종목을 동일 비중으로 보유한 수준

닷컴버블 당시 약 80개 보다 더 낮은 수치로, 시장 집중도는 당시보다 더 높은 상태

* 미국 지수 전체의 방향성은 AI 빅테크들에게 달려있다고 봐도 무방

[Afterule @Afterule]

[01:24:50]
"One was don't die at 25 and get buried at 75. Yes. What do you mean?"

[01:24:56]
"So that's a quote by Ben Franklin. As you know, I have no original ideas. So Ben Franklin uh said that many people die at 25 and are buried at 75. And basically what that's saying is that you've stopped growing and you've stopped kind of doing things and you're kind of just coasting.

You know, I had discussed the stock with Charlie uh in my last meeting with him and he was buying that stock 6 days before he died. Okay, he was 99.9 years old. He didn't know he was going to die in 6 days. But when you have a 99.9 year age, you know, life expectancy is not 20 years or 10 years. Okay, but he was... I saw Charlie make investments and bets and decisions ignoring his mortality like... like he was 25. He was making the bets as if he was... he was 25.

And so I think that um living till the very end, truly living is really important. So we... we want to be pursuing our passions. We want to be getting our music out. We want to be doing the things that we want to do for this very finite time we have here."

https://t.co/TfAfaK3WrF

[Afterule @Afterule]

@thdouglas11 @leffim @dekeedii https://t.co/uFqNB37da6

[Afterule @Afterule]

@mishals1101 @agoodnoteymtfam @conorsingapore https://t.co/uFqNB37da6

[Afterule @Afterule]

@gupta_yash99 @alenruizr @chainriffs https://t.co/uFqNB37da6

[Afterule @Afterule]

@chococream358 @_simonasays @pragsz28 https://t.co/uFqNB37da6

[Afterule @Afterule]

@billbain23 @li20020227mrlee @uche_omj https://t.co/uFqNB37da6