Leo

Tech Investor; not financial advice

[Leo @thee_bad_guy8]

Never settle

[Leo @thee_bad_guy8]

Nailed it

[John Intel @intelfabs]

AI memory demand remains extremely strong.
Supply is still constrained.
UBS thinks shortages extend into 2027.
Buybacks could become enormous.
Wall Street earnings estimates are still too low.
Investors should focus less on the next quarter and more on the multi-year earnings power of Micron. $MU

[Leo @thee_bad_guy8]

@jukan05 Thank you! You were getting lost among other people I follow with the last one

[Leo @thee_bad_guy8]

@HiCagr LFG @DavidSacks the AI Czar we need

[Leo @thee_bad_guy8]

@beffjezos @bubbleboi @FBI @AI_DHS @ATFHQ @pmarca Yes they are but they just need to be shamed with sunlight so the absurdly of their statements are exposed

[Leo @thee_bad_guy8]

@PatrickMoorhead @LisaSu @JensenHuang @elonmusk @sama @tim_cook Yup I thought the same. All good! Thank you for ask your insights on AI! 🙏🏻❤️

[Leo @thee_bad_guy8]

@AndrewCurran_ He better cure cancer asap

[Leo @thee_bad_guy8]

@immanencer @AndrewCurran_ @hari65535 This one got my good well done sir 😂

[Leo @thee_bad_guy8]

This is what we’ve been waiting for

[Semiconductor Insider @SemiconductorsX]

UBS put another large number on Micron’s next two prints, and a bigger one on the buyback that starts when CHIPS restrictions lift.

The firm says the DRAM and NAND gap widens into calendar 2027. DRAM fulfillment is about 60%.

Server DDR bit demand is seen up about 80% year on year. Server plus storage SSD bits are modeled up more than 100% in 2027. AI memory stays tight. Supply does not catch it.

FQ4 revenue is near $52 billion. Gross margin is around 87.6%. FQ1 guide is seen at $58–59 billion with another 100 basis points of margin. Street still sits under that.

CHIPS Act limits on buybacks expire December 9, 2026. UBS models a quarterly repurchase starting near $20 billion in FQ2:27, then a ramp toward $40–50 billion a quarter. 2027–29 EPS go to about $212, $277 and $219. Price target stays $1,625.

That is a shortage model plus a capital-return model. The first needs the 60% fill rate to hold.

If FQ1 is already $58–59 billion, does a $20 billion quarterly buyback in FQ2 change the stock more than another 100 basis points of margin?
$MU

[Leo @thee_bad_guy8]

@TradexWhisperer But did they lock in lower prices with the LTAs?

[Leo @thee_bad_guy8]

Excited to see the terms

[Alex @Alex_Intel_]

Another reason to believe that the Intel Hynix JV situation should be resolved by the midterms or end of the year is that the 35% tax credit ends December 31st 2026.

If you take steps before the end of the year to start your semiconductor manufacturing project then you get the credit going forward. Tax credit is 35% off everything, equipment, buildings etc.

Hynix would be stupid to miss the deadline to announce it's investment.

$INTC

[Leo @thee_bad_guy8]

@DrNHJ LFG

[Leo @thee_bad_guy8]

Repurchase 40-50 billion a quarter $MU

[루팡 @DrNHJ]

UBS , 마이크론( $MU) ‘매수(Buy)’ 의견, 목표주가 1,625달러

“마이크론 주식은 여전히 주가 모멘텀과 업황 지속성(duration) 사이에서 논쟁이 치열한 종목이지만, 펀더멘털은 계속 개선되고 있습니다.

최근 실시한 공급망 점검 결과를 보면 수요와 공급 간 격차는 2027년까지 계속 확대될 것으로 보입니다.

구체적으로 보면,
DRAM 공급 충족률(fulfillment)은 여전히 약 60% 수준
2027년 서버 DDR 비트 수요 증가율은 전년 대비 약 +80%
2027년 서버 및 스토리지용 SSD 비트 수요 증가율은 전년 대비 +100%를 넘어설 가능성
이 있다고 판단합니다.

따라서 마이크론이 이미 장기공급계약(LTA)에 대한 구체적인 내용을 공개했고, 이러한 계약들이 실적의 추가적인 상방을 일부 제한하기 시작했다는 점을 고려하면, 투자자들이 앞으로 집중해야 할 부분은 수요가 얼마나 오래 지속될 수 있는지, 그리고 특히 주주환원(capital return)이 될 것입니다.
주주환원 측면에서는 CHIPS Act와 관련된 자사주 매입 제한이 2026년 12월 9일 종료됩니다.

저희는 마이크론의 분기별 자사주 매입 규모가 2027 회계연도 2분기(2월 종료) 약 200억 달러 수준에서 시작해 빠르게 분기당 400억~500억 달러 수준까지 확대될 수 있다고 보고 있습니다.

다만 회사 입장에서는 아직 시점이 오기 전에 이를 먼저 공개하고 싶어 하지 않을 가능성이 높습니다. 따라서 이번 실적 발표에서 자사주 매입과 관련해 아주 많은 새로운 내용이 나올지는 확신하기 어렵습니다.

2026 회계연도 4분기(8월 종료)의 경우 저희는
매출 약 520억 달러 — 회사 가이던스 상단을 소폭 상회
매출총이익률(GM) 약 87.6% — 기존 가이던스 대비 약 160bp 상회
를 예상합니다.

또한 2027 회계연도 1분기 매출 가이던스는 약 580억~590억 달러, 매출총이익률은 여기서 다시 약 100bp 상승할 것으로 예상합니다.

결론적으로 저희는 실적 전망치를 다시 한번 상향하고 있습니다.

이번 상향의 가장 큰 이유는 메모리 업황 호조가 예상보다 더 오래 지속될 것으로 보는 점과 더 큰 규모의 자사주 매입입니다.

이에 따라 마이크론의 예상 EPS를
2027년: 약 212달러
2028년: 약 277달러
2029년: 약 219달러
로 상향했습니다.

이는 모두 현재 시장 컨센서스를 크게 웃도는 수준입니다.

대규모 자사주 매입을 감안하면, 저희는 2028년 말 마이크론의 주당 유형장부가치(TBV/share)가 약 360달러에 이를 것으로 예상합니다.

더욱 중요한 점은, 이번 메모리 사이클이 정점을 지난 이후 2028년 하반기부터 오픈마켓 메모리 가격이 약 80% 하락한다고 가정하더라도, 사이클 저점에서의 연환산 EPS가 마이크론이 다음 분기 실적 발표에서 제시할 것으로 예상되는 연환산 EPS보다도 여전히 높을 것으로 보고 있다는 점입니다.

이는 매우 강력한 의미를 갖는 전망이며, 결국 시장도 이러한 점을 주가에 반영할 것으로 판단합니다.

저희는 목표주가 1,625달러를 유지합니다.
이 목표주가는 저희의 2029년 예상 EPS 약 219달러에 차기 12개월(NTM) PER 약 8배를 적용한 뒤 1년 할인한 수준에 불과합니다.

또한 2028년 말 예상 주당 유형장부가치(TBV/share)의 약 4.5배로도 목표주가가 뒷받침됩니다.
따라서 마이크론에 대한 매수(Buy) 의견을 재확인합니다.

[Leo @thee_bad_guy8]

@amitisinvesting You live in Jersey?

[Leo @thee_bad_guy8]

@AlexHormozi You’re so good at this. It’s awesome

[Leo @thee_bad_guy8]

This is awesome. Please continue looking into theses revolutionary heath breakthroughs

[Dario Amodei @DarioAmodei]

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).

More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.

I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

[Leo @thee_bad_guy8]

@Alex_Intel_ Wow that’s great insight!

[Leo @thee_bad_guy8]

Intel $200 per share

[Ben Bajarin @BenBajarin]

Very good note on $INTC from Ben Reitzes at Melius.

Wish I would have done this myself specific analysis myself but credit where its due. A few key nuggets.

Book Value Growth: With a consolidated net PP&E of $105.7B as of June and a $20B equity raise, the foundry book value could exceed $120B by 2028.

Valuation Multiple: Applying a 4x book value multiple (which is a discount to TSMC's 9x but a premium to GlobalFoundries' 2.1x) results in a valuation of $540B, or over $100 per share.

Earnings Potential: A target of $4.00 EPS for the products business by 2028 is considered "very doable," requiring an operating income run rate of roughly $26-27B.

[Leo @thee_bad_guy8]

@BenBajarin Wow that was great. Thanks for sharing

[Leo @thee_bad_guy8]

@theallinpod Very cool