sanjay charagulla

[Steve Grasso @grassosteve]

Most investors don’t realize these leveraged ETFs are for short-term, daily trading, not long-term holdings, they rebalance daily which causes volatility decay and compounding losses when markets move against them or fluctuate. They’ve existed for years and many don’t have a clue of the capital destruction they can result in if you don’t know the product you’re holding.

[Really American 🇺🇸 @ReallyAmerican1]

BREAKING: Jessica Tarlov leaves her Fox News co-hosts speechless as she drops a truth bomb list of the issues Americans are protesting Trump for on No Kings Day. "America doesn’t have a king. But Trump acts like one."

Tarlov is a national treasure.🔥

[Dan Niles @DanielTNiles]

Last wk, Mag7 -0.8% & my broader AI index -1.7% but S&P only -0.3%. This bolsters my belief that the best risk adjusted way to stay invested & benefit from rate cuts driving a slow mkt melt up is by staying diversified. Having said that, Q3 earnings should be strong for Mag7.

$AAPL
Positives include: 1) good demand for the iPhone 17 family, 2) even better products coming in 2026 w/ AI infused Apple Intelligence on a foldable phone, and 3) favorable Google anti-trust remedies leaving Apple in an enviable position with AI companies that want to partner.

$AMZN
Positives include: 1) higher income consumer demand that is remaining strong, 2) benefits from recent advertising momentum such as the recent deal with $NFLX, and 3) potential for AWS growth to improve driven by Anthropic in 2H.

$GOOGL
Positives include: 1) recent favorable anti-trust remedies 2) their dominant position in consumer facing AI as shown by Gemini passing ChatGPT to become the top free app in the Apple App Store on 9/12, and 3) the lowest valuation of the Mag7 and inline with the S&P PE multiple of 25x.

$META
They have increased their own revenue forecasts consistently over the past three years as they have used AI to drive engagement & ad monetization. Now with WhatsApp showing ads to their 1.5B daily active users starting in June, the next layer of rev growth has begun.

$MSFT
Microsoft should see a solid quarter given they have the right of first refusal for OpenAI workloads. These were clearly strong given the $ORCL forecast of Cloud Infrastructure revenue growth of 70% per year through FY30.

$TSLA
Customers are trying to get in front of the expiration of EV tax credits at the end of September while optimism abounds for autonomous robots and robo-taxis. But Q4 could be difficult given the current pull forward in EV demand. The over 300x PE discounts a lot of positives.

$NVDA has an October month end qtr
I believe Nvidia will get approval to ship a derivative of Blackwell chips to China given their recent investment in $INTC will be viewed favorably by the US government. This is not in their guidance. Also since Nvidia gave Q3 guidance on 8/27, the deal announced with OpenAI of $100B is additive to everything they have spoken about so far. I still believe these “circular investments” and a more discerning funding environment for cash burning entities will be a problem at some point in 2026. But in the near-term, the above two factors I believe will matter more and should take the stock to new all-time highs.

[Meidas_Charise Lee @charise_lee]

Our country has fallen‼️ #Fascism

[Tara Setmayer 🌻 🇺🇸 @TaraSetmayer]

Introducing Charlie Kirk, in his own words.
@senecaprojectus

[Brian Krassenstein @krassenstein]

Every American needs to Watch what Obama just said about the Kirk assassination.

[sanjay charagulla @sanjaycha]

@ElonMuskNews47 A

[sanjay charagulla @sanjaycha]

@Billyjensen @realDonaldTrump And he wants Nobel peace prize 😱

[sanjay charagulla @sanjaycha]

@musk_news13 No

[Barchart @Barchart]

Will DeepSeek end U.S. Tech Dominance?

[naiive @naiivememe]

Crypto Guys waiting for 20 Jan

[Buitengebieden @buitengebieden]

Dog having his own fun.. 😊

[Dan Niles @DanielTNiles]

PCE came in 0.1% lower than forecast driving down the dollar and treasury yields which had surged following the FOMC mtg on 12/18. W/ many oversold conditions & positive seasonality typical to end the yr, I believe we saw the lows for December for the S&P this morning.

I like small & mid-cap stocks as the best risk adjust way to participate versus the mega-cap tech stocks through year-end and into 2025. Earnings should grow for those combined sectors on average for the first time since 2022 in 2025 given the US first, pro-growth policies of the incoming administration. These same policies are likely to act as headwinds for the more export exposed large cap names in the S&P500 by comparison.

The performance of the mid & small cap sectors have also lagged dramatically since the AI driven tech surge over the past two years. The S&P500 is +53%, S&P Midcap +28% and S&P Smallcap +22% from 12/31/22 through 12/19/24. As a result, valuations are more compelling as well at 19x PE for mid and 20x for small versus 25x for the S&P. The valuation of the mid & small sectors should also benefit from any AI digestion phase in 2025 driving investment dollars back into these less loved sectors of the market.

[The Kobeissi Letter @KobeissiLetter]

Key Events This Week:

1. S&P Global Services PMI data - Monday

2. November Retail Sales data - Tuesday

3. Fed Interest Rate Decision - Wednesday

4. Q3 2024 GDP Data - Thursday

5. November Existing Home Sales data - Thursday

6. November PCE Inflation Data - Friday

Are you ready for Fed week?

[sanjay charagulla @sanjaycha]

India loves Trump 💕
#realdonaldtrump #elonmusk #chamat

[Steve Grasso @grassosteve]

He acts like Taylor Swift is the new owner of the Chief’s

[Dr_Gingerballs @Dr_Gingerballs]

The semiconductor industry is screaming at us that AI is all hype. Yes, lots of orders for H100 GPUs, but even an AI datacenter still needs CPUs, memory, and HDs. Then you have AI implementation, such as in computer vision and controls, robotics, etc, which require specialized chips for efficient compute.

The companies that provide these things are all telling us that AI is not going to deliver the types of growth people are foaming at the mouth for.

$INTC revenues down 14% YoY. Net earnings down 78% YoY. They make the CPUs that go in the servers.

$AMD releases earnings next week which are pivotal, but Q3 showed revenues down 8% YTD, earnings down 85% YTD. They also make CPUs that go in the servers.

$MU makes memory. Revenues up 15% YoY but they have had negative earnings for over a year.

Samsung memory chip earnings have dropped 78% YoY.

$WDC also makes hard drives and memory. They just posted revenues down 2% YoY and more than a year of net losses.

$TXN makes all sorts of chips necessary for AI, such as computer vision processors and robot automation chips. Their revenues are down 13% YoY and their net income is down 30% YoY.

In short, hardware sucks for AI. Except for 1 company. $NVDA. Revenue up 206% YoY, net income up 1200% YoY. Even $TSMC, the company that makes the chips for $NVDA, has had a 1.5% YoY revenue decline and an earnings decline of 19% YoY.

$NVDA has so far this year sold about $30B of datacenter equipment. These are primarily A100 and H100 chips, which are 5nm processes. $TSMC reports that about 35% of chips they ship are 5nm in 2nd half 2023. Before that it was variable (as apple moves down to 3nm and others move from 7 to 5), but the lowest number was around 20%. So let's give $NVDA the benefit of the doubt and say 15% of the chips they make are likely for $NVDA and lets say half are for data centers. All told this year, that's $5B of revenue for $TSMC to make A100 and H100 chips.

Then on the $NVDA side, they have made around $30B on AI chips this year, putting their margin around 600%. Others have estimated 800%. So nobody, not even $NVDA's supplier, has ANY pricing power to accommodate this demand. Nobody is making money except for $NVDA.

Then we move to the cloud side. Pricing is pretty transparent for some specialty LLM AI cloud services like Coreweave and Lambda Labs. They have been selling compute on the H100 for about $100/day. If they are paying $40k a pop, then it will take over a year just to break even on the chip, and most likely will take 2-3 years. This is longer than the likely life of the chip. So the cloud providers also do not have the pricing power to make money off of AI.

At one point I looked through the listed AI partners for $ORCL, who has been given preferential treatment by $NVDA since they aren't trying to compete with their own chips. The partners of any significant size are all companies that $NVDA has heavily invested in, or Larry Ellison's own bio institute. Hardly a who's who of titans of organic demand.

In fact, everywhere you look, most of the activity is localized to private start ups. Many of them are funded in part by $NVDA. I have even found some start ups that boast having more H100 chips than their entire raised capital to date. There's little transparency into their books, and many of them likely are not profitable, nor never will be.

So to summarize. The people who make hardware for AI compute are all losing money except for $NVDA. The people who provide cloud compute to train LLMs are selling H100 compute time at a loss. The people using the cloud compute are likely running on fumes with venture capital money, some of which came from $NVDA.

So the industry is absolutely running on hype, as people scramble to capture the money flowing out of VC funds. To be fair, there are some cool things coming out of these LLMs. Nearly instant voice translations are pretty cool. The chat bot is sort of neat. I've heard some good things about the code generation, although admittedly reviews are very mixed and polarized. The image generation seems cool but it looks likely that they will be required to pay royalties to artists. None of these things is $1T a year industry.

But that's not the hype people have been sold. They have been sold on the imminent creation of artificial general intelligence. An intelligence so advanced it can out-perform a human in cognitive and perhaps physical tasks. The problem? LLMs cannot, by themselves, ever achieve this. They are just extremely large regressions to existing data. They simply collage together an output based on an input. It cannot reason. It cannot think. It cannot truly create something outside of its training. It's missing fundamental components of intelligence that nobody has solutions for.

So you have a fundamental misunderstanding by investors about the capabilities of the technology, fueled by a technologically ignorant media, on one side. And mega-cap technology companies willing to spend vast amounts of money to maintain dominance (and find palatable excuses to raise prices without drawing too much ire for anti-trust practices) on the other side. The result is a fever dream where only those hyping the technology appear to be making any money, and not the people building it.

Eventually the fever will break and we will be left with a massive misallocation of capital and a lot of broken hearts.

Buyer beware.

[Zacks.com @ZacksResearch]

Bitcoin ETFs Are Almost Here: What Investors Should Know. $BLK $COIN $MSTR $MARA $BITO https://t.co/l09fuI8OIe