LG DOUCET

I've been hosting crypto podcasts for 5+ years. The @tbpn deal stands out for one simple reason: they treat it like a real show. Studio. Professional setup. They actually dress the part. This goes way beyond the dorks who sit at home with toys and books as their background

@MilkRoadAI where can i learn about this in podcast form

This Morgan Stanley chart is probably the most important single slide in investing right now (Save this). Morgan Stanley just raised its 2027 and 2028 hyperscaler capex estimates by 9% and 10% respectively and the new numbers: $1.23 trillion in 2027 and $1.4 trillion in 2028. Those figures include SpaceX terrestrial compute capex for the first time, a new entrant Morgan Stanley is now treating as a legitimate hyperscaler class spender alongside Amazon, Google, Microsoft and Meta. To put the trajectory in context: hyperscalers spent $261 billion in 2024 and now projected to spend $1.4 trillion in 2028 which is a 5x increase in four years. Now, who captures this spending and how could you benefit from all of this? NVIDIA is the most direct beneficiary, every dollar of AI compute capex disproportionately flows through GPU purchases first. Goldman Sachs estimates semiconductor sales revisions for 2026 are already up approximately 60% as a result of these capex uplifts and the $1.2-1.4 trillion spending level creates demand visibility essentially locked in 18-24 months forward. Micron and SK Hynix are the memory layer, every NVIDIA GPU requires HBM, high bandwidth memory stacked directly on the chip package and there is no substitute. Micron's HBM3E is now shipping at scale into the GB300 ecosystem and SK Hynix continues to dominate HBM supply overall. As capex scales toward $1.4 trillion, HBM demand scales in direct proportion and Micron is the most accessible US-listed pure-play on this dynamic. Broadcom captures the custom silicon and networking layer. Google TPUs, Amazon Trainium, Meta MTIA, every major hyperscaler custom AI chip program runs through Broadcom for networking silicon and packaging. As total capex grows and custom silicon share within it grows, Broadcom's addressable market expands on both axes simultaneously. Marvell captures the optical DSP and custom ASIC layer specifically the photonic engines and SerDes that connect GPU clusters together at scale. Fiscal 2027 revenue is tracking toward $11 billion, $15 billion guided for fiscal 2028, driven almost entirely by hyperscaler AI commitments already in backlog. Vertiv and Eaton capture power and cooling infrastructure, every GPU cluster requires thermal management and power distribution at roughly $0.30-0.40 of infrastructure spend per dollar of compute. At $1.4 trillion total capex, that translates to $400-550 billion flowing into power and cooling infrastructure over the next two years. The infrastructure suppliers get paid regardless of whether AI ROI materializes while the hyperscalers bear the risk. That asymmetry is the core reason the picks and shovels trade remains compelling even at current valuations. Milk Pro Subscribers are up massively on these trades and come join us using the link below to get our full AI trades and today is the last day to get 33% off.

I just remembered when Al Franken was on with Tom and Christina 4 years ago and he became a couples therapist out of nowhere for a minute and it got REAL in there for a second

@NickDiFabio1 lol put some respect on this mfer’s name man

This 82 year old man explaining his regrets & how he’d live his life over again might change how you view your life forever…

@SpeculatorArt @0xDith camera angle needs to be closer

My most liked tweet in years And its reply where I show the Zipper

@postingthistime @MoonOverlord

@LgDoucet @MoonOverlord Lg degen mode activated

Robinhood chain stole Base’s motion

@_fat_ugly_rat_ People in the Yukon:

This is genuinely the best part of our entire planet

@ThoughtZap @MorEdge_Insight Huge part of it

@LgDoucet @MorEdge_Insight The real terror was knowing that it was set up just the day before by carnies.

I absolutely love rollercoasters… but this is just fucking nuts😱🤯 Dinoconda at China Dinosaurs Park in Jiangsu is the world's fastest 4D coaster and one of only three regular 4D coasters on earth. Just watch those seats spinning and flipping… and those corkscrews… that’s CRAZY.

The next leg of the AI trade isn't the hyperscalers anymore. It's the applications sitting on top of them. And Tesla is one of the few companies with an AI application that could genuinely explode. Here's the part investors are underweighting: $TSLA is unusual among the application layer because it's also building its own infrastructure underneath, its own data centers, its own chips, acting almost like a mini hyperscaler the way SpaceX does. Most AI application companies are entirely dependent on renting compute from someone else. Tesla controls more of its own stack than any other consumer-facing AI application in the market which means less margin leakage as robotaxi volume scales. Robotaxis have been a slow bleed for years specifically because the technology was ready before the regulatory environment was. But what changed now is that Tesla has gotten a legal green light: Texas just changed its laws to allow robotaxis to operate statewide. The market is still pricing Tesla primarily on car deliveries and margins. It is not yet pricing a statewide robotaxi launch as a distinct, high-margin software business layered on top of the hardware. Our analysts are ready for that repricing. $TSLA is the most popular position inside Milk Road PRO. If you want to see what other assets our analysts hold, join PRO with a 33% discount. (link below) FT @MelvinInvests @KyleReidhead @WhiteCollarExit.

2 years of an AI infra trade bull market I think this continues into 2027 But this time, I think the application layer will bull market with it (biotech, financials, some SaaS, robotics) our analysts nailed $MU, $NBIS, $BE and more you can track their real time portfolios at Milk Road PRO and its currently 33% off less than a $1/day to have 5 analysts share their market research, give you investment ideas and show their trades live And you have access to each of them inside the private PRO community it would be crazy to do this bull market alone when a product like this exists See my link in bio to subscribe to Milk Road PRO at 33% OFF (just 3 days left)

Melvin @MelvinInvests: the only humanoid robot you can actually buy right now is not Tesla. It trades as a SPAC, ticker $CCXI. Agility Robotics, relisting as $AGLT, has logged 65,000 real operational hours across 9 customers and $300M in signed contracts. Tesla Optimus has deployed zero commercial hours. Agility also sources 75% of its parts in the US while Tesla leans on China. Elon Musk @elonmusk says Optimus will be Tesla's biggest product ever. Maybe. But it has not shipped a single paid hour yet. If you want deployed-humanoid exposure today, there is one name. Melvin holds it, so verify the 65K hours and $300M yourself before following in. Why a warehouse tote-mover is the highest-conviction robotics trade: podcastalpha.substack.com/p/missed-micro… Source: Milk Road AI - youtube.com/watch?v=XWZ3rf…

The relevant unit of AI demand is changing from tokens per answer to attempts per task! This is the deeper bull case for AI infra, and it's why I'm not scared of falling token prices. (Save this) Jensen Huang explains it well. When intelligence gets cheaper and faster, agents can explore a larger search space: testing more approaches, using more tools, checking their work, and retrying until they find a better answer. $NVDA productized exactly that. Its new NemoClaw stack bundles an open model, an agent workflow layer, and a secure sandbox where agents can actually run tasks. The data speaks for itself: A 0.86 score at $4.48 on LangChain's agent benchmark vs. $43.48 for the next-closest model. Put simply, NVIDIA turned agents from a build-it-yourself project into an off-the-shelf product, and that pulls forward adoption for every company waiting on agentic capabilities. That flips how investors should read AI efficiency. A 10x decline in inference cost doesn't mean companies spend 90% less. It means an agent gets 10x more attempts, or that millions of smaller tasks become economical to automate for the first time. That creates a flywheel: - Cheaper intelligence = more economical attempts - More attempts improve the result - Better results justify deploying more agents - More agents create more total compute demand And notice NVIDIA's position in it. The stack is open and model-neutral, but NVIDIA now sits at every layer of the loop: it makes agents cheaper in software and captures the extra demand in hardware. TLDR: Bullish AI infrastructure. Bullish AI adoption.

@Molson_Hart @tferriss

@tferriss Throw all this stuff out and find a good woman and have kids.

There is a process that I have used, and still use, to reignite life... Create two timelines—6 months and 12 months—and list up to five things you dream of having (including, but not limited to, material wants: house, car, clothing, etc.), being (be a great cook, be fluent in Chinese, etc.), and doing (visiting Thailand, tracing your roots overseas, racing ostriches, etc.) in that order. If you have difficulty identifying what you want in some categories, as most will, consider what you hate or fear in each and write down the opposite. Do not limit yourself, and do not concern yourself with how these things will be accomplished. For now, it’s unimportant. This is an exercise in reversing repression. Be sure not to judge or fool yourself. If you really want a Ferrari, don’t put down solving world hunger out of guilt. For some, the dream will be fame, for others fortune or prestige. All people have their vices and insecurities. If something will improve your feeling of self-worth, put it down. Drawing a blank? In that case, consider these questions: 1) What would you do, day to day, if you had $100 million in the bank? 2) What would make you most excited to wake up in the morning to another day? Don’t rush—think about it for a few minutes. If still blocked, fill in the five “doing” spots with the following: — one place to visit — one thing to do before you die (a memory of a lifetime) — one thing to do daily — one thing to do weekly — one thing you’ve always wanted to learn What does “being” entail doing? Convert each “being” into a “doing” to make it actionable. Identify an action that would characterize this state of being or a task that would mean you had achieved it. People find it easier to brainstorm “being” first, but this column is just a temporary holding spot for “doing” actions. Here are a few examples: 1) Great cook —> make Christmas dinner without help 2) Fluent in Chinese —> have a five-minute conversation with a Chinese co-worker Determine three steps for each of the dreams in just the 6-month timeline and take the first step now. Define three steps for each dream that will get you closer to its actualization. Set actions—simple, well-defined actions—for now, tomorrow (complete before 11 A.M.) and the day after (again completed before 11 A.M.). Once you have three steps for each of the four goals, complete the three actions in the “now” column. Do it now. Each should be simple enough to do in five minutes or less. If not, rachet it down. If it’s the middle of the night and you can’t call someone, do something else now, such as send an e-mail, and set the call for first thing tomorrow. If the next stage is some form of research, get in touch with someone who knows the answer instead of spending too much time in books or online, which can turn into paralysis by analysis. The best first step, the one I recommend, is finding someone who’s done it and ask for advice on how to do the same.

@KyleReidhead @FuturumEquities INSANE

This is INSANE! Datacenter Capex to grow to 2.27T in 2027 according to @FuturumEquities the last few years analyst say: "we'll see Capex growth into next year, then it will taper off" and yet, every year the guidance raises higher I'm telling you, everyone is underestimating the demand for compute we've barely touched the surface for the application of artificial intelligence the biggest application is autonomy, in both agents and in robotics and neither of those have started yet we need orders of magnitude more compute to handle this analysts are telling you it tapers off in 2028 now, just like in 2025 they told you it would taper in 2027 they will continue to be wrong because we need to provide compute and intelligence for billions of agents and eventually billions of robots the demand is almost endless, especially as we find more ways to turn intelligence into ROI positive models the dips in neoclouds, memory and semis are for buying this is a multi-year build out that will not stop until well into the 2030's the sooner your realize that, the sooner you can start making real money