Chinnu Ancha

[𝗥𝗘𝗧𝗥𝗢 - 𝗩 - 𝗝𝗨𝗡𝗞𝗜𝗘 @Retro_V_Junkie]

𝐎𝐧 𝐭𝐡𝐞 𝐒𝐢𝐥𝐯𝐞𝐫 𝐆𝐥𝐨𝐛𝐞 (𝟏𝟗𝟖𝟖, 𝐀𝐧𝐝𝐫𝐳𝐞𝐣 Ż𝐮ł𝐚𝐰𝐬𝐤𝐢)

"The Same Chaos. The Same Absence of Truth."

An Astronaut Arrives on a Planet with a Society Founded by Cosmic Explorers. But finds their Descendents Enslaved by Bird-Monsters called Szerns 🇵🇱🤘🏻

[RonnieV @TheRonnieVShow]

I can’t stop buying $ZETA.

Why I think Zeta Global could become a Palantir-style AI operating system for marketing, data, business intelligence, and GEO.

Thesis. Valuation. Targets. Chart setup.

Watch here 👇
https://t.co/sKQt2ZOnBN

[True North @TNorth]

Crappy Timing.

"They lost $16B of collateral value in 5 days..."

@PunterJeff breaks down $MSTR retiring the convertible bond.

[The Bitcoin Historian @pete_rizzo_]

JUST IN: MICHAEL SAYLOR'S STRATEGY JUST REVEALED THEY CAN COVER 32 MORE YEARS OF DIVIDENDS WITH ITS #BITCOIN RESERVE

THE MACHINE NEVER STOPS

DIP IS TEMPORARY 🔥

[Chinnu Ancha @ChinnuAncha]

@alexandrosM how often do you spend time thinking about the Roman empire?🤔

[Ryan Hogue @RyansMethod]

$MSTR — The Setup for a Massive Move Is Here… But Be Patient

[Justin Banks @RealJGBanks]

$MRVL next to make new MILLIONAIRES

[Chinnu Ancha @ChinnuAncha]

@cantonmeow im ready to make out now Cat

[Alessandro Palombo @thealepalombo]

This is the map I made for myself.

12 rural areas in Italy where I'd actually buy a house and start a farm. Each one within an hour of a real city.

I think geography is about to be redrawn. One hour from a city, plus self-driving cars, will completely change where it makes sense to live.

Since you asked, I'm building the same list for Italian coastal towns.

Which one should I look into next?

[Chinnu Ancha @ChinnuAncha]

@matthughes13 MSTR is looking so cheap right now, especially if you can hold this for a few years… I am stacking!

[Chinnu Ancha @ChinnuAncha]

@cantonmeow capital getting divested from crypto to AI, this incidentally coincides with crypto winter and in the past it was ponzi schemes but if this time around it’s just seasonality, I think it shows crypto is holding up well!

[Kai @kai_xbt]

Threadguy explains why SpaceX money will never rotate back into crypto

"Everyone thinks the people who made a fortune on SpaceX are going to take profits and rotate it into crypto. That's not what happens. The complete opposite happens."

"If you put money very early into SpaceX and made a lot, you are now programmed to invest in bleeding-edge tech and venture. You're going to put that money into robotics, not crypto. Nobody who just made a billion dollars is shoving a meaningful amount into crypto. It's not happening."

[Ryan Hogue @RyansMethod]

The Setup For $400,000 Bitcoin & $1,200 $MSTR is About to Begin

[One Chair @OneChairPod]

What if everyone is measuring $MSTR wrong?

In this conversation with @_Adrian, founding member of True North (@TNorth), we challenge some of the biggest assumptions in the Bitcoin Treasury space:

• Why mNAV is really a sentiment metric
• Why Bitcoin per share isn't a valuation metric
• Why Strategy shouldn't defend its mNAV
• The real story behind Strategy selling 32 $BTC
• Why $STRC and $SATA may evolve very differently than investors expect
• Why AI is attracting some of Bitcoin's capital

One of the most thought-provoking $MSTR conversations we've had.

If Adrian is right, investors may be looking at $MSTR completely wrong.

$MSTR $ASST $MPJPY

00:00 Adrian Morris' Bitcoin Journey & Why He Bought MSTR
05:02 The Real Meaning of mNAV (Market Sentiment)
10:10 Why Bitcoin Treasury mNAVs Eventually Collapse
11:22 Should Bitcoin Treasury Companies Defend Their mNAV?
15:56 The Fatal Flaw in mNAV Buybacks
17:57 Why Strategy Should NOT Sell Bitcoin to Buy Back Shares
20:15 Why Did Strategy Sell 32 Bitcoin?
23:55 MSTR Myths, Margin Calls & X Misinformation
25:38 Why Bitcoin Per Share May Be Misleading Investors
31:09 The Endgame for Bitcoin Treasury Companies
34:42 The Future: REITs, Options & Bitcoin Financial Products
36:46 STRC, SATA & Bitcoin Preferred Shares Explained
41:22 Does STRC Guidance Even Matter?
43:12 Why SATA Outperformed STRC
45:42 Daily Dividends: Innovation or Hype?
48:30 Will STRC & SATA Eventually Cut Dividends?
54:54 Is AI Stealing Capital From Bitcoin?
01:00:51 Can Bitcoin Become AI's Security Layer?
01:03:56 Adrian's Message to Bitcoin Investors

Watch the full episode 👇

[Northwise Project @InvestNorthwise]

In February, we released our first full $NBIS model to X with and set a staggering $1,250 end of 2029 price target ($644 present value @ 18% discount rate) for our premium members.

We have decided to open our full model for free this week. (link in first comment)

Although all of our reports are free for readers, our price targets, portfolio allocations, present value calculations, and buy/hold/trim/sell zones are generally gated.

Before we release our new $NBIS model next week, we have decided to open up our most popular model to date for everyone on the X community who has supported our work on $NBIS and many other names in AI infra.

We appreciated you and hope you gain something on the way we think, what we got right, and more importantly what we got wrong.

It's not easy to set a target so high with confidence when Nebius was trading under 100 a share at the time, but it's much easier when you put in the work, do the research, and actually base price targets on real numbers.

The original report was published when most public analysis still treated Nebius as a GPU rental business.

Our report instead modeled the company around power availability, connected MW, ARR per MW, utilization, customer funding, enterprise mix, and dilution.

Several parts of that framework were correct.

We correctly identified Nebius as a vertically integrated AI infrastructure platform rather than a simple reseller of GPU capacity.

We correctly identified power and energization as the primary operating constraints.

We correctly modeled revenue as a function of connected and monetized MW rather than applying a simple revenue growth rate.

We correctly treated hyperscaler contracts as both revenue sources and financing instruments.

We correctly identified customer prepayments, deferred revenue, operating cash flow, secured financing, and asset-backed financing as central components of the capital stack.

We correctly identified Aether and the broader software layer as important to utilization, orchestration, customer integration, and long-term margin quality.

We correctly expected enterprise, AI-native, and inference workloads to become more important over time.

We correctly argued that equity outcomes would differ significantly across AI infrastructure companies depending on power control, capital structure, dilution, depreciation, and software integration.

We were materially above most public and Wall Street valuation estimates. Our original public model included:

Bear case: $752

Base case: $1246

Bull case: $1760

Those estimates were based on long-term infrastructure throughput and earnings power rather than near-term revenue alone.

Several assumptions now appear too conservative.

ARR per MW may be ramping faster than we expected.

The original model assumed ARR per MW would increase gradually as rack density improved, utilization rose, and enterprise and inference mix expanded.

Our modeled midpoint assumptions were:

2026: $9M per MW

2027: $11M per MW

2028: $13M per MW

2029: $15M per MW

The current revenue and ARR trajectory suggests the starting point and slope may both need to move higher.

Contracted power has expanded faster than expected.

The original report assumed more than 3GW of contracted power by the end of 2026.

The disclosed pipeline has since expanded beyond that level, increasing the potential long-term capacity base.

Contracted power is not the same as energized capacity, but it increases the top of the future deployment funnel.

The energization schedule may have been too conservative.

The old model assumed approximately:

2026: 900 connected MW

2027: 1,500 connected MW

2028: 2,000 connected MW

2029: 2,650 connected MW

That schedule already appeared aggressive at publication.

New site announcements, construction progress, and disclosed capacity targets suggest the ramp may occur faster or reach a larger endpoint than our original base case.

Customer funding appears stronger than expected.

The original model assumed that prepayments and contract-related cash flow would fund a meaningful portion of the buildout.

The increase in deferred revenue and operating cash flow suggests that customer commitments may be contributing more funding, and contributing it earlier, than our original assumptions.

We will distinguish carefully between deferred revenue, cash prepayments, working capital movements, and operating cash flow in the update.

Enterprise and AI-native mix may be ahead of our original assumptions.

The old model assumed the following revenue mix:

2026: 85% hyperscaler, 15% cloud and enterprise

2027: 80% hyperscaler, 20% cloud and enterprise

2028: 72% hyperscaler, 28% cloud and enterprise

2029: 65% hyperscaler, 35% cloud and enterprise

Current customer activity and product development suggest enterprise, inference, healthcare, life sciences, and AI-native workloads may be scaling faster than this path assumed.

The capital structure has become more complex.

The old model used scenario-based equity issuance assumptions.

The updated model must now include:

Basic share count

Prefunded warrants

Convertible notes

Potential conversion dilution

Interest expense

Cash raised

Customer funding

Secured financing

Asset-backed financing

The old share-count framework is no longer detailed enough.

CapEx will need to move higher.

The original model used approximately $18B as the midpoint of 2026 CapEx.

A larger contracted power base and faster site development may require higher spending.

Higher CapEx can increase long-term value if the capacity is efficiently funded and monetized. It can also increase execution and financing risk. The update will evaluate both sides.

The original report correctly identified the structure of the opportunity and was materially ahead of the market on valuation.

The new report will update the assumptions where Nebius has moved faster than expected and add greater precision where the original model relied on incomplete information.

[Podcast Alpha @PodcastAlphaX]

Not NVIDIA. Not OpenAI. Eli Lilly.

Jordi Visser (@jvisserlabs) says a 150-year-old pharma company in Indianapolis has the best shot at becoming the world's largest company within five years.

The case: ~1,000 NVIDIA Blackwell GPUs in a private data center, a co-innovation lab with Jensen Huang, an AlphaFold partnership via Isomorphic Labs, Toon Lab in Silicon Valley, and 150 years of proprietary metabolic disease data no general model can replicate.

That last moat is the one that matters. You cannot train your way to that dataset. Eli Lilly has it.

The investor gap: the stock is filed under healthcare. The thesis is AI application. That category mismatch is where the repricing happens.

The full breakdown on why $LLY is the AI trade the market hasn't priced: https://t.co/PYftzrateR

Source: Anthony Pompliano Podcast - https://t.co/juzZcMeM4V

[Jordi Visser @jvisserlabs]

For some, last week felt like the beginning of an AI bubble unwind.

For me, it looked like the end of the agentic infrastructure fireworks show.

The buildout is not over.

But the easy phase where every chip, memory, power, and infrastructure name worked is probably over.

Now the rotation shifts to the next layer of Jensen Huang’s five-layer cake:

Applications.

That is where ROI shows up.

That is where specialized AI models matter.

And that is why Eli Lilly may be one of the most important AI stories in the market.

GLP-1 cash flow.
Proprietary data.
LillyPod.
TuneLab
Specialized AI.
Peptides=API Keys
Human software.

This week’s video is about why this is rotation, not a bear market and why the next phase of AI may look very different from the last one.

Watch here:https://t.co/NX74tzApzP

[Bilaal- BD investing @bdinvestingg]

Copper can’t keep up . AI data centers need light — and photonics companies can’t build fast enough to keep up.

$LITE – Makes the lasers inside every optical transceiver. No LITE, no light.
$AAOI – Builds transceivers that convert electrical signals to optical. The translator.
$MRVL – Designs the silicon brains that control photonic data flow at speed.
$GLW – Invented optical fiber. Still makes the glass the internet runs through.

[Mr Zzz @__MrZzz__]

$ETH
Mark Newton @MarkNewtonCMT noted today that Ethereum is now flashing Tom DeMark TD Sequential buy setup, which looks for a completed downtrend exhaustion pattern before a potential reversal higher - first time since Feb'26.

[Herbert Ong @herbertong]

🚨 Elon Musk says SpaceX is going public because the company is entering a major investment phase

Speaking at J.P. Morgan, he highlighted plans for 100,000+ next-generation Starlink satellites, AI data centers in space, and continued development of Starship, which he believes can dramatically lower the cost of reaching orbit.

Musk says these projects could open the door to entirely new industries in space. 🚀 $SPCX

00:00 Welcome to JPMorgan
02:45 Why SpaceX Goes Public
03:28 Starlink V3 and Space Energy
07:46 Moon to Mars Roadmap
10:18 Starship Reusability Breakthrough
12:06 Next Gen Starlink Plans
13:54 AI Data Centers in Orbit
15:07 Terafab and US Chip Supply
16:35 AI Strategy and Grok
18:38 Culture and Building the Bench