This media is not supported in your browser
VIEW IN TELEGRAM
$MSFT introduced a new quantum chip aimed at building a useful quantum machine by 2029.
You own the stock for Azure and improving Copilot monetization but quantum remains a free call option attached to the story.
You own the stock for Azure and improving Copilot monetization but quantum remains a free call option attached to the story.
🚨 New models from Microsoft. 💪 7 new models include: In OpenRouter and also runs on RTX Spark! 💥
» MAI Thinking-1
▪ 35B MoE medium-sized model
▪ 97% AIME 2025
▪ 53% SWE Bench Pro
▪ runs on Mia200 chip
▪ 256K context window
» MAI Code-1-Falsh (coding model) → VSCode
» MAI Image-2.5
» MAI Image-2.5-Flash
» MAI Transcribe-1.5
» MAI Voice-2
» MAI Voice-2-Flash
» MAI Thinking-1
▪ 35B MoE medium-sized model
▪ 97% AIME 2025
▪ 53% SWE Bench Pro
▪ runs on Mia200 chip
▪ 256K context window
» MAI Code-1-Falsh (coding model) → VSCode
» MAI Image-2.5
» MAI Image-2.5-Flash
» MAI Transcribe-1.5
» MAI Voice-2
» MAI Voice-2-Flash
👀1
This media is not supported in your browser
VIEW IN TELEGRAM
$MSFT previewed its in-house Cobalt 200 chip at Build, underscoring the company’s push to optimize AI and cloud workloads with custom silicon.
$NVDA CEO Jensen Huang also joined the conference via livestream, highlighting the deep partnership between Microsoft and Nvidia in the AI infrastructure race.
$NVDA CEO Jensen Huang also joined the conference via livestream, highlighting the deep partnership between Microsoft and Nvidia in the AI infrastructure race.
WHERE THE ENTERPRISE AI STACK IS BEING BUILT
1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127
2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface
3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web
4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows
1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127
2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface
3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web
4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows
✍1
🇨🇭 Global One SI - Prompt
🚨BIG NEWS FOR CRYPTO The CLARITY Act is now officially on the Senate Legislative Calendar. This means the bill is eligible to move to a full Senate floor vote. One step closer to regulatory clarity. 🚀
😔 Observing buyers on Twitter, we can see that in 2022, Michael Saylor's sale of #BTC coincided with the local low. Will history repeat itself?
🇨🇭 Global One SI - Prompt
🚨 New models from Microsoft. 💪 7 new models include: In OpenRouter and also runs on RTX Spark! 💥 » MAI Thinking-1 ▪ 35B MoE medium-sized model ▪ 97% AIME 2025 ▪ 53% SWE Bench Pro ▪ runs on Mia200 chip ▪ 256K context window » MAI Code-1-Falsh (coding…
Microsoft unveiled MAI-Thinking-1.
So Microsoft now has a full in-house pipeline for building stronger reasoning models again and again.
Microsoft calls this system a “hill-climbing machine,” meaning it keeps improving the data, training setup, rewards, safety tests, and evaluations as one connected process.
Strong for its size, including 97.0% on AIME 2025, 87.7% on LiveCodeBench v6, and 52.8% on SWE-Bench Pro.
MAI-Thinking-1 is the first model from that process, using 35B active parameters inside a 1T total parameter mixture-of-experts model, where only part of the model runs for each token.
The base model was trained from scratch on 30T mostly human-generated tokens, with Microsoft saying it avoided third-party model distillation during pre-training.
After that, the team used reinforcement learning, which means the model practiced tasks and improved from feedback, to teach math reasoning, coding, tool use, helpfulness, and safety.
So Microsoft now has a full in-house pipeline for building stronger reasoning models again and again.
Microsoft calls this system a “hill-climbing machine,” meaning it keeps improving the data, training setup, rewards, safety tests, and evaluations as one connected process.
Strong for its size, including 97.0% on AIME 2025, 87.7% on LiveCodeBench v6, and 52.8% on SWE-Bench Pro.
MAI-Thinking-1 is the first model from that process, using 35B active parameters inside a 1T total parameter mixture-of-experts model, where only part of the model runs for each token.
The base model was trained from scratch on 30T mostly human-generated tokens, with Microsoft saying it avoided third-party model distillation during pre-training.
After that, the team used reinforcement learning, which means the model practiced tasks and improved from feedback, to teach math reasoning, coding, tool use, helpfulness, and safety.
This media is not supported in your browser
VIEW IN TELEGRAM
Satya Nadella on Microsoft’s Fairwater data center, an AI superfactory.
at today's Microsoft Build 2026 keynote.
its vertically designed, two-story AI data center architecture.
Instead of spreading compute only across a flat floor, Microsoft can place racks in three dimensions, packing far more GPUs densely while preserving fast network access.
This helps the cluster behave more like one massive AI machine, with low latency and high bandwidth between GPUs.
The other major point is its cooling efficiency: its cooling loop is filled once and can operate with effectively zero ongoing water consumption, using roughly the annual daily-water equivalent of a single restaurant.
at today's Microsoft Build 2026 keynote.
its vertically designed, two-story AI data center architecture.
Instead of spreading compute only across a flat floor, Microsoft can place racks in three dimensions, packing far more GPUs densely while preserving fast network access.
This helps the cluster behave more like one massive AI machine, with low latency and high bandwidth between GPUs.
The other major point is its cooling efficiency: its cooling loop is filled once and can operate with effectively zero ongoing water consumption, using roughly the annual daily-water equivalent of a single restaurant.
👀1
🇨🇭 Global One SI - Prompt
Humanoid robotics is turning into a capital race. Tesla sits at $1.54T market cap Figure is shown at a $39B valuation SoftBank Robotics is backed by a $306B parent company and UBTECH is already public in Hong Kong. Then comes the serious private stack: Unitree…
Among
->Tesla
->Unitree
->Boston Dynamics
->Figure AI
which company will be the first to turn humanoid robots into paid daily workers outside controlled demos?
->Tesla
->Unitree
->Boston Dynamics
->Figure AI
which company will be the first to turn humanoid robots into paid daily workers outside controlled demos?
🇨🇭 Global One SI - Prompt
Photo
The $800 billion capex number just doubled (Save this).
Six months ago, the 14 largest publicly traded data center operators had a combined FY2027 capex consensus sitting around $450 billion.
As of February 2026, that number has been revised to $800 billion, nearly doubled in a single consensus cycle, according to the Newmark report that just dropped.
That's the market finally catching up to what demand actually looks like.
And demand is legitimately staggering, Newmark's base case is that AI inference alone could require 250 gigawatts of capacity to serve mainstream adoption.
For reference, the entire US data center fleet was running at roughly 30 GW in 2025.
So the upside scenario isn't 2x or 3x but rather an order of magnitude. The report calls it aggressive but feasible and separately notes that AI training is expected to triple annually through 2030, none of which is included in that 250 GW figure.
Now here's who actually gets paid, Nebius is the cleanest expression of this trade.
It's a full stack AI cloud company, GPU clusters, cloud platform, developer tooling built almost entirely by former Yandex engineers who had to rebuild from scratch after Russia's assets were stripped out in a $5.4 billion separation deal in 2024.
The stock resumed trading on Nasdaq in October 2024 at around $20 per share and today it's trading near $279.
That's roughly a 14x move in under two years for anyone who was paying attention.
The fundamentals justify the move, and then some. Q1 2026 revenue came in at $399 million, 684% growth year over year, 75% quarter over quarter.
This is not a business hunting for customers but rather demand is outrunning capacity.
So Nebius raised the bar it was already clearing.
Capex guidance for 2026 was lifted to $20–25 billion, up from a prior range of $16–20 billion, they signed a $27 billion contract with Meta.
They raised over $6 billion of capital in a single quarter, pushing cash above $9 billion.
The power story makes it even more defensible.
Nebius now has contracted power capacity above 3.5 gigawatts, with a new Pennsylvania site adding 1.2 GW alone and power is the binding constraint in AI infrastructure right now, it's what's stopping everyone else from building faster.
Nebius has the power, they also have the GPU supply relationships and they have the contracts.
That combination is genuinely hard to replicate in any reasonable timeframe.
Milk Road Pro members already know this, NBIS was a featured position in our AI infrastructure coverage well before the Q1 print.
Members who sized it appropriately are sitting on massive gains.
Six months ago, the 14 largest publicly traded data center operators had a combined FY2027 capex consensus sitting around $450 billion.
As of February 2026, that number has been revised to $800 billion, nearly doubled in a single consensus cycle, according to the Newmark report that just dropped.
That's the market finally catching up to what demand actually looks like.
And demand is legitimately staggering, Newmark's base case is that AI inference alone could require 250 gigawatts of capacity to serve mainstream adoption.
For reference, the entire US data center fleet was running at roughly 30 GW in 2025.
So the upside scenario isn't 2x or 3x but rather an order of magnitude. The report calls it aggressive but feasible and separately notes that AI training is expected to triple annually through 2030, none of which is included in that 250 GW figure.
Now here's who actually gets paid, Nebius is the cleanest expression of this trade.
It's a full stack AI cloud company, GPU clusters, cloud platform, developer tooling built almost entirely by former Yandex engineers who had to rebuild from scratch after Russia's assets were stripped out in a $5.4 billion separation deal in 2024.
The stock resumed trading on Nasdaq in October 2024 at around $20 per share and today it's trading near $279.
That's roughly a 14x move in under two years for anyone who was paying attention.
The fundamentals justify the move, and then some. Q1 2026 revenue came in at $399 million, 684% growth year over year, 75% quarter over quarter.
This is not a business hunting for customers but rather demand is outrunning capacity.
So Nebius raised the bar it was already clearing.
Capex guidance for 2026 was lifted to $20–25 billion, up from a prior range of $16–20 billion, they signed a $27 billion contract with Meta.
They raised over $6 billion of capital in a single quarter, pushing cash above $9 billion.
The power story makes it even more defensible.
Nebius now has contracted power capacity above 3.5 gigawatts, with a new Pennsylvania site adding 1.2 GW alone and power is the binding constraint in AI infrastructure right now, it's what's stopping everyone else from building faster.
Nebius has the power, they also have the GPU supply relationships and they have the contracts.
That combination is genuinely hard to replicate in any reasonable timeframe.
Milk Road Pro members already know this, NBIS was a featured position in our AI infrastructure coverage well before the Q1 print.
Members who sized it appropriately are sitting on massive gains.
✍1
🇨🇭 Global One SI - Prompt
Microsoft unveiled MAI-Thinking-1. So Microsoft now has a full in-house pipeline for building stronger reasoning models again and again. Microsoft calls this system a “hill-climbing machine,” meaning it keeps improving the data, training setup, rewards,…
This media is not supported in your browser
VIEW IN TELEGRAM
Mustafa Suleyman, Microsoft AI: 7 new Microsoft Models, no end in sight when it comes to development, orders of magnitude in the next few years
👀1
🇨🇭 Global One SI - Prompt
$LASE One defense name quietly getting stronger. The company just announced its Laser Shield Anti-Drone (LSAD) system was selected as a top submission by government reviewers under the MEIA Vulcan Call for Solutions. Now invited to an exclusive technical…
$LASE Congrats BULLS! We Did it 💵💵💵💵💵💵
👏1💯1
$FCEL Most traders see a beaten-down hydrogen stock.
I’m starting to see something very different.
The monthly chart is quietly building one of the most important setups in years.
What’s happening?
→ multi-year downtrend being challenged
→ volume exploding to levels not seen in years
→ OBV breaking out aggressively
→ TTM Squeeze turning positive
→ institutional accumulation becoming visible
The most important level sits near $28.
A monthly close above that area would completely change the long-term structure.
And here’s the interesting part:
The world is entering an era where AI infrastructure, data centers and power demand are exploding.
Everyone talks about chips.
Few talk about energy.
Even fewer talk about alternative energy infrastructure.
If this monthly breakout completes, FCEL could finally get the freedom it has been waiting for after years of compression.
The move hasn’t happened yet.
But the chart is starting to suggest that 2026 could be the year the market begins to look at FCEL very differently.
#FCEL #Hydrogen #AIInfrastructure #Energy #Power #Stocks #Investing
I’m starting to see something very different.
The monthly chart is quietly building one of the most important setups in years.
What’s happening?
→ multi-year downtrend being challenged
→ volume exploding to levels not seen in years
→ OBV breaking out aggressively
→ TTM Squeeze turning positive
→ institutional accumulation becoming visible
The most important level sits near $28.
A monthly close above that area would completely change the long-term structure.
And here’s the interesting part:
The world is entering an era where AI infrastructure, data centers and power demand are exploding.
Everyone talks about chips.
Few talk about energy.
Even fewer talk about alternative energy infrastructure.
If this monthly breakout completes, FCEL could finally get the freedom it has been waiting for after years of compression.
The move hasn’t happened yet.
But the chart is starting to suggest that 2026 could be the year the market begins to look at FCEL very differently.
#FCEL #Hydrogen #AIInfrastructure #Energy #Power #Stocks #Investing
🇨🇭 Global One SI - Prompt
$ENPH starting to look increasingly important inside the entire AI-power infrastructure narrative. People still underestimate how much energy architecture will be required to sustain: → AI compute → GPU clusters → edge inference → distributed data infrastructure…
$ENPH Remember when nobody wanted to talk about energy?
Everyone wanted AI.
Nobody wanted the power layer behind AI.
Now look at the chart.
The weekly breakout I highlighted is no longer a setup.
It’s becoming a trend.
The market is finally starting to understand something:
AI without power is just a theory.
Data centers.
Grid modernization.
Distributed energy.
Intelligent power management.
Those themes are getting bigger every quarter.
The AI story isn’t ending.
It’s expanding.
And $ENPH is quietly becoming one of the biggest beneficiaries of that expansion.
As expected.
Everyone wanted AI.
Nobody wanted the power layer behind AI.
Now look at the chart.
The weekly breakout I highlighted is no longer a setup.
It’s becoming a trend.
The market is finally starting to understand something:
AI without power is just a theory.
Data centers.
Grid modernization.
Distributed energy.
Intelligent power management.
Those themes are getting bigger every quarter.
The AI story isn’t ending.
It’s expanding.
And $ENPH is quietly becoming one of the biggest beneficiaries of that expansion.
As expected.