No BS. No regurgitating other users AI slop. Long on asymmetric bottlenecks. Swing trading overreactions. DM me ur thesis! Nothing I post is financial advice!
Nasdaqistan
Joined June 2023
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$NBIS $VRT $NVDA
Power is AI's biggest bottleneck.
So here's a $NBIS stat almost nobody talks about.
Give Nebius and the average data center the same 100 MW from the grid:
Nebius: 80 MW reaches the GPUs
Industry average: 65 MW
+23% more compute from the same power.
Bonus:
→ ~100x less water per kWh at its best site
Rough math: at Nebius's $20M+ of revenue per MW per year, those extra 15 MW are worth ~$300M a year for every 100 MW it connects.
When power is the bottleneck, every MW you don't waste is a MW you can sell.
$NBIS $NVDA $CRWV $IREN $DRAM
This is a huuuuge milestone!
Same GPUs. Up to 5x faster AI training.
That's what @nebiustf just did with software alone.
They built an AI auto-tuner: it finds what's slowing each reinforcement learning (RL) run, predicts which fixes will help, and tests them on real runs.
Results across 8 jobs (4 models × math + agentic coding) on B300s:
- Every job got faster: 1.4x to 4.9x
- Median 3.5x: a week of training done in ~2 days
- DeepSeek-V4.1-Flash ran faster on 40 GPUs than before on 56
- Training quality checks held up
And fixing one bottleneck can expose another: faster inference did nothing once the sandboxes became the limit. Cramming more sandboxes onto one host made trials slower.
Why it matters for $NBIS:
- RL is where post-training is heading. Their CTO: "SFT is kind of a yesterday thing."
- Lead author Di Jin (@kingsquare2) co-founded Eigen AI and worked on Llama post-training at Meta. Nebius bought Eigen for $643M in May. Six months later, the team is shipping.
- When RL gets 3.5x cheaper, teams run more of it. Jevons, again.
$NBIS $NVDA
Nebius spent ~$1.2B in 8 months assembling an inference stack.
Search. Speed. Hardware. Cold starts.
Most of what an AI agent needs to run, now under one roof.
It's truly a stroke of genius.
Feb → Tavily: web search for AI agents (~$400M)
May → Eigen AI: faster inference + post-training ($643M)
Aug → First cloud on Nvidia's Groq 3 LPX
Oct → Inferize: models load in seconds ($100-150M)
Yet most people still value $NBIS like a GPU landlord.
$NBIS $CRWV $IREN $WULF $CIFR $PLTR
Neocloud is the new N-word.
If Nebius is a Neocloud, Palantir is just a consulting firm.
A note for anyone staring at red screens tonight:
Days like today feel like the ground is giving way. It isn't.
Here's what happened: after the midterms, calls in Washington for a pause on advanced AI and a ban on superintelligence hit a market that was already stretched. Fear spread fast, and the Nasdaq slid into correction territory within hours.
Two things can be true at once. The regulatory risk is real and deserves attention. But the speed of today's selling says more about crowded positioning and forced liquidations than about the long-term value of these businesses.
Keep the timeline in perspective. The new Congress doesn't take its seats until January. Any sweeping ban would need to pass both chambers, bridge real disagreement among lawmakers, survive a possible veto, and answer hard questions about America's place in the global AI race. Tougher rules are likely. Shutting the industry down is not.
So what should you do tonight? .
Don't make permanent decisions in a temporary panic. Make sure you're not overleveraged, that your position size lets you sleep, and that your horizon is measured in years, not hours.
Every major technological shift has had days like this. The people who came out ahead were rarely the ones who sold into the scariest headline.
Take a deep breath. The story isn't over.
Allegedly, Leopold's hedge fund blew up again.
Aschenbrenner means burning ashes in German.
So something that got burnt gets burnt again.
Can't make this shit up.
$SOXX
>enough red light therapy for today
>close my brokerage app
>start doomscrolling on reddit, stupid gifs, laughing at people getting hurt
>suddenly this message pops up
>really can't escape it. peak capitulation, I guess.
Life-changing feature!
Will look forward to using it in 2035 when it's available in the EU!
$ANT
The biggest bottleneck is expertise.
Starting today, every engineer who joins Serval begins with a weeklong, $10,000 paid vacation. We’re building the next generational company. We need generational talent to see it through.
Our mission has always been to empower people across every team to turn their understanding of a problem into working, trustworthy automations. Making that function inside the largest enterprises takes world-class engineering.
That’s what you’d be joining us to do. There are foundational decisions still to make, and the engineers making them will shape the future of how big we can build this company.
We want you to arrive rested and ready to do the best work of your life. So your first week is a chance to get away.
And if a @getserval employee refers you, both you and that employee receive $30,000, on top of the vacation.
Come build with us: serval.com/careers
I don't usually post about price action, but I believe he's right.
Markets are getting absolutely hammered, what we really need is one proper day of capitulation.
Capitulation is when fear takes over, investors throw in the towel, and selling becomes indiscriminate. Even fundamentally strong stocks get dumped.
How do you spot it?
• Extreme selling volume, well above average.
• VIX spikes as panic reaches extreme levels.
• Market breadth collapses, with almost everything red.
• A violent intraday reversal as buyers absorb forced selling.
The ideal scenario? A massive flush at the open, followed by aggressive buying and a strong close.
That's often how durable bottoms begin.
Capitulation doesn't guarantee the bottom is in, but sometimes the market needs to exhaust the sellers before it can recover.
We don't need another slow bleed. We need the market to finally wash out the fear.
$NBIS $NVDA $CRWV $MSFT
Modal CEO @bernhardsson gave the clearest explanation of AI margins I've heard.
"Almost no company today has software margins anymore."
The reason is "almost topological":
"You have applications, you have models, you have infrastructure, you have cloud, you have Nvidia. When a customer pays a dollar at the top of the stack, it has to flow through a lot of different layers, and at every layer there's going to be maybe 20%, 30% gross margins."
His own long-term target: 40-60%. "We're never going to get to software margins."
ELI16: every middleman between your $1 and the GPU takes a cut.
Now look at $NBIS. Nebius designs its own servers, writes its own firmware, runs its own cloud, sells its own tokens through Token Factory, and owns some of its data centers.
The more layers you own, the fewer cuts you pay.
Source: youtube.com/watch?v=1dwyL6Iz…
$NBIS $NVDA $CRWV $MU $SKHY
Modal CEO @bernhardsson predicted the AI pullback.
And he's fine with it.
"There's going to be a pullback, 20%, 30% in the stock market. Everyone's going to freak out and like people are going to say it's the end of the world. I'm almost certain it's going to happen."
"I'm okay with that because I think the long-term opportunity is so f***ing big."
His numbers explain the confidence:
→ Modal grew 50x in 12 months and still grows 30-40% per month
→ He expects both open and closed model inference to grow "5x, 10x, 20x over the next 12 months"
→ Asked if AI can be overbuilt in the next 5 years: "Probably not."
He's honest about the other side too: hardware prices are "like gravity." They always come down in the very long run. Just not yet.
Bookmark this for the next red week.
Source: youtube.com/watch?v=1dwyL6Iz…
$NBIS is diversified in at least 7 ways.
If you can count all 7 you will sleep well in times like these.
Let's see:
1. Across the stack
GPU cloud → managed Kubernetes & Slurm → Token Factory inference → post-training (Eigen AI) → GPU snapshots (Inferize) → search for agents (Tavily) → sandboxes.
2. Across countries
Finland, France, Iceland, UK, Netherlands, Israel, US. Saudi Arabia next (NEOM, from Q4 2027). Partners now selling in Türkiye, the UAE and Saudi, plus first customers in Asia-Pacific.
3. Across customers
Hyperscalers (Microsoft up to $19.4B, Meta up to $27B), AI labs, enterprises, startups and universities. Plus distributors like TD Synnex.
4. Across pricing
Multi-year contracts, on-demand, dynamic spot, preemptible, per-token.
5. Across hardware
Hopper → Blackwell → Vera Rubin, plus the first cloud on Nvidia's Groq 3 LPX for ultra-fast inference.
6. Across how it builds
Owned data centers, leases, self-builds, and an asset-light model where partners bring the capital.
7. Beyond the AI cloud
→ Avride: robotaxis with Uber and Hyundai in Dallas, delivery robots for Uber Eats. Uber and Nebius committed up to $375M to it last year.
→ ClickHouse: a reported ~28% stake in a database company valued at $15B in January. Roughly $4B on paper.
→ Toloka (AI training data, backed by Bezos Expeditions) and TripleTen (tech education).
So less dependence on any single customer, country or chip generation.
And the stakes double as a funding source: Nebius has framed ClickHouse as an asset to help fund the AI buildout.
$NBIS $NVDA
Inference may no longer be vegan🐭
A Lightspeed investor says she saw two startups in one week running AI inference on living brain cells from mice.
Amber Yang: "using brain cells, like actual physical brain cells from mice to basically become an inference layer."
Apparently some teams have already trained very small models on them.
Sounds like sci-fi. The reason behind it is very real 👇
"In order to achieve the next level of intelligence, you have to get inference costs at very, very low prices."
Her other big focus? "Optimized inference" companies.
That's how valuable cheap inference has become. Investors are looking at everything from better kernels to living neurons.
For $NBIS, that's the race Token Factory is running. The Eigen AI team Nebius bought says optimization gets 4-5x more tokens out of the same GPU.
Source: youtube.com/watch?v=XRB8kqjQ…
$NBIS $CRWV $IREN $NVDA $ORCL
"Today, compute is like oxygen."
That's SkyPilot CEO Zongheng Yang. His software runs GPU fleets across providers for AI teams.
What his infrastructure partners told him last week:
"Every new allocation that comes online has like 15 or 20 companies in line."
What AI startups do right after raising: "they call up like 10 providers, 20 providers, and they try to get whatever allocations come online the fastest." US, India, Europe. Whatever's available.
His conclusion: "It's no longer an option to be multi-cloud. By necessity, you have to do it on day one."
For $NBIS: in a market this tight, capacity sells as soon as it's online. The real race is how fast you can bring it online.
Source: youtube.com/watch?v=dV7_xVS3…
$NBIS $AMZN $MSFT $GOOGL $ORCL $CRWV
Same GPU. Same setup. Same networking. 10% slower.
SkyPilot's CEO on a customer that moved between two hyperscalers:
"Same GPU SKU, same setup, same networking setup, but somehow the performance difference was like 10%."
His point: providers differ a lot in "reliability, in how they operationalize the cluster... how they are validated."
On a $100M training budget, 10% is $10M.
He also name-checked Nebius: "Nebius provides managed Kubernetes. Other providers may not have that. Nebius also provides managed Slurm."
And Nebius engineers contributed the Nebius integration to SkyPilot's open-source project.
For $NBIS: the chip is the same everywhere. What Nebius builds around it shows up in performance, and in which provider customers pick.
Source: youtube.com/watch?v=dV7_xVS3…
