A day at the Park

inspire others, seek the truth, be the future

[Olivier Crottaz CEFA @crofin67]

COPPER

In 2050, annual copper demand forecast to be greater than all copper consumed from 1900 - 2022

Global Refined Copper Consumption 1900-2022

[Insiderwave @insiderwave]

🚨BREAKING

Joshua Kushner, a multi billionaire, co founder of Oscar Health, early investor in Instagram and future owner of The Lakers

Just revealed his funds’ current investments in the recent 13F filing.

It includes these 5 stocks…

[CooperBaggs 💰🍞 @edgaralandough]

IF YOU'RE A MILLENNIAL (BORN 1981–1996), AND YOU'RE REBUILDING YOUR LIFE, START WITH THESE 20 THINGS.

1. Get bloodwork done this month. Vitamin D, thyroid, iron, B12 — the whole panel.

[Genius Thinking @GeniusGTX]

This mathematician built the AI company that started the image-generation boom, Stability AI.

On André Duqum's podcast, he shared 8 bold predictions on how AI changes humanity:

1) Desk jobs will be replaced by it in 1,000 days

[Fareed Zakaria @FareedZakaria]

For decades, Sweden was known for its inclusive welfare state and its strong voice on human rights.

But recently, the country has come to resemble something very different: an ultra-capitalist haven, with an increasingly tough approach on immigration.

I explained what changed:

[Christophe Barraud 🇫🇷 🇲🇨 @C_Barraud]

🇺🇸 Alphabet, Amazon, Meta and Microsoft now have more than $2.4 trillion in future off-balance-sheet commitments - WSJ
*Not hidden debt but mainly leases that have not yet commenced and contractual commitments to purchase capacity, energy, servers, cloud services or infrastructure.

➡️ CAPEX tells us what these companies are spending today while contractual commitments tell us, at least partly, what they have already committed to spend tomorrow. Getting out of a data-center lease or a multi-year supply agreement is much harder. A significant portion of future compute demand is therefore already locked in.

💰 This reinforces, in my view, the thesis around the financialization of the AI boom. A data center backed by a long-term contract with Google, Meta or Microsoft becomes a much easier asset to finance. Those future cash flows can support debt, private credit, dedicated financing vehicles and potentially securitization. This is the logic behind the financing ecosystem now developing around Nvidia and major financial institutions.

✅ In the short term, this is very supportive for the entire AI value chain because it gives the investment cycle enormous inertia. Even if hyperscalers wanted to slow spending sharply, part of their future expenditure is already contractually committed. This provides visibility for Nvidia, Broadcom, memory manufacturers, data centers, electrical equipment suppliers and even power producers. This is why I remain cautious about the repeated calls for an imminent end to the AI boom as the machine is now much more deeply committed than it was even two years ago.

⚠️ However, this is also what could make the system more vulnerable over the medium term. The more rigid these commitments become, the more important the question of returns will be. Will AI-generated revenues ultimately be large enough to justify all the capital being committed today? If compute demand keeps exploding, there is no real issue, but if AI revenues start to slow while data-center capacity continues to come online, utilization rates could fall, compute pricing could come under pressure and infrastructure refinancing could become more difficult. In that scenario, the same financial mechanism that is amplifying the boom today could eventually start working in reverse.

This probably reduces the risk of the cycle stopping abruptly over the next few quarters while increasing the cost of any long-term overbuilding mistake. The music can therefore keep playing for quite a while precisely because Wall Street is now building the financial plumbing that allows it to continue.

*WSJ link: https://t.co/7sNbi7Uj9P

[dylan ツ @demian_ai]

I’ve been at @nebiusai for two years.
Today feels like one of those days we’ll look back on.

When people look at AI infrastructure, they often reduce the story to one thing: GPUs.

But what we’re building at Nebius is better understood through an equation:

$/MW × MW activated × speed-to-revenue × funding efficiency + software leverage

And in Q2, every part of that equation moved.

1. First: $/MW.

Our 2026 base was around $12M in annual contract value per MW.
- The deals signed in Q2 came in above $20M/MW.
- Short-term capacity opportunities are now reaching $40–50M/MW.
- Pricing on older gen GPUs increased by more than 30% vs Q1.
- Our first Blackwell capacity auction cleared 15% above the highest price we had achieved before, and 20% above the existing pipeline.

That’s what real pricing power looks like in a market where certainty, scale and speed matter.

2. Second: MW activated.

We raised our 2026 contracted-power target from more than 4GW to 5GW, and expect to deploy more than 1GW of new capacity annually starting in 2027.

But contracted power isn’t live compute.

Between the two sit permitting, construction, energy, cooling, networking, GPUs, orchestration, testing and customer onboarding.

The unglamorous (and brutally difficult lol) work required to turn land and electrons into reliable production infrastructure.

After two years at Nebius, I’ve come to believe that this conversion engine is one of the real moats.

3. Third: speed-to-revenue.

-> Q2 revenue reached $582M: +454% YoY and +46% QoQ.

the ai cloud rev reached $575 M. ARR reached 3B$ (adjusted EBITDA margin reached 50%)

And this was before most of our planned 2026 capacity comes online in the second half.

Demand is no longer only a pipeline story.

- Total contract value won in Q2 grew nearly 4× QoQ. TCV from new customers more than 9×'d
- four landmark agreements averaged more than $1B each (with Reflection, Cohere, a US AI neolab and a major US quant firm)

These weren’t passive inbound wins. They took competitive processes, technical POCs and trust earned across multiple engagement cycles.

Customers chose scale, performance, reliability, technical support and the ability to grow with us.

This is becoming a strategic-partner business, not a GPU-rental business.

4. Fourth: funding efficiency.

Roughly 70% of Q2 deals included customer prepayments.

Across the four landmark agreements, those prepayments cover 50–60% of the associated capex.

Expected payback fell to 1 year and 10 months, compared with 2–3 years historically.

Add more than $9B of expected customer prepayments in 2026 and our first ~$775M asset-backed facility at SOFR +250bps, and the flywheel becomes:

Signed demand → prepayments → infrastructure → contracted cash flows → cheaper financing → more infrastructure.

That is a long way from “build it and hope.”

But let’s be precise: this remains infrastructure.

Q2 capex was approximately 5.7B$. Converting GW into production compute requires enormous capital and near flawless execution. The breakthrough isn’t that the business suddenly became capital-light. It’s that more of the build is becoming contracted, prepaid and financeable.

5. And then comes the final part: software leverage.

@nebiustf production inference workloads more than tripled in Q2.

Aether 3.6 strengthened the core cloud platform. @tavilyai brings the real time information agents need. Echo creates a natural language interface for operating infrastructure. @Eigen_AI_Labs and @clarifai deepen our inference capabilities.

This part is particularly personal for me because much of my own work sits close to Token Factory.

The opportunity isn’t simply selling GPU hours, it’s serving the complete AI lifecycle:

Training → post-training → inference → grounding → agents.

Training creates inference demand. Inference creates demand for tuning, evaluation and optimisation. Agents multiply model calls and require reliable access to real-time information.

A platform that serves that entire loop can improve customer outcomes, infrastructure utilization and economics simultaneously.

So the complete equation is:

Higher $/MW
× more MW activated
× faster conversion into revenue
× smarter funding
+ more software per unit of compute.

Each term reinforces the others.
And that’s why today’s announcement is so meaningful.

Two years ago, much of this existed as ambition, architecture and an extraordinary group of people willing to build from first principles.

Today:
- $3B ARR. 50% AI-cloud adjusted EBITDA margin
- More than $40B in customer commitments.
- A 5GW contracted-power pipeline.

Still early, but very real.

I’m incredibly proud of what this team has built, and even more excited by what’s ahead.

The Nebius story isn’t “more GPUs.”
It’s turning power into compute, compute into intelligence, and intelligence into products people use every day.

Strategy → execution → compounding.

We’re just getting started. $NBIS

Disclosure: I work at Nebius. This post is based exclusively on today's public earnings materials and public earnings call. The interpretations are my own and do not represent official company guidance or investment advice. Personal views, not investment advice.

Public sources:
https://t.co/DsFTpShaK2

https://t.co/fOqXoWDB8Z
https://t.co/bYmCk6du4k

[Walter Hudson @WalterHudson]

Top 1% paid ~ 38.4% of all federal income tax.

Top 10% paid ~70.5%
Top 50% paid ~96.7%
Bottom 50% paid ~3.3%

Define "fair share."

[A day at the Park @Adayatthepark]

@felixprehn Workbook

[A day at the Park @Adayatthepark]

@TheLongApe Better buy QQQI then its about 600-700k

[A day at the Park @Adayatthepark]

@HedgieMarkets Without OpenAi Orcle will have 100 other waiting in line. On top of that. Oracle has data, real world data that they can use to train and further develop healthcare technology for example. Oracle is a better and safer bet than an OpenAi, Anthropic, etc

[Shay Boloor @StockSavvyShay]

Data center capacity is projected to add ~106GW from 2026 through 2030 reaching 174GW and implying more than $5T of total spending.

That buildout could drive 50–60% of new U.S. power capacity through 2030 spanning $GEV turbines, $BE on-site fuel cells, $CEG and $VST generation and $VRT cooling and electrical infrastructure as hyperscalers work around years-long grid queues.

Once the power is secured, spending flows into $NVDA and $AMD accelerators, $AVGO and $MRVL custom silicon and networking, $TSM manufacturing and HBM from $MU, $SKHY and Samsung.

I also think as AI shifts toward more distributed and latency-sensitive inference in 2027 then demand should broaden beyond mega-campuses into edge and colocation facilities while $OKLO and $SMR offer longer-term nuclear optionality.

[Michael Saylor @saylor]

Fiat currency is the problem. Companies, institutions, securities, and technologies that strengthen Bitcoin are part of the solution. We can debate ideas without mistaking allies for enemies.

[A day at the Park @Adayatthepark]

https://t.co/vLSWY8dnId