Zeeshan Ansari | Web3Go (🟣,🟣)

Blockchain is for AI, AI is for Humanity

I am good boy
#BTC

[RAX Finance @RaxFinance]

The listed price is not the traded price.

Public marketplaces show H100 at $3.25/hr.
Real year-long contracts clear at $2.10–2.70.

Why the gap?

Bilateral negotiations are confidential.
Nobody publishes what actually traded.

Price discovery needs transparency.

🔗 Join the Waitlist: https://t.co/LuaHr8A0uT

[Zeeshan Ansari | Web3Go (🟣,🟣) @Zeeshan00108]

#5 RWA ANCHOR

⚓️ Real-world connection secured! Just got the #5 RWA ANCHOR fragment from RaxFinance.

The bridge between physical and digital is set. Ready to integrate and see the final form! 💎
https://t.co/Z1Om5jDzAl
#RaxFinance #RWA #Blockchain

[Zeeshan Ansari | Web3Go (🟣,🟣) @Zeeshan00108]

#4 AI MATRIX

🤖 Entering the Matrix! Secured the #4 AI MATRIX fragment from my latest @RaxFinance unboxing.
Who else is collecting these fragments? LFG! 📈
https://t.co/Z1Om5jDzAl
#RaxFinance #AI

[RAX Finance @RaxFinance]

Commodity formation playbook:

Gold → London Fix (1919) → futures market
Oil → posted prices → spot market → Brent/WTI
Electricity → bilateral → pools → day-ahead markets

Compute → ?

We're watching the pattern repeat in real-time.

🔗 Join the Waitlist:
https://t.co/0sXOt71OLm

[RAX Finance @RaxFinance]

The debate on AI compute has moved.

From "will demand exist?"
To "do the unit economics work?"

GPU pricing, financing costs, utilization.
Contracted power, density, offtake.

A 1GW factory only works if it can be rented, powered, and paid back.

Some names are being marked down on returns.
Others are being marked up for contracted power, density, and long-term offtake.

The market is underwriting compute like real assets now, not like a theme.

Via @wallstengine

[RAX Finance @RaxFinance]

Compute is not a commodity yet.

Why? No standard unit.

Every GPU purchase is different:
• Region differs
• Provider differs
• Price differs — $1.99 to $5.99 for the same H100
• Contracts are locked and hard to resell

@RaxFinance is building a standard:
1 cT = 1 GPU-hour
Backed 1:1 by real machines.
Trade it or redeem it for compute.
Same unit, anywhere in the pool.
Making compute fungible and tradable.

🔗 Join the Waitlist:
https://t.co/0sXOt71OLm
💬 Drop your address below.
🎁 Surprise Unboxing Opportunities await.

[RAX Finance @RaxFinance]

BlackRock: AI compute could be tokenized.
Translation:
compute is about to get a market.

The world’s largest asset manager just named the future.
We’re building the unit it needs.

1 cT = 1 GPU-hour.

Fungible. Redeemable. Tradable.
That’s the layer.

[RAX Finance @RaxFinance]

A founder's dilemma:

"Signed 12-month GPU contract.
Raised $2M seed.
Built for 3 months.
Funding talks fell through.

Now: 9 months left on contract.
$27K/month burning.
Can't resell. Can't transfer.
Just... stuck."

This isn't rare. This is Tuesday in AI.

[RAX Finance @RaxFinance]

🛠️ Builder’s Note #01

An index without a pool is a number nobody can transact on.

A GPU cloud without an index is capacity nobody can price.

@RaxFinance is building all three:
✓ The machines — custodied in partner data centres
✓ The unit — cT, backed 1:1 by those machines
✓ The venue — where cT can actually clear

One place where GPU compute is:
Minted → Priced → Traded → Redeemed

A benchmark, once referenced, becomes a market standard.

🔗 Join the Waitlist:
https://t.co/0sXOt71OLm
💬 Drop your address below.
🎁 Surprise Unboxing Opportunities await.

[Zeeshan Ansari | Web3Go (🟣,🟣) @Zeeshan00108]

I'm pre-registered for the DAC mainnet launch. Mission card in hand — see you at the gate. @dac_chain https://t.co/agFkqHLpbu

[4Dlabs @4Dlabs_Official]

We’re excited to share that 4D Labs has received investment from @yzilabs through @EASYResidency Season 2. ⚡️

We’re building the real-world embodied data layer for physical AI — turning real-world interaction into training-ready embodied data. 🦾

AI has learned to speak.
Now it needs to learn how to act.

A new chapter begins.

[RAX Finance @RaxFinance]

AI companies have a compute problem:

Sign a 12-month GPU contract.
Need it for only 3 months.
→ $30K/month still gets burned.

40% of your reserved capacity sits idle.
You’re locked into the contract.
→ Capital stays tied up.

@RaxFinance is building a marketplace for tradable compute capacity.

Have 9 months of H100 capacity left?
Monetize the unused portion.

Need compute for a shorter period?
Access available capacity without committing to a long-term contract.

Compute capacity shouldn’t have to sit idle.

[RAX Finance @RaxFinance]

A 1GW AI factory is being described as a $50B -a-year rental machine.

Jensen’s CNBC numbers make the unit economics clearer:
• ~$50–60B to build
• ~$50B in annual rental revenue
• capital that can pay itself back on a very short cycle if utilization holds

That is why this is no longer just a GPU cycle.
It is power + chips + cooling + financing, packaged as a factory that sells compute by the year.

When the world’s largest compute company starts quoting AI factories in gigawatts and rental revenue, the asset is the factory itself.

Compute is becoming the scarce, cash-flowing layer underneath the entire AI buildout.

https://t.co/FTU9qT9Axv

[RAX Finance @RaxFinance]

AI companies have a compute problem:

Sign a 12-month GPU contract.
Need it for only 3 months.
→ $30K/month still gets burned.

40% of your reserved capacity sits idle.
You’re locked into the contract.
→ Capital stays tied up.

@RaxFinance is building a marketplace for tradable compute capacity.

Have 9 months of H100 capacity left?
Monetize the unused portion.

Need compute for a shorter period?
Access available capacity without committing to a long-term contract.

Compute capacity shouldn’t have to sit idle.

Coming Soon 👉 https://t.co/LuaHr8A0uT

[RAX Finance @RaxFinance]

2026: AI compute is becoming the next oil.

Just like crude oil has:
• Spot markets
• Futures contracts
• Regional benchmarks

@RaxFinance is building the same for AI compute:
• Collateralized lending (Vault)
• Trading marketplace
• Regional price index

Tokyo vs Mumbai: 2x price spread on GPUs
A trillion-dollar market is being born 🌱

Join the waitlist → https://t.co/0sXOt71OLm

[RAX Finance @RaxFinance]

RaxFinance is teaming up with @SmartXTerminal 👀

SmartX is the personalized social trading app for memes, perps, stocks, and prediction markets.

And we’ve got something special for the Rax community 👀

📝 Step 1: Box Hunt

Complete each Partner task to earn 5 Boxes!

👉 https://t.co/bMdxOHjgoZ

With Testnet approaching, this could be one of your last great chances to make a final push!

📝 Step 2: Complete the Survey & Win Rewards

🎁 Earn 5–10 additional Boxes for completing the survey.

🏆 The best answers to our open-ended question will receive an additional 2–10 USDT.

👉 https://t.co/xB5D44f8Zj

⏰ Campaign ends September 20, 2026.

Don’t miss the final sprint!

[RAX Finance @RaxFinance]

A permanent framework is moving closer.

When the rules get clearer, capital usually gets more comfortable showing up.

The RWA ecosystem stands to benefit most if that shift continues.

Via @a16zcrypto

[RAX Finance @RaxFinance]

AI compute is not priced by hardware alone.

The economics depend on a combination of:

• GPU supply
• Workload demand
• Utilization
• Energy costs
• Capital efficiency

As AI infrastructure scales, understanding these variables becomes increasingly important.

Because the next compute opportunity may not be about owning more GPUs.

It may be about understanding where demand is heading.

[RAX Finance @RaxFinance]

More GPUs don't always mean more compute.

Utilization matters.

A data center running 10,000 GPUs at 30% utilization has very different economics from one running the same fleet at 80%.

As AI workloads evolve, the value of compute will increasingly depend on:

• GPU utilization
• Workload demand
• Energy costs
• Hardware efficiency

Compute capacity tells us what exists.
Utilization tells us how much of it is actually being put to work.

[RAX Finance @RaxFinance]

Compute and energy are becoming increasingly inseparable.

Every additional AI workload requires more than GPUs.

It requires electricity, cooling, networking, and physical infrastructure.

As AI demand grows, access to reliable power may become just as important as access to compute capacity.

In the AI economy, megawatts and megaflops are becoming two sides of the same equation.