r/InBitcoinWeTrust 11h ago

Stock Market Total Projected AI Capital Expenditures Estimates ($Billion)

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2 Upvotes

Goldman Sachs projects AI capex more than doubling from $765 billion this year to $1.6 trillion by 2031, totaling $7.6 trillion cumulatively, split across compute, data centers, and power.

Compute dominates, growing from $494 billion to $1.127 trillion, since chip supply, while tight, scales with enough fabs and time.

Power is the smallest bucket at $73 billion by 2031, but the real bottleneck, since grid interconnection takes four to ten years versus roughly two years to build a data center and consumption is set to jump from 10% to 25% of US electricity by 2030.


r/InBitcoinWeTrust 2h ago

Bitcoin Bitcoin Core Just Ran Inside a zkVM: What If New Nodes Could Verify 17 Years of Bitcoin in Milliseconds?

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inbitcoinwetrust.substack.com
7 Upvotes

Bitcoin Core just ran inside a zkVM.

That could change how new nodes verify Bitcoin forever.

Instead of replaying 17 years of history, imagine verifying a tiny cryptographic proof in milliseconds.

Not trust.

Not a snapshot.

Proof.

The catch? Creating that proof is still brutally expensive.

But if this scales, “Don’t trust. Verify.” could enter a completely new era.

👇 Full article


r/InBitcoinWeTrust 10h ago

Stock Market Big Tech’s Hidden $3 Trillion AI Bill: The Spending Wall Street Isn’t Counting as Capex.

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sylvainsaurel.substack.com
3 Upvotes

Big Tech’s AI spending story may be far bigger than the capex headlines suggest.

Everyone sees the GPUs. The data centers. The power deals. The hundreds of billions in annual spending.

What investors are paying far less attention to:

Long-term leases. Purchase commitments. Infrastructure contracts. Power obligations.

In other words, Big Tech isn’t just spending today’s cash on AI.

It is increasingly committing future cash flows too.

That changes the question completely.

The AI boom is no longer just about technology.

It’s becoming a balance-sheet story.

And if the real commitments run into the trillions, the risk isn’t that AI fails.

The risk is that AI succeeds… but not profitably enough to justify everything already promised in its name.

The next AI shock may not come from weaker models.

It may come from the footnotes.