Qubic (QUBIC)

OGAudit Web3 Research: @_Qubic_ $QUBIC - The Chain Where Mining Trains AI Instead of Burning Electricity?

Qubic has been building useful blockchain compute since its April 2022 mainnet launch, directly converting mining hash power into AI training cycles:

- A quorum based architecture with 676 elected Computors running identical hardware validates network state, achieving feeless transactions with sub second finality, Useful Proof of Work aligns computational effort with meaningful tasks like distributed AI model training and validation via Aigarth, a native decentralised AI running on top of Qubic rather than burning energy on arbitrary hash puzzles like Bitcoin.
- A governance approved halving at Epoch 175 increased the weekly burn rate to 57.5%, cutting net emissions from 900 billion to 425 billion QUBIC per week addressing concerns the 200 quadrillion max supply could be reached four years ahead of schedule. A “Supply Watcher” dynamically adjusts emission and burn rates in real time while QEarn staking

Honest take:

What is $QUBIC? A thread for those who keep asking 🧵

676 Computors. Quorum consensus. Zero fees. AI training.

Let me break it down.

@_Qubic_

Not financial advice.

AI data centers are on track to consume up to 70% of the world’s memory chips this year.

The knock-on effect is already here: memory prices are climbing, and laptops and phones are likely to get more expensive because of it.

The cause is one assumption the whole industry runs on:

More intelligence means more scale.

Bigger models.
More memory.
More data centers.

Every high-bandwidth chip the big memory makers produce is roughly three conventional chips they choose not to make.

Qubic bets against that assumption on two fronts.

First, the science.

Qubic’s Neuraxon research argues that brute-force scale is not intelligence at all.

Intelligence is efficiency: how well a system learns the unfamiliar, not how much it memorized.

You do not get there by buying more memory.

Second, the compute.

Qubic’s Useful Proof of Work runs AI training across thousands of machines that already exist, owned by the people securing the network.

It does not need to win a bidding war for scarce data center silicon because i