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Mark the date. On April 1st, 2026, Qubic flips the switch on Dogecoin mining, and the entire mining architecture of the network changes with it.
How Qubic Mining Worked Before Dogecoin
If you've been following Qubic, you know the network has always been about making computation useful. This transition takes that philosophy from promising to proven. Here's the full picture.
How Qubic Mining Worked Before Dogecoin
Under the previous model, Qubic miners split their time between two tasks. Roughly 50% of compute time went toward mining Monero (XMR). The other 50% went toward training Aigarth, Qubic's own AI. CPUs toggled back and forth, and while the system worked, neither task got the full attention of the hardware running it.
What Changes With Dogecoin Mining on Qubic
Dogecoin uses the Scrypt hashing algorithm, which runs on ASIC hardware: dedicated machines built for that specific type of work. Qubic's AI training runs on CPUs and GPUs. Different hardware. Different jobs. No overlap.
That single architectural fact changes everything. Instead of splitting time, the network runs both workstreams in parallel:
ASICs mine Dogecoin, 100% of the timeCPUs/GPUs train Aigarth, 100% of the time
No more alternating. No more compromises. The old interleave model is retired for good. And older Scrypt ASICs that have been sitting in closets, machines like the Antminer L3+ that can't turn a profit on standard Doge pools, suddenly have a reason to exist again. The ASIC layer is purely additive: new revenue for the network without touching existing CPU/GPU miner rewards.
Why Qubic's Shift to Dogecoin Mining Matters
It would be easy to frame this as "Qubic now mines a different coin." The significance runs deeper.
Full resource utilization. Under the old model, AI training only had access to half the network's compute cycles. Now it gets 100%. That's a straight doubling of throughput dedicated to Aigarth.
Hardware specialization. ASICs do what ASICs are built for. CPUs and GPUs do what they're built for. The network stops forcing general-purpose hardware into a hashing role it was never optimized for.
A new revenue stream without cannibalization. Dogecoin mining introduces external value into the Qubic economy. New money flows in and feeds directly into the buyback mechanism (more on that below).
Horizontal scalability proven. If Qubic can absorb ASIC miners running Scrypt alongside CPUs running AI workloads, the door opens for future hardware categories to plug in the same way. Dogecoin marks the beginning of a new era for Qubic's mining architecture, the first proof that multiple hardware categories can plug into the network and run in parallel.
Oracle Machines get their first real-world stress test. Every Dogecoin share submitted to the network gets validated through Qubic's decentralized Oracle Machines, not by a single pool operator. That creates real on-chain transaction volume and proves that Oracle infrastructure works under production load.
Qubic Dogecoin Mining: The 3-Phase Transition Plan
The core team is not flipping a switch overnight. The move from XMR to DOGE follows a three-phase rollout designed to protect network stability. Each phase lasts roughly 1 to 2 epochs, giving computors and miners time to adjust.

Phase 1: Testing (1 to 2 Epochs)
The network keeps running XMR mining as-is while Dogecoin enters a live testing phase on mainnet.

What this means for you: Nothing changes on the revenue side. Computors earn from XMR exactly as before. Dogecoin runs in the background, proving the full pipeline works (dispatcher, pool connections, oracle validation) without affecting earnings. This is the safety net phase.
Phase 2: Migration (1 to 2 Epochs)
Computors get to choose: stick with XMR or opt into Dogecoin mining. Both options coexist, but XMR begins its phaseout.

What this means for you: The decision point. Computors who opt into Doge start receiving rewards through the new system. XMR miners can still earn, but incentives shift: top-ups move to the Doge side. The migration is voluntary, but the economics clearly favor moving over.
Phase 3: Final State
XMR mining is fully removed. The dispatcher is turned off. Dogecoin and AI training run the network.

What this means for you: The target architecture. ASICs mine Doge around the clock. CPUs and GPUs train Aigarth around the clock. The network reaches its most efficient configuration to date.
How the Qubic Dogecoin Buyback Mechanism Works
All that mined Dogecoin needs to go somewhere useful. Here's how:
ASIC miners produce DOGE through the networkThe DOGE gets sold on the marketProceeds are used to buy back QUQU is distributed to computors based on their participation
There's also an optional layer the community is shaping: computors can vote to allocate a percentage of QU emissions directly to Doge miners. The Doge buyback can top up rewards to approximately 110% of the base rate. Any remaining buyback that isn't distributed gets burned.
The result is a self-reinforcing loop. Dogecoin mining generates external revenue, that revenue flows back into QU demand, and the burn component keeps long-term supply pressure in check. For more on Qubic's tokenomics, see the halving page.
Qubic Dogecoin Mining: Current Development Progress
The team isn't theorizing. They're proving it works in the real world.
Doge Connect is the protocol bridging ASIC miners to the Qubic network. The draft protocol is ready, the repo is live on GitHub, and a test miner is available. The first successful test share already passed through the full pipeline. For a deep dive into the technical architecture, read the full Dogecoin mining explainer.
Computor documentation with technical specs for pool participation is available in the Doge Connect repository.
Workflow testing is running through the complete chain. Computors and pools are already testing in preparation for launch. Full details were covered in the March 5 All-Hands Recap.
What to Expect When Qubic Dogecoin Mining Goes Live
Computors and pools are already testing behind the scenes. April 1st is when the stats start showing up on mainnet.
If you were around for the early days of XMR mining on Qubic, you've seen this movie before. The network ramps gradually. Miners connect, configurations get dialed in, hashrate climbs day by day. Slow and steady wins the race.

The architecture is proven. The testing is done. Give it room to breathe and the growth curve will speak for itself.
How to Start ASIC Mining Dogecoin on Qubic
If you've got Scrypt ASIC hardware (or you're thinking about picking some up), here's how to get started:
Get the hardware. You need a Scrypt-compatible ASIC miner. Popular options: the Bitmain Antminer L7 (widely available secondhand), the Antminer L9 (current gen, best efficiency), and the Goldshell Mini-DOGE Pro (compact, good for home setups). Older machines like the L3+ work too. Check CoinWarz for current Scrypt miner profitability.
Set up your miner. Connect via Ethernet (most ASICs don't support Wi-Fi), access the web interface, update firmware, and configure pool settings. The official Dogecoin mining guide covers the basics.
Connect to Qubic. Follow the computor documentation in the Doge Connect repo to configure your miner for the Qubic network. Details on pool structure and connection specifics will be confirmed closer to launch.
Join the conversation. Head to the #dogecoin channel on Discord to coordinate with other miners and the core team.
Whether you're dusting off an old L3+ or buying your first ASIC, the network has room for you.
Before April 1st: Join the Live Preview on March 30th
Two days before DOGE mining goes live, the people who built it are pulling back the curtain.
Join Joetom (Core Tech Lead) and Raika (DOGE Lead Dev) for a live walkthrough of the full technical architecture, the three transition phases, and what launch day actually looks like in real time. Hosted by Stephanie (DefiMomma), Head of Marketing & Growth.
No script. No spin. Just the engineers answering your questions on the eve of one of the most anticipated launches in Qubic's history.
Monday, March 30, 2026 at 11:00 AM EDT / 3:00 PM UTC Live on X · YouTube · Linkedin
RSVP here to get a reminder
What's Next for the Qubic Network
This transition was designed in the open, built with community input, and governed by computor vote. The roadmap is clear, the code is tested, and April 1st is coming fast.
Qubic started with a simple idea: computation should be useful. Dogecoin mining is the next chapter, where the network stops choosing between AI and mining and starts doing both, fully, at the same time.
April 1st. Not a joke. But first, March 30th.
See you on mainnet.
Stay connected: GitHub
#Qubic #Dogecoin‬⁩ #AI #AGI #UPoW

Written by Qubic Scientific Team

How Information Flows in Traditional Artificial Neural Networks
In the artificial intelligence models we know, information enters, is encoded, is transformed through algebraic matrices, and produces outputs. Even in the most advanced architectures such as transformers, the principle is the same: the signal passes through a series of well-defined operations within a structured system. The model functions as a directed processing circuit, from left to right, input-output, or from right to left, through backpropagation for adjustments and training.
The results, as we well know, are spectacular. By working over millions of language parameters, AI is capable of giving magnificent answers, along with some hallucinations, however. But if the goal is not to process inputs and produce outputs, but to build systems capable of maintaining an internal dynamics, adapting continuously, reorganizing themselves, regulating their learning, and sustaining intelligence as a property of the tissue, current AI falls short.
Although people sometimes speak of language models as imitations of the brain, in reality this is more of a comparative metaphor than a simulation of computational neuroscience. Biological systems do not handle information from left to right and vice versa. Information propagates through a network, feeds back on itself, and also oscillates, is dampened, or is reinforced depending on the context.

Fig 1. Left-right information flow in traditional artificial neural networks
Not Only Neurons: The Role of Astrocytes in Brain Function and Synaptic Plasticity
We usually associate cognition and intelligence with the functioning of neurons, their receptors, and neurotransmitters. But they are not the only cells in the nervous system. For a long time, astrocytes were considered nervous system cells devoted to support, cleaning, nutrition, and stability of the environment. Today we know that they actively participate in regulation; in fact, a term is used: tripartite synapse, in which they actively participate by detecting neurotransmitters, integrating signals from multiple synapses, modulating plasticity, and modifying the functional efficacy of the circuit.
A living network is not composed only of neurons that fire, but also of astrocytes that regulate how, when, and how much the system changes. In biology, computing is not only about emitting a signal but also about modulating the terrain where that signal will have an effect. Recent research has demonstrated that astrocytes can perform normalization operations analogous to self-attention mechanisms found in transformer architectures — linking astrocyte–neuron interactions directly to attention-like computation in artificial intelligence systems.

Fig. 2 Biological astrocytes and tripartite synapse 
Astrocytic Gating in Neuraxon: Bio-Inspired Neural Network Architecture
Neuraxon is an architecture that tries to recover and emulate the functioning of the brain and to compute functional properties that classical artificial networks have oversimplified.
As we have explained in previous volumes of this academy, Neuraxon does not work only with input, output, and hidden neurons in the conventional sense. It introduces units with states that emulate excitatory, inhibitory, or neutral potentials (-1, 0, +1). In addition, it does so within a continuous TEMPORAL dynamics where we take into account context and the recent history of activation. The network is no longer a sum of layers but resembles more a system with internal physiology. For deeper context on how these foundational elements work, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time and NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence.
We have explained how Neuraxon models transmission through fast, slow, and neuromodulatory receptors — a mechanism explored in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI. But now we also model the regulation of plasticity through astrocytic gating.
How Astrocyte-Gated Multi-Timescale Plasticity (AGMP) Works
Astrocytic gating introduces a gate inspired by the role of astrocytes in the tripartite synapse. The idea is to introduce a local, slow, and contextual filter that determines when a synaptic modification should be opened, dampened, or blocked. It is as if the system can consider whether there is permission for a change. This approach directly addresses the stability-plasticity dilemma, one of the most fundamental challenges in continual learning for neural networks.
Eligibility Traces and Local Synaptic Memory
How does it work? Through a kind of eligibility trace. It is a local memory that says, "something relevant has happened at this synapse." It is updated with a decay over time and with a function between presynaptic and postsynaptic activity. That is: the synapse accumulates local evidence of temporal coincidence or causality. From there, there is a global broadcast-type signal, such as an error, a possible reward, or something dopamine-like. The astrocytic gate selects whether the neuron is in a learning state. In future versions, astrocytes could modulate thousands of synapses if this provides a computational advantage.
This approach is consistent with recent advances in neuromorphic computing, including the Astrocyte-Gated Multi-Timescale Plasticity (AGMP) framework proposed for spiking neural networks, which similarly augments eligibility-trace learning with a slow astrocyte state that gates synaptic updates — yielding a four-factor learning rule (eligibility × modulatory signal × astrocytic gate × stabilization).
Endogenous Regulation: Why Neuraxon Is More Than a Conventional Neural Network
Neuraxon within QUBIC does not compete in scale or task performance. It works through an architecture with endogenous regulation. By incorporating astrocytic principles, it begins to behave like a network with internal ecology. That is: a system where it matters not only which units are activated, but which domains of the tissue are plastic, which are stabilized, which areas are damping noise, which are consolidating regularities, and which are preparing to reorganize themselves. For a comprehensive overview of how biological and artificial neural networks compare, see NIA Volume 4: Neural Networks in AI and Neuroscience.
For Aigarth and QUBIC, the goal is not to accumulate more parameters, but to introduce more levels of functional organization within the system.
Why Astrocytic Gating Matters for Aigarth and Decentralized AI
Aigarth is not a static model but an evolutionary tissue through an architecture capable of growing, mutating, pruning, generating functional offspring, and reorganizing its topology under adaptive pressures. In that context, Neuraxon contributes something: a rich computational microphysiology for the units that inhabit that tissue.
This has implications for robustness, adaptability, and memory. Also for scalability. In large architectures, the problem is not only that there are many units, but how to coordinate which parts of the system are available for reconfiguration and which must maintain stability.
In roadmap terms for QUBIC, the goal is to build systems where intelligence emerges not only from neuronal computation, but also from the coupling between fast processing, slow modulation, and structural evolution. You can explore these dynamics firsthand with the interactive Neuraxon 3D simulation on HuggingFace Spaces, where you can build, configure, and simulate a Neuraxon 2.0 network from scratch.
Fig 3. Neuraxon astrocytes gating - AGMP formulation
Scientific References
Allen, N. J., & Eroglu, C. (2017). Cell biology of astrocyte-synapse interactions. Neuron, 96(3), 697–708.Halassa, M. M., Fellin, T., & Haydon, P. G. (2007). The tripartite synapse: Roles for gliotransmission in health and disease. Trends in Molecular Medicine, 13(2), 54–63.Kofuji, P., & Araque, A. (2021). Astrocytes and behavior. Annual Review of Neuroscience, 44, 49–67.=Perea, G., Navarrete, M., & Araque, A. (2009). Tripartite synapses: Astrocytes process and control synaptic information. Trends in Neurosciences, 32(8), 421–431.Woodburn, R. L., Bollinger, J. A., & Wohleb, E. S. (2021). Synaptic and behavioral effects of astrocyte activation. Frontiers in Cellular Neuroscience, 15, 645267.=Vivancos, D. & Sanchez, J. (2026). Neuraxon v2.0: A New Neural Growth & Computation Blueprint. ResearchGate Preprint.
Explore the Full Neuraxon Intelligence Academy
This is Volume 5 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon and Qubic's approach to brain-inspired, decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized

Artificial General Intelligence, or AGI, represents the next milestone in the evolution of artificial intelligence. Unlike narrow AI, which excels at specific tasks like voice recognition or image classification, AGI aspires to replicate the versatility of human intelligence — thinking, reasoning, and adapting across a wide range of challenges.

But is it truly possible for a machine to think like a human?

Supporters of AGI envision a future where machines can understand complex ideas, learn continuously, and solve problems much like humans do. If achieved, AGI could revolutionize nearly every aspect of society — from science and medicine to education and the economy. However, replicating the depth and flexibility of the human mind remains one of the most complex scientific challenges of our time.

A major point of contention in the AGI debate is whether machines can or should be conscious or self-aware. Some researchers argue that without these human traits, AGI can never truly replicate human thinking. Others maintain that even without consciousness, an AGI that behaves like a human is sufficient to achieve its purpose.

As progress continues, we are also confronted with profound ethical dilemmas. What rights, if any, should AGI have? How do we ensure these systems act in humanity’s best interests? And most importantly — who gets to decide how AGI is used?

AGI could become one of humanity’s greatest achievements, but it could also pose serious risks if left unchecked. Issues like decision-making autonomy, privacy invasion, and unintended consequences must be addressed as the technology evolves.
In summary, while the potential of AGI is immense, we must approach its development thoughtfully and responsibly. Whether AGI can ever truly think like a human remains uncertain — but its impact on our future is undeniable.

#AGI