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Qubic's June 3rd “Tech on Deck” AMA centered on a development that will reshape how the protocol interacts with the outside world: Outsourced Computation. Core developers FNordSpace and Raika joined moderator Joetom to break down the architecture, explain the authorization model, and lay out a roadmap targeting a July 29 go-live date. The session also offered a look at the realities of building on Qubic's unconventional codebase, drawing over 3,500 live viewers.
What It's Like Building on Qubic's Bare Metal Architecture
Before diving into Outsourced Computation, the team discussed what $Qubic core development actually involves. The picture that emerged is one of constant adaptation. Network health dictates the day: if the chain gets stuck or tick speeds degrade, everything else pauses until the issue is resolved.
The Qubic codebase runs directly on UEFI with no standard C++ library available. That constraint colors everything. Basic functionality that most C++ developers take for granted has to be implemented from scratch. AI coding tools help catch small bugs, but they routinely suggest patterns that won't compile in a bare metal environment. The sandbox system for smart contract execution carries its own assumptions about memory and state that AI tools aren't aware of. As Raika noted, developers often end up guiding the AI more than the AI guides them.
Both developers shared war stories. FNordSpace's first public release caused the network to tick once and freeze, a small bug that shook his confidence in those early days. Raika spent months tracking a concurrency bug in the pending transaction pool before isolating it. These experiences drove process improvements: the team now has structured testing plans, better unit test coverage, and network recovery mechanisms. Raika also built a contract verification tool that has become essential as the volume of community-submitted Qubic smart contracts grows.
Outsourced Computation: From On-Chain Observer to Cross-Chain Actor
FNordSpace led the technical deep dive, drawing on three weeks of close collaboration with CFB to design the architecture. He opened with an important correction: Outsourced Computation has nothing to do with renting out compute power, to clarify any confusion. He confirmed this with CFB multiple times.
The concept fits alongside a system Qubic already has. Oracle Machines bring external data into the chain. A smart contract can query them for something like the current Bitcoin price and receive a verified answer. Information flows inward. Outsourced Computation reverses that flow. It allows a Qubic smart contract to send an authorized instruction outward, where an external system acts on it. FNordSpace put it simply: Oracle Machines gave smart contracts eyes and ears. Outsourced Computation gives them hands.
One critical difference separates the two systems. Oracle Machines return results. Outsourced Computation does not. It is fire-and-forget. The smart contract issues an intent, the network authorizes and delivers it, and the OC's job is done. If the contract needs confirmation that the action succeeded, it queries an Oracle Machine separately.
How the Qubic Outsourced Computation Invocation Works
The authorization process has four stages. A smart contract invokes an OC during execution and pays a fee. Every computor executing the contract at that moment records the request parameters into a core data structure. Computors then independently sign the request. Once 451 of the 676 computors have signed, the request is authorized. This two-thirds quorum threshold ensures no instruction leaves the chain without broad consensus, and the signature bundle lets external systems independently verify that the Qubic network sanctioned the action. Finally, each computor forwards the signed bundle to its processor, a separate machine built to carry out the specific off-chain task.
The processor is where the action happens, and it's developed by the smart contract developer, not by Qubic. The protocol provides the authorization machinery and the delivery mechanism. What the processor does with the authorized instruction is entirely up to whoever builds it.
Cross-Chain Actions, Custody Transitions, and Real-World Use Cases
Three categories of use cases emerged from FNordSpace's work with CFB. Cross-chain actions allow a Qubic smart contract to trigger transactions on Bitcoin, Ethereum, or other blockchains through a purpose-built processor. Custody transitions become possible, where a contract could rotate the authorized signers on a multisig wallet in another chain. And external service integrations open the door to real-world interactions: a user pays a contract in QU, the contract verifies the payment, and an OC triggers an action like unlocking a bike or releasing escrowed funds.
Blockchain bridges stand out as a near-term application. Current Qubic bridges depend on middleware that constantly monitors the tick chain. With OC, a smart contract could push instructions to the other chain directly, eliminating the need for an always-on listener and reducing reliance on third-party middleware that users can't easily audit.
One design constraint worth noting: because all 676 computors forward the authorized bundle to their processors, the receiving system must handle deduplication. A single instruction from Qubic arrives as 676 identical requests, and the destination needs logic to recognize them as one order. This can be handled at the processor level, but it has to be accounted for in the design.
Qubic Token Burn and Fee Model
The economics follow the same pattern as Oracle Machines. Each OC invocation costs a fee paid by the smart contract, and that fee is burned, permanently removing QUBIC tokens from circulation. Alongside existing execution fees and Oracle call fees, Outsourced Computation adds another deflationary channel that scales with network usage.
Outsourced Computation Roadmap: Timeline to July 29 Go-Live
The development roadmap is staged and concrete. Architecture documents were in review with CFB at the time of the AMA. Basic implementation started June 3. A mock OC targets the testnet by June 17 and mainnet by July 1, exercising the full invocation and authorization path without real-world actions. The production release, opening the protocol for developers to build on, is set for July 29. If the system stabilizes earlier, the team indicated the schedule could move up.
Qubic's Three Infrastructure Pillars Near Completion
With Outsourced Computation approaching its go-live, Qubic's smart contract infrastructure reaches a milestone. Smart contracts handle on-chain logic. Oracle Machines bring external data in. Outsourced Computation sends authorized intent out. Together, these three pillars give Qubic smart contracts bidirectional communication with the outside world at the protocol level.
The community now has a clear set of dates to watch: testnet mock in mid-June, mainnet testing in early July, and a full go-live on July 29. A follow-up Tech AMA covering release management and additional developer topics is planned. For anyone building on Qubic or evaluating where the protocol is headed, the next eight weeks will be telling.
#Qubic #SmartContracts #CrossChain #Oracle #CryptoNews

#Neuraxon Intelligence Academy — Volume 10
By the Qubic Scientific Team
If we build an artificial system and want to know whether it is intelligent, what exactly do we measure? We think we know when we hear that ChatGPT-5 announces it has beaten DeepSeek and then that Claude sweeps Gemini.
But the question is still there, intact. Measuring artificial intelligence is not measuring speed or temperature. We have no unit of measurement, as strange as that may seem.
In psychology we have been dealing with this problem for over a century. Artificial intelligence has been at it for a decade. And it does so in a hurry, with a lot of money at stake and with a constant temptation: to declare victory.
The g Factor: A Single Number to Summarize General Intelligence
At the beginning of the 20th century, Charles Spearman realized that when a child performed well in one subject, they tended to perform well in the others, even if they were subjects with no apparent relation. The scores correlated with one another, all of them positively. He called that pattern the positive manifold, and he deduced that there must be a common latent factor behind all those disparate abilities: the factor g, or general intelligence (Spearman, 1904).
The idea is seductive. If all cognitive tests load onto a single factor, it is enough to extract that factor through factor analysis to have a summary measure of general capacity. In human practice, that first factor usually explains between 40 and 50 % of the variance in performance (Detterman & Daniel, 1989; Deary et al., 2009).
But watch out, because here lies the first trap. The g factor is populational. It does not measure the individual, but variance within individuals (Hernández-Orallo et al., 2021). To say that a specific subject has so much g is, strictly speaking, a mistake. g emerges when comparing many subjects, not when examining one. Like personality, you are the most extroverted of your age group. And you remain so at 50 relative to your group, even if in intensity you are less extroverted than at 20.
What Does IQ Really Measure? Understanding Intelligence Scores
But then, what does IQ measure?
It measures a relative position. The scale is calibrated on a sample with mean 100, standard deviation 15. An IQ of 130 is not an absolute amount of intelligence stored inside someone's head; it is the assertion that this person is two standard deviations above the mean of their normative group. The number is attached to the individual, yes, but its meaning is populational. It is a position in a ranking, not a content.
Your height is absolute: you are 180 centimeters tall even if you are the last human being on Earth. Your IQ is not: being above the mean requires a mean, and a mean requires others. No one can be more intelligent than the average on a desert island.
Now one understands why transferring this to AI is so delicate. When someone computes a g for a set of large language models (LLMs), that factor is an artifact of the set they chose. We are measuring a position in a table, and we present it as if it were an internal property of the system.
Applying the g Factor to Artificial Intelligence: A Dangerous Temptation
The temptation to transfer all of this to AI was irresistible. Gignac and Szodorai proposed that, if the performance of models across varied tasks correlates positively, it should be possible to identify a general factor of capacity in artificial systems as well. And indeed, several recent works apply factor analysis to test batteries in LLMs and find a unidimensional g factor that remains stable across models, batteries and extraction methods (Ilić, 2023). It sounds like confirmation. It is wise to be suspicious.
The appearance of a dominant first factor does not prove that there exists a general capacity analogous to the human one. It proves that the scores of those models covary. And they covary for a very shallow reason: they share architecture, they share training corpus, they share optimization recipes. A large, well-trained model does everything better than a small, poorly trained one, across all tasks at once. That is enough to manufacture a beautiful positive manifold that tells us nothing about cognitive generality. It tells us about the scale of computation. WATCH OUT: The factor we extract may simply be a factor of size disguised as intelligence.
The brain, moreover, does not concentrate intelligence in a single module. A multitude of specialized subsystems process in parallel and, when a piece of information wins the competition, it becomes globally available to the rest of the system, which can then recombine it for new purposes (Baars, 1988; Dehaene & Changeux, 2011). What we call generality is global availability: putting a piece learned in one context at the service of a problem in another. It is not a stored scalar number; it is a pattern of access and integration. This is the kind of functional architecture that Neuraxon tries to emulate — modular subsystems with continuous-time dynamics and multi-timescale plasticity, rather than a monolithic transformer.
François Chollet and the Modern Approach: Measuring What You Still Don't Know How to Do
Against the psychometric legacy, François Chollet proposed in 2019 a conceptual turn. His argument, in On the Measure of Intelligence, is that we were measuring the wrong thing.
Traditional AI benchmarks reward skills, specific competencies on concrete tasks. But a skill can be bought with data and computation: it is enough to train sufficiently on a task to master it. Intelligence, Chollet maintains, is not skill, but efficiency in the acquisition of skills: how much you learn from how little, when facing a genuinely new task (Chollet, 2019).
Intelligence is what you do when you don't know what to do.
This distinction changes everything. A system that solves a million problems because it has seen ten million similar ones is not intelligent. An intelligent system is the one that, facing a problem for which it could not prepare, discovers the structure and adapts with few examples. The measure stops being the final result and becomes the slope of learning.
ARC-AGI: The Benchmark That Tests Genuine AI Reasoning
ARC-AGI was born from that idea, and its most recent version, ARC-AGI-3, takes it further. It is not a question-and-answer test. It is a set of interactive environments, like mini-videogames, in which the agent explores an unknown world, deduces what the objective is without being told in natural language, builds a model of the environment and adapts its strategy step by step (ARC Prize, 2025).
The design principles are explicit: environments 100 % solvable by humans, with no preloaded knowledge or hidden instructions, and with enough novelty to prevent memorization. What is scored is not getting it right, but efficiency in the acquisition of skill over time.
It is the opposite of the g factor: instead of looking for what a system already masters and summarizing it, it looks for what it still does not know how to do and measures how much it costs it to learn it.
Data Contamination: Why LLM Benchmark Scores Are Inflated
The ultimate reason why Chollet's approach matters, and why the g factor applied to LLMs is so slippery, has a technical name: data contamination. If the exam, or something almost identical, was in the notes the student studied, their grade does not measure what they can reason. It measures what they have memorized.
Language models are trained on books, forums, code repositories, articles, practically all the available text. The benchmarks with which we then evaluate them are published on the internet. The conclusion is that fragments of the tests end up inside the training data, which violates the separation between training and evaluation and inflates the scores (Xu et al., 2024; Deng et al., 2024). Empirical audits have detected contamination levels ranging from 1 % up to 45 % in widely used benchmarks, and the problem grows over time (Li et al., 2024).
It is not a minor problem of a couple of leaked questions. In benchmarks as cited as MMLU or GSM8K, part of what we interpret as reasoning may be pure memorization (Chen et al., 2025). When decontamination techniques are applied that rewrite the leaked items without altering their difficulty, accuracy drops: in one study, 22.9 % on GSM8K and 19.0 % on MMLU (Zhu et al., 2024).
Paraphrased items, or even ones translated into another language, dodge the superficial-overlap detectors and continue to inflate the results (Yang et al., 2023; Yao et al., 2024). The usual solutions (paraphrasing, translating, tweaking the context) are assumed to be effective without having been validated rigorously. And for most open models we cannot even check anything, because their training data is not published. We are grading exams without knowing what the student studied.
Here one understands why ARC-AGI chose the path it chose. An interactive, novel environment, with no natural-language instructions and designed to prevent brute-force memorization is, by construction, resistant to contamination.
So, What Should We Measure to Evaluate Machine Intelligence?
The g factor is a populational property that, applied to models that share architecture and corpus, runs the risk of measuring the scale of computation and not generality. The lesson for whoever builds artificial systems is not to choose between the g factor and ARC-AGI as if they were rival teams. It is to understand what question each one answers. A factor analysis can be useful to describe the internal structure of a system's performance, as long as the first factor is not confused with an essence of intelligence. And an ARC-type protocol is indispensable for what really matters: checking whether the system generalizes beyond what it saw, or merely recites.
When we evaluate a system only by its final answer, we are measuring it with our eyes closed to its temporal dimension: planning, the updating of beliefs, the integration of evidence across many steps. It is exactly what ARC-AGI-3 decided to score, and exactly what a static exam cannot see.
Why Brain-Inspired AI Architectures Like Neuraxon Take a Different Path
If intelligence is not a stored number but the efficient integration of specialized subsystems, as suggested by the parieto-frontal integration theory (P-FIT) and the global availability of the workspace in the brain…
If that integration is above all a temporal phenomenon, with time scales…
Then a system built on modular architectures with functional spheres, plasticity across multiple temporal scales and continuous dynamics does not need to be evaluated by asking it to recite answers.
The correct question is not how many benchmarks it beats, but with what efficiency it acquires new behavior, over time, in environments for which it was not prepared. That is the direction Neuraxon tries to take. To compute time – that is, adaptation – not memorized answers that simulate being a good student, when in reality, it already knows the questions.
#AI #AGI #Qubic #TechTrends
References
Chollet, F. (2019). On the Measure of Intelligence. arXiv:1911.01547.Deary, I. J., Penke, L., & Johnson, W. (2009). The neuroscience of human intelligence differences. Nature Reviews Neuroscience.Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227.Detterman, D. K., & Daniel, M. H. (1989). Correlations of mental tests with each other and with cognitive variables. Intelligence.Gignac, G. E., & Szodorai, E. T. (2024). Defining and identifying a general factor of ability in AI systems.Guttman, L. (1955). The determinacy of factor score matrices with implications for five other basic problems of common-factor theory. British Journal of Statistical Psychology.Hernández-Orallo, J., et al. (2021). General intelligence disentangled via a generality metric for natural and artificial intelligence. Scientific Reports.Honey, C. J., et al. (2012). Slow cortical dynamics and the accumulation of information over long timescales. Neuron, 76(2), 423–434.Ilić, D. (2023). Unveiling the General Intelligence Factor in Language Models: A Psychometric Approach. arXiv:2310.11616.Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence. Behavioral and Brain Sciences.Spearman, C. (1904). "General intelligence" objectively determined and measured. American Journal of Psychology, 15, 201–293.Roberts, M., et al. (2024). Temporal evidence of contamination from training cutoff dates.Schönemann, P. H. (2008). A Rejoinder to Mackintosh and some Remarks on the Concept of General Intelligence. arXiv:0808.2343.Xu, C., et al. (2024). Benchmark data contamination of large language models: a survey.Yang, S., et al. (2023). Rethinking benchmark and contamination for language models with rephrased samples.Zhu, Q., et al. (2024). Inference-Time Decontamination: Reusing leaked benchmarks for LLM evaluation. Findings of EMNLP 2024.ARC Prize (2025). ARC-AGI-3: An interactive reasoning benchmark. Technical Report.
Explore the Full Neuraxon Intelligence Academy Series
This is Volume 10 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, Aigarth, 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.NIA Volume 5: Astrocytes and Brain-Inspired AI — How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Volume 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained — Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems — The science behind Qubic, Aigarth, and Neuraxon's emergent complexity and self-organized criticality.NIA Volume 8: Brain Criticality and the Branching Ratio in Neural and Artificial Networks — Why a branching ratio near 1 and self-organized criticality are bioinspired design principles in Neuraxon.NIA Volume 9: The Origins of the g Factor: From Education and Neuroscience to Artificial Intelligence — Explores the origins of the g factor across education, neuroscience, and AI.
$Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram.