upow

Written by The Qubic Team

Blockchains are powerful systems for verifiable computation, but they have a fundamental limitation. They can only work with data that already exists on-chain. If a smart contract needs to know the current price of Bitcoin, the outcome of a sports match, or the weather in Tokyo, it has no way to find out on its own.
Oracle Machines solve this problem. Qubic is introducing its native oracle infrastructure, giving smart contracts direct access to real-world information.
An Oracle Machine serves as middleware between Qubic Core Nodes and external data sources. It handles requests leaving the blockchain and delivers verified data back in a form the network can trust.
Think of it as a three-layer system:
Qubic Core Nodes - where smart contracts live and executeOracle Machine Node - the middleware layer that handles routing, caching, and validationExternal Oracle Services - price feeds, weather APIs, event data providers
When a smart contract needs external data, it sends a query to the Oracle Machine. The Oracle Machine checks its cache, forwards the request to the appropriate external service if needed, and returns the result to the blockchain in a standardized format.
This architecture keeps external complexity isolated from the core protocol, while enabling smart contracts to access real-world information reliably.

Technical Architecture
The Oracle Machine system uses a modular design with clear separation of concerns:

Core Modules:

How Data Flows Through the System
The request lifecycle follows a clear sequence:
Qubic Core Node sends OracleMachineQuery
      ↓
NodeConnection receives and validates
      ↓
RequestHandler checks cache
      ↓
InterfaceClient forwards to oracle service
      ↓
Oracle service fetches data (e.g., from CoinGecko API)
      ↓
Response cached and returned to Qubic Core node as OracleMachineReply
      ↓
Qubic Core nodes generate one OracleReplyCommitTransaction per Computor
      ↓
Quorum verifies the oracle reply based on commits of the Computors
      ↓
Verified oracle reply is revealed on the chain by a OracleReplyRevealTransaction
   
The caching layer is particularly important. Frequently requested data (like popular trading pair prices) can be served instantly from cache, reducing latency and external API load. The TTL-based system ensures data stays fresh while optimizing performance.
Oracle Interface Types
Oracle Machines support different interface types, each with its own query and reply structure. The system will launch with The Price and the Mock interface. More oracle interfaces will be added soon.
Price Interface (Index 0)
The Price interface fetches currency pair data from providers like CoinGecko.
Query Structure (Example):
Oracle: Provider identifier (e.g., CoinGecko)
Timestamp: Query timestamp
Currency1: Base currency (e.g., BTC)
Currency2: Quote currency (e.g., USD)
Note: This is an example. It may need to be revised and a precision requirement will likely be added.
Reply Structure (Example):
Numerator  Price numerator (sint64)
Denominator: Price denominator (sint64)
The numerator/denominator format preserves precision for financial calculations without floating-point errors.
Mock Interface (Index 1)
Useful for automated and manual testing.
Two Ways to Request Data
Smart contracts and users can interact with Oracle Machines in two distinct modes:
One-Time Query
You submit a request, the Oracle Machine fetches the data, and you receive your answer. This works well for situations where you need a specific piece of information, at a specific moment.
Example use case: A prediction market contract needs to know who won last night's basketball game to settle bets.
Subscription
A smart contract can subscribe to receive ongoing updates from an oracle. Instead of asking for the current price every time, the contract receives automatic updates at regular intervals.
Example use case: A DeFi protocol needs continuous price feeds to calculate collateral ratios and trigger liquidations.
Request Tracking
Every oracle request gets a unique tracking ID for correlation between queries and replies. Query status can be:

Timeouts ensure the system keeps moving. If an oracle fails to respond within the defined window, the request is marked as failed, rather than waiting indefinitely.
Fees and Economics

This structure aligns with Qubic's tokenomics - where fees are burned rather than redistributed, creating deflationary pressure while incentivizing efficient operation.
What This Enables
Oracle Machines open up categories of applications that were previously impossible to build on Qubic. Combined with Qubic's feeless transactions and high-speed execution, developers can now create:
Prediction Markets: Automatic resolution based on verified real-world outcomes. Sports results, election outcomes, and event occurrences can now settle contracts without manual intervention.
DeFi Protocols: Reliable price feeds enable lending protocols, synthetic assets, and automated market makers. Liquidations can trigger based on accurate, timely price data from providers such as CoinGecko.
Insurance Applications: Parametric insurance contracts can pay out automatically when verified conditions are met such as weather events, flight delays, or other measurable occurrences.
Gaming and NFTs: Real-world data can influence in-game mechanics. Sports NFTs could update based on actual player performance.
For more potential applications, see Qubic Use Cases.
Building New Oracle Services
The Oracle Machine system is designed for extensibility. Third-party developers can add new oracle services by implementing the BaseOracleService interface.
To create a new oracle service:
Define interface structures in Qubic Core (query/reply formats)Create service implementation inheriting from BaseOracleServiceImplement data providers for external APIsAdd configuration entriesRegister in the build system
The oracle-machine repository includes reference implementations and detailed documentation for building custom oracle services.
This modular architecture means the range of available data sources will expand as the ecosystem grows - without requiring changes to the core protocol.
How Oracle Machines Fit Into Qubic's Vision
Oracle Machines represent another step toward Qubic's goal of building truly intelligent smart contracts. Combined with Useful Proof of Work (uPoW) and Aigarth - Qubic's decentralized AI initiative, oracles give smart contracts the ability to observe and respond to the real world.
As described in Qubic's About page:
"Oracle Machines will be used to make Qubic Smart Contracts even smarter by resolving events through trustworthy data such as stock prices, sports scores, or sensor readings and much more. Also Oracles will give Aigarth the ability to observe the outer world."
This positions Qubic uniquely among Layer 1 blockchains; not just as a transaction settlement layer, but as infrastructure for AI-powered applications that interact with external reality.
Performance Specifications

The InterfaceClient maintains persistent connections to oracle services with automatic reconnection on failure, ensuring reliability even when external services experience brief outages.
*The values are for reference only and predicted under the testing environment. Actual Values may differ when Oracles are live. 
Getting Started for Developers
Developers interested in building with Oracle Machines can explore:
Qubic Documentation  -  Comprehensive technical guidesOracle Machine Repository  -  Source code and implementation detailsSmart Contracts Guide  -  How Qubic smart contracts workDeveloper Introduction  -  Getting started with Qubic developmentQubic Dev Kit  -  Set up your local testnetQubic CLI  -  Command-line tools for interacting with the networkGitHub Organization  -  All open-source repositories
For support, join the Qubic Discord community where developers actively collaborate.
Looking Ahead
Oracle infrastructure is foundational technology. Most users will never interact with Oracle Machines directly. Instead, they will use applications that rely on oracles behind the scenes.
Oracle Machines are currently in final testing on Qubic mainnet. Once testing is complete, the infrastructure will be ready for developers and applications to integrate.
Stay updated on Qubic developments through:
Qubic Blog  -  Latest news and technical updatesTwitter/X  -  Real-time announcementsTelegram & Discord  -  Community discussions
Oracle Machines are coming soon. Get ready to build something that matters.
#Qubic #Oracle #UPoW #AI #DeAI

Written by Qubic Scientific Team

A commentary on Nick Bostrom’s latest paper by Qubic Scientific Team
Reframing the Superintelligence Debate: Surgery, Not Roulette
He has just published a new working paper, Optimal Timing for Superintelligence: Mundane Considerations for Existing People (2026), in which he shifts the central question. Rather than asking whether we should develop superintelligence, Bostrom focuses on when it is optimal to do so. For anyone following the rapidly evolving intersection of AI and blockchain, his framework carries profound implications for how we design the infrastructure that will underpin artificial general intelligence (AGI).
Reframing the Superintelligence Debate: Surgery, Not Roulette
The starting point of Bostrom’s paper is both elegant and disruptive. He reframes the polarized “AI yes vs. AI no” debate entirely. Developing superintelligence, he argues, is not like playing Russian roulette. It is more like undergoing a risky surgery for a condition that is already fatal.
What is that condition? The current state of humanity itself. Consider the baseline: approximately 170,000 deaths occur each day from aging, disease, and systemic failures. An aging global population faces irreversible biological deterioration. Incurable diseases, including oncological, neurodegenerative, and cardiovascular conditions, continue to burden millions. We confront unmitigated global risks, from climate instability, to systemic institutional corruption, to the erosion of democratic quality. Pandemics, wars, and the collapse of entire systems remain ever-present threats.
Given these realities, Bostrom argues that framing the choice as “zero risk without AI” versus “extreme risk with a superintelligence” is simplistic. The more rigorous question is: Which trajectory generates greater expected life expectancy and greater quality of life for people who already exist?
By anchoring his analysis in the real, present conditions of human life, Bostrom sidesteps philosophical abstractions and theological speculation. He is talking about you, your family, and the people alive right now.
Life Expectancy, Mortality Risk, and the Case for Artificial General Intelligence
When we are young, the annual risk of dying is extremely low. Biologically, we are far from death in most cases. But as we age, the probability of dying climbs relentlessly due to biological deterioration.
If superintelligence could radically reduce or even eliminate aging, as Bostrom proposes, your annual mortality risk would stay at the level of a healthy young person. Your mortality would stop increasing over time. In that scenario, life expectancy becomes extraordinarily long.
From this vantage point, the expected value of superintelligence compensates for its high risks. But what happens if we delay until the technology becomes perfectly “safe”? What if we accumulate the probability of dying with each passing year? The question becomes: is it more rational to accept the probability of catastrophe from early deployment, given that AI safety progress is exponential, or to accept the certainty of accumulated deaths from delay?
Temporal Discounting and the Cost of Waiting
Bostrom introduces the concept of temporal discounting (ρ), a well-studied principle in decision theory. Humans systematically value present outcomes more than future ones. This is why we stay in unsatisfying jobs, relationships, and patterns: the effort of change feels large, and the reward feels distant.
But here an interesting inversion occurs. If life after AGI is not merely longer but dramatically better, with radical improvements in health, cognitive capacity, and quality of life, then temporal discounting actually punishes waiting. Every year of delay is a year spent in a qualitatively worse condition when a far superior state is accessible.
Quality of Life and Risk Aversion in AGI Deployment
Bostrom’s model does not assume longevity alone. It incorporates substantial improvements in well-being. If quality of life doubles after the transition to superintelligence, the balance shifts decisively toward earlier deployment. He then layers in risk aversion metrics (CRRA and CARA), acknowledging that if we are more sensitive to extreme losses, the window where “launch now” remains advisable narrows and optimal delays lengthen.
This is not reckless accelerationism. It is calibrated decision-making under uncertainty, the kind of analysis that should inform how we govern the path to artificial general intelligence.
Two-Phase Deployment: Swift to Harbor, Slow to Berth
One of the paper’s strongest contributions is its division of the AGI transition into two distinct phases:
Phase 1: Reaching AGI capability. Move as quickly as is responsible toward building a system that demonstrates general intelligence.
Phase 2: A strategic pause before full deployment. Once the system exists, introduce a controlled delay to study it, test it under real conditions, and solve technical safety problems that were previously only theoretical.
Bostrom’s hypothesis is that once an AGI system actually exists, a “safety windfall” occurs. Researchers can observe real behavior rather than speculate about it. Safety progress accelerates dramatically because the problems become empirical rather than abstract. The motto he coins: swift to harbor, slow to berth.

Who Benefits Most from an Earlier Transition to Superintelligence?
Bostrom does not treat optimal timing as universal. Older people, the seriously ill, and those living in precarious conditions have fewer expected years remaining. For them, the potential benefit of a rapid transition to superintelligence is far greater. Younger people with decades ahead can tolerate more waiting.
If you apply a prioritarian logic, giving greater weight to those who are worse off, the optimal timeline shifts forward. Bostrom also explicitly rejects the common assumption that beyond a certain age, additional life adds no value. That judgment, he argues, is rooted in our experience of current aging and deterioration. It does not account for a scenario of genuine rejuvenation, one of the central promises of a superintelligent future.
Institutional Risks: Why AI Governance Infrastructure Matters
In the final sections of his paper, Bostrom introduces critical institutional warnings. The most reasonable scenario, he suggests, is one in which the technological leader uses its advantage for safety. But he also flags the dangers of national moratoria, international prohibitions, and the competitive dynamics that arise when multiple actors race toward AGI under geopolitical pressure.
His analysis implicitly assumes an ecosystem where computational power tends to concentrate. In such an environment, the risks compound: militarization of compute resources, compute overhang (massive reserves ready to be activated under competitive pressure), and the perverse incentives of extreme centralization. These are not abstract concerns. The current trajectory of AI development, dominated by a handful of hyperscale cloud providers and corporate laboratories, creates precisely this concentration.
Implications for Qubic: Why Decentralized AI Infrastructure Reduces Existential Risk
If we take Bostrom’s framework seriously, the foundational question shifts from “when to launch AGI” to what kind of infrastructure reduces the risks associated with that launch. This is where Qubic’s architecture becomes directly relevant to the global conversation about superintelligence safety.
The Centralization Problem in Current AI Development
If superintelligence is built on centralized infrastructures, dependent on enormous data centers, opaque training pipelines, and corporate control, the risk profile expands beyond the purely technical. It becomes geopolitical. Concentration of compute makes the kind of adaptive governance Bostrom considers essential during the critical pre-deployment phase far more difficult. It also creates exactly the type of compute overhang he warns about: massive computational reserves ready to be activated at once under competitive pressure.
How Qubic’s Distributed Compute Architecture Addresses These Risks
Qubic dilutes that structural bottleneck. Its architecture distributes computational power across a global network rather than concentrating it in a single node. Qubic does not depend on an LLM-type architecture trained opaquely in mega data centers. Instead, it leverages Useful Proof of Work (uPoW), where miners contribute real computation to the training of its AI core, Aigarth, rather than solving arbitrary hash puzzles.
This design choice has direct implications for Bostrom’s analysis. A less centralized infrastructure reduces the probability of the abrupt, competitive deployment scenarios he warns against. Distributed compute means power is not located in a single facility that can be militarily captured, nor in a corporate laboratory under unilateral control. That structural resilience expands the space for Bostrom’s Phase 2: the strategic pause where real testing, incremental improvement, and adaptive governance can occur before full deployment.
For a deeper understanding of how Qubic’s approach to AI differs from mainstream models, explore Neuraxon: Qubic’s Big Leap Toward Living, Learning AI and the recent analysis That Static AI Is a Dead End. Google Confirms.. These posts illustrate how Qubic is building intelligence through a fundamentally different paradigm: one designed for continuous learning, distributed resilience, and real-world adaptation on a decentralized network.
Decentralized AI and Blockchain: Structural Alignment with AGI Safety
From Bostrom’s perspective, Qubic’s potential does not lie simply in being “decentralized” as a branding exercise. It lies in modifying the structural variables that determine optimal timing for superintelligence deployment. By distributing compute, by building consensus protocols that align miner incentives with genuine AI training, and by making the entire process open-source and auditable, Qubic creates the kind of infrastructure that makes the transition to AGI structurally safer.
If you’re interested in how Qubic’s CPU mining model and distributed compute network are evolving, the Dogecoin Mining on Qubic deep dive explains the latest expansion of Useful Proof of Work, and Qubic’s 2026 Vision details the broader infrastructure roadmap now underway.
The Hardest Problem: Building AGI That Learns from the World
Imagining utopian and dystopian scenarios is valuable. It is, in fact, the best path to creating futures aligned with human needs and values. But looking away, waiting aimlessly, or accelerating without restraint all fail to provide the necessary reflections.
Perhaps the most difficult challenge right now is not so much weighing the risk of accelerating the transition and modeling it. For now, the hardest task is building a general artificial intelligence capable of learning by itself from different dynamic environments, creating representations of the world, and acting within it. That is precisely the challenge Qubic’s Neuraxon framework is designed to address, not by training on static datasets behind closed doors, but by evolving in the open, learning from real-world complexity on a decentralized network anyone can participate in.
References and Sources
1. Bostrom, N. (2026). Optimal Timing for Superintelligence: Mundane Considerations for Existing People. Working paper, v1.0.
https://nickbostrom.com/optimal.pdf
2. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
3. Bostrom, N. (2003). Astronomical Waste: The Opportunity Cost of Delayed Technological Development. Utilitas, 15(3), 308–314.
4. Yudkowsky, E. & Soares, N. (2025). If Anyone Builds It, Everyone Dies.
5. Hall, R. E. & Jones, C. I. (2007). The Value of Life and the Rise in Health Spending. Quarterly Journal of Economics, 122(1), 39–72.
6. Qubic Scientific Team. Neuraxon: Qubic’s Big Leap Toward Living, Learning AI.
https://qubic.org/blog-detail/neuraxon-qubic-s-big-leap-toward-living-learning-ai
7. LessWrong community discussion: Optimal Timing for Superintelligence
https://www.lesswrong.com/posts/2trvf5byng7caPsyx/optimal-timing-for-superintelligence-mundane-considerations
#Qubic #AGI #UPoW #Dogecoin‬⁩ #DeAI

Scris de Echipa Qubic

Consolidarea rețelei Qubic prin recompense pentru noduri ușoare
Introducere
Rețeaua Qubic și-a construit reputația pe viteză, atingând 15,5 milioane de tranzacții pe secundă verificate de CertiK. În spatele acestei performanțe se află o rețea de mașini puternice care rulează protocolul direct pe hardware bare metal. Deși eficientă, această arhitectură prezintă o provocare: cerințele hardware au limitat cine poate participa la susținerea rețelei.
Paza rețelei Qubic este proiectată să schimbe asta. Prin introducerea opțiunilor de noduri ușoare cu cerințe hardware mai scăzute, inițiativa elimină barierele de intrare și face participarea la rețea accesibilă pentru toată lumea. Mai mulți participanți înseamnă o rețea mai puternică și mai descentralizată.

Co-fondatorii Stripe, Patrick Collison și John Collison, au lansat o profeție șocantă în scrisoarea publică din 2025: **Blockchain-ul ar putea necesita atingerea a 10 miliarde TPS (tranzacții pe secundă) pentru a susține cu adevărat era tranzacțiilor pe internet dominată de agenții AI**.
Aceasta nu este doar o afirmație la întâmplare, ci provine dintr-o perspectivă profundă a uneia dintre cele mai mari infrastructuri de plată din lume, Stripe. În 2025, Stripe a procesat un volum total de tranzacții de până la 1,9 trilioane de dolari, echivalentul a aproximativ 1,6% din PIB-ul global. Mai important, au observat că agenții AI se transformă rapid din 'unelte auxiliare' în 'decidenți autonomi' - de la completarea formularelor, căutarea preferințelor, până la cumpărarea activă, plata și chiar negocieri complexe, agenții AI sunt pe cale să preia cea mai mare parte a volumului de tranzacții pe internet.

În lumea tehnologiei, T3chFest nu este un loc pentru hype gol. Gazduit la Universidad Carlos III de Madrid, acesta servește ca unul dintre cele mai riguroase "teste de litmus" tehnice pentru proiecte, analizate de peste 1.800 de dezvoltatori și arhitecți de sistem de elită. Printre zeci de vorbitori, Qubic a apărut ca cel mai anticipat nume—nu doar datorită sprijinului său masiv din comunitate, ci și datorită unui cadru arhitectural capabil să redefinească intersecția dintre Blockchain și Inteligența Artificială.

Qubic aduce mineritul Dogecoin în rețeaua sa. Nu ca o caracteristică secundară sau o idee de moment, ci ca o expansiune fundamentală a ceea ce poate face protocolul. Arhitectura este finalizată. Testarea începe în martie. Obiectivul lansării mainnet este 1 aprilie 2026.
Aceasta este imaginea de ansamblu a modului în care va funcționa mineritul Dogecoin pe Qubic, ce înseamnă pentru rețea și de ce minerii existenți ar trebui să fie atenți.
Care este modelul de minerit al Qubic?
Qubic nu minează doar de dragul mineritului. Rețeaua funcționează pe un concept numit Dovada Utilă a Muncii, unde puterea computațională servește unui scop real: antrenarea Aigarth, inițiativa de cercetare AI a Qubic. Minerii contribuie cu puterea de procesare pentru a avansa modelele AI, iar rețeaua îi recompensează cu QUs pentru acea muncă.

UPOW x DOGECOIN: Zori ale Motorului Universal de Calcul!
Cel mai anticipat obiectiv al anului 2026 pentru ecosistemul $QUBIC a fost dezvăluit: mineritul Dogecoin ($DOGE ) vine oficial în rețeaua Qubic. Acesta nu este doar o altă integrare; este un salt uriaș spre transformarea Qubic într-un adevărat Motor Universal de Calcul.
📍 Stare Actuală: Unde Ne Aflăm
✅ Faza de Design: Finalizată.
✅ Planul Proiectului: Finalizat.
⚙️ În Curs: Două fluxuri de lucru se desfășoară în paralel—Implementare tehnică (Coordonarea computerelor) și Planificare de afaceri (Propunerea comunității).

Acesta este un moment pivotal pentru industria AI. Anunțul lui Sam Altman privind parteneriatul OpenAI cu Departamentul de Război (DoW) marchează nașterea oficială a "Complexului AI-Militar."
În timp ce narațiunea se concentrează pe "siguranță" și "responsabilitate," pentru cei din spațiul descentralizat, acesta este semnalul suprem. Asistăm la bifurcația inteligenței: AI Centralizat Controlat de Stat vs. AI Decentralizat deținut de Rețea.
Iată o analiză detaliată a motivului pentru care acest pas făcut de OpenAI face misiunea Qubic mai critică ca niciodată.

Scris de echipa științifică Qubic
Academia de Inteligență Neuraxon — Volumul 3

1. Neuromodularea în Creier: Fundamentul Inteligenței Adaptive
Neuromodularea se referă la setul de mecanisme care reglează modul în care funcționează sistemul nervos în orice moment dat, fără a schimba arhitectura sa de bază. Datorită neuromodulării, creierul poate învăța rapid sau lent, poate deveni explorator sau conservator și poate rămâne deschis la noutăți sau se poate concentra pe ceea ce este deja cunoscut. Conexiunile nu se schimbă; ceea ce se schimbă este modul în care aceste conexiuni sunt utilizate. Acest concept este esențial pentru înțelegerea inteligenței inspirate de creier și a arhitecturii din spatele Neuraxonului Qubic.

În timp ce lumea este obsedată de "Blockchain vs. Finanțe Tradiționale", o revoluție mai profundă are loc sub capotă la Qubic. Nu este doar un alt registru; este o reproiectare fundamentală a modului în care computerele procesează informația. Prin abandonarea standardului binar de 80 de ani pentru logica ternară echilibrată, Qubic a deblocat un nivel de eficiență pe care Big Tech abia începe să-l înțeleagă.
1. Rebeliunea Vizionară: "Fii încrezător în matematică, nu în CEO"
Povestea Qubic a început în aprilie 2022, condusă de Sergey Ivancheglo (CFB). Soluția sa pentru a atinge adevărata AGI? Programarea bare-metal. Qubic interacționează direct cu hardware-ul, eliminând greutatea sistemelor de operare. Acest lucru asigură zero latență și maximum de debit, permițând rețelei să atingă un teoretic 15 milioane de Tranzacții pe secundă (TPS).