{"slug": "tether-believes-intelligence-should-not-be-a-service-people-rent", "title": "Tether believes intelligence should not be a service people rent", "summary": "Tether's AI research team argues that cloud-hosted AI systems force users to rent intelligence, creating availability bias, high costs, and data sovereignty risks. They advocate for local, edge-optimized AI that runs on user-grade devices, converting intelligence into a portable capital asset. The team cites over 4,200 US data centers, projected $1.3 trillion AI capex by 2030, and rising data breach concerns as evidence of cloud AI's shortcomings.", "body_md": "The world is advancing toward a future in which over 10 billion humans coexist with trillions of autonomous agents in a superintelligent universe. However, the cloud-hosted systems that currently dominate AI’s operational models lack the architectural elasticity to support this growing demand for compute resources. Championed by managed GPU Clusters and centralized data centers, cloud AI relies on infrastructure that expands the operational scope of AI, requiring users to literally “rent” intelligence or the tools to develop it.\n\nTether argues that this system has a weak advantage, similar to scaling a database by simply buying a bigger server. AI scaling, in contrast, amplifies intelligence and availability. Cloud-hosted AI falters in both cases. Infinite scalability and universality in AI are therefore driven by the ability to deploy intelligent systems in any environment using readily available toolsets.\n\nTether’s AI research and development team believes that this “ability” is inherent in edge-optimized, local AI: self-hosted infrastructure for AI inference, training, and development on user-grade devices. Essentially, local AI converts intelligence into a portable capital asset readily available to the user, rather than a rented utility, as with cloud-hosted AI.\n\nOver 4,200 (43% of the [world’s](https://www.datacentermap.com/datacenters/) data centers) are located in the United States; eight times more than second-placed UK and third-placed Germany, which hosts more than twice as many data centers as all of Africa. This availability bias cuts across several other core infrastructure for AI development and routine use, impacting cost-effectiveness, performance, and data sovereignty.\n\nFurthermore, data centers and other AI infrastructure, swamped with resource demands from unicorns and AI startups, continually adjust rental fees to offset operating costs and generate revenue. By extension, the high capital expenditure (CapEx) required for AI infrastructure is forcing companies to restructure their balance sheets. Major AI companies are expected to spend [$500 billion](https://www.goldmansachs.com/insights/articles/why-ai-companies-may-invest-more-than-500-billion-in-2026) on capital costs this year, a 30% increase from 2025 records. This is projected to reach $[1.3 trillion by 2030](https://www.investing.com/news/stock-market-news/ai-capex-to-exceed-half-a-trillion-in-2026-ubs-4343520), growing at 25% per annum.\n\nBeyond availability and cost-efficacy, reliance on third-party infrastructure introduces additional operational factors and points of failure. Unplanned outages and abrupt changes in usage terms could significantly affect developers and end users.\n\nIn contrast, local AI operates autonomously, runs on infrastructure available to everyone, and works on any system. Plainly, they are agnostic, run on-premise, and eliminate barriers to availability.\n\nIn an ideal scenario, anyone should be able to confidently use AI tools without worrying about what happens to their data post-execution. However, third-party access to user data opens channels for data mismanagement, this time, more advanced due to the quality of data unintentionally supplied by users during model training, fine-tuning, and inference.\n\n34% of cybersecurity leaders [identified](https://www.statista.com/chart/35663/main-cybersecurity-concerns-related-to-ai/) “data leaks through generative AI” as their top concern for 2026, surpassing hacker capabilities for the first time. Elsewhere, the majority of the [670 data breach](https://www.bitsight.com/underground/data-breaches) incidents reported in the first quarter of 2026 are either directly AI-driven or involve centralized data management infrastructure.\n\n“Third-party” in this context includes AI companies and as-a-service infrastructure providers contracted to serve as a liaison between AI tools and users. Tens of the former are already headed to court in [major class-action lawsuits](https://www.mckoolsmith.com/newsroom-ailitigation) over the handling of user data.\n\nLocal AI positions users as the only control point. User data is stored on-device and managed by software that has zero contact with external systems.\n\nTether AI’s research and development focuses on advancing machine intelligence as a readily available utility worldwide through technologies that let anyone build or use AI tools anywhere.\n\nTether believes that efficient, self-hosted AI can transform machine intelligence into a new element of the periodic table that powers new possibilities in dynamic systems. This (self-hosted AI) will set in a new paradigm in which superintelligence is a foundational element owned by the user. However, effective agnostic, on-premise AI can only be achieved through creative engineering. This includes modifications from the model level to the complete architecture that accommodate design differences. Tether is leading innovations in pursuit of this. It is also contributing to open-source efforts to localize AI and abstract its complexities. This expands opportunities for even more advancements in local and edge-first AI.\n\nThe idea is to decouple AI from the current siloed, controlled, and fragile model. Tether is re-engineering Artificial Intelligence and modifying existing technologies to achieve infinite, scalable intelligence.\n\nThe first task here is to build a base infrastructure that operates as a self-governed unit. To this end, Tether developed the [Pear runtime](https://pears.com/) and co-founded [Holepunch](https://holepunch.to/). Pear Runtime and Holepunch employ decentralized resource networks, databases, and communication protocols to achieve a serverless P2P backend for edge applications.\n\nNext, Tether addresses the heavy computational overhead of AI models by developing resource-efficient models and infrastructure that run locally on user-grade devices and across heterogeneous environments. It launched QVAC (QuantumVerse Automatic Computer), an AI research team, and a development framework for local-first and edge-first AI research and development.\n\nThis unit has led the development of:\n\nBuilding on the QVAC framework, Tether’s AI coverage has expanded to include tools for deploying intelligence across diverse systems, from personal computers to interfaces for controlling smart homes and appliances. This includes runtime environments, training data, fine-tuning frameworks, and edge-optimized AI applications.\n\nTether has built a suite of tools that put local AI into practice across every layer of the stack, including:\n\nDevelopers equipped with these can implement local AI integrations across diverse systems through a serverless backend, models that can run on resource-limited systems, and UI modules that simplify usage. This is further reinforced by the [QVAC SDK](https://qvac.tether.io/dev/sdk/), which consolidates all of Tether’s AI-related achievements to date. QVAC SDK is a toolkit of prebuilt modules for components of the QVAC AI infrastructures. It provides usage guides, integration contexts, and functional samples. This enables developers to build intelligent on-premises applications for any system without requiring permissions.\n\nArtificial intelligence is arguably the most human-targeted internet-based technology in history. In an AI-dominated future, the current user base will be only a fraction of the demand scale. However, Institutional capital expenditure capacity isn’t unlimited, and the bloat in rental costs has no ceiling.\n\nTether’s local-first approach to AI acknowledges the relevance of machine intelligence to humans and its deep connection to everyday life. In response, it is dedicated to developing a universally accessible AI that is modular enough to be embedded in the fabric of any device, system, or environment. This ranges from industrial servers to the smallest chip in a light bulb.\n\nIn all of these cases, the ‘users’ own their AI, can build on their own terms, without permission or external constraints, choose their own biases, and control how their data is used. Practically, this is the only way to ensure that superintelligence is successfully delivered to the billions of humans it is meant for.\n\n**Subscribe to the ****QVAC newsletter**** to learn more about Tether’s breakthroughs in AI**", "url": "https://wpnews.pro/news/tether-believes-intelligence-should-not-be-a-service-people-rent", "canonical_source": "https://www.cio.com/article/4193647/tether-believes-intelligence-should-not-be-a-service-people-rent.html", "published_at": "2026-07-07 16:36:47+00:00", "updated_at": "2026-07-07 16:41:29.945216+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-ethics", "ai-safety", "ai-policy", "ai-research"], "entities": ["Tether", "Goldman Sachs", "UBS", "Statista", "BitSight", "McKool Smith"], "alternates": {"html": "https://wpnews.pro/news/tether-believes-intelligence-should-not-be-a-service-people-rent", "markdown": "https://wpnews.pro/news/tether-believes-intelligence-should-not-be-a-service-people-rent.md", "text": "https://wpnews.pro/news/tether-believes-intelligence-should-not-be-a-service-people-rent.txt", "jsonld": "https://wpnews.pro/news/tether-believes-intelligence-should-not-be-a-service-people-rent.jsonld"}}