Licensing can be complicated, particularly when enterprises are forced to double-pay because the software they already own is only licensed for a specific environment, and moving it requires a whole different licensing model. Without proper portability rights, they need to make additional financial investments to run the same workloads in a different home.

AWS says its new Bring Your Own Media (BYOM) service eliminates this duplication. Customers can now reuse their existing Microsoft SQL Server media and licenses on Amazon Relational Database Service (RDS) with no additional licensing fees.

This means enterprises no longer have to justify workload migrations to the cloud against existing licensing commitments and infrastructure investments, AWS said.

“What used to be a licensing barrier between operational SQL Server data and agentic AI is now gone,” AWS data engineer Srikanth Katakam and product marketing manager Colleen Betik wrote in a blog post announcing the service.

Avoiding the double-licensing problem

To drive true value, agentic AI apps must have access to elastic GPU capacity as well as direct, low-latency access to the data they need to reason and make decisions. But, AWS argued, that can be difficult in self-managed data centers and on premises infrastructure with limited access to agentic AI cloud-native services. But a lot of enterprise data lives in Microsoft SQL Server, and until now customers have had to pay for a second license to use a fully managed cloud service like RDS.

Now, with BYOM on Amazon RDS for SQL Server, enterprises can reuse their existing SQL Server Enterprise Edition or Standard Edition licenses through a “lift-and-shift” model. This brings their data into a fully-managed environment.

According to the licensing distribution terms, enterprises must provide their licensed SQL Server Release to Manufacturing (RTM) media to Amazon RDS. They can then upload that media to Amazon S3 and launch BYOM instances. Enterprises must configure AWS License Manager to perform automatic instance tracking.

From there, RDS automates patching, backups, high availability, and monitoring, while providing direct access to agentic AI and analytics services native to AWS. The platform also reports vCPU usage to provide visibility into SQL Server license usage, Katakam and Betik wrote.

“You do not need to choose between protecting your SQL Server licensing investments and giving your data a path to the AWS analytics and agentic AI services redefining what’s possible in the cloud,” they said. 

It’s important to note that this service is only available to enterprises with Microsoft Software Assurance (SA), an add-on which supports mobility and rehosting of existing licenses. Enterprises must verify that their SQL Server licenses comply with Microsoft’s licensing agreement, and that they have a SQL Server License (Standard or Enterprise) with SA. They also must submit a License Mobility Verification Form to Microsoft; it, in turn, will notify AWS once verification is complete.

AWS emphasized that enterprises remain responsible for compliance; it does not block operations if license limits are exceeded.

More control over workloads, loosening Microsoft’s grip

The advantage of this service, explained Mike Leone, principal analyst at Moor Insights & Strategy, is that enterprises can get a Microsoft SQL Server workload onto a managed AWS service without rewriting it for Aurora or paying for the license twice. This means they can modernize on their own schedule rather than being forced into rewrites before they’re ready.

“For a lot of shops, that control over timing is worth more than the license saving itself,” Leone said.

This also loosens Microsoft’s grip on enterprise workloads, because organizations finally have somewhere else to run SQL Server using licenses they already own, he noted. Realistically, their dependency shifts to AWS rather than disappearing entirely, as their data ends up living next to AWS’s AI services.

“For a lot of teams, that’s a trade they’re glad to make for the managed infrastructure and the AI tooling they get in return,” Leone said.

Yaz Palanichamy, a senior advisory analyst at Info-Tech Research Group, also pointed to significant improvements stemming from the migration. Notably, it allows organizations to transition away from static, linear, or reactive storage capabilities towards more dynamically intelligent environments.

“The key is to balance the unification of transactional data towards managed AI and machine learning pipelines,” he said.

Enterprises take on new responsibilities

The catch? Leone noted that enterprises now own the task of licensing compliance. Staying current on SA, the Microsoft maintenance contract that makes any of this legal, and the license mobility rules, “become your headache instead of something baked into the bill,” he said.

Furthermore, he added, lift-and-shift has a way of turning into “lift-and-forget” when enterprises move SQL Server as-is and never actually modernize, “so you end up carrying all the old baggage onto a shinier platform.”

Palanichamy also pointed to skyrocketing AWS costs, since operationalizing AI agents requires a considerable amount of data ingestion and querying. This can potentially be cost prohibitive on RDS for SQL Server.

“One aspect to consider would be the relative time to production value, so that enterprises can better handle the management of volatile AI workloads,” he said.

Just one challenge in the AI race

Although AWS frames licensing complexity as a roadblock to agentic AI, analysts say it’s really just one piece of the puzzle.

Organizations are not in an AI-ready state due to a number of factors, Palanichamy noted. This could be total cost of ownership (TCO)-related, a lack of appropriate acceptable use policies (AUPs), or concerns around security and compliance.

If and when enterprises are ready to adopt AI, they must perform thorough needs analysis/process optimization benchmarking exercises to determine what workflows could benefit from the technology, he said, and, conversely, flag workflows where AI could prove “an impediment or potential disservice.”

Leone also pointed out that moving SQL Server onto RDS doesn’t “magically make your data agent-ready,” nor does it make it smarter. The data proximity and managed plumbing are what actually support agents. Sitting data next to Bedrock and the rest of AWS’s AI services means builders “stop wasting time shipping the data around or building one-off integrations every time an agent needs it.”

Ultimately, though, licensing isn’t the real issue when it comes to AI; it’s more of a migration problem. The real roadblocks are messy data, questionable governance, and teams that aren’t ready to run it in production, and, Leone said, “no change to a license bill touches a single one of them.”

Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action.

That extra autonomy is the value proposition. It also introduces the cost problem.

A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understood at the level of “agent instances,” not just model calls.

For the estimates below, I am using a blended token cost of $3 dollars per million tokens. This is not intended to reflect a single vendor’s list price. It is a blended planning figure that assumes a mix of input and output tokens, reasoning steps, retrieval-augmented generation, summarization, tool calls, memory updates, and occasional use of larger context windows. Some enterprises will pay less through volume discounts or by routing work to smaller models. Others will pay more by using premium models, long-context prompts, web browsing, large document ingestion, and repeated reasoning loops.

The basic formula is straightforward. If an agent consumes 2 million tokens per day, it consumes 730 million tokens per year. At $3 per million tokens, that single agent costs about $2,190 per year in token burn. That number sounds surprisingly low until you multiply it by the number of agents, workflows, and users, plus the surrounding infrastructure required to run these systems safely.

What an agent really costs

In the model used here, the annual token-only cost per agent ranges from about $1,095 to $3,833, depending on the use case.

• A lightweight HR recruiting or onboarding agent that consumes one million tokens per day costs about $1,095 per year.
• A more demanding software engineering agent that consumes 3.5 million tokens per day costs about $3,833 per year.
• Customer support agents are roughly $2,190 per year.
• Legal contract agents cost about $2,409 per year.
• Security triage agents run about $2,738 per year.
• Research agents are around $3,066 per year.

These figures are useful but incomplete. They include only LLM token consumption and exclude orchestration platforms, vector databases, observability, model evaluation, security controls, workflow monitoring, human review, enterprise application integration, data pipelines, audit logging, prompt management, and the engineers needed to build and maintain the systems. In real deployments, I would expect the all-in operating cost to be two to five times the raw token cost. For regulated or mission-critical environments, the multiplier can be even higher.

This is where many agentic AI business cases become less clear. The model call may be inexpensive, but the system around the model is not. An agent that can update a customer relationship management system, approve a refund, generate a purchase order, or recommend a security containment action needs guardrails, permissions, logging, rollback mechanisms, and human escalation paths. These are not optional features. They are the difference between a demo and an enterprise system.

Use cases that make economic sense

Customer support is one of the more obvious use cases. A typical support automation deployment may use eight agents: an intake classifier, a knowledge retrieval agent, a response drafting agent, an escalation agent, a quality review agent, a CRM update agent, a sentiment detection agent, and an analytics agent. At two million tokens per agent per day, each agent costs about $2,190 per year in token burn, bringing the annual total to roughly $17,520. If that system deflects even a modest number of tickets or improves agent productivity, the economics can be attractive.

Sales development is another practical example. A five-agent system for account research, lead enrichment, email personalization, CRM updates, and follow-up scheduling may consume 1.2 million tokens per agent per day. That results in an annual cost of about $1,314 per agent, or $6,570 for the full agent team. This can be compelling if it improves pipeline quality, but it can also be wasteful if agents generate low-quality outreach at scale. The cost of brand damage is not measured in tokens.

Software engineering is more expensive but potentially more valuable. A 12-agent system covering requirements analysis, architecture, code generation, testing, review, security checks, documentation, CI debugging, refactoring, release notes, dependency analysis, and hot-fix support may consume 3.5 million tokens per agent per day. That works out to about $3,833 per agent annually or roughly $45,990 for the full system. Compared with engineering salaries, the token cost is small. The real question is whether the system reliably improves throughput without increasing defects, security vulnerabilities, or maintenance complexity.

Security operations also fit the agentic model because the work is repetitive, time-sensitive, and context-intensive. A 10-agent security triage system could include agents for alert triage, log analysis, threat intelligence, endpoint investigation, network investigation, incident summarization, ticketing, compliance evidence, escalation, and post-mortem. At 2.5 million tokens per agent per day, the annual token cost is about $2,738 per agent or $27,375 for the system. This is easy to justify if it reduces alert fatigue and accelerates response, but risky if the agents hallucinate causality or bury critical signals in confident summaries.

Finance, legal, healthcare administration, market research, HR, and supply chain are also viable:

• A six-agent finance close system may cost about $9,855 per year in token burn.
• A four-agent legal contract review system may cost about $9,636.
• A seven-agent healthcare administrative workflow may cost about $13,797.
• Competitive intelligence using six agents may cost $18,396.
• HR recruiting and onboarding using five agents may cost $5,475.
• Supply chain planning and exception management using eight agents may cost $21,024.

Across all 10 example use cases I’ve mentioned in this section, the model assumes 71 agents and a total annual token burn of about $175,638.

The comparison with traditional AI

The economics of agentic AI should always be compared with simpler approaches. Traditional AI, workflow automation, rule engines, robotic process automation, and non-agentic LLM calls are often cheaper, easier to govern, and more predictable. Agentic AI is usually overkill for tasks like classification, extraction, summarization, routing, or drafting within a narrow context. A deterministic workflow with a single model call can do the job at a fraction of the cost and risk.

Agentic systems make sense when the process requires judgment across multiple steps, dynamic planning, tool use, exception handling, and adaptation to incomplete information. They are valuable when the path to the answer cannot be fully scripted in advance. They are less valuable when enterprises use them as a trendy substitute for basic automation.

The best architecture is usually hybrid. Use traditional automation where the process is stable. Use non-agentic AI where the task is bounded. Use agentic AI only where autonomy creates measurable leverage. That means fewer agents, tighter scopes, explicit budgets, model routing, token monitoring, and human checkpoints for high-impact decisions.

The financial mistake many organizations will make is to treat agents as digital employees with near-zero marginal cost. They are not. They are probabilistic software components that consume tokens, trigger tools, create operational dependencies, and require supervision. The token bill may be manageable. The governance bill may not be.

Agentic AI can absolutely be worth the money. In many cases, the annual token burn for a useful agent team is less than the loaded cost of a single employee. But that is not the same as saying it is cheap. Companies must measure agent cost per completed business outcome, not cost per prompt or cost per model call. In the end, the question is not how much an agent costs. Ask yourself the right question: Does the autonomy an agent provides outweigh the complexity it introduces?

For the past two years, enterprises have focused on grounding AI systems in internal documents, databases and knowledge repositories. Microsoft now contends that the next challenge is giving those systems reliable access to the outside world as they move into production.

At its ongoing annual Build conference, Microsoft unveiled Web IQ, a new suite of AI-native APIs designed to connect AI agents and applications to real-time information from across the web, including web pages, news, images and videos.

The goal is to help developers build more accurate and context-aware AI systems while reducing the complexity of integrating web search, retrieval and grounding capabilities into enterprise applications, the company wrote in a blog post.

The APIs already underpins grounding for Microsoft Copilot and ChatGPT, and unlike traditional search APIs are designed to retrieve highly relevant information while minimizing token consumption, helping reduce both inference costs and response latency, Microsoft said.

Reducing the cost and complexity of web grounding

That focus on reducing inference costs and response latency to deliver Web IQ’s search capabilities will be valuable for CIOs and developers, said Phil Fersht, chief analyst at HFS Research.

“Developers have typically stitched this together themselves using search APIs, web scraping, retrieval-augmented generation, vector databases, custom ranking logic, crawling tools and separate orchestration layers. That works, but it is messy, brittle and expensive to maintain,” he said.

“The hard part is not just finding a web page. It is retrieving the right evidence, ranking it, selecting useful passages, reducing token waste, respecting publisher controls and doing all this fast enough for multi-step agents,” he added.

An agent running in production will typically run at least five or ten retrieval steps and in such scenarios latency and cost can become “ugly,” said Moor Insights and Strategy principal analyst Mike Leone.

“I’ve watched plenty of teams handle this by gluing a search API onto a model and hoping the model can sort through whatever HTML comes back, which isn’t ideal,” he said.

Simplifying development of applications

Beyond the infrastructure and cost advantages, Web IQ could also simplify the AI application building process developers, said Stephanie Walter, practice lead of the AI stack at HyperFrame Research.

“The value of Web IQ is that Microsoft is trying to make web grounding a reusable, agent-native service rather than a bespoke integration,” Walter said.

That reusability, Fersht said, will matter to CIOs as most enterprises do not want every development team rebuilding its own web grounding stack: “They need a common capability that is governed, scalable, low-latency and economically efficient.”

Still, Microsoft is not entering an untapped market and there are several offerings already available, including OpenAI web search, Google grounding, Perplexity APIs, Bing Search APIs, Azure AI Search, vector databases, and custom RAG pipelines.

However, Microsoft might have an advantage with IQ, despite Google’s strength in search and web-scale knowledge, OpenAI’s tooling push, and AWS’ Bedrock-data-service-partner integrations combo, Fersht said.

“Microsoft’s differentiation is that it can combine Bing’s global web index with Azure AI, Copilot, Foundry, Microsoft 365 and enterprise developer channels,” he said.

The other advantage is the existence of existing IQ offerings, such as Work IQ, Fabric IQ, and Foundry IQ, all of which are targeted towards adding more business context for agentic systems, Fersht added.

But uptake, Walter said, will depend on whether these integrations translate into better production outcomes.

Web IQ is currently available in limited access for select Azure customers. Enterprises can request access to Web IQ via their Microsoft account team or submit a form.

Beyond direct API access, developers can also configure the model-agnostic Web IQ as an MCP tool within Foundry IQ, a Microsoft spokesperson said.

Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.

Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.

The European Union has now published a set of measures aimed at boosting Europe’s tech industry to help reduce reliance on US and Chinese suppliers for AI, cloud, and semiconductors. The proposals include rules to restrict the use of US hyperscalers for certain public sector procurement purposes, but stop short of banning them outright.

“Technological sovereignty does not mean protectionism. Europe remains grounded in openness, partnership, and fair competition,” Henna Virkkunen, executive vice president for Tech Sovereignty, Security and Democracy, said in a statement Wednesday. “At the same time, Europe wants to be in the position to make its own choices, avoiding dependence on single dominant suppliers, especially from non-like-minded countries.”

The European Technological Sovereignty Package — released after several delays — includes two legislative proposals: the Cloud and AI Development Act and Chips Act (CAIDA) 2.0 and the Open Source Strategy and Strategic Roadmap for Digitalization and AI in Energy.

CAIDA aims to triple data center capacity in the next five to seven years by easing restrictions for deployments across the EU. It also includes rules that, if enacted, would require EU public bodies to meet certain sovereignty criteria for cloud service procurement related to certain sensitive workloads.

Amid ongoing trans-Atlantic tensions and a long-time deep reliance on US tech providers, European organizations have become increasingly wary of a “kill switch” that would cut off access to digital services. There are also concerns that US hyperscalers could be compelled to share data with US government under the CLOUD Act and Foreign Intelligence Services Act (FISA), even when data centers are located in Europe.

The CAIDA proposals include four levels of criteria for suppliers; the most basic includes data center infrastructure located and operated in the region – something  many US cloud suppliers already provide – with stricter rules around supplier ownership, full control over the software stack, and more stringent cybersecurity certification.

The majority of existing EU public sector workloads (70%) fall under the first level, with 20% at level 2, and 9% at level 3. Only a small proportion (1%) of the most sensitive workloads would require level 4.

Other proposals include the Chips Act 2.0, a follow-up to the 2023 legislation that sought to improve semiconductor production capabilities; the updated version now aims to boost research and spur demand for domestically produced processors. 

The legislative proposals must be negotiated by the European Parliament and Council of the European Union before adoption.

Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.

Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.

IBM has launched a tool designed to help customers assess cloud-sovereignty risks and meet regulatory compliance requirements. 

The Sovereignty Risk Profile launch comes as digital sovereignty becomes a higher priority for organizations concerned about where data is stored and processed. According to an IBM survey, 93% of executives believe sovereignty needs to be part of their business strategy.  

Via the new tool, customers can set up policies related to regulatory and business requirements — such as where data resides and how it’s protected, for instance. These policies can be applied to specific cloud workloads, regions, or zones in the Sovereignty Risk Profile tool, allowing users to track sovereignty requirements “in real time,” IBM Cloud product manager Janet Van said in a blog post, with “visibility into configurations, encryption posture, and environmental controls.” 

It’s then possible to assess compliance and decide what workloads meet sovereignty requirements. 

Tracking the factors that contribute to sovereignty is a challenge for many organizations, said Holger Mueller, vice president and principal analyst at Constellation Research. “It is very difficult, as you don’t know about the details of the stacks; sometimes, even the location of data is not fully transparent,” he said.

The Sovereignty Risk Profile “addresses many of the compliance-related requirements associated with data residency and encryption, while also tackling sovereignty from a resilience and concentration-risk perspective,” said Dario Maisto, senior analyst at Forrester.

However, the monitoring tool can only do so much to address digital sovereignty concerns, he said. While it can help organizations identify and report on potential issues, it “does not help [make] clients more or less sovereign, per se: it has only the potential to tell that a sovereignty problem is there.”

Broader questions around digital sovereignty remain difficult to address, he said, as there’s no universally accepted definition of the concept and limited legislation to establish clear requirements. 

Mueller described a spectrum of sovereignty issues that depend on factors such as whether data is stored, processed, and backed up in a customer’s own country, as well as whether staff that operate the data are domestic nationals. “Then there is the sovereignty of the software supply chain — but here everybody is dependent,” he said.

To further complicate matters, while several US hyperscalers sell sovereign-branded cloud services to European customers — with local staff and infrastructure —  concerns remain about the potential for extra-jurisdictional access to data, due to the US CLOUD Act and the US Foreign Intelligence Surveillance Act (FISA).

The Sovereignty Risk Profile is available within IBM’s Security and Compliance Center Workload Protection. It’s the latest in a range of IBM Cloud products aimed at addressing customers’ sovereignty concerns, including the recently launched IBM Sovereign Core software platform. 

Meta has raised the possibility that it could be joining the likes of Amazon, Microsoft and Google in offering cloud services at some point in the future — although potential customers shouldn’t be adding the company to their suppliers list just yet.

When asked about plans for offering such services at the company’s annual shareholders meeting,  Meta CEO Mark Zuckerberg said there was a possibility of the company competing with the major hyperscalers. “It’s definitely on the table.”

He explained that different companies were approaching Meta asking for the company to offer an API service or to buy compute services at a premium price. “We haven’t done it yet, because we think we have a use for the compute, but when we feel we have overbuilt, then that is an option that we have.”

Meta has been active in developing its data centers over the past few years, so there will be a possibility of some excess capacity. It is also developing its own AI chips.

For the moment, though, the company may well need all the capacity it can build: Zuckerberg said that the launch of Muse Spark, a new AI model from Meta Superintelligence Lab, had resulted in large increases in Meta’s AI usage.

This article first appeared on Network World.

“The future of AI should be accessible, available, and open to people and builders everywhere, and it should not require an absurd amount of resources only available to a handful of cloud providers,” Paolo Ardoino, CEO, Tether.

About 700 million people use generative AIs like Gemini and ChatGPT weekly, but adoption is far from uniform. McKinsey’s 2025 State of AI survey found that nearly half of respondents from companies with more than $5 billion in revenue have reached the AI scaling phase, compared with just 29 percent of those from companies with less than $100 million in revenue, a gap that only widens further down the chain, locking out smaller businesses, developers, and everyday users.

Retail and small businesses are limited to basic AI utilities that their facilities can power, such as text-based inference and multimedia generation, using base models. That is billions of end users, and developers locked out of full utilization and development of intelligent software due to high infrastructure demands.

Tether’s edge-first LoRA fine-tuning framework for Microsoft’s Bitnet LLM is an important step towards developing an infrastructure system that supports billions of AI agents and intelligent machines. By reducing the computational overhead of machine learning and enabling consumer-grade devices to perform advanced operations, Tether’s edge-first approach ensures greater leverage for the larger population.

Imagine a 13-billion-parameter model being fine-tuned on everyday handheld devices like Samsung S25 and iPhone 16, as well as on regular personal computers. The breakthrough combines resource-efficiency and platform-agnostic techniques to develop a fine-tuning framework for the ternary-quantized LLM.

Behind Tether’s Bitnet fine-tuning framework

Bitnet LLM was born out of the vision of an intelligent AI model that doesn’t consume outrageous computing resources even at full precision. Earlier attempts at resource-efficient AI relied on trade-offs, such as running small-parameter models at higher precision or larger-parameter models at lower precision, but neither approach fully solved the problem.

Bitnet takes a more fundamental approach. The result is a model that achieves linear efficiency while consuming only a fraction of the computing resources traditionally required.

The challenge, however, is that contemporary GPUs are optimized for the very floating-point operations Bitnet eliminates, creating a hardware compatibility gap. Compounding this, Bitnet was originally confined to its own Bitnet.cpp inference engine, limiting its broader utility. Tether’s breakthrough addresses both constraints at once by integrating a Vulkan and Metal GPU backend that unlocks true cross-platform capabilities for BitNet inference and LoRA fine-tuning on heterogeneous consumer GPUs, including mobile GPUs. Bitnet can now run on more mature, widely supported inference engines without sacrificing its efficiency advantages.

Vulkan’s cross-platform nature is key here. Unlike CUDA, which ties developers to NVIDIA hardware, Vulkan runs across a broad range of GPUs and operating systems, opening Bitnet to genuinely multi-platform deployment. Tether’s Bitnet fine-tuning framework implements a dynamic tiling technique to mitigate limitations in Vulkan driver buffer allocation on mobile GPUs.

The dynamic tiling algorithm technique was first applied in the fine-tuning framework for QVAC Fabric LLM, the AI model that powers Tether’s QVAC Workbench application.

This implementation demonstrates the efficiency of this approach: fine-tuning a 13-billion-parameter model across a range of consumer devices with varying GPU configurations.

The Bitnet LLM Fine-tuning framework is Tether’s latest achievement and part of a broader expansion into open-source AI and communication technologies that challenge current, slow, fragile, and controlled systems. These developments are open-sourced and packaged as modules in the QVAC SDK for easy deployment and to help developers build edge-first AI applications without needing anyone’s permission.

Tether envisions superintelligence as a foundational element possessed by its owner and is enforcing this through:

Local-first AI

Synonymous with decentralized AI, “Local-first” AI aims to create sovereign AI solutions that do not rely on centralized infrastructure, such as data centers, to operate. They are considered cost-effective, relatively more sustainable, and unarguably more private than centralized AI. Tether is building AI applications that rely entirely on the device’s resources. These applications store data in device memory and use its processors for advanced operations, such as fine-tuning and inference.

P2P computing network for AI inference

Tether’s AI applications are built on the Pear runtime. Pear is a tooling platform for fully P2P applications that can operate without servers. Pear leverages the Holepunch tech stack. Holepunch is purpose-built for stable, direct communication between devices. Pear enables delegated inference for AI applications such as QVAC Workbench. Delegated inference enables a unified, dynamic workstation architecture where compute tasks are fluidly distributed between mobile and desktop environments, allowing either device to offload high-intensity processing to the most capable system. That is, you can start a task on your mobile device and delegate it to your desktop or laptop for completion.

AI for everyone

The only way to scale intelligence to the needs of a ten-billion-strong society is to push it to the edge. This, in turn, depends on the progress made by experiments aimed at cost-effectively localizing AI computation.

Billions of AI agents and countless AI applications deployed by developers in every region of the world, running effectively on user-owned resources, is the only way we can democratize superintelligence and avoid creating another ‘luxury’ cutting-edge technology controlled by unicorns and fully accessible only to elites.

Tether is pioneering limitless superintelligence for an ever-growing society and applications. Follow the journey to truly local and edge-first AI solutions