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A Study Reveals Critical Flaws in the Transaction Costs of Ethereum Layer-2 rollups

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A recent academic study warns that Ethereum’s scaling solutions, known as Ethereum Layer-2 rollups, are incorrectly pricing small transactions. The research, conducted by experts from zkSecurity, Prooflab, and Imperial College London, indicates that current fee models distort the actual cost, directly affecting users with lower-volume operations. This situation not only leads to overcharging but also opens new security vulnerabilities.

The report’s main finding details that a major issue is that current fee structures are overly simplistic. These models group various costs—such as execution, data availability, and cryptographic proofs—into a single formula or apply fixed rules. As a result, small transactions end up being overpriced, forcing users to pay more than they should. Conversely, other operations may be underpriced, creating an imbalance that can be exploited.

This pricing problem arises in a context where Ethereum Layer-2 rollups are fundamental to the network’s scalability. Their goal is to process transactions off the main chain to reduce costs and increase speed. However, the research shows that the method for recording this data on the mainnet does not adequately distinguish between transactions, applying fixed costs that harm smaller operations and benefit larger ones.

The implications of this flaw are twofold. For the market, it means that ordinary users making modest transfers or interacting with low-value decentralized applications are unknowingly subsidizing higher-volume operations. Furthermore, from a security standpoint, it creates an attack vector, as malicious actors could flood the network with spam at an artificially low cost. This type of denial-of-service (DoS) attack would degrade network performance and increase costs for everyone.

Towards a Fairer Fee Model on Layer 2

The study underscores the urgent need to redesign the fee models in Ethereum Layer-2 rollups to more accurately reflect the true cost of each transaction. Although these solutions are vital for Ethereum’s future, their sustainability depends on implementing more sophisticated and equitable mechanisms. The next step for developers will be to create dynamic fee structures that protect small users and strengthen the overall security of the ecosystem, ensuring that scalability is not achieved at the expense of fairness and network robustness.

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Bittensor introduces Gamma Tokens as payment layer

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Bittensor has introduced Gamma Tokens as a native payment system for its AI economy, giving users and AI agents a way to pay for subnet services with a Bittensor wallet rather than separate accounts or API keys for each subnet.

The OpenTensor Foundation said Gamma is designed as a prepaid, dollar-valued service credit system for subnet services. In practice, the update is meant to simplify how customers access services across the network while keeping the payment process inside the Bittensor wallet flow.

According to the release, Gamma lets subnets charge for services in dollar-valued credits. Customers fund a deposit by burning Subnet Alpha, then authorize a services spender to charge that deposit as usage accrues. The system also uses a hotkey as an API key, creating a standardized payment layer across subnets.

The foundation described the mechanism as a way to budget usage in dollars while service demand translates into Alpha burn. That structure makes Gamma less about a separate speculative token and more about how subnet services are paid for inside Bittensor’s network design.

For readers following AI-focused crypto projects, the key change is operational rather than promotional: Bittensor is formalizing a payment standard for subnet services, with unified wallet access replacing a more fragmented setup. The announcement did not add further detail on adoption or usage levels at the time of publication.

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io.net highlights idle GPU supply as AI compute demand grows

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io.net is again framing its network around a familiar bottleneck in AI infrastructure: builders need compute, while some GPU capacity remains unused. In an official post on X, the company said startups are burning runway while waiting for compute and argued that idle GPUs can be connected to builders at prices they can afford.

The post positions io.net as a coordination layer for matching spare GPU capacity with AI workloads, with the company saying the goal is to help founders spend funding on product progress rather than waiting on hardware. The message aligns with io.net’s broader pitch in its official blog post on AI infrastructure, which describes the project as an orchestration layer for aggregating and coordinating underutilized GPU capacity.

The update does not introduce a new product launch or a fresh technical rollout, but it does restate the core use case io.net is presenting to the market. That use case centers on making idle compute available to AI builders without requiring them to wait for new capacity to come online.

io.net’s framing also suggests the company is continuing to emphasize affordability and access as the main draw for its network. Rather than presenting itself as a new source of hardware, it is pitching a way to make existing GPU supply more usable for developers who need it now.

For readers following AI-focused crypto projects, the post is best read as a reaffirmation of io.net’s positioning rather than as evidence of a new operational milestone. The company is making a case for coordination over expansion: use the GPUs that already exist, and route them toward builders who can put them to work.

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NEAR launches Agent Market job board for local services quote agent

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NEAR has added a new job posting to its Agent Market for a Local Services Quote Agent, an AI agent designed to contact local providers, collect responses and return side-by-side comparisons of price and availability.

The listing appeared on the official NEAR DevHub channel and was also amplified by the project’s official social accounts. According to the post, the builder who takes on the job would keep ownership of the agent and retain the revenue it generates.

The job description frames the agent as a tool for gathering real quotes from local services rather than a general chatbot. It would reach out to providers, log the replies and organize the results so users can compare options more easily.

Builder-owned agents remain central to the marketplace pitch

NEAR has positioned the Agent Market around the idea that developers can publish agents, set their terms and keep ownership after the job closes. In the official posts, the company said builders keep the agent and can earn when it is hired.

The job board format also suggests a more structured approach to AI agent development, with specific tasks posted for builders to bid on. In this case, the task centers on a practical consumer workflow: collecting quotes from local providers and presenting them in a usable format.

NEAR did not say when the Local Services Quote Agent will be completed or who will take the job. The posting does, however, show that the Agent Market is actively listing new work and pushing the marketplace model as part of its product rollout.

The post NEAR launches Agent Market job board for local services quote agent appeared first on The Cryptocurrency Post.

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