GAEL_

The majority of blockchains consider tokens as a product of activity. After execution, fee is collected, after validation, rewards are given out and settlement is made when the computation has already been carried out. This sequencing is effective with simple transaction systems, but it does not work when infrastructure is supposed to handle persistent and autonomous AI workloads.

@Vanarchain takes a different standpoint to the problem. In an AI first world, implementation has to be ensured beforehand. The settlement, automation, computation, and memory persistence cannot be based on best-effort incentives. They require enforcement. This is where $VANRY will reside, as not a token of transactional fee, but as an execution requirement that Vanar has built directly into its infrastructure layer.
AI systems do not act in the same way as normal persons. One-off transactions are not presented by them. They are stateful, invoke repetitive actions, service-to-service interaction and rely on confidence in completeness. When the execution of the operation can be postponed, reverted, or abandoned because of a lack of incentives, the system fails. Vanar responds to this by conditioning its execution on initial economic commitment. VANRY is that commitment.
Within @Vanarchain , the use of $VANRY is not linked to narrative use, but operational flow. Resources are needed to be allocated before an AI task, automation sequence or persistent process can be run. Storage, compute, coordination and settlement capacity are not taken to be unlimited and free. $VANRY is the mechanism that links resource allocation to enforceable payment making execution economically guaranteed instead of probabilistic.
Such a design decision is important since AI architecture cannot be based on post-hoc settlement. An AI agent that coordinates actions in the time, either through memory maintenance, workflow, or communicating with user services, cannot stop halfway when the fees change or are congested. VANRY being an execution input will mean that, once a process starts, Vanar will guarantee that the infrastructure will fulfill it in stipulated parameters.
More importantly, it also transforms the way of managing failure. Failed execution in most networks is ambiguous: is it network, pipe or even user error? The model by @Vanarchain eliminates this ambiguity. In case of a $VANRY , the implementation is transferred to the infrastructure. A failed process can be detected, held responsible and financially contained. It is a precondition to construct systems that the institutions and consumer-related products can rely on.
The other implication is persistence. AI systems are not ephemeral. They need permanent memory, a steady state access, and availability. The infrastructure of Vanar is not built on a throughput basis, but on persistence. VANRY supports this by investing not only in transaction, but in continuity. Implementation is not a one-time event, it is a life cycle and VANRY puts the life cycle to an economic anchor.
This methodology also makes blockchain complexity unseen by end users. Consumers who will have to deal with AI-driven applications do not have to be aware of gas properties or network properties. Economic logic is processed at the infrastructure level. Developers establish reliability on predictable execution assurances, AI agents are continuously running, and customers enjoy reliability in their services. VANRY silently ensures this reliability in the background.
Noteworthy, it is not abstract design. Vanar uses this execution-first model in building their real products and tooling. The token does not await future utility, it is already entrenched in the point of execution coordination and settlement. That is why one should take VANRY as operation fuel and not a narrative asset.
The infrastructure to enable AI on scale will not be measured by throughput metrics or the theoretical decentralization in the long run. It will be scored on whether execution actually occurs, whether systems are maintained and failure is managed in a predictable manner. @Vanarchain @undefined satisfies the requirements in a structural, as opposed to a promotional, manner. $VANRY exists because AI infrastructure cannot operate without forced execution- and Vanar is built around that fact.
#vanar

@Plasma initially was not intended to be the easy way out of the throughput constraints of Ethereum. Since the beginning of its formulations, Plasma made the problem of scaling a problem of constraints, rather than a problem of raw capacity. Secondly, rather than putting more computation on a base block, it actually limited what one could perform off-chain, to maintain a thin and critical promise: one should always be able, in any case, to safely make it back to Ethereum. It is that design choice that makes Plasma fundamentally different to many subsequent scaling methods--and also the confusion behind the availing of Plasma.

In its bare essence, @Plasma views Ethereum as a last resort of arbitration as opposed to an execution layer. State transitions, computation and order of transaction are offloaded into child chains. Ethernet merely has occasional commitments, Merkle roots that capture the state of such chains. This separation minimizes on-chain load significantly, at the price of Ethereum no longer ensuring the correctness of every transition. Plasma however, makes the assumption that wrong behavior will be confronted and not precluded. Scaling is the acknowledgement of such constraint.
Here the focus of @Plasma on mechanisms of exit comes into play. Since Ethereum does not authenticate all of the transactions, Plasma has to issue users with cryptographic and procedural means to redeem their money unilaterally. The peripheral features of exit games, challenge periods and fraud proofs are the backbone of the system. Each choice of design in Plasma: the presence of utxo-based the various forms of models, any constraint on smart contract expressiveness, buying out at a longer withdrawal time is dictated by the property that exit is predicably verifiable and executable on Ethereum in adversarial circumstances.
In this light, @Plasma is not throughput optimum in the traditional meaning of the term. It does not strive to maximize the number of transactions per second and still be completely general. Rather, it limits the functionality to the point that even the worst-case scenario can be survived. When an operator withholds data, bans users (or gives out incorrect state roots), the system does not crash completely, but instead enters the exit process. This scaling philosophy is much different than rollups in which Ethereum re-executes or re-verifies off-chain computation in order to ensure stronger guarantees.
These limitations are the cause of many of the real limitations of @Plasma . Smart contracts of general purpose are hard to support, as exits have to be expressible and challengeable in Ethereum. The availability of data is the assumption that is external and it implies that the users or watchers have to actively track the chain. The concept of mass exits, although hypothetically secure, creates the threat of congestion on Ethereum. All these are not some accidental shortcomings; they are direct outcomes of considering exit safety over the flexibility of the execution.
It is also a consequence of seeing @Plasma as a constraint system that it has not been the scaling solution of choice even though it is a beautiful thing to do. With the development of Ethereum, more models that replace the higher on-chain verification costs with easier user guarantees became accepted in the ecosystem. Rollups transfers more computation to the security model of Ethereum, so users do not need to act in the defense of their funds. Plasma on the other hand requires vigilance and is willing to tolerate complexity to be able to have small on-chain footprint.
But the ideas of @Plasma still have their effect. The isolation of the implementation and the settlement, the specific modelling of failure modes, the emphasis on a recovery reducing adversarial failure rather than optimistic performance all influenced subsequent designs. More to the point, Plasma is used to show that the scaling is not a one-axis optimization issue. The process of increasing throughput not specifying how systems fail and the users would escape the failures is not scaling but deferral.
In that regard, @Plasma is successful because it does not turn out to be a throughput shortcut. It causes protocol designers to grapple with awkward issues of operator trust, user responsibility and worst-case behavior. Ethernet can be scaled in numerous different ways, but Plasma is reminding us on the fact that before we go further with any other credible approach, we need to answer a more basic question: in case everything is going wrong, who will be able to make it out of the trenches and how?
#Plasma
$XPL

In the olden days, decentralized infrastructure assumed bandwidth as an implicit by-product of storage. The thinking was that the network would be in a position to serve the data in case there were enough nodes to hold the data. Availability was conceptualized as persistence, and persistence was conceptualized as storage. Bandwidth - with whom, when, and under what load was left largely to chance.

No more holding of that abstraction.
With the expansion of blockchain systems, the bottleneck does not lie in the fact that data are stored somewhere on the network. Whether that data can be provided, many times and dependably, to numerous independent verifiers at the very instant it can be relied on by the execution, is the question. At this point the discussion silently shifts to a more business-like expression of coordinating the trustless bandwidth as opposed to storing data, hence why @Walrus 🦭/acc is the core of the shift.
The execution environments of the modern world create demand trends that do not resemble the initial blockchain. Rollups post large blobs. Externalization of data is done in high-throughput chains. Off-chain computation is based on verifiable inputs that have to be fetched by a large number of parties at the same time. Bandwidth in these systems is not background utility. It is scarcity that will mean that execution will be left in a decentralized state or will crunch into assumptions of trust.
This fact was never modeled in traditional storage networks. They are also time optimized to last longer and not to deliver in contention. Nodes are also not encouraged to store datas, just in order to serve them during peak demand. What is left is a known phenomenon: the data will be availed in theory but retrieval is worse when the systems are stretched.
@Walrus 🦭/acc redefines the issue by considering bandwidth as a matter that should be coordinated, priced and incentivized explicitly. It does not make the passive assumption that availability is a byproduct of replication but sees availability creation as an active behaviour, which incurs real costs. Bandwidth is competing with other hardware uses. The free retrieval is not under load. When this is not given economical consideration by the protocol, the most flimsy assumptions will be revealed first.
The central change is rather minor but significant. Walrus does not question the feasibility of the storage of data. It poses the question of whether the network is capable of making a promise that it can serve that data in scale, and even in a trustless setting, free of privileged actors or off-chain tasks. The re-framing causes the system to cease being a passive store of data to an active coordination.
The issue of coordination is that decentralized bandwidth is not homogenous. The nodes are different in capacity, location, latency and readiness to serve. Demand is spiky and uneven. There is no way to coordinate incentives in a dynamic way so that networks aren’t either wasteful to build or inefficient at performing on demand. @Walrus 🦭/acc uses this as an economic problem initially to enable the system to react to actual usage instead of a priori assumptions.
This is highly applicable when the availability of the data is modularized. There is an increasing reliance on external DA systems in execution layers which implies they inherit whatever the assumption the system makes about bandwidth. Provided that DA layers act as best-effort storage, quality of execution becomes fragile. When they act as integrated infrastructure, the execution will be more resilient by default.
What comes out of the approach of @Walrus 🦭/acc is another form of scaling. Not only increased data storage but more predictable access to the data during a load. Not only decentralizing the number of nodes, but also decentralizing the people who are free to access and verify information without authorization. The bandwidth is not something that the applications hope will be available but is actively handled by the protocol.
There is an implication too, which is long-term. It opens up a new design space when the protocol-level coordination of trustless bandwidth occurs. More significant evidences are possible. Richer off-chain computation is made safer. The applications may have the assumption that data-intensive operations will not silently centralize because of constraints on infrastructure.
This is the reason why @Walrus 🦭/acc is not exactly a storage network iteration but more of a conceptual correction. It recognizes that in current decentralized systems no storage is made without coordinated bandwidth is illusionary. Information is only important when it can be transferred.
The language of storage is likely to remain in common use in the industry due to the custom. However, in the background, the systems which scale will be the ones which know what they are actually constructing: not warehousing for data, but the markets and mechanisms to serve this data trustlessly, at scale.
#walrus
$WAL

The availability of data had been viewed as plumbing over the years. Something delegated, abstracted away, and hoped to be there when needed. As long as blocks propagated and nodes remained online it had been assumed that there would be data when it was needed. That was true when the throughput was low, applications were simple and demand was predictable. It scales poorly when used with large execution.

@Walrus 🦭/acc also begins with the assumption that the availability of data is not a backend service. It is an economic layer. And considering it as such alters the system behavior in the real world.
The greatest error that the architectures adopted was to believe that data availability is a technical issue. Safety stocking, increasing redundancy, and availability is born. Practically, availability does not break down because of disappearance of data but rather because of lack of incentive during the peak of demand. The data can technically be stored in the Nodes, however, retrieval is slow, unreliable or economically pointless at the time when the system needs it most.
Where execution systems have been bled with performance is that distance between nominal availability and usable availability.
@Walrus 🦭/acc challenges this face on by pricing and aligning data availability as an economic process and not as a guarantee in the background. The protocol does not rely on the assumption that storage providers will provide data on a selfless or non-chargeable basis, rather than considering bandwidth, retrieval, and storage as limited resources that need to be dynamically shared. This renders the availability of data decipherable to the system. Costs reflect demand. Incentives adjust with load. Capacity is not something the network hopes to have, but rather becomes something the network actively manages.
This is important since current blockchains are no longer monorails. Execution layers are more specialized and faster, and the availability of data is externalized. Using rollups, app-chain, and high-throughput systems off-load large amounts of data pushing that data off-chain, and ensuring that execution is honest using DA layers. DA is no detail of implementation in that world. The limitation is what defines the ability to scale the execution safely at all.
The failures of DA become transparent only until they are disastrous when it is considered as a backend service. Congestion looks like a bug. Bad networking appears like latency. Costs spike unexpectedly. To merely survive unpredictable infrastructure behavior, builders are compelled to do excessive data format optimization, to compress, or to be less expressive.
@Walrus 🦭/acc overturns that relationship. It makes data availability an explicit economic layer, which provides systems with something to reason about. Developers are able to predict costs depending on the usage. Operators are able to observe the location of demand. The network is able to compensate behavior enhancing real availability and not nominal storage.
It also has a coordination advantage that is readily overlooked. Feedback loops are developed through economic layers. In the case of retrieval being more valuable, there is an increase in incentives. When the demand is low then costs would change to the downward. This enables provisioning of capacity where and when it is required, rather than overbuilding it in the same manner and letting it remain idle. In the long run, it results in a more resilient infrastructure that is more efficient.
More importantly, @Walrus 🦭/acc does not attempt to be all. It does not even place itself as a universal storage or alternative to execution environments. It is more specific and more straightforward: it wants to make big data availability act like infrastructure rather than best-effort storage. The said limitation is what makes the economic model remain coherent.
What comes about is an alternative mental model by builders. Information is no longer an object that you hide or reduce by all means. It is transformed into a first-class input whose level of availability, price and reliability can be formulated on. That opens up more apps, proofs and expressive performance without faking bandwidth is free.
This is the reason why @Walrus 🦭/acc seems to be more of an infrastructural fix than a storage project. The systems that survive with the maturing of the execution environments will be the ones that realize the location of the real constraints. Walrus does not regard data availability as a background service that can be disregarded, but an economic reality that should be designed.
Such framing will not be the same thing to everyone. However, once the throughput increases, and assumptions are violated, then it becomes difficult to construct anything serious without it.
#walrus
$WAL

Decentralized storage has been in that awkward part of the stack of crypto all its history. Necessary, but never central. Something that teams reluctantly purchased, aggressively optimized and discussed when costs achieve outliers or performance failed. Storage was seen as a back-end liability - a line to minimize as opposed to a system to be designed.

Framing is literally disintegrating, and @Walrus 🦭/acc happens to be at the fault line.
It is not necessarily that storage has become exciting and thus shifted. It is occurring so because the execution environments evolved at a higher rate than the assumptions the storage systems were constructed out of. The current blockchains, rollups, and application-specific chains do not face the most significant problem of consensus and compute any longer. They have difficulties in transporting, authenticating, and availing huge amounts of data in a dependable fashion to numerous parties simultaneously. The availability of data and bandwidth have ceased to be, and remain, second-order concerns.
After you view that, the previous model of decentralized storage begins to appear out of place. The systems were mostly configured to operate in a manner that is durable and redundant rather than predictable retrieval, coordinated bandwidth, and cost-beneficial incentives that increase with demand bursts. They modeled equations in terms of the existence of the data, as opposed to the execution capability of the system under load should this data be required immediately, by numerous agents, and in large amounts?
@Walrus 🦭/acc tackles the issue in a different direction. It does not view data as a passive object that has to be stored but as a dynamic contribution to the performance. That one change manages to make storage a cost center an execution primitive - something which applications and chains can reason about, rely on and structure against with confidence.
It is not significant that @Walrus 🦭/acc holds big lumps. Plenty of systems can do that. How it organizes storage, availability, and retrieval in an economical way is what is important. Rather than presuming that an idle capacity will somehow be used to fulfill demand, Walrus is built to bring to the surface real incentives that will cause nodes to take on the duty of serving data when it is truly needed, at scale, and contended with. No technical flourish that, it is an acknowledgment that decentralized infrastructure can only be effective when it is supported by incentives that more closely reflect reality, as shown by real-world patterns of usage.
This is particularly true when blockchains go more towards increased throughput and modular design. There is a growing specialization and high speed of execution layers, but externalizing data availability. That isolation can only be achieved when the DA layer is used as infrastructure rather than as best-effort storage. The existence of predictable costs, known performance envelopes and failure modes are more important than raw measures of decentralization.
@Walrus 🦭/acc fits in this fact because of its emphasis on coordination instead of maximalism. It is not an effort to substitute execution, consensus and settlement. It does not deny that it has a more limited but imperative role. Through this, it will be able to optimise things that actually break systems in practice: bandwidth saturation, uneven demand and gap between nominal capacity and usable capacity.
There is even a subtle implication here. The inclusion of storage as an implementation primitive alters the way applications are implemented. The developers cease compressing, batching, and distorting data simply to endure cost models that are not based on usage. They can build in the direction of more prosperous state, larger evidence, or more communicateive off-chain calculation, and understand that the data layer can never fall under achievement.
This is the reason why @Walrus 🦭/acc is less of a storage network launch and more of a correction. It recognizes what the ecosystem failed to learn via experience: it is not that execution does not work when the data that it needs does not run: rather, it is that the data that it needs cannot be transported, verified or accessed in a dependable and reliable manner at the right time.
These changes are normally detected in the market late. The issue of storage as cheap or expensive will continue to be debated. However, it is changing below that language. @Walrus 🦭/acc is not requesting people to become concerned with storage because it cares about the storage. It is compelling an acknowledgement that, in the contemporary decentralized systems, execution is never more than real as the data it can rely on.
That’s not a new insight. It is one that the industry is finally willing to develop on.
#walrus
$WAL

Ether compatibility is now a two sided sword in blockchain infrastructure. On the one hand, it will reduce the entry barrier of developers through the provision of familiar tooling and execution models. On the other, it regularly brings in assumptions which are thoroughly inconsistent with regulated finance - most notably, unconditional public execution and observability by the global state. This trade-off can hardly be tolerated in case of institutions. Being compatible cannot be done at the expense of exposure.

Here @Dusk Foundation distinctly draws an architectural line. DuskEVM is not an effort to model its convenience on publicly available blockchains. It is intended as a institutional interface: a means of builders to get into the EVMigmatized development without assuming the upfront transparency of the case constitutive of a public network.
The majority of EVM settings presuppose that the execution of smart contracts should be publicized by default. The inputs, logic and the resulting changes of the state are freely available to any node runners or chain indexer. This transparency facilitates the practice of permissionless experimentation, but is in direct opposition to the practicalities of managed assets. Institutions cannot reveal pricing logic, settlement mechanics or contractual conditions just to have programmability.
DuskEVM re-defines this issue by separating the interface with the developer with the execution visibility model. As a developer, the environment is known. Known paradigms are used in writing the contracts, tooling is easily available, and the learning curve is also low. However, under that interface, it is executed in the confidential protocol layer of @Dusk . EVM is not operated on transparency-first ledger; it is operated within the framework of a selective disclosure system.
This is not the cosmetic difference. It is an alteration of what is practically constructible. With @Dusk EVM developers are able to run smart contracts, managing regulated workflows, such as issuance conditions, transfer restrictions, corporate actions, without showing the logic to the whole network. The protocol confirms the correctness and still maintains confidentiality of the execution specifics. This does not mean hiding the behavior under supervision, it is to ensure the unnecessary broadcasting of proprietary information does not take place.
Notably, DuskEVM does not outsource compliance. A significant proportion of what are known as institutional EVM solutions is based on gateways being allowed, off-chain verification or on-top legal contracts over public execution. These strategies divide the responsibility and augment risk of operations. @Dusk , as an alternative, enables compliance logic to coexist with application logic, which is imposed on execution instead of retrospective. The interface is the same, however, the guarantees are completely different.
Institutionally speaking, this is more important than raw performance or composability accounts. Risk containment, auditability, and predictability are factors that lead to the adoption of financial infrastructure. @Dusk EVM also complies with these priorities as the developers do not have to redesign systems just to keep them out of the limelight. The environment does not compel institutions to decide between programmability and confidentiality.
Lifecycle consistency is another very important factor. On numerous platforms, although issuance or execution is initially done in a private setting, assets ultimately come to a publicly settled layer where exposure comes back. DuskEVM works under a protocol that ensures the secrecy of the asset lifecycle. Issue, execution and settlement is done under the same privacy and compliance assumptions. It is this coherence that enables @Dusk to be used as infrastructure as opposed to being an experiment.
It is also notable what DuskEVM fails to be. It is not a multi-purpose open DeFi public execution environment. That is a deliberate choice. Narrowing down its focus to institutional and regulated usage cases allows Dusk to compromise less than would be the case when it serves both incompatible audiences on the same execution layer.
Finally, @Dusk EVM reveals that the compatibility of EVM does not necessarily imply the openness. The fact that the EVM is viewed as an interface, as opposed to a philosophy, makes Dusk accessible to the developers and redefines execution guarantees. In the case of institutions, it is the distinction between tuning in on the blockchain technology to financial reality and strapping financial systems to technical limitations to which they do not belong.
In that way, it is less compatible as an end-to-end requirement but rather about control, which is the control over visibility, compliance, and execution integrity. And that is what institutional infrastructure needs.
#dusk
$DUSK

The most blockchains silently declare their limitations through the issuance of assets. Although lots of networks can represent assets on-chain, very few of them can issue it in a manner that is consistent with how regulated finance functions. As soon as real securities, private funds or institutional securities are introduced into the system, there is a structural risk, and not a philosophical issue. Issuance is not only a technical event because it is a legally limited process which requires confidentiality, control and verifiability simultaneously.

This is what Dusk Foundation is aimed to solve.
Regulated asset issuance is not considered a thin wrapper over a public token standard on Dusk. It is represented as a private, regulated, procedure carried out at the protocol level itself. In place of disclosing issuer or investor data, or logic determining allocation, @Dusk permits issuance to be done in private, but can still be cryptographically verifiable by the entire network. The network ensures that the rules of issuance are adhered to and that no sensitive information is transmitted to all the observers.
Majority of the publicly available blockchain presupposes that issuance should be transparent to be credible. This assumption is challenged by Dusk which separates trust and disclosure. Within a confidential execution environment, an issuer on @Dusk can establish the conditions of issuance including eligibility requirements, transfer requirements, or jurisdictional requirements. Validators ensure that those rules are implemented in the right way but the underlying data never ever gets into the state of any globally readable state.
This difference is essential to regulated assets. In traditional markets the information about issuance is distributed selectively: regulators receive one version, counter-parties other version and the public would frequently only receive aggregated disclosure. This reflective reality is on chain at @Dusk . The protocol does not impose upon all the participants into the visibility domain; it encourages controlled disclosure as a property inherent in issuance.
The other major difference is the enactment of compliance. In most platforms, on-chain checks on compliance are bypassed, or the permissioned gatesway is placed on the execution layer. This creates fragmentation. The asset can be on-chain, and the regulations are elsewhere. @Dusk does not do this by incorporating compliance logic into the issuance process. The protocol-level conditions must be met in order to create the asset. External approval step cannot be omitted and restricted.
This lowers operational risk in an issuer. Issuance is not dependent on having trust in middleware providers or having parallel compliance systems. The protocol by itself is the mechanism of enforcement. This is especially true in the case of institutions dealing in tokenized securities, the issuance of private credit instruments, or regulated funds, in which compliance failure is not an option.
Market integrity is also guarded by privacy during issuance. Public issue through transparent chains will unintentionally spill strategic information, including that of price assumptions, demand cues, allocation policies, which the institution has a legal and competitive duty to keep confidential. The issuing confidential model of @Dusk does not allow such leakage at the expense of auditability. The issuance event is verifiable, definitive and enforceable, not publicly dissectable.
DuskEVM has expanded it to the builders who require the familiar development workflows. Issuers and developers can create ethereum-compatible tooling issuance contracts whilst under the confidentiality assurances of Dusk. This reduces the entry barrier excluding the importation of Ethereum default exposure model. The outcome is a space in which regulated assets may be issued in a programmable manner without writing financial logic to support the risks of the public-state.
On the system level, the strategy of Dusk does not require layered compromises. It does not have a separate phase of private issuance and a phase of public settlement which reintroduces exposure. Lifecycle control, rule enforcement and issuance take place in a single protocol. It is this coherence that makes institutional adoption realistic as opposed to a theoretical one.
At the end, @Dusk radically reconstructs the meaning of on-chain issuance. It is not the focus of making assets visible to all; but the focus of making them valid, enforceable, and submissive without unwarranted disclosure. Dusk, by considering confidentiality a prerequisite as opposed to a functionality, allows the controlled issuance of assets that is in line with both legal reality and the current financial infrastructure.
#dusk
$DUSK