Exciting News: NEAR Introduces Staking for AI Usage! Tokens Won't Be Consumed!

📌 Quick Summary in 3 Lines

• NEAR has introduced a new payment method called "staking-based" for AI usage!

• By simply locking NEAR tokens, users will receive monthly AI computing credits.

• This connects tokens with AI usage, but whether it will truly become popular remains to be seen!

Why has this happened? NEAR's new payment method is making waves!

NEAR has started a "staking-based payment model" at NEAR AI! With this, everyone can lock their NEAR tokens (which means depositing them) and receive monthly AI computing credits (similar to points for using AI) instead of traditional cloud billing (like regular internet service fees) or credit card payments.

According to official information, this system will allow access to 43 different AI models, including well-known ones like OpenAI (the company behind ChatGPT!), Anthropic, and Google. This is crucial because tokens will not be consumed! Users will receive computing credits based on the amount of locked NEAR tokens.

This is more interesting than just saying, "We have more payment methods," right?

NEAR is trying to directly link the utility of tokens (what they can be used for) to AI usage. Instead of encouraging everyone to buy tokens for speculative purposes (hoping they will increase in value), they are giving tokens a role that is useful for using AI.

However, it is still uncertain whether everyone will truly adopt this method on a large scale. Nevertheless, the direction of this design is definitely worth paying attention to!

This isn't just a payment method! It's a new way to use tokens! {#i}

Payments for using AI actually involve some very complicated issues.

Users and developers usually pay using cloud accounts, credit cards, monthly subscriptions, invoices, or platform-specific credits, right? These methods work fine for regular software, but they don't fit well for autonomous AI agents (programs that make decisions and act on their own), people familiar with cryptocurrencies, or apps that want to access programs without regular billing.

Thus, NEAR's model aims to solve this problem by using "staking" (locking tokens) as a payment layer.

Instead of using tokens directly, everyone just locks their tokens (deposits them). The amount locked determines the monthly computing credits. This fundamentally changes the relationship between holding tokens and being able to use the product.

Users are not just paying fees. They are committing their funds to the network and receiving AI computing access as a reward.

This could make a lot of sense for developers who already hold NEAR tokens, those creating AI agents, and anyone looking for reasons to use tokens beyond staking yields (profits from deposited funds) or governance (participating in network operations).

Why is "no token consumption" so important? It's perfect for AI agents! {#_AI}

The fact that tokens are not consumed is really important.

If the model required consuming NEAR every time an AI model was used, it wouldn't be much different from a regular pay-per-use system, right? However, with the token locking method, users can retain ownership (meaning the tokens are theirs) while receiving credits, which completely changes the economic structure.

Thanks to this, users might feel like "it doesn't cost that much." Of course, there is still an "opportunity cost" (the cost of not being able to use the tokens for other purposes while they are locked, and the potential profits or uses lost due to market price fluctuations).

So, this model is like a membership or access system backed by staking.

This represents a new type of token utility. Cryptocurrency networks have been searching for utility models that do not rely solely on speculation (profit motives) or inflationary rewards (distributing tokens excessively, which lowers their value) for years.

And from the perspective of "autonomous AI agents," it is even more forward-thinking.

If AI agents operate independently, calling AI models, using tools, paying for services, and making decisions within a software environment, they will need a payment method (payment rails) that can be controlled programmatically. Traditional billing systems work well for human-managed accounts, but considering that software agents will be continuously operating, they become very clunky and inconvenient.

This is where cryptocurrency payment methods (crypto rails) might come in handy.

With a staking-based computing model, AI agents and development environments could access AI resources based on locked funds without relying on recurring credit card payments or centralized authentication (like IDs or passwords).

This is still in its early stages, though. There are still many unresolved issues regarding permissions, security, fraud prevention, cost predictability, and user experience. However, this direction aligns perfectly with NEAR's overarching strategy to focus on the infrastructure for AI and AI agents.

But we can't celebrate just yet! There are still many challenges ahead! {#i-2}

The point is simple. "Launching" a service and being "adopted by users" are entirely different things.

NEAR may have introduced a smart computing credit model, but whether users will genuinely prefer to use it will be proven by the market in the future. Developers will also compare it with various methods like direct API billing, cloud credits, open-source AI models, enterprise contracts, and other cryptocurrency-based computing markets.

Additionally, this model needs to be made clearer.

• For example, how much do you need to lock to receive how many credits?
• Which AI models can be used, and at what cost?
• How predictable are the credits received over time?
• Can the team develop this without worrying about token price volatility?
• Will this system attract new users who are not already part of NEAR's ecosystem?

Whether these questions can be resolved will determine whether this becomes a truly usable use case or ends up being a niche experiment.

Ultimately, what NEAR aims for is a "truly usable token."

What makes NEAR's AI payment model interesting is that it gives tokens a highly practical role.

Cryptocurrencies have long struggled to explain why a token is necessary beyond governance (participation in operations), gas fees (transaction fees), staking, and incentives (rewards). However, by linking token staking to access to AI computing, NEAR has created a more concrete narrative around token utility.

Of course, this doesn't guarantee success. However, it is certainly more useful than a vague branding of "AI-related!".

If users lock NEAR and genuinely receive computing credits for the AI models they use, then those tokens become part of the product cycle. This is precisely what many networks are trying to achieve. It's not just about market cycles (price fluctuations) but also about the demand for tokens linked to actual usage.

NEAR's staking-based computing payments have just begun, but they point to a much more practical model of cryptocurrency and AI than most of the hype circulating in this field.