How the Government’s ‘AI for Everyone’ Differs from Existing Free Chatbots: Domestic Models, Privacy, and Launch Schedule

The government’s “AI for All” initiative is not simply a project to create yet another free generative AI chatbot. The core of the publicly announced plan is to build a general-purpose chatbot that anyone can use without concerns about cost or usage limits, an operating structure centered on domestically developed AI models, and AI agents connected to public services.

However, as of August 3, 2026, the operator bidding process is still underway, and the actual operator, terms of use, privacy policy, and performance have not been finalized. Therefore, what can currently be compared is not a finished product, but the project requirements and goals presented by the government.

AI for All Project Overview and Schedule

The Ministry of Science and ICT is accepting bids for the “AI for All Project” from July 13 to August 11, 2026. The publicly announced implementation schedule is as follows.

This schedule represents the project’s implementation targets. It may change depending on the results of the bidding and development processes, security verification, or integration with public institutions.

Under the plan, the scope of the service is broadly divided into two areas.

• General-purpose AI chatbot: A public service providing common generative AI functions such as answering questions, drafting documents, summarization, and translation
• Public AI agent: A function that finds public information, provides guidance on administrative procedures, and, in the future, assists with the use of public services within permitted limits

Whether public AI agents will actually take the place of administrative dispositions or eligibility determinations is a separate issue. Providing information, assisting with applications, querying institutional systems, and making automated decisions with legal effect involve different levels of authority and responsibility, so they must be distinguished in the official service.

What Does the 70% Domestic AI Model Requirement Mean?

The publicly disclosed project requirements state that domestically developed AI models must account for at least 70% of all models used and must include an independent foundation model. This can be interpreted not as a requirement to exclude foreign models entirely, but as a standard designed to ensure that domestic models form the primary foundation of the service.

An independent foundation model means an internally developed base model that has been pre-trained on large-scale data and can be used for multiple tasks, rather than a service that simply resells another company’s model or changes only its interface. However, this may not mean that the training data, open-source components, and even synthetic data generated using external models must all be domestic technologies.

To evaluate the 70% requirement, the method used to calculate the ratio must be disclosed.

• Is it calculated based on the number of user queries?
• Is it based on the number of tokens processed or GPU usage?
• Are calls for individual functions such as search, translation, and image generation calculated separately?
• How are cases reflected when a domestic model fails and the request is switched to a foreign model?
• In an agent system combining multiple models, which stage is used as the basis?

If these standards are unclear, the nominal share of domestic models may differ from their actual share of usage. For the official service, regularly disclosing the list of models used, routing principles by purpose, and the results of ratio calculations would help with verification.

The Role of 512 B200 GPUs

The 512 NVIDIA B200 GPUs provided by the government are core computing resources for running large-scale AI models and responding to user requests. For a free service accessed by many users simultaneously, sustained inference-processing capacity and stable resource allocation are especially important, even more so than model training.

The expected benefits of GPU support include:

• Reducing the burden of initial infrastructure investment
• Securing inference capacity to accommodate increases in concurrent users
• Supporting performance testing and routing across multiple domestic models
• Measuring demand and costs during the beta-service period

However, actual user capacity or response speed cannot be calculated from the number of GPUs alone. Throughput varies significantly depending on model size, input and output length, precision, quantization, batch processing, caching, search systems, and reserve capacity for incident response. GPU support is a resource for service quality, not an indicator that guarantees quality itself.

Supported resources must also be distinguished from long-term operating expenses. After the GPU support period ends, the operator must continue to cover server rental fees, electricity costs, network expenses, security, and customer-support personnel. To keep the service free after 2027, a clear structure is needed to specify whether costs will be covered through government budgets, contracts with public institutions, enterprise services, APIs, or other sources of revenue.

Does Free and Unlimited Literally Mean No Restrictions?

The free and unlimited service described in the plan means that it aims to offer a service in which users do not pay subscription fees or face ordinary limits on the number of questions. It is difficult to interpret this as meaning that there will be no technical restrictions whatsoever.

Large-scale public AI services generally require the following types of operational controls.

• Preventing bulk requests made through automated programs
• Blocking attacks, spam, illegal content generation, and service disruptions
• Preventing a single user from consuming excessive resources
• Applying temporary speed limits or queues during service incidents
• Allocating resources differently between high-cost functions and ordinary text functions

The official terms should therefore clearly specify the scope of “unlimited” use, the fair-use policy, conditions for speed limits, and whether high-volume commercial use is permitted. The fact that the service is free also does not mean that the government guarantees the accuracy of every answer or the results of using the service.

User Prompts and Privacy Issues

AI chatbot prompts may contain not only names and contact information, but also sensitive information such as health, income, family relationships, civil complaint details, contracts, and company secrets. In particular, AI connected to public services may process data with a higher likelihood of identifying individuals than an ordinary chatbot.

If the use of processed user prompts for service improvements or revenue models is being considered, the following details must be disclosed first.

Removing a name or replacing it with a pseudonym does not automatically make all information anonymous. This is because an individual may be re-identified when a rare civil complaint case is combined with location, occupation, and family relationships.

Data minimization needs to be incorporated into the basic design of public AI. Merely warning users not to enter sensitive information such as resident registration numbers, financial information, or medical information is not sufficient; automated detection and masking, short retention periods, access controls, encryption, and audit logs are also required.

It cannot be concluded that monetization of prompt data has been finalized. However, if long-term operating expenses are to be covered through a data-based business, it must be made clear whether citizens who do not consent to data use can still access core public AI functions and how their data will be handled after consent is withdrawn.

Comparison with ChatGPT, Gemini, and Claude

The biggest difference between AI for All and existing global chatbots lies not in current performance, but in policy goals and operating structure. ChatGPT, Gemini, and Claude are commercial services that are already available, whereas AI for All is still at the planning stage, ahead of operator selection and beta testing.

The existence of a free tier does not mean that service conditions are the same. Global chatbots may impose separate limits on specific high-performance models, file processing, image generation, or deep-research functions. Conversely, AI for All may also apply fair-use standards to maintain stability and prevent abuse.

How Should Performance and Responsibility Be Verified?

The share of domestic models and the number of GPUs are input requirements, while the results experienced by users require separate evaluation. Once the beta service begins, it should be possible to review at least the following indicators.

Performance Indicators

• Accuracy of Korean-language factual queries and long-document comprehension
• Source citations and up-to-dateness of legal and policy information
• Rate of hallucinations that generate content without supporting evidence
• Average response time and success rate during peak periods
• Downtime, recovery speed, and whether data was lost
• Accessibility for people with disabilities and usability on mobile devices and low-speed networks

Public-Service Safety Indicators

• Clear distinction between informational guidance and decisions with legal effect
• Procedures for reporting and correcting incorrect answers
• A function for transferring users to the responsible institution or a human counselor
• Change history when models or data are modified
• Checks for discriminatory errors based on gender, age, region, disability, and other factors

Responsibility Structure

Errors in general conversational answers, errors in public-institution data, and errors in actions performed by agents may have different causes. The official terms and operating rules must specify who, among the government, operating company, model developer, and data-providing institution, will investigate and correct such errors and respond to user harm.

Key Questions to Check Before and After Launch

For AI for All to become public infrastructure that is meaningfully different from existing free chatbots, it must be able to answer the following questions.

1. In what units is the 70% domestic-model share calculated, and who verifies it?
2. Under what conditions are questions sent to foreign models, and are they transferred overseas?
3. What fair-use standards apply to free and unlimited use?
4. Where and for how long are original prompts stored?
5. Can users opt out of model training or commercial analysis?
6. What funding sources will cover GPUs and operating expenses after 2027?
7. What can and cannot public AI agents do?
8. Who is responsible when incorrect administrative guidance or a personal-data breach occurs?

Ultimately, what distinguishes AI for All will not be determined solely by the label “a free chatbot created by the government.” Meaningful comparison with existing services will only be possible when the actual use of domestic models, the accuracy of public information, a design that prioritizes privacy over profit, long-term funding, and the responsibility framework are disclosed.