{"content_id":"3qqtqnndtt","slug":"korea-ai-for-all-vs-free-chatbots","locale":"en","schema_type":"Article","category":"comparison","category_name":"Comparison","title":"How the Government’s ‘AI for Everyone’ Differs from Existing Free Chatbots: Domestic Models, Privacy, and Launch Schedule","summary":"The government’s ‘AI for Everyone’ initiative aims to provide a free, unlimited general-purpose chatbot and public AI agents to all citizens. The share of domestic models, B200 GPU support, long-term operating costs, and standards for handling prompt data will determine its actual differentiation and reliability.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["‘AI for Everyone’ is a public AI service initiative targeting a beta launch by the end of September 2026 and an official launch within the year.","The project requirements specify that domestic AI models must account for at least 70% of usage and that a proprietary foundation model must be included.","The 512 NVIDIA B200 GPUs provided by the government will support the initial inference infrastructure, but they do not automatically guarantee service quality or sustainability.","The phrase ‘free and unlimited’ refers to pricing policy, while actual operations may require restrictions to prevent security risks, overload, and abuse through automation.","If user prompts are to be used for monetization, the service must clearly disclose data minimization practices, purposes of use, retention periods, and whether data is shared with third parties."],"content_markdown":"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.\n\nHowever, 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.\n\n## AI for All Project Overview and Schedule\n\nThe 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.\n\n| Stage | Scheduled time | Items to check |\n|---|---:|---|\n| Operator bidding | July 13–August 11, 2026 | Participation requirements, technical and operational proposals, privacy protection measures |\n| Operator selection | August 2026 | Final operator and consortium structure, models to be used |\n| Beta service | Late September 2026 | Actual usage limits, response quality, incident response, scope of data collection |\n| Official service | Within 2026 | Scope of public-service integration, long-term operating policy, official terms |\n\nThis 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.\n\nUnder the plan, the scope of the service is broadly divided into two areas.\n\n- **General-purpose AI chatbot:** A public service providing common generative AI functions such as answering questions, drafting documents, summarization, and translation\n- **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\n\nWhether 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.\n\n## What Does the 70% Domestic AI Model Requirement Mean?\n\nThe 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.\n\nAn **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.\n\nTo evaluate the 70% requirement, the method used to calculate the ratio must be disclosed.\n\n- Is it calculated based on the number of user queries?\n- Is it based on the number of tokens processed or GPU usage?\n- Are calls for individual functions such as search, translation, and image generation calculated separately?\n- How are cases reflected when a domestic model fails and the request is switched to a foreign model?\n- In an agent system combining multiple models, which stage is used as the basis?\n\nIf 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.\n\n## The Role of 512 B200 GPUs\n\nThe 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.\n\nThe expected benefits of GPU support include:\n\n- Reducing the burden of initial infrastructure investment\n- Securing inference capacity to accommodate increases in concurrent users\n- Supporting performance testing and routing across multiple domestic models\n- Measuring demand and costs during the beta-service period\n\nHowever, 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.\n\nSupported 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.\n\n## Does Free and Unlimited Literally Mean No Restrictions?\n\nThe 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.\n\nLarge-scale public AI services generally require the following types of operational controls.\n\n- Preventing bulk requests made through automated programs\n- Blocking attacks, spam, illegal content generation, and service disruptions\n- Preventing a single user from consuming excessive resources\n- Applying temporary speed limits or queues during service incidents\n- Allocating resources differently between high-cost functions and ordinary text functions\n\nThe 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.\n\n## User Prompts and Privacy Issues\n\nAI 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.\n\nIf the use of processed user prompts for service improvements or revenue models is being considered, the following details must be disclosed first.\n\n| Item to check | Explanation required |\n|---|---|\n| Scope of collection | What is stored among prompts, attachments, answers, and access logs? |\n| Purpose of use | Is it used for service delivery, safety reviews, model training, statistics, or commercial analysis? |\n| Legal basis and choice | How are mandatory processing and optional training or analysis distinguished? |\n| Retention period | When are the original and processed data deleted, respectively? |\n| Provision to third parties | What data is transferred to participating companies or public institutions? |\n| De-identification measures | How is the risk of re-identification assessed and reduced? |\n| Exclusion from model training | Can users exclude their conversations from training? |\n| Deletion and access | How can users review and delete stored conversations? |\n| Transfer to external models | Are any requests sent to foreign models or overseas servers? |\n\nRemoving 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.\n\nData 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.\n\nIt 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.\n\n## Comparison with ChatGPT, Gemini, and Claude\n\nThe 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.\n\n| Comparison item | AI for All | Free ChatGPT, Gemini, and Claude services |\n|---|---|---|\n| Current status | Operator bidding and implementation planning stage | Already available to general users |\n| Pricing policy | Presented with the goal of being free and unlimited for all citizens | Free tiers are available, but model, feature, and usage limits vary by service |\n| Main models | Requirement for at least 70% domestic models, including an independent foundation model | Centered on models developed and operated respectively by OpenAI, Google, and Anthropic |\n| Public-service integration | One of the core goals | Focused on general conversation and productivity functions, with limited integration into domestic administrative services |\n| Infrastructure | Plan to use 512 government-supported B200 GPUs | Uses each company’s global cloud and AI infrastructure |\n| Privacy rules | Official policy must be reviewed after the operator is selected | Vary according to each company’s terms, region, account, and plan |\n| Performance verification | Actual measurements possible after the beta launch | Available through public services, but performance differs by model and plan |\n| Responsibility structure | Roles of the government, operator, and connected public institutions must be distinguished | Governed by the service company’s terms and applicable laws |\n\nThe 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.\n\n## How Should Performance and Responsibility Be Verified?\n\nThe 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.\n\n### Performance Indicators\n\n- Accuracy of Korean-language factual queries and long-document comprehension\n- Source citations and up-to-dateness of legal and policy information\n- Rate of hallucinations that generate content without supporting evidence\n- Average response time and success rate during peak periods\n- Downtime, recovery speed, and whether data was lost\n- Accessibility for people with disabilities and usability on mobile devices and low-speed networks\n\n### Public-Service Safety Indicators\n\n- Clear distinction between informational guidance and decisions with legal effect\n- Procedures for reporting and correcting incorrect answers\n- A function for transferring users to the responsible institution or a human counselor\n- Change history when models or data are modified\n- Checks for discriminatory errors based on gender, age, region, disability, and other factors\n\n### Responsibility Structure\n\nErrors 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.\n\n## Key Questions to Check Before and After Launch\n\nFor AI for All to become public infrastructure that is meaningfully different from existing free chatbots, it must be able to answer the following questions.\n\n1. In what units is the 70% domestic-model share calculated, and who verifies it?\n2. Under what conditions are questions sent to foreign models, and are they transferred overseas?\n3. What fair-use standards apply to free and unlimited use?\n4. Where and for how long are original prompts stored?\n5. Can users opt out of model training or commercial analysis?\n6. What funding sources will cover GPUs and operating expenses after 2027?\n7. What can and cannot public AI agents do?\n8. Who is responsible when incorrect administrative guidance or a personal-data breach occurs?\n\nUltimately, 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.","content_html":"\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eHowever, 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#ai-for-all-project-overview-and-schedule\" class=\"anchor\" id=\"ai-for-all-project-overview-and-schedule\"\u003e\u003c/a\u003eAI for All Project Overview and Schedule\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStage\u003c/th\u003e\n\u003cth\u003eScheduled time\u003c/th\u003e\n\u003cth\u003eItems to check\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eOperator bidding\u003c/td\u003e\n\u003ctd data-label=\"Scheduled time\"\u003eJuly 13–August 11, 2026\u003c/td\u003e\n\u003ctd data-label=\"Items to check\"\u003eParticipation requirements, technical and operational proposals, privacy protection measures\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eOperator selection\u003c/td\u003e\n\u003ctd data-label=\"Scheduled time\"\u003eAugust 2026\u003c/td\u003e\n\u003ctd data-label=\"Items to check\"\u003eFinal operator and consortium structure, models to be used\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eBeta service\u003c/td\u003e\n\u003ctd data-label=\"Scheduled time\"\u003eLate September 2026\u003c/td\u003e\n\u003ctd data-label=\"Items to check\"\u003eActual usage limits, response quality, incident response, scope of data collection\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Stage\"\u003eOfficial service\u003c/td\u003e\n\u003ctd data-label=\"Scheduled time\"\u003eWithin 2026\u003c/td\u003e\n\u003ctd data-label=\"Items to check\"\u003eScope of public-service integration, long-term operating policy, official terms\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\n\u003cp\u003eUnder the plan, the scope of the service is broadly divided into two areas.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeneral-purpose AI chatbot:\u003c/strong\u003e A public service providing common generative AI functions such as answering questions, drafting documents, summarization, and translation\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePublic AI agent:\u003c/strong\u003e 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\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhether 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-does-the-70-domestic-ai-model-requirement-mean\" class=\"anchor\" id=\"what-does-the-70-domestic-ai-model-requirement-mean\"\u003e\u003c/a\u003eWhat Does the 70% Domestic AI Model Requirement Mean?\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eAn \u003cstrong\u003eindependent foundation model\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003eTo evaluate the 70% requirement, the method used to calculate the ratio must be disclosed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIs it calculated based on the number of user queries?\u003c/li\u003e\n\u003cli\u003eIs it based on the number of tokens processed or GPU usage?\u003c/li\u003e\n\u003cli\u003eAre calls for individual functions such as search, translation, and image generation calculated separately?\u003c/li\u003e\n\u003cli\u003eHow are cases reflected when a domestic model fails and the request is switched to a foreign model?\u003c/li\u003e\n\u003cli\u003eIn an agent system combining multiple models, which stage is used as the basis?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#the-role-of-512-b200-gpus\" class=\"anchor\" id=\"the-role-of-512-b200-gpus\"\u003e\u003c/a\u003eThe Role of 512 B200 GPUs\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eThe expected benefits of GPU support include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eReducing the burden of initial infrastructure investment\u003c/li\u003e\n\u003cli\u003eSecuring inference capacity to accommodate increases in concurrent users\u003c/li\u003e\n\u003cli\u003eSupporting performance testing and routing across multiple domestic models\u003c/li\u003e\n\u003cli\u003eMeasuring demand and costs during the beta-service period\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHowever, 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.\u003c/p\u003e\n\u003cp\u003eSupported 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#does-free-and-unlimited-literally-mean-no-restrictions\" class=\"anchor\" id=\"does-free-and-unlimited-literally-mean-no-restrictions\"\u003e\u003c/a\u003eDoes Free and Unlimited Literally Mean No Restrictions?\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eLarge-scale public AI services generally require the following types of operational controls.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreventing bulk requests made through automated programs\u003c/li\u003e\n\u003cli\u003eBlocking attacks, spam, illegal content generation, and service disruptions\u003c/li\u003e\n\u003cli\u003ePreventing a single user from consuming excessive resources\u003c/li\u003e\n\u003cli\u003eApplying temporary speed limits or queues during service incidents\u003c/li\u003e\n\u003cli\u003eAllocating resources differently between high-cost functions and ordinary text functions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#user-prompts-and-privacy-issues\" class=\"anchor\" id=\"user-prompts-and-privacy-issues\"\u003e\u003c/a\u003eUser Prompts and Privacy Issues\u003c/h2\u003e\n\u003cp\u003eAI 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.\u003c/p\u003e\n\u003cp\u003eIf the use of processed user prompts for service improvements or revenue models is being considered, the following details must be disclosed first.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem to check\u003c/th\u003e\n\u003cth\u003eExplanation required\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eScope of collection\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eWhat is stored among prompts, attachments, answers, and access logs?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003ePurpose of use\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eIs it used for service delivery, safety reviews, model training, statistics, or commercial analysis?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eLegal basis and choice\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eHow are mandatory processing and optional training or analysis distinguished?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eRetention period\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eWhen are the original and processed data deleted, respectively?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eProvision to third parties\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eWhat data is transferred to participating companies or public institutions?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eDe-identification measures\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eHow is the risk of re-identification assessed and reduced?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eExclusion from model training\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eCan users exclude their conversations from training?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eDeletion and access\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eHow can users review and delete stored conversations?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to check\"\u003eTransfer to external models\u003c/td\u003e\n\u003ctd data-label=\"Explanation required\"\u003eAre any requests sent to foreign models or overseas servers?\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eRemoving 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.\u003c/p\u003e\n\u003cp\u003eData 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.\u003c/p\u003e\n\u003cp\u003eIt 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#comparison-with-chatgpt-gemini-and-claude\" class=\"anchor\" id=\"comparison-with-chatgpt-gemini-and-claude\"\u003e\u003c/a\u003eComparison with ChatGPT, Gemini, and Claude\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComparison item\u003c/th\u003e\n\u003cth\u003eAI for All\u003c/th\u003e\n\u003cth\u003eFree ChatGPT, Gemini, and Claude services\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003eCurrent status\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eOperator bidding and implementation planning stage\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eAlready available to general users\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003ePricing policy\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003ePresented with the goal of being free and unlimited for all citizens\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eFree tiers are available, but model, feature, and usage limits vary by service\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003eMain models\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eRequirement for at least 70% domestic models, including an independent foundation model\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eCentered on models developed and operated respectively by OpenAI, Google, and Anthropic\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003ePublic-service integration\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eOne of the core goals\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eFocused on general conversation and productivity functions, with limited integration into domestic administrative services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003eInfrastructure\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003ePlan to use 512 government-supported B200 GPUs\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eUses each company’s global cloud and AI infrastructure\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003ePrivacy rules\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eOfficial policy must be reviewed after the operator is selected\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eVary according to each company’s terms, region, account, and plan\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003ePerformance verification\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eActual measurements possible after the beta launch\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eAvailable through public services, but performance differs by model and plan\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison item\"\u003eResponsibility structure\u003c/td\u003e\n\u003ctd data-label=\"AI for All\"\u003eRoles of the government, operator, and connected public institutions must be distinguished\u003c/td\u003e\n\u003ctd data-label=\"Free ChatGPT, Gemini, and Claude services\"\u003eGoverned by the service company’s terms and applicable laws\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-should-performance-and-responsibility-be-verified\" class=\"anchor\" id=\"how-should-performance-and-responsibility-be-verified\"\u003e\u003c/a\u003eHow Should Performance and Responsibility Be Verified?\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#performance-indicators\" class=\"anchor\" id=\"performance-indicators\"\u003e\u003c/a\u003ePerformance Indicators\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eAccuracy of Korean-language factual queries and long-document comprehension\u003c/li\u003e\n\u003cli\u003eSource citations and up-to-dateness of legal and policy information\u003c/li\u003e\n\u003cli\u003eRate of hallucinations that generate content without supporting evidence\u003c/li\u003e\n\u003cli\u003eAverage response time and success rate during peak periods\u003c/li\u003e\n\u003cli\u003eDowntime, recovery speed, and whether data was lost\u003c/li\u003e\n\u003cli\u003eAccessibility for people with disabilities and usability on mobile devices and low-speed networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#public-service-safety-indicators\" class=\"anchor\" id=\"public-service-safety-indicators\"\u003e\u003c/a\u003ePublic-Service Safety Indicators\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eClear distinction between informational guidance and decisions with legal effect\u003c/li\u003e\n\u003cli\u003eProcedures for reporting and correcting incorrect answers\u003c/li\u003e\n\u003cli\u003eA function for transferring users to the responsible institution or a human counselor\u003c/li\u003e\n\u003cli\u003eChange history when models or data are modified\u003c/li\u003e\n\u003cli\u003eChecks for discriminatory errors based on gender, age, region, disability, and other factors\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#responsibility-structure\" class=\"anchor\" id=\"responsibility-structure\"\u003e\u003c/a\u003eResponsibility Structure\u003c/h3\u003e\n\u003cp\u003eErrors 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#key-questions-to-check-before-and-after-launch\" class=\"anchor\" id=\"key-questions-to-check-before-and-after-launch\"\u003e\u003c/a\u003eKey Questions to Check Before and After Launch\u003c/h2\u003e\n\u003cp\u003eFor AI for All to become public infrastructure that is meaningfully different from existing free chatbots, it must be able to answer the following questions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIn what units is the 70% domestic-model share calculated, and who verifies it?\u003c/li\u003e\n\u003cli\u003eUnder what conditions are questions sent to foreign models, and are they transferred overseas?\u003c/li\u003e\n\u003cli\u003eWhat fair-use standards apply to free and unlimited use?\u003c/li\u003e\n\u003cli\u003eWhere and for how long are original prompts stored?\u003c/li\u003e\n\u003cli\u003eCan users opt out of model training or commercial analysis?\u003c/li\u003e\n\u003cli\u003eWhat funding sources will cover GPUs and operating expenses after 2027?\u003c/li\u003e\n\u003cli\u003eWhat can and cannot public AI agents do?\u003c/li\u003e\n\u003cli\u003eWho is responsible when incorrect administrative guidance or a personal-data breach occurs?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUltimately, 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.\u003c/p\u003e\n","tags":["Personal data","Generative AI","AI for Everyone","Homegrown AI","Public AI"],"faqs":[{"question":"Is AI for All available for immediate use now?","answer":"No. As of August 3, 2026, it is at the stage of soliciting operators. The publicly announced target schedule is to select an operator in August, launch a beta service at the end of September, and officially launch by the end of 2026, though the actual schedule may change."},{"question":"Does AI for All really have no usage limits whatsoever?","answer":"Free and unlimited access is a policy goal intended to eliminate the burden of costs and limits on the number of questions for general users. However, fair-use restrictions or temporary rate limits may be necessary to prevent automated bulk requests, attacks, spam, and excessive resource consumption, and the specific criteria should be checked in the official terms of service."},{"question":"Does AI for All use only domestic models?","answer":"The publicly announced requirements are that domestic AI models account for at least 70% of usage and that a proprietary foundation model be included. This does not mean that no foreign models will be used, and further clarification is needed on which unit, such as the number of questions or tokens, will be used to calculate the 70%."},{"question":"How many people can use the service simultaneously with 512 B200 GPUs?","answer":"The number of concurrent users cannot be determined from the number of GPUs alone. Actual throughput varies significantly depending on the model size, response length, quantization, batch processing, cache, image features, and reserve capacity, so measurement results from the beta service are needed."},{"question":"Does AI for All perform better than ChatGPT, Gemini, and Claude?","answer":"It is too early to tell. Since AI for All is still in the pre-development stage, it must be compared under the same conditions during the beta service using the same set of questions, accuracy of up-to-date information, Korean-language processing, response speed, and hallucination rate."},{"question":"Will conversations entered into AI for All be used to train models?","answer":"This cannot be stated with certainty until the operator and official privacy policy are finalized. At launch, users should check the scope of prompt storage, purposes of training and analysis, retention period, provision to third parties, and methods for opting out of training and requesting deletion."},{"question":"Are responses from the public AI agent official government decisions?","answer":"General guidance provided by AI does not automatically constitute an official decision by an administrative agency. Legally effective dispositions or eligibility determinations require separate authority and procedures, so the service must clearly distinguish between guidance, application assistance, and actual administrative processing."},{"question":"Can it continue to operate for free after 2027?","answer":"Whether it can remain free over the long term depends on its funding structure after GPU support ends. The methods used to cover costs, such as government funding, contracts with public institutions, enterprise services, or APIs, as well as whether prompt data will be used commercially, must be disclosed transparently."}],"sources":[{"url":"https://msit.go.kr/eng/bbs/view.do?bbsSeqNo=42\u0026mId=4\u0026mPid=2\u0026nttSeqNo=1285\u0026sCode=eng","title":"Ministry of Science and ICT announcement regarding the AI for Everyone Project","type":"data_point"},{"url":"https://www.korea.kr/news/policyNewsView.do?newsId=148968693\u0026pWise=sub\u0026pWiseSub=R5","title":"Korea Policy Briefing policy news on the AI for Everyone Project","type":"data_point"},{"url":"https://www.yna.co.kr/view/AKR20260713108901017","title":"Yonhap News Agency coverage of the AI for Everyone Project","type":"source"}],"images":[{"id":446,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NTI2OCwicHVyIjoiYmxvYl9pZCJ9fQ==--09e349af3b98c333427cf970dd48d95bcd11b120/ai-fa2b6553.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"시민들이 정부 청사와 보안 금고, 서비스 아이콘 및 클라우드 AI 챗봇을 바라보는 일러스트","caption":"정부 주도 AI 서비스의 공공성, 보안, 다양한 활용 분야를 상징적으로 보여준다.","description":null},"en":{"alt":"Citizens facing a government building, secure vault, service icons, and cloud-based AI chatbots","caption":"The illustration symbolizes the public services, security, and varied uses of a government-led AI platform.","description":null},"ja":{"alt":"市民が政府庁舎や金庫、サービスアイコン、クラウド型AIチャットボットを見つめるイラスト","caption":"政府主導AIの公共性や安全性、幅広い活用分野を象徴的に表している。","description":null},"es":{"alt":"Ciudadanos ante un edificio público, una caja fuerte, iconos de servicios y chatbots de IA en la nube","caption":"La ilustración simboliza los servicios públicos, la seguridad y los diversos usos de una IA gubernamental.","description":null},"id":{"alt":"Warga menghadap gedung pemerintah, brankas, ikon layanan, dan chatbot AI berbasis cloud","caption":"Ilustrasi ini melambangkan layanan publik, keamanan, dan beragam penggunaan AI yang dipimpin pemerintah.","description":null},"pt":{"alt":"Cidadãos diante de prédio público, cofre, ícones de serviços e chatbots de IA na nuvem","caption":"A ilustração simboliza os serviços públicos, a segurança e os diversos usos de uma IA governamental.","description":null},"zh-hant":{"alt":"民眾望向政府建築、保險庫、服務圖示與雲端AI聊天機器人的插畫","caption":"插畫象徵政府主導AI服務的公共性、安全性與多元應用。","description":null},"de":{"alt":"Menschen vor Regierungsgebäude, Tresor, Dienstsymbolen und cloudbasierten KI-Chatbots","caption":"Die Illustration steht für öffentliche Dienste, Sicherheit und vielfältige Einsatzfelder einer staatlichen KI.","description":null}}},{"id":447,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NTI3NCwicHVyIjoiYmxvYl9pZCJ9fQ==--c60624924e6c8e4db50691d6bbbdda6178aeebaa/ai-307b2c1b.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"서버와 AI 신경망, 개인정보 보호 방패, 정부 청사, 단계별 출시 과정을 그린 일러스트","caption":"정부 AI 서비스의 보안, 공공 인프라, 단계적 출시를 상징적으로 보여준다.","description":null},"en":{"alt":"Servers, an AI network, a privacy shield, a government building, and a staged rollout","caption":"The illustration links a government AI service with security, public infrastructure, and phased deployment.","description":null},"ja":{"alt":"サーバー、AIネットワーク、個人情報保護の盾、政府庁舎、段階的な展開を描いた図","caption":"政府AIサービスの安全性、公共基盤、段階的な提供開始を象徴している。","description":null},"es":{"alt":"Servidores, red de IA, escudo de privacidad, edificio público y fases de lanzamiento","caption":"La ilustración relaciona la IA pública con la seguridad, la infraestructura y un despliegue gradual.","description":null},"id":{"alt":"Server, jaringan AI, perisai privasi, gedung pemerintah, dan tahapan peluncuran","caption":"Ilustrasi ini mengaitkan layanan AI pemerintah dengan keamanan, infrastruktur publik, dan peluncuran bertahap.","description":null},"pt":{"alt":"Servidores, rede de IA, escudo de privacidade, prédio público e etapas de lançamento","caption":"A ilustração associa a IA governamental à segurança, à infraestrutura pública e ao lançamento gradual.","description":null},"zh-hant":{"alt":"伺服器、AI神經網路、隱私防護盾、政府大樓與分階段推出流程","caption":"插圖呈現政府AI服務的資安防護、公共基礎設施與分階段上線。","description":null},"de":{"alt":"Server, KI-Netzwerk, Datenschutzschild, Regierungsgebäude und stufenweise Einführung","caption":"Die Illustration verbindet staatliche KI mit Sicherheit, öffentlicher Infrastruktur und schrittweiser Einführung.","description":null}}}],"published_at":"2026-08-03T16:45:50+09:00","updated_at":"2026-08-03T16:45:50+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/korea-ai-for-all-vs-free-chatbots"}