{"content_id":"9wku4zj6qp","slug":"naver-vs-kakao-ai-strategy-maus-monetization","locale":"en","schema_type":"Report","category":"trends","category_name":"Trends","title":"Comparing Naver and Kakao's AI Strategies: Monetization and Investment Efficiency Matter More Than MAU","summary":"Reports that Google app's monthly active users have surpassed Naver's are not enough to determine the winner among Korean platforms. Whether Naver's full-stack AI or Kakao's service-focused AI strategy has the advantage must be assessed by considering usage frequency, transaction conversion, inference costs, company revenue, and capital efficiency together.","sponsorship_disclosure":null,"affiliate_disclosure":null,"commerce_disclosure":null,"author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["MAU only shows the number of people who use an app at least once a month; it does not directly explain usage frequency or revenue contribution.","Naver is pursuing both technological control and the enterprise market through an integrated strategy connecting its own models, cloud, data centers, and services.","Rather than completely abandoning in-house technology development, Kakao is moving toward combining external models from OpenAI and others with internal models while focusing on KakaoTalk-based services.","The winner between the two strategies should be determined by AI revenue, transaction conversion rates, inference costs per user, and return on capital rather than model performance.","A company's long-term strategy and prospects should not be judged solely by whether it participates in public AI projects or by the ranking of a single app."],"content_markdown":"Reports that the Google app has overtaken the Naver app in monthly active users in South Korea are highly symbolic. However, app user numbers alone do not directly indicate search competitiveness, platform loyalty, advertising revenue, or the success of an AI business.\n\nNaver and Kakao’s AI strategies are also overstated when reduced to a simple choice between “proprietary models versus external models.” Naver is expanding the scope of its control from models to cloud infrastructure and services, while Kakao is focusing on combining powerful external models with its own technology to enable users to complete real actions within KakaoTalk.\n\n## How to Interpret Reports of the Google App Taking the Lead in MAU\n\nMAU is the number of unique users who use a service at least once during a one-month measurement period. It is useful for understanding reach, but it does not show how often a user visited, what the user did, or whether the user generated revenue.\n\n| Metric | Description | What to examine in platform analysis |\n|---|---|---|\n| MAU | Number of unique users active during a month | How broadly the service reaches |\n| DAU | Number of unique users active during a day | Whether it is used repeatedly as part of daily life |\n| DAU/MAU | Average daily share of monthly users who are active | Whether habitual use and user loyalty are high |\n| Number of sessions | Number of times the app was launched during a given period | Whether repeat visits occur |\n| Time spent | Time spent in the app or service | Whether content consumption is deep |\n| Conversion rate | Share of searches or impressions that lead to reservations, purchases, inquiries, and other actions | Whether usage translates into economic value |\n| Revenue per user | Average revenue generated by one user | How efficiently traffic is monetized |\n\nApp statistics also vary depending on the research firm and aggregation method. Factors to check include the coverage of Android and iOS, the treatment of preinstalled apps, criteria for excluding background activity, sample adjustment methods, and whether multiple apps from the same company are aggregated. The Google app can serve multiple entry purposes beyond search, including voice search, image search, recommended content, and connections to Gemini.\n\nAccordingly, “the Google app ranks No. 1 in MAU” can be read as a sign that Google’s mobile touchpoints have expanded, but it is not evidence that Naver’s search, advertising, and commerce businesses have immediately fallen behind. Multiple quarters should be tracked using the same criteria, while DAU, usage time, search share, transaction conversion, and revenue should be compared together.\n\n## Why Search Intent Matters More Than Search Share\n\nNot all searches have the same value. Fact-checking or document summarization can be completed quickly through general-purpose search and generative AI services such as Google, ChatGPT, and Gemini. By contrast, searches closer to action—such as comparing products immediately before a purchase, finding local stores, or checking reservation availability—favor platforms that connect reviews, prices, maps, and payment systems.\n\nWhat is regarded as Naver’s strength is not merely the volume of Korean-language documents. The key is its structure connecting user discovery and transactions through services such as blogs, cafes, maps, Place, shopping, and reservations. Information about local businesses and user reviews accumulated over long periods may be difficult for general-purpose AI to replace immediately at the same level.\n\nHowever, these assets should not be viewed as a permanent defensive barrier.\n\n- If review credibility declines or promotional content increases, search satisfaction will fall even if the volume of data is large.\n- If generative AI clearly cites sources while comparing information from multiple sites, the need to search within portals may decline.\n- External models can also strengthen local data through partnerships, licensing, the public web, and information provided by businesses.\n- If AI answers resolve questions directly on the search results page, traffic moving to content pages and advertisements may decrease.\n\nWhat Naver must protect is not simply search volume, but reliable, up-to-date information and a complete user journey that leads through to reservations and purchases.\n\n## Why Naver’s Corporate Value Does Not Rise on AI Investment Alone\n\nStock-price movements cannot be explained by a single specific cause. Interest rates, market risk appetite, earnings forecasts, regulation, competition, and capital allocation are reflected simultaneously. However, the questions investors seek to answer when a mature platform begins large-scale AI investment are relatively clear.\n\n1. Can it maintain the growth rates of its existing search advertising and commerce businesses?\n2. How much will increases in GPU, data center, and R\u0026D expenses reduce operating margins?\n3. Can AI search generate new revenue without cannibalizing existing advertising products?\n4. Will enterprise AI and cloud revenue grow enough to recoup investment costs?\n5. Is there an appropriate balance between investment and shareholder returns such as share cancellations and dividends?\n\nNaver can be compared to a large retailer or an aircraft carrier changing course, but an analogy does not prove causation. Naver can repeatedly sell intangible data and software, whereas retailers have a high proportion of inventory and store costs. The core of the comparison should be limited to their shared position in “a transition period requiring investment in new infrastructure while maintaining existing cash-generating businesses.”\n\nIn the market, visibility into the path to recouping investment is more important than the scale of the investment. Even if AI features increase user numbers, economic performance may remain weak if inference costs rise faster than revenue.\n\n## Comparison of Naver and Kakao’s AI Strategies\n\nThe two companies have made different choices because their platforms have different starting points and revenue structures.\n\n| Comparison category | Naver | Kakao |\n|---|---|---|\n| Core assets | Search, content, commerce, maps, cloud | KakaoTalk, messaging relationship network, and affiliated mobility, payment, and content services |\n| AI direction | Connect proprietary foundation models and infrastructure to services | Combine external models and proprietary technology for use in user services |\n| Representative assets | HyperCLOVA X, NAVER Cloud, data centers, Korean-language and local service data | KakaoTalk distribution network, Kanana family of technologies, collaboration with OpenAI, connections among affiliated services |\n| Priority market | Consumer services and enterprise and public-sector cloud and AI | KakaoTalk-centered consumer AI and action-oriented services |\n| Advantages | Control over data, models, and infrastructure; security and localization; ability to serve enterprise customers | Rapid service deployment, use of leading models, reduced initial infrastructure burden |\n| Key risks | Large capital expenditures, infrastructure burden from technological changes, delays in recouping investment | Dependence on external models, API costs and bargaining power, insufficient service differentiation |\n| Conditions for success | Growth in enterprise revenue and high infrastructure utilization | Increased KakaoTalk usage and conversion into actions such as reservations, purchases, and ride requests |\n\n### Naver: A Full-Stack Strategy That Expands the Scope of Control\n\nNaver connects proprietary models such as HyperCLOVA X with NAVER Cloud, data centers, and consumer services. This strategy is compelling to customers for whom data location, security, Korean-language and local context, and enterprise-specific customization are important. The sovereign AI market, in which countries and institutions place importance on their own language, regulations, and data control, is also a potential business area.\n\nHowever, “full-stack” does not mean independently producing every semiconductor and piece of software. Collaboration with external suppliers is necessary for GPUs, key components, and parts of the software ecosystem. Success depends less on whether each layer is internally owned than on how efficiently those layers are integrated and whether paying customers are secured.\n\n### Kakao: A Strategy Leveraging Its Distribution Network and End-to-End Action Completion\n\nKakao announced a strategic partnership with OpenAI and has pursued a direction in which AI goes beyond answering conversations within KakaoTalk to assisting users with tasks. If it can reduce the steps from messages to schedules, searches, reservations, purchases, and ride requests, Kakao’s network of connected services could become an advantage.\n\nHowever, it would be inaccurate to conclude that Kakao does not invest at all in its own LLM or AI infrastructure. Its approach is closer to a hybrid strategy that develops proprietary AI models and services while also using leading external models. Using external models can accelerate development, but Kakao must manage costs, personal data processing, incident response, and dependence on suppliers.\n\n## Metrics for Determining Which Strategy Has Won\n\nA business winner cannot be determined solely by model evaluation scores or app download numbers. The following metrics must be checked repeatedly over the same periods.\n\n| Area | Metrics to examine for Naver | Metrics to examine for Kakao |\n|---|---|---|\n| Users | AI search usage rate, repeat visits, search satisfaction | Users of AI features, changes in time spent and repeat visits on KakaoTalk |\n| Transactions | Shopping, Place, and reservation conversion rates | Completion rates for tasks such as reservations, payments, and taxi requests |\n| Revenue | AI advertising, cloud and enterprise AI revenue | AI paid services and transaction contributions to affiliated services |\n| Costs | GPU utilization, inference costs, data center investment efficiency | External model API costs, service cost per user |\n| Competitiveness | Enterprise customer retention, recurring revenue from overseas contracts | Differentiated features available only within KakaoTalk |\n| Capital efficiency | Operating cash flow relative to AI investment | Whether the lower investment burden leads to actual profit improvement |\n\nIn the short term, a strategy using external models may appear faster and less expensive. If usage increases significantly or supplier prices rise, the economics of proprietary infrastructure may improve. Conversely, if technology generations change rapidly and demand is uncertain, large-scale proprietary infrastructure becomes a burden. Neither approach is always superior; outcomes vary according to demand, utilization, and margins per service.\n\n## Why South Korean Software Competitiveness Cannot Be Explained Solely as a Stage Following Hardware Deployment\n\nThe explanation that GPU and data center investment expands first in the AI industry and application software grows afterward captures part of the trend. However, it is difficult to say that software monetization begins only after infrastructure has been built. Cloud, advertising technology, games, enterprise software, and mobile services are already competing simultaneously with hardware.\n\nThe following conditions should be distinguished when evaluating the software competitiveness of South Korean companies.\n\n- Can services proven in the domestic market be expanded into other languages and regulatory environments?\n- Can products be sold as repeatable subscription services rather than one-off deployments?\n- Can they build APIs and ecosystems for use by overseas developers and enterprises?\n- Do they have global sales, technical support, and partner systems?\n- Have they incorporated privacy, copyright, and AI safety requirements into product design?\n\nNaver’s overseas AI and cloud business may demonstrate potential, but individual partnership announcements must be distinguished from sustained revenue. Actual competitiveness can be evaluated when verifiable business metrics such as contract size, recurring revenue, customer retention, and profitability are disclosed.\n\n## How Should Participation in Public AI Projects Be Interpreted?\n\nPublic campaigns, free-use support programs, or consortium projects that use similar names such as “AI for All” may have different participation requirements depending on the announcement date and the administering institution. To assess a particular company’s participation or non-participation and its motives, official announcements, partner institutions, budgets, service periods, and procurement documents must first be reviewed.\n\nKakao’s participation may help expand user touchpoints, while Naver’s non-participation may indicate a focus on its independent business. However, unless a company has officially disclosed its motives, commercial benefits, technological direction, or promotional objectives cannot be asserted as facts. Participation in a public project is only one clue to strategy and does not prove model competitiveness or long-term profitability.\n\n## An Easily Overlooked Battleground: Control Over Agent Transactions\n\nAn issue easily missed in simple model comparisons is where an AI actually completes a transaction after understanding the user’s intent. If an AI agent goes beyond recommending a restaurant and handles the reservation and payment, the platform’s revenue structure changes.\n\nFour types of control are important in this process.\n\n1. **Intent data:** Can the platform determine what users are searching for and intend to purchase?\n2. **Transaction connections:** Can it access sellers, stores, mobility, and payment systems in real time?\n3. **User consent:** Does it transparently obtain and securely manage personal data and payment permissions?\n4. **Revenue distribution:** How is value divided among model providers, platforms, content creators, and sellers?\n\nNaver starts from the connection between search and commerce data, while Kakao starts from its conversational relationship network and access to daily-life services. The final contest is likely to be decided not by more fluent answers, but by the ability to complete trustworthy actions in fewer steps and generate sustainable profit in the process.\n\n## Three Possible Development Scenarios\n\n### When Naver’s Integrated Strategy Has the Advantage\n\nThis would occur if enterprises and public institutions prioritize data control and localization, Naver secures high utilization of its own infrastructure, and cloud and AI revenue grows faster than investment costs.\n\n### When Kakao’s Service Strategy Has the Advantage\n\nThis would occur if foundation models rapidly become commoditized and less expensive, while Kakao uses KakaoTalk’s distribution power to quickly spread action-oriented AI for reservations, purchases, ride requests, and similar tasks.\n\n### When the Two Strategies Converge\n\nNaver may also combine external models where necessary, while Kakao may operate cost- and security-sensitive functions internally, causing both to converge on hybrid structures. In the actual market, various combinations between purely proprietary development and complete external dependence are likely to compete.\n\n## Conclusion\n\nThe rise in the Google app’s MAU is a warning sign for Naver, but not a declaration of defeat. Naver’s key challenges are the quality of its search and transaction data, recouping its AI infrastructure investment, and expanding in the enterprise market. For Kakao, it is important to use external models while creating experiences possible only within KakaoTalk and controlling service costs and supplier dependence.\n\nThe two companies’ strategies are not a simple contest between a heavy approach and a light one. One prioritizes control and the long-term economics of infrastructure, while the other prioritizes speed of distribution and service conversion. The winner will not be the company ranked No. 1 in MAU, but the company that turns AI into recurring revenue and cash flow.","content_html":"\u003cp\u003eReports that the Google app has overtaken the Naver app in monthly active users in South Korea are highly symbolic. However, app user numbers alone do not directly indicate search competitiveness, platform loyalty, advertising revenue, or the success of an AI business.\u003c/p\u003e\n\u003cp\u003eNaver and Kakao’s AI strategies are also overstated when reduced to a simple choice between “proprietary models versus external models.” Naver is expanding the scope of its control from models to cloud infrastructure and services, while Kakao is focusing on combining powerful external models with its own technology to enable users to complete real actions within KakaoTalk.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-interpret-reports-of-the-google-app-taking-the-lead-in-mau\" class=\"anchor\" id=\"how-to-interpret-reports-of-the-google-app-taking-the-lead-in-mau\"\u003e\u003c/a\u003eHow to Interpret Reports of the Google App Taking the Lead in MAU\u003c/h2\u003e\n\u003cp\u003eMAU is the number of unique users who use a service at least once during a one-month measurement period. It is useful for understanding reach, but it does not show how often a user visited, what the user did, or whether the user generated revenue.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMetric\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eWhat to examine in platform analysis\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eMAU\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eNumber of unique users active during a month\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eHow broadly the service reaches\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eDAU\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eNumber of unique users active during a day\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eWhether it is used repeatedly as part of daily life\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eDAU/MAU\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eAverage daily share of monthly users who are active\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eWhether habitual use and user loyalty are high\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eNumber of sessions\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eNumber of times the app was launched during a given period\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eWhether repeat visits occur\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eTime spent\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eTime spent in the app or service\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eWhether content consumption is deep\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eConversion rate\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eShare of searches or impressions that lead to reservations, purchases, inquiries, and other actions\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eWhether usage translates into economic value\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Metric\"\u003eRevenue per user\u003c/td\u003e\n\u003ctd data-label=\"Description\"\u003eAverage revenue generated by one user\u003c/td\u003e\n\u003ctd data-label=\"What to examine in platform analysis\"\u003eHow efficiently traffic is monetized\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eApp statistics also vary depending on the research firm and aggregation method. Factors to check include the coverage of Android and iOS, the treatment of preinstalled apps, criteria for excluding background activity, sample adjustment methods, and whether multiple apps from the same company are aggregated. The Google app can serve multiple entry purposes beyond search, including voice search, image search, recommended content, and connections to Gemini.\u003c/p\u003e\n\u003cp\u003eAccordingly, “the Google app ranks No. 1 in MAU” can be read as a sign that Google’s mobile touchpoints have expanded, but it is not evidence that Naver’s search, advertising, and commerce businesses have immediately fallen behind. Multiple quarters should be tracked using the same criteria, while DAU, usage time, search share, transaction conversion, and revenue should be compared together.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#why-search-intent-matters-more-than-search-share\" class=\"anchor\" id=\"why-search-intent-matters-more-than-search-share\"\u003e\u003c/a\u003eWhy Search Intent Matters More Than Search Share\u003c/h2\u003e\n\u003cp\u003eNot all searches have the same value. Fact-checking or document summarization can be completed quickly through general-purpose search and generative AI services such as Google, ChatGPT, and Gemini. By contrast, searches closer to action—such as comparing products immediately before a purchase, finding local stores, or checking reservation availability—favor platforms that connect reviews, prices, maps, and payment systems.\u003c/p\u003e\n\u003cp\u003eWhat is regarded as Naver’s strength is not merely the volume of Korean-language documents. The key is its structure connecting user discovery and transactions through services such as blogs, cafes, maps, Place, shopping, and reservations. Information about local businesses and user reviews accumulated over long periods may be difficult for general-purpose AI to replace immediately at the same level.\u003c/p\u003e\n\u003cp\u003eHowever, these assets should not be viewed as a permanent defensive barrier.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf review credibility declines or promotional content increases, search satisfaction will fall even if the volume of data is large.\u003c/li\u003e\n\u003cli\u003eIf generative AI clearly cites sources while comparing information from multiple sites, the need to search within portals may decline.\u003c/li\u003e\n\u003cli\u003eExternal models can also strengthen local data through partnerships, licensing, the public web, and information provided by businesses.\u003c/li\u003e\n\u003cli\u003eIf AI answers resolve questions directly on the search results page, traffic moving to content pages and advertisements may decrease.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhat Naver must protect is not simply search volume, but reliable, up-to-date information and a complete user journey that leads through to reservations and purchases.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#why-navers-corporate-value-does-not-rise-on-ai-investment-alone\" class=\"anchor\" id=\"why-navers-corporate-value-does-not-rise-on-ai-investment-alone\"\u003e\u003c/a\u003eWhy Naver’s Corporate Value Does Not Rise on AI Investment Alone\u003c/h2\u003e\n\u003cp\u003eStock-price movements cannot be explained by a single specific cause. Interest rates, market risk appetite, earnings forecasts, regulation, competition, and capital allocation are reflected simultaneously. However, the questions investors seek to answer when a mature platform begins large-scale AI investment are relatively clear.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCan it maintain the growth rates of its existing search advertising and commerce businesses?\u003c/li\u003e\n\u003cli\u003eHow much will increases in GPU, data center, and R\u0026amp;D expenses reduce operating margins?\u003c/li\u003e\n\u003cli\u003eCan AI search generate new revenue without cannibalizing existing advertising products?\u003c/li\u003e\n\u003cli\u003eWill enterprise AI and cloud revenue grow enough to recoup investment costs?\u003c/li\u003e\n\u003cli\u003eIs there an appropriate balance between investment and shareholder returns such as share cancellations and dividends?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNaver can be compared to a large retailer or an aircraft carrier changing course, but an analogy does not prove causation. Naver can repeatedly sell intangible data and software, whereas retailers have a high proportion of inventory and store costs. The core of the comparison should be limited to their shared position in “a transition period requiring investment in new infrastructure while maintaining existing cash-generating businesses.”\u003c/p\u003e\n\u003cp\u003eIn the market, visibility into the path to recouping investment is more important than the scale of the investment. Even if AI features increase user numbers, economic performance may remain weak if inference costs rise faster than revenue.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#comparison-of-naver-and-kakaos-ai-strategies\" class=\"anchor\" id=\"comparison-of-naver-and-kakaos-ai-strategies\"\u003e\u003c/a\u003eComparison of Naver and Kakao’s AI Strategies\u003c/h2\u003e\n\u003cp\u003eThe two companies have made different choices because their platforms have different starting points and revenue structures.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComparison category\u003c/th\u003e\n\u003cth\u003eNaver\u003c/th\u003e\n\u003cth\u003eKakao\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eCore assets\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eSearch, content, commerce, maps, cloud\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eKakaoTalk, messaging relationship network, and affiliated mobility, payment, and content services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eAI direction\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eConnect proprietary foundation models and infrastructure to services\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eCombine external models and proprietary technology for use in user services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eRepresentative assets\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eHyperCLOVA X, NAVER Cloud, data centers, Korean-language and local service data\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eKakaoTalk distribution network, Kanana family of technologies, collaboration with OpenAI, connections among affiliated services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003ePriority market\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eConsumer services and enterprise and public-sector cloud and AI\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eKakaoTalk-centered consumer AI and action-oriented services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eAdvantages\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eControl over data, models, and infrastructure; security and localization; ability to serve enterprise customers\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eRapid service deployment, use of leading models, reduced initial infrastructure burden\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eKey risks\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eLarge capital expenditures, infrastructure burden from technological changes, delays in recouping investment\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eDependence on external models, API costs and bargaining power, insufficient service differentiation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison category\"\u003eConditions for success\u003c/td\u003e\n\u003ctd data-label=\"Naver\"\u003eGrowth in enterprise revenue and high infrastructure utilization\u003c/td\u003e\n\u003ctd data-label=\"Kakao\"\u003eIncreased KakaoTalk usage and conversion into actions such as reservations, purchases, and ride requests\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca href=\"#naver-a-full-stack-strategy-that-expands-the-scope-of-control\" class=\"anchor\" id=\"naver-a-full-stack-strategy-that-expands-the-scope-of-control\"\u003e\u003c/a\u003eNaver: A Full-Stack Strategy That Expands the Scope of Control\u003c/h3\u003e\n\u003cp\u003eNaver connects proprietary models such as HyperCLOVA X with NAVER Cloud, data centers, and consumer services. This strategy is compelling to customers for whom data location, security, Korean-language and local context, and enterprise-specific customization are important. The sovereign AI market, in which countries and institutions place importance on their own language, regulations, and data control, is also a potential business area.\u003c/p\u003e\n\u003cp\u003eHowever, “full-stack” does not mean independently producing every semiconductor and piece of software. Collaboration with external suppliers is necessary for GPUs, key components, and parts of the software ecosystem. Success depends less on whether each layer is internally owned than on how efficiently those layers are integrated and whether paying customers are secured.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#kakao-a-strategy-leveraging-its-distribution-network-and-end-to-end-action-completion\" class=\"anchor\" id=\"kakao-a-strategy-leveraging-its-distribution-network-and-end-to-end-action-completion\"\u003e\u003c/a\u003eKakao: A Strategy Leveraging Its Distribution Network and End-to-End Action Completion\u003c/h3\u003e\n\u003cp\u003eKakao announced a strategic partnership with OpenAI and has pursued a direction in which AI goes beyond answering conversations within KakaoTalk to assisting users with tasks. If it can reduce the steps from messages to schedules, searches, reservations, purchases, and ride requests, Kakao’s network of connected services could become an advantage.\u003c/p\u003e\n\u003cp\u003eHowever, it would be inaccurate to conclude that Kakao does not invest at all in its own LLM or AI infrastructure. Its approach is closer to a hybrid strategy that develops proprietary AI models and services while also using leading external models. Using external models can accelerate development, but Kakao must manage costs, personal data processing, incident response, and dependence on suppliers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#metrics-for-determining-which-strategy-has-won\" class=\"anchor\" id=\"metrics-for-determining-which-strategy-has-won\"\u003e\u003c/a\u003eMetrics for Determining Which Strategy Has Won\u003c/h2\u003e\n\u003cp\u003eA business winner cannot be determined solely by model evaluation scores or app download numbers. The following metrics must be checked repeatedly over the same periods.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eArea\u003c/th\u003e\n\u003cth\u003eMetrics to examine for Naver\u003c/th\u003e\n\u003cth\u003eMetrics to examine for Kakao\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eUsers\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eAI search usage rate, repeat visits, search satisfaction\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eUsers of AI features, changes in time spent and repeat visits on KakaoTalk\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eTransactions\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eShopping, Place, and reservation conversion rates\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eCompletion rates for tasks such as reservations, payments, and taxi requests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eRevenue\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eAI advertising, cloud and enterprise AI revenue\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eAI paid services and transaction contributions to affiliated services\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eCosts\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eGPU utilization, inference costs, data center investment efficiency\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eExternal model API costs, service cost per user\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eCompetitiveness\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eEnterprise customer retention, recurring revenue from overseas contracts\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eDifferentiated features available only within KakaoTalk\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Area\"\u003eCapital efficiency\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Naver\"\u003eOperating cash flow relative to AI investment\u003c/td\u003e\n\u003ctd data-label=\"Metrics to examine for Kakao\"\u003eWhether the lower investment burden leads to actual profit improvement\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eIn the short term, a strategy using external models may appear faster and less expensive. If usage increases significantly or supplier prices rise, the economics of proprietary infrastructure may improve. Conversely, if technology generations change rapidly and demand is uncertain, large-scale proprietary infrastructure becomes a burden. Neither approach is always superior; outcomes vary according to demand, utilization, and margins per service.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#why-south-korean-software-competitiveness-cannot-be-explained-solely-as-a-stage-following-hardware-deployment\" class=\"anchor\" id=\"why-south-korean-software-competitiveness-cannot-be-explained-solely-as-a-stage-following-hardware-deployment\"\u003e\u003c/a\u003eWhy South Korean Software Competitiveness Cannot Be Explained Solely as a Stage Following Hardware Deployment\u003c/h2\u003e\n\u003cp\u003eThe explanation that GPU and data center investment expands first in the AI industry and application software grows afterward captures part of the trend. However, it is difficult to say that software monetization begins only after infrastructure has been built. Cloud, advertising technology, games, enterprise software, and mobile services are already competing simultaneously with hardware.\u003c/p\u003e\n\u003cp\u003eThe following conditions should be distinguished when evaluating the software competitiveness of South Korean companies.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCan services proven in the domestic market be expanded into other languages and regulatory environments?\u003c/li\u003e\n\u003cli\u003eCan products be sold as repeatable subscription services rather than one-off deployments?\u003c/li\u003e\n\u003cli\u003eCan they build APIs and ecosystems for use by overseas developers and enterprises?\u003c/li\u003e\n\u003cli\u003eDo they have global sales, technical support, and partner systems?\u003c/li\u003e\n\u003cli\u003eHave they incorporated privacy, copyright, and AI safety requirements into product design?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNaver’s overseas AI and cloud business may demonstrate potential, but individual partnership announcements must be distinguished from sustained revenue. Actual competitiveness can be evaluated when verifiable business metrics such as contract size, recurring revenue, customer retention, and profitability are disclosed.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-should-participation-in-public-ai-projects-be-interpreted\" class=\"anchor\" id=\"how-should-participation-in-public-ai-projects-be-interpreted\"\u003e\u003c/a\u003eHow Should Participation in Public AI Projects Be Interpreted?\u003c/h2\u003e\n\u003cp\u003ePublic campaigns, free-use support programs, or consortium projects that use similar names such as “AI for All” may have different participation requirements depending on the announcement date and the administering institution. To assess a particular company’s participation or non-participation and its motives, official announcements, partner institutions, budgets, service periods, and procurement documents must first be reviewed.\u003c/p\u003e\n\u003cp\u003eKakao’s participation may help expand user touchpoints, while Naver’s non-participation may indicate a focus on its independent business. However, unless a company has officially disclosed its motives, commercial benefits, technological direction, or promotional objectives cannot be asserted as facts. Participation in a public project is only one clue to strategy and does not prove model competitiveness or long-term profitability.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#an-easily-overlooked-battleground-control-over-agent-transactions\" class=\"anchor\" id=\"an-easily-overlooked-battleground-control-over-agent-transactions\"\u003e\u003c/a\u003eAn Easily Overlooked Battleground: Control Over Agent Transactions\u003c/h2\u003e\n\u003cp\u003eAn issue easily missed in simple model comparisons is where an AI actually completes a transaction after understanding the user’s intent. If an AI agent goes beyond recommending a restaurant and handles the reservation and payment, the platform’s revenue structure changes.\u003c/p\u003e\n\u003cp\u003eFour types of control are important in this process.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eIntent data:\u003c/strong\u003e Can the platform determine what users are searching for and intend to purchase?\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTransaction connections:\u003c/strong\u003e Can it access sellers, stores, mobility, and payment systems in real time?\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUser consent:\u003c/strong\u003e Does it transparently obtain and securely manage personal data and payment permissions?\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRevenue distribution:\u003c/strong\u003e How is value divided among model providers, platforms, content creators, and sellers?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNaver starts from the connection between search and commerce data, while Kakao starts from its conversational relationship network and access to daily-life services. The final contest is likely to be decided not by more fluent answers, but by the ability to complete trustworthy actions in fewer steps and generate sustainable profit in the process.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#three-possible-development-scenarios\" class=\"anchor\" id=\"three-possible-development-scenarios\"\u003e\u003c/a\u003eThree Possible Development Scenarios\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#when-navers-integrated-strategy-has-the-advantage\" class=\"anchor\" id=\"when-navers-integrated-strategy-has-the-advantage\"\u003e\u003c/a\u003eWhen Naver’s Integrated Strategy Has the Advantage\u003c/h3\u003e\n\u003cp\u003eThis would occur if enterprises and public institutions prioritize data control and localization, Naver secures high utilization of its own infrastructure, and cloud and AI revenue grows faster than investment costs.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#when-kakaos-service-strategy-has-the-advantage\" class=\"anchor\" id=\"when-kakaos-service-strategy-has-the-advantage\"\u003e\u003c/a\u003eWhen Kakao’s Service Strategy Has the Advantage\u003c/h3\u003e\n\u003cp\u003eThis would occur if foundation models rapidly become commoditized and less expensive, while Kakao uses KakaoTalk’s distribution power to quickly spread action-oriented AI for reservations, purchases, ride requests, and similar tasks.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#when-the-two-strategies-converge\" class=\"anchor\" id=\"when-the-two-strategies-converge\"\u003e\u003c/a\u003eWhen the Two Strategies Converge\u003c/h3\u003e\n\u003cp\u003eNaver may also combine external models where necessary, while Kakao may operate cost- and security-sensitive functions internally, causing both to converge on hybrid structures. In the actual market, various combinations between purely proprietary development and complete external dependence are likely to compete.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#conclusion\" class=\"anchor\" id=\"conclusion\"\u003e\u003c/a\u003eConclusion\u003c/h2\u003e\n\u003cp\u003eThe rise in the Google app’s MAU is a warning sign for Naver, but not a declaration of defeat. Naver’s key challenges are the quality of its search and transaction data, recouping its AI infrastructure investment, and expanding in the enterprise market. For Kakao, it is important to use external models while creating experiences possible only within KakaoTalk and controlling service costs and supplier dependence.\u003c/p\u003e\n\u003cp\u003eThe two companies’ strategies are not a simple contest between a heavy approach and a light one. One prioritizes control and the long-term economics of infrastructure, while the other prioritizes speed of distribution and service conversion. The winner will not be the company ranked No. 1 in MAU, but the company that turns AI into recurring revenue and cash flow.\u003c/p\u003e\n","tags":["AI","Generative AI","AI Data Center","AI Agents","Technology strategy","Platform Business"],"faqs":[{"question":"If Google's app MAU is higher than Naver's, does that mean it has taken the top spot in South Korea's search market?","answer":"That conclusion cannot be drawn definitively. App MAU shows monthly reach, but the number of searches, search market share, repeat usage, time spent, and advertising and commerce revenue are separate metrics. The scope of each research firm's data collection and its samples by operating system should also be examined."},{"question":"Why is Naver considered strong in local search and product discovery?","answer":"Because it connects maps, Place, shopping, and reservations, as well as user-generated content from blogs and cafes. However, this advantage could weaken if the reliability of reviews and the freshness of information decline or if general-purpose AI improves its local data."},{"question":"What does Naver's full-stack AI strategy mean?","answer":"It is a strategy to expand the scope of operational control by connecting its own foundation models, cloud, data centers, and services such as search and commerce. It does not mean independently producing every semiconductor and technology; external GPUs and technology partnerships are also necessary."},{"question":"Has Kakao stopped developing its own AI models and become solely dependent on OpenAI?","answer":"It is inaccurate to view this as complete dependence on external providers. Kakao's approach is closer to a hybrid strategy of developing its own AI technology while combining it with external models, including OpenAI, to accelerate service launches and improve quality."},{"question":"Is using external AI models always cheaper than operating your own data center?","answer":"No. External APIs may be advantageous when initial demand is low or uncertain, but as usage grows, API call costs and provider dependence can become burdensome. It can also be difficult to recoup investments in proprietary infrastructure if utilization is low or model generations change rapidly."},{"question":"What metrics should be used to determine the winner between Naver's and Kakao's AI strategies?","answer":"AI-related recurring revenue, transaction conversion rates, user retention rates, inference cost per user, enterprise customer retention, data center utilization, and cash flow relative to investment should all be considered. It is difficult to assess business performance based solely on model evaluation scores or a single metric such as MAU."},{"question":"Why are AI agents important to the revenue of the two platforms?","answer":"Because if AI goes beyond providing answers and completes reservations, purchases, payments, and ride requests, it can generate transaction fees and advertising conversions. In this case, consent for the use of personal information, payment security, liability for errors, and integration with third-party businesses become key requirements."},{"question":"Can a company's strategy be assessed based on whether it participates in public-sector AI projects?","answer":"Participation can be taken into consideration, but it is not decisive evidence. The project purpose, budget, duration, and participation requirements in the official notice should be reviewed, and reasons for nonparticipation or commercial motivations not disclosed by the company should not be asserted as fact."}],"sources":[{"url":"https://support.google.com/analytics/answer/12253918?hl=ko","title":"Google Analytics User Metrics Guide","type":"source"},{"url":"https://www.navercorp.com/investment/earningsRelease","title":"NAVER Earnings Presentation Materials","type":"data_point"},{"url":"https://www.kakaocorp.com/ir/referenceRoom/earningsAnnouncement","title":"Kakao Earnings Presentation Materials","type":"data_point"},{"url":"https://openai.com/index/kakao/","title":"Kakao and OpenAI announce strategic collaboration","type":"source"},{"url":"https://clova.ai/hyperclova","title":"NAVER CLOVA HyperCLOVA X","type":"source"},{"url":"https://dart.fss.or.kr/","title":"Financial Supervisory Service Electronic Disclosure System DART","type":"source"}],"images":[{"id":920,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MTI0MTEsInB1ciI6ImJsb2JfaWQifX0=--998773982819b81514ec23a3337c5105ea00914a/ai-393096c7.webp","is_representative":true,"generation_method":"ai_photo","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"식당 카운터에서 분석 대시보드가 표시된 태블릿을 조작하는 여성","caption":"한 여성이 매장 카운터에서 각종 지표와 그래프가 담긴 대시보드를 확인하고 있다.","description":null},"en":{"alt":"Woman using a tablet displaying an analytics dashboard at a restaurant counter","caption":"A woman reviews performance metrics and charts on a tablet at a busy counter.","description":null},"ja":{"alt":"飲食店のカウンターで分析ダッシュボードを表示したタブレットを操作する女性","caption":"女性が混雑した店内でタブレット上の指標やグラフを確認している。","description":null},"es":{"alt":"Mujer usando una tableta con un panel de análisis en el mostrador de un restaurante","caption":"Una mujer revisa métricas y gráficos de rendimiento en una tableta junto al mostrador.","description":null},"id":{"alt":"Perempuan menggunakan tablet dengan dasbor analitik di konter restoran","caption":"Seorang perempuan meninjau metrik dan grafik kinerja pada tablet di konter yang ramai.","description":null},"pt":{"alt":"Mulher usando tablet com painel de análise no balcão de um restaurante","caption":"Uma mulher consulta métricas e gráficos de desempenho em um tablet junto ao balcão.","description":null},"zh-hant":{"alt":"女子在餐廳櫃檯操作顯示分析儀表板的平板電腦","caption":"一名女子在繁忙的櫃檯前查看平板上的績效指標與圖表。","description":null},"de":{"alt":"Frau bedient am Restauranttresen ein Tablet mit Analyse-Dashboard","caption":"Eine Frau prüft an einem belebten Tresen Leistungskennzahlen und Diagramme auf einem Tablet.","description":null}}},{"id":921,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MTI0MTcsInB1ciI6ImJsb2JfaWQifX0=--4476ee610b82e54d44efa0d37589d01277937f8b/ai-1123da3f.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"사용자 규모와 수익, AI 인프라를 저울과 대시보드로 비교한 인포그래픽","caption":"AI 서비스의 사용자 지표와 수익화·투자 효율을 함께 분석하는 장면이다.","description":null},"en":{"alt":"Infographic comparing user scale, revenue, and AI infrastructure with a balance and dashboard","caption":"The illustration shows user metrics, monetization, and investment efficiency being assessed together.","description":null},"ja":{"alt":"ユーザー規模、収益、AIインフラを天秤とダッシュボードで比較する図解","caption":"AIサービスの利用指標と収益化、投資効率を総合的に分析している。","description":null},"es":{"alt":"Infografía que compara usuarios, ingresos e infraestructura de IA con una balanza y paneles","caption":"La ilustración analiza conjuntamente las métricas de usuarios, la monetización y la eficiencia de inversión.","description":null},"id":{"alt":"Infografik perbandingan skala pengguna, pendapatan, dan infrastruktur AI dengan neraca dan dasbor","caption":"Ilustrasi ini menilai metrik pengguna, monetisasi, dan efisiensi investasi secara bersamaan.","description":null},"pt":{"alt":"Infográfico compara escala de usuários, receita e infraestrutura de IA com balança e painéis","caption":"A ilustração analisa em conjunto métricas de usuários, monetização e eficiência de investimento.","description":null},"zh-hant":{"alt":"以天平與儀表板比較用戶規模、收益及AI基礎設施的資訊圖","caption":"圖中呈現對AI服務用戶指標、商業變現與投資效率的綜合分析。","description":null},"de":{"alt":"Infografik vergleicht Nutzerumfang, Umsatz und KI-Infrastruktur mit Waage und Dashboards","caption":"Die Illustration bewertet Nutzerkennzahlen, Monetarisierung und Investitionseffizienz gemeinsam.","description":null}}}],"published_at":"2026-08-27T18:29:09+09:00","updated_at":"2026-08-27T18:29:09+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/naver-vs-kakao-ai-strategy-maus-monetization"}