The Digital Hangul Defect and its impact
on the investment climate of Korea's ICT-sector

1. Framework

There is no precedent in the history of technology for an architectural defect of an alphabetic language persisting for 40 years and affecting every dimension of IT. The case in which an alphabetic‑syllabic script was digitized as though it were ideographic is unique, and its economic consequences have no ready‑made template for evaluation.

This document does not attempt to express in monetary terms problems whose cost cannot be reliably assessed, because the associated expenditures have never been separately recorded or studied in direct relation to the architectural defect. This, however, does not imply that real economic consequences are absent; it means only that they cannot be reliably extracted from open sources. Such indicators include:

lost profits and missed opportunities;

adjacent industries and multiplier effects;

hidden custom solutions, situational workarounds, and patents not available in the public domain;

commercial secrets of companies;

direct associated expenses (full labor costs and provision of working conditions, electricity, server and computing capacity, consulting, intermediation and subcontracting, premises and additional services, taxes, value added) — even though these items factually account for 30–90% of expenditure in each category.

2. The Day‑One Architectural Defect

In 1987, when the KS C 5601 standard was created, Korean script was digitized not as an alphabetic system but as a collection of ready‑made syllables — effectively “ideographs.” This decision was cemented in Unicode in 1993.

Key comparison with China.

The Chinese language is objectively more complex: it contains thousands of ideographs and has no alphabetic nature. Its digitization required colossal effort, and Chinese engineers performed this work as well as was possible for an ideographic system. Moreover, two paths existed in China and both have survived to the present day: Pinyin, created by state structures and institutions, and the Wubi system created by the independent developer Wang Yongmin, which neither the Chinese government nor the global community ignored but adopted as one of the standard input methods.

The Korean language is a combinatorial writing system, created in 1443 as a direct replacement for Chinese characters and numbering 33 letters today. It is simpler than Chinese because its nature is combinatorial and alphabetic‑syllabic. Yet it was digitized in the same manner as Chinese — initial‑medial‑final — although in Chinese this is justified by the reading structure of ideographs, whereas in Korean it led to the same symbols being perceived differently, even though in print and logic they are used as letters.

3. Technological Consequences

This section examines only losses documented in open sources, analyzed through the lens of the Day‑One Architectural Defect. In the studies, patents, and rankings themselves this defect is often presented as a “complex environment” or “challenges” — abstract notions that do not reflect the real problem or its consequences. The cost estimates given are approximate, because truly objective data are not publicly available. The figures and analysis presented below do not deny the fact that mature high‑level solutions and libraries exist for the Korean segment. However, the low‑level defect has never been fully rectified by these methods; therefore the consequences of digitization continue to affect the Korean ICT sector to this day.

3.1. Normalization (NFC/NFD) and Search

Mechanism. Korean text is represented in Unicode in two incompatible forms: NFC (pre‑composed syllables) and NFD (sequences of jamo letters). Any operation — string comparison, full‑text search, sorting, indexing, regular expressions — requires prior normalization, i.e., reduction to a single form. Without it, even semantically identical words may fail to match. Standard engines (SQLite FTS, Elasticsearch, Solr) and regular‑expression libraries cannot work correctly with Korean without connecting external components such as ICU (International Components for Unicode).

No other Tier‑1 alphabetic language faces such a representation dichotomy. English, Vietnamese, Russian — all of them have a single encoding form, and search engines work natively. Chinese, being ideographic, also does not have the NFC/NFD problem. Arabic and Hindi require specialized but already established tools (arabic_reshaper, python‑bidi, Devanagari fonts), yet none of them faces the need for constant manual re‑configuration at the level of every company. Korean, on the other hand, is processed as an ideographic system while remaining an alphabet. It is precisely this contradiction that generates a whole class of costs.

Concrete manifestations:

Cross‑platform incompatibility. Operating systems use different normalization forms: macOS — NFD, Windows and Linux — NFC. A file saved on a Mac cannot be found by search on Windows without additional processing. Apple users have been reporting this problem since 2024 (Apple Discussions, 2024). Developers are forced to write code that accounts for the peculiarities of each OS.

Degradation of search engines. SQLite FTS cannot correctly search Korean text without ICU; open bug reports from 2026 confirm the relevance of the problem (SQLite FTS5 CJK, issue #4). Elasticsearch and Solr also require connecting external analyzers, which increases server load and complicates configuration.

Higher cost of database operations. Oracle (which dominates the Korean market with a share > 50%) and other DBMS use ICU for processing Korean. Every normalized query consumes 10–20% more CPU time. Licensing payments to foreign vendors, including Oracle and Microsoft, reach $5–10 billion/year, and part of these expenses falls precisely on normalization support.

Unique legacy problems of the Korean segment. The EUC‑KR and ActiveX standards, which emerged as a consequence of the 1987 architectural error, still require support in government and financial systems. The incompatibility of EUC‑KR with Unicode and the absence of basic ActiveX support in modern systems create tangible obstacles to unifying Korean ICT solutions and bringing them into line with global standards.

Integration. Unlike a standard Tier‑1 alphabetic language, where search functions without additional configuration, supporting Korean requires connecting ICU, writing wrappers, and testing on different platforms. This generates unique costs in both development and operation. Company size and duration of presence in the Korean market are factors that increase these costs. Small companies use off‑the‑shelf current tools, medium‑sized ones use customized versions of those tools, while large and international corporations are forced to create their own solutions that account for compatibility and legacy problems. A large company or international corporation working for export considers Korean as important, but only one of many languages. The need to unify the Korean segment with global language standards and internal support systems directly prevents the reduction of unit costs through volume.

Quantitative assessment. Additional computing resources for normalization consume 10–20% of CPU time in search and server systems.

3.2. Input Methods (IME)

Dozens of documented bugs in terminals and IDEs demonstrate that even the simplest task — solved for all alphabetic languages as a priority during digitization and subsequently eliminated globally through Unicode unification — remains an open question for the Korean segment. The volume of losses cannot be quantified, but it is a clear illustration that losses occur even where the problem is solved for far more complex languages.

Examples of bugs (GitHub):

Warp Terminal (#6891) — Korean input breaks after updates.

Claude Code (#12528) — after switching languages, characters decompose into jamo.

Claude Code (#18291) — jamo do not display until the syllable is completed.

Gemini CLI (#2638) — input lag, characters drop.

Ghostty (#5404), OpenCode (#303), BossTerm (#87) — dozens of open bugs.

Cursor — a user stated: “Fixing just this one issue alone would probably double my productivity” (Cursor Forum, 2025).

3.3. Artificial Intelligence: Tokenization and Data Quality

The architectural defect also affects Korea’s AI industry in two directions: economic (the quantity of tokens consumed and the effectiveness of final answers) and qualitative (dual data representation, difficulty of verification through standard benchmarks). These two directions are interconnected and form a vicious circle. Since the defect is low‑level, its solution can only be implemented at the same level of abstraction where the problem arises — namely, at the level of C++ drivers intended for AI models. Higher‑level fixes, which are currently chosen as the primary approach, will yield no economic or qualitative effect, or will not deliver improvements commensurate with the costs.

3.3.1. Economic Dimension

Korean text requires 1.6–2.59 times more tokens than English, and falls in roughly the same range as Chinese, when processing the same volume of information. However, the quality of answers and results from Korean models is significantly lower than those of American and Chinese models. Data in NFD and NFC formats are unrelated from a tokenization standpoint, and any upper‑level solutions will not change the actually established token‑handling format. Expanding the language sample does not remove the problem: in languages with a single representation form, such as English or Chinese, discrepancies are excluded by the architecture itself, and tokenization works predictably. As a result, other languages are economically more efficient in terms of costs and expected results.

Examples:

SK Telecom developed its own tokenizer for the A.X 4.0 model, requiring 33% fewer tokens than GPT‑4o. However, this did not lead to a qualitative improvement in answers. Spending fewer tokens on an incorrect answer means that the same or more tokens must be spent on the next iteration — and so on until a satisfactory result is achieved. Quantity and quality are interconnected: the problem cannot be solved in only one dimension.

The independent benchmark EntropyMath (December 2025, Sogang University / Deep Fountain) shows that the best Korean model, K‑EXAONE‑236B‑A23B, consumes ~25.2k tokens per task, whereas the global leader GPT‑5.2 (high) consumes only ~12.6k tokens (EntropyMath Leaderboard, 2026). A twofold difference — a direct consequence of the architectural defect.

3.3.2. Qualitative Dimension

Beyond direct costs, the architectural defect leads to a systematic decline in the quality of the data on which Korean AI models are trained and tested. This creates an illusion of progress and masks real lag. Furthermore, the defect has spawned an institutional trap in which corporations are forced to compete on knowingly worse terms.

3.3.2.1. KMMLU — the Main Korean Model Evaluation Benchmark

The original KMMLU benchmark (Korean Massive Multitask Language Understanding) contained 35,030 questions and was created as an analogue of the English MMLU. In 2025 it was discovered that 7.66% of the data contained errors: “leaked” answers, vague formulations, non‑existent references. Contamination — overlapping of test questions with training data — was also detected.

An attempt to fix it — KMMLU‑Redux. Out of 35,030 questions only 2,587 remained (less than 7.4% of the original volume). Then KMMLU‑Pro (2,822 questions based on professional examinations) was created.

All current versions are developed by LG AI Research — the very same company that creates the EXAONE model, which complicates the verification and assessment of EXAONE’s objectivity through their own benchmark.

LG EXAONE is LG’s flagship model. According to LG Display data, the total number of accumulated patents linked to the model exceeds 70,000. However, according to the documentation, its tokenizer operates at the level of individual UTF‑8 bytes. The documentation mentions complex multi‑level processing systems (MeCab, SuperBPE, vocabulary expansion to 150K), but it does not follow from it how these systems address the architectural defect of Hangul. The text states that the complexity of the environment and the challenges were taken into account during development. Consequently, it is impossible to draw conclusions about whether the problem has been solved.

3.3.2.2. Academic Approach: Publications, Patents, and Their Quality

Korea ranks 6th in the world by the number of AI publications (58,913 articles in 2020–2024) and 4th by high‑tech patents (IMD WDCR 2025).

Assessing the impact of publications and patents on the global AI industry through other indicators gives a more accurate picture:

Citations. While 6th by volume, Korea ranks only 22nd in citations per article and 26th in Field‑Weighted Citation Impact (Chosun Ilbo, 2025). Publications exist, but the interest of external researchers and scholars is lower than expected given the high volume of work and grant support.

Corporate research. Samsung, with 59 publications in top‑tier journals, occupies only 12th place, behind Alphabet (820), Microsoft (414), and Meta (385).

International collaboration. 33%, 43rd place, demonstrating low opportunities for joint work in AI. The experience of solving the problems of dual data representation and other complexities specific to the Korean segment turns Korean researchers into narrowly specialized experts.

3.3.3. Independent Benchmark: EntropyMath

EntropyMath is an evolutionary multi‑user evaluation system developed by a research group from Sogang University and Deep Fountain. The benchmark generates high‑entropy tasks specifically designed to “crack” LLMs and test their ability for logical thinking rather than simple memorization (Sogang University, 2025; entropymath.com).

A research group headed by Professor Kim Jong‑rak performed independent testing of 5 Korean and 5 foreign LLMs on CSAT problems and additional tasks.

Key results (as of 2026):

Global leaders (GPT‑5.2 high, Gemini‑3‑Pro‑Preview) dominate the top of the table.

Korean models (K‑EXAONE‑236B‑A23B, Solar‑Pro 2, HCX‑007, EXAONE‑4.0‑32B) occupy the lower half.

K‑EXAONE‑236B‑A23B consumes ~25.2k tokens per task — twice as many as GPT‑5.2 high (~12.6k). This is direct confirmation of the quantitative damage from the architectural defect.

4. Comparative Analysis

The analysis of IT solutions for different languages is drawn from open sources and discussions. In addition to direct comparison, a new variable is introduced — Ease of Integration (Out‑of‑the‑box Readiness) — an assessment of how easy it is to integrate a language into one’s product or system. The fewer external and additional solutions are required to provide basic functionality, the higher the score. English is set at 10 and serves as the reference point (the time since the issue was resolved for English, coupled with the number of add‑ons required; for English these are almost always zero).

Summary Table


Detailed justification (based on search data):

I. Ease of Integration

Korean (1/10): Absolute leader in the number of “add‑ons”. New patents continue to be registered in 2024‑2025 (Samsung, KEPCO, Naver). Special libraries exist (libhangul), which, however, have variations and adaptations for different systems and are not a standard or universally accepted solution. The dual representation (NFC/NFD) and the EUC‑KR legacy interact with each other only through the use of external tools.

Arabic (4/10): Requires a whole set of working tools: arabic_reshaper for correct display, python‑bidi for bidirectional text, special tokenizers, and mandatory CTL/RTL support everywhere.

Hindi (5/10): Requires special fonts with complex ligature support (Devanagari), special handling of grapheme clusters in rendering engines, and adapted NLP tools.

Chinese (5/10): Requires separate libraries for word segmentation and specific tokenizers for search (ICU/trigrams).

Vietnamese (8/10): After the adoption of Unicode the problem virtually disappeared. Only isolated tools like Unikey remain.

English (10/10): Zero add‑ons — everything is supported natively.

II. Input Methods (IME)

Korean (2/10): The most problematic sector from the perspective of bug trackers. claude‑code (#12528, #59426), warp (#6891), OpenCode, Ghostty.

Arabic (6/10): Requires manual tweaks in VS Code terminal, Matplotlib, and other environments because of RTL/Arabic shaping. The problem is known, but its solution is hindered by implementation complexity, not by an architectural defect.

Hindi (7/10): Mostly stable, but there are specific rendering bugs for composite characters (matras) in individual libraries such as PyMuPDF.

Chinese (7/10): Pinyin is stable, but input of rare characters still requires improvement.

Vietnamese (8/10): Rare bugs in terminals, not directly related to the language.

English (10/10): Benchmark of efficiency.

III. LLM Tokenization and API Cost

Korean (3/10): The problem is actively researched (“Korean Penalty”, 2024); dozens of patents are emerging.

Arabic (4/10): The situation is worse than Korean. The “token tax” reaches 230% (token tax ×3.3). Special tools like Tokenizer Lab are being created.

Hindi (5/10): Standard tokenizers are inefficient because of complex conjuncts. New works regularly appear (WWHO architecture).

Chinese (7/10): An area of active research, but there are fewer breakthrough problems.

Vietnamese (9/10): Diacritics slightly increase token consumption, but this is not a systemic problem.

English (10/10): Benchmark of efficiency.

IV. Search (FTS, Regexp, SQLite)

Korean (4/10): Dual representation (NFC/NFD) prevents setting up search that is both high‑quality and cheap in terms of clock cycles. Bug reports in SQLite FTS (issue #4, 2026) confirm the relevance of the problem.

Arabic (6/10): Requires handling of diacritics and special symbols, but the main solutions are known.

Hindi (6/10): Similar to Arabic, requires understanding of grapheme clusters.

Chinese (7/10): The main problem is word segmentation. Solved with ICU.

Vietnamese (9/10): No problems.

English (10/10): Benchmark of efficiency.

V. Embedded Systems and IoT

Korean (4/10): Storing 11,172 ready‑made syllables overflows memory, and 67 graphemes work either independently or require additional NFC‑NFD decomposition conditions when data processing is needed. Discussions of this problem are active on forums such as SEGGER.

Arabic (5/10): Mandatory RTL/CTL support on low‑power devices is a complex task.

Hindi (6/10): Complex ligatures require more memory than Latin script.

Chinese (6/10): Requires storing thousands of characters.

Vietnamese (9/10): Latin script + diacritics; few problems.

English (10/10): 26 letters in kilobytes of memory.

VI. E‑commerce

Korean (5/10): Difficulties with input on local platforms (Naver, Coupang) mean that finding the necessary goods and brands requires precision, while transliteration of foreign names and proper nouns remains an open question even from the standpoint of professional linguistics.

Arabic (6/10): RTL localization is a well‑established but more complex process.

Hindi (6/10): Problems are solved within the framework of India’s general multilingual support.

Chinese (7/10): Adaptation for local platforms is well‑tuned.

Vietnamese (8/10): Localization is stable.

English (10/10): Global standard with no problems.

Conclusion of the comparative analysis

The combination of the technological losses described above creates conditions that reduce the efficiency of any Korean IT product and solution, and consequently the return on investment.

5. Investment Landscape of Korea’s ICT Sector in 2026

The Korean economy possesses a substantial margin of safety owing to manufacturing and microelectronics, where investment attractiveness is steadily growing. The IMD World Competitiveness Ranking shows growth and stability: Korea as a whole consistently ranks around 20th place, which testifies to the high quality of business culture, entrepreneurial activity, and government regulation. However, this study considers only the ICT sector, which since 2025 has not demonstrated comparable stability and attractiveness, operating under the same macroeconomic conditions as the rest of the economy. The IMD World Digital Competitiveness Ranking placed Korea’s ICT at 15th according to its external indicators and methodology; its previous position was 6th (2023-2024), and in certain sub‑indicators the ranking had already reached 30th or below at the time of analysis.

For comparison: Japan consistently occupies 30th place or lower. From the viewpoint of investment logic, Korea appears to an external investor as an unstable ICT market for capital allocation. The IMD study assessed only external signs, yet even they show a decline.

The Korean ICT sector previously enjoyed a high level of trust in the international investment and venture market, but it is important to consider that investor behavior reliably follows signals from independent sources such as IMD. This means reduced activity, higher capital costs, and a shift of funds into more stable assets. These losses cannot be calculated; however, one can take an approximate conservative reference point that allows determining the extent of market losses.

The Korean ICT sector previously enjoyed strong confidence in international investment and venture capital markets. However, investor behavior inevitably hinges on external independent data, such as the IMD ranking. Reduced activity, rising capital costs, and capital flight to more stable assets — these losses cannot be precisely quantified, but a conservative estimate can be established to gauge the scale of damage to the market.

Method of Analysis

A precise causal link between specific technical problems in each area of Korean ICT and their direct economic consequences can only be established through a thorough analysis of internal information from market participants. For the analysis of the ICT sector as a whole, a general scientific approach was adopted, based on data from the IMD ranking, whose purpose is the objective comparison of countries using comparable criteria; the IMD methodology incorporates repeated verification, research, and observation. Mill's method was then applied to these data, on the assumption that the sum of technical causes and economic consequences has already been reflected in the external audit — the IMD ranking. The rule of inductive logic states: if the systems being compared are identical in all parameters but differ in only one factor and yield different results, then that factor is the root cause.

The Korean economy shows stability in the overall IMD ranking (top 20) over an extended period, with growth or minor fluctuations across most indicators, despite the international situation, economic conditions, and shifts in markets and production. The decline in the ICT subindex, however, reflects a downturn across all system-critical corporations and systems. The only isolated variable present in 100% of the ICT sector oriented toward the Korean market is the low-level defect in the digital representation of Hangul, which has become part of every ICT solution and, by the totality of its consequences, is the most likely cause of the ICT sector's decline in 2025.

Legal Context

Since 2024, two regulatory changes have been in force in South Korea that directly affect the personal liability of directors.

First, the Financial Services Commission (FSC) launched the Corporate Value-Up Program, aimed at enhancing shareholder value and transparency.

Second, the Commercial Act (Article 382-3) has been refined so that the fiduciary duty of directors to act in good faith and in the interest of the company is now interpreted as including the protection of the interests of all shareholders.

This means that top management bears the same costs as the Korean ICT sector as a whole. Its performance is assessed through the prism of external indicators and authoritative institutions. Thus, solving the problems of Hangul representation becomes one of the key priorities in a context where the influence of technological issues and the internal economics of corporations has already manifested on a macroeconomic scale.

Forecast

If IMD registers a further fall to 30th place or below, and external investors restructure their portfolios on the basis of such data, the ICT sector — even within Korea — will face at a minimum a decline to Japanese indicators (annual growth of 9% instead of the previous 11%). Similar declines occurred in other countries such as the United Kingdom or Thailand, but their declines were caused by infrastructure problems, government regulation, and external factors. Japan also has a complex representation of language in the digital world, which makes the estimate closer to reality.

In figures for 2026:

South Korea, nominal GDP: ~$1.9 trillion.

IT services market: $40.1 billion.

IT sector growth rate: 11.0% per year.

At 11% growth, the absolute market increase is $40.1 bn × 0.11 = $4.411 bn.
If the rate drops to 9%, the increase is $40.1 bn × 0.09 = $3.609 bn.
Minimum forgone profit due to the slowdown alone: $4.411 bn – $3.609 bn = $802 million per year.

This estimate does not include venture investments (2025: $2.03 bn), which are an important component of the Korean economy; they are difficult to forecast, but a similar reaction to that of standard investments can be expected with confidence. A 1% drop in investment ($20.3 million) would cut funding to 4–40 startups, depending on their stage of development.

The above calculation represents a minimum estimate of forgone profit based on the assumption that the IT sector’s growth rate falls to the Japanese level. The real figure may be higher if the ranking decline proves steeper or affects adjacent sectors.

The figures presented demonstrate that the consequences of the digital defect are already manifesting and becoming noticeable even under cursory analysis.

6. Fixing the Defect Without Replacing Legacy Systems

The SAMSEGI project is a low‑level mathematical solution for Korean language processing that eliminates the root architectural error without requiring the abandonment of existing infrastructure. Unlike high‑level workarounds, SAMSEGI restores the original combinatorial nature of Hangul at the level of elementary operations, ensuring the identity of any syllable’s representation in all contexts.

Principle of Operation

SAMSEGI is based on a system of 33 atomic numerical tags that uniquely correspond to the letters of Hangul. Each tag is equal to itself regardless of position in a syllable, which removes the fundamental Unicode problem where the same letter is encoded differently depending on its role (initial, medial, final). Any syllable is converted into a combination of tags in O(1) time without lookup tables or dynamic memory allocation. Mathematical compatibility guarantees: UNICODE (NFC/NFD) ≡ SAMSEGI_TAG, EUC‑KR/CP949 ≡ SAMSEGI_TAG — at any level of abstraction and with any legacy.

Experimental Verification of Efficiency

For an objective assessment, a direct comparison of the cycle cost of seven methods of syllable decomposition into graphemes was carried out (10 million random syllables, x86‑64 processor, MSVC /O2 compiler). The standard algorithmic Unicode method with branching is taken as the baseline (100%). Results:

Unicode algorithmic (baseline) — 100%

Unicode tables (branching tables) — 23%

Unicode flat (fully optimized table) — 14%

SAMSEGI SYMBOL‑TO‑MATH (pure math, unoptimized version) — 117%

SAMSEGI+NFD (grapheme‑optimized, compatible with Apple NFD and SIMD) — 80%

SAMSEGI+NFD flat (pre‑computed table) — 7%

SAMSEGI SYMBOL‑TO‑MATH flat (pre‑computed table) — 7%

SAMSEGI SYMBOL‑TO‑MATH is the reference implementation of SAMSEGI without optimizations, using division and remainder (expensive operations, ~20 cycles each) and a minimal number of branches. Despite a cycle cost higher than baseline Unicode, it does not depend on external tables and is ideally suited for working with the legacy EUC‑KR encoding, where 2,350 syllables are stored outside system organization. It has a SIMD‑capable version but without algorithmic acceleration. It is 17% slower than algorithmic Unicode.

SAMSEGI+NFD demonstrates how SAMSEGI integrates with the NFD grapheme representation: NFC syllable → three NFD graphemes → substitution with SAMSEGI grapheme tags → SAMSEGI letter tags via a single division, adapted for SIMD instructions. The key difference is the replacement of cumbersome branching with an equivalent of mathematical bracket expansion at the moment of swapping grapheme encodings for grapheme tags. Hence the number of operations inside SAMSEGI itself drops to a single division. It is 20% faster than algorithmic Unicode.

SAMSEGI+NFD flat and SAMSEGI SYMBOL‑TO‑MATH flat are fully ready low‑level products, achieving 7% of the cycle cost of baseline Unicode, or an acceleration of more than 14×. They are pre‑computed decomposition tables requiring neither division nor branching and can be directly embedded into any software product. The best result of the most optimized Unicode is 14%, which takes twice as many cycles as SAMSEGI+NFD flat and SAMSEGI SYMBOL‑TO‑MATH flat (7%).

The product includes:

All variants of integration functions and formats in C++98/11 for Windows and Apple (support for all relevant encodings NFC, NFD, EUC‑KR, optimization for SIMD, with and without tables, via union or exclusively at the product level) for any business and development tasks.

A complete Hangul map with a mathematical description of the algorithmic approach.

Instructions for input and output values.

The solution is low‑level. For the system it is a comprehensible mathematical function, adapted to specific code and business tasks, and correcting only the single element — the Hangul Architectural Defect.

Integration and Compatibility

SAMSEGI is compatible both at the product level and at the system level with UTF‑8/16 via union, and does not require changes to file systems or abandonment of legacy code. In its original form it covers about 70% of the industrial problems associated with the cost and quality of Hangul processing, but does not solve file‑system‑level issues, since file systems operate at a layer that precedes the union call. For operating systems and search engines working at the kernel level, SAMSEGI can become a correction mechanism, but only in an adapted form.

Economic Value

The price of the asset for a partner is determined during negotiations, taking into account factors important to the specific partner: company size, segment (pure ICT, adjacent or internal solutions), presence in the Korean market, the amount of EUC‑KR legacy, and the existence of proprietary high‑quality Hangul solutions requiring no improvement, provided they are proven to solve the problem and not require optimization. The client receives the full scope of the solution and after the contract is entitled to use it at its discretion within its own product or ecosystem, retaining the variable name samsegitag and the table name samsegitable for usage identification, without the right to transfer to third parties.

An elegant mathematical solution based on the combinatorial logic of the language is the only way to fix the defect and significantly reduce technical debt without drastic changes to systems, products, and legacy.