To discuss Donymony, we first need to understand the global search engine market and the e-commerce market. The primary tradable goods in e-commerce are physical items, mostly at the retail level. For buying and selling bonds or stocks, there is no direct connection to e-commerce; instead, financial software like HTS (Home Trading System) is used.

The global e-commerce revenue reached $2.6996 trillion in 2021, and it is projected to experience a rapid surge, reaching $3.2453 trillion by 2025. Additionally, when we consider worldwide stock trading volume, it refers not to market capitalization but to the actual total amount traded. Unfortunately, despite the global market capitalization being $103 trillion in June 2021, there is a lack of statistical data from relevant institutions or international organizations regarding the actual daily stock trading volume. Therefore, while the exact figures for daily and annual stock trading volume remain uncertain, we can make an approximate estimation.

As of April 12, 2022, the market capitalization of the KOSPI (Korea Composite Stock Price Index) stands at 2091 trillion won, with a daily trading volume of 10 trillion won. The market capitalization of the KOSDAQ (Korea Securities Dealers Automated Quotations) is 399 trillion won, with a daily trading volume of 7 trillion won. Considering that South Korea is classified as a middle-income developed country rather than an advanced one, let’s align our perspective with the Korean market. Combining the KOSPI and KOSDAQ market capitalizations, we get a total of 2491 trillion won, while the daily trading volume amounts to 17 trillion won. Assuming 250 trading days per year (considering business days), the annual trading volume reaches 4250 trillion won, which is twice the market capitalization. If we convert this to international trading figures, it implies trading approximately $206 trillion worth of stocks annually.

Donymony has seamlessly integrated e-commerce into a search site. It is designed to allow users to buy and sell items directly without the need for separate trading systems or traditional e-commerce platforms.

Google dominates the global search engine market. As of 2020, Google holds a staggering 91.54% of the worldwide search engine market share. Even when combined, the second-place contenders—Bing, Yahoo, and Baidu—account for just over 5%. Naver, on the other hand, has the lowest market share among the top 10, standing at a mere 0.07%. While it’s true that Baidu in China and Naver in the korean market have unique internal market structures different from other countries, globally, Google remains an absolute leader in the search engine market.

Since its launch in 1998, Google’s search engine has undergone countless updates, continuously refining its algorithms. In 2018 alone, it implemented a staggering 3,243 algorithm updates. As a result, Google currently provides the most comprehensive search environment among all existing search engines. Despite the expanding spectrum of evolving ad formats and social media services, some still harbor doubts about the search engine market. However, one thing remains clear: Google, as the dominant user in the market, relentlessly strives for improved search quality through ongoing research and experimentation, maintaining its overwhelming user trust.

The characteristic of Google and other search engines is that they datafy the content access frequency of existing users. When a new user enters a search query, the system analyzes it by comparing it with the data accumulated from the content access of existing users. Based on this comparison, the system effectively analyzes and detects the information that the new user is seeking, ranking it according to the access volume of web pages desired by previous users. In other words, it’s a system that leverages the legacy of content access frequency by existing users to evaluate and display rankings based on the sentences or words the current user is searching for.

However, in reality, most users may not be particularly interested in the search volume or legacy of existing searchers. For those who simply want to know what others have seen or what interests them, conventional search engines like Google and others can be useful. However, the primary concern for most search engine users, as well as most people, is likely to be money and their unstable and unpredictable future.

Moreover, Donymony combines search functionality with e-commerce features. It not only displays items related to the currently searched keywords but also evaluates their future value. If a customer invests, it calculates whether the value will rise or fall in the upcoming future, potentially resulting in either losses or gains. In essence, Donymony is a search site where commerce and predictive search capabilities intersect.

Amazon is arguably the most prominent e-commerce site. Users can buy and sell various items through Amazon. Donymony, on the other hand, has systematized the ability to directly buy and sell anything from search results, excluding these conventional e-commerce sites. Whether it’s retail, wholesale, or direct transactions with producers, Donymony enables users to take action regardless of the context.

Some people aim to implement AI politics by using the law as the basis and means, interpreting legal content through AI systems. However, the series of events carried out in PERT·CPM inherently include legal elements, so there is no need to separately interpret the law.

The question is whether to use the law as a means of AI politics, or to use PERT·CPM + NNAIS, which discusses actions within events themselves. Which is more advanced and efficient — the law, or PERT·CPM + NNAIS?

Using probabilistic and statistical methods, the four main areas of PERT — time, cost, personnel, and resources — are categorized. The sequence and direction are determined through infinite generation PERT·CPM (A.I WORK). Feedback is provided through NNAIS (A.I and HUMAN WORK).

Utilizing generative AI for PERT*CPM + NNAIS in politics. Infinite planning and scheduling through PERT*CPM, with feedback mechanisms from NNAIS to verify.

LLM2+ means the combination of LLM.1 (large language model) and LLM.2 (large logic model). A country with the third largest territory and population, based on the United States, can govern and control the world smoothly and strongly without additional costs through AI politics using PERT·CPM + NNAIS, supported by a military force of 500,000–1,000,000 civil servants and 1,000,000–2,000,000 soldiers.

A country with the third largest territory and the third largest population in the world, based on the United States, can govern and control the whole world smoothly and strongly without additional costs through AI politics using PERT*CPM + NNAIS (feedback on quality, process, safety, cost, design, specifications, etc.), supported by a military force consisting of 500,000 to 1,000,000 civil servants and 1,000,000 to 2,000,000 soldiers specially.