If you invest in American stocks, you have probably heard of Palantir at least once. It holds the title of being the most secretive company in the world, so many investors may not know what Palantir really does and invest without knowing. People think it looks cool even if they roughly search or watch videos. I also invested after a rough look and ended up cutting losses. However, today, I will take a very interesting look at why Palantir is receiving so much attention for its AI and how it can become the world's number one in AI field revenue. Hello, I'm the Tech Farmer. First of all, Palantir is a company that sells high-priced platforms like Apollo and Foundry that we are good at. Understanding each platform is good, but we understand the word that is the basis of all these platforms. That word is ontology.

In simple terms, it is organizing and analyzing the relationships between data and representing them in a form that computers can process. If you listen to the end of this video, I believe you will understand what it means. Let's take a car as an example. If there is an object called a car, we can express how fuel-efficient this car is and how many people can ride in it. Also, if there is an object called driving, we can define the time of departure and arrival, and if there is an object called a parking lot, it has characteristics such as when it was built and how many cars it can accommodate. In other words, organizing objects related to each other, such as cars, driving, and parking lots, into a hierarchical rule is called an ontology. It's about giving data limits and categorizing them. It may not be easy to understand right away, but if you keep watching, you will be able to understand. And ontology...

The collected data contains information that is not abstract but can be agreed upon by anyone. For example, the fuel efficiency of a car and the number of passengers are data that anyone would consider correct. I tried to use ChatGPT for YouTube video production in the past because it took more time to determine whether the ChatGPT responses were real or fake. This is because ChatGPT is not yet fully ontologized. The ontology seen earlier contains information that anyone would consider correct. There is not a single incorrect feature among its characteristics. Therefore, when asking a question, even if the correct answer is given, there may be subtly incorrect information inserted in between. For example, when I asked to be informed about who General Yi Sun-sin is, it is written at the end that even his tomb has not been found. When we see this, we still do not know who General Yi Sun-sin is.

It may seem that the tomb cannot be found, but in Asan City, Chungcheongnam-do, there is actually the tomb of General Yi Sun-sin. This means that there is some fake information mixed into the data of General Yi Sun-sin. However, currently, AI cannot distinguish between the real and fake parts. That is why ontology is important because it needs to define concepts clearly as actual existing words. Looking at ontology and the given data, if all 100 out of 100 people say it is right or wrong, it can be considered data. When abstract or virtual concepts are extracted and input into the computer, it is necessary to verify whether the data is correct before inputting. For example, when considering the statement Will your head become smarter through this training method?, will everyone say the same thing about how to make your head smarter? Also, when interpreting the meaning of being smart when young as a student and being smart after aging, are these two interpretations the same?

In the past, being good at studying was considered smart, but nowadays, people who speak well at work or earn money through investments can be seen as smart. For example, some may argue that not smoking can increase concentration and make a person smarter, but this claim has not been proven in any research papers. This is how fake information can spread. Without clear rules and ontology, we are more likely to fall into misinformation and speak incorrectly. It's like what we saw earlier, like a teacher. Palantir Ontology aims to correct these issues and build a digital twin. A digital twin is a representation of the real world transformed into digital form and implemented in the computer space. In other words, all data should be inside the computer. If there is no ontology, a digital twin cannot be built.

In the online world, fake information and real information will be mixed, so Palantir emphasizes the importance of ontology and is building a digital twin. This way, it can help deal with fake information, accurately understand the company's issues, and assist in making the right decisions. Let's take a look at an actual event at Tesla, for example. Recently, Elon Musk showed a scene of autonomous driving with the Beta 12 version live. Starting from this Beta version, autonomous driving is not done through code but by training AI with video data. In other words, it's not that the command go on a green light is inputted through code, but rather, the AI learns from numerous video data where people start on a green light and makes decisions on its own. From this live video, we can see points related to Palantir. This means that even with a vast amount of ordinary data,

It's saying that good quality data is needed because it doesn't help in the driving room, meaning that quality data needs to be filtered out. So, there is separate software for filtering quality data. In this video, Elon Musk is mentioning an interesting example. One of the slightly funny challenges we've had is that, since the car is being trained on what humans do, humans almost never fully stop at a stop sign. So when they get to a stop sign, humans actually almost never go to zero miles an hour. They may think they did, but usually, they are doing at least a few miles an hour coming up to a stop. Sometimes that bothers the regulators. Some quite insist that we go to a complete stop at stop signs. When we looked at the data, only 5% of the time do humans actually stop, even lower than that, 5%. Wow. So basically, people almost never fully stop at stop signs. In Korea, cars should come to a complete stop at bus stops, but generally, Americans do not stop at the stop sign. In Korea, when making a right turn, you should stop in front of the pedestrian crosswalk and then proceed, but most people just go. In other words, if you input AI to just enable autonomous driving.

Autonomous driving can stop at bus stop signs and avoid accidents. As mentioned earlier, autonomous driving must be done safely, but if it is not operated safely, fake data can be generated, so it is necessary to collect high-quality data that can be used at bus stops and train AI. They talk about curation. Data curation means selecting useful data. Ontology may be confusing. As mentioned earlier, to curate data, which is the process of acquiring data, you need to go through the ontology process that enriches the meaning of the data. In other words, even Elon Musk mentioned that ontology is difficult. No matter how excellent Tesla's AI technology is in the autonomous driving market, if a lot of cost and time are spent on ontology like this, the development of robotaxis and Tesla's vision may slow down. And Elon.

What if Musk is managing Palantir by forming a partnership to distinguish high-quality data? There are rumors that Tesla and Palantir have actually formed a partnership. Please subscribe and like. If you look now, you can see a domain with Tesla's name added to Palantir's domain. This is a domain created by Palantir. Just for your information, Palantir doesn't have a free version or a test version; you must sign a contract to provide the domain and Foundry software. Alternatively, it could simply be a test site created by Palantir employees. Nonetheless, when Elon Musk and Palantir CEO Peter Thiel heard about the PayPal Mafia, they could see how challenging the software developed by Palantir is. They could understand how difficult the software developed by Palantir is, considering the software Palantir is working on. The world's top autonomous driving company's AI supercomputer, Dojo.

Despite the difficulties, Palantir software can be seen to help customers select and give meaning to numerous data tailored to their goals, enabling effective decision-making by deriving conclusions. Another example is the ongoing war in Ukraine. How has Ukraine been able to continue the war against Russia, one of the world's most powerful military forces? It is because of Palantir. By comprehensively analyzing numerous satellite data, information on the Russian military, and the dynamics of the war so far, Palantir has been able to inform the Ukrainian military of possible locations of the Russian army. By attributing meaning and categorizing the numerous sporadic data that arise during the war, ultimately, it extracts the desired goal of the customer. In this case, the goal is precisely the location of the Russian army. It is similar to using a map pack in the game StarCraft.

Using this kind of technology to generate revenue, Palantir can be considered almost the only company doing so. However, Palantir had a problem with Ontology. It was difficult for customers to build it directly. Because customers need to input the exact purpose they want into the Palantir program, someone who knows the company or industry well needs to set up the Ontology. However, customers know what they want, but they have no idea how to Ontologize it in the Palantir program. Therefore, Palantir sends FDE developers to the customer companies until the customers can build the Palantir program themselves. The picture now is with an FDE dispatched to Doosan Infracore. The person in blue is the Palantir FDE. While staying at the company, they understand the flow of the industry.

At the same time, we explain how to build a customized ontology for the company and how to utilize software. Therefore, developers spend a long time at the company. This means that they have to customize each one for Palantir customers. Therefore, the biggest problem with this method is that it takes too long, making mass production difficult. Customers who initially signed contracts with Palantir cannot use Palantir programs immediately. It takes time to build them through dispatched developers. Even after the dispatched developers have built it, they continue to manage it due to maintenance issues. It usually took about six months. However, last year, Palantir made this announcement: In response to the advancement of AI, Palantir introduced the Palantir AI platform AIP, which supports customers to use large-scale language models along with existing machine learning technologies.

It was released. During the performance announcement in the third quarter of last year, the Palantir CEO said that by using AIP, the ontology construction that used to take six months with existing dispatched developers was completed in just a few days. Previously, because ontologies differed by industry, FDE had to be sent for customization one by one. However, by using AI to do the customization work, it is possible to complete the customization in a short period. In this way, Palantir can sell the program in large quantities. In fact, after Palantir introduced AIP in June last year, on August 28th, an analyst from Bank of America acknowledged Palantir's capabilities through various customers and analyzed that they are in a unique position to enable AI implementation. However, the current target price of institutions mostly shows a sell recommendation at $1. For Palantir, solving companies' problems in the ontology.

The reason why Palantir was able to quickly enter the market that solves problems is due to its complementarity. To do ontology, you need to analyze all the data of companies, which of course includes confidential information. However, Palantir gained recognition by handling such confidential information in a way that customers can trust. There are only three companies that have been granted Impact Level 6, the highest level of handling classified data by the U.S. Department of Defense, and Palantir, Microsoft, and Amazon are among them. The fact that they started by targeting U.S. government agencies, not private companies, shows how excellent their stability and complementarity are. However, is Palantir's software truly unique and exceptional? No matter how much they have been in the field for 20 years, the current market is growing rapidly in AI, so we can see how excellent Palantir's stability and complementarity are. Nevertheless, Palantir is dominant in the software industry and truly unique.

I also think that following Big Tech could pose a significant risk to Palantir's stock price. Along with Palantir, let's take a look at Microsoft and Amazon, both of which can handle the highest-level classified information from the U.S. Department of Defense. Microsoft recently provided a feature in its GPT-integrated CoPilot that enables quick decision-making. Furthermore, using CoPilot's AI library, it is said to promote more strategic, data-driven thinking and decision-making. At the 2022 re:Invent event, the Vice President of AWS Data and Machine Learning announced that they have added features that allow customers to make data-driven decisions easily. In other words, Big Tech companies have started to incorporate features that can help solve customer problems, as mentioned earlier in the case of Palantir Ontology. However, as seen before, even Tesla is curating data to solve problems but still faces many issues. Amazon and SpaceX also have similar challenges.

I think there will be problems. First of all, it is because it is impossible to distinguish good data as well as Palantir. However, recently, Meta is trying to advance in the AI market with a huge amount of money, such as purchasing 350,000 NVIDIA chips. Therefore, if Palantir creates a program that automatically ontologizes like Palantir AI feature, it could have a significant impact on Palantir's revenue. Currently, this is considered a dominant task in this field, but because big companies can easily catch up with money, it is essential to consider performance in investing in Palantir. The information I have explained so far may be somewhat subjective or abstract when it comes to investing in stocks, but performance data can be objectively viewed because it is represented by precise numbers. Therefore, in the next video, let's dissect all of Palantir's performance and analyze its future growth.

I have analyzed a company after a long time. I will upload the second video by analyzing it as quickly as possible. Have a great day. Thank you. Please note that this is not a recommended video, but my personal opinion. I have opened a Naver Premium Content channel where I share my personal stories, stock trading records, Korean stocks, real estate, and the process of actually making money that I couldn't cover on YouTube. Thank you for watching my videos so far. Please subscribe and like.

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