The heat of artificial intelligence seems to make people forget the sound of the "bubble" bursting

Introduction

Introduction

Looking back at history, betting on hot new topics can quickly bring your portfolio to disaster, and this AI theme is no exception.

While the valuation of leading companies is suspected of a bubble when an emerging industry develops, other small-scale companies are more risky. Due to the complexity and uncertainty of AI technology, it is difficult to conduct a reliable risk assessment.

Shares of chipmaker Nvidia have risen recently, with a market value of more than $1 trillion at one point as investors excited about demand for chips made by its so-called "generative artificial intelligence." As investors look for big winners from the latest popular themes, smaller AI-related stocks also rose sharply.

The development of artificial intelligence has affected the entire U.S. stock market, and against the backdrop of the Federal Reserve's continued interest rate hikes and rising U.S. Treasury yields, the tech-heavy Nasdaq has risen by about 25% this year, while the Dow Jones has risen by only 1%.

The widespread application of AI technology has brought higher profitability and competitive advantage to technology companies. Many tech companies are already applying AI technology to products and services, such as Google, Microsoft, and Apple. The investment and innovation of these companies in the field of artificial intelligence has made them outstanding in the market and has also contributed to the rise of the NASDAQ index.

Due to the wide range of applications of artificial intelligence technology and its relatively small impact on the economic outlook, technology stocks have become a target for investors in the face of a poor economic outlook in the United States.

The advent of ChatGPT has changed everyone's 2023

Recall that ChatGPT was launched to an unsuspecting public last November and surpassed 100 million users in two months, making it the fastest-growing consumer app ever. In addition to botched jokes, botched poetry, it has been used and even abused by students, lawyers, CEOs, and programmers. The first version of GPT-3 was introduced late last year.

GPT is a natural language processing technology developed by OpenAI that made its public debut on June 11, 2020. The technology employs a transformer architecture that uses large-scale pre-trained neural network models to produce high-quality, human-readable text output.

GPT-3 caused a huge stir because of its ability to generate real language that could not only answer questions, but also perform a variety of tasks such as dialogue, translation, and even simulate human thought processes through language. Compared with previous natural language processing technologies, the performance and performance of GPT-3 have been significantly improved, which has greatly promoted the development of artificial intelligence and natural language processing.

Before the advent of GPT-3, OpenAI had released several versions of GPT models and achieved remarkable results on various natural language processing tasks. However, these models have some limitations, such as the need for a large amount of data to train, difficulty in adapting to new domains and tasks, and potential bias. Therefore, OpenAI has improved on these problems and introduced a more powerful GPT-3 model.

The key innovation of the GPT-3 model is its size. According to OpenAI, the model was pre-trained using 175 billion parameters, making it the largest natural language processing model before. This enables GPT-3 to understand and generate more complex, abstract language constructs, with greater versatility and adaptability.

In addition to scale, GPT-3 also introduces some new technologies and improvements, such as dynamic control of output length, multi-mode input, zero-sample learning, etc., making the model more intelligent and flexible. For example, GPT-3 can automatically adjust the length of the answer according to the context, avoiding too short or too long answers; At the same time, it also supports a variety of input methods, including text, pictures, sound, etc., which can better adapt to various application scenarios.

Since GPT has sparked an AI craze, investors naturally want to find companies that will profit from what has been called the biggest technological development since the internet. But there are two problems here. First, whether AI has been overhyped. The second is the lack of liquidity, after all, the limited number of stocks such as AI has led to an abnormal surge in some stocks.

We remember that AI must have been overhyped before. In 2010, IBM's Watson system was invited to compete on the quiz show Jeopardy! This show is a question answering game that requires players to answer questions rather than simply memorize knowledge. Watson systems use natural language processing technology and machine learning algorithms to analyze and understand massive amounts of data and generate possible answers.

During the competition, Watson System competed with two former champion human contestants in three rounds of fierce competition. In the end, the Watson system beat a human contestant with a high score, becoming the first computer program in history to win the Jeopardy competition.

This event has attracted widespread attention and media coverage, and is considered a major breakthrough in the field of artificial intelligence. IBM has also introduced this technology to the commercial field and has carried out several commercial applications based on Watson. Over time, however, some industry observers began to question the real value and business prospects of the Watson system, and IBM's stock price fell by more than a third.

Borrowed from the 2000 dot-com bubble

There is nothing wrong with AI itself, the problem lies in unrealistic expectations.

Suffice it to say that the hype cycle can lead the public to expect too much from AI and think that it will solve all problems. However, in practice, AI technology also faces many challenges and limitations when applied. For example, in some fields, such as healthcare and finance, AI needs to comply with stricter regulations and standards to ensure its safety and reliability.

Let's start by pulling our thoughts back to the late nineties and early century, when high expectations and unrealistic valuations for the future of the Internet led to inflated stock prices and market values of Internet companies, which eventually triggered a massive economic crisis.

During this period, many internet companies raised money through IPOs and expanded their businesses at an alarming rate. However, these companies are often loss-making or never profitable, but their stock prices skyrocket in a short period of time. Many people are betting that Internet companies will be able to achieve high profits in the future and bring huge returns.

However, this speculation eventually led to the collapse of the market. When investors begin to realize that the companies they invest in simply cannot be profitable or have no business model at all, they start selling stocks, causing the stock price to fall. This situation spread rapidly, causing the market value of the entire Internet industry to shrink significantly, causing some companies to close down and many others to lose a lot of market value.

At that time, the stock prices of many Internet companies were overvalued, and some typical cases include:

• Microsoft's MSN was worth more than $50 billion when it launched its IPO in 1999. But by the time the dot-com bubble burst, its market value had fallen to less than $10 billion in 2002.
• Amazon, an e-commerce giant dominated by retail books, played a major role in the stock price boom in 1999. However, the company lost $304 million in 2000 and $567 million in 2001. Despite this, Amazon managed to weather the dot-com bubble and become one of the world's largest online retailers.
• Netscape, which has search engine technology, was one of the most iconic companies during the dot-com bubble. In 1995, it launched its first commercial web browser, followed by an IPO in 1996. Soon, the company was acquired by AOL, but due to the market depression, its value also shrank significantly.

But are these companies out of business now? No. The dot-com bubble has dealt a huge blow to the global economy, but it has also had a certain positive impact in the long run. The crisis prompted investors to be more cautious about assessing the true value of their companies and forced entrepreneurs to rethink their business models and profit models. Since then, the Internet industry has gradually moved towards a more robust and sustainable development path, and these large enterprises have become larger and larger, in fact, many with market capitalizations exceeding trillions of dollars.

For example, some emerging artificial intelligence technologies may be difficult to widely adopt because they are not mature enough or lack sufficient practical value, which is similar to the rise of the Internet at that time.

Google Translate is an example, and it is already widely used. However, even in this case, this AI system still has some limitations, such as the complexity of language and cultural differences. Therefore, we need to be aware of the limitations of AI technology and see it as a tool that provides behind-the-scenes improvements, rather than a perfect solution.

In addition, since virtually all AI is data-driven, collecting massive amounts of data and using machine learning to output results on top of that, one way U.S. regulators may be dealing with AI-related harms may be to limit the collection and use of data in AI systems. This is similar to the data privacy regulatory measures proposed for social media companies. Given these similarities, internet platforms are expected to lobby vigorously against such data restrictions.

How to distinguish real from fake AI technology & introduction of major related companies around the world

While we acknowledge that AI is a promising technology, it has been widely used in many fields, including natural language processing, computer vision, machine learning, and more. As AI technology continues to evolve and improve, it will continue to bring more innovation and change to various industries.

However, the AI market also faces some challenges and risks. Due to the complexity and high cost of AI technology, few listed companies focus on this area. This could limit investors' options, making it difficult to find true AI leaders.

Due to excessive hype, some companies may exaggerate the capabilities and prospects of their AI technology to attract investors' attention. However, these overhyped companies may not have a real technological advantage, or their technology needs more time and resources to perfect.

For potential AI investors, most investors are very lacking in this knowledge, so they cannot assess the technical strength, business model, market prospects and other factors of the invested company, in addition, from the current market reaction, investors do not notice the bubble risk of the AI market, most of them just blindly follow the trend.

In this regard, we have sorted out the giants and major related companies in the current artificial intelligence industry, when an emerging industry develops, the valuation of leading companies is suspected of bubbles, and other small-scale enterprises are more risky.

At present, in the artificial intelligence market, NVIDIA is a company that has attracted much attention, and its GPU chips have become the core of many artificial intelligence applications. But with its share price already up 160% and trading well above future earnings expectations of 44 times, who can say it hasn't faced higher risk and valuation pressures?

In addition to NVIDIA, some other large technology companies such as Google, Amazon, Microsoft, etc. have also laid out in the field of artificial intelligence and are already providing similar cloud services. The strength and resources of these companies also make them important players in the AI market, but equally, the stock prices of these companies can also be affected by the excessive speculation of the AI market, and investors need to evaluate carefully.

Stocks associated with AI Here are some of the major giants:

• NVIDIA (NVDA): NVIDIA is a GPU-centric computer technology company whose products are widely used in artificial intelligence.
• Alphabet/Google (GOOGL): Google has used its massive data and powerful algorithms to build a wide range of AI applications.
• Amazon and Microsoft offer cloud computing services, including artificial intelligence solutions.
• Alibaba (BABA): Chinese e-commerce giant and one of the world's largest artificial intelligence companies. Its artificial intelligence technology has been applied in various fields, such as finance, logistics and retail.
• Facebook (FB): One of the world's largest social media platforms with many innovative applications in the field of artificial intelligence. For example, deep learning algorithms are used to analyze user data to recommend more personalized content.
• Tencent (TCEHY): Chinese Internet giant and one of the world's largest game development companies. Its artificial intelligence technology is widely used in game development and human-computer interaction.
• Qualcomm (QCOM): The world's leading chip manufacturer whose Snapdragon chip family is widely used in smartphones and other mobile devices. The company is also launching more and more AI chip solutions.
• Apple (AAPL): A world-renowned technology company whose Siri voice assistant and Face ID face unlock technology are based on artificial intelligence. In addition, Apple is constantly exploring the application of artificial intelligence technology in other fields.
• IBM (IBM): International Business Machines Corporation is one of the world's leading technology services companies, and its artificial intelligence technologies include the Watson intelligent question answering system, Deep Blue chess computer, and more. IBM continues to invest in artificial intelligence and actively promotes research and development.
• Tesla (TSLA): Tesla is a company that focuses on electric vehicles and clean energy, and is mainly used in artificial intelligence for autonomous driving technology. Its Autopilot system is based on deep learning algorithms that can help vehicles drive autonomously.
• Baidu (BIDU): Baidu is one of the largest search engine companies in China and one of the largest artificial intelligence companies in the world. Its artificial intelligence technology mainly covers intelligent search, speech recognition, image recognition and other aspects.
• Intel (INTC): Intel is one of the world's largest semiconductor chip manufacturers, and its artificial intelligence technology mainly covers chips, servers, software, and more. The company continues to introduce new AI chip solutions to meet customer demand for high-performance computing.
• Salesforce (CRM): Salesforce is a cloud-based enterprise software company whose artificial intelligence technology is primarily used in customer relationship management and sales automation. The company continues to introduce new AI products and solutions to improve customer experience and business efficiency.
• Advanced Micro Devices (AMD): AMD is a world-renowned semiconductor chip manufacturer whose artificial intelligence technologies mainly cover graphics processors and computer vision. AMD's GPU chips are widely used in machine learning, deep learning, and more.

With the continuous development of artificial intelligence technology and the continuous expansion of application scenarios, more and more enterprises and investors have poured into this industry. However, before investing in AI projects, we need to understand the complexity of technology, data and resources, as well as the high nature of construction and operating costs. Without the right business model and application scenarios, even if there is a large investment, it may not be able to realize the benefits.

The complexity of AI technology is a very important factor. Artificial intelligence involves many different kinds of technologies, such as machine learning, deep learning, natural language processing, computer vision, and many more. These technologies require deep expertise and large amounts of data to support their effective application. In addition, in order to use these technologies, companies need to have highly specialized teams to manage and maintain these systems, which also requires significant expenses and costs.

The complexity of data and resources is also a factor that AI projects must consider. In the field of artificial intelligence, large-scale data sets and powerful computing resources are essential. For businesses and investors, accessing these data and resources takes a lot of time and money. Moreover, issues such as data quality, privacy, and security also need to be fully considered and addressed.

In addition to the complexity of technology, data, and resources, the right business model and use cases are critical to the success of AI projects. Businesses need to have a deep understanding of market demand and customer value, determine the most appropriate business model, and combine it with AI technology. Only by finding the right business model and application scenarios can the economic viability of AI projects be ensured.

While realizing business models and application scenarios, investors must consider the operating costs required by the enterprise. These costs include aspects such as human resources, equipment, infrastructure, R&D and maintenance. When implementing AI projects, it is essential to have sufficient funding and resources to ensure that the project can function properly and generate benefits.

How investors can face the artificial intelligence market rationally and maturely

Artificial intelligence is one of the fastest growing areas in the global technology industry today, and it is gradually penetrating many different industries and driving change in various industries. Investors are also starting to pay attention to this area and hope to seize the opportunities of the artificial intelligence track and achieve high-yield investment returns. But at the same time, the risks in the field of artificial intelligence cannot be ignored.

For investors, the first step should be to understand the basics of artificial intelligence and master its core technologies and application scenarios. The basic technologies of artificial intelligence include machine learning, deep learning, etc., and it is also necessary to consider algorithm optimization and data quality in specific applications. When investing in AI companies, it is necessary to understand the technology, data sources and application scenarios used, and evaluate their technical level.

After understanding these basic professional knowledge, investors can look for potential investment targets from the following aspects:

• Invest in companies with core technologies and rich patents. The essence of AI technology is algorithms and models, so excellent AI companies usually have a large number of core technologies and patents, which can provide effective protection for them to strive for a larger share in the market. For example, Google's technical prowess in the field of machine learning is one of the strongest in the world, with numerous patents.
• Invest in companies with rich data accumulation and successful application cases. Data is the foundation of AI development, and data quality and scale are extremely important for model performance. Therefore, it may be less risky to invest in a company that has a large amount of data accumulation and is successful in real-world application scenarios. For example, Meituan Dianping is a data-driven company that has grown rapidly in a short period of time and has become one of the domestic local Internet giants.
• Invest in startups with innovative products and solutions. While start-ups are risky, the future can be very promising if they can develop innovative products or solutions.
• Invest in companies that apply AI in traditional industries. Artificial intelligence can bring great changes and improvements to traditional industries, so investing in companies that apply artificial intelligence technology in traditional industries is also a good choice.

In addition to the above points, investors also need to pay attention to the risks in the field of artificial intelligence. In the field of artificial intelligence, data quality and scale are critical to the development of technology. If the data quality is poor or the source of the data is limited, it will have a big impact on the application of artificial intelligence.

Due to the complexity and uncertainty of AI technology, it is difficult to conduct a reliable risk assessment. For example, the performance of a machine learning model is affected by many factors, such as the quality and size of the dataset, model selection, algorithm optimization, etc., which are difficult to accurately evaluate.

Investors need to consider the growth potential and future development prospects of the company. The field of artificial intelligence is constantly developing and growing, with broad application prospects. Investors need to assess whether the company has enough room for growth in the next few years and whether there is sufficient market demand.

- Summary -

The field of artificial intelligence is an area of both risk and opportunity. Investors need to understand the relevant technology, core competitiveness, business model and team, and comprehensively consider market demand and future development prospects to find targets with investment value. At the same time, it is also necessary to pay attention to the risks and challenges in the field of artificial intelligence, and fully evaluate and manage issues such as personal privacy protection and data security that may be involved.