1. Introduction

Artificial Intelligence (AI) is an industry experiencing extensive growth with significant advancements across multiple fields. It has also become a helpful tool in modern society. The AI landscape can be broadly divided into four key areas: machine learning, robotics, artificial neural networks, and generative AI. According to Statista, the global AI industry is projected to grow at an annual rate of 28.46%, reaching an estimated market size of $184 billion by 2024 [1]. This rapid expansion creates vast opportunities for venture capitalists to invest and secure high returns as the demand for AI technologies surges across various sectors. According to an Early Young report, 95% of senior leaders reported that their organizations are already investing in AI [2]. The proportion of companies spending $10 million or more on AI technology is anticipated to nearly double in the next year, rising from 16% to 30%. AI startups, particularly in China and the United States, attracted the bulk of venture capital interest, accounting for over 80% of global AI investments in 2020 [3]. Venture capital investment in AI has grown at a remarkable pace. In 2012, total VC funding in AI was approximately $3 billion. By 2020, that figure skyrocketed to around $75 billion, reflecting the increasing confidence in AI's transformative potential [4]. This surge in investment underscores AI as a major force in innovation, with venture capitalists eager to support companies that are pushing the limits of what's possible with technology. The U.S. and China are currently the two leading countries with venture capitalists investing in the AI field. They were also the net beneficiaries of venture capital investment with US investors earning around 40 billion and Chinese investors earning around 19 billion between 2012 and 2020. This paper will investigate the steps and considerations in investing in artificial intelligence as well as the challenges behind it.

2. Market Trends

The AI market is experiencing rapid growth, with leading companies such as OpenAI driving significant advancements in recent years. OpenAI’s ChatGPT generative chatbot is revolutionizing the industry and represents a major breakthrough in artificial intelligence. Firstly, ChatGPT enables human-like text generation with coherence and contextual accuracy, which are capabilities that were challenging for AI models in the past [5]. Additionally, ChatGPT can perform a wide range of tasks across various natural language processing domains, such as text completion, answering questions, and dialogue generation, making it highly adaptable to different circumstances. Another important aspect of ChatGPT is its ability to understand and maintain context in conversations, resulting in more relevant responses even in complex or ambiguous situations. This advancement is crucial for improving conversational AI. Furthermore, the model's cross-domain capabilities allow it to generate text in multiple languages and across various fields, making it a valuable tool for breaking down language barriers across different fields. ChatGPT can also adapt to user preferences and conversational styles to enhance personalized interactions [5]. These advancements make ChatGPT a pivotal innovation in the AI industry, transforming how humans interact with AI and opening new possibilities for applications across various sectors.

In terms of investment, prior to 2015, U.S. AI startups were the dominant players in the global AI landscape, raising the majority of venture capital funds and setting the pace for innovation across various sectors. However, after 2015, the global AI investment landscape shifted dramatically with the emergence of Chinese AI firms. A wave of Chinese startups entered the market and quickly captured a significant share of total AI funding. By 2018, Chinese companies were receiving up to 50% of global AI venture capital, marking a rapid rise that rivaled U.S. dominance [3]. These Chinese companies focused largely on capital-intensive sectors such as “mobility and autonomous vehicles” and “robots, sensors, and hardware”, which accounted for two-thirds of their investments in 2017.

Venture capital investment in AI is categorized across various industries. In the U.S., AI investments were spread across multiple sectors, with mobility and autonomous vehicles receiving 30%, healthcare 13%, business processes 11%, IT infrastructure 10%, and media/social platforms 8%. In contrast, Chinese investments were more concentrated, with 41% in mobility and autonomous vehicles, followed by 14% in media, 13% in robotics and hardware, 8% in IT infrastructure, and 7% in business processes [3].

The autonomous vehicles industry was a significant investment focus in both the U.S. and China. Before 2017, China dominated investments in this sector, but after 2017, the U.S. took the lead. While autonomous vehicles remained a major focus, the healthcare, drugs, and biotechnology sectors experienced substantial growth, nearly doubling in 2020. The COVID-19 pandemic further underscored AI’s potential to advance drug research, enhance diagnostic accuracy, and automate healthcare processes. Therefore, there is increasing investment in the field of AI in healthcare.

3. Challenges

Additionally, investing in artificial intelligence is particularly appealing to venture capitalists due to its potential to create vast learning opportunities and drive technological advancements. However, along with these benefits come significant challenges. First, there is a degree of information asymmetry in AI ventures since the industry is still developing, highly dynamic, and relatively unstable [6]. AI technologies are vulnerable to security risks, including privacy concerns and the potential for personal data breaches [6].

Venture capitalists must carefully assess the extent of the associated risks before investing in AI firms. There are different risks across different stages of a company that the venture capitalists may invest in. For example, at an early stage, the company will encounter product risk, team risk, etc. Startup AI companies are more susceptible to these challenges as exemplified by the downfall of LeEco, a Chinese AI company founded by Jia Yueting [7]. The company faces many challenges. Firstly, the company pursued an aggressive diversification strategy, venturing into multiple industries such as electric vehicles, smartphones, video streaming, and virtual reality without first securing a strong foothold in any one area. This rapid expansion overextended the company's resources, resulting in significant operational inefficiencies. LeEco's growth was primarily financed through debt and the increasing cost created a debt burden that became unsustainable. In addition to financial and operational mismanagement, LeEco faced serious team challenges. Jia Yueting's unwillingness to relinquish control or bring in experienced management increases the company's difficulties. Corporate governance issues further undermined its stability, including a controversial transaction involving Jia's wife, which damaged the company's reputation and paved the way for its eventual failure [7].

On the venture capitalist side, those with experience are better equipped to manage the risks inherent in AI technology and to evaluate how advancements in AI may impact the long-term growth of their portfolio companies [4]. Venture capitalists with a strong combination of technical expertise, deep market insights, and risk management capabilities are more proficient at unlocking the value of AI investment opportunities while effectively navigating the complexities of a rapidly evolving technological landscape.

Given the fast-paced development of the AI field, experienced investors with a background in both AI and venture capital are in a stronger position to make informed investment decisions. AI ventures experience greater uncertainty and risk, resulting in lower venture capital investment per deal compared to non-AI ventures. However, this effect is moderated by factors such as the venture’s age, the expertise of the investor, and the maturity of the country’s AI industry. This helps to highlight that experience in AI and a supportive regulatory environment can help close the funding gap for AI ventures.

Moreover, there are limited partners since many investors tend to invest in established funds with solid reputations and thus are less likely to invest in these high-risk funds in artificial intelligence [8]. Furthermore, another challenge is that the typical venture capital funding cycles are often too short to realize returns from AI investments. According to Boston Consulting Group, 81% of deep tech entrepreneurs believe that investors lack the necessary scientific or engineering expertise to properly evaluate these ventures [8]. Artificial intelligence, being part of the deep tech sector, demands significant early-stage resources, which makes the conventional venture capital approach of spreading bets across many pitches ineffective for this domain.

4. Investment Considerations

4.1. Common VC Decision Process

After understanding the risk associated with it, it is important to understand the investment criteria used by venture capital firms. Venture capitalist is a highly risky associate job but with great risk comes great returns.

The first step in the venture capital process is deal sourcing, where the goal is to identify and generate promising investment opportunities. The most valuable deals often come from referrals by entrepreneurs or companies in which the fund has previously invested. The next step is deal selection. Venture capitalists typically evaluate factors such as the management team, the entrepreneur's background and personality, the product or service being offered, market dynamics, financial health, customer interest, technological and strategic adaptability, the business's stage, exit potential, return on investment, syndicate investors, and how well the opportunity aligns with the fund's investment goals. While financial performance and customer traction tend to be decisive, factors like the quality of the management team, market barriers, and overall business potential are often more predictive of long-term success. Venture capitalists gather this information through various methods, such as interviews, surveys, and discussions with the company's stakeholders. After deal selection comes the valuation step, where the VC estimates the company's potential value at exit and assesses its stake in it. Common valuation methods include risk factor summation, liquidation value, and valuations based on tangible assets. Following this is the deal structuring phase, which involves negotiating and formalizing the terms and conditions of the investment. This includes ownership and cash flow rights, control rights, liquidation preferences, employment terms, and syndications with other investors. Post-investment value-add is the next stage, where venture capitalists actively support the company's growth by helping to identify key partners, providing strategic guidance, and leveraging their networks. Finally, the exit phase involves making decisions on the exit strategy, focusing on the optimal exit route—such as an IPO or M&A—and determining the best timing to maximize returns [9].

4.2. VC Investment Decision Process Across Countries

This section will analyze the criteria investment in the United States, China, and Germany for technology startups, with a particular focus on AI startups. Venture capital investment decisions are inherently complex and involve balancing multiple risks and rewards, especially in fast-moving and unpredictable markets like technology. In the case of technology startups, venture capitalists typically focus on a few core aspects that are seen as indicators of potential success. These include the startup’s capacity for disruptive innovation of the novelty and groundbreaking of the technology or business model as well as its ability to protect intellectual property, a critical factor in maintaining a competitive edge [10]. Another essential consideration is the scalability of the business: investors want to ensure that, once the startup finds product-market fit, it can grow rapidly and dominate its sector. These factors have traditionally guided investment in tech startups, but AI-focused ventures bring additional layers of complexity that venture capitalists must navigate.

Oliver Gottfried and fellow researchers in 2023 provide a comparative analysis of how venture capitalists in different countries approach their investment decisions, highlighting both similarities and notable differences influenced by the economic, cultural, and institutional contexts unique to each region [10]. In the United States, venture capitalists tend to prioritize the experience and expertise of the founding team. A startup’s leadership is seen as a key indicator of potential success, particularly in terms of navigating the challenges of scaling a company. U.S. investors are also highly focused on growth potential, seeking out companies that can rapidly expand and dominate markets. This reflects the broader venture capital ecosystem in the U.S., which is heavily oriented toward high-risk, high-reward investments, particularly in Silicon Valley, where innovation and disruption are highly valued.

In contrast, venture capital investment decisions in China are shaped by different factors. Personal relationships play a critical role in Chinese business culture, and this extends to the venture capital sector [10]. Chinese investors often place significant importance on their relationships with the founding team, viewing trust and personal connections as key determinants of investment decisions. Additionally, Chinese venture capitalists must navigate a complex regulatory environment, meaning that alignment with government strategies is often crucial for success. Startups that align with national priorities—such as AI development, which the Chinese government has heavily promoted— are more likely to receive funding. Furthermore, the volatile and fast-paced nature of the Chinese market means that investors tend to focus on exit strategies, prioritizing startups that offer a clear path to a profitable exit in a short timeframe. This combination of personal relationships, government alignment, and exit strategies sets the Chinese venture capital landscape apart from that of other regions.

Germany’s venture capital market, while smaller in comparison to the U.S. and China, is steadily growing [10]. German investors tend to focus more on the quality and technological innovation of the product itself, reflecting the country’s strong industrial and engineering traditions. The emphasis is less on the founding team’s experience and more on the product’s technical robustness and its potential for innovation. German venture capitalists seek out startups that have developed a well-engineered, high-quality product with significant potential for growth, particularly within the domestic market. This focus on the product over the team contrasts with the U.S. approach, where the team’s ability to lead and scale the company is often seen as more important than the technical specifics of the product at early stages.

5. Conclusion

In conclusion, the growing artificial intelligence (AI) industry offers vast opportunities for venture capital investment, driven by advancements in machine learning, robotics, neural networks, and generative AI. With a projected market size of $184 billion by 2024, the AI sector continues to attract substantial venture capital interest, especially in countries like the U.S. and China. However, despite its potential, AI investment comes with significant challenges, including information asymmetry, security risks, and uncertainty regarding returns. Experienced investors with technical expertise and a strong understanding of market dynamics are better equipped to navigate these challenges and unlock AI’s long-term value.

Market trends indicate that both U.S. and Chinese venture capitalists are heavily invested in sectors like autonomous vehicles and healthcare, with U.S. investments showing a broader industry focus compared to China’s concentration on mobility and robotics. Despite these differences, both countries continue to lead global AI investments, leveraging their regulatory environments and market conditions.

Challenges such as the short venture capital funding cycles and a lack of deep tech expertise among investors further complicate AI investments. Addressing these challenges requires venture capitalists to adapt their investment strategies, prioritize long-term growth, and work closely with AI ventures to mitigate risks. Investment considerations in AI vary across countries, shaped by economic, cultural, and institutional factors. U.S. venture capitalists emphasize growth potential and the leadership team’s expertise, while Chinese investors focus on personal relationships and government alignment. In contrast, German investors prioritize product quality and technical innovation. These regional differences highlight the complexity of investing in AI, underscoring the need for venture capitalists to tailor their approaches based on the unique conditions of each market.

Overall, while AI presents a highly promising investment landscape, success will depend on investors’ ability to navigate the associated risks, leverage expertise, and align with regional dynamics to capitalize on AI’s transformative potential.