Written by Yang Jianyong

As one of the former "Four Little Dragons" of AI, Yuncong Technology once attracted much attention and enjoyed immense popularity. Unfortunately, years have passed, but revenue performance remains sluggish, and sustained losses have not improved.

From 2020 to the first half of 2024, Yuncong Technology's revenue was 755 million, 1.076 billion, 526 million, 629 million, and 120 million yuan, respectively. Notably, revenue in 2022 halved, and while 2023 showed some recovery, the scale remained smaller than before.

Regrettably, this year's performance has declined again, accompanied by expanded losses. In the first half of 2024, revenue decreased by 26.35% year-on-year, with losses reaching 356 million yuan, an increase of 16% from the 306 million yuan loss in the same period last year. Revenue cannot fully cover costs, leading to continuous operating losses.

Data shows that from 2017 to the first half of 2024, Yuncong Technology's cumulative losses exceeded 4.2 billion yuan. Interestingly, the company had projected profitability by 2025 in its prospectus. However, given current revenue and market conditions, turning a profit in 2025 seems unrealistic and unlikely.

In response to investor inquiries about achieving profitability by 2025, as repeatedly stated by the company, Yuncong Technology replied that the performance forecasts in the prospectus for 2022-2025 are preliminary estimates based on the company's operating plans and assumptions about specific conditions for profitability. These include factors like successful fundraising, no unexpected events disrupting normal operations, and no force majeure, and do not constitute a profit forecast or commitment.

When asked about when profitability can be achieved, Yuncong Technology stated its commitment to optimizing business structure, controlling costs, and expanding markets. It aims for high-quality development and turning a profit by reducing losses.

Not just Yuncong Technology, other AI unicorns also face long-term losses, which is one of their biggest challenges.

Cambricon lost 530 million yuan in the first half of the year. From 2017 to the first half of 2024, its cumulative losses exceeded 5.4 billion yuan. There is still no guarantee of profitability in the coming years. As a technology-intensive field, chip development requires substantial R&D funding, with nearly 5 billion yuan invested in R&D over the past four years. High-quality R&D investment is crucial for long-term chip industry growth and enterprise development.

SenseTime, the leader among the "Four Little Dragons" of AI, also sustains losses, with 2.477 billion yuan in the first half of 2024. However, amid the generative AI wave, SenseTime's revenue began to recover, reaching 1.74 billion yuan in the first half of 2024, up 21.4% year-on-year. Generative AI revenue was 1.05 billion yuan, up 255.7%, becoming the largest business segment, accounting for 60% of total revenue.

The strong performance of generative AI business is largely due to SenseTime's investment in AI infrastructure, with 54,000 GPUs in operation. Its large model infrastructure, SenseCore, has a total computing power of 20,000 petaFLOPS. This has enabled it to provide comprehensive enterprise-level generative AI solutions and reap market benefits. According to IDC, SenseTime holds a 16% market share in large model platforms and applications, ranking second in the industry.

Large models are technology- and capital-intensive. Facing the new wave of AI brought about by large models, Yuncong Technology once proposed a private placement plan to raise 3.635 billion yuan for the development of its "Industry Elf" large model project. However, after 17 months, the planned 3.6 billion yuan refinancing was terminated.

The race for AI large models is ambitious but Cruel realistic. After a near-frenzied development in 2023, the hype around large models has cooled. Competing in large models requires significant financial resources, as training and deploying them is costly. Moreover, commercializing large model technology is challenging, with even companies like OpenAI struggling to break even.

However, as large model technology advances, AI is moving towards general AI development, accelerating the global adoption of digital and intelligent technologies. This bodes well for the generative AI market, with IDC projecting global enterprise spending on generative AI solutions to reach $143 billion by 2027. The breakthrough in large model technology heralds a new era of growth and significant opportunities for AI vendors.

Yet, ultimately, the competition lies in the ability to implement technology. Yuncong Technology's sluggish revenue and expanding losses highlight the challenges of AI implementation. Overall, factors like technology implementation, sustained losses, and intensified competition paint a bleak outlook for Yuncong Technology's prospects, posing challenges to its sustainable development and operating performance.

Yang Jianyong is a contributor to Forbes China and expresses his personal views. He focuses on in-depth analysis of cutting-edge technologies such as IoT, cloud services, AI, and smart homes.