Sora Concept Stocks Soar, Can They Break the 'Roller Coaster' Curse?
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On February 16, OpenAI released its first text-to-video model, Sora, once again igniting the AI industry. Sora can produce videos up to one minute long, not only accurately presenting details but also generating characters with rich emotions, marking a significant milestone for the AIGC industry. Elon Musk responded on social media with "gg humans," implying that AI is so powerful that humans must concede defeat.
The popularity of Sora has also shifted attention back to domestic discussions about who could create a "Chinese version of Sora," leading to a surge in Sora concept stocks, with many related stocks hitting the 20% daily limit. The question has evolved from "Why wasn't ChatGPT born in China?" to "Why wasn't Sora born in China?"
Compared to similar products like Runway and Pika, the most intuitive observations about the videos generated by Sora are twofold: First is duration - leaping from typically 3-second clips to full-minute videos. This one-minute duration already meets basic requirements for short videos and advertising, effectively unveiling the prelude to AI automation in the film industry while illuminating commercialization and mass adoption pathways for large AI models.
Second is visual fidelity - Sora generates videos with significantly higher realism than peers, whether depicting human skin textures, facial expressions, or animal fur with authentic detail. Its sophisticated handling of camera angles and scenes is particularly noteworthy. Most crucially, it evolves text-to-video generation from single shots to producing multiple stylistically consistent scenes.
According to OpenAI's technical report, Sora adopts a "Diffusion+Transformer" architecture for its video generation model. As ICBC Credit Suisse analysis indicates, compared to traditional U-Net diffusion models, Transformer architecture offers stronger parameter scalability - performance accelerates with increased parameters while supporting training data of arbitrary resolution, aspect ratio, and duration without quality degradation from compression. This represents truly milestone progress. Sora can sometimes simulate actions that affect the world state in simple ways. For example, a painter can leave new brushstrokes on a canvas that persist over time, or a person can eat a hamburger and leave bite marks. In other words, Sora can "simulate the world," becoming a highly capable simulator for developing both physical and digital worlds, including the objects, animals, and people within them.
However, as a simulator, Sora currently has many limitations. For instance, it cannot accurately simulate many basic physical interactions, such as glass shattering. This "shortcoming" also provides some relief to those anxious about "AI replacing humans," allowing them to face technological progress more calmly.
According to Morgan Business Research, in China, neither the suddenly popular Sora concept stocks nor the "experts" making quick money by selling Sora courses—as the first beneficiaries of the Sora concept—are likely to achieve significant success through this trend. This isn't meant to pour cold water on technological progress. In the torrent of tech development, whenever a new trend emerges, everyone is busy spinning stories about the future or capitalizing on information asymmetry. However, from the perspective of developmental patterns, looking at the past provides much clearer insights than predicting the future. The explosive popularity of ChatGPT a year ago serves as a prime example. From the sudden emergence of concept stocks to the "hundred-model battle," after a year of "separating the wheat from the chaff," the trend in the secondary market is far from optimistic.
Let's take Danghong Technology, which rode the waves of both ChatGPT and Sora, as an example. When the Sora frenzy hit, on February 8, Danghong Technology's stock price rebounded from its bottom, surging by 13.49% that day. Subsequently, the company experienced two consecutive 20% limit-up rallies. By the close on February 21, Danghong Technology's stock had risen by over 70% in just four trading days. This price movement was almost identical to what happened when the ChatGPT trend emerged last year.
On January 31 last year, Danghong Technology stated on its interactive platform: "The company is actively promoting the application of AI technology across various industries, including AIGC, NLP, and other technologies and products related to ChatGPT, which have been gradually refined and implemented in some business areas." Riding the tailwind of ChatGPT, its stock price soared from 27.87 yuan to a peak of 69.99 yuan by March 28. As the hype around ChatGPT began to fade and leading players successively launched their large model products, Danghong Technology's stock price entered a downward trend, even hitting a low of 16 yuan on February 6 this year—a 77.14% drop from last year's peak.
The rollercoaster-like stock performance can be partly explained by Danghong Technology's financial reports. In 2021, the company's net profit was 61.3475 million yuan, a sharp year-on-year decline of 40.34%. By 2022, its net loss widened to 98.5883 million yuan, plummeting 260.7% year-on-year. As of the third quarter of 2023, Danghong's net loss reached 96.1575 million yuan, down 132.01% year-on-year, with almost no possibility of reversing its profitability.
Danghong Technology is just one example among many 'concept stocks' in the R&D phase with immature technologies: they are related to cutting-edge fields but not leading in them, their product commercialization remains unsatisfactory, and their financials struggle to balance or continue to bleed losses. Thus, 'concept stocks' are far from synonymous with 'high-quality stocks'—their reputation for 'riding the hype' and 'pump-and-dump schemes' overshadows their actual capabilities. As one of the leading companies in the AI industry, iFLYTEK is a classic case of a 'concept stock.' From a product perspective, the Spark large model was among the earliest domestic ChatGPT-like models released in China, continuously iterating and integrating commercial applications with its learning tablets and business laptops. According to iFLYTEK's 2023 financial forecast, its net profit has also shown steady growth.
However, the secondary market has not experienced the anticipated sustainable growth for iFLYTEK. Instead, each technological iteration has been met with a 'darkest hour' in the secondary market:
On the afternoon of August 15, 2023, the iFLYTEK Spark Cognitive Large Model V2.0 upgrade was announced. The next day, iFLYTEK's stock price closed down 8.62%. On the morning of October 24, the 'iFLYTEK Spark Cognitive Large Model V3.0' was released, claiming to surpass ChatGPT overall and exceed GPT-4 in the medical field. That same day, iFLYTEK's stock price closed down 9.97%. On January 30, 2024, iFLYTEK held an upgrade conference for the Spark Cognitive Large Model V3.5, where Chairman Liu Qingfeng officially unveiled the new-generation model, Spark V3.5. The following day, iFLYTEK's stock price closed down 6.66%. This is not hard to understand. For tech companies, the easiest time to tell a compelling story is before product implementation. iFlytek has consistently emphasized 'surpassing' in its promotions. However, with ChatGPT setting a precedent, every release and iteration of domestic large models provides users with better opportunities for comparison.
Repeatedly falling short in user experience makes it increasingly difficult to convince users of the 'surpassing' vision. Technical iterations and innovations that bring no surprises only lead to disappointment, making it hard to maintain a moat in the secondary market.
What people anticipate are sudden 'surprises,' not long-planned 'responses.' Are you disappointed with domestic enterprises in related fields? To say no would be insincere. However, the disappointment does not stem from "backward" technology but rather from the behaviors of many domestic companies—making empty promises, jumping on bandwagons, manipulating capital, and exploiting retail investors.
Technologically speaking, whether it's groundbreaking large model products like ChatGPT and Sora or the design and production technologies of various high-end chips, the delays due to late starts and the "no rice, no meal" predicament caused by product and technology blockades are understandable. As many joke, many domestic companies developing large models can't even afford their electricity bills.
Taking computing power chips as an example, according to estimates by third-party data agency SemiAnalysis, OpenAI used approximately 3,617 HGX A100 servers, containing nearly 30,000 NVIDIA GPUs. Moreover, following Moore's Law, with technological and product iterations, OpenAI's demand for computing power will grow exponentially. This will be a terrifying financial requirement, one that is difficult for small and medium-sized enterprises to sustain. For most domestic companies, a better path would be to build applications based on existing open-source large models or their ecosystems. However, possibly driven by capital pressures, many have instead embarked on the path of surpassing OpenAI.
It's important to note that beyond GPUs, OpenAI's investor Microsoft helped build customized computing clusters for large models, which further enhances the efficiency of these GPUs. With such resources and investment, OpenAI still took over eight years to develop the groundbreaking GPT-4, which still has its flaws.
This shows that the path of large models will be extremely long and difficult. Even for well-funded companies like Tencent, Baidu, Huawei, and Alibaba, they are affected by the US 1017 new regulations, which restrict the export of GPU chips including A100, H100, A800, and H800 to China, creating a bottleneck. In reality, whether it's OpenAI, Microsoft, Google, or the numerous domestic companies engaged in the "Hundred Models War," all face difficulties in product implementation and commercialization. Coupled with the absence and lag in ethical, legal, and regulatory frameworks, it's challenging for them to monetize their technologies and products in the short term.
The "paradigm revolution" sparked by ChatGPT and Sora has not only fueled speculative capital but has also made Nvidia, the seller of computing chips, the real beneficiary.
Financial reports show that Nvidia's revenue for the fourth quarter reached $22.1 billion, a 265% year-over-year increase. Net profit soared to $12.3 billion, up 769% year-over-year, with diluted earnings per share at $4.93, marking a 765% increase compared to the previous year. In the secondary market, NVIDIA stood out with a staggering 239.02% annual increase in 2023, followed by another 46.63% surge in 2024 (as of February 16). NVIDIA's stock price has reached a new all-time high, pushing its market capitalization to $1.8 trillion, surpassing Google and Amazon to become the world's fourth most valuable company. This explains why, beyond investments in large models, many leading companies are turning their attention to chips.
In early February, a Meta spokesperson revealed that its second-generation self-developed AI chip, Artemis, will officially enter production this year, initially for use in data centers. However, the production capacity and computing power of self-developed chips cannot meet demand in the short term. Meta plans to deploy 350,000 H100 GPUs by the end of this year, bringing the total number of GPUs in use to over 600,000.
OpenAI CEO Sam Altman aims to raise $7 trillion to enter the chip industry, seeking self-sufficiency by controlling the upstream supply chain. Meanwhile, SoftBank Group founder Masayoshi Son, after a long hiatus, is seeking to raise $100 billion (approximately RMB 7 trillion) to establish an AI chip company to challenge NVIDIA. In other words, this AI battle isn't simply about the metaverse, virtual humans, or large models. It's about concentrating global attention, capital, and resources through disruptive technologies to reshuffle and redefine the world's technological discourse. For domestic enterprises in China, this remains an arduous and long-term mission.
Of course, we all believe that Chinese tech companies will eventually break through the "bottleneck" constraints, and China's AI technologies and products will stand at the forefront of the world. However, before any milestone "surprises" materialize, all claims of "intention to surpass" or "aspiration to become" can only be defined as cowardly acts of "exploiting one's own people."
In the AI era, talent, data, and chips are the true cornerstones for training and development. As humanity transitions from "carbon-based" to "silicon-based," we need to pursue technological advancement with down-to-earth determination. Disclaimer: This article is based on the company's statutory disclosure content and publicly available information for commentary, but the author does not guarantee the completeness or timeliness of such information.
Additionally: The stock market involves risks, and caution is required when entering. This article does not constitute investment advice, and investment decisions should be made at your own discretion.