This article answers the core question: why does Alibaba continue to invest in leading AI video startups even when it already has HappyHorse, a first-tier self-developed AI video model. Its key insights are as follows:

1. AI video is an inherently compute-intensive industry. The more powerful a model is and the more users it serves, the exponential growth in demand for GPU and cloud computing resources. Alibaba’s core goal in investing in these startups is to lock in long-term computing power orders for Alibaba Cloud. HappyHorse demonstrates Alibaba’s technical prowess, while the invested startups generate a steady stream of computing demand.

2. In the AI era, big tech’s core competition is for infrastructure access, not dominance of a single model. HappyHorse acts more like Alibaba’s technical flagship store. Alibaba will emerge as the biggest winner as long as all AI video companies run their operations on Alibaba Cloud, so it does not need to enter a zero-sum competition with its portfolio companies.

3. Investing in different technical routes also allows Alibaba to secure strategic optionality for the future, avoiding the risk of its single model being obsolete amid technological iterations. Alibaba essentially acts as a low-risk track enabler, and future competition in the AI industry will center on computing power.

This article outlines the latest industrial landscape and development trends of the AI video track, offering valuable insights for brands to grasp AI-driven marketing trends and build new content capabilities. Key takeaways are as follows:

1. AI video is currently the most heavily invested track by Chinese internet giants, with nearly all leading AI video startups backed by big tech capital. This will drastically speed up industry technological maturity and commercialization, and AI video will become the core content production tool for brand marketing in the future.

2. The growing computing power demand for AI video will gradually be met amid big tech’s ecological layout. In the future, the cost for brands to produce high-definition, long-form customized marketing videos will keep declining, while production efficiency will improve significantly. Brands can build up AI content production capabilities in advance.

3. Technological iteration in the AI video industry is currently extremely fast, with industry leaders changing every few months. More products adapted to brand marketing scenarios will emerge in the future. Brands can keep tracking industry developments and seize the efficiency upgrade opportunities brought by AI for marketing.

This article sorts out the latest capital trends and industrial logic of the AI video track, and summarizes opportunity and risk alerts for sellers布局 AI-related businesses. Key insights are as follows:

1. AI video is currently the core focus of big tech capital布局, and the industry will enter a period of rapid expansion. Sellers can explore application opportunities of AI video in product display, marketing promotion, content marketing and other links, and build relevant capabilities in advance to capture traffic dividends.

2. The core of competition in the AI industry has shifted from model capability to computing power, and computing power cost will become the core cost of AI-related businesses. When choosing AI tool partners, sellers should prioritize products backed by big tech cloud services, to get more stable services and more controllable costs.

3. Technological iteration in the AI video industry is extremely fast, so do not blindly overinvest in a single technical route to avoid losses from rapid tool obsolescence. At the same time, you can capture various tool subsidies and traffic support opportunities during the industry expansion period to reduce your own trial-and-error costs.

This article analyzes the latest development logic of the AI industry, and offers plenty of insights for factories advancing digital transformation and exploring new business opportunities. Key takeaways are as follows:

1. The core of competition in the current AI industry has shifted to computing infrastructure. As big tech companies gradually布局 their ecosystems, the threshold for AI application deployment will continue to decrease. Factories will be able to access AI technology at lower costs for product design, promotional video production, production process optimization and other links, to accelerate digital transformation.

2. The rapid boom of the AI video track has generated massive demand for supporting industries, including GPU and server hardware manufacturing, AI video post-production and other related needs. Factories with matching capacity can adjust their business布局 accordingly to secure new stable orders.

3. Big tech companies like Alibaba are opening up their cloud ecosystems to attract enterprises of all types. The threshold for factories to connect to big tech cloud services will further lower, and factories can also access support resources from big tech ecosystems. Factories can seize this opportunity to accelerate their own digital upgrading process.

This article sorts out the latest development trends of the AI track and core customer pain points, and provides clear direction for various technology service providers to expand their businesses. Key insights are as follows:

1. The core of competition in the current AI industry has shifted from model R&D to computing infrastructure. AI video is inherently an industry with extremely high computing power demand, and the core pain point of AI enterprises has shifted from insufficient model technology to stable, low-cost computing power supply. Service providers can develop targeted solutions around computing power supporting services to capture new growth opportunities.

2. Technological iteration in the AI video industry is extremely fast, and all AI startups need to lock in long-term stable computing power cooperation in advance. Service providers can launch customized computing packages, cloud deployment services and computing cost optimization solutions for AI startups to match the core needs of this customer group.

3. Future growth of the AI industry will center on computing power. Cloud service providers can learn from the models of Alibaba and Microsoft: use capital ties to lock in long-term computing demand from high-quality AI clients in advance, to achieve sustained and stable business growth, while diversifying the risk of relying on a single technical route.

This article interprets the new strategic logic of cloud platforms in the AI era, offering valuable insights for various platforms in strategic layout and risk mitigation. Key takeaways are as follows:

1. In the AI era, the core of platform competition has shifted from user access in the mobile internet era to computing infrastructure access behind AI models. In the future, as long as more AI models run on a platform, the platform can share the growth benefits of the entire AI industry, without forcing its self-developed model to hold a monopoly position.

2. Current technological iteration in the AI track is extremely fast, and no company can guarantee its model will remain leading permanently. Platforms can adopt a strategy of "flagship self-developed model + diversified investment layout": this retains the company’s own technical advantage, while allowing it to布局 different technical routes in advance through investment, mitigates the risk of single technology failure, and retains sufficient optionality for the future.

3. For cloud platforms, self-developed top-tier models act more as "flagship stores" to demonstrate technical strength. The core goal is to prosper the entire AI ecosystem, attract more model companies to settle in and drive growth in computing business. This ecological model is more robust than monopolizing model R&D, and can generate long-term stable returns.

This article reveals the new investment logic of big internet companies and new industrial development trends in the AI era, offering high reference value for industrial research. Key insights are as follows:

1. The AI industry has emerged with a brand-new business logic unseen in the previous internet era. Unlike past investments in startups that pursued capital gains after IPO, cloud providers’ core goal beyond equity returns when investing in AI model companies is to lock in their long-term growing computing demand. This is a unique industrial cooperation model in the AI era.

2. The core of competition in the AI industry has shifted. In the mobile internet era, big tech competed for C-end user access; in the AI era, they compete for access to underlying computing infrastructure. Competition in the AI industry will center on computing power over the next decade, completely reshaping the previous internet industry landscape where competition revolved around traffic.

3. Technological iteration in the current AI video track is extremely fast, with industry leaders changing every few months. The "flagship model for technical demonstration + full ecosystem investment layout" model adopted by big tech is a new business model adapted to current industry characteristics. It can both demonstrate the company’s technical strength and allow it to share the growth benefits of the entire industry, making it worthy of in-depth research.