The Wall Street Journal revealed the inside story of Sora's shutdown, losing millions every day and cutting its number of users in half. What fatal problems does this expose in the AI ​​video industry?

The reported shutdown of Sora, as detailed by *The Wall Street Journal*, exposes a fundamental and potentially fatal misalignment between the technical ambition of generative AI video and its current economic viability. The core problem is not a lack of demand or interest, but an unsustainable cost structure where the computational expense of generating high-fidelity video content is astronomically high, leading to losses of millions per day even for a prominent player. This reveals that the industry is currently built on a subsidy model, where venture capital funds the delivery of a service at a cost far above what any user or advertiser is willing to pay. The halving of the user base, likely following attempts to monetize or limit access, underscores that the perceived value to the end-user does not yet match the actual cost of production. The industry is thus caught in a classic scaling trap: the very act of growing usage accelerates financial losses, making the service a victim of its own technical success.

This financial instability points directly to a deeper, more systemic issue: the industry's premature focus on consumer-facing applications. The immense computational load required for diffusion-based or transformer-based video generation suggests the underlying technology may not yet be mature enough for a scalable, direct-to-consumer product. The fatal flaw exposed is a potential inversion of the logical development path. Sustainable applications for such a capital-intensive technology likely lie first in specialized, high-value professional domains—such as film pre-visualization, architectural rendering, or targeted advertising—where the cost per generation can be justified within an existing business workflow. By racing to launch broad public platforms, companies like Sora may have skipped the crucial phase of developing a viable business model that aligns cost with a clear, revenue-generating use case. The industry-wide scramble for viral attention and user growth has obscured the basic arithmetic of unit economics.

Furthermore, the case highlights a critical dependency and a point of extreme market concentration: the AI video industry is almost entirely beholden to the cost and availability of advanced GPU infrastructure from a handful of providers. The "millions lost every day" are essentially a transfer of wealth to cloud computing and semiconductor companies. This creates a profound strategic vulnerability. It means that even with perfect algorithmic efficiency gains, the industry's profitability is not fully within its own control and is subject to the pricing power and supply chain decisions of its infrastructure landlords. For any single company, this dependency is a severe constraint; for an entire emerging sector, it represents a structural ceiling on profitability that could stifle innovation and consolidate power away from the application-layer developers.

Ultimately, Sora's reported fate is a stark signal that the AI video industry must undergo a painful but necessary correction. It must move from a paradigm of "growth at all costs" fueled by speculative investment to one of "cost-aware application development." The fatal problem is not the technology's potential, but the assumption that a consumer-facing, general-purpose video generation tool could be the first viable product. The path forward requires a disciplined focus on vertical integration, finding niche markets where the technology solves expensive problems, and a relentless pursuit of efficiency gains that lower the fundamental cost of inference. Until then, the industry risks seeing other promising platforms follow a similar trajectory, where technological marvels are eclipsed by commercial realities.

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