The real economic value of generative video lies in advertising, not filmmaking. Unlike movies with finite consumption, there is unlimited demand for personalized, diverse ad content. This makes advertising a perfect fit for the technology's scalable content creation capabilities.
Despite base models improving, they only achieve ~90% accuracy for specific subjects. Enterprises require the 99% pixel-perfect accuracy that LoRAs provide for brand and character consistency, making it an essential, long-term feature, not a stopgap solution.
Fal maintains a performance edge by building a specialized just-in-time (JIT) compiler for diffusion models. This verticalized approach, inspired by PyTorch 2.0 but more focused, generates more efficient kernels than generalized tools, creating a defensible technical moat.
To operate thousands of GPUs across multiple clouds and data centers, Fal found Kubernetes insufficient. They had to build their own proprietary stack, including a custom orchestration layer, distributed file system, and container runtimes to achieve the necessary performance and scale.
Fal strategically chose not to compete in LLM inference against giants like OpenAI and Google. Instead, they focused on the "net new market" of generative media (images, video), allowing them to become a leader in a fast-growing, less contested space.
Fal's revenue growth wasn't gradual but occurred in massive leaps tied to specific model releases. SDXL drove their first million in revenue, while Black Forest Labs' Flux models catapulted them from $2M to $10M in revenue in a single month.
Distilled models like SDXL Lightning, hyped for real-time demos, failed to gain user retention. The assumption they'd be used for 'drafting' proved wrong, as users consistently prefer waiting for the highest possible quality output, making speed secondary to final results.
Unlike streaming text from LLMs, image generation forces users to wait. An A/B test by one of Fal's customers proved that increased latency directly harms user engagement and the number of images created, much like slow page loads hurt e-commerce sales.