AI Video Entrepreneurship: How the Xiyang Stone Team Earns Six-Figure Income with ‘Kling’

Monetization Angle

The Xiyang Stone team treats AI video as a business, not a content hobby: Kling, an AI video generation tool, sits at the center of a production operation that has earned a reported six-figure income. The monetization is entrepreneurial: sell video production that would traditionally require crews, locations, and equipment, but deliver it with an AI pipeline where a small team covers concept, generation, and finishing. A lower cost per finished minute means the team can price competitively, take on more projects, or reinvest the margin in capacity. The income is the output of the machine, and the machine is the workflow.

Best For

Small teams or founding pairs who want to build a video business without traditional production resources. It suits people with complementary roles, one on creative direction and clients, one on generation and editing, and enough budget for AI tool subscriptions. It is a poor fit for a lone hobbyist: the six-figure result comes from operating like a studio, with delivery deadlines, quality control, and repeatable pipelines, not from occasional viral clips.

Difficulty

Moderate to high, and the difficulty is mostly business rather than tooling. Kling dramatically lowers the skill threshold for producing good-looking footage, but a studio still has to win work, scope it, hit deadlines, and keep quality consistent enough that clients come back. Prompt craft matters, since teams that learn to control the tool's output reliably ship faster and cheaper, but positioning, pricing, and process discipline are what separate a six-figure operation from a well-equipped amateur.

Workflow

The team model matters as much as the tool. Running generation, editing, and client handling as separated roles is what converts Kling's cheap output into reliable delivery, and the workflow below is built to be repeated as a standard operating procedure rather than reinvented per project. Every step exists to protect the two things clients pay for: consistency and deadlines.

• Define the studio's product line first, meaning the kinds of video work you will and will not take, so every Kling capability is developed against a sellable service rather than a demo reel.
• Split roles on the team: creative direction and scripting, prompt-driven generation in Kling, and editing or finishing, so each stage has an owner accountable for quality.
• Storyboard and script every project before generating anything, because shot-level planning is what keeps AI production from burning credits on improvised attempts.
• Build a prompt library for recurring shot types such as product scenes, environments, and camera moves, so proven prompts become reusable studio assets.
• Generate multiple takes per shot in Kling and treat selection as its own discipline: keep only footage that matches the storyboard's tone and holds visual consistency.
• Finish in a standard editor with color grading for uniformity, cutting to pacing, and sound design, since raw AI footage needs a unified pass to feel like one production.
• Run a QA review against the brief before delivery: check continuity, brand accuracy, and whether every required message survived the edit.
• Collect structured client feedback after delivery and fold it into the prompt library and storyboarding templates for the next project.
• Track cost and time per finished minute across projects, and steer the product line toward the video types where the team's margin is best.
• Reinvest early profit into capacity, such as more tool capability, better assets, and deeper specialization, rather than drawing it all out, because the moat compounds with the pipeline.

Case Review

The Xiyang Stone case is the strongest form of the AI video thesis: not one person making clips, but a team earning a six-figure income by industrializing AI generation. Kling matters because its output quality per unit of cost is the economic engine; footage that once needed a shoot now comes from prompts, and the team's cost structure undercuts traditional production while still charging professional rates. The risks are the mirror image of the strengths. As Kling-class tools spread, footage itself commoditizes, and studios compete on price unless they own something defensible, whether a specialized niche, a prompt and pipeline library, or client relationships with repeat work. The case's real lesson is organizational: the income followed the decision to run delivery as a process with roles, QA, and feedback loops. Teams that treat the AI tool as staff rather than magic are the ones that get paid like a studio.

Kling remains Kuaishou's AI video generation platform for text-to-video and image-to-video, and Kuaishou is still its home. For current capabilities, Kling AI's official page is the reference: An 资产 (assets) section and a 全部工具 (all tools) menu round out the app; the page is a JavaScript app shell, so these UI labels are the substance of the landing page (https://app.klingai.com).

Tools Used

• Kling — the AI video generation engine at the center of the team's production pipeline. As its official page puts it: Primary tools in the top navigation: 图片生成 (image generation) and 视频生成 (video generation, badged NEW) (https://app.klingai.com)
• Storyboarding and scripting documents — plan every shot before credits are spent on generation.
• A standard video editor — grading, pacing, and sound design that unify raw AI footage into one piece.
• Project tracking tools — keep roles, deadlines, and per-project feedback organized across the team.