- SHELL+28Positive
MyShell.AI (@myshell_ai)
When AI Models Are Everywhere, What Becomes Scarce? For the past few years, much of the AI conversation has revolved around models. A new release arrives with a higher benchmark score, a larger context window, better reasoning, faster generation, or a lower price. Then another model catches up a few weeks later. By 2026, that cycle has become almost routine. Stanford’s 2026 AI Index shows how quickly leading models are converging on several major benchmarks. At the same time, the number of capable models available to creators keeps growing across text, image, video, voice, and agents. For creators, that changes the question. Access to powerful AI is becoming easier. Choosing what to do with it is becoming more important. The creative edge is moving up the stack Creators are already working this way. Adobe’s global creator research found that 86% of creators surveyed were using generative AI, while 60% had used more than one generative AI tool in the previous three months. That tracks with how AI creation increasingly works in practice. A creator might use one model to explore visual directions, another to generate images, a video model to animate them, and a voice model to finish the piece. What starts to matter more is the combination: which tool you choose, when you switch, what you keep, and how you shape the final result. Two creators can start with access to the same models and end up somewhere completely different. That gap is where taste, judgment, and creative direction begin to matter. More generation creates more decisions Lower generation costs make experimentation much easier. A single brief can produce ten visual directions in minutes. A character can be tested across different voices, formats, and styles. An idea can move from a still image to video, an interactive agent, or a small AI application without requiring a full production team. That gives creators a much wider field to work with. It also creates more decisions. Which version actually fits the brief? Which result feels generic? Which detail is worth refining? When should the creator stop generating and start editing? When is a different model a better fit? These choices increasingly shape the quality of the final work. The ability to generate becomes the starting point. The ability to curate becomes part of the craft. Shared tools can also create shared aesthetics There is another side to widespread access. When large numbers of creators use the same models, similar prompts, and the same recommendation systems, creative output can begin to cluster around familiar patterns. Researchers from the University of Mannheim and MIT have started examining this question through AI-assisted creation on YouTube: what happens to creative diversity when creators receive AI-generated ideas, hooks, and content directions from the same system? It is an important question for the next phase of AI creation. As AI becomes embedded in more creative workflows, originality will depend increasingly on what creators do after the first generation. Selection matters. Iteration matters. Context matters. So does knowing when to ignore the obvious suggestion and take the work somewhere else. The creator role is expanding AI is also changing what a creator can build. Creation no longer stops at an image, a song, or a video. A creator can design a character with its own voice and behavior. They can build a reusable workflow. They can create an agent, an interactive experience, or a small AI-native product that other people return to. That brings creative work closer to product building. The creator is making choices across models, interfaces, interactions, and distribution. In many cases, one person can now prototype something that previously required several specialists. This expands the idea of the creator economy itself. The next generation of creators may be producing media, tools, agents, experiences, and applications at the same time. From model capability to creator experience A useful way to look at the emerging stack is: Models → Tools → Creators → Experiences → Users Models keep pushing the technical ceiling higher. Tools make those capabilities easier to access. Creators decide how those capabilities should be combined, shaped, and presented. Users experience the result. MyShell sits in this layer of the ecosystem. Across MyShell Art, ShellAgent, and the wider MyShell creator ecosystem, creators can work with different AI capabilities and turn them into images, videos, agents, workflows, and AI-native applications that can be published and shared with others. As the model layer keeps moving faster, this creator layer becomes more interesting. There will always be another model release. When powerful AI becomes widely available, scarcity moves elsewhere. Toward taste. Toward ideas. Toward judgment. Toward the ability to turn a growing set of capabilities into something people actually want to use, experience, and share.