Advanced generative media workflows are not simple text-to-video prompts. Top customers chain an average of 14 different models for tasks like image generation, upscaling, and image-to-video transitions. This multi-model complexity is a key reason developers prefer open-source for its granular control over each step.

With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.

Figma CEO Dylan Field predicts we will look back at current text prompting for AI as a primitive, command-line interface, similar to MS-DOS. The next major opportunity is to create intuitive, use-case-specific interfaces—like a compass for AI's latent space—that allow for more precise control beyond text.

Current text-based prompting for AI is a primitive, temporary phase, similar to MS-DOS. The future lies in more intuitive, constrained, and creative interfaces that allow for richer, more visual exploration of a model's latent space, moving beyond just natural language.

Advanced multimodal AI can analyze a photo of a messy, handwritten whiteboard session and produce a structured, coherent summary. It can even identify missing points and provide new insights, transforming unstructured creative output into actionable plans.

While today's focus is on text-based LLMs, the true, defensible AI battleground will be in complex modalities like video. Generating video requires multiple interacting models and unique architectures, creating far greater potential for differentiation and a wider competitive moat than text-based interfaces, which will become commoditized.

An AI-generated image is no longer a final product. It's the starting point that can be branched into countless other formats: videos, 3D assets, GIFs, text descriptions, or even code. This 'infinite branching' approach transforms a single creative idea into a full-fledged, multi-format campaign.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

Exceptional AI content comes not from mastering one tool, but from orchestrating a workflow of specialized models for research, image generation, voice synthesis, and video creation. AI agent platforms automate this complex process, yielding results far beyond what a single tool can achieve.