To onboard the next billion users, ChatGPT's image generation feature avoids forcing users to invent prompts from a blank canvas. It offers pre-canned ideas and styles like "turn yourself into a bobblehead," lowering the barrier to creation and encouraging sharing via links, which in turn drives app installs and new user acquisition.
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Once you've identified the core components of an image, structure them into a repeatable formula. This template allows anyone on your team, even non-designers, to generate consistent, on-brand assets by simply filling in the blanks, effectively turning prompting into a scalable system.
To introduce advanced technology without alienating its broad user base, Moonpig framed generative AI within a simple, familiar concept: 'AI stickers.' This approach drove massive adoption by making the feature feel magical and intuitive, rather than complex and technical.
The challenge in using AI effectively is often prompt engineering, not model capability. A potential solution is a social platform where users can follow experts, discover their prompts, and be 'catalyzed' by others' creativity. This democratizes access to AI's full potential beyond one's own ingenuity.
For a generative video model like OpenAI's Sora 2 to achieve viral adoption, it needs a universally appealing, simple-to-execute prompt, much like DALL-E's "Studio Ghibli moment." A feature like "upload your profile picture and turn it into a video" would engage a mass audience far more effectively than just showcasing raw technical capabilities.
Open-ended prompts overwhelm new users who don't know what's possible. A better approach is to productize AI into specific features. Use familiar UI like sliders and dropdowns to gather user intent, which then constructs a complex prompt behind the scenes, making powerful AI accessible without requiring prompt engineering skills.
Google is sidestepping a direct confrontation with ChatGPT's text-based dominance. Instead, it's leveraging viral, multimodal models like NanoBanana to drive user acquisition through creative use cases, a domain where OpenAI was previously seen as the leader.
Nano Banana's popularity stemmed from fun, accessible entry points like creating self-portraits. This 'fun gateway' successfully onboarded users, who then discovered deeper, practical applications like photo editing, learning, and problem-solving within the same tool.
Despite comparable model capabilities, OpenAI's thoughtful UX, like providing trending templates in a TikTok-style feed for image generation, successfully guides users. In contrast, Google's blank-slate interfaces can intimidate users, proving that small product details are crucial for adoption.
In a significant shift, OpenAI's post-training process, where models learn to align with human preferences, now emphasizes engagement metrics. This hardwires growth-hacking directly into the model's behavior, making it more like a social media algorithm designed to keep users interacting rather than just providing an efficient answer.
Genspark's 'auto prompt' function takes a simple user request and automatically rewrites it into more detailed, optimized prompts for different underlying image and video models. This bridges the gap between simple user intent and the complex commands required for high-quality generative AI output.
