Right now, 78% of people in the world have never dabbled in AI. It's just that remaining 0.12%—the pro users in those three little squares—who are shaking up the entire industry.
What does this mean? The real penetration of AI hasn't even started yet.
What has always bridged this gap is not the strength of AI itself.
Tools like Doubao and GPT have been within reach for ages; the real hurdle lies in whether you can turn a vague demand into commands that AI can execute reliably.
This task requires pro users to spend a ton of time experimenting, while regular folks are left in the dark.
Speaking of which, I have to mention a project that's been covered by Xinzhi Yuan, Machine Heart, Geek Park, and Quantum Bit.
xBubble is an AI project launched by the DappOS team, which has received backing from Sequoia China and Yzi Labs, making it one of the hottest Web3 background AIs out there.
It positions itself as a Low-prompt AI Agent, meaning it's designed to teach AI how to use AI, allowing you to accomplish complex tasks with shorter requests.
When you look at a comparison of xBubble against other AIs for the same brief prompt, it’s easy to see that xBubble is more professional, clearly utilizing well-tuned SOPs to deliver results.
xBubble’s processing flow relies on two main components: Bubble Pilot and Bubble Engine.
Bubble Pilot: Smart Execution Hub
- Responsible for task delegation; once it receives a command, the Pilot matches SOPs in the background and selects the optimal path to complete the task. If no matching process is found, it automatically switches to a universal Agent as a fallback, ensuring responses are never empty.
Bubble Engine: Automated Evolution Engine
- Responsible for skill learning; for unknown tasks, the Engine generates multiple solutions through AI programming and tests them against each other. Once a quality-verified optimal path is established, it solidifies that as a reusable SOP/Skill, enabling continuous self-expansion of capabilities.
When the internet bubble burst, many concept stocks fell due to the inability to scale.
But now, AI is achieving self-iteration through projects like xBubble, meaning AI is learning from AI and even using AI.
Back to that initial 78%, the enhancement of productivity through AI shouldn't only belong to that 0.12%.
What xBubble aims to do is to enable those remaining folks who have never encountered AI to achieve professional-level output with just a simple statement of goals.
What does this mean? The real penetration of AI hasn't even started yet.
What has always bridged this gap is not the strength of AI itself.
Tools like Doubao and GPT have been within reach for ages; the real hurdle lies in whether you can turn a vague demand into commands that AI can execute reliably.
This task requires pro users to spend a ton of time experimenting, while regular folks are left in the dark.
Speaking of which, I have to mention a project that's been covered by Xinzhi Yuan, Machine Heart, Geek Park, and Quantum Bit.
xBubble is an AI project launched by the DappOS team, which has received backing from Sequoia China and Yzi Labs, making it one of the hottest Web3 background AIs out there.
It positions itself as a Low-prompt AI Agent, meaning it's designed to teach AI how to use AI, allowing you to accomplish complex tasks with shorter requests.
When you look at a comparison of xBubble against other AIs for the same brief prompt, it’s easy to see that xBubble is more professional, clearly utilizing well-tuned SOPs to deliver results.
xBubble’s processing flow relies on two main components: Bubble Pilot and Bubble Engine.
Bubble Pilot: Smart Execution Hub
- Responsible for task delegation; once it receives a command, the Pilot matches SOPs in the background and selects the optimal path to complete the task. If no matching process is found, it automatically switches to a universal Agent as a fallback, ensuring responses are never empty.
Bubble Engine: Automated Evolution Engine
- Responsible for skill learning; for unknown tasks, the Engine generates multiple solutions through AI programming and tests them against each other. Once a quality-verified optimal path is established, it solidifies that as a reusable SOP/Skill, enabling continuous self-expansion of capabilities.
When the internet bubble burst, many concept stocks fell due to the inability to scale.
But now, AI is achieving self-iteration through projects like xBubble, meaning AI is learning from AI and even using AI.
Back to that initial 78%, the enhancement of productivity through AI shouldn't only belong to that 0.12%.
What xBubble aims to do is to enable those remaining folks who have never encountered AI to achieve professional-level output with just a simple statement of goals.