AI Poses an Existential Threat to Bubble.io
A Note to the Bubble Team and Fellow Bubblers
Let me state clearly from the start that, as a dedicated Bubble user and developer whose professional success is directly tied to this platform, I deeply respect what the team has achieved over the past decade. I’m sharing this perspective from a place of experience and commitment—not as a complaint but as a warning—because I strongly believe Bubble is heading toward an existential crisis. My goal here is to initiate an important and perhaps difficult conversation about Bubble’s future, addressing technological economics, structural disadvantages, and shifts in user expectations.
The accelerating capabilities and rapidly falling costs of AI-powered coding agents are swiftly rendering traditional visual abstraction layers obsolete. Bubble must fundamentally reimagine its value proposition beyond merely bridging ideas and code or face irrelevancy within the next 3–5 years.
The Economics Are Undeniable
At the core of this issue is an accelerating technological and economic trend that favors code-first, AI-powered agentic platforms over Bubble’s closed ecosystem.
Consider that, between 2022 and 2024, model inference costs plummeted nearly 280× while GPUs improved their FLOPS-per-watt performance by roughly 40 percent each year—and with Gartner forecasting global generative AI spending to hit $644 billion in 2025, every dollar now buys exponentially more “intelligence.”
These accelerating curves mean that every few months, a dollar buys significantly more computational power. Platforms like Replit, which operate directly on code, automatically capitalize on these physics-driven gains to deliver ever-more powerful results—whereas Bubble’s visual-to-code bridge simply can’t leverage each round of hardware and model improvements at the same pace.
The New Reality for the Target Audience
Bubble’s core users, non-technical founders, face a shifting paradigm. The choice is no longer between code complexity and Bubble simplicity. Instead, users face:
- Option A: Invest significant time and effort to learn Bubble’s visual editor, database logic, and workflow system.
- Option B: Clearly articulate your vision in plain English and have an AI agent translate it directly into a fully operational, scalable app.
Learning Bubble is certainly easier than learning to code, but far from effortless. If a secure, scalable MVP can be achieved by iterating in natural language, the incentive to learn Bubble’s visual system quickly drops to 0.
Why the ‘Last Mile’ Is Not Safe
Arguments for Bubble’s current strengths—deployment ease, security, and robustness—are temporary advantages, not permanent moats. Historically, Bubble has excelled at solving the “last mile” problem, but this advantage is rapidly diminishing.
Building a robust, secure, and scalable application indeed requires far more than just generating code. Bubble advocates frequently point to deployed Replit apps exhibiting security flaws or scalability challenges. However, this perspective fundamentally underestimates the trajectory of AI by assuming its potential is limited to generating code.
For Bubble’s advantage to persist, agentic AI’s progress would need to stagnate—improving only marginally at code generation. This assumption fundamentally misunderstands the trajectory of AI. Integrating frontier models (such as Gemini 2.5 Pro or OpenAI’s o3) immediately elevates generated code quality, robustness, and security. Specialized agents are already emerging for critical operational tasks—security enhancement, automated deployment, scalable database management—closing the operational advantage Bubble historically maintained.
Bubble’s Deeper Structural Disadvantages
Bubble’s attempts at AI integration face inherent structural disadvantages:
Visual-Code Dependency
Bubble translates visual abstractions into code, inherently limiting the pace and scope of its AI capabilities. Competitors like Replit, whose agents directly manipulate code, instantly leverage any new language, process, or architecture without requiring pre-engineered visual infrastructure. Bubble must continuously engineer every visual component and underlying functionality before AI can utilize them effectively, creating a perpetual gap in agility and responsiveness.
Proprietary Grammar and Training Costs
Bubble’s AI must be trained on its proprietary, non-public platform. Achieving world-class AI capabilities requires fine-tuning on extensive Bubble-specific data, incurring substantial costs, making it harder to stay competitive with platforms using general-purpose models.
Development Speed Constraints
Bubble must simultaneously develop advanced AI features and the underlying platform infrastructure. In contrast, competitors instantly integrate powerful new LLMs as they become available. Any advancement in AI immediately translates into enhanced user capabilities for Replit, whereas Bubble faces significant internal engineering delays.
The Lock-in Liability
Bubble’s proprietary nature, once a strength for customer retention, becomes a liability when users can’t easily export their logic to AI-enhanced environments. This creates friction for users who want to leverage the latest AI capabilities.
Acknowledging Current Strengths
To be clear: Bubble remains superior for many use cases today. Complex database relationships, sophisticated workflows, and enterprise-grade applications are still more reliably built in Bubble. The platform’s decade of refinement shows in its stability and feature depth.
But this advantage is eroding at an accelerating pace. Today, Replit’s agent already accomplishes in minutes tasks that take hours in Bubble.
Two Futures for No-Code
Consider two potential futures over the next five years:
Scenario One: Continued AI Acceleration
AI intelligence significantly improves, and costs plummet. Platforms like Replit evolve rapidly, easily managing multi-agent tasks and solving “last mile” issues like security and deployment. For Bubble, this scenario is existentially threatening; its visual bridge becomes irrelevant, forcing a difficult and fundamental reinvention.
Scenario Two: Technological Stagnation
AI capabilities plateau, energy costs stabilize or rise, and agentic platforms fail to deliver meaningful advancements. Bubble’s value proposition remains stable, serving non-technical creators reliably in a world of limited AI progress.
I have very high conviction that Scenario Two will not occur. All available evidence strongly points in the opposite direction.
The Uncomfortable Conclusion
So, I genuinely ask the Bubble team and community: How can this future be avoided? Is there any plausible scenario where agentic, natural-language-driven AI does not render Bubble’s visual abstraction obsolete within five years?
I invite the counterargument. Bubble has built an extraordinary bridge, but the world around it is rapidly learning how to fly.
