Artificial intelligence (AI) has ushered in a new era of software creation, one where ideas move from concept to interface in minutes rather than weeks. Tools like Lovable make this feel almost magical, turning prompts into polished screens while you sip your coffee. But for institutions, governments, and investor-backed ventures, the real question isn’t how fast something can be generated, it’s whether it will still work, scale, and comply a year from now.

At riivo, we put this tension to the test. We built the same MVP twice: once in Bubble, once in Lovable. What we found is that both platforms have strengths, but they play in very different leagues.

Skip ahead: We need a custom solution to our business challenges, fast.

1. AI Has Changed the Starting Line, Not the Finish Line

AI-native builders like Lovable are outstanding at one thing: getting you started. They remove the barrier of the blank canvas and turn abstract requirements into something visual, something you can react to. This is a powerful evolution, especially for teams who need to socialise ideas quickly or secure early stakeholder buy-in.
But early momentum isn’t the same as long-term viability. Beyond the glossy prototypes, institutions need architecture that behaves predictably, complies with regulations, and evolves safely. That’s where Bubble still holds the strategic upper hand.
Where they differ
AI-driven speed (Lovable) vs structured stability (Bubble)
Instant UI (Lovable) vs durable, maintainable logic (Bubble)
Ideation tool (Lovable) vs production-ready engine (Bubble)

**Case study:** **AI and Digital Innovation: The Driving Forces Behind TTT Financial Group’s Industry Leadership**

2. The Build Test: Lovable’s Speed vs Bubble’s Depth

We approached both builds with the same documentation, flows, and expectations, and watched how each platform interpreted them.
**Lovable** delivered screens almost immediately, often beautiful ones, which felt impressive on first glance. But as soon as the MVP required deeper nuance, such as multi-stakeholder logic, state transitions, or conditional workflows, the cracks began to show.
**Bubble** moved at a more deliberate pace, but every step was intentional and verifiable. When we defined a workflow, it behaved exactly as expected. When we added permissions, they were enforceable and transparent. And when the system grew in complexity, Bubble absorbed it without collapsing under its own weight.
Practical distinctions
Lovable is brilliant for visualising ideas but struggles with consistent logic over time
Bubble requires more setup but rewards you with reliability and precision
AI interpretation (Lovable) introduces risk vs deliberate configuration (Bubble)

**Interesting:** **Vibe Coding Without Regrets: Bubble vs Lovable vs Power Platform (What to Use, When, Why)**

3. Code Ownership: Freedom vs Stability

Code ownership is one of the clearest dividing lines between the two platforms.
**Lovable** gives you real, exportable code - React, Tailwind, Supabase - that you can host anywhere and extend with traditional engineering. For teams with internal developers, this unlocks long-term flexibility and avoids platform dependence.
**Bubble** takes the opposite approach: it keeps its runtime proprietary. While you can export data and some structures, you can’t walk away with a deployable codebase. On the surface, that may feel restrictive, but it’s also what allows Bubble to provide a managed, stable, fully maintained environment without handing you DevOps headaches.
What this means in practice
Lovable = maximum flexibility, maximum responsibility
Bubble = guarded ecosystem, predictable behaviour
Institutional teams must choose between autonomy and managed stability

4. Business Logic: Where Lovable’s AI Still Misfires

Business logic is the backbone of any serious application, and this is precisely where AI-generated development still falls short.
**Lovable** can infer simple flows, but as soon as complexity enters the picture, the system’s interpretations become inconsistent. Small changes in phrasing led to different logic paths, and earlier assumptions often vanished without warning.
**Bubble**, however, brings a visual logic engine that leaves nothing to chance. Option Sets, custom states, conditional UI, and workflow triggers mean everything is explicit, reviewable, and maintainable. In environments where workflows must align with compliance, financial controls, or operational standards, this clarity is non-negotiable.
Observed challenges in Lovable
State management was fragile and often unpredictable
Dependencies between screens broke during iterations
Logic became more opaque with each AI-generated adjustment

**Related:** **Why 95% of AI Projects Fail, and How To Prevent It From Happening to You**

5. Authentication: The Breaking Point

Authentication is rarely glamorous, but it’s essential, and it’s often where early-stage development tools reveal their limitations. We required a simple, passwordless phone-number login flow.

Bubble handled it with built-in logic.

Lovable, however, spiralled into a tangle of architectural conflicts with Supabase, temporary passwords being created behind users’ backs, dead code, and security concerns.

When a platform begins breaking fundamental access flows, the problem is structural, not cosmetic. Authentication isn’t optional for institutions; it must be airtight, predictable, and easy to audit.

What went wrong in Lovable

Overwritten hidden passwords created compliance risks
Custom OTP logic clashed with Supabase’s architecture
Multiple authentication flows contradicted each other
Significant development hours were wasted before the solution was abandoned

6. Bubble’s AI Agent Builder: Speed Without Sacrificing Integrity

Bubble’s new AI Agent Builder is perhaps the most exciting development in this space because it blends AI assistance with human-governed clarity. Rather than generating opaque code, the Agent Builder creates workflows directly within Bubble’s visual environment. This means nothing is hidden, everything is editable, and every action is explainable.

This is exactly what institutions want: AI acceleration without black-box behaviour. Bubble is not racing towards a future of autonomous code generation; it’s moving towards AI-enhanced control.

Why this matters

AI suggestions remain fully transparent
All logic stays within Bubble’s standard, compliant framework
It avoids the technical debt AI-generated code often introduces

7. How Institutions Should Use Both Tools

One lesson from this build test is that Lovable and Bubble aren’t competitors, they’re complementary. Institutions can leverage both, but at different stages of the lifecycle. Lovable is the perfect tool for early exploration, allowing teams to visualise ideas at high speed. Bubble is the environment where those ideas become robust, governable systems that can serve real users.

When used together, teams reduce design cost, speed up approvals, and still deliver stable, compliant products.

A practical hybrid model

Ideate in Lovable → fast UI, instant stakeholder alignment
Build in Bubble → predictable, secure, maintainable systems
Enhance using AI Agent Builder → safe AI-driven iteration

8. The Institutional Bottom Line

Brands are judged on whether their systems are secure, compliant, scalable, maintainable, and trustworthy. Lovable accelerates the early creative phase, but it can’t yet satisfy the deeper structural needs of public bodies, enterprises, or investor-backed programmes.

Bubble, on the other hand, may not deliver instant gratification, but it delivers lasting outcomes. And when the stakes involve audits, data governance, or multi-year reliability, durability wins over dazzle every time.

Ultimately.

Lovable changes how ideas begin
Bubble determines whether those ideas survive

Again Confirmed That AI Is a Partner, Not a Shortcut

The future of software development isn’t purely AI-driven or purely human-led, it’s the combination of both. Lovable proves how quickly AI can turn imagination into interface. Bubble proves how structure, clarity, and governance turn software into an asset.

At riivo, we don’t chase hype. We build for environments where systems must stand up to scrutiny, scale responsibly, and earn stakeholder trust. AI will continue to shape the landscape, but the platforms that succeed will be the ones that embrace AI without abandoning rigour.

Because innovation without accountability is just experimentation, and institutions need more than that.

Let’s talk.

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Bubble vs Lovable: What Our Build Test Revealed About AI’s Limits

1. AI Has Changed the Starting Line, Not the Finish Line

Where they differ

2. The Build Test: Lovable’s Speed vs Bubble’s Depth

Practical distinctions

3. Code Ownership: Freedom vs Stability

What this means in practice

4. Business Logic: Where Lovable’s AI Still Misfires

Observed challenges in Lovable