Which company became a unicorn example of vibe coding and grew rapidly?

Lovable became the standout example, offering a platform to describe software and generate it from prompts. It reached dramatic growth, including a $1.8B valuation and $4B inbound offers, though with questions about margins and scalability.

What are the main risks of vibe coding according to the video?

The video highlights hollow confidence, unreliable AI-generated code, security risks, and the need for human oversight. It also notes volatility, cost, and the potential for buggy or dangerous software if not properly checked.

What are some examples of security issues mentioned?

Examples include a multiplayer game's use of Python’s pickle module for networking, which could enable remote code execution, and an app called T that leaked data due to weak encryption.

Is Lovable offering discounts for Cold Fusion viewers?

Yes. Lovable announced a 20% discount via lovable.dev with the code fusiont20 for Cold Fusion viewers.

What does the video say about the state of vibe coding in 2025?

The video argues that vibe coding is here to stay in some use cases but comes with serious limitations and risks; ambition is high but human oversight and expertise remain essential.

Are We Really Ready for AI Coding?

ColdFusion

Science & Technology6 min read22 min video

Nov 7, 2025|260,287 views|11,285|1,381

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Key Moments

TL;DR

AI coding is powerful but risky; human oversight remains essential.

Key Insights

Vibe coding lets you describe apps and generate prototypes quickly, lowering entry barriers.

Lovable exemplifies rapid growth and mass adoption, but raises questions about margins, costs, and sustainability.

The wider ecosystem (Cursor, AnySphere, YC cohorts, big tech) shows rapid mainstreaming, with lasting flaws beneath the hype.

AI-generated code can be unreliable, insecure, or hallucinated, demanding rigorous human review and testing.

The human experience shifts toward prompt-based workflows, risking burnout and a need for new, nuanced skills.

Fundamentals of coding and security remain essential; AI should augment, not replace, skilled development.

ORIGINS AND RISE OF VIBE CODING

Vibe coding marks a shift from writing code to describing desired software outcomes, leveraging the Transformer-era capabilities that power ChatGPT and related models. Its rise traces from copilots and code generators in the early 2020s to a philosophy in 2025 that treats building software as a conversation rather than a syntax-heavy craft. The idea, popularized by Andre Carpathy, is to embrace the vibes, iterate rapidly, and accept that the code may emerge with minimal direct keyboard input. This approach boosts confidence and speeds up initial prototyping, enabling non-technical founders, designers, and students to generate working concepts quickly. But the promise comes with caveats: AI-generated code can be misleading, incomplete, or insecure, and without careful human review, hidden flaws can lurk in the foundation. The narrative around vibe coding emphasizes both the democratization of software creation and the new need for disciplined oversight, as the model-driven outputs require interpretation, validation, and sometimes substantial refactoring.

LOVABLE: THE SYMBOL OF THE MOVEMENT

Lovable represents a high-water mark for vibe coding, illustrating how far the model-driven approach can push the envelope. Born in Sweden in late 2023, Lovable offered a chat- or text-based surface that promised to translate descriptions directly into functional software, effectively expanding who can build. The results were breathtakingly fast: in its first year it reported over $100 million in annualized revenue and more than 10 million projects, with a $200 million Series A at a $1.8 billion valuation and inbound offers that suggested a multi-billion potential. The platform framed software as a frictionless surface area expansion—‘any language to build your software’—and touted speed as a business advantage: ship something this week, iterate with real users. A hands-on testing narrative showed Lovable delivering a mobile/desktop-ready prototype with reasonable responsiveness, but design refinement proved challenging: initial outputs were bland, changing layouts after the fact was hard, and some UI tweaks (like dark mode) required nuanced prompting. A practical takeaway was a clever method: feeding inspiration images into ChatGPT as text to refeed Lovable, yielding better, though still imperfect, designs. Lovable thus anchored the hype in tangible results while revealing the practical limits of automated design at speed.

ECOSYSTEMS AND ECONOMICS BEHIND AI CODING

The vibe coding wave did not stay confined to Lovable. Other players swiftly amplified its momentum: Cursor positioned as an intelligent autocomplete with memory, and AnySphere reportedly reached a multi-billion valuation by commodifying AI-assisted code generation into an ongoing workflow. In Israel, Base44, a no-code AI builder, was acquired by Wix, signaling strategic confidence in AI-enabled tooling. YC’s 2025 batch reportedly saw significant MVPs built largely with AI-generated code, with some claims of extremely high proportions of code being machine-written. Tech giants followed: Microsoft embedded Copilot Everywhere, while Google integrated natural-language code generation into Vertex AI. The pattern was clear—describe, ship, and iterate became a mainstream capability across startups and large ecosystems alike. Yet economics remained a constraint: per-call fees to model providers, memory and compute costs, and the challenge of maintaining healthy margins as the scale of usage grows. The broader takeaway is that vibe coding moved from a novelty to a platform-level phenomenon, inviting competition, collaboration, and ongoing scrutiny of cost, reliability, and architectural quality.

HUMAN EXPERIENCES: JOY, BURNOUT, AND LEARNING

A central tension in the vibe coding era is the human experience behind the automation. A prominent voice in the space, CJ, described a once-thrilling thrill of AI-assisted coding turning into fatigue, as the back-and-forth with an LLM often erased the satisfaction of solving problems independently. He spoke of inconsistent results, even with repeated prompts, and warned that silent model updates could disrupt workflow. The sentiment reflects a broader critique: the culture around prompt engineering can feel like a new religion, where the same tasks yield different outputs across days or model versions. The emotional impact—loss of pride in craftsmanship and the sensation of being a ‘prompter’ rather than a creator—contributes to burnout and prompts some to step back and code manually for a while. Despite these challenges, many celebrate the efficiency gains and potential for rapid iteration, but the consensus is that sustained productivity requires discipline, robust testing, and a willingness to blend human expertise with AI assistance.

RISKS: SECURITY, RELIABILITY, AND SKEPTICISM

Security and reliability remain the most consequential risks of vibe coding. The same ease that lets anyone generate a prototype also enables hallucinations, phantom APIs, and fragile architectures that look plausible on the surface. The video recounts concrete concerns: an AI-generated multiplayer game relied on Python’s pickle module, creating a vector for remote code execution; unencrypted deployments leaked millions of messages and images; and a general trend toward copy-and-paste development reduces fundamental understanding of what the software actually does. Critics argue that many tools are just variations on the same underlying models, leading to a ‘magic trick’ effect where users chase novelty rather than solid engineering. The takeaway is not to reject AI, but to acknowledge that vibe coding requires validation, security-by-design, and rigorous review. For critical systems, reliance on the model without audits is dangerous; for simple pages or MVPs, it can be helpful if used with caution and proper safeguards.

A PATH FORWARD: RESPONSIBLE USE AND EDUCATION

The overarching conclusion is nuanced: vibe coding is here to stay, but its true value depends on responsible usage. For straightforward projects like landing pages or early MVPs, tools like Lovable can drastically accelerate experimentation and reduce the entry barrier. For mission-critical software, the risk calculus favors disciplined practices: combine AI-generated scaffolding with human-led architecture decisions, security reviews, and comprehensive testing. The future may resemble a conversation-based operating system for building software, where AI handles routine scaffolding and humans steer architecture and quality. Education remains essential; fundamentals of software design, security, and debugging are not optional luxuries but prerequisites for meaningful AI collaboration. The message is to balance creativity with caution: harness the speed and reach of vibe coding while maintaining rigorous standards, invest in ongoing learning, and treat AI-generated code as a draft requiring review, refinement, and robust validation before it reaches production.

Mentioned in This Episode

●Software & Apps

●Companies

●Concepts

●People Referenced

Vibe Coding Cheatsheet: Do's and Don'ts

Practical takeaways from this episode

Do This

Start with a simple MVP prompt describing the app you want.

Iterate by feeding back text descriptions of inspiration images to Lovable.

Validate AI-generated code with human review and testing.

Use AI for rapid prototyping, but don’t skip fundamentals.

Avoid This

Rely on AI alone for production-grade code without oversight.

Ignore security risks or assume all AI-generated code is safe.

Common Questions

Vibe coding is a style of building where you describe what you want and the AI writes the code. It promises faster prototyping and easier access for non-technical founders, but it can produce fragile, insecure, or incorrect code without human oversight.