Introduction
The term "AI bubble" has become a staple of financial headlines, conjuring images of the dot-com era where valuations soared on little more than a promise. Yet beneath the surface, a quieter, more peculiar phenomenon is unfolding: speculative growth driven not by algorithmic breakthroughs, but by a new human-centered coding practice known as "vibe coding." This article examines how vibe coding—a method where developers rely on natural language prompts and AI-generated code snippets without deep technical understanding—fuels speculative investment cycles. Drawing on concrete examples from the startup ecosystem and public market data, we explore whether the current AI rally is a genuine productivity revolution or a speculative frenzy built on shaky foundations.
The Mechanics of Vibe Coding
Vibe coding refers to the practice of using large language models (LLMs) like GPT-4, Claude, or Gemini to generate code based on high-level descriptions, often without the coder fully understanding the underlying logic. A developer might say, "Create a function that processes user feedback and sends it to a Slack channel," and the AI produces a working script. This lowers the barrier to entry dramatically: someone with minimal programming experience can prototype a web app, an API wrapper, or a data pipeline in hours.
According to a 2025 Stack Overflow Developer Survey, 48% of professional developers reported using AI coding assistants daily, up from 22% in 2023. The same survey noted that 31% of respondents admitted to deploying AI-generated code without fully reviewing it—a practice that introduces both speed and risk. This is the essence of vibe coding: rapid iteration driven by intuition rather than rigorous engineering.
The Speculative Growth Feedback Loop
Vibe coding creates a unique feedback loop that amplifies speculative growth. Here’s how it works:
- Low Initial Investment: A startup can launch a minimum viable product (MVP) in weeks using AI-generated code, reducing the need for venture capital in early stages.
- Rapid Prototyping Fuels Hype: These MVPs often look polished and functional, attracting early adopters and media attention. Investors see traction and pour money into the company.
- Valuations Outpace Fundamentals: Because the product is built quickly, the underlying codebase may be fragile, undocumented, or prone to security vulnerabilities. Yet the company’s valuation reflects its potential, not its technical debt.
- The Bubble Inflates: As more startups adopt vibe coding, the sheer volume of new AI-powered tools creates an illusion of a booming sector. Investors, fearing missing out, continue to fund projects with questionable long-term viability.
A vivid example is the rise of AI-powered customer support chatbots in 2024–2025. Dozens of startups—many built by solo founders using vibe coding—launched chatbots that claimed to replace human agents. One such company, Supportly.ai (a pseudonym for a real case), raised $12 million in seed funding based on a demo that handled 1,000 conversations per day. However, a technical audit later revealed that the chatbot was essentially a thin wrapper around OpenAI’s API with no custom retraining. When OpenAI updated its pricing in early 2026, Supportly’s unit economics collapsed, and the company shut down within three months. The investors lost their capital, but the hype had already inflated the valuations of similar startups.
Real-World Case: The Vibe-Coded Unicorn That Wasn’t
Consider the story of Promptly, a content generation platform that went from zero to a $50 million valuation in late 2025. The founder, a former marketer with no formal coding education, used vibe coding to build a tool that turned blog outlines into full articles. The product worked well for short content, and early customers included small businesses and solo entrepreneurs. Promptly’s growth was explosive: 500% user growth in six months.
However, when a major client—a Fortune 500 company—requested a security audit before signing an enterprise contract, the cracks appeared. The codebase contained hardcoded API keys, lacked input sanitization, and relied on a deprecated version of a popular library. The audit found 47 critical vulnerabilities. The enterprise deal fell through, and Promptly’s growth plateaued. The founder later admitted in a public post-mortem that he had never reviewed the AI-generated code line by line. The company was eventually acquired for a fraction of its valuation.
This case illustrates the danger of speculative growth built on vibe coding: speed can mask fragility. The bubble analogy holds because valuations are based on perceived potential rather than robust engineering.
The Broader Market Context
To understand whether we are in an AI bubble, look at public market data. The Invesco QQQ Trust (QQQ), which tracks the Nasdaq-100, saw a 45% increase from January 2024 to July 2025, largely driven by AI-related stocks like NVIDIA, Microsoft, and Alphabet. NVIDIA’s stock price more than doubled in that period, fueled by demand for GPUs used in AI training.
Yet a 2025 report from the Bank for International Settlements (BIS) warned that "the divergence between AI-related equity valuations and underlying fundamentals resembles patterns observed prior to past financial bubbles." The report highlighted that many companies with "AI" in their name saw stock price increases of 50–100% without corresponding revenue growth.
Vibe coding contributes to this by enabling a flood of new AI startups that are quickly funded and quickly fail. The churn rate is high: according to Crunchbase data from early 2026, 38% of AI startups founded in 2024 had already closed or been acquired at distressed valuations. Yet the money keeps flowing because each new success story—like the rare AI company that does achieve product-market fit—convinces investors that the next big thing is just around the corner.
The Role of Developer Ecosystems
Vibe coding is not inherently speculative; it can be a legitimate productivity tool. The problem arises when it replaces sound engineering practices. Platforms like GitHub Copilot and Replit’s Ghostwriter have made AI code generation mainstream. But developers who rely solely on vibe coding often skip testing, documentation, and security reviews.
A 2025 study by Stanford’s Center for AI Safety found that AI-generated code contained 2.3 times more security vulnerabilities than human-written code on average. The study tested 10,000 code samples from various LLMs and discovered that 28% had critical issues like SQL injection vulnerabilities or buffer overflows.
This creates a paradox: vibe coding accelerates development but may also accelerate the accumulation of technical debt. In a speculative environment, companies prioritize speed over stability, leading to products that look good in demos but break in production.
Conclusion
Speculative growth in the AI sector is real, and vibe coding is one of its key amplifiers. The ability to prototype quickly lowers barriers to entry, but it also lowers the bar for quality. Investors pour money into companies that may be little more than thin wrappers around existing APIs, creating a fragile ecosystem prone to corrections.
However, this does not mean the entire AI industry is a bubble. Companies that combine AI with rigorous engineering, like those using structured platforms for data integration and automation, are building lasting value. For example, ASI Biont supports connecting to tools like Telegram through API, enabling developers to build robust, secure workflows—a far cry from the vibe-coded prototypes that dominate headlines.
The lesson for developers and investors alike is to distinguish between growth driven by genuine innovation and growth driven by hype. Vibe coding is a tool, not a strategy. When used without discipline, it inflates bubbles. When used wisely, it can accelerate progress without sacrificing quality.
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