I'm writing this from the perspective of someone completely immersed in the creation process. I've spent the last few weeks building my applications and products with the help of vibe coding tools. I have plenty of thoughts and insights on this topic that I'll describe in the coming days. I'll discuss several areas today, but what struck me most is this: the barrier to entry for those who want to build is very low. If you can identify a problem and know how to monetize it, the possibilities are phenomenal.
AI is fundamentally transforming the way we build products, introducing new programming paradigms and accelerating development at unprecedented speed.
The AI coding tools market reached $4.86 billion in 2023 and is projected to grow to $26.03 billion by 2030, with a CAGR (2024-2030) of 27.1%, while 76% of programmers are already using or planning to use AI tools in their work.
What is Vibe Coding?
"Vibe coding" – essentially building software by vibing with an AI assistant rather than hand-writing every line. In practice, vibe coding means I can simply tell an AI what I want my app to do (often in plain English/Polish, in my case) and watch it spit out working app/code. As Merriam-Webster explains, "vibe coding" is this new phenomenon where you build apps by just telling an AI program what you want, with the coder often not needing to understand the code in detail and accepting that some bugs may remain. In other words, it's less about typing code and more about guiding the AI.
This sounds almost magical and in simple cases, it often is. With today's tools (I've been using things like Replit's AI, Cursor's AI, Bolt.new, GitHub Copilot, etc.), even non-experts can prototype apps in minutes. For example, Replit's Agent can take a natural-language prompt and build a runnable web app, while Cursor and Copilot can autocomplete code and even refactor entire projects with the right prompts. I also have used Claude for challenging the output.
Origins
In concrete terms, vibe coding is basically AI-assisted programming taken to an extreme. It originated with AI researchers like Andrej Karpathy and gained popularity when journalists noted that hobbyists can now build websites and apps by just giving prompts to an AI model. Kevin Roose (New York Times) called it "writing code by telling an AI what you want", and even major firms are experimenting: reports suggest about 25% of startups in Y Combinator's Winter 2025 had "codebases that were 95% AI-generated," essentially vibe-coded by their founders. (Of course, these are very early experiments – many are still buggy or one-off prototypes.)
Vibe coding can be thrilling. As written at the very beginning - it dramatically lowers the barrier to building software: if you have an idea, you can often generate a crude working version immediately. You don't need to manually write every function or deal with syntax – you just describe the feature and let the AI handle the boilerplate. In this sense, it's akin to those "software for one" projects where someone builds a tiny custom tool for themselves with AI help (yep: my school). It has democratized coding: as Merriam-Webster puts it, "You don't have to know how to code to vibe-code – just having an idea, and a little patience, is usually enough."
Traditional programming focuses on "how" - implementation details, manual coding, and deep understanding of code.
Vibe coding focuses on "what" - desired outcomes, conversational development, and trusting AI capabilities.
However, there are caveats. Experts warn that blindly accepting AI-generated code can lead to serious issues. If you don't really understand the output, you might introduce subtle bugs, security holes, or unmaintainable spaghetti code. Having foundations and prior exposure to code was very helpful for me.
Analysts note that vibe coding might be fine for quick throwaway projects or learning, but building a robust, complex product still requires human oversight and engineering rigor. This was my experience – I reached out to a friend, showed him a demo, and asked how to improve or deploy the application.
In my own experience, I see that AI (for now) doesn't fully replace the developer's job. It's a tool for prototyping and learning, not a magic wand for final products – at least not yet.
Impact on Product Building
The development process is becoming democratized. You know what you want to build. You write a prompt. You provide context. You provide corrections. You iterate. You become an architect of prompts. You define problems and validate solutions. You describe, present your vision, and improve. And boom!
Changes in workflow:
Traditional flow: Requirements → Design → Code → Test → Deploy
Vibe coding flow: Describe → Generate → Iterate → Improve → Deploy
New possibilities:
Voice-to-code programming with tools like SuperWhisper
Conversational debugging by copying error messages
Rapid prototyping with instant visual feedback
Adoption Statistics
76% of programmers are using or planning to use AI tools (up from 70% in 2023)
60-75% feel more fulfilled with AI assistance
55% faster task completion with GitHub Copilot
Stack Overflow Developer Survey 2024 (65,000+ responses):
82% of programmers use AI tools primarily for writing code
82% of respondents used generative AI in at least two different phases of the development process
26% incorporated AI into four or more development stages
New capabilities
Teams of 10 people are now capable of work that previously required 50-100 engineers
Workflow automation: Repetitive processes and tasks are automated through tools like n8n
Platforms Pros and Cons
The key to vibe coding is the platform you use. Here's a quick rundown of some popular AI-assisted coding tools I've tried, along with their strengths and weaknesses:
Replit
A browser-based IDE that runs entirely in the cloud. It's very accessible (no local setup needed) and great for prototyping and collaborative learning. The Ghostwriter AI in Replit offers code completion and suggestions in many languages. It supports 50+ languages and even hosts your app for you, which is handy. I consider it easy to jump in. Builds complete applications from natural language prompts. For very complex tasks, you still need to prompt carefully or code manually. Also, running entirely in a browser means heavy projects can get sluggish.
Capabilities: Generates full codebases, sets up environments, configures databases, and handles deployment.
Cursor
A relatively new AI-native code editor. Cursor is a standalone app (built on a VS Codebase) that is entirely AI-first. Unlike Copilot (an extension), Cursor is its own desktop IDE with a deeply integrated AI agent. It indexes your whole codebase, so its suggestions can draw on project-wide context. Cursor's AI offers multiple interaction modes: you can have a chat that reads your files, asks questions, or lets it apply changes across files. It even supports several AI engines (GPT-4, Claude, Google Gemini, etc.), giving you flexibility. In my tests, the Cursor feels powerful. Consistent at multi-file tasks and has advanced features like "Tab" (an autocomplete that suggests entire diffs).
Lovable
Lovable AI is an in-browser platform that generates full-stack web apps from plain English prompts. Sweet spot is rapid prototyping. It automates mundane setup (UI + backend + deploy) so you can focus on user flow. Lovable prioritizes turnkey output and offers a compelling, well-integrated package for builders who want to skip boilerplate. Its limitations are similar to other AI builders.
Bolt.new
A chat-based full-stack AI coder for web apps. Bolt is like Google Docs meets coding: you open a chat, describe the app and features you want, and it generates the front-end and back-end code for you in real-time. It supports modern JS frameworks and integrates with tools like GitHub and Figma. In practice, I've found Bolt useful for generating boilerplate and simple UIs. But many bugs catch me in the process.
GitHub Copilot
An AI assistant that plugs into traditional code editors (VS Code, JetBrains IDEs, etc.). Copilot excels at inline code completion: you write a comment or start a function, and it predicts what you want. It has evolved to include chat ("Copilot Chat"), multi-file edits, and even autonomous "agent" features, basically acting like a junior developer. Copilot works with popular development setups, which is a big plus (you don't have to switch tools). Overall, it's a solid, cost-effective way to get AI help in your normal workflow.
Pricing Comparison
Replit
Starter: Free (Limited AI access (trial access to Agent and Assistant)
Replit Core: $20 USD/month (annual) or $25 USD/month
Teams: $40 USD/user/month
Enterprise: Custom pricing
Cursor
Hobby: Free
Pro: $20/month (new model: "unlimited with rate limits")
Ultra: $200/month (new plan, 20x more)
Teams: $40/user/month
Bolt.new
Free: 150K tokens daily, 1M monthly
Simple Pro: $20/month (10M tokens)
Pro 50: $50/month (26M tokens)
Pro 100: $100/month (55M tokens)
Pro 200: $200/month (120M tokens)
GitHub Copilot
Free: new limited plan
Pro: $10/month
Pro+: $39/month (new plan)
Business: $19/user/month
Enterprise: $39/user/month
Lovable
Free: 10 editions daily
Starter: $20/month
Launch: $50/month
Claude
Free: ~30 prompts daily
Pro: $20/month
Max: $100/month (5x more) or $200/month (20x more) - new plan
Team: $30/user/month
Enterprise: Custom pricing (~$60/seat 70+ users)
Some Reflections
All these tools point to a bigger shift: building software is getting easier but also more crowded. Demos and prototypes that would have taken weeks or months can now be spun up in minutes with AI. Yet industry real product development – with polish, reliability, and scalability – remains hard. As one recent analysis from a16z noted, it's relatively trivial nowadays to create a flashy AI demo, but the gap from a demo to a full-fledged product is "even wider" in practice. In other words, AI has made early prototyping easy, but the last mile of development (robust testing, edge cases, security, integration) still demands serious work.
Likewise, the bar for success seems to have changed. Old benchmarks (like 3× growth or hitting $1M ARR) are no longer enough. In fact, observers say the breakout sweet spot has shifted: the fastest-growing AI-driven startups today are often achieving 10× year-over-year growth or more. This doesn't mean every idea should be scaled 10-fold, but it highlights how big and fast winners move in the AI era.
Another big trend is democratization: the barrier to entry for building apps has plunged. Thanks to plummeting compute costs and user-friendly AI IDEs, a flood of new apps is emerging. It's no longer only professional developers who can ship software. Suddenly, a hobbyist can build a personal health tracker or a productivity bot by themselves; a founder can code an MVP without hiring a full team. I wrote a traveling app, an orchestrator for subscription management, and an investment tracker. Some for fun, for the pure sake of building, and to solve problems.
In this environment, speed matters more than ever. You need to iterate quickly and launch fast to capture users' interest before someone else does something similar. In my own work, I've found that taking an idea from concept to demo to working beta in a weekend can make all the difference in getting early feedback and traction.
However, one must be realistic: rapid prototyping alone might not give a robust product with a sustainable moat. With so many new products, we still need real moats to endure.
In practical terms, this means designing your product so it solves a deep problem or integrates so tightly with customers that switching to an alternative (even one with fancy AI) is hard. In other words, you still need to answer, "Why would users stick with this app?" beyond just "It was built with cool AI." It's a reminder I keep coming back to: speed and AI can get you to market, but product vision and execution build a real business.
Challenges and Limitations
25-70% of AI-generated code contains vulnerabilities
Vibe coding increases security risks due to a lack of code review
Need for human oversight for critical decisions
AI tools can fill the role of a junior on the team. No one wants juniors anymore. If the "pool" of juniors shrinks, there will also be fewer seniors.
Risks related to a superficial understanding of what was written also emerge.
Summary: Riding the AI Wave
From my perspective, we're in an exhilarating but tricky moment. Tools for vibe coding are multiplying. This creative empowerment is unprecedented; every week brings something new to the AI space. At the same time, I've learned to stay grounded. The polished, robust products will still require traditional skills: architecture design, testing, understanding user needs, and ensuring security and performance. The ecosystem of product-building is evolving, but fundamentals remain.
I'll keep using AI tools (Replit, Copilot, Cursor, etc.) to prototype and iterate at warp speed. As a builder, I'm excited by these possibilities, yet mindful of the craftsmanship needed to turn those AI prototypes into products that truly last.