The Vibe Coding Imperative for Product Managers

As a Technology Product Manager with years of experience building products, I’ve witnessed many shifts in the industry. But nothing compares to the transformation we’re seeing now with “vibe coding,” a term coined by Andrej Karpathy in February 2025. This isn’t a new tool or methodology—it’s a fundamental change in how we turn ideas into products, how we approach product development, from initial ideation to strategic execution. For product managers working with AI, understanding vibe coding isn’t optional; it has become a competitive necessity.

What Exactly is Vibe Coding?

Vibe coding is a groundbreaking shift in software development, where, instead of writing code line by line, you describe what you want in plain English. The AI figures out the rest. As Karpathy puts it, “It’s not really coding—I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works. It’s a way to fully give in to the vibes, embrace exponentials, and forget that the code even exists.”¹

Powered by advanced large language models (LLMs) like GPT-4, vibe coding flips traditional programming on its head. Think of it like having a conversation with a good developer who never gets tired, never gets frustrated, and can instantly turn your ideas into working software. You say, “I need a login page that remembers users,” and minutes later, you will have one. The barrier between having an idea and seeing it work has almost disappeared.

Key characteristics of vibe coding include:

• Natural language input: You describe, not code.
• Multimodal interaction: Utilizing voice, text, or even images.
• Conversational development: Iterating through dialogue.
• Agentic autonomy: The AI provides suggestions, flags issues, and learns your style.

The Tech Behind the Magic
Vibe coding combines several technological advances in AI:

• Multimodal AI Models: Models like OpenAI’s GPT-4o and Google Gemini 1.5 process various inputs (text, voice, images) and turn them into working code.¹⁰
• Conversational Memory: AI systems such as LangGraph and OpenDevin use vector embeddings—numerical representations of data—to remember past interaction preferences and design decisions for smarter iterations as they remember what you’ve built before and can carry context across entire projects.¹¹
• Reinforcement Learning with Human Feedback (RLHF): This technique trains AI models using reward systems that prioritize output to align with human values and nuances. With this, the models do not just write
  code but write code the way humans want it written.⁷
• Integrated Development Environments (IDEs) with AI-native workflows: Platforms like Replit, Cursor, and Ghostwriter are more than just code editors, they are conversation partners. Tools like Serenade or superwhisper even allow spoken instructions, simulating a dialogue and making the whole process feel more like brainstorming than programming.

Why It’s a Big Deal: Reshaping Product Management
Vibe coding is transforming the role of product managers, transforming how we prototype, discover insights, and strategize:

• Rapid Prototyping: McKinsey reports that generative AI can reduce product time-to-market by 5% and increase productivity by 40%. This means that what once took weeks can now be accomplished in days or even hours.
• Democratized Creation: Vibe coding empowers non-technical product managers, designers, and business stakeholders to contribute directly to prototype creation, fostering a more collaborative environment. In the
  2025 Vibe Coding Game Jam, sponsored by Bolt and Lambda, non-technical teams created playable games using AI-generated code, demonstrating the accessibility of this approach.
• Iterative Discovery: AI can analyze vast datasets like social media, industry reports and user behavior data to identify trends and validate ideas quickly. For example, AI-driven tools can track real-time user
  interactions with prototypes, providing immediate feedback for refinement.⁸

These benefits are already evident: GitLab’s 2024 survey found that 78% of development teams use AI-assisted coding,³ with projections that 80% of developers will need AI skills by 2027.

The Catch—It’s Not Perfect
To be honest, vibe coding isn’t perfect. Despite its advantages, it has certain limitations as well.

• Unpredictable Code: AI-generated code can be unpredictable. When the same prompt is run multiple times, different outputs will be generated.
• Security Concerns: Studies show 25% to 70% of AI code has vulnerabilities like SQL injections. Simon Willison warns that “vibe coding your way to a production codebase is clearly risky without understanding the underlying code.”⁵
• Debugging and Maintenance: Without grasping the generated code, fixing bugs can be challenging. Karpathy himself admits to pasting error messages back to the AI or making random tweaks to fix bugs. ¹

With this context, the key is knowing when to use vibe coding and when to stick with traditional development. It’s excellent for prototyping, experimentation, and rapid iteration, but can falter for enterprise systems or mobile apps without human oversight.

New Skills and the Evolution of the PM Role

The advent of vibe coding and AI-native development calls for upskilling product managers’ skillsets to thrive in this AI-native landscape.

AI Literacy and Machine Learning Fundamentals: Product managers must have a strong conceptual understanding of AI and machine learning, including concepts like model training, bias, and performance metrics to make informed decisions about AI tools.

• Prompt Engineering Expertise: Mastering the art of crafting clear, concise, and comprehensive prompts is the key to guide AI code generation effectively.
• Data Fluency and Analytical Thinking: Product managers must be highly data-fluent, capable of identifying necessary data, understanding data pipelines, and deriving actionable insights.
• Human-AI Collaboration and Orchestration: The future of product development involves seamless human-AI collaboration. Product managers must orchestrate interactions and determine when to delegate to AI versus when human creativity is indispensable.
• Strategic Storytelling with AI: Product managers must translate complex AI concepts into compelling narratives for diverse audiences.

What’s Next? The Future of Vibe Coding

The future of vibe coding is electric, with several developments on the horizon. Like, potentially integrating voice commands, visuals, or even AR. LLMs are expected to become smarter, cutting errors and boosting precision.
In the next 12-to-24 months, there will be some interesting developments:

• AI-native IDEs like Cursor and DevIn will become standard tools.
• Building on Anthropic’s interpretability research, emotion-aware agents that adapt based on tone or user excitement, can potentially become standard in IDEs by 2027.⁹
• Agentic DevOps, with AI agents managing workflows from development to deployment, streamlining operations autonomously.
• Multimodal Vibe Design, using a combination of voice, gestures, sketches, and written prompts to design software, making the process even more intuitive.

These advancements will further blur the lines between human and machine, making vibe coding an integral part of product development.

Bottomline
Vibe coding is more than a technological advancement; it’s a mindset shift. It’s about dreaming big because the barriers between ideas and execution are dropping and letting AI catch the vibe. It is about liberating ideation from
syntax and inviting non-coders into the software development conversation. The strongest skill won’t be writing a perfect code, but thinking clearly, communicating intention, and vibing with machines that are finally listening and understanding what you’re trying to build.

The conversation between human and machine has started. The question isn’t whether you should join it—it’s how quickly you can learn to make it productive.

Disclaimer: This article was developed with the assistance of AI tools, including ChatGPT and Claude, which were used to structure references and summarize supporting details that reinforce the article’s central argument. However, all content has been written, reviewed, and edited by the author to ensure accuracy, originality and to reflect the author’s perspective.

References:

1. Karpathy, A. Vibe Coding, X, February 2, 2025
   https://x.com/karpathy/status/188619218480814938
2. GitLab, Agentic AI, self-hosted models, and more: AI trends for 2025.
   https://about.gitlab.com/the-source/ai/ai-trends-for-2025-agentic-ai-self-hosted-models-and-more/
3. GitLab, 2024 Global DevSecOps Report
   https://about.gitlab.com/developer-survey/
4. DeepLearning.AI, Vibe Coding 101 with Replit
   https://www.deeplearning.ai/short-courses/vibe-coding-101-with-replit/
5. Willison, S., Not all AI-assisted programming is vibe coding (but vibe coding rocks), March 19, 2025
   https://simonwillison.net/2025/Mar/19/vibe-coding/
6. Democratizing Code with AI, The Second Wave of AI Coding is Here
   https://podcasts.apple.com/us/podcast/the-second-wave-of-ai-coding-ishere/id1523584878?i=1000705500088
7. OpenAI, Training Language Models to Follow Instructions with Human Feedback, OpenAI Research, 2022
   https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf
8. GitHub, Octoverse: The state of open source and rise of AI in 2023, November 8, 2023
   https://github.blog/news-insights/research/the-state-of-open-source-and-ai/
9. Anthropic, Interpretability Dreams, Anthropic Research, May 24, 2023 https://transformer-circuits.pub/2023/interpretability-dreams/index.html
10. OpenAI, Introducing GPT-4o, OpenAI Blog, May 13, 2024
    https://openai.com/index/gpt-4o-and-more-tools-to-chatgpt-free/
11. LangChain, Build an Agent
    https://python.langchain.com/docs/tutorials/agents/
12. How generative AI could accelerate software product time to market, McKinsey & Company, 2023.
    https://www.mckinsey.com/industries/technology-media-andtelecommunications/our-insights/how-generative-ai-could-accelerate-software-product-time-to-market

Vivek Sunkara is a Technology Product Manager at Citi, transforming Risks & Controls data into actionable insights that drive strategic growth. A BCS Member, IEEE Senior Member, IETE Fellow, and ACM professional member, he is an ‘AI-first’ product leader focused on building products and emotionally resonant user experiences.