Recently, Sebastian Siemiatkowski, CEO of Klarna, a global payment solutions company offering “buy now, pay later” services, shared how AI tools like Cursor have revolutionized prototype development. He highlighted the growing trend of vibe coding, where AI assists in generating code through natural language prompts, streamlining workflows and reducing reliance on technical teams. This approach is becoming a key skill for developers, with major companies increasingly seeking proficiency in AI-powered coding tools.
In a conversation with Mpost, Ahmad Shadid, CEO of O.XYZ—an agentic, full-stack AI development ecosystem—shared his insights and expertise on the evolution of this trend.
Ahmad Shadid noted that non-technical leaders now have the opportunity to turn ideas into clickable demos within hours, thanks to AI-powered tools. This accelerates product discovery and reduces the translation gap between business intent and engineering. However, the risks include a false sense of feasibility, as prototypes may conceal underlying issues like feasibility, security, and technical debt. Additionally, leaders may become overly focused on what the tool can generate, overlooking what is viable from a strategic or technical perspective.
He also shared the most common pitfalls teams face when using AI-generated code and offered insights on how to mitigate these risks.
“Unsafe input handling and weak authentication patterns are among the top issues. These security concerns can be mitigated by enforcing SAST/DAST in CI, security linters, dependency scanning, and threat modeling on features that originate from AI. Data leakage in prompts can be reduced by routing through approved providers who redact and protect secrets, and using privacy-preserving prompt gateways,” said Ahmad Shadid to Mpost.
“It’s not just the AI-generated code. When a person is not an engineer or a coder, they often lack a comprehensive understanding of how software is built and what the system architecture looks like. The AI is only as good as the prompt, right? So they aren’t able to prompt the AI properly, and this can result in security threats and issues like APIs in the frontend, public databases,” he continued.
Additionally, the expert added that something a lot of engineers complain about is that when the context becomes too large or when something becomes too complex, the AI starts to hallucinate. It begins to make changes in the code that weren’t needed or that weren’t explicitly asked for. AI also generates thousands of lines of code. Imagine trying to keep up with random codebase changes across thousands of lines of code.
“Ultimately, regular time-boxed ‘no-AI’ reviews are essential for keeping the fundamentals fresh and combating skill atrophy,” he said.
Commenting on whether reliance on AI-driven coding could eventually reshape how software engineers are valued and hired across industries, with “vibe coding” becoming a sought-after skill even in job listings, Ahmad Shadid said that, “The less raw typing, the more system design, code review, debugging, security, and data/AI orchestration make up for product sense. We’ve also seen a shift from ‘implement X from scratch’ to ‘critique, harden, and extend AI-produced code,’ plus architecture and incident drills. The rise of ‘AI pair-programming leads,’ ‘code custodians,’ and platform engineers who build guardrails in AI-generated software shows increasing uptake of AI-driven coding.”
“Novices often skip the fundamentals and jump right into prompt engineering with no idea about what they want to achieve. On the other hand, experienced engineers gain leverage, generating more time for architecture, reliability, and suitable product outcomes. Explicit learning tracks, a ‘read-before-write’ culture, and periodic ‘manual mode’ exercises can help ensure efficient and ethical use of AI for writing code,” he noted.
One of the concerns is that vibe coding tools could eventually replace traditional coding workflows. However, the expert noted that vibe coding tools are just too simple to replace full-on coding workflows.
“Will it form part of coding workflows from now on? Sure, product teams really benefit from this to just quickly put on a frontend and check different UX designs, sure, freelance developers and hobbyists can quickly put together something, but it cannot replace the whole workflow. In fact, development right now is facing some challenges, especially as AI becomes more and more powerful,” he said to Mpost.
“We just simply can’t catch up, tools can’t catch up, and we’re facing a tool fragmentation crisis where developers now need 4, 5 tools as part of their workflow. Every time you switch, you lose context, you just can’t keep up, and AI can’t keep up; you can’t follow through with all the changes in one tool and the other, etc.,” Ahmad Shadid continued.
To put it simply, the current vibe coding tools and platforms still have a very long way to go before replacing traditional coding workflows. These tools are still incomplete.
Ahmad Shadid highlighted that current development tools and environments are prepared to safely integrate AI-powered coding: “IDE integrations, strong code-completion, decent refactors, and repo-aware assistants all play a major role in producing AI-generated cod,” he said to Mpost. “However, enterprise-scale gaps exist. A unified auditability of AI suggestions, robust policy enforcement with cost controls, and seamless on-prem/private model options could potentially create major gaps at the enterprise level,” the expert added.
As more executives embrace AI tools for fast prototyping, this could help democratize innovation within companies. However, it also carries the risk of oversimplifying the complexity of software engineering.
Ahmad Shadid believes that with more people involved in the ideation process, companies can validate ideas faster and improve cross-functional collaboration. This allows more ideas to be developed and refined into stable solutions, giving creators the freedom to bring their concepts to life through software.
“The use of AI tools for prototyping underestimates the complexity of reliability, operability, and scale, making demo-driven decisions that could lead to failure if left unchecked. The tools make it easy to prototype, but hard to ship without engineering quality gates,” the expert highlighted.
Furthermore, companies should allow non-engineers to operate in isolated environments that run the applications quietly and privately. Using dummy/synthetic data as well as zero production credentials may help minimize data leakage risks.
“Clear system identification strategies, such as throwaway repos and separate namespaces, assist in leveraging the AI programs in isolation. Approved stacks, secured scaffolds, built-in tests, and linting provide a secure platform for the scalability and resilience of the application,” Ahmad Shadid said to Mpost.
The post O.XYZ’s Ahmad Shadid On The Promise And Pitfalls Of AI-Powered Coding Tools: Balancing Innovation With Security And Complexity appeared first on Metaverse Post.
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