Why Your AI Product Is Failing (And How to Fix It)

The Core Argument

Guided by host Sanjay Saini, 26-year IT veteran Vinod Sharma argued that most AI products fail because builders jump straight into complex architectures without validating the market or mastering the basics. He emphasized that building AI products is like playing with Lego blocks—start small with desktop AI tools, validate user willingness to pay using principles from "The Mom Test," and scale into complex agentic systems only when necessary. Furthermore, Vinod highlighted the rapid emergence of the "Forward Deployed Engineer," a role combining business analysis, domain expertise, and AI orchestration to build hyper-customized enterprise solutions.

“Senior developers doubting AI today are like traditional tailors who doubted the first sewing machines. The craftsmen who survived weren't necessarily better tailors; they were the ones who learned to operate the machine.” Vinod Sharma (01:01:20)

Detailed Answers

1. Could you give a brief intro about yourself and your background?

Vinod Sharma brings over 26 years of software development experience. After spending 12 years in traditional project management away from hands-on technology, he successfully returned to coding in 2024 using AI tools like v0, Bolt, and Lovable. He now builds and scales advanced agentic AI ecosystems alongside his business partner in Bali.

2. What has been the evolution of your AI product building journey?

Vinod started by building standard web applications, eventually progressing to native iOS and Android apps using AI. He quickly realized the true power of AI lies in building autonomous skills and agents. Today, he develops centralized agentic systems that aggregate data from platforms like Jira, GitHub, and ServiceDesk to autonomously detect and fix production issues.

3. What are the biggest challenges you face when building AI products?

The primary challenges are data privacy, token management, and AI hallucinations. Vinod stressed the importance of strictly controlling what proprietary data is exposed to an LLM. Additionally, while early tokens were cheap, scaling an application requires meticulous optimization of context windows to prevent runaway API costs.

4. What AI coding tools do you recommend for beginners and technical users?

For technical users, Vinod highly recommends using Claude's extension inside VS Code or the Claude Desktop app. For those who are newer to coding or prefer a comprehensive environment that handles local execution automatically, he suggests using CodeX, which has rapidly become a powerful, all-in-one AI IDE equipped with mobile continuity.

5. How do you validate an AI product idea before building it?

Referencing "The Mom Test," Vinod warned against building a product just because AI makes it easy to generate the code. He advised talking to 25-50 potential users in person to understand their pain points before writing a single line of code. If users are not willing to actually pay for the solution, the product is a vanity project not worth scaling.

6. How should product engineers start their AI journey today?

Stop writing massive Product Requirement Documents (PRDs) for theoretical apps. Vinod's advice is to pick one tool (like CodeX) and build a simple, personal portfolio website. Treat AI building like Lego blocks: start with tiny, functional pieces, learn how the AI agent behaves in the sandbox, and gradually increase complexity.

7. What is a Forward Deployed Engineer (FDE) and why is it in high demand?

An FDE is essentially an evolution of a Business Analyst combined with a Subject Matter Expert and AI architect. They go directly to client departments (like Supply Chain or HR), understand their specific manual workflows, and build hyper-customized AI solutions end-to-end to automate those exact, niche processes.

8. How do you build an AI-native mindset within an organization?

Stop doubting the AI. Vinod compared current senior developers doubting AI's coding abilities to traditional tailors who doubted the first sewing machines. The craftsmen who survived weren't necessarily better tailors; they were the ones who learned to operate the machine. To build an AI mindset, organizations must deploy sandboxes and start experimenting immediately without fear.

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