I’ve been using AI tools for several years. Looking back at my prompt history recently, I noticed something that stopped me in my tracks: the change isn’t just in the complexity of what I’m asking — it’s in the entire abstraction level.
Then vs Now
Early prompts were transactional and tactical. Write this regex. Debug this function. Explain what this error means. The AI was a smart search engine that could also write code.
Recent prompts look nothing like that. Reposition this platform as an ecosystem play. Build a technology strategy with a layered approach. Research competitive dynamics in Indian banking initiatives. Develop a compliance framework for the DPDP Act.
The transformation extends beyond complexity to purpose. I’m no longer asking AI to build things — I’m asking it to help me decide what to build and why.
The AI as Shadow Board of Directors
In a startup, you are often the only person in the room when you need to make a hard call. The loneliness of that is real. AI has quietly become my shadow board of directors — a counterpart that pushes back on assumptions, surfaces frameworks I hadn’t considered, and helps me think through the second-order consequences of decisions.
When I’m researching something like tokenization efforts in Indian fintech, I don’t want definitions. I want a dialogue. I want to understand the regulatory gap, the practical implementation barrier, and the market opportunity in one conversation.
The Meta-Learning
The competitive advantage in India’s fintech sector no longer comes from faster code generation. The real value is in using language models to navigate complex regulatory systems — bridging the gap between what the Banking Regulations Act says and what a credit underwriter actually does on a Tuesday morning.
AI is eating my operational bottlenecks. The entrepreneurial journey feels less isolated than it used to.