It’s undeniable that as you transition from a junior to a senior role, the time required to complete the same task inevitably increases. This is because we have to consider potential side effects and the scalability of our code—decisions often based on the information provided by the client and their attitude at the time.
The key difference between a senior and a junior engineer is that a senior can deliver solutions that are far less likely to cause issues after deployment. Through experience, they can anticipate potential problems and address them before implementation, which allows for better communication with clients.
That said, most clients just want to see immediate results. I totally understand that mindset, but it creates a real dilemma—how do we meet client demands within a limited timeframe while ensuring scalability and avoiding unintended side effects? That’s the toughest part.
And honestly, even today’s AI can’t really solve this problem for me.
Speaking of AI, OpenAI’s Sam Altman is reportedly considering launching high-end AI agents priced at $2,000 and $20,000 USD. I have no idea if they would actually help with my challenges, but one thing’s for sure—those price tags are way out of reach for individual users like me.
AI is quickly turning into a pay-to-play game, a stark contrast to when Google’s free search engine first came onto the scene. That’s why I think it’s important to take a balanced approach. After all, hardware keeps advancing, and new AI chips are constantly being developed—maybe in the future, running AI locally will become the norm.
Gotta work late today. Just sharing some thoughts—let’s chat more tomorrow. Good night!
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