How Much Should We Rely on AI?

AI Makes Starting Easier

AI has changed how we build, think, and start. For me, it has made it so much easier to shift between fields, start new projects quickly, and let large language models (LLMs) guide me depending on my commands.

It’s not just about switching fields — AI is also amazing for one-time projects. For example, I once helped a friend with web scraping. I was hesitant to start because I didn’t have much experience with scraping, and it wasn’t really my interest. But this time, AI guided me through the process. Thanks to my coding knowledge, I knew how to ask questions, what information to give AI, and how to solve errors. Without AI, I might never have started.

But here’s the truth:
I don’t let AI do everything for me.

It’s great for repetitive tasks, or busywork, but every task has its own “soul”—its unique niche that reflects me as a developer. By keeping the core ideas mine, I’ve developed a better understanding of my work and made it more modular and reusable. If I relied on AI fully, I’d just be producing copies of work, not creating something new.

Using AI Wisely: Better Code, Less Waste

And there’s a bigger picture: relying on AI fully just produces copies, which require server computation and electricity. One person’s copies may seem small, but billions add up — more energy, more emissions, more pollution. By keeping the core work mine and reusing it, I reduce AI usage, learn more and minimize environmental impact.

Thinking vs. Decision-Making

Sometimes I have a vivid idea. I’ll share it with an LLM and ask it to ask me questions back. This makes me think harder, and thinking is vital — it’s what differentiates us from AI.

AI is a decision-maker from data.
Humans are thinkers.

What’s crucial is that my idea is still mine. I’m not “stealing” from AI models, which are built from other people’s perspectives. I’m using AI to challenge my thoughts, not to replace them.

AI as an Assistant, Not a Leader

Doing the same stuff again and again can be overwhelming, and here AI shines. It automates repetitive tasks, freeing my time. But I still know what I’m doing; it’s under my ownership.

AI is my personal assistant.
It is not my leader.

Never Lose Your Identity

This is the most important part. Authenticity still matters. AI may be faster than someone else, but in the end, authenticity wins.

AI helps us start easier. Even this post is polished with AI, but all the ideas and thinking are mine. This is my thought. This is authentic.

Takeaway

Use AI to start faster, automate the boring, and spark better questions. But keep your identity, your thinking, and your authenticity at the center. That’s how you rely on AI without losing yourself.

(Follow me if you want more thoughts and lessons from my AI + dev journey.)

Comments

[Cyber Safety Zone]

Thanks for sharing this — it’s a great overview of how document intelligence has come so far! A few takeaways and thoughts:

• I really liked how you traced the history: from card catalogs → early IR systems → semantic/NLP/AI. It helps put into context why modern document intelligence is so compelling. (DEV Community)
• The “beyond keywords” section resonates: as businesses scale, it’s no longer enough to simply retrieve documents — extracting meaning (entities, relationships, structure) becomes crucial. (DEV Community)
• I appreciated your discussion of the challenges — black-box models, accuracy especially with low-quality inputs, bias, integration. All very real. (DEV Community)

A couple of additional points I’d love to see expanded:

1. Data governance & privacy — with so many documents, some sensitive, how do organizations ensure compliance, secure handling, auditability, and also ethical use (especially with training data)?
2. Model maintenance — as layouts, styles, and document types evolve (new formats, languages, etc.), how do we ensure models don’t degrade over time? Continuous retraining, human-in-the-loop?
3. Benchmarking & evaluation — it'd be interesting to compare how commercial tools (or open source ones) stack up in real-world tasks: extraction accuracy, speed, cross-document consistency.

Overall, loved the article — excited to see where this field goes next, especially with multimodal docs and more explainable AI.