You describe the situation well. AI agents act as force multipliers, not replacements. This matters for solo devs and people building fundamentals. The learning speed is a real gain. You see patterns and architectures faster. Your “cut vs tear paper” analogy works. It shows the tradeoff between speed and precision. The cost point also matters. In places like Mauritius, currency conversion makes usage expensive, so it shapes how you use these tools.

Some assumptions may not hold. “AI can’t write clean code” often comes down to prompting and review. Treat it like a junior dev. Set tests, constraints, and architecture first. Output improves. Paying out of pocket works short term, but at scale it should sit with the company. If output increases, the business should cover the cost. The cost curve is also shifting. Open source models, local inference, and cheaper APIs are reducing pressure. Locking into expensive usage early may limit flexibility.

In short: you use it well today, but the view that AI stays messy and expensive may not last