Zerocapped
Use ChatGPT, Claude, Gemini & Codex inside Lovable
Start new thread

AI was supposed to make building cheaper. Is it actually doing that for you?

by•

AI has made it ridiculously easy to go from “I have an idea” → “I’m building it.”

But something I've noticed while building products myself:

The deeper you get into a project, the more the costs start stacking up.

AI credits. Multiple subscriptions. Different models for different tasks. Usage limits. And sometimes you burn through credits just trying to fix what the AI generated in the first place. 😅

It made me wonder whether AI is actually making software development cheaper, or just changing where we spend the money.

For those actively building with AI:

Has AI genuinely reduced your development costs? Or are you spending more than you expected?

Would especially love to hear what happens once you move beyond prototypes and start building real products.

68 views

Add a comment

Replies

Best

I would say it's not cheap. In fact, I'm spending more than I would on hiring an employee. But it enables us to build fast and iterate quickly. AI can build things faster than any human.

That’s interesting, spending more than hiring, but building faster than any human is probably the trade-off we’re actually heading toward.

At that point, AI isn’t necessarily reducing the cost of development. It’s letting us buy time and speed with compute.

And if speed is creating enough value, paying more can absolutely make sense. But I wonder where that equation starts breaking down. As you build more products, run more agents, and iterate more frequently, the faster you want to move, the more you have to spend.

Ideally, we shouldn’t have to choose between moving fast and keeping development costs predictable. If we could maintain the speed you’re getting now without the cost increasing alongside every additional iteration, that seems like a pretty meaningful unlock for scaling.

Curious, if you could keep your current AI development speed but make the cost predictable, would that materially change how much you build or experiment with?

 Yes, I think we’re moving toward a time when the cost of LLMs for coding will decrease significantly. For example, Cursor Grok 4.5 Very Fast is one of the best and cheapest models, and I’ve tested it extensively. To my surprise, it works really well.

Once cheaper LLMs become more accurate and capable, it will be possible to iterate much faster while keeping a close eye on the cost.

Cheaper to start, but possibly more expensive to finish. Prototyping feels basically free, but when you come to verification and submission, things slow down massively. What brought my costs down was mostly being stricter with myself and trusting less. Smaller tasks, telling it where to stop and checking before merging. Still way cheaper than hiring though.
Exactly. And I think the interesting part is that you’ve already found a workaround: being stricter with the AI to control the cost. Smaller tasks, stopping it at the right point, checking before merging… all of that works, but it also means we’re adapting the way we build just to avoid wasting credits and rework. That makes me think there’s a real problem to solve here. What if we could keep the speed and freedom of prototyping even when the product gets serious, without constantly thinking about credits, limits, or whether the next iteration is worth the cost? For people building and shipping continuously, I feel like solving that could make scaling with AI a completely different experience. Curious, if the cost/credit limitation disappeared, would you actually use AI more aggressively throughout the whole development process?
Not really. Cheaper credits would make me sloppier early, not faster overall. The constraint isn’t the meter, it’s that I’m the only one reviewing what comes out. Free generation doesn’t fix that, it just gives me more to check.

That’s a fair distinction. Free generation doesn’t remove the cost of being wrong. You still have to review, test, and decide what actually ships.

Where I think “limitless” starts to matter is at the scale layer, especially as we move toward more agentic development.

If one agent can generate, test, refactor, document, or even run multiple tasks in parallel, suddenly usage itself becomes infrastructure. You can buy back a huge amount of time but at what cost? If every extra iteration or agent adds another meter running in the background, eventually cost becomes the thing deciding how fast you’re willing to move.

So maybe the real opportunity isn’t making AI free so we can be sloppier. It’s making the cost predictable enough that good builders can scale their process without the meter becoming the bottleneck.

I’m curious how you see that playing out with agents. Do you think review becomes the main bottleneck

anyway, or does cost eventually become just as important once we’re running multiple agents continuously?

Agreed, that’s probably where it matters most. Though for a solo dev the agentic thing has the same problem, just bigger. More agents running means more output landing on the same one person to verify. Maybe that changes with better automated checks. Right now the review step is still human.