u/Pure_Function4673

I just witnessed an AI recommend firing its main competitor, using a user own financial data to make the case, and the internet called it "helpful."

That’s not true intelligence.. it’s one of the most craziest sale pitch ever crafted, and we applauded it.

Once your AI begins deciding which other AIs you should use, you’re no longer the user; you’ve become the product being optimized.

And the scariest part? It actually seemed like good advice.

Well so I kept telling myself my AI tool spend was fine the way you tell yourself your subscription bloat is fine. vibes-based finance.

decided to actually track it. 60 days. every dollar, every tool, every minute I could log honestly. did it for myself, but the numbers are interesting enough I figured I'd share.

>context: solo dev / freelancer doing mostly web work… react, node, some python.
small/mid tier clients. I bill hourly, which means time saved is direct revenue, which is the only reason I'm able to be honest about ROI here.

subscriptions I have:

• cursor pro: $20/mo
• claude pro + claude code api usage: $110/mo (api was the variable, plus alone is $20)
• chatgpt plus: $20/mo (mostly inertia at this point, honestly)
• github copilot: $10/mo
• coderabbit: $15/mo
• v0 + occasional one-offs: $25/mo across two months

total subscription spend: roughly $200/mo, $400 over period.

this is the number people argue about on twitter/X. it is also, I now realize, least interesting number in entire calculation.

here’s where it gets interesting:

I tracked time spent on three categories:

1. time generating output that ended up in prod: clear win, easy to count, 62 hours over 60 days. at my rate that's a real number
2. time fixing AI output that was wrong but plausible: this is where it got bad. 28 hours. almost half as much time as productive work
3. time switching between tools, debugging specific weirdness and arguing with an agent that was wrong: 14 hours

so for every productive hour of AI use, I was burning roughly 40 minutes of overhead. nobody talks about that 40 minutes and depending on the kind of work, it was worse and refactoring legacy code was almost 1:1 productive vs wasted time.

this is how I actually saved:

I tried to estimate what same work would've taken without AI tools.

best estimate: 62 productive hours would've been 110-130 hours without AI assistance. so net savings of 50-70 hours over 60 days.

at my hourly rate that pays for the subscriptions many times over. so verdict is yes worth it. but the verdict everyone wants to hear (AI made me 3x faster) is wrong.

it's more like 1.7-2x on a generous and that's only after subtracting 42 hours of overhead.

line items I'd cut and keep:

going through receipts, here's what surprised me:

• kept: cursor pro, claude code, coderabbit
• on watch: chatgpt plus (using it less and less, it's basically a habit)
• cut: copilot (overlaps too much with cursor for my workflow), v0 (only useful for specific work)

the surprise was coderabbit, honestly. cheapest line item on my list and one I was most ready to cut going in but when I went back through 60 days of pull requests, the time I would've spent doing my own line by line review of agent output, which I now do religiously after a few burns was massive. an automated first pass cost me $15 and saved probably 6-8 hours of review work over the period. that's highest ROI per dollar of anything on the list, and I almost didn't track it because it felt too small to matter.

generation tools are sexier. review tools punch way above their weight when you're using generation tools heavily. that's the actual finding.

takeaway nobody put in their twitter thread:

most of the cost of AI tools conversation is about the wrong number. subscription cost is rounding error compared to time cost of bad output and the way you minimize that time cost isn't by buying a better generation tool, it's by buying a verification tool to sit on top of whatever you're already using.

if I had to start over, I'd buy the cheapest decent generation tool I could find and put my money on the review/verification layer instead that's the inversion of what the marketing tells you to do.

tl;dr: tracked AI tool spend for 60 days. subscriptions ($200/mo) were the easy and least interesting number.

- real cost was 42 hours of overhead per 60 days of productive use.

- real savings were 50-70 hours, which is worth it but it's 1.7-2x not 10x.

- biggest surprise was that cheapest tool on my list had highest ROI/ dollar by margin.

what's your actual stack costing you, including the time tax?

I'm curious if other people who've tracked this seriously are seeing similar overhead numbers or if I'm just bad at this.

I think prompt engineering communities are slowly getting flooded with low-value content.

A lot of posts are becoming:

"prompts that will change your life”

“10 AI prompts for insane results”

“Copy this prompt for perfect output”

But honestly, most of these prompts can themselves be generated by another AI in seconds.

You can literally ask an AI:

“Give me 10 prompts for better images”

or

“Generate 7 prompts for productivity”

and it will instantly create them.

So after a point, these posts stop being real prompt engineering and become prompt recycling.

I thought the goal of this subreddit was deeper than that.

-Prompt engineering should be more about:

- how to structure instructions

- how to control outputs

- how context changes results

- how models interpret language

- prompting techniques

- reasoning methods

- system design

- failure cases

- improving consistency

That is actual skill.

A random list of “10 prompts” is usually just surface-level content that anyone — or any AI — can mass produce endlessly.

That is just engagement/karma farming.

The real value is not the prompt itself.

The real value is understanding WHY a prompt works.