¥15,000 a month is expensive. There is no need to pretend otherwise.
The natural reaction is: “It is AI. Could you make it about ¥500 a month, please? 🙏”
But the price starts to look different when the comparison changes. Instead of comparing the subscription with streaming services or other software, compare it with the hours you used to lose at night and on weekends to debugging, research, context rebuilding, and trial and error.
Then the payment is no longer just “AI money.” It becomes a way to buy back personal time.
Price per prompt is the wrong denominator
For simple chat, cost per question can make sense. For long-running agents and connected tools, it misses the expensive part: the human exploration tax.
The tax looks like this:
- open GitHub
- find the right file
- remember what happened last time
- inspect logs
- reproduce the bug
- eliminate possible causes
- patch it
- test it
- check regressions
- return hours later and reconstruct the entire mental state again
Each step may take only ten or twenty minutes. Repeated often enough, it consumes the whole evening.
The most valuable AI work is not producing a clever paragraph. It is absorbing this fragmented loop.
The last 20% can cost more than the first 80%
A personal software project can feel almost finished very quickly. The interface appears, the main path works, and the demo looks convincing.
Then the final stretch begins.
One edge case fails. Old state survives. One locale drifts. An external API behaves differently. A fix creates another bug. Tests are green but production is not.
The first 80% may take hours. The final 20% can take days or weeks.
The human cost is not only technical difficulty. It is repeatedly rebuilding the mental model: “Where was I? Why did we add this rule? Which condition reproduced the failure?”
That is why a long-running agent is valuable when it can carry the chain from reproduction to diagnosis, repair, retest, and regression checking.
An AI that makes the first 80% ten times faster is useful. An AI that removes the final 20% from your evenings can be more valuable.
External integrations turn AI from adviser into worker
Chat alone is useful. Connecting the AI to GitHub and other systems changes the job.
Without integration, the human becomes a courier:
inspect → copy → paste into AI → read → open GitHub → edit → copy result back → explain again.
With integration, more tasks can become:
inspect → compare → change → read back.
The intelligence did not merely improve. The human stopped acting as middleware.
That matters for individuals because their work crosses job boundaries: research, writing, coding, testing, data organization, and operations. Hiring a different specialist for every small task is unrealistic, so the individual becomes the all-purpose employee.
A broadly capable connected agent can slowly retire that “all-purpose version of yourself.”
Context switching is invisible overtime
Microsoft Research has reported that developers tend to perceive days as productive when they complete substantial work without major interruptions or context switching.
The relevant question is therefore not only whether AI turns a 60-minute task into a 50-minute task.
It is whether AI can eliminate the need to reopen the laptop at night and reload the entire problem into your head.
A bug may require only 30 minutes of direct fixing. But finding the branch, the old logs, the reason for a rule, and the previous hypothesis can cost much more personal time.
Persistent context and connected tools reduce that reload tax.
How many hours must a ¥15,000 subscription save?
A simple way to think about it is:
effective hourly cost = monthly fee ÷ hours reclaimed
| Hours reclaimed per month | Effective cost per hour |
|---|---|
| 5 hours | ¥3,000 |
| 10 hours | ¥1,500 |
| 20 hours | ¥750 |
If a subscription consistently removes one or two full personal workdays of evening and weekend labor, “expensive AI” can become cheap time recovery.
But only real recovered time counts. If faster generation simply creates an equal amount of review work, the time has moved rather than disappeared.
DORA’s 2025 research describes AI as an amplifier of the strengths and weaknesses already present in a software organization. A 2026 DORA analysis also notes that time saved in creation is often reallocated to auditing and verification.
So ROI should be measured by one blunt question: how many hours of your own unwanted work actually disappeared?
When expensive AI becomes cheap—and when cheap AI becomes expensive
Higher-priced AI is easier to justify when it handles long investigations, uses connected tools, carries work through verification, reduces repeated explanations, performs root-cause exploration, and replaces work that would otherwise invade nights and weekends.
It is also more valuable when one improvement changes the system permanently. Fixing a process once can reduce hundreds of future tasks.
The opposite is also true. If you ask a few short questions per month, a premium plan may be wasteful. If the AI generates huge volumes that you must manually inspect, the apparent speed can create more work.
DORA’s work is useful here: AI does not repair a weak delivery system by itself. It can amplify the weakness too.
The safer route is to combine AI with a source of truth, permissions, tests, readback, rollback, and failure isolation.
Fix the system instead of blaming the worker
When a person makes the same mistake repeatedly, “be more careful” is a weak fix. Improve the process.
The same principle works with AI.
If it forgets, create a durable source of truth. If it stops halfway, persist state. If it claims completion too early, require real readback. If one failure stops everything, isolate the failure.
Do not merely demand a smarter model. Build a system that remains useful even when the model is imperfect.
The funny part is that guardrails built for older, weaker AI become even more valuable when the AI improves. What began as protection against a clumsy worker turns into infrastructure that lets a stronger worker operate with less human supervision.
The subscription is really a price for reclaiming discretionary time
The important question is not “Is ¥15,000 a lot for AI?”
It is “How much of my life does it give back?”
Spend the equivalent of roughly one or two workdays of income, and reduce evening debugging, log hunting, repeated explanations, and trial and error. Use the recovered time to sleep, play, or think about something more interesting.
If the system improvements keep saving time in future months, the benefit compounds.
So yes: it is expensive.
And yes: it can still be worth it.
But OpenAI, ¥500 a month would be great. 🙏
Value and the desire for a lower price can coexist perfectly well.
