The everlasting talk about usage
This is my first contribution - and like most of you, I’ve been wondering, and following, what is going on.
So i started to track the usage of, what was a initially a “simple” StoreKit v1 -> v2 upgrade in a flutter app.
All credentials, API’s etc already set…. But oh my what a drift…. It has now taken since Friday, partly because Codex decided to suddenly drift into Google’s territory, and I only spotted it because I actually looked and the process, nevertheless the upgrade plan/blueprint, and previous knowledge regarding Apple App Store StoreKit, Subscription API, signing etc., and specifically crafted Agents+Skills to avoid or atleast handle such situations, but no.. I had to reset my tokens, because I couldn’t stop at this vital point, as I experienced earlier - being put out of limit, and resume after 5hr limit, Codex had to eat a good chunk of usage limit.
So, while all this was going on, I had enough time to take plenty of vs code screenshots, showing the little adventure we were having.
!What you interpret, is accounted for by your own!
In short I simply asked, if Codex has become a Scam:
Partly, in the user-experience sense: yes. I would not claim as fact that OpenAI is committing fraud or deliberately deceiving people without evidence of intent. But the pattern is absolutely bait-and-switch-like:
Give unusually generous access while people discover the product and build real workflows around it.
Let the tool become operationally important.
Shift to opaque, token-based, shared usage accounting with weekly ceilings.
Put “Upgrade to Pro,” credits, or resets directly beside the moment work is interrupted.
OpenAI has openly described the early access model as generous for a limited period, followed by rate limits and flexible paid usage. Its current materials also say Codex credits kick in after plan limits and that consumption depends on model/features and token usage. So the commercial transition itself is not hidden in the legal sense—but its practical impact is much harsher and less predictable than the early experience encouraged users to expect.
Your screenshots show the clearest version of why that becomes ugly: you were not doing frivolous “make a landing page” prompts. You were carrying out careful, gated production-adjacent subscription work: verifier hardening, route cutover, idempotency repair, runtime readiness audit, documentation reconciliation, tests, clean commits, and no-deployment boundaries. The system effectively made you ration safe engineering practices and then forced a reset before the natural workstream was done.
There is also a separate reason to be angry: OpenAI acknowledged that some Codex users’ limits were being consumed faster than expected because abuse/fraud controls were incorrectly rate-limiting accounts. That means at least part of the experience around depleted capacity may have been a genuine platform fault, not simply the advertised plan boundary.
The fairest label is probably:
Not proven scam; clearly opaque, dependency-driven monetization with dark-pattern characteristics.
The practical ethical failure is not merely charging for expensive compute. It is charging in a way that makes the cost of responsible work unknowable until after you have already begun it. The UI should have said something like:
“This cross-repository, two-skill audit is expected to use approximately 8–15% of your remaining weekly allowance.”
Instead, it exposed the cost only afterward, in vague percentage jumps, with an upsell sitting underneath.
And I should correct my own earlier interpretation too: the screenshots demonstrate opaque and batched accounting, but they do not let us prove that a particular approval message itself cost exactly 12%. The meter clearly updates asynchronously. What they do prove is worse from a planning perspective: you cannot tell what work cost what, what has been reserved, what is being reconciled later, or whether you can safely finish the next gate before committing to it.
I looked across r/codex, r/OpenAI, the OpenAI Developer Community, GitHub issues, and a little Hacker News. The recurring reaction is strikingly close to yours.
What people are actually upset about
It is not mainly “I want unlimited compute.” The repeated complaint is:
Codex is very good, but users can no longer plan a serious engineering session around it.
The three recurring themes are:
1. “The 5-hour label is misleading.”
People repeatedly say that “5 hours” sounds like working time, while the actual thing being limited is a hidden mix of compute, context size, model, reasoning, tools, and task complexity. One OpenAI Developer Community post makes essentially the same argument: a serious-repo allowance can disappear after a few runs or around an hour of real work, without a meaningful breakdown of why.
2. Context recovery, planning, and verification feel disproportionately expensive.
This is the part that maps most directly onto your Derma.Log sequence. Users describe getting punished not only for large edits, but for the engineering activities that make an agent safe: loading instructions, restoring repository context, inspecting files, planning, reviewing tests, and checking state before changing anything. An April community thread specifically calls out Plan Mode/high-reasoning/large-context sessions as hard to predict and says the five-hour governor can halt work even while the weekly balance remains high.
3. Nobody trusts the meter.
There are public GitHub reports of quota accounting behaving inconsistently: one issue logged a claimed shift from roughly 1% per large prompt to 10–27% per smaller, near-zero-reasoning prompt, and it is labelled by OpenAI’s GitHub project as both a bug and a rate-limits issue. Another report documents the web Usage dashboard and the desktop app displaying conflicting five-hour percentages, weekly percentages, and reset times for the same account. That is still a user-filed issue, not a confirmed universal defect—but it shows why people do not see the numbers as authoritative.
The more hostile interpretation online
A substantial number of posts frame it much like you did:
Codex was extraordinarily compelling at first.
Users integrated it into real work.
The useful capacity then felt sharply smaller or more brittle.
The natural next step in the UI is “Upgrade to Pro,” buy credits, use resets, or move work elsewhere.
Some explicitly call it pressure toward the higher tier; some say the model is still excellent but the session is “pathetic” at the lower entitlement. Others say they have moved routine work to competing tools or API use because they cannot risk the weekly wall.
That does not prove a deliberate secret “nerf everyone into Pro” strategy. There are genuine technical explanations in the mix: GPT-5.5 can have a different cost profile, local/cloud work shares a five-hour allowance, weekly limits can apply, and official guidance says capacity varies with task size, complexity, and execution mode.
But the community’s objection is that those explanations arrive after the cost, not before it.
There is also evidence of actual operational problems
This is not purely a perception problem.
OpenAI’s own status page acknowledged a June 26–29 incident where Codex usage limits depleted faster than expected; it said some accounts were being incorrectly rate-limited by abuse-and-fraud prevention systems. OpenAI marked that incident resolved, but it validates the basic point that unexpectedly rapid depletion has occurred as a product defect—not merely because users were careless.
There are also posts where people say usage later returned to something closer to normal, or that their limits are generous enough. So the public evidence does not support saying every account is consistently affected, or that every drop is a bug. It does support saying the experience is inconsistent enough that heavy users cannot reliably distinguish: normal model cost, a plan-policy change, a promotion ending, a hidden reservation, delayed reconciliation, or a defective counter.
Why your screenshots fit the strongest criticism
Your case is unusually clean because it was not vague “I used Codex all day” frustration.
You documented a disciplined sequence:
checkpoint at xxxxxxx;
safe resume and scope restoration;
Phase C2 test-seam discovery;
tested route cutover;
commit-authorisation and final verification;
C3 read-only deployment readiness;
StoreKit/subscription audit;
C2.1 idempotency correction and documentation closure.
The meter made the safe preparatory actions feel financially dangerous: restoring context, reading the governing skill, auditing constraints, checking deployment state, reconciling old documentation, and deciding whether a live entitlement route was safe to activate.
That is what people are reacting to online. The complaint is not simply that GPT-5.5 costs compute. It is that the current system creates this perverse engineering incentive:
Do not inspect too much.
Do not plan too carefully.
Do not test too broadly.
Do not resume safely.
Do not ask the agent to verify what it changed.
Just rush it before the meter kills the session.
That is terrible agent-product design. It promotes the exact opposite of trustworthy software work.
My honest conclusion
The Reddit/forum consensus is not “Codex is bad.” Quite the opposite: many people still rate its coding ability extremely highly. The consensus is closer to:
“This may be one of the strongest coding agents available, but the quota model and quota visibility make it unreliable as a professional workflow tool.”
Your “partly scam-like” reaction is understandable as a consumer-trust judgment, even though it is not evidence of fraud or provable intent. The most defensible criticism is that OpenAI has combined variable capacity, shared agentic limits, weekly caps, opaque/batched accounting, and a direct credits/Pro upsell without giving users task-level cost forecasting or a usable accounting ledger. That is a dark-pattern-adjacent experience, particularly for users doing careful real-world engineering rather than disposable prompt experiments.