How is it misleading if this would be the consumer's cost?

Eventually Codex's subscription subsidization will diminish to near-zero, like the rest of the providers.

It's extremely important that people understand how expensive these models currently are. Even $300k in raw API costs is alarming for the output.

Hey guys, I’m super good at using tokens.

Business: Amazing, that’s great what did you do?

I ran 50 instances and had them all fix the same bugs at the same time and then analyzed the results of all 50 runs to have AI score each of the attempts, then sort them, then compare them to each other in a round robin tournament style double elimination to ensure I got the best result. Then I had AI convert this into a skill, and then ran all 50 attempts again and repeated the process to ensure that I had the absolute best result. It was amazing and I used 1.3 billion tokens!

Business: That is amazing! What did you fix?

A spelling mistake on the About page.

I think its less misleading this way because every other reader would have to pay $1.3M to emulate his workflow for a similar size project. His discounted internal costs are relevent only to openai.

Even at unlimited budget, there is a crossover where outsourcing thinking to the machine costs more than the machine.

What I mean by this:

1. Intern, analyst, junior, or offshore level coding is cheaper when done by the machine.

// Side note: There is good reason the industry invests in suboptimal output from this set which moves to the "cost" column when using an LLM, but nobody's accounting for that.

2. For the interns, analysts, junior, or offshoring to do the right thing costs a multiple of the coding effort: the PdM/PjM stuff of course, but also the Stakeholder, Product Owner, Architect, Principal Engineer, QA, and SRE stuff.

3. If you are not a principal or staff engineer level engineer, you are likely unqualified to catch and fix the errors LLMs make across engineering, much less these other PDLC (product development lifecycle, which includes SDLC and SRE) loop.

4. For LLM output to be useful, your 'harness' has to incorporate all of that as well, which because it's so much harder than transliterating spec-to-code, balloons tokens exponentially.

5. Today it is faster, more efficient, and costs less, to work with LLMs "XP" (eXtreme Programming) style, pairing with the LLM actively co-creating and co-reviewing, steering for more effective turns.

So, your options are:

- ship garbage while costing less than a median first world SWE

- pair with the LLM actively for the benefits of XP

- add enough harness and steering the LLM costs more than SWEs, and still needs a human loop “move fast and break things to find out what's broken” style

I would expect that within a couple years, these other disciplines can be baked in enough the machine costs less for everything but surprises.

> unless they're subsidizing all this

They literally are. (If by "all this" you mean the subscription future bait-and-switch plans.)

But even going with the $5k - $6k monthly usage on a $200 codex subscription even going over their limits is also unrealistic in the long term and that is just ONE person.

Lets say I was at the casino and was spending a lot on casino chips but I also happen to work at the casino. I'm not really losing money whether if I win / lose since I'm using the houses money and there's little risk involved on every dice roll or press of the button. The risk is far higher if I don't have that level of access and continue to spend the same amount of money on lots of tokens (or casino chips, spins or button presses.)

The same is true here with these agents. Some companies will realize that they can no longer afford to spend millions a month on tokens or even startups spending $5k - $6k per person per month on tokens.

I can only see local efficient models making sense on recovering from this unnecessary spending or even light gambling on tokens.