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Comment by serjester | Hacker News Reader

Assuming a highly motivated office worker spends 6 hours per day listening or speaking, at a salary of $160k per year, that works out to a cost of ≈$10k per 1M tokens.

OpenAI is now within an order of magnitude of a highly skilled humans with their frontier model pricing. o3 pro may change this but at the same time I don’t think they would have shipped this if o3 was right around the corner.

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If you start paying someone and give them some onboarding docs, to a first approximation they'll start doing the job and you'll get value.

If you attach a credit card to o3 and give it some onboarding docs, it'll give you a nice summary of your onboarding docs that you didn't need.

We're a long way from a model doing arbitrary roles. Currently at the very minimum, you need a competent office worker to run the model, filter its output through their judgement, and act on it.

Right, value per token is much more important (but harder to quantify). A medical AI that could provide a one-paragraph diagnosis and treatment plan for rare / untreatable diseases could be generating thousands of dollars of value per token. Meanwhile, Claude has probably racked up millions of tokens wandering around Mt. Moon aimlessly.

“Untreatable” disease.

Yet somehow the AI knows a treatment?

Miraculously, it does know the treatment! The question is, does it have malpractice insurance?

I can just imagine all the executives and actuaries in Lloyd’s of London just salivating and wringing their hands: ”that’s how we get in on the AI fad too!”

The treatment is a cranial amputation. No more symptoms!

I think that’s the remarkable thing - even with all of its flaws and its insane pricing, there’s plenty of people that will pay for it (myself included).

LLM’s are good at a class of tasks that humans aren’t.

More like: every time you tell o3 to do something, it will first reread the onboarding docs (and charge you for doing so) before it does anything else.

> Assuming a highly motivated office worker spends 6 hours per day listening or speaking, at a salary of $160k per year, that works out to a cost of ≈$10k per 1M tokens.

I guess...if by office worker you mean a manager that does nothing but attend meetings and otherwise talk to people. For other workers you probably want to count the token equivalent of their actual work output and not just the chatting.

I suspect inner monologue is the useful metric for token count. I don't know if (any, let alone most or all) human brains think in token-like chunks, but if we do, and that's at 180/minute, thats 180x60x5x48 (working weeks/year) = 20,736,000 tokens/year. At that rate, $160k/year would be ~$7700/million tokens.

My guess is that this is better than a human who would cost $16k/year to hire. But with the logarithmic improvements in quality for linear price increases, I'm not sure it would be good enough to replace a $160k/year worker.

Just noticed I missed the 8x in the LHS, but the total is correct:

> 180x60x5x48 (working weeks/year) = 20,736,000 tokens/year

How do you reconcile issues such as the o1 pro model erroring out every 3rd attempt at an extremely large context? (that still fits but is near the limit)

Every time I try to get this thing to read my codebase and onboarding docs (about 40k line angular codebase) it is "pull your hair out" failing leading to frustration.

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