alt Hacker NewsI take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly.
I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value. At a corporate level, I'd much rather hire a junior engineer who spends $100-$200/month and becomes productive then try and rationalize $100k/year in token spend.
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It's easily explained. People are losing their skill in real time and literally cannot develop anymore without AI. That's it.
I'm working on some serious data analysis + realtime async code, and I use 200-400 million tokens a day with Claude Code alone (via ccusage). The complexity of the code seems to have a big impact on the number of tokens used. On simpler projects I use many fewer tokens.
My programming endurance is much greater now (2-3x focused hours per day), my productivity per hour is multiples higher, and I code seven days a week now because it's really exciting.
All told, I would pay for these tools as much as I would pay for full-time human programmer(s).
> I'd much rather hire a junior engineer who spends $100-$200/month
I'd much rather hire a junior engineer at $1.20/hour too! Can you hook me up with your contract services provider?
Obviously I know you're talking about AI costs only. But the idea of doing that analysis without looking at the salary of the person running the tool seems to be completely missing the point.
Now, sure, there are legitimate arguments to be made about efficacy and efficiency and sustainability and best practices. But, no, $100k/year absolutely doesn't need to be "justified" if it works. That's cheaper than the alternative, and markedly so.
They keep forgetting to put "make no mistakes", "think deeply" and "get it right the first time" in their prompts.
When people have no ability to understand what they are doing, they will just rerun it endlessly hoping they get something passable. When that doesn't happen they burn money.
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In your fictional world you hire a junior who will write code manually, right?
First , I interview people, Junior skills in manual coding dropped sharply this year. These are people who started they school manual and switched mid-course. In two years there will be no such people.
well, that will never happened anymore in this world unless we will go back to caves, especially for juniors. Junior that writes good code is already a dying unicorn.
The outcome will be ... you will hire a junior ... who will burn more tokens, and chances of mistakes with less expensive model, less tokens are even higher.
The fully loaded cost of a senior engineer is already well past 400k. +5k a month is not that much if it helps them be XX% more productive. Personally at a different big tech I'm in the mid 4 digits AI spend per month and it helps me a lot, basically all coding has been trivialized and I work on an extremely large codebase. I'm spending more time on things closer to direct value generation like data analysis and experiment tweaking rather than spending time moving a variable across 10 layers of abstraction and making sure code compiles.
You are probably guiding them step by step and reading the results. Maybe you also sit and wait for the results.
Agents can iterate on a problem for hours if they can see their results and be given a higher level goal to evaluate their progress toward.
When you have an agent working for minutes or hours, never wait on it. Use that time to spin up another agent.
You can also spin up several agents in parallel to attempt the same item of work and compare their results to choose which to work off for next steps, instead of rolling the dice on a single option at a time and gambling that it's better to refine that first attempt instead of retrying from the start several more times.
And if you are doing manual QA manually, you're missing out on having e.g. Codex's "Computer Use" or "Browser Use" automate your manual verification steps and collecting a report for you to review more quickly. Codex can control multiple virtual cursors simultaneously in the background without stealing focus, to parallelize this.
If you want to use up more tokens to get more done (though more outside of your control and ability to review of course), that's how.