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Comment by jasonthorsness | Hacker News Reader

Claude Code works well for lots of things; for example yesterday I asked it to switch weather APIs backing a weather site and it came very close to one-shotting the whole thing even though the APIs were quite different.

I use it at home via the $20/m subscription and am piloting it at work via AWS Bedrock. When used with Bedrock APIs, at the end of every session it shows you the dollar amount spent which is a bit disconcerting. I hope the fine-grained metering of inference is a temporary situation otherwise I think it will have a chilling/discouraging effect on software developers, leading to less experimentation and fewer rewrites, overall lower quality.

I imagine Anthropic gets to consume it unmetered internally so I they probably completely avoid this problem.

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a couple weekends ago i handed it the basic MLB api and asked it to create some widgets for MacOS to show me stuff like league/division/wildcard standings along with basic settings to pick which should be shown. it cranked out a working widget in like a half hour with minimal input.

i know some swift so i checked on what it was doing. for a quick hack project it did all the work and easily updated things i saw issues with.

for a one-off like that, not bad at all. not too dissimilar from your example.

> I use it at home via the $20/m subscription and am piloting it at work via AWS Bedrock. When used with Bedrock APIs, at the end of every session it shows you the dollar amount spent which is a bit disconcerting. I hope the fine-grained metering of inference is a temporary situation otherwise I think it will have a chilling/discouraging effect on software developers, leading to less experimentation and fewer rewrites, overall lower quality.

I’m legitimately surprised at your feeling on this. I might not want the granular cost put in my face constantly but I do like the ability to see how much my queries cost when I am experimenting with prompt setup for agents. Occasionally I find wording things one way or the other has a significantly cheaper cost.

Why do you think it will lead to a chilling effect instead of the normal effect of engineers ruthlessly innovating costs down now that there is a measurable target?

I’ve seen it firsthand at work, where my developers are shy about spending even a single digit number of dollars on Claude Code, even when it saves them 10 times that much in opportunity cost. It’s got to be some kind of psychological loss aversion effect.

Years of being hammered to reduce reduce reduce in monthly cloud spend certainly makes us cost-averse. In reality you are completely correct wrt the real value, and leadership will always see this (hopefully), but I still don’t want to hear my name spoken in our monthly budget review.

I think it’s easy to spend _time_ when the reward is intangible or unlikely, like an evening writing toy applications to learn something new or prototyping some off-the-wall change in a service that might have an interesting performance impact. If development becomes metered in both time and to-the-penny dollars, I at least will have to fight the attitude that the rewards also need to be more concrete and probable.

once upon a time - engineers often had to concern themselves with datacenter bills, cloud bills, and eventually SaaS bills. We'll probably have 5-10 years of being concerned about AI bills before the AI expense is trivial compared to the human time.

"once upon a time"? Engineers concern themselves with cloud bills right now, today! It's not a niche thing either, probably the majority of AWS consumers have to think about this, regularly.

This guy has not been hit with a 100k/mo cloudwatch bill

I’ve managed a few 8 figure infrastructure bills. The exponential decrease in hardware costs combined with the exponential growth in software engineer salaries has meant that these bills become inconsequential in the long run. I was at one unicorn which had to spend 10% of Cost of Goods Sold on cloud. Today their biggest costs could run on a modest Postgres cluster in the cloud thanks to progressively better hardware.

100k/mo on cloud watch corresponds to a moderately large software business assuming basic best practices are followed. Optimization projects can often run into major cost overruns where the people time exceeds the discounted future free cash flow savings from the optimization.

That being said, a team of 5 on a small revenue/infra spend racking up 100k/mo is excessive. Pedantically, cloud watch/datadog are SaaS vendors - 100k/mo on Prometheus would correspond to a 20 node SSD cluster in the cloud which could easily handle several 10s of millions of metrics per second from 10s of thousands of metric producers. If you went to raw colocation facility costs - you’d have over a hundred dual Xeon machines with multi-TB direct attached SSD. Supporting hundreds of thousands of servers producing hundreds of millions of data points per second.

Human time is really the main trade-off.

Datadog has entered the chat.

AI bills are already trivial compared to human time. I pay for claude max, all I need to do is save an hour a month and I will be breaking even.

$200h * 8 * 5 * 4 * 12 = $384 000 per year.

You're like in the top 0.05% of earners in the software field.

Of course, if you save 10 hours per month, the math starts making more sense for others.

And this is assuming LLM prices are stable, which I very much doubt they are, since everyone is price dumping to get market share.

Across most anglosphere countries and tech cities - wages and salaries far outstrip what you can get for AI. AI is already objectively cheaper than human talent in rich countries. Is it as good? Yea I'd say it's better than most mid to junior engineers. Can it run entirely by itself? No, it still needs HITL.

Again, those prices aren't stable.

Nobody is investing half a trillion in a tech without expecting a 10x return.

And fairly sure soon those $20/month subscriptions will sell your data, shove ads everywhere AND basically only allow you to get that junior dev for 30 minutes per day or 2 days a month.

And the $200/month will probably be $500-1000 with more limitations.

Still cheap, but AI can't run an entire project, can't deliver. So the human will be in the loop, as you said, so at least a partial cost on top.

What’s different is all the open weight models like Kimi-k2 or Qwen-3 Coder that are as good and, depending on the task, better than Anthropic’s Sonnet model for 80% less via openrouter [1] and other similar services.

You can use these models through Claude Code; I do it everyday.

Some developers are running smaller versions of these LLMs on their own hardware, paying no one.

So I don’t think Anthropic and the other companies can dramatically increase their prices without losing the customers that helped them go from $0 to $4 billion in revenue in 3 years.

Users can easily move between different AI platforms with no lock-in, which makes it harder to increase prices and proceed to enshitify their platforms.

[1]: https://openrouter.ai/

The wages aren't stable either. There's going to be gradual convergence.

Oh, by the way, this entire discussion revolves around LLMs being an exponential tech. Real life only works with sigmoids.

Not gonna happen. The competition for AI models is approaching commodity.

The percentages in the field are skewed, FAANG employ a vast number of engineers.

No, they don't. FAANG probably employs 400 000 programmers worldwide, and I think the US alone probably has about 3-4 million programmers. Worldwide there are probably 30 million.

And even for FAANG, an SDE for them in Spain makes 60-100k total comp, not 400k.

on the other hand, it could also you mean you are overpaid

You will start seriously worrying about coding AI bills within 6 months

You will start seriously worrying about coding AI bills within 6 months

Nope.

More open models ship everyday and are 80% cheaper for similar and sometimes better performance, depending on the task.

You can use Qwen-3 Coder (a 480 billion parameter model with 35 billion active per forward pass (8 out of 160 experts)) for $0.302/M input tokens $0.302/M output tokens via openrouter.

Claude 4 Sonnet is $3/M input tokens and $15/M output tokens.

Several utilities will let you use Claude Code to use these models at will.

Where are you seeing those prices? Cheapest one I see (and it has horrible uptime/throughput numbers) is $0.40/$1.60, and it's more like $2/$2 on average.

Why is that?

Because at some point, Anthropic needs to stop hemorrhaging money and actually make some.

Uber, founded in 2009 was finally profitable in 2023. That's a bit longer than 6 months.

Different situation. Uber's entire strategy was to "disrupt" the transportation industry by undercutting everyone, with the promise that eventually adoption (and monetization via data, advertising, etc.) would be large enough for fees not to rise so much as to push consumers and drivers into the established taxi industry. Anthropic, on the other hand, is a competitor brand in a brand-new industry, one with heavy reliance on capex and exploding employee salaries, for that matter.

Totally different than except for the fact that it shows that "money" is able to wait 14 years, which is 28x longer than 6 months.

And when Anthropic raises the prices enough, people will jump ship.

That's why you don't pay the yearly license for anything at this point in time. Pay monthly and evaluate before each bill if there's something better out already.

and that jumping ship didn't happen IRL, cursor ARR jumped 50% after their pricing change.

> it shows you the dollar amount spent which is a bit disconcerting

I can assure you that I don’t at all care about the MAYBE $10 charge my monster Claude Code session billed the company. They also clearly said “don’t worry about cost, just go figure out how to work with it”

Meanwhile I ask it to write what I think are trivial functions and it gets them subtly wrong, but obvious in testing. I would be more suspicious if I were you.

Ask it to write tests first and then implement based on the tests, don't let it change the tests.

On more than one occasion I've asked for a change, indicated that the surrounding code was under test, told it not to change the tests, and it off it goes making something that is slightly wrong and rewrites the tests to compensate.

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