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>Code review is a fantastic mechanism for catching bugs and sharing knowledge "Sharing knowledge" is one of the first phrases in the article, and highlighted a

>Code review is a fantastic mechanism for catching bugs and sharing knowledge "Sharing knowledge" is one of the first phrases in the article, and highlighted as a key benefit of code review. But the loss to human-capital from this process is never examined in the post. > Trivial reviews (typo fixes, small doc changes) cost 20 cents on average They did around 25,000 of these runs (about 20% of total). So CF spent $5k in the period making language models run through PRs which were <10 lines long. I get that CF engineers are paid well, but the labour cost of having an intern/entry level engineer spend ~30-60s looking through these is likely close to $0.20, and that engineer builds some human-capital while they're at it.

alain94040 5mo ago
the labour cost of having an intern/entry level engineer spend ~30-60s looking through these is likely close to $0.20 Did you do the math? Your estimate feels way off. First, I doubt an intern would process one PR in 30s. Maybe 2-3 minutes, to read 10 lines carefully looking for typos and indentation mistakes. We pay interns close to $100K these days (in a company like CloudFlare), so that's ~80c/minute. My estimate is therefore closer to $1.6 per PR. About 10X. You are correct that there is a residual value with the intern, over time they would start learning (a little bit) about the code base.
- MeetingsBrowser 5mo ago
  $100k is probably at the top end. Even at Cloudflare, interns are more likely to be making $25-40/hour according to levels.fyi, which works out to $52-83k.
onlyrealcuzzo 5mo ago
Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after). Are you sure it's going to make sense to pay someone to do that moving forward? I don't think it's worth it now, by the way. It's definitely not going to be worth it in the near future. Can we even blink for $0.002? What happens when the next 90% increase in efficiency happens??
- fg137 5mo ago
  Would like to know where that 90% number comes from, and if it matches historical trend.
  - onlyrealcuzzo 5mo ago
    https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_cost_is_decreasing_by_10x_each_year_for/ https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_co... See Chart 13 here: https://www.rdworldonline.com/ais-great-compression-20-charts-show-vanishing-gaps-but-still-soaring-costs/ https://www.rdworldonline.com/ais-great-compression-20-chart... See here: https://epoch.ai/data-insights/llm-inference-price-trends https://epoch.ai/data-insights/llm-inference-price-trends LLMs are so comically inefficient compared to the human brain that it is pretty easy to imagine this trend continuing for several more 90% drops. If LeCun's JEPA or GRAM turn out to be a thing, we could see a 3-4 order of magnitude drop in a single release cycle / generation. Keep in mind that performance per watt on the hardware side - at the same time - is still doubling every ~24 months - and this doesn't factor that in.
    - dingnuts 5mo ago
      [dead]
- FuckButtons 5mo ago
  > Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after As far as I can see, token costs have been steadily increasing over the past few months, so I’m not sure that buying the hype that another 90% cost reduction is just around the corner is warranted.
  - wetoastfood 5mo ago
    Doesn’t seem like token costs, specifically, are increasing. Opus cut its token pricing by 66% 6 months ago and it had previously been that higher price consistently for a year and a half (since that model launch). GPT’s latest model is harder to track since it’s not named, but it’s historically inline with its history. Not to mention what’s happening with other models like DeepSeek, GLM, and Kimi. It seems to me the bigger change in costs is based on token appetite. People are discovering agentic capabilities are stronger than they used to be and use cases have broadened because of that. They’ll eventually discover too that these alternative models offer 95% of the intelligence at 20% of the price.
notawhitemale 5mo ago
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alain94040 5mo ago

the labour cost of having an intern/entry level engineer spend ~30-60s looking through these is likely close to $0.20 Did you do the math? Your estimate feels way off. First, I doubt an intern would process one PR in 30s. Maybe 2-3 minutes, to read 10 lines carefully looking for typos and indentation mistakes. We pay interns close to $100K these days (in a company like CloudFlare), so that's ~80c/minute. My estimate is therefore closer to $1.6 per PR. About 10X. You are correct that there is a residual value with the intern, over time they would start learning (a little bit) about the code base.

MeetingsBrowser 5mo ago
$100k is probably at the top end. Even at Cloudflare, interns are more likely to be making $25-40/hour according to levels.fyi, which works out to $52-83k.

onlyrealcuzzo 5mo ago

Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after). Are you sure it's going to make sense to pay someone to do that moving forward? I don't think it's worth it now, by the way. It's definitely not going to be worth it in the near future. Can we even blink for $0.002? What happens when the next 90% increase in efficiency happens??

fg137 5mo ago
Would like to know where that 90% number comes from, and if it matches historical trend.
- onlyrealcuzzo 5mo ago
  https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_cost_is_decreasing_by_10x_each_year_for/ https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_co... See Chart 13 here: https://www.rdworldonline.com/ais-great-compression-20-charts-show-vanishing-gaps-but-still-soaring-costs/ https://www.rdworldonline.com/ais-great-compression-20-chart... See here: https://epoch.ai/data-insights/llm-inference-price-trends https://epoch.ai/data-insights/llm-inference-price-trends LLMs are so comically inefficient compared to the human brain that it is pretty easy to imagine this trend continuing for several more 90% drops. If LeCun's JEPA or GRAM turn out to be a thing, we could see a 3-4 order of magnitude drop in a single release cycle / generation. Keep in mind that performance per watt on the hardware side - at the same time - is still doubling every ~24 months - and this doesn't factor that in.
  - dingnuts 5mo ago
    [dead]
FuckButtons 5mo ago
> Well, AI costs are definitely going to go down at least 90% in the next ~18 months for the same quality of output (and probably 90% again in the 24 months after As far as I can see, token costs have been steadily increasing over the past few months, so I’m not sure that buying the hype that another 90% cost reduction is just around the corner is warranted.
- wetoastfood 5mo ago
  Doesn’t seem like token costs, specifically, are increasing. Opus cut its token pricing by 66% 6 months ago and it had previously been that higher price consistently for a year and a half (since that model launch). GPT’s latest model is harder to track since it’s not named, but it’s historically inline with its history. Not to mention what’s happening with other models like DeepSeek, GLM, and Kimi. It seems to me the bigger change in costs is based on token appetite. People are discovering agentic capabilities are stronger than they used to be and use cases have broadened because of that. They’ll eventually discover too that these alternative models offer 95% of the intelligence at 20% of the price.

notawhitemale 5mo ago

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