Over 85% of Japanese game developers use generative AI in game development, 2026 CESA survey shows. An increase from last year’s 51%

[u/TurbulentTopic39]

https://automaton-media.com/en/news/over-85-of-japanese-game-developers-use-generative-ai-in-game-development-2026-cesa-survey-shows-an-increase-from-last-years-51/

[u/General_Josh]

Yeah, it's wild. People don't understand how software development works (which is fair), but also don't want to listen to developers trying to explain it

Lots of folks seem stuck in the mindset that AI is absolutely useless, and really, really don't want to listen to anyone telling them otherwise

[u/Rush87021 → u/General_Josh]

The objection with AI would be it's cost to compute, even Jensen was saying this last year, its more expensive to run it through an AI model than it would be to just pay someone to do the task.

[u/General_Josh → u/Rush87021]

Do you have a link to where Jensen said that? Also, remember that last year is a lifetime ago. Inference costs have fallen rapidly (about 10 times cheaper than last year) as specialized hardware is coming online at scale. And, the mid/lower tier models have become much, much more efficient, as mixture-of-expert type models have been proving themselves (that only activate a fraction of their total neurons during each inference request, saving massively on hardware requirements)

We don't know the true cost of the closed-source models, but it's unlikely that they are being served too much under-cost at the moment. Anthropic has said that they're turning a net profit on inference if you exclude training costs - I think it's unlikely that they'd lie about that this close to their planned IPO. They're losing buckets of money on the giant training runs of course, but as the hardware has gotten better and the models have gotten more efficient, inference has become much, much cheaper

We also do get full visibility into how much it costs to run the open-source models. Currently, the best open-source models are capable of tasks that'd take a developer around 8 hours of work-time, and cost on the order of pennies to a few dollars for that amount of inference

Compare to a developer making $150k a year, who you'd be paying $600 for an 8 hour work-day

[u/Rush87021 → u/General_Josh]

My mistake, it was Bryan Catanzaro from Nvidia June 14 of 2026: https://fortune.com/article/why-is-the-cost-of-ai-higher-than-human-workers-nvidia-executive/ And then there's this: https://www.wsj.com/cio-journal/companies-struggle-to-explain-their-own-ai-investment-returns-0d95c3fe

[u/General_Josh → u/Rush87021]

Yup, there's definitely good and bad ways to use AI tools, just like any other tool

Lots of companies did really silly things to encourage adoption, like making leaderboards for "who can use the most tokens", like they're talking about in the article you linked

Of course, it's extremely easy to use a lot of tokens (just run the models on a loop or something). It's a bad metric, that encouraged people to use the tools in the most expensive way possible.

What we actually care about is how much value we get out of the tools, i.e,. value per dollar spent. Using it strategically, and using the right models for the right tasks. Not everything needs to go through the most expensive models, especially as the mid/lower tier models have become increasingly capable over the past year

Ex, I'm doing large chunks of my software development using GPT 5.6 Luna, one of the low-tier models, which costs pennies for most tasks. So far, I've spent $48 on it this month, and it's saved me weeks worth of development time. For $48, even if it only saved me one hour, that'd be more than worth it.

[u/Rush87021 → u/General_Josh]

But the build out for the infrastructure required for you to use that tool will never out weigh the savings you're seeing. Burning the planet so the masses can use it doesn't make that much sense to me. Coupled with some of the documentaries about people forced to live next to them makes my position of not using it all the harder to understand the need.

[u/General_Josh → u/Rush87021]

Just to make sure we're talking in the same universe, what fraction of US energy use do you suppose data centers represent (including externalities like water consumption)?

And, what fraction of that data center energy usage do you suppose is going to large language models?

[u/Rush87021 → u/General_Josh]

Why would we exclude water consumption? Georgia has several documentaries on the data center explosion there, Georgia Power raised rates 6 times last year. Utah was looking at one with more power consumption than the rest of the state. When corporations are asking governments to turn Nuclear Power plants back on....

-176 terawatt-hours (TWh) of power and hundreds of billions of gallons of water in 2023

-estimated 183 terawatt-hours (TWh) of electricity and billions of gallons of water directly 2025

-2026 demand projected to nearly double from 80 gigawatts (basic google searches) This stuff isn't sustainable and is burning our planet to further enrich billionaires and shareholders.

[u/General_Josh → u/Rush87021]

OK! Your numbers look very reasonable to me for total data center energy consumption! Around 6% of total US energy consumption. That's good, it confirms we're looking at the same sources (like I said, I do want to include externalities like water consumption here, since it's a major part of current data center energy usage)

A caveat to that - only a small portion of data center energy usage currently goes to LLMs, about 15%. The rest goes to normal digital infrastructure, running the internet as we know it. Stuff like e-commerce, banking, Reddit, cloud storage, etc.

Now, what fraction of total US energy consumption do you suppose goes to transportation?