The point of integrating AI into games is to provide further diversity within the game.
Think Skyrim. By default you’re limited to 3-4 discussion options, right? Imagine now, if you will, that you could just… type in anything, including emotional markers, and have the characters respond interactively to the statement and tone. No longer are you bound by limited dialogue in RPGs.
visual generative AI will just spice up the visuals - hopefully. Things like repetitive textures and such will disappear as the game generates brand new textures for each grid element. Or create tons of background characters without the need to specify them. The list goes on.
I dream of AI being used to generate cool, theme appropriate NPCs for an indie dev that doesn’t have an army of talented writers at their disposal.
But honestly let’s face it, that’s never what they’ll aim for. It’s all about not paying people, for these assholes. They’ll happily fire their talented writers of they can use AI instead, never once wondering who’s gonna feed the AI in the first place.
And so it is a matter of principle to say no to AI in general, because you know they’ll always pick the worst possible way to use it.
You have optimism. It could be used for good like you describe, but most likely will be used by EA to cut artists out, slash costs, make as much profit as possible and suffocate the company until the private equity owners move on to the next company.
No, that’s exactly what this is about. They came right out and said as much. It won’t work, but they’ll cause a lot of damage in the process of failing.
Which is a different article about a (somewhat) unrelated topic.
Using AI for development is already out there, and you can’t put that genie back in the bottle. As an engineer I’m already using it in my daily work for tons of things - I’ve built separate agents to do a number of things:
read work tickets, collate resources, create a work plan, do the initial footwork (creating branches, moving tickets to the right states, creating Notion document with work plan and resources)
read relevant changes in UI design documents and plan + execute changes (still needs some manual review but e.g. with Android Jetpack Compose, it makes 90-95% of the needed work and requires minimal touch-up)
do structural work - boilerplates, etc.
write unit and integration tests, and already working out a UI test automation agent
do code reviews on changes, document them, and write appropriate commit messages
do PR reviews - I still review them myself but an extra eye is always helpful
guess what, AI didn’t replace me, it just allowed me to focus on actually thinking up solutions instead of doing hours of boilerplate stuff.
AI isn’t the enemy in software development. Companies who think they can replace engineers with AI. Middle managers will sooner be on that date, as they were mostly useless anyway.
A project costs X amount because of the standard template of pay per time unit Y multiplied by timeframe in time unit Z.
Simply said if you have 100 people working on the project, that costs 100Y per hour. If the project takes 6 months (approx. 960 hours), you multiply the two and get that your costs are 96000Y.
Now the two ways to reduce this is to either reduce the number of employees, with AI you can get rid of maybe 2/3, reducing the expenses to 32000Y…
Or since AI speeds up almost every workflow by about 8 to 10 times, you can keep all the people, but cut down project time from 6 months to about 2 months, which doesn’t just reduce the expenses by the same 2/3 but also increases potential profits for the same 6 month period by 200%, as instead of one product you’re releasing three.
Cutting jobs ain’t the only way to reduce costs with AI.
Your experience counts for jack shit. There is zero evidence that AI is substantially improving efficiency. There is some that suggests its effect is negative.
Great for you. You did say “almost every workflow”. How many workflows exist beyond your own lived experience? Do you work on games, do you know all the workflows there? Citation absolutely fucking needed.
I have worked on games, and have a good understanding of the workflows involved.
You’ll obviously still need to do the creative parts manually (and should!) but the majority of the work involving the engine core build and the specific game coding, that can all be sped up borderline exponentially.
But I’m glad that someone with absolutely no understanding of the topic does their best to call out those who do show some experience on the topic just because they don’t get a neatly pre-chewed and pre-digested reply detailing all the information they lack and are unwilling to look it up themselves. As a next step would you like me to cut your steak up and feed it to you byte by byte, or tuck you in at night?
Procedural generation with appropriate constraints and a connected game that stores and recalls what’s been created can do this far better than a repurposed LLM. It’s hard work on the front end but you have a much better idea of what the output will be vs. hoping the LLM “understands” and remembers the context as it goes.
I agree that the results will be different, and certainly a very narrowly trained LLM for conversation could have some potentials if it has proper guardrails. So either way there’s a lot of prep beforehand to make sure the boundaries are very clear. Which would work better is debatable and depends on the application. I’ve played around with plenty of fine tuned models, and they will get off track contextually with enough data. LLMs and procedural generation have a lot in common, but the latter is far easier to manage predictable outputs because of how the probability is used to create them.
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That’s… not what this is about?
The point of integrating AI into games is to provide further diversity within the game.
Think Skyrim. By default you’re limited to 3-4 discussion options, right? Imagine now, if you will, that you could just… type in anything, including emotional markers, and have the characters respond interactively to the statement and tone. No longer are you bound by limited dialogue in RPGs.
visual generative AI will just spice up the visuals - hopefully. Things like repetitive textures and such will disappear as the game generates brand new textures for each grid element. Or create tons of background characters without the need to specify them. The list goes on.
I dream of AI being used to generate cool, theme appropriate NPCs for an indie dev that doesn’t have an army of talented writers at their disposal.
But honestly let’s face it, that’s never what they’ll aim for. It’s all about not paying people, for these assholes. They’ll happily fire their talented writers of they can use AI instead, never once wondering who’s gonna feed the AI in the first place.
And so it is a matter of principle to say no to AI in general, because you know they’ll always pick the worst possible way to use it.
You wish, this will be about money.
You have optimism. It could be used for good like you describe, but most likely will be used by EA to cut artists out, slash costs, make as much profit as possible and suffocate the company until the private equity owners move on to the next company.
No, that’s exactly what this is about. They came right out and said as much. It won’t work, but they’ll cause a lot of damage in the process of failing.
Which is a different article about a (somewhat) unrelated topic.
Using AI for development is already out there, and you can’t put that genie back in the bottle. As an engineer I’m already using it in my daily work for tons of things - I’ve built separate agents to do a number of things:
guess what, AI didn’t replace me, it just allowed me to focus on actually thinking up solutions instead of doing hours of boilerplate stuff.
AI isn’t the enemy in software development. Companies who think they can replace engineers with AI. Middle managers will sooner be on that date, as they were mostly useless anyway.
How do they reduce costs with AI if not by eliminating jobs?
By improving the cadence of projects.
A project costs X amount because of the standard template of pay per time unit Y multiplied by timeframe in time unit Z.
Simply said if you have 100 people working on the project, that costs 100Y per hour. If the project takes 6 months (approx. 960 hours), you multiply the two and get that your costs are 96000Y.
Now the two ways to reduce this is to either reduce the number of employees, with AI you can get rid of maybe 2/3, reducing the expenses to 32000Y…
Or since AI speeds up almost every workflow by about 8 to 10 times, you can keep all the people, but cut down project time from 6 months to about 2 months, which doesn’t just reduce the expenses by the same 2/3 but also increases potential profits for the same 6 month period by 200%, as instead of one product you’re releasing three.
Cutting jobs ain’t the only way to reduce costs with AI.
Citation fucking needed.
My own fucking experience. Which I’ve already explained in detail above.
Your experience counts for jack shit. There is zero evidence that AI is substantially improving efficiency. There is some that suggests its effect is negative.
And your comment also counts for jackshit since you provided no evidence of your claims, not even your own experience.
So you can fuck right off, buddy.
Great for you. You did say “almost every workflow”. How many workflows exist beyond your own lived experience? Do you work on games, do you know all the workflows there? Citation absolutely fucking needed.
I have worked on games, and have a good understanding of the workflows involved.
You’ll obviously still need to do the creative parts manually (and should!) but the majority of the work involving the engine core build and the specific game coding, that can all be sped up borderline exponentially.
But I’m glad that someone with absolutely no understanding of the topic does their best to call out those who do show some experience on the topic just because they don’t get a neatly pre-chewed and pre-digested reply detailing all the information they lack and are unwilling to look it up themselves. As a next step would you like me to cut your steak up and feed it to you byte by byte, or tuck you in at night?
Procedural generation with appropriate constraints and a connected game that stores and recalls what’s been created can do this far better than a repurposed LLM. It’s hard work on the front end but you have a much better idea of what the output will be vs. hoping the LLM “understands” and remembers the context as it goes.
Sorry but procedural generation will never give you the same result as a well tuned small LLM can.
Also there’s no “hoping”, LLM context preservation and dynamic memory can be easily fine-tuned even on micro models.
I agree that the results will be different, and certainly a very narrowly trained LLM for conversation could have some potentials if it has proper guardrails. So either way there’s a lot of prep beforehand to make sure the boundaries are very clear. Which would work better is debatable and depends on the application. I’ve played around with plenty of fine tuned models, and they will get off track contextually with enough data. LLMs and procedural generation have a lot in common, but the latter is far easier to manage predictable outputs because of how the probability is used to create them.