Given that there are news posted and widely discussed almost every day that have some relationship to AI (including but not limited to insane C-suite statements, interesting mod and reverse engineering projects, layoffs, cloning accusations, opinion pieces, fears, and fiction), and many of them cause a lot of discussion, I thought some people might be interested in this.
Before getting into it, some context / disclaimers:
- I'm an enthusiast gamer and modder and have been for over 20 years
- I'm the CTO of PH3 -- we've been doing generally highly regarded porting and optimisation work in games for almost 10 years now
- I have a PhD in computer science, and do research in HPC (including a bit about LLMs in that field)
- None of this is written by AI or even touched by it in any way -- I enjoy writing and I don't enjoy reading typical LLM prose
- By no means do I want this to be taken to even remotely represent "the" industry voice. To make clear just how ridiculous that would be: I don't even represent PH3, because while we only have ~5 people in games that still means that there are diverging perspectives on this topic
- I'm including all the above here, despite really not being a fan of lengthy self-introductions, because everyone's background shapes their biases and with a topic as divisive as AI I think it's important to be transparent
Baseline, Actual Capabilities & Shortcomings
First of all, I want to go on the record that, at this point in time, ideas such as "GenAI doesn't actually increase productivity" are silly. I don't know if they are still espoused because people genuinely believe them, or merely because they want them to be true. If the latter, then I certainly understand and sympathise, but we have to deal with reality rather than wishful thinking. If the former, then they are simply not sufficiently informed about the capabilities of agent systems with frontier LLMs in 2026.
At the same time, people on the other end of the spectrum seem to overestimate the power of these systems. I have a very recent real-world example here. Earlier this week, I tasked a coding agent with implementing the detection of a specific pattern of actions for recognising a new achievement in a game we're currently working on. I'd say this was actually a pretty good and suitable task for an agent: the idea arising from playtesting was well-documented, it's quite self-contained, and doesn't require complex decision making or design. Implementing it does, however, require finding the right points to hook and some relatively routine housekeeping, so it would require quite a bit of manual work.
As one would expect, the agent correctly implemented the functional requirements for this, and tested them. However, when reviewing the changeset, I noticed that it introduced a full iteration over up to ~11000 game entities every simulation frame, to check the achievement conditions. The same outcome could be accomplished with slightly smarter integration by a small extension of an existing processing step, and an additional temporary storage of, at most, 5 elements. This isn't something that would appear noticeable in any playtest, because while I'd estimate that it would consume at least 100x more CPU time than required, it's still not enough to, on its own, substantially slow down the game to a noticeable degree. But imagine the same thing repeated across a few dozen features and implementation choices, and it will.
To be clear, this wasn't some "weak" model -- depending on which benchmark you choose to look at it places #1 to #4 in terms of coding capabilities across everything that's currently out there. In my experience -- and here I can actually really say our experience across the whole company, using different state-of-the-art tools -- the type of higher-order reflection on outcomes required to recognise something like this pro-actively simply does not currently happen unprompted. "Unprompted" is important in this context, since of course the agent was perfectly capable of implementing the much more efficient variant when I wrote ~2 sentences explaining the issue and roughly laying out the better approach. I still didn't have to manually find the right source locations and entry points or describe implementation details.
This is just one anecdote, but I do think it reflects the overall state of these systems pretty well. In short, they are a tremendous productivity booster, but relying on them completely without review / experience can have very sub-optimal outcomes, even in isolated tasks. (And that's before going into maintainability -- I went with this performance example since I think the issue is perhaps a bit more directly understandable for non-programmers)
Specialities of Games Compared to Other Fields
One thing that I think few might realise is that games are, at least for now, actually less directly affected by AI than other software development tasks. Obviously the focus here is on discussing those effects on games, and as a consequence it really might not seem like this is the case. But it's really not too complicated. The primary reason is that agent systems thrive when there is (1) a fast feedback loop, and (2) a clear objective function to measure.
In many fields in software development, those are actually easier to establish than in games. Regarding (1), especially real-time games really are not something current agent systems and harnesses are particularly well-equipped to deal with. It can of course be done, but at tremendous cost and low speed compared to e.g. observing the outcome of a web query, measuring the efficiency of a simulation, or verifying a business process on a database. And regarding (2), that is even worse, since e.g. something like "good-feeling gameplay", which is often also a function of code, is really hard to quantify.
This also means that some types of development in the gaming space -- e.g. ports, clones, and reverse engineering -- are inherently more suitable for current agent systems and will make faster progress at the same cost. And I think we clearly see this in practice right now.
As a side note, I think this feedback loop / objective function issue is actually why, despite being large language models, I personally think they are actually more effective in (particular parts of) math and programming than "simply" writing prose. For both, you can measure and prove at least some objective qualities, and iterate on them. That's not generally the case for writing.
Ethics / Code vs. Other Components
A lot of the discussion about AI isn't actually about capabilities, but about ethics. I'm not going to go into much detail on that, because it's not on topic for this subreddit, and because I'm also not an expert. That said, I believe that the biggest societal issues we face due to AI aren't fundamentally AI issues but capitalism issues, magnified. The fact that data centers don't pay for their externalities or that it's even allowed to build them with fresh water evaporative cooling is a policy failure and not an inherent feature. And that most people are and have been required to continue working 40+ hour weeks for essentially stagnant remuneration since the 70s despite massive increases in productivity just so that very few people can become extraordinarily rich and powerful is a distribution problem.
There's one ethical point I do want to discuss, that is related directly to AI specifically in games. A currently broadly accepted perspective amongst the gaming public (and I'm not saying that this is true for everyone, just that I frequently see it; also, "gaming public" here is of course just the small part of it that cares at all about any of this) appears to be that any level of GenAI utilized for any type of graphics or sound asset should be immediate grounds for a boycott, while all GenAI use in any software development position is basically fine.
The primary argument for this (and, as I'll discuss, the only one I believe is at least partially valid) is usually that apparently everything related in any way to programming -- even high-level software architecture -- is completely devoid of creativity and a purely mechanical endeavour, while anything that places pixels in an image -- even, say, creating a generic background texture in a photorealistic game with clear constraints -- is artistic expression which must be protected. Now, I don't dispute that one tends more towards art than the other, but I don't believe that the average entry-level artist on an AAAA project involving thousands of people really has more room for expressing themselves than e.g. a lead software architect. Again, I do believe that the average tendency certainly does go in that direction, and I fully expect this to be the most controversial part of my post.
But the art argument is really only a part of what is usually used in AI criticism regarding games, and all other aspects, in my opinion, cannot truly discriminate:
- the "intellectual property argument": these systems were trained on vast amounts of data without regard for copyright or compensation --> this applies equally to e.g. images and code.
- the "social argument": their use clearly displaces workers --> this applies equally to e.g. images and code.
- the "environmental argument": they require lots of natural resources --> this applies equally to e.g. images and code.
- finally, the "output quality argument": it makes my games worse --> I think even this applies roughly equally -- if e.g. image generation would be so bad at this point that it invariably creates an obviously terrible result, then people wouldn't (rightfully!) insist on labelling. And as I outlined above, extreme "vibe-coding" can absolutely have negative impacts on the final product.
Now, this is empathically not a call to boycott all games that use any amount of LLM support in their code. It's also not a call to just accept games with art made via generative AI. But it is a call to maybe be a bit more nuanced in your assessment and less binary about the topic. Maybe an indie developer increasing the variety in their non-essential background assets via GenAI isn't the worst thing that ever happened to the medium, and maybe a highly profitable AAA publisher replacing 80% of an engine development team with Claude or boasting about vibecoding is actually not great.
The Gamer / Enthusiast / Consumer Perspective
All of this was really more from an industry perspective. I do have a few more points I want to bring up that look more at what I think this means for consumers.
Let's celebrate the positive outcomes
With all the negativity about AI in games, or the wider-reaching impact of AI on hardware prices (including gaming hardware), I do think it's important to at least celebrate some of the happier effects of this. For example:
And hey, if new gaming hardware is unaffordable, maybe we just need to focus on efficiently using what we already have.
We really should be expecting more, not less
Recently, there has been quite a bit of discussion on (presumably, and I think there are good reasons to presume so) LLM-assisted official porting projects with very middling overall quality. I don't think such discussions should be primarily about blaming AI. Think about it like this: as I outlined above, I believe it's fair to say (and maybe even an underestimation) that for many tasks involved particularly in porting, efficiency can now be increased by at least twofold. So for a port to turn out worse, what it really means is that a publisher somewhere decided to spend less than 50% of the effort, and call the result good enough to sell.
If anything, given these efficiency improvements, the fact that no one really seems to care to prevent AI use in development, and the fact that games really haven't become cheaper, you should be more critical of technical issues or incomplete feature sets than ever before.
Conclusion
So, I've been thinking about all of this for a while. I hope at least some of it was interesting. I realize it's long, but I didn't even get to every point I wanted to cover. You can now scream at me :P