Reliable insider information about AI in casino game development is surprisingly hard to get. Publicly, the conversation is still cautious. Privately, most people close to production already know the answer: AI is being used across the sector, at many different studios, to many different extents, and almost nobody wants to talk about it plainly.
That silence creates a misleading picture. From the outside, AI can still look like a speculative trend or a layer of marketing language. Inside studios, that is already outdated. The real shift is not whether AI has arrived in casino game creation. It has. The real shift is how deeply it has entered the workflow, and what happens when a company reorganizes itself around that fact rather than treating it as an add-on.
Martin Tibensky, CEO of Casarecce, a Malta-based AI-native game design studio serving game studios, says the biggest misunderstanding is that people still talk about AI as though it were mainly a support tool sitting on top of a traditional production model.
“The truth is that most studios are already using AI in some form,” Tibensky says. “They are just using it to different extents, and very few want to talk about it plainly.”
That difference in extent matters. Some teams are using AI to accelerate isolated tasks. Others are pushing it further into the art and design pipeline, using it to compress work that previously required significant human headcount. The companies with the biggest operating advantage are likely to be the ones in the second group.
That is what makes Casarecce a useful example. It is built to handle outsourced game design work for game studios rather than sell directly to online casinos. Key components of game development that once depended on large teams can now be handled differently, faster, and at greater scale when AI sits at the center of the process.
That does not mean the whole production chain disappears into automation. It means the labor profile changes. In the parts of the workflow where AI is effective, it can replace headcount that used to be necessary to move production forward. Once that happens, the studio begins to operate on a different basis.
For years, scale in game design was closely associated with people. More output generally meant more artists, more designers, more layers of review, and more elapsed time between one version and the next. Casarecce is built on a different assumption. Instead of treating growth in capacity as primarily a staffing problem, it treats it as a problem of workflow design and compute.
That matters because casino content is a category where variation, iteration, and visual execution all matter at once. A team may need to explore multiple themes, test different directions, revise rapidly, and push a concept toward something production-ready without losing coherence on the way. In a conventional pipeline, that creates drag. In an AI-native studio, the same process can move at a very different pace.
“What has changed is not just that certain tasks can be done faster,” Tibensky says. “It is that the design process itself starts to move differently. You can test more directions, kill weaker ideas earlier, and get to something usable much sooner than before.”
This is the point that often gets lost in broad industry discussion. The real effect of AI is not just speed in the abstract. It is the ability to compress large parts of art and design work that used to require substantial teams. That changes both quantity and quality: more options can be explored, more material can be compared, weaker ideas can be discarded earlier, and stronger ideas can be refined further before a studio commits serious downstream time to them.
In practical terms, that means more bandwidth, tighter feedback loops, and shorter gaps between iterations. It means a studio can cover more creative ground without having to add people at the same rate. That is why the relevant comparison is no longer simply between companies that use AI and companies that do not. It is between companies that have redesigned the way work gets done and companies that are still organized around a more labor-heavy model.
Studios that stay with the older model will not vanish overnight, and not every part of the process will shift at the same speed. But they are likely to move slower, and in a crowded market that matters. What used to count as fast will not continue to count as fast once buyers, developers, and production teams become used to a different baseline.
Casarecce’s significance, then, is not just that it uses AI. Many companies now do. What makes it notable is that it was built around the idea that AI can replace headcount across key parts of the art and design process, creating a fundamentally different kind of studio. More output is one consequence. Lower dependence on large human teams is another. The more interesting point is that quality does not have to fall with that shift. In the right workflow, it can improve.
That is also why insider information remains scarce. Once a company recognizes that its process advantage comes from replacing costly, time-intensive parts of production with AI-driven ones, it has little incentive to explain too much in public. The result is an industry where the change is already real, already widespread, and still oddly underdescribed.
For casino game development, that may be the clearest way to understand the moment. AI is no longer the experiment. It is becoming the hidden normal. The real competitive divide now is between studios that have adapted to that shift, and studios that have not.