Cubeia, a Swedish software firm, is advancing its use of AI in development, reaching the third stage of its journey. The company has transitioned from permitting developers to use AI tools at their discretion to implementing a structured AI-driven development process. This evolution prompts a significant question: how should the organization function when coding is no longer the primary constraint?
Stefan Grenstad, COO of Cubeia, affirms, "We’re doing this 100%." With AI creating additional capacity, the company must now determine how to leverage this newfound efficiency. Six months ago, the focus was on swapping human-written code for AI-generated code. Now, the conversation has shifted toward refining the development pipeline, redefining roles for developers and quality assurance (QA) staff, and reshaping how teams interact with clients.
The journey began with a hands-off approach to AI in its first phase, allowing developers the freedom to utilize it as needed. The second phase introduced structure, requiring all team members to use the same AI entities, which led to resolving issues regarding quality assurance and agent collaboration while helping staff adapt to their new working environment. Grenstad believes the organization has largely succeeded in these endeavors. "During the hybrid period in Q1, Cubeia solved 259 issues. Once it moved to the AI-driven process, that figure rose to 421 – a 62% increase. Larger projects increased from 17 to 58. So the answer is yes: moving to AI-driven development has increased our output tremendously," he shares.
The central inquiry now is how Cubeia will utilize this increased capacity. Grenstad notes, "Because we’re not spending as much time coding, we’re spending more time on the business: talking about value and understanding the domain." Rather than simply producing requested projects, teams are now expected to comprehend the purpose behind their work and the value it should deliver.
Paul Crisp, Cubeia's head of marketing, illustrates this shift, stating, "If the objective is 10% more traffic, for example, how do we measure it? We need to establish a baseline, implement the change, measure it, and see whether it goes up by 10%. If it only goes up by 1%, maybe we need another iteration because we haven’t fulfilled the objective."
Another evolution in Cubeia’s AI approach involves responding to customer desires. Grenstad explains, "We [recently] started asking, 'What does this mean for the customer? What can the customer actually do with this?'" One client, for example, built its own casino landing page and sought to use Cubeia’s APIs for integration with the platform. "Another wanted to use Cubeia’s player account management system to build its own functionality, including personalized bonuses," he recalls. This shift indicates that Cubeia aims to provide access to its platform and data, allowing customers to leverage their own AI developments, marking a significant strategic change.
As part of this restructured development approach, Cubeia is experimenting with a rapid-response team to handle smaller customer requests alongside a team focused on larger, long-term projects. Current team sizes range from five to eight members, but Grenstad imagines that these could eventually shrink. "In my dream, it would be two- or three-person teams," he says. Developers will have the flexibility to transition between immediate response projects and long-term initiatives based on their interests and contributions.
Despite the increase in capacity, challenges remain. With AI agents generating diverse work streams, Cubeia must find an equilibrium regarding individual workloads and review processes. "The bottleneck becomes the person reviewing everything. For sensitive or critical parts of the system, we still look closely at the implementation, particularly from an architecture, performance, security, and reliability perspective. But we no longer apply that level of manual code review to everything," Grenstad explains.
This shift in AI utilization also affects hiring. Grenstad asserts that for teams focusing on the player experience, understanding the iGaming landscape can prove more valuable than traditional technical skills. "I’d rather take someone who’s good in the domain but doesn’t know any Java," he states.
The changing landscape prompts reflections on how future developers will fit into the industry if conventional entry points, like junior Java developers, become less common. Grenstad identifies a concern regarding the potential imbalance of experienced developers without new talent, emphasizing, "That’s a super-interesting question. How do we fill up with younger people over time? That’s something we’ve been discussing."
Additionally, Cubeia is reshaping its traditional development processes. Grenstad describes the past model where a developer built something and handed it to QA, stating that QA is now integrated throughout the development workflow. Product engineers assess from the outset what elements can be automated while QA personnel enhance automation and pinpoint coverage gaps. This evolving responsibility spans from problem identification through to implementation and results measurement.
Despite these advancements, Cubeia still grapples with uncertainties. Some team members miss the hands-on coding aspect of their roles. Grenstad acknowledges, "We still have people who loved the coding part – solving problems with code and writing beautiful code. They’re struggling with the change. For some people, an important part of their work that they genuinely loved is now gone."
There are also concerns regarding reliance on AI providers. Grenstad raises questions about potential pricing increases or quality issues from AI supplier Claude, stating that navigating these uncertainties will be part of Cubeia’s learning process. He expresses confidence that the company will adapt as these challenges arise, stating, "We’re confident that the question marks will be solved as we work through them. We’ll see the problems, learn how to manage them, and adapt."
Cubeia's initial aim was straightforward: remove coding as a bottleneck in software development. Yet, the journey has revealed that coding is just one piece of a more complex puzzle. As this bottleneck shifts, the organization is confronted with new demands regarding domain knowledge, product strategy, quality control, prioritization, and customer understanding. Consequently, technology's role has shifted to the background as Cubeia focuses on how to best utilize it moving forward.
