Digicode’s president and global CEO, Max Maslii, has been working in applied artificial intelligence since the early 2000s. In an exclusive interview with SiGMA News, he explains why the AI revolution is the logical outcome of decades of development, and what it means for iGaming operators that have yet to build their own AI models.
Not an explosion, but an accumulation
When people talk about the AI “explosion” of 2022–2024, it creates the impression that the technology appeared from nowhere. Maslii regards this as an illusion.
“We were working on image recognition projects to convert satellite imagery into topographic maps as far back as 2001. These were very powerful technologies that produced remarkable results,” he recalls.
The systems of that era could distinguish roads from terrain, and the outputs were immediately deployed in industrial solutions. AI has long been solving complex problems, just in niche B2B scenarios that remained invisible to the wider public.
Maslii identifies the radical reduction in computing costs as the key driver of the current wave. Where every processor cycle had to be carefully accounted for in the early 2000s, the picture today is the reverse: “Nobody thinks twice about an extra 60 processors or 500 gigabytes of memory. You can generate code that is hundreds of times less efficient than what an experienced engineer would write, and it will still run.”
“The real revolution is not in the ideas. It is in the combination of affordable hardware, big data, and mature algorithms.”
Labour market: junior roles under pressure
The impact of AI extends well beyond engineering. Maslii describes a structural shift in the labour market, with demand for junior specialists performing routine tasks falling sharply.
“The need for entry-level professionals is declining significantly, as AI can be trusted with simple tasks,” he says.
At the same time, demand is growing for experienced professionals who can set tasks for AI systems, review outputs, and think architecturally. This creates the risk of a generational gap: straightforward work migrates to machines, while the natural career path from junior to senior level becomes less defined.
A second paradox is the explosive growth in product and content volume against a backdrop of unchanged user attention. “If every person can build their own project in a week, what happens then?” Maslii asks. In the context of gambling, the answer is clear: competition for player retention will intensify.
The Ukrainian school: mathematics plus motivation
Maslii, who was born and lives in Ukraine, attributes the competitiveness of Ukrainian IT talent to three factors: a strong academic grounding in mathematics, tight links between universities and the real business world, and high professional motivation.
“Academic study combined with hands-on industry practice produced very strong results and created tens of thousands of IT professionals in the market,” he says.
Digicode ran its own training courses and brought people up from scratch. Alongside state education, a private talent pipeline developed in Ukraine, directly tied to live projects. In Maslii’s view, the defining quality of Ukrainian specialists is a willingness to go beyond the scope of the assigned task. That characteristic is especially valuable in AI projects that require systems-level thinking.
Private AI models as a competitive edge in iGaming
In iGaming, Maslii treats the question of whether to adopt AI as settled: “All companies will use AI, one way or another. If you do not, the cost of sales will become too high to be competitive.”
He describes an emerging market architecture in which a handful of global players invest billions in foundational models, while everyone else builds proprietary solutions on top of them. It is those private models that will become an operator’s primary competitive advantage, he argues.
“A private AI model that communicates with players correctly, anticipates their behaviour, needs, preferences, and potential, will take an iGaming company to the highest level of profitability.”
By the end of 2025, AI had penetrated deepest into CRM, segmentation, and communications. The next areas of development are game design, offer generation, and anti-fraud. Maslii singles out fraud prevention in particular: “AI will be on both sides: on the side of the industry, and on the side of the player, who will use their own tools to try to beat the system.”
AI against AI
Mass AI automation creates new vulnerabilities. Maslii acknowledges that rapid development cycles involving generative models often introduce security weaknesses.
“In iGaming, as in other sectors, AI can fight AI. Bad actors find openings; security specialists look for ways to counter them while keeping the system operational,” he explains.
For gambling, historically a high-risk sector from a fraud perspective, this means that operating without AI-based monitoring and protection tools will become untenable within a few years.
Chaos engineering as an operating model
Digicode has grown from a handful of people to several hundred, deliberately maintaining a flat structure and a culture of rapid adaptation. Maslii describes his approach as “chaos engineering”.
“It is an attempt to describe a process that looks like chaos, but is in fact a structured, directed chaos. The participants move like improvising particles, but each one knows where they are going and can change direction very quickly,” he explains.
In a market where the AI stack, regulatory requirements, and operator demands shift every six months, this model keeps the company relevant.
The battle of models has already begun
Max Maslii leaves no room for ambiguity: refusing to adopt AI in the coming years will be equivalent, for an operator, to refusing automation during the industrial revolution. Competition will be decided not by offer copy or manual configurations, but by private models that can deeply understand players and manage their lifecycle.
Those who begin building this capability today will, in three to five years, hold a structural advantage that will be extremely difficult to close. The Ukrainian development school is one possible route to achieving that. But the fundamental choice belongs to operators: build their own AI expertise, or concede ground to those who are already building theirs.
This article was originally published in Russian on 19 May.
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