Stop looking in the Rear-View Mirror

Max, what gaps convinced you there was room for Blask?

The gap wasn’t subtle. iGaming is a hundred-billion-dollar industry that makes decisions like it’s 2005. Operators were running on gut feel, quarterly reports, and whatever their affiliates told them. The data that did exist was locked inside a handful of large operators who had no reason to share it, or it arrived so late that by the time you read the report, the market had already moved.

The problem we kept hitting: track a market by individual domains and you’re left with thousands of fragments. One operator runs fifty domains across forty jurisdictions. Make sense of that? You can’t. So we asked: what if you tracked player attention instead of operator domains? That’s the insight Blask is built on.

Data that arrives three months late and requires a data science team to interpret is archaeology.
Which experiences outside gambling have most influenced your approach?

Fintech shaped my instincts more than anything. When you’re building financial data products, accuracy is the product. If your numbers are off by two percent, you’re done. I brought that discipline directly to how we think about data quality at Blask.

Crypto, specifically Kryptex, taught me how to operate in unregulated, fragmented markets where there’s no authoritative data source. You triangulate from multiple signals and stay honest about your confidence levels. That turns out to be the core problem in iGaming offshore markets.

The music degree is the wilder card. But music production is about signal and noise, literally. You keep asking: what matters, what’s interference, and what silence you’re misreading as a signal? I think about data the same way.

How do you define the difference between a market intelligence platform and a traditional analytics dashboard?

A dashboard answers “what happened?” Intelligence answers “what should I do next?” Most analytics tools in this industry are sophisticated rearview mirrors. They’ll show you GGR trends, session counts, churn rates, all looking backward. That works for audits, not for decisions.

Here’s the thing: by the time a metric shows up in a quarterly report, the operators who were paying attention already knew. They saw it coming in search behaviour, in player acquisition trends, in competitive signal shifts. The lagging report confirms what the leading indicator told you six to 12 weeks earlier.

Blask is built around leading indicators. Think of the Blask Index like a stock ticker, the Dow Jones for iGaming brands. A stock price tells you what the market believes will happen next. You stop watching the rearview mirror and start watching the road.

How difficult is it to build accurate models across regulated, grey and offshore markets?

Hard. I won’t pretend otherwise.

Regulated markets are the easy part: licensing data, tax records, mandatory reporting in some jurisdictions. The challenge there is latency and fragmentation. Grey markets are harder because the signal exists but there’s no ground truth to validate against. Offshore is hardest, because you’re working with behavioural inference and you have to be precise about what you know versus what you’re estimating.

Triangulation is what works. You combine search intent data, player acquisition signals, affiliate traffic patterns, and brand visibility metrics. No single source holds up in isolation. When they converge, you have confidence. When they diverge, you have an interesting question.

Our models are better in some markets than others, and we tell clients that. If you’re using Blask data to make a major investment decision in a market where our signal density is lower, we say so. If you’re not critically evaluating the information in front of you, you’re setting yourself up to be someone’s fool.

What are the biggest blind spots operators still have?

Three blind spots, and they’re related. First: operators optimise the customers they already have and miss the ones about to arrive. Most operators are drowning in first-party player data but have almost no visibility into the funnel they haven’t captured yet. They know everything about people who converted. They know almost nothing about why high-value players chose a competitor.

Second: they track revenue and miss attention. Revenue tells you what happened. Player search behaviour, brand comparisons, game engagement tells you what’s about to happen. Changes in share of search precede financial outcomes by thirty to ninety days. Most operators aren’t watching that clock.
Third: geography. Operators think in single markets; players don’t. A brand losing ground in one market is often pushing harder elsewhere. If you watch only your domestic metrics, you read half the picture.

What does AI change inside a platform like BLASK, beyond automating reports?

I have a standard answer for AI hype questions. AI isn’t a magic wand. It’s a messy black box filled with entropy and unpredictability. At Blask, we don’t treat it like a holy grail. It’s another tool, one we use only when it’s the fastest, cheapest, and most efficient way to get to the right answer.

AI raises the ceiling on signal processing. Analysing brand-level behavioral data across hundreds of markets blows past anything humans can do in spreadsheets. At that data volume, you need machines. AI changes the question you can ask. Without it, you ask “what happened to brand X in market Y last quarter?” With it, you ask “which brands in emerging markets are showing the early signals that preceded breakout growth?” Those are different decisions, made at different moments.

You still need human judgment to interpret the output. We supervise every output and run sanity checks. We don’t trust the machine without scrutiny, and our clients shouldn’t either.

How do you balance AI-driven forecasting with human expertise?

The framing I’d push back on is “balance.” It implies a tradeoff: more AI means less human. That’s not how we operate. The bots work for us, not instead of us. AI scales pattern recognition across enormous datasets. Humans handle context, judgment, and catching the moments when a model is confidently wrong.

Concrete example: an algorithm flags a brand as showing strong growth signals in a market. An analyst with regional context knows the brand just ran a one-off promotion that inflated short-term search interest. The signal is real. The interpretation requires knowledge the model doesn’t have.

For operators making major capital allocation or acquisition decisions, this matters. Our role is to show you what the data says. Your role is to apply judgment to what it means for your situation. Anyone who tells you the AI will just give you the answer is selling you something.

Are behavioural and attention-based indicators becoming more valuable than revenue figures alone?

Already more valuable for certain decisions. The question is which ones. GGR and market share are the right metrics for accounting. They tell you what happened, and you need to know what happened. But they can’t tell you what will happen. You can’t build a forward-looking strategy on a lagging indicator.
Here’s the kicker: operators competing on GGR data alone are watching each other’s rearview mirrors and calling it strategy. They find out they’ve lost market position around the same time the news breaks.

Player attention data (search volume, brand comparison behavior, content engagement) moves thirty to ninety days ahead of financial outcomes. Monitor that and you anticipate competitive shifts instead of chasing them. You move first instead of catching up. The industry will keep using GGR. The operators who build an advantage in the next cycle are already treating revenue figures as one input among several.

Has the industry become more sophisticated in how it uses competitive intelligence?

Honestly? The gap between the most sophisticated operators and the average operator has widened. The top tier, operators running serious data operations and building internal intelligence functions, have leveled up over the last three or four years. Partly regulation forcing more rigor. Partly market maturation making the gut feel more expensive.

The middle and long tail of the market? Still running on instinct and whatever affiliates tell them. Operators still make major market entry decisions on surface-level analysis. Operators still acquire without serious competitive intelligence on the target market.

The story is consistent: operators making decisions on instinct describe their process as “experience” and “market feel.” Sometimes that’s real expertise. More often it’s a polished name for skipping the work.
The data infrastructure to do this now exists. The question is whether the organisation will follow.

What does the future of gambling intelligence look like?

I want to push back on the “powered entirely by AI” framing. It leads to the wrong conclusion. Operators will still drive their strategic decisions. The intelligence layer gets more real-time, more predictive, broader in market coverage. The quality of your questions still determines what you get out.

The latency collapses. The gap between a market signal appearing and an operator having an actionable read on it goes from weeks or months to days or hours. That changes the competitive dynamic, because the advantage of seeing a trend first compounds quickly.

The second shift is coverage. Right now, even sophisticated operators have deep intelligence in their core markets and thin coverage everywhere else. As the data infrastructure improves, that asymmetry narrows. The operators building that discipline now will lead when real-time global coverage becomes standard.

In the markets where the best operators play, the intelligence will look unrecognizable in five years. In markets where instinct still dominates, not much changes. Both will be true at the same time, in the same industry.