Scottish football recently teetered on the brink of a significant change, approaching a league champion outside the traditional dominance of Celtic or Rangers for the first time since Aberdeen's triumph in 1985. Heart of Midlothian F.C. made a valiant effort but ultimately fell short on the final match day of the season. Nonetheless, their journey was remarkable, particularly when considered alongside their annual turnover of £24.4 million, dwarfed by the financial powerhouses Rangers at £94.1 million and Celtic at £143.6 million.
At the heart of this narrative is Tony Bloom, who recently completed a £9.86 million investment for a 29% non-voting stake in Hearts on 25 June 2025. His pivotal move, however, occurred much earlier, when Hearts became the exclusive Scottish partner of Jamestown Analytics, a football data firm within Bloom's expansive network.
Bloom, founder of Starlizard and recognized widely as the top football bettor globally, has uniquely transitioned his professional betting acumen into formal sports ownership. His influence is evident in the ascension of Brighton from League One to a competitive fixture in the Premier League and in Union Saint-Gilloise's first Belgian title win in nine decades. His 19.1% stake in Melbourne Victory suggests he is replicating this model in Australia as well.
Bloom’s ventures highlight a broader betting ecosystem where improved data and sharper models lead to better performance. While top betting groups handle significant amounts of money, they usually keep their strategies and models closely guarded to maintain their competitive edge.
Waterhouse VC notes a growing trend as individuals and teams strive to establish serious betting operations, with artificial intelligence (AI) acting as a key driver.
AI’s appeal to recreational bettors—often referred to as 'squares'—lies in its potential to narrow the gap between them and professional bettors, or 'sharps'. An increasing array of AI-enhanced betting tools facilitates price comparisons, identifies arbitrage opportunities, tracks performance, and helps users understand market movements. These tools can prove valuable for users, and with widespread distribution, they could become lucrative business ventures.
Moreover, AI has streamlined processes for data scraping, coding, cleaning datasets, building models, and generating reports. The pressing question for Waterhouse VC and other established players is whether AI will enable new entrants to compete or merely enhance efficiencies for existing operators.
Success in betting hinges on a team's ability to generate substantial profits over time while managing limits, execution, costs, and market fluctuations. At the elite level, professional betting remains a high-volume, low-margin pursuit; small advantages only matter when they can be reliably scaled. Although AI may help some teams appear sophisticated and potentially improve their operations, achieving sustained success remains a formidable challenge.
Every new market instrument comes with the promise of increased accessibility. Just as poker training materials simplified learning and retail trading apps opened financial markets, betting exchanges and prediction markets have enhanced transparency and participation. Similarly, in football data-driven recruitment has become commonplace, yet clubs adept at analytical methods, like those within Bloom’s network, have maintained their lead.
Currently, AI serves as an extension of this trajectory, making research and analysis quicker and more accessible. However, the primary beneficiaries of these advancements tend to be groups that already possess a solid understanding of models, data, and market dynamics. In adept operations, AI can enhance the processes of data cleaning, testing, monitoring, and reporting. Ultimately, the effectiveness of the output depends on the expertise of the people interpreting it.
For less experienced bettors, AI can inadvertently lead to overconfidence, presenting incomplete data in polished formats that mask weaknesses. The ease of analysis doesn’t equate to the quality of the insights being drawn.
One betting group in Waterhouse VC’s network summed it up: “AI has made it much easier for people who think they have an edge. The reality is that those who couldn’t build a model before still can’t build one with AI. The AI produces an answer that is detailed but still wrong.”
It’s true that AI cannot convert weak modellers into skilled ones today, but its future advancements will be tested as technology evolves. It’s essential to understand that serious betting syndicates operate on proprietary datasets honed over years—resources that newcomers lack and AI cannot create. Portfolio firms are already utilizing AI to deepen their competitive advantages, thus widening gaps that challengers may find difficult to bridge.
Another syndicate articulated a similar sentiment: “Small datasets make it very hard for AI to beat traditional managed stats. That won’t change.” In betting, a model’s value is determined by its resilience in real-world conditions. Their viewpoint is that relying on AI to develop credible betting models necessitates a human expert scrutinizing each detail, someone versed in constructing neural models and predictive machine-learning systems rather than just understanding the software.
One group acknowledged that AI could be a game changer for young and intelligent individuals lacking financial resources to hire a development team, shifting the primary limitation from capital to skill set. For those without foundational expertise, AI’s ultimate value hinges on the user’s capabilities to validate the analysis, ensuring data integrity, accuracy in backtesting, proper correlation assessments, and the existence of genuine liquidity.
Once a viable model is in place, AI can significantly enhance efficiency by automating tasks related to updating, reporting, and monitoring. Professional betting operations require diligent upkeep, including dataset refreshes, performance tracking, price monitoring, anomaly detection, and reporting. AI accelerates these processes.
One operation noted, “We are not replacing any analyst who leaves; we are putting in AI agents instead.” The competitive edge lies in the initial model, data, and processes rather than in the technology alone. The integrity of serious betting models is safeguarded by keeping them within private systems, instead of exposing them to public scrutiny, which diminishes their value.
For weaker bettors, AI may yield seemingly sophisticated analyses, while for proficient groups, it amplifies their existing strengths. The notion that AI could transform ambitious bettors into figures like Tony Bloom may be fanciful, but the real potential lies in AI-driven products that deliver advantages to both retail and professional bettors.
At the retail level, effective products will enable bettors to make more informed decisions: comparing prices, tracking performance, identifying mispriced opportunities, and interpreting market dynamics. These tools don’t need to create professional bettors to generate value. With compelling products and solid distribution, they can attract substantial audiences.
In the domain of operators and syndicates, AI-driven tools streamline trading processes—helping to track prices across sharp books, execute swiftly, manage risks, and safeguard margins as the market expands. The presence of more participants allows retail bettors to maximize their gambling budgets through enhanced tools, leading to reduced losses and longer player lifespans.
Both scenarios increase the demand for the essential infrastructure that underpins betting, including data feeds, trading tools, market-making, compliance, and risk management. Waterhouse VC continues to invest worldwide in both public and private entities throughout the wagering and gaming ecosystem.
Since its inception in August 2019, Waterhouse VC has reported a gross total return of +3,806% (73% annualized) as of 30 April 2026, assuming that all distributions are reinvested.
