Mandula Moments: Risks and opportunities in an AI-driven world (Part 3)

Continuation from Part 2

Concentration of Power: The question of “who will own and control” the world’s data is not just about personal privacy – it’s also about market power and geopolitics. Currently, a handful of big tech companies control disproportionate slices of global data, giving them enormous influence and competitive advantage. Data network effects can create natural monopolies, where the more data a company has, the better its services become, attracting more users who generate even more data. This dynamic raises antitrust and fairness concerns: smaller firms or new entrants may struggle to compete against data-rich incumbents, potentially stifling innovation.

Furthermore, control over data is becoming a geopolitical issue. Data has become a strategic commodity in the race for global power. Nations are keenly aware that dominance in AI and data analytics could translate to economic and military advantages. As one analysis put it, the ability to amass and exploit data has placed it “squarely at the convergence of an increasingly frantic race between nations for global power.” The United States and China, for example, are often seen as leaders in the AI-driven data race – the US with its tech giants and startup ecosystem, and China with its massive population, surveillance systems, and tech champions – while Europe has focused on regulating data use. This competition could lead to a fragmentation of the digital world as countries enforce data protectionism. Already, 75 per cent of countries have implemented some form of data localisation or sovereignty rules that compel certain data to be stored within national borders. Such policies, intended to protect privacy or national security, can complicate global data flows and raise costs for businesses operating internationally. In the worst case, we could see an Internet Balkanised by nation-state data regimes, undermining the free exchange that fueled the digital boom. The risk here is a world where data is tightly controlled by the few – whether corporations or governments – rather than broadly shared for common good.

Environmental and Infrastructure Strain: An often-overlooked aspect of the data explosion is its environmental footprint and infrastructure demands. Storing and processing zettabytes of data requires vast data centers, which consume significant electricity and water for cooling. Data centers worldwide already account for about 1.5 per cent of global electricity consumption (as of 2024) and this share is expected to double by 2030 due to surging AI and compute needs. This means nearly an entire country’s worth of power (comparable to Japan’s annual electricity use) will soon be devoted just to data centers. In addition, large data facilities can guzzle millions of gallons of water per day for cooling servers, which may strain local water supplies. The carbon footprint of big data and AI is non-trivial, especially if the energy comes from fossil fuels – training one cutting-edge AI model can emit as much carbon as several cars do in their lifetimes. As data volumes grow, sustainability becomes a risk: without greener IT practices, the data industry could hinder climate goals.
There is also the practical challenge of building enough infrastructure (networks, storage, computer hardware) to manage the flood of data. The need for continual upgrades and new facilities could reach trillions of dollars in investment. One study estimates nearly $7 trillion in global investment may be needed in data infrastructure (like data centers) by 2030 to keep up with demand. Organizations that cannot keep pace with these infrastructure needs may find themselves overwhelmed by the data they collect.

Workforce Disruption: Finally, the data-AI revolution is poised to disrupt labor markets. Automation and AI driven by big data can perform tasks that once required humans, potentially displacing workers in certain roles. Many routine or data-intensive jobs (from factory and warehouse work to data processing and administrative tasks) are being augmented or replaced by AI algorithms and robotics. Studies project that by 2030, around 22% of current jobs could be disrupted by automation trends. The World Economic Forum’s Future of Jobs analysis (2025 edition) anticipates 92 million jobs globally may be displaced by 2030, but also 170 million new jobs created, especially in tech, data, and care sectors – a net gain but with significant churn.

Those who can adapt and gain data-related skills may benefit, but workers without access to retraining could be left behind. Thus, while society as a whole may gain economically, there will be winners and losers in the transition. Managing this workforce shift – through education, upskilling, and social safety nets – is a critical challenge to ensure the data revolution is inclusive and doesn’t exacerbate inequality. Business leaders need to anticipate how AI and data will transform job roles and help their workforce prepare for new skill requirements, while policymakers must address potential unemployment or transition effects in vulnerable industries.

In summary, the risks of the data boom are multi-faceted. Privacy can be eroded, security threats are rampant, biases can be baked into systems, power can concentrate, and unintended consequences (environmental or social) can emerge. Who will be harmed by it? Potentially, any of us – if our personal data is misused or stolen; if we are judged unfairly by a biased algorithm; if we lose a job to automation without support; or if we live under intrusive surveillance. Conversely, failing to use data where it could help (for example, not sharing data in a pandemic for contact tracing due to privacy fears) can also cause harm. The challenge is to maximise data’s benefits while mitigating its harms through prudent management, ethical practices, and forward-looking policies.

And as it should be crystal clear by now, the global factoring industry must take a leadership role in the commercial finance ecosystem if it wants to remain a proven solution for secure working capital for corporates and SMEs for years and decades to come.

To be continued in Part 4.