AI
Are businesses introducing governance because they genuinely understand and control AI, or because governance gives them the confidence that they do? Often the latter. Governance frameworks are frequently built to satisfy auditors and boards rather than to genuinely constrain model behaviour, because true technical control is still an unsolved research problem, not a policy one. A governance committee can create confidence without creating capability.
Could AI create a new form of corporate addiction, where organisations know the risks but cannot stop using it?
Yes, and the addiction framing is apt: known risks, and continued use – driven by competitive fear rather than conviction. It ' s a coordination problem: no single CEO or business owner can unilaterally slow down without ceding ground, so rational individual behaviour produces collectively risky momentum, structurally similar to arms races or dot. com-era spending.
As AI becomes more convincing, will people stop challenging it, and could that become one of AI’ s greatest business risks? This may be one of its most underrated business risks. Fluency is being mistaken for correctness, and confident, well-formatted output disarms the instinct to challenge it. It is the opposite of how humans typically signal uncertainty. Organisations need to deliberately engineer scrutiny back in, rather than assume it will happen naturally.
Will cyberattacks increase as businesses become more AI-driven, and would it be easier to steal from these systems? Almost certainly, yes. AI-driven systems expand the attack surface while simultaneously lowering the skill floor for attackers who can now use AI to write exploits. Defensive AI helps, but offence-defence balance historically favours attackers early in a technology adoption curve.
When AI gets it horribly wrong, who is held accountable? This is a well-documented governance failure mode, and it’ s structurally worse with AI because decisions are distributed across data scientists, vendors, deploying managers, and the model itself. In practice, when something goes wrong, accountability usually lands on whoever is easiest to fire.
In 2023, the ratio of AI developers to safety alignment researchers was 30:1. Will the call for a global“ pause on AGI development” help effective regulation? The structural concern stands regardless of the exact number: organisations calling for regulation while continuing to race is a rational but selfundermining position, since regulation only bites if it is binding on all competitors simultaneously. �
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