- Alignment & Safety -AI systems pursuing subtly wrong objectives create product liability exposure at scale. Enterprises must build elaborate human-in-the-loop safeguards that erode the efficiency gains AI was supposed to deliver.
- Concentration of Power-AI development has consolidated around a handful of frontier labs and cloud providers, creating structural barriers to entry higher than any in the history of computing.
- Misinformation – Generative AI has industrialised synthetic content creation. The cost of convincing fakes has dropped to zero, corrupting evidentiary standards and increasing friction in every commercial interaction.
- Labour Disruption- This wave targets cognitive, credentialed work — writing, coding, legal research, financial modelling. Entry-level knowledge work is being hollowed out faster than new career ladders are being built.
- Governance & Accountability – AI governance is a patchwork of inconsistent national regulations. Liability for AI-caused harm remains unresolved in most jurisdictions, paralysing adoption in healthcare, legal, and finance.
The thread connecting all five commercially is trust erosion. Companies that solve these issues-rather than navigate around them -will have the most durable commercial positions.
Every prompt entered into a public LLM externalises organisational knowledge. Because public AI providers often have unclear data retention and training policies, businesses risk losing control over proprietary information while gaining little visibility into how that data is used.
Public LLM risks: IP leakage via prompts, unclear data retention policies, compliance exposure, shadow AI adoption, and no per-call audit trail. Public AI systems are externally controlled platforms with their own commercial incentives.
Private LLM advantage: deployed behind the organisation’s firewall — full AI capabilities while keeping data, knowledge, and competitive advantage secure and under organisational control. AI sovereignty rather than AI dependency.
The rapid adoption of public LLMs mirrors the shadow IT problems of previous decades — but at a much faster scale, with much higher-value IP at stake.
