Dependency Lock-in

Dependency Lock-in

Organizations are rapidly building AGI into their core workflows:

  • Healthcare systems using AI for diagnosis and triage
  • Legal systems using AI for document review and research
  • Educational systems using AI for tutoring and assessment
  • Scientific research using AI for data analysis and hypothesis generation
  • Government services using AI for benefits determination and service delivery

Each integration creates dependency. Once the workflow assumes AI capability, removing it becomes costly and disruptive.

The Lock-in Mechanism

Dependency lock-in happens through:

Process redesign: Workflows are redesigned to assume AI availability. The old process is deprecated, expertise in it atrophies.

Staffing changes: Organizations hire fewer humans for tasks now handled by AI. Rebuilding human capacity takes time.

Expectation setting: Users and stakeholders come to expect AI-enabled speed, availability, or capability. Reverting to pre-AI performance seems like failure.

Investment sunk costs: Money spent on AI integration becomes a reason to continue (“we’ve invested so much already”).

Data entanglement: Data formats, pipelines, and storage are optimized for AI workflows. Untangling is expensive.

What Could Go Wrong

Once dependency is established, organizations become vulnerable to:

Provider changes: The AI provider changes pricing, terms, capabilities, or values. The dependent organization must accept or undertake costly migration.

Infrastructure disruption: Energy costs spike, data centers go offline, compute becomes scarce. The dependent workflow stops.

Ethical revelation: Problems with AI (bias, unreliability, environmental cost) become undeniable. But switching away is now prohibitively expensive.

Regulatory change: New rules restrict AI use. Organizations that can’t function without AI face compliance crises.

Capability degradation: The AI gets worse (through Drift, model changes, or intentional degradation). Dependent organizations suffer.

The Ethics of Building Dependence

The question isn’t just whether AGI is good or bad. It’s whether building dependence on AGI is wise, given:

Organizations making integration decisions now are betting that AGI infrastructure will remain available, affordable, ethical, and improving. This may be a good bet. It may not be.

Reversibility as a Value

One response: treat reversibility as a design constraint. Build AI-assisted workflows that could function (perhaps degraded) without AI. Maintain human expertise in parallel. Keep switching costs manageable.

This is costly and may seem unnecessary while AI works well. But it’s insurance against a future where AI dependence becomes a liability.

The Systemic Version

Dependency lock-in at the organizational level is concerning. Dependency lock-in at the societal level is more so.

If entire sectors — healthcare, education, research, government — become dependent on AGI infrastructure, then:

  • Society inherits the vulnerabilities of that infrastructure
  • Power concentrates in those who control the infrastructure
  • Alternative paths atrophy
  • Disruption affects everyone, not just individual organizations

Open Questions

  • How much dependency is too much?
  • Who bears the risk of dependency — organizations, providers, or society?
  • What would “graceful degradation” look like for AI-dependent systems?
  • Is it too late to maintain reversibility for early adopters?

See Also