Concern 893 · Working draft · editorial review in progress
Concentration risk in cloud providers
Cloud concentration creates organizational risk when important AI services depend on a small set of infrastructure providers or on the same underlying provider.
Understanding the concern
Two products with different names may rely on the same cloud region, identity service or network. Buying from both may therefore provide less backup than it appears. Concentration can also affect pricing and negotiating power, although the level of risk depends on the actual architecture and alternatives.
The UK's cloud-market investigation examines competition and dependency concerns. An everyday analogy is ordering from two shops whose deliveries use the same blocked bridge. A resilience plan needs to trace the shared dependencies and decide which failures matter most. Using several clouds can add complexity, so the goal is a workable recovery plan, not diversity for its own sake.
An example
An organization's primary AI tool and backup both stop because they depend on a common cloud service.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The market report and provider incident reports establish specific contexts. They do not show that every multi-provider arrangement has the same concentration risk.
UK Competition and Markets Authority 2025 · Market investigation
Cloud services market investigation: final report ↗ (opens in a new tab)Findings concern UK public cloud infrastructure, including switching barriers. Not a count of all global AI suppliers.
Amazon Web Services · Provider incident reports
AWS Post-Event Summaries ↗ (opens in a new tab)Reports include the October 2025 DynamoDB disruption and dependencies. Evidence of particular outages, not proof that every AI service is affected or will fail together.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Which dependencies are truly independent, and what can keep essential work going if a shared provider fails?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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