What Happens When Access to Intelligence Is No Longer Your Decision?
When Access Isn’t Guaranteed
When Anthropic temporarily withdrew access to its Fable model following a US Government directive, the restriction lasted only a matter of weeks. Anthropic then restored access.
For many organizations, the incident came and went with little impact. For others, it highlighted something much bigger than the availability of a single AI model. It showed that access to a frontier AI capability could change almost overnight, driven by decisions beyond the control of either the customer or the technology provider.
Who controls access to AI matters more than where it runs.
For Kishore Jayaram, Chief Transformation Officer at Kinetic IT, that is the conversation organizations should be paying attention to.
“The model came back, which was a good outcome,” he said. “What stayed with me was the discussion it prompted. It reminded organizations that access to an important capability can change because of decisions made somewhere else. As AI moves into production, that’s something leadership teams should understand.”
Jayaram encourages organizations to start with one practical question. “If a critical AI capability was available on Friday and then suddenly unavailable on Monday morning, what would we do?” he said. “It’s a practical way of understanding how dependent you’ve become, whether you have alternatives, and how quickly you could adapt if circumstances changed.”
A New Kind of Dependency
Historically, organizations have managed technology risk through procurement, vendor governance, cybersecurity, and business continuity planning. Those disciplines remain essential. As AI becomes part of critical operations, they need to extend to how organizations select, integrate, and manage AI capabilities.
What has changed is the nature of the dependency itself. Organizations are not just relying on infrastructure or software anymore. Instead, they are relying on capabilities that sit inside rapidly evolving ecosystems, driven by commercial decisions, regulation, and geopolitical events well outside their control.
That broadens the questions leaders are asking. Rather than focusing only on whether a capability delivers value today, executive teams increasingly weigh how resilient that decision will be over time. They also ask how quickly they could adapt if circumstances changed, and whether critical services stay resilient as the technology underneath them keeps evolving.
AI now belongs in the same planning conversation as infrastructure failures, supplier changes, and regulatory shifts. That’s because the commercial arrangements behind AI capabilities and the conditions under which they are available will keep evolving.
Jayaram said, “Frontier AI is still evolving quickly, and the organizations developing these models don’t control every variable that can influence availability. Building flexibility into your architecture from the outset makes it much easier to respond as technology, regulation, and commercial arrangements continue to evolve.”
Planning for What You Actually Need
That is also changing how organizations evaluate AI itself. Capability remains important, but it is no longer the only consideration. Cost, flexibility, governance, and the ability to adapt over time are now part of the decision as well. Jayaram believes organizations should start with the business outcome they are trying to achieve and work backward, rather than assuming every use case needs the most advanced frontier model available.
“We spend a lot of time talking with customers about minimum viable capability,” he said. “Not every use case needs the most advanced model available. Start with the outcome you’re trying to achieve, then ask what’s the minimum level of capability needed to achieve it. That usually leads to better architectural decisions, gives organisations more flexibility as the market evolves, and makes it much easier to adapt when circumstances change.”
AI will keep evolving, bringing new models, new providers, and new opportunities to innovate. That is good news for organizations because it creates more choice and more ways to apply AI. It also means today’s decisions will need revisiting as the technology continues to evolve. That’s exactly why Jayaram believes strategic decision-making matters more now than it used to.
“AI is accelerating the speed at which assumptions can be tested,” he said. “The organizations that will be best placed to take advantage of it over the long term are the ones building flexibility into their architecture, understanding their dependencies, and making decisions that preserve choice as the technology continues to evolve.”
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