Artificial Intelligence promises independence but increasingly delivers dependence. As the technology weaves itself into the fabric of modern business, mentioned in 45% of Q3 2025 earnings calls, many businesses find themselves not more autonomous, but more tethered to a handful of dominant cloud providers.
This is the paradox of the Agentic Age, a new era in which AI agents can act on our behalf, process data, and orchestrate workflows in carefully sandboxed environments without human involvement. The potential to improve businesses is extraordinary. But instead of enabling greater independence and operational flexibility, today’s AI adoption patterns often pull companies into tighter reliance on a handful of dominant cloud providers. The risk is that innovation slows, costs rise, and businesses are left working to someone else’s roadmap rather than their own.
Dependence disguised as progress
The dominant players in the market have created ecosystems that make AI adoption appear seamless, bundling storage, infrastructure, and pre-trained models into integrated packages that can be deployed quickly, but the very convenience that attracts teams under pressure to “do something with AI” hides structural constraints that become apparent over time.
As organisations embed themselves more deeply into a single ecosystem, leaving becomes increasingly difficult. Even as data egress fees begin to be phased out under the EU Data Act, a shift likely to ripple globally, the challenge of disentanglement runs deeper than cost. Entire architectures become engineered around a provider’s proprietary systems, making it hard to migrate or even modularise elements of a stack. Over time, innovation and deployment cadence start to follow the provider’s roadmap rather than the business’s own, constraining operational autonomy. Performance improvements are often limited to combinations of products within the same ecosystem, and access to GPUs is rationed in favour of the largest, most profitable customers.
The resulting concentration of control has broader consequences across the market, as startups outside major tech hubs struggle to access affordable infrastructure, limiting grassroots innovation. At the same time, established enterprises find themselves confined to large models that are often overengineered for their actual needs. These off-the-shelf models, trained on billions of parameters, force businesses to consume unnecessary compute and cost simply because their stack supports them, expending time, energy, and budget on scale they don’t need while waiting on provider timetables for updates and new features.
Reintroducing choice
Rejecting hyperscalers entirely is neither practical nor necessary, they serve a purpose. The issue is thinking that the purpose they serve is all-encompassing. Businesses that want to retain independence must ensure that AI strategies are shaped internally rather than being dictated by a provider’s ecosystem. The future of AI workloads will be defined by open standards, independent cloud providers, and adaptable AI models.
Open standards establish neutral protocols and abstraction layers between workloads and the underlying infrastructure, allowing companies to avoid lock-in while also protecting digital sovereignty, offering more than technical convenience and becoming a foundation for strategic flexibility and control.
By contrast, independent cloud providers offer transparent pricing and a focus on performance rather than bundled services, giving businesses the ability to diversify their infrastructure, reduce risk, and access competitive terms while retaining the freedom to innovate on their own schedules.
Complementing these approaches, lightweight, open-source AI models can be tuned to the specific needs of the business, require fewer resources, and scale incrementally.
Combined, these approaches restore choice, allowing enterprises to utilise hyperscaler resources where appropriate without surrendering strategic autonomy, and giving organisations the freedom to pivot, scale, and innovate according to their own priorities.
Building the Agentic Age on our terms
The Agentic Age will ultimately be defined by the businesses that make AI their own, shaping agents around their customers, training them on proprietary data, and rooting them within the business’s strategy in ways that drive innovation and competitive advantage. Dependence on a single ecosystem may feel safe, but independence unlocks adaptability and control in a world where AI is no longer optional.
As the Agentic Age unfolds, businesses must decide whether to move through it as passengers or navigate it as pilots. Taking command means building on infrastructure you control, deploying models you can adapt, and maintaining the flexibility to evolve to your business’ – not your provider’s – demands. The companies that thrive will be those that refuse to outsource their strategic future.
