2026: Agentic AI and the Edge go into production. Between sovereign cloud, GPU and specialized LLM, companies must make strategic choices in the face of alternative hyperscalers.
The cloud and AI are entering a maturity phase where architectural choices are no longer technical arbitrations, but strategic decisions. We are currently experiencing a pivotal year marked by three fundamental trends: an accelerated consolidation of the market, the concrete landing of the sovereign cloud and a profound restructuring of the value chain around GPUs and AI models.
A “neo-cloud” market under high pressure
The first strong trend that marks the start of the year is the concentration of the market around a small number of players capable of operating on a global scale. Indeed, more than 80% of NVIDIA and AMD GPU capacity is expected to be captured by a handful of “neo-cloud” and alternative cloud providers, those that combine capital, scale and aggressive go-to-market.
The success of these players relies on their ability to raise funds, quickly deploy large GPU clusters and establish themselves among large AI companies. Those who do not reach this critical mass risk being marginalized, or even bought out. For user companies, it is therefore crucial to integrate this consolidation dynamic into their multi-cloud strategy: they must select long-term partners, while ensuring not to create excessive dependence on a limited number of suppliers.
Sovereign Cloud: from speech to execution
Second strong signal: the sovereign cloud will cease to be a vague concept and become a priority operational project.
Until now, sovereignty has been supported by political declarations and a few pilot projects, hampered by the absence of a precise framework. In 2026, governments should align their sovereign cloud initiatives with their digital strategies by supporting start-ups, academic research and AI ecosystems.
In practice, companies will see the emergence of more concrete offers, structured around measurable objectives (data localization, control of dependencies, support for local innovation), rather than marketing labels. CIOs and CISOs will have a key role in translating these sovereignty requirements into architectural decisions and choice of partners.
The end of universal models: specialized LLMs make a sensational entrance
This year will also see a rebalancing between very large general models and smaller models, optimized for targeted uses.
Thinking that all enterprise AI will only revolve around ChatGPT, Anthropic or Perplexity would be a mistake. On the contrary, operational use cases will increasingly rely on more compact models, specialized by business or domain, optimized for inference, and therefore faster, less expensive and simpler to integrate.
For business and IT departments, the objective is no longer to have a large model, but to know how to select, train or adapt targeted models, integrated into existing workflows and aligned with security and sovereignty constraints.
The era of heterogeneous GPUs and openstacks
Another structuring development: the end of material homogeneity.
Companies will compose portfolios of heterogeneous GPUs, mainly mixing NVIDIA and AMD, supplemented by specialized chips (Cerebras, Groq, etc.). Future value will no longer reside only in hardware, but in the ability to orchestrate this complexity. This will involve the use of “agentic” AI frameworks (such as n8n, Arize) and inference platforms (such as Fireworks, Baseten). These tools are essential to facilitate testing phases, iteration and scale-up.
This heterogeneity offers an advantage: it reduces dependence on a single supplier and makes it possible to optimize each use (training, real-time inference, edge, etc.). In return, it imposes a strong discipline in platform engineering and standardization (APIs, monitoring, security).
The rise of alternative hyperscalers
Between historical hyperscalers and small specialized players, a third category is emerging: alternative hyperscalers. These promise to combine classic public cloud functions (computing, storage, networking) with advanced AI infrastructure services, in an open and flexible ecosystem. These new players target businesses that want the power of a hyperscaler, without technology lock-in or over-reliance on a single proprietary stack.
These players offer organizations a credible option for building truly balanced multi-cloud strategies, combining performance, cost, flexibility and sovereignty.
Enterprise AI finally goes into production
After two years dominated by POCs and presentations, enterprise AI finally appears to be entering a phase of large-scale deployment.
The rise of platform engineering plays a key role in the industrialization of uses, with the establishment of standardized pipelines, robust test environments, observability capabilities and model governance frameworks. The decisions are gradually shifting from data teams to developers, who are more inclined to adopt open source building blocks and composable architectures. This convergence between open ecosystems, alternative hyperscalers and heterogeneous hardware helps to remove several obstacles (costs, technological dependence, scaling) and opens the way to truly transformative use cases, well beyond office co-pilots.
Agentic AI arrives at the edge
2026 will mark the advent of agentic AI at the edge of networks. This revolution will begin with increased specialization: inspection drones, industrial sensors and autonomous vehicles will integrate on-board models combining business expertise and real-time responsiveness.
While the deployment of generic agents will be more gradual, the opportunity for the industrial, energy and transport sectors is immense. The issue? Gain reliability and decision-making autonomy thanks to local analysis, freeing yourself from systematic dependence on the central cloud.
In 2026, decision-makers will no longer be able to simply “follow” cloud and AI trends: they will have to make structuring choices about their partners, their architectures and their AI models. Those who treat sovereignty, material diversity and alternative hyperscalers as real strategic levers (and not as constraints) will be best placed to transform these changes into a sustainable competitive advantage.
