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Enterprise AI Sovereignty: Total Control of the Agent Stack Is Imperative, According to Cohere
1. Executive Summary
The VB Transform 2026 conference, held at the Hotel Nia in Menlo Park, was the epicenter of crucial debates on the implementation of generative AI agents in the enterprise space. One of the highlights was the conversation between Rachad Alao, vice president of product engineering at Canadian startup Cohere, and Matt Marshall, CEO and editor-in-chief of VentureBeat. Alao presented a compelling thesis: enterprise AI sovereignty, especially for organizations with mission-critical systems like banks, hospitals, and governments, demands absolute control over the entire agent stack.
This definition of sovereignty transcends merely downloading an open-weight model or running an application behind a corporate firewall. For Alao, it implies rigorous oversight over data residency, the underlying infrastructure (including GPUs and private clouds), the governance systems that route requests between models, and the connection tools, search capabilities, and agent frameworks operating on enterprise data. The implication is clear: the ability to switch providers and maintain operational autonomy depends on this granularity of control. This approach positions itself as a direct response to growing concerns about third-party dependency and data security in the age of AI.
Furthermore, Alao challenged one of the most widespread economic premises in the sector: the idea that the rapid decline in per-token inference costs would weaken the argument for optimizing smaller models or local control. He argued that while per-token costs may decrease, total token consumption is skyrocketing exponentially as companies transition from simple chatbots to complex AI agents. These agents, capable of reasoning, using tools, searching internal systems, and executing multiple steps before delivering a response, demand significantly greater processing power. This paradigm shift underscores the need for a sovereignty strategy that encompasses not just the model, but the entire AI value chain, redefining the economics of enterprise AI.
2. Deep Technical Analysis
Rachad Alao's vision of AI sovereignty delves into the technical complexities of modern agent systems, proposing a control framework that spans every layer of the technology stack. Traditionally, sovereignty in the context of AI has been interpreted narrowly: either through the use of open-weight models like Llama 4 or Gemma 4, or by running proprietary models like GPT-5.6 or Claude Fable 5 within private cloud or on-premises environments. However, Alao argues this is insufficient for organizations handling sensitive data and critical operations.
Full stack control begins at the most fundamental layer: hardware infrastructure. This includes Graphics Processing Units (GPUs) and private cloud infrastructure. The ability to determine where these GPUs physically reside and who operates them is crucial for data security and regulatory compliance. For a bank or hospital, knowing that their AI workloads run on servers located in a specific jurisdiction and under their direct control, or that of a trusted partner with strict sovereignty agreements, is a non-negotiable requirement. This contrasts with reliance on large cloud providers, which, while offering scalability, often operate with multi-tenant architectures and distributed geographic locations that can complicate traceability and control.
Ascending the stack, Alao emphasizes the importance of governance systems. These systems are responsible for routing requests between different AI models, ensuring that the right data reaches the appropriate model and that usage and access policies are consistently applied. In a complex enterprise environment, an agent may need to interact with multiple models, from a GPT-5.6 for general reasoning, to a DeepSeek-V4-Pro for coding tasks, or a Qwen 3.7-Max for global language processing. A robust governance system under direct control allows the organization to dictate which model is used for which task, how inputs and outputs are managed, and how interactions are audited, mitigating risks of bias, privacy, and security.
The next critical layer consists of connectors and search tools. AI agents do not operate in a vacuum; they need access to internal databases, document management systems, CRMs, ERPs, and other proprietary data sources. Connectors are the bridges enabling this interaction. If these connectors are owned by third parties or outside the company's control, a vulnerability point is introduced. Similarly, search tools that allow agents to retrieve relevant information from internal systems must be configurable and controllable by the organization. This ensures that agents only access authorized information and that search results are accurate and compliant with internal policies, preventing exposure of sensitive data or generation of erroneous responses based on unverified information.
Finally, agent frameworks, which are the structures orchestrating the behavior and logic of AI agents, form the top component of the stack. These frameworks define how an agent reasons, plans, executes actions, and uses tools. Models like Claude Fable 5 or Gemini 3.5 Flash may be the agent's intelligence, but the framework is its nervous system. Having control over this framework means the company can customize agent behavior, integrate specific enterprise tools, define complex workflows, and crucially, audit and modify agent logic as needed. This is vital to prevent unwanted emergent behavior and ensure agents act in alignment with the organization's goals and values.
Alao's argument about the exponential increase in token consumption reinforces this need for technical control. As agents evolve from simple conversational interfaces to systems performing multi-step reasoning, external tool calls, complex database searches, and information synthesis, the number of tokens processed per interaction skyrockets. A basic chatbot might consume a few hundred tokens per turn; an agent investigating a customer problem, querying multiple databases, generating a report, and suggesting actions could consume tens of thousands or even hundreds of thousands of tokens. This explosion in token utilization makes cost optimization and operational efficiency at every stack layer imperative, not just the per-token inference cost. Sovereignty, in this context, is not just a matter of security or compliance, but also of long-term economic viability.
3. Industry Impact and Market Implications
Cohere's stance, articulated by Rachad Alao, has profound implications for the AI industry and the enterprise market. By elevating the definition of AI sovereignty to total control of the agent stack, Cohere not only differentiates itself from many competitors but also sets a new standard for enterprise expectations, especially those in highly regulated sectors. This approach could reshape AI acquisition and deployment strategies, moving them away from purely cloud-based solutions and toward hybrid or even fully on-premises models.
For AI providers, this vision presents both a challenge and an opportunity. Companies like OpenAI with GPT-5.6 (Sol, Terra, Luna), Anthropic with Claude Opus 4.8 and Claude Fable 5, and Google with Gemini 3.5 Flash, have focused much of their enterprise offering on access to powerful models through cloud APIs. While they offer deployment options in virtual private environments or with enhanced data controls, the degree of control over the underlying infrastructure and agent frameworks often remains limited. Cohere's proposal suggests that true sovereignty requires much greater transparency and customizability at every layer, which could push these giants to develop more disaggregated offerings or allow greater control over their technology stacks.
The AI infrastructure market will also feel the impact. Demand for GPUs and private cloud solutions, which can be directly controlled by companies or by managed service providers with strict sovereignty agreements, could experience a boom. This would benefit companies like NVIDIA, which manufactures the GPUs, and providers of private or hybrid cloud solutions. The need for robust and customizable AI governance systems, as well as open-weight or highly configurable connection tools and agent frameworks, will also create new opportunities for startups and specialized software providers.
For companies, especially banks, hospitals, and governments, the implication is clear: evaluating AI solutions must go beyond the power of the base model. They must consider the ability to control data residency, the jurisdiction of operations, infrastructure ownership, and the flexibility to customize and audit each component of the agent stack. This could lead to increased investment in internal talent with expertise in AI engineering and operations (MLOps), as well as greater demand for consultants specialized in AI sovereignty and regulatory compliance.
Finally, the AI economy will be redefined. The drop in per-token costs, while real, is offset by the exponential increase in token usage by complex agents. This means efficiency will be achieved not only through cheaper models, but also through smarter management of the entire stack. Companies that manage to optimize their agent architectures to minimize token consumption, whether through the selection of smaller, more efficient models (like Gemma 4 for the edge or Mistral Large 3 for the EU) or through smarter agent orchestration, will gain a significant competitive advantage. Sovereignty, in this context, becomes a key factor for the long-term economic sustainability of AI initiatives.
4. Expert Perspectives and Strategic Analysis
Cohere's vision of AI sovereignty resonates with a growing industry concern about vendor lock-in and data security. Cybersecurity and regulatory compliance analysts have warned for years about the inherent risks of outsourcing critical functions without adequate control. AI, being a transformative technology that handles highly sensitive data and makes decisions with significant implications, amplifies these risks exponentially.
From a strategic perspective, Cohere's proposal aligns with the trend toward disaggregation and modularity in software architecture. Instead of relying on monolithic solutions from a single vendor, companies seek to build their systems from interoperable components that can be swapped or customized. This is particularly relevant in the AI space, where innovation is rapid and the ability to adapt to new technologies (such as the next generation of models or agent frameworks) is crucial. Full stack control allows companies to integrate the best available models, whether proprietary like Grok 4.5 or open-weight like Llama 4, without being tied to a closed ecosystem.
However, implementing full-stack AI sovereignty is not without challenges. It requires significant investment in infrastructure, talent, and processes. Not all organizations have the capacity or resources to manage their own GPU farms or develop their own AI governance systems from scratch. This opens the door to a managed sovereignty model, where specialized providers offer solutions that guarantee the required control and transparency, but manage the operational complexity on behalf of the client. This is a growing market niche that Cohere, with its enterprise focus, is well-positioned to exploit.
The discussion also highlights the tension between rapid innovation and the need for control. The most advanced AI models, such as GPT-5.6 or Claude Mythos 5, are often developed and deployed first in cloud environments, where scalability and computational power are unmatched. Opting for full stack control may mean slower access to the latest innovations or the need to invest in massive computational resources to replicate cloud performance. The strategic decision for each company will be to find the right balance between the technological cutting edge and the level of sovereignty that its operations and regulations demand.
In this context, recommendations for companies are clear. First, conduct a thorough audit of their AI sovereignty needs, identifying the most sensitive data, the most critical operations, and the applicable regulations. Second, evaluate AI providers not only by the power of their models, but also by their ability to offer control over each layer of the stack. Third, consider investing in internal AI engineering and MLOps capabilities to manage and customize stack components. Finally, explore hybrid models that allow leveraging cloud scalability for less sensitive workloads, while maintaining strict control over critical components in private environments.
5. Future Roadmap and Predictions
Cohere's vision of full-stack AI sovereignty is not an anomaly, but a harbinger of the future direction of the enterprise market. In the next 12 to 24 months, we foresee an intensification of demand for solutions that offer greater control and transparency in AI deployment. Major cloud providers and proprietary model developers will be forced to offer more granular options for data management, jurisdiction selection, and customization of agent frameworks. This could manifest in more robust sovereign cloud offerings, where clients have explicit control over the physical location of data and infrastructure, or in the availability of model versions optimized for on-premises or edge deployments, such as Gemma 4 (12B).
The rise of complex AI agents will continue to drive the need for token consumption optimization. As agents become more sophisticated, capable of multi-modal reasoning and interacting with an even wider range of tools and systems, efficiency in token processing will become a key differentiator. This will foster research and development of smaller, more efficient models, as well as agent orchestration techniques that minimize redundant calls and optimize the use of computational resources. We will see greater adoption of agent architectures that combine large, powerful models (like Claude Fable 5) with smaller, specialized models for specific tasks, managed by intelligent agent frameworks.
Interoperability and open standards will play a crucial role. For companies to truly control their agent stack, components must be interchangeable. This means increased pressure for agent frameworks, connectors, and governance systems to adopt open standards, enabling organizations to mix and match the best solutions from different vendors. Open-weight models like Llama 4 and Mistral Large 3 will continue to be fundamental in this strategy, providing a foundation upon which companies can build and customize their AI solutions without relying on restrictive licenses or proprietary APIs.
Finally, regulation will play an increasingly important role in shaping AI sovereignty. As governments around the world implement stricter regulatory frameworks for AI (such as the EU AI Act), the ability to demonstrate control over the agent stack, traceability of AI decisions, and data residency will become a legal requirement. This will not only drive the adoption of sovereignty solutions but also create a market for AI compliance tools and services that help companies navigate this complex regulatory landscape.
6. Conclusion: Strategic Imperatives
The discussion at VB Transform 2026, led by Rachad Alao of Cohere, has crystallized an inescapable truth for the future of enterprise AI: sovereignty is not a luxury, but a strategic imperative. For organizations operating in critical sectors, control over the complete AI agent stack, from physical infrastructure to orchestration frameworks, is essential to ensure security, privacy, regulatory compliance, and operational autonomy. Ignoring this reality means exposing oneself to unacceptable risks of vendor lock-in, data leakage, and lack of control over systems that will soon be the heart of their operations.
Companies must act immediately to reassess their AI strategies. This means going beyond simply selecting models and considering the complete architecture of their agent systems. Investment in controlled AI infrastructure, robust governance systems, and customizable agent frameworks is no longer an option, but a necessity. Those organizations that proactively embrace this vision of full-stack sovereignty will be best positioned to innovate securely, manage costs effectively in the face of rising token consumption, and maintain a competitive advantage in an ever-evolving AI landscape. The era of plug-and-play AI without control is over; the era of sovereign AI has begun.