When Jack Dorsey posted four words on X on October 24, 2026,  “there is no moat”,  he captured a growing concern across the technology industry: whether traditional sources of competitive advantage can remain durable in a market where software and artificial intelligence capabilities are becoming easier to access and reproduce.

For decades, the technology sector relied on the concept of the economic moat, popularized by Warren Buffett. Proprietary software, closed ecosystems, high development costs and specialized technical capabilities helped companies protect their market positions. During the early years of the generative AI boom, many expected these advantages to migrate toward the developers of large language models or companies building products around them.

But the economics of software development are changing. Open-weight models, increasingly capable development tools and AI-assisted coding are lowering barriers to building and adapting software. Businesses can access powerful models without developing them from scratch, while competing products can reproduce features that once required substantial engineering investment. Technology remains important, but the ability to build a product is becoming less reliable as a standalone source of differentiation.

The question is where enterprise value moves when access to technology becomes less exclusive. In many cases, it moves toward the infrastructure that makes AI useful within an organization: data architecture, governance, system integration and the orchestration of business processes.

Large enterprises cannot simply connect an off-the-shelf AI model to decades of fragmented information and expect reliable results. Energy providers, telecommunications companies, banks and industrial organizations operate across complex systems, with different data formats, access permissions and operational requirements. Their challenge is not only selecting the right model. It is ensuring that the model can access relevant information, work within appropriate controls and contribute to business processes without compromising security or reliability.

As a result, the ability to integrate AI into the operational environment becomes a critical part of its value. Data must be organized and governed, existing systems must communicate, and AI applications must be designed around specific business objectives. The underlying model may change, but the architecture that connects technology to the organization determines whether the solution can deliver lasting value.

This shift is also creating opportunities for technology companies across Europe, including in Southeastern Europe. As enterprises move beyond experimentation and seek to integrate AI into their core operations, specialized expertise in data governance, system integration and enterprise architecture is becoming increasingly relevant. Companies that can address complex business requirements and turn AI capabilities into reliable applications have an opportunity to contribute to the next phase of enterprise AI without competing directly with global players in foundation model development.

Cloudevo, based in Athens, operates at the intersection of this opportunity. Working across data analytics, artificial intelligence, machine learning and cloud technologies, the company focuses on connecting technological capabilities with the practical needs of businesses. In sectors such as energy, infrastructure and media, this means looking beyond the model itself to the data, systems and operational requirements needed to make AI useful in real-world environments.

This approach also highlights the importance of flexibility. As new models and AI services become available, enterprises need architectures that allow them to evaluate and adopt different technologies where appropriate. A model-agnostic design can help reduce dependence on a single provider, while robust governance and integration practices help ensure that changing the underlying technology does not undermine the wider solution.

Dorsey’s statement should not be interpreted as meaning that every competitive advantage has disappeared. Rather, it challenges the assumption that proprietary technology alone can protect a business from competition. In enterprise AI, differentiation increasingly depends on how effectively companies connect models to their data, systems and operational priorities.

The next generation of enterprise AI solutions will be defined not only by the capabilities of the models behind them, but also by the quality of the architecture, the reliability of the implementation and the business outcomes they enable. When intelligence becomes more accessible, knowing how to put it to work becomes a more important part of the competitive equation.