Why AI’s Next Phases Will Favor Independent Players

As AI moves from its initial infrastructure buildout phase to widespread implementation, these smaller technology companies find themselves uniquely positioned to capture value. The focus is shifting from infrastructure to execution, where access to data and the ability to orchestrate it effectively are becoming critical advantages.

This shift is further accelerated by the rise of AI agents, which rely on seamless data integration across systems to deliver results. Independent providers, known for their flexibility and cost-efficiency, are well-positioned to support this phase—helping companies scale their AI capabilities with adaptable solutions.

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The Three Stages of AI Evolution

Investors need to recognize that the AI revolution is unfolding in three distinct stages to understand where the opportunities lie.

Stage 1—the GPU buildout phase—has largely benefited industry giants like Nvidia, driving large-scale model training and powering the initial wave of AI advancements.

In Stage 2, the model training and implementation phase of AI-driven solutions, smaller, independent technology companies are playing a crucial role. AI companies require efficient, cost-effective storage, compute and infrastructure to support model training, execution, and large-scale data management—and they often prefer providers who aren’t building competing AI models themselves. Success in this phase depends on infrastructure and the ability to consolidate and orchestrate data effectively. Companies embracing a multi-cloud approach to streamline data pipelines, integrate diverse data sources, and eliminate silos are well-positioned to enable faster model iteration and improved performance.

Take Decart, whose video-generating AI model achieved over 10 times greater efficiency than competitors. Initially using traditional cloud providers, Decart transitioned to independent providers, scaling their data storage from 5 to 50 petabytes in months. This shift optimized their data pipelines, improving cost efficiency and accelerating model refinement.

This trend is not isolated: AI companies consistently double their data usage annually as they refine and expand their models. As Stage 2 progresses, a key success factor will be a strategy incorporating a cost-efficient, multi-cloud approach to data consolidation, orchestration, and scalable infrastructure.

The Value of Independence

This preference for independent providers isn’t just about cost. While smaller providers often offer services at 60-80% below market leaders’ prices, independence is equally important. As AI becomes more competitive, companies are increasingly cautious of relying on hyperscalers that may develop their own competing models.

The benefits of independence extend beyond cost savings. Companies are adopting open cloud strategies to enable workload portability across providers and avoid vendor lock-in. This shift toward greater flexibility particularly benefits smaller, independent providers offering more interoperable, adaptable solutions.

Organizations adopting multi-cloud or hybrid cloud approaches are well-positioned to achieve greater agility and cost optimization. Independent providers supporting these approaches, especially those focused on cost-efficient AI implementation and independent deployment solutions, are primed to play a pivotal role in Stage 2 and beyond.

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Looking Ahead to Stage 3

Building on the cost efficiencies, multi-cloud strategies, and data orchestration successes of Stage 2, Stage 3 marks the shift toward monetizing AI solutions through domain-specific applications. Companies will leverage their industry expertise and data assets to create tailored solutions that drive real business value. A key enabler in this phase will be autonomous AI agents—intelligent systems that integrate and act on data across platforms—allowing companies to automate processes, generate insights, and deliver targeted solutions in healthcare, finance, manufacturing, and retail. For example, Salesforce’s Agentforce enables businesses to deploy AI agents that autonomously manage tasks across sales, service, marketing, and commerce.  These agents go beyond traditional automation by analyzing data, making context-driven decisions, and executing tasks—such as resolving customer cases, qualifying sales leads, and optimizing marketing campaigns—enhancing both efficiency and customer engagement. This evolution marks the rise of Agentic AI, where systems operate with greater autonomy, adaptability, and contextual understanding to drive more advanced outcomes.

Ultimately, Stage 3 is about transforming AI advancements into business outcomes. While hyperscalers will remain key players, independent providers are well-positioned to help companies scale their AI capabilities with cost-efficient, adaptable, and multi-cloud solutions. By offering open, flexible systems without vendor lock-in, these providers will empower organizations to fully capitalize on AI, driving industry-specific innovations and unlocking new opportunities across sectors.

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