AI David and Goliath

Mark Dando at SUSE considers how to break Big Tech’s grip on innovation

 

As AI continues to advance at pace, there is a critical battle over who gets to shape its future. On one side, a handful of tech giants are consolidating power and building proprietary AI ecosystems that limit access and control. On the other, a growing movement of open-source developers, smaller enterprises, and community initiatives are pushing for a more transparent, collaborative, and decentralised approach.

 

This tension between monopolised and grassroots AI development will define how the technology evolves - and who truly benefits from its progress.

 

 

Breaking away from the AI monoculture

In recent years, AI has been dominated by just a few major players. These organisations control access to data, infrastructure, and foundational models, creating what many now call an "AI monoculture." This concentration of power restricts innovation, locks users into narrow ecosystems, and raises costs for organisations trying to integrate AI in meaningful ways.

 

But a shift is underway. New government policies, emerging open-source models, and growing demand for flexible AI infrastructure suggest that the balance of power could change - if we make the right choices now.

 

The UK, home to the world’s third-largest AI market, has a real opportunity to lead this shift. The Government’s new AI Opportunity Action Plan outlines how AI can boost productivity and improve people’s everyday lives. But whether this potential is realised equitably will depend on how much room is made for open ecosystems that empower independent developers and enterprises to innovate freely.

 

 

Why open source matters

Open source and AI are natural allies. Both are rooted in collaboration and shared innovation. Open AI models - those that can be freely used, modified, and distributed - help accelerate progress, improve transparency and broaden access beyond the corporate elite.

 

A truly open AI ecosystem allows organisations to tailor models to their needs, maintain control over their data and avoid vendor lock-in. It also fuels a broader innovation cycle: the more organisations adopt and contribute to open source AI, the more demand there is for secure, enterprise-ready open infrastructure, creating a virtuous circle of progress.

 

We’ve seen this play out across enterprise software, where open source has helped businesses reduce costs, enhance flexibility, and build more resilient digital environments. The same principles apply to AI. Organisations need the freedom to deploy and manage AI workloads at scale, choose the tools and models that best fit their goals, and maintain sovereignty over their data and infrastructure.

 

The alternative - keeping AI development locked behind the gates of a few tech giants - carries significant risks. Proprietary models limit visibility into how decisions are made, restrict opportunities for customisation, and often come with high costs for access or integration.

 

As AI becomes more embedded in core business processes, these limitations will become more problematic. Organisations will find it harder to adapt models to their needs, respond to new challenges, or ensure alignment with ethical and regulatory requirements.

 

We’re already seeing the consequences of this approach. Businesses are facing higher costs and reduced flexibility while concerns about bias, transparency, and safety continue to grow. Without meaningful alternatives, the risk is that AI innovation becomes centralised, opaque, and disconnected from the needs of most users.

 

 

Open source in action

Open source AI is not a theoretical ideal - it’s already reshaping the way organisations build and deploy intelligent systems. A recent McKinsey study found that 63% of organisations already use open source AI tools, rising to 72% in the tech sector. Those adopting it for competitive advantage are 40% more likely to do so than their peers, with key benefits including lower costs and faster innovation cycles.

 

Community-driven infrastructure projects, such as those supported by the Linux Foundation, are also gaining ground. These initiatives bring together developers, organisations, and users to co-create trusted, transparent AI systems that prioritise security, openness and resilience.

 

Open standards and shared development models also improve safety. When communities can examine, test, and improve code collectively, they reduce the risk of hidden flaws and increase trust in the technology. With AI still in its early stages, this kind of transparency is critical.

 

 

The role of regulation

While open-source innovation is essential, it can’t thrive in isolation. Effective regulation should also support decentralisation and openness - not unintentionally reinforce the dominance of existing players.

 

There are encouraging signs. Last year, the White House acknowledged the benefits of open source AI in encouraging innovation and broader participation. The UK has an opportunity to follow suit by ensuring that regulation enables access, transparency and customisation rather than entrenching the status quo.

 

A strong AI ecosystem will require regulatory guardrails - but those guardrails should be designed to protect users without shutting out independent developers and smaller players.

 

The decisions made in the next few years will shape AI development for decades. If we want a future where innovation is ethical, fair and inclusive, we must resist the gravitational pull of centralised control and invest in open ecosystems that serve everyone.

 

Governments, enterprises and developers have a role to play in ensuring that AI development remains transparent, collaborative and democratic. Open source is more than just a model - it’s the foundation of a future where technology works for the many, not just the few.

 

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Mark Dando is General Manager, EMEA North at SUSE

 

Main image courtesy of iStockPhoto.com and yuriz