Is Google Pulling Ahead of OpenAI in the AI Race?

Google’s expanding AI partnerships, product integration, and recent technical progress are fuelling growing debate over whether it has quietly moved ahead of OpenAI in the global race to deploy large-scale artificial intelligence.

Matched Since 2022

Google and OpenAI have been closely matched since late 2022, when OpenAI’s release of ChatGPT reshaped public and commercial expectations of what generative AI could do, yet the balance of momentum now appears to be shifting as Google converts years of research into deployed systems at scale.

How Google Recovered From a Slow Start

When ChatGPT launched in November 2022, it caught much of the technology industry, including Google, off guard. Despite Google’s long history in machine learning and AI research, OpenAI’s product arrived first with a highly accessible conversational interface that rapidly reached over 100 million users within months.

Google’s response was swift but initially uneven. For example, the company accelerated internal development under what chief executive Sundar Pichai later described as an urgent shift in priorities, whereby teams were reorganised, projects were refocused, and products that had been in research phases for years were pushed towards public release.

Early versions of Google’s Bard chatbot struggled to match ChatGPT’s reliability, leading to public missteps that reinforced the perception that Google was playing catch-up. Behind the scenes, though, the company continued investing heavily in foundation models, custom AI chips, and infrastructure that would later underpin its Gemini model family.

Gemini and Google’s Integrated AI Strategy

Google’s launch of the Gemini model family signalled a change in approach by moving away from a standalone chatbot towards a set of foundation models designed to operate across mobile devices, consumer services, and large-scale cloud infrastructure.

This approach appears to reflect a kind of key philosophical difference between Google and OpenAI. For example, OpenAI has focused primarily on developing increasingly capable general-purpose models, which are then distributed via ChatGPT, APIs, and selected partnerships. Google, by contrast, has emphasised deep integration across its existing products, including Search, Android, Chrome, Gmail, Docs, and Google Cloud.

The result is that Gemini is not just a single AI product, but a layer embedded across services used daily by billions of people. Google has argued that this allows it to deploy AI features more safely and more consistently, refining them in specific contexts rather than relying on one general interface.

Gemini – “Natively Multimodal”

In public communications, Google has been keen to stress that its Gemini AI is designed to be “natively multimodal”, meaning it can work with text, images, audio, and video from the outset rather than treating those as add-ons. This capability has become increasingly important as businesses look to automate workflows that involve documents, meetings, images, and structured data together.

The Significance of Apple’s Gemini Decision

One of the clearest external signals of Google’s renewed standing emerged in mid 2025, when Apple confirmed it had selected Google’s Gemini models as a foundation for parts of its AI strategy, including planned upgrades to Siri and its wider “Apple Intelligence” platform, following months of reported negotiations.

In a joint statement announcing the partnership, the two companies said Apple had concluded that Google’s AI technology offered the most capable foundation for its needs, while still allowing Apple to run Apple Intelligence primarily on device and through its Private Cloud Compute infrastructure in line with its long-standing privacy and security requirements.

This was widely interpreted as a setback for OpenAI, which already has an integration with Apple platforms through ChatGPT features in macOS and iOS. Choosing Google for foundational models suggests Apple values stability, scale, and long-term integration over cutting-edge experimentation.

The decision also appears to reinforce Google’s strength in enterprise-grade AI infrastructure, with Apple’s focus on privacy, reliability, and global scale seeming to align more closely with Google’s long-standing cloud-first approach than with OpenAI’s faster, more consumer-led release cycle.

Benchmarks, Capability, and Credibility

AI model benchmarks remain quite a contentious topic, as results can vary depending on test design and optimisation. However, it seems that independent evaluations published by academic researchers and industry analysts have shown Gemini models performing competitively, and in some cases outperforming, comparable GPT models across reasoning, multimodal understanding, and coding tasks.

That said, OpenAI continues to lead in certain creative and conversational use cases, particularly where developer tooling and ecosystem maturity are concerned. OpenAI’s API adoption remains strong, and Microsoft’s integration of GPT models into products such as Copilot has given OpenAI unparalleled reach within enterprise environments.

The difference increasingly lies in how these capabilities are delivered. For example, Google has prioritised gradual rollout through familiar tools, reducing friction for users who may not actively seek out AI products. OpenAI has relied more heavily on direct user engagement with ChatGPT and developer-driven experimentation.

Why Infrastructure Really Matters

It’s worth noting here that Google’s position is also shaped by its control over large-scale AI infrastructure, including one of the world’s largest global computing networks and its in-house Tensor Processing Units, which are specialised chips designed for machine learning workloads.

This level of vertical integration is essentially what allows Google to train and deploy models at scale while managing cost, energy use, and availability more tightly than companies that rely entirely on third-party infrastructure, a factor analysts increasingly point to as a constraint on sustained AI development.

OpenAI, despite strong backing from Microsoft, remains more exposed to external infrastructure decisions, a relationship that has enabled rapid progress so far but appears to introduce strategic dependencies that Google is largely able to avoid.

Governance and Trust

Enterprise adoption increasingly depends on governance, compliance, and long-term support rather than headline-grabbing demos. With this in mind, Google has certainly invested heavily in AI safety frameworks, model evaluation, and policy tooling designed to meet regulatory expectations in Europe and the UK.

However, it seems that OpenAI has faced more visible scrutiny around governance, leadership changes, and transparency, none of which necessarily undermine its technology but which do affect risk assessments for large organisations.

For large organisations, purchasing decisions increasingly appear to be shaped less by who releases new models first and more by long-term stability, governance, and confidence that platforms and suppliers will remain consistent over time.

Where OpenAI Still Leads

Despite Google’s momentum, it should be noted that OpenAI remains a pretty formidable competitor. For example, ChatGPT continues to set the standard for conversational AI, and OpenAI’s research output continues to influence the wider field. The company’s ability to rapidly iterate and release new features has driven much of the innovation seen across the sector.

Microsoft’s backing also ensures that OpenAI models are deeply embedded in workplace software used by millions, particularly in the UK enterprise market.

The current dynamic is less about one company winning outright and more about diverging strengths. Google appears to be excelling at scale, integration, and infrastructure-driven deployment, while OpenAI remains strong in rapid innovation and developer engagement.

It could be said, therefore, that what has changed is the assumption that OpenAI holds a clear and unassailable lead. With Gemini embedded across platforms and endorsed by partners as demanding as Apple, Google could be said to have repositioned itself not as a follower, but as a central force shaping how AI is delivered, governed, and trusted at global scale.

What Does This Mean For Your Business?

What now appears to matter most is not a single benchmark result or product launch, but how effectively AI capabilities are being embedded into real services, governed at scale, and sustained over time. For example, Google’s recent progress suggests it has been able to translate long-standing strengths in infrastructure, distribution, and enterprise trust into tangible momentum, while OpenAI continues to set the pace in innovation speed, developer engagement, and conversational experience. The picture that emerges isn’t one of a clear winner, but of two companies optimising for different definitions of leadership as the market matures.

For UK businesses, this distinction is likely to become increasingly important. Organisations adopting AI tools are moving beyond experimentation and into decisions that affect procurement, compliance, data handling, and long-term supplier relationships. Google’s approach may appeal to firms prioritising stability, regulatory alignment, and tight integration with existing productivity platforms, while OpenAI’s ecosystem remains attractive for teams seeking flexibility, rapid capability gains, and access to cutting-edge features. The choice is becoming less about which model is most impressive in isolation and more about which provider fits operational reality.

For other stakeholders, including developers, regulators, and platform partners, the evolving balance between Google and OpenAI reinforces how the AI race is shifting away from spectacle and towards execution. As generative AI becomes embedded into everyday tools rather than standing apart from them, influence is likely to be shaped by who can deliver reliable systems at scale, earn sustained trust, and adapt to regulatory pressure without slowing progress. In that context, the question is no longer simply who is ahead today, but who is best positioned for the next phase of AI adoption.