Microsoft CEO Satya Nadella recently sparked debate by suggesting that advanced AI models are on the path to commoditization. On a podcast, Nadella observed that foundational models are becoming increasingly similar and widely available, to the point where “models by themselves are not sufficient” for a lasting competitive edge. He pointed out that OpenAI – despite its cutting-edge neural networks – “is not a model company; it’s a product company that happens to have fantastic models,” underscoring that true advantage comes from building products around the models.
In other words, simply having the most advanced model may no longer guarantee market leadership, as any performance lead can be short-lived amid the rapid pace of AI innovation.
Nadella’s perspective carries weight in an industry where tech giants are racing to train ever-larger models. His argument implies a shift in focus: instead of obsessing over model supremacy, companies should direct energy toward integrating AI into “a full system stack and great successful products.”
This echoes a broader sentiment that today’s AI breakthroughs quickly become tomorrow’s baseline features. As models become more standardized and accessible, the spotlight moves to how AI is applied in real-world services. Firms like Microsoft and Google, with vast product ecosystems, may be best positioned to capitalize on this trend of commoditized AI by embedding models into user-friendly offerings.
Widening Access and Open Models
Not long ago, only a handful of labs could build state-of-the-art AI models, but that exclusivity is fading fast. AI capabilities are increasingly accessible to organizations and even individuals, fueling the notion of models as commodities. AI researcher Andrew Ng as early as 2017 likened AI’s potential to “the new electricity,” suggesting that just as electricity became a ubiquitous commodity underpinning modern life, AI models could become fundamental utilities available from many providers.
The recent proliferation of open-source models has accelerated this trend. Meta (Facebook’s parent company), for example, made waves by releasing powerful language models like LLaMA openly to researchers and developers at no cost. The reasoning is strategic: by open-sourcing its AI, Meta can spur wider adoption and gain community contributions, while undercutting rivals’ proprietary advantages. And even more recently, the AI world exploded with the release of the Chinese model DeepSeek.
In the realm of image generation, Stability AI’s Stable Diffusion model showed how quickly a breakthrough can become commoditized: within months of its 2022 open release, it became a household name in generative AI, available in countless applications. In fact, the open-source ecosystem is exploding – there are tens of thousands of AI models publicly available on repositories like Hugging Face.
This ubiquity means organizations no longer face a binary choice of paying for a single provider’s secret model or nothing at all. Instead, they can choose from a menu of models (open or commercial) or even fine-tune their own, much like selecting commodities from a catalog. The sheer number of options is a strong indication that advanced AI is becoming a widely shared resource rather than a closely guarded privilege.
Cloud Giants Turning AI into a Utility Service
The major cloud providers have been key enablers – and drivers – of AI’s commoditization. Companies such as Microsoft, Amazon, and Google are offering AI models as on-demand services, akin to utilities delivered over the cloud. Nadella noted that “models are getting commoditized in [the] cloud,” highlighting how the cloud makes powerful AI broadly accessible.
Indeed, Microsoft’s Azure cloud has a partnership with OpenAI, allowing any developer or business to tap into GPT-4 or other top models via an API call, without building their own AI from scratch. Amazon Web Services (AWS) has gone a step further with its Bedrock platform, which acts as a model marketplace. AWS Bedrock offers a selection of foundation models from multiple leading AI companies – from Amazon’s own models to those from Anthropic, AI21 Labs, Stability AI, and others – all accessible through one managed service.
This “many models, one platform” approach exemplifies commoditization: customers can choose the model that fits their needs and switch providers with relative ease, as if shopping for a commodity.
In practical terms, that means businesses can rely on cloud platforms to always have a state-of-the-art model available, much like electricity from a grid – and if a new model grabs headlines (say a startup’s breakthrough), the cloud will promptly offer it.
Differentiating Beyond the Model Itself
If everyone has access to similar AI models, how do AI companies differentiate themselves? This is the crux of the commoditization debate. The consensus among industry leaders is that value will lie in the application of AI, not just the algorithm. OpenAI’s own strategy reflects this shift. The company’s focus in recent years has been on delivering a polished product (ChatGPT and its API) and an ecosystem of enhancements – such as fine-tuning services, plugin add-ons, and user-friendly interfaces – rather than simply releasing raw model code.
In practice, that means offering reliable performance, customization options, and developer tools around the model. Similarly, Google’s DeepMind and Brain teams, now part of Google DeepMind, are channeling their research into Google’s products like search, office apps, and cloud APIs – embedding AI to make those services smarter. The technical sophistication of the model is certainly important, but Google knows that users ultimately care about the experiences enabled by AI (a better search engine, a more helpful digital assistant, etc.), not the model’s name or size.
We’re also seeing companies differentiate through specialization. Instead of one model to rule them all, some AI firms build models tailored to specific domains or tasks, where they can claim superior quality even in a commoditized landscape. For example, there are AI startups focusing exclusively on healthcare diagnostics, finance, or law – areas where proprietary data and domain expertise can yield a better model for that niche than a general-purpose system. These companies leverage fine-tuning of open models or smaller bespoke models, coupled with proprietary data, to stand out.
OpenAI’s ChatGPT interface and collection of specialized models (Unite AI/Alex McFarland)
Another form of differentiation is efficiency and cost. A model that delivers equal performance at a fraction of the computational cost can be a competitive edge. This was highlighted by the emergence of DeepSeek’s R1 model, which reportedly matched some of OpenAI’s GPT-4 capabilities with a training cost of under $6 million, dramatically lower than the estimated $100+ million spent on GPT-4. Such efficiency gains suggest that while the outputs of different models might become similar, one provider could distinguish itself by achieving those results more cheaply or quickly.
Finally, there’s the race to build user loyalty and ecosystems around AI services. Once a business has integrated a particular AI model deeply into its workflow (with custom prompts, integrations, and fine-tuned data), switching to another model isn’t frictionless. Providers like OpenAI, Microsoft, and others are trying to increase this stickiness by offering comprehensive platforms – from developer SDKs to marketplaces of AI plugins – that make their flavor of AI more of a full-stack solution than a swap-in commodity.
Companies are moving up the value chain: when the model itself is not a moat, the differentiation comes from everything surrounding the model – the data, the user experience, the vertical expertise, and the integration into existing systems.
Economic Ripple Effects of Commoditized AI
The commoditization of AI models carries significant economic implications. In the short term, it’s driving the cost of AI capabilities down. With multiple competitors and open alternatives, pricing for AI services has been in a downward spiral reminiscent of classic commodity markets.
Over the past two years, OpenAI and other providers have slashed prices for access to language models dramatically. For instance, OpenAI’s token pricing for its GPT series dropped by over 80% from 2023 to 2024, a reduction attributed to increased competition and efficiency gains.
Likewise, newer entrants offering cheaper or open models force incumbents to offer more for less – whether through free tiers, open-source releases, or bundle deals. This is good news for consumers and businesses adopting AI, as advanced capabilities become ever more affordable. It also means AI technology is spreading faster across the economy: when something becomes cheaper and more standardized, more industries incorporate it, fueling innovation (much as inexpensive commoditized PC hardware in the 2000s led to an explosion of software and internet services).
We are already seeing a wave of AI adoption in sectors like customer service, marketing, and operations, driven by readily available models and services. Wider availability can thus expand the overall market for AI solutions, even if profit margins on the models themselves shrink.
Economic dynamics of commoditized AI (Unite AI/Alex McFarland)
However, commoditization can also reshape the competitive landscape in challenging ways. For established AI labs that have invested billions in developing frontier models, the prospect of those models yielding only transient advantages raises questions about ROI. They may need to adjust their business models – for example, focusing on enterprise services, proprietary data advantages, or subscription products built on top of the models, rather than selling API access alone.
There is also an arms race element: when any breakthrough in performance is quickly met or exceeded by others (or even by open-source communities), the window to monetize a novel model narrows. This dynamic pushes companies to consider alternative economic moats. One such moat is integration with proprietary data (which is not commoditized) – AI tuned on a company’s own rich data can be more valuable to that company than any off-the-shelf model.
Another is regulatory or compliance features, where a provider might offer models with guaranteed privacy or compliance for enterprise use, differentiating in a way beyond raw tech. On a macro scale, if foundational AI models become as ubiquitous as databases or web servers, we might see a shift where the services around AI (cloud hosting, consulting, customizations, maintenance) become the primary revenue generators. Already, cloud providers benefit from increased demand for computing infrastructure (CPUs, GPUs, etc.) to run all these models – a bit like how an electric utility profits from usage even if the appliances are commoditized.
In essence, the economics of AI could mirror that of other IT commodities: lower costs and greater access spur widespread use, creating new opportunities built atop the commoditized layer, even as the providers of that layer face tighter margins and the need to innovate constantly or differentiate elsewhere.
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