Will AI models become commodities?

Microsoft CEO Satya Nadella recently proposed that advanced AI models are on the road to commodification, sparking debate. On the podcast, Nadella observed that the underlying model became increasingly similar and widely available, so “The model itself is not enough” For lasting competitive advantage. He pointed out that although Openai has cutting-edge neural networks – “Not a model company; this is a product company that happens to have great models“emphasizing the real advantage comes from building products around models.

In other words, having only the most advanced model may no longer guarantee market leadership, because at the rapid pace of AI innovation, any performance clues can be short-lived.

Nadella’s perspective plays a role in an industry where tech giants are training more and more models. His argument hints at a shift in focus: Companies should direct energy to bringing AI into scope, rather than obsessing with model first “A complete system stack and excellent successful products.”

This responds to a broader sentiment that today’s AI breakthroughs will soon become the baseline feature of tomorrow. As models become increasingly standardized and accessible, the spotlight will shift to the way AI is applied in the real world. Companies like Microsoft and Google have a huge product ecosystem, preferably taking advantage of this trend of commoditized AI by embedding models into user-friendly products.

Expand access and open models

Not long ago, only a few labs could build state-of-the-art AI models, but the speed of exclusivity is rapidly disappearing. Organizations and even individuals have access to the capabilities of AI, thus promoting the concept of model as a commodity. AI researcher Andrew Ng can be compared to the potential of AI as early as 2017 “New Power”, When electricity becomes the ubiquitous commodity of modern life, AI models may become the basic utility for many providers.

The latest proliferation of open source models has exacerbated this trend. Meta, the parent company of Facebook, for example, has released powerful language models like Llama to Inspur for free to researchers and developers. Reasoning is strategic: Through open AI, Meta can stimulate wider adoption and gain community contribution while undermining competitors’ proprietary advantages. Even recently, the AI ​​world exploded with the release of the Chinese model DeepSeek.

In the field of image generation, the stable diffusion model of stable AI shows how quickly a breakthrough can be transformed into commercialization: within a few months of its opening in 2022, it became a household name in Generative AI, available in countless applications. In fact, open source ecosystems are exploding – there are thousands of AI models publicly available for repositories like hugging faces.

This ubiquitousness means that organizations no longer face secret models for individual providers or no paid binary options at all. Instead, they can choose from the model menu (open or commercial) menu, or even choose on their own, just like selecting items from a catalog. There are a lot of signs that Advanced AI has become a widely shared resource rather than a strictly protected privilege.

Cloud Giant Turns Artificial Intelligence into Utilities Service

Major cloud providers are the main enablers and drivers of AI commodification. Companies such as Microsoft, Amazon and Google offer AI models for on-demand services, similar to utilities delivered on the cloud. Nadella pointed out “The model is being commoditized [the] cloud,” Emphasize how the cloud enables wide access to powerful AI.

Indeed, Microsoft’s Azure Cloud has a partnership with OpenAI, allowing any developer or enterprise to leverage GPT-4 or other top-level models via API calls without having to build their own AI from scratch. Amazon Web Services (AWS) has taken a step forward in the bedrock platform that acts as a model market. AWS BedRock offers basic models from several leading AI companies (from Amazon’s own models to models from humans, AI21 Labs, Stability AI, and others), all accessible through a hosting service.

This “many models, one platform” approach exemplifies commodification: customers can choose models that suit their needs and convert providers relatively easily, as if they were buying a product.

In fact, this means that businesses can rely on cloud platforms to always have state-of-the-art models, just like electricity on the grid – if new models make headlines (e.g., like a startup breakthrough), the cloud will be available immediately.

Differentiate between the model itself

If everyone has access to similar AI models, how can AI companies distinguish themselves? This is the key to the commodity debate. The consensus among industry leaders is that value will be application AI, not just algorithms. Openai’s own strategy reflects this shift. In recent years, the company has focused on providing polished products (CHATGPT and its API) as well as enhanced ecosystems such as fine-tuning services, plug-in add-ons and user-friendly interfaces, rather than simply publishing original model code.

In fact, this means providing reliable performance, custom options, and developer tools around the model. Similarly, Google’s DeepMind and Brain teams (now part of Google DeepMind) are introducing their research into Google’s products such as search, office applications and cloud APIs – embedding AI to make these services smarter. The technical complexity of the model is of course important, but Google knows that users will eventually care about the experience enabled by AI (better search engines, more helpful digital assistants, etc.) rather than the name or size of the model.

We also see companies different through specialization. Instead of a model to rule them, some AI companies build models for specific domains or tasks that can claim excellent quality even in a commercialized landscape. For example, AI startups focus only on healthcare diagnostics, finance or law – areas where proprietary data and domain expertise can generate Better The niche model is better than the general system. These companies stand out by using open models or smaller custom models and fine-tuning of proprietary data.

Another form of differentiation is efficiency and cost. A model that provides the same performance in a small fraction of the computational cost may be a competitive advantage. The emergence of DeepSeek’s R1 model highlights this, and the model reportedly matches some of OpenAI’s GPT-4 features, with training costs below $6 million, significantly below the estimated $10 billion spent on the GPT-4. This efficiency improvement shows that although Output In different models, a provider can differentiate itself by achieving these results cheaper or faster.

Finally, there is a competition to build user loyalty and ecosystem around AI services. Once an enterprise has penetrated a specific AI model into its workflow (with custom tips, integrations, and fine-tuning data), switching to another model is not frictionless. Providers such as OpenAI, Microsoft and other companies are trying to improve this stickiness by providing a comprehensive platform from the developer SDK to the AI ​​plugin market, making it more of a full-stack solution to AI than a swap commodity.

Companies are advancing to the value chain: when the model itself is not a moat, differentiation comes from everything around the model – data, user experience, vertical expertise, and integration into existing systems.

Economic Chain Reaction of Commodified AI

The commodification of AI models has a significant economic impact. In the near term, it is driving the cost reduction of AI capabilities. With multiple competitors and open alternatives, the pricing of AI services has always been reminiscent of the classic commodity market.

Over the past two years, OpenAI and other providers have cut prices for accessing language models. For example, Openai’s GPT series’ token pricing has dropped by more than 80% from 2023 to 2024, due to competition and increased efficiency.

Likewise, new entrants who offer cheaper or open models will force incumbents to offer less price – whether through free tiers, open source versions, or bundled deals. This is good news for consumers and businesses that adopt AI, as advanced features are becoming more affordable. This also means that AI technology spreads faster throughout the economy: when something becomes cheaper and more standardized, more industries incorporate it into innovation (as with cheap commodified PC hardware in the 2000s, it led to the explosion of software and internet services).

We are already in the wave of AI in areas such as customer service, marketing and operations ready-to-use models and service-driven. Thus, even if the profit margins of the model itself shrink, wider availability can expand the overall market for AI solutions.

Economic dynamics of commercialized AI (Unite AI/Alex McFarland)

However, commodification can also reshape the competitive landscape in challenging ways. For established AI labs that have invested billions of dollars in developing cutting-edge models, the prospect of these models producing only transient advantages raises questions about ROI. Instead of selling API access only, they may need to tweak their business models, for example, focusing on enterprise services, proprietary data advantages, or subscription products built on top of the model.

There is also a weapon competition factor: the window to narrow the novel model can be monetized when others (even by the open source community) quickly reach or surpass any breakthroughs in performance. This dynamic prompts companies to consider alternative economic moats. One such moat is integrated with proprietary data (uncommodified) – AI adjusted for the company’s own rich data may be more valuable than any off-the-shelf model.

Another is regulatory or compliance features, where the provider may provide models with guaranteed privacy or compliance usage to distinguish in a way that goes beyond the original technology. On the macro scale, if the underlying AI model becomes ubiquitous like a database or a web server, we might see a change Serve Become the main revenue generator around AI (cloud hosting, consulting, customization, maintenance). Cloud providers have benefited from the need to increase the need for computing infrastructure (CPUs, GPUs, etc.) to run all of these models, which is a bit like the profits of power used by users, even if the devices are commercialized.

Essentially, the economics of artificial intelligence can reflect the economics of other IT commodities: lower costs and greater access stimulate widespread use, thus creating new opportunities at the commoditization layer, even if the providers at that layer face stricter profits and need to be constantly innovated or constantly innovated or differentiated elsewhere.