Are AI models now available as products?

Microsoft CEO Satya Nadella has recently sparked debate by suggesting that advanced AI models are on the path to commodity. In the podcast, Nadella observed that the basic models are increasingly similar and widely available. “The model itself isn’t enough.” For a lasting competitiveness. He pointed out Openai despite its cutting-edge neural networks – “It’s not a model company. It’s a product company that just happens to have great models.“It emphasizes that there are real benefits from building products around models.

In other words, simply having the most advanced model can no longer guarantee market leadership, as performance leads can be short-lived amid the rapid pace of AI innovation.

Nadella’s perspective has weight in an industry where the tech giants race to train bigger models. His argument means a shift in focus: Instead of falling into the supremacy of the model, companies should turn their energy to integrate AI “Complete system stack and successful product.”

This reflects the broader sentiment that today’s AI breakthrough will become a baseline feature of tomorrow. As models become more standardized and accessible, spotlights move to how AI is applied in real services. Companies like Microsoft and Google have a vast product ecosystem and may be best positioned to take advantage of this commoditized AI trend by embedding models into user-friendly products.

Expanding access and open models

Not long ago, only a handful of labs could build cutting-edge AI models, but their exclusiveness is rapidly fading. AI capabilities become increasingly accessible to organizations and individuals, promoting the concept of models as commodities. AI researcher Andrew Ng used AI potential as an example in 2017 “New electricity” Just as electricity has become a ubiquitous product that supports modern life, it suggests that the AI ​​model could become a fundamental utility available to many providers.

The recent growth of open source models has accelerated this trend. For example, Meta (the parent company of Facebook) has made waves by publicly releasing powerful language models such as Llama for free to researchers and developers. Inference is strategic. By open-sourcing AI, Meta can promote wider adoption and gain community contributions while undermining the unique benefits of its rivals. More recently, the AI ​​world exploded with the release of Chinese model Deepseek.

In the area of ​​image generation, stable diffusion models of stability AI showed how quickly breakthroughs are commoditized. Within months of its open release in 2022, it has become a well-known name for generation AI available in countless applications. In fact, the open source ecosystem is exploding. Tens of thousands of AI models have been released in facial-like repository.

This ubiquitousness means that organizations will no longer face binary choices to pay for a single provider’s secret model. Alternatively, you can choose from the menu (open or commercial) of the model (open or commercial) or fine-tune your own menu, just like you would select products from the catalog. The vast number of options is a strong indication that advanced AI is becoming a widely shared resource rather than a closely guarded privilege.

Cloud Giants transform AI into a utility service

The major cloud providers are key enablers and drivers for AI commercialization. Companies such as Microsoft, Amazon, Google offer AI models as on-demand services similar to utilities delivered in the cloud. Nadella pointed it out. “The model is commoditized in the cloud.” It highlights how the cloud makes powerful AI widely accessible.

In fact, Microsoft’s Azure Cloud has a partnership with Openai, allowing developers and businesses to leverage GPT-4 or other top models via API calls without building from scratch. Amazon Web Services (AWS) is taking it a step further with the Bedrock platform, which serves as a model market. AWS Bedrock offers a selection of basic models from multiple major AI companies, from Amazon’s proprietary models to models such as Humanity, AI21 Labs, and Stability AI.

This “many models, one platform” approach exemplifies commoditization. Customers can select the model that suits their needs and switch providers relatively easily, as if they were shopping for their products.

In fact, that means that businesses can rely on cloud platforms to ensure that cutting-edge models are always available, just like electricity from the grid. If a new model grabs headlines (such as a startup breakthrough), the cloud will deliver quickly.

Distinguish beyond the model itself

How do AI companies differentiate when everyone has access to similar AI models? This is at the heart of the commoditization debate. The consensus among industry leaders is that it is worth it application Not just AI and algorithms. Openai’s own strategy reflects this change. The company’s focus in recent years is not simply releasing raw model codes, but delivering sophisticated products (ChatGPT and its APIs) and enhanced ecosystems, including fine-tuning services, plug-in add-ons, and user-friendly interfaces.

In reality, it means providing reliable performance around the model, customization options, and developer tools. Similarly, Google’s Deepmind and Brain team are currently part of Google Deepmind, channeling research into Google’s products, including search, office apps, and cloud APIs. Embed AI to make these services smarter. While technical refinement of models is certainly important, Google knows that they ultimately care about an effective experience through AI (better search engines, more useful digital assistants, etc.) rather than model names or sizes.

In addition, companies are differentiated through specialization. Instead of one model that controls them all, some AI companies can build models tailored to a particular domain or task, and even claim excellent quality in commoditized landscapes. For example, there are AI startups that focus solely on healthcare diagnostics, finance, or law. Better A model that is more of its niche than a general-purpose system. These companies, coupled with their own data, take advantage of the fine tweaks of open or smaller bespoke models to stand out.

Another form of differentiation is efficiency and cost. Models that offer equal performance at a portion of computational costs can be competitive. This was highlighted by the emergence of Deepseek’s R1 model. This reportedly coincides some of Openai’s GPT-4 features with training costs of less than $6 million, dramatically lower than the estimated $100 million spent on the GPT-4. Such an increase in efficiency suggests that output Of the different models, they may be similar, but one provider can distinguish themselves by achieving these results cheaper or faster.

Finally, there is a competition to build user loyalty and ecosystems around AI services. Once your business has deeply integrated a particular AI model into your workflow (using custom prompts, integrations, fine-tuned data), you cannot switch to another model. Providers such as Openai, Microsoft are trying to increase this stickiness by providing a comprehensive platform, from developer SDKs to AI plug-in marketplaces.

Companies are increasing their value chain. If the model itself is not a moat, the distinction arises from everything surrounding the model: data, user experience, vertical expertise, and integration into existing systems.

Economic ripple effects of commoditized AI

Commoditizing AI models has important economic implications. In the short term, the cost of AI features is reduced. With multiple competitors and open alternatives, AI services pricing lies in a downward spiral reminiscent of the classic commodity market.

Over the past two years, Openai and other providers have dramatically reduced the price of access to language models. For example, the token price for the GPT series Openai fell by more than 80% between 2023 and 2024. This is a reduction caused by increased competition and increased efficiency.

Similarly, new entrants offering cheaper and open models will force them to offer less expensive existing people through free tiers, open source releases and bundle deals. This is good news for consumers and businesses adopting AI, as advanced features become increasingly affordable. It also means that AI technology is spreading faster across the economy. If something gets cheaper and more standardized, more industries will incorporate it to encourage innovation (in the 2000s, cheap commoditized PC hardware led to an explosion of software and internet services).

We are already seeing the wave of AI adoption in sectors such as customer service, marketing and operations. Therefore, wider availability could potentially expand the overall market for AI solutions, even if the profit margins of the model itself shrink.

However, commercialization can also reconstruct a competitive situation in challenging ways. For established AI labs that have invested billions in developing frontier models, the prospects of these models offering only temporary benefits raise questions about ROI. For example, rather than selling API access alone, you may need to adjust your business model focusing on enterprise services built on top of the model, the benefits of your own data, or subscription products.

There is also an element of arms competition. When performance breakthroughs are quickly filled or surpassed by others (or the open source community), the windows that monetize new models become narrower. This dynamic encourages businesses to consider alternative business circles. One such moat is integration with its own data (not commoditized). AI, tailored with the rich data of AI companies themselves, is more valuable to the company than ready-made models.

The other is a regulatory or compliance feature. The provider may provide a model that guarantees privacy or compliance for the use of the enterprise. On macroscales, shifts may be displayed when the underlying AI model is as ubiquitous as a database or a web server. service AI peripherals (cloud hosting, consulting, customization, maintenance) are the major revenue generators. Already, cloud providers are benefiting from an increased computing demand for their computing infrastructure (CPUs, GPUs, etc.) to run all of these models. This is a bit like how utilities make profits from the way they use them, even if electrical appliances are commoditized.

Essentially, the economy of AI can reflect the economy of other IT products. Reducing costs and increasing access will encourage widespread use, creating new opportunities built on top of commoditized layers, even when providers in that layer face closer margins or need to innovate or differentiate elsewhere.