Will AI Models Become Commodities? (The Case for Aggregators)

A Familiar Pattern Is Emerging

In the early days of cloud computing, AWS had a massive lead. Then Azure caught up. Then Google Cloud. Today, the core services, compute, storage, databases, are nearly identical across all three. The products became commodities, and competition shifted to pricing, ecosystem, and convenience.

The same pattern is starting to appear in AI models. And if it continues, it will reshape how people buy and use AI tools.

The Quality Gap Is Shrinking

In 2023, GPT-4 was clearly the best general-purpose AI model. Nothing else came close. By 2024, Claude 3.5 Sonnet and Gemini 1.5 Pro had narrowed the gap significantly. By early 2026, we have GPT-5, Claude (latest version), Gemini Ultra, and strong competitors from Mistral, DeepSeek, and others.

The differences between these models still exist, but they're getting smaller. Ask any of the top five models a general knowledge question, and the answers are roughly equivalent. Ask them to write an email, summarize a document, or explain a concept, and you'd struggle to consistently identify which model produced which output.

Specialized tasks still show meaningful differences. Claude is better at long-document analysis. GPT-5 has stronger mathematical reasoning. Gemini handles multimodal input more naturally. But for the 80% of tasks that most people use AI for, the top models are increasingly interchangeable.

What Commoditization Means

When products become commodities, several things happen:

Price becomes the primary differentiator. If the product is essentially the same across providers, customers buy the cheapest option. We're already seeing this with the popularity of open-source models and cheaper API pricing from newer providers.

Distribution matters more than product quality. When every model is "good enough," the companies that make their models easiest to access and use will win. This is why OpenAI invested so heavily in ChatGPT's consumer experience and why Google bundled Gemini into its entire product ecosystem.

Bundling and aggregation become attractive. Cable TV bundles exist because no single channel is worth a standalone subscription for most viewers, but a bundle of 200 channels is. If no single AI model is worth $20/month for most users, a bundle of 350+ AI models for $10/month (or $8/month billed annually) starts to make more sense.

The Case for Aggregators

If models are becoming commodities, the value shifts from the model itself to the layer that sits between users and models. This is the aggregator layer, and it offers several advantages:

Model selection. Instead of committing to one provider, users can choose the best model for each task. Need careful analysis? Use Claude. Need speed? Use a smaller, faster model. Need image understanding? Use Gemini. One subscription covers all use cases.

Price optimization. Aggregators can route queries to the most cost-effective model that meets the quality threshold. A simple factual question doesn't need GPT-5; a smaller model handles it fine at a fraction of the cost. This keeps user costs down while maintaining quality.

Insurance against obsolescence. If you're subscribed to ChatGPT and a competitor releases a better model, you're stuck or you have to switch. With an aggregator, the new model simply appears as another option. You're never locked into a model that's fallen behind.

Admix is building on this thesis. By offering 350+ AI models through a single interface, they let users treat AI models as interchangeable commodities while still accessing each model's specific strengths. The user doesn't need to care about the model provider. They just need the best answer.

The Case Against Commoditization

Not everyone agrees that models will become commodities. The counterarguments are worth considering:

Frontier capabilities keep diverging. While the average quality is converging, the cutting edge keeps moving. Each new model release introduces capabilities that competitors don't have yet. If these frontier features matter, then the latest model from each provider still has a unique value.

Ecosystem lock-in is real. OpenAI has custom GPTs, persistent memory, and deep integrations with Microsoft products. These ecosystem features create switching costs that prevent commoditization even if the core model is no longer uniquely superior.

Data moats matter. Models trained on different data have different knowledge and capabilities. Google's models benefit from Search data. Meta's models benefit from social media data. These data advantages may prevent full convergence.

Where Things Are Heading

My read on the situation: the base capabilities of AI models will continue to converge. The top 5-10 models will be roughly equivalent for most tasks. But frontier capabilities and specialized features will continue to differentiate them.

This creates a two-tier market. For most users doing everyday tasks, models are already commodities and the aggregator model makes economic sense. For power users and enterprises needing cutting-edge capabilities, individual provider subscriptions still make sense.

The interesting question is how large each segment is. If 80% of users are doing "commodity" tasks and 20% need frontier capabilities, aggregators win the volume game even if they don't capture the high end.

Historical Parallels

This pattern has played out in multiple industries:

• Airline seats: Most passengers book the cheapest flight. Aggregators like Kayak and Google Flights won by making comparison easy.
• Insurance: Comparison sites captured distribution by showing that most policies are interchangeable.
• Music streaming: Spotify won partly by offering everything in one place. Most people don't care which label distributes a song.

In each case, the product became commoditized first, and then distribution and aggregation became the competitive battleground. AI models appear to be on the same trajectory.

What This Means for Users

If you're currently paying for a single AI subscription, it's worth testing whether an aggregator gives you equivalent quality at a lower price. If you're paying for multiple subscriptions, the math almost certainly favors consolidation.

The AI provider you subscribe to matters less than it did two years ago. What matters is having access to the right model for the right task. That's the value proposition of aggregation, and it gets stronger as models become more similar.

Try Admix free and run the same prompts through different models. If you can't tell a meaningful difference for your typical tasks, you have your answer about commoditization.