AI as the 'New Electricity': Are Foundational Models Destined to Become Commodities?

High risk… high reward?

Yet, as we project the evolution of frontier models, we encounter a series of complications that show the path forward might not be as straightforward as simply scaling up.

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‍Eroding Differentiation

On the surface, LLMs built on transformer architectures seem to be hitting a performance threshold. Despite increasing training data and compute power, further investments in these models are yielding diminishing returns. This trend suggests that foundational models may become less of a market differentiator in the future. In a market where every model offers similar levels of excellence, competition would shift from "who's better" to "who's more cost-effective," similar to the commoditization of electricity.

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Another model or architecture breakthrough (or even entirely new concepts, like liquid neural networks or hybrid concepts) might unlock the next performance plateau (and give the inventor an edge).

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Competitive pressure and free alternatives

The competitive landscape is fiercely crowded, with over 25 public LLMs offered by a diverse array of providers, hinting at an upcoming market consolidation.

Additionally, open-source models are gaining traction, particularly those from Meta, which challenges proprietary models by providing high-quality alternatives at no cost. Let me emphasize: One of the leading AI research companies, Meta, is giving away its models for free.

Why would Meta offer its models for free? While there are plenty of reasons for open sourcing software, a few stick out for Meta:

1. Attract & retain talent: Open-sourcing their models allows Meta to attract talented AI researchers, similar to OpenAI's strategy when they were still "open"
2. Reduce competitor’s moat: If they decide to compete in the foundational model game in the future, this move definitely makes their competitor’s life harder
3. Improve brand marketing and community support: Meta’s move has definitely changed public perception about the company (eventually also beyond the tech-bubble)
4. Improve model development : Meta develops these models for its diverse range of products anyway. Unlike Microsoft or Google, Meta does not need to safeguard its cloud business, which gives it more freedom in sharing its models and benefit from a larger dev community around the model development
5. Maybe most importantly: Go-to-market with a freemium strategy: What if the Llama 2 models are only the freemium predecessors for a later larger (cloud based) commercial model that is coming?

It is also worth noting that Llama’s license agreements explicitly restrict competitors from using it and they don’t allow using Llama models to improve competitor models.

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Low defensibility and lock-in challenges

From a technical user standpoint, switching between LLM APIs is as simple as changing a line of code. However, integrating a new model into existing applications requires thorough re-testing and re-tuning, a task easier said than done (and the source of many new startups typing to tackle LLM validation and ops).

Because of this, companies like OpenAI are working hard to create ecosystems that encourage developer and user loyalty through services that offer higher lock-in, such as custom models, fine-tuning capabilities and marketplaces (e.g., custom GPTs and plugins).

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Specialization vs. Generalization

While the advantage of using state-of-the-art models like GPT-4 for prototyping is undeniable (they are called “frontier” for a reason), many specialized applications can achieve satisfactory results with smaller (and cheaper!) fine-tuned models. This nuance suggests that foundational model providers might be overlooking significant opportunities in the production and inference segments going forward, potentially missing out on valuable revenue and data.

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