The AI Bubble Isn't One Big Burst – It's a Cascade: Understanding the Three Layers of AI Risk

The question on everyone's lips right now: Are we in an AI bubble? While many are quick to point to soaring valuations and massive investments, the reality, according to recent analysis, is far more nuanced. It's not a single, monolithic bubble poised for one dramatic pop. Instead, the AI landscape is comprised of three distinct layers, each with its own unique risk profile and timeline for potential disruption. Understanding these layers is crucial for anyone building, investing in, or simply trying to comprehend the future of artificial intelligence.

Layer 3: The Wrappers – First to Feel the Pinch

The most vulnerable segment of the AI ecosystem consists of companies that are essentially repackaging existing AI capabilities. These are the businesses that leverage APIs from major AI labs like OpenAI, add a user-friendly interface and some clever prompt engineering, and then charge a premium for what often amounts to a glorified wrapper around a powerful foundation model. While some, like Jasper.ai, have seen rapid initial success, their position is inherently fragile.

Why are these companies so vulnerable?

• Feature Absorption: Large tech giants like Microsoft and Google can easily absorb these functionalities into their existing, widely-used products. Imagine your $49/month AI writing tool becoming a free feature in Office 365 overnight.
• The Commoditization Trap: As foundation models become more powerful and accessible, the unique value proposition of these wrappers diminishes. Their reliance on external models means they can lose value rapidly if those models are updated or improved.
• Zero Switching Costs: Without deep integrations, proprietary data, or significant workflow lock-in, users can easily switch to competitors or directly to the underlying AI models, leaving these wrappers with no defensible moat.

The timeline for significant failures in this layer is estimated to be between late 2025 and 2026, as users begin to question the value of paying for commoditized AI features.

Layer 2: Foundation Models – The Precarious Middle Ground

This layer includes the companies actually building the large language models (LLMs) such as OpenAI, Anthropic, and Mistral. These players possess more defensible technological moats, including expertise in model training, access to significant compute power, and performance advantages. However, their position is far from unassailable.

Challenges for Foundation Model Providers:

• Sustainability of Moats: The core question is whether their current advantages can remain sustainable as AI models converge in capabilities and potentially commoditize.
• Engineering as a Differentiator: The competitive edge will increasingly shift to inference optimization and systems engineering. Companies that can efficiently scale AI inference will likely lead, not just those with the largest training runs.
• Circular Investment Dynamics: Investments can create a self-reinforcing loop. For example, chip manufacturers investing heavily in AI companies that then purchase their chips could artificially inflate demand.

This layer is expected to see significant consolidation between 2026 and 2028, with a few dominant players emerging as smaller competitors are acquired or shuttered.

Layer 1: Infrastructure – Built to Last?

Contrarian to the immediate hype, the AI infrastructure layer – encompassing companies like Nvidia, data centers, cloud providers, and AI-optimized storage – is considered the least bubbly segment. While the sheer volume of investment here is staggering, infrastructure has a unique characteristic: it retains value regardless of which specific AI applications succeed.

Why Infrastructure is More Resilient:

• Enduring Value: Just as fiber optic cables laid during the dot-com bubble eventually powered the internet as we know it, today's AI infrastructure will power whatever AI applications ultimately emerge, even if today's dominant ones falter.
• Real Demand: Companies like Nvidia are seeing immense, tangible demand for their hardware, reflecting genuine, long-term investments in AI capabilities.
• Fundamental Innovation: Modern AI infrastructure goes beyond simple storage, integrating the entire memory hierarchy to support complex inference workloads, representing a fundamental architectural shift.

While there might be some short-term overbuilding or inefficiencies (around 2026), the long-term value retention of this layer is expected to be robust as AI workloads continue to expand over the next decade.

The Cascade Effect: A Roadmap for the Future

Instead of a single, dramatic crash, the AI boom is more likely to unfold as a cascade of failures, starting with the most vulnerable wrapper companies. This will be followed by consolidation in the foundation model layer, and finally, a normalization of infrastructure spending that remains elevated in the long run.

For builders and companies operating in the AI space, the key takeaway is to avoid being a mere wrapper. The real advantage lies not just in accessing LLMs, but in owning the user experience, building deep workflow integrations, and establishing a strong distribution moat. The AI revolution is undeniable, but survival will depend on understanding which layer of the AI ecosystem you inhabit and building for long-term resilience.