It’s the question on everyone’s minds and lips: Are we in an AI bubble?
It’s the wrong question. The real question is: Which AI bubble are we in, and when will each one burst?
The debate over whether AI represents a transformative technology or an economic time bomb has reached a fever pitch. Even tech leaders like Meta CEO Mark Zuckerberg have acknowledged evidence of an unstable financial bubble forming around AI. OpenAI CEO Sam Altman and Microsoft co-founder Bill Gates see clear bubble dynamics: overexcited investors, frothy valuations and plenty of doomed projects – but they still believe AI will ultimately transform the economy.
But treating “AI” as a single monolithic entity destined for a uniform collapse is fundamentally misguided. The AI ecosystem is actually three distinct layers, each with different economics, defensibility and risk profiles. Understanding these layers is critical, because they won’t all pop at once.
Layer 3: The wrapper companies (first to fall)
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The most vulnerable segment isn’t building AI – it’s repackaging it.
These are the companies that take OpenAI’s API, add a slick interface and some prompt engineering, then charge $49/month for what amounts to a glorified ChatGPT wrapper. Some have achieved rapid initial success, like Jasper.ai, which reached approximately $42 million in annual recurring revenue (ARR) in its first year by wrapping GPT models in a user-amiable interface for marketers.
But the cracks are already showing. These businesses face threats from every direction:
Feature absorption: Microsoft can bundle your $50/month AI writing tool into Office 365 tomorrow. Google can make your AI email assistant a free Gmail feature. Salesforce can build your AI sales tool natively into their CRM. When large platforms decide your product is a feature, not a product, your business model evaporates overnight.
The commoditization trap: Wrapper companies are essentially just passing inputs and outputs, if OpenAI improves prompting, these tools lose value overnight. As foundation models become more similar in capability and pricing continues to fall, margins compress to nothing.
Zero switching costs: Most wrapper companies don’t own proprietary data, embedded workflows or deep integrations. A customer can switch to a competitor, or directly to ChatGPT, in minutes. There’s no moat,no lock-in,no defensibility.
The white-label AI market exemplifies this fragility. Companies using white-label platforms face vendor lock-in risks from proprietary systems and API limitations that can hinder integration. These businesses are building on rented land, and the landlord can change the terms, or bulldoze the property, at any moment.
The exception that proves the rule: Cursor stands as a rare wrapper-layer company that has built genuine defensibility. By deeply integrating into developer workflows, creating proprietary features beyond simple API calls and establishing strong network effects thru user habits and custom configurations, Cursor has demonstrated how a wrapper can evolve into somthing more significant. But companies like Cursor are outliers,not the norm - most wrapper companies lack this level of workflow integration and user lock-in.
Timeline: Expect significant failures in this segment by late 2025 through 2026, as large platforms absorb functionality and users realize they’re paying premium prices for commoditized capabilities.
Layer 2: Foundation models (the middle ground)
The companies building LLMs – OpenAI, Anthropic, Mistral – occupy a more defensible but still precarious position.
Economic researcher richard Bernstein points to OpenAI as an example of the bubble dynamic, noting that the company has made around $1 trillion in AI deals, including a $500 billion data center buildout project, despite being set to generate only $13 billion in revenue. The divergence between investment and plausible earnings “certain
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The artificial intelligence (AI) landscape is experiencing rapid growth, but success isn’t guaranteed for all companies involved. Understanding the current market dynamics and avoiding investment in unsustainable “bubbles” is crucial for long-term viability, according to a recent VentureBeat article.
The AI Revolution and Market Bubbles
The AI revolution is demonstrably underway, with increasing investment and adoption across various industries. However, not all companies capitalizing on this trend will succeed; discerning between genuine innovation and speculative bubbles is critical.
Val Bercovici, CAIO at WEKA, highlights the importance of understanding where a company operates within the AI ecosystem and identifying potential bubble risks to avoid becoming a casualty of the unavoidable shakeout.
WEKA and its Role in Data Infrastructure
WEKA is a data platform company focused on providing high-performance,scalable data infrastructure for AI and other data-intensive workloads.
The company positions itself as enabling organizations to accelerate AI initiatives by providing the necesary data infrastructure to handle the massive datasets required for training and deploying AI models. WEKA’s technology is designed to address the challenges of data movement and access, which are often bottlenecks in AI workflows.
In November 2023, WEKA announced a $100 million Series D funding round led by Green lake, bringing the company’s total funding to $285 million. WEKA Press Release
Understanding the Importance of Data Infrastructure for AI
Data infrastructure is the foundation upon which AI applications are built. It encompasses the hardware, software, and networking components required to store, process, and access the large datasets needed for AI model training and inference.
Effective data infrastructure is essential for several reasons: it enables faster training times, supports larger and more complex models, and ensures data accessibility for AI developers and data scientists. Without robust data infrastructure, organizations may struggle to realize the full potential of their AI investments.
According to a 2023 report by Grand View Research, the global AI infrastructure market was valued at USD 29.31 billion in 2022 and is projected to reach USD 192.84 billion by 2030, growing at a CAGR of 26.1% from 2023 to 2030. Grand View Research – AI infrastructure Market Analysis
Identifying and Avoiding AI Bubbles
An AI bubble occurs when investment in AI companies exceeds their underlying value, driven by hype and speculation rather than fundamental business metrics.
Several factors can contribute to AI bubbles, including excessive venture capital funding, unrealistic expectations about AI’s capabilities, and a lack of clear business models. Companies operating in these bubbles may experience rapid growth followed by a sharp decline when the hype subsides.
To avoid investing in AI bubbles, investors and companies should focus on businesses with strong fundamentals, including a clear value proposition, a lasting business model, and a demonstrated ability to generate revenue. It’s also important to assess the competitive landscape and identify companies with a differentiated offering.
A 2024 report by PitchBook indicates that while AI funding remains strong, investors are becoming more selective, focusing on companies with proven traction and clear paths to profitability. PitchBook – AI Funding Trends 2024
val Bercovici is CAIO at WEKA.
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