# The AI Cambrian Explosion or a Transformer Monoculture?
The last few years in artificial intelligence have felt like a whirlwind. From ChatGPT writing poetry to Midjourney creating photorealistic art from a simple prompt, the pace of innovation is staggering. Many have likened this period to a “Cambrian Explosion”—a moment of rapid, diverse evolutionary expansion. On the surface, this analogy holds. We see an incredible proliferation of AI *applications* branching into every conceivable domain.
But as a practitioner in the field, I urge us to look deeper, beneath the application layer. When we examine the architectural bedrock upon which this explosion is built, the picture changes dramatically. What we find isn’t a diverse ecosystem of competing designs, but a startlingly uniform landscape. We are not in a Cambrian Explosion; we are in the age of a **Transformer Monoculture**.
### The Unseen Homogeneity
The transformer architecture, first introduced in the seminal 2017 paper “Attention Is All You Need,” is the engine driving nearly every major breakthrough we see today. Large Language Models (LLMs) like GPT-4 and Llama 3 are transformers. Vision Transformers (ViTs) now dominate computer vision tasks. Even the diffusion models behind generative art are heavily reliant on transformer components.
This architectural dominance is not accidental. The transformer’s core innovation—the self-attention mechanism—proved exceptionally effective at processing sequential data and, crucially, was highly parallelizable. This allowed it to scale to unprecedented sizes, and in deep learning, scale is often a direct path to capability. The result is an incredibly powerful and versatile architecture.
The standardization around a single, powerful architecture has its benefits:
* **A Unified Ecosystem:** Researchers and engineers share a common language. Tools like PyTorch, TensorFlow, and libraries like Hugging Face are heavily optimized for transformers, accelerating development.
* **Compounding Gains:** Improvements made to the core architecture by one research lab can be quickly adopted and built upon by others, creating a powerful feedback loop.
* **Focus on Application:** With the foundational model architecture largely “solved,” companies can focus their resources on fine-tuning and building innovative products on top of this stable base.
### The Risks of a Monoculture
However, this homogeneity creates significant, often-understated risks. In agriculture, a monoculture—like planting only one variety of potato—is incredibly efficient until a single blight arrives to wipe out the entire crop. The same principles apply to our technological ecosystems.
**1. Architectural Fragility and Stifled Innovation**
The entire field is placing a massive bet on the transformer and its scaling laws. What if a fundamental limitation is discovered? What if we find a class of problems where the attention mechanism is inherently inefficient or flawed? With so much research funding and talent focused on iterating on this single design, we risk creating architectural blind spots. Truly novel, non-transformer ideas struggle to get the funding and attention needed to mature, potentially starving the next paradigm-shifting architecture of oxygen before it can even take root.
**2. The Massive Barrier to Entry**
Transformers are data-hungry and computationally voracious. Training a state-of-the-art foundational model requires access to supercomputing-levels of GPUs and costs hundreds of millions of dollars. This reality has centralized foundational AI development in the hands of a few hyperscale tech companies. This resource barrier prevents smaller, more agile teams from competing at the architectural level, concentrating power and narrowing the range of perspectives that shape the future of AI.
**3. Diminishing Returns**
We may already be seeing the diminishing returns of simply scaling up existing transformer models. Each leap in capability requires an exponential increase in compute and data, an unsustainable trajectory. True, long-term progress will require not just bigger models, but *better* and more efficient architectures.
### Cultivating Architectural Biodiversity
The solution isn’t to abandon the transformer. It is an undeniably powerful tool that will remain a cornerstone of AI for years to come. Instead, we must actively cultivate **architectural biodiversity**.
This means encouraging and funding research into fundamentally different approaches. We’re seeing promising sparks in areas like State Space Models (e.g., Mamba), which offer a different approach to sequence modeling with potential for greater efficiency. Graph Neural Networks, neuro-symbolic methods, and other hybrid models all represent different evolutionary paths.
The “Cambrian Explosion” of AI applications is real and exciting. But to ensure the long-term health, resilience, and continued progress of our field, we must ensure it is built on a rich and diverse foundation. The next truly revolutionary leap in AI may not come from a bigger transformer, but from a completely different architecture we’ve yet to properly explore. It’s our responsibility to ensure we’re planting more than one kind of seed.
This post is based on the original article at https://techcrunch.com/2025/09/23/do-startups-still-need-silicon-valley-hear-from-the-founders-and-funders-challenging-old-assumptions-at-techcrunch-disrupt-2025/.
