There is No Moat for Frontier AI Labs

Introduction

A couple of years ago, big AI labs like OpenAI, Anthropic, Google DeepMind, and Meta seemed to have a big lead. They had cutting-edge research, big money, and top-tier AI researchers. But AI moves so fast that it’s hard for any group to hold on to that lead forever.

A year ago, open-source models were behind the big labs by six to eight months. Now, DeepSeek R1 has practically closed that gap to just a few weeks. Once a breakthrough happens, others can replicate it faster than you’d think. The only way to stay truly ahead is to come up with step change algorithmic innovation, like what happened when the Transformer paper first came out. Simply scaling up a model isn’t enough to build a strong moat.

Scaling Alone is Not a Defensible Strategy

Frontier labs have relied on massive datasets and huge compute clusters to make extremely large models. It can definitely lead to breakthroughs. For example, GPT-4 showed a big jump in capabilities thanks to more data and more parameters.

But once that big model is made, other teams can catch up at a fraction of the original cost by using model distillation, RL, training optimization, specialized fine-tuning, or compression. Distillation, for example, uses a larger teacher model to train and teach a smaller one with similar performance. As soon as one lab releases a big model, smaller groups can follow behind and copy or improve upon it. In weeks or a few months, that big lead is gone.

DeepSeek R1 has shown that through efficient training techniques, reinforcement learning , and model distillation, comparable reasoning capabilities can be achieved at a fraction of the cost. This reveals a crucial insight: mere computational scaling without algorithmic innovation does not create a defensible business model. While well-funded frontier labs can build increasingly large and sophisticated models through computational power alone, smaller players can leverage model distillation to create similarly capable smaller models much more cost-effectively.?True competitive moats in AI appear to emerge primarily from fundamental algorithmic breakthroughs, such as the transformer architecture, which occur infrequently.

Early Mover Advantage Won’t Last

ChatGPT was the first major AI system to capture the public’s attention in late 2022. Because it was first, OpenAI gained a huge user base, a lot of brand recognition, and a real source of revenue. But being first doesn’t mean you will dominate forever. Others in the market, especially open-source contributors, have been moving quickly. Meta's open-source LLaMA models exemplify this effect, creating downward pricing pressure that forced OpenAI to reduce GPT-4 inference costs by over 90% within 18 months. DeepSeek R1 is poised to drive similar cost reductions for reasoning models.

The Cost of Intelligence Is Dropping Fast

It used to be super expensive to run advanced AI: you needed teams of PhDs, lots of infrastructure, and enormous compute budgets. That’s still true if you want to push the absolute cutting edge, but some things are changing:

• Model Distillation: Compresses big models into smaller ones.
• Reinforcement Learning (RL): Uses reward signals to guide and improve model performance, helping it learn more effective or aligned behaviors without massive additional dataset requirements.
• Low-Rank Adaptation (LoRA): Lets you fine-tune big models quickly, for less money.
• Quantization: Uses fewer bits for model parameters, making things cheaper and sometimes faster.

These methods allow more people to run powerful AI models on smaller hardware—even on a laptop. That’s driving down the cost of AI and bringing more people into the space.

Who Makes Money?

If no one can keep a moat by just building bigger and bigger models, who profits from AI?

1. Hardware Providers (e.g., Nvidia): At least in the near term, they’ll make money because training and running big models requires specialized hardware. Over time, competition from new chip makers might reduce margins.
2. Cloud Providers: They can sell Model-as-a-Service and profit from the increased demand for compute. Whether it’s hosting a proprietary model or an open-source one, big cloud platforms can benefit from this surge.
3. AI-Native Applications: Companies that build new kinds of software on top of AI stand to benefit the most. It’s similar to the smartphone revolution—companies like Uber and Airbnb reshaped whole industries by using the unique features of mobile devices. We haven’t yet seen the groundbreaking apps that fully leverage next-generation AI.

Frontier Labs Are Running in Circles

These major labs pour money into building the next bigger model, only to have open-source or smaller labs distill or replicate that achievement soon after. That cycle repeats. Meanwhile, the huge training costs add up. If it takes $100 million to train your next model, but someone else can replicate 80-90% of the performance for a fraction of that, your business is always under threat.

AI will be Democratized

We’ve seen many examples where open-source efforts quickly close the gap with bigger labs. Once code or model weights are available—either released intentionally or leaked—global communities spring into action, refine methods, compress model sizes, and find new ways to make things faster or cheaper. This constant wave of open-source innovation pushes down AI’s cost curve.

We’re headed toward a world of cognitive abundance, where powerful AI is widely accessible. Frontier labs can jump ahead for a short time, but it’s never a stable lead. In many ways, that’s good for society because it prevents any one group from controlling all the benefits of AI.