ChatGPT vs. DeepSeek: The Evolution of AI and the Cost of Innovation
Artificial intelligence is evolving rapidly. From established models like ChatGPT to emerging challengers like DeepSeek, the race is no longer just about capability—it’s about efficiency. While both systems excel at generating human-like responses, their underlying architecture and approach to openness set them apart.
DeepSeek’s recent innovations—Mixture of Experts (MoE), Memory-Level Attention (MLA), and PTX optimisation—demonstrate how AI is becoming leaner and more cost-effective. However, one fundamental truth in technology is often overlooked: the cost of being first is always higher than the cost of following.
What Makes DeepSeek Different?
DeepSeek has implemented three key innovations to enhance performance and efficiency:
Mixture of Experts (MoE) – Instead of activating all parameters simultaneously, MoE selectively engages only the necessary parts of the model for each task. This allows the AI to use less energy while maintaining high-quality responses.
Memory-Level Attention (MLA) – Traditional AI models require significant memory to process and store context. MLA introduces memory compression techniques that reduce power consumption while preserving accuracy over long conversations.
PTX Optimisation – AI models rely heavily on GPUs, which have fixed architectures. DeepSeek optimises GPU execution at the software level, ensuring maximum efficiency and reducing operational costs.
These innovations improve performance while dramatically cutting costs. But why weren’t they implemented earlier?
The Cost of Innovation vs. The Cost of Replication
Pioneering new technology is always the most expensive route. When OpenAI developed ChatGPT, it wasn’t merely refining an existing model—it was establishing an entirely new category of AI-powered interactions. This meant:
Massive R&D investment – Every breakthrough required thousands of hours of research, experimentation, and data processing.
Infrastructure scaling – OpenAI had to build and optimise vast computing clusters to support ChatGPT, long before efficiency became a priority.
Market validation risks – Nobody knew whether AI chatbots would achieve mass adoption. OpenAI took on the financial risk of proving their value.
In contrast, DeepSeek and newer AI companies can build on these foundational advances. They have the advantage of hindsight, allowing them to optimise where OpenAI had to experiment. They do not need to bear the cost of early-stage failures—only to refine and improve existing methodologies.
Open vs. Closed: A Key Distinction
A significant difference between ChatGPT and DeepSeek is their approach to openness.
ChatGPT (OpenAI): While OpenAI initially embraced open-source principles, later models like GPT-3 and GPT-4 are not open-source. Access is restricted via APIs and a subscription model, with no public release of model weights or training data.
DeepSeek: DeepSeek has taken a more open approach, releasing some of its models with open weights. DeepSeek-Coder, their code generation model, and DeepSeek LLM provide transparency and flexibility for developers.
This highlights another truth about innovation: first movers must commercialise their work to justify their substantial upfront investment, whereas later entrants can afford to be more open because they are optimising rather than pioneering.
History Repeats Itself
This cycle has played out time and again in the tech industry:
Apple vs. Android – Apple revolutionised the smartphone market. Android followed with an open-source model that leveraged Apple’s innovations while optimising costs.
Tesla vs. the EV industry – Tesla spent billions proving that electric vehicles could be viable. Now, legacy automakers are capitalising on Tesla’s groundwork to produce EVs more efficiently.
SpaceX vs. the aerospace industry – SpaceX pioneered reusable rockets at great expense. Now, other aerospace companies are copying the model at a fraction of the cost.
The Bigger Picture
The difference between ChatGPT and DeepSeek isn’t just about efficiency—it’s about the economics of innovation. OpenAI, as the trailblazer, absorbed the highest costs to bring AI into the mainstream. DeepSeek, like other latecomers, has the luxury of standing on these achievements, refining efficiency without the same pioneering expenses.
It’s easy to look at newer models and assume they’re simply “better” or “cheaper.” But in reality, they are only possible because of the heavy lifting done by those who came first. Innovation isn’t just about who builds the best product—it’s about who pays the cost to make it possible in the first place.
