The rapid evolution of Artificial Intelligence has been nothing short of extraordinary. Over the last few years, we have seen models grow in size, complexity, and capability at an exponential rate. However, recent research and industry observations suggest that the current trajectory of AI development—simply making models larger and feeding them more data—may be hitting a significant wall.
As businesses consider how to integrate AI into their long-term strategies, it is important to understand that the next generation of frontier models might require vastly more investment for only fractional improvements in performance.
The Problem with Scaling: More is Not Always Better
For a long time, the prevailing wisdom in the AI industry was that increasing the number of parameters and the volume of training data would lead to a corresponding increase in intelligence. While this held true for several years, we are now seeing the emergence of the “Law of Diminishing Returns”.
A significant paper from the Massachusetts Institute of Technology (MIT) titled “AI: Why ‘Meek,’ Low-Budget Models Could Soon Match High-Budget Performance”, released on 30th January 2026, highlights a growing concern. The study demonstrates that as top-tier models face decreasing returns to compute scaling, the performance gap between high-budget and more modestly resourced “meek” models is shrinking. We are reaching a point where a model that costs exponentially more to train may only perform a few percentage points better than its predecessor on key benchmarks like the MMLU.
Averaging and the Loss of Detail
The core of the issue lies in how Large Language Models (LLMs) learn. These systems are designed to predict the next most likely word or concept based on vast datasets. In doing so, they often rely on “averaging” the information they receive.
As we discussed in our previous post on 24th October 2025, Why Your AI Chatbot Sounds So Average and Why It Matters, this reliance on the “most likely” outcome leads to a plateau in quality. The recent MIT research reinforces this, pointing out that even when models are trained on large amounts of data, choosing the “best average model” can lead to failures in specific settings where high average performance hides individual errors. This creates a ceiling where performance flattens out regardless of the additional compute power applied.
The Economic Reality for Businesses
In our previous discussions regarding the “Fast, Cheap, Quality” triangle, we noted that you cannot have all three. In the context of AI, we are seeing a shift where “Fast and Quality” is becoming extraordinarily expensive.
For an organisation, this means that the most expensive AI tool on the market may not necessarily provide a significant competitive advantage over a well-optimised, mid-sized model. If the current pathway leads to models that are larger and more expensive but only fractionally better, the focus for businesses should shift from “bigger is better” to “smarter and more efficient is better”.
Strategies for a Stronger AI Posture
Rather than waiting for a “perfect” all-knowing AI that may never arrive, organisations should consider focusing on how they implement current technologies:
- Data Quality Over Quantity: Instead of relying on a model to know everything, consider focusing on high-quality, proprietary data through methods like Retrieval-Augmented Generation (RAG).
- Specialised Models: Consider implementing smaller models that are fine-tuned for specific business tasks, which can be faster and more cost-effective.
- Security-First Integration: As AI models become more integrated into business processes, the surface area for cyber attacks increases. Protecting the data that feeds these models is just as important as the output they produce.
How Vertex Can Help
Navigating the rapidly shifting landscape of AI and cybersecurity requires a balanced approach. At Vertex, we help businesses understand the risks and rewards of new technologies, ensuring that your path toward innovation does not compromise your security posture.
If you are looking for expert guidance on how to secure your AI implementations or wish to discuss a tailored cybersecurity strategy for your organisation, please contact the team at Vertex Cyber Security for more information.