In 2019, OpenAI released a model so large it redefined what many thought was possible. The 1.2 billion parameters in GPT-2 seemed enormous—until the following summer, when GPT-3 arrived, over 100 times bigger. From that moment, the AI field entered the era of scaling laws: a relentless push to make models larger, train them on more data, and pump them full of compute power.
Scaling worked wonders, at least for a while. By January 2020, OpenAI researchers demonstrated that AI performance didn’t just improve with scale; it improved predictably, consistently, and dramatically. They published the now-famous scaling laws paper, revealing that pouring more parameters, data, and compute into a model resulted in smooth, steady performance gains. For AI, bigger was unquestionably better.
Google’s DeepMind soon expanded on these insights. In 2022, they added a critical dimension: the idea that models weren’t just about size, but about balance. If GPT-3 had been trained on more text, it might have been much more powerful. DeepMind’s experiments with their own models, including Chinchilla, revealed that undertrained, overgrown models were less efficient than smaller models trained on enough data. The result was a revelation: you don’t need a monster model if you have the right recipe.
This was a turning point. It confirmed that the scaling laws weren’t limited to text-based models—they applied to image generators, protein predictors, even mathematical reasoning systems. The field had a blueprint for predictable improvement, and it felt like the future would be nothing but incremental but steady growth. Just keep scaling, and everything would get better.
Or so we thought.
Is Bigger Still Better?
In the past year, whispers within the AI community suggest the scaling strategy might be losing steam. As researchers pushed their latest models—investing eye-watering sums in GPU clusters and unprecedented training datasets—the returns started to diminish. Some experiments faltered, producing models no better than their predecessors. Rumors surfaced of large-scale training runs that didn’t yield the groundbreaking results the labs had hoped for.
Then came the practical constraints. Training data isn’t infinite, and much of the internet’s high-quality content has already been consumed. The pool of truly novel, valuable data for training the next generation of models is shrinking. Without a fresh influx of diverse, high-quality material, the old scaling strategy could stall.
A New Scaling Paradigm
Rather than training ever-larger models from scratch, researchers are exploring a different kind of scaling: not scaling the model’s size, but scaling its reasoning capacity. OpenAI’s recent advances with “chain-of-thought” reasoning models hint at a future where models improve by thinking longer and harder, rather than simply growing bigger.
Imagine a high school math student. Instead of simply trying to remember more formulas (scaling parameters), they spend more time solving challenging problems step-by-step, refining their thought process. Similarly, OpenAI’s latest models don’t just have more parameters; they’re allowed to “think” more during test time, leveraging greater compute power to tackle increasingly complex tasks.
When OpenAI introduced their latest model, it wasn’t just a marginal improvement. It shattered previous benchmarks, excelling at tasks that were once far out of reach. This shift—from scaling up model size during training to scaling reasoning capabilities during usage—could be the next great leap for artificial intelligence.
What’s Next?
The AI community is at a crossroads. The old playbook—make the model bigger, train it longer—might no longer be enough. Instead, we may be witnessing the birth of a new era in AI research, one where success hinges on how models think, not just how large they are. This shift promises not just incremental gains, but entirely new frontiers of capability.
What we’re seeing is a transformation in our understanding of intelligence itself. It’s not just raw size or scale, but the combination of data, reasoning, and the ability to adapt to new challenges. If this trend continues, the next wave of AI systems could unlock breakthroughs in science, engineering, and even the fundamental ways we interact with technology.
The era of scaling isn’t over—it’s evolving. And that evolution might take us places we never dreamed possible.
Inspired and based on: https://youtu.be/d6Ed5bZAtrM?si=cmPa8EpPJGEH94oO