Leading vs. Following: Innovations in Reasoning AI

In the rapidly evolving world of Artificial Intelligence, there’s a fascinating dynamic between being a trailblazer and the art of the “second mover”—that is, leveraging existing breakthroughs to drive efficiency and innovation. Recent developments in reasoning AI models illustrate this perfectly.

The Leader’s Journey

Leading the charge in reasoning AI, OpenAI’s groundbreaking models O1 and O3 exemplify the pinnacle of innovation in this field. Unveiled in September 2024, O1 marked a historic shift by introducing a “chain-of-thought” process that enables the model to deliberate internally before answering—mimicking human-like reasoning to tackle complex tasks in mathematics, coding, and science. This transformative achievement, however, came at an extraordinary cost in terms of compute power and financial investment, underscoring the steep price of pushing AI’s frontiers. Building on this foundation, OpenAI launched O3 in December 2024, a next-generation reasoning model that further refines these capabilities. O3 has shattered performance benchmarks—from scoring impressively on expert-level science tests to achieving top competitive programming ratings—demonstrating that even higher expenditures can yield leaps in accuracy and problem-solving prowess. Together, O1 and O3 stand as monuments to the state-of-the-art in reasoning AI, driven by colossal investments that continue to redefine the limits of machine intelligence.

The Second Mover Advantage

While first movers like OpenAI have set high benchmarks with their resource‑intensive models, second movers are redefining the game by refining, optimizing, and drastically cutting costs. DeepSeek R1, for example, leverages innovative training techniques and a mixture-of‑experts architecture to deliver performance comparable to premium models—but at only about $5.6 million in GPU costs using 2,048 Nvidia H800 GPUs over 55 days. Similarly, NovaSky’s Sky‑T1-32B‑Preview demonstrates that top‑tier reasoning can be achieved in just 19 hours on 8 H100 GPUs for under $450, thanks to meticulous data curation and fine‑tuning of open‑source models. Adding to these advances, the s1 project from SimpleScaling employs “budget forcing” and test‑time scaling on a modest 1,000‑example dataset to boost reasoning accuracy by up to 27% on competition benchmarks—all while dramatically reducing compute usage. s1’s training was performed in about an hour using 16 H100 GPUs which cost approximately $50. These examples illustrate how second movers are not only matching the capabilities of their high‑cost predecessors but are doing so more efficiently, thereby democratizing access to advanced AI and driving a new era of innovation.

Hands-On Exploration

Not to be outdone, I’ve managed to replicate the process used to develop s1 to create my own reasoning model in just 40 minutes at a cost of only $0.40 using 2× RTX 3090s 😂

Full disclosure: I used Qwen2.5-0.5B, not the larger Qwen2.5-32B.

The results may be comical, but even at such a small scale, the base model exhibits notable reasoning capabilities. Curious to see for yourself? You can clone and test the model on HuggingFace and review the training results here. This hands-on experimentation is a perfect example of the second mover strategy—taking established advancements and pushing them further through creative, cost-effective methods.

Broader Context

This approach ties into a larger narrative about technological evolution. While first movers pave the way with groundbreaking research, second movers refine and optimize. This cycle of innovation is essential for the sustainable growth of AI.

The journey from costly, resource-intensive models to highly efficient, second mover implementations demonstrates that innovation doesn’t always have to break new ground from scratch. Sometimes, by building upon the successes of the past and fine-tuning existing technologies, we can achieve remarkable progress—both in performance and cost-effectiveness.

As the AI industry continues to evolve, the interplay between leadership and adaptation will remain a critical driver of progress. Whether you’re a pioneer or a savvy optimizer, there’s a role to play in shaping the future of Artificial Intelligence.

For more insights on scaling and efficiency in modern AI, check out Simple Scaling to read more about the development of S1.