A deep analysis with Lablup CEO Shin Jungkyu on the meaning and background of GPT-OSS — the open-source LLM model that OpenAI suddenly released in August 2025 — its actual technical characteristics, and the changes it brings to the AI ecosystem and industry at large. The conversation covers the global ripple effects of the open release, the specific architecture and performance of GPT-OSS, and the rapid transformation of programming driven by AI, delivered through firsthand voices and experiences — essential insights for anyone working with AI.

1. The Meaning and Background of the GPT-OSS Release

The conversation begins with the news that OpenAI released an open-source language model called GPT-OSS in August 2025. The panelists note that "the last time OpenAI opened a model was during the GPT-2 era, and they had developed exclusively in closed form for years since then," and discuss the background and significance of this release.

CEO Shin Jungkyu explains two major factors:

"One was the enormous socioeconomic pressure to release models openly, and the other was that competitors like DeepSeek had preemptively released their technical recipes alongside recent advances in Omni-series reasoning models, diluting OpenAI's technological edge."

In particular, as competing models like DeepSeek opened up their advanced reasoning capabilities and training process know-how, OpenAI and NVIDIA took hits in terms of market positioning and capital raising. Ultimately, the context emphasizes that open source became essential for differentiation, technological leadership, and investment attraction.

Internally, company members are estimated to have held opinions like:

"I want to release the model openly. OpenAI is a peculiar organization built around ideals, and the management's persistent focus on closed services must have significantly impacted morale."

While there were multiple triggers for OpenAI to proceed with early release, market conditions and internal strategy alignment somewhat delayed the announcement.

2. Adoption and Impact of the GPT-OSS Open Model: The Sovereign AI Perspective

As developers on Facebook, Twitter, and other platforms began stating that "this model will play an important role in Sovereign AI solutions," discussion proceeds on the impact GPT-OSS will have on each country's domestic AI and enterprise LLM development.

CEO Shin Jungkyu evaluates the fact that actual performance, architecture, training methods, and recipes (know-how) were fully disclosed down to the format, code, and architecture level as "unprecedented confidence and transparency."

"Unlike China's open-weight releases, this time they used the Apache license with truly no commercial restrictions. The fact that they revealed this much of their core means OpenAI understands this model's limitations."

• Training resources: The fact that the model can be trained in about 44 days on 2,000 H100 GPUs
• Code and format: A PyTorch-based codebase that isn't overly complex yet reflects real-world experience
• Degree of openness: While China has recently restricted open-source licenses with "country-of-use limitations," OpenAI has secured unprecedented leverage

These factors create opportunities for countries like Korea that are preparing to pursue Sovereign AI.

3. Technical Characteristics of GPT-OSS and Ecosystem Evolution

3.1 The MoE (Mixture of Experts) Paradigm

As of 2025, the biggest trend in AI model architecture is the MoE (Mixture of Experts) structure. GPT-OSS also comes in two versions — 120B and 20B — reflecting various architectural experiments.

"This model isn't excessively deep in layer count. Instead, it adjusts structure through the number of experts — small sub-networks. Recent models like DeepSeek, Qwen, and Kimi K2 share this strategy."

Here, "expert" doesn't mean specialists in math or language but rather "small models deployed across GPUs, where specific experts are partially activated for specific inputs to produce combined output values."

"In production services, this has taken the form of expert farms, where GPU resource allocation efficiency is the key."

3.2 Serving Infrastructure Innovation: GPU Farm, Composable Architecture

CEO Shin Jungkyu emphasizes how AI model serving has been revolutionized over the past 1-2 years.

• From an era of loading the entire model on a single GPU
• To separating into prefill GPU farms (for token preprocessing) and decode GPU farms (for token generation) — Composable Architecture has become mainstream.

"Thanks to structural changes from context caching and similar innovations, separating GPU farms makes whole-system optimization much easier and has also significantly impacted hardware interconnect design."

Details are shared on hardware requirements from open AI models and actual cloud/on-premise infrastructure changes, including NVIDIA's latest Blackwell architecture and Rubin and Feynman developments.

3.3 Rapid Spread of New Technologies: Softmax, Attention Sink

GPT-OSS also applies innovations like modified softmax functions — specifically Attention Sink (Quiet Attention).

"Mathematically it's simple, but in practice, adoption will spread rapidly. When a major player implements it, everyone follows."

Additionally, technical elements like local attention structures are expected to quickly shift toward data-centric agendas.

3.4 Explosion of Model Development and Open-Source Market

In just a few weeks, 50 foundation models were released in rapid succession, signaling the arrival of a "Model Cambrian Explosion."

"When 10 foundation models come out, 100 specialized models and 10,000 fine-tuned models follow. On ecosystems like Hugging Face, they've already surpassed 2 million."

This is the backdrop for intensifying technical competition and the catch-up game.

4. Economic Perspective: BEP, Coding, Multimodal, and Market Changes

The business perspective on AI industry changes increasingly takes center stage.

4.1 BEP (Break-Even Point) and AI Business

"The big change is that model companies are starting to see BEP (Break-Even Point) for the first time."

When BEP becomes visible, the attitudes of developers and executives fundamentally change. Currently, the top revenue source is code generation tokens, and soon multimodal (image, voice, video) data will become a new revenue stream for AI companies.

"Anthropic's API revenue alone jumped 20-30x in three months. As the multimodal market opens up and more companies reach BEP, especially infrastructure and chip companies like NVIDIA, growth will accelerate further."

4.2 Surge in Model and Token Scale

Current frontier labs are generally capping serviceable models at around 200B to 250B parameters, with internal distillation estimated to exceed 1T (one trillion) parameters.

5. Fundamental Changes in Development Culture and Work Driven by AI

CEO Shin Jungkyu reflects on his recent programming experience, candidly sharing how much the introduction of AI-based coding tools has changed the very nature of coding.

"I worked on projects in different ways in February, April, and June. After using ChatGPT, Copilot, and eventually Claude Code, I realized each time that the 'definition of coding' itself was changing."

He once thought coding was an "art," but as the latest LLMs explosively accelerate programming automation:

"Changes as drastic as the transitions from vacuum tubes to OMR cards to mouse cursors are happening on a monthly basis."

Ultimately, he says "these changes are irreversible, so the only option is to experience and enjoy them before anyone else." He reveals that at his company, all employees — including non-developers — are actively leveraging AI's productivity and leverage through AI coding hackathons, documentation automation, and presentation material generation.

"I'm not depressed at all these days. These changes in the world are irreversible, so I'm enjoying the joy of having an army that does work for me, as long as I have the resources."

6. The New Standard Set by GPT-OSS and the Future of the AI Market

Finally, the economic and technical ripple effects of GPT-OSS and frontier labs' strategies are summarized.

• The fact that a 100B-class model can be trained in 44 days on 2,000 H100s will become the new "baseline"
• "The level of frontier labs isn't as far away as Mars."
• GPT-OSS confidently acknowledges its limitations, and its stronger moat (real competitive advantage) lies in what it hasn't revealed

"What they don't show is the moat (sustainable competitive advantage). What they share with us is a signal to 'just take it.'"

The true competitive advantage in today's AI market and ecosystem is determined by leverage and utilization ability — that is, "how efficiently you're utilizing your tokens."

Conclusion

GPT-OSS is an important milestone representing technological transparency, market moats, and industrial structural changes in AI history. An era has opened where anyone can more easily jump into LLM development, and the conversation concludes powerfully with the message that only "those who enjoy the change" will be the protagonists of the next era.

"If it's coming anyway, let's all enjoy it!" "Along with AI, we hope you actively embrace the new definitions of work and opportunity."

Key Terms:

• GPT-OSS, Open-Weight, OpenAI, Sovereign AI, BEP, MoE, Expert, GPU Farm, Softmax, Attention Sink, Composable Architecture, Multimodal, Token, Automation, AI Productivity, Model Moat, Frontier Lab