Scaling laws for pre-training are not dead and continue to provide predictable, significant returns, forming a core pillar of OpenAI's strategy despite industry narratives to the contrary [58, 71, 98].
AI development is best advanced along two complementary axes: unsupervised learning for broad knowledge and a newer, equally important reasoning paradigm (powered by RL) for deliberate, multi-step thought [2, 22, 37].
The ultimate goal of AI research is to create models that can automate scientific discovery and conduct end-to-end research, thereby creating a self-sustaining cycle of innovation [59, 63, 80, 101].
OpenAI's competitive advantage is rooted in its research culture, which prioritizes high-risk, frontier research and talent density over simply replicating competitor benchmarks or engaging in salary-based bidding wars [51, 88, 128].
Agentic AI is the most effective application of today's most capable models, and improving agentic behavior is fundamentally linked to enhancing the underlying model's reasoning capabilities [15, 31, 46].
More than 2 years ago
Chen states that OpenAI made an 'unpopular bet' to focus on applying reinforcement learning (RL) to language models, laying the groundwork for its reasoning paradigm [95].
Last 1.5 to 2 years
According to Chen, OpenAI discovered and began scaling a 'new, very exciting paradigm through reasoning,' shifting its research focus to include a second axis beyond unsupervised learning [2, 35].
Last 6 months
Chen notes a renewed focus on pre-training, with OpenAI 'supercharging' these efforts, suggesting a strategic re-investment in scaling foundational models alongside the newer reasoning work [121].
Q2, Current Year
OpenAI's research division implemented a hiring freeze to increase talent density and force a strategic evaluation of existing teams, as stated by Chen [106].
Present Focus
Chen describes the immediate six-month goal as continuing to scale the reasoning paradigm, particularly by leveraging more test-time compute [38].
Near Future
Chen discusses the imminent release of models like GPT-5, which he describes as the first to marry the pre-training and reasoning paradigms, representing a significant inflection point in capability [45, 107].
▶The Unwavering Faith in Scaling
Chen consistently refutes the 'scaling is dead' narrative, asserting that scaling laws for pre-training have held for nearly 10 orders of magnitude and will continue to yield returns with more compute, data, and algorithmic efficiencies [58, 71, 98]. He views this as a core, and perhaps underestimated, tenet of OpenAI's strategy.
OpenAI's capital expenditure on compute is likely to remain aggressive, as its research leadership believes the returns on this investment are far from diminishing and key to maintaining their lead.
▶The Dual-Axis Strategy: Pre-training and ReasoningJul 2026
Chen describes OpenAI's strategy as scaling along two complementary axes: unsupervised learning (pre-training) for foundational knowledge and reinforcement learning (RL) for reasoning capabilities [2, 22, 20]. He positions models like GPT-5 as the first to truly integrate these two paradigms, suggesting a synthesis of world knowledge with deliberate 'thinking' time [37, 45].
Analysts should track developments in both pre-training efficiency and RL-based reasoning, as Chen positions the synthesis of these two areas as the key to the next generation of models and a core part of OpenAI's R&D thesis.
▶Automating Science as the Endgame
A primary organizational goal articulated by Chen is to develop models capable of automating scientific discovery and conducting end-to-end research [59, 63, 80]. He sets concrete timelines for models to act as 'AI interns' and eventually full researchers, viewing this as a direct path to AGI and a way to solve currently intractable problems [86, 101, 43].
Chen's focus on automating research suggests OpenAI's long-term commercial strategy may involve not just selling API access but also leveraging AGI for proprietary scientific and technological breakthroughs, creating a powerful, self-sustaining innovation loop.
▶Research Culture as a Competitive Moat
Chen emphasizes that OpenAI's ability to retain talent against lucrative offers from competitors is due to its unique research culture [125, 128]. This culture prioritizes fundamental, high-risk research over short-term product goals, fosters internal talent, and encourages open idea sharing to out-innovate rivals [51, 55, 109, 126].
While competitors may compete on compensation, OpenAI's ability to maintain its mission-driven focus on AGI and its distinct research environment is its primary, and perhaps most durable, defense against talent drain.