OpenAI intentionally releases powerful technologies like Sora in stages, viewing it as the "GPT-3.5 moment for video." This approach avoids "dropping bombshells" and allows society to gradually understand, adapt to, and establish norms for the technology's long-term impact.

Unlike traditional engineering, breakthroughs in foundational AI research often feel binary. A model can be completely broken until a handful of key insights are discovered, at which point it suddenly works. This "all or nothing" dynamic makes it impossible to predict timelines, as you don't know if a solution is a week or two years away.

For the first time in years, the perceived leap in LLM capabilities has slowed. While models have improved, the cost increase (from $20 to $200/month for top-tier access) is not matched by a proportional increase in practical utility, suggesting a potential plateau or diminishing returns.

Users mistakenly evaluate AI tools based on the quality of the first output. However, since 90% of the work is iterative, the superior tool is the one that handles a high volume of refinement prompts most effectively, not the one with the best initial result.

The advancement of AI is not linear. While the industry anticipated a "year of agents" for practical assistance, the most significant recent progress has been in specialized, academic fields like competitive mathematics. This highlights the unpredictable nature of AI development.

A paradox of rapid AI progress is the widening "expectation gap." As users become accustomed to AI's power, their expectations for its capabilities grow even faster than the technology itself. This leads to a persistent feeling of frustration, even though the tools are objectively better than they were a year ago.

AI progress was expected to stall in 2024-2025 due to hardware limitations on pre-training scaling laws. However, breakthroughs in post-training techniques like reasoning and test-time compute provided a new vector for improvement, bridging the gap until next-generation chips like NVIDIA's Blackwell arrived.

For consumer products like ChatGPT, models are already good enough for common queries. However, for complex enterprise tasks like coding, performance is far from solved. This gives model providers a durable path to sustained revenue growth through continued quality improvements aimed at professionals.

After facing backlash for over-promising on past releases, OpenAI has adopted a "low ball" communication strategy. The company intentionally underplayed the GPT-5.1 update to avoid being "crushed" by criticism when perceived improvements don't match the hype, letting positive user discoveries drive the narrative instead.