Despite perceptions of rapid acceleration, a large-scale analysis by Google DeepMind and EPOC that stitches together many benchmarks over time shows that general AI capability progress has been remarkably linear. This suggests AI is currently a better tool, not an expanding population of researchers.

People mistakenly dismiss AI's current inaccuracies as proof of its limitations. This is like calling a stumbling toddler stupid. AI is in a rapid learning phase and will soon be sprinting, creating opportunities for those who understand this developmental stage.

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.

While AI progress is marketed in revolutionary "step-changes" (e.g., GPT-3 to GPT-4), the underlying reality is more like compounding interest. A continuous stream of small, incremental improvements are accumulating, and their combined effect is what creates the feeling of an exponential leap in capability over time.

With past shifts like the internet or mobile, we understood the physical constraints (e.g., modem speeds, battery life). With generative AI, we lack a theoretical understanding of its scaling potential, making it impossible to forecast its ultimate capabilities beyond "vibes-based" guesses from experts.

Contrary to the "bitter lesson" narrative that scale is all that matters, novel ideas remain a critical driver of AI progress. The field is not yet experiencing diminishing returns on new concepts; game-changing ideas are still being invented and are essential for making scaling effective in the first place.

The belief that AI progress will be slow often stems from a strong prior that 'things are just always hard and slow.' This 'bottleneck objection' leads skeptics to assume unforeseen drag factors will always emerge, causing them to dismiss detailed scenarios for rapid acceleration without engaging with the specifics.

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.

The narrative of Artificial General Intelligence (AGI) being just a few years away is fading. Experts like Andrej Karpathy are now suggesting current machine learning paradigms have limits, reframing AI's progress as impressive but not on an immediate path to uncontrollable superintelligence.