According to @karpathy, Richard Sutton questions whether LLMs are truly bitter-lesson‑pilled because they depend on finite, human-generated datasets that embed bias, challenging the idea that performance can scale indefinitely with more compute and data, source: @karpathy. Sutton advocates a classic RL-first architecture that learns through world interaction without giant supervised pretraining or human teleoperation, emphasizing intrinsic motivation such as fun, curiosity, and prediction-quality rewards, source: @karpathy. He highlights that agents should continue learning at test time by default rather than being trained once and deployed statically, source: @karpathy. Karpathy notes that while AlphaZero shows pure RL can surpass human-initialized systems (AlphaGo), Go is a closed, simplified domain, whereas frontier LLMs rely on human text to initialize billions of parameters before pervasive RL fine-tuning, framing pretraining as "crappy evolution" to solve cold start, source: @karpathy. He adds that today’s LLMs are heavily engineered by humans across pretraining, curation, and RL environments, and the field may not be sufficiently bitter‑lesson‑pilled, source: @karpathy. Actionably, he cites directions like intrinsic motivation, curiosity, empowerment, multi‑agent self‑play, and culture as areas for further work beyond benchmaxxing, positioning the AI‑agent path as an active research narrative, source: @karpathy.

According to Jesse Pollak (@jessepollak), the creation of 'smart coins' will be evident when users are naturally inclined to engage with them, and the norm becomes holding these coins for the long term. This statement implies that the success of a cryptocurrency could be measured by the level of intrinsic motivation it creates among its holders, leading to stable investment patterns. Such a trend could reduce volatility and increase the market value of these assets over time, making them more attractive to traders looking for long-term gains. However, it is important for traders to verify these factors through market data and behavioral analytics before making trading decisions.