In AI discussions lately, fast takeoff, the singularity, and recursive self-improvement (RSI) are all major topics of focus. There is some truth to them regarding what’s currently unfolding in the AI industry. Two or three labs are merging into an oligopoly, controlling access to the top AI models and holding the resources needed to create the next generation. Today’s AI tools are rapidly reshaping engineering and research roles. In numerous respects, conducting AI research has become significantly easier. Scaling the training of large language models further presents formidable technical challenges that must be overcome. Super-human coding assistants are making these tasks accessible, shattering many previous assumptions about what it took to build them. This is positioning us for a year (or longer) of swift advancements at the forefront of AI. We’re also at a point where language models are already exceptionally capable. In reality, they’re capable enough for many highly valuable knowledge-work tasks.
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