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Many NLP researchers are experiencing an existential crisis triggered by theastonishing success of ChatGPT and other systems based on large language models(LLMs). After such a disruptive change to our understanding of the field, whatis left to do? Taking a historical lens, we look for guidance from the firstera of LLMs, which began in 2005 with large $n$-gram models for machinetranslation. We identify durable lessons from the first era, and moreimportantly, we identify evergreen problems where NLP researchers can continueto make meaningful contributions in areas where LLMs are ascendant. Among theselessons, we discuss the primacy of hardware advancement in shaping theavailability and importance of scale, as well as the urgent challenge ofquality evaluation, both automated and human. We argue that disparities inscale are transient and that researchers can work to reduce them; that data,rather than hardware, is still a bottleneck for many meaningful applications;that meaningful evaluation informed by actual use is still an open problem; andthat there is still room for speculative approaches.

First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models