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    Stanford University's release of the Alpaca model - which basically showed that an inferior foundational LLM can mimic a superior model cheaply - has massive business and science implications. Though "self-instruct" - i.e. the pattern of fine-tuning models with auto-generated demonstrations/training data - is nothing new, Stanford showed that it could be done at the scale of LLMs, and that it only costs $600! If Stanford's results are valid (and that's a big "if"), here are the major implications.
  • Published on
    Is AI adoption actually beneficial for tech stocks? While enthusiasm for AI has piqued investor interest in tech stocks again, i the long-term impact of AI on tech valuations may not be as positive as initially thought. In this article, we'll explore why AI adoption may actually be bearish for the average tech stock, particularly for IaaS, PaaS, and SaaS companies. We'll delve into the risks associated with rapid AI advance, such as execution risk, reduced cash flow visibility, and a shift in data and compute gravity. Additionally, we'll discuss how the rise of AI may reduce the demand for white-collar workers, leading to fewer seats and less revenue for SaaS companies. Ultimately, while the impact of AI on tech valuations may not be immediate, strategic mishaps will inevitably lead to re-pricings over multiple earnings cycles.