https://archive.is/xrXKT https://archive.is/xrXKT subtitle: "After a decade’s worth of innovation, new models can change the world the way the internet did."

https://archive.is/xrXKT https://archive.is/xrXKT subtitle: "After a decade’s worth of innovation, new models can change the world the way the internet did." "A common failure scenario in earlier-generation AI startups (which I call the AI mediocrity spiral) highlights a few major factors at play. In order for a startup’s AI-based application to have sufficient accuracy early on, the company hires humans to perform the function it hopes the AI will automate over time. Often, this is part of an escalation path where a first cut of the AI will handle the most common use cases, and humans manage the long tail of less-common ones. Early investors tend to be more focused on growth than on margins. To raise capital and keep the board happy, the company continues to hire people rather than invest in the automation—which proves tricky anyway because of the aforementioned complications. By the time the company is ready for growth-level investment, it has already built an entire organization around hiring humans in the loop, and it’s too difficult to unwind. The result is a business that shows relatively high initial growth, but maintains a low margin and, over time, becomes difficult to scale. Fortunately, this doesn’t seem to be the case with the current wave of generative AI applications such as ChatGPT and the foundation models such as GPT-4 that power them. While still very early, we’re already seeing use cases in large existing markets with orders-of-magnitude improvement in time, cost and performance. This has led to some of the fastest-growing technology and product adoption in the history of the software industry. We may be experiencing what is likely the start of a new supercycle on par with the advent of the microchip or the internet. For one reason, accuracy isn’t that important for many applications. When a model is generating novel images or engaging in entertaining banter, being correct simply means appealing to or engaging the user. In other popular uses, like helping developers write code, the user is the human in the loop—iterating and providing the feedback to improve the generated answers."