Last year, I focused my 2022 predictions on becoming a better decision-maker. In 2023, the theme is loss functions.

The loss function in optimization is a function that serves as a proxy for the underlying performance measurement. In many cases, it's one of the most important components of any form of machine learning.

It's also sometimes referred to as the cost function, objective function, error function, or reward function, depending on what you're doing. Those terms capture the essence of what I'd like to get out of my 2023 predictions – a measured error and a way to derive a path to improvement (i.e., a metaphorical gradient).

Private Equity comes for SaaS – This is already starting to happen for those watching Thoma Bravo. SaaS multiples will continue to readjust, and many companies will be left without a clear path forward – no window to IPO, no additional capital at their current (e.g., 2021 high) valuations, but with real intrinsic value and revenue. If it does happen, it will be hard to judge in 2023 whether or not this was a good move (for founders or PE).

Questions:

A wave of new generative AI companies and the march to monetization for existing ones – ChatGPT, Stable Diffusion, and GPT-3 have created renewed excitement in AI. There has been a large amount of capital flowing to startups in these areas, so we'll see products in this space (companies funded are a leading indicator of products launched). Meanwhile, existing products that have captured attention (pun intended) but not monetization will inevitably have to monetize (OpenAI, Stability AI).

Questions:

Questions:

LLMs fine-tuned code enables developer productivity in various places – I'm very excited about the application of LLMs to developer workflows. First, chain-of-thought in LLMs dramatically increased when code was introduced into the training data (OpenAI's Codex). See How Does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources for a good explanation. I imagine GitHub Copilot is already bringing in significant revenue (even for GitHub). A few of this year's thoughts on how it could play out.

Other predictions and questions