The Flow of Investments in the LLM Space

The development of Large Language Models (LLMs) is heavily dependent on the resources available to the teams working on them, and investments play a crucial role in determining the success of these models. The recent trend in media coverage of LLMs focuses on the investment amount raised. Microsoft’s promise to renew and increase its stake in OpenAI with a $10 billion series or Amazon’s recent new stake of $4 billion in Anthropic are examples of the significant investment that can affect the growth of LLMs. The share of funding in the Artificial Intelligence (AI) and Machine Learning (ML) sectors has increased significantly over the last year. Conversely, uncovering a trend in the founding of text analytics is challenging. The post-money valuations in the AI and ML sectors have also increased from virtually inexistent to up to 15% of the total valuations (in the private sector, two types of valuations are generally available: the one done before an investment round, the pre-money valuation, and that done after the post-money valuation. Since the latter incorporates the latest information, it is generally used to compute returns and other metrics on investment rounds). However, this increase is accompanied by significant volatility, likely due to uncertainties regarding investors’ expectations. The log distribution of investment amount in each field shows significant outliers, and the majority of investors and investees are present in the US.