VC activity has remained consistent throughout the VC downturn since Q3 2022, “leaving aside the tech giant megadeals that commit upfront funds for future cloud purchases,” according to an update from PitchBook.
PitchBook notes in a report that they have “tracked $22.3 billion invested in Q4 2023, including an outlier $2.0 billion deal size for Anthropic, led by Alphabet.”
According to the PitchBook report, median valuations have remained above the market midpoint, “while equity sold has fallen lower, demonstrating founder-friendly conditions.”
As stated in the research report, deal count has “remained much lower than pre-downturn levels, even given the excitement about GenAI over the past five quarters.”
These totals would assuredly “be much lower without tech giants’ involvement in GenAI deals, with GenAI leaders raising $6.0 billion in Q4 across only 194 deals.”
The momentum in horizontal platforms “led the segment to set a VC record in 2023 with $33.0 billion raised, demonstrating the value of basic innovation.”
Q3 2023’s bump in M&A did “not sustain into Q4, as both deal value and count fell.”
PitchBook also mentioned that they have “tracked only $2.7 billion in disclosed VC exit value in Q4, which will likely settle as the lowest quarter since Q1 2019.”
The PitchBook report pointed out that tech giants “remained dormant in M&A given their focus on partnerships with leading large language model (LLM) startups.”
Exceptions are said to reportedly “include NVIDIA’s pending acquisition of Run:ai, AMD’s acquisition of nod.ai in ML operations (MLOps), IBM’s acquisition of Manta in database management, and ServiceNow’s acquisition of UltimateSuite in predictive analytics.”
The recent successful listing “for AI connectivity hardware company Astera Labs will encourage other companies riding the GenAI tailwind to go public.”
More broadly, impressive results “for AI companies in public markets should encourage further listings, although our review of the IPO pipeline for 2024 does not heavily feature AI companies given their ability to stay private.”
Horizontal platforms empower end users “to build and deploy AI & ML algorithms across a variety of use cases.”
The PitchBook report further noted that these platforms directly apply scientific “advances in AI & ML research to commercial applications.”
Companies in this segment “have differentiated AI & ML approaches and are built with AI & ML from the ground up—this is also referred to as AI-first.”
Furthermore, some horizontal platforms are used “to improve AI & ML algorithms but do not use AI & ML themselves.”
Subsegments include:
- AI core: Building blocks of AI & ML deployments, including developer tools needed to build and deploy models to production. Categories within this subsegment include AI as a service (AIaaS) AI & ML developer tools, AI platform as a service (PaaS), automated ML (autoML), cognitive computing, data preparation platforms, quantum AI, and TinyML.
- Computer vision: The use of AI & ML to analyze visual data and make meaningful predictions about both the physical world and digital images. The technology can be used across use cases to label and make predictions about visual data. Key products utilizing computer vision across a range of verticals include AI-enabled augmented reality, computer vision as a service, facial recognition, geospatial analysis, and visual data labeling software.