Databricks, a data storage and management startup, has entered into an agreement to acquire MosaicML, an OpenAI competitor, for $1.3 billion. The deal is expected to close on July 31, 2023, subject to customary closing conditions and regulatory clearances.
MosaicML provides a fully managed infrastructure and software tools to let customers train large-scale artificial intelligence models more efficiently.
The acquisition is structured as a stock deal, meaning that investors in MosaicML will receive Databricks stock as compensation. This includes the ARK Venture Fund, which holds MosaicML through a Simple Agreement for Future Equity (SAFE).
The fair value of MosaicML within the ARK Venture Fund’s portfolio has appreciated due to the acquisition announcement.
Upon the deal’s closing, owners of MosaicML stock, including the Ark Venture Fund, will have their MosaicML shares converted into Databricks stock. The conversion will occur at a value equal to the new, current valuation of MosaicML within the ARK Venture Fund’s portfolio.
Overall, the acquisition of MosaicML by Databricks allows startups and corporations to build their own AI models and provides more control over their data. The deal is expected to enhance Databricks’ offerings and tap into the growing AI market.
Disclosures
We offer this news related to MosaicML because it is part of the total ARK Venture Fund portfolio. To see the most updated portfolio, please click here. Holdings subject to change. Not a recommendation to buy, sell, or hold any specific security.
ARK’s statements are not an endorsement of any company or a recommendation to buy, sell or hold any security. ARK and its clients as well as its related persons may (but do not necessarily) have financial interests in securities or issuers that are discussed. Certain of the statements contained may be statements of future expectations and other forward-looking statements that are based on ARK’s current views and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance, or events to differ materially from those expressed or implied in such statements.
