SoftBank, a diverse technology company out of Japan is mapping out its future in artificial intelligence(AI). Even as its Vision Fund arm actively invests in use cases of AI, SoftBank has opted for a different trajectory when it comes to investments in AI infrastructure.
Image Source: Softbank.jp
Role of the Vision Fund
SoftBank’s Vision Fund, whose business model resembles that of a venture capital firm, has been one of the most active investors in SoftBank. However, fresh deployment was lower by 60% in the June quarter at $620 million. This is not so, as it has been seen that there is a lot of stock investment going on from the conglomerate’s side. This soft bank lost a massive $1.9 billion of investments during the same period, mainly by using its resources on the balance sheet.
AI Infrastructure vs. Use-Cases
The current strategy is to acquire AI infrastructure through direct purchase using SoftBank’s balance sheet. This strategy focuses on generating value over the long term. AI infrastructure is comprised of products such as AI chips and data centers as well as energy solutions required for operating these centers. For instance, SoftBank bought a UK-based AI chip startup in Graphcore recently and it is strategically ahead of NVIDIA in the competition. Infrastructure investments are long-term investments as they typically take time to deliver value upon completion.
The Vision Fund specializes in the use of artificial intelligence in those applications. These are specific instances of the use of Artificial Intelligence technology. For example, the Vision Fund invested in the search engine startup company Perplexity in the range of $10-20 million. Perplexity delivers are written by its AI and are not confined to basic search engine answers. They are no longer suited to venture capital structures owing to shorter holding periods which are compatible with the timescales of innovation.
Tenure Flexibility and Value Creation Potential
Infrastructural investments are best held for longer periods in the infrastructural investments category. SoftBank itself has flexibility with its balance sheets, there is no restriction to the period in the fund. This gives SoftBank freedom in managing AI infrastructure projects for it does not have to look at the expectations of the external investors. Arm which SoftBank later acquired from Vision Fund and NVIDIA are some examples of how successful infrastructure investments can yield great value. The balance sheet investments of SoftBank also seem to pursue the same goals.
Vision Fund-backed AI use-cases
Yanolja, a South Korean firm that operates in the travel industry, uses generative AI for analysis. It gathers information from several providers to offer a united, pertinent landing page for tourists. This means improving customer journeys through an integrated experience rather than solely focusing on the commission-heavy online channels that Yanolja intends to transform through its business model. Furthermore, they have generative AI tools to keep travelers connected with them and give them advice and support during the journey.
OakNorth, a credit intelligence business, is reluctant to incorporate generative AI into its main product. Current generative AI models pose accuracy and reliability problems in the compliance-focused environment OakNorth operates. The state’s healthcare marketplace, Covered California also incorporates Document AI to reduce some of the documentation and verification for those applying for coverage.
Conclusion
SoftBank’s practice of investing in AI infrastructure out of its cash reserves and at the same time funding AI use cases via Vision Fund places it favorably. Such developments show that whether it be in the financial markets, e-commerce, technology, or AI, SoftBank is invested in making technology’s future.
