SoftBank Group is in discussions to invest up to $30 billion more in OpenAI, according to a Wall Street Journal report. The additional capital would be part of a $100 billion funding round, valuing the artificial intelligence company at roughly $830 billion. This follows SoftBank's earlier $41 billion investment in December for an 11% stake.
SoftBank Doubles Down on AI Bets
CEO Masayoshi Son has been pursuing an aggressive AI strategy. The potential fresh capital would deepen SoftBank's ties to OpenAI, which is also a partner in the Stargate project — a $500 billion initiative to build AI data centers. Stargate is seen as critical to U.S. competitiveness in AI against China. SoftBank and OpenAI co-invested through a $1 billion SB Energy deal.
OpenAI's costs for training and running models are rising sharply, driven by rapid adoption and increasingly sophisticated systems. The company has been raising capital at a breakneck pace to fund its compute needs.
SoftBank declined to comment on the negotiations. Other Vision Fund deals have slowed as the firm focuses resources on the OpenAI relationship, the report noted.
AI Crypto Sector Reacts
CoinGecko's AI category currently shows a total market cap of $31.6 billion and 24-hour volume of $2.4 billion, with a modest 2.7% daily gain. Within this niche, several tokens moved sharply on the news.
Worldcoin (WLD) surged more than 15% in a single session, fueled by speculation that OpenAI might adopt its iris-based verification for a bot-free social platform. If OpenAI integrates Worldcoin's proof-of-personhood rails, WLD could see sustained on-chain usage. However, regulatory headwinds around biometrics and data localization remain a key risk.
Fetch.ai (FET) tends to outperform during AI risk-on phases. Its autonomous agent stack is used in logistics and DeFi automation, with FET required for transaction fees. The narrative could weaken if alternative middleware or Layer2 solutions capture market share.
Render (RNDR) is leveraged to GPU scarcity. Its marketplace model benefits from fragmented AI inference workloads across decentralized compute networks. Competition from centralized clouds and other DePIN GPU networks may compress margins over time.