Allbirds rebrands as Smartbird, shifting focus to AI infrastructure with Nadia Carlsten as CEO and a $100 million financing boost.
Allbirds Transitions to Smartbird, Emphasizing AI Infrastructure
Allbirds, previously known for its eco-friendly footwear, has officially rebranded as Smartbird and divested its shoe division to concentrate on artificial intelligence infrastructure. This strategic pivot was announced on Wednesday, marking a significant departure from its original business model.
The San Francisco-based company, listed on Nasdaq under the ticker BIRD, has completed the sale of its footwear and apparel lines, a move initially disclosed in April. Nadia Carlsten, a seasoned expert in AI computing infrastructure, has been appointed as the new president and CEO, succeeding Joe Vernachio, who has resigned from the position.
In a notable market reaction, BIRD shares jumped 52%, reaching $5.99 following the announcement. Earlier this year, shares had fluctuated from $2.49 to a high of $24.31, reflecting a 46% increase year-to-date despite some volatility.
Carlsten brings extensive experience from her previous role as CEO of DCAI, where she played a crucial role in launching an AI supercomputer in collaboration with Nvidia. Her background includes significant positions at SandboxAQ and Amazon Web Services, where she contributed to the development of Amazon's quantum computing service. She holds degrees in chemistry and physics from the University of Virginia, as well as a doctorate in engineering from the University of California at Berkeley.
The company has also expanded its convertible financing facility from $50 million to $100 million to support its AI transformation. This funding will facilitate the creation of managed AI computing clusters tailored for business clients, with Smartbird already engaging potential customers and planning initial deployments.
Carlsten emphasized the timing of Smartbird's entry into the market, stating, "AI is rapidly becoming essential for organizations across all sectors, yet many lack the means to deploy and manage the infrastructure required for these workloads." She highlighted the opportunity to meet the growing demand for enterprise-grade AI infrastructure, which balances control and performance without the costs associated with hardware ownership.
