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Willow Care, the company behind Willow Voice, selected CoreWeave for reinforcement-learning infrastructure and forward-deployed support, according to SiliconANGLE’s October 8, 2026 report. Willow says the arrangement lets its team focus on evaluating and aligning its speech-recognition system instead of managing the underlying infrastructure. The report describes the approach, but does not publish Willow-specific performance results.
How Willow Voice turns speech into formatted text
Willow Voice combines speech recognition with a small language model that post-processes the recognized words into text formatted as the user might have written it. Willow co-founder and CTO Lawrence Liu described that formatting step as a key part of the product:
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“I think the main part of our secret sauce is we have this really, really small LLM that post-processes everything that you say,” Liu said. “Now it’s able to format everything correctly, take your inputs and change it to exactly what you would’ve written.”
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SiliconANGLE reports that Willow is fine-tuning its speech-recognition model on quiet, real-world audio. Liu’s example was a person whispering in a coffee shop. That example illustrates the kind of speech the team is addressing; it is not a complete description of Willow’s training data or data practices.
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Why Willow chose CoreWeave
Willow selected CoreWeave for reinforcement-learning infrastructure and forward-deployed support, SiliconANGLE reported after interviewing Liu at CoreWeave’s Fully Connected event. Liu said the infrastructure means Willow does not have to manage everything “under the hood,” allowing the team to concentrate on evaluation alignment.
“The great thing about CoreWeave’s [reinforcement learning] infrastructure is we don’t have to worry about anything under the hood,” Liu said. “The only thing we really need to focus on at this point is eval alignment.”
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In practical terms, the reported choice is about infrastructure and support for Willow’s training work. The article does not provide a head-to-head provider comparison, disclose the CoreWeave configuration or contract, or establish that CoreWeave is faster or cheaper for Willow.
What the report says about training and inference
Liu said Willow’s work is mostly training at present, while demand for inference is growing faster as more people use dictation. This is his account in an event interview, not a published usage dataset. The article does not quantify training volume, inference demand, or workload scale.
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What is—and is not—known about results
SiliconANGLE’s report does not give Willow-specific measurements for accuracy, latency, cost, model size, or before-and-after performance. It also does not disclose Willow’s model name, training-data quantities or composition, evaluation protocol, or measured outcomes. Those omissions mean the report explains Willow’s infrastructure decision and product approach, but does not establish how much the choice improved the service.
CoreWeave separately reported nearly 1.4x faster training and 40 percent lower costs, with no impact on model quality, for its Serverless RL offering in an October 8, 2025 announcement. Those were CoreWeave’s company-reported comparisons with local H100 GPU environments; they are not Willow results or an independent comparison. Read CoreWeave’s Serverless RL announcement.
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Will keyboards disappear?
Liu described a long-term ambition for speech to replace keyboard input, saying, “Within two years, we want it such that you don’t even need keyboards anymore,” he said. “You can just speak and your computer understands.” That is a product vision, not evidence that keyboards will disappear or that Willow has already achieved that outcome.
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Reporting context
The account is SiliconANGLE’s coverage of an interview with Willow co-founder and CTO Lawrence Liu at CoreWeave’s Fully Connected event. SiliconANGLE says TheCUBE was a paid media partner for event coverage and that neither CoreWeave nor other event sponsors had editorial control. Read SiliconANGLE’s report.
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