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Block has demonstrated Dessa, a voice AI model trained on human speech that can listen and speak at the same time. Its natural-sounding responses drew praise and concerns about scams; the model remains a research preview, with no public release date or independent test results disclosed.
Block has demonstrated Dessa, a voice AI model that speaks directly from audio and can respond while listening, drawing both praise for its natural-sounding conversation and concerns that realistic AI voices could aid deception. The company says the model runs locally on certain MacBook Pro computers, but it remains a limited research preview, with no public release date announced.
Block says Dessa was trained from scratch on human speech, rather than text, and can handle interruptions, laughter and changes in vocal tone while it speaks. That differs from systems that first turn speech into text before generating a spoken answer. In company demonstrations, Dessa laughs mid-sentence and reacts to a surprise with apparent emotion. Block said the conversations were recorded live, without someone directing the model’s responses.
According to Block, training used a single Nvidia GB200 NVL72 system, and the model can run locally on a MacBook Pro with an M4 Max chip or better. The company has not published its model size or benchmark results. The audio samples available so far are company demonstrations, not independent evaluations, and the initial research preview is going to a small group of AI safety researchers.
The demonstrations prompted sharply different reactions online. Binance founder Changpeng Zhao pointed to possible educational uses, while filmmaker Justine Bateman and journalist Shiv Aroor raised concerns about deception and scam calls. Those comments reflect reactions to the demos, not evidence that Dessa has been used in fraud.
Humanlike Voices, New Trust Risks
Dessa highlights a practical tension in voice AI: more natural conversation could make spoken interfaces easier to use, but could also make it harder for listeners to recognize when a voice is artificial. The concern is especially relevant to phone calls, where people may make decisions based on a speaker’s apparent identity or emotional tone. The reported reactions are warnings about potential misuse, not proof of a specific scam involving this model.
Block says speech-based systems may eventually recognize cues such as distress or pain that can be lost in a text transcript. If that capability develops, it could support more responsive applications, including education. Its value and risks will depend on how the model performs outside selected demonstrations, how access is managed and what safeguards accompany any broader release.
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How Dessa Differs From Text-Based Voice AI
Many voice assistants use a sequence of steps: convert a person’s speech into text, generate a text response, then synthesize that response as audio. Block describes Dessa as processing audio directly, allowing it to listen while speaking and respond to vocal qualities and interruptions in real time. The company’s demos are intended to show those conversational features, but do not by themselves establish performance across different speakers, settings or tasks.
Dessa comes from Block, the company behind Square and Cash App, as well as the Bitkey bitcoin wallet and Proto bitcoin-mining hardware unit. Block’s AI research lab reports directly to CEO Jack Dorsey and includes former employees of the Toronto-based AI startup Dessa, which Square acquired in 2020. Block has not said when or whether the current research preview will become a public product.
“Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.”
— Jack Dorsey, Block co-founder and CEO, writing on X
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Performance and Access Remain Unknown
Block has not disclosed Dessa’s model size, benchmark results or public release date. The published audio remains company-provided material, so independent assessments of its quality, reliability and limitations are not yet available. The company has also not detailed how it will prevent impersonation or other misuse if access expands.
It is also unclear how well the model handles different accents, noisy environments, emotionally difficult conversations or situations beyond the demonstrations. Block says researchers will assess risks the company may have overlooked, but it has not released findings from that review. No confirmed use of Dessa in scams is reported in the source material.
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Safety Review Before Wider Access
The next step identified by Block is access for a small group of AI safety researchers, who are expected to examine potential risks. Their work may help clarify how the system behaves beyond company demonstrations, although Block has not provided a timetable or specified what findings will be published.
For now, there is no announced public launch date. Further details on independent testing, safeguards, model specifications and access plans would help establish what the demonstrations show—and how the potential benefits and risks may change if the technology becomes more widely available.
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Key Questions
What is Block’s Dessa?
Dessa is a voice AI model that Block says was trained on human speech and processes audio directly, rather than first converting speech to text.
Can Dessa run on a personal computer?
Block says it can run locally on a MacBook Pro with an M4 Max chip or better. The company has not published independent tests confirming performance across devices or use cases.
Is Dessa available to the public?
No public release date has been announced. Block says the model is a research preview and is initially being shared with a small group of AI safety researchers.
Has Dessa been used in scams?
The source report does not document any confirmed scam involving Dessa. Concerns about fraud are potential risks raised in response to the demonstrations, not evidence of reported misuse.
What details has Block not released?
Block has not disclosed the model’s size, benchmark results or public release date. Its available audio samples are company demonstrations rather than independent evaluations.
Source: rss
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