According to The Verge, yes: Google’s experimental Foresight app for macOS transcribes meetings and audio files without an internet connection. It is free, but it is still experimental and many details are not yet known.
What is Google AI Edge Foresight?
Google AI Edge Foresight is an experimental note-taking application from Google. The Verge reports, citing earlier coverage by TechCrunch, that it can transcribe meetings and existing audio files and then summarise the content. The app sits under the Google AI Edge name, which Google uses for its work on running artificial intelligence on users’ own devices rather than on remote servers. The word “experimental” matters. It suggests Google is testing an idea in public, not launching a finished, supported product. Experimental apps often change quickly, have rough edges and are sometimes withdrawn. Anyone thinking of using it for important work should keep that in mind and avoid relying on it as their only record of a meeting.
What does “completely offline” actually mean for a note-taking app?
Most AI transcription services upload recorded audio to a company’s servers, where large models turn speech into text and produce summaries. An offline app does that work on the computer itself. According to The Verge’s report, Foresight handles both transcription and processing locally, so the audio does not have to leave the machine for the core task. In practice, the recording, the transcript and the summary can all be created without a network connection, for example on a train or in a building with poor connectivity. Offline processing is not the same as having no network activity at all. The available reporting does not say whether the app checks for updates, collects usage data or connects online for any other reason. Users who want certainty should check the app’s own privacy information.
Who can use it, and what does it cost?
The Verge reports that Foresight is free and that it runs on macOS. The available material does not mention versions for Windows, Linux, Android, iOS or the web, so availability on those platforms is not known. Nor does it give minimum hardware requirements, such as which Mac processors are supported or how much memory is needed. Those details matter, because running AI models locally uses the computer’s own processing power. Older or lower-specification machines may run such software slowly or not at all. Before installing it, it makes sense to read Google’s official listing for system requirements, supported languages and any account sign-in the app may need. “Free” also says nothing on its own about long-term pricing, which for an experimental product is not known.
What is EmbeddingGemma 2 and why does it matter here?
The Verge reports that Foresight uses EmbeddingGemma 2, one of Google’s on-device models. Gemma is the name Google gives to a family of openly available models designed to be small enough to run outside large data centres. An embedding model turns text into numerical representations that capture meaning. Software uses these to search, group or compare passages, for example to find where a topic came up across several transcripts. The available reporting does not explain exactly which parts of Foresight’s work EmbeddingGemma 2 handles, or whether other models are involved in turning speech into text. What it does show is the general approach: Google is using compact models that a laptop can run, rather than sending data to the cloud.
How does it compare with Granola and Wispr Flow?
The Verge places Foresight alongside Granola and Wispr Flow, two existing AI tools built around capturing speech and turning it into usable text. Granola is generally described as an AI meeting-notes tool. Wispr Flow is generally described as a voice dictation tool. The main difference the reporting highlights is that Foresight works entirely offline. A full feature-by-feature comparison is not possible from the available information. The reporting does not cover transcription accuracy, speaker identification, language support, integration with calendars or video-call software, or export formats. Readers deciding between these tools should compare them directly on the features they need. They should also check each provider’s own documentation on where audio is processed and stored, as this varies between services.
Why would someone choose offline transcription over a cloud service?
Privacy is the most obvious reason. When audio never leaves your computer, there is less risk of sensitive conversations being stored on, or accessed through, a third party’s servers. This can matter for confidential business discussions, legal or medical conversations, or any organisation with strict rules on data handling. Reliability is a second reason: an offline tool keeps working without Wi-Fi or mobile data. A third is control: files stay where you put them, subject to your own backup and deletion habits. Offline processing does not make data automatically secure, though. Transcripts saved on a laptop are only as safe as that laptop. Disk encryption, a strong login password and careful sharing still matter.
What are the likely trade-offs of running AI on your own computer?
Models small enough to run on a laptop are generally less capable than the largest models in data centres. Depending on the task, that can mean less accurate transcripts or simpler summaries, although how Foresight performs specifically has not been established in the available reporting. Local processing also uses the computer’s processor, memory and battery, so long recordings may take time to process and may slow down other work. Storage is another consideration: audio files and transcripts build up on the device, not in an online account. Finally, there is no cloud copy unless you make one. If the laptop is lost or fails, recordings that were never backed up may be gone. These are general features of on-device AI, not confirmed findings about this app.
What should you check before recording a meeting with it?
Whatever tool you use, the most important step is getting consent. Recording conversations without telling participants can breach workplace policies, and in some jurisdictions it can breach the law. Tell everyone at the start that the meeting is being recorded and transcribed, and say how the notes will be used. Next, check your organisation’s rules on software. Many employers restrict which applications can be installed or used with company information, even when the software runs offline. Then test the app on a short, low-stakes recording to see how well it handles your microphone, the room and the accents and speaking styles involved. Finally, read through any summary before sharing it. Automated summaries can leave out nuance or misattribute points.
How can you try an experimental tool like this sensibly?
Treat Foresight as something to evaluate rather than depend on. A practical approach is to run it alongside your existing method for a few meetings, then compare its transcripts and summaries with your own notes. Keep an eye on errors with names, technical terms and figures, as these are common weak points in automatic transcription. Decide in advance where transcripts will be stored and how long you will keep them, so sensitive material does not pile up on your machine indefinitely. Keep the app updated through official channels only, and download it only from Google’s own sources. If it does not meet your needs, uninstall it and delete any stored recordings you no longer require.
What is still unknown about the app?
A lot. The available reporting does not say which languages are supported, how accurate transcription is, whether it can tell speakers apart, or how it captures audio from video-call software. It does not give system requirements or say whether versions for other operating systems are planned. It is also unclear whether the app stays free, how long the experiment will run, or whether it might become part of a wider Google product. Details of any optional data collection are not covered in the material either. Until Google publishes fuller documentation, or independent testers report their findings, judgements about its quality should be provisional.
Sources and further reading
- The Verge: technology news report on the release of Google’s offline note-taking app
- TechCrunch: earlier technology news coverage of the same app, cited by The Verge
- Google AI Edge: official developer material on Google’s on-device AI tools and models
- Google’s Gemma model documentation: background on the Gemma family of openly available models
Surfaced from the rss:verge signal “offline AI transcription app”. AI-assisted draft, editorially reviewed.
