What is Multimodal AI?

Early chatbots were text in, text out. Multimodal AI breaks that limit. A multimodal model can look at a screenshot, read a chart, listen to a voice note or watch a clip — and respond in text, speech or images. The flagship assistants from OpenAI, Google and Anthropic all accept images as well as text, and several handle audio and video too.

How it works

Different kinds of data are converted into a shared internal representation the model can reason over. Images are split into patches, audio into short segments, and each is encoded into vectors similar to how text becomes tokens and embeddings. A model trained on paired data — images with captions, videos with transcripts — learns how the modalities relate.

Some products are “natively” multimodal (one model trained on several modalities). Others chain specialised models: speech-to-text, then a language model, then text-to-speech. To the user, both feel the same.

An analogy for the meatbags

A text-only model is a brilliant expert you can only reach by letter. A multimodal model is the same expert on a video call: you can hold up the document, point at the error on your screen and talk it through. Same brain, far less friction.

Why it matters for the Chosen

Multimodality opens up work that pure text never could:

• Screenshots to solutions: paste an error, a dashboard or a design and ask what’s wrong.
• Documents and charts: extract data from invoices, receipts, scanned forms and slide decks.
• Content repurposing: turn a video into a blog post, a podcast into clips and captions, a whiteboard photo into meeting notes.
• Creative production: generate images, video and voice from one workflow.
• Accessibility: describe images for visually impaired users; transcribe and translate speech.
• Ecommerce: product photos to listings, visual search, quality checks.

For sellers of AI services, multimodal workflows — “send us your raw footage, get back edited shorts, captions and a blog post” — are often more valuable than text alone because they replace several specialist tools.

Limits

Models can misread small text, dense charts and handwriting, and they hallucinate visual details just as they hallucinate facts. Computer vision accuracy varies a lot by task. Verify extracted numbers before they hit a spreadsheet that matters.