The eighth stop on my AI roadmap is the one that opens up once text is solid. Everything before this was words in, words out. But the models are now multimodal: they take images and audio as input, and separate models generate speech, pictures, and video. Same ideas, more senses. This stop is a map of what each one is, what is genuinely production-ready in 2026, and what still falls apart.
Same assistant, more senses
Picture the assistant from earlier stops, the one who could only read and write. Now they can listen to a voice note, look at a photo you hand them, reply out loud, sketch something, and cut a short clip. Each of those is a different tool with different maturity, and the useful thing is knowing which is a commodity you can lean on and which is still a party trick.
flowchart LR T[Text model at the core] --> V[Vision: reads images] T --> S[Speech in: transcription] T --> O[Speech out: text to speech] T --> I[Image generation] T --> M[Video generation]
Seeing: vision is now a commodity
Reading images is the easy win. Every major model provider now accepts an image alongside your prompt through the same API you already use: hand it a screenshot, a receipt, a product photo, and ask about it. No separate service, no new skill. If your app has images and questions about them, this is a single extra field in a call you already make.
Listening and speaking: voice crossed the line
Two halves. Speech to text (transcription) is dominated by Whisper-style models, with real-time services for live use, and it is reliable enough for production. Text to speech has crossed the uncanny valley: the best current voices are hard to tell from a human recording.
Put them together with a text model and you get a voice agent, and the piece that makes it feel natural is a realtime audio API: low enough latency that a conversation flows instead of lurching. This is the pattern behind phone agents and voice assistants.
NOTE
Latency is the whole product in voice. A great answer that arrives two seconds late feels broken in a conversation. Stream the audio out as it generates, exactly as you stream text, and measure the time to first sound.
Drawing and filming: impressive, with sharp edges
Image generation is mature for the common cases: product mock-ups, illustrations, variations on a style. The open models let you run it yourself; the hosted ones are a single API call. It still stumbles on precise text inside an image and on very specific layouts.
Video generation is production-ready for short cinematic clips, and that qualifier matters. The current generation handles a few seconds of controlled camera and character work well, and still fails on hands, on legible text in the frame, and on keeping a character consistent across a long sequence. Treat it as a tool for short, curated pieces, not a replacement for a video pipeline.
WARNING
Generation output needs human review before it ships, every time. Hands with six fingers, text that is almost English, a logo that is nearly yours: these are common, and a model will not flag them. Review is not optional at this stop.
The tools you actually reach for
| Job | Popular tools | Note |
|---|---|---|
| Vision (image in) | Any major provider’s model | Same API as text; add an image to the prompt |
| Speech to text | Whisper (open), Deepgram | Whisper is free/OSS; Deepgram for real-time |
| Text to speech | ElevenLabs, OpenAI TTS | Hosted, paid; free tiers to start |
| Realtime voice | OpenAI Realtime API, and equivalents | Low-latency conversational audio |
| Image generation | Flux, Stable Diffusion (open), hosted APIs | Open models run locally; hosted pay per image |
| Video generation | Runway, Kling, Google Veo | Short clips; review output |
More senses, same rules
The lesson from this stop is that the fundamentals from the text stops carry over completely. Latency still decides the experience, so stream. Output can still be confidently wrong, so review it before it reaches anyone. Cost still scales with volume, so budget. What changes is only the input and output format. Treat each new modality as the same model with a new sense, not a new kind of magic, and the earlier discipline keeps working.
New senses, old discipline.
Sources
- Best-AI.org: multimodal AI, vision, voice and video explained (2026) for what multimodal models are and the 2026 state of each modality
- Ofox: multimodal AI APIs 2026, vision, TTS and transcription guide for vision as a commodity, transcription, and text to speech
- AIapps: best multimodal AI tools 2026, text, image, video and voice for the tool landscape and video generation limits