Mini-Vibe Check: ChatGPT Voice Mode

Plus a workflow for managing agents while doing the dishes, why AI needs to be more social, and who to follow on X for design inspiration.

Laura Entis is a staff writer at Every.

Is voice mode ready for real work?

Last week, voice mode was blowing up Every’s Slack.

Powered by GPT-Live, OpenAI’s new voice model, the feature lets you have natural conversations with ChatGPT, complete with interruptions, follow-up questions, and redirections. Within the ChatGPT desktop app, voice mode can find the right task or thread based on spoken context, kick off new threads, check on existing work, and send more complex tasks to GPT-5.5.

After Dan took to X to evangelize voice mode’s powers for writing and revising an essay, the team put the feature through its paces. We used it to fix user-reported bugs, draft article outlines, do meal prep, draw connections between what we were reading and what we were building, book airline tickets, and orchestrate agents while cooking.

What works: There’s a lot to love about voice mode, which allows you to get work done without having to sit at a keyboard.

One of its biggest strengths is that it lets you read and ask questions aloud or connect what you’re reading to another file or project. Engineer Lee Knowlton uploaded a PDF of Designing Data-Intensive Applications , a book about building large-scale data systems, while voice mode had access to the codebase he was working on. As a result, he could keep reading while asking questions aloud, exploring unfamiliar ideas, and connecting the book’s insights to his own code. The result was a more fluid way to learn.

“Shifting from text to voice is different from shifting from text to text for me,” he says. “Reading something and then having a conversation, or asking a quick question, is different from typing something and then having to parse more text.”

What could be better: During a walk, COO Brandon Gell found that the mobile app’s voice mode could read a thread’s visible history but didn’t have access to important context outside the thread. Currently, voice can control local Codex work through Remote connections—but only while the host computer is awake, online, and running the desktop app. Without that connection, voice can’t access the host’s Codex projects, files, or tools.

Further complicating matters, the mobile app also has an “ordinary voice mode,” which can use the current cloud conversation but not the local context available through Remote. Are you confused? We’re confused.

The model’s ability to distinguish between speech intended for it and ambient conversation was also inconsistent. Lee found it good at filtering out exchanges with his wife, while engineer Tyler Nishida had the exact opposite experience. And the lag time can make it hard to use as a writing or editing partner (I found voice mode impressive but functionally too laggy to help me write this piece, for example). Finally, although GPT‑Live can delegate complex tasks to a frontier model in the background, some responses still felt shallow compared with responses from a text chat set to GPT-5.6 Sol.

Final verdict: Voice mode is, as Dan puts it, “a whole new world”—one in which you can direct agents away from a computer. But there are kinks to work out.

“It’s both not quite there yet and obviously the future,” Lee says. “A week ago, I couldn’t imagine a version of this that was really good, and now I can.”

‘AI & I’: The next big opportunity in AI is social

Sarah Tavel has spent her career studying consumer technology cycles, and she thinks she’s spotted the next one. A former Pinterest product manager and current Benchmark partner, she’s betting the next wave of AI products won’t just be smarter, they’ll be social. On this week’s AI & I, we’re revisiting our April 2025 conversation with Sarah, who argues that even power users are still using AI products like ChatGPT in a rudimentary way. But the gap isn’t the models: It’s that nobody has built a way for users to learn from each other. Sarah thinks whoever captures and shares that knowledge will create the next big product.

Here are the highlights:

Somebody has to build the “follow” button for prompts. Sarah remembers searching Reddit for prompts to help interpret blood test results. That’s when she saw the opportunity. Imagine following trusted experts in healthcare, finance, or law the way you follow creators today—and automatically gaining access to the prompts they use. Prompt libraries aren’t new. They appeared shortly after ChatGPT launched. But Sarah thinks they arrived too early, serving mostly solopreneurs and marketers before mainstream users had developed meaningful AI habits. Now, she sees a second chance.
Technical builders build the first wave; product geniuses will build the next. Looking across consumer technology, Sarah sees a familiar pattern. Google was primarily a technical breakthrough. Facebook was less technical and more polished. By the time Pinterest and Snap emerged, “the CEOs weren’t technical at all—they were product geniuses,” she says. She believes AI is following the same trajectory. Today’s leaders are largely infrastructure companies. Tomorrow’s winners may be the people who understand community, product design, and human behavior.
How to spot if a startup actually has network effects. Sarah looks for strong network effects in startups she backs—but she’s learned most claimed ones aren’t real. Founders will describe a flywheel that sounds like Amazon’s or Uber’s. The tell, she says, is whether each step actually speeds up the next one or just sounds like it should: “The biggest thing is when you really look at what the articulation of the flywheel is—it’s words, but not accelerators.”

This episode is a must listen for anyone who wants to understand why the biggest AI product hasn’t been built yet—and what it might take to build it.

Steal this workflow

Manage an agent team while you do the dishes

Lee’s workflow shows how voice mode changes the way we interact with agents...