How the 4 New AI Models Change How You Work
episode
The AI Daily Brief: Artificial Intelligence News and Analysis
34 min
1 speaker
2 chapters
transcribed 2 months ago
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How did four new AI models arrive this week and why does it matter?
Today on the AI Daily Brief, how the count of four new models we got access to this week will change how you work. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Blitzy, and Airtable. To get an ad-free version of the show, go to patreon.com slash ai-dailybrief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. Welcome back to the AI Daily Brief. Among the many changes that AI is bringing to the professional world, one of them is an almost total obliteration of the previously agreed upon idea that you could actually slow down a little bit over the warm summer months. While not every white-collar professional would agree that July and August are a time for resting and vacations and catching up, it's pretty undeniable that it's a season where things quiet down a bit.
Except in AI land, where almost especially now that the previous cadence was thrown off by the government's interference, we are in for what I believe will just be an absolute cavalcade of models, many of which, as you'll see, I think have fairly significant implications for how we work. The month of models got off to a big start with the return of Fable, which while yes, of course, technically was released in June, for all intents and purposes for most of us, it actually feels like an early July release. And yet this week we added a whole new slate of models to the roster, including OpenAI's first answer to Fable, 5.6 Sol, a new entrant from Grok, their first since they hooked up with Cursor called Grok 4.5, a new model from Cognition, SWE 1.7, which continues a trend that we saw with Cursor and Composer 2.5,
And finally, the model that we're going to start with today, GPT Live. Now, you might have seen this announcement floating around social media. It's a set of cute and charismatic grannies talking to chat GPT's new live model in a way that's meant to represent just how much more natural and conversational the new model feels. Now, this is not at all the main point of the show, but it is worth noting as a side story that I think and many think that this content was some of the more effective we've seen from OpenAI. Not Boring's Paki McCormick wrote, Now that particular choice I also think reflects one of the big underlying points of this announcement, which is an evolution in how they imagine consumers interacting with AI.
We'll come to that in just a minute, though. Let's talk about the actual model that was released first. The model is called GPT Live and it comes in two flavors, GPT Live 1 and GPT Live Mini. It is built on what they call a full duplex architecture, meaning OpenAI says that it can listen and speak at the same time. And a big part of the emphasis here is in how the interaction model has changed. The earliest versions of ChatGPT Voice were built on something called a cascaded voice system. Basically, that model was actually three models chained together. The user would speak, and then a speech-to-text model would transcribe that speech, an LLM in the background would produce a response, and then a text-to-speech model would convert it back into speech.
This approach, OpenAI wrote, enabled us to talk to frontier AI models for the first time, but the complexity came at a cost. Information could be lost across models, and responses were slow and stilted. Now, the next version, which was built into ChachiBT Advanced Voice Mode, was a turn-based model system. It generated audio within a single model, as in it didn't have to go through that speech-to-text and text-to-speech translation, which made a big difference in terms of latency, but it still operated in a discrete turn-based way, i.e. the model had to wait for the user to stop speaking before it started, which created all sorts of challenges because when turn detection was based on silence, something like a brief pause in thought or background noise could be mistaken for the end of the user talking, which could lead to the model interrupting in weird ways, keeping things feeling stilted and not all that natural.
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