GPT-5-Codex and the Year of Agentic Coding

0:00Today on the AI Daily Brief, OpenAI introduces GPT5 Codex and we discuss why 2025 is so clearly the year of agentic coding. And before that, in the headlines, thanks to Nana Banana, Gemini has topped the App Store. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:24All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Blitzy, Robots and Pencils, KPMG, and Super Intelligent. To get an ad-free version of the show starting at just$3 a month, head on over to patreon.com slash ai daily brief. I'll also be setting up subscriptions on Apple and Spotify soon. And if you're interested in sponsoring the show, we are more or less sold out for 2025. There are a couple little slots here and there, but we are getting deep into 2026 now. The audience is large, growing, awesome, and very powerful in their own organization.

0:53So if you want to get access to them, shoot us a note at sponsors at AIDailyBrief.ai. With that, let's dive in. Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. We kick off today with a big update in the AI competition, which tells us something I think about consumer usage. TLDR is that Gemini has hit number one in the App Store, overtaking ChatGPT. ChatGPT has basically had a stranglehold on the number one spot since its release in 2023, with just a very few times where its dominance was broken. So this is a huge milestone for Google's flagship AI app.

1:27For many analysts, this is the validation they've been looking for that Google is starting to take the lead on AI. Justin Patterson of KeyBank Capital wrote, If Gemini can remain on the top of the App Store charts, we believe more investors will start to view Gemini as a strong core offering with incremental use cases that complement as opposed to cannibalize the core surge experience. Now, of course, for those of you in the know, which is all of you AI Daily Brief listeners, the proximate cause of Gemini's surge is growing interest in the nano banana image model. Josh Woodward, the VP of the Gemini app, said that they added 13 million first-time users in just four days last week.

2:01Now, as has happened with other AI image models in the past, we start to see these trends around a specific type of generation. The one that's happening right now is turning base images into 3D figures. Here's one of an image of Cristiano Ronaldo that turns into a 3D figure sitting on a desk and in a packaging box. Woodward noted that the trend had caught on in India last week to drive a wave of new downloads. Now, of course, we saw this happen before with the massive uptick in ChatGPT usage after their latest image generation model earlier this year came out, specifically around the studio Ghibli trend that turned everything into a Ghibli-looking animation.

2:38Han Nguyen of Fractal MCP commented, Gemini topped the App Store charts thanks to Nano Banana. It doesn't surprise me one bit. Humans are visual creatures. She continued, What does surprise me is how Meta, with all its data, hasn't released a decent image or video generation model. It's even behind ByteDance. Now, of course, Meta has recently announced partnerships with Black Forest Labs and with MidJourney, so clearly they are aware of the problem. And the same could also be said for Apple, who are now serving as the product surface for Google's most successful product in some time. The market is certainly paying attention.

3:09Alongside the rise of Gemini, Google's valuation has reached$3 trillion for the first time. And the turnaround has been significant. Google is up more than 70 % since the April lows. 18 months ago, Google AI was all about glue-on pizza and ridiculous ahistorical images, but since then the company's valuation has doubled. Citigroup analyst Ron Josie boosted his price target for the stock, saying, an accelerated product development cycle that is beginning to emerge with greater Gemini adoption across both its ads and clouds business. He added, we believe Google is executing better across its halo of products, experiencing greater demand, and delivering improved profitability.

3:46Now that doesn't mean everything is rosy. Google appears to be going through some of the same sort of restructuring we talked about at XAI last week, with Wired reporting that they have fired over 200 contractors working on AI products. Wired said that these contractors were working on AI ratings work, including evaluating, editing, and rewriting the Gemini's chatbot response to make it sound more human and intelligent. Basically, it sounds like a data annotation team of some time. Now, it is not exactly clear what the proximate cause of this was. As we discussed yesterday, there is a shift going on more broadly from generalist AI data annotators to more specialists.

4:17so maybe this is part of that or maybe it's something else. Whatever the case, there are definitely some shifts going on behind the scenes that I think are worth keeping an eye on. Lastly today on the Google front, one of the company's top executives has defended the company's use of AI overviews in search as promoting customer choice. Yesterday, after the publisher of Rolling Stone sued Google, the first comments from the company were very brief and generic. However, now VP of Government Affairs and Public Policy, Markham Erickson, gave a much more vigorous defense of AI overviews at the Wired AI Power summit in New York on Monday.

4:46Erickson said, I don't want to speak about the specifics of the lawsuit, but I can speak to our philosophy here, which is we want a healthy ecosystem. The 10 blue links serve the ecosystem very well, and it was a simple value proposition. We provided links that directed users free of charge to billions of publications around the world. We're not going to abandon that model. We think there's use for that model. It's still an important part of the ecosystem. However, he continued, user preferences and what users want is also changing. So instead of factual answers and 10 blue links, they're increasingly wanting contextual answers and summaries.

5:16We want to be able to provide that too, while at the same time driving people back to valuable content on the internet. So it's a dynamic space. Ultimately, our goal is to ensure that we have an overall healthy ecosystem. Now, this topic is going to get more contentious. Absolutely, there are going to be more legal battles around it. However, I think that the core idea that there are certain types of searches for which the summary is a much better value proposition for users is absolutely undeniable. The question is going to be what the right type of business model around it is and how the relationship between the summarizers and the publishers evolves to match the new reality.

5:49Next up, a small but pretty interesting story out of Eleven Labs. The company is introducing a new managed service called Eleven Labs Productions. The service offers AI-generated dubbing, captions, transcripts, and audiobooks with a human producer to verify and polish the final product. The goal is to deliver a production-ready result, and more importantly, to bring everything under one roof for an end-to-end customer experience. Co-founder Maddy Staniszewski said that 11 Labs have already partnered with 500 producers, more than the company's full-time employee count. Pricing starts at$2 a minute.

6:20And while some folks on Twitter had the take represented by BT Norris, wait, 11 Labs is offering human services? Is it over? For anyone who has interacted with enterprises or businesses of any kind when it comes to AI, it is so clear that there is a massive gap and opportunity for last mile services that provide just a little bit more assurance on top of the core gains that come from AI generation. I don't think it's at all surprising to see a company that builds the software also offering the service in some native way. I think even as the ecosystem of next generation service providers develops, you are going to see the platforms themselves offer some version of this kind of managed service around their core products in many, if not all cases.

7:02Lastly today, one that I'm just going to touch on briefly because I think it's likely that we come back to it in more detail and maybe even a main episode later this week, the CEO of Fiverr has announced 250 layoffs, which represent about 30 % of the company, alongside an announcement that the company is moving back into startup mode. Back in April, Fiverr CEO Mika Kaufman was one of the CEOs that came out and shared an internal note about the potential disruption of AI. Now, what was interesting about his note was one, the candor. It featured the line, here's the unpleasant truth. AI is coming for your jobs.

7:33Heck, it's coming for my job too. What was also interesting about this is that although we had similar notes from the CEOs of Shopify and Duolingo around the same time, Miga's original note did not have any firings or restructurings. It was basically a warning slash wake-up call that change was likely to come and an invitation to come and talk about it. Now, six months later, it kind of feels like this is part two of that same idea. And underlying the message is basically the big reminder that the way that people build and release things is just simply changing. Now, to many, it seems like Fiverr is directly in the firing line of AI.

8:06So many of the things that people used to hire freelancers for are exactly the type of things that they're now using generative AI for. And so it's not surprising that a marketplace for hiring those freelancers is feeling the pinch. At the same time, in their most recent reporting for the second quarter, Fiverr actually saw a 15 % year-on-year increase in revenue. The company stated AI-related services are booming, with surging demand especially around AI agents, workflow automation, and vibe coding. Anyway, the point is that we are in the midst of some big changes. For now, though, that is going to do it for today's headlines.

8:34Next up, the main episode. AI changes fast. You need a partner built for the long game. Robots and Pencils work side by side with organizations to turn AI ambition into real human impact. As an AWS certified partner, they modernize infrastructure, design cloud native systems, and apply AI to create business value. And their partnerships don't end at launch. As AI changes, Robots and Pencils stays by your side, so you keep pace. The difference is close partnership that builds value and compounds over time. Plus, with delivery centers across the U.S., Canada, Europe, and Latin America, clients get local expertise and global scale.

9:10For AI that delivers progress, not promises, visit robotsandpencils.com slash AI Daily Brief. What if AI wasn't just a buzzword, but a business imperative? On You Can With AI, we take you inside the boardrooms and strategy sessions of the world's most forward-thinking enterprises. Hosted by me, Nathaniel Whittemore, and powered by KPMG, this seven-part series delivers real-world insights from leaders who are scaling AI with purpose. From aligning culture and leadership to building trust, data readiness, and deploying AI agents. Whether you're a C-suite executive, strategist, or innovator, this podcast is your front-row seat to the future of enterprise AI.

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10:26increasingly towards an annual AI planning model where we map out how you can create an action map of your organization's agent opportunities that represents an executable backlog of AI and agent use cases that you can deliver on over the course of the next year. Additionally, for those end of year budgets, we have worked out deals with a number of partners where we can pre-lock in general implementation packages even before you figured out exactly what use cases are going to require them. If you'd like to learn more about superintelligence agent readiness audits and this new end of fiscal year plan, visit us at bsuper.ai, click get started, and make sure to use the word fiscal somewhere in the description.

11:02Welcome back to the AI Daily Brief. Today we are talking about the launch of GPT-5 Codex. It is, as it sounds, a new version of GPT-5 that is specifically optimized for agentic coding. Sam Altman tweeted yesterday that it is faster, smarter, and has new capabilities. The team, he said, has been absolutely cooking. So today we're going to talk about GPT-5 codex, what it changes, how it works, and what the initial responses are. But before we do, I want to contextualize it in a theme which is becoming abundantly clear. And to do that, we actually have to go back to last year. At the end of the summer of 2024, heading into the fall of 2024, there was a lot of discussion around whether AI was hitting a wall.

11:43Now, the nuanced version of this discussion was about whether pre-training was hitting a wall. All the way back then, people were expecting some new model from OpenAI. We had heard about something called Orion in and had all these rumors. And yet there was no indication that they were going to release a major update with all the leakage suggesting that they just weren't as happy with the progress that they had made. And yet that was a very narrow slice of what was actually going on. Because of course, while we didn't get GPT-5 back then, what we did get was the new paradigm of reasoning models.

12:12O1 was first previewed in September and then over the course of the fall started to come to production, and as it did so, it clearly opened up some new agentic possibilities that hadn't been there before. Then, of course, there was Claude 3.5 Sonnet. It was initially released back in June of 24, and in the subsequent ones became the standard for people using LLMs to code. And around the same time that we're talking about 01 and all these questions of hitting a wall, we also got an updated version of Claude 3.5 Sonnet, which had major improvements in this field of software engineering. Between the initial release version of 3.5 Sonnet and the October reversion, its performance on Sweebench Verify jumped from 33.4 % to 49%.

12:50It was also around this time that we started to see some user behavior shift. It wasn't just that people were using Claude to code, but that dedicated applications for AI coding started to become successes. Bolt launched around this time, October 2024, quickly grew to 5 million, and in the first two months grew to 20 million in ARR. And yet, of course, from a narrative perspective, as we came into 2025, the story was all about agents in general. Everyone thought that this was going to be the year when we started to see the first embers of digital employees working alongside human employees. And certainly we have seen some major developments in particularly enterprise adoption of agents.

13:27Between Q4 of last year and Q1 of this year, agent pilots jumped massively. And then between Q1 of this year and Q2 of this year, enterprise agent deployments actually tripled. And yet I would argue that part of the malaise that we've been experiencing for the last month or so, is that when push comes to shove, at least from a maturity perspective, 2025 hasn't been the year of agents, at least not in general. What it has been is the year of one very specific type of agent, which is, of course, the coding agent. In February, we had Andre Karpathy coin the term vibe coding. He wrote, there's a new kind of coding I call vibe coding, where you fully give into the vibes, embrace exponentials, and forget that the code even exists.

14:06It's possible because the LLMs are getting too good. When I get error messages, I just copy-paste them in with no comment. Usually that fixes it. The code grows beyond my usual comprehension. I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug, so I just work around it or ask for random changes until it goes away. I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works. It wasn't long after that in addition to Bolt, we started to see other platforms like Lovable and Replit just absolutely shoot to the moon in terms of their ARR. And of course, as this was happening, we were getting updates to the tooling as well.

14:39A big one happened back in February when Anthropic announced Claude Code, which was an agentic coding tool that allowed people to work directly within their terminal rather than using some other platform like Cursor. We started to hear from CEOs that AI tools were doing a meaningful percentage of the code that was getting pushed. And we got a very bold prediction from Dario Amadei, who back in March said that within three to six months, AI could be writing 90 % of the code that software developers were deploying. Now, obviously, there's been a lot of debate around the accuracy of that prediction, which I've covered in previous shows.

15:08But certainly when it comes to just about any chart you can show about adoption or revenue around coding, the proof is in the pudding. Anthropic in particular has been an enormous beneficiary. After taking a couple of years to get up to a billion dollars in revenue around the December-January timeframe, Anthropic rocketed from that$1 billion in ARR all the way up to$5 billion in ARR by the end of the summer. Indeed, as much as some other tools like Gemini 2.5 and 2.5 Flash were occasionally used for certain types of coding projects, Anthropic has been largely dominant when it comes to AI coding.

15:41And of course, given how significant this use case is, this was certainly not something that OpenAI could abide. Which brings us to the release of GPT-5. Now we've covered the release of GPT-5 endlessly, but the important reminder for our purposes here is that it was so clear right out of the gate that the use case that they cared about was coding. I even tweeted back then, OpenAI is betting about 700 million new people are about to become coders. It was in fact the first bullet that they talked about in describing the new features in the announcement blog post, ahead even of creative writing.

16:14And while the first couple weeks after the release of GPT-5 were spent in a combination of the dust-ups around the deprecation and reintroduction of 4.0 and a more general resurgence of the talk of performance walls, it wasn't long before people started to come around. In the middle of August, As Sean Wang tweeted, Watching the timeline flip on GPT-5 sentiment from negative to positive is pretty funny. He also pointed out that the same thing happened to O1 on a pretty similar timescale. And more than anything else, the thing driving the vibe shift was around OpenAI's coding tool, Codex. At the beginning of September, Sam Altman tweeted, Really cool to see how much people are loving Codex.

16:49Usages up 10x in the past two weeks. Lots more improvements to come, but the momentum is so impressive. On the same day, Jan Pellig tweeted, Codex CLI hype is real. GPT-5 High in Codex is great, stays on track much longer than Opus, never gives up on your task, much longer context window, etc. And as recently as yesterday, we talked about how some discussion had shifted to how significant the development of coding agents were going to be in the quest for AGI. And as I was pressing send on that piece, we got this update from OpenAI, the release of a new version of GPT-5 Codex. OpenAI writes, GPT-5 Codex was trained with a focus on real-world software engineering.

17:26It's equally proficient at quick interactive sessions and independently powering through long complex tasks. Its code review capability can catch critical bugs before they ship. Now, one of the major differences between the GPT-5 model that runs ChatGPT and GPT-5 Codex is the way it implements reasoning. Unlike GPT-5, the Codex version doesn't have a model router. Instead, it's able to adjust its reasoning effort in real time as it works on a task. So the idea here is that whereas the router in ChatGPT has to decide at the outset how much computational power and time to use on a particular problem, instead GPT-5 codecs can dig into a problem and then decide a few minutes in whether it needs to add more or less power to solving it.

18:09Basically, it uses a version of dynamic thinking so it can modulate its reasoning effort based on the complexity of the task. OpenAI also claims a big jump in prompt adherence, saying that you should be able to, quote, just tell it what you need without writing long instructions on style or code cleanness. Now, it wouldn't be a model release without the benchmarks. And GPT-5 Codex does have a modest bump on the SWE Bench Verified test, jumping from 72.8 % to 74.5%. And yet, as we're seeing with basically all new releases right now, the most common benchmarks just really aren't telling the full story about model improvement anymore.

18:40To that end, OpenAI devised a custom code refactoring eval based on large established repositories. While GPT-5 High got 33.9 % on those code refactoring tasks, GPT-5 Codex jumped all the way up to 51.3%. So when it comes to the significance, there are a few things here. Variable thinking like we were just talking about is clearly one of the big parts of this. SWIX posted the token usage distribution chart during internal testing and commented, this is the most important chart on the new GPT-5 Codex model. We're just beginning to exploit the potential of good routing and variable thinking. Easy responses are now greater than 15 times faster, but for the hard stuff, 5-codex now thinks 102 % more than 5.

19:20Same model, same paradigm, but bending the curve to fit the non-linearity of coding problems in LLM use cases. And this idea of token efficiency is really starting to come to the fore. For a long time, it's sort of been like everyone just uses the most state-of-the-art thing, especially on the development side, but as use cases get more complex, and that complexity is expressed in token consumption, and token consumption is expressed in cost, and cost is starting to be expressed in shifting business models that are based on usage rather than bulk rates, the efficiency of models is starting to matter alongside just raw capability.

19:52Swix again writes, developers are going to prefer the model that sips or spends tokens according to task difficulty. Why spend$200 on a chonky plan if you can get$20 on a competitor who takes efficiency seriously? This is perhaps the most important hparam of inference time compute. AI engineer Daniel Mack actually thinks that the variable thinking and the ability to adjust effort while in the midst of solving the problem shows, as he puts it, a spark of metacognition. He says this represents AI models beginning to think about their own thinking process, the exclusive domain of human minds until now.

20:24More simply from a business standpoint, Theo writes, GPT-5 codex is, as far as I know, the first time a lab has bragged about using fewer tokens. Hope this becomes a trend. Now, the other big part of this announcement has to do with autonomy. You heard in that sentence at the top of the description in their blog post that GPT-5 Codex was trained to focus on independently powering through long, complex tasks. But when they say long, complex tasks, they mean really long, complex tasks. Alongside the announcement, OpenAI released a conversational podcast, and one of the team members said, one of the things that this model exhibits is an ability to go on for much longer and really have that grit that you need on these complex refactoring tasks.

21:02But at the same time, for simple tasks, it actually comes way faster at you and is able to reply without much thinking. And so it's this great collaborative where you can ask questions about your code, find where this piece of code is that you need to change or better understand, plan, but at the same time, once you let it go on to something, it will work for a very, very long period of time. We've seen it work internally up to seven hours for very complex refactorings. We haven't seen other models do that before. Brasser X writes, GBT5 codecs just shattered a frontier in autonomous AI work. A few weeks ago, the record was around 200 minutes of continuous independent coding.

21:35That came from Replit, by the way, and even that was crushing the previous high. Browser continues, Now, OpenAI claims Codex can push through 7-plus hours nonstop on complex tasks, iterating, fixing test failures, and delivering working implementations. That's not just a speed boost. It's a shift towards agents that don't just chat but persist, staying on a problem until it's solved. We may be watching the early shape of true autonomous dev agents emerging. What happens when this stretches to days or weeks? Now, again, that 200 minutes that they're referring to was the claim from Replit on the release of Agent 3 last week.

22:06Before that, the best data we had on long-horizon tasks was from Meter. Replit CEO Amjad Massad said at the time, the Meter paper that says that the length of tasks AI can do is doubling every seven months radically undersells the scaling that we're seeing at Replit. It might be true if you're measuring one long trajectory for a single model class, but this is where an agent research lab's alpha is. We build multi-agent architecture and use different models from various providers to tap into their latent abilities across various tasks. The point being that the Agent 3 system is capable of these 200 minutes.