Opus 4.6 and ChatGPT 5.3-Codex Are Here and the Labs Are at War

0:00Today on the AI Daily Brief, we've got not one but two new models that show exactly where the leading model labs priorities. lie. And before that, in the headlines, looks like we're going to spend a cool two-thirds of a trillion dollars on AI infrastructure this year. 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. Firstly, thank you to today's sponsors, KPMG, Scrunch, Superintelligent, and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. If you are interested in sponsoring the show or really want to learn anything else about the show, you can head on over to AIDailyBrief.ai. 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 Google and Amazon rounding out big tech earnings with a very unified message.

0:56AI CapEx is accelerating faster than ever. Both companies lifted CapEx forecast significantly. Google guided AI spending between$175 and$185 billion for this year, vastly outstripping estimates of$115 billion. This level would double Google's already high$91 billion in CapEx for 2025. Amazon, though, came in over the top the following evening, guiding$200 billion in CapEx for 2026 for a 60 % jump. With Google, Amazon, Microsoft, and Meta all lifting expectations, we now have$650 billion in projected AI CapEx for 2026 from just these four. That's now more than the inflation-adjusted cost of the multi-decade U.S.

1:36interstate highway project anticipated to be spent in a single year. It's about two and a half Apollo moon missions or four and a half international space stations. Now, on the actual earnings, there was a slight divergence in performance. Google reported annual revenue of$400 billion for the first time. They saw an 18 % increase in overall revenue year-over-year and a 48 % jump for their cloud division. Still, Google Cloud was a$17.7 billion business for the quarter, which puts them firmly in third place behind Microsoft, Azure, and AWS. At the same time, they recorded by far the fastest growth rate and were the only hyperscaler that increased their pace of growth.

2:11Amazon's story was slightly less positive. Net profit was$21.2 billion right in line with expectations. top-line revenue growth was 13.6 % for a slight beat, reaching$213.4 billion for the quarter. AWS revenue growth was 24%, their fastest growth rate in three years, bringing division revenue to$35.6 billion for the quarter. While the numbers were fine, they didn't necessarily speak to massive monetization of AI bets, and CEO Andy Jassy spent much of the earnings call justifying the massive ramp-up in CapEx. He told investors, I think this is an extraordinarily unusual opportunity to forever change the size of AWS and Amazon as a whole.

2:47We see this as an unusual opportunity and we're going to invest aggressively to be the leader. Later in the Q &A section, he pushed back against an analyst who questioned the conviction. Jassy commented, This isn't some sort of quixotic top-line grab. We have confidence that these investments will yield strong returns on invested capital. We've done that with our core AWS business. I think that will very much be true here as well. Similar to Microsoft, both Amazon and Google said they were capacity-constrained in their cloud businesses. They claimed that stronger growth would have been possible if they had more GPUs on racks in 2025.

3:17Still, both companies saw a big drop in share price following their earnings calls, with Google falling 6 % on Wednesday night and Amazon losing 11 % on Thursday night. Now, one interpretation of this is investors being uncomfortable with spending at these levels regardless of AI-derived revenue. But there is also something more going on here that is worth exploring. For decades, hyperscalers have been doing hundreds of billions of dollars in stock buybacks each year, peaking at over a trillion dollars in 2023. Most analysts expect the hyperscalers to reduce or even end buybacks this year. CapEx plans also seem likely to require debt funding across the board for the first time.

3:52Steve Goldstein, the Euro Bureau chief of Market Watch, wrote, It's funny that we had a decade of no stock buybacks or evil, and now that companies are actually ramping up CapEx, it's no, not like that. Quantian summed it up, Investors have officially remembered that doing CapEx means you can't spend money on buybacks and decided they don't like it anymore. Architect pointed out that Stanley Druckenmiller has argued in the past that high corporate capex, such as spending on factories, inventory, and equipment, acts on a drag on financial assets because it drains liquidity from the financial system.

4:20Now, this is a super important point. We tend to think of these investors as sending signals about what they find to be a reasonable amount to spend on AI. But what they really might be saying is not that they necessarily think it's wrong to spend that on AI. They just don't like that it's not there to spend on them. I think we're going to see a lot more of this debate play out, but keep that in mind as you try to interpret markets' reactions to the hyperscalers over time. Speaking of Amazon, the company is considering a deep partnership with OpenAI, including using their models to power Alexa.

4:49As previously reported, Amazon is in talks to take part in OpenAI's latest funding round. They are in fact rumored to be considering an investment as large as$50 billion, which would be about half the money that OpenAI is seeking to raise. The information now reports that Amazon isn't just interested in an equity stake or a compute partnership, but is looking to get privileged access to OpenAI's tech. Sources said that OpenAI's models could bolster Amazon's AI products, including the Alexa voice assistant, under a proposed deal. The process would require post-training OpenAI models to tune them for Amazon's use cases, and would also require OpenAI to supply dedicated researchers and engineers to the process.

5:22That could be the hitch, as that would obviously divert resources to some extent away from their own ambitions. At this point, and I'm not sure how much to make of it, with a spokesperson for OpenAI saying we are focused on our strong existing compute partnership with Amazon. One other little nugget from the earnings report, Gemini has, according to Google, hit 750 million monthly active users. In December, Google said that Gemini's user base had surged from 450 million to 650 million in the final quarter, making this another substantial jump for January. The latest figure we have for OpenAI came from Sensor Tower, with their data showing that ChatGPT had 110 monthly million active users as of November.

5:57Now, there is a little bit of a question mark around how some of these companies are measuring user numbers. Meta, for example, claims 500 million monthly users for Meta AI, but that's presumably including quite a few people who stumble across the assistant in Instagram or WhatsApp. Google, however, was clear that these numbers are only counted using the Gemini app. CEO Sundar Pichai said in a statement, the launch of Gemini 3 was a major milestone and we have great momentum. One quick nugget of fundraising news, 11 Labs has secured a half billion in new funding at an$11 billion valuation. The round triples 11 Labs' previous valuation from their last funding round, which closed in January of last year.

6:33In terms of what comes next, it sounds like they're interested in moving into video. Co-founder Maddy Staniszewski said, The intersection of models and products is critical, and our team has proven time and again how to translate research into real-world experiences. We plan to expand our creative offering, helping creators combine our best-in-class audio with video and agents, enabling businesses to build agents that can type and take action. Finally, speaking of taking action, a story that deserves way more time than it gets in this headlines, but which I'm sure we will come back to. In addition to the news, which will be the subject of our main episode today, OpenAI also announced a new platform called Frontier.

7:08The goal is basically to help businesses deploy AI co-workers. OpenAI writes that it is a new platform that helps businesses build, deploy, and manage AI agents that can do real work. Frontier gives agents the same skills people need to succeed at work, shared context, to onboarding, hands-on learning with feedback, and clear permissions and boundaries. That's how teams move beyond isolated use cases to AI co-workers that work across the business. Basically, Frontier is a combined orchestration, governance, and optimization platform for OpenAI's agents. It allows users to manage the skills that each agent has access to, share context between agents, and set permissions and boundaries.

7:42OpenAI noted that AI leaders across every industry are rapidly rolling out agentic deployments, adding that what's slowing them down isn't model intelligence, but how agents are built and run in their organizations. They noted that the capability gap between leading performance and live deployments is actually growing due to increased complexity around agent governance. Frontier is designed to give a unified platform to control all the things around the AI model that goes into a successful agentic deployment. Context, data access, skills management. Now, this is something that we are going to necessarily talk a lot more about, but I just wanted to flag a little bit of commentary around this chart that was flying around Twitter and in particular financial circles.

8:17For those of you who are listening, not watching, it's a chart that shows at the bottom your enterprise system of record, and then five layers above it. Business context, agent execution, and evaluation and optimization right above it, then agents above that and interfaces above that. Investor Gokul Rajaram writes, check out where systems of record sit in this diagram from OpenAI Frontier. At least three, if not four layers of context and intelligence sit between them and the end business application. It's one of the clearest representations of how AI companies plan to build next-gen systems of action on top of existing systems of record, and why the markets are so worried about the future of software companies.

8:53Buko Capital put it even more simply, quite a visual from OpenAI. Your system of record is a dumb pipe and we will layer five rows of value on top of it to steal the relationship and all the economics along with it. No wonder SaaS is in the gutter. Like I said, there is so much more to explore about Frontier, but we have not one, but two model releases to talk about. So we are going to close the headlines there and move on into our main episode.

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12:23Oh my goodness. Look, it is a good day around here when we get news about upcoming models. It's a great day when we actually get a new model that we get to play with. And it is just something else entirely when within 20 minutes of each other, two of the leading frontier labs drop dueling frontier models, in this case with an incredibly clear focus. The models are, of course, Claude Opus 4.6 from Anthropic and GBT 5.3 Codex from OpenAI. And for sure, the first thing that people noticed is the sequence. Now, none of the frontier labs have ever been above trying to get out in front of one of their peers when it comes to a big announcement, but we've never seen anything like this.

13:00Vercel's Alley writes, Anthropic vs. OpenAI is like Kendrick vs. Drake, but for nerds. This indeed was a common refrain. Ayush wrote, Anthropic drops a model and OpenAI responds 15 minutes later. This is basically a rap beef now. Peter Yang writes, How did Opus 4-6 and GPT-5-3 drop back-to-back in like 20 minutes? Is this a new Cold War or something where there are spies in each company? The answer, Peter, is almost certainly yes. But obviously way more important than the popcorn ball watching of the competition is what the models actually have for us. On the Opus side, Anthropic says there are key coding improvements, including better code review and debugging skills to, quote, catch its own mistakes.

13:39Interestingly, it does also feel like Anthropic, because Claude is so associated with coding, used the chance to build on the themes that they've been exploring with Claude Cowork to talk about how Opus 4.6 was better for everyday tasks as well. Still with a focus, of course, on white-collar work, but things like running financial analyses, doing research, and using and creating documents, spreadsheets, and presentations. In fact, in their presentation of the benchmarks, they actually put knowledge work right up front. On the benchmark side, they claim the leading score on the agent decoding benchmark, Terminal Bench 2.0, and also claim the top spot on the leaderboard for Humanity's Last Exam.

14:13Now, Humanity's Last Exam began as a general knowledge test, but is increasingly a measure of reasoning and tool use capability. In terms of new features, Opus 4.6 now supports million-token context windows. And for folks who feel like they constantly run against those limits, that has got to be welcome news. They've also introduced something that I'm really excited about called Agent Teams. Now, Anthropic seemingly got the memo that Swarms was sort of an intimidating name, because they are explicitly trying to move away from the naming of Agent Swarms and calling it Teams instead. The feature basically allows users to set a whole team of clods to work on a particular problem, including a coordination layer to ensure they're working on separate tasks that all contribute to the whole.

14:52They write that agent teams are most effective for tasks where parallel exploration adds real value. Some of the examples they give are cross-layer coordination, where you've got coding changes that span front-end, back-end, and tests. But they also point to research and review, where, for example, multiple teammates can investigate different aspects of a problem simultaneously then share and challenge each other's findings. The difference between sub-agents and agent teams basically comes down to the extent to which you need your agents to communicate with one another. Anthropic sums up, use sub-agents when you need quick, focused workers that report back, use agent teams when teammates need to share findings, challenge each other, and coordinate on their own.

15:28Anthropic has also added a feature called adaptive thinking. It allows the model to pick up on context clues to determine how much reasoning effort to expend on a particular task. Users also have manual controls available to dial up or down the amount of reasoning effort being deployed. Now, to demonstrate the power of their new agent swarm slash agent teams, they tested a fully autonomous coding task. Anthropic writes,

16:18the standard Rust library. Anthropic also noted that this version of Claude was also created by Claude, with the AI model now the key driver of all coding within Anthropic. Now there are a bunch of additional features that they talked about. Opus 4.6 shows significant improvement on long context retrieval and long context reasoning, which should lead to a much better experience in long horizon tasks as well as coding work that requires the model to access large code bases. Overall, they said, with Opus 4.6, we found that the model brings more focus to the most challenging parts of a task without being told to, moves quickly through the more straightforward parts, handles ambiguous problems with better judgment, and stays productive over longer sessions.

16:53So that was 4.6, and then literally 15 minutes later, OpenAI dropped GPT Codex 5.3. Now the first thing you might notice is that we don't have not Codex 5.3 yet. And I think it's quite clear that the choice to release the coding-to-inversion of GPT-5.3 as a standalone before the release of the regular GPT 5.3 tells you everything you need to know about how these labs see the importance of various use cases. OpenAI says that this model advances both the coding performance of GPT Codex 5.2 and the reasoning and knowledge abilities of regular GPT 5.2 in a single package. Similar to Anthropic, OpenAI's models are now the core of the development team.

17:32With OpenAI writing, GPT 5.3 Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations. Our team was blown away by how much Codex was able to accelerate its own development. Max Stoiber from the ChatGPT team shared that a recently announced feature where ChatGPT had full support for MCP apps was built entirely with GPT-5.3 Codex. Max writes zero lines of code written by hand. Most times, the Codex CLI worked autonomously for hours and implemented parts of this first try.

18:06Now, on the benchmark, self-reported of course, Codex 5.3 is a significant jump on SweetBench Pro compared to the previous version. And maybe even more notably than performance, the performance that was achieved was achieved with far fewer tokens, demonstrating OpenAI's work on token efficiency. The model claims a new absolutely state-of-the-art score of 77.3 % on TerminalBench 2.0, which beats Codex 5.2 at 64%, and is, if true, much higher than Opus 4.6 at 65.4%. For their autonomous coding demo, OpenAI showed off a racing game. Codex 5.3 used the web game skill and was fed generic prompts throughout the process like fix the bug and improve the game.

18:43The result demonstrated that Codex 5.3 can churn through a task using millions of tokens without human intervention. Like Anthropic, OpenAI also highlighted various non-coding work that Codex 5.3 excels at. While for developers, the model is trained to be able to debug, deploy, write PRDs and tests and a lot more, basically supporting the entire development lifecycle, For non-programmers, they showed off tasks like a set of financial advice slides, a retail training document, and a fashion presentation deck. Now, interestingly, on GDPVAL, Codex 5.3 was very similar to previous models, but on the OS World benchmark, which measures compute use in real-world tasks, Codex 5.3 scored 64.7%, which almost doubles the performance of GPT-5.2.

19:23Summing up, OpenAI wrote,

19:51Basically summing up, you got two labs which are both reaffirming everything we talked about on the Code AGI is Functional AGI episode. That by expanding the capability set around coding, it unlocks use cases that are far beyond coding and are core to economically valuable knowledge work. So what were the first impressions? The companies that had early access to these models? Big surprise, like them. AJ Orbach from Triple Whale said, we've had early access to Claude Opus 4.6 and have been testing it over the past week. It's the best model in the world for front-end report design, ad anatomy and copywriting, and orchestrating other tools to actually build creatives.

20:24What's kind of crazy is how good these models are getting at long-running agentic work. Box's Aaron Levy said that overall, Opus 4.6 represented a 10 % jump over Opus 4.5 on their hardest knowledge work tasks. In terms of the features that people were most excited about, on the Opus side, many, many focused on this 1 million token context window. Now, as Manlo Ventures' Didi Das pointed out, part of why people were excited about this is that it came with, as he put it, insane-stated-the-art performance on all the long-context benchmarks. In other words, this isn't one of those claims of a long-token context window, where functionally it actually doesn't work.

20:59Others honed in quickly on the Swarm slash Team mode. McKay-Wrigley shared a test between Opus 4.6 with the Teams mode and 4.6 without it, and found that the Teams mode was 2.5 times faster and done better. He also pointed out, Reminder that Swarms is available in the Claude Agent SDK as well. You can build Swarms into any product literally right now. Kieran Klassen from Every thinks the implications are even bigger. He wrote, been running agent swarms for a few weeks now. I think this is the future, but I'm relearning what feature development even means. On the 5.3 codex side, the things that people were taking note of were things like the new token efficiency.

21:34Andy Henney writes, the biggest 5.3 codex news is that it's roughly three times more token efficient. 5.3 high is smarter than 5.2 high, but uses one third of the tokens, making it faster and letting your weekly limit last about three times longer. Good job. But of course, the real question is, which of these models is better? Perennial early tester Simon Willison kind of shrugged his shoulders. In his blog post about them, he said, I don't have much interesting to say about these models yet, to be honest. They're both incremental improvements on their predecessors and very capable. Maybe my favorite tweet, which maybe you'll get right away but did take me a second, came from the prime agent who wrote, I cannot believe how much better 5.3 is than 4.6.

22:13After some internal testing, results show it's 15.2 % better. Now, rather than explain, I'll let you try to work that one out for yourselves. Some people did try to actually make a comparison, though, and many did it with their own benchmarks. Neil Chudley writes, TLDR, Opus 4.6, 1 million context, enterprise and knowledge work, agent teams, and clawed code, not benching as high as Codex 5.3. Although he does point out that he doesn't really care about self-reported benchmarks. On 5.3 Codex, he writes, wins code benchmarks, faster, mid-task steering, but less than half the context window of Opus.