What We Learned About Amazon’s AI Strategy

Today on the AI Daily Brief, what we learned about Amazon's AI strategy from AWS reInvent, and before that in the headlines, more updates about forthcoming open AI models. 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, Robots and Pencils, Blitzy, Robo, and Super Intelligent. To get an ad-free version of the show, go to patreon.com slash ai daily brief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, shoot us a note at sponsors at ai daily brief dot AI. Once again, now is your last chance to lock in 2025 rates. So shoot us a note again at sponsors at ai daily brief dot AI. And with that, let's get into some updates that dropped basically as soon as I finished recording yesterday. Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. We are in an extremely fast-moving period right now. Yesterday's episode was, of course, all about OpenAI's Code Red and what it meant in terms of how they were shifting priorities and trying to push forward to improve ChatGPT as well as release some new models that could help shift the narrative momentum once again. Just after I finished recording, we got more information about one of those models, which is codenamed Garlic. Sources told the information that GARLIC is the result of a new pre-training run. They said that Chief Research Officer Mark Chen had recently informed staff that GARLIC was performing well in internal benchmarking, compared against Google's Gemini 3 Pro and Anthropics Opus 4.5. Coding and reasoning tasks were a particular strength. Chen said that the model improved upon OpenAI's previous best and much larger pre-trained model GPT-4.5. Chen said that the model would be released as soon as possible, which the information interpreted as early next year, possibly as GPT 5.2 or GPT 5.5. Now, Garlick is apparently a separate model to Shallow Pete, which had been mentioned in Sam Altman's October memo, where he warned staff to expect some rough vibes after the release of Gemini 3. Altman pitched Shallow Pete as OpenAI's response to the new Google model and a way to win back momentum. Chen explained to staff that Garlick incorporates bug fixes first deployed in the Shallow Pete pre-training run, suggesting that OpenAI has cleared their problems with pre-training. In a recent research note, semi-analysis claimed that OpenAI had not completed a successful full-scale training run on a new foundation model since GPT-40 in May of last year. That made Google's proclamation that Gemini 3 was an advancement in pre-training all the more impactful. If OpenAI has fixed this problem, then it paves the way for more significant advancements in the base model, rather than just relying on the reinforcement learning process. Now, while some assume that GARLIC is the model set for release next week as part of OpenAI, code red. Others are saying this isn't the case. Chris, chatgbt21 on Twitter writes, the model releasing next week is not garlic. Garlic will be a separate model that performs well. The truly new pre-trained model that they have deeply cooked and innovated on, which is expected to be strong across all areas, is scheduled for early 2026, roughly January to March. So for those trying to keep track at home, the rumors are that we're getting a new reasoning model next week, although it won't be the fully new pre-trained model. But separately, OpenAI has fix their issues with pre-training and can now make further advancements on the base model, which they expect to show off early next year. I'm sure by the time that this comes out, we will have even more information. But if Gemini 3 weren't enough pressure, it increasingly appears that at least among developers, Anthropik's Opus 4.5 was probably the most important model released in the past month. The longer people sped with this model, the more they like it. Everywhere you look, there are tweets like this one. Rishikesh writes, the more I try Opus 4.5, the more I feel like Anthropic is right about software engineering dying. It's unbelievably good. Ivan Fioravanti writes, I think Claude with Opus 4.5 can easily brute force any software engineering problem and solve it in one way or another. This model is really strong. Yassin MTB writes, the gap between Opus 4.5 and every other model is insane. Justin Schroeder writes, honestly, Opus 4.5 feels like the biggest jump in coding models I've seen to date. It's really, really good. Stuart Chaney writes, Opus 4.5 has taken us to the next level. I can now offload 8 to 10 linear tickets at a time with no humans in the loop until after the PR is reviewed in GitHub. The step up in quality is exceptional. Pretty insane to think where this will be 12 months from now. Pietro Chirano had the simplest take. Opus 4.5 is alien tech. Now one other story regarding Anthropic. The company has acquired developer tool startup Bunn to accelerate Claude Code. Bunn produces a JavaScript runtime that's dramatically faster than competitors. The product is an all-in-one developer toolkit that combines runtime, package manager, bundler, and test runner. Anthropic wrote that Bunn has, quote, become essential infrastructure for AI-led software engineering, helping developers build and test applications at unprecedented velocity. By bringing the Bunn team on board, Anthropic hopes to work on rebuilding the developer stack with an AI-first approach. They wrote, Bunn will be instrumental in helping us build the infrastructure for the next generation of software. Together, we will continue to make Claude the platform of choice for coders and anyone who relies on AI for important work. Bunn will remain open source and Anthropic will continue to invest in the platform to ensure it remains a top choice. And while deal terms weren't disclosed, Anthropic did use the occasion to announce that Claude Code had reached a billion dollars in ARR. Chief Product Officer Mike Krieger writes, Claude Code reached a billion dollars in run rate revenue in only six months, and bringing the Bunn team into Anthropic means we can build the infrastructure to compound that momentum and keep pace with the exponential growth in AI adoption. On the Anthropic front, the company is apparently joining the race to go public as they prepare for an IPO next year. The Financial Times reports that Anthropic has engaged lawyers and major investment banks to prepare for a 2026 IPO. The report also stated that they're in the middle of negotiating a private funding round at more than a $300 billion valuation. That round would include that $15 billion commitment from Microsoft and NVIDIA last month and could see the valuation go as high as $350 billion. Anthropic offered a very non-committal statement telling the FT it's fairly standard practice for companies operating at our scale and revenue level to effectively operate as if they are publicly traded companies. They added that no decisions had been made on whether or not to go public or timing for the IPO. Still, the news sets up a new competitive race dynamic next year, this time between OpenAI and Anthropic to list on the public markets. The Financial Times wrote, Anthropic's investors are enthusiastic about an IPO, arguing that Anthropic can seize the initiative from its larger rival OpenAI by listing first. Now, whichever order the IPO is happening, they're likely to be the two biggest public listings in history setting 2026 and or 2027 up to be another set of huge years for AI markets Lastly today a big new set of model releases comes from Mistral Mistral announced the new Mistral 3 open model family on Tuesday The lineup includes updates to the small Mistral models available in 3 billion, 8 billion, and 14 billion parameter sizes. Each small model has three different variants, a base model, as well as fine-tunes for reasoning and agentics. The smallest model can run on devices like smartphones and normal laptops. And while the small models are all very strong in their class, Mistral's new large model is also notable. Called Mistral Large 3, the model is a 675 billion parameter model that uses a mixture of experts' architecture with 41 billion active parameters. Mistral's benchmarking shows that the model is competitive with DeepSeek 3.1 and KimiK2, outperforming slightly on reasoning and scientific knowledge, and lagging a little on coding. The large model delivers best-in-class performance for multilingual prompts outside of English and Chinese, which is one of Mistral's big focuses. The other new feature is native multimodal capabilities across the entire family. Multimodality has proven to be a very useful feature in Google's models, allowing them to apply reasoning to image analysis and use cases like transcription. And since most of the Chinese open source models have been deploying image models as a separate system, native multimodality could be a big point of differentiation for Mistral. Mistral noted that they carried out the training run on a cluster of just 3,000 NVIDIA H200s, tiny compared to the clusters operated by the leading US labs, which contain over 100,000 GPUs. The big gap in the lineup is the lack of a reasoning model. That means that although Large 3 beats the Chinese non-reasoners in an apples-to-apples comparison, it falls short of the state-of-the-art performance of the Chinese reasoning models. Mistral chose to highlight their small models as a big step forward. They wrote, The next wave of AI won't be defined by sheer scale, but by ubiquity, by models small enough to run on a drone, in a car, in robots, on a phone, or a computer laptop. Speaking with VentureBeat, chief scientist and co-founder Guillaume Lampel discussed the use case for small models and how it fits with Mistral's business model. Mistral is now targeting enterprises that are experiencing failure with the large proprietary models. He said, sometimes customers say, is there a use case where the best closed source model isn't working? If that's the case, then they're essentially stuck. There's nothing they can do. It's the best model available and it's not working out of the box. And in those situations where those leading proprietary models are failing, Mistral is trying to deploy engineering teams to work directly with their customers. Said Lample, in more than 90% of cases, a small model can do the job, especially if it's fine-tuned. So it's not only much cheaper, but also faster, plus you have all the benefits. You don't need to worry about privacy, latency, reliability, and so on. In fact, it appears that a lot of Mistral's business is coming from companies who build agents on top of large closed-source models, only to find the result is cost-prohibitive. Lample said, they come back to us a couple months later because they realized we built this prototype, but it's way too slow and way too expensive. Some were disappointed with the release. AI content creator Theo writes, it's kind of sad to see the slow death of Mistral. Their new model is one, dumber than DeepSeek, two, three times more expensive than DeepSeek, and three, slower than GPT-5. Others, however, say, wait just a second. Anji Midha writes, these were trained on 3000H200s, a practice cluster, and yet, state-of-the-art zone. Mistral's 18K GB200 cluster comes online soon. Today's releases are a warm-up for the Mistral 4 family. It'll be an interesting few months for Frontier Open models. Certainly something we will be watching here. However, for now, that is going to do it for today's headlines. Next 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. 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Whatever the case, we're launching a new type of assessment called Plateau Breaker that, as you probably guessed from that name, is about breaking through AI plateaus. We'll deploy voice agents to collect information and diagnose what the real bottlenecks are that are keeping you on that plateau. From there, we put together a blueprint and an action plan that helps you move right through that plateau into full-scale deployment and real ROI. If you interested in learning more about Plateau Breaker shoot us a note contact at bsuper with plateau in the subject line Welcome back to the AI Daily Brief. Today, we are talking about everything that Amazon has unveiled so far at their AWS reInvent event. This is, of course, AWS's big annual event. And since AWS is the part of Amazon that is most connected to the broader AI world, it as the event where we most often get updates from Amazon around their AI strategy. You may or may not remember that back in 2022, AWS was actually planning on releasing something akin to ChatGPT that they were then calling Bedrock. But after ChatGPT launched on November 30th of 2022, and they saw how far ahead it was, they scrapped those plans and actually reconstituted entirely what Bedrock meant. Since then, Amazon hasn't exactly found its place in the AI narrative, even if with their cloud business, which remains the number one in the world, they have been a key part of the story structurally for many enterprises. At last year's AWS reInvent, we got a new family of Amazon models called Nova, which seemed to be making a bet on the idea of an expanding diversity of enterprise workloads, where the vectors of competition would not only be state-of-the-art performance, but also efficiency and performance for the cost. At this year's event, we have gotten, well, a little bit of everything. So the question was, and this was the question that we were asking in our December preview show, what this event and its announcements might do for Amazon's positioning relative to its cloud and model provider peers, and frankly, just how much enterprises have to care about all of this that's going on. Let's start with Amazon's update to their Nova family with the appropriately named Nova 2. As I mentioned, the Nova models were first released last year and consisted of four text models of various sizes as well as an image model. Nova 2 has done away with the dedicated image model by switching to a native multimodal architecture. The family includes a small reasoning model called Nova 2 Lite and a large reasoning model called Nova 2 Pro. There's also a dedicated speech-to-speech model called Nova 2 Sonic and a model called Nova 2 Omni that Amazon is referring to as a unified multimodal reasoning and generation model. In other words, Nova 2 Omni can process text, image, video, and speech inputs while generating both text and images. Now, Amazon is touting this as an industry first, and certainly being able to handle native video and speech inputs could open up a number of new use cases. Benchmarks were only shared for the Lite and Pro models and seemed decent, if unsplashy, across the board. There are a handful of categories, where the Nova models outrank models of the same class from Anthropic, OpenAI, and Google, but they tend to be clustered around specialized features like multimodal perception. Tool calling was also very strong, meaning these models could be useful as the foundation for agents. Notably, the models fell far short of state-of-the-art on Sweebench Verified, meaning that these are not going to become the new coding models of choice. If the benchmarks are nothing to write home about, in aggregate they seem to combine into a decent frontier model and certainly a big improvement over Nova 1. Independent benchmarking firm Artificial Analysis showed that Nova 2 Pro is in the same ballpark as Claude 4.5 Sonnet overall, and Nova 2 Lite is slightly ahead of Claude 4.5 Haiku. The models are not competitive with Gemini 3 Pro, GBT51, or Claude 4.5 Opus, but the question is whether they need to be. Artificial analysis noted that Nova 2 Pro completed their benchmark run at around 80% of the cost of Claude 4.5 Sonnet and about half the cost of Gemini 3 Pro. Alongside the new models, AWS launched a new service called Nova Forge that allows companies to train their own versions of the Nova family of models. The service is not cheap, starting at $100,000 a year. However, it is a pretty different type of offering, with Amazon providing access to various pre-training and post-training checkpoints. The idea is that enterprises can feed their own proprietary data, as well as industry-specific data, to come up with a frontier model customized to their needs. Chris Lowe, the CTO of Reddit, provided a testimonial for the service saying that it's quote, already delivering impressive results. He continued, we're replacing a number of different models with a single more accurate solution that makes moderation more efficient. The ability to replace multiple specialized ML workflows with one cohesive approach marks a shift in how we implement and scale AI across Reddit. Now, in terms of reactions, frankly, it's kind of too early to get much, and the way that Amazon rolls out models doesn't make it a lot easier. As Professor Ethan Mollick put it, Since Amazon makes it very hard to experiment with its new models, I haven't tried Nova 2 Pro yet. So it seems fine. They have never been at the cost-performance frontier, and the new Nova 2 continues to generally lag other AIs with scattered higher scores on some agentic benchmarks. On Nova Forge, there was a little bit more intrigue. AI entrepreneur Eddie Gray wrote, I need to research more, but if what they say is true, Amazon is the first to do this. AWS Nova can now take a company's own proprietary data and let that data train their own LLM just for the customer to use at a large scale. It can also allow them to strategically bring in external data sets as needed to merge them with their data. The result is a much more valuable LLM model tailored to each company and customer. So the things that are interesting to me about this set of announcements is that they sort of both represented doubling down on theses, which, while haven't being disproven yet, have at the very least taken longer to come to fruition than some might have expected. It seemed pretty clear when Nova was released that Amazon's bet was that as AI workloads matured and got more diverse, there was going to be a need for models that were not state-of-the-art but were more efficient and cost-effective for certain categories of use cases. That thesis may end up proven correct, but it certainly hasn't been the major emphasis this year when it comes to enterprise AI. In many cases, we've still been living at the state-of-the-art, and enterprises have been focused on the new capabilities that come online with each new SOTA model release. However, while the thesis hasn't fully played out yet, it seems somewhat inevitable to me that when we do reach full-scale across the enterprise, there will be far more cost consciousness and consideration of the economics of AI deployments as we get more specific about what different use cases need which type of capabilities. On the Forge front, there has been this sense since the very beginning of ChatGPT that enterprises customizing their own models, either fully training them from scratch, or having them plugged into novel datasets via RAG, or generally whatever other strategies have been available to connect the proprietary and non-public data of a company with the underlying models. And while the uses for this seem intuitive, again, they just haven't been the mainstream in where enterprises are. Once again, I wouldn't be ready personally to write off the thesis that this would be valuable at some point, but we're still very much in the early stages of discovery around what the demand for that type of product looks like The point in both these cases though is that what looks like a shoulder announcement now could end up paying off for Amazon at some point in the future Now moving on to the 2025 watchword of agents AWS previewed a trio of specialized agents There is Kiro, a software development agent, AWS security agent that can automate application security, and AWS DevOps agent for IT operations. Kiro was pitched as a coding agent that can work for days without human intervention. Now, AWS wasn't clear on whether this was a neutral harness that could be driven by a proprietary model or if it was locked to the Nova models. Still, people are pretty eager to see how strong AWS's version of this type of long-horizon coding agent is once it's actually released. The security agent got a lot of attention as this is a big gap in the current AI coding space. The idea is to have an always-on proactive agent that can autonomously hunt for bugs and exploits. Amazon says that it can operate at every stage of the development process from design to deployment. Shelley Kramer of AR Insights was in the crowd and posted, there's every reason for the spontaneous applause that happened when Matt Garman announced the launch of AWS Security Agent. This is incredibly significant as it delivers security feedback at every stage of development, ensuring that potential issues are caught early, reducing the risk of costly rework and strengthening overall product security. The DevOps Agent, meanwhile, is designed to be the first actor during a triage situation. If your application goes down, the agent can step in, route alerts to the correct people, and get to work diagnosing and maybe even fixing the issue. Not a glamorous agent, but the kind of thing that could be invaluable for software developers. And I will say that I think that Amazon's agent strategy, at least, becomes a little bit more clear when you see these all together. These agents are designed to function as self-contained digital workers that can extend your team. They are not generalist agents. They are specific to a type of work. And it's very clear that Amazon is making a bet on practical, real integration here. Together, these agents mark the beginning of a new era in software development. These frontier agents don't just make teams faster, they fundamentally redefine what's possible when AI works as an extension of your team, delivering outcomes autonomously across the software development lifecycle. Now, I mentioned at the beginning of the show that Bedrock was originally going to be the name of their chatbot, but instead became the name of their platform where Amazon allowed their cloud customers to access multiple models all from a single place. And when it comes to this re-invent, the Bedrock big release is actually a ton of little releases. The Bedrock platform added 18 open-weight models, including the latest Mistral 3 model family. But one thing that was not here is any sort of update that adds access to the proprietary models from companies like OpenAI. We'll talk a little bit more towards the end about what that suggests for their strategy and whether there might be something that's changing there. AWS also used the event to formally launch their Tranium 3 Ultra Server and tease their next-generation Tranium 4 chips. The Ultra Server is their data center-scale unit that can host 144 chips. AWS said that thousands of Ultra servers can be networked to provide up to a million coherent Tranium 3 chips. And while this sounds impressive, it is not an apples-to-apples comparison to the thousand-strong NVIDIA clusters, so we'll have to see how the chips perform in the wild. AWS said that Tranium 3 was four times faster, had four times as much memory, and are 40% more efficient than the previous generation. Interestingly, Tranium 4 will be fully compatible with NVIDIA's NVLink Fusion networking system, meaning AWS chips will be interoperable with NVIDIA GPUs. Amazon didn't announce a timeline for the Tranium 4 release, so we'll have to wait until next year to see how they stack up. But in a sign of the narrative times, rather than being written off as previous Amazon chip releases had been, the Wall Street Journal was quick to declare Tranium quote another threat to NVIDIA. Investors have of course been hooked recently on the narrative that Google's TPUs could disrupt NVIDIA's market dominance, so Tranium apparently fits right alongside that story. Now in the real world it pays, I think, to be circumspect of whether either of these chips can gather significant market share, but it is a sign of the times that investors are taking the threat to NVIDIA seriously. One curveball was the announcement of a new product called AI Factories. With this product, AWS is getting into the on-premise compute sector. The idea is that companies and governments can supply their own data center, while AWS supplies the AI servers and hardware management. The service can also be tied to other AWS cloud services, giving customers something of the best of both worlds. The product, of course, reflects a growing concern over security and data sovereignty. By hosting their own hardware, customers can ensure they're not sending their data to an AI company at all, with the models hosted on their own hardware in their own facilities. Throwing some cold water on the idea that Tranium is somehow about to take over the industry, this product is a partnership with NVIDIA, who will be the exclusive hardware provider. Now, it's clearly a response to market demand, so it'll be interesting to see how many companies start setting up their own private clouds using this white label service. Taking a step back, in a lot of ways, I think that this reInvent is in some ways a doubling down on the long-term vision of enterprise AI that Amazon has been pursuing. It has a lot of incremental updates and developments, some of which will be very valuable to business customers, but it doesn't feel to me like any core thesis has changed. To the extent that anything has changed, there does seem to be some new amount of flexibility and openness to not trying to lock people into the AWS ecosystem. The information wrote a piece called In a Reversal, AWS Makes It Easier for AI Customers to Use Rival Clouds. And while they presented as a concession to the reality of being out-competed in AI for Amazon, I think that there's a broader thing going on underneath, which is just that it's going to be very hard for anyone in such a fast-moving field where leadership changes on a nearly weekly basis to try to play the old-style games of lock-in. I think companies are assessing that customers simply will not accept that. And so there's going to be a lot of really interesting new types of frenemy relationships. The rising tide truly is lifting all boats. And to me, at least, I think it makes sense to reevaluate the old enterprise playbooks in the way that Amazon seems to be doing. Now, in terms of what this all adds up to for enterprise listeners and people who are trying to figure out how much they have to be paying attention, the way that I would put it is this. I don't think that there's anything here that means that all of a sudden you have to rush out and start paying attention to any one thing that was announced. However, I do think that for many enterprise buyers, being at least familiar with what Amazon has cooking, not just now, but in terms of the trajectory of where they're headed, feels like good proper due diligence. But of course, I'm sure we'll hear more throughout the week. And if there is anything notable, I will do an update. For now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time, peace. Our Antes Antes