What We Learned About Amazon’s AI Strategy

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 is

the event where we most often get updates from Amazon around their AI strategy. You may or may not remember

strategy. You may or may not remember that back in 2022, AWS was actually planning on releasing something akin to Chat GPT that they were then calling

Bedrock. But after Chat GPT launched on

Bedrock. But after Chat GPT 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

enterprises. At last year's AWS reinvent, we got a new family of Amazon models called Nova, which seem 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

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

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

multimodal architecture. The family includes a small reasoning model called Nova 2 Light and a large reasoning model called Nova 2 Pro. There's also a dedicated speech-to-pech 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

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

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 light and pro models and seemed decent, if unsplashy across the board. There are a handful of categories

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

for agents. Notably, the models fell far short of state-of-the-art on Sweetbench verified, meaning that these are not going to become the new coding models of choice. If the benchmarks are nothing to

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 Light is slightly ahead of Cloud 4.5 Ha coup. The models are not competitive

coup. The models are not competitive with Gemini 3 Pro, GBT 5.1 or Cloud 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 Cloud 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

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

Slow, the CTO of Reddit, provided a testimonial for the service, saying that it's quote already delivering impressive results. He continued, "We're replacing

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 Mllik 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

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

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

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

represent a doubling down on thesis which while haven't disproven yet have at the very least taken longer to come to fruition than some might have expected. It seemed pretty clear when

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 proving 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 soda model release. However, while the thesis

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

costconsciousness 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

capabilities. On the forge front, there has been this sense since the very beginning of chatbt that enterprises customizing their own models, either fully training them from scratch or

having them plugged into novel data sets 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

models. And while the uses for this seem intuitive, again, they just haven't been the mainstream and where enterprises are. Once again, I wouldn't be ready

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,

like. The point in both these cases, though, is that what looks like a shoulder shrug 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 Kira, a software development agent, AWS security agent that can automate application security, and AWS DevOps agent for IT operations. Kira 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

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

deployment. Shelley Kramer of AR Insights was in the crowd and posted, "There's every reason for the spontaneous applause that happened when Matt Garmin announced the launch of AWS Security Agent. This is incredibly

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,

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

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.

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

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 life cycle. Now, I mentioned at the beginning

cycle. 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 reinvent, the Bedrock big release is actually a ton of little releases. The Bedrock platform added 18

releases. The Bedrock platform added 18 openweight models, including the latest Mistl 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

ultraserver is their data center scale unit that can host 144 chips. AWS said

that thousands of ultra servers can beworked to provide up to a million coherent tranium 3 chips. And while this sounds impressive, it is not an applesto apples comparison to the thousand strong Nvidia clusters. So we'll have to see

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,

previous generation. Interestingly, Tranium 4 will be fully compatible with Nvidia's NV Link 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

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

Nvidia. Investors have of course been hooked recently on the narrative that Google's TPUs could disrupt Nvidia's market dominance. So Tranium apparently

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

Nvidia seriously. One curveball was the announcement of a new product called AI factories. With this product, AWS is

factories. With this product, AWS is getting into the onremise compute sector. The idea is that companies and

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

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

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 present it 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 fastmoving 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

accept that. And so there's going to be a lot of really interesting new types of frenmy relationships. The rising tide

frenmy relationships. The rising tide truly is lifting all boats. And to me at least, I think it makes sense to re-evaluate the old enterprise playbooks in the way that Amazon seems to be doing. Now, in terms of what this all

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.