Why agentic AI makes the case for a systems and silicon provider, and how large the markets around it can become

AMD's AI strategy was set well before the industry started talking about agents, and we do not think the company needs to reinvent it. The pieces have been there for years in its CPU, GPU, networking, and software investments. We have long viewed AMD as a company building toward complete systems, and agentic AI makes the case for that direction easier to see. The work now is execution, which means putting those pieces into systems customers can deploy and run efficiently.

The CPU work AMD has done in cloud infrastructure extends naturally to the work that surrounds AI agents. Those servers already host accelerators, run applications, and move data. Agents add new demands as they call models, use company software, and check their own results, but much of the underlying work draws on the same foundation. The architecture gives AMD room to adapt to those workloads, and its networking investments keep data moving between the parts of the system. New software and integration work is still required. The difference is that AMD is building on an installed base it already has.

So when we size the agentic opportunity, we think about AMD as a systems and silicon provider. For the operators and enterprises buying this infrastructure, the value comes from how the parts work together and how easily they fit into what is already in place. AMD can work with customers and partners on an integrated design, or it can supply the compute and networking for a system the customer helps shape. We consider that choice more important than the number of products in the catalog, because customers arrive with different applications, power limits, and supplier relationships. The markets growing around those requirements are how we size it.

At its Advancing AI event in July 2026, AMD put its total addressable market at approximately $2 trillion by 2030 across data center, PCs, edge, and embedded processors. That is AMD's estimate. The direction it describes, steadily expanding demand for compute, is consistent with what we see in our own models, but we are examining a narrower set of markets here: server CPUs, merchant GPUs, networking, and private AI infrastructure. Those definitions differ from AMD's, so our forecasts describe where we expect spending to grow without attempting to reconcile to the $2 trillion figure or to predict AMD's revenue. [1, 3–6]

A strategy built around the data center

The July 2026 event showed how AMD's data center investments are coming together. The company presented CPUs, GPUs, networking, and software as parts of one coordinated system for training, inference, and agentic AI, and it extended its infrastructure roadmaps through 2030. We read that as the continuation of a direction AMD has pursued for years. The Pensando acquisition, completed in 2022, is a useful marker, because it brought networking and distributed data center services into the portfolio well before the current wave of agent adoption. [1, 2]

The portfolio has grown and the AI market is far larger than it was, but the reason to invest across the data center has stayed the same. A customer buying a faster processor still needs the rest of the system to keep that processor supplied with work, and it still has to install the system, connect it to existing applications, and support it over time. An industry can only scale as fast as its deepest bottleneck, and the same is true of a single deployment: the accelerator is only as productive as the host, the network, and the software around it. AMD's approach gives customers a choice between integrated designs developed with partners and individual components for their own infrastructure. Helios, for example, is a reference design for partner systems rather than a requirement to buy every part of the data center from AMD. [9]

Agentic AI adds more work around the model, which is why we think this broader approach is well timed. An agent may call a model several times, use company applications, and check the result before it finishes a job, and each of those steps draws on infrastructure beyond the accelerator. As that activity grows, we expect more spending on the servers and networks that support it. That surrounding demand is an explicit part of what we model.

The markets AMD is positioned to serve

The simplest way to frame the opportunity is to look at each market on its own terms. Our models point to annual server CPU silicon value of approximately $251 billion by 2030, a merchant GPU supplier opportunity approaching $1 trillion, and roughly $190 billion in AI networking silicon. These are Creative Strategies outlooks, separate from AMD's estimate. The more interesting point is that AI is adding infrastructure demand to all three markets at the same time, so AMD has more than one way into the same customer's budget. [3–5]

Our latest CPU forecast reaches approximately $251 billion of annual silicon value in 2030, of which about $208 billion is merchant CPU sales. The remaining $43 billion is the estimated value of processors that cloud operators design for their own use, which is not revenue available to a merchant supplier. AMD's opportunity is substantial without counting internal chip value as an addressable sale, and counting it would overstate the market open to any merchant supplier. The forecast also remains conditional on how much agent activity turns into sustained CPU work and new equipment purchases, and that bridge from activity to purchases is the assumption we watch most closely. [4]

Exhibit 2: Scope of each 2030 outlook

Source: Creative Strategies. See source and forecast definitions at the end of this paper.

Most of the growth in that forecast comes from value rather than volume. Packages about double, but silicon value roughly quadruples from about $62 billion in 2026 (Exhibit 3), because the sockets doing the most AI work are the most expensive and we expect CPU prices to keep rising while capacity stays tight. AMD's own published 1,000-unit list prices show the premium for accelerator-host parts: the 64-core EPYC 9575F lists at $11,791, against $9,826 for the 64-core general-purpose EPYC 9555. These are product list prices, not realized AMD ASPs or inputs to our 2030 forecast. [12] By 2030 we expect accelerator hosts to account for close to half of CPU value and agent-native CPU racks (Vera, Arm AGI, AMD and Intel agentic racks; about 25 million CPUs) for nearly a third. [4]

Our Agentic CPU Guidebook follows that work through three places: the CPUs hosting accelerators, the separate environments running agent tools, and the business applications those tools call. A coding agent illustrates the pattern. It asks a model for a change, runs tests, queries a service, and then asks the model what to do next. Some of that activity fits in servers the customer already owns. As sustained demand consumes that spare capacity, the customer has a reason to add equipment, or to buy a larger system at replacement. More agents therefore create an opportunity for CPU suppliers, but the number of agents alone does not tell us how many processors customers will buy. We would size it from the work instead: the peak rate of tasks, the CPU time each task consumes, and the utilization an operator is willing to run at, with existing headroom netted out.

For EPYC, this creates several routes into the same customer's spending. AMD can supply the host in an accelerator system and compete for the tool and application servers around it, including in deployments where another supplier serves the model. The CPU opportunity therefore does not depend on winning the GPU socket, and we think that point is less appreciated than it should be. Faster cores help when a task is waiting on a dependent tool result, while more cores help when many jobs are ready to run at once, and both need enough memory behind the cores. We see that range of requirements as a reason AMD's broad CPU portfolio fits the market, independent of any view on share.

Merchant GPUs are the largest market in this discussion. Our supplier models point to annual GPU and compute revenue approaching $1 trillion by 2030 for the merchant GPU suppliers, with custom accelerators left out of the figure. The scale reflects our expectation that more applications will use models continuously, and that inference creates recurring demand well beyond the initial work of developing and training those models. [3]

Instinct gives AMD a way to compete for that spending as customers add inference capacity. Its chiplet design brings compute and memory interfaces together in the package, which supports the memory capacity and bandwidth that model serving requires. More memory can let a model fit on fewer GPUs or leave room for more concurrent sessions, although the benefit depends on the model and the serving configuration. We expect buyers to compare performance and cost on their own workloads, and AMD has a large and growing market in which to make that case. [10]

Our AI networking silicon outlook reaches roughly $190 billion by 2030. As customers build more compute, they also need to connect it and move data through it efficiently. AMD participates in parts of that market through its networking portfolio, and it does not need to supply every networking component for those investments to pay off across its data center business. [5] Slow communication leaves expensive compute waiting, so networking sets the return on a much larger investment in processors and servers. AMD's networking products let it compete for part of this spending while also improving how its own compute performs inside the customer's system.

Adding these forecasts into a single AMD market number would hide differences in what each one covers and where the company actually competes. Read side by side, they show AMD with relevant products in several growing markets at once, and that is the basis for our view that the portfolio is pointed where spending is going.

Enterprise AI creates room to grow with partners

We also expect enterprises to run some AI on infrastructure they own while continuing to use cloud services for other work. A business with sensitive data and applications already in its data center has good reasons to keep some inference close to those systems. As usage becomes more predictable, it also gains a clearer view of how much equipment it needs and whether it can keep that equipment busy enough to justify owning it.

Our server model puts annual private AI hardware spending at roughly $63 billion of complete accelerated servers by 2030 in the base case. That figure includes privately owned systems in colocation as well as equipment on the customer's premises, so it is broader than strictly on-premises spending. We treat it as another view of where demand develops and do not add it to the CPU, GPU, and networking forecasts, since those components already sit inside the systems customers buy. [6]

Dell and HPE give enterprises a way to buy and support AMD-based AI infrastructure through supplier relationships they already have. The opportunity extends beyond those two companies. Cisco, Lenovo, and Supermicro also bring EPYC-based systems into enterprise environments, with accelerator support varying by product. These partners turn silicon into equipment a customer can install, connect, and operate, and AMD can grow through EPYC in the servers that support enterprise AI and through Instinct where customers choose AMD for model serving. [7, 8, 11]

The same strategy reaches customers through more than one route. Large cloud operators buy for their own services, specialist AI clouds offer access to accelerator capacity, and enterprises buy systems for their own facilities or for private deployments in colocation, where the provider supplies the space, power, and cooling the customer may lack on premises. Each route has a different buyer and a different deployment schedule, but all of them create demand for compute and networking. We see the partner network as the way AMD reaches that demand without having to own every part of delivering the service.

Inference changes how customers judge the investment

Our thesis is that inference becomes the main recurring AI workload for data center operators as more applications use models in daily work. Training creates and improves the models, and inference puts them to work across customers and tasks. Agents add to that demand through repeated model calls, longer inputs carrying tool results and company context, and additional reasoning before they act. More demanding jobs may also call for a larger model, although a smaller model can still be the better choice for routine work. Infrastructure demand therefore has several sources of growth beyond a simple count of users or requests.

Our conviction is stronger on the direction of that demand than on its timing at any one operator, and we are not assuming inference already represents most data center activity. Adoption will track how much work companies trust AI to do. Drafting material for a person to review creates one pattern of demand, while allowing an agent to carry out a complete process creates another, with many more calls to models and company systems along the way.

Once inference becomes a regular service, its cost becomes part of running the business. A cloud operator has to supply answers at a price customers will pay, and an enterprise has to get enough value from the work to cover its infrastructure and operating expense. That puts total cost of ownership into the buying decision, and TCO here covers the equipment, the power, the software, the deployment work, and how much of the capacity is actually used. We keep coming back to utilization in that calculation. A lower-priced system that spends too much time waiting can cost more per completed task than a higher-priced system serving steady demand, and the arithmetic in Exhibit 6 shows how quickly that happens. Faster CPU execution can shorten a test or database operation that holds up the next step, while better host processing can keep an accelerator supplied with requests. Those are different benefits and should be kept apart, since a GPU serving other customers need not sit idle while one agent waits on a tool. The comparison has to follow the work through the system and include the cost of operating it.

Lower inference costs should also let companies apply AI to tasks that are too expensive today. Our growth outlook assumes that this broader use, together with more demanding tasks, absorbs enough of the efficiency gains to keep spending rising. There is a real sensitivity here. GPU revenue could fall below our forecast even as customers get more AI output, because the price of producing that output may fall faster than usage grows (Exhibit 7). This is the variable we find hardest to model, and it cuts both ways: if usage responds to lower prices faster than we assume, the upside surprise is just as real.

Power availability adds another limit on how much work an operator can serve. With a fixed amount of power, the customer has to weigh how much output a system delivers within that limit alongside its purchase price. Getting more work from existing capacity is worth the most while the customer is waiting on a new building or a new power connection, and suppliers that improve that result give operators a reason to evaluate their systems on more than the price of the chips.

Why the broader approach fits this market

AMD's position across compute and networking gives it more than one lever on the cost and performance of the customer's system, and software and system design decide how much of that benefit reaches the application. With inference running day after day, customers will have an ongoing measure of whether the equipment is serving enough work at a competitive cost. That makes this a prove-it story, and it is the test AMD's broader approach will be judged on.

Customers also have applications and supplier relationships they want to keep as they add AI. AMD's open approach gives them room to work with system and software partners, choose individual components, or adopt an integrated design where that saves time. Openness and integration are compatible. AMD can integrate the parts that need to work closely together and leave the rest of the deployment open, which is how its systems work and its merchant component business support each other. The counterargument is fair. Breadth is not proof of lower cost, and we do not assume that every AMD system will carry a lower total cost of ownership. Buyers will test their own workloads and account for the effort of moving software and reaching production. Our reason for viewing the portfolio as an advantage is narrower. It gives AMD more ways to address the cost and performance limits in that comparison than any single product would.

Two developments would change our view. The first is evidence that existing headroom absorbs agent activity for much longer than we expect, which would delay the bridge from activity to new CPU purchases. The second is unit cost declines outrunning usage growth for more than a year or two, which would pressure the GPU outlook even as AI output rises. We will be watching both.

Our strongest conviction is that AMD's strategy fits the direction of demand. The size and timing of these markets remain sensitive to adoption, software efficiency, and how quickly customers can bring new capacity into service, and those assumptions can move our forecasts. They do not remove the need for processors, networking, and software that work well together. AMD is positioned to compete for that spending, and how much it wins will depend on the systems it and its partners deliver.

From the market opportunity to the technology

The market outlook gives us a starting point for evaluating the technology. Our CPU forecast reflects more work across the data center, the merchant GPU outlook reflects more use of models, and the networking forecast reflects the need to connect that capacity. Enterprise deployments add another route to customers through AMD's partners. Across all of them, recurring inference puts more weight on the cost of operating the complete system.

AMD has been investing toward those requirements for years. The next question is how its products and architecture deliver on the strategy, from the compute portfolio and Helios to networking and software. Paper 2 takes up that technology discussion and how those parts work together to support the market outlined here.

Sources and forecast definitions

[1] AMD, "AAI 2026: AMD Delivers Full-Stack Compute for the Agentic AI Era," July 23, 2026. AMD estimates an approximately $2 trillion total addressable market in 2030 across data center, PCs, edge, and embedded processors. This is AMD's forward-looking estimate, not a Creative Strategies forecast. Directional consistency refers to the expansion of compute demand, not a like-for-like validation of the total. https://ir.amd.com/news-events/press-releases/detail/1294/aai-2026-amd-delivers-full-stack-compute-for-the-agentic-ai-era

[2] AMD, completion of Pensando acquisition, May 26, 2022. https://www.amd.com/en/newsroom/press-releases/2022-5-26-amd-expands-data-center-solutions-capabilities-wit.html

[3] Creative Strategies AI Accelerator Core and AI Industry TAM models, October 2026. "Approaching $1 trillion" refers to 2030 accelerator package value, including HBM, for the identified merchant GPU suppliers. It excludes custom ASICs and is a directional outlook, not an exhaustive merchant GPU market size or a supplier share forecast.

[4] Creative Strategies Server CPU TAM Forecast, October 2026, companion model to the Agentic CPU Guidebook. Annual server CPU silicon value in 2030, combining merchant CPU revenue and the imputed value of processors cloud operators design for their own use. It covers general-purpose, accelerator-host, and agent-native CPU demand; memory and complete systems are outside the figure. Captive value is not merchant supplier revenue.

[5] Creative Strategies Core Networking Model, October 2026. Semiconductor content inside AI networking hardware. It is distinct from the broader finished-hardware market and should not be added to it.

[6] Creative Strategies AI Server Three Markets Model, October 2026. Complete accelerated servers owned by enterprises and institutions, including privately owned equipment in colocation. It excludes cloud-owned infrastructure, facilities, power and cooling, software, and services. It is not AMD revenue and is not additive to the component forecasts.

[7] Dell, "Dell and AMD Are Expanding What's Possible for On-Premises AI," May 7, 2026. Supports the partner and deployment discussion, not the size of our market forecast. https://www.dell.com/en-us/blog/dell-and-amd-are-expanding-what-s-possible-for-on-premises-ai/

[8] HPE, HPE and AMD compute solutions portfolio, accessed September 14, 2026. Product coverage supports the partner discussion and does not imply that every HPE private AI offering uses AMD accelerators. https://www.hpe.com/us/en/solutions/amd.%20html.html

[9] AMD, Helios overview and FAQ, accessed September 29, 2026. Helios is a reference design for OEM and ODM partner systems. https://www.amd.com/en/products/rackscale-solutions/helios.html

[10] AMD, CDNA architecture and Instinct MI350 microarchitecture, accessed September 29, 2026. Chiplet and HBM integration support the architecture discussion, not a universal competitive performance claim. https://www.amd.com/en/technologies/cdna.html; https://rocmdocs.amd.com/en/latest/reference/gpu-arch/mi350.html

[11] AMD, "The Agentic Era Runs on a CPU Foundation," July 2026, and EPYC OEM portfolio information. https://www.amd.com/en/blogs/2026/the-agentic-era-runs-on-a-cpu-foundation-and-it-s-called.html; https://www.amd.com/content/dam/amd/en/documents/epyc-business-docs/infographics/cpu-you-choose-can-make-or-break-your-investment.pdf

[12] AMD, 5th Gen AMD EPYC launch announcement, October 10, 2024, and AMD public pricing table, accessed October 6, 2026. Published 1KU USD list prices: EPYC 9575F (64 cores), $11,791; EPYC 9555 (64 cores), $9,826. The 9575F is designed for GPU host systems. Product list pricing is illustrative, not a market ASP or forecast assumption.

Exhibits 6 and 7 are Creative Strategies illustrations built on hypothetical, indexed inputs to show a mechanism. They are not measured results or forecasts.

© 2026 Creative Strategies. Forecasts are Creative Strategies outlooks, not AMD guidance. Product capabilities are described from public AMD documentation and are not claims of universal performance or measured customer savings.

We're in the age of preference. RTX Spark gets Windows close enough to the performance I've liked so much in the M5 Macs that the computer I want to use becomes a much more interesting question. The operating system, the design, and the configuration I prefer can carry a lot more weight now.

Apple has been building chips with this approach for about five years: a powerful CPU and GPU sharing a large pool of memory in a laptop. For the combination of performance and local AI capability I wanted, that meant buying a Mac. RTX Spark is the first Windows competitor that makes this feel like a real choice to me across AI, CPU performance, and gaming.

I know some people don't like seeing every new Windows chip compared directly with a Mac, they are different markets. In this case, I think the comparison is useful. You'll see M5 Max results throughout this review because they show the performance we had available before, alongside what RTX Spark now offers. These machines are really close in what they let me do, even when individual benchmarks have larger gaps. The reason I want to do this is simple, and why I think this preamble is important, a frame of reference to show how close these are and what is now available in the Windows ecosystem.

And there are a lot more options. Six OEMs are building computers with RTX Spark, with different designs and configurations running Windows. Linux could add another option, depending on support. We've gone from looking to one manufacturer's product lineup for this particular combination to having a much broader selection of computers. We'll talk more about those designs later. For now, I want to look at what the chip makes possible, with the benchmarks as one part of deciding which of these computers someone might actually prefer.

The CPU still matters for AI

At one point while testing RTX Spark, I had 14 projects compiling and 15 coding subagents running WHILE playing a game. The fans were spinning, but I could still browse the web without the computer feeling overwhelmed. I really liked that. There was a ridiculous amount of work happening in the background, and I could keep using the machine.

This is increasingly how I use a computer. I have agents writing code, builds running, and applications open so I can see whether any of it actually works. Even when the model is running in the cloud, the computer on my desk has plenty to do. Add a local model, and the GPU and memory become a much bigger part of the experience too.

The coding agents in that busy session were using cloud models. My computer was doing the builds, running the tools, and opening the applications they produced. That distinction matters because it's easy to talk about AI performance as if the only thing happening is a model generating tokens. When an agent writes code, the CPU still has to do the work that follows.

NVIDIA's 20-core N1X configuration has ten Arm Cortex-X925 cores and ten Cortex-A725 cores. In my Geekbench 7.1 results, single-core performance was close to the Core Ultra 9 386H, with a much stronger multicore result. M5 Max was ahead of both.

My test results. Higher is better.

Spark scored about 36% higher than the Intel system in multicore. Against M5 Max, it was about 30% lower in single-core and 32% lower in multicore. I also wanted to see how those scores translated into the development work I use these machines for.

I tested compilation, bundling, and type-checking across Go, Rust, TypeScript, and Python. These are the sorts of jobs that keep happening while a coding agent works through a project.

My average test times. Lower is better.

The Mac finished every one of these tests faster. Kubernetes took about 42 seconds on Spark and 25 on M5 Max. TypeScript type-checking took almost twice as long on Spark. The bundling test also favored the Mac, although we're talking about roughly a third of a second there.

I care more about the longer waits. An agent can build, find a problem, make a change, and build again several times before it's done. Saving time on each pass helps, even if the model itself is running somewhere else.

Spark still felt comfortable to use with all that work going on. I haven't run the same multitasking session side by side with the Mac, so I can't rank them on responsiveness. But I was happy with how much I could leave running without making the rest of the computer unpleasant to use.

A Blackwell GPU with room for local models

The larger N1X configuration has 6,144 CUDA cores. For local AI, what interests me just as much is pairing that Blackwell GPU with up to 128GB of unified memory. Having access to NVIDIA's GPU software with that much system memory opens up more room to experiment with local models.

The memory is shared by the CPU and GPU, so Windows and my other applications need some of it too. The GPU's allocation also matters. A model needs space for its weights, working memory, and the context it keeps as a conversation grows. Fitting the model into memory doesn't mean every possible conversation or workload will fit alongside it.

Memory speed matters as well. NVIDIA specifies up to 300GB/s of memory bandwidth. There's also a 600GB/s bidirectional NVLink C2C connection between the CPU and GPU dies, but that's a different connection. The larger number isn't the bandwidth available for reading model weights from system memory.

CUDA is a big part of why this combination appeals to me. It also doesn't remove the need to check software support. This is a Windows Arm system, and the runtime needs to support the model, precision, and operations I want to use. How well those pieces work together matters a lot more to me than the CUDA core count by itself.

How long am I actually waiting?

When I run a language model locally, there are two parts of the response to think about. First it processes the prompt, which is called prefill. Then it generates the answer, which is called decode. Both can happen on the GPU. The CPU work I described earlier is the work around the model, like compiling code and running applications.

Prefill can process many input tokens in parallel. Decode generates the answer a little at a time and, in many low-concurrency workloads, spends much of its time waiting on memory. Caching, batching, and the model itself can change that balance. NVIDIA has a useful explanation of how this works.

I used AgentPerf to look at decode speed and end-to-end throughput. Both figures below are output tokens per second, but end-to-end throughput includes the time spent waiting before those output tokens arrive.

My initial AgentPerf results, using the 128GB Spark configuration with the larger GPU. I still need to reconcile model precision, runtime settings, and replay configuration across the systems, so these are preliminary comparisons.

If I only looked at decode speed, the Mac would win every test here. With Qwen 3.8 Flash Next, it generated tokens about twice as fast. Include the rest of the request, though, and the two were within about 2% of each other. This is one of the main benefits of RTX Spark, the fp4 CUDA accelerators really helps with prefill performance. While Qwen 3.8 Flash N

The Qwen 3.8 27B run with DFlash 2 went further: Spark recorded 2.61 times the end-to-end throughput despite decoding more slowly. I can't pin that difference on prefill or the GPU from these totals alone. Cache behavior and runtime settings can affect the result too. With Liquid LM, the Mac was ahead on both measures.

This is why I want to see more than decode speed when I'm looking at local AI performance. I care about how quickly the answer arrives, including the time before it starts appearing. A faster stream of text doesn't help as much if I've spent longer waiting for it to begin.

AgentPerf Local replays recorded agent conversations to measure model-serving speed. It doesn't score the quality of the answers, and its default mode doesn't execute the tools. These results cover the model's response; the development tests above cover some of the work that follows.

NVIDIA's AI results

NVIDIA's own testing gives another useful example of that initial wait. In its Qwen3.8-27B AIPerf run on a 64GB ProArt P16, a 64K-token input and 1K-token output took 136.37 seconds to reach the first token and 197.14 seconds overall.

That's more than two minutes before the answer starts, or about 69% of the total time. NVIDIA disabled prompt caching for this test, so an ongoing conversation that reuses cached context can behave differently. It's a useful reminder of how much the prompt and cache settings matter when comparing results.

NVIDIA also supplied image and video generation results from a 128GB ProArt system. These are its measurements; I haven't reproduced them myself.

A roughly 15-second image generation with FLUX2.Klein is the kind of use I'd be interested in trying locally. The settings matter here: all three workloads use NVFP4, and the table includes the steps and output sizes. I'd want to look at the output quality alongside the time before comparing them with another system or a higher-precision run.

Where I land

The big takeaway for me is how close these machines are in the performance they make available for this work. M5 Max finished my development tests faster, and there are meaningful differences between individual results. I still came away thinking Spark is a really good alternative for someone who wants to do this work on Windows.

For the combination of performance, large unified memory, and local AI capability I wanted in a laptop, a Mac had been the obvious choice. Spark gives me another option. I can get NVIDIA GPU acceleration and a large shared memory pool in a Windows machine I can also use to build and test Windows applications.

Operating-system preference matters a lot here. Some people prefer Windows, and having a Mac finish a benchmark faster doesn't change the operating system they want to use. They should have good options for this kind of work too.

With Spark, that choice also extends to different manufacturers and different laptop designs. Someone can pick the size, design, and operating system they prefer while getting performance that's close to what has made the M5 Macs so compelling. It doesn't need to win every test to be a good choice. Having this level of performance available across more Windows laptops is a big deal to me.

Gaming

I also played Control Resonant while two coding agents worked on native Windows applications. One was working on GPU shaders for a UI graphic, and the other was improving an agent workflow. The game felt smooth, and I didn't notice frame drops in that session. I wasn't recording frame times, but I liked being able to play while that work continued.

NVIDIA's supplied gaming results include 135 average FPS in Cyberpunk 2077 on a 64GB ProArt P16. That's at 2560×1440 output with RT Low, DLSS Balanced, and 3× frame generation, so the number includes upscaling and generated frames. It's NVIDIA's measurement; I haven't reproduced it myself.

Alongside HomeView, Apple announced Apple Home Collection, a lineup of cameras, a video doorbell, a door lock, thermostats and sensors designed with LG Electronics and Schneider Electric, as well as a new Apple TV 4K and HomePod mini 2.

A small screen with a clear job

Seeing HomeView in person in New York, the first thing that struck me was how small it is. The rumored images made it look far bigger. It is a square 7-inch screen on a domed speaker base, and it reads as a cross between Apple Watch and the iMac G4.

The form factor makes clear that Apple designed HomeView to sit apart from the devices people already use for consumption at home, such as iPhone, Mac, Apple TV and iPad. Its role is to give a view of the home and the people who live in it.

The household and the individual

HomeView brings together everyone who lives in the home through their Apple ID and iPhone. This is what I hoped Apple would deliver. The household, whether a family or roommates, shares photos, music and control of the home, and guests can join in. Each person also gets their own experience. HomeView recognizes up to six faces, and someone's messages or calendar only show up when that person is in front of it with their iPhone nearby, a sensible safeguard for a screen anyone in the house can walk up to.

Apple made a deliberate choice here, and it makes HomeView a smart home hub in a different form than many of us, me included, expected.

Apple Watch DNA

When the rumored images circulated, all I saw was a watch, and the final product confirms it. You swap faces on HomeView the way you do on your wrist, whether that is a photo album, an ambient art face or a grid of camera feeds, and widgets sit where complications would. The screen even adjusts how much it shows depending on how close you are standing. A lot of what Apple learned on Apple Watch carries over, the same pattern I wrote about with Apple Vision Pro.

Continuity of design does more than let Apple reuse what it has perfected. A familiar interface lowers the learning curve, which matters most for a device entering a new category, as HomeView does.

Built for privacy, priced for more than one

HomeView runs on Apple's A18 chip. Camera and microphone data is handled in Secure Exclaves, protected areas of the chip kept separate from the rest of the system, so neither apps nor Apple can access it. Siri AI requests are processed either on HomeView itself or in Private Cloud Compute, Apple's private cloud, where the data is not stored and is not accessible to Apple. The sound is great, especially for a device this compact.

The attention to detail shows in the use cases Apple demonstrated. In the kitchen, HomeView turns recipes from the web into step-by-step instructions, with the ingredients first and then each step. You advance with a swipe, or ask Siri to move forward when your hands are covered in dough.

HomeView can sit on a counter or go on the wall, as a separate model that uses mounts from Schneider Home. Both versions cost $299. At that price, Apple might be encouraging homes to own more than one: one by the bed, one in the kitchen. It adds to the Apple devices a household already owns with little risk of cannibalizing them.

Apple Home Collection, a first with LG and Schneider Electric

Apple Home Collection is a lineup of smart home accessories designed with LG Electronics and Schneider Electric, a first for Apple. LG covers cameras, a video doorbell, a smart lock, a thermostat and a sensor, and Schneider Home adds its own thermostat and room sensor, so buyers can pick the one that fits the energy setup they already have.

The Apple influence is visible in the design, the simplicity, and of course the privacy, since neither the partners nor Apple can see your Home data. Setup is a tap with your iPhone, with no extra app or account, and none of the engineering degree the smart home still seems to require. The details that stood out to me address frustrations people actually have with the smart home. Accessories update themselves when you change your Wi-Fi password, and the indoor camera physically tilts down and stops monitoring as soon as the first person gets home.

The lineup builds on Thread and on Matter, which Apple has supported for several years, with an Apple layer on top. I hope this helps other partners see where the opportunity in the home sits.

Apple TV 4K and HomePod mini 2

Apple TV 4K gets a solid update. Siri AI turns finding something to watch into a conversation, and you can ask questions about a show while it plays without leaving it. With far-field microphones built in, you no longer need the remote to talk to Siri.

HomePod mini 2 gets Siri AI later through a software update and, like HomeView, recognizes the different people in a household, in this case by their voice. It also gains Sound Glow, a light on top that moves with the music and takes its colors from the album art, a small touch that helps a speaker feel at home on a shelf.

The surround sound setup is where the simplicity shows. Four HomePod mini 2 speakers can now pair with Apple TV 4K, and they work out where they sit in the room and tune themselves, so people get a home theater without fiddling with settings. Anyone who has tried to calibrate a traditional surround system will appreciate that. HomePod (2nd generation) works in the same setup too.

Who it is for, and the real competition

HomeView speaks to two groups. The first is people deeply invested in both Apple products and the smart home. The second is Apple users who want a simple device and are less tech-savvy than most early adopters. Easy FaceTime and easy pairing of smart devices serve them well.

For Apple, that widens the connected home beyond early adopters. It also sets up the real competitive fight. Many iPhone users turned to Alexa for the home. HomeView's use cases are familiar, and a familiar, private device tied to the iPhone already in their pocket gives those users a reason to bring the home back to Apple.

The invite for today's event read "Welcome home," and the welcome was for Siri AI. HomeView, Apple TV 4K and HomePod mini 2 all bring Siri AI into the rooms where people spend their time, through far-field microphones, voice and face recognition, and a screen that shows answers at a glance. Apple Home Collection gives Siri more of the home to act on, from the front door to the thermostat. People have already been enjoying Siri AI on the go, through iPhone and Apple Watch. With this lineup, Apple is making Siri AI part of everyday life at home.

The name

Google is simply calling it Gemini. In its analyst Q&A, the company said Spark, its consumer agent, has folded into it, and that the agent is the same whether you reach it from Workspace, the desktop or the command line. Google's comparison is Gmail: one product for consumers and businesses, with different controls and licensing behind it.

That choice makes sense. The Gemini app has 950 million monthly active users, according to Alphabet's Q2 results, so employees meet the agent already knowing the brand. Microsoft made the same choice with Copilot: at general availability, Copilot Cowork runs on Anthropic models, with OpenAI's GPT 5.5 available to Frontier customers, all under the Copilot name.

The name already covers several products. Google sold Gemini as Workspace add-ons until it folded AI into Workspace plans in January 2025, and Gemini Enterprise has been the name of its agent platform since it launched last October. The models keep the Gemini name too, and Google says capability labels such as Gemini 4 Argon will separate model names from product names. An agent called Gemini that sends some of its work to Claude depends on that separation sticking. Where Workspace ends and Gemini Enterprise begins is still unclear, and Google's own explanation covers capabilities more than cost. One name works for users. Buyers still need Google to spell out which SKU includes what.

Workspace integration

Inside Gmail, Docs, Sheets, Slides, Drive, Chat and Calendar, Gemini works inline with the same memory, skills and controls it carries elsewhere. Asked to set up a meeting with "the usual regional event leads," it works out who they are from a Chat space and an old thread, checks calendars and starts the email. Workspace Intelligence flags requests the agent can take on, such as a manager asking for an update deck, and offers a one-click handoff.

The more consequential piece is the coworker agent. Describe a role and Gemini creates an agent with its own Workspace account: email address, calendar, Drive and a directory entry. Colleagues @mention it in Chat or tag it in a comment, its edits appear under its own name in version history, and it sees only what is shared with it.

That visibility is what regulated customers need. It also raises questions the keynote left alone: who reviews the agent's work, who answers for it when it gets something wrong, and how a team's sense of shared ownership shifts when one of its members is software. Those are management questions, and they will decide how far coworker agents spread inside a company.

On price, Google says there is no incremental seat license for the agent in Workspace or Gemini Enterprise. That extends its January 2025 decision to fold Gemini into Workspace plans, which Google said left a Business Standard customer paying $2 more a month than Workspace without Gemini. Microsoft has taken the other route. As I argued in July in Copilot Seats Are the Bridge for Agents, the Microsoft 365 Copilot seat is best read as an installation base: it is the prerequisite for Copilot Cowork, which Microsoft bills on usage on top of the license. Google's analyst Q&A shows both companies end up in a similar place. When the Gemini agent steps outside Workspace to run cross-system work, it draws on Gemini Enterprise's consumption-based capacity. That work is billed on usage, which is why Google launched project-level spend caps with the agent. Google has yet to say whether a coworker agent with its own account consumes a seat.

Competitor landscape

Microsoft took Copilot Cowork to general availability on June 16. OpenAI launched Frontier on February 5. Anthropic made Claude Cowork generally available on April 9. All four now make the same promise: hand over an outcome, get finished work back, and govern the agent the way you govern an employee.

Anthropic is in an unusual position. Its engine powers Microsoft's Cowork, its models run inside Google's agent, and it sells its own product to the same buyers. Each vendor builds on what it already sells. Microsoft works inside the Microsoft 365 tenant, OpenAI sells an operating layer backed by forward-deployed engineers, and Anthropic starts from the knowledge worker's desktop. Google leads with data and infrastructure: a Knowledge Catalog for shared business definitions, a lakehouse that queries Amazon S3 and Azure Data Lake without variable egress fees, and TPUs it says deliver 80% better price-performance than the previous generation.

Google is also going after Microsoft customers directly. The agent works inside Microsoft 365, Teams and Slack, so a company standardized on Office can buy it without moving. Ryanair, rolling out Workspace and Gemini Enterprise to 35,000 employees under a dual-cloud strategy, is the kind of mixed estate Google is after.

Gemini vs. Microsoft Copilot deployment

Microsoft reported more than 30 million paid Microsoft 365 Copilot seats in its July earnings, up from 15 million in January. Against the more than 450 million paid Microsoft 365 commercial seats Microsoft reported in January, that is under 7%, so even strong growth shows how slowly a per-seat add-on spreads. Microsoft also says Agent 365 had nearly 40 million agents registered two months after launch.

Google reports different metrics. Its last seat figure was 8 million Gemini Enterprise seats in February. Since then it has cited nearly 90% of the Fortune 100, nearly 500 customers processing over a trillion tokens each, and the Department of Defense putting Gemini in front of 3 million personnel. A Fortune 100 company counts whether one team uses Gemini or everyone does, and with Gemini bundled into Workspace there is no add-on seat to count. 

Model choice is open, while the memory, context and skills an agent builds up stay in Google's layer, and that is where switching costs will be. The same holds for Microsoft. Whichever agent a company lets learn its work first becomes the hardest one to replace.

Google Play is the right call

For years, Amazon asked tablet buyers to live without Gmail, Google Docs and much of the Play catalog in exchange for a lower price. That bargain confined Fire tablets to entertainment and kept them out of consideration for anything resembling work. Amazon's advantage was always going to come from its services, so maintaining a parallel app store on tablets made little sense.

I am glad to see Amazon make this move. With Google Play settling the app question, Amazon can put its effort where it has something other tablet makers cannot match: Alexa+ and its content.

Alexa+ at the center

Alexa+ is built into every model. Users call it by voice or with an Ask Alexa button, and it appears over whatever app is open. The more interesting feature is On-Screen Intelligence, which lets Alexa read what is on screen and act on it, such as pulling the dates from a sports schedule into a calendar and flagging conflicts. Live sessions keep a hands-free conversation going without repeating the wake word, and users can share the screen so Alexa follows along as they browse.

Amazon deserves credit for the defaults. Every On-Screen Intelligence feature is off until the user opts in, a visual indicator shows whenever Alexa can see the screen, and each feature can be switched off in Settings. Screen-aware assistants are where AI on personal devices is heading, and starting from opt-in is how a company earns the trust these features require.

The open question is habit. Google Play puts Gemini and ChatGPT one download away on the same device. Alexa+ has the advantage of being built in, free for Prime members in the US, and connected to the user's Amazon history and smart home. It still has to be useful often enough that people reach for it first.

Content, and a reading mode worth copying

Every tablet connects Prime Video, Kindle, Audible, Amazon Music and Amazon Luna out of the box, with a For You feed that brings together what the user is watching, reading and listening to. This is Amazon's core strength, and the new home screen puts it front and center.

The feature I find most compelling is the Kindle reading mode. Open a book and the tablet silences notifications, reduces blue light and stretches battery life to up to 20 hours, with three display themes ranging from natural color to grayscale. On the 12 Pro, the Pro Pen can highlight and annotate pages, and those notes sync with Kindle Scribe. For consumers who own a Kindle and also carry a tablet, this mode could make a single device enough. Amazon is likely comfortable with that consolidation, since the reader stays in Kindle and spends more on the tablet. Tablets have become devices of constant interruption. A mode designed around focus speaks to how many people feel about their screens, and other platforms should take note.

Hardware that can compete

This lineup also shows the influence of Panos Panay, who joined Amazon in 2023 to lead Devices & Services after nearly two decades at Microsoft, where he built the Surface line. His priorities run through these tablets: premium materials, careful display engineering and accessories designed alongside the device, delivered at prices Amazon's customers will accept. Under Panay, Amazon devices increasingly stand as products in their own right, offering a rich experience at an affordable price.

The Alexa Tablet 12 Pro is the device that changes Amazon's position, and its design choices show who Amazon has in mind. The 3:2 display gives more vertical room than the widescreen shape most Android tablets use, which pays off in books, documents and web pages. The 120Hz refresh rate makes scrolling and pen input feel responsive, and the optional Nanomatte glass, developed with Corning, cuts glare while keeping text sharp, a real benefit for anyone who reads outdoors or works under harsh office lighting. Rated battery life of up to 16 hours of video covers a full workday and the flight home. Choosing the USI 2.0 pen standard is a practical call too, since compatible third-party pens will work with it.

The rest of the lineup covers the price points where Fire tablets used to live. The Alexa Tablet 11, at $329.99, is the entertainment model, with a sharper and smoother display than buyers have come to expect at that price. The Alexa Tablet 8, at $229.99, is light enough to hold in one hand through a long reading session, which makes it the most natural candidate for the Kindle consolidation described above. The Kids versions add a protective case, a year of Amazon Kids+ and a redesigned Parent Dashboard, carrying Amazon's strongest family product into the new lineup.

The iPad Air math

The 12 Pro starts at $499.99 with 8GB of RAM and 128GB of storage, or $549.99 with Nanomatte. Apple's iPad Air with M4, released in March, starts at $599 for the 11-inch model and $799 for the 13-inch. Add Amazon's $149.99 Pro Keyboard and $89.99 Pro Pen and the complete 12 Pro setup comes to about $740. An 11-inch iPad Air with Magic Keyboard ($269) and Apple Pencil Pro ($129) lands near $1,000, and the 13-inch configuration passes $1,200.

For that money, Amazon delivers a larger screen than the 11-inch Air, a 120Hz refresh rate Apple reserves for the iPad Pro, and an anti-glare display option Apple only offers on its most expensive iPad Pro configurations.

The iPad Air keeps real advantages. The M4 is a proven chip, while Amazon describes its processor only as a 4nm 8-core part. iPadOS has a deep catalog of apps designed for large screens, and Android tablet apps remain uneven. Apple supports iPads with software updates for many years, and Amazon's announcement includes no update commitment. Creatives built around Procreate and the Apple Pencil will stay where they are.

The buyer Amazon can win is the Prime household that uses a tablet for streaming, reading, browsing and some email and documents. For that person, the iPad Air has been the default upgrade because nothing comparable existed at a lower price. Amazon now has a credible alternative.

A second run at business

Amazon also positions these tablets for work. The 12 Pro supports multiple windows side by side with its keyboard and trackpad, offers separate work and personal profiles. Amazon tried to reach business users once before with Alexa for Business, launched through AWS in 2017 to bring the assistant into offices and meeting rooms. It never found traction, and Amazon wound it down.

Amazon Quick, AWS's agentic AI workspace for business users, is now a high priority for AWS, and it gives Amazon the business software anchor it lacked when Alexa for Business launched. A tablet that can support Quick, work profiles and runs the Google Play apps companies already use gives Amazon devices a credible path back into the workplace, starting with organizations already on AWS. The work still ahead is in device management, security certifications and enterprise sales, areas where Apple and Samsung are well established.

Amazon spent years selling tablets close to cost to sell everything else. This lineup asks people to value the tablet itself, with Google Play removing the old objections and Alexa+, Kindle and Prime Video giving them reasons to choose it. The hardware makes the case. Software updates and app quality over the next two years will decide whether buyers trust it.

Microsoft opened its Windows and Surface event in San Francisco on October 7 with Satya Nadella, Microsoft CEO, and Pavan Davuluri, executive vice president of Windows and Devices, laying out the case for hybrid AI. Many in the room were there for the Surface Laptop Ultra. The opening framing explains why that device, and the RTX Spark laptops from Microsoft's partners, need to exist.

The argument for splitting AI work between the device and the cloud usually rests on privacy and economics: keep sensitive data local and stop paying for cloud inference on every task. Nadella and Davuluri added a third point. Hybrid will become the default workflow for users and agents alike, with work moving between the PC and the cloud depending on what each task needs. Microsoft calls this hybrid intelligence: a local model handles the work suited to the PC, and a cloud model adds capability when the task needs it. GitHub is building the idea into developer tools with HydraFusion, which routes work between local and cloud models in the GitHub Copilot app, Copilot CLI and Visual Studio Code, with an experimental preview due later in October. Accept that premise and the new hardware has a clear job. It is where the local half of that workflow runs.

Trust Starts at the Operating System

The second theme of the day was trust, and Microsoft approached it from two directions. The technical one is Microsoft Execution Containers, or MXC, which the company introduced at Build in June. MXC isolates agents from the user's desktop, clipboard and input devices, ties each agent to its own local or Entra-backed identity, and enforces the file and network access a developer declares before the agent acts. MXC became generally available on Windows 11 today, so organizations can now set which files and networks an agent can reach and have Windows enforce those limits while it runs. Microsoft also showed MXC integration for OpenClaw as part of a simpler setup for popular agents.

In the fireside chat with Nadella that closed the event, moderated by Sriram Krishnan, Jensen Huang, Nvidia CEO, called MXC the cornerstone of the agentic era. His endorsement carries weight because Nvidia silicon now sits inside Windows PCs built to run agents locally. A sandbox that IT departments can manage with the identity and policy tools they already use is what makes a powerful local machine one an enterprise will let agents operate on.

Trusting the Agent Itself

The second direction is harder to engineer. Users have to trust the agent they are running before they will let it act for them. That trust has three parts: confidence the agent can do the job, confidence it followed instructions correctly, and confidence the data it touched stayed private and secure.

Delegation between people works the same way. A manager gives a new hire small tasks, checks the output, and hands over more as the results hold up, until checking stops feeling necessary. Agents will earn autonomy on that curve. Each task done right lowers the bar for the next one, and each visible mistake raises it. MXC covers the security part. Capability and accuracy get earned one task at a time, which makes the earliest experiences disproportionately important.

Meeting Consumers Where They Are

Trust matters for consumers too, and most of them are still uneasy about agents. Microsoft is meeting them at that level with the new search bar in Windows, which can now take actions such as sending a message or changing a PC setting. Search actions start rolling out today to Windows Insiders in the Experimental channel, with examples including switching to dark mode, turning on Do Not Disturb and arranging windows. Optional Copilot integration in taskbar Search follows in select markets later this year.

Small, low-stakes tasks are the right place to start. Every time AI gets a simple action right, it builds the confidence consumers will need before they consider agents for areas such as health and finances. Those are also the areas where many people will want the work done locally, which brings the argument back to hybrid AI and to the hardware that makes local processing practical.

A Workstation for Local AI

The Surface Laptop Ultra starts at $2,599 with Nvidia's RTX Spark platform, an 18-core N1X CPU, a 5,120-core Blackwell GPU, 24GB of unified memory and 512GB of storage. Higher configurations step up to a 20-core Grace CPU and 6,144 GPU cores, and Microsoft says the machine can run local agent workflows on models of up to 120 billion parameters. The 15-inch PixelSense Ultra touchscreen reaches 2,000 nits of peak HDR brightness, the chassis is under 18mm thin and 4.5 pounds, and the SSD is removable and upgradeable to 2TB. The top configuration, with 128GB of memory and a 1TB SSD, reaches $5,899.99. Preorders opened today and units ship October 16, alongside RTX Spark laptops from ASUS, Dell, HP, Lenovo and MSI. The Surface RTX Spark Dev Box, a compact desktop with 128GB of unified memory that ships with Visual Studio Code, GitHub Copilot, WSL and other developer tools set up, follows in November at $5,999.

These prices put the category out of reach for most PC buyers. But, honestly, they are not for everyone. They are AI workstation. Sure The Surface Laptop Ultra can handle gaming well, but its primary role is as the home for local AI workloads, with up to 128GB of unified memory available to large models running on the device. That is the configuration Apple has owned with the MacBook Pro. Microsoft names the target outright: its launch promotion offers up to $1,000 back on a qualifying trade-in through November 23, marketed as breaking up with your MacBook. Windows buyers who want unified memory and serious on-device AI performance in a laptop now have a credible alternative, from six brands.

Reinventing the PC

Huang closed the conversation by telling Nadella, Davuluri and the Microsoft team that together they have reinvented the computer as we know it. Anyone judging RTX Spark machines on whether every Windows app runs natively on Arm is applying the test of the PC we have used for decades. The reinvention Huang described is about agents, and agents will get work done in a different way from how we move between apps today.

Huang also pointed out that agents can draw on every feature these devices have, while people are limited to the features they know about. Most of us use a fraction of what our hardware can do. Running on a machine like the Surface Laptop Ultra, an agent will extract far more from it than its owner ever could.