Compute choice, fungibility, and the cost of correctly completed work

As companies put AI into everyday work, we expect attention to shift toward everything required to complete a task. An agent may use a model to interpret a request, retrieve company information, call an application, and then check the result. Those steps use different parts of the system, so the cost depends on how well the software assigns the work and how much time is spent waiting between steps.

We see a broader role for AMD here. EPYC, Instinct, networking, and ROCm each address a different part of that work, and software and system partners help customers put them into service. The question we are most interested in is how those investments change the cost of running an application over time. The customer needs a system that handles today's work and can change models or add capacity without the application being rebuilt each time.

Our thesis that the future of compute is fungible starts with that need for choice. By fungible, we mean that operators and customers can direct a workload to whichever compute serves it best without rebuilding the application around each change. Some of that is already here. An application can use a model through an API without choosing the processor that serves each request, and routing services already let customers select among providers behind a common interface. That gives AMD an opportunity to supply the infrastructure beneath services customers already use, as well as the systems they buy directly. The provider still chooses and prepares the hardware, but much of that work no longer has to be repeated by every application developer. [8]

The assumptions behind our view

Our view rests on four assumptions. We expect enterprises to use a mix of models and tasks with different cost and response-time needs, and those differences give customers reasons to choose where the work runs, although the savings will depend on the application. We also assume that common APIs, which already support choice between model endpoints, keep widening that choice. How much work can actually move between endpoints depends on model availability, application features, data rules, and the provider's supported systems, and it does not require different GPUs to share one model instance.

The economics further assume that customers have enough work to justify the capacity they buy. Low utilization or repeated integration work can outweigh a lower hardware price, and an application still has to deliver value to the business. Finally, AMD's software and partners give customers more than one way to use its hardware, and we expect the strongest economic benefit where a supported configuration meets the application's quality and response-time needs while reducing the cost or power required to serve it.

Software gives customers more ways to choose compute

Customers can use AMD through a hosted inference service, a new internal application, or infrastructure they operate themselves. A common model API makes the first route relatively simple for the application team, because the service provider handles the hardware and serving software. Buying equipment gives the customer more control over data and capacity, with more responsibility for operating it. Both routes widen the set of workloads AMD can serve without asking customers to replace everything they already run.

The application still has to return an acceptable answer, handle tool calls correctly, and meet the company's data and security rules. Those are normal requirements for deploying AI on any platform. A provider or system partner that has already done the hardware integration reduces the work left for the customer, which means the same processor can be easier to adopt through a supported service than through a system the customer assembles itself.

We see AMD's work on integrated systems such as Helios and its open software interfaces as two parts of the same effort. Helios provides a rack design that partners turn into systems for training and inference. Common interfaces help those systems fit the applications customers already run, and other AMD components can be bought separately for the surrounding infrastructure. The customer gets to decide how much integration to buy from a partner and how much to control itself. [1, 9]

The combination works because the customer's existing software has value. A new inference endpoint can serve an application without changing the databases or business services around it, and an EPYC server can support those services regardless of which accelerator serves the model. AMD can therefore compete at more than one point in the workflow, and each system should be judged on the performance and cost of the work it takes on.

Software makes the hardware available to applications

It helps to separate the software layers (Exhibit 1). ROCm is AMD's GPU software platform, including runtimes, libraries, and development tools; CUDA fills a comparable role in NVIDIA's ecosystem, although their capabilities and interfaces are not identical. Many developers work above those layers, through frameworks such as PyTorch and serving engines such as vLLM. The framework expresses the model's operations, the serving engine manages requests and execution, and hardware-specific software runs the work on the GPU. Supporting familiar frameworks lets AMD reach developers without asking all of them to write directly against a new low-level platform. [9]

AMD Inference Microservices, or AIMs, package model serving into containers with profiles for supported hardware and models. For language models, AIMs expose an OpenAI-compatible API, as NVIDIA NIM also does, so an application using the shared API features may need relatively few changes to use another endpoint. The model name, authentication, tool behavior, and any service-specific features still have to match what the application expects. A common interface reduces migration work without making every endpoint identical. [2, 10]

AMD's enterprise software stack brings container deployment and serving tools together, with Kubernetes managing where containers run and the resources assigned to them. AMD also works with platform partners including Red Hat, SUSE, Canonical, VMware, and Nutanix so that customers can use familiar operating, virtualization, and container environments. Support depends on the specific product and configuration, and not every partner offers the same stack. For an enterprise, the benefit is being able to add AI to the environment its teams already know how to operate. [3, 11]

Cloud operators and enterprises will use that support differently. A provider with its own software team will choose and tune the infrastructure beneath an API. An enterprise is more likely to want a partner to supply the supported system and operating environment. AMD can serve both without pushing the hardware work onto the application developer. At that point openness shows up in deployment time and support effort, which is where customers feel it, and not only in the terms of a software license.

Model routing and hardware placement solve different problems

Choosing the model is one decision. A routine classification may be handled by a much smaller model than a complex planning task, so sending both to the same expensive service wastes resources when the smaller model already meets the need. Tools such as LiteLLM route and balance requests among endpoints, and vLLM documents a Semantic Router integration that selects models according to the request. That integration is still labeled a preview, but it shows model selection and serving starting to converge. [12]

Deciding where to run that model involves a different set of questions (Exhibit 2). A server that already has the model loaded and the relevant context cached may serve a request more efficiently than an idle machine that has to load the weights and repeat earlier work. The software has to account for that preparation along with the queue, the available memory, and the expected response time. Looking only for the least busy GPU misses part of the cost.

A business can use that approach to send routine, permitted work to a privately hosted model and reserve a frontier service for the tasks that need it. The router has to account for answer quality and data policy as well as price. A cheaper response that triggers retries or human correction may cost more across the full task, so the savings have to be measured after the work is accepted rather than at the first model call.

This is also why our compute-fungibility thesis distinguishes between routing requests and splitting a model across different hardware. A router selects an endpoint, and the provider or serving system determines which devices execute the model behind it. Customers get real choice between services without mixing accelerator vendors within a single model instance. The more closely the hardware has to cooperate on the same model, the more its memory, communication, and runtime support decide the outcome.

Memory and communication set the useful capacity

The work changes during inference itself (Exhibit 3). Processing a prompt, called prefill, leans heavily on compute. Generating the response, called decode, puts more pressure on memory bandwidth. Meta's published inference work describes those differences and the potential to use different hardware for the two stages. The balance depends on the model, the context length, and how many requests are processed together, so a single hardware ratio will not suit every service. [4]

Separating those stages means the information built during prefill has to reach the system generating the answer. The gain from using hardware suited to each stage has to cover the transfer time, the network traffic, and the added scheduling work. We would expect that tradeoff to look different for a large, steady service than for a smaller deployment that already fits on one server.

Memory capacity also governs how many requests a system can serve at once. Alongside the model weights, transformer serving uses a key-value cache to retain information from earlier tokens, and longer contexts and more concurrent sessions consume more of that memory. Reusing a shared prompt prefix avoids some repeated processing, and lower-precision representations reduce memory use when they preserve the required quality. AMD's ROCm optimization guidance addresses these serving choices, which need to be tuned to the workload. [5] Exhibit 4 shows why agents feel this more than chat applications do. Agentic inputs carry tool results and company context, so they tend to run long, and at long context lengths the cache rather than the weights sets the ceiling on concurrent sessions.

For Instinct, memory capacity and bandwidth affect how much work the customer can serve from the GPUs it buys. More memory may allow a model to fit on fewer GPUs or leave room for additional sessions, although that benefit depends on what else limits the system, including compute throughput and queues. Sharing or partitioning a GPU also helps smaller workloads use capacity, provided the available isolation and resource limits meet the customer's requirements.

A mixture-of-experts model adds communication between the GPUs hosting its experts as tokens are processed, and Meta describes this exchange as a source of inference latency. That is one reason faster links need to be considered alongside how work and experts are distributed. For AMD, networking is tied directly to the cost of serving the model, because time spent waiting on data is time the accelerator is not doing the work the customer bought it to do. [4]

Agent execution needs its own operating controls

Our Agentic CPU Guidebook follows the surrounding work across accelerator hosts, separate tool environments, and existing business services. A coding agent may wait for a build or test before making its next model call, so faster execution shortens that dependent step, while more cores help when many independent jobs are ready at once. EPYC gives AMD a way to serve both requirements across these pools, including work generated by models running on another supplier's accelerators. [13]

The number of hardware threads does not tell us how many agents a server can support. Some environments spend most of their time waiting. Others run builds or tests that use several cores. Sharing execution capacity or suspending inactive environments changes how much equipment the operator needs, and memory may limit the number of active jobs before the CPU is fully used. We would size the system from the work, its memory needs, and the required response time, rather than treating every agent as a permanently occupied core.

Existing x86 applications also give AMD a familiar base for the business services agents call, and enterprises can keep much of their software, identity management, and operating practice as they add agent workflows. They still need permissions for each tool, a record of actions, and controls on resource use. VM isolation, confidential computing, and application permissions each solve a different problem. Offloading network services can free CPU time, but it does not decide whether an agent should be allowed to change a business record.

On premises and cloud can serve the same business

We expect enterprises to decide where AI runs according to the work they need done. Steady inference on sensitive company data is a reason to consider private capacity. Unpredictable demand, or the need for a particular model, points toward a cloud service. Owning equipment in the company's facility or in colocation carries different responsibilities from renting access to a model in the cloud, even when both serve the same business.

Private equipment has to be paid for and supported even when demand falls. The customer needs to include deployment, power, ongoing support, and capacity held for failures or peaks in its cost. A cloud service charges for the work sent to it, plus data movement costs where they apply and the effort of maintaining the connection. How steadily the company uses its own capacity is therefore a large part of deciding which work belongs there, and Exhibit 5 shows how quickly the answer flips as utilization falls.

Dell, HPE, Cisco, Lenovo, and Supermicro give enterprises routes to AMD-based servers through established supplier relationships. Their offerings differ, including which systems support Instinct acceleration, so customers choose a supported configuration rather than a generic AMD platform. These partners also help size installations and work through the power, cooling, network, and support requirements of an existing data center, and the same equipment may run on premises or in a private deployment at a colocation facility. [6, 7, 14]

We also see a reason to plan for expansion without buying everything at once. Preparing a site for a later upgrade can avoid disruption, but unused capacity still costs money, so the customer needs a plausible view of the workloads and equipment it will add. A modular installation lets it expand as demand develops instead of committing immediately to the largest system it might eventually need.

Measure the cost of correctly completed work

We would start by dividing the full cost of the service by the number of tasks completed to the required standard and within the required time (Exhibit 6). That cost includes failed attempts, retries, and human review, since the customer pays for those along with the successful runs. Counting only accepted results keeps additional attempts from making a system look more productive than it is.

Power is a real limit on how much service an operator can deliver. Tokens per second per watt compares model serving under matched conditions. Energy per correctly completed task also captures the work around the model. More useful output within the same power cap can let an operator serve additional demand before a new facility is available. An agents-per-watt measure helps only when it describes active work and completed output, since a count of waiting environments does not establish productive capacity.

The customer should track both typical response time and slow requests. The median, or p50, describes the middle request, and the p99 is the response time within which 99 percent of requests finish. A system can look good at the median and still leave too many users waiting when queues build (Exhibit 7). Memory capacity, cache behavior, and scheduling influence those delays, so they belong in the comparison alongside peak throughput. More memory helps when it relieves a real working-set limit, but it is not evidence on its own that every AMD configuration delivers better tail latency.

CPU performance reaches those measurements in two different ways. Faster host processing can raise GPU output when request handling or input preparation is the bottleneck, and vLLM documents scaling its API servers for that situation. Faster tools can instead shorten an individual agent's task while the GPU fleet stays busy serving other users. Both improvements have value, but only the first addresses a direct limit on feeding the accelerator. [13, 15]

A full-system comparison combines these effects. The customer needs to know how many tasks finish correctly within the required time, what resources they consume, and what it costs to keep the service running. Measured that way, CPU execution, GPU serving, memory, and networking are all judged against the same result, and no one has to assume that improving a single component improves every workload.

Our view

We expect common interfaces and better serving software to keep widening the compute choices available to enterprises. Some of that choice already exists in hosted model services, where the application does not need to know the processor behind the endpoint, and customers operating their own infrastructure have another route through supported systems and familiar software platforms. Our compute-fungibility thesis depends on those choices becoming easier to use. It does not require every processor to become interchangeable.

AMD has several ways to participate: EPYC in the work around inference, Instinct in model serving, and networking and software that help customers use the capacity well. Helios gives partners an integrated design for large model workloads, and individual components reach the rest of the deployment. What we find most interesting about that position is that the supplier running the model does not automatically win every part of the work the model creates.

The opportunity grows as enterprises put agents into daily work. Our view depends on that activity justifying new capacity after efficiency gains and existing headroom are taken into account, and that is the part of our view that is hardest to model. Systems that maintain quality and response time while lowering the cost of completed work give customers a reason to expand, and AMD's portfolio and partners position it to compete for those purchases.

Sources

The compute-fungibility and CPU-execution discussion reflects Creative Strategies research and analysis. New and revised sources accessed September 29, 2026; retained sources are dated individually.

[1] AMD, "AAI 2026: AMD Delivers Full-Stack Compute for the Agentic AI Era," July 23, 2026. https://ir.amd.com/news-events/press-releases/detail/1294/aai-2026-amd-delivers-full-stack-compute-for-the-agentic-ai-era

[2] AMD Enterprise AI, AIMs Overview, version 2.2.2. https://enterprise-ai.docs.amd.com/en/v2.2.2/aims/overview.html

[3] AMD Enterprise AI, Reference Stacks. Supported configurations and deployment requirements apply. https://enterprise-ai.docs.amd.com/en/latest/reference-stacks.html

[4] Meta Engineering, "Scaling LLM Inference," October 17, 2025. https://engineering.fb.com/2025/10/17/ai-research/scaling-llm-inference-innovations-tensor-parallelism-context-parallelism-expert-parallelism/

[5] AMD ROCm 7.2.4 documentation, vLLM V1 performance optimization. Version-specific serving guidance, not a universal performance comparison. https://rocm.docs.amd.com/en/docs-7.2.4/how-to/rocm-for-ai/inference-optimization/vllm-optimization.html

[6] Dell, "Dell and AMD Are Expanding What's Possible for On-Premises AI," May 7, 2026. https://www.dell.com/en-us/blog/dell-and-amd-are-expanding-what-s-possible-for-on-premises-ai/

[7] HPE, HPE and AMD compute solutions portfolio. https://www.hpe.com/us/en/solutions/amd.%20html.html

[8] OpenRouter, Provider Routing. Endpoint selection is distinct from hardware qualification. https://openrouter.ai/docs/guides/routing/provider-selection

[9] AMD, Helios overview and open software ecosystem. https://www.amd.com/en/products/rackscale-solutions/helios.html

[10] NVIDIA, NIM for LLMs 1.12 API reference. Common API support does not establish identical model or feature behavior. https://docs.nvidia.com/nim/large-language-models/1.12.0/api-reference.html

[11] AMD, "The Importance of Open Ecosystems for Agentic AI Deployment," July 30, 2026. https://www.amd.com/en/blogs/2026/the-importance-of-open-ecosystems-for-agentic-ai-development.html

[12] LiteLLM Router, and vLLM Production Stack Semantic Router integration. The latter is labeled preview in the documentation reviewed. https://docs.litellm.ai/docs/routing; https://docs.vllm.ai/projects/production-stack/en/latest/use_cases/semantic-router-integration.html

[13] Creative Strategies, Agentic CPU Guidebook V5, September 28, 2026. CPU pools, execution time, memory, and provisioning analysis.

[14] AMD, EPYC OEM portfolio information. https://www.amd.com/content/dam/amd/en/documents/epyc-business-docs/infographics/cpu-you-choose-can-make-or-break-your-investment.pdf

[15] vLLM 0.25.1, Optimization and Tuning. https://docs.vllm.ai/en/v0.25.1/configuration/optimization/

Exhibits 4, 5, and 7 are Creative Strategies illustrations built on stated or hypothetical inputs to show a mechanism. They are not benchmarks, 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.

Good and Ready

Windows PCs have offered touch for years, and Apple itself has spent well over a decade teaching people to use touch on iPhone and iPad while keeping it off the Mac. In 2010, Steve Jobs argued that reaching up to a vertical laptop screen would leave your arm tired, and that clip gets replayed every time the topic comes up.

Since then, AI has changed how we engage with content, from talking to our devices to pointing at what we want help with, and reaching for the screen fits that way of working. There is also a generation that grew up with touch as its first interface, and many of them tap a laptop screen by instinct and are puzzled when nothing happens.

As is often the case, Apple moved when it was good and ready, with a clearer use case today and an OLED display that can support it.

Why the Display Matters

PROLED XDR is the foundation of this launch, giving colorists, photographers and designers a screen they can trust for HDR and color work away from the studio. Apple also reduced the reflections OLED panels usually struggle with, which matters more now that people will be putting their fingers on the screen.

The display is also what makes touch work, with the sensor built into the panel itself so the response feels immediate, and a sturdier hinge that keeps the screen steady when you tap it.

OLED also helped Apple cut weight, taking half a pound off the 14-inch and 0.7 pounds off the 16-inch, and you notice it on a laptop you will be reaching toward more often.

Touch That Feels Familiar

Apple built Touch on the gestures Mac users already use on the trackpad, so scrolling, zooming and dragging work the way you expect. The handful of additions, like tapping a window to fill the screen or flicking it into a tiled layout, are easy to pick up. iPad users will feel at home too, so the learning curve is short wherever you are coming from.

The more conservative part of the Apple base is uneasy about touch on the Mac, much like the negative reaction some loyal users had to iPhone Duo next to the traditional iPhone, and two things should ease that concern. First, Apple kept macOS designed around the keyboard and trackpad, so anyone who has no interest in touching the screen can keep working exactly as they do today. Second, many of the skeptics will see the display in person and come away won over by the color, the brightness and how little it reflects.

Because macOS treats a touch like a click, most existing apps work from day one without any developer effort. The more interesting examples sit in Apple's own creative apps, where touch removes a step: playing an instrument on screen in Logic Pro, drawing directly on the canvas in Pixelmator Pro, or dragging clips into place in Final Cut Pro. Bringing the Dynamic Island to the Mac also gives touch a natural spot for quick interactions like pausing music.

Apple updated its gesture APIs for developers, and Microsoft, Adobe, Blender and Canva are among those adding Touch support, with their apps arriving later this year. Translated clicks get users started, and sustained use will depend on apps designed specifically for Touch.

For the Pro and the Aspiring Pro

Apple's event in Los Angeles included a set of demos showcasing creators across film, photography, music, art and fashion. ILM showed how its artists build virtual production worlds on the Mac before they reach the LED wall. Photographer Jeremy Cowart ran his tethered camera, LED walls and projection from one machine. Grammy-winning mixer Fab Dupont worked in spatial audio. Designers Susan Fang and Orelio de Jonghe used computational design and 3D printing to create pieces that look grown, and botanical artist Azuma Makoto imaged flowers in CT and X-ray to design his installations. It was the most hands-on computing launch I have attended.

It was interesting that AI did not come up in the use cases Apple chose to highlight, even though the M5 Max can run large language models. In the demos, several creators went out of their way to point out that what they were showing was made without AI. For a creative community wary of what AI means for its work, Apple kept the spotlight on the creators themselves, with the Mac as the tool they work on.

The positioning targets two audiences at once: the professional who needs the power of M5 Pro and M5 Max and a display they can rely on away from the studio, and the aspiring creator who wants the same tools as the people in those demos. Pricing starts at $2,899 for the 14-inch with M5 Pro and $3,399 for the 16-inch, with M5 Max models from $4,499 and $4,799. Pre-orders opened October 28, with availability on November 6.

Touch speaks to both audiences. For the aspiring creator who grew up on an iPad, it makes the Mac feel less foreign, and for the professional it adds a new way into tools they already know. How far it goes depends on how many developers use the new gesture APIs to build for Touch directly.

As someone who travels with an iPad Pro, I am now wondering whether a 14-inch MacBook Pro could take its place, and Apple has never been shy about letting its own products compete with each other. Touch also brings back a question I first asked when the Mac moved to Apple silicon. I expected that transition to bring iPad apps and a new wave of developers to the Mac App Store, and that growth never quite materialized. A Mac you can touch gives developers a stronger reason to make the move, and this time the Mac App Store might finally see the growth I was expecting back then.

T-Mobile today announced T-Mix, a plan construct that replaces fixed bundles with Picks: an allowance customers spend on streaming, gaming, AI and travel benefits, wireless features and device promotions, and can reassign four times a year. The three plans, Standard, More and Loaded, are available in November. The thinking behind the new plans makes a lot of sense, and the execution looks more considered than previous attempts in the industry. Whether it lowers churn the way T-Mobile expects depends on two things it has yet to prove at scale: that customers beyond its most engaged users will work with the choices, and that the AI guidance planned for after launch arrives soon enough to help the ones who won't.

Why choice matters now

In its announcement, T-Mobile says 84% of consumers it surveyed want more control over what they pay for, and that customers change plans on average only once every six years, which leaves them living with fixed benefits long after their needs have moved on. Carriers have spent a decade folding streaming services, hotspot allowances, roaming and satellite connectivity into premium tiers, and bundles that looked generous at launch now read as cost to households watching every bill. People are also holding their phones for about three years, so value tied to the next upgrade it not utilize in a way to show real return to customers. 

Every T-Mix plan includes the network, core talk, text and data, and T-Mobile's member perks, including T-Mobile Tuesdays. Picks sit on top. T-Mix Standard starts at $55 a month for one line with one Pick, More at $75 with two, and Loaded at $95 with three, with more Picks as accounts add lines or move up a tier. Partner Picks include Apple TV, Apple Music, DashPass by DoorDash, Delta SkyMiles, ESPN Unlimited, Google Gemini, Hulu, Netflix, Pandora, Paramount+, SiriusXM, Xbox Game Pass and YouTube Premium. Picks also cover wireless benefits such as T-Satellite and a new Optimized Video Calls feature that uses a network slice to prioritize video calls, as well as device promotions, including a new phone on Us.

Flexibility as a retention tool

The most consequential design choice is the remix cycle. Wireless plans, and broadband even more so, tend to lock customers into a decision for a year or longer, often followed by a price surprise when a promotion ends. T-Mix lets customers change partner Picks four times a year, during three change windows plus an annual remix, at no cost and on the same monthly plan. Wireless benefits like T-Satellite can change at any time. A family can park Paramount+ and pick up T-Satellite for a summer of national parks, then switch back in the fall. When the kids' viewing habits change, or an adult child finally leaves the family plan, the plan adjusts with them.

That is where the churn argument comes in. Customers leave carriers when the value they perceive drifts away from what they pay. A plan that can be reshaped four times a year gives them a reason to adjust within the relationship before they consider shopping elsewhere. In the briefing, T-Mobile also talked about trapped value: a family offered a phone promotion on every line loses it on any line that does not need a phone. With T-Mix, customers choose when, and on which line, Picks go toward a new phone, so a family can spend them on services while it waits for the right device.

There are limits. Device promotions hold Picks for the length of the promotion; in the briefing, T-Mobile pointed to 36-month terms, longer than the 24-month installment plans customers know, and that works as the same retention lever carriers have always relied on. Changing a partner benefit outside a change window may carry a fee. Unused Picks do not roll over to the next quarter, and once a year customers must reselect their benefits. The fine print also warns that customers may need to cancel existing subscriptions themselves to avoid paying twice, a friction point for exactly the households T-Mix wants to win over.

Why this attempt looks different

T-Mobile is not the first carrier trying to offer perks. Verizon tried with myPlan. The structural difference is that Verizon charges for each perk on top of the line price, so every choice is also a wallet decision. T-Mix includes the benefits in the plan price and asks customers only to decide what they want. T-Mobile puts the value of T-Mix Loaded at an average of at least $120 per line per month in plan features and benefits.

The bigger difference is the channel. A flexible plan only works if changing it is easy, and T-Mobile has spent three years training customers to use T-Life for T-Mobile Tuesdays, movie tickets and account management. In the briefing, T-Mobile said the app sees about 30 million interactions in a slow month and more than 40 million in a busy one. Picks appear as a wallet, selections are a tap, and T-Mobile activates the partner services on the back end. Customers get SMS and in-app reminders before each window, and T-Mobile said that outside the annual reselection, doing nothing keeps the current Picks in place.

T-Mobile shared that a pilot in 37 Kansas City stores, running since October 1, has averaged 19 to 20 minutes per switching transaction, and that about 30% of customers left with Picks unspent, often to decide at home with their families. Those numbers come from three weeks in one market with newly trained store staff. The national test will be the customer who rarely opens T-Life, and the care rep handling the call when a phone ends up in a lake five days after the window closed.

AI is the piece that makes it scale

AI already shows up in T-Mix as a Pick, through Google Gemini. Its more important role will be guidance. Choice helps until it overwhelms, and consumers stall quickly when faced with too many options. When I asked in a briefing whether T-Mobile plans to use AI to give customers a starting point, the answer was yes, though not at launch.. T-Mobile already knows whether an account travels to Canada or Mexico and, with permission, which services customers stream on their phones. It is building an AI agent that will suggest Picks to existing customers based on usage, with a lighter version for prospects switching from other carriers.

This is what would separate T-Mix from flexible plans that came before it. A plan that can change every 90 days only delivers if someone notices when it should change. Usage-based suggestions turn the remix window into a prompt customers can act on and move the work of optimizing the plan onto the carrier. They also require trust. T-Mobile will need to be explicit about what data the agent uses, keep it opt-in, and avoid steering picks toward the services with the best wholesale terms. Recommendations that visibly save customers money will make T-Mix a relationship people stay in. Recommendations that feel like upselling will make it one more menu to ignore.

The industry conversation is moving from AI models to AI systems, and what gets measured is moving with it, from a single inference request to a complete workflow that includes reasoning, retrieval, orchestration, tool execution, memory, and security. Agentic AI, in which software agents plan and carry out tasks on their own, is driving that change.

We think agentic AI gives AMD's years of data center investment a broader use. It brings the CPU and the front-end network into the workflow alongside the GPU and its scale-up and scale-out fabrics, and it calls for more than one CPU profile: hosts for the accelerators, sandboxes for agent tools, and the general-purpose servers that run the enterprise systems agents call into. AMD's portfolio spans those roles, and it offers customers a choice between integrated designs developed with partners and individual components for systems they build themselves.

This paper makes four arguments. First, agentic AI changes the IT operating model. Enterprises designed their systems to support employees, processes, and applications, and they now have to support software agents that generate additional model calls and execute work across those same systems. Sustained growth beyond existing capacity creates demand for more infrastructure. Second, the infrastructure requirement is a system. Agent workflows span CPUs, GPUs, DPUs, networking, memory, and software, and their economics depend on utilization and execution across all of them. Third, AMD's portfolio maps to the workflow. EPYC serves the host, sandbox, and general-purpose roles; Instinct serves inference and training; Pensando supports networking and infrastructure services; and Helios brings compute and networking together in a rack design for model workloads, with separate execution and application servers completing the wider deployment. [1] Fourth, the metric customers use is changing. Token speed is still part of it, but buyers will increasingly judge systems on how quickly they turn tokens into completed business actions, measured through tokens per watt, agents per watt, utilization, and total cost per business outcome.

1. Agentic AI changes the IT operating model

A chatbot answers a question. An agent is given a task and is expected to finish it, and that difference changes what infrastructure an enterprise has to build. When an enterprise deploys a chatbot, the operational question is how quickly and how well the model responds. When it deploys agents, the question becomes whether the system can execute. An agent given a task will plan, call one or more models, retrieve enterprise data, run code, query databases, invoke APIs, check policies, and often spawn sub-agents for subtasks, and each of those steps touches infrastructure that a single inference request never would.

An enterprise designs its IT systems around its employees, its processes, and its applications. Agentic AI adds software that can use those systems repeatedly within a single task. An agent working a prospect list may query the CRM, gather information, draft a response, and update a record, which creates inference demand as well as work for the general-purpose servers behind the applications. The purchases that follow depend on how much of that activity becomes sustained work and how much existing capacity can absorb.

The pattern repeats across the enterprise. In sales and customer workflows, an agent researches a prospect, retrieves account context, updates the CRM, drafts a follow-up, and schedules the next action, and most of the value comes from coordinating work across data, applications, and APIs, with the email itself the smallest part. In IT operations, agents interpret requests or incidents, query knowledge bases and databases, invoke applications, carry out permitted actions, verify outcomes, and maintain the controls and audit trail that production environments require, which makes orchestration, tool execution, policy enforcement, and compliance full parts of the AI workload. In software development and testing, agents move beyond writing code to interpreting test specifications, operating software and browsers, running tests, and feeding results into CI/CD pipelines; AMD Enterprise AI includes an agentic testing blueprint built around this multi-step execution model. In finance and back-office processes, agents retrieve documents and enterprise data, perform calculations, work with business applications, apply permission and approval policies, and validate results.

The model call is one step among many in every one of these workflows. The GPU remains essential, but cost and speed also depend on orchestration, general-purpose compute, front-end services, networking, and memory movement. Our thesis is that inference becomes the main recurring AI workload as agents make repeated use of models in production, and that the infrastructure around the model grows with it.

2. From AI models to AI systems

AMD has argued for some time that the future of AI would depend on the entire infrastructure stack. Agentic AI makes that systems view more relevant, and it favors open, heterogeneous architectures in which CPUs, GPUs, networking, memory, and software work as one compute fabric. AMD describes itself as a full-stack AI solutions provider, and the portfolio supports the description (Exhibit 2).

Exhibit 2: AMD's portfolio mapped to the agentic deployment

Source: AMD product documentation [1, 3, 4, 6]; Creative Strategies.

AMD pairs that breadth with a merchant CPU portfolio serving all three CPU roles and an ecosystem of system and software partners. We take each layer in turn and then tie them to the economics that decide agentic deployments.

3. The CPU foundation: host, agent sandbox, and general purpose

Agentic AI expands the CPU work around model serving. CPUs host accelerators, execute tools, and run the enterprise systems agents call. We describe those as three workload roles, which do not necessarily map to three separate CPU architectures or to a fixed number of servers.

Host CPUs feed and coordinate accelerators. Per-core performance, memory, and I/O become the constraint whenever request handling or input preparation limits GPU output, and the serving software must also be able to use the CPU capacity that is available. [2]

Agent sandbox CPUs run tools and code in controlled environments. More cores help when many independent jobs are ready to run, while faster cores shorten dependent steps such as a build or a test, and memory, isolation, and scheduling determine how much of that capacity is usable. An agent waiting on a remote service need not occupy a physical core, and a demanding tool may use several, so thread count is not a count of productive agents. [2]

General-purpose CPUs run the systems agents call into: databases, ERP, CRM, web services, and the rest of the enterprise estate. Persistent enterprise tool execution, such as database calls, happens on these servers, and agentic traffic adds load to systems that were sized for people.

AMD's EPYC portfolio offers performance and density choices for this mix of work. The 6th Gen EPYC family reaches up to 256 cores and 512 hardware threads, but those specifications describe execution resources rather than a guaranteed agent count. Customers need to match core performance, memory capacity and bandwidth, and the power setting to the work each pool runs. [3]

Core speed is not only a host question. A tool result on the critical path holds up the next step of an agent's task, so faster execution can improve completion time even when parallel capacity is plentiful. Many independent tasks, by contrast, benefit from more workers, provided memory keeps pace. This is the wide-versus-fast question, many slower cores against fewer fast ones, and Exhibit 4 shows how two configurations with the same aggregate capacity can produce materially different response times under a burst of work. Both requirements often show up in the same pool, so we would measure the complete tool operation under load instead of picking one CPU metric for every sandbox. [2]

EPYC also brings an established enterprise software environment and a broad OEM base. Agents will call databases and business services that already run on x86 servers, which lets companies keep their existing applications and much of their operating practice. Extending that environment to new execution pools can reduce the work of adoption, although new deployments still need their own capacity and security planning.

4. Instinct GPUs and enterprise inference

The GPU still generates the tokens. AMD's Instinct MI350 series (MI355X, MI350X, and MI350P) and the MI450 series, led by the MI455X, carry the company's acceleration roadmap into the agentic era. What changes with agentic AI is what enterprises need from the accelerator.

Enterprise agentic workloads rarely look like one very large model serving one very large queue. They look more like fleets of specialized models: small language models tuned to a task, retrieval models, safety models, and an occasional frontier-class model for difficult reasoning steps. Serving that mix efficiently rewards two things AMD has prioritized, memory capacity and flexible partitioning.

Instinct's chiplet package integrates compute dies, I/O, and high-bandwidth memory. The MI350X and MI355X, for example, combine 288 GB of HBM3E with up to 8 TB/s of peak theoretical memory bandwidth. More memory can hold larger working sets or additional model sessions, and supported GPU partitioning gives customers another way to divide capacity. How much that is worth depends on the model, the concurrency, and the isolation requirements, not on memory capacity alone. [4]

The token economics depend on the application. A smaller model tuned or selected for a defined task can reduce serving cost when it meets the required quality, although it does not necessarily use fewer tokens, and any retries or human correction belong in the comparison. The target is a lower cost per correctly completed task.

Right-sizing the model is half the opportunity, and routing between models is the other half. An enterprise can send routine, permitted work to a privately hosted model and use a frontier service for harder requests. The router has to weigh answer quality, data policy, and response time alongside price, and the enterprise then has to measure whether that choice actually lowers the cost of the completed workflow.

The Instinct roadmap serves training as well, but we weight this paper toward inference because that is where agentic demand concentrates. Enterprises will train and fine-tune specialized models occasionally and serve them continuously.

5. Helios: rack-scale co-design

Helios is the most complete example of AMD's rack-scale approach to model workloads. It brings EPYC CPUs, Instinct GPUs, Pensando networking, and ROCm into a reference design that OEM and ODM partners can use to build their own systems, and it supports large-scale inference and training. Tool execution, enterprise applications, and parts of the front-end network remain outside that model-serving rack and need their own infrastructure. [1]

Rack-scale integration reduces the number of hardware and software combinations a partner has to assemble for the model workload. That can make deployment and support more manageable, and it gives AMD and its partners room to optimize communication and power across the rack. The customer still has to connect that capacity to the rest of its agent workflow, including the systems that hold data and execute actions.

Helios also shows how AMD combines integration with customer choice. Partners can build from the rack reference design, while other customers use individual AMD components in systems shaped around their own requirements. AMD and HPE have described scale-up Ethernet work with Broadcom using UALoE, alongside the wider open ecosystem. Open interfaces give partners more room for design and interoperability, although any particular configuration still needs support and qualification, and openness does not guarantee that every component can be upgraded independently. [1, 5]

Across the rack, the objective is utilization, resilience, and cost efficiency.

6. Networking: scale-up and scale-out

Networking connects every layer of the agentic system, and it divides into three distinct paths: the front-end network that connects agents to enterprise systems and users, the scale-up fabric inside the rack, and the scale-out network across racks.

Inside the rack, the scale-up links connecting GPUs within a model-serving system reduce the time spent moving data and coordinating work. AMD and its partners have described UALoE for Helios scale-up. The supported protocol and topology determine memory access and synchronization, and a connected rack should not be assumed to be one universally cache-coherent memory space. [5]

Across the cluster, the Pollara 400 and Vulcano 800 AI NICs address scale-out communication at different product generations. The gain from more network bandwidth depends on the model and on how work is distributed, so communication overhead, congestion, and recovery from failures need to be measured in the deployed topology rather than inferred from a NIC's peak link rate.

The value of scale-out bandwidth depends on the model architecture (Exhibit 7). Dense models process every token through the entire model, so inference performance is driven primarily by compute and memory bandwidth, and networking helps mainly when a model spans multiple systems. Mixture-of-experts models work differently. Each token is routed to a subset of experts that may be distributed across many GPUs or servers, so network bandwidth and latency become binding. Faster scale-out networks reduce expert-routing overhead, improve utilization, and keep responses fast. As enterprise-grade reasoning models increasingly adopt mixture-of-experts architectures, scale-out networking moves from a training concern to an inference concern, which is where agentic demand is growing.

The front-end network is the third path, and it is where agent traffic grows fastest.

7. Pensando and the programmable front end

The DPU sits on the agentic execution path, where agents reach enterprise systems, and traffic on that path grows with every agent an enterprise puts to work. Every agent action that touches an enterprise system, whether a database query, an API call, or a workflow trigger, crosses the front-end network and crosses a policy boundary. At enterprise scale, the front-end network has to enforce security, segmentation, and load balancing on a very large number of machine-generated, multi-tenant flows at line rate. Doing that enforcement on host CPUs consumes the same cycles the enterprise wants to spend running agents.

AMD's Pensando Salina 400 DPU is built for this layer, with 400G throughput, PCIe Gen 5, dual 400GE ports, a P4-programmable data plane, Arm cores, and DDR5 memory. Salina offloads SDN, stateful firewalling, encryption, load balancing, NAT, and NVMe-over-Fabrics storage services from the host, which keeps infrastructure services at line rate and returns CPU cycles to agent execution.

The more interesting part is programmability. Agentic infrastructure requirements are still forming, and a programmable data plane lets operators adapt supported packet processing and infrastructure policies without replacing the silicon for every change. Application identity and permission decisions still require software above that data plane, so programmability does not substitute for governing what an agent is allowed to do.

More agent activity can increase front-end traffic and infrastructure services work, which gives operators a reason to consider offload where it frees useful CPU capacity. We would base that decision on traffic, security requirements, and the cost of running those services on the host, rather than on an assumed DPU attach rate.

8. Open ecosystem and software

Enterprises will not deploy agentic AI through one uniform architecture. They will deploy across public cloud, on-premises, and hybrid environments, with software stacks assembled from open-source frameworks, ISV platforms, and internal tooling. AMD's open approach fits that mix.

AMD's enterprise software stack spans ROCm, supported AI frameworks and serving software, and tools for container deployment. Many developers use PyTorch or vLLM rather than programming directly against the underlying GPU runtime, so familiar frameworks and common APIs reduce the application changes required, while partners help enterprises use their existing virtualization and container environments. Support remains specific to the model, the software version, and the hardware configuration. [6]

An integrated platform reduces deployment work, and open interfaces give customers more options for connecting applications and choosing suppliers. A customer can have both. Software licensing and support costs belong in the system comparison. Openness alone does not prove a lower bill or eliminate migration work. The benefit we would point to is that customers keep their options as applications, models, and capacity needs change.

9. The economics: tokens per watt, agents per watt, and total cost

Customers will judge the architecture by what it does to their economics, and agentic AI puts four efficiencies in play at once: token efficiency in the GPUs, agent efficiency in the CPUs, power efficiency across the system, and utilization across the cluster. Buying the fastest accelerator optimizes one of the four.

Token throughput per watt measures the serving side under stated conditions. For the CPU side, useful task throughput and energy per completed task are more informative than the number of configured agents, and waiting sessions, retries, and jobs that miss response-time targets should not make a system look more productive than it is. The full comparison includes the supporting servers and network as well as the accelerator. [2] Each layer of the AMD system maps to a customer metric (Exhibit 10).

Exhibit 10: Architecture capabilities and the customer results to measure

Source: Creative Strategies.

The table connects engineering measurements to the business result. More capacity is valuable when it produces additional accepted work within the required response time, and lower power has value when it reduces operating cost or allows more work within a fixed site limit. We would measure those results across the task rather than infer them from a component specification.

AMD's portfolio gives customers several ways to improve that result: CPU execution for tools and applications, GPU capacity for model serving, and networking for the data moving between them. Helios integrates the model-serving rack, while AMD components and partners serve the surrounding systems. For any buyer, the test is how well those choices fit its complete deployment.

Conclusion

We think of agentic AI as a new population of workers inside enterprise IT: software that plans, executes, and completes work across the systems the enterprise already runs. Supporting that population changes the IT operating model, and it changes what enterprises need to buy. The fastest chip is one input to that decision. The larger question is which system completes the most work across the whole workflow for each dollar and each watt.

AMD has been investing across the requirements that agentic AI brings together. EPYC serves hosts, agent execution, and enterprise applications; Instinct supplies model compute and memory; and Pensando supports networking and infrastructure services. Helios adds a rack-scale design for model workloads, and an open software and partner ecosystem connects AMD's components to the wider deployment.

Our view is that the rise of agentic AI supports the direction of AMD's investments. Enterprises planning deployments should measure token throughput, CPU execution, power, utilization, and response time against the cost per correctly completed business task, and they should buy for the whole workflow, whether that means an integrated rack, individual components, or a mix of suppliers. Paper 3 turns to the software layer and the economics of choosing where that work runs.

Sources

[1] AMD, Helios overview and FAQ, accessed September 29, 2026. https://www.amd.com/en/products/rackscale-solutions/helios.html

[2] Creative Strategies, Agentic CPU Guidebook V5, September 28, 2026; vLLM 0.25.1, Optimization and Tuning. https://docs.vllm.ai/en/v0.25.1/configuration/optimization/

[3] AMD, "The Agentic Era Runs on a CPU Foundation," July 2026. Thread resources are not measured agent throughput. https://www.amd.com/en/blogs/2026/the-agentic-era-runs-on-a-cpu-foundation-and-it-s-called.html

[4] AMD, Instinct MI350 microarchitecture. Specifications quoted for MI350X and MI355X, not all family members. https://rocmdocs.amd.com/en/latest/reference/gpu-arch/mi350.html

[5] AMD and HPE, expanded collaboration on open rack-scale infrastructure. https://newsroom.amd.com/news/amd-and-hpe-expand-collaboration-to-advance-open-r/

[6] AMD, "The Importance of Open Ecosystems for Agentic AI Deployment," July 30, 2026. https://www.amd.com/en/blogs/2026/the-importance-of-open-ecosystems-for-agentic-ai-development.html

AMD architecture and ecosystem illustrations (Exhibits 5, 8, and 9) are reproduced from AMD materials. They show the proposed architecture and partner coverage, not independently measured performance. Creative Strategies diagrams explain workload and network roles. Exhibit 4 is an illustration built on hypothetical inputs. Unconfirmed customer savings and comparative benchmark claims are excluded from this paper.

© 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.

A message to developers, and one to Apple

Netflix, Oura, Claude and Instagram, to name a few, have all gone through a full redesign to take advantage of both screens. They are the apps I hope other developers will look at when figuring out how far to push their own redesign. Basic tablet support is fine. Apps run on the inner display, scale sensibly and get the job done. That level of support will not drive deeper engagement with your app, though. My message to developers is to update, and to lean into AI tools to help with the work. My message to Apple is to make that move a no-brainer for them: continue to provide tools that make customization easier, and make sure the simulator experience is a great one, especially given the supply constraints, as smaller developers may not have been able to secure a unit on preorder day.

Dedicated apps are key to success for both Apple and developers. The Duo's appeal reaches beyond early adopters to lifestyle and fashion buyers and more. What these users have in common is disposable income, both to buy the device and to pay for apps that deliver a great experience.

How it sits in the hand

Aside from the work Apple did on the display to limit the crease, the first thing I noticed was the weight. If you have held a Galaxy Z Fold 8, you will feel the difference. If you are coming from an iPhone Pro Max, the Duo feels dense more than heavy. I put that down to it being shorter, so it sits differently in your hand, especially if you have small hands like mine. Closed, the 5.4-inch cover display still offers about 90 percent of the screen area of an iPhone 18 Pro.

Open it, and the weight is distributed so evenly that you stop noticing it. The thinness is what you notice. Content looks great on either screen, and the range of postures lets you consume it the way you prefer, depending on where you are. I know some people are unhappy with the nano-texture matte finish on the inner display. I have not found that I enjoyed content any less because of it.

Familiar, with a little adjusting

I want to focus most of my first impressions on the people I think make up the bulk of those interested in the Duo: current Apple users who have never owned anything but an iPhone. They will pick up a device that looks very familiar and behaves a little differently. The learning curve is small, and there is a short adjustment period, mostly because of new gestures. With controls moved to the side in iOS 27, you tap to open Control Center instead of swiping down. To go back, you swipe from left to right or use the arrow on the right.

Unlocking also moves to Touch ID in the side button, since the Duo has no Face ID. I know some people have complained about that, but it has been a non-issue for me. The way most people will hold the device, a finger rests by the sensor most of the time, so there is no wait to unlock. When you are not holding it, Apple Watch unlock fills the gap, and I am willing to bet most people buying a Duo own an Apple Watch.

Camera and battery

The camera is usually a point of contention on foldables, which rarely match the camera systems on their makers' top models. The Duo pairs a 48MP Fusion main camera with the same Fusion ultra wide as the 18 Pro, and has no dedicated telephoto. It is certainly not my iPhone Pro Max. For selfies, and for anything that did not call for long zoom or very low light, it has done what I needed for both photos and video. One thing to get used to is hand positioning. Because the device is shorter, it is easier to end up with a finger over the lens.

Battery life has been good. Once the phone finished setting up and indexing for Siri AI, I usually ended my day with between 10 and 20 percent left, which is not far off what I was getting on the iPhone 18 Pro Max.

Daily driver

I use different phones all the time, and I have made no secret that my SIM card has not left an iPhone since the iPhone launched. So I was very curious to see whether a foldable iPhone could become my main device.

On Android, the closest I got was the Samsung Galaxy Z Fold 8. Its form factor helped me decide which posture to use for which task, which made its learning curve lower than the Galaxy Z Fold Ultra's, as I wrote in my report for Creative Strategies. Even so, the effort was not offset by enough apps taking advantage of both screens. I spend less time on Android, so a direct comparison with the Duo would not be a fair one.

With the Duo, the apps are starting to justify the effort. On a flight this week, I watched the end of the first episode of The Diplomat on Netflix, answered emails with the Duo fully open since Gmail has not been optimized yet, read the latest Diligence Stack report, and worked through content ideas with Claude.

I can see myself keeping it as my daily driver. When I travel, my iPad Pro and laptop still come with me, and the Duo lets me get much more done before I open either one.

I also find I don't feel compelled to unfold the Duo as often as I expected. Some of that is habit, after years of using a traditional iPhone. Some of it comes from how good the cover display's form factor is, and it makes me wonder whether the iPhone mini might find a new life in this shape.

The camera is where I would like to see Apple do more, using computational photography to close the gap with the iPhone Pro camera a little further. It is really the only compromise I feel I am making between the two.

Before getting into the specifics of the iPhone Duo, I want to talk about some of the objective benefits of a foldable device. There are genuine productivity advantages to two apps at once. Most of what I was doing was taking notes during calls while having a full size screen, or in a lot of cases scrolling X while copying posts on specific topics to my notes. We, at Creative Strategies, have been working a lot more on financial modeling around data centers and AI infra build out. Being able to keep two Safari tabs or my email and Calendar side by side is really nice. This is genuinely useful productivity that makes working on my phone far easier than any other device, including traditional foldables. For entertainment, it is also fantastic. Last week I was heading into Manhattan on the train, and there was something fantastic about watching a show on Apple TV with the larger display. I want to specifically call out Apple TV here, because they tend to do the best job at supporting HDR content, which makes the colors and brightness on the Duo far more striking and better looking than it would be in other apps. Disney+ and Netflix also tend to do a great job, but Apple TV is second to none here.

Oh and I want to go on a bit tangent here about the iPhone Duo's nano texture and how cool it is. Normal nano-texture (Studio Display, iPad Pro, MacBook Pro) is etched into the glass. The goal of nano texture is to refract external light across the display, so the reflection doesn't get back to your eye causing glare. The downside is these micro-grooves (at the nanometer level) also refract some of the light coming out of the display panel itself, causing some of that shimmer and lost contrast, as well as reduction of clarity. On iPhone Duo, the nano-texture coating is etched into the polymer cover using maskless laser lithography that essentially writes a micro-lens over each pixel individually. Yes, each pixel. With this technology, it let's the light from the pixel be directed through the nano-texture layer while also refracting the external light being reflected against the surface. This allows the display to keep the contrast, color reproduction, clarity of text and fine detail, while also getting rid of the reflections and glare on the surface. But think about this, laser etching at a nanometer level a micro-lens into a custom plastic blend to fix the issues of normal matte coatings. It's just so cool.

When Apple was talking about the size and shape, the company mentioned that quite a bit of this was based around scaling content between the two display sizes, 5.4-inch and 7.6-inch. What's interesting is the way content scales vs. most phones, the controls move to the right side when folded and unfolded, and when in split screen it moves it to the right or left depending on it's position. This is important because content that is normally at the top and bottom of your display like time, signal, navigation controls are moved to the left and right leaving the viewable content roughly identical between an iPhone 18 Pro and iPhone Duo. Depending on the app, there can be more content or roughly the same, very rarely less.

Speaking of apps here, there's some nuance to apps and how a developer would go about supporting the iPhone Duo. There are essentially three categories of iOS apps relevant here: apps compiled with iOS 26 SDK, iOS 27 SDK, and iOS 27.1 SDK. Building for iOS 27 did require apps to support adaptive screen sizes for iPhone app mirroring, and that did lay the groundwork for iPhone Duo support in iOS 27.1. If an app uses Apple's native SwiftUI and doesn't have too much custom built on top, it tends to be one-click support by just downloading and using Xcode 27.1. Apple also gave developers agent skills built into Xcode to help optimize apps, these skills were done by the design and engineering teams at Apple and tend to be very good to get your app there with a single prompt in Claude Code.

The reason I want to bring this up is because over the ~6 days I've been using my iPhone Duo, apps went from not at all optimized (except for Perplexity and Claude), to nearly half of them being updated. I spent the past week or so chatting with developers to see what's going on with their apps, and most should be ready for customer pickup on Friday. While the reviewers experiences probably weren't that great, I have no doubt that customer experiences a few days later will be far better. This is to say, Apple has a great track record of developer support and getting their apps supported in the age of AI agents, this should totally be fine.

This software support for iPhone Duo, developer community and tools, and AI being so extremely good at Apple software development is a great sign in the age of AI. Apple stands to be one of the biggest winners of AI software development when it's incredibly simple to just update using AI to support an entirely new form factor like iPhone Duo, and other platforms (like HomeView, CarPlay, Apple Watch, Siri, etc) are likely to benefit as well. It is under-estimated how easy it can be for supporting these new platforms when Apple provides agent skills and AI-first developer tooling, assuming developers take advantage.

Some quick things too just to get them out of the way:

• Performance is great, A20 Pro is the best mobile chip and performs well
• Cameras are good, they are iPhone cameras! I do miss the telephoto, but ultrawide and main sensor are pretty great
• Battery life is outstanding, roughly the same or better than my iPhone 18 Pro
• Connectivity is great, the phone has the Apple C2 modem and N1 WiFi/Bluetooth chip
• The displays are bright, they are fantastic, and I love nanotexture on the internal display
• The crease is probably the best on a foldable, but is more visible than I initially saw at the Apple event
• The phone can get warm, but never uncomfortably so. The vapor chamber stops it from throttling, though
• iOS feels buggier than normal iOS. Random re-springs, UI elements being placed in the wrong place, URL bar in Safari disappearing. None of these are too prevalent or too annoying, but worth mentioning it's not as bug-free as the iPhone 18 Pro Max I was using prior

Oh here are some camera samples!

If you want more specifics on these, I highly recommend looking for some other reviews from creators or journalists who likely went into far more detail on some of these topics!

What I think is most important about the device and why I think this device will be a hit for Apple is just because it's fun to use. Genuinely, it is such a pleasure in a way that I don't think many other foldables have been. It's really hard to articulate that, and I don't know if everyone will feel it in the same way I do, but I'm going to try my best!

When you pick it up, it feels premium. The edges are soft with no sharp corners, the beveled titanium rails of the frame feel incredibly comfortable in the hand. It has weight to it, a heft like you might feel with a premium watch. It's not off putting, but you feel it. The phone has a feeling of intention and purpose. It's one of those product that feels premium, you don't feel the seam between the glass and the frame. The components flow into each other. It feels extremely premium.

It's slightly wider than what you would normally expect, it looks strange but cool at first. As you use it, the design makes sense with the controls shifted to the right side under the hole punch. It's simple and makes it easy to reach. It also looks really cool.

I think part of the reason I like this slightly wider form factor is the changes made to the OS and where system UI elements are placed, so you get this unique feeling with the new width while not having the downside of traditionally wide form factors of cutting off content and less viewable space. It's actually similar to an iPhone 18 Pro!

This gets into something else I don't know if I've experienced before, a sense of freedom. Generally with a foldable Android device, there is some lag or change of experience when you fold and unfold. You have to essentially intentionally choose which display you want to use. Sometimes apps wouldn't adapt either, so you would need to save your work, open the display, then have to close then open the app again for it to adapt to the larger size. On the iPhone Duo, it's seamless and it's fast. It's so seamless and fast, I found myself doing it to do simple things like fill out a form a little quicker or close it to type on a smaller screen then open again a moment later. It was freeing and felt like I didn't need to think about it very much. It feels like how a foldable should be, and it's not something I really realized mattered to me or even changed my habits until using the iPhone Duo. It sounds like a small thing, but that animation, having two screens on at once, and the overall speed (plus frankly better app developer frameworks) make this all possible.

Something I've also found myself enjoying a lot about the iPhone Duo is actually the fact that multi-tasking is a bit more limiting. This may sound strange, but I think there's a few reasons for it. First, it keeps the user (me, in this case) from being able to do something stupid. Opening a tiny window of an app is not a good experience, though it can show you some content that you may like, like maybe a single post on X at a time while having Gmail and Chrome open. This makes each window slightly worse, but I did it because I want to and could. Generally, I don't do this because there's a reason just because I can. I don't speak for every user here obviously. Being forced into just 2 apps means both apps are given better space. It also means app developers only need to focus on optimizing for 2 (maybe 3) windows! The front display and split screen are the same, then full width tablet mode apps on the foldable Duo display. Yes, you have vertical internal Duo display too, but I didn't really find myself using it (and it's the same shape as the outside screen). Being more limited gives developers less work to do and more time to optimize for the form factors! It tends to just be better.

There are also just fun animations around, like when you turn off the under display camera it looks like a ripple where you're dropping a stone into water or popping up from below the surface. When it disappears, it looks like it's just shrinking away. When you unfold the Duo, the idea is that the screen you're looking at mostly stays the same, and the content expands to the left to fill in the now available space. Sorta like you're lifting a glass panel to show more content. When you do this on the home screen, the left side apps actually settle from the animation a moment after the right, since the left side is settle physically a moment later. There's so many fun animations and tweaks and experience throughout, it's just fun.

The iPhone Duo is the first phone in a long time, probably 7 years 1 month (since the Galaxy Fold hit the US market), since I've had an experience of a fun being fun to use and just makes me smile every time I touch it, open it, and use it. Genuinely, I pick this thing up and just smirk a little. Do you know how long it's been since technology was fun or had this reaction for me? As someone that sees and uses practically everything, it's been a LONG time. iPhone Duo was the first one that did it.

It's not just me! When I got it, I was heading into Manhattan, I opened the MTA Train Time app for my ticket so it could be scanned. The guy scanning looked at it and said "woah what is that" and my reply was just "oh the iPhone Duo." His response, with literally no other details about it or knowing it existed before this interaction was "I'm buying that." I told him that it preordered opened Friday and was going to tell him a bit more, his response was "I don't even care, I'm getting that" as he walked away to scan the next persons ticket.

Obviously, anecdotal, but I think the general sentiment stands. People love their iPhones, people love cool technology, when you mix the two there interest is going to be there.

And this is where we get into the more analytical research/analyst view on the iPhone Duo. I posted a quick video on this a few days ago on X, but I view Apple has having what is essentially three categories of iPhone now. They have what I think you could consider: value, pro, and luxury.

And on a quick tangent, all products are bought on the perception of value. Different people have different values. Some may value camera more than weight, others may value performance more than display, others may value low price over good features. All of these are still value inherently, and users all have different values that shape their purchasing decisions. Every product needs to hit these for each person to get any adoption.

The iPhone 17e and iPhone 17 are what I would consider value phones. These focus on providing you the best experience at the lowest cost for what they provide. This doesn't mean cheap, but for the price you are paying, you are getting the most they can provide. I think both fit into that category extremely well.

The next category is pro, with 18 Pro and 18 Pro Max. These are devices that target creators and gamers, the ones that want the undebatable best performance, quality, battery, etc. This is purpose built for it, with thicker bodies and material like aluminum designed to handle heat that sustain that performance. These are devices who's value proposition target people who need what it offers, and for those people, it is a great value.

The last category is luxury, these devices are what I think focus on the experience or form factor. These devices are, obviously iPhone Air and iPhone Duo. These are what I think base their value proposition on a unique experience and device. iPhone Air is about being strikingly thin and light in the hand, it's truly an unreal experience to hold and use one of these. It feels like nothing! The iPhone Duo, obviously, is it's folding design.

I think it's worth mentioning this because this has allowed Apple to also allow what I think is the majority of users to have options outside of the Pro iPhone that aren't the value ones. I'm a user that doesn't want the iPhone 17, there's no reason I couldn't use it, but I want more. I want better performance from an A__ Pro chip and I want something that's just a little more. What the 18 Pro Max offers me, I really like! The cameras, performance, and battery are the best. What I value more than that is something unique and fun. I'm price insensitive and like technology. That's the iPhone Duo. I'm one of the targets for the iPhone Duo.

This market for the iPhone Duo isn't small, some equity research I've read leading into the iPhone Duo puts Apple's supply chain targets for iPhone Duo between 11 and 17 million units over the next year. For reference, the entire foldable market up until today is around ~18M units. I believe, genuinely, Apple will sell as many devices as they can manufacture. This is a proven category, there is enough demand for it to sustain itself and financially justify it's existence.

I think Apple's focus on fine detail mixed with experience, as well as fixing the issues that have plagued foldables. It does not fix them all, cameras are still slightly worse than the Pro iPhones (A for Duo vs. A+ for Pro), the crease and feeling of softness could still use a tad bit of work, but overall I do think this is the best foldable to date and I think Apple truly nailed this thing. I have enjoyed every second of using it, and I believe they will continue to nail this form factor.

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.

Qualcomm came to Maui for their annual Snapdragon Summit with the clearest version of its AI strategy I have heard from the company, and with the same structural limit it has always had. Apple can promise AI that works across devices because it designs the silicon, writes the operating system, and ships the hardware. Qualcomm controls the first of those three. Everything else it delivers through Google, Microsoft, and the phone, PC, and wearable makers that build on Snapdragon. The most interesting thing about this year's Summit was how deliberately Qualcomm is organizing itself around that dependency.

The phone stays at the center

Qualcomm President and CEO Cristiano Amon used his opening keynote to address the question that follows every new AI wearable. "Your phone is not going anywhere," he told the audience, arguing that the phone is unique to you, always with you, and has been personalized to you, which makes it the device that knows you best and therefore essential for agentic experiences. Other devices become endpoints: the agent sits at the center, with your phone, PC, glasses, wearables and car all serving as endpoints for it.

It is the same point John Ternus landed at Apple's iPhone event earlier this month, when he described the iPhone as the intelligent personal hub. As I wrote then, that is an expansion argument. Settling the succession question frees a company to add devices around the phone. Sergio Buniac, speaking on stage in his new Qualcomm role, described smartphones becoming the AI hub for our agents, connected to a platform of devices that touch our lives every day.

Android's Best Shot at an Apple-Like Experience

Apple's cross-device experience works because one silicon team designs the chips in the iPhone, Mac, iPad, Watch and AirPods, and every product is built to work with the others. Android brands have always assembled their experiences on top of a mix of chipmakers and platforms, which makes that kind of continuity harder to deliver. Qualcomm is the closest Android gets to a shared foundation, and in that sense, Qualcomm is the merchant equivalent of Apple silicon.

The announcements this week filled in the pieces. The new flagship phone platforms include a Sensing Hub designed to continuously process voice activity and personal context at very low power, the kind of always-on context a hub needs. Snapdragon Sound Elite Gen 2 supports earbuds, over-ear headphones, open-ear audio, audio glasses, hearing devices, and camera-equipped earbuds. Googlebook, a new laptop category built around Gemini Intelligence, launches with Snapdragon X Elite, with Dell and HP among the first manufacturers. Features like Cast My Apps, which opens apps from an Android phone on the laptop without installing them separately, are the phone-as-hub idea in practice. On the Windows side, Microsoft took the Summit main stage to introduce the next Surface Pro 12-inch and Surface Laptop 13-inch on Snapdragon X2 Plus, putting Snapdragon in the newest laptop launches on both Google's and Microsoft's platforms.

Googlebooks from Acer, Asus, Dell, HP and Lenovo are available for pre-order on both Intel Panther Lake and Snapdragon X Elite, so Qualcomm has competition on the laptop. What Intel lacks is the phone, the earbuds, and the glasses. Snapdragon now spans smartphones, PCs, XR, wearables and automotive. When the same platform sits in the phone someone carries, the laptop they work on, the earbuds they wear, and the car they drive, Android brands have a common foundation for the continuity Apple users take for granted. Apple builds that experience with its own chips across its own products. Qualcomm gives Android the opportunity to build it across brands.

Bringing a technology to market

When I talk about Qualcomm, I always point out that its role in making a technology mainstream has extended well past the chip into everything needed to bring it to market. This week that meant healthcare. Qualcomm and Global Health Connector launched the Personal AI Health Alliance, with founding members including Optum Health, Scripps Health, Merck KGaA, and the Digital Medicine Society. The goal is to speed up commercialization of continuous, privacy-supporting health solutions that use on-device AI to provide wellness insights between clinical visits.

Wearables have been collecting health data for years, and turning that data into clinical value has been slow. An alliance is a starting point. Its value will show in products that clinicians trust and health systems adopt.

A more integrated Qualcomm

Now more than ever, I see a company internally integrated enough to thrive on a better-together story. Buniac's appointment is part of that. He was named group general manager of mobile computing and XR, bringing device experience from his years as Motorola's CEO, which brings phones and XR under one group leader and matches the hub thesis.

The partnerships showed up again. Google's Rick Osterloh, on stage with Amon, said the move to AI-driven computing requires deep collaboration across every level of technology, with Gemini understanding your context whether you are in the car, on your phone, or at your desktop. Mastercard described building a trust layer for agentic commerce. Xiaomi and Motorola announced phones on the new platforms, and Honor showed its AI robot phone. With the Modular acquisition, Qualcomm plans to open source Mojo and MAX so developers can optimize AI across hardware, including other chipmakers' silicon.

Fandom as a trust strategy

The marketing and branding story has become central, and Qualcomm opened the Summit with it. Before any chip was mentioned, Qualcomm's Don McGuire talked about community. The Snapdragon Insiders community has grown to 23 million members, Snapdragon creators reached a combined 405 million people over the past year, and Qualcomm cited research that 92% of tech buyers trust peer recommendations over traditional marketing.

AI has an image problem. Consumers are wary of what it does with their data, their jobs, and their attention. Leaning into communities and fandom through Manchester United, Mercedes F1, and creators helps, provided the message stays genuine and transparent. That condition applies to the products as much as the messaging. In the mobile keynote, Qualcomm described an image signal processor that can add demographic information to the pixels it analyzes while identifying and tracking individuals as they move. Capabilities like that need careful explaining to the fans Qualcomm is courting.

Qualcomm has built the audience. The products arriving over the next year have to earn the trust it is asking that audience to give.

Disclosure: Qualcomm covered my travel costs to attend Snapdragon Summit

My configuration has 256GB of unified memory, but memory alone only gets a model onto the machine. The model needs to be fast enough to use, and the rest of the computer needs to keep up with it. M3 Ultra could run capable local models, but I could feel the desktop hitch when I pushed inference and tried to work at the same time. On M5 Ultra I have not had that problem in my use. The CPU is great, the GPU is great, and honestly it is hard to really push this thing. It's funny, a few days before the M5 Ultra was announced I was actually talking to a few friends and was telling them after nearly 18 months I was finally able to really hit the limits of M3 Ultra. Well, I have yet to hit that for M5 Ultra.

It's funny, if you've been following Creative Strategies for a bit, you may notice our website got a slight redesign recently. This was actually done using GPT 5.6 Sol and GPT 6 Astra recently, but both of these were running on the M3 Ultra Mac Studio I've been using for the past year or so. The reason I'm mentioning it is this was actually a full revamp of all the infrastructure hosting it, and part of that was creating a ton of simultaneous containers in OrbStack to test, convert data formats to retain articles when moving from WordPress to Ghost, testing and optimizing our self-hosting optimizations for Railway Docker containers, and help with design for it. Given each container needed its own database, worktree for real time updates for different UI changes, and needed to be available in real time for the agents, it was very memory and CPU intensive. I'm not sure any other computer I have would have been able to handle this as well! It did really push the 512GB memory M3 Ultra to its limits, believe it or not. This isn't super relevant to this report on the M5 Ultra, but it's an anecdote that I feel sorta explains where these ultra-level SoCs and workstations are incredibly useful regardless of where the inference happens. 

You own the CPU too

There’s a lot of debate right now about owning a GPU for inference. Should you buy the hardware and run your own models? Should you rent the GPUs? Should you just use an API? Those are fair questions, and yes, owning the GPU can be great. What gets missed is that with a Mac Studio you also own the CPU that runs everything around the model. The agents, compilers, Xcode, the apps they are controlling, and the rest of your work have a place to run. For me, this is the best CPU and GPU combination I’ve had in one machine. 

What makes the power of this machine so useful is that it is not locked into one job. I can push local inference, then move straight to development, or have the CPU and GPU working at the same time. As a consumer, that makes it my main computer rather than a dedicated AI appliance. For an enterprise, it could be a CPU hub, a GPU hub, or both. If models or inference software move in a different direction, the Mac is still an extremely capable computer. That is a lower-risk bet than committing the whole machine to one workload.

The GPU is obviously powerful too. Apple lists roughly 1.2TB/s of memory bandwidth for M5 Ultra, and its GPU includes Neural Accelerators for matrix operations. That memory bandwidth and compute are why a large local model can feel interactive instead of merely fitting in memory. Of course, the model and the software matter just as much. Apple’s specifications and its explanation of Neural Accelerators cover the hardware side.

Model architectures and inference frameworks change constantly. The machine stays the same. I can tune the software for a model I care about today, then use the CPU for development tomorrow, and I don’t need to predict exactly which model or framework will win three years from now to find the computer useful.

Ok, the local AI part

There are two things that decide whether I actually want to use a local model: can it do the work, and can it do that work quickly enough? Qwen 3.8 Flash Next has been very good for me in OpenCode 2. In my earlier agent testing I saw around 40 tokens per second on M3 Ultra, with waits of roughly 5–20 seconds before an agent turn got moving. On M5 Ultra it was around 75 tokens per second, with waits closer to 1–5 seconds. Over one turn that may not sound dramatic. Over an agent run with tool call after tool call, you feel every one of those waits. In practice this actually ends up being 5-8x faster on M5 Ultra.  

Those are numbers from my agent workflow, not some universal speed claim for every Qwen setup. Context length, cache reuse, quantization, and the inference software all change the result. The 149-tokens-per-second decode figure I get into below is from later kernel tuning, so I wouldn’t put it on the same chart as the earlier 75-tokens-per-second agent observation without labeling the workloads.

I’ve also been able to keep multiple subagents doing real work at once. I haven’t done a controlled concurrency test yet, so I’m not going to give you a neat throughput number. What I can say is that the machine no longer feels like I have to choose between letting an agent work and getting on with my own work. 

M3 Ultra could run these models. That wasn’t the problem. The problem was that I was spending too much time waiting for the next useful thing to happen. M5 Ultra changes that enough that leaving the model loaded in the background now makes sense for how I work.

The cache still matters. A lot.

Here’s the part that gets lost when people only compare tokens per second: an agent isn’t one continuous stream of text. It reads context, calls a tool, gets something back, and starts another turn. Whether it can reuse the work it already did on that context makes a massive difference.

Prefill is the work of processing the input before the model generates anything. If the server can reuse cached context, there is less to process on the next turn. If that cache breaks, the model has to chew through those input tokens again. It doesn’t matter that decode is fast if you’re sitting there waiting for prefill.

OpenCode 2 has given me better cache reuse in my setup, which is one of the reasons I’ve liked using it with Qwen 3.8 Flash Next. But I’ve still seen a cache break turn into a 30–40 second wait on a long context, even on M5 Ultra. You notice very quickly how much the software matters.

That’s also why I don’t think it makes sense to talk about the hardware as if the performance is fixed on launch day. The silicon gives you a ceiling, but the kernels and the rest of the inference stack decide how close you get to it. I’ve been working on that part directly.

Writing kernels for M5 Ultra

A model is a series of operations on numbers: matrix multiplications, attention, normalization, and so on. A GPU kernel is the code that actually carries out one of those operations, or a group of them, across the GPU. How you divide the work, reuse data, and move it through memory has a lot to do with the speed you end up seeing. MLX supports custom Metal kernels, so you can tune the operations that matter for the model you are running.

Take a matrix multiplication. A good kernel breaks it into tiles, reuses data in fast on-chip storage, and keeps the matrix hardware doing useful work. You can also combine compatible operations instead of writing an intermediate result to memory just to read it back. That’s fusion. Metal’s tensor operations can use the Neural Accelerators inside M5-class GPU cores, but you still have to feed that hardware well. The same chip can give very different results depending on the software.

This is one of my favorite parts of using the Mac Studio. I’ve had Opus 5 and GPT 6 Astra writing and optimizing Metal kernels for Qwen 3.8 Flash Next. Give the agents the Metal 4.1 programming guide, a token budget, and the actual M5 Ultra to test against, and they can write code, compile it, run the model, look at the result, and try again. The GPU runs the model, but the CPU is hosting the whole loop that makes the GPU faster.

In this round of tuning, I took prefill from around 2,400 to 4,000 tokens per second on the same machine. That’s a 66.7% increase without changing the hardware. Decode is around 149 tokens per second in this optimized setup. I’m still working on it, and I think there’s more to get out of the chip.

What does that mean for an actual task? At those prefill rates, the same input takes about 40% less time to process. If your agent spends a lot of time on long prompts or cache misses, that is a meaningful change. If it spends most of its time in tools, the overall improvement will be smaller. This is why an enterprise deploying a particular model has a reason to optimize for its own context lengths and workload, not just run one benchmark and call it done.

What I mean by owning the CPU

A good example of what I mean happened yesterday. I was on a plane, using the Mac Studio remotely while I had agents working on the Qwen model. The CPU was running the agent harness, compiling Metal code, managing the tools, and hosting the rest of the development environment. The GPU was where I was testing the optimizations. I could check in while traveling, but the interesting part was how much the machine itself was doing.

Then the optimization work finished and the machine moved on to my hobby projects in Xcode. It didn’t need a new purpose. It was already a macOS development machine with a great CPU, memory, storage, and all the apps I use. That’s the part I keep coming back to when people frame this as a question of whether to own a GPU. You’re also getting the host that can do real work before, during, and after inference.

On M3 Ultra I could sometimes feel the desktop hitch or lag when a model was running and I was trying to use the computer for something else. I haven’t noticed that on M5 Ultra in my use so far. I’m sure there are workloads that could push it there, but being able to leave a model loaded, call it when I need it, and keep working normally changes how useful the whole setup is.

For a consumer, this can just be your computer. For an enterprise, maybe it is a developer workstation, maybe it is a local inference node, maybe it is the CPU hub coordinating several jobs, or maybe it is all of those things. That flexibility matters because nobody knows exactly which models, inference frameworks, or deployment patterns will make the most sense in a few years. If one part of the stack changes, the other part of the machine doesn’t suddenly stop being useful.

And yes, the CPU is fast

My Geekbench 7.0.0 Pro result was 3,666 single-core and 51,448 multi-core, on macOS 27.0 build 26A428. The screenshot identifies the machine as Mac17,15. That is a strong CPU result, and one reason I don’t want to treat the CPU as a footnote in a discussion about inference.

A benchmark score is nice, but what I actually notice is the computer doing the work around the model. It is building code, running Xcode, hosting agents, and staying responsive while inference is happening. I’d need a comparable M3 Ultra Geekbench 7 run before using these scores to make a precise generation-over-generation claim.

One result that really stood out was building a Rust project from scratch. It took about six minutes on M5 Ultra versus 20 minutes on M3 Ultra. That’s a strange enough difference that I think something else may be going on, but it was striking to see in my own work.

I wouldn’t attribute all of that to the silicon without repeating it with the same project revision, Rust toolchain, dependencies, caches, and build settings. It is a workflow observation for now. Still, it’s another reminder that this CPU is doing real work for me, not just sitting there to launch GPU jobs.

It’s a whole computer

Another example: I used a local model through OpenCode to build a benchmark app for Apple’s Foundation Models framework in Xcode. The model ran locally, called tools in Xcode, wrote the app, built it, tested it, and kept going through that loop on the Mac Studio. Think about what is happening there. The model and the application it is building are on the same machine, and the CPU and GPU each have work to do.

I already have an M3 Ultra cluster that I use remotely through a personal inference proxy, so I’m not opposed to separating the model from my working computer. I can access that cluster from my M5 Max MacBook Pro or the Mac Studio. With M5 Ultra, though, I’ve found more value in having a capable model and the rest of my work on the same machine. Even when I’m away from it, I can reach the full computer remotely.

None of this means I think every agent should run on a local model. For many tasks, I still want a frontier cloud model doing the main reasoning, with a local model as a tool when that makes sense. But there are also jobs I want to run entirely on my own machine, and I don’t want to stitch together a bunch of smaller models just to make that possible. Having enough memory and speed to run one capable model when I choose to is a big deal.

The same goes for privacy. Local isn’t automatically the right answer for everyone. Privacy is about choosing where the data goes and who you trust with it. If a team needs an air-gapped system, or wants certain documents and tools to stay on hardware it controls, this Mac can host the model and the applications together. If the workflow can use a cloud model, it can do that too. The point is having the choice.

To be blunt, this thing is insanely good. Qwen is fast enough for real agent work, the CPU can build and host everything around the model, and I can keep using macOS as my main computer without feeling it slow down. I can use it for inference, make it a CPU development hub, or do both at once. There are certainly heavier workloads that could push it, and I want to try clustering for larger models, but in my work so far the M5 Ultra Mac Studio feels frankly overpowered.

There has been a lot of discussion about what comes after the smartphone. AI creates new ways to interact with technology, so naturally people want to know what new hardware those interactions will require. But I think it is worth taking a step back and asking what we actually need from that hardware.

It already has so much of what would make personal intelligence useful. Your messages, calendar, photos, contacts, and the apps you use to get things done are accessible through it. Your Watch and AirPods connect to it. It has cameras to understand what you are looking at, microphones to capture what you hear, and a display to show you something when a spoken answer is not enough. You already carry it, charge it, and know how to use it.

Having data on a phone does not automatically mean an AI system can access all of it. Permissions, app integration, and the ability to retrieve the right information still matter. But the iPhone is already where many of those relationships come together. That is a significant starting advantage.

In my earlier piece on agents, I argued that what will sell is products and the features on those products. We can build increasingly capable agents, but consumers still need a reason to use them. Asking someone what they want to automate in their life can be surprisingly difficult. Helping them remember something they just heard is much easier to explain.

Apple’s Audio Intelligence features are a good example of this. Live Rewind lets you bring up text from the previous 15 seconds by double clicking the Watch’s Digital Crown. Someone mentions a book, and you miss the name. You can catch it and save it. Siri Recap serves a different purpose, producing high-level takeaways from conversations that you can view in the Siri app.

These are small, understandable things. They address moments people already experience, on a device that is already on their wrist. You do not have to figure out an entire new workflow before finding a use for them. Apple also does it in a far more secure way, with a new Secure Exclave that holds a ~15s audio buffer, sends it to the iPhone directly to transcribe it, and the iPhone sends the text back to the watch and the buffer disappears. There is no saving to storage, there are no uploads. From watch to phone to watch, and gone. 

The Watch also makes the broader hub idea more concrete. The iPhone does not need to be in your hand for its ecosystem to be useful. The Watch gives you an interaction on your wrist. AirPods give you a way to talk and listen when your hands are full. The phone provides the screen and access to your apps when you need to go further. Different devices can do what suits them while remaining part of the same experience.

This is why I think new form factors can actually strengthen the phone’s position. A new way to capture information or interact with an assistant can connect to the device that already has your context. It does not have to recreate that entire relationship from scratch. The question for new hardware becomes what useful role it plays in your life and how well it works with everything else.

There is another distinction here that matters: the location of the product experience and the location of the intelligence doing the work are separate decisions.

Calling the iPhone a personal intelligence hub does not require every model to run on it. Apple described both on-device models and more advanced models accessed through Private Cloud Compute. The phone can bring together the user’s context, permissions, and interactions while different workloads run where they make sense.

That is consistent with the broader point I made about local models. There are useful jobs for smaller models running close to your data. Sound recognition is one example Apple described running on the Watch itself. More complex work can have different requirements. We should evaluate those choices based on whether they deliver a reliable feature, at an acceptable cost, with appropriate privacy protections. The user’s experience is what ultimately needs to work.

Audio makes the personal context story especially interesting because so much of our lives never becomes a message or a calendar entry. A recommendation from a friend, something discussed in a meeting, or a detail from a conversation may be useful later. Giving people a simple way to retain some of that information could make their devices considerably more helpful.

But we should be precise about what Apple has described. This is not yet evidence of an assistant that remembers every conversation and automatically acts on everything it hears. The current actions for recaps are saving and sharing them. Creating a calendar event directly from a recap is not a supported action. There is still work between capturing something useful and reliably doing something with it. I think in the current environment until this technology is better understood by the public, going on the side of caution at the risk of having a less useful proactive product is the right decision. 

Apple also described a deliberate limit on what it retains. Its approach to recaps favors high-level notes without speaker attribution or a retained audio recording. That is a meaningful product decision. People may want help remembering a conversation without wanting a permanent recording of it.

That choice also makes the quality of the AI important. If the takeaway is wrong and there is no recording to check, the convenience starts to fall apart. Privacy, accuracy, and utility have to work together. Simply having the right devices and access to context does not solve that.

This is where Apple still has to execute. The personal intelligence hub is a compelling direction, but people will judge it through the moments when they ask for help. Does it find the right information? Does it understand what they mean? Can it complete the action without creating more work? Those are the tests that turn a good strategy into a product people rely on.

What I find encouraging is that the strategy starts with how people already live. They have their apps, their devices, their conversations, and their habits. Making those things work better together is a very large opportunity on its own.

The iPhone is exceptionally well suited to sit at the center of that. It already connects so much of your life. Apple’s opportunity is to make those connections useful in ways that previously required you to do the work yourself. That is a future for personal AI I can see people actually wanting.

And on a slightly different note, I have been arguing this idea that we don't need a new form factor and fundamentally a smartphone, smartwatch, and earbuds are a perfect starter pack for AI experiences. Voice and conversations with earbuds in, it can use the display on the watch to show and alert you to information, if you need to show the AI something pulling out your phone to use the camera is natural, it handles privacy well by matching actions the consumer normally takes, and isn't trying to sell extra hardware without a propose. I'm glad Apple started this route. I think consumer trust for dedicated AI hardware is non-existent, I think smart glasses are not going to end well and the experience isn't really there quite yet, and just making your phone feel more intelligent in an age where AI is being blamed for near universal price inflation in technology is bridging the gap better than I think many would expect. Apple did a good job and I'm glad they're going on what I think is the right path. 

And the price

The Duo starts at $1,999 for 256GB, running up to 2TB. Speculation had run to $2,500, which would have placed it above every Android foldable and framed it as a halo device. At $1,999 Apple lands it where everybody else is. It also sits $700 above the iPhone 18 Pro Max at $1,299. The price signals that while Apple might be supplied constraint it will sell as many as it can make. It also says that this is a category Apple is seriously pursuing.

The hardware holds up

The hinge is over a hundred precision components with carbon fiber support and a custom variable torque profile, plus integrated magnets that give it a sharp close.The display work is where the crease question gets answered. Apple brought its nano-texture finish to iPhone for the first time, using a custom polymer it claims is up to 40 percent stiffer than what the industry uses, with high-strength glass above and below the OLED panel, viscoelastic adhesives between the layers, and a titanium plate underneath. The result is matte, perceptually flat, and resistant to the dents and fingerprints that have affected plastic-topped foldables.

The software is the differentiator

Android foldables have had capable hardware for several generations. What they have lacked is an operating system that treats the second screen state as a design target in its own right.In iOS 27, the essential controls move to the side to preserve vertical space for scrolling content. The dock moves with them. The status bar has been redrawn as a compact element that tucks into whichever corner it needs. Liquid Glass was reworked for the new aspect ratio.There is no Face ID on the Duo and to some people this seems like a step back. But considering all the different orientations you can hold the phone at Touch ID built into the side button is the least frictionless experience especially as you can enroll different fingers depending on how you hold the device. Considering the changes of Duo bu=yers also having an Apple Watch are high, Apple added Apple Watch unlock for added conveninece.Separately, the FaceTime camera on the inner display sits under the panel and stays hidden until a call comes in.

App support? Check!

Netflix is the clearest example. Its clips feature runs on the closed display for scrolling through content, and opens to the inner display to watch. The app renders its own reflections that respond as you move the hinge, which is the kind of detail developers only build when they expect the hardware to matter. Zoom put participants and shared content on screen together. Slack showed the full workspace alongside messages. Apple also opened a Core AI framework for on-device models, which a content creation app was already using in the labs.Developers showed up for the buyers. A $1,999 device selects for exactly the customers a subscription business wants, and that math works at volumes well below iPhone Pro.

Who buys it, and what Apple did not say

Most iPhone Duo buyers will come from inside Apple's own base, upgrading from Pro or Pro Max. Some Android switchers will cross over for the software and the app support. Samsung can match the panel and the hinge.Apple did show productivity. Split View puts two apps side by side on an iPhone for the first time, and you can pair apps to return to them, run two Safari windows, and drag content between them. The framing around it stayed light. The center of the pitch was the same one Apple used at the original iPad launch: you already love this device, here it is with more room.That framing carries risk. iPhone customers have been pragmatic about industrial design for fifteen years and grew up on the glass rectangle. Android buyers showed more appetite for form-factor experimentation, which is part of why foldables took root there first.

Why now

Apple does not enter markets early. It waited on the larger-screen iPhone until the market had proven the demand, and it has done the same here. Foldables have been shipping on Android since 2019.What has changed is what a bigger screen is for. Apple has spent years building a content business it wants people to actually sit and watch. It now has a version of Siri that, from early beta, looks materially more capable than what it replaced. Those two things put pressure in the same direction, toward more display, and toward a device you interact with conversationally as much as by tapping.The framing across the whole lineup was iPhone as the intelligent personal hub, and John Ternus used it to close out the question everyone keeps asking about what comes after the phone. His answer is that nothing comes after it. The phone is the hub, and more devices arrive around it.That is an expansion argument. Watch unlock on the Duo is a small feature and a large signal: a device you already own authenticating a device you just bought. The audio intelligence work on Series 12 comes at it from the other side, putting ambient AI on the wrist and leaning on the phone for the context that makes it useful. Each device Apple adds pulls from the same personal context and gives something back to it.So the question for next year stops being what replaces the iPhone and becomes what attaches to it.

There is no iPhone 18

This is the first iPhone launch where only the higher end of the lineup arrives in the fall. Pro, Pro Max, and Duo. The iPhone 17 stays on sale as the entry point.One reading is scheduling. Apple brings the base model in the spring, closer to when the mass-market Android devices land, and gets a second moment in the calendar instead of one crowded September.The other reading is about cost. Memory prices have moved against everyone building phones this year, and a base iPhone 18 would have to absorb that. Raising the price of the model that sells to the segment already telling us they do not need a new phone every year is a hard sell. Holding the 17 at a $100 more serves the buyer better than a more expensive 18 would.

October is the number that matters

IPhone Pro preorders open Saturday with availability September 18. Duo preorders open October 16, with availability October 23 in more than 70 countries and October 30 in 28 more. That is a five-week gap, and nobody buys a foldable without trying it for size. Buyers who would otherwise take a Pro now have a reason to wait, and it has nothing to do with price.If Pro preorders come in soft, the temptation will be to read weak iPhone demand. The likelier explanation is deferred decisions, and the number to watch is combined Pro and Duo sell-through in November.Apple has a partial hedge here. iPhone Handoff lets one number live on two iPhones, active on whichever you pick up. At launch that is T-Mobile in the US and Deutsche Telekom in Germany, with more carriers follow.

The camera is still the compromise

The Duo runs a dual camera system: a 48MP Fusion Main camera with an integrated optical-quality 2x telephoto, a fast aperture, a large sensor and sensor-shift OIS, plus the same 48MP Fusion Ultra Wide as the 18 Pro, which also handles macro. It shoots 4K120 in Dolby Vision. Apple built form-factor-specific features around it: Duo FaceTime, Duo Preview so your subject can see the framing on the outer display, and Smart Take, which watches the scene and fires when people are posing.There is no dedicated telephoto. But Apple said they learned from iPhone Air about how many people depend on the ultra wide. That is a fair account of the tradeoff, and it is still a tradeoff.Battery is the better story. Two cells with custom silicon rebalancing them so they present as one, delivering 31 hours of video on the inner display, 44 on the outer, and 24 hours of typical use per charge with the displays used equally. Fifty percent charge in around 20 minutes wired, 30 minutes on MagSafe or Qi2. That puts it level with the iPhone 18 Pro on everyday use, which is not where foldables have historically landed.Both objections are smaller than they have been on any previous folding phone.

What is left is the question Apple cannot engineer past: whether its own customers, told for fifteen years that the glass rectangle was the answer, want a phone that folds.

Google has a new strategy around this, where the news embargo and videos from tech creators come out a few hours before the actual event. The last year it was around 2 hours before, this year it's 8 hours before. These events were also hosted by celebrities and influencers (who are also celebrities).

Normally, events are pretty simple: live stream with a keynote and the news comes out when it starts (Samsung) or as it's going (Apple). Google did this up until last year as well! The reason I consider this important context is this is the standard launch, everyone does it like this. Every tech brand did it like this, even car brands and others like Taco Bell started to copy this format. Taco Bell example may be a bit off because it was more of a parody of it, but you get the point.

These events for tech brands tend to work well because the market already knows about the products. Samsung and Apple are the largest smartphone brands in terms of volume, together they ship around 40-45% of all smartphones, around half a billion phones annually. There is interest in these phones from demographics that don't know or understand technology, you find normal people (think your parents, grandparents, friends, random person on the NYC subway) talking about the new iPhone and to a lesser extent Samsung Galaxy, but they are still aware of it.

Google is in a different boat, while they have been growing Pixel for 11 generations now, Pixel is still not a well known brand in the same way the others are. It is more in a way that those people are aware that Google makes phones and those phones are called Pixel, but not nothing about them. When someone will go to buy a phone, this makes choosing a Pixel uncomfortable vs. the reliable devices, Samsung and Apple. It becomes a familiarity thing.

Making these devices more familiar to the masses becomes extremely difficult. It's a leap of faith to upgrade devices, let alone swap the brand you have been using for years, regardless of that experience being good or bad. A known bad experience is better than an unknown for a lot of people.

Now, the reason this preamble is included is pretty simple: because of the aforementioned market conditions, Google needs to build trust, they need to build familiarity, and they need to build recognition. There are many ways to try to do this, one way could be by becoming a fashion brand and start with high style designs and lifestyle, this is what Nothing is trying. Another could be going for the total extremes in their products to make the best of the best and market themselves, this is what a lot of Chinese OEMs try.

Google, on the other hand, is taking an approach I think I like a lot more. They are trying to appeal to consumers with public figures and celebrities that people know and trust, building products with and around their needs, and using them to grow awareness. The Steph Curry Fitbit Air and Pixel Watch 5 are perfect examples. If Steph Curry is helping make it, you can trust that it's good. If Alex Cooper starts to talk about the Pixel 11 and it's camera, you can trust that the camera is good. These events are now a showcase of the products and how the products are used, rather than a keynote trying to explain tech specs.

You can try to combine these into a single event and keynote, and it's extremely difficult. Arguably a single fruit-company is the only one to do it well. Google decided to separate it, in the morning (10am today) Google released all of the specs, reviewers posted their hands-on videos, and media/influencers/press received their devices. This afternoon at 6pm, Trevor Noah hosts the Made by Google event. There is an 8-hour window where media, press, fans, enthusiasts can all be excited and enjoy the devices, like they otherwise would be.

This is an exciting launch, regardless of the initial coverage kinda calling it boring. I haven't used the phones yet (I have no clue when I will get mine), but over the past few days I talked to a ton of people who used it at the early content capture. Every comment was about how the camera processing is really good to the point they consider this a multi-generational leap back to what the Pixel's used to feel like around Pixel 4, the thing felt fast, didn't overheat, and battery life was good. These are basically my complaints from Pixel 10 Pro XL. This initial coverage gives the enthusiasts (hello, that is me) time to be excited! I don't need to see someone on stage for this to be true and exciting.

This afternoon, with actual live event, that's time to get others excited and interested. I know about the Pixel series, do I need to see Steph Curry, Trevor Noah, Alex Cooper, and everyone else whose names I am not familiar with using the features I already know about? Absolutely not, but I will enjoy every second of it.

You know who needs to see that? My mother who is thinking about getting a Pixel 11 Pro to replace her aging Galaxy S24. My girlfriend who didn't know Google made phones until I told her about this event. My neighbor who hasn't looked at anything but an iPhone in years. We as tech enthusiasts and creators and fans of technology take for granted the knowledge and education we have about these devices from pure genuine interest. These are names they recognize, maybe people they follow or care about seeing. Maybe it's just curiosity to see how what Google must be spending a fortune on.

Google needs to build that same genuine interest and trust to make Pixel a trusted choice over the known devices. I think this is a great way to do it. Some find it cheesy, but it's not meant for them. That's completely ok. If you're reading this, it's probably not meant for you either. Also ok. What is important is understanding why they make these choices. I could be totally wrong, but this is a strategy that I think is working exceptionally well for Google and makes the launches just far more interesting than looking at specs.

and just to acknowledge it, yes the specs are minor iterative upgrades but the reality is there really isn't that much more to improve with current technology. Tensor G6 has new IP in the SoC to help power the new imaging pipeline, that imaging pipeline has a massive leap in image quality. The battery life is better thanks to the chip too, even without silicon carbon batteries. The display is brighter, the thing runs cooler, there are more intelligent Gemini features. The Pixel Watch 5 uses a new large sensor model architecture for health tracking that can detect insulin resistance trends and is far more accurate. I don't care if it doesn't look different, that's huge! What else could you want or need? What else would make it better? I can't think of anything that I'd want. The iterative upgrades look small on paper and might not be exciting but they create new experiences and solve pain points that existed before, and no longer exist. That is exciting. Smartphones are mature, the market is mature, how to market them, how to talk about them, how to get people interest in them is different in a mature market. Google did that well.

That sequence explains why The next measure of AI momentum is work transformed, published by Jared Spataro on the Microsoft 365 blog the morning after FY26 Q4 earnings, spends its customer proof points on agent counts. Premera Blue Cross has built more than 900 agents, most of them by employees outside IT. Microsoft’s own supply chain team runs more than 70 across planning, sourcing, fulfillment and logistics. Jared Spataro closes on the growth and governance of agents as the measure that matters next, which is a long way from the hours-saved framing Copilot launched with.

What the seat actually installs

On the January earnings call Satya Nadella described the data underneath Microsoft 365 as the most important database any company running Microsoft has, pointing to the tacit information it holds about people, relationships, projects and artifacts. Copilot Chat users on the bundled tier do not get Work IQ grounding. The paid seat is the thing that turns that database into something an agent can query with permissions attached.

This makes broad deployment a technical requirement in its own right. A partial rollout produces a partial context graph, and agents grounded in a partial graph produce generic output. Enterprises that bought a pilot allocation of 500 seats to test ROI were, without knowing it, testing the version of the product least likely to work. The economics that looked unjustifiable at $30 per user for time savings look different when the seat is the prerequisite for an agent that closes a workflow.

Agent 365 extends the same logic to identity. Agents carry their own credentials and permissions, Entra governs them, and Microsoft Scout, introduced in June, runs in the background under that model. The governance layer is the asset competitors cannot assemble quickly, because it depends on already sitting inside the enterprise directory.

The trajectory, with two qualifiers

Microsoft had never published a Copilot paid seat count before January 2026. It disclosed 15 million that month, 20 million in April, and more than 30 million this week, with net adds going from 5 million to 10 million quarter over quarter. Customers running more than 50,000 seats grew more than sevenfold year over year. NHS England is deploying to 505,000 clinicians and staff, HSBC has committed to 200,000 seats, and EY is putting the E7 suite in front of 400,000 employees.

Q4 is Microsoft’s fiscal year end, when large deals cluster to capture year-end discounting on exactly the product the vendor wants to push, so the 50 percent sequential jump needs one more quarter before it reads as a run rate. And the attach rate stays small. Against a Microsoft 365 commercial base of roughly 464 million paid seats, derived from the 6 percent annual growth Microsoft reported against the 450 million figure it last published in January, 30 million works out to about 6.5 percent. The seat count doubled in six months and more than 93 percent of the installed base has not bought.

Comprehensive stopped being the differentiator

Microsoft’s portfolio breadth is real. Microsoft 365 Copilot, GitHub Copilot, Security Copilot, Copilot Studio, Agent 365, Dynamics, Power Platform, Azure AI Foundry. No competitor matches that span from developer tooling through security to the knowledge worker.

Breadth stopped sorting the field sometime last year. Google has Gemini Enterprise, Workspace, the Gemini Enterprise Agent Platform that replaced Vertex branding at Cloud Next, Code Assist, and a security line that now includes Gemini 3.5 Flash Cyber. Andy Jassy has been describing AWS share gains in terms of a top to bottom stack since 2025, and Quick Suite put Amazon directly into the knowledge worker layer. Every hyperscaler claims full coverage now. What separates Microsoft is the installed base and the directory, which is a distribution argument.

What this means for a Copilot evaluation

For anyone still running a Copilot pilot, the allocation size is the thing to reconsider. Testing 500 seats against a time-savings baseline measures the configuration least likely to produce a return, because the context graph underneath it is thin and the agents built on it stay generic. An evaluation that produces a usable answer covers a full function, targets one workflow that crosses systems, and measures completion rather than minutes saved.

Fiscal year end matters to the negotiation. The cohort above 50,000 seats grew more than sevenfold this year, which tells you volume pricing exists at thresholds Microsoft has an incentive to reach. Buyers sitting between 5,000 and 50,000 seats have more room than list price suggests, and the leverage is largest in the quarter Microsoft is trying to close.

Microsoft is competing with its own supplier

The most revealing line in the Copilot post sits in footnote 2. Microsoft ran 125 test runs across 12 prompts comparing Copilot Cowork against Claude Cowork with the Microsoft 365 connector, both running Opus 4.8, and reports its own product came in 30 to 40 percent cheaper. A post celebrating 30 million paid seats does not normally carry a competitor cost benchmark in its footnotes.

The threat that footnote names is a model provider reaching Microsoft’s own surfaces through a connector, without Microsoft’s seat and without Microsoft’s margin. Copilot Cowork runs on agentic technology Microsoft brought in from Anthropic, and Microsoft booked a $3.2 billion gain on its Anthropic investment in the same quarter it published the comparison.

Nadella made the architecture explicit on the same call. Answering UBS analyst Karl Keirstead, he said the platform design requires you to “keep your harness separate from the model,” with memory and context held externally so that any model is swappable at any time. He described models as an input to the enterprise, and the goal as a firm that controls its own human capital and token capital.

That is the grounding argument stated as vendor strategy, and it names the layer Microsoft intends to own. It also explains the benchmarking. Footnote 2 puts Copilot Cowork against Claude Cowork on cost. On the same call Nadella claimed MAI-Cyber-1-Flash outperforms a much larger Mythos model at half the cost when paired with Microsoft’s multi-agent security harness. Two comparisons against Anthropic in the same week, from a company holding a stake in it.

Cowork’s multi-model design lets a task pull whichever model fits, which is Microsoft declaring models a commodity input it intends to arbitrage. The position holds while the harness stays proprietary and the context stays inside Microsoft’s directory. Thirty million seats are what keep it there.

A skeptic might argue Samsung is moving early to get ahead of Apple’s rumored foldable iPhone, which is expected to resemble the Fold8 and arrive in the fall. Look at the portfolio, though, and this is the right time to expand the family. Flip owners now have something to upgrade to without jumping all the way to the Ultra. The Fold8 also brings a content consumption audience to a lineup that has spanned the lifestyle appeal of the Flip and the productivity of the Ultra. The price points tell that same story. The Fold8 Ultra starts at $2,099.99 and the Fold8 starts at $1,899.99, while the Flip8 starts at $1,199.99. The $200 gap between the two Folds is narrow. The Ultra remains the best of the portfolio, but a buyer stepping down to the Fold8 gives up little, which is why the two prices sit so close.

The device’s positioning shows in its design ratios. Folded, the cover screen suits the quick moments people return to most, from messaging to short-form video. Unfolded, the main display suits games and films, and rotated, the same display works for reading articles and e-books. At 201 grams, it is Samsung’s lightest Z Fold yet.

As I said in my first hands-on video, the moment you hold the Fold8 it becomes clear how much easier it is to figure out what you will do with the device. It reminds me of the Surface Duo, but where the Duo split content across two separate panels with a hinge running down the middle, the Fold8 gives you one continuous screen. I liked the Duo concept already, and the single unbroken surface delivers what the two-panel design was reaching for.

The engineering that makes the form factor possible

The thinness matters for more than looks. It is the visible result of a display structure Samsung rebuilt from the panel out, and it is the part of this launch that will be hardest for competitors to match quickly.

Samsung calls the new structure Flex Titanium. The company selected titanium because it is strong, stable, and resistant to permanent deformation, the same properties that make it useful in aerospace. The engineering problem was processing a rigid material so it could survive repeated folding without losing that strength. Samsung’s answer uses two titanium layers. A titanium plate absorbs pressure and shock while reducing unevenness across the display surface, with a precisely engineered lattice pattern in the folding area to allow flexibility despite titanium’s natural rigidity. A second titanium-alloy layer sits just below the panel to minimize the crease. To let that layer bend within the display stack, Samsung refined the film to tens of micrometers, roughly a third the thickness of a human hair.

The payoff is measurable. Flex Titanium reduced display thickness by about 10 percent. That is what lets the Fold8 Ultra reach 4.1mm unfolded and the Fold8 land at 201 grams. Just as important, the reclaimed space does not go to waste. It creates room for a larger battery and improved thermal design, which is what makes the Ultra’s performance possible in a thinner body. This is the system-level point Samsung keeps returning to, and it holds up. You cannot pull thinness, battery, and sustained performance apart and credit any one of them. They depend on the same reclaimed millimeters.

The panels themselves back this up. All three devices use Dynamic AMOLED 2X with adaptive refresh down to 1Hz, and the Fold8 reaches up to 3,000 nits with a new anti-reflection finish that Samsung tied directly to the Flex Titanium redesign. The flatter display, reduced crease visibility, and low-reflection finish are the parts a user actually notices. The titanium structure is why they are there.

The battery follows the same logic. Samsung introduced a silicon-carbon anode to raise energy density, then redesigned the cathode, electrolyte, and separator around it to manage silicon carbon’s higher reactivity. Paired with the space Flex Titanium reclaimed, that is how the Fold8 Ultra reaches a 5,000mAh battery without adding thickness. The Fold8 Ultra and Fold8 also get 45W fast charging, a first for the Fold line. The Snapdragon 8 Elite Gen 5 for Galaxy runs across all three devices, but silicon and processor headroom mean little in a foldable if the thermal envelope cannot support them. The point of the structural work is to give that chip somewhere to breathe.

The naming and the segment

Rumors were set on the new design being called the Fold8 Wide. Samsung made the right call leaving the qualifier off the model that could become its volume seller. A qualifier would have signaled compromise on the device meant to carry the broadest appeal.

Samsung’s trifold launched earlier this year at a price that meant a limited release. With the Fold8 added, the Galaxy Z family now spans four form factors. This category clearly matters to Samsung, and the investment continues.

There are rumors this Flip may be the last of its line. I do not usually deal in rumors, but I will share a thought. The Flip design has been central to establishing foldables as a credible category for mass market consumers. It made the form factor approachable at a price that did not require a productivity justification. At $1,199.99, the Flip8 remains the entry point to the family by a wide margin. I expect the Fold8 to do well, but its price will give some consumers pause in a way the Flip never has. Walking away from that audience now, rather than a few years from now when the market has matured, would be a mistake.

The broader investment question

There is a broader investment question coming into view. Samsung is already pointing at it. The company positions the new Z series to connect with its Galaxy Watch line and upcoming intelligent eyewear. As Samsung brings smart glasses to market, it needs to think about the total spend a buyer is taking on. The choice should not come down to a foldable and no glasses versus a foldable and glasses. If both categories matter to Samsung, the portfolio has to give buyers a way to enter both without forcing an either-or on price.

What stands out in Samsung’s positioning is that AI is a supporting actor here, not the main character. That is the right call at a moment when sentiment around the technology has turned negative. Samsung leans on Galaxy AI and partner experiences such as Gemini Intelligence, with app automation across more than 40 apps and services, but the use cases it puts forward are about helping users get to a better experience rather than making the AI the reason to buy. A new AI Assistant Activity dashboard gives users a single view of the automations running on their behalf, which fits the same posture of keeping the user in control.

I am eager to see whether the Fold8 is finally the foldable that wins me over.

That is not because benchmarks are useless. They are useful. But many of them are still optimized around narrow tasks, coding-heavy environments, academic-style reasoning problems, or synthetic prompts that do not look much like how knowledge workers actually use AI systems. The more practical question is different:

If I connect an AI agent to my company’s data, give it tools, and ask it to produce a useful piece of work, how close does it get to something I can actually use?

That is the question behind the Diligence Stack Agent Bench.

The benchmark is built around a view we have had for a while: outside of coding, one of the most common enterprise agent patterns will not be “let the model roam the open internet and hope for the best.” It will be something more structured. Companies will create MCP servers, internal tools, and knowledge systems that connect their proprietary data to applications like ChatGPT, Claude, and their own internal agents.

That means agent performance is not just about the model. It is also about the surrounding infrastructure: the retrieval system, the quality of the knowledge base, the tools available to the agent, the artifact-generation environment, and the cost structure of the workflow.

The model matters enormously. But the system around the model increasingly matters just as much.

This exercise mirrors the evaluation process we believe many organizations are undertaking, and many more will undertake, as enterprise AI moves from experimentation to adoption. The decision is not simply which model scores highest. Organizations must decide which models to adopt, which platforms or agent systems will route work among them, and how to manage token costs without compromising output quality. Evaluating models inside a realistic knowledge-work environment is therefore not an academic exercise; it is a practical proxy for how enterprises will select, deploy, and govern AI systems.

My Experience With the GPT-5.6 Series

I have been tasking GPT-5.6 Sol with demanding work for about a month, and it is incredible. It has an exceptional understanding of how to use and control subagents, it is remarkably intelligent, and, most importantly, it keeps working. When you give it a task, it continues until that task is complete instead of stopping at a plausible first draft.

I have only had access to GPT-5.6 Terra and Luna for a few days, but the early results are equally compelling. For knowledge work, Terra is undefeated on the combination of price, speed, and value.

Luna may be the best everyday model, period. It is better than the open-weight models we have tested while being significantly cheaper. On factuality-focused work, it can outperform Claude at a fraction of the price. The tradeoff is presentation: if you need a polished final artifact, you may have to do some additional work to make the output look better.

With Luna and Terra in the mix, frontier-level performance is finally affordable enough that you do not have to worry about the price on most tasks. Most of the daily work we ran through these models cost cents rather than dollars, while still producing usable, factually accurate results. That is not true of most models in the same price range, where lower cost usually comes with a clear drop in reliability. This is the first time token budgeting starts to look like a problem that can be solved by better, cheaper models rather than tighter limits: use capable models like Luna and Terra for the majority of work, and reserve the most expensive systems for the smaller set of tasks that truly need them.

If you are looking for quick factual answers and research, I have not found a better model. When that research needs to become a polished, actionable deliverable, you can feed it to GPT-5.6 Terra or Sol and have the stronger model synthesize and present it. That gives you the best of both worlds at a fraction of the price.

Overall, I would take the GPT-5.6 series over Claude for these workflows. Based on the pricing I have seen, I expect it can deliver equal or better work for roughly one-fifth to one-tenth of the price.

Why We Built the Benchmark

The Diligence Stack Agent Bench exists because we wanted to evaluate models and agents in a practical knowledge-work environment.

The core question is not whether a model can answer a trivia question, summarize a clean document, or solve a toy reasoning problem. The question is whether a model can take a messy, real-world knowledge-work task and produce a useful first draft.

For example:

If I ask an agent to build a financial model with a specific set of variables, source the assumptions from internal research, produce an Excel file, write a PDF report, and explain the confidence level behind the output, can it give me something I can actually use?

That is the bar.

In most knowledge-work environments, AI outputs are not replacing the entire workflow yet. They are usually the first step. A model or agent creates a draft, model, memo, deck, or report that a human reviews, edits, and improves. But that first draft still matters. The better it is, the less human cleanup is required. In some cases, if the draft is strong enough, it may be close to final.

That is what this benchmark tests: how close a model or agent can get to a complete, usable work product when given access to internal data and a computer-like tool environment.

The Two-Pronged Setup

When we started building the agent system that became the foundation of this benchmark, we began with a simple question: what does a knowledge-work agent actually need access to?

For Diligence Stack, the answer was three core data sources:

1. Historical Creative Strategies research and internal notes
2. Professional investment-grade research collected over time
3. Financial models and related structured files

There are a few obvious ways to give an agent access to this kind of data. You can upload files to Box, Google Drive, Dropbox, or another document system and let the connector handle search and retrieval. We tried versions of that. It works. But it is not ideal.

The problem is that most enterprise data was not designed for agents. PDFs have headers, footers, watermarks, irrelevant boilerplate, broken tables, embedded images, and inconsistent formatting. Presentations are even messier. Audio and video are generally worse. A basic connector can search over that content, but it is rarely optimized for high-quality agent retrieval.

So we built a better pipeline.

The Diligence Stack Knowledge Base Pipeline

The knowledge base pipeline is designed to structure unstructured data so agents can use it efficiently.

At a high level, it takes uploaded files, cleans them, splits them where appropriate, removes junk, extracts useful structure, and makes the content easier for agents to search and reason over. That means removing things like watermarks, repeated headers, useless page artifacts, and other noise that makes retrieval worse.

When images appear in files, the system generates detailed descriptions of those images so the agent can reason over charts, diagrams, screenshots, and visual content. The original files remain available, but the agent is no longer forced to treat them as opaque blobs.

The system is designed to handle essentially every major file format, including PDFs, documents, spreadsheets, slides, images, audio, and video.

In practice, that means you could upload a four-hour Zoom recording, 200 PDFs of old emails, a folder of financial models, an audio recording of your dog barking, and a pile of research notes, and the agent should still be able to find and use the relevant information.

Under the hood, the system uses Gemini embedding models from Google for multimodal embedding, Convex for database storage, Gemini 3.1 Flash Lite for data processing, and a pipeline architecture designed by GPT-5.6 Sol to be robust across different downstream models.

The point is not just to make one model perform better. The point is to make the whole agent environment better.

Why This Matters

This matters because it lets us give any agent access to high-quality internal knowledge through MCP or through our own agent environment.

Instead of asking a model to search raw files in a generic document store, we can provide a purpose-built knowledge base that has already been cleaned, structured, embedded, and optimized for agent retrieval.

That changes the economics and performance of the workflow.

In our testing, searching through the Diligence Stack knowledge system instead of using the more generic agent search pattern saved roughly five minutes on simple requests. We do not yet have fully audited cost numbers across every configuration, but the likely savings on large-model requests are meaningful, potentially in the range of several dollars per request depending on the model, search pattern, and context size.

That adds up quickly.

Faster retrieval means lower latency. Better retrieval means fewer wasted tool calls. Cleaner context means lower token usage. Better source selection means stronger outputs. The economics are not subtle: better infrastructure makes agents cheaper, faster, and more useful.

The Diligence Stack Agent

We then built this knowledge system into our own agent.

The Diligence Stack agent has access to its own computer-like environment, tools to create documents, tools to create spreadsheets and financial models, access to the full knowledge bases, and the ability to download and inspect original files. It is designed for longer-horizon work, not just short chat responses.

That distinction matters.

A normal chatbot can answer a question. A useful knowledge-work agent needs to investigate, retrieve, synthesize, model, format, check its work, and produce artifacts. It needs to behave more like a junior analyst with tools than a text box with a memory.

In the Diligence Stack Agent Bench, this setup performed well. In one benchmark run, our internal agent outperformed Claude Cowork running Claude 5 Fable on the same task, while also coming in at a lower price. Creative name for the benchmark? Maybe not. Useful signal? Absolutely.

Why the Harness Matters

One of the clearest lessons from this testing was that the model is only one part of the result. The harness—the tools, retrieval system, context management, execution environment, and revision loop wrapped around the model—can materially change both output quality and cost.

We tested this directly with Claude 5 Fable. To make the comparison as controlled as possible, we gave Cowork access to identical tools through MCP and the exact same skills available to our Diligence Stack harness. In this benchmark run, Claude 5 Fable still produced a better result inside our harness than it did inside Claude Cowork. The Cowork run scored worse and cost more, while our harness used the same model more efficiently and produced a stronger deliverable.

That does not mean Cowork will be worse for every task, or that one run settles the question. It does mean model comparisons need to account for the environment in which the model is operating. A strong model inside an inefficient harness can waste tool calls, consume more tokens, retrieve weaker context, and still produce a worse final output. A well-designed harness can make the same model both cheaper and more useful.

What the Benchmark Measures

The benchmark is designed around practical output quality.

We are not asking, “Which model sounds smartest?” We are asking, “Which model produces the most useful work product when connected to internal data and tools?”

The grading framework evaluates several dimensions:

Prompt and deliverable completion. Did the model actually do what was asked? Did it produce the requested files, sections, analysis, and supporting materials?

Factual accuracy and verification. Are the claims correct? Are the numbers internally consistent? Does the model avoid hallucinating facts, companies, timelines, or financial assumptions?

Source grounding and priority. Did the model use the right sources? Did it prioritize the Diligence Stack knowledge base and internal materials before reaching for weaker external context? Did it cite and trace its assumptions appropriately?

Analysis and actionability. Did the output merely summarize information, or did it synthesize it into something useful? Does the report help a decision-maker understand what matters, what is uncertain, and what to do next?

Artifact quality and auditability. Are the PDF, Excel file, model, or other deliverables usable? Are they readable, well-structured, and traceable? Can a human inspect the assumptions and understand how the output was created?

Tool strategy and robustness. Did the agent use tools intelligently? Did it search efficiently, inspect the right files, recover from missing information, and avoid wasting time on low-value steps?

Cost and token efficiency. How expensive was the run? How much did the model spend to reach the result? Cost is tracked separately from quality because the cheapest bad answer is still a bad answer, but price matters once output quality clears a usable threshold.

In the current scoring framework, the final quality score is weighted most heavily toward completion, factual accuracy, source grounding, and analytical usefulness. Design, auditability, tool behavior, and cost efficiency also matter, but the benchmark intentionally does not let a cheap model win if the output is not useful.

That is the right tradeoff. In real knowledge work, the highest-cost outcome is not paying a few extra dollars for a model. The highest-cost outcome is a polished but wrong deliverable that wastes a senior person’s time.

How We Think About Pricing

Pricing is part of the benchmark, but it is intentionally a smaller part of the weighted score.

The reason is simple: output quality matters most. A cheap run that produces a weak, incomplete, or inaccurate deliverable is not actually cheap. It just moves the cost downstream to the human who has to fix it. In knowledge work, the expensive failure mode is not spending a few more dollars on a model. The expensive failure mode is trusting a polished but wrong output, or getting a draft that still requires hours of senior cleanup.

So we treat price as an important constraint, not the primary objective.

The goal of the benchmark is to help users understand which models produce the best outputs, and then decide which of those outputs are worth the price for their specific workflow. For some tasks, the highest-scoring model may be worth paying for because it gets meaningfully closer to a usable final deliverable. For other tasks, a slightly lower-scoring model may be the better choice if it delivers 90% of the quality at a much lower cost.

That is why we look at both absolute quality and cost-adjusted performance. The right answer is not always “use the cheapest model” or “use the highest-scoring model.” The right answer is usually: find the best output quality available at the price you are willing to pay.

This is especially important for agentic workflows because costs can compound quickly. Long-running agents search, retrieve, reason, inspect files, generate artifacts, and revise outputs. A model that is slightly more expensive per token can still be the better economic choice if it uses tools more efficiently and produces a cleaner first draft. Likewise, a cheaper model can be attractive if the task is lower stakes, more repetitive, or easier for a human to review.

In other words, price matters. But price only matters after the output clears the bar of being useful.

Why We Didn’t Include Open Models

We also tested open models extensively, but did not include most of them in the current benchmark results.

The reason is straightforward: almost all of the open models we tested produced materially worse outputs while also costing more to run in this agent environment. That is a difficult combination to recommend. Lower quality can be acceptable when it comes with a meaningful cost advantage, but lower quality at a higher price does not offer users a useful tradeoff.

DeepSeek V4 Pro was the one clear outlier. It was far cheaper than the other open models we tested, although the quality tradeoff is visible in its benchmark score. For lower-stakes, high-volume, or highly reviewable tasks, that price difference may still make it interesting.

This is not a permanent exclusion. We will keep testing open models and add more of them as their performance and economics improve. For this version of the benchmark, however, including a longer list of models that were both weaker and more expensive would not have helped readers make better decisions.

Where GPT-5.6 Fits

The GPT-5.6 series performed strongly in this environment because the task rewarded more than raw fluency. It rewarded long-context handling, source discipline, synthesis, spreadsheet construction, report writing, and the ability to produce usable artifacts in a single run.

GPT-5.6 Sol and GPT-5.6 Terra were the strongest quality leaders in our testing, with GPT-5.6 Luna standing out as a strong value option. The key takeaway is not simply that one model “won.” It is that stronger models produced more complete first drafts, handled ambiguity better, and were more effective at turning messy internal data into structured outputs.

That is exactly what matters for agentic knowledge work.

There is also a broader point here: as models improve, the benchmark ceiling rises. Tasks that previously required a human analyst to spend hours gathering sources, cleaning up assumptions, creating tables, and writing the first report draft can now be pushed much further in a single agent run. The output is still not perfect. But the baseline is moving fast.

What We Learned

The biggest lesson from the benchmark is that model quality and system design compound.

A better model inside a weak retrieval environment can still waste time and miss key information. A strong knowledge base paired with a weaker model can produce decent results, but may still fall short on synthesis, judgment, and artifact quality. The best results come from pairing strong models with purpose-built data infrastructure and tools.

That is the direction we think enterprise AI is heading.

The winning setup will not be a generic chatbot pointed at a file dump. It will be a structured knowledge system, exposed through MCP and internal tools, connected to high-quality models, and evaluated against real work products.

That is the premise behind Diligence Stack Agent Bench.

We are not trying to measure whether a model can perform well in an artificial sandbox. We are trying to measure whether it can do useful work in the kind of environment businesses are actually building.

And on that front, GPT-5.6 looks like a meaningful step forward.

The practical readout is simple: for knowledge-work agents, the gap between “interesting demo” and “usable first draft” is closing. The models are getting better, but the infrastructure around them is what turns that improvement into something businesses can actually use.

I am not much of a controller person, and gaming handhelds have never fit into my life. I have owned a Nintendo Switch and used a few Windows/Steam handhelds, but they always felt like a compromised way to play games I could run better somewhere else. The battery was fine, the controls were fine, the performance was fine. None of them made me want to keep using the device after I had done the testing.

The Claw did. After a few days with Spider-Man 2, I stopped thinking about what I was giving up and started thinking about the game. That is the entire point of this category, and this is the first handheld I’ve used that nailed it for me.

The larger point is not that one handheld changed my mind. This category now feels mature enough to grow beyond the people who were already sold on it. The performance is there. The controls are there. Windows has a controller-first interface without giving up the PC game library. Thunderbolt turns the same device into a docked gaming system or a normal PC. These products no longer feel like experiments.

That maturity is arriving while gaming hardware gets more expensive. The tools and process complexity behind leading-edge silicon cost more, memory and NAND supply are tight, and OLED and high-refresh displays remain premium parts. A desktop or gaming laptop built around the hardware people now expect is not heading back toward cheap either.

That makes flexibility more valuable. A handheld like this can be the machine in your hands, the gaming system connected to a TV, and the Windows PC on a desk. It brings the games you already own across Xbox, Epic, Steam, and other stores. As every route into high-end gaming costs more, having more ways to use the same hardware makes the whole category more compelling.

There is still a catch. MSI lists this 32GB/1TB model for $1,799. I can rave about the product and still tell most people not to spend $1,799 on a handheld. Both things are true. I care more about the product here; others can care more about the value. The Claw is not the mainstream handheld. It is proof that the category is ready for more products, more prices, and more people.

My excuse to play Spider-Man 2

Spider-Man 2 is a perfect game for this machine. It is demanding, it looks fantastic, and its movement and combat work naturally on a controller. More importantly for me, it is a game I had wanted to play for years. A portable device that I needed to use to review (and more importantly, try to understand Intel’s G series chip here) is a perfect excuse to justify playing this game.

I ran the game at 1920×1200 on the High preset with ray tracing, V-sync, and frame generation turned off. The display was set to 120Hz and XeSS upscaling was enabled. Most of my gameplay sat around 45 to 50 fps, with some scenes climbing into the 50 to 60 fps range. That is enough for this kind of game. It felt smooth, the controls felt immediate, and I never found myself reaching for the settings menu instead of playing.

I want to be careful with the inevitable console comparison. This does not mean the Arc G3 Extreme is a PlayStation 5 in your hands. The PC and console versions use different settings, the exact XeSS mode matters, and an average frame-rate counter does not tell you about frame pacing or image quality. What I can say is simpler and more useful: a demanding former PlayStation exclusive ran well enough on an 8-inch handheld that I restarted the game and kept playing it.

I spent some time trying to figure out what a comparison would be for this performance. After a few minutes of searching, this compares pretty well to an RTX 4070 laptop; it’s within the same ballpark. I don’t mean to say this as a bad thing or saying it’s better or anything, but rather the RTX 4070 laptop chip is a really good chip! There are really good gaming laptops with this GPU. The Claw 8 with Intel G3 Extreme is close to it. That’s really cool.

The result held up elsewhere. In the built-in Cyberpunk 2077 benchmark, the Claw returned about 44 fps at 1200p on the High preset across several runs. I did not spend an afternoon finding the perfect low-medium-custom soup to inflate the number. On an 8-inch screen I would rather start with a sensible preset, turn on the right upscaler, and play.

Frame generation is available if you want more visible smoothness, and this form factor makes a good case for it. A handheld has a hard power ceiling. If the GPU can render a solid base frame rate and use generated frames to make motion look smoother on a 120Hz screen, that is a good trade in a single-player game. Generated frames are not the same as rendered frames, though, and they do not magically improve input latency.

One thing worth being precise about is how frame generation interacts with input latency, because the bigger frame-rate number oversells the responsiveness. A natively rendered 120fps game samples your input 120 times a second, so the controls feel as immediate as that frame rate suggests. A game running at 60fps samples input half as often, so its latency is roughly double. If you take that 60fps game and use frame generation to reach 120fps, the motion looks like 120fps, but your input is still tied to the 60fps base frames. The generated frames are interpolated, so the technique actually adds a little latency on top of that 60fps baseline rather than removing any. The point is that frame gen buys you a smoother-looking image, not native-120fps responsiveness. As long as the base frame rate is solid, which it is here, I have no problem with fake frames.

Arc G3 Extreme is built for this job

The Intel Arc G3 Extreme is based on Panther Lake, but Intel did more than put a laptop chip in a smaller box. Arc G3 is Intel’s first family of purpose-built handheld SoCs, and the parts were rebalanced around what a game needs inside an 8W to 35W power envelope.

The CPU has two performance cores, eight efficiency cores, and four low-power efficiency cores. The GPU is an Arc B390 based on Xe3 with 12 Xe-cores, 96 XMX engines for AI work such as XeSS, 12 ray-tracing units, and 16MB of cache. The compute tile is built on Intel 18A. There is enough CPU here to feed a game, but the design is plainly biased toward graphics.

That matters because a handheld has one small pool of power and heat to divide. Every watt the CPU takes is a watt the GPU cannot use. Intel’s Intelligent Bias Control manages that split, and at 14W or below it can park the two P-cores entirely so the E-cores handle the CPU side while the GPU keeps more of the budget. This is the kind of mechanism that disappears when it works. You do not think about core parking while swinging through Manhattan. The frame rate holds together.

Intel’s own testing says Arc G3 Extreme at 35W averaged 42% faster than the Ryzen AI Z2 Extreme at 35W across 36 games at 1080p High, using 2x upscaling where supported. Intel also says its chip at 17W roughly matched the Z2 Extreme at 35W across that same test set. Those are Intel’s numbers from pre-production systems, not mine, so treat them accordingly. The direction matches what I felt using the Claw, but I did not run a controlled 36-game comparison of my own.

XeSS is the other half of the platform. Super Resolution lets the GPU render from a lower internal resolution and reconstruct the image for the 1200p screen. Multi-Frame Generation can add up to three generated frames between traditionally rendered ones in supported games. On a desktop with a giant GPU, these features can feel optional. On a handheld, they are how you turn a limited power budget into a better-looking and smoother game. See the above point on input lag and frame gen, it’s still great!

Intel finally has the CPU, GPU, process technology, packaging, drivers, and upscaling stack working toward one clear product. That is why Arc G3 feels more important than another integrated-GPU bump. It is a complete handheld platform.

MSI built the right hardware around it

The chip would not matter if the machine were miserable to hold. MSI did a good job here.

The Claw has an 8-inch 1920×1200 IPS touchscreen with a 48 to 120Hz variable refresh range. I initially wrote down that it was OLED because it looked that good. It is not. Color and contrast are still excellent, VRR keeps sub-60 fps games looking smoother, and 1200p is the right resolution for this size. I never wanted more pixels.

The textured plastic feels better than I expected, and the flared grips make the 785g body easier to hold than the number suggests. The Hall-effect sticks and triggers feel like proper controller parts. MSI rounded the ABXY buttons, changed the D-pad to a metal-dome design, and upgraded the haptics with new linear motors. I am not good enough with a controller to lecture anyone about competitive input latency, but the whole control surface felt normal in the best possible way. I did not feel like I was using a tiny PC with controller parts glued to the sides.

The fans are audible under load, as they are on every handheld in this class, but they never became the dominant sound. The two front-facing speakers help. Turn the volume up a little and the game covers most of the fan noise without headphones.

My informal battery experience landed around three to three and a half hours while playing and using the machine normally away from a charger. This was not a controlled rundown with fixed brightness and a scripted path, so do not read it as one. The 80Wh battery is large for a handheld, and Intel also has an Endurance Gaming mode that targets 30, 40, or 60 fps to reduce power. I did not run the full matrix needed to turn that into a stronger battery claim.

The rest of the hardware is unusually complete. My unit has 32GB of LPDDR5X memory and a 1TB SSD. MSI moved to a standard M.2 2280 slot, so storage upgrades should be cheaper and easier to find. There is Wi-Fi 7, a microSD Express reader, a fingerprint sensor in the power button, and two Thunderbolt 4 ports across the top.

Those Thunderbolt ports deserve their own section.

The one-cable dock changes the product

Intel sent an OWC Thunderbolt Go Dock with the review kit. It does not come in the retail box, but it showed me one of the Claw’s best tricks.

I plugged the dock into my monitor, connected the Claw with one cable, and used it the same way I would use a Nintendo Switch. The cable handled the display, peripherals, and charging. Dock it and play. Unplug it and walk away.

That sounds obvious because Nintendo solved it years ago, but it has not been the default experience for Windows handhelds. Thunderbolt makes the setup predictable, and having two ports means charging or docking does not consume the only high-speed connection on the machine.

It also changes what the Claw can be. This is a full Windows 11 PC with 32GB of memory. Add a keyboard, mouse, and monitor and it can handle ordinary desktop work, development, or local AI experiments in a way a closed console cannot. I did not replace my workstation with it, and I am not going to pretend a handheld is the ideal machine for every job. The point is that the option is real and the setup takes one cable.

Full Windows matters before you ever reach a desk too. The newer Xbox full-screen experience gives the Claw a controller-first home screen while keeping access to games from Xbox, Epic, Steam, and the rest of the PC ecosystem. I am apparently in the minority here, but I like buying games on Epic. A device that can bring that library along without asking me to repurchase games is more useful to me than one built around a single store.

Windows can still show its desktop-shaped edges, and a console-first interface does not remove every prompt or launcher. It is much closer than it used to be. More importantly, I did not spend the review fighting the operating system. I spent it playing Spider-Man.

Why PC handhelds will keep growing

The Claw feels like a mature answer to a real problem: how do you make PC gaming portable without asking people to abandon their library, their accessories, or the option to use the machine as a computer? There is no single trick here. The category works because the chip, controls, battery, software, display, and docking finally work together.

The cost backdrop matters too. TSMC says the tools for leading-edge nodes are becoming more expensive and the process complexity keeps increasing. Micron expects DRAM and NAND supply to remain tight beyond 2026, with enough pressure to weigh on PC shipments. Display companies are moving more of gaming toward high-refresh OLED, which remains a premium part. None of that makes a $1,799 handheld cheap. It does mean the old assumption that a handheld only makes sense as an inexpensive second device is becoming less useful.

For some people, this can be the gaming PC. It can live on the couch, go in a bag, dock to a television, and connect to a monitor and peripherals at a desk. A desktop will still deliver more performance for the money, and a laptop remains better if you need a built-in keyboard and larger screen. The handheld earns its place through the number of situations where it works.

This particular machine sits at the expensive end of the category. The important part is what happens next. Arc G3 gives more manufacturers a capable platform to build around, Windows is finally treating handhelds as a real product class, and every new design creates another chance to balance performance, size, display, and price differently. The category does not need every product to be cheap. It needs enough good options that more people can find the one that fits.

The best thing I can say about the MSI Claw 8 EX AI+ is that it got out of the way. It gave me a reason to restart Spider-Man 2 and ran it well enough that I stopped watching the frame counter. When I finished testing, I kept playing.

Intel nailed Arc G3 Extreme. MSI nailed the hardware around it. This is the best Windows handheld I have used and the first one that did not feel like a downgrade. I love the thing.

I do not think every PC gamer is about to replace a desktop or laptop with a handheld. I do think many more people will choose one as gaming hardware gets more expensive and the handheld becomes capable in more places. A machine that works in your hands, on a television, and at a desk is easier to justify than one with a single job.

The Claw 8 EX AI+ is too expensive to take this category mainstream by itself. It is mature enough to show why something like it will. There are other versions with Intel’s Lunar Lake, and there are likely more affordable SKUs on the way. It’s only a matter of time!

A Transaction Layer That Never Existed

HTTP 402 has been in the web’s specification since 1991, a status code reserved for payment-required responses that never found a use case at scale. The reason is straightforward: until recently, the web’s consumers were humans, and humans had browsers, credit cards, and login sessions. The payment infrastructure built around those assumptions works well enough. Agents have none of those things. They cannot log in, cannot click through a payment form, and have no persistent identity that maps to a billing relationship. The result is that agents currently operate on the web the way someone might walk through a store taking things without any mechanism to pay, not out of bad intent but because no checkout exists for them.

That is the structural problem AWS is addressing. The WAF AI traffic monetization capability returns a machine-readable HTTP 402 response using the x402 open protocol, a standard developed by Coinbase for machine-to-machine payments. An agent that hits a monetized resource gets a price manifest in JSON: the cost in USDC, accepted blockchain networks, destination wallet, and payment timeout. Any x402-compatible agent runtime can complete that flow autonomously. No human needs to be in the loop.

This is not AWS inventing something from scratch. x402 is an open protocol, and Coinbase built the facilitator infrastructure. What AWS contributes is operationalizing it at cloud scale, at the network edge, across CloudFront’s global distribution network, with no changes required to origin infrastructure. That is a meaningful difference between an interesting protocol and something enterprises can actually deploy.

The Traffic That Pays Nothing

The urgency behind this is real. AI bot traffic now exceeds 50% of web traffic for many content providers, with AI-specific crawlers growing more than 300% year-over-year. Unlike traditional search crawlers like Googlebot or Bingbot, which returned measurable referral traffic, AI bots consume content to generate summaries and responses with little to no traffic sent back. The web’s existing monetization model has no answer for that. Blocking agents entirely is a short-term response that trades one problem for another. Licensing deals negotiated individually are not scalable. A programmable transaction layer is the only structural solution.

AWS WAF Bot Control already classifies over 650 distinct AI bot and agent types, including GPTBot, Claude-Web, and Perplexity-Bot. The monetization capability lets content owners price by content path, bot category, or verification tier. That last variable is worth pausing on. AWS distinguishes between verified agents, whose identity is confirmed through cryptographic signatures or documented IP ranges, and unverified agents, recognized through behavioral fingerprinting but not cryptographically confirmed. Pricing those two tiers differently creates a commercial incentive for AI companies to formally identify their agents. That is a shift in the accountability dynamic that goes beyond content monetization.

Why Amazon Is the One Building This

Amazon’s durable advantage has never come from selling things. It comes from owning the infrastructure that others use to sell things, and taking a position on every transaction that flows through it. AWS, Amazon Marketplace, Fulfillment by Amazon, Amazon Pay, Buy with Prime. The pattern across two decades is consistent. Find a commerce flow, build the indispensable intermediary layer, and embed deeply enough that switching becomes costly.

The WAF monetization announcement follows that logic. The feature is available at no additional charge to existing AWS WAF customers. AWS takes no fee on content revenue, with disbursement going directly to the content owner’s wallet. That looks generous until you consider that the value Amazon captures is not transactional margin. It is infrastructure position and the data visibility that comes from sitting at the center of every agent commerce flow.

What This Does Not Solve

It would be a mistake to read this as AWS completing the transaction layer for the agentic web. What they have built handles one well-defined use case: a content owner pricing and collecting payment from an agent requesting access to a protected resource. That is an important piece of infrastructure. It is not a complete answer.

Agent identity at scale remains unsolved. The verification tier distinction AWS draws today is a start, but a functional agent economy requires identity frameworks that travel across platforms, not just within AWS’s classification system. Liability is unresolved. When an agent transacts autonomously, the legal and financial accountability structures that govern human commerce do not map cleanly. Interoperability is an open question. x402 is an open protocol but adoption outside of AWS and Coinbase is early. A transaction layer that only works within a subset of the agent ecosystem is not a transaction layer for the web.

None of that diminishes what AWS shipped. It is the most serious production-grade infrastructure move on this problem to date. But the agentic web will need contributions from standards bodies, regulators, and the broader industry before the transaction layer is genuinely complete. AWS has built the first significant piece. The rest is still ahead.

At WWDC, the expectation is for a long list of new features rolling across every OS Apple supports in its portfolio. This year, Apple reframed that. Rather than leading with what is new, the keynote centered on perfecting and homogenizing the experience across products, addressing the kind of friction that quietly erodes user satisfaction over time, and laying the groundwork for what was the main focus of the event: Apple Intelligence and the new Siri AI.

Perfecting the Experience

Craig Federighi framed the releases around three explicit priorities: making platforms more responsive and easier to use in daily life, advancing trust and safety, and delivering a meaningful leap forward for Apple Intelligence with a new Siri architecture. That ordering was deliberate. Performance came first, and it showed throughout the keynote.

Across iOS, macOS, iPadOS, and watchOS, Apple’s updates centered on responsiveness and polish. App launches are up to 30% faster, with the system preloading key data apps need when opened, applying to third-party apps as well. Photos appear in libraries up to 70% faster after being shot. AirDrop transfers are up to 80% faster. File browsing and transfers from iPad to external drives are up to five times faster. These are the moments users encounter daily, and their cumulative effect on satisfaction is significant even when users cannot name the specific change that made things feel better.

Apple also rebuilt the foundation of search across iOS, iPadOS, and macOS, re-architecting the Search Index that powers Spotlight, Photos, and Mail to be more stable, more comprehensive, and faster at indexing new content. In Mail, a new ranking system surfaces more relevant results in top hits, so the email you need is more likely to appear first regardless of how long ago it was sent. Search is one of those features that users only consciously notice when it fails. Making it work reliably is the kind of improvement that adds up quietly into trust.

Apple extended these gains to older hardware as well, bringing an optimized CPU Scheduler all the way back to iPhone 11, meaning iOS 27 supports the same iPhone models as iOS 26 and extends Apple’s already notable device longevity record. At a time when sustainability commitments are under scrutiny across the industry, keeping older hardware performant is both a user satisfaction story and an environmental one.

The design layer received attention too. Liquid Glass, introduced last year as Apple’s most ambitious cross-platform design update, and one that did not please everyone, was refined based on user and developer feedback, with improved diffusion behind complex content for better readability. A new slider in settings lets users adjust Liquid Glass from ultra clear to fully tinted, applying the preference across apps including third-party ones that have already adopted it. None of this generates headlines, but it signals a company willing to revisit and improve rather than defend a prior decision.

Apple Intelligence and Siri AI: Grounded in the Real World

On Apple Intelligence, the company showed compelling everyday use cases that may not be revolutionary but are built to hold up outside a demo environment.

The Google collaboration on Siri AI generated the most discussion coming out of the keynote. Creative Strategies’ Max Weinbach explained it clearly after the keynote: Apple’s new advanced on-device model for A19 Pro runs 20 billion parameters on 12 gigabytes of memory, loading only the active parameters needed at any given moment. That model architecture is unprecedented. Apple does not lack AI research capability; it lacks large foundational models, which is what Google contributes to the collaboration. The reason Apple’s AI work has been underestimated is straightforward: companies like OpenAI, Anthropic, and Google publish benchmarks aggressively because they are selling AI as a service to enterprises and need to compete publicly. Apple is not selling that, so it has not played that game. The collaboration with Google fills the large model gap without Apple ceding control of its own architecture.

Apple chose to show what this means in practice rather than where it sits on a benchmark. The demos were grounded in real tasks: finding content by describing it naturally, drafting and editing in context, taking action across apps without requiring users to navigate between them manually.

Apple continued with the same architectural philosophy that has defined its approach to AI from the start: ground Apple Intelligence in the OS and in Apple’s own apps, then expose APIs so developers can bring those capabilities into their products. Apple Intelligence uses a new system orchestrator to coordinate across system-wide capabilities, including personal context understanding, broad world knowledge that can reach the web via Private Cloud Compute, and App Actions that draw on an App Toolbox to find the right tools from installed apps to complete a request. The result is AI that surfaces where users already are, which limits some of what Apple can announce at any given moment but also limits the gap between what Apple promises and what users actually experience.

The Siri AI app is the most structurally significant announcement from an architecture standpoint. It serves as the persistent home for user interactions with Siri, where conversations live, are searchable, and are accessible across devices. Apple is giving users a coherent AI identity that travels with them rather than a series of disconnected exchanges that disappear after each session. That continuity across devices matters practically, but it also reflects a considered view of how an AI relationship should work: one personality, one history, accessible from every device a user owns.

What stood out across the keynote was how grounded it was, and how grounded the demos were in particular. Apple showed real situations without overpromising what the technology does in those situations. Federighi framed Apple’s position directly: truly helpful AI must be centered around the user and their needs, integrated deep into the products people use every day, grounded in personal context and the apps they rely on, and designed with privacy at every step.

After leaving consumers waiting for a meaningfully improved Siri, Apple had to deliver. Releasing Siri in the developer beta on the day of WWDC keynote, with public availability in English in the US later in the year, points to a September launch alongside the new iPhone models on iOS 27. The stakes were high and the pressure was real. While we are only seeing the first glimpse through the beta, the signs are promising.

The company reported record third-quarter revenue of $15.8 billion, up 12% year over year, driven by demand for AI infrastructure and networking products. The announcements at Cisco Live made a clear statement about what Cisco has become and where it is positioning itself: the infrastructure layer for the agentic era, the same role it played for the internet.

The Playbook Has Run Before

Cisco lived this transition once,. When it built the networking backbone the internet ran on, it hit a constraint that had nothing to do with technology. There were not enough engineers in the world who knew how to deploy, configure, and manage what Cisco was selling. So Cisco built the talent pipeline itself. Networking Academy launched in 1997 with 64 institutions across seven U.S. states. It has since reached 28 million students across 195 countries. The CCNA, first awarded in 1998, became the industry benchmark for network engineering. Cisco did not wait for the talent market to catch up. It created the talent market.

The same logic is now being applied to AI. Cisco has committed to providing AI and digital skills training to one million student participants in the United States over the next four years, running from the beginning of fiscal 2026 through the end of fiscal 2029. The certification portfolio has been rebuilt accordingly. The CCNA v2.0 blueprint is organized around four pillars: network infrastructure, troubleshooting and problem-solving, a security-first mindset, and understanding the role of AI in network management and operations. AI literacy is now treated the way networking and cybersecurity once were: as a baseline competency, not a specialization.

This is a strategic investment. Cisco needs the people who will deploy its AI infrastructure to know how to use it. Enterprises need to trust that those people are qualified. Cisco is building both sides of that equation simultaneously, as it did with the internet generation.

The Infrastructure Argument

AI agents generate 450% more network traffic per task than a human performing the same work, and AI traffic overall is expected to triple in the next three years. The network is the constraint of this transition, not a background utility. Cisco Cloud Control is a new management platform bringing together networking, security, observability, and collaboration under a single interface, built for an environment where AI agents operate continuously across enterprise systems and need to be managed, secured, and audited in real time. Cross-domain telemetry sits at its core: data flowing across any connected system is compiled and viewable in one place, so humans and agents can act on coordinated, contextual information to manage uptime, control agent behavior, and prevent token overspend.

Hybrid Mesh Firewall extends unified protection across networks, applications, and both Cisco and third-party firewalls, designed to limit the blast radius in an environment where AI agents have extended the security perimeter and can introduce attack vectors at machine speed. Patel framed the underlying challenge as an “AI trust deficit,” the gap between what enterprises want to do with AI and what they currently feel confident deploying. The full portfolio is structured around closing it.

The Leadership Structure That Made This Possible

Patel’s consolidation of product strategy under one role is visible in what was announced last week. Networking, security, observability, and collaboration are no longer separate conversations with separate roadmaps. They are one platform. Liz Centoni as EVP and Chief Customer Experience Officer leads the organization responsible for ensuring enterprises can realize what Cisco is selling. Her keynote brought on stage two customers operating in environments where the margin for error is essentially zero. For GEODIS, Cisco IQ replaced guesswork with full visibility across 12,000 devices and a clear end-of-life roadmap. For GlobalFoundries, where maintenance windows are not an option, Cisco IQ cut vulnerability assessment from days to hours. Both cases made the argument that an integrated platform delivers where point solutions cannot.

The through-line from 1987 to 2026 is consistent. Cisco has understood, at each major infrastructure inflection, that the technology alone is insufficient. You need the infrastructure. You need the security model. You need the platform to manage it at scale. And you need the people qualified to operate all of it. Cisco built that full stack for the internet era, including the workforce development program when the market could not supply the talent on its own. The conditions are the same now. The difference is that Cisco arrives at this moment with a leadership structure finally organized to execute it as a single coherent strategy rather than a collection of business units making separate bets.

I was watching the movie The Hitman’s Bodyguard a couple of weeks ago, and one of the characters says “boring is always best.” While that might be a line written for an action film, it’s also a perfect rule of thumb for consumer technology. When a laptop is meant for everyday users, the best place it can be is a spot where you can’t think of a single major issue. Almost every laptop has some weird, annoying quirk that ruins the experience. I can’t find any of those game-breaking flaws on this machine. It just works, it’s comfortable, and it’s easily my default recommendation.

Refinements over the First Generation

I spent a lot of time with the first-generation Snapdragon Yoga Slim 7x, and it was a bit of an odd machine. The trackpad felt mushy, the speakers were mediocre, and the 14.5-inch chassis was slightly wider than it needed to be.

This second-generation model fixes those issues. Lenovo trimmed the screen size down to 14.0 inches. That half-inch reduction makes the footprint a lot smaller. It now weighs 1.17 kg and measures 13.9 mm thick, which is a much more comfortable size to throw in a backpack.

Lenovo didn’t mention it in their marketing, but they completely redesigned the trackpad. Clicking the old and new models side-by-side reveals the change immediately. The new one has a tactile, crisp click that feels vastly superior to the mushy mechanism from last year. The keyboard layout is roughly identical, but it feels like they made a subtle tweak to the key travel that makes typing more comfortable. They also upgraded the audio system to four stereo speakers (two 2W woofers and two 2W tweeters). It sounds clean and punches well above its weight class for a thin-and-light laptop.

I actually passed the first-generation Snapdragon laptop down to my dad when he needed an upgrade. He chose it over several other laptops because it hit all his basic needs. He was a big fan of it, but when I showed him this new version, he liked it even more (but I am keeping this one for myself). The refinements are subtle, but they add up to a much more polished product.

Elite Silicon without the Extreme Compromise

The silicon landscape on Windows on ARM has split into distinct tiers. Under the hood, this laptop runs the standard Snapdragon X2 Elite chip. Qualcomm also offers a flagship X2 Elite Extreme, but I think the standard Elite is the smarter choice for a thin-and-light laptop.

To understand why, we have to look at how these chips are packaged. The Snapdragon X2 Elite Extreme uses on-package memory, which sits right next to the silicon to deliver higher memory bandwidth (228 GB/s, against 152 GB/s on the standard Elite) and better native efficiency. In a vacuum, that should mean better battery life.

However, Qualcomm has to justify the “Extreme” branding. In practice, they push the Extreme chip’s clock speeds higher to secure impressive peak performance numbers in synthetic benchmarks. Pushing those clocks spends the efficiency gains, which means the battery life ends up leveling out between the Elite and the Elite Extreme.

The Extreme chip is destined for larger, heavier chassis designs that can handle the thermal output of sustained high clock speeds. For a 1.17 kg thin-and-light like the Yoga, the standard X2 Elite is the right fit. It keeps the chassis thin, stays cool, and runs almost completely silent (the fans are barely audible in daily use).

To be clear, none of this is confirmed by Qualcomm, but given what I know about the silicon and have experienced myself, I tend to believe this is true.

The Screen Configuration Mismatch

My review unit came configured with the Snapdragon X2 Elite, 32 GB of RAM, and a 1 TB SSD, but it was paired with the base WUXGA (1920 x 1200) 60Hz OLED display. Lenovo sent me this unit directly, and while it’s a fine screen, I don’t think this display configuration makes sense.

This 60Hz 1200p OLED belongs on the lower-end Snapdragon X2 Plus laptops. If you’re configuring a laptop with Qualcomm’s premium X2 Elite chip, you deserve a premium display. Lenovo offers a gorgeous 2.8K (2880 x 1800) 120Hz OLED screen as an upgrade option on their website.

The markup on Lenovo’s website to jump from the 60Hz WUXGA screen to the 120Hz 2.8K display is $60. That’s a rounding error on a laptop in this class, and with a gap that small, Lenovo should just kill the base 60Hz display option on the Elite configurations entirely. Pairing a high-end chip with a low-refresh-rate screen is a compromise that doesn’t need to exist. If you’re custom-building this on Lenovo’s site, spend the $60 and get the 2.8K display. It’s a no-brainer.

Real-World Performance: The Agent Benchmark

I’m not going to dump standard benchmark numbers here. We already covered those in my Snapdragon X2 Elite Extreme reviews, and synthetic benchmarks don’t tell you what a laptop is actually like to use.

Instead, my ultimate test for real-world performance has been running local AI coding agents. For the past few weeks, I’ve been working on a side project using Claude Code and a local co-work agent. The project is an implementation of SwiftUI that compiles natively on Windows and Windows on ARM. Unlike other ports, I wanted this to have its own custom GPU rendering engine (similar to how GPUI works in Rust) rather than mapping to WinUI 3.

This is a heavy workload. It requires constant compiling and building of native Swift code on the machine. Any CPU bottlenecks immediately slow down the loop between agent runs.

I’ve run this project on my main desktop (which rocks an RTX 5090 and Intel Core i9-13900K) and several heavy desktop-replacement laptops. When I ran it on this Snapdragon X2 Elite Yoga, I was stunned. It’s one of the fastest laptops I’ve ever used for this workflow. The CPU compiles native Swift code incredibly fast. The agents finished their tasks so quickly that it felt like a night-and-day difference in how much work they could get done. A lot of this is purely from the higher performance per core, not every workload is using all the cores to compile.

I also used a co-work agent to build TypeScript and Electron applications, which are notorious for compiling slowly on Windows. On this machine, those builds are snappy. When you pair that level of performance with great battery life, fast Windows Hello face recognition, and a thin chassis, it makes the Yoga an incredible development machine.

The Easy Default

Qualcomm’s chips make this laptop what it is. The thinness, the silence of the fans, the performance under agent workloads, and the outstanding battery life are all direct results of the Snapdragon X2 platform.

But Lenovo deserves credit for nailing the chassis design. The product you touch and feel is great regardless of the configuration you choose. Whether you buy the base Snapdragon X2 Plus SKU or custom-build a top-tier Elite model, the physical experience is excellent.

We’re missing easy defaults in the Windows laptop space. It’s hard to find a machine that doesn’t have some glaring flaw that will annoy you or break your workflow. Lenovo built a laptop that has none of those. It’s an easy recommendation for almost anyone. If I’m throwing a Snapdragon laptop into my bag, it’s going to be this one.