For decades, every major breakthrough in computing has been built around a simple premise: A human sits at the centre of the machine.
Whether using a mainframe, a personal computer, a smartphone, or the internet, people have always been the active participants. Humans initiated actions, searched for information, opened applications, and decided what happened next. Computers responded.
That assumption is beginning to change with the rise of artificial intelligence (AI) agents.
A new generation of AI systems can increasingly reason, plan, execute tasks, monitor information, and interact with software systems on behalf of users. Rather than waiting for instructions, these systems can operate continuously in the background, pursuing objectives defined by humans.
As a result, technology companies are redesigning everything from computer chips and operating systems to cloud platforms and search engines for a future in which software increasingly works on behalf of humans rather than waiting for commands.
This is why Nvidia chief executive Jensen Huang believes AI agents will become the “largest consumers of computing resources” and why Qualcomm chief executive Cristiano Amon has described 2026 as “the year of agents”.
The shift, if realised, could become as significant as the move from desktop computing to mobile computing.
How have computers always been built around humans?
Every major computing era has been defined by the way humans interacted with machines.
In the mainframe era, users entered commands into terminals and computers processed them. The personal computer era replaced command lines with graphical interfaces, allowing users to click icons, open files, and navigate software visually.
The internet expanded access to information, but retained the same basic model. Humans searched for information, clicked links, and navigated websites manually.
The smartphone era compressed these interactions into applications. Users tapped icons, opened apps, entered information, and moved between services to complete tasks.
Across each of these transitions, one principle remained unchanged: humans were the active participants and computers were reactive systems.
AI agents reverse that relationship.
Instead of waiting for instructions at every step, software can increasingly operate independently, carrying out tasks and making decisions within boundaries defined by the user.
Why do AI agents change the rules of computing?
The significance of AI agents lies in what differentiates them from traditional software.
They are not simply chatbots that respond to prompts, nor are they digital assistants limited to answering questions. Agentic systems can reason, plan, execute tasks, monitor information sources, coordinate software services, and perform multi-step workflows with limited human involvement.
Consider travel planning.
Today, a user might search for flights, compare prices, monitor fare changes, coordinate schedules, and make bookings manually across multiple websites and applications.
An AI agent could potentially handle those steps autonomously, monitoring fares over time, evaluating options against user preferences, coordinating with calendar availability, and completing bookings once specified conditions are met.
The implications extend far beyond travel. Similar systems are being developed for research, software development, financial analysis, customer support, enterprise workflows, and more.
If software begins acting on behalf of users rather than merely responding to requests, the computing infrastructure supporting it must change as well.
If machines become users, why must chips change?
The first signs of that transformation are appearing in semiconductors.
The initial wave of generative AI was powered primarily by graphics processing units (GPUs), which proved highly effective for training large language models and running inference workloads.
Agentic systems, however, introduce new requirements.
Unlike chatbots, agents operate continuously. They interpret goals, gather information, execute code, coordinate application programming interfaces (APIs), and maintain context across multiple tasks. These workloads require significantly higher concurrency, memory bandwidth, and persistent processing capabilities.
Hardware designed around intermittent human interactions is not optimised for such workloads.
This challenge has prompted chipmakers to rethink processor architecture.
Nvidia recently introduced Vera, which the company describes as the first high-performance datacentre CPU built specifically for agentic AI workloads. Nvidia claims the processor delivers up to 1.8 times the performance of leading x86 datacentre CPUs that have historically powered enterprise computing.
Despite being a new entrant to the CPU market, Nvidia has already secured support from companies including OpenAI, Anthropic, xAI, Oracle Cloud Infrastructure, and ByteDance.
This transformation is also reaching consumer devices.
Apple’s M-series silicon architecture combines CPU, GPU, Neural Processing Unit (NPU), and unified memory into a single design optimised for AI processing. The latest M5 family places particular emphasis on memory bandwidth and on-device AI execution.
Within the Windows ecosystem, Qualcomm’s Snapdragon X platform and Nvidia’s RTX Spark architecture are pursuing similar goals. Nvidia’s platform combines a Blackwell GPU, a 20-core Arm-based CPU, and up to 128 GB of unified memory, delivering memory bandwidth of up to 300 GB/s.
For Qualcomm, the transition extends beyond PCs. The company envisions smartphones, laptops, vehicles, and smart glasses functioning increasingly as endpoints connected to persistent AI systems operating across local and cloud environments.
Why may apps matter less in an agent-driven world?
The rise of AI agents also challenges one of computing’s most enduring concepts: The application.
For decades, software has been organised around applications because humans needed visual interfaces to interact with digital systems. Operating systems existed largely to help users launch, switch between, and manage these applications.
AI agents do not necessarily require those interfaces. Instead, they can interact directly with software services through APIs, retrieving information, executing transactions, and coordinating workflows without opening traditional applications.
As a result, technology companies are exploring agent-first operating systems where applications become backend execution layers while agents serve as the primary interface.
Google’s vision for Android 17 shows how this transition could reach billions of smartphone users. The upcoming version of Android is expected to deepen Gemini’s integration into the operating system, allowing AI systems to understand on-screen context, access system-level functions, and coordinate actions across multiple apps.
Rather than requiring users to manually switch between applications, Google is increasingly positioning Gemini as an orchestration layer capable of completing tasks that span messaging, calendars, maps, shopping, travel, and productivity services.
The broader goal is to make the smartphone less app-centric and more intent-centric. Instead of opening several apps to organise a trip, schedule a meeting, or compare products, users could simply describe an objective and allow an AI system to determine which services need to be accessed and in what sequence.
In this model, applications remain important, but increasingly operate behind the scenes as execution engines rather than primary user interfaces.
Microsoft’s Project Solara offers a glimpse of a similar future.
Described as a chip-to-cloud platform designed for a multi-agent world, Solara is built around a lightweight operating environment that coordinates local and cloud-based agents rather than traditional desktop software.
To demonstrate the concept, Microsoft introduced two reference designs: A wearable smart badge aimed at frontline workers and a desk companion device designed to provide persistent access to enterprise AI systems.
Underlying both is the concept of “Just-in-Time” user interfaces, where generative AI dynamically assembles interfaces according to context rather than relying on pre-built application layouts.
In effect, the interface itself becomes generated on demand.
How are search, cloud and the internet being rebuilt?
The transformation extends beyond devices and operating systems.
For decades, the internet has operated on a reactive model. Users searched for information, opened websites, gathered data, and repeated the process whenever new information was needed.
Agentic systems introduce a different model.
Rather than asking questions repeatedly, users define objectives and allow software systems to pursue them continuously. An AI agent could monitor competitors, track regulatory filings, compare products, follow market developments, or gather research while the user is offline.
Google’s overhaul of Search reflects this shift.
The company is repositioning Search from a tool that helps users locate information towards one that increasingly interprets, summarises, monitors, and delivers information directly.
Gemini, Google’s foundational AI model, is simultaneously becoming an intelligence layer across Search, Android, Gmail, Chrome, Workspace, and other products.
The goal is to move computing from episodic interactions towards continuous execution.
What changes if computers start working for us?
The implications extend beyond technology infrastructure.
If AI systems become capable of coordinating software autonomously, the importance of individual applications could diminish. Users may increasingly interact with a single AI layer rather than managing dozens of separate apps.
Search behaviour could also change fundamentally. Instead of directing users to websites, AI systems may increasingly gather, synthesise, and present information directly.
That shift raises questions for publishers, creators, and businesses that rely on search traffic.
Work itself could be reshaped. Research, scheduling, reporting, coding, customer service, and administrative tasks may increasingly be delegated to software operating in the background.
Even devices could evolve. Smartphones and PCs may become gateways into persistent AI systems rather than destinations where work is performed.
These are the questions increasingly occupying technology executives because they determine not only how computing evolves, but also where value is created across the digital economy.
Why may the transition take longer than Big Tech expects?
Despite the momentum, significant obstacles remain.
Cost remains one of the largest challenges. Agentic workloads require substantial computing resources, making large-scale deployment expensive.
Reliability presents another hurdle. AI systems continue to generate inaccurate outputs and hallucinations, limiting their ability to operate autonomously in sensitive environments.
Security concerns are equally significant. Systems capable of taking actions on behalf of users introduce new risks if compromised or manipulated.
Regulatory questions also remain unresolved. As AI systems gain greater autonomy, questions around accountability, liability, and oversight become increasingly complex.
These constraints suggest the transition may unfold more gradually than some industry leaders anticipate.
The next computing shift
For decades, computers have been tools that waited for human instructions.
The technology industry’s largest companies are now betting that the next era of computing will look different: Machines acting on behalf of people, interacting with other machines, and operating continuously in the background.
How quickly that vision becomes reality remains to be seen, but one thing is clear: Technology companies are redesigning the foundations of computing for a future in which AI systems may become a major new consumer of computing resources alongside human users.
 "Computers were built for humans. Big Tech is redesigning them for AI")
