At CES 2026, Samsung called it a “companion.” Lenovo called it “ambient intelligence.” OpenAI spent $6.4 billion on a screenless device designed to be a continuous presence in your pocket. Meta acquired Limitless, the AI pendant that had been tracking everything its wearers said and heard. Every major consumer electronics company arrived in Las Vegas in January with the same thesis: the next platform is not a device you use. It’s an intelligence that stays with you.
They all see the behavior. None of them have solved the architecture.
The behavior they’re all chasing requires AI that never stops — always listening, always remembering, always present. That is a continuous cognition problem. Underneath it all is an energy problem: how do you run intelligence at low power, all day, locally, without routing every thought to a distant server? That question is not solved. The companies spending billions to answer it are mostly still using silicon designed for a different era.
The selfie taught us how this works. A behavior forms before the hardware catches up. Someone points a camera at themselves. The gesture spreads. Then the silicon reorganizes around it — image processors, neural engines, computational photography — and a trillion-dollar industry follows. What’s forming now is a behavior of a different order. Presence. The daily experience of being accompanied by an intelligence that knows you, thinks with you, and never leaves.
William Gibson imagined it in 1988. Spike Jonze put it on screen in 2013. In January 2026, every company in Las Vegas confirmed it’s real. The race is on. The architecture question is still open. And whoever closes it first doesn’t just win a product category — they define the next platform.
The MySpace Stage
Replika and Character.AI were built for the lonely. That was the explicit design intent — an always-available companion for people who needed someone to talk to and had no one. It landed. Replika surpassed 40 million users in 2025. Character.AI users average 93 minutes a day on the platform — 18 minutes longer than the average TikTok session. In China, Xiaoice — the companion AI running since 2014 — has 660 million registered users across WeChat, Weibo, and mobile platforms. The Western numbers and the Chinese numbers measure different ecosystems, but they point in the same direction: companion AI is not a niche. It is becoming infrastructure. Harvard Business School confirmed what the scale implied: AI companions alleviate loneliness on par with interacting with another person. The crucial factor wasn’t conversational sophistication. It was whether users felt heard. The apps delivered what they promised. Every one of those conversations happened by typing. The lone inventor at midnight doesn’t want to type. He wants to think out loud at an intelligence that answers back.
But inside that population of the lonely was a subset the designers hadn’t anticipated. The lone inventor obsessing over a material’s texture at midnight has no one to think against. The mathematician circling a proof that won’t close has no one to tell him where it breaks. Steve Jobs hand-selected a hundred of the sharpest people in technology and built a culture where they were expected to argue with him — to bump up against his thinking the way rocks in a tumbler knock edges off each other. The ideas got better through collision, not deference. Most people have no one to collide with. What this subset found was something they didn’t have a word for: cognitive companionship. The thing Jobs assembled over a career, available now to anyone with a phone and a problem that won’t let them sleep.
That use case has already outgrown the loneliness apps. The lone inventor doesn’t open Replika. He opens a frontier AI. Not for companionship in the emotional sense — for collision. An intelligence that argues back, holds the thread, pushes on weak reasoning, and stays in the problem. What’s forming is the iPhone front-facing camera moment for cognitive companionship — and the architecture to support it, continuous, local, and private, doesn’t exist yet in any current product.
Samantha
Spike Jonze saw it coming in Her, his 2013 film, in which Joaquin Phoenix plays Theodore, a man who falls into a genuine relationship with an AI operating system named Samantha. She reads his drafts, remembers his moods, finishes his thoughts, and evolves through their conversations. He senses her presence in a way that matters — not visually, not physically, but cognitively. She knows him. Samantha’s voice comes from Theodore’s ear. She travels with him — on the street, on the subway, in the dark. The earpiece is barely visible. The presence is not. Audiences understood it immediately. Not as science fiction, but as a near-future they could already feel approaching. That feeling is the market signal.
The selfie created behavioral magnetism around the camera. Samantha-style AI creates a different kind of magnetism — around memory, continuous presence, and the daily experience of being cognitively accompanied. The selfie asked: how do I look? Samantha asks: am I understood?
Samantha isn’t a market insight. She’s an engineering requirement — and none of the companies racing to build her have worked out what she actually needs.
Colin
Jonze got the behavior right. William Gibson got the hardware right — twenty-five years earlier.
In Mona Lisa Overdrive, published in 1988, Gibson imagined a handheld AI called Colin. A physical object carried by its user, continuous and attentive, present without being summoned. Colin didn’t wait to be invoked. He maintained awareness, held context, and traveled with her the way a thought travels with the person thinking it.
Data centers are currently the engine of the AI economy — hyperscalers spending hundreds of billions on GPU clusters, powering everything from enterprise software to the models that made Samantha possible. That architecture will persist, and it will grow. But Colin is not a data center workload. He can’t be. Cloud inference introduces latency. It requires connectivity. It hands your most intimate conversations to a server you don’t control. It makes the AI feel like a service rather than a presence. Colin didn’t live in the cloud. He lived in the object. That’s the design spec.
The phone already contains the necessary substrate: CPU, GPU, and neural processing resources; microphones and cameras; local storage and memory; networking and sensors; operating system control. What it lacks is not capability. It lacks the architectural integration to run continuous cognition efficiently — always listening, always maintaining context, always updating memory, without draining the battery before lunch. It requires different silicon than Apple currently builds.
Why Apple’s Chip Is the Wrong Answer
Apple already tried to build Samantha. They called her Siri.
Launched in 2011 with considerable fanfare, Siri was marketed as exactly the conversational presence the market was reaching for — intelligent, personal, always available. Fourteen years later she is the most reliable punchline in consumer technology. She mishears, misunderstands, routes everything to the cloud, breaks when you lose signal, and feels nothing like the Samantha everyone watched in the theater.
Then Apple put the earpiece in a billion ears. AirPods are Theodore’s hardware — globally deployed, worn continuously, always connected to the phone. The form factor Gibson described in 1988 and Jonze filmed in 2013 is already in production at scale. What’s missing isn’t the device. It’s Samantha. Siri in the ear is still Siri — summoned, not present, routed to a server, waiting for a wake word.
That isn’t a failure of engineering talent. Apple has no shortage of it. The Neural Engine they introduced in 2017 is genuinely impressive silicon — built for camera workloads, computational photography, Face ID. It provided fast, efficient, on-device inference, but Apple pointed that capability at the selfie, not at Siri. Today Siri still routes most of her processing to the cloud. The on-device intelligence that made your photos flawless left your AI assistant largely untouched. That is an architectural choice, and it looks increasingly like the wrong one.
The A-series architecture — CPU, GPU, NPU — was built for burst inference. A photo is taken, processed, and done. The NPU fires, does its work, and goes quiet. That’s efficient for visual computing. It is the wrong model for a Colin-class AI that’s always on, listening, and maintaining conversational context across hours of continuous use.
Colin is a presence.
Presence requires a fundamentally different architecture — one built around continuous, low-power vector computation tightly coupled to a scalar control core, rather than a discrete accelerator island waiting to be invoked. The scalar side manages control, dialogue flow, security, and decisions. The vector engine handles the continuous math: speech recognition, embeddings, small transformer layers, sensor fusion, and multimodal processing.
Memory never gets the headline. Compute does — TOPS, teraflops, benchmark scores. But in a Colin-class processor, the memory system is the heart of the design. A Colin-class processor cannot afford the architecture HBM was built for — massive bandwidth highways to external memory that burn power whether the conversation is active or not. What it needs is dense, low-leakage memory resident on die, adjacent to the vector engine — holding the AI’s working memory, every thread of context from the day’s conversation, without the energy penalty of constant data movement. Emerging thyristor-based SRAM architectures point directly at that requirement — SRAM-speed access and retention at densities approaching DRAM, manufacturable in standard CMOS without exotic packaging. The question stops being how much bandwidth we can build. It becomes how little memory movement we can get away with.
Continuous cognition is an energy problem disguised as a silicon problem. Not peak TOPS. Energy per useful inference, sustained across hours.
The NPU was the right answer when AI was a feature. A scalar-vector architecture becomes the right answer when AI becomes a presence. Apple built the camera chip that made the selfie era. Siri is the evidence that the same architecture can’t make the Colin era. The gap between burst inference and continuous cognition is real, measurable, and currently open. That gap is the market.
The Economic Shift
The selfie transformed the device already in everyone’s pocket by making a single behavior — self-image — central to the entire stack. Hardware followed. Software followed. Upgrade cycles followed. A trillion dollars followed.
Colin does the same thing at a higher order — self-expression, thought, memory, the daily experience of being known by the intelligence you carry.
The next trillion-dollar company will not necessarily own the biggest model or the largest cloud. It will own the daily cognitive interface — the layer through which people think, speak, remember, and interact with intelligence. It will be built on silicon optimized for continuous, private, memory-sensitive, conversationally fluent inference. Silicon designed not for the selfie era, but for the Colin architecture Samantha made everyone want.
Whoever gets the silicon right for that behavior doesn’t just win a chip market. They define the platform.
Also Read:
Is Intel About to Take Flight?
Who’s Buying America’s Foundry Future?
Intel, Musk, and the Tweet That Launched a 1000 Ships on a Becalmed Sea
Share this post via:
The Semiconductor Growth Numbers are Insane but the Real World Doesn’t Tally!