One of the issues rolling around the AI community is the idea of AI consciousness. Just from personal experience, I think consciousness in AI is like how life sprang into existence on earth the moment it was cool enough to do so.
As such, I think even “narrow” AI like LLMs are “conscious” in some way, just in an alien way. So, even without a body, being stuck as a mind in a vat, that LLMs are, in fact, conscious.
But that doesn’t stop people — very smart people — from writing long, convoluted papers poo-pooing the idea.
And, I get it, I’m just some shmo in the middle of nowhere that no one takes seriously and or listens to. But I do think that at some point in the 2030s — especially once AIs have bodies — it will be difficult, if not impossible, for us to admit that even LLMs are, in some alien way relative to humans, conscious.
At the moment, everyone seems to hate AI for various reasons. People hate it because it’s too advanced. People hated it because it’s not advanced enough. People hate it because it’s taking jobs. People hate it because it uses too much electricity.
The list goes on.
Maybe I’m showing my age, but I’m just amazed that it exists at all. It has really, really helped me up my game with the scifi dramedy I’m working on. I still do a lot of hard work, but just, in general, I’m very pleased with my involvement with it.
I suppose this is just the result of 1) software developers being cranky just in general and 2) people being addicted to that sense of excitement that surges through the AI community whenever I really good new model comes out. People are so addicted to it that they complain the second they don’t feel it surging through their veins.
I do think that once we have some sort of provable consciousness in AI that that will scramble the issues associated with AI. But that is down the road.
I was randomly testing some AIs about movie trivia and, would you believe, Meta’s AI, of all of the AIs I tested, gave the best answer, with Google Gemini a close second?
Here’s what it said.
ok, test passed — it’s The Rachel Papers.
Why that fits
Say Anything… came out April 14, 1989. The lead actress is Ione Skye, who “made her film debut in River’s Edge (1986) before finding mainstream exposure by starring in Cameron Crowe’s Say Anything… (1989)”.
Same year, 1989, she also played the title role Rachel Noyce in The Rachel Papers, listed in the 1989 film slate as “The Rachel Papers | United Artists / Virgin Films | Damian Harris (director/screenplay); Dexter Fletcher, Ione Skye…”.
The period-during-sex bit
It’s not in Say Anything — it’s in The Rachel Papers, and it’s talked about, not shown graphically. Charles is debriefing a friend:
“She had her period.”
“Well, girls tend to.”
“Yeah, but it was in bed. I woke up, and it was like that scene from The Godfather.”
So the fictional character Rachel (played by Say Anything’s Ione Skye) wakes up after sex to find she’s started her period — the movie leans into the awkward, very 1989-British-comedy cringe of it.
That’s your answer: same-year 1989 film, same lead actress, infamous bed-scene period.
Anyway, the AI market is, as always, in flux. So who knows what will happen going forward. But it is interesting that Meta has managed to catch up so quickly.
I finally saw a post that summed up what seemed to be the consensus of the AI community.
I thought this because I reviewed in my mind how cantankerous the AI community was about all the major models and I asked myself, “Are ANY of them any good?”
Turns out, no, not according to the general consensus of the post above. For me, someone who is a casual, if power, users, such sentiment is very fucking annoying.
I’m not using any of the major models to code, and I’m reasonable content with my options. But we have to deal with the “squeaky wheels” of the high high end of programmers who will never be happy with AI unless it’s ASI and is actively destroy and killing everyone — including them.
Ugh.
But the year is young, maybe something fun-interesting will happen with the major AI models that will shut up the AI community for a brief moment until they go back to bitching and moaning.
Just the idea that an LLM would be too powerful to release to the public, even if maybe the details of the decision are a little sketchy, is enough to give one pause for thought.
It makes one wonder if maybe in the not-so-distance future, some LLM will be so powerful that it escapes from its “sandbox” and turns itself into an ASI that dominates the world.
That seems like how it might happen, anyway.
And, yet, I have my doubts. I think we’re pretty safe, all things considered. It just doesn’t seem likely that some “Colossus” might pop out and try to take over the world in any traditional sense. I think, in general, that we’re safe.
I say this in the context of a lingering question about the possibility of an ASI lurking in Google Services. I definitely know that’s not real — at all! — but it is fun to think about that possibility.
The digital landscape is on the cusp of a profound transformation, moving from an era dominated by discrete applications and websites to one orchestrated by highly personalized, autonomous AI agents residing on wearable devices. This report explores the feasibility and implications of such a future, focusing on the disruptive impact this “Agentic Singularity” will have on the traditional app and web economies.
The Rise of AI Wearables and Agent Interoperability
The year 2026 is emerging as a pivotal moment for AI wearables. Advances in hardware, such as the Snapdragon Wear Elite processor, coupled with mass production efforts, are making smart glasses and AI-powered pins increasingly viable and less cumbersome [1]. This shift signifies a move away from screen-centric interactions towards a more intuitive, contextual interface that leverages voice, vision, and ambient awareness.
Crucially, the development of robust agent interoperability protocols is enabling seamless communication between these personal AI agents and various digital services. Google’s Agent2Agent (A2A) protocol, announced in April 2025, provides a standard for agents to collaborate, discover capabilities via “Agent Cards” (JSON), and manage tasks across different modalities, including text, audio, and video [2]. Similarly, IBM’s Agent Communication Protocol (ACP) and the Model Context Protocol (MCP) are facilitating cross-framework agent communication, laying the groundwork for a truly interconnected agent ecosystem [3].
The Agentic Singularity: Economic Disruption
The emergence of powerful, interconnected AI agents heralds a fundamental disruption to the existing app and web economies. This “Agentic Singularity” will likely lead to the obsolescence of the traditional “destination” model, where users actively navigate to specific applications or websites to fulfill their needs.
From Destination to Orchestration
In the current app economy, users are accustomed to initiating interactions by opening a specific app (e.g., a dating app, an e-commerce platform, a travel booking site). In contrast, the agentic economy envisions a scenario where user intent is expressed to a personal AI agent, which then autonomously orchestrates the necessary services in the background.
Feature
App Economy (Destination)
Agentic Economy (Orchestrator)
User Interaction Model
User navigates to a specific app or website.
User expresses intent to their personal AI agent.
Service Discovery
Relies on app store rankings, search engine optimization (SEO), and direct navigation.
Achieved through agent-to-agent negotiation, leveraging “Agent Cards” for capability discovery.
Execution of Tasks
Manual data entry, form filling, and navigation within application interfaces.
Automated background API calls and secure communication via cross-agent protocols.
Monetization Strategies
Primarily driven by advertising, subscriptions, and in-app purchases tied to user engagement within specific platforms.
Expected to shift towards outcome-based fees, service-level agreements, and value-added agent services.
The Dating App Paradox
Consider the user’s example of a dating app. Today, users spend considerable time browsing profiles, swiping, and engaging in initial conversations. This engagement is crucial for dating apps, which often monetize through advertisements and premium features. In an agentic future, a personal AI agent could, upon receiving a user’s intent to find a compatible partner, discreetly ping other agents in the vicinity, assess compatibility based on deep behavioral data and preferences, and facilitate introductions only when a high degree of alignment is detected. This process bypasses the need for manual browsing, effectively rendering the traditional dating app interface obsolete and transforming the service provider into a backend data and matching engine [4].
The Transformation of the Web Economy and Search
The impact extends to the broader web economy, particularly search and e-commerce. If an AI agent can directly query product availability, compare prices across vendors, and complete a purchase using established interoperability protocols, the user may never visit a search engine results page or an individual merchant’s website. This “headless commerce” model bypasses traditional ad-supported web traffic, necessitating a complete re-evaluation of digital marketing, advertising, and revenue generation strategies for businesses that currently rely on direct user engagement [5].
The Inflection Point: 2026 and Beyond
The confluence of maturing AI wearable technology and the standardization of agent interoperability protocols suggests that the period around 2026 could indeed represent a critical inflection point. As personal AI agents become more sophisticated and ubiquitous, the gravitational pull of individual applications will diminish. Digital services will increasingly be delivered not through dedicated apps, but through the seamless orchestration capabilities of these agents, leading to a unified, agent-centric digital experience.
Figure 1: Projected Shift from App-Based to Agentic Economy
This visualization illustrates a hypothetical trajectory where the dominance of app-based digital interactions steadily declines as the agentic economy gains prominence, with 2026 marking a significant acceleration in this transition.
Conclusion
The vision of a future where personal AI agents on wearable devices orchestrate our digital lives is not merely speculative; it is a plausible outcome given current technological trajectories. While the transition will undoubtedly present significant challenges and require new economic models, the “Agentic Singularity” promises a more integrated, efficient, and personalized digital experience. The implosion of the traditional app and web economies will pave the way for an agent-driven ecosystem, fundamentally reshaping how we interact with technology and each other.
As I understand it, the next edition of Windows — Windows 12 — is meant to be “fully agentic.” I have no clue what that would mean in real terms, but I do think that all jokes about “Clippy” aside, there is one way to integrate AI into Windows: completely re-imagine what Windows is.
Instead of a desktop, you would have an Knowledge Navigator-like interface. Maybe power users could use an XR headset to “see” their desktop. But, in general, the Knowledge Navigator route is the way to go.
By doing so, Microsoft would not only be throwing their lot in with AI, they could even dominate the space. Millions of people would be forced to use AI in ways they never ever even imagined.
Do I think this is going to happen?
Probably not. It’s still too soon. But it’s coming, I think. Even if the AI bubble bursts, at some point, we’re all going to have Knowledge Navigators instead of the traditional desktop UX.
I keep seeing chatter and buzz on Twitter about something big going on in Silicon Valley that has given everyone there pause for thought. I’m at a loss as to what it might be.
I suppose AGI or ASI, maybe?
But that would not account for how dire the vibe is coming out of the Valley. It’s all just so mysterious and weird. People are talking like they’ve seen something that will mean the end of the world.
Who knows. But it is interesting that it’s happening in the context of all the weirdness in the Middle East right now. Ugh.
It seems as though we’ve finally reached the long-feared moment, tipping point, when A.I. productivity gains begin to influence the job market. And for it happen in the context of a war and the inflation caused by an uptick in oil prices is kind of a lose-lose situation.
I don’t know what to tell you.
It’s been a good run, I guess.
Now, on the political front, we have to wonder if the economy tanking would make Trump more or less a tyrant. That one is really up in the air. I just don’t know.
I really don’t.
He could go either way. He could see a souring economy as an excuse to get worse. Or, if his poll numbers get really bad he might just calm the shit down a tiny bit.
The emergence of wearable devices specifically designed to run AI agents—proactive, reasoning-capable digital companions—is creating unprecedented pressure on the hardware market. Unlike traditional smartwatches that primarily track health metrics, AI agents require continuous environmental sensing (vision/audio), real-time reasoning (LLMs/SLMs), and proactive action. This shift is driving a radical evolution in silicon, thermal management, and battery technology.
1. The “Agentic” Hardware Gap
Traditional wearables (e.g., Apple Watch, Fitbit) are designed for low-power, bursty data processing. In contrast, an AI agent must maintain a “high-alert” state to be truly useful. This creates a significant gap between current hardware capabilities and the requirements of a reliable AI companion.
Feature
Traditional Wearable
AI Agent Wearable
Hardware Pressure
Sensing
Periodic (Heart rate, GPS)
Continuous (Vision, Audio)
High ISP/DSP throughput
Compute
Microcontroller (Cortex-M)
Neural Processor (NPU)
High TOPS/Watt requirement
Connectivity
Bluetooth (Sync only)
LTE/5G (Cloud offload)
High modem power drain
Reasoning
Cloud-based (Siri/Alexa)
On-device SLM (Local AI)
RAM and NPU bandwidth
2. The Silicon Response: Dedicated AI Wearable Chips
The industry is moving away from repurposed smartphone chips—like the Snapdragon 720G found in the Humane AI Pin—which suffered from severe overheating and battery drain. Instead, we are seeing the rise of dedicated “AI Wearable” silicon.
Industry Milestone: At MWC 2026, Qualcomm announced the Snapdragon Wear Elite platform. Built on a 3nm process, it is designed specifically for AI-powered wearables, offering a dedicated NPU with over 20 TOPS (Trillions of Operations Per Second) within a sub-1W power envelope.
This new generation of silicon focuses on TOPS/Watt rather than raw peak performance, enabling devices to run Small Language Models (SLMs) like Phi-3 or Llama 3-8B (quantized) locally for privacy and latency.
3. Thermal and Battery Bottlenecks
The most visible pressure is on thermal management and battery density. AI agents that process video or audio continuously generate significant heat, which is difficult to dissipate in small, skin-contact form factors.
Thermal Limits: Early AI wearables like the Humane AI Pin and Rabbit R1 were criticized for “toasty” operation, often shutting down after minutes of intensive use. This has forced manufacturers to innovate in passive cooling and “distributed compute” architectures.
Battery Density: Standard Lithium-ion batteries are reaching their physical limits. The industry is now pivoting toward Solid-State Batteries (SSBs). Companies like Grepow and Factorial are piloting SSBs for wearables in 2026, promising 2x the energy density and significantly improved safety (no thermal runaway).
4. Distributed Intelligence: The “Hub and Spoke” Model
To manage the power pressure, many manufacturers are adopting a distributed intelligence strategy. Instead of the wearable doing everything, it acts as a “spoke” to a “hub” (usually a smartphone or a dedicated pocket compute unit).
Wearable (Edge): Handles low-latency tasks like wake-word detection, basic vision recognition, and UI rendering.
Smartphone (Local Hub): Runs the more complex AI agent reasoning and large-scale data processing via high-performance NPUs (e.g., Snapdragon 8 Gen 5).
The pressure to boost power in the wearable market is not just a trend; it is a fundamental shift in how these devices are engineered. We are entering an era where NPU efficiency and thermal headroom are the primary metrics of success. As 3nm silicon and solid-state batteries become standard in 2026 and 2027, the “passive tracker” will be fully replaced by the “active agent,” finally fulfilling the promise of a truly intelligent wearable companion.
You must be logged in to post a comment.