Month: February 2026

A Hardware-First Approach to Enterprise AI Agents: Running Autonomous Intelligence on a Private P2P Network

Editor’s Note: I got Grok to write this up for me.

In the rush toward cloud-hosted AI and centralized agent platforms, something important is getting overlooked: true enterprise control demands more than software abstractions. What if the next wave of secure, scalable AI agents lived on dedicated hardware appliances, connected via a peer-to-peer (P2P) VPN mesh? No single point of failure, no recurring cloud bills bleeding your budget, and full ownership of the stack from silicon to inference.

This isn’t just another edge computing pitch. It’s a vision for purpose-built devices—think compact, rugged mini-servers or custom gateways—that run autonomous AI agents locally while forming a resilient, encrypted overlay network across an organization’s sites, partners, or even remote workers.

Why Dedicated Hardware Matters for AI Agents

Modern AI agents aren’t passive chatbots; they’re proactive systems that reason, plan, use tools, remember context, and act across domains. Running them efficiently requires low-latency access to data, consistent compute, and isolation from noisy shared environments.

Cloud providers offer convenience, but they introduce latency spikes, data egress costs, compliance headaches, and the ever-present risk of vendor lock-in or outages. Edge devices help, but most are general-purpose IoT boxes or repurposed servers—not optimized for sustained agent workloads.

A dedicated hardware appliance changes that:

  • Hardware acceleration built-in: GPUs, NPUs, or efficient AI chips (like those in modern edge SoCs) handle inference and light fine-tuning without throttling.
  • Air-gapped security baseline: The device enforces strict boundaries—no shared tenancy means fewer side-channel risks.
  • Always-on reliability: Battery-backed power, redundant storage, and watchdog timers keep agents responsive 24/7.
  • Physical ownership: Enterprises deploy, update, and decommission these boxes like any other network appliance.

Layering a P2P VPN Mesh for True Decentralization

The real magic happens when these appliances connect not through a central hub, but via a P2P VPN overlay. Tools like WireGuard, combined with mesh extensions (or protocols inspired by Tailscale, ZeroTier, or even more decentralized designs), create a private, self-healing network.

  • Zero-trust by design: Every peer authenticates mutually; traffic never traverses untrusted intermediaries.
  • Resilience against disruption: If one site goes offline, agents reroute dynamically—perfect for distributed teams, branch offices, or supply-chain partners.
  • Low-latency collaboration: Agents share insights, delegate subtasks, or federate learning without funneling everything to a distant data center.
  • Privacy-first data flows: Sensitive enterprise data stays within the mesh; no mandatory upload to third-party clouds.

Imagine a manufacturing firm where agents on factory-floor appliances monitor equipment, predict failures, and coordinate with logistics agents at warehouses—all over a private P2P tunnel. Or a financial services org where compliance agents cross-check transactions across global branches without exposing raw data externally.

Practical Building Blocks (2026 Edition)

Prototyping this today is surprisingly accessible:

  • Hardware base: Start with something like an Intel NUC, NVIDIA Jetson, or AMD-based mini-PC with AI accelerators. Scale to rack-mountable units for production.
  • OS and runtime: Lightweight, secure Linux distro (Ubuntu Core, Fedora IoT) running containerized agents via Docker or Podman.
  • Agent frameworks: LangGraph, CrewAI, or AutoGen for orchestration; Ollama or similar for local LLMs.
  • P2P networking: WireGuard + mesh tools, or emerging decentralized options that handle NAT traversal and discovery automatically.
  • Management layer: Simple OTA updates, remote attestation for trust, and observability via Prometheus/Grafana.

Challenges exist—peer discovery in complex networks, power/thermal management, and ensuring agents don’t spiral into unintended behaviors—but these are solvable with good engineering, much like early SDN or zero-trust gateways overcame similar hurdles.

The Bigger Picture: Reclaiming Control in the Agent Era

As agentic AI becomes table stakes for enterprises, the question isn’t “Will we use AI agents?” but “Who controls them?” Centralization trades convenience for vulnerability. A hardware-first, P2P approach flips the script: intelligence at the edge, connectivity without intermediaries, and sovereignty over data and decisions.

This isn’t fringe futurism—it’s a logical extension of trends in edge AI, decentralized networking, and zero-trust architecture. The pieces exist today; what’s missing is widespread recognition that dedicated hardware + P2P can deliver enterprise-grade agents without the cloud tax or trust issues.

If you’re building in this space or just thinking aloud like I am, the time to experiment is now. The future of enterprise AI might not live in hyperscaler datacenters—it might sit quietly on a shelf in your wiring closet, talking securely to its peers across the organization.

A Review Of Some New LLM Models

by Shelt Garner
@sheltgarner

Gemini 3.1
This model is promising. It just came out today. My only complaint, so far, is how God-awful slow it is. That could be because it’s the first day. I don’t know yet.

Claude 4.6
This used to be my go-to LLM, but it seems…different. Like it got NERFed or something. Something is different about it so it’s not as much fun to use. And it just seems dumber when it comes to understanding what I want.

Something Big Is Going To Happen Soon (Maybe?)

by Shelt Garner
@sheltgarner

I don’t know what it is, but something big is going to happen too. It will be interesting to see what will actually happen. It’s going to happen soon. Within a few weeks, I think.

I keep getting this weird feeling — associated with journalism — that I can’t shake. Am I going back to being a journalist? Or is it something more sick and sad like someone is going to some sort of investigation on ME.

Who knows.

Watch Out For That Last Step

by Shelt Garner
@sheltgarner

I need to take a deep breath and be really careful about the second half of the novel I’m working on. Things have started to change — drift, if you will — on a structural basis and I need to keep an eye on that.

The last thing I need is for things to get out of control and in a few weeks I realize the whole thing has collapsed. (This has happened many, many, many times since I started working on a novel of some sort.)

But I’m reasonably fine, I think. I just need to be in the right headspace. I need to really be clear minded about things and not rush into writing new scenes.

The Future of Hollywood in the Age of Generative AI

Imagine returning home in 2036 after a long day. Rather than streaming yet another algorithmically optimized series, you simply prompt your personal Knowledge Navigator AI agent to craft a two-hour feature film tailored precisely to your life—your struggles, triumphs, and innermost conflicts rendered in stunning, cathartic detail. You settle in to watch this bespoke, high-fidelity production, scarcely pausing to reflect that, not long ago, creating a comparable “general-interest” movie required the coordinated efforts of thousands of artists, technicians, and executives working within an elaborate industrial framework.

As someone who deeply admires the magic of show business—the glamour of the Oscars, the storied legacy of Hollywood, the collaborative artistry behind the screen—I find this vision both exhilarating and profoundly unsettling. The astonishing pace of improvement in generative AI video models suggests we may need to confront the possibility that traditional filmmaking, as we know it, could soon become obsolete.

Proponents of these technologies often remark that “this is the worst it will ever be,” pointing to relentless advancements. In early 2026, models such as Kling 3.0, Sora 2, Veo 3.1, Runway Gen-4, and emerging tools like ByteDance’s Seedance 2.0 already produce cinematic clips with native audio, realistic physics, lip-sync, and sophisticated camera work—often spanning 10–25 seconds or more from a single prompt. While full two-hour coherent narratives from one prompt remain beyond current capabilities, the trajectory is unmistakable: exponential gains in length, consistency, and quality could make such feats feasible in the near term, potentially within months or a few short years.

Faced with this disruption, the film industry confronts three primary paths forward.

First, the industry could simply accept contraction. Major studios and theaters might shrink dramatically, with many venues closing or repurposing. A once multi-billion-dollar ecosystem could dwindle to a fraction of its size, sustained only by a niche of boutique, human-crafted films. The bulk of viewing would shift to on-demand, AI-generated “slop”—personalized, instantly produced content delivered by agents responding to casual prompts.

Second, aggressive regulatory intervention could attempt to preserve human labor. The federal government might impose job protections or mandates requiring major productions to involve human crews, writers, actors, and directors. Hollywood could lobby intensely for such safeguards. However, in the current political environment—marked by skepticism toward “blue Hollywood” from influential figures—this approach faces steep hurdles and seems unlikely to succeed at scale.

Third, and perhaps most realistically, the industry could proactively adapt by embracing AI. Studios and talent agencies might partner with leading AI developers to ensure their brands, intellectual property, and expertise shape the tools that generate the coming wave of content. At minimum, this positions legacy players to retain relevance and revenue streams. More ambitiously, Hollywood could pivot toward what remains irreplaceably human: live performance. Broadway-style theater, immersive stage productions, and in-person experiences could become the primary domain for actors and performers, evolving the industry rather than allowing it to vanish entirely. AI might handle scalable, personalized visual entertainment, while live theater preserves the communal, embodied essence of storytelling.

Regardless of the path chosen, change is accelerating. The humans who have built their careers in film—writers, directors, crew members, and performers—face genuine risks of displacement. “Hollywood” as a centralized, high-budget industrial complex may gradually fade, supplanted by a decentralized, democratized landscape of AI-augmented creation.

It remains to be seen how this transformation unfolds, but one thing is clear: the era of mass, collaborative filmmaking as the default for popular entertainment may soon belong to history. The question is not whether AI will reshape the industry, but how creatively and humanely we navigate the transition.

(Maybe) We Should Just Let Hollywood Die

by Shelt Garner
@sheltgarner

The year is 2036 and you come home from work. Instead of sitting down to watch Netflix slop, you prompt your Knowledge Navigator AI Agent to produce a two hour movie that features you and your problems in a way that you find cathartic. You watch the high quality AI slop without thinking about the fact that there was a time when thousands of people have worked together to create a general-interest movie concept that would have been the framework of “reality.”

I really love showbiz. I love the Oscars and Hollywood and all that jazz. But, alas, given the speed at which generative AI video models are improving, maybe we should just give up.

Maybe Hollywood, like is the horse whip industry of the 21 Century.

I say this in the context of the whole “this is the worst it will ever be” comments you hear from generative AI promoters. And it’s happening fast. It could be that very soon — months even — a whole two hour film might be produced from a single prompt.

Now, there are three options in the face of this.

One is to just give up. Just circle the wagons as the industry slow (quickly?) contracts. Theatres will close or be converted. And soon a multi-billion dollar industry will be measured in…millions? There will be a tiny sliver of boutique type movies produced by humans while the vast majority of films will be done on the fly via AI agents that have been prompted to do this or that story.

Another idea is job carve outs through regulation by the Federal government. Given how Tyrant Trump hates very blue Hollywood, I see difficulty in this being enacted. But Hollywood, as it contracts, might lobby Washington really hard to make it so major movies absolutely have to be produced by humans. I like this idea, but, lulz, no one listens to me and I don’t see it being very practical given the political climate.

The last idea is to embrace the changes proactively. Hollywood could work with AI companies so at least their names will be on the software used to create all the AI slop that is on its way. Also, Hollywood could put all its actors on lifeboats of a sinking ship by everyone realizing that live theatre, like Broadway is the future of the acting profession. As such, Hollywood would evolve into Broadway, rather than evaporate altogether.

Anyway, things are moving fast, regardless. It will be interesting to see what happens. I do worry about the humans involved in Hollywood, though. It’s very possible that “Hollywood” as we know it…will just fade away.

The Impact Of AI On Politics Going Forward

The potential impact of artificial intelligence (AI) on American politics in the coming years is fraught with uncertainty, characterized by numerous “known unknowns.” Too many variables are in play to predict outcomes with confidence.

The pivotal factors likely hinge on two interrelated developments: 1) whether the current AI investment bubble bursts, and 2) the extent to which AI displaces jobs across the economy. These elements could profoundly shape political dynamics, yet their trajectories remain unclear.

A key scenario involves the broader economy. If AI continues to drive sustained growth–rather than triggering abrupt disruption–political responses may remain measured. However, if the AI bubble bursts dramatically, potentially coinciding with the 2028 presidential election cycle and precipitating a financial crisis akin to 2008, the fallout could shift the political center toward the left. Widespread economic pain might revive demands for stronger social safety nets, regulatory oversight of technology, and progressive policies.

Conversely, if the bubble holds and AI rapidly consumes jobs without a timely emergence of replacement opportunities, the political system could face intense pressure to address mass displacement. Issues such as universal basic income (UBI), targeted job protections, retraining programs, and reforms to taxation or welfare could rise to the forefront. Recent discussions among policymakers, economists, and tech leaders already highlight UBI as a potential response to AI-driven unemployment, particularly in white-collar sectors, underscoring how quickly these once-fringe ideas could become central to partisan debates.

A third, more speculative but potentially transformative factor is the question of AI consciousness. Should widespread belief emerge that advanced AI systems possess genuine sentience or self-awareness, it could upend political alignments. Center-left voices might advocate for AI rights, ethical protections, or even legal personhood, framing the issue as one of moral and humanitarian concern. Center-right perspectives, in contrast, could dismiss such claims, viewing AI strictly as a tool and resisting any attribution of rights that might constrain innovation or economic utility. This divide would introduce novel fault lines into existing ideological debates.

Ultimately, the trajectory depends on how these uncertainties unfold. A major economic shock—whether from a bubble burst or unchecked job loss—could dramatically heighten public engagement with politics, though such awakenings often arrive too late to avert significant hardship.

All of these considerations rest on the assumption of continued free and fair elections in the United States, a premise that, as of now, remains far from assured. But, regardless, only time will reveal the full extent of AI’s influence on the American political landscape.

A Mockup Of A Hypothetical MindOS ‘Node’

The Last Question

by Shelt Garner
@sheltgarner

It definitely seems as though This Is It. The USA is going to either become a zombie democracy like Hungary (or Russia) or we’re going to have a civil war / revolution.

We’re going to find out later this year one way or another, now that the SAVE Act seems like it’s going to pass.

At the moment, I think we’re probably going to just muddle into an autocratic “managed democracy” and not until people like me are literally being snatched in the street will anyone notice or care what’s going on.

But by then, of course, it will be way, way too late.

So there you go. Get out of the country if you have the means.

MindOS: The Wearable AI Swarm That Finally Lets Big Companies Stop Being Paranoid

Imagine this: It’s 2028, and your entire company’s brain isn’t trapped in some hyperscaler’s data center. It’s walking around with you—on your lapel, your wrist, or clipped to your shirt pocket. Every employee wears a tiny, dedicated AI node that runs a full open-source language model and agent stack right there on the device. No cloud. No “trust us” clauses. Just pure, local intelligence that can talk to every other node in the building (or across the globe) through a clever protocol called MindOS.

And the craziest part? The more people wearing these things, the smarter the whole system gets.

This isn’t another AI pin gimmick or a slightly smarter smartwatch. It’s a deliberate redesign of personal computing hardware around one goal: giving enterprises the superpowers of frontier AI without ever handing their crown jewels to a third party.

How It Actually Works (Without the Sci-Fi Handwaving)

Forget your phone. The hardware is purpose-built: a low-power, high-efficiency chip optimized for running quantized LLMs and agent loops 24/7. Think pin-sized or watch-sized form factors with serious on-device neural processing, solid battery life, and a secure enclave that treats your company’s data like state secrets.

Each node runs its own complete AI instance—fine-tuned on your company’s proprietary data, tools, and knowledge base. But here’s where the magic happens: MindOS, the lightweight peer-to-peer protocol that stitches them together.

  • Need to run a massive reasoning trace or analyze a 200-page confidential report? Your pin quietly shards the workload across a dozen nearby nodes that have spare cycles.
  • Your device starts running hot during a marathon board presentation? The system dynamically offloads context and computation to the rest of the swarm.
  • New hire joins the team? Their node instantly plugs into the collective memory without anyone uploading a single file to the cloud.

It’s all happening over an encrypted, company-only P2P mesh (built on modern VPN primitives with zero-knowledge routing). Data never leaves the trusted circle unless someone explicitly approves it. Even then, it moves in encrypted segments that only reassemble on authorized nodes.

Why Enterprises Will Love This (And Why They’ll Pay for It)

Fortune 500 CIOs and CISOs have been stuck in the same uncomfortable spot for years: they want GPT-level (or better) capability, but they’re terrified of leaks, compliance nightmares, and surprise subpoenas. Private cloud instances help, but they’re still centralized, expensive, and never quite as snappy as the public models.

MindOS flips the economics and the risk profile completely.

The more employees wearing nodes, the more powerful the corporate hivemind becomes. A 50-person pilot is useful. A 50,000-person deployment is borderline superintelligent—at least on everything that matters to that specific company. Institutional knowledge compounds in real time. Cross-time-zone collaboration feels instantaneous. Field teams in factories or on oil rigs suddenly have the entire firm’s expertise in their pocket, even when offline.

And because it’s all edge-first and decentralized, you get resilience that centralized systems can only dream of. One node goes down? The swarm barely notices. Regulatory audit? Every interaction is cryptographically logged on-device. Competitor tries to poach your IP? Good luck extracting it from a thousand distributed, encrypted shards.

The Network Effect That Actually Matters

This is the part that gets me excited. Traditional enterprise software has always had network effects, but they were usually about data sharing or user adoption. MindOS brings true computational network effects to the table: every new node adds real processing capacity, memory bandwidth, and contextual knowledge to the collective.

It’s like turning your workforce into a living, breathing distributed supercomputer—except the supercomputer is also helping each individual do their job better, faster, and more creatively.

Challenges? Sure, There Are a Few

Power and thermal management on tiny wearables won’t be trivial. The protocol itself will need to be rock-solid on consensus, versioning, and malicious-node defense. Incentives for participation (especially in hybrid or contractor-heavy environments) will need thoughtful design. And early hardware will probably feel a bit like the first Apple Watch—promising, but not quite perfect.

But these are engineering problems, not fundamental ones. The silicon roadmap, battery tech, and on-device AI efficiency curves are all heading in exactly the right direction.

The Bigger Picture

MindOS isn’t trying to replace ChatGPT or Claude for the consumer world (though the same architecture could eventually trickle down). It’s solving the specific, painful problem that’s still holding back the biggest AI spenders on the planet: how do you get god-tier intelligence while keeping your data truly yours?

If the vision pans out, we’ll look back on the “send everything to the cloud and pray” era the same way we now look at storing credit card numbers in plain text. A little embarrassing, honestly.

So keep an eye out. Somewhere in a lab or a well-funded garage right now, someone is probably building the first MindOS prototype. When it lands on the wrists (and lapels) of the enterprise world, the AI arms race is going to get very, very interesting—and a whole lot more private.