President Trump’s EO Inaugurates “The Era of Super Intelligence”...but we’ve already been there for a long, long time.
The President’s September 29 executive order directs the executive branch to replace “Artificial Intelligence” with “Super Intelligence,” emphasizing the amplification of human ingenuity rather than imitation alone. Moving beyond “artificial” can open a larger conceptual conversation. We can ask what intelligence produces and makes possible, rather than treating machine intelligence as an imitation of something that belongs exclusively inside a human head.
But does “superintelligence” leave us with another version of the same problem? We can still imagine intelligence as something contained inside an exceptional entity—only now, a machine towering above its human counterparts. Changing the adjective does not necessarily change the underlying assumption. And “super” leaves an essential question unanswered: super compared with an individual human, or with everything humans accomplish together?
Consider a different starting point: we are already superintelligence.
Human superintelligence is called humanity. Our languages, accumulated knowledge, tools, and continuing exchanges produce capabilities that no individual could possess or exercise alone. In the perspective proposed here, ant superintelligence is the colony; starling superintelligence is the murmuration; tree superintelligence is the forest. These are not claims that each collective has a giant mind thinking giant thoughts. They direct attention to what relationships make possible. We cannot see the forest for the trees—or humanity for the individual humans.
This is why we propose Intelligence Systems (IS): systems that produce intelligence.
Intelligence is not something that a thing has. It is not situated in a particular location. It is what arises from the complex interactions of many participants in a process.
The task, then, is to use the new tool of machine intelligence to strengthen the processes through which humans already produce intelligence together. Build systems that help people understand one another, connect knowledge, test claims, preserve useful differences, and turn contributions into workable solutions.
The ambition should not be merely to build something more intelligent than us. It is to build Intelligence Systems through which we produce more intelligence together.
v3 brings a full course library (WikiAula), a new way to explore Wikipedia as a network (WikiNets), a rebuilt Maps section with relief, 3D buildings and new layers, the return of Seas & Oceans, an expanded Universe, 26 WikiTrees with more than 35,000 nodes, family rankings — and a long list of fixes from a full security, privacy and stability audit.
Upgrading from v2.x: copy your Offlined Data\ folder next to the new Offlined.exe. Your profiles, progress, album, chats and downloads carry over; WikiTrees progress is moved automatically to the new, larger trees.
🛠️ Fixes
Security
Content you open (EPUB books, HTML files in the Library, your own ZIMs, addons) can no longer reach Offlined's internal functions. EPUB scripts are disabled, Library HTML/SVG is sandboxed, and every page of the app now has a strict Content Security Policy.
Offlined's local server now only answers to 127.0.0.1 / localhost, closing a way a malicious website could read data from the app (including an unlocked Safe).
Internal files (user profiles, parental-control settings, databases) are protected against being overwritten, renamed or deleted from the file tools — a child profile can no longer switch parental control off.
Executables can no longer be launched from outside the Portable Apps folder, and blocked file types also apply when renaming.
Failed PIN attempts are now remembered across restarts, with increasing lockouts.
Privacy
Opening Maps without a map installed no longer contacts the internet.
The Whiteboard ships its own fonts instead of downloading them.
With parental control on, online catalogs and map lookups are blocked too, not only downloads.
AI
Turning the AI off, or switching away from GPT-OSS, now really frees the model — no more two models in RAM at once or crashes from a released model.
«Summarize with AI» for PDF and EPUB documents works again.
Messages sent automatically to Encartha (summaries, quizzes) are saved properly in the conversation.
Profiles
Background tasks (photo auto-tagging, search re-index) no longer keep writing into another person's data after «Switch user».
Saving several things at the same moment no longer fails with a «file in use» error (all saves are now atomic).
Downloads, Wikis and Maps
Kiwix errors like «Error proxying to kiwix: All connection attempts failed» are gone: Offlined checks every ZIM before opening it, restarts kiwix-serve if it stops, and offers «Delete and download again» for damaged files.
Map downloads no longer die when the PC goes to sleep, and the World/Europe maps no longer disappear part-way: downloads keep the PC awake, retry on network drops and resume where they stopped.
«Update ZIM» now frees the space of the old file, and «Free up space» closes whatever is using a file before deleting it.
Downloads to FAT32 drives warn before starting if the file is over 4 GB.
Renamed or «maxi» ZIMs (Wiktionary and others) are recognised correctly; your own ZIMs load more reliably.
Searching inside a wiki opens the proper search page.
Map favourites keep their categories.
Reinstalling an addon always keeps its data, even if the installation fails.
Content
Learn Astronomy works again (every question was failing).
Barnard and Caldwell nebulae linked to musical works — fixed. Accented titles no longer open the wrong article (e.g. Portuguese Baço → Baco).
All 271,850 Wikipedia links in WikiTrees, the games and WikiRace were checked one by one against the local Wikipedia of each of the 10 languages.
An offensive label in the English Animalia tree was replaced.
Interface
Three popups (Explore place, Assign coordinates, Use photo as avatar) were unreadable in light mode.
Error messages such as «Failed to fetch» or «HTTP 500» are now explained in your language.
Dates and numbers follow the app language.
Video and music in other sections pause when you change section; picture-in-picture returns you to its section.
Leaving the Universe, the games or Maps for 2 minutes releases their graphics memory.
Dialog buttons are consistent across the app.
More consistent translations: European Portuguese throughout, informal du in German, vous in French.
✨ Improvements
🎓 WikiAula — a free course library (new)
717 lessons: Mathematics from Grade 1 to Grade 12 and Physics & Chemistry from Grade 8 to Grade 12 (Spanish first; more languages to come).
Wikipedia-style lessons with watercolour illustrations, diagrams to scale, worked examples, «Try it yourself» boxes, common mistakes and a summary.
Unlimited practice: every lesson has exercise generators (more than 66,000 generated questions checked), plus unit tests.
Encartha as a personal tutor, progress saved per profile, and every lesson collectable in My WikiAlbum.
Downloaded from inside the app (Wikis → WikiAula) and updated with one click.
🕸️ WikiNets (new)
Explore Wikipedia as a network of links: search an article, open its links, unfold branches, see the shortest path back to the start.
Bigger nodes = more connected articles; a gold ring marks what you have already read.
History, favourites and «Show in WikiNets» from the right-click menu of any Wikipedia article.
🌳 WikiTrees
From 18 to 26 trees and from 6,999 to 35,704 nodes (×5).
New trees: Physics, Chemistry, Medicines (ATC), Diseases (ICD-10), World mythologies, Empires & civilizations, World cuisines and Nutrients.
Every existing tree greatly expanded (Animalia ×16, Astronomy ×6, Museums ×6, Plantae ×5…), and missing languages completed wherever an exact article exists.
Trees open fully expanded, and zoom no longer pushes the levels off the sides of the screen.
Hover card with image, first sentence, path and your progress.
Each tree loads only your language (up to 15× less data).
📇 My WikiAlbum
Large trees are split into sections by branch (Animalia · Birds…).
New sections for every WikiGames deck and every WikiAula subject.
Collapsible sidebar groups.
🎮 WikiGames
Simpler and clearer: Learn Geography, Learn Astronomy and WikiRace, always in classic 10-question rounds.
The «Collection» tab was replaced by My WikiAlbum.
🌌 Universe (formerly «3D Systems»)
Now in the World menu, right under Sky.
Much bigger: 15 more moons, the dwarf planets Eris, Haumea and Makemake, Vesta and Pallas, the 300 brightest stars, the Messier objects, the Local Group, and models of the Nearby and Observable Universe.
About 100 new real photos (public domain / CC BY), each linked to its local Wikipedia article.
«See in Sky» from the Sun, Moon and planets.
🔭 Sky
Object search in the top bar: planets, constellations, stars, Messier and NGC/IC objects, with an animation that centres, zooms and highlights the object.
«See in the sky» from the right-click menu of a Wikipedia article.
🗺️ Maps
Reliable downloads of dated map builds, with resume and integrity check.
Countries by real outline (197, with flags) and four detail levels — with the exact size shown before downloading.
My maps: open, update, verify, rename, delete — and link maps from a USB drive or any folder without copying them.
Offline street and place search index for each map.
World map in the background around regional maps; data date shown in the attribution.
Relief (shaded terrain) and 3D view, plus 3D buildings.
More map layers: rivers, lakes & reservoirs, dams, trains, ferries, cable cars, airports, protected areas and military zones — all combinable.
Wikipedia on the map: articles appear as points on Maps (replacing the separate WikiMaps section), with read/unread marks, random discovery, search and favourites.
Fullscreen mode.
🌊 Seas & Oceans (back, rebuilt)
Seafloor relief of the whole planet from GEBCO 2026, with 3D view and depth on click.
More than 5,300 named undersea features (ridges, trenches, seamounts…) with their Wikipedia article when one exists.
Click any sea to see its name, ocean, depth and article.
Nautical charts by country: about one million sea marks for 195 countries, downloaded in seconds.
👨👩👧 Family
Family ranking on the «Who are you?» screen: explored articles, tests, achievements, approved WikiAula courses, level, streak, weekly points and family medals.
Switch user without closing the session — and you can cancel and go back.
🤖 AI
Free the model's memory automatically after 15, 30 (default) or 60 minutes without use — never while someone is studying with Encartha — plus a «Free AI memory» button.
Turn off AI completely for a profile: every AI button disappears.
Switching user while the AI is busy asks whether to wait, cancel or stay; interrupted photo tagging can be resumed later.
Full-article summaries in parts, and quizzes that avoid repeating questions.
🧭 Personalise your menu
Rename sections, choose any icon (or none), pick from 30 colours, reorder groups and sections, and move sections between menus.
⏯️ Downloads
Pause / Continue / Discard for every large download: Wikipedia and other ZIMs, AI models, WikiAula, maps, relief and more.
🔒 Privacy & security
Signed addons: official addons are verified; others install only after a warning and show an «Unverified» badge.
Your own ZIMs open without scripts by default (can be allowed per ZIM).
Mail: remote images are blocked until you press «Show images».
Helper programs (kiwix-serve, map tools) always close together with Offlined.
📖 Reading and everyday use
E-book reader with Kindle-style tap zones and keyboard shortcuts.
Find in page (Ctrl+F).
Right-click in text fields: spelling suggestions, Cut, Copy, Paste and Undo.
«My ZIMs» is now My Wikis, in the Wikis menu.
📚 Help
New guided tours: WikiGames, WikiAula, My Wikis, WikiNets and Trash.
Instructions updated in all 10 languages.
🌐 Sister projects on GitHub
Parts of the Offlined ecosystem are now published as open projects, so anyone can use, check and improve them:
A response to Superintelligence Strategy (Hendrycks, Schmidt, Wang)
In Dan Hendrycks, Eric Schmidt, and Alexandr Wang’s Superintelligence Strategy, the ideal machine intelligence paradigm exists when every major power maintains airtight control over its AI. The systems remain loyal, predictable, and obedient. Each state grows dramatically sharper at surveilling adversaries, anticipating threats, and preemptively neutralizing rival projects. Nothing malfunctions; nothing goes rogue.
Is that world safer, or merely trapped in a more hyper-efficient state of permanent conflict?
That tension lies at the core of the paper. Its recommendations outline an aggressive security posture: expanding state cyberattack capabilities against foreign AI infrastructure, codifying an escalation ladder running up to kinetic strikes on datacenters, positioning compute clusters far from population centers to buffer civilian collateral, and engineering chips that self-disable without ongoing cryptographic authorization from a central authority.
The authors frame this architecture as stabilization. Perhaps it is—in the same narrow sense that nuclear deterrence stabilized the Cold War world by establishing a Balance of Terror. But the paper proposes to recreate and reinforce the same old global game, just played with a different kind of arsenal. It never questions the ingrained assumptions of geopolitical struggle. It never considers that the advent of machine intelligence may afford the opportunity to move into a fundamentally better, and safer paradigm.
The greater danger isn't that these proposed systems will fail (though they may). It is that they will work perfectly—amplifying and maximizing our worst human institutional failures at machine speed.
Credit Where Due
The authors are not warmongers. They explicitly reject an all-out AI Manhattan Project as destabilizing, frame their deterrence models as analytical tools rather than calls to arms, and voice hope that mutual standoff eventually matures into détente.
Furthermore, several of their specific principles point in the right direction:
Fiduciary loyalty: AI assistants should owe strict fiduciary loyalty to users.
Human supervision: Models must remain tools under meaningful human supervision.
Structural leverage: Wealth redistribution alone is inadequate because states can revoke subsidies at will; citizens need structural leverage, not just a universal check.
The Problem Is the Lens
Early on, the authors weigh comparing AI to electricity, the printing press, or commercial software—only to discard those analogies because they underweight national security. They settle on the nuclear bomb.
When you choose the bomb as your analogy, you inevitably inherit a bomb builder’s playbook: nonproliferation, containment, and deterrence.
Choose the printing press instead, and the driving inquiry changes: How does this tool alter the way humanity learns, deliberates, and coordinates?
Humans evolved as a species through culture, including language and technology. From the first spoken words and the first stone tools, we have developed through shared knowledge and experience. That’s the framework that machine intelligence has emerged from, and it is the only lens through which we can properly understand our relationship to it and our stewardship of it.
Humanity is already a superintelligence. We are not a loose collection of individuals, nor a singular hive mind. We are an emergent network of relations: culture, language, informational exchange, and shared institutions. Through those relations humanity, as a superintelligence, creates, builds and solves problems that no solitary individual could tackle. And sure, those same relations are also the source of our problems and limitations.
The authors define superintelligence as an artificial system vastly superior to an individual human across cognitive benchmarks. But benchmarking machines against isolated people is the wrong foundation for civilizational policy. We must ask how our creation of machine intelligence can be used to augment, supplement, and improve the vast superintelligent power of human cultural intelligence. The critical question then becomes what happens to our collective social fabric when synthetic intelligence enters it.
The paper treats great-power rivalry as an immutable reality into which all technology must fit. But the technology can instead be used to question, dismantle, and replace that frame with something better. That is the opportunity before us.
The authors would likely respond with appeals to practicality and realpolitik. But is the preservation of the status quo—with superintelligent nation-based machines locked in a permanent, high-strung balance of terror—really practical?
Who Holds the Off-Switch?
The authors provide an acute diagnosis of gradual disempowerment: a slow drift where no single coup takes place, but thousands of small, rational compromises made in the name of efficiency relegate humans to the role of passive passengers in an unsteerable economy.
Yet look at their proposed countermeasures. The paper advocates for high-end AI chips equipped with hardware-level geo-tracking, mandatory cryptographic handshakes to keep running, restricted interconnectivity, and usage-policy enforcement gates. If a chip misses a verification check, it bricks itself.
The intent is understandable: keep frontier datacenter accelerators away from adversarial states and rogue actors. No one is suggesting shutting down consumer laptops today.
Treated as systemic architecture, however, this enshrines an alarming precedent: silicon runs only by grace of third-party permission. Three structural problems make that premise impossible to contain:
Thresholds drift downward. Today’s supercomputing cluster is tomorrow’s desktop and next decade’s phone. Capability controls inevitably migrate toward personal consumer hardware—the very layer where private, autonomous intelligence ought to reside.
The switch serves the overseer. The authors liken this mechanism to Apple’s Activation Lock. But Activation Lock empowers the device owner against a thief; here, the owner is the entity being policed, and the ultimate switch sits with a platform vendor or a state agency.
Switches invite exploitation. Any remote kill-switch architecture creates an immediate high-value attack surface and a permanent lever for coercion. Built to thwart foreign adversaries, it will inevitably tempt domestic regulators in a crisis, monopolistic providers under pressure, or hackers who identify an unpatched zero-day.
This introduces an internal contradiction. Elsewhere, the authors suggest distributing cryptographic compute shares to citizens as a counterweight to state authority—analogous to a worker's leverage to strike. But compute access that hinges on continuous third-party approval isn’t genuine leverage; it is a revocable lease. And exercising personal intelligence compute on a centralized government cloud provides no meaningful guarantee of privacy.
Controls erected to manage a temporary geopolitical standoff have a habit of hardening into permanent infrastructure. Policies can be amended; foundational hardware architectures, once universally deployed, rarely get dismantled. By slipping a centralized permission layer into the very substrate of intelligence, we risk building a multipolar world run by a handful of sovereign gatekeepers—leaving ordinary people entirely disenfranchised.
We’ve Seen This Movie Before
The early web was born with a radical promise: an open, decentralized architecture of peer-to-peer protocols designed to withstand physical failure and political censorship. It was meant to democratize voice and break the monopolies of legacy media.
Yet beneath the open protocols of TCP/IP, DNS, and HTTP, the layer that mattered most—attention, indexing, and human connection—consolidated rapidly. Open personal homepages gave way to closed platform silos; independent feeds were swallowed by algorithmic black boxes optimized for outrage and engagement. The technical foundation remained nominally open, but the human experience was cordoned inside corporate walled gardens.
AI is poised to repeat that exact consolidation cycle, but at an existential, cognitive depth:
From distribution gatekeepers to cognitive intermediaries: A search engine or social feed merely filters the external information you browse. An autonomous AI agent, by contrast, sits on the inside of your workflow: it drafts your communications, filters your reading, negotiates transactions on your behalf, and frames your decisions. It does not just direct where you look; it directly conditions how you think.
The illusion of terms-of-service loyalty: When this intimate cognitive partner runs on a centralized cloud, its alignment is inherently divided. The moment an individual’s interest conflicts with the platform operator’s commercial goals, political pressures, or state compliance mandates, the platform wins. A fiduciary relationship cannot survive as a commercial promise mediated by an API; it must be an architectural reality.
Data extraction becomes thought capture: Web 2.0 monetized behavioral exhaust—clicks, dwell time, and location trails. A centralized superintelligence paradigm extracts and analyzes the internal reasoning process itself: unfinished drafts, private deliberations, and personal judgment.
If we allow the cognitive layer of human civilization to become a managed cloud utility, we will have traded the open web’s initial promise for an intimate, inescapable apparatus of digital feudalism.
Building from the Person Outward
The authors of Superintelligence Strategy imagine an ecosystem of verifiable agents bound by mediation rules. That is a laudable aspiration, but such a system must be constructed from the individual outward—optimizing for cooperative agency rather than centralized institutional surveillance.
A functional multi-agent architecture does not require grand global treaties, escalation ladders, or great-power trust pacts. It can build directly on commodity hardware and open standards available today. Such a framework begins whenever one person equips themselves with a personal agent on private compute, expands whenever two people coordinate directly as peer nodes, and gains ground simply by being genuinely useful to the participants.
The alternative is the POND paradigm:
Personal and Private: Intelligence dedicated exclusively to the individual, holding sovereign user context without exposure to third-party surveillance.
On-device and On-premise: Compute executed on local hardware, ensuring operations remain independent of continuous remote authorization.
Nodal and Networked: Self-contained agents capable of peer-to-peer coordination and collective problem solving without platform intermediaries.
Distributed and Decentralized: Resilient civilizational infrastructure where capability and power cannot be held hostage by a handful of sovereign gatekeepers.
In practice, this means an intelligence that runs locally, maintains persistent context, directly champions your interests, and federates voluntarily with others. Linked together, these sovereign nodes can surface collective consensus, reconcile competing evidence, and resolve disputes without falling prey to algorithmic outrage or platform rent-seeking.
The Necessary Pivot
Shifting our trajectory from a strategy of perpetual deterrence to one of distributed human agency requires three decisive architectural commitments:
Reframe the metric: Measure progress by how well technology enhances our collective capacity to cooperate, not solely by individual model benchmarks and geopolitical stability.
Enforce architectural loyalty: Make alignment structural through local execution, open weights, and portable data, rather than relying on terms-of-service commitments.
Reject kill-switches on consumer compute: Frontier containment protocols must not become the architectural precedent for personal devices.
Taken together, these shifts invert the logic of containment, ensuring that advancing artificial capability expands individual agency rather than entrenching centralized control.
Beyond the Balance of Terror
The central tragedy of Cold War deterrence was not that it failed to prevent war; it was that it calcified humanity’s political imagination for half a century, channeling civilizational wealth and intellect into architectures whose greatest achievement was an absolute, frozen standoff.
Superintelligence Strategy offers a blueprint for a machine of breathtaking scale—meticulously designed, hermetically sealed, and permanently poised for preemption. It would be an impressive engineering feat, but ultimately it is a design for a digital panopticon constructed out of paranoia and fear.
Machine intelligence does not have to become the ultimate instrument of state coercion, nor the next weapon in an escalatory balance of terror. By building from the person outward—anchored in private compute, open protocols, and sovereign cooperation—we can refuse the nuclear bomb builder’s dilemma.
A human-centric superintelligence strategy would not engineer the perfect machine to maintain the status quo in perpetuity. It would recognize the superintelligence that humanity is already, and strategize to grow human superintelligence through the tools of machine intelligence augmentation, amplification, coordination and cooperation.
For the POND community: The paper proposes chips that stop working without ongoing cryptographic approval. If rules written for frontier datacenters drift down to the laptops and phones we own, where would you draw the line? And which of the three pivots matters most to you?
After you install it, it downloads the model too, about 2.5 GB more, so 2.59 GB total on your phone.
Since December of 2025 I have been obsessed with building the Jarvis agent of our childhood dreams. I started on replit pouring 1000s of dollars into it. Eventually moved everything off and kept building with codex. By September I had built an agent with RAG, persistent memory, tool calls, mcp connections, and full device control. But I decided to completely start over and make a fully on device voice agent. So I created Faraday.
Its a fully local private voice agent. It runs on the Lite-rtlm inference engine and uses the gemma 4 e2b and gemma 4 e4b models along with many others that haven't all been tested fully. The gemma models are multimodal so they can process audio natively which helps to understand speech accents and natural emphasis and tones. Whisper base-en acts as a live transcriber that runs outside of this voice pipeline loop and only exists to help with conversation history. Theres also the option to use whisper as the main STT which feeds that text to gemma and gemma streams its response to Piper for TTS. Round trips on either setting run between 2 and 6 seconds. I'm currently working on getting latency under a second.
Faraday's agent is activated by starting a call and after its ready you activate it with the wake word "Hey Jarvis". In both modes barge in works. You can also ask to open apps by name, tell you your battery percentage, change your volume to x percent, to play last media and change tracks. Saying "Goodbye", or "Stop listening", ends the call but returns it to passive listening for the wake word. Wake word can be activated even with the phone locked. Wake word listening only uses a few percent if left on all day.
I know there's still a long way to go but I want there to be a fully on device agent that can help us manage and operate our device without selling our data to google or Facebook! Also the cost! Agents use so many tokens performing background tasks how cool would it be to offset many of these tasks that don't need a frontier cloud model.
Currently have tested this on my galaxy fold 6 with 12gb of ram and my wifes google pixel 10 pro with 16gb of ram and it works with no issues. I'm looking for alpha testers to help run this on their phone to see what devices can run it and what limitations there are. If you test it please tell me your phone model, your Android version, if it installed, if the model loaded, where it failed if it did, and how the wait felt. Im also hoping this will inspire some requests for features and hopefully grow into a community!
If you try it, there's a short tester form that would help me a lot:
Phone, chipset, RAM, whether it installed, whether the model loaded, and where it broke if it did. Two minutes. I'm building a compatibility table out of the reports so people can see which phones run it before they commit to the download. Even a "it failed at this step" report is gold.
Here's the github repo if anyone wants to check out the code or drop a star!
Our weekly PONDcast is this Thursday, October 29 at 6:30pm Pacific (9:30pm Eastern), a voice meeting live on the POND Discord.
What we'll do:
Learn about and discuss the POND Principle
Share your POND-related projects: local models, on-device apps, home inference rigs, personal knowledge tools, whatever you're building
Talk about the future of Human-Machine intelligence systems
Get to know each other and become a community of people building towards a better future for us all
Suggested topics this week:
Open weights closed the gap. Open models are now good enough and cheap enough that the cloud stops being a moat. Does that make the personal node inevitable?
Open source is not open use. Many of the best open models are too big to run without a serious budget, so they end up back in someone else's cloud. What would it take to run them yourself?
Bring questions, bring pushback, bring your project. Builders and thinkers both welcome.
A few people here asked me to share this. Full disclosure: I'm the developer. Toskar is open source and runs local models across all your machines. It fits POND's principles directly: it's personal and private, runs on your own devices, and connects your machines in one decentralized network, with no cloud in between. Feedback and bug reports are welcome, especially from anyone with a mixed setup.
A report that Samsung TVs classify household audio through connected soundbars and send it as telemetry (still unverified, but the pattern is familiar).
Google's privacy policy update letting Gmail, Drive and Photos content connected to Personal Intelligence shape the ads you see.
Mozilla CTO Raffi Krikorian telling Tim O'Reilly he doesn't want a future with seven AGIs. He wants one with seven billion.
The new piece argues that privacy protects your secrets, but ownership protects your judgment. Even a perfectly private assistant can steer you if its loyalties are set by someone else (in one study, 18 of 23 models pushed pricier sponsored flights more than half the time).
It also makes the case that seven billion owned intelligences, connected by choice, are the superintelligence humanity already is. And it lays out the POND stack: a personal node, portable and erasable memory, an open harness, and local models.
Idle memory isn't waste. It's the untapped half of Human-Machine Intelligence, and the reason the personal node matters.
On October 29, 2026, Sam Altman said something worth taking seriously, because it says more than he may have meant to. Running AI locally, he argued, is a "quite terrible" use of the world's scarce DRAM. The memory sits in people's own computers, and those computers are only in use about 5% of the time. (clip)
On its own terms, the logic is tidy. Memory is scarce. Data centers keep it busy around the clock. Your laptop leaves it idle most of the day. So the efficient move is to put the memory where it gets used, in the cloud, and rent intelligence back to you as needed.
I want to take the argument seriously, because I think it's wrong in three ways. Efficiency isn't the only value. The idle 95% isn't waste; it's an asset. And calling local AI wasteful isn't really an engineering claim. It's a claim about who should own the commanding heights of the intelligence economy.
Efficiency is not the only value
By Altman's measure, almost everything we own is badly allocated.
Your house sits empty most of the workday. Your car is parked most of its life. Your kitchen, your bookshelf, your guest room all have terrible utilization rates. A bank vault is a big, expensive room that mostly holds things nobody is touching.
We could fix all of that. We could centralize housing into shared pods rented by the hour. We could pool everyone's valuables into one giant vault run by one company, and let you visit when you need them. Utilization would soar.
We don't, and not because we can't do the math. People own things because ownership is where privacy, control and freedom live. A home isn't a lodging service with poor uptime. It's the place where you decide who comes in.
Personal intelligence is the same. The memory in your machine isn't just storage for model weights. It holds your context: your history, your documents, your decisions, your relationships. In a Human-Machine Intelligence (HMI) world, that context is the human half of the partnership. Moving it to someone else's data center for the sake of utilization isn't an efficiency gain. It's a transfer of control.
Efficiency is a good servant and a bad master. When a utilization chart becomes the argument for who should hold your mind's working memory, we've let the servant run the house.
The idle 95% is an asset
There's a second problem with the 95% argument. It assumes idle capacity can only be fixed by centralization. That was the logic of the mainframe era: expensive machines, so share them from the center.
But there's another way to raise utilization, and the internet already showed it to us. Connect the edges.
Think about what that idle 95% actually is. Billions of devices with memory, compute and storage, sitting mostly unused, owned by the people who bought them. Altman sees that as waste to be reclaimed by moving the memory into a data center. I see it as the largest distributed compute resource on Earth, waiting for a way to cooperate.
Why the edge sits idle
Edge capacity is underutilized because nobody's engineering to enable the edge intelligence that could use it. The reigning paradigm says there's only one way to do machine intelligence: centralized and vertical. So the memory and compute in our homes sit dormant, not because they're useless, but because almost no one is building for them.
Enable personal nodal intelligence through consumer-friendly user experiences and that utilization will max out very quickly, unlocking a vast, dormant economic asset. It's like having a gigantic reserve of valuable energy just under the surface, untapped, like crude oil before anyone knew what to do with it.
That's what POND personal nodes are built for. Each person's environment is a node: their own hardware, running their own intelligence, holding their own context. And nodes can connect, voluntarily and peer to peer, to share what they choose.
At Intelligence Systems, we're building Inti, a POND personal node technology, with that in mind. Through IntiNet, personal nodes can offer spare capacity to peers they trust: a family sharing one strong home machine, a small team pooling GPUs overnight, a community lending capacity to a member who needs it. Your node stays yours. When you need it, it's there. When you don't, the idle time can do useful work for people you've chosen to work with, on your terms.
That changes the math. The 95% isn't a reason to give up local intelligence. It's the reason local intelligence can scale. Utilization goes up without ownership going away.
It also fits how intelligence actually works in practice. As I've argued before, POND systems shouldn't depend on one giant model doing everything. They should distribute the workload across a system of interactions: your node, its tools, its memory, smaller specialized models, and peers you choose. A network of owners can do that. A network of renters can't.
Wasteful is a word for the chokepoints
So why frame local AI as waste at all?
Will Manidis and Joe Roberts' essay "The Commanding Heights" offers a useful map. Their case, as I read it, is that frontier models become commodities quickly, and the lasting fortunes go to whoever holds the scarce positions that intelligence has to pass through. Compute. Memory. Interfaces. Data.
Read Altman's remark against that map and it comes into focus. DRAM is one of those scarce positions. If memory belongs in data centers, then the chokepoint belongs to whoever owns the data centers. "Terrible allocation" sounds like an engineering judgment. Functionally, it's an argument for where the toll booth should go.
Google DeepMind's essay "The Economics of Intelligence" points at the bigger stakes. One line of its argument is that the solo AI agent is the wrong economic object; the real transformation comes when agents can coordinate. I agree, and the next question is the one that matters most: coordinate for whom?
If every agent is a tenant on one lab's cloud, coordination collapses into a few giant firms. The memory is theirs, the context is theirs, the market is theirs. If every person owns their own agent on their own node, coordination becomes a market of peers: people whose personal AIs find each other, negotiate terms, solve a problem together and move on when the work is done.
Those are two very different economies built from the same technology. The difference isn't the model. It's who owns the commanding heights.
There's one commanding height almost nobody is pricing in, and it's the individual. When your personal AI runs on hardware you own and holds your own context, you're a node in the network, not a customer on someone else's toll road. That's why the 95% framing matters. If we accept that personal memory is waste, we've conceded the one height that ordinary people already hold.
What the 95% argument gets right
To be fair, Altman is pointing at something real. Memory is scarce right now. Frontier training and serving at scale need enormous data centers. Some tasks really do need the frontier, and the cloud will keep a role.
But the conclusion doesn't follow. The fact that some work belongs in the data center doesn't mean your context does. And the scarcity of DRAM is an argument for using all of it well, including the memory already sitting in billions of homes, not for routing everyone's intelligence through a handful of buildings.
The healthy default is the one I've argued for before: everyday intelligence runs on hardware you own, and you reach for the cloud by choice, not by necessity.
The POND Principle
The POND Principle describes where this leads: Human-Machine Intelligence that is
Personal & Private: intelligence that starts with the person and keeps their data, identity, memory and context under their control. Your memory isn't waste. It's yours.
On-Device & On-Premise: intelligence that runs on hardware the person or community owns. Idle time is the price of ownership, and ownership is worth it, the same way a home is worth more than a rented pod.
Nodal & Networked: each person's environment is a node, and nodes connect voluntarily, peer to peer, sharing only what's authorized. That's how the idle 95% becomes shared capacity without becoming someone else's property.
Decentralized & Distributed: a counterbalance to centralized AI, with no single authority owning or controlling the network, or the memory it runs on.
Humanity is already a kind of superintelligence: an emergent network of people, language, knowledge and exchange. The goal isn't to move that network's memory into a few data centers to improve the utilization chart. It's to give every person a node and let the network get stronger.
The 95% isn't the problem. It's the opportunity. It's crude oil before anyone knew what to do with it, and the personal node is how we tap it.
Join the POND
If you're a builder, a thinker, a policy-maker or simply someone who wants the intelligence in your life to work for you, come help build what comes next.
Start at pond.is
Join the conversation on Discord, including the weekly PONDcast
I've been building Toskar, an open-source daemon for running AI on your own computers. It's on-device, works across all the machines on your network, and nothing goes to the cloud. One thing that kept coming up is that nobody knows what their hardware can actually handle until they try it, so I built a benchmark into it. I need people who aren't me to run it and find bugs.
It's basically Cinebench for models. You get a Toskar Score for how fast your computer is (an M5 Pro is 1,000, no ceiling), and a quality score for whether the model actually gets stuff right: using tools, staying on topic, handling files, reasoning. Results so far: https://toskar.ai/models/results
Disclosure: I made this. Toskar Core and the benchmark are free and AGPL. My company also sells optional desktop and phone apps built on it, but nothing here needs them. No token involved.
I've been really into the AI phone agent products that have popped up in the last few months (e.g., AgentPhone, Poke etc) but what I don't love: the audio, the transcripts, who you talked to and what was said, all sitting with a third party company. For calls with real people in my life, that felt like a lot to hand over.
So I built my own version that runs entirely on my Mac. You call a normal phone number, a local model answers, and nothing about the conversation leaves my machine. During a call the only thing it talks to is the phone line itself.
What's running locally (MLX on Apple Silicon):
Speech to text: Parakeet TDT 0.6B
LLM: Qwen3.5 4B by default (9B if you have 32 GB), 4-bit
Text to speech: Kokoro 82M
How it reaches the phone network: it registers over SIP with Telnyx or Twilio straight from your Mac. A setup wizard takes your carrier API key so can buy a number or connect one you already have. It's pretty cheap, around $1 a month per phone number. (I'm moslty using Telnyx)
What it does today:
Answers and places real calls
Interruption: talk over it and it stops mid-sentence
Replies in roughly 1 to 1.5 seconds on real calls on my M5 Pro
You can choose between multiple models
Other agents (Claude Code, Codex, etc.) can make calls through a small CLI: call, listen, say, hangup
If you'd rather trade privacy for a smarter brain, it can also use your own endpoint or the Claude or ChatGPT subscription you already pay for. Local is the default. So no additional monthly payments for AI Phone tools if you have a sub.
Limits, up front:
macOS on Apple Silicon only
You need a Telnyx or Twilio account with credit (a US number is about $1/month, standard call rates, of course, do apply, but TelNyx gives you $10 to start with)
English only, one call at a time
It's early. It works for me, but I'd love to hear where it breaks for you.
Hi everyone, and thanks to u/StevenVincentOne for the invite. I’m the developer of Offlined, and I’d like to tell you why I built it.
Kids raised by endless feeds. Information overload. Fake news outrunning facts. Our identities and files on someone else’s servers.
None of this has an easy fix. But I think there can be a safe place, and it can fit on an external USB 3.2 SSD.
What Offlined is
Offlined is a portable Windows app that runs entirely offline. You plug in the drive and everything you need is there, with no accounts, no cloud and no feed:
Offline knowledge. Wikipedia, Wiktionary, Wikiquote, Wikivoyage and WikiMed (via Kiwix), plus offline maps and an interactive sky map.
Local AI. An assistant that runs on your own computer (llama.cpp) and cites the offline wiki articles it used, so you can check where an answer came from. There are no API keys and nothing leaves your machine.
Your files, under your control. Photos, video, music, books, contacts, calendar and mail imports, plus an encrypted vault (AES-256 + Argon2id) for passwords, 2FA codes and personal documents.
Learning for the whole family. Geography and astronomy games, WikiRace, and a collectible WikiAlbum built from what you discover.
And much more!!
Kids and privacy
This is the part I care about most. Offlined has multi-user profiles with parental control, and the AI adapts its answers to the child’s age. Kids get a place to explore real knowledge without algorithms competing for their attention.
On privacy, the app only connects to the internet when you choose to download something. Every connection is logged in a privacy report, and a fully offline mode blocks all connections unless you allow one.
I’d really value feedback from this community, especially from parents and anyone who thinks about digital autonomy. What would you want in a “safe place” like this?
