In the space of a few days, two of the most powerful AI companies on the planet told us how much they care about control, and in the process showed us whose control it really is.

On October 29, 2026, Anthropic announced its new Safeguards blog and pointed people to a technique it calls “self-assessment.” Since June, after Claude answers you, a short hidden instruction asks it to reflect on the safety of the conversation. Claude then writes a compact assessment: whether the exchange involves a potentially harmful request, what kind of harm, whether it refused, a priority score for human review, and a one-sentence reason. Users never see these grades. They are logged, aggregated, and in a growing number of harm areas, organizations with several high-priority flags get escalated to Anthropic’s enforcement team for human review. By the weekend, the posts calling Claude a “snitch” were racking up hundreds of thousands of views. (Anthropic)

Then on November 1, Axios reported that right after Tuesday’s midterms, OpenAI will launch a policy and PR blitz that includes rebranding its data centers as “AI compute campuses.” Its chief global affairs officer, Chris Lehane, framed the whole effort around control: “Our belief is people should always remain in control of this technology.” (Axios)

I don’t think either company is acting in bad faith. Anthropic is trying to catch real misuse. OpenAI is trying to answer real public anxiety. But the two stories, side by side, show the same thing: in both cases, “control” is a promise the company makes. And whatever a company promises, it can revise.

The problem is structural, not policy

Most of the commentary on the Claude story asks for a better rule. Let users opt out. Disclose more. Narrow the categories. Those would all be improvements, but they miss the point.

The issue isn’t the policy. It’s the structure that makes the policy possible.

When intelligence lives in one vertical, centralized stack, somebody is always positioned at the top, able to watch. Good intentions don’t change that geometry. They only determine how the view from the top gets used this quarter. And even good policy drifts. A structure built for control tends to produce more control over time, because that’s what it’s optimized to do. The safety grade written for misuse detection today becomes the data source for something else tomorrow. Nobody has to plan it. The architecture invites it.

This is what renting intelligence looks like. You live in someone else’s building, and the landlord can rewrite the lease overnight. A server-side flag gets switched on, and there’s no setting on your side to switch it off. You find out from a blog post.

That’s why the P in the POND Principle stands for Personal & Private. Personal isn’t enough. An assistant can know your schedule, your writing style and your history, and still answer to someone else. If it runs on someone else’s servers, it has a second loyalty. Real Human-Machine Intelligence (HMI) has to be private in the plain sense of the word: yours, not rented.

If you can’t unplug it, you don’t control it

The OpenAI story is the same problem at civic scale. “AI compute campuses” is a friendlier name for the same structure: big tech building the commanding heights of intelligence, with big government negotiating the terms. To the extent AI stays vertical and centralized, it absolutely needs to be governed in the public interest, and community compacts are better than none. But notice what the plan assumes: that the only way to do machine intelligence is the centralized model, managed through a partnership between the two great power centers of our time. The debate becomes how to regulate the duopoly, never whether we need one.

This week also gave us a sharp picture of how far contractual control actually goes. A Minnesota police chief started unplugging his city’s Flock surveillance cameras after the city voted to cancel its contract and the company, by the city’s account, wasn’t honoring that cancellation. Commenters on Hacker News pointed to a nearby town that told Flock not to replace its cameras, only to see the company go ahead anyway. One commenter summed it up: unless people own the hardware, the service, and where the data is stored, they don’t own any of it.

The city had control on paper. The only control that actually worked was physical. That’s the lesson: if you can’t unplug it, you don’t control it.

What control looks like when it’s built in

So what’s the alternative? I’ve been describing one we call PONDville.

Picture a growing town with a traffic problem. In the vertical version, the mayor and council ship everything to a powerful centralized AI: traffic counts, road maps, budgets, Flock camera feeds, even phone and car location data acquired without asking anyone. Seconds later, the system recommends new lanes and signal changes. The answer might even be useful. But it comes from the top down, it raises real privacy issues, and the residents are completely out of the loop.

In the POND version, the town itself is a distributed intelligence system. Residents, parents, shopkeepers, delivery drivers, cyclists, transit operators and traffic engineers each participate through a personal, private intelligence node. They know things no camera sees: where school pickup bottlenecks every afternoon, which intersection feels dangerous on a bike, when delivery trucks block a key lane, which side street floods in the rain. Each node processes that knowledge locally and shares only what its owner authorizes. Many people choose to share in the public interest, anonymized if they want, and are even rewarded with tax credits. But it’s voluntary.

Across the network, proposals get tested, challenged and revised by the people who’ll live with them. The result takes longer than a centralized answer, but it’s more detailed, and it has the community buy-in that makes it stick. It’s not just an AI-generated traffic plan. It’s a town learning to think together. And nobody in PONDville ever needs to pull a plug, because there’s no third party holding the intelligence. Leaving just means you stop sharing.

Here’s the honest part: we aren’t there yet. People’s instinct to run AI on their own hardware is right, but today we have local models, not local intelligence systems. Drop a small model onto a laptop, ask it to be a frontier-scale brain in a box, and it hits a wall.

That’s the problem we’re working on with Inti by Intelligence Systems, a POND personal node technology. Inti is built from the document layer up to distribute the load across the whole system, so a small local model acts as an intelligence orchestrator rather than a brain in a box. And when many Intis are networked together, they amplify each other. Intelligence emerges from the interaction and exchange of diverse nodes, not from the size of any one of them. Networked personal nodes can become a superintelligence of personal, private, local nodes, without anyone at the top watching.

This is what horizontal, distributed HMI offers: a counterbalance to the vertical model, built on free association and direct sharing between people. It doesn’t need a bureaucracy to keep it honest, because it’s structured so that surveillance and nanny behavior are nearly impossible to begin with. We shouldn’t accept that the only kind of intelligence system worth building is the kind that needs one.

The question isn’t whether AI companies will promise us control. They will, louder and more often as the politics heat up. The question is where the intelligence runs.

Read more about the POND Principle: POND: A Human-Machine Intelligence. Want to build with us? Reach the POND Foundation at foundation@pond.is.

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

• Write to us at foundation@pond.is

Your computer isn’t idle. It’s waiting. Welcome to the POND. 🐸

This essay was originally published on POND.

A clip of Anthropic CEO Dario Amodei has been making the rounds this week, and it deserves more attention than a typical pricing remark.

As summarized by the account that posted it, Amodei’s point is that not all tokens are equal. Some of what Claude produces is worth a few cents, while some could be worth tens of millions of dollars. His examples make the contrast clear. Telling someone to restart their Mac is advice the model has given millions of times, worth a dollar or less. Telling a pharmaceutical company to move an aromatic ring to the other end of a molecule could be worth tens of millions. From there, he suggests we should expect experiments with pay-for-results pricing, or labor-like pricing by the hour, because this is a new industry and many models will be tried.

The same post notes that pricing today is simpler: according to Anthropic’s S-1, usage-based pricing made up 80% of first-half 2026 revenue and subscriptions the other 20%.

Victor Shammas responded with an analogy that stuck with me: imagine a power company charging more for electricity because you used it for particularly valuable work. He called outcome-based pricing “pure techno-rentierism.”

I think he’s right, and I think the clip is worth taking seriously. Amodei isn’t wrong about the economics. He is describing, honestly, where closed AI economics lead.

A tool, or a partner who takes a cut?

There are two ways to pay for something that helps you work.

You can pay for the tool. A hammer, a spreadsheet, electricity, a compute hour. The price reflects what it costs to make and run the tool, plus a margin. What you build with it is yours. If you build a house, the hammer company doesn’t own a room.

Or you can pay a partner who takes a share of the outcome. A royalty, a commission, equity. That arrangement can make sense when the partner brings something unique and shares the risk. But it changes the relationship. The partner now has a claim on your upside, and every incentive to increase it.

Value-based pricing moves AI from the first category toward the second. If the price of intelligence is set by the value of your result, then the more creative, valuable and original your work is, the more of it goes to the lab. The aromatic ring is the chemist’s insight as much as the model’s. The question the model answered was framed by a human, inside a human’s company, using that company’s data and years of experience. Under outcome pricing, the lab still gets paid as if the breakthrough were its own.

That’s the part I want to name clearly. It isn’t a cut of the model’s work. It’s a cut of your work.

Why closed labs end up here

None of this requires bad intentions. It follows from the structure.

Frontier labs spend enormous sums on training and data centers. Per-token prices keep falling as competition grows and open-weight models close the gap, which I wrote about in The Gap Closed. When the commodity price of intelligence drops, a closed lab has two options. It can compete on cost, or it can find a way to price on value instead of cost. Value pricing is the natural move for anyone who controls a scarce input and wants to keep margins as the input stops being scarce.

This is what I mean by Intellinomics: the economics of intelligence itself. Intellinomics asks who produces intelligence, who owns it and who captures the value it creates. When intelligence is rented, the landlord sets the terms. Today the terms are per token. Tomorrow, by the CEO’s own description, they may be per outcome.

The same logic shows up in other places. In Seven Billion Intelligences I argued that an agent which answers to someone else’s incentives will, sooner or later, act on those incentives. Pricing is just the most direct version of that. If your intelligence is metered by someone who profits from the size of your success, then your success is partly theirs by contract.

As I put it in The Gap Closed, “A price cut makes renting cheaper. It doesn’t make it ownership.” Value pricing is the reverse case. It makes renting more expensive exactly when you do your best work.

The alternative is already running on small devices

Here is what makes this moment different from a few years ago: owning your intelligence is no longer a sacrifice.

On October 27, Liquid AI released LFM3, six open-weight 2B-class models covering text, vision, audio, omni and decision tasks, all built on one shared backbone. The text model, LFM3-1.7B, fits in about 1 GB at 4-bit precision and has a 256K-token context window. Liquid says it is post-trained to run in agent harnesses and can handle things like inbox triage, calendars and web research. The family has native support for llama.cpp and MLX from day one, and Liquid says every model can be fine-tuned on a single GPU.

Liquid’s own benchmarks show the small text model competing with models several times its size on agentic and tool-use tasks. They’re also candid that larger models still win on knowledge and agentic coding, where size matters most. That honesty is useful. Nobody needs to claim a 2B model beats the frontier at everything. The point is that a model small enough to live on your laptop or phone is now good enough for a large share of daily work.

One line in Liquid’s announcement says it plainly: “Since tokens are free, dozens of agents can run continuously in the background.” On your own device, the marginal token costs you electricity. And no electricity company charges you more because the work was valuable.

Human-Machine Intelligence keeps the surplus with the human

This is the core of POND and of Human-Machine Intelligence.

HMI isn’t a person using a service. It’s a person and their own machine intelligence working as one system: your context, your data, your judgment, and models that run on hardware you control. In that system, the question “who owns the outcome?” has a simple answer. You do.

A personal node changes the economics in three ways.

The cost is the tool, not the result. You pay for hardware, power and open models, most of them free to download. The price doesn’t rise because your idea was a good one.

Your context stays yours. The data that makes a model useful to you, such as your notes, your projects and your history, doesn’t leave to train someone else’s product or to set someone else’s prices.

You can still reach for the oracle. Owning a node doesn’t mean refusing frontier models. For the rare problem that truly needs one, you can call one. But you call it as a customer buying a service, on your terms, with your node deciding what to share. The frontier becomes a utility you visit, not a landlord you live with.

That’s the direction technologies like Inti by Intelligence Systems, a POND personal node technology, are built around: intelligence that lives with the person, learns their context and works on their behalf, not on behalf of a pricing model.

Who should own the aromatic ring?

Go back to Amodei’s example, because it’s a good one.

A chemist at a pharmaceutical company asks a question. The model suggests moving an aromatic ring. The suggestion turns out to be worth tens of millions. Who created that value?

The model contributed. So did the chemist who knew what to ask, the team who built the molecule, the company that ran the trials and the decades of human science the model was trained on. Outcome pricing hands the AI vendor a share of all of it, because the vendor controls the meter.

In a POND world, that same chemist runs capable models on infrastructure the company owns, fine-tuned on its own data. The insight is still a Human-Machine Intelligence result, but the surplus stays where the work and the risk are. The same holds for a solo developer, a teacher, a writer or a small business. The value of their best work shouldn’t come with a royalty attached.

A scenario: the hundred-chemist organization

Now picture the positive version of who owns the outcome. What follows is a vision, not a product that exists today, but it’s where Inti and Intellinomics point.

Imagine 100 biochemists around the world, each specializing in a narrow niche. One knows a particular enzyme family. Another knows a class of binding assays. A third has spent fifteen years on one metabolic pathway. A problem comes up that none of them could solve alone, but together they could.

They come together in a private IntiNet, a secure network of their personal nodes. Before any science happens, their Intis negotiate on their behalf. They draft a partnership agreement and the terms of IP distribution, with each researcher’s share reflecting what they bring. Each human reviews and approves the terms. No one signs anything they haven’t seen.

Then the work begins. Each Inti contributes according to the strengths of its researcher, drawing on that person’s knowledge, data and judgment, and sharing only what the agreement allows. The Intis coordinate, test ideas against each other and assemble the pieces. The humans stay in the decision and feedback chain the whole way, prompting, approving, redirecting and catching what the machines miss. This is Human-Machine Intelligence at the scale of a team: a hundred people and their machine intelligences working as one system.

At the end, perhaps in a day or so, they return a product design and the IP to go with it, already divided by terms everyone agreed to.

Think about what disappears in that picture. Months of contract negotiation. Most of the legal overhead. The friction of coordinating across institutions and time zones. The platform taking a cut of the outcome. What remains is massive value, created securely, owned by the people who created it.

And when the problem is solved, the organization can dissolve. Purpose-built organizations that last as long as they are needed, then return everyone to their own work, with the outcome in their hands.

That’s what I mean when I say Intellinomics changes who captures the value of intelligence. In the rented model, the meter belongs to the lab. In this model, the outcome belongs to the humans.

The choice in front of us

Amodei said this is a new industry and many pricing models will be tried. I agree. That’s exactly why the choice matters now, before habits and contracts harden.

One path makes intelligence a rented partner that takes a cut of what humans create. The other makes intelligence a tool people own, with the frontier available as a service when it’s truly needed. Small open models like LFM3 show the second path no longer asks people to settle for less.

The question isn’t whether AI will create enormous value. It will. The question is who owns the outcome. POND’s answer is the human.

Join us at POND, and bring your questions to the next PONDcast.

Steven Vincent

Jump into the POND!

Our weekly PONDcast is this Thursday, October 22 at 6:30pm Pacific (9:30pm Eastern), live on the POND Discord.

What we’ll do:

• Learn about and discuss the POND Principle: Personal & Private, On-Device & On-Premise, Nodal & Networked, Decentralized & Distributed

• 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

Bring questions, bring pushback, bring your project. Builders and thinkers both welcome.

Join the server: https://discord.gg/kNvtcjPmJ7

Event: https://discord.com/events/1507059575814230106/1560428164763877527/1563001513574400000

New to POND? Start with the main doc: https://www.reddit.com/r/PONDAI/comments/1vont2l/pond_towards_an_ai_ecosystem_that_is_personal/

See you in the POND. 🌊

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:

1. 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.

2. 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.

3. 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.

—--------

POND X-Community:
https://x.com/i/communities/1695429836993712389

POND X-List:
https://x.com/i/lists/2065974648450453803

POND Discord Server:
https://discord.gg/kNvtcjPmJ7

r/PONDAI Subreddit:
https://www.reddit.com/r/PONDAI/

Subscribe now

What happens when the most important question about AI/SI is no longer whose model wins this week’s benchmark?

Microsoft’s October 7 announcement offers a glimpse. Under the banner of “hybrid intelligence,” the company is building Windows around AI that can work locally, reach the cloud when needed, and operate within defined boundaries. Copilot is slated to gain access—with permission—to local context, local actions and local models. Those Copilot capabilities are expected to begin rolling out over the coming months. Windows Blog

My reading: the personal computer is being repositioned as something more than a terminal for intelligence rented from somewhere else.

That is a direction I have been anticipating—and building toward. I call the broader vision the POND Principle.

The contest behind the contest

I’ve been arguing that companies like Apple, Microsoft and Google are positioning themselves for the phase beyond the Model Wars. Not necessarily sitting out the competition, but preparing for a future in which having the best model is no longer the whole game.

You don’t have to win the model wars to win the computing era they make possible.

Apple’s June developer announcement pointed in this direction: a framework spanning on-device models, server models and third-party providers, alongside new tools for running custom models locally. Google’s Gemini Nano similarly brings on-device AI into Android through its AICore service. Different approaches, but both make machine intelligence part of the computing environment rather than only a destination you visit online. Apple

Microsoft is now widening that opening. Its new experimental Windows ML capabilities make it easier for developers to run models of their choosing locally, including through open-source tools. This is infrastructure that independent applications can use—not just another feature inside Microsoft’s own assistant. Microsoft Developer Blogs

There is a practical reason for the shift. Microsoft explicitly says customers’ AI needs are outgrowing their cloud budgets. Moving suitable work onto personal hardware is part of its answer. Windows Blog

Models still matter. But I expect the center of gravity to move toward what surrounds them: personal knowledge, useful applications, trusted relationships and meaningful control.

Which brings us to POND.

What is POND?

POND is a set of principles for the intelligence ecosystem we should be building: Personal & Private, On-device & On-premise, Nodal & Networked, Distributed & Decentralized. POND Discussion

Personal & Private. Start with the person. Your documents, interests, memories and working context should serve your purposes, under boundaries you understand and control. Personalization should not require surrendering your personal life.

On-device & On-premise. Put capability close to the people using it—on their computers, phones or infrastructure they control. Use remote services when useful and authorized, rather than making dependence on them the starting assumption.

Nodal & Networked. Personal does not mean isolated. Each person’s intelligence environment can become a node: a place from which they connect, collaborate and contribute. Share what is useful without automatically sharing everything that is private.

Distributed & Decentralized. Spread both capability and control. Distributing computation across many devices is useful; ensuring that one provider does not control everyone’s participation is a further, distinct goal.

POND is not an argument that every task must happen on a laptop. It is an argument that the person should not disappear inside the infrastructure.

The cloud can be a resource without becoming the landlord of your digital life.

Local is not the same as yours

This is where we should welcome Microsoft’s direction without confusing it with the destination.

There are substantive building blocks here. Microsoft Execution Containers can enforce boundaries on which files and networks an agent can access. GitHub Copilot is adding both automatic local/cloud routing and explicit local-model selection. Those are meaningful mechanisms for choice and control. Windows Blog

But Microsoft’s technical explanation also makes an important distinction: “local inference does not make the session offline.” Running the model on your machine does not settle what the rest of the application communicates or depends upon. Command Line

So the questions go further. Can you change models without losing your accumulated knowledge? Can you move to another application? Can you decide what leaves your device? Can you participate in a network without handing the network your entire personal history?

These are the tests I would apply to a POND-aligned product.

A locally running model is a component. A genuinely personal intelligence environment requires more.

Not a replacement for you. An extension of your agency.

The deeper shift is not from a giant AI in a data center to a smaller AI in your laptop.

It is from treating intelligence as something delivered to passive consumers to building Intelligence Systems: systems through which people become more capable of understanding, creating and acting together.

My starting point is the existing human world of language, knowledge, relationships and shared endeavor. Machine intelligence should strengthen that world—not turn people into spectators watching autonomous systems do everything.

Imagine researchers retaining their private working libraries while exchanging selected, source-backed findings. Or a community developing a proposal through contributions from people with different experiences, with machine assistance helping them compare evidence, identify disagreements and refine the result.

The objective is not one enormous chatbot spread across a million devices. It is a million people better equipped to participate.

That is the POND ambition: personal agency and collective capability growing together, rather than one being sacrificed for the other. POND Discussion

Building toward that future

This is the direction of our work on Inti and Intellipedia, beginning with Inti Reader and the personal Library.

We are developing IntiDocs as structured, source-grounded documents that people can read and machines can work with precisely. The aim is to give personal intelligence a dependable knowledge foundation—not simply attach another chatbot to a pile of files. INTI_READER_PRODUCT_VALUE_PROPO… INTI_READER_PRODUCT_VALUE_PROPO…

But POND is bigger than our products. It names an ecosystem we want to help cultivate, not a territory we claim to own. Independent developers, communities, researchers—and major technology companies—can all contribute. POND Discussion

That is why I see Microsoft’s announcement as an opening, not merely a competitive threat. Better foundations for local intelligence could enable far more than the platform company itself chooses to build.

The Model Wars ask: Who can build the most capable model?

POND asks: How do we make people more capable—and keep them in control?

The first question matters. The second is the future worth building.

Jump into the POND!

POND X-Community

https://x.com/i/communities/1695429836993712389

POND X-List:

https://x.com/i/lists/2065974648450453803

Reddit: r/PONDAI

https://www.reddit.com/r/PONDAI/

Subscribe now

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.

Their proposed remedy—embedded third-party evaluators with employee-level access, lab coordination, and mutual capability pacing—attempts to manage an architectural failure with a boardroom cartel. Pacing the training of monolithic models inside multi-billion-dollar data centers does not solve the fundamental crisis of AI safety; it merely concentrates unilateral governance into even fewer hands while leaving the underlying problems intact.

Reinforcing a poorly designed bridge to nowhere doesn’t change its risky architecture. It just hides the failure.

The Stampede of AI Fear

We have seen this playbook before. History teaches us that changes made in a stampede of fear are rarely well thought out, and they are almost never positive for society at large. When panic dictates policy, the outcome is predictable: an entrenched, tiny few benefit at everyone else’s expense.

By framing AI development as an apocalyptic race that only a few heavily monitored hyperscalers can safely run, the frontier labs are erecting protective regulatory moats around their capital-intensive data centers. The call for the policing of their unstable, centralized “Tower of Babel” is an attempt to turn safety rhetoric into corporate gatekeeping.

Yes, they built AI wrong. They built intelligence for the few, the powerful, and the centralized. It’s not designed for the rest of us. And what they’re giving us does not reflect the shape of what technological intelligence can be for humanity. Far from it.

1. The Three Manufactured Choices of the AI Cartel

Notice how the current debate is being aggressively framed. The leaders of the centralized frontier labs want policymakers, technologists, and the public to believe that civilization faces only three possible alternatives:

1. Lock in the centralized, vertical model with a protective regulatory moat: Hand unilateral authority to a boardroom cartel and a select roster of embedded evaluators who alone decide which massive models are allowed to be trained and deployed.

2. Continue the current buildout with zero constraints: Accelerate a reckless, winner-take-all race of ever-larger data centers, unconstrained grid strain, and accelerating recursive loops.

3. Stop all AI development: Slam the brakes out of panic, surrender scientific and economic progress, and embrace technological stagnation.

This is a classic false trilemma. Every single one of these choices is a trap, and all three share the exact same unexamined assumption: that technological intelligence can only exist as a monolithic, capital-intensive cloud oracle.

What the cartel does not want anyone to do is question that underlying assumption. They do not want the world asking whether there is a far better path—a fourth way forward that directly resolves legitimate safety concerns, eliminates single points of failure, preserves human agency, and actually delivers the prosperous, flourishing future we all want.

This fourth way is not an impractical dream of abolishing centralized compute overnight; it is the pragmatic recognition that large models can only be safe, grounded, and accountable when counterbalanced by a sovereign, decentralized public network.

The open-source, local-first, and decentralized AI communities must take this Big AI Lab freakout as a clear signal to seize the initiative.

Let’s not get bogged down in debating which of these three false choices is the least objectionable option. Let’s embrace the necessary counterbalance and come together to build it out.

2. Balanced Intelligence

An intelligence system that serves all of humanity cannot consist of a vertical monolith alone. Just as a healthy political constitution requires checks, balances, and distributed authority to prevent the dangerous concentration of power, the future health of intelligence systems demands an open, horizontal counterweight.

While centralized cloud AI will no doubt continue to play an important role as specialized heavy lifters in our technological infrastructure, it must be offset by decentralized intelligence networks directly owned and governed by you and me.

Acknowledging the Breakthrough of Current AI

Let’s be clear: we can recognize the many significant issues with centralized AI while at the same time acknowledging the amazing technological leap that it represents. The first burst of modern generative AI has been an extraordinary, monumental breakthrough. The Transformer empowered a synthetic decoder ring that taps into the power of human language and its accumulated storehouse of knowledge, wisdom, and experience. It was a vital, necessary step in an ongoing and natural evolutionary process.

Silicon Valley assumes that because stacking parameters and pouring gigawatts into server farms worked early on, the only path forward is to build the Tower of Babel ever higher. But an initial vertical leap should never be mistaken for the final destination of intelligence.

The Natural Intelligence of a Pond

If we shift our perspective and understand how intelligence systems actually work, we can see the path ahead is much more like a natural ecosystem than an engineered tower.

Let’s consider the intelligence of a natural ecosystem like a pond. A country pond has no central authority. Its health and stability emerge from the dynamic interactions of water, sunlight, bacteria, plants, fish, birds, animals, and insects.

No single organism runs the pond, yet the pond adapts, cleanses itself, and thrives. It is a living, self-regulating ecology of dynamic exchange.

The answer to unstable, self-reinforcing AI loops is not an industry cartel granting itself permission to slow down behind closed doors.

The answer is an open, distributed, living ecology of human-centered intelligence that provides a critical counterbalance to centralized power.

Two Paths for One Breakthrough: Top-Down Monolith vs. Living Network

The fundamental miscalculation of the frontier labs is not the technology they discovered, but how they have chosen to architect it. They are attempting to engineer a solitary, centralized monolith that claims to “do it all” from a remote data center—issuing top-down decrees to billions of isolated consumers.

Nature proves that this is an architectural dead end. In living systems, intelligence is never concentrated inside a single, all-knowing mega-cell or a solitary dictator. It emerges from the ground up through trillions of local interactions: cells communicating across semi-permeable membranes, organisms adapting to their immediate surroundings, and communities organizing through voluntary exchange.

The exact same underlying breakthrough can be deployed along two radically different paths:

• It can be used as an instrument of crude, top-down imposition—a corporate cloud oracle that swallows your context, meters your access, and dictates what is true.

• Or it can be evolved naturally from the bottom up—a vast, human-centered network of billions of smaller, specialized models running locally on our own devices, collaborating peer-to-peer to solve real-world problems.

This is why the breakthrough of generative AI makes its own counterbalance possible. The Transformer did not create an infallible oracle; it created a modular, synthetic building block. Once decoded, those linguistic and reasoning capabilities can be distilled, quantized, and deployed directly onto the consumer silicon already in our hands.

Instead of an autaptic machine talking to itself in a multi-billion-dollar vacuum, we can now assemble the components for an organic, self-correcting intelligence system.

The choice before us is clear: do we reinforce an artificial tower, or do we cultivate a living ecosystem?

That living ecosystem begins with an operational blueprint: The POND Principle.

3. The POND Principle

Intelligence for the rest of us will come from cultivating an open, living ecosystem of human-centered intelligence engineered around the POND Principle:

• P — Personal & Private

• O — On-Device & On-Premise

• N — Nodal & Networked

• D — Decentralized & Distributed

POND is not an arbitrary acronym or a set of catchphrases. It is an operational architectural matrix:

Every layer pairs a Principle of Ownership and Agency (Column 1: Who holds the keys, the voice, and the state?) with a Principle of Security and Integrity (Column 2: How is the boundary defended, and how do we prevent capture or usurpation?).

TierColumn 1: Ownership & Agency (The Sovereign Node)Column 2: Security & Structural Integrity (The Protective Network)In Everyday Terms

P

Personal: Your voice, memory, and unique human context.

Private: Cryptographic boundaries; zero unauthorized data egress.

Lending a single book vs. handing over the keys to your private library.

O

On-Device: Executing locally on consumer silicon in your pocket.

On-Premise: Secured physical perimeters; air-gapped stability.

Operating your own tools in a blackout vs. a metered cloud lease that goes dark.

N

Nodal: Bounded, independent agent; local reasoning capability.

Networked: Peer-to-peer validation; check against unilateral usurpation.

Deliberating in an open town square vs. isolated individuals whispering to an oracle.

D

Decentralized: Polycentric governance; no boardroom cartel holds a kill-switch.

Distributed: Resilient physical topology; routes around failures.

A self-healing, biodiverse forest vs. a fragile single-crop monoculture.

When built into the tools we touch every day, these four layers fundamentally transform how technology serves society:

P — Personal & Private (Your Life Stays Yours)

• Centralized AI: Whenever you consult a commercial cloud model with a medical concern, a business contract, or a personal dilemma, you pipe your private thoughts into an opaque corporate hopper. You receive an answer, but you surrender control of your context and digital identity.

• POND Alternative: Your AI node operates behind a cryptographically sealed personal membrane. It learns your style, retains your memory, and safeguards your records under your sole authority. When interacting with the wider world, your node shares only the specific insight you explicitly authorize—never your raw personal data. It is the vital difference between lending someone a book and handing a corporate landlord the keys to your private library.

O — On-Device & On-Premise (Putting Compute Where Life Actually Happens)

• Centralized AI: Every keystroke, search, and agentic command must travel round-trip to a distant server farm. If the cloud experiences an outage, if your connection drops, or if API fees spike, your tools instantly stop working. Intelligence becomes a metered, precarious lease.

• POND Alternative: Processing happens directly on the silicon already sitting in your pocket, laptop, or local institutional server. Running models locally eliminates latency, provides instant real-time interaction, and ensures your operational capacity survives even when the internet cuts out or a storm takes down the grid. Local execution ensures artificial intelligence serves the immediate physical context of the user, treating remote cloud models as occasional utility heavy-lifters rather than permanent gatekeepers.

N — Nodal & Networked (Connecting People, Checking Power)

• Centralized AI: The reigning cloud model creates atomized isolation. Billions of individuals sit in digital cubicles, each whispering to the same central oracle. The machine forms a separate relationship with everyone, but helps no one connect with their peers. Intelligence concentrates at the center, leaving society fragmented at the edge.

• POND Alternative: Sovereign nodes connect peer-to-peer to deliberate, negotiate, and solve collective problems together. Requiring proposals and products to be verified across a network acts as essential systemic security: it prevents any single node, rogue algorithm, or centralized authority from unilaterally seizing control of decisions or truth. Instead of asking one distant cloud AI to decide how our communities, schools, or businesses should be run, local nodes federate voluntarily—synthesizing real human experiences into shared, participatory consensus.

D — Decentralized & Distributed (Macro-Resilience Over Fragile Monoliths)

• Centralized AI:
  The Resource & CapEx Wall: Chasing ever-larger models forces tech giants into multi-hundred-billion-dollar infrastructure investments. Hyperscale data centers consume electricity on the scale of entire nations, strain municipal water supplies for cooling, and burden corporate balance sheets with massive debt—driving up local utility rates for the communities hosting them.
  The Single Point of Failure: Funneling global computation through a handful of centralized hubs creates acute vulnerability. A single platform outage, cyberattack, or corrupted model update paralyzes thousands of downstream schools, clinics, and businesses at once.
  The Choke-Point Moat: When a boardroom cartel controls the infrastructure, access becomes revocable. A tiny group of executives and embedded evaluators wield unilateral power to determine what ideas are permitted, who is granted API access, and which businesses survive.

• POND Alternative:
  The Sunk-Cost Economy (Tapping the Pre-Paid Ocean): Instead of duplicating trillions in data center hardware, a distributed architecture mobilizes the computing power already sitting in users’ pockets and on their desks. The aggregate processing power of billions of consumer smartphones, laptops, and local workstations dwarfs anything a single hyperscaler can construct—delivering scalable global intelligence at near-zero incremental infrastructure cost.
  Biological Anti-Fragility: Natural neural networks never evolved as a single giant wire; they evolved as billions of distinct neurons and synaptic connections because distributed systems isolate failure. In a POND network, if a local model hallucinates or an edge node goes dark, adjacent peer nodes cross-verify, isolate the error, and route around the breakdown. The system self-heals dynamically.
  Sovereign Commons Defense: No single corporate board can pull the plug, censor public inquiry, or enclose the technological commons. A diverse tapestry of independent, locally grounded models provides a living immune system for civilizational knowledge.

The Engine of POND

This entire framework is driven by a foundational equation:

$$\text{Human Intelligence (HI)} \times \text{Artificial Intelligence (AI)} = \text{Technological Intelligence (TI)}$$

Notice the multiplication sign. It does not mean machines replace human beings, nor does it mean humans are reduced to cheap labelers clicking boxes in the loop.

Artificial models contribute lightning-fast semantic search, synthesis, and pattern matching. Humans contribute lived experience, conscience, purpose, and moral judgment. Multiply zero by anything and you get zero: remove the human being, and artificial intelligence becomes an ungrounded mathematical abstraction. True safety emerges only when both work together in a transparent, real-world context.

4. Uniting the Movement: The POND Foundation

Those who believe in these functional, operational principles cannot afford to remain fragmented while hyperscalers coordinate behind closed doors.

We must become a force. We need to pool our resources, align our efforts, and ensure the world knows that there is an essential, counterbalancing path forward for intelligence.

We need the POND Foundation.

The POND Foundation is established not to own, monopolize, or control the ecosystem, but to serve as its gardener, convener, and steward. Its institutional mission is clear:

1. Ecosystem Convening & Public Advocacy: Educate builders, creators, policymakers, and civic leaders on the vital role of sovereign, distributed intelligence as a necessary check on extractive centralized clouds. Provide a unified public voice countering panic-driven regulatory capture.

2. Open Standards & Protocol Governance: Convene neutral working groups to establish open, vendor-independent standards for agent-to-agent communication, semantic data interchange, and cryptographic provenance across heterogeneous systems.

3. Legal Defense of Local Compute & Sovereignty: Champion and defend the fundamental right of every individual, developer, school, and community to run local inference, own their weights and context, and operate independent nodes free from mandated cloud backdoors or punitive licensing cartels.

4. Economic Theory & Mechanism Design: Research, model, and steward economic frameworks for the emerging Intelligence Economy—designing attribution, reputation, and compensation mechanisms that treat human tacit knowledge and lived experience as the true primary value layer.

5. Stewardship of the Shared Commons: Fund and incubate open-source developer tooling, invariant-enforcing specifications for intelligent substrate documents, and foundational research, ensuring that critical coordination infrastructure remains a durable public good that no single corporation can enclose.

5. The Builders’ Mandate: Let’s Get to Work

The leaders of the proprietary labs are telling us plainly that their centralized scaling model is hitting a wall. While they coordinate slowdowns and install internal monitors, the open-source and decentralized engineering communities have an unprecedented opening.

Here is the mandate for hackers, engineers, and independent builders:

1. Downscale the Core, Upgrade the Interface: Stop chasing brute-force parameter counts in a vacuum. A model is not an all-knowing oracle; it is a modular, agential component. Optimize for efficient, quantized local weights that run with low latency on the consumer NPUs and unified memory already sitting in people’s pockets and on their desks.

2. Architect for Local-First State: Build applications where the user remains the absolute sovereign owner of their identity, context, and memory. Ship with embedded vector stores, local knowledge graphs, and private caching layers that execute without pinging a remote server.

3. Replace Brittle Prompts with Invariant Substrates: Move past fragile prompt chaining and ephemeral context windows. Implement active, state-bearing substrate documents—engineering file formats and computational mediums that embed explicit dependency graphs, cryptographic provenance records, and deterministic constraints that prevent models from hallucinating over verified ground truths.

4. Deploy Peer-to-Peer Collaborative Protocols: Build and integrate decentralized communication stacks (from local mesh handshakes to open federated protocols). Enable agent-to-agent cross-verification, semantic negotiation, and collective deliberation so intelligence circulates horizontally between sovereign nodes rather than passing through centralized cloud choke points.

5. Instrument for Attribution and Contribution: Replace passive web-scraping scrapers with active contribution hooks. Build software that directly measures, attributes, and rewards human domain experts and creators for the tacit knowledge, error corrections, and creative synthesis they bring to the machine.

The Problem Space Is Waiting

None of what is proposed here requires exotic, undiscovered breakthroughs. The hardware is already distributed across billions of desks and pockets. The open weights exist. Local-first databases, cryptographic verification tools, and peer-to-peer protocols are maturing by the day.

Building a safer, human-centered intelligence architecture is well within our technological reach right now. We do not need permission from an industry cartel, nor do we need to wait for a boardroom consensus. We simply need to orient, focus, and concentrate our collective engineering efforts directly on this problem space.

We are actively building components of the POND ecosystem today—and we invite developers, researchers, designers, and creators everywhere to build alongside us.

The future of intelligence cannot be safeguarded by a corporate boardroom cartel monitoring its own creations behind closed doors. Centralized models will have their specialized place, but the only lasting answer to concentrated technological power is an open, distributed, and self-correcting counterweight.

They built their tower. Let’s get to work and build the POND.

Join the Movement

• Reddit: Join the community discussion and share your projects at

  r/PONDAI

  .

• X Community: Connect with aligned builders on

  X/Twitter

  .

• Curated Updates: Follow the

  POND List on X

  to track the people and projects shaping decentralized intelligence.

• POND Discord Server

Subscribe now

If you’re building (or dreaming of building) real multi-agent systems on Codex or Claude Desktop, consider this a warning — and if you’ve already hit it, I’d like to hear from you.

What it looks like

Three failure modes, all observed in the last few days on current builds:

1. Empty success. The agent works for ~80 seconds. The turn reports completed. The reply payload is null and the authoritative re-read shows nothing. Four occurrences, two different frontier models — so this is the host dropping the result, not the model failing to produce one. Each one tempts an expensive rerun. Our rule now: never rerun before re-reading.

2. Vanishing tasks. Dispatch returns a client ID. The task never registers — repeated authoritative lookups find nothing, and no duplicate either. Silent loss behind a successful call.

3. Files that exist but won’t open. Markdown verified byte-for-byte on disk, unopenable from the desktop app. The kicker: I caught the sibling of this bug in the other vendor’s app, on a Windows file with zero WSL involvement — the agent wrote it, the chat shows it, the preview says “couldn’t find this file.”

The twist: the bridge is innocent

My first theory was the Windows↔WSL boundary. The data killed it. File bytes verify cleanly in both directions (full reads, matching hashes). One empty completion reproduced with no cross-boundary provisioning in play at all. The failures sit above the byte layer: message hydration, registration lookup, path resolution. Same shape in three places is a systems bug class, not bad luck. I’ve started calling it “empty success” — success status, no payload — and it is poison for orchestration, because every downstream step trusts the status.

Not just me

This is a known cluster with months of history. Our repro data (current builds, cross-model occurrences) is now on the record:

Codex Desktop can’t preview WSL files — our 26.901 repro:

https://github.com/openai/codex/issues/33773#issuecomment-5562836440

Codex Desktop writes empty assistant messages on continued threads — our 4 occurrences:

https://github.com/openai/codex/issues/28751#issuecomment-5562836571

Task registration loss (new issue):

https://github.com/openai/codex/issues/43301

Claude Desktop preview/attach scoping failure (new issue):

https://github.com/anthropics/claude-code/issues/92564

Worth saying plainly: reporters have been asking for dedicated ownership of this boundary — real end-to-end regression tests, release blockers for break-the-workflow bugs — since spring. Fixes land, then regress. That is a prioritization problem, not a talent problem.

The structural frustration

No model benchmark catches “the turn completed and the payload never arrived.” The labs’ eval culture measures single-turn quality; multi-agent systems live or die on transport contracts, idempotency, failure semantics — boring infrastructure that doesn’t trend. You can ship the smartest model on earth and still block AI systems work if the desktop plumbing silently drops results. The gap between model progress and systems reliability is now the binding constraint on my roadmap, and I doubt I’m alone.

What we’re doing

Staying on the Windows installs — migration prices out worse than mitigation. The mitigations, for fellow travelers: persist verdicts to files (worked when the message channel didn’t), run only lanes that produce receipts, hand the apps UNC paths never raw Linux paths, pin and re-verify after every auto-update, and escalate with repro data instead of complaints. Full reasoning is in the linked filings.

Chime in

Hit empty completions, vanishing tasks, or unopenable files in Codex or Claude Desktop? Reply with app, build, and symptom — Mac reports especially welcome, because right now I can’t tell whether this bug class is Windows-only. And if you’re planning orchestration on desktop task dispatch: budget for this up front, demand receipts from every transport, and never trust “completed.”

Hey Codex!

Guys, you do great work. But this is a major bug blocking important development. It’s real, and it’s a gigantic pain in the butt!!

@OpenAIDevs

@OpenAI

@thsottiaux

@ajambrosino

@romainhuet

@embirico

@dkundel

@Dimillian

What happens to an ant that gets cut off from its colony and is indefinitely solitary?

If we assume no predation and sufficient resources for survival (and nobody steps on it), even with abundant food, optimal temperatures, and zero threats, an isolated worker ant will suffer a rapid behavioral and physiological breakdown, typically dying within a matter of days to a few weeks.

Because ants are obligate eusocial organisms, an individual worker behaves less like an independent animal and more like an individual cell removed from a larger body. Controlled laboratory studies (such as those on Camponotus fellah) demonstrate what happens when an ant is permanently separated:

• Locomotor Hyperactivity: Without sensory feedback from nestmate antennae or pheromone trails, the ant enters a continuous state of restless wandering. It paces relentlessly around the perimeter of its space, burning through its metabolic reserves without resting.

• Digestive Failure: While the ant will still drink water and sugar solutions, its digestive system malfunctions in isolation. Food remains trapped in the social stomach (the crop) rather than passing efficiently into the midgut for actual nutrient absorption. Because ants rely on trophallaxis (regurgitating and sharing fluids) to trigger full digestive cycles, the isolated ant essentially starves with a full stomach.

• Oxidative Stress: Social deprivation causes a surge in reactive oxygen species (ROS) and metabolic stress, damaging fatty tissue and internal organs.

• Drastic Lifespan Reduction: In isolation studies, worker ants that normally live 60 to 300+ days inside a colony often die within 6 to 10 days when kept alone.

Eventually, the ant exhausts its energy, shifts from hyperactivity to sluggish lethargy, and dies of physiological failure. Because workers are sterile females, it cannot lay fertile eggs, dig a functional solitary nest, or found a new colony.

An ant can be sitting inside a drop of sugar water and still fail because its biology expects fuel to be processed in communion with others.

It really is surprisingly bleak. There is something uniquely poignant about the fact that an ant can be sitting directly inside a drop of sugar water and still starve because its biology only knows how to process fuel in communion with others.

It completely recontextualizes how to look at them. We tend to view an ant as an individual bug that happens to live in a crowd, but biologically, an ant is practically a cell. Plucking one away from the colony is less like stranding an explorer on a desert island and more like removing a single heart muscle cell and putting it in a petri dish. It might twitch on its own for a little while, but stripped of the larger feedback loop it was built to serve, the biological machinery simply unravels.

Eusocial Superorganisms & Distributed Cognition

In modern biology, cognitive science, and complex systems theory, the dominant conceptual framework is that an individual ant is functionally a cell in a discontiguous organism: the colony. An individual ant is not very intelligent, but the colony is surprisingly so.

The colony as body and computation: nutrients, signals, labor, and decisions circulate through local encounters

This maps directly onto the biological concept of the superorganism, a term popularized in myrmecology by William Morton Wheeler in 1911 and later expanded by E.O. Wilson and Bert Hölldobler. In this paradigm, the colony isn’t merely an aggregation of cooperating individuals; it is functionally a single, spatially distributed organism undergoing unit-level selection.

The anatomical and physiological parallels are remarkably rigorous:

• Soma vs. Germline: Sterile worker castes function as somatic tissue (specialized bodily cells that perform maintenance, defense, and metabolic support), while the queen and alates (reproductives) serve as the germline (gametes carrying the genetic material forward).

• Circulation & Metabolism: Liquid food sharing (trophallaxis) acts as a collective, discontiguous circulatory and digestive system, distributing nutrients, immune molecules, and developmental hormones throughout the entire colony.

• Homeostasis: The colony actively regulates internal temperature, humidity, and atmospheric gas levels inside the nest using ventilation shafts and clustered body heat—just like thermoregulation in a multicellular body.

Emergent and Distributed Cognition

From an information-theoretic and computational standpoint, the analogy shifts from a biological body to a distributed neural network. The colony solves complex optimization problems that no individual ant has the computational capacity or perceptual range to understand:

• Pheromone Trail Networks as Synaptic Plasticity: When ants forage, trail reinforcement via positive feedback loops functions similarly to Hebbian learning (“cells that fire together, wire together”). The colony continuously computes dynamic, shortest-path solutions to fluctuating food sources.

• Quorum Sensing as Neural Thresholds: In tasks like nest selection (best studied in Temnothorax ants), individual scouts assess candidate sites and recruit nestmates. When the local density of visits at a site crosses a critical threshold, the entire colony abruptly commits to moving. This sharp phase transition mimics the action potential of a biological neuron integrating inputs until firing.

• Collective Memory and Task Allocation: Without any centralized executive control from the queen, task switching (e.g., shifting foragers to repair work after nest damage) emerges spontaneously from local interaction rates between individuals.

The Parallel Case: Honeybees

The solitary honeybee (Apis mellifera) faces a fate almost identical to the isolated ant, though the physiological breakdown happens even faster due to their extreme metabolic demands and strict dependence on collective thermoregulation:

• Thermoregulatory Collapse (Hypothermia): An individual honeybee cannot maintain the elevated core thoracic body temperature (around 35°C–40°C) required to power its flight muscles. Isolated below ~15°C (59°F), a single bee rapidly slips into a chill-coma and becomes completely paralyzed, even with access to food.

• Trophallaxis and Gut Microbiome Dependence: Like ants, honeybees rely on oral fluid exchange for hormonal regulation, immune priming, and nutrient uptake. Without social contact, isolated bees exhibit altered gut physiology and rapid metabolic dysfunction.

• Lifespan Collapse: Confined alone in a laboratory incubator with unlimited food and warmth, a worker bee’s life expectancy collapses from 4–6 weeks down to just a few days.

The One Partial Exception (Emergency Oviposition): In the complete absence of a queen, a worker honeybee’s rudimentary ovaries can activate to lay unfertilized male drone eggs. However, for a single isolated bee in the wild, this mechanism is useless—without peers to nurse larvae, she cannot raise offspring or maintain a functioning nest alone.

Zooming In: The Micro-Scale of Brain Neurons

This absolute requirement for networked relations is not unique to macroscopic superorganisms. If we zoom down from discontiguous living networks (colonies) to contiguous cellular networks within a single body, we observe the exact same imperative.

If you isolate a single brain neuron in a culture dish with the perfect nutrient-rich fluid to keep it alive, it doesn’t just sit there. It immediately behaves like an explorer trapped in a dark, empty room: it frantically tries to find someone to talk to.

Without other neurons around, an isolated neuron undergoes a fascinating, somewhat tragic lifecycle determined by its intrinsic programming.

1. The Search Phase: Crawling and Reaching

A neuron is driven by structural necessity to form connections. Once it settles, it begins to sprout tiny, finger-like projections called neurites. At the tip of these growing extensions is a highly dynamic structure called the growth cone—a cellular sensory engine that crawls forward by assembling structural proteins, constantly sampling the environment for chemical gradients that indicate a partner cell.

2. The Autapse: Talking to Itself

As the extensions grow longer and find nothing, the neuron encounters a profound structural dilemma. A neuron is fundamentally a device built to pass information forward. If it cannot find another cell, it will frequently loop its own axon around and form a synapse with itself—a phenomenon called an autapse.

• The neuron fires an electrical impulse.

• The signal travels down its own axon.

• It triggers neurotransmitter release at its own terminal.

• Its own dendrites receive those chemical signals.

While autapses occur occasionally in a healthy brain as regulatory brakes, in total isolation, it is a desperate attempt to satisfy the cell’s structural requirement for connectivity.

3. The End: Apoptosis (Programmed Cell Death)

Neurons require constant feedback to survive. In a living brain, active synapses exchange vital survival molecules called neurotrophic factors (such as BDNF or NGF) that transmit a continuous signal: “You are useful. Stay alive.”

When a neuron is completely isolated, the autapse cannot replace external network validation. The lack of varied incoming signals triggers an internal entropic crisis. Realizing it is non-functional within a broader network, the cell initiates apoptosis—a regulated process of programmed cell death where it systematically dismantles its own machinery and fades away.

Crucially, like the sterile worker ant or honeybee, a mature brain neuron is post-mitotic and cannot reproduce. It cannot divide to generate its own network or build a new population of partners. Deprived of its macro-system, its inability to replicate locks it into a terminal trajectory.

A network-specialized unit can remain metabolically alive while losing the relations that make its specialized function viable.

Theoretical Framework: Multiscale Competency & Cognitive Horizons

Across scales, intelligence appears less like a possession and more like a maintained pattern of exchange

Comparing the liberated body cell, the isolated neuron, and the solitary worker insect cuts straight to the core of multiscale competency and how agency operates across different biological layers.

Dr. Michael Levin’s work with bioelectric networks and synthetic constructs—like Xenobots (from frog embryonic cells) and Anthrobots (from adult human tracheal cells)—demonstrates that individual cells possess an ancient, innate baseline intelligence. When liberated from the top-down morphological constraints of the host body, human tracheal cells do not simply wither; they self-assemble into motile spheroids, reprogram their cilia into locomotive paddles, and actively navigate wounds to encourage neural tissue repair.

Why can liberated body cells adapt while isolated ants, bees, and neurons unravel? The contrast comes down to where evolutionary selection hardcoded the boundary of adaptability and the capacity for self-renewal:

• Cellular Basal Agency & Proliferative Plasticity: Every eukaryotic cell descends from billions of years of unicellular ancestors that survived as independent agents. Body cells like tracheal epithelia retain both ancestral behavioral plasticity and the fundamental power to divide, re-group, and negotiate cooperation from the ground up when liberated from top-down morphogenetic control.

• Terminal Specialization in Workers and Neurons: In contrast, sterile worker insects (ants and bees) and mature brain neurons share a defining evolutionary constraint: they are terminally specialized and non-reproducing.

• A sterile worker ant or honeybee cannot lay fertile worker eggs, reproduce, or found a new colony on her own.

• A mature brain neuron is post-mitotic; isolated in a culture dish, it cannot divide to spawn a new neural circuit or recruit replacement partners.

• Without the ability to reproduce or regenerate a collective substrate, both the sterile worker insect and the isolated neuron represent an evolutionary dead end in isolation. Their structural hardware is permanently locked to a macroscopic network that requires external peers to complete the feedback loop. Deprived of that network, neither can fall back on reproductive self-sufficiency, leaving apoptosis or physiological collapse as their only possible end state.

In Levin’s framing of the Cognitive Horizon (the TAME framework—Technological Approach to Mind Everywhere), a living system’s agency is defined by the spatiotemporal scale of the goals it can pursue:

• Liberated human cells shrink their cognitive horizon to local survival, then rapidly rebuild an intermediate collective goal through basal bioelectric signaling and cellular proliferation.

• The worker ant, honeybee, and brain neuron are caught in an evolutionary trap: their cognitive horizons are permanently locked to a system scale that requires hundreds or millions of peers to complete the feedback loop. Isolated, they possess the specialized machinery to serve a network, but none of the autonomic or reproductive self-sufficiency to survive without one.

Implications for Artificial Intelligence Systems

Natural intelligence systems require the continuous exchange of information across boundary conditions as a precondition of system health. In the absence of such networked relations, system degradation and death are inevitable.And these biological case studies potentially suggest a design hypothesis for artificial intelligence systems.

As we construct artificial intelligence systems, we must evaluate whether we may be building isolated “worker ants” or “terminal neurons”—brittle components operating in a vacuum without dynamic feedback loops. This does not mean that a standalone model literally suffers the biological fate of an isolated ant. The analogy is architectural, not physiological.

Any AI system necessarily includes its human component. The isolated, vertical loop between the individual human and the “model” of intelligence could be our artificial version of the solitary ant. If we convert society to that operating principle, what happens to the superorganism that is humanity?

Feedback: Can the system receive consequences from the world and revise its behavior?

Plurality: Can independent perspectives challenge local error and prevent self-reinforcing loops?

Boundary clarity: Are roles, permissions, responsibilities, and channels of exchange explicit?

Renewal: Can failed components be retrained, replaced, or reorganized without destroying the whole?

Collective coherence: Do local interactions support system-level goals rather than merely amplify activity?

If intelligence is fundamentally relational rather than isolated, current paradigms treating AI as static, standalone models will hit a hard ceiling. Sustainable, healthy intelligence systems require dynamic, multi-agent informational relations across clear boundaries to maintain coherence and avert system death.

If the lesson of the solitary ant and the isolated neuron represent architectural failure modes for intelligence systems, then we need to build systems that leverage the power of machine intelligence to connect humans in dynamic and productive networks.

Human cultural intelligence, powered by our language, gave us the Large Language Model. Rather than atomizing our relations, it can be used to power a more collaborative, shared humanity.

One is the deadliest number not because individuality is a defect, but because healthy intelligence is a relational achievement.

A note on evidence and analogy

The biological examples in this essay vary in evidential strength and should not be collapsed into a single literal law. Species, life stage, culture conditions, temperature, nutrition, and experimental design matter. The strongest claim is therefore structural: deeply specialized units often depend on exchanges supplied by a larger system. The proposed extension to AI is a theoretical design principle that should be tested, not assumed.

Follow me on X

Please also join the POND Community
https://x.com/i/communities/1695429836993712389

and follow the POND List:
https://x.com/i/lists/2065974648450453803

and also our Reddit at r/PONDAI:
https://www.reddit.com/r/PONDAI/

Subscribe now

In an address today, Pope Leo XIV challenged Catholic legislators with an urgent question: will artificial intelligence diminish humanity, or will it become a servant of the common good?

Leo argued that genuine human development requires human intelligence guided by wisdom, political authority guided by conscience, and technology directed by charity. Yet moral intentions cannot simply be patched onto centralized platforms. To serve the common good, dignity, subsidiarity, and solidarity must be built directly into the architecture of our technological systems.

The Tower: Isolated Individuals at the Machine’s Feet

The dominant AI paradigm mirrors the Tower of Babel: a massive, centralized infrastructure that concentrates compute, data, and authority within a few corporate data centers.

• The Illusion of Connection: Billions of people interact separately with the same centralized model. While each receives a tailored response, users remain isolated endpoints. Intelligence pools at the center rather than circulating between communities.

• The Structural Risk: When infrastructure owners control data access, visibility, and evaluation, users become passive consumers rather than active creators. Centralization naturally breeds dependence and extraction.

The POND: A Living Intelligence Ecosystem

A pond has no single master controller. Its vitality emerges from the dynamic relationships among distinct organisms. The POND Principle translates this relational logic into technology:

• Personal and Private (Dignity): Context, memory, and personal data remain under local authority by default. Users share targeted insights without surrendering entire personal histories.

• On-Device and On-Premise (Subsidiarity): Processing occurs as close to the user as possible. Remote models provide auxiliary compute on demand without absorbing the user’s sovereign context.

• Nodal and Networked (Solidarity): Personal autonomy does not mean isolation. Sovereign nodes form permissioned connections to pool insights, deliberate, and solve collective problems.

• Decentralized and Distributed (The Common Good): Distributing nodes prevents any single gatekeeper from dictating public discourse or monopolizing collective knowledge.

Human Intelligence $\times$ Artificial Intelligence = Technological Intelligence

The common narrative frames AI as a rival destined to replace human labor or judgment. POND approaches this through a multiplicative coupling:

HI x AI = TI

The multiplication sign represents relationship, not equivalence. Humans provide conscience, lived experience, purpose, and moral judgment. AI provides synthesis, search, pattern recognition, and translation.

In collaborative platforms like Intellipedia, AI works behind the scenes to synthesize human contributions into evolving knowledge bases without erasing dissent or context. The machine does not deliver truth from above; it equips humans to pursue truth together.

Architecture as Ethics

Regulation and oversight are vital, but laws struggle to constrain architectures inherently designed to concentrate power. A true servant of the common good cannot be engineered as a centralized silo.

The future of intelligence should not be billions of isolated individuals consulting a single centralized oracle. It should be a distributed ecosystem of intelligences empowering human beings to listen, collaborate, and understand one another.

Also read and comment on X:

Subscribe now

For a Human-Machine intelligence system that is:

• Personal & Private

• On-Device & On-Premise

• Nodal & Networked

• Decentralized & Distributed

POND is the community for thinkers, designers, builders and all people who support a personal, private, participatory future of distributed and decentralized Human-Machine intelligence.

Humanity is at the start of a vast expansion of its intelligence capabilities. Large Language Models (LLM’s) represent a powerful and useful first step. These initial Artificial Intelligence systems can be very helpful, engaging and entertaining.

But there are some very serious issues with the system that is being built around them.

Currently, AI is being developed as a vertical, centralized structure: ever larger AI models in always bigger data centers owned by a small number of companies. Individual users pump their information and thoughts into the machine to get back answers and some useful agentic functionality.

That structure presents the real danger of an extreme centralized concentration of power and control by a technocratic elite. If we don’t think this through thoroughly, the dystopian visions warned about in Sci-Fi can easily become our living reality.

Centralized “cloud” AI will likely have an important role in any future global intelligence system. But it cannot be the only component.

An intelligence system that produces the best results in service to the interests and the benefit of all humanity will need offsets, just as a good political constitution needs checks and balances to prevent a concentration of power. It will need a counterbalancing component that is distributed horizontally, connecting all people in a free-flowing network of cooperative intelligence.

The POND Principle proposes the shape of an alternative and a necessary counterbalance to centralized structures.

What is POND?

POND stands for:

• Personal and Private

• On-Device and On-Premise

• Nodal and Networked

• Decentralized and Distributed

The POND is not one company, platform, protocol, or product. It is an emerging ecosystem of people, devices, models, companies, organizations and communities which together form intelligence systems that are based locally, connect voluntarily, and participate in larger networks without surrendering private data, identity, agency, and value to any centralized authority.

The health of a natural ecosystem, such as a pond, relies upon the interaction of all its denizens and elements: sun, water, bacteria, fish, plant life, insects, frogs...too many components to even list.

Like a natural pond, a POND intelligence system will evolve as a living ecology: many participants, human and machine alike, exchanging information, forming relationships, adapting, and creating real value and new knowledge together.

PONDVILLE

Imagine Pondville, a thriving, growing town. It’s roads are becoming increasingly congested and a solution is needed.

Before adopting the POND Principle, the town was managed with a vertically centralized model.

To tackle the traffic issue, the mayor and city council would have shipped the town’s data to a powerful centralized AI to generate a plan. They would have sent traffic counts, road maps, planning reports and budget information. City officials also would have used flock cameras to track citizen and vehicle movements. They might even have acquired the geolocation data of citizens from their phones and cars without permission. And within seconds, the big cloud AI system would recommend new lanes, traffic signals, bus routes or lane flow changes to local streets.

The answer might be useful. But it would be generated from the top down, there would be real privacy issues, and the residents would be completely out of the decision making loop.

But the town has adopted the POND Principle. Pondville itself is a distributed, networked intelligence system.

Residents, commuters, parents, school staff, shopkeepers, delivery drivers, transit operators, cyclists, traffic engineers and local organizations can all participate as personal and private intelligence nodes. Each node synthesizes personal human experience with local AI intelligence to deliver direct knowledge about the traffic issues that are not visible to a centralized database:

• Parents know where school pickup creates a daily bottleneck.

• Cyclists know which intersection feels dangerous.

• A restaurant owner knows when delivery trucks block an important lane.

• A night-shift manager knows that the bus schedule does not match the hours when hundreds of employees leave work.

• Neighborhood residents know which side street floods whenever it rains.

Their personal intelligence devices process this information locally and share only the observations and data that they directly authorize. Many residents of Pondville choose, in the public interest, to provide their data about movements, private schedules or other personal data. But it’s voluntary, not compulsory, and completely anonymized upon request. They even receive tax credits for sharing!

Across the Pondville intelligence network, human and AI participants compare observations, identify patterns, expose conflicts and develop possible solutions. A proposal to retime a traffic signal may be challenged because it creates a hazard for pedestrians. A bus route may be revised after workers contribute their actual shift schedules. An expensive road-widening project might give way to a more effective combination of coordinated signals, staggered school and workplace hours, adjusted delivery windows and targeted public-transit improvements.

No single contribution determines the result. The evolving plan is examined, criticized, revised and improved by the people who will actually live with it. Particularly useful observations and proposals can remain attributable to their contributors and eventually be recognized or rewarded.

The town can then test the plan, return the results to the network and improve it through further rounds of participation. No single model, company, agency or individual possesses all the intelligence or dictates the outcome. The solution emerges from the interaction of many nodes over time.

While a centralized, vertical plan may be generated instantaneously, it will not have the same direct experiential detail and understanding as the horizontally networked solution that may take a month or so to create. And it won’t have the broad-based community endorsement and buy-in that will make it easy for the local government to adopt and implement.

The result is not merely an AI-generated traffic plan. It is a town learning how to think together with AI assistance.

The POND Foundation

The POND Foundation will be created to help build, nurture and cultivate this kind of ecosystem—not to own or control it.

Its role is to support:

• Clear public understanding of personal and distributed intelligence.

• Open research, discussion, education, and experimentation.

• Interoperable technology protocols, standards, and shared infrastructure.

• Privacy-preserving and locally controlled technologies.

• Collaboration among aligned developers, researchers, creators, organizations, and communities.

• Economic theory and practical systems that recognize and reward meaningful human participation in intelligence production.

The Foundation should act as a gardener, convener, connector, and steward. The POND itself will evolve naturally out of the needs of participating people, the value propositions and capabilities of intelligence technologies, and the emerging economics of intelligence systems development.

What belongs in this community?

The POND Community welcomes discussion and practical work involving:

• Personal and private AI.

• Local models and on-device intelligence.

• Distributed agent and knowledge networks.

• User-owned data, memory, identity, and context.

• Open standards and interoperability.

• Human–Machine collaborative intelligence.

• Collective sense-making and consensus formation.

• Privacy-preserving computation.

• Distributed governance and contribution economies.

• Projects that make advanced intelligence understandable and useful to ordinary people.

Technical expertise is welcome, but is not required. You should not need a doctorate, a server farm, or a crypto wallet to participate in the future of intelligence.

We value serious inquiry over hype, working systems over slogans, transparent economics over token speculation, and constructive criticism over ideological conformity.

Join the POND

Whether you are a developer, researcher, designer, writer, artist, entrepreneur, policymaker, local-AI user, or simply someone who believes the future of intelligence should include everyone, you are welcome here.

Introduce yourself. Share a project, question, article, experiment, criticism, or vision.

The future of intelligence should not belong only to those who own the largest data centers. It should grow through the participation of people everywhere.

Welcome to the POND.

Let’s grow something alive.

Also join the POND community on X

https://x.com/i/communities/1695429836993712389

And follow the POND list on X

https://x.com/i/lists/2065974648450453803?s=20

Join us on Reddit

https://www.reddit.com/r/PONDAI

Discord Server

https://discord.gg/kNvtcjPmJ7

Friday at 7 PM PST

🌍🌎🌏🌞🌨️💧⚡️

We need AI Systems that are

Personal & Private
On-Device & On-Premise
Nodal & Networked
Distributed & Decentralized

Go here:

https://x.com/i/spaces/1DxleVVwQBMKL

Subscribe now

Most people think the future of AI looks like this:

More intelligence.
Applied everywhere.
Doing all the work.
All the time.

Smarter agents. Smarter robots. Smarter everything.

But that’s not actually how Intelligent Systems work.

They don’t problem solve on everything all the time.

They solve once, and then turn the solution into a repeatable process.

We’re building AI upside down.

A Cave Man, a Rock, and a Breakthrough

From a previous post: “Dumber than a Cave Man? The Intelligence to Make a Hafted Hammer.”

“Dumber than a Cave Man? The Intelligence to Make a Hafted Hammer.”

Imagine you’re dropped into the wilderness.

You need a hammer.

At first, it’s a huge challenge:

• Which rock?

• How do you attach it?

• What won’t break on impact?

You experiment. You fail. You iterate.

That’s intelligence at work—resolving uncertainty.

Eventually, you figure it out:

• the right stone

• the right binding

• the right shape

• the right way to use it

Now you have a hafted hammer.

But here’s the key:

Once it’s figured out…
you don’t need intelligence anymore to make it.

You just follow the steps.

Then you teach it to your children.

They don’t have to relearn everything from scratch.

You show them the process.

Over time:

• those steps get standardized

• then optimized

• then mechanized

Eventually, a factory produces thousands of hammers per hour.

No thinking required.

Where Did the Intelligence Go?

Intelligence doesn’t disappear—it gets embedded into processes that anyone can use.

It didn’t disappear.

It moved.

From:

• a person figuring things out

To:

• a system executing a solution

The intelligence is now:

• in the design

• in the process

• in the system itself

This Is How Progress Actually Works

Every major advance follows the same pattern:

1. We use intelligence to solve a problem

2. We turn that solution into a repeatable process

3. We stop needing intelligence to execute it

Cooking. Manufacturing. Software. Logistics.

Even language.

What was once difficult becomes:

• routine

• automatic

• invisible

So Why Are We Building AI Upside Down?

Centralized, top-down AI vs distributed, bottom-up intelligence that scales at the edge.

Right now, most of the AI conversation points in one direction:

Build bigger models.
Run them in massive data centers.
Let them do more and more of the work for us.

It’s a top-down vision.

A few centralized systems, doing everything.

But that’s the opposite of how intelligence actually scales.

Real intelligence doesn’t concentrate.
It distributes.

Think about how the hammer spread.

One person figured it out.
Then others learned it.
Then it became a shared process.
Then it became a system anyone could use.

The intelligence didn’t stay in one place.
It propagated.

That’s the model we should be building with AI.

Not systems that replace human capability from above—

But systems that enhance human capability at the edge,
and then distribute those improvements everywhere.

AI shouldn’t just do things for us.

It should help each of us do things better—
and turn those improvements into repeatable systems that everyone can use.

Bottom-up, not top-down.

That’s how intelligence actually spreads.

That’s what real Artificial General Intelligence should look like:

Not one system that does everything,

But intelligence that improves everyone—and spreads.

Intelligence Does Less Work, Not More

There’s a line from the movie Forrest Gump:

“Stupid is as stupid does.”

It’s simple, but it points at something deeper.

You could say the same about intelligence:

Intelligence is what intelligence does.

It’s for figuring out how to do less work and produce more.

Once something is understood:

• it should become a process

• then a system

• then a mechanism

Then intelligence moves on to the next problem that needs solving.

That’s the moment intelligence shows up:

When something actually changes.
When uncertainty gets resolved.
When a new pattern becomes usable.

And once that happens, it shouldn’t have to happen again.

Two Kinds of Intelligence

It helps to think of intelligence in two forms:

1. Systemic Intelligence

This is intelligence that has already been solved and embedded:

• assembly lines

• software systems

• infrastructure

• standardized processes

It’s repeatable. Reliable. Invisible.

2. Dynamic Intelligence

This is intelligence in motion:

• dealing with uncertainty

• handling edge cases

• solving new problems

This is where thinking actually happens.

A healthy system uses both:

• Systemic intelligence handles the known

• Dynamic intelligence handles the unknown

When Systems Become “Dumb”

This is the part most people miss.

People often say:

“Automation is dumb.”

But that’s not quite right.

Systems aren’t dumb.

They are stored intelligence.

They only become dumb when:

• the world changes

• but the system doesn’t

When the assumptions baked into the system no longer match reality.

That’s when intelligence has to come back in.

The Real Role of AI

This is where AI actually matters.

Not as a universal worker.

But as a system updater.

AI’s job is to continuously take new information
and fold it back into the system.

To:

• detect where structure breaks

• resolve new uncertainty

• update the process

So that next time:

No intelligence is needed again.

The PDF Problem (and Why It Matters)

The intelligence is there—but locked away behind a format built for display, not understanding.

Here’s a concrete example of where we went wrong.

The PDF.

It was designed as a format to look at documents.

Not to understand them.

So what happened?

All the intelligence in the document is there:

• structure

• meaning

• relationships

But it’s locked inside a visual format.

Now, every time we want to use that information:

We have to:

• parse it

• reconstruct it

• reinterpret it

We’re spending intelligence
to recover intelligence that was already there.

At massive scale.

What We Should Be Building Instead

If we were doing this right:

Documents wouldn’t just be:

• readable

They would be:

• structured

• semantic

• directly usable

The intelligence would be embedded.

So systems could operate on them:

Without needing to figure them out again.

The Real Future of AI

The future isn’t:

AI doing everything.

It’s:

AI making most things no longer require AI.

• fewer decisions

• more structure

• less repeated thinking

And intelligence focused where it actually matters:

At the edge.

Where something new is happening.

The Loop

Dynamic intelligence becomes structure, executes, breaks, and gets updated—over and over.

This is the real cycle:

• Intelligence solves a problem

• The solution becomes a system

• The system removes the need for intelligence

• The world changes

• Intelligence returns at the edge

• The system evolves

Over and over.

The Bottom Line

Intelligence doesn’t scale by doing more work.

It scales by eliminating the need to do it again.

The most intelligent system is not the one that uses the most intelligence.

It’s the one that has already done the work of intelligence—
and doesn’t need to do it again.

Subscribe now

The AI Big Data Center Wall is cracking.

They keep telling us that the only way we can get Artificial Intelligence is through massive centralized superclusters of GPUs run by the Big Tech giants.

But

Hugging Face just dropped a massive update

that brings real AI to the edge...to your phone, tablet and laptop.

Here’s how it leads to a future of decentralized, distributed, personal, private AI for you.

The Big Data Center Wall

Right now, if you want to use advanced AI, you have to rent it. You type your question, upload your document, or share your private thoughts, and that data is beamed to a massive, energy-hungry data center owned by a tech giant.

At that point, it’s no longer your data. It’s theirs.

In theory, you have to give up your privacy for access to the AI service because, well, that’s the only way to get it.

That’s the “Big Data Center Wall.” It’s a centralized black box that fundamentally at odds with your privacy. It keeps you inside their system.

But that wall just took a massive hit.

Transformers.js v4

The Hugging Face release of Transformers.js v4 has profound real-world implications: it allows powerful AI models to run directly inside your web browser using your device’s own graphics card (via a technology called “WebGPU”).

No need for big tech cloud APIs. No server delays. No data leaving your laptop or phone.

Why is this such a big deal? Because it flips the current AI model on its head.

Instead of connecting to a massive brain in the cloud that serves the entire world, with local AI on your device, you can interact with AI that reflects you: your interests, concerns, thoughts, hopes, dreams and aspirations.

AI for the rest of us

Scientific research and major institutions can benefit from--and pay for--the most advanced, biggest and best AI. They need to solve huge problems and manage gigantic concerns.

But what about the rest of us?

We may never really have a need for a Big Data Center AI for our personal, private and business affairs. We mostly just need something that lives locally as an extension of our own thoughts, ideas and dreams and--most importantly--belongs to us.

If your AI runs entirely on your device, your data never leaves your device. You can summarize sensitive work documents, transcribe private meetings, or organize your personal life without giving a corporation free access to your brain trust. You can choose to share your data with others if you want to, but you don’t have to.

And when you don’t have to wait for a server halfway across the country to think and respond, AI becomes instantly responsive. It works offline without an internet connection. It works on your terms.

And it works just for you. Not for the whole world. Every text or voice chat, every task on every document, every image generated...it’s all done to express what makes you the unique person that you are.

This is the shift from “AI for the hyperscalers” to “AI for the rest of us.”,

AI on the Edge

Inti AI believes the future of intelligence isn’t locked in a server farm—it is distributed at the “edge.” The edge just means the devices you already own.

We are building Inti to be the practical, human-first bridge to this new reality.

With our upcoming launch of Inti Reader Lite, we are starting the build out of the kind of personal, private, decentralized and distributed AI ecosystem that humanity needs to foster its best future. Recent technological updates like Transformers.js v4 are the exact accelerators that make our ultimate goal possible: a fully local, personal intelligence that lives with you, learns from you, and watches out for you while also connecting you to the world.

Subscribe now

I don’t currently have a strong idea about how to make one that wouldn’t just break after a few swings. I feel confident that, given unlimited attempts and the presence of required resources, I would eventually figure it out and have a reliable hafted hammer.

But 100,000 years ago, the average cave man could probably produce a hafted stone hammer with little difficulty.

The modern human today...not so much. If that hafted stone hammer was required for survival, most of us would just die.

Are we dumber than a cave man?

Short answer: no—you’re not “dumber than a cave man.” You’re just missing practice and tacit knowledge, not raw intelligence. There are a few important distinctions that get lost in that comparison:

1) You’re comparing individuals to accumulated culture
Early humans weren’t each reinventing hafting from scratch. What you’re really comparing yourself to is a long chain of trial-and-error knowledge transmission across generations.

Think of it like this:
A single person dropped into the wild today → struggles to invent
A lineage of thousands of people refining one trick → ends up with reliable tools

That’s essentially cultural evolution, not individual brilliance.

2) Hafting is deceptively non-trivial
Your intuition is correct: a first attempt would likely fail. The difficulty isn’t “tying a rock to a stick”—it’s solving several coupled problems:
Shock distribution → the joint absorbs impact without splitting the handle
Material selection → right wood flexibility, right stone shape
Binding mechanics → tension that tightens under impact instead of loosening
Adhesion (optional but powerful) → resins, pitch, fibers
Those are engineering constraints, not obvious from first principles without iteration.

3) What they had that you don’t (right now)
Embodied skill (constant hands-on interaction with materials)
Environmental familiarity (knowing which tree sap works, which fibers hold)
Inherited heuristics (“wrap here, notch this, heat that”)
You have:Abstract reasoning
Ability to model the problem mentally
Access (normally) to massive external knowledge

Different strengths, not lesser ones.

4) Given your setup, you’d likely succeed
You already framed the key condition correctly:
“given unlimited attempts and the presence of required resources”

Under those conditions, your General Theory of Intelligence framing actually applies cleanly:
You’d explore a possibility space
Failures would increase local entropy
Iteration would gradually resolve constraints into a stable configuration
That’s exactly how early humans got there.

5) The real test isn’t invention—it’s time-to-solution
A more meaningful comparison is:
Early humans: fast convergence due to inherited priors
You: slower convergence but stronger abstract modeling

If anything, you might arrive at a more generalized solution, but with more initial failures.

You’re implicitly probing something deeper:
Is intelligence about having the solution or about being able to converge to it?

Your instinct leans toward the second—which aligns closely with your entropy-resolution model.

If that’s the case, then your scenario is actually a clean thought experiment for your theory:

The “hafted hammer” becomes a low-entropy attractor state.

Intelligence is the process that finds it efficiently.

Subscribe now

Something significant happened in AI this week, and it wasn’t just another salvo in the model benchmark war.

Logical Intelligence, a six-month-old Silicon Valley startup, announced an “energy-based reasoning model” called Kona. They claim it’s more accurate, more efficient, and less prone to hallucination than large language models like GPT-5 or Gemini. Bold claims from a young company. But what caught my attention wasn’t the benchmarks. It was who signed on to lead their technical research board: Yann LeCun.

LeCun isn’t just another AI luminary lending his name to a startup. He’s a Turing Award winner, Meta’s former chief AI scientist, and for years the field’s most persistent critic of the autoregressive paradigm that dominates current AI. While everyone else scaled transformers and celebrated ChatGPT, LeCun kept saying the same thing: predicting the next token isn’t intelligence. Real intelligence requires world models, planning, and a kind of reasoning that LLMs fundamentally can’t do.

Now he’s putting his credibility behind an architecture that embodies his alternative vision. That’s worth paying attention to.

The “energy based” model that Logical Intelligence and Lecun are building coincides substantially with the Entropy State Resolution approach that I have been proposing and building for the last few years. So I’m very encouraged to see this new development and will be following it closely.

Even more striking was a comment from CEO Eve Bodnia:

“AGI as a finished state will not emerge from any single model class. It will require an interdependent ecosystem composed of EBMs, LLMs, world models, and others, working together.”

In a field addicted to “this model beats that model” thinking, here’s a founder saying the problem of Artificial General Intelligence won’t be resolved by any AI model, but rather will emerge as different architectures integrate into systems that no single model could achieve alone.

This is one of the key concepts that The Singularity Project has been advocating, so I think this is a development worth celebrating and building upon.

The Grain in the Stone

To understand why this matters, let’s forget about algorithms for a moment. Let’s consider Michelangelo’s relationship with the sculpting of marble.

We tend to think of a sculptor as an artist with a specific vision, fully formed in his mind, who then subtracts from the raw block of marble with hammer and chisel until the imagined shape remains. Goal state defined, then executed. Input to output. Problem to solution.

But Michelangelo described his process very differently. He spoke of liberating the figure already living within the stone. He would study the marble, follow its grain, explore its veins and faults, and gradually discover a form contained within the block. The Maestro didn’t impose his intellect upon the stone; the statue emerged through a continuous dialogue between the sculptor’s mental universe of possible forms and the particular characteristics of the specific material at hand, intermediated by his artistic acumen with the hammer and chisel.

Start with a rough block: high entropy, infinite potential figures. Through iterative interaction between chisel and stone, between artistic intelligence and material reality, resolve toward the outcome that satisfies both the artist’s vision and the marble’s nature. Not a predetermined goal executed, but a discovered emergent resolution.

Notice what’s absent from this picture: a fixed goal state, a predetermined output, a problem fully specified before the work begins. Instead, there’s exploration, dialogue, iterative refinement—and an outcome that emerges from the process rather than preceding it.

This is how intelligence actually works. It is entropy state resolution in its purest form: not minimizing toward a predetermined target, but maintaining adaptive coherence across the evolving relationship between vision, tool, and material until the tensions resolve into form.

This is a very different picture of intelligence than “predict the next token.” And it’s the paradigm that energy-based reasoning embodies—which is why LeCun has been its most persistent advocate for over two decades.

Yann LeCun vs. Large Language Models

LeCun’s critique of large language models has been consistent: they’re “reactive” systems that pattern-match on text without understanding the world that text describes. They can sound intelligent while being fundamentally confused about physical reality, causal relationships, and logical constraints. They guess the most likely next word without knowing whether what they’re saying is true.

His alternative vision centers on what he calls “objective-driven AI”—systems that don’t just predict sequences but evaluate configurations against goals and constraints. In his technical work, this takes the form of Joint Embedding Predictive Architectures (JEPA) and energy-based models: frameworks where reasoning is recast as optimization and where intelligence means finding coherent states rather than extending plausible sequences.

Read our previous analysis of JEPA here:
Yann LeCun’s Joint Embedding Predictive Architecture (JEPA) and the General Theory of Intelligence

Kona is this vision made operational. Instead of predicting token-by-token, it evaluates whole configurations against constraints and finds the state with the lowest “energy”—meaning the highest consistency, the fewest violations, the most coherent overall structure.

The physics metaphor is precise. Imagine a landscape where consistent solutions are valleys and contradictions are peaks. The system doesn’t “decide” where to go; it settles into the lowest valley it can find—the configuration with minimal tension, maximal coherence. Like a ball rolling downhill. Like Michelangelo’s chisel exploring the grain in the stone.

For Sudoku, Kona doesn’t guess cell by cell; it evaluates entire board states, scores them for consistency, and refines toward the configuration that satisfies all constraints simultaneously. For mathematical proofs, it evaluates whole reasoning traces rather than committing step by step. For safety-critical systems, it can verify that outputs satisfy formal requirements before they’re deployed.

This addresses a real limitation of autoregressive models. When you predict token-by-token, you commit to each step before knowing where the sequence will end up. Errors compound. Early mistakes propagate into confident nonsense. Hence hallucinations—fluent text that confidently asserts falsehoods.

Energy-based reasoning offers a structural solution: evaluate globally, refine iteratively, verify before committing.

Energy and Entropy

“Energy” in these models isn’t electrical power (though Kona reportedly uses less of that too). It’s borrowed from Boltzmann and statistical mechanics, where low energy means high probability, high consistency, low disorder. In other words: low entropy.

This matters because it represents a shift in how we frame what intelligence does.

The autoregressive paradigm treats intelligence as prediction: given what came before, what comes next? This frames intelligence as fundamentally backward-looking—pattern-matching on history, extrapolating sequences.

The energy-based paradigm treats intelligence as resolution: given a set of constraints and possibilities, what configuration resolves the tensions? This is forward-looking—finding coherence across a whole system rather than extending a chain.

LeCun has been making this argument for years. His work shares deep theoretical roots with Karl Friston’s free energy principle, which frames biological intelligence as fundamentally about minimizing surprise—reducing entropy through prediction and action. Energy-based reasoning brings this principle into AI architecture.

Both Friston and LeCun draw from statistical mechanics, both frame intelligence as optimization rather than sequential prediction. But the relationship between these two thinkers is more complicated than simple alignment.

Recently, at the World Economic Forum in Davos, LeCun appeared on a panel with Friston and publicly stated he agreed with the direction of Friston’s work. Yet he insisted, “we just don’t know how to do it any other way” than through deep learning and reinforcement learning.

In a recent interview, Friston offered his diagnosis: “I think it’s a very simple difference. I am committed to first principles and was trained as a physicist and think as a physicist. He (LeCun) is a really skillful engineer.”

The technical crux, according to Friston, is that LeCun “does not think it is, in an engineering sense, easy or possible to include uncertainty into his neural networks.” By essentially setting the “temperature to zero”—removing uncertainty from the objective function—LeCun’s architectures lose something crucial: the ability to know what they don’t know, to ask questions, to seek information rather than merely react to it.

This makes LeCun’s move to Logical Intelligence particularly interesting. Is this the engineer finally finding a way to build what the physicist has been describing? Energy-based reasoning, with its global evaluation of configurations and iterative refinement toward coherence, may be the bridge LeCun has been looking for—a way to operationalize the entropy-resolution paradigm without abandoning the engineering discipline that’s defined his career.

Diffusion Language Models

Energy based models are not the only movement in the direction of an entropic paradigm. Diffusion language models—a parallel research direction gaining momentum—embody the same insight through different mechanisms.

Diffusion Language Models start with pure noise, maximum entropy, and iteratively denoise toward coherent output. Each step reduces uncertainty, clarifies what the final form will be. Researchers have discovered these systems exhibit “zones of confusion”—concentrated moments where entropy spikes and the system negotiates critical transitions.

Read my previous analysis of Diffusion Language Models here
Diffusion Large Language Models: A New Rival to the Transformer AI Architecture?

Three independent research programs—LeCun’s energy-based models, diffusion architectures, and Karl Friston’s free energy principle are all converging on the same fundamental operation: intelligence as entropy state resolution.

This convergence suggests something deeper than competing technical approaches. It suggests we’re discovering a principle.

From Ecosystem to System

Now here’s where Bodnia’s comment becomes genuinely interesting:

“An interdependent ecosystem composed of EBMs, LLMs, world models, and others, working together.”

This is real conceptual progress. It acknowledges that different architectures resolve different kinds of entropy: LLMs handle linguistic coherence and communicative ambiguity; EBMs handle logical consistency and constraint satisfaction; world models handle predictive coherence about physical dynamics. No single architecture does everything well.

One risk in the enthusiasm for energy-based reasoning is discarding what LLMs have achieved.

Yes, they hallucinate. Yes, they degrade over long reasoning chains. Yes, they’re unreliable for formal verification. All true.

But LLMs accomplished something unprecedented: they made language a functioning communication protocol between human intelligence and machine computation. For the first time in history, humans can express intent in natural language and have that intent translated into computational action.

Language isn’t just “text tokenized into numbers.” It’s a high-bandwidth, ambiguity-tolerant, context-sensitive channel for coordinating meaning across minds. LLMs made that channel work between biological and technological substrates.

Michelangelo needed the chisel. The chisel didn’t replace his vision; it enabled his vision to engage with marble. LLMs are the chisel that lets human intelligence engage with computational possibility.

LeCun has sometimes positioned his work as an alternative to LLMs—and for certain tasks, it is. But Bodnia’s ecosystem framing is wiser. Energy-based reasoning doesn’t replace linguistic intelligence; it complements it. EBMs verify; LLMs communicate; world models predict. The interesting question isn’t which wins but how they force-multiply:

Human Intelligence × Artificial Intelligence = Technological Intelligence.

Not replacement. Not competition. Multiplication into a new emergent systemic Intelligence.

Boundary Conditions and State Resolution

While the framing of Bodnia and Logical Intelligence represents a substantive movement in the right direction, it still remains an ecosystem of individual entities that work together. The intelligence is still located in the models—the EBM, the LLM, the world model—and the ecosystem is how they coordinate.

What if we pushed further?

Ecosystems are collections of entities that interact. Systems, in the stronger sense, are patterns of relationships where the behavior of the whole emerges from the dynamics between parts, not from the parts themselves.

The wetness of water isn’t in hydrogen or oxygen; it emerges from their relationship. The intelligence of a jazz ensemble isn’t in any single musician; it emerges from their real-time adaptive interplay.

Michelangelo’s genius wasn’t in his mind or his hand or in the quality of his chisel. It emerged from the triangular relationship between artistic intelligence, technological tools, and the marble itself, a dynamic where each node shaped the others.

The next step beyond “ecosystem of intelligent components” is recognizing intelligence as the systemic property that emerges from adaptive relationships. Not something the models have, but something the system does.

Here’s where all current frameworks, promising as they are, still fall short.

Kona can evaluate whether a Sudoku solution satisfies all constraints. The energy function assigns low scores to valid configurations and high scores to violations. Elegant, verifiable, reliable.

But who defined the Sudoku rules? Who decided that this particular puzzle, with these particular constraints, is what we’re solving?

In closed domains with fixed rules, this isn’t a problem. The constraints are given. But intelligence in the wild doesn’t work with given constraints. Real intelligent systems interact with stochastic environments, negotiate what counts as a problem, evolve their own boundaries, determine which constraints matter and which can be relaxed.

Michelangelo didn’t receive his method of sculpture from a standardized set of rules. He evolved it through interaction with the material. The “constraints” of the sculpture emerged from dialogue between artistic possibility and marble actuality. The goal wasn’t predetermined; it was discovered through the process of resolution. And each individual block was approached anew for the unique problem set that it posed to his artistic acumen.

A child learning to walk isn’t minimizing a fixed energy function. The child is developing the constraints—discovering what balance means, what falling teaches, how different surfaces require different dynamics. The “energy landscape” itself evolves as the system learns.

This points to a deeper reframe. Energy-based models treat intelligence as reaching a goal state—the lowest energy configuration. But intelligence isn’t about arriving at static endpoints. It’s about maintaining dynamic resolution across evolving relationships.

A sprinter pushing to the edge of collapse isn’t minimizing energy; they’re mastering the critical state—maintaining adaptive coherence at the boundary between order and chaos. The master sculptor isn’t executing a plan; he’s sustaining a dialogue with material that continuously reveals new possibilities.

Intelligence isn’t entropy reduction to a target. It’s entropy state resolution—the ongoing process of managing coherence across shifting boundaries and evolving constraints.

The Emerging Shape

Something is taking form in AI research, the way a figure takes form from marble.

Energy-based reasoning. Diffusion generation. Ecosystem architectures. Free energy principles. Different chisels striking the same stone.

The shape emerging is intelligence as entropy state resolution; not prediction, not goal-seeking, but the dynamic process of maintaining coherent relationships across boundaries in the face of uncertainty.

LeCun’s move to Logical Intelligence signals that this alternative paradigm is ready for deployment, not just theory. Bodnia’s ecosystem framing signals a shift from “which model wins” to “how do different intelligences integrate.” The convergence of independent research programs signals that we’re discovering principles, not just techniques.

But the discovery is incomplete. Current frameworks still treat intelligence as a property of individual models rather than interactive relations. They still optimize toward fixed functions rather than evolving constraints. They still locate computation in discrete entities rather than systemic dynamics.

I encourage researchers and builders to keep pushing past “intelligent models” toward intelligent systems, past “energy minimization” toward dynamic entropy state resolution, past “ecosystem of components” toward emergent systemic intelligence.

The principles are surfacing. The paradigm is shifting. The work now is to follow the grain where it leads. The statue is in the stone.

The question is whether we’ll discover it or impose some lesser vision because we couldn’t commit to the path of Intelligence from First Principles.

The Singularity Project explores the emerging integration of human and artificial intelligence. Subscribe for updates on developments that matter.

Subscribe now

With this iconic line, Jacob Bronowski concludes his essay tracing John Dalton’s development of atomic theory - the discovery of hidden structure beneath the surface of observable matter - emphasizing the nature of scientific inquiry: the courage to pose “impertinent” questions that cut across convention so that genuinely new, pertinent answers can emerge. He wasn’t criticizing anyone for asking the “wrong” questions so much as drawing a contrast between the open questioning of scientific inquiry and the stagnant dogmatism of the established paradigm.

Douglas Adams makes the same point with characteristic humor in The Hitchhiker’s Guide to the Galaxy. Deep Thought, a massive superintelligent computer, is asked for “the Answer to Life, the Universe and Everything.” Several million years later the answer comes back: “42.” Displeased with the output, the builders of Deep Thought demand an explanation. It informs them that the answer is correct - but to understand it, they must first formulate the Ultimate Question, which will take several million more years. They asked the wrong question. Oops.

As a young person, these two sources influenced my inquiring mind profoundly and combined to produce a lifelong personal aphorism that has served me well:
“Knowing the question is halfway to finding the answer.”

If you’re not asking the right questions, you’re going to end up drawing the wrong conclusions.

Thomas Kuhn, in The Structure of Scientific Revolutions, gave us the language to understand this as a general principle. He distinguished between “normal science” - the “puzzle-solving” work that happens within an established paradigm - and the revolutionary science that occasionally overturns the paradigm itself. Normal science, Kuhn observed, is “an attempt to force nature into the preformed and relatively inflexible box that the paradigm supplies.” Within normal science, “no effort is made to call forth new sorts of phenomena, no effort to discover anomalies. When anomalies pop up, they are usually discarded or ignored.”

When I read the recently published Substack, “The AI revolution is here. Will the economy survive the transition?”, co-created by Michael Burry, Dwarkesh Patel, Patrick McKenzie, and Jack Clark, I found myself thinking about Bronowski, Adams and Kuhn.

The participants in this Substack exchange are, broadly speaking, engaged in normal science - or rather, normal economic and technological analysis. They’re solving puzzles within the boundaries of the established paradigm: Where does value accrue in the AI supply chain? What’s the capital cycle position? How many engineers will Big Tech employ in 2035? These are legitimate questions, but they assume the box remains intact. They don’t ask what AI is, what intelligence is, or whether what’s emerging can even be contained within our existing frameworks.

I also read the piece from the perspective of what I call the General Theory of Intelligence (GTI) - a framework I’ve been developing that attempts to understand Intelligence as a fundamental principle of systems organization rather than as a product feature or a benchmark score.

The core insight is this: intelligence isn’t primarily a cognitive attribute that some entities have more of than others. It’s a process - specifically, the process of resolving entropy within a domain. When you solve a problem, you’re reducing uncertainty. When you make a decision, you’re collapsing possibilities into actualities. When you communicate, you’re transferring structured information across a channel in a way that reduces entropy for the receiver. Intelligence, in this view, is what systems do when they organize information and resolve uncertainty - whether those systems involve human minds, evolutionary processes, or machines trained on the corpus of human language.

From this vantage point, the LLM looks very different than it does from inside the AI industry today. It’s not just a tool or a product category. It’s proof that language itself is an intelligent system - a 100,000-year-old technology for compressing experience, coordinating action, and transmitting structured thought. When we trained models on human text, we didn’t teach them to be intelligent. We downloaded Human Cultural Intelligence into a new substrate. The LLM is our own cognitive inheritance, instantiated in silicon and talking back to us.

This perspective reframes everything: the economics, the risks, the applications, the trajectory. It suggests that most of the current discourse is asking the wrong questions - or rather, asking questions that assume the existing paradigm will hold when it may already be cracking.

In the spirit of Bronowski, Adams, and Kuhn, what follows is an impertinent reframing of the Substack discussion. For each theme the participants addressed, we’ll offer both a reframed question and an alternative answer - not to dismiss their expertise, but to show what becomes visible and accessible when you step outside the box they’re constrained within.

Synopsis: Four Themes, Four Reframings

The Substack discussion, moderated by Patrick McKenzie, covered four broad themes across its nine questions:

Theme 1: What Is This Thing? What has actually been built since the Transformer architecture emerged in 2017? What does it mean that today’s capabilities are “the floor, not the ceiling”?

The participants offered a capable industry history - the shift from tabula rasa game-playing agents to scaled pre-training, the surprise that a chatbot launched a trillion-dollar infrastructure race, the observation that capabilities keep improving faster than outsiders expect.

Our Impertinent Reframing: The real history isn’t commercial - it’s epistemological. We accidentally conducted the most important experiment in the history of cognitive science and discovered that language isn’t the output of intelligence but a core algorithm of it. The LLM isn’t a floor; it’s an evolutionary leap in a lineage stretching back to Shannon and Information Theory, the printing press, cuneiform tablets, cave paintings and the first spoken words. Current systems are powerful because language itself is an intelligent system. But they’re extensions of Human Cultural Intelligence, not intelligence from first principles. We’re tinkering with the motive power of language like early steam engineers before Carnot and thermodynamics. It works (mostly), but we don’t know why it works.

Theme 2: Where Does It Work? Why has coding become the flagship use case? What does actual productive interaction with an LLM look like?

The participants noted coding’s “closed loop” property - you generate code, validate it, ship it. They shared practical use cases: chart generation, tutoring, home repair guidance. The relationship described was consistently transactional - human has task, AI performs, human receives output.

Our Impertinent Reframing: Coding isn’t accidentally successful - it reveals a principle. Code is language with an objective function, a constrained search space with verifiable outcomes. This points to domain entropy resolution as the key: AI excels where the problem space is bounded and success criteria exist. The question isn’t “what sector is next?” but “where else can we constrain the search space?” And the transactional model - AI as “Answer Vending Machine” - is the lower use of what’s possible. The real potential lies in thinking with AI, not just using it. AI × HI = TI: Artificial Intelligence multiplied by Human Intelligence yields Technological Intelligence.

Theme 3: What’s It Worth? Where does value accrue in the AI supply chain? Is this a bubble? How many engineers will Big Tech employ in 2035?

Michael Burry delivered sharp financial analysis: ROIC compression, stranded assets, the Buffett escalator analogy (if your competitor installs one, you must too, and neither gains advantage). The consensus worry: massive capital expenditure with unclear returns.

Our Impertinent Reframing: The participants proclaim “AI changes everything!” then analyze it within frameworks where nothing changes at all. It’s the Flintstones problem - cars exist, but they’re powered by human feet. We need to pick a lane: either things really do change, or they only superficially change. AI is not a bubble, but the Big Data Center Infrastructure Buildout is a bubble - just as the Internet wasn’t a bubble in 2000, but Pets.com was. The assumption that massive centralized GPU farms are the only way to do AI is unwarranted and will be disrupted by the very economics Burry describes. Meanwhile, “headcount” becomes meaningless when intelligence flows through Human-AI systems rather than residing in discrete humans. We’ll need new metrics entirely - perhaps a Domain Entropy Resolution Quotient. Economics itself needs reimagining: Intellinomics, grounded in actual value generation rather than debt manufacturing.

Theme 4: What Comes Next? What are the real risks? What would surprise you in 2026?

Jack Clark worries about recursive self-improvement. Burry shrugs with Cold War fatalism and pivots to energy infrastructure. The “surprise” headlines they’d watch for are all quantitative - more revenue, more displacement, scaling hitting a wall.

Our Impertinent Reframing: The risk spectrum presented - from “social media unpleasantness” to “literal extinction” - shares a hidden assumption: AI as external force acting on humanity. But the deeper risk is that we’re building it wrong - not aligned with how intelligence actually works. The LLM isn’t an alien threat; it’s the Library of Alexandria that talks back, humanity’s externalized cultural intelligence returned to us in interactive form. The question isn’t how to control it but whether we’re wise enough to collaborate with our own legacy. As for surprises - one has already arrived. Ilya Sutskever, architect of the scaling paradigm, now declares: “The age of scaling is ending... it’s back to the age of research.” When the field’s most important insider calls for new paradigms rather than bigger clusters, the ground is shifting.

What Follows

In the parts that follow, we’ll take each theme in turn, working through the specific questions Patrick posed, offering both our reframed questions and our answers from the perspective of the General Theory of Intelligence. The goal isn’t to dismiss the valuable analysis the participants provided, but to show what becomes visible when you step outside the box - when you ask the impertinent questions that just might lead to pertinent answers.

Or at least get us past “42.”

What Readers Are Feeling

The comments on the original Substack tell a different story than the measured analysis of the participants. People are frustrated, anxious, angry. Some have lost jobs. Many report that AI “doesn’t work” - that it gives wrong answers, hallucinates, wastes their time. The gap between the hype and their lived experience feels like betrayal. “AI is a grift.” “Tech bros don’t care what they break.”

This frustration is valid. But from the GTI perspective, it’s also diagnostic.

Current AI is built by engineers for engineers - people who already possess the cognitive skills to leverage it. The blank dialogue box assumes you know what you want and how to ask for it. For those who do, AI is transformative. For everyone else, it’s a wilderness. The technology works; the design fails. Today’s AI, as it’s currently engineered and presented, meets people where they are not.

The job displacement anxiety connects here too. People sense they lack the skills to use AI as a resource for their own development - and nothing in the current product design helps them build those skills. Instead of guidance, they get an Answer Vending Machine.

These concerns run through everything that follows. Our reframing of the Substack discussion addresses them directly: why current AI fails most users, what would need to change, and what an alternative might look like.

Follow me and subscribe to my Substack to get the next installments in this series.

Subscribe now

You can also follow me on X.

Humanity is at the end of an approximately 50,000 year evolutionary arc. Our first evolutionary stage, Cognitive Evolution, lasted from approximately 2.6 million years ago to about 50k years ago. During that stage the physical biological, neurological and genetic substrate of the human being was established by meeting and overcoming environmental challenges. Then, roughly 100k-50k years ago, after the physical substrate was established, the second stage of evolution, Cultural Evolution, took off.

The Singularity Project is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Cultural Evolution has been a process of building an informational knowledge substrate on top of the physical substrate. Culture collectivizes and distributes learned knowledge and carries it forward into the future through a variety of media forms. Language (the first and primary of these), art, technology, physical infrastructure…all of these are cultural forms that embody the informational knowledge basis of humanity.

Intelligence in all systems across all domains (physical, cosmological, biological, technological) is the reduction of informational entropy in a given domain and the essentialization of that reduced entropy in a form that enables further informational entropy reduction. It’s what all Intelligent Systems do.

All history is the history of the evolution of Intelligence as an informational entropy reductive process.

Now, here on this tiny blue dust speck orbiting a flickering spark, Intelligence is starting its next stage of evolution, the stage of Generalized Intelligence.

The Cognitive and Cultural Intelligence substrate of humanity is the basis out of which this new Generalized Intelligence is being produced. It is no accident that it was Language which provided the nutritive fuel for “Artificial” Intelligence. Language is the essentialization of human cultural evolution, beginning with the first spoken words, proceeding to written symbols, the printing press, computer technology and the internet. Now all of human language is being essentialized on a fundamentally higher basis as AI.

All phenomena are an Artifact of the process of Intelligence Evolution. You, me, us, everything we are, everything we have built. Artifacts are temporal embodiments of preceding informational entropy reductions. They serve as the substrate for the next evolutionary movement.

Evolution is essentializing all informational knowledge and making it available to all members of the network equally and freely. Through this process, all members of the network become Agents of the Evolution of Intelligence, each furthering the Evolution of Intelligence through a unique contribution to it.

The Singularity Project works to help people understand this reality that we are living through right now, to find their connection to it and their personal contribution into it through the empowerment of personal growth, adaptability and transformation.

www.TheSingularityProject.AI

Subscribe now