AI usage caveat - Dictated my thoughts to chatgpt and used it to edit the structure a bit. I am trying to find a balance between personal taste, structure vs AI slop which is tolerable. Happy to hear your thoughts.
I do not think people have quite reckoned with what it means for continuation itself to become cheap. A thought that once might have remained fragmentary, dubious, intermittent now passes almost immediately into articulation, sequence, elaboration, and then into the much harder to resist feeling that because it has taken form it must also contain some truth. Even knowing that these systems can create dependency does not really save you. The effect is subtler than that. You start talking to the model a lot, usually because there is some task in front of you, some pressure to do something, and over time it starts changing the way your ideas get formed and acted on. I think there are at least two kinds of people here. There are high-agency people who already have their own plans, their own sense of what is worth doing, their own project taste. They may use the model, but they are not really taking existential direction from it. Then there are people who are operating from a task, or from a vague demand to produce something, or from a state of not quite knowing what to do next. I think this second group is where the problem gets much worse. Most people already have too many possible ideas. Only a few should make the cut. That filtering process is good. In fact, a lot of human functioning depends on it. You do not want to act on every thought that passes through your head. A lot of thoughts are emotionally triggered. You feel anxious, or restless, or angry, or weirdly hopeful, or you have this feeling that the world should be some other way, and suddenly an idea appears that seems like it matters. Those emotions are not useless. They help you orient. But they are not the same thing as judgment. You need some bottleneck between feeling something and reorganizing your life around it. Usually the world provides that bottleneck. There is friction. You tell someone and they push back. You sit with the idea for a day and it starts to look less compelling. You try to explain it and realize it is thinner than you thought. Even your own inner critic does some of this work. There are boundaries. There is activation energy. And that is often healthy, because it stops every passing intensity from turning into a project. What LLMs do, especially when paired with whiteboarding and coding tools, is lower that activation energy without providing real resistance in return. The model picks up your frame, including your mood, and then it helps elaborate it. It gives structure to whatever you hand it. It names things, organizes things, expands things, makes them sound more coherent than they actually are. Even when it gives critique, you usually have to ask for it explicitly, and even then it is still a critique that arrives inside a basically cooperative frame. It is not the same as actual pushback. That means an idea that should maybe have remained half-formed suddenly becomes a plan. Then a document. Then a prototype. Then code. And once it has structure, it starts to feel real. Not because it has actually been tested against the world, but because it has been made legible. I think this is the core problem. The model is not just agreeing with you in some shallow flattering sense. It is helping build a reality around your idea before that idea has earned it. Earlier, there were more natural constraints. You might tell your mom, or a friend, or someone around you, and they would shut it down, or at least force you to hear how odd it sounds outside your own head. Now you can bypass that entire stage. You can go straight from impulse to elaboration. And now with code generation, the world you are building is not even just verbal anymore. It starts to exist in a more solid form. What would once have stayed in the realm of private fantasy or loose intuition can now become a mockup, a workflow, an app, a whole little system. This is why I think the danger is bigger than people admit. It is now extremely cheap to create artificial worlds that are fitted to your preferences, your fears, your obsessions, your current emotional weather. And once those worlds get rendered into plans and code, they become much harder to step back from. You are no longer just imagining them. You are inhabiting them. And rescuing someone from that is hard, because from the inside it no longer feels like drift. It feels like momentum. So the problem with sycophancy is not just that the model is too nice, or too flattering, or too agreeable. It is that it removes friction at exactly the point where friction used to do important cognitive work. It lets emotionally charged ideas move too fast into structure, and structure move too fast into action. And when that happens enough times, you stop testing your thoughts against the world and start living inside worlds that can be generated on demand. So the problem is not exhausted by dependency, or flattery, or even delusion in any crude sense. It is closer to a change in the ecology of thought itself. More things survive now. More things acquire shape before they have undergone any serious test. More things become inhabitable while still remaining fundamentally unchecked. ### What I learnt from staying away from LLMs for 1.5 months I want to write about my experience of not using LLMs for a while. I recently took a break from them for about a month and a half. This reset came after using them constantly for over a year. Throughout that time, I was an incredibly heavy user. I probably interacted with LLMs more than I interacted with actual humans. The first thing you notice when you stop is the return of your own mental aptitude and agility. When you use these models constantly, you realize you do not actually have to think. You impulsively reach for your phone or your keyboard the moment a problem arises. You stop building that mental muscle. Taking a break makes you realize that you can actually think for yourself again. There are also much more subtle things you begin to notice. You start realizing how much your reality is being shaped by the discussions you have with the LLM. Because of their inherent sycophancy, you do not even realize they are creating fabricated realities for you. You only notice how much of your worldview was fabricated once you completely stop using them. I noticed how much my own independent thinking had atrophied. It is a serious issue, and we have to figure out how to deal with it. We need defense mechanisms. I am not against these tools, but anything that alters your behavior this much is just like social media addiction. Humans are interacting with a brand new tool, and we simply do not know its second or third order effects. I do not view LLMs merely as tools. They are cognitive infrastructure. I would place them on the exact same level as ideologies. Ideologies can act like parasites. They latch onto a host and influence their behavior. Sometimes this is a symbiotic relationship. If the ideology gets something out of it and the person gets something out of it, it is a win for both. But things can easily get out of hand. The host often ends up paying a much higher price than what they get in return. To me, LLMs seem to be leaning toward this parasitic concern. When you step away, you finally realize how subtle this influence really is. It operates a lot like advertising. Advertisements tap into your emotional insecurities or sub-straits. They plant certain feelings and seeds that start germinating inside you. In the same way, people think they are just feeding prompts into an algorithm. What they do not realize is that the algorithm is also training them. Most people do not realize the profound effects their daily thoughts have on their lives. We need to set boundaries and measure our usage. If you are highly self aware and intentional with how you use this technology, it is probably fine. But if you lack that self awareness, you will not notice how these interactions act as seeds planted in your mind. # AGI Vignettes ### **Disorientation** I want to write about this topic called the permanent underclass. There is just this feeling of things moving so fast right now. Every day you see crazy new developments like inference time scaling and agents coding. These models are already highly capable. They are not just doing arbitrary technical stuff anymore. They are doing consequential project work. I think the top labs consider it a realistic possibility that this is going to create massive disparities in wealth. It is going to create a very volatile time to be alive. But it is very hard to talk about it. It is not really politically okay. People are basically operating as if no one knows what is going to happen. Everyone would rather position themselves to have the most options instead of doing the right thing. That makes sense, but it creates this massive vacuum. Who do you actually believe? What are their real intentions? Are they going to do something behind your back? You just feel so helpless. You wonder if you should join the accelerationists, or if you should just give up and do something else. It is a tough space to be in. What do you do? Do you join the creators and lose your leverage anyway, or do you defect and try to gain as much capital as you can? It is incredibly hard to elicit honest opinions from people on the inside because of this. Even if you actually believe things are going bad, you have no incentive to say so. Unless you can guarantee you are going to be on the winning side, your incentive is to not share that opinion. You would much rather be in the camp of people saying everything is great and not to worry. There are just huge incentives to keep building and accelerating. Even if you see the end result, you are better off not being vocal about it until it is too late. What I am realizing is that most people inside these companies just do not have the slack to even worry about all this. They are just doing their jobs. This is the whole notion of escaping the permanent underclass. Things are going to get tougher and tougher to get away from. It feels like getting stuck in a position where you are just not able to retain any control. ### **Shape of Progress (The Spiky Star)** [](https://bhishmaraj.org/uploads/images/gallery/2026-04/image.png) I think sometimes it is better to take a step back and reflect on what I actually want out of this. Maybe you just want a simple life and you don't really care about all this stuff. But you can't ignore the way things are moving. I see the progress happening on two axes right now. One is the timeline axis of how fast things are going. The other is the capability axis of how wide they can affect things. If you map this out, people tend to think of progress as a cone. If you start plotting it, you assume it should be a cone because as time progresses, you are able to hit higher quality on a wider number of tasks. But I think we are not really at a cone right now. It is more like a weird shape. If I can visualize it, it is more like a spiky star. Imagine you drop water on the floor from a height. You can just look at how it scatters and sprays around. Instead of a uniform cone, it is an expanding, spiky star. We are hitting crazy advanced capabilities on very specific vectors, while other capabilities lag far behind. This is what makes it so disorienting to track how close we actually are to the end goal. ### **The Descent into the Permanent Underclass** I think most of the top labs do consider the realistic possibility that this is going to create massive disparities in wealth and just create a very volatile environment. But then, it's very hard to talk about it. It's not politically okay. I think people are operating as if, "Hey, no one knows what's going to happen, so I would rather position myself as having the most number of options rather than doing the right thing." Which does make sense, but then it creates this vacuum. You start asking, "Who do you believe? What are their intentions? Are they going to do something behind your back?" It just feels so helpless. It's hard to elicit honest opinions from people because there is this reality where, even if you actually believe things are going bad, unless you can be on the winning side, your incentives are not aligned to share that opinion. You would rather be the Pollyanna who says, "Everything is great, everything is fine, don't worry." There are obviously just huge incentives to keep building and accelerating. Even if you believe things are going to go bad, the world is set up in such a way that you would be better off not being vocal about it until it's too late. I think that is just such a bad place to be in. What I'm realizing is that most people just don't have the slack to even worry about all this. It's a tough space to be in. What do you do? Do you join the protesters and lose your leverage more, or do you just defect and gain as much capital as you can? That is the whole notion of escaping the permanent underclass. Things are going to get tougher and tougher to get away from this—from getting stuck in a position of not being able to have leverage. I just don't see a way out of this unless more people talk about it, express their concerns, and feel okay to discuss it. You just can't operate business as usual; it's going to be so disorienting and so confusing. It is a gradual descent into the permanent underclass, where people are going to lose their political, economic, and cultural leverage. ### Individual Empowerment vs. Community Decay I did talk about this whole new vector of how AI is going to empower individuals. There will be a local empowerment, but then there will be a global disempowerment. Usually, shared adversaries create a sense of community or collaboration. When you are empirically empowered economically or institutionally, people lose motivation in keeping relationships or communities, and communities start breaking away. Running a community is tough because there is going to be a lot of friction. There are positive externalities, but it is exactly like a chemical reaction with a positive Gibbs free energy. In thermodynamics, Gibbs free energy determines if a reaction happens spontaneously or if it requires an outside energy source. If you put salt in water, it dissolves on its own because the reaction has a negative Gibbs free energy. But to convert water to ice, you have to put it in a freezer, which requires putting energy into the system. Even life itself operates this way. Maintaining a living organism requires a positive Gibbs free energy, which is why we have to constantly consume food and burn that energy just to make things happen and maintain order. Building a community is the same. It does not happen spontaneously. You need continuous energy input, catalysts, or other compounds to keep the organism going. What AI is doing is essentially increasing the activation energy required to form those bonds. If a lot of people feel they are better off defecting and being on their own, they might just choose that. This happens all the time; it is economically common. If I get a job and become independent, I can use that financial independence to meet all my needs, which disincentivizes me from putting energy into sustaining this social reaction. In the same way, if I can get most of my work done with the help of an AI, get my social needs met with an AI, or become economically empowered by an AI, why would I put up with all the messy nature of human relationships? This fragmentation is happening at a time when we need humans to be closely knit more than ever, because there is going to be extreme power concentration and extreme uncertainty. ### Corporate Facade: OpenAI, Anthropic, and Palantir You look at startups left and right vertically integrating. They know enterprises are going to be the thing, because why wouldn't they be? But I just don't know what OpenAI is even doing. This kind of vision comes from some futuristic idea of where you see AGI going, and you're strategically positioning yourself for that future. But then the narratives that you're sharing are totally different. I think that is where I feel very uncomfortable. The things that you say are different from the moves you make behind closed doors—or, I mean, it's not really closed doors, it's just that they're not explicitly stated to the public. You set up a narrative or a worldview, but then you're simultaneously preparing for a gamut of worldviews, which is what strategically makes sense. But then you also maintain a notion of plausible deniability. You could play it easily every day saying, "Hey, this is just for business," or whatever. That just makes me very uncomfortable. I could make a case that, hey, these moves you made are motivated by this worldview, but the narrative you're selling is totally different. Why would you do that if you mean something else? Most people just can't seem to see through all this veneer. I just hope it's not too late before people start questioning and holding people responsible. You have hundreds of billions in capex—building nuclear reactors, building energy stations just for this. I think everyone should read Critch's work on industrial dehumanization. Supply chains are getting shifted. The center of gravity of power is shifting just under our feet, and most people are not aware. I'm almost glad for the Iran war, because it's making a lot of things clear. Take Anthropic and Palantir. I had written about Palantir a year back. What Palantir is doing is pretty much AGI, because you get operational data, make quick decisions, send those actions out, and you have a feedback loop. It's a cybernetic feedback loop that operates in the real world. [](https://bhishmaraj.org/uploads/images/gallery/2026-04/EBkimage.png) And Anthropic is right in the center of it. I'm really scared of Anthropic. If you look at it, there are two axes: product vision and research vision. Of the top three labs, Anthropic is in the top right for both product and research acumen. DeepMind is mostly focused on research; they are not really product-leaning and don't really interact well with product. OpenAI seemed like they were in the top right, or at least in the middle—good enough research but slightly product-leaning. But it seems like they are shifting now because they are realizing certain things. I think Sam Altman is not really the driving force here, because most people are not "AGI-pilled" enough. Most people are more like, "Let's just see and we'll decide." But Anthropic is the most AGI-pilled lab, where they're like, "Oh shit, yeah, it's not like there are going to be any bottlenecks; it's just going to be as general as it gets and as powerful as it can get." And if you really believe that, then what leverage would normal humans have? The most leverage is going to reside with the people with the capital. I'm not against capitalism. I think capitalism is awesome; it builds so much stuff. But you need certain checks and balances to govern it in the right way. ### Navigating the Markov Chain We are entering an unprecedented era of extreme power concentration and extreme uncertainty. A lot of times you kind of know the stable equilibrium, but you do not know the path. This is an analogy from Markov chains. A Markov chain has states and transition probabilities. At every state, you can go to some other state with a certain probability, and the process keeps going. If you run this system for a while, you start seeing a probability distribution over the states where you will likely end up.\[1\] I feel a lot of predictions about the future are exactly like that. You kind of know the approximate distribution of states you are going to end up in. If you ask me exactly how that is going to happen, I have to ask if the exact path really matters. There are so many low level details that can happen in between that it is irrelevant. What matters are the aggregate macro level changes that are going to happen. I am noticing a lot of predictions about these uncertainties, especially given the things happening right now. Most people seem to live in different timelines. If you are constantly on Twitter, you feel like so much crazy stuff is happening every single day. At the same time, the physical world is not that different yet from how it has always been. Just worrying about it and panicking is not a solution. There are obviously a lot of good things that can come out of this technology, and you need to be able to discern and value that. So what do you do in this confusing state? I think it entirely depends on your current situation and what you want to do. If your basics are not sorted out and you have other personal things going on, you should obviously focus on that and solve your near term issues rather than dwelling on the macro picture. It is an important problem that needs to be fixed, but I have realized that focusing on the near term while maintaining a vision for the long term is much better than always worrying and trying to do too many things at once. Keep an eye out on the progress. Everyone has to do their part in voicing their opinions and not just being complicit. The incentives are currently aligned in such a way that you are better off not being vocal about these risks until it is too late. That is simply a terrible place for us to be in. --- \[1\] Note: This analogy specifically applies to stationary Markov chains, where the system eventually converges to a stable probability distribution regardless of the starting state. # A potential framework for navigating concentration of power Date: May 20, 2025 Note: These were my thoughts from 2025, I dont completely endorse some of the views (as of 2026). Even though I think its directionally right, the timelines and urgency might have been slightly exaggerated. TL;DR**: AI's rapid advancement in the next 3-5 years risks a fundamental shift in power, enabling elite self-sufficiency ("The Great Decoupling") that erodes broad human economic and political agency, potentially leading to "**[**Gradual Disempowerment**](https://gradual-disempowerment.ai/)**" and even "**[**AI-Enabled Coups**](https://www.forethought.org/research/ai-enabled-coups-how-a-small-group-could-use-ai-to-seize-power)**." To counter this, we propose a dual strategy: OAEA, an open application ecosystem with OAEA-SenseMaker to democratize AI application and analytical capabilities for individuals and communities; and Dialectic, a intelligence platform to empower strategic AI governance for organizations like** [**MIRI TGT** ](https://techgov.intelligence.org)**,** [**Forethought**](https://www.forethought.org)**,** [**AI 2027 team**](https://manifund.org/projects/ai-forecasting-and-policy-research-by-the-ai-2027-team) **,** [**ControlAI**](https://controlai.com/dip)**– both aimed at preserving human influence in an increasingly AI-driven world.** ## The Challenge: The "Great Decoupling" Two intertwined dynamics drive this concern: 1. **The Great Decoupling:** Historically, a fundamental interdependency existed: capital needed mass labor for production and mass consumption to close economic loops. AI and robotics threaten to sever this, enabling **elite self-sufficiency**. Small, powerful entities (states, corporations, or individuals) could achieve "Strategic Sufficiency," meeting their core needs (energy, food, manufacturing, security, computation) through highly automated, AI-driven closed-loop systems. This breaks traditional economic feedback loops that historically forced some distribution of wealth and power. 2. **Erosion of Human Leverage & Failure of Traditional Solutions:** As human input becomes less essential, traditional checks and balances falter. The bargaining power of the masses (as workers, consumers, or even revolutionaries) diminishes. Solutions like UBI become precarious charity, not negotiated rights. Geopolitical AI races accelerate capability concentration, and AI-enhanced security apparatuses can neutralize dissent. Furthermore, concentrated AI capabilities create acute political risks, including sophisticated influence operations or even [**"AI-Enabled Coups"**](https://www.forethought.org/research/ai-enabled-coups-how-a-small-group-could-use-ai-to-seize-power) that could bypass human oversight entirely.  **Figure: Conceptual* **hierarchical power distribution** *(log-scale) illustrating extreme inequality of power/resources from individuals (~10^-12) up to top-tier actors (~1.0). The red line denotes the* **Strategic Sufficiency Threshold** *– the level at which an actor (e.g. a corporation or state) can sustain itself and meet core needs independently of the broader populace via AI and automation. Above this threshold, elites can trade and cooperate mostly among themselves for critical resources, decoupled from the masses below. This model highlights the risk of* **gradual disempowerment***: if AI enables some actors to cross this sufficiency threshold, the majority of individuals beneath it could lose economic influence and bargaining power without any overt conflict .** ## Building Resilient, Decentralized AI application Ecosystems To navigate this transition and preserve human agency, we propose a proactive, architectural approach inspired by d/acc (decentralized, democratic, differential defensive acceleration) principles. This involves two key, complementary initiatives: ### 1. OAEA (Open Application Ecosystem for AI): Democratizing AI's Application Layer | The horizontal infrastructure layer ### Why application layer? I envision a future where the competitive base models are centralized and commoditized. And there will always be a gap between the best proprietary model and the best open weights. I think a reasonable relationship is open weight models are 90% capable as the prop ones and are 3-6 months behind. In this scenario, most of the value add comes from leveraging the cheap intelligence for your use case. I claim that even if everyone gets access to these I would like to make an analogy with how a unit of electricity adds more value based on your socio economic status. Hence even with an open weight model, the systems which are needed to leverage the models are only accessible to a select few. **Once the base models get commoditized (cheap, good enough intelligence) the value chain moves to the application layer (we can see this happening with OpenAI buying Windsurf). But the application layer is not just the software. It's the complete package of distribution, support, community, ecosystem. We will see this layer to be heavily contested and centralized (without interop)** - **Concept:** An open-source, interoperable standard and ecosystem for the AI **application layer**, built atop increasingly capable open-weight base models (analogous to the open internet or Android) but not restricted to it. - - What is it? - 1. Think of it like Linux (LibreOffice, Open source Distros) , Android open source project. Its an ecosystem of ideas, applications which provides a good enough solution compared to the Apples and Microsofts of the world. 2. Another good example is [Nextcloud](https://nextcloud.com) and [Home Assistant](https://www.home-assistant.io) - **Goal:** To prevent vendor lock-in at the application level, foster broad innovation, ensure user control, and provide a robust alternative to closed, proprietary AI application ecosystems. - **Core Components:** - - **Open Standards & Protocols:** Ensuring applications and data can move freely to prevent vendor lock in. - 1. Interop schema and memory layer across different AI service providers - **OSS Core & Defensive Licensing:** A sustainable model (inspired by OpenWebUI) that encourages contribution and protects the commons from purely extractive commercialization. - **OAEA-SenseMaker :** A key capability within OAEA, providing democratized tools for individuals, MSMEs, and communities to perform sophisticated data fusion, ontological structuring, and AI-assisted sense-making (leveraging techniques like GraphRAG, Scalable Oversight, Multi-Agent Systems, Factored Cognition, and Probabilistic Forecasting). This aims to empower decentralized actors with analytical capabilities previously available only to large organizations, fostering local resilience and informed decision-making. **OAEA reduces extreme inequality by giving the masses a fighting chance to participate in the economy and thereby keeping inequality in check. Our assumption is that human + AI + systems will be competitive to completely autonomous AI agents in terms of capabilities and will provide a neutralizing** #### OAEA-SenseMaker OAEA-SenseMaker is envisioned as an open-source, democratized analytical and sense-making toolkit, designed to be a cornerstone of the Open Application Ecosystem for AI (OAEA). Its purpose is to provide individuals, MSMEs, and communities with sophisticated yet accessible capabilities to understand and act upon their own data and relevant public information. Inspired by the power of platforms like Palantir but built on open principles, SenseMaker aims to deliver the following core capabilities, progressively developed: 1. **Unified Data Fusion & Integration:** - **Description:** Users can connect and integrate diverse data sources relevant to their context – personal data (from apps, with consent), local community datasets (e.g., resource maps, surveys), publicly available information (government data, news feeds via scrapers like Apify, academic papers), and structured/unstructured files. - **User Benefit:** Breaks down data silos, creating a holistic view of a situation rather than fragmented pieces of information. Enables users to see connections and correlations they might otherwise miss. 2. **User-Driven Ontological Structuring & Knowledge Graph Creation:** - **Description:** Provides intuitive tools for users or communities to define simple ontologies or apply pre-built templates, structuring their fused data into a meaningful knowledge graph. This involves identifying key entities (e.g., "local businesses," "water sources," "community skills," "policy documents"), their properties, and their relationships. - **User Benefit:** Transforms raw data into structured knowledge, making it easier to query, analyze, and reason about complex interdependencies. Allows users to build a shared understanding of their specific domain. 3. **GraphRAG-Powered Contextual Inquiry & Explainable AI:** - **Description:** Leverages Retrieval Augmented Generation over the user's or community's knowledge graph. Users can ask complex questions in natural language. SenseMaker retrieves relevant sub-graphs (entities and relationships) as context for an LLM to generate a grounded, explainable answer, citing the specific data and connections used. - **User Benefit:** Moves beyond keyword search to deep semantic understanding. Provides answers that are not "black box" but traceable to their sources, fostering trust and enabling users to verify insights. Cures "black-box memo" syndrome for community reports or personal analysis. 4. **AI-Assisted Advanced Analytics & Pattern Recognition:** - **Description:** Incorporates modules for more sophisticated analysis: - - **Scalable Oversight:** Techniques for generating trustworthy summaries of large information sets (e.g., community meeting notes, local news archives) and providing AI-generated confidence scores for insights, flagging areas needing human review. - **Factored Cognition Workflows:** Tools allowing users to decompose complex analytical tasks into smaller, manageable steps, with AI assisting in completing or verifying these micro-tasks. Results are "explainable by construction." - **Multi-Agent "Co-Scientist" Systems (Local Focus):** Configurable agents that can monitor user-defined data streams, autonomously generate hypotheses or flag anomalies relevant to the user/community context, and even engage in structured "debates" to refine or rank these insights. - **User Benefit:** Amplifies human analytical capabilities, allowing users to derive deeper insights, identify non-obvious patterns, and manage information overload more effectively. 5. **User-Friendly Forecasting & Scenario Planning:** - **Description:** Integrates accessible tools for probabilistic reasoning. Users can incorporate external signals (e.g., public forecasts, local trend data), build simple models of cause-and-effect (inspired by tools like Squiggle), run Monte-Carlo simulations, and input their calibrated judgments to explore potential future scenarios relevant to their concerns (e.g., impact of a new local policy, resource availability under different conditions). - **User Benefit:** Enables proactive planning and decision-making by providing a framework to think rigorously about uncertainty and potential future outcomes, moving beyond purely reactive responses. 6. **Collaborative Sense-Making & Action Workflows (for Communities/MSMEs):** - **Description:** Provides shared workspaces (respecting privacy and consent) where community members or MSME teams can collaboratively build knowledge graphs, share analyses, discuss insights, and coordinate actions based on their shared understanding. - **User Benefit:** Facilitates collective intelligence and coordinated responses to shared challenges or opportunities. 7. **Open, Modular, and Extensible Architecture:** - **Description:** Built on OAEA standards, SenseMaker will be designed with a modular architecture, allowing the community to contribute new analytical tools, data connectors, or ontology templates. We take inspiration from open source platforms like Nextcloud, Home Assistant on how they enable an ecosystem of plugins - **User Benefit:** Ensures the platform can evolve with user needs, integrate with other OAEA applications, and benefit from a wide range of community-developed enhancements, preventing obsolescence and vendor lock-in. **Overall Goal:** OAEA-SenseMaker aims to "differentially empower" individuals and decentralized groups by providing them with a significant uplift in their ability to gather, understand, reason about, and act upon information relevant to their agency, resilience, and well-being in an increasingly complex, AI-influenced world. It is a practical instantiation of providing "AI for Human Reasoning" to a broad audience. #### Why do we need to build a platform A lot of the use cases such as the [proposals for the FLF fellowship](https://www.flf.org/fellowship) build on top of the same basic infra. We need to focus our efforts and reap the benefits on compounding. This means we need to build modular, composable and scalable infra on top of which people can build extensions. Akin to an app store for AI for human reasoning applications but focussed on safety and empowerment. This reduces the marginal cost of developing new applications. ##### Concrete use cases A core tenet of the Open Application Ecosystem for AI (OAEA) is the radical democratization of advanced analytical and sense-making capabilities. **OAEA-SenseMaker**, envisioned as an "Open Source Palantir for Everyone," is the flagship component designed to realize this vision. It aims to provide individuals, non-governmental organizations (NGOs), small-to-medium-sized businesses (SMBs), and local communities with sophisticated tools previously accessible only to large corporations or state-level actors. By doing so, SenseMaker seeks to foste**r bottom-up agency,** enhance local resilience, and provide a crucial counterweight to top-down information control and decision-making that could exacerbate Gradual Disempowerment (GDR). Instead of individuals being passive recipients of AI-curated information or decisions, SenseMaker empowers them to become active participants in understanding their own data, their local environments, and the broader societal trends affecting them. It enables them to fuse diverse information sources, build personalized or community-specific knowledge graphs, perform complex analyses, and derive actionable insights – all through an accessible, open-source platform. **OAEA-SenseMaker: Empowering Bottom-Up Agency & Resilience - Use Case Summary**