In The Lab Podcast

Milan Cheeks

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Technology
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Aug 14

When AI Gets a Body

Beyond the Screen: 5 Surprising Ways Physical AI Will Rewrite Our Reality 1. Introduction: The Great Escape from the Glass For years, artificial intelligence has been a ghost in the machine, a disembodied intellect trapped behind a pixelated cage. We have grown accustomed to it as a mere information processor—a silent partner that summarizes emails and generates art. But the era of the “screen-only” AI is coming to an abrupt end. As intelligence acquires agency through sensors and limbs, it is shattering the digital glass ceiling to manipulate our physical reality. This is not just an upgrade; it is a fundamental phase shift. We are transitioning from the world of information work to the era of physical labor, where AI transcends its software roots to become a participant in the world of atoms. 2. When Software Becomes Labor For decades, the mechanical engineering of robotics has been ready, but the intelligence remained the bottleneck. In a structured factory, a robot is a prisoner of its script: If This → Do That. But drop that same machine into the entropy of a random kitchen, and it founders. It cannot find the cup; it cannot navigate the clutter; it cannot adapt to a world that isn’t bolted to the floor. We are now witnessing the birth of a new paradigm: Understand → Decide. Empowered by computer vision and reasoning engines, the machine no longer waits for a pre-written command. It perceives the environment. It plans its trajectory. It acts with intent. The machine no longer repeats; it planfully executes. “AI stops being just software. It becomes labor.” 3. We Aren’t Building a New World; We’re Building Robots for This One Why the obsession with the humanoid form? It isn’t vanity; it’s strategic efficiency. We have spent millennia sculpting a world specifically for the human silhouette. To automate our lives, we don’t need to redesign civilization; we need machines that can navigate the infrastructure we have already built. By mimicking our form, these agents can utilize our existing tools and operate within our sacred spaces without requiring a single architectural change. The humanoid robot is the ultimate “universal adapter” for our existing world, including the following: * Homes, Kitchens, and Private Living Spaces * Hospitals, Offices, and Schools * Warehouses, Factories, and Logistics Hubs * Industrial Tools and Human-Centric Equipment * Stairs, Doors, and Transportation Systems 4. One Identity, Five Bodies As software and robotics converge, the boundaries between our devices will evaporate. We are approaching a reality where your personal AI isn’t a collection of disparate apps but a singular, continuous intelligence inhabiting a variety of physical shells. This heralds the next great “Platform War”: the race to build the operating system for physical intelligence. This isn’t just about managing files; it is about managing identity, permissions, security, and memory across your phone, your car, and your home assistant. When you tell your phone to “have the groceries put away,” the OS must orchestrate the hand-off between the digital intent and the physical execution. The devices are different, but the intelligence is a single, persistent entity. “You’re interacting with your intelligence through five different bodies.” 5. The Economics of Physical Abundance Human labor is the invisible tax embedded in every product we consume. AI has already begun to drive the marginal cost of cognitive work toward zero; robotics will now do the same for physical movement. The “multiplier effect” of combining these two forces—abundant intelligence and abundant matter—is staggering. Intelligence alone cannot build a shelter or harvest a crop. AI can design a high-efficiency home, but the bridge between design and reality requires the ability to move atoms. By connecting intelligence to matter, we can automate the actual building, harvesting, and manufacturing processes across sectors like agriculture, construction, manufacturing, hospitality, and logistics. We are no longer just optimizing the world; we are automating its creation. 6. From Performer to Orchestrator This shift forces a radical reckoning with the nature of human work. As machines master the “doing”—from sorting inventory to cleaning facilities—the value of human labor migrates toward higher-order orchestration. We are moving from being performers of tasks to deciders of outcomes. Our value will no longer be measured by the strength of our backs or the speed of our data entry but by our judgment, leadership, and entrepreneurship. However, this transition brings us to the high-stakes ownership question. Will these productive assets be centralized in the hands of a few monolithic organizations, or will individuals own the productive machines that perform their labor? The future isn’t just about the automation of work; it is about who owns the means of that automation. “The future of robotics isn’t only about automation. It’s also about ownership of automation.” 7. Conclusion: The Era of Moving Atoms The chatbot era was merely the prologue. We are entering the age of “physical intelligence,” where AI stops merely talking and starts building, transporting, and producing. We are moving beyond the era of the digital assistant and into the era of the digital teammate—an entity that doesn’t just offer advice but possesses the agency to change the physical state of your world. As the line between tool and teammate disappears, and your AI stops answering your questions and starts commanding your reality, we must ask ourselves: When the ghost finally gets a body, who is really in charge of the machine? Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 13

The End of One-Size-Fits-All Education

The End of the Factory Model: 7 Ways AI is Rebuilding How We Learn For more than a century, the modern classroom has been defined by a fundamental limitation: scarcity. We did not design our education system because it was the best way for humans to learn; we designed it to solve a logistical problem. To achieve scale, we built a factory model of one teacher, one curriculum, and one pace for thirty students. In this system, the student who is completely lost but too embarrassed to raise their hand is the “collateral damage” of efficiency. Today, we are witnessing the first great cracks in this monolith. For the first time in history, artificial intelligence is removing the constraint of “one-to-many,” signaling a shift from a system of mass-produced education to one of personalized abundance. 1. Interest-Led Intelligence: The End of the Generic Curriculum AI does more than just deliver content; it deconstructs the monolithic curriculum. In the traditional model, the curriculum is a static requirement to which every student must submit. AI inverts this power dynamic, creating an adaptive experience where the subject matter meets the student in their own world. If a student struggles with the abstract nature of mathematics, the AI can pivot, explaining those same principles through the lens of basketball statistics. For the student fascinated by the stars, physics is no longer a series of dry formulas, but the mechanics of rockets and planets. This is a radical philosophical pivot: we are moving from a world where the student is forced to adapt to the system, to a world where the system adapts to the student. 2. Healing the “Compounding Problem” of Education Our current model suffers from a compounding failure where students fall behind because they lack foundational concepts. If a student misses a step in multiplication, division becomes a hurdle, and algebra eventually becomes a wall. At this point, the student often adopts a self-limiting identity: “I’m just bad at math.” An AI tutor heals this by providing a “stop and go backward” functionality—an act of systemic empathy that a human teacher managing thirty students simply cannot afford. The AI identifies the specific gap, perhaps a concept missed years prior, and explains it differently until mastery is achieved. This isn’t because the student failed; it’s because the system finally learned to adapt. “Maybe they were never bad at math. Maybe they missed one foundational concept three years earlier.” 3. From Time-Based Schooling to Mastery-Based Learning The logical result of healing these gaps is a shift away from “semester-based” schooling toward a mastery model. Our current system is organized around the calendar: you spend four months in a seat, and then you move on, regardless of what you actually know. AI facilitates a model where the objective is understanding, not synchronization. The shift is best defined by the change in the questions we ask: * “Did you spend four months studying?” vs. “Do you understand the material?” Allowing students to move at different speeds—whether they need three weeks or three months to master a concept—is the ultimate form of educational equity. It ensures that no student is held back by the group, and no student is left behind by an arbitrary deadline. 4. The Evolution of the Teacher: From Transmitter to Guide The fear that technology replaces humans ignores the reality of the modern classroom. AI is positioned to make teachers more vital than ever by liquidating the administrative burdens that currently consume their days. AI can absorb the repetitive tasks: * Grading assignments and tracking worksheets * Managing administrative paperwork * Answering the same foundational questions dozens of times * Developing basic lesson plans By automating the mundane, we free the human teacher for the premium roles machines cannot replicate: mentorship, encouragement, judgment, and social development. The teacher evolves from an information-delivery system into a guide who fosters curiosity and human connection. 5. The Shift from “Memorizing Facts” to “Asking Questions” For generations, school rewarded the brain’s ability to act as a storage device. However, when every student carries a device with access to more information than any human could memorize, the goal of education must change. In an AI-augmented world, information is a commodity, but inquiry is a superpower. The priority shifts toward higher-order skills: critical thinking, scientific reasoning, and knowing when an AI’s output is flawed. At the center of this new hierarchy is the cornerstone of all future success: Learning how to learn. The goal is no longer to know the answer, but to know how to find and verify it. 6. The Great Equalizer: Democratizing Instruction, Not Just Information There is a vital distinction between the internet’s impact and AI’s potential. The internet democratized access to information (textbooks and Wikipedia), but information is just the raw material. AI democratizes access to instruction. As the source material notes, “Information gives you the material. A great teacher helps you understand it.” Historically, personalized, patient tutoring was a luxury reserved for the elite, costing $200 an hour. AI offers that same level of elite calculus or physics instruction for pennies. This compresses the inequality of geography and economics, making world-class intelligence available to any student with a screen. “The internet democratized access to information. AI could democratize access to instruction.” 7. Education as a Lifelong Relationship: The “Self OS” The final shift is the death of the “22-year sprint.” We are moving toward a “Self OS” model—a personal intelligence that isn’t trapped inside a single website, but belongs to the individual. This AI grows alongside the person, maintaining a portable record of what they have mastered, what they have forgotten, and how they learn best. Education becomes a lifelong relationship with one’s own potential, adapting to needs across the decades: * At 30: “Teach me accounting.” * At 40: “Help me understand machine learning.” * At 55: “Teach me Spanish.” * At 70: “Explain quantum physics to me.” From Scarcity to Abundance We are transitioning from a system built on the scarcity of expertise to one built on the abundance of personalized attention. As the constraints of the factory model fall away, we can finally stop asking the industrial-age question: How do we teach millions of students simultaneously? Instead, we can begin to answer the only question that has ever mattered: How does this specific human learn best? We are moving toward a classroom of thirty students, thirty learning paths, and thirty different speeds. One classroom—and finally, thirty personal teachers. This is not just a technological upgrade; it is the redesign of education around the individual. Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 12

The Last Generation of Programmers

Why the Next Great Programming Language Isn’t a Language at All For roughly eighty years, the history of computing has been defined by a lopsided relationship: humans adapting themselves to machines. In the early days, programmers worked in the cold, unforgiving basement of hardware, managing registers, memory addresses, and processors directly through assembly instructions. To make a computer perform, we had to learn its dialect, slowly climbing a ladder of abstraction from machine code to C, then to Python and JavaScript. This trajectory has always moved in one direction: away from the hardware and closer to human thought. We are now witnessing the final collapse of that abstraction layer. We are entering a period where the relationship is reversing—where the machine finally begins to adapt to us. In this new era, the ultimate goal of software development is shifting fundamentally. Source code, once the sacred artifact of the programmer’s craft, is becoming a mere implementation detail. We are moving toward a world where the “how” of a system is automated, leaving humans to focus entirely on the “what.” The Vanishing Middleman: Source Code as an Implementation Detail We have been here before. When the first compilers were introduced, programmers who were used to manual memory management feared a loss of control. Yet, we eventually learned to trust the compiler to handle the hundreds of machine-level operations required to execute a single high-level command. We stopped looking at the assembly because the abstraction worked. Today, AI coding follows a familiar but inefficient chain: Human intent → AI → Source code → Compiler → Machine code. This process still treats source code as a necessary bridge, a readable script that humans feel they must audit. But as AI models become more sophisticated, this bridge is becoming a bottleneck. We are moving toward a direct chain: Human intent → AI → Executable system. In this model, the AI determines the architecture, generates the necessary representation, tests it, and validates it. Just as modern developers rarely inspect the assembly instructions of a C++ compiler, future creators will no longer need to peer into the plumbing of source code. The Age of the Ephemeral: Software with a Thirty-Minute Lifespan Our current economy is built on software as a permanent asset. We “collect” apps and pay for subscriptions because software is expensive, time-consuming, and difficult to produce. We keep tools on our devices because of the high friction of creation. When the marginal cost of creating software drops toward zero, the concept of a “permanent application” begins to evaporate. If you need a specific tool for a single thirty-minute meeting, the AI can generate it on the fly, let you use it, and then discard it. Software becomes a temporary utility rather than a digital heirloom. “When creation approaches zero marginal cost, software can become ephemeral.” This shift toward the ephemeral means we will stop “owning” software in the traditional sense. Instead, we will inhabit temporary digital environments designed to solve a single, fleeting problem. Beyond the App Store: The Rise of Generated Experiences The traditional “app store” model—where we download a generic tool built for millions—is dying. It is being replaced by generated experiences tailored to a specific individual’s intent at a specific moment. We will no longer “install” software; we will simply request outcomes. Consider the act of budgeting. Instead of navigating a complex third-party app, you might simply say, “Help me understand where my money went this month.” The AI generates the specific interface and logic required to answer that request instantly. The same applies to project management; the system organizes tasks and highlights delays by creating a bespoke dashboard that only exists as long as the project does. The Death of Syntax: Intent as the Final Language As AI handles technical execution, the definition of “programming” is being rewritten. The primary skill of a developer is shifting from the mastery of syntax to the clarity of intent. The technical barrier is being replaced by the depth of the creator’s vision. Imagine building a multiplayer strategy game. In the old world, you would spend months writing functions for resource management and pathfinding. In the new world, you define the high-level constraints: a game where civilizations evolve through technological eras and every non-player character possesses a persistent memory. The new “leverage” for developers lies in knowing what should exist and how it should behave. The skill is no longer in telling the computer how to move bits, but in defining the rules of the world those bits create. The Engineering Pivot: From Building to Verifying If we stop reading the code, we face a “Black Box” problem. We cannot blindly trust AI-generated systems, which makes the transition from “building” to “verifying” the most significant engineering challenge of our time. This is the “one enormous challenge” that will define the next decade of tech. Engineering will move upward into the realm of rigorous validation. This includes a full suite of specialized disciplines: Security, Testing, Formal verification, Sandboxing, Permissions, and Observability. We are likely moving toward a future where one AI builds the software while a completely independent, secondary AI system continuously verifies that the system is safe and performing as intended. “The computer begins adapting itself to us... the disappearance of the requirement that humans understand how computers want to be programmed.” The Intelligent OS as an Intent Manager This evolution will ultimately transform the Operating System from a file-and-app manager into an “intent manager.” A system like the proposed “SELF OS” would act as an orchestrator of intelligence rather than a launcher of icons. When a user expresses a goal, the OS decides which resources are required to fulfill it. It might pull from an existing database, trigger a specialized AI agent, or generate a piece of temporary software to bridge a gap. The user never needs to know which method was used or whether a single line of code was written; they simply experience the outcome. Conclusion: The Final Programming Language The trajectory of computing is a story of closing the gap between thought and execution. We have climbed from machine code to assembly, to high-level languages, and now to the edge of the AI era. The final programming language will not be a language at all—it will be human intent. The requirement that humans must speak “computer” is finally vanishing. When the machine understands our descriptions of the world and can make them real, we will have reached a fundamental turning point in human history. In a world where the technical barrier to entry has vanished, the ultimate question remains: Who gets to be a “creator” when the only thing standing between a vision and a finished system is the ability to describe it? Gemini Notebook can be inaccurate; please double check its responses. Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 11

The Human Operating System

Amplified Humanity: Why the Future of AI is About You, Not the Machine 1. Introduction: The Wrong Question The current discourse surrounding artificial intelligence is frequently defined by a visceral, existential dread. We see it in the headlines and feel it in our professional anxieties: the fear that we are building the architects of our own obsolescence. This is not a new phenomenon. History is a steady rhythm of such tremors, from the Luddites of the Industrial Revolution to the white-collar panic that accompanied the arrival of the personal computer and the internet. Yet, we are consistently guilty of asking the wrong question. By focusing on whether AI will replace us, we ignore the more profound paradigm shift at hand. We are not facing a substitute for the mind, but a radical expansion of it. This is a transition from human intelligence as a finite, isolated resource to a new era of cognition—one where technology acts as a mechanism to amplify our inherent potential, allowing us to become exponentially more capable than ever before. 2. From Digital Storage to Cognitive Partnership For decades, our relationship with computers has been fundamentally transactional and archival. We treated our devices as sophisticated filing cabinets—receptacles for the documents, photos, and bookmarks we didn’t want to lose. This “storage-based” era is ending. We are now entering a stage of cognitive partnership, where the computer moves from remembering information to helping us reason through it. In this new dynamic, AI doesn’t just hold data; it synthesizes it. It connects disparate ideas, suggests creative possibilities, and identifies patterns that would otherwise remain invisible to the naked eye. This evolution transforms our devices from external tools into something far more intimate. As we delegate the heavy lifting of processing and pattern recognition, our devices become extensions of thought itself, acting as a functional augmentation of our own reasoning capabilities. 3. The End of the “Twenty-Four Hour” Limit Human intelligence is extraordinary, yet it has always been tethered to biological constraints. We suffer from “Finite Intelligence”—the reality that we forget crucial details, miss fleeting opportunities, and possess a brain that simply cannot learn everything in a single lifetime. No matter how brilliant the individual, we are all restricted by the same twenty-four-hour limit. AI-enhanced capacity solves for this biological bottleneck. It doesn’t physically add hours to the day, but it fundamentally alters the cognitive load we carry during those hours. By providing high-fidelity support, a personal AI collaborator manages the administrative friction that typically drains human energy: * Summarizing every meeting and conversation for instant recall. * Organizing complex projects and cross-referencing vast amounts of data. * Researching any topic and rapidly learning new tools on our behalf. * Scheduling and managing the logistics of a complex life. This is the end of the “limited” human. It is not about working more, but about ensuring that no insight is lost and no opportunity is missed because of the natural limits of our memory or time. 4. Reclaiming Our Humanity Through Automation There is a persistent misconception that more technology inevitably leads to less humanity. However, when we look closer, we see that technology is at its best when it removes the friction between people and what matters most. By automating the “busywork” of life, we are not becoming more machine-like; we are reclaiming the space to be more human. The shift is a simple but powerful trade-off. By reducing the time spent on repetitive tasks, administration, and the endless searching for information, we unlock a massive surplus of human energy. This allows for a transition from “managing” to “building,” where we prioritize: * Creativity over repetition. * Relationships over administration. * Purpose over the friction of daily logistics. “Technology has always been at its best when it removes friction between people and what matters most.” 5. The Rise of the “One-Person Team” Perhaps the most democratizing aspect of this era is the collapse of the barrier between having an idea and executing it. In the past, the asymmetry of resources meant that a single person could only go so far; to truly build something meaningful, you needed a massive organization or a full team. You needed a designer, a marketer, a lawyer, an accountant, a developer, and a researcher. The rise of the “One-Person Team” changes this. While human experts will always be vital for high-level strategy, AI allows an individual to access these specialized capabilities autonomously. This shifts the balance of power from resource-heavy institutions to the individual creator, empowering anyone with a vision to compete at a scale that was previously impossible without a corporate infrastructure. 6. A Future of Billions of Super-Capables At SELF Labs, the vision for the future is not the emergence of a single, centralized “Superintelligence” that dictates our lives. Instead, it is the rise of billions of “Amplified Humans.” This philosophy centers on Personal Intelligence—infrastructure that belongs to the individual rather than a distant corporation. By building systems around collaboration rather than just automation, we create an environment where people can participate instead of simply consume. This is a future where every person is equipped with an intelligent operating system that understands their intent, making them more capable of contributing their unique value to the global economy and society. 7. Conclusion: The Greatest Invention The history of progress is a lineage of tools that expanded our reach. The printing press expanded knowledge; electricity expanded productivity; the internet expanded communication. AI is the natural successor to this legacy, designed to expand human potential itself. By removing the administrative and cognitive burdens that have historically held us back, we are entering an era where our limitations are no longer defined by our resources or our biological memory, but by the depth of our imagination. “Perhaps the greatest invention of the AI era won’t be artificial intelligence. It will be amplified humanity.” As your personal operating system begins to shoulder the weight of the mundane, you are left with a more profound responsibility. When your finite limitations are finally removed, what legacy will you choose to build? Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 10

The AI Orchestra: Why the Future Isn't One AI Model

The vision of a single, all-knowing AI monolith is an architectural dead-end. While the “Monolith Myth” promises a single, massive brain that handles every digital interaction, history tells a different story. The internet is not a single server, the global economy is not a single company, and your computer is not a single program. We are currently fighting “computational gravity.” The idea that we can solve every problem with one giant model is a recipe for siloed intelligence and inefficiency. The future of AI is not a solo performance; it is an orchestra of specialized intelligences working in harmony. Efficiency Over Size (Bigger Isn’t Always Better) While models grow larger and smarter each year, we are hitting a wall of practicality. Massive models require staggering amounts of energy, compute, and infrastructure. Attempting to use a trillion-parameter model to set a simple calendar reminder is like using a rocket engine to power a bicycle. The “orchestra” model solves for the physics of energy and the speed of light. Efficiency requires matching the task to the appropriate level of model power to avoid unnecessary latency and cost. “Not every task requires the most powerful model available.” The Rise of the Specialist Just as a hospital relies on cardiologists, neurologists, and radiologists rather than a single general practitioner for every procedure, the AI ecosystem is moving toward domain-specific optimization. Generalist models are jacks of all trades, but specialists are the masters of the future. We are seeing the rise of Vision models, Reasoning models, and models specifically fine-tuned for coding, medicine, and law. These “instruments” are becoming exceptionally good at their individual tasks, often outperforming generalist models in their respective fields because they are built for depth rather than breadth. The Operating System as the Conductor As these specialists proliferate, the role of the Operating System (OS) must undergo a fundamental shift. We are moving away from an OS that simply launches applications and toward one that orchestrates intelligence through a Personal Intelligence Layer. This is no longer just about traffic control; it is about context. The OS acts as a conductor, making routing decisions based on its knowledge of your unique life—your calendar, your documents, your preferences, and your finances. It doesn’t just choose the smartest model; it chooses the smartest model for you. This removes the “cognitive load” from the user, as the orchestration happens silently in the background. Intelligence Without Friction To see the orchestra in action, consider a complex life event like buying a house. Traditionally, this is a friction-heavy process of juggling multiple experts. In an orchestrated future, you have one seamless conversation while multiple specialized models play their parts: * Contract Model: Reviews legal documents for red flags and hidden clauses. * Financial Model: Estimates mortgage rates and analyzes your long-term financing. * Research Model: Analyzes neighborhood data, school ratings, and local trends. * Insurance Model: Summarizes various coverage options and risk profiles. * Scheduling Model: Coordinates appointments with agents, inspectors, and banks. To the user, this feels like a single, frictionless interaction. Behind the scenes, it is a highly coordinated symphony of specialized intelligences. Modular Innovation and SELF OS A modular approach to AI accelerates innovation by lowering the barrier to entry. In a monolithic world, a startup must build a whole brain to compete. In a modular world, they only need to build a “virtuoso” violin—a superior specialized model that can be integrated into the existing system. The SELF OS represents this coordination layer. By focusing on connecting and routing rather than replacing, SELF OS allows local and cloud intelligence to coexist. Every new breakthrough in a specialized field becomes another instrument in the orchestra, instantly making the entire system more capable without requiring a total overhaul. Conclusion: The Most Important Software Ever Built The future of artificial intelligence does not belong to a single model that does everything. It belongs to the “conductor”—the intelligent operating system capable of coordinating thousands of specialized intelligences into a functional, harmonious system. This orchestration layer is likely to become the most important piece of software ever built. As the OS transitions from a tool into a Personal Intelligence Layer, we must look beyond the technology. How will your identity and economic agency shift when your OS knows your preferences better than you do, and can instantly coordinate a thousand specialists to execute your vision? The era of the monolith is over; the symphony is just beginning. Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 9

The Internet Is About to Become Intelligent

The Death of the Click: Why the Internet Is About to Start Thinking for Itself For over three decades, we have been conscripted into a form of digital manual labor. We search, we click, we scroll, and we scavenge, treating the internet as a sprawling, passive storage unit. But we are at the precipice of a seismic shift: the web is evolving from a repository of fossilized data into a living system of intelligence that doesn’t just store information—it understands it. The Intentionality Shift: When Software Anticipates the Goal The static web is a relic—a graveyard of passive containers waiting for human resuscitation. In the “old way,” the burden of effort is on the user to navigate complex menus and bridge the gap between a need and a result. The “new way” replaces this scavenger hunt with the simple expression of intent. Instead of clicking through endless real-estate filters or insurance comparison tables, you simply state your goal. * “I’m looking for a home under $500,000.” * “I need insurance for my new business.” * “Help me choose the best laptop for AI development.” Instead of navigating a labyrinth, you have a conversation. The internet connected information. Artificial intelligence may connect intentions. This represents a fundamental inversion of the human-technology relationship. We are moving away from a world where humans must learn the language of software, and toward an era where software masters the nuance of human goals. Beyond the Chatbot: The Rise of the True Agent The next evolution of the digital experience replaces static landing pages with intelligent representatives. These are not the brittle, frustrating FAQ bots of yesteryear; they are “true agents” integrated directly into the core business logic of a company. Because these agents are tethered to the company’s internal systems, they possess a real-time understanding of: * Products * Policies * Inventory * Pricing * Documentation * Scheduling For the user, this means the end of “I’ll get back to you.” A true agent doesn’t just talk; it performs. It can check live stock levels, adjust pricing dynamically based on your specific needs, and actually complete tasks that previously required a human middleman. The End of Search: From Scavenging to Orchestration We are witnessing the death of search as we know it, replaced by “orchestration.” In the current paradigm, finding an answer is a labor-intensive process of visiting dozens of sites and manually synthesizing fragmented data. It is exhausting, inefficient work. In the near future, your personal AI will act as a conductor. It won’t return a list of blue links for you to investigate; it will communicate directly with retail, banking, and healthcare agents to return with “answers, not links.” By handling the orchestration of data across the web, AI removes the cognitive tax of comparison and decision-making, delivering the result without the research. The Invisible Economy: Software Talking to Software The most radical transformation is happening where you can’t see it. The internet is becoming an “AI-to-AI” economy, where the primary users are no longer humans navigating interfaces, but intelligent systems collaborating with one another. Your personal assistant won’t just remind you of a task; it will negotiate the solution by talking to other software. Consider the speed of an internet where AI handles the friction: * Scheduling complex meetings. * Automating grocery replenishment. * Booking multi-leg travel. * Negotiating better subscription rates. * Coordinating healthcare paperwork. The internet becomes less about humans navigating websites... And more about intelligent systems collaborating. AI-Native: The New Architecture of Logic At SELF Labs, we believe the future isn’t about “bolting” AI onto existing frameworks—it’s about AI-native architecture. This is a vision where software is built around intelligence from its first line of code. This transformation will rewrite the DNA of operating systems, websites, and mobile phones alike. We are moving away from managing software and toward a reality where software manages the complexity of our lives for us. The Final Transformation The history of the web is being rewritten in real-time through four primary evolutions: * Pages → Conversations: From static reading to interactive dialogue. * Websites → Agents: From passive destinations to active representatives. * Search → Reasoning: From finding links to logical synthesis. * Information → Intelligence: From data storage to active understanding. As we move from being digital managers—burdened by the friction of “doing”—to creative architects of our own time, we face a profound question: If software begins managing the complexity of our lives for us, what will we do with the time and mental energy we get back? Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 8

The End of Software: Why You’ll Soon Own Intelligence Instead of Apps

We are currently enduring a state of profound digital friction. Our lives are cluttered with dozens of disconnected applications, siloed data, and a relentless “subscription tax” that demands payment for tools that remain stubbornly indifferent to who we are. We have become high-paying renters in our own digital lives, managing updates and learning complex interfaces for software that is essentially blind to our needs. But the era of the static tool is ending. We are witnessing a fundamental shift from “Software-as-a-Service” to “Intelligence-as-an-Extension.” The glass ceiling of static code is breaking, giving way to a world where the value of your technology isn’t found in the apps you download, but in the intelligence you own. From Blind Tools to Perceptive Partners Traditional software is fundamentally reactive. It sits on your device, a hollow vessel waiting for specific instructions. Whether it’s a spreadsheet or a photo editor, the experience is identical for every user because the software lacks context. It doesn’t know your goals, it doesn’t remember your preferences, and it doesn’t understand your world. AI breaks this cycle by introducing the pillars of cognitive partnership: memory, reasoning, and context. It is perceptive where software is blind. As it works alongside you, it absorbs your writing style, your project history, and your priorities. It evolves from a passive utility into an active partner that grows more capable the longer it exists in your orbit. “Two people using the same AI eventually have two completely different assistants.” The Switching Cost of a Digital Soul In this new paradigm, hardware becomes a disposable vessel. We are approaching a moment where you will choose a new phone or computer not because of its silicon specs, but because of the intelligence living inside it. The device is merely the physical gateway to a growing, personal digital consciousness. While switching hardware will become a triviality, switching “intelligence” will become an existential challenge. You can easily replace a screen, but you cannot easily replace a system that has spent years learning the nuances of your life. Technology is transitioning from a tool you use to a digital extension of your very self. Personal Intelligence: The New Digital Gold The industry is obsessed with the “arms race” of chips, massive models, and data centers. They are looking at the factory, but they are missing the product. The supreme digital asset of the new era is Personal Intelligence. It is your specific memory. It is your unique experience. It is your private history of reasoning. It is your personal understanding of the world. This asset—cultivated through years of interaction—will become the most valuable property you possess in a digital economy. The Moral Imperative of Digital Sovereignty Ownership has always been the prerequisite for freedom. We own our homes, our businesses, and our data to ensure we are not subjects of a higher power. Yet, today, we are drifting toward a “Cloud Feudalism” where we rent our intelligence via monthly subscriptions. If you stop paying, your digital brain is lobotomized. SELF Labs operates on a different moral imperative: personal AI must belong to the individual. It shouldn’t be locked inside a corporate cloud or tethered to a specific vendor’s whim. Your intelligence should move with you, grow with you, and exist as your sovereign property. You shouldn’t just use intelligence; you must own it. The Death of the App-Centric Operating System The “App Store” model is a relic of a fragmented past. In the current OS, the user is the manual bridge between disconnected silos. The vision for SELF OS flips this hierarchy. We are moving toward Intelligence Ecosystems where your personal AI is the primary interface—the center of your digital life. In this ecosystem, applications are no longer the destination. They are merely backend tools—plugins and utilities that your intelligence calls upon to execute specific tasks. You stop managing apps and start managing your intelligence. Conclusion: The Question for the Next Generation The transition from computers as “collections of software” to “collections of intelligence” is the defining shift of our time. The winners of the last decade built the best stores to rent us tools. The winners of the next decade will be those who empower individuals to own their own minds. We are moving past the era of the utility and into the era of the partner. As the boundaries between our intentions and our technology blur, the metrics of digital success will change forever. “The most important question won’t be, ‘What apps do you have installed?’ It will be, ‘What intelligence do you own?’” Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 7

The Architecture of Abundant Intelligence

Introduction: The High Price of Being Smart For ten millennia, the velocity of human progress was tethered to a single, rigid constraint: the number of “smart people” we could get into a room. Throughout economic history, we have successfully scaled physical labor via the steam engine and connection via the internet, but the core engine of value—human intelligence—remained stubbornly scarce and expensive. We are now witnessing a fundamental shift in the factors of production. Artificial intelligence is not merely a new tool; it represents the collapse of the marginal cost of cognitive labor. We are transitioning into an era where intelligence is being decoupled from human biology and repurposed as a cheap, abundant layer of cognitive infrastructure. This is the end of expensive thinking. Intelligence is the “Hidden Cost” in Everything You Buy To a strategist, every price tag tells a story of intellectual overhead. Most of what we consume—from a skyscraper to a legal contract—is actually a bundle of human decision-making and expertise. This “premium on middle-man expertise” has historically been the primary bottleneck in the global economy. Consider the professionals whose specialized knowledge previously drove project costs into the stratosphere: * Architects and Engineers who manage structural complexity. * Attorneys and Accountants who navigate regulatory and financial mazes. * Creative Strategists, Designers, and Developers who build brand and digital presence. * Medical Professionals who interpret biological data. When AI automates the repetitive elements of these roles, it removes the “intellectual tax” that currently inflates the cost of living. By eroding this bottleneck, we can move toward a reality where personalized education, sophisticated legal drafting, and medical diagnostics are no longer luxury goods. However, a visionary must also remain grounded: while the cost of “thinking” may plummet, the physical economy—housing, energy, and raw materials—will still face traditional scarcity and regulatory constraints. The Rise of the “Super-Individual” The democratization of intelligence creates a new “liquidity of expertise.” In the legacy economic model, achieving enterprise-scale output required massive organizational structures to manage cognitive load. Today, that equation is being inverted. We are seeing the rise of the “Super-Individual”—a single founder empowered by AI to act as a full-stack enterprise. This shift provides small businesses with asymmetric access to capabilities that were once the exclusive domain of Fortune 500 companies. It isn’t just about speed; it’s about a fundamental change in how we explore the boundaries of the possible. “A designer can explore hundreds of concepts in an afternoon. Researchers can analyze information in minutes that once took weeks.” The Critical Tug-of-War Over Ownership As cognitive labor becomes a commodity, the strategic focus shifts from the creation of intelligence to its ownership. We face a binary choice: will this cognitive infrastructure be concentrated within the proprietary silos of a few “hyper-scalers,” or will it be decentralized? Ownership of personal AI and participation in decentralized intelligent networks is the only mechanism by which individuals can capture the value of this new era. Without ownership, the “abundance” of AI becomes a subscription service where the economic gains are siphoned away by the providers of the infrastructure. To share in the benefits, we must transition from being mere users of intelligence to being owners of the assets that produce it. Earning a Living Beyond the Traditional Job The evolution of income is moving from the sale of time to the management of assets. We have progressed from physical labor to the creator economy and gig work, but the AI era introduces a paradigm shift: renting out “intelligent infrastructure.” In this new economy, language is no longer a barrier to doing business, effectively expanding the global marketplace to every corner of the planet. As the “selling of hours” becomes less viable, individuals will create value through: * Renting Assets: Contributing compute power and “renting” personal data or hardware to decentralized networks. * Systemic Curation: Training agents, refining specialized models, and managing complex AI ecosystems. * Digital Architecture: Operating the intelligent infrastructure and designing high-value digital experiences. This mirrors historical transitions that redefined our world: just as electric light replaced the scarcity of candles, digital cameras ended the era of film, and GPS made paper maps obsolete, AI is making the “access to answers” an ambient resource rather than a paid service. A Shift Toward “Deeply Human” Work The pervasive fear of technological unemployment misses the strategic forest for the trees. Technology consistently shifts the focus of human effort rather than eliminating it. By offloading repetitive cognitive tasks to the infrastructure layer, we liberate human capital for work that is “deeply human.” Our economy will likely pivot toward activities that AI cannot synthesize: building high-trust relationships, creating original art, teaching, caring for others, and solving the “wicked problems” of science and exploration. The value of empathy, intuition, and leadership will likely experience an “unnatural” appreciation as cognitive tasks become commoditized. Conclusion: Building the Future We Want AI is the new “roads and bridges” of the 21st century. Much like the interstate highway system transformed commerce or electricity revolutionized manufacturing, an abundance of intelligence will reshape the fundamental cost structure of human life. However, this transition is not a destiny; it is a choice. The outcome depends on our collective ability to navigate policy, competition, and ownership models. We have the potential to either centralize power to an unprecedented degree or to distribute opportunity to every person on Earth. “The greatest impact of artificial intelligence may not be that it replaces human work. It may be that it makes intelligence so abundant that opportunities once reserved for a few become accessible to billions.” As we architect this new economic landscape, the question is no longer whether intelligence will become abundant. The question is: Will you be a mere consumer of this new infrastructure, or will you be an architect of the world it creates? Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 6

Why the Best Interface is One You’ll Never See: The End of the App Era

Every interface you’ve ever used exists because computers couldn’t understand what you wanted. For decades, we have accepted this as the natural order of things. We adapted our human brains to the rigid logic of machines. First, we memorized syntax for the command line. Then, we learned to navigate the spatial metaphors of the graphical user interface. We transitioned to touchscreens that put the internet in our pockets and voice assistants that brought us natural language. Yet, despite this evolution, the cognitive load remains staggering. We are still forced to translate our high-level human goals into the granular language of software. We spend our lives toggling between windows, hunting through menus, and organizing folders. The burden of combining these disconnected tools into meaningful work falls entirely on you. This is the hidden tax of the digital age: we have become the manual labor for our own devices. We are now at a tipping point where this dynamic finally flips, moving from a software-centric world to one defined by intent-based interaction. You Never Actually Wanted the App Applications are not the goal of computing; they are a temporary compromise. We have tolerated them only because computers lacked the intelligence to assist us directly. Every application is its own isolated world with its own navigation, settings, and learning curve. This fragmentation creates a mental tax every time you switch tasks. The truth is that nobody actually wants to “open a banking app”—they want to pay a bill. Nobody wakes up with the desire to “launch a travel app”—they want to visit their family. The app is a middleman, a siloed interface that stands between you and your objective. “Applications were never the goal. They’re simply tools we’ve accepted because computers lacked a better way to help us.” The Shift from Language to Intent The breakthrough of Large Language Models (LLMs) isn’t just that computers can now “talk.” The real revolution is that they can finally understand intent. In the app-centric era, if you wanted to prepare for the next day, you had to manually open a calendar, a weather app, a map, and an email client. You were the orchestrator. In an intent-based system, you simply express a goal: “Help me prepare for tomorrow.” The system doesn’t ask which app you want to open; it begins reasoning about your objective. This transition from “software” to “intention” marks a fundamental shift in the human-computer relationship. The machine finally takes on the burden of processing, leaving the human to focus on the outcome. Apps as “Invisible Infrastructure” In the near future, the hundreds of disconnected apps on your phone will cease to be destinations. Instead, they will become “invisible infrastructure”—background services utilized by a single, cohesive intelligence. This is the core philosophy behind SELF OS. Rather than managing a fragmented collection of interfaces, you interact with “One Intelligence” built on four technical pillars: Reasoning, Context, Memory, and Intelligent Orchestration. Because the system remembers your relationships and reasons through your goals, the interface can effectively disappear. On a typical busy day, this invisible layer handles the logistics autonomously: * Intelligent Orchestration: Checking your calendar and cross-referencing it with real-time traffic data. * Proactive Planning: Suggesting exactly when to leave to arrive on time. * Contextual Review: Monitoring unread emails and summarizing only the ones that impact your current schedule. * Information Synthesis: Organizing the specific documents and data points you need for your upcoming tasks before you even ask for them. The Law of Diminishing Visibility In technology, power and visibility are inversely proportional. The more sophisticated a system becomes, the more it recedes into the background. Today, we no longer think about internet protocols, graphics drivers, or file systems. These haven’t gone away; they have simply been buried under a better abstraction layer. Intelligence is the ultimate abstraction. It sits above the messy plumbing of software, shielding us from the friction of the “how” so we can focus on the “what.” We are reaching a point where we will stop thinking about apps entirely, just as we stopped thinking about the “file system” on our phones. “The future of computing isn’t about finding better apps. It’s about reaching a point where you no longer need to think about them at all.” Reaching the Vanishing Point Every major leap in computing has been defined by the removal of friction. The mouse removed the need for typed commands; touch removed the need for physical buttons; voice removed the need for typing. Artificial Intelligence is the final step in this evolution because it removes the interface itself. As we move toward a world of autonomous action and intent-based systems, we regain something precious: human agency. We are stepping out of the role of “device managers” and back into the role of creators and decision-makers. How will you spend your time when the digital overhead of your life no longer requires your constant attention? The most sophisticated technology is the one you don’t have to see. Ultimately, the best interface is the one that disappears. Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 5

The Operating System That Pays You

For decades, operating systems have managed our devices—but they’ve never generated value for their owners. In this episode, we explore a different future. What if your phone wasn’t just a tool you paid for, but an asset that could participate in the AI economy? We discuss idle computing power, distributed AI, edge inference, hybrid intelligence, and why SELF OS is being designed around a simple idea: Your operating system shouldn’t just work for your device—it should work for you. Body Modern smartphones contain powerful CPUs, GPUs, and AI accelerators, yet most of that computing power sits idle throughout the day. As artificial intelligence continues to reshape computing, we believe operating systems need to evolve beyond launching applications and managing files. SELF OS explores a future where intelligent devices become active participants in a decentralized AI infrastructure. Rather than relying exclusively on massive cloud data centers, billions of capable devices could contribute compute when appropriate, helping power the next generation of artificial intelligence. It’s a different way to think about ownership. Not just owning hardware. Owning participation in the intelligence economy. This episode explores why the operating system of the future may become far more than software—it may become infrastructure. Call To Action If this vision resonates with you, subscribe to follow our journey as we build SELF OS and explore what the future of AI-native computing could look like. Every episode documents the ideas, architecture, and philosophy behind building the next generation of operating systems. Purchase a SELF Phone today! https://selflabs.xyz/self-phone Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 4

Why Apps Are Dying

For nearly two decades, apps have organized our digital lives. Need a ride? Open an app. Need to send money? Open another app. Need to book a flight, schedule a meeting, edit a document, order food, or manage a project? Find the right icon, open the right interface, and complete the task step by step. We have become the orchestration layer between dozens of disconnected systems. I believe AI changes that. In this episode, I explore why the next major shift in computing may move us away from app-first experiences and toward intent-first computing. Instead of asking which app to open, you simply describe what you want accomplished. Your Agent Avatar becomes the primary interface. SELF OS interprets the request, gathers the right context, chooses the appropriate intelligence, connects to the necessary tools, and coordinates the task across applications and services. The apps may still exist. But they increasingly become infrastructure used behind the scenes. In this episode, I break down: * Why app switching creates unnecessary friction * How Agent Avatars could become the next interface after touchscreens * The difference between a chatbot and an operating-system-level AI * How Aura gives your Agent Avatar persistent memory and context * How Role Model AI coordinates specialized agents behind one interface * How APIs, MCPs, ACPs, and browser automation allow AI to take action * How SELF OS routes tasks between local SLMs, larger models, and Resonatia * Why developers may eventually build capabilities for agents instead of standalone apps * What the “death of apps” actually means for users and software companies The death of apps does not mean software disappears. It means people stop spending so much time operating software manually. The future interface may not be a grid of icons. It may be a personal intelligence that already knows your tools, understands your context, and can act across your digital life on your behalf. The next operating system may not ask: “Which app do you want to open?” It may ask: “What do you want to accomplish?” Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe
Aug 3

Earn Passive Income With SELF OS

What if your phone, laptop, or gaming PC could earn value while it sits idle? In this episode, I break down one of the core ideas behind SELF OS: transforming unused personal computing power into part of a distributed AI network. Instead of relying entirely on centralized data centers, SELF OS is designed to route workloads across local devices, trusted nodes, Resonatia’s distributed infrastructure, and the cloud based on privacy, performance, availability, and cost. Users who choose to contribute their available compute could participate as node operators and receive rewards through the SELF ecosystem. We explore: * How SELF OS routes tasks between local SLMs, larger models, Resonatia, and the cloud * Why billions of idle devices represent an overlooked computing resource * How node operators could earn by contributing unused compute * The role of Essence in rewards and machine-to-machine payments * Why the future of AI infrastructure may be shared rather than fully centralized * How personal devices could evolve from passive consumers into productive digital assets This is not about promising effortless money or replacing cloud infrastructure. It is about building a system where people can voluntarily contribute resources, understand how they are being used, and participate in the value their hardware helps create. The future of AI should not only be powered by people’s devices. People should have the opportunity to benefit from powering it. Get full access to In the Lab by SELF Labs at selflabs.substack.com/subscribe

12 Episodes

Stay up to date on our research and development. selflabs.substack.com

Creator

Milan Cheeks
Years Active

2026
Episodes

12
Rating

Clean
Copyright

© Milan Cheeks, SELF Labs Inc
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In The Lab Podcast