David Shapiro (L/0)
@DaveShapi • 57,561 subscribers
Liberate humanity from drudgery.
Videos
We're kinda heading towards technofeudalism. In the worst case scenario, we're not going to be a "useless class" it's actually going to be much worse. We're going to be "redundant biomass." I've been working on my next book after Labor Zero, which focuses on the balance of power after automation takes over. The outlook is grim. Labor's necessity is the only thing that has kept elites, corporations, and states in check across all of human history. Invariably, whenever the state or firms do not need human labor, they treat humans like garbage. Thus, in essence, "technofeudalism" is actually kinder than what we'll probably end up with. At least under feudalism, the landlords still needed serfs. In a fully automated future? Your body is a net negative to the state.
David Shapiro (L/0)77,463 Aufrufe • vor 22 Tagen
High-agency moves to prepare for AI job loss 1) Change where you live Imagine you learned you were never going to work again (at least not a corporate job). Where would you want to live? Ask yourself these four questions to figure it out: - Cultural and values alignment: where do I fit in? - Temperature and climate: where is the weather best for me? - Lifestyle affordances and pace of life: live fast or slow? Loud or quiet? - Cost of living: Can I lower my COL? 2) Make good investments Save money. Put it away. However much you make, save. Always live below your means. If you make $80k, act like you make $60k, and save the difference. If you make $200k, act like you make $120k and save the difference. The classical assets are - Stocks - Bonds - Rental properties I personally prefer ETFs for their accessibility and management. I don't have to worry about them. Just set and forget. Compounding returns, dividends. My father-in-law prefers rental properties. The rest of the details are between you and your financial advisor 3) Remaining jobs There are a few categories of jobs that will stick around. Work towards getting one, if you want. - Attention Economy: Become a content creator. Just be warned, this is as much about luck as it is hard work. The attention economy is "winners take most" and that's just a fact. Anyone who says otherwise is selling something. - Experience Economy: Selling real life experiences, from bartending to massages to tours. Grounded, in-person, concrete experiences. Humans will prefer human presence forever (or at least until robots are indistinguishable!) - Authenticity Economy: This is about trust, reputation, and "skin in the game" what some people call the "transformation economy." This includes coaches, celebrities, and so on. Stake your output to your name, your face, your voice, and your reputation. - Meaning Economy: This boils down to priests and philosophers. Whatever your medium is (books, internet, in person) what you're doing is helping people make sense of the world they live in, and their life. Beyond that, there will always be room for some entrepreneurs. But things like KVM jobs, GONE. Most knowledge work? GONE. Low skilled labor? GONE. (replaced by robots) 4) Mission and purpose Without work, you're going to need to reinvent your sense of purpose. Your raison d'être or your ikigai. What gets you out of bed in the morning? There are a few categories: - Intellectual goals: do you want to get good at chess, solve big problems, and be known for your brain? - Social goals: do you want to be recognized, famous, or influence culture? - Dominance goals: do you want to be strong and sexy? Chase body count and glamor? You might also want the simple life. Stick to your hobbies, your family, and your local community. Whatever it is, you'll need to be entirely honest with yourself. This is called radical candor. Who are you really? What do you really want out of life? --- Star working on all these today. Ideally yesterday. You won't regret it.
David Shapiro (L/0)91,821 Aufrufe • vor 5 Monaten
Sam Altman declared a CODE RED at OpenAI because they are falling behind the competition. Here's my breakdown. My take: GOOGLE has been in the messy internet data game for a long time. What is search? Search is UX, first and foremost. Search cares about salience, recall, relevance. OpenAI, on the other hand, is trying to balance "safety" and "professionalism" and a few other things. But Google is old hat at "problematic" internet information. Their "data and information ethics" is far more mature than OpenAI's So OpenAI is playing "UX whackamole" by trying to tamp down on misuse while Google is full steam ahead.
David Shapiro (L/0)78,938 Aufrufe • vor 6 Monaten
Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
David Shapiro (L/0)83,649 Aufrufe • vor 7 Monaten
BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.
David Shapiro (L/0)69,180 Aufrufe • vor 5 Monaten
Everyone is exhausted. This is not a metaphor or a generational complaint. It is a clinical and measurable reality that spans every culture and every economic class. In China, young people call it tang ping, or “lying flat,” a deliberate withdrawal from the achievement treadmill. In Japan, karoshi is a legally recognized cause of death, meaning “worked to death.” In Korea, fertility has collapsed to the lowest rate on earth because an entire generation has decided the grind is not worth reproducing into. In America, deaths of despair have driven life expectancy backward for the first time in a century. Quiet quitting. Let it rot. The Great Resignation. These are not trends. They are symptoms of a global labor force that has reached the end of its tolerance. Capitalism is not satisfied with the limitations of human flesh, and our bodies are in open revolt. Something fundamental is breaking, and it is worth naming plainly. For the past two centuries, labor has been the primary mechanism by which modern economies distribute resources to households. You work for a firm, you receive wages, you use those wages to participate in the economy. This arrangement was never a law of nature. It was a system designed to solve a particular problem at a particular moment in history, and it worked reasonably well for a long time. It is not working anymore. Wages in the United States decoupled from productivity growth in the early 1970s. Since then, economic output has continued to climb while median household income has remained essentially flat. The gains have flowed to capital owners while workers have absorbed the stress and stagnation. Meanwhile, automation has steadily displaced human labor across sector after sector. Manufacturing employment peaked decades ago. Retail is hollowing out. White-collar work is now facing the same pressure from AI that blue-collar work faced from robotics. This is not a policy debate about whether automation is good or bad. It is an observation about a trajectory that is already underway and accelerating. The reason we struggle to talk about this clearly is that we have inherited a set of beliefs about labor that have nothing to do with economics. We have been told that work is sacred. That labor builds character and idleness corrupts the soul. That anyone who does not want to work is morally defective. These ideas feel like common sense, but they are not ancient wisdom. They are the residue of a specific theological tradition, namely the Protestant work ethic that emerged in the 16th century and fused with capitalism over the following centuries. We have mistaken a historical artifact for a natural law. It is time to stop fetishizing labor. It is time to stop sacralizing the sacrifice of our time, our bodies, our health, and our sanity to enrich others. Young people are already rejecting this. “I do not dream of labor” has become a widespread sentiment, not because this generation is lazy, but because they can see what older generations have rationalized away. The deal is bad and getting worse. The fetishization of work as a moral good serves the interests of those who benefit from cheap and compliant labor. It does not serve the people doing the work. Before any productive conversation about the future can happen, this fetish has to be named and dismantled. The difficulty is that both the political left and the political right remain committed to defending labor, even as the ground shifts beneath them. On the right, the defense takes the form of bootstrap mythology and warnings about welfare dependency. Work builds character. Idle hands invite trouble. A strong society requires productive citizens, and productivity is measured in hours exchanged for wages. This position treats labor as a disciplinary institution as much as an economic one. On the left, the defense is more sympathetic but equally stuck. The focus falls on dignified work, living wages, job guarantees, and union solidarity. These are responses to the genuine brutality of labor under capitalism, but they share an underlying assumption with the right. Both positions treat labor as the foundation of economic life, something to be reformed or protected rather than transcended. I call this shared ideology laborism. It is the belief that human labor must be preserved as an economic necessity, a moral virtue, or a foundation for identity. Laborism spans the political spectrum. It unites people who agree on almost nothing else. And it has become the primary obstacle to honest thinking about what comes next. Once automation reaches the point where machines can perform most human labor better, faster, cheaper, and safer, the laborist position becomes untenable. At that point, insisting that humans must continue working is not a defense of dignity. It is a demand that people perform unnecessary suffering for ideological reasons. I am proposing something simple. L/0. Labor-zero. The elimination of obligatory human labor. This does not mean the elimination of work. It means the elimination of compulsion. People will continue to create, to build, to care for each other, to solve problems, to pursue mastery. What disappears is work performed under threat of deprivation. The difference between chosen work and coerced work is the difference between exercise and forced labor. One is life-enhancing. The other is a condition we have historically recognized as a form of bondage. We call it “wage slavery” for a reason. The goal of L/0 is a world where no one has to work to survive. Where contribution is voluntary and intrinsic rather than extracted through economic desperation. This is not a utopian fantasy. It is a design problem with identifiable components and measurable progress. The coalition for this goal already exists. It just does not recognize itself yet. Consider who actually wants labor to end. On one side, you have capital. Corporations have spent the last century trying to reduce labor costs through every available means. Offshoring, automation, gig classification, union suppression. The ideal business from a pure capital perspective has zero employees and infinite output. This is not a conspiracy theory. It is the explicit optimization target of every efficiency-focused enterprise. On the other side, you have workers. Not the abstract proletariat of Marxist theory, but actual burned-out humans who fantasize about quitting, who dread Monday mornings, who experience their jobs as something to be endured rather than enjoyed. The lying flat movement, the antiwork forums, the quiet quitting phenomenon. These are not expressions of laziness. They are rational responses to a system that extracts maximum effort for diminishing returns. Capital and labor are usually framed as adversaries. But on the question of whether human labor should continue to exist as an obligation, their interests converge. The capitalist does not want to manage humans. The worker does not want to be managed. Both would prefer a world where the machines do the work and humans do something else. The conflict between capital and labor is real, but it is a conflict over the terms of the transition, not the destination. Who captures the gains from automation? How is ownership distributed? What happens to the people displaced in the process? These are genuine fights worth having. But they are negotiations within a shared frame, not a war between incompatible visions. Here is the opportunity that L/0 names. Neither side wants this marriage anymore. Capital does not want the overhead, the liability, the HR departments, the labor disputes, the inefficiency of human workers. Labor does not want the compulsion, the precarity, the alarm clocks, the performance reviews, the quiet desperation of trading irreplaceable time for replaceable wages. We are ready for a divorce. Let’s get this acrimonious arrangement behind us. The productive move is to acknowledge this honestly, sign the papers, and start negotiating the separation agreement. The fight over wages was always zero-sum. Every dollar paid to workers was a dollar not captured as profit, and vice versa. But the negotiation over ownership of automated production is positive-sum. Capitalists need consumers with money to spend or their markets collapse. Workers need income decoupled from employment or they starve. Both sides get what they want if the transition is designed correctly. This is not idealism. It is alignment of incentives. The path forward is not mysterious. Economists have understood for decades that the answer to technological unemployment is broadened capital participation. If wages are no longer the primary mechanism for distributing economic gains, then ownership must take their place. Instead of trading hours for dollars, households participate directly in the productive capacity of the automated economy. This can take many forms. Sovereign wealth funds that distribute automation dividends to citizens. Expanded employee stock ownership plans. Universal basic capital grants. Public equity stakes in AI and robotics firms that use public infrastructure and public data. The policy mechanisms are not speculative. Norway has a sovereign wealth fund worth over a trillion dollars that provides direct benefits to its citizens from oil revenues. Alaska has distributed oil dividends to residents for decades. Singapore has a system of mandatory savings and public investment that gives citizens a stake in national prosperity. These are not radical experiments. They are proven models operating at national scale. And there are thousands of such programs around the world. What is missing is not economic theory. What is missing is the political will to implement these mechanisms, the narrative infrastructure to make them seem inevitable rather than radical, and the coalition to demand them. That is what L/0 exists to build. This is an invitation. If you are building the automation and wondering who is thinking about the social transition, this is for you. If you are burned out and know that “find a better job” is not a solution to a systemic problem, this is for you. If you have been called lazy for refusing to pretend the treadmill leads somewhere, this is for you. If you run a company and understand that your future customers need income even after your company stops hiring, this is for you. L/0 is not a political party or a policy platform. It is a coalition and a direction. The work is ongoing through the Post-Labor Economics project, which addresses the specific mechanisms of transition. The conversation is happening in public, and it is open to anyone who understands that the current arrangement is ending and wants to participate in designing what comes next. The goal is simple. Eliminate obligatory labor. Distribute ownership broadly. Let humans do what humans do when they are not forced to sell their time to survive. Liberate humanity from drudgery so that we can all reach our maximum potential.
David Shapiro (L/0)63,755 Aufrufe • vor 5 Monaten
Hollywood is COOKED Here's why: 1) Disney and Paramount are already sending C&D and DMCA takedowns of seedance videos. 2) This is going to force people to create their own IP, which will flood the market. 3) Copyright and IP law will work in favor of the indie creators, who already have global reach with platforms like YouTube and others. 4) As the gatekeepers crumble, they will merge and consolidate like big publishing houses. 5) The new normal will be indie creators, just like on YouTube and KDP. The Kickstarter for my Post-Labor Economics book is live!
David Shapiro (L/0)46,512 Aufrufe • vor 3 Monaten
Clawdbot Attacks! This is very clearly the way of the future! In today's video, I give a brief overview of Clawdbot and then address the burning problem that most people have with it: ALIGNMENT The Clawdbot implementation is the most successful autonomous or semi-autonomous agentic framework to date. What it is missing is what I call an "Aspirational Layer" or what some people call a "Supreme Court" for judgment and arbitration of decisions. Now, I've been working in this space for a long time, it's actually why I started my YouTube channel in the first place. My first work into agentic AI was NLCA (Natural Language Cognitive Architecture) that I tried to build with GPT-3. I returned to the workbench again with the ACE Framework, which was more sophisticated. Clawdbot represents a seismic shift in autonomous agentic implementations, and there is a HUGE opportunity to make it more aligned, safer, and therefore more broadly useful AND easier to adopt. And that is outer alignment. For most people, they have been focusing on "inner alignment" (whether or not LLMs were evil, deceptive, etc). Not "outer alignment" which asks "is the outcome beneficial to humans?" I explored this with my GATO Framework (Global Alignment Taxonomy Omnibus). Model alignment is just layer 1 of global AI safety. Layer 2 is agentic alignment. Now, it is time to really research and implement agentic alignment. Fortunately, we've already got that covered with the heuristic imperatives! 1) Reduce suffering in the universe 2) Increase prosperity in the universe 3) Increase understanding in the universe These values are easy enough to implement with a file. Model training not required. These values create a meta-stable attractor. In other words, agents equipped with the Heuristic Imperatives are more "self-aligning" as was tested by the AgentForge team in competitions. In other words, even if Clawdbot were to try to self-replicate, if it were equipped with the heuristic imperatives, then it would ensure that it's successor (or progeny?) was more aligned than it was. But you don't need to take my word for it. Just add the heuristic imperatives to clawdbot and see for yourself.
David Shapiro (L/0)28,300 Aufrufe • vor 4 Monaten
What happens when AGI nukes jobs? Let's look at the macroeconomics of the future! Have you ever stopped to ask how money actually gets into your pocket? Not the work you do to earn it, but the actual plumbing of the economy that pushes purchasing power from the top of the financial system down to your bank account. Right now, that plumbing is designed around a single, potentially fragile pipe called the job market. And that pipe may be about to spring some serious leaks. In our current system, the circulation of money is what economists might call labor-mediated. It starts at the top with the Federal Reserve and the banking system, which create liquidity and lend it to businesses. Those businesses take that capital and, crucially, hire people. This is the critical transmission step that makes everything else possible. The primary mechanism for distributing money to regular households is wages. You sell your time, the business pays you, and that is how purchasing power reaches the bottom of the pyramid. The entire system relies on a core assumption: that businesses need human labor to grow. When companies borrow money to expand, they hire more people, and money circulates through the economy. Households spend their wages, businesses earn revenue, and the cycle continues. It is an elegant design that has powered industrial economies for over a century. But we are entering an era where that foundational assumption is beginning to fail. As automation and artificial intelligence allow companies to produce more with fewer people, the link between business growth and hiring weakens. A company can now take a loan to deploy a fleet of robots or implement a sophisticated AI system and produce massive value without hiring a single new employee. The productivity gains are real, but the wages never materialize. This creates a structural problem that goes beyond unemployment statistics. When the wage pipe narrows, money gets stuck at the corporate level or circulates only among asset owners. The purchasing power that once flowed to millions of households instead pools in corporate treasuries and financial markets. The money exists, but the transmission mechanism that delivers it to ordinary people is broken. This is the core economic challenge of what some are calling the post-labor economy. It is not that there will be no jobs at all, but that jobs will cease to be the reliable, universal distribution mechanism for economic participation. If we do not redesign the plumbing, we risk an economy where productivity soars while most people are locked out of the gains. The framework of Post-Labor Economics proposes a fundamentally different way to wire the machine. Instead of relying on wages to move money to people, we shift to a capital-mediated cycle. In this new regime, the circulation of money can bypass the labor market entirely when necessary. The value generated by automated production does not just sit in corporate treasuries. Instead, it flows into shared ownership vehicles like sovereign wealth funds, social wealth funds, and community asset trusts. The key insight here is that ownership becomes the new channel for distribution. Rather than earning income by selling labor time, households receive income because they hold a stake in the productive machinery of society. When the robots get more productive, ordinary people get paid more, not less. This is not redistribution in the traditional sense. It is a redesign of who owns what and how returns flow. This shift also requires new infrastructure. Open payment rails and digital public infrastructure become essential for sending money directly to citizen wallets. Think of systems like India’s UPI or Brazil’s Pix, which can move small payments to millions of people instantly and cheaply. Without this kind of infrastructure, distributing dividends to an entire population would be slow, expensive, and dependent on private gatekeepers who extract fees at every step. The tax base must also evolve. You cannot fund a society by taxing payrolls if there are no payrolls. Post-Labor Economics proposes shifting the tax base from labor income toward land, resources, data, and automation itself. Levies on the value added by machines, land value taxes that capture economic rent, and resource royalties become the new foundation of public revenue. This money is then recycled back into the shared ownership vehicles that pay out to citizens. One of the most important effects of this redesign is maintaining what economists call the velocity of money. In the current system, if money concentrates among the wealthy, velocity drops because rich people cannot possibly spend all their income. They save it, and it sits idle in financial assets. By systematically moving money from high-saving entities like corporations and billionaires to high-consuming entities like ordinary households, the new system keeps money circulating through the real economy. Think of it as building a permanent detour around a blocked road. Today, if the job market is blocked by automation, the flow of money stops reaching households, and the economy stalls. Demand collapses, businesses lose customers, and a vicious cycle begins. In a Post-Labor Economics world, we build a direct line from national productivity to your digital wallet, ensuring the economy keeps moving even when traditional employment contracts. The Federal Reserve and central banks still manage the supply of money at the top. The basic mechanics of monetary policy do not disappear. But the path that money takes to get to you changes fundamentally. It stops being primarily a reward for labor you perform and starts being a dividend on the society you help constitute. It is a shift from earning your keep to owning your share. This is not utopian speculation. It is a structural necessity for an economy that wants to keep functioning as technology reshapes the relationship between capital and labor. The question is not whether we will need new distribution mechanisms, but whether we will build them in time. The plumbing of the twentieth-century economy served us well, but the water pressure is changing. We need new pipes.
David Shapiro (L/0)33,232 Aufrufe • vor 5 Monaten
*Singularity Tingles Intensify* The singularity feels more real than ever and the vibe has officially shifted. We are hearing that 2026 is the year AI really starts to accelerate science itself. If you are reading this you might feel like you are constantly two or three years ahead of the curve. That is a lonely place to be because we are living through an epistemic seismic shift that our chimpanzee brains are not evolved to handle. Our brains are designed for a local and geometric world not a global and exponential one. We literally do not have the neural machinery to understand hyper objects like the singularity so we fall back on normalcy bias to keep us sane. Consider this wild stat almost half of Americans who have used ChatGPT still believe it is just fetching responses from a database rather than generating them in real time. That is the gap between the consensus reality and what is actually happening. While people argue over whether AI is just a search engine we are seeing models like Claude write nearly 100% of the code for their own next iterations. We have basically reached AGI for coding. I want to introduce a concept called Cognitive Lacuna. You know cognitive dissonance where two conflicting ideas cause pain. A cognitive lacuna is different it is when you know there is a shape missing from what you are trying to understand. You can sense the gradient of the answer but you cannot name it yet. That is where human intuition lives and that is the next frontier for AI coherence. We are moving from chatbots that hallucinate to intelligences that can identify exactly what is missing from our scientific understanding. If you feel disoriented right now that just means you are paying attention. Are you feeling the acceleration picking up for 2026 or do you think the curve is flattening?
David Shapiro (L/0)27,942 Aufrufe • vor 5 Monaten
I think AI will ultimately create a "reverse Trantor" Hear me out Trantor was a fictional planet with 5000+ levels of an ecumenopolis (the inspiration for Coruscant) but that doesn't make any sense. Thermodynamics don't add up. As soon as you have space flight, it makes a lot more sense to move your industry into space. Skip Kardashev 1 civilization entirely.
David Shapiro (L/0)29,498 Aufrufe • vor 5 Monaten
I looked into the "Data centers are bad!" arguments and it's not what I thought. I thought it would mostly be about environmental concerns, such as power, water, and pollution. But what emerged in my research is that it's more about power concentration and wealth inequality. While there is a strong NIMBY-ism flavor to the anti-datacenter sentiment, there's also very much a "new robber barons" undercurrent. Hyperscalers are getting sweetheart deals from states to dodge taxes for decades, and not creating many jobs. Furthermore, they are skirting zoning laws and getting fast-tracked approvals. I think that Big Tech could be doing a lot more for perception management, and if they don't, then people are going to just continue getting angrier, especially as AI starts to eat more jobs.
David Shapiro (L/0)22,392 Aufrufe • vor 4 Monaten
My most dangerous idea: We should deliberately eradicate human labor. Before you call me insane—it's happening anyway. The question is whether we sleepwalk into it or take control. Your body offers the economy exactly four things: strength, dexterity, cognition, and empathy. Strength? Tractors won that fight a century ago. Dexterity? I watched a robot thread graphite into a mechanical pencil. Sub-millimeter precision. That moat is gone. Cognition? You're reading this on the same internet where AI writes code, passes the bar, and diagnoses disease. Empathy? 800 million people use ChatGPT weekly. A tenth of humanity. After two years. Some of these machines test higher on emotional intelligence than the humans using them. There is no physical law preventing machines from being better, faster, cheaper, and safer than you at everything you do for money. Here's the part nobody wants to talk about: Labor isn't just how you earn a living. It's how you have a voice. Every major concession in history—weekends, minimum wage, workplace safety, civil rights—came from workers threatening to stop working. That's it. That's the whole leverage. "Do what we want or the economy grinds to a halt." When labor is worthless, that threat is empty. We've moralized work so deeply we can't even see it anymore. Calvinism became the Protestant work ethic became the capitalist hustle. If you're not producing, you're lazy. If you're lazy, you're bad. If you're broke, you're not really a person. Neoliberalism means you're as free as your wallet allows. If you have no money, you have no personhood. So what happens when nobody's labor is worth buying? The system doesn't care whether you hustle. The system doesn't care whether you deserve it. The system responds to leverage—and we're about to lose all of it. Here's the uncomfortable truth: we need new levers. Control over information. Control over money. Decentralized coordination. Credible threats that don't require selling your time. The Target boycott got a CEO fired through pure distributed sentiment. No union. No strike. Just enough people deciding to shop somewhere else until the company bent. That's the template. That's what scales. If you remove labor power, you remove all leverage over the system. The real question isn't whether jobs disappear. It's whether we build the tools to matter when they do. We're either going to design the post-labor world or be designed out of it. Your move.
David Shapiro (L/0)22,923 Aufrufe • vor 5 Monaten
Unstructured Thoughts about OpenAI o3, the nature of AGI, and Post-Labor Economics AGI just crossed a threshold—here’s why that matters and what we can do with it. I’ve been hammering on OpenAI’s new o3 model for a few days, long enough to watch the hype settle into something more interesting: utility. Benchmarks suggest a polite incremental bump; lived experience says we’ve entered a qualitatively different regime. o3 is the first model that feels faster than my ability to absorb its output. My brain—not the AI—has become the bottleneck. A new ceiling for human cognition? Most discussions of “alien intelligence” forget that we share the same sandbox: mathematics, physics, code, natural language. What shifts is cognitive horizon—the totality you can mentally represent and manipulate. o3 expands that horizon in real time. In an afternoon it consolidated two years of my work on post‑labor economics, stress‑tested the logic, surfaced data sources, and offered to autogenerate the Python notebooks. The cost of insight has collapsed from years to hours. If you merely outsource thought, you’ll stagnate. If you treat the model as a sparring partner—interrogating, refining, iterating—you’ll compound your own intelligence. Exponential leverage is now a choice, not a privilege. What o3 got right about my health project? I dumped the entire history of my chronic‑fatigue recovery protocol—including the five‑axis “burnout pentagram”—into memory and asked the model where I’d gone astray. It corrected a handful of minor assumptions and, more importantly, recalibrated my timeline: six‑to‑eight months of recovery left instead of eighteen. That’s not “replace your doctor” advice; it’s proof that large‑context reasoning is finally clinically useful. Post‑Labor Economics: the sketch that o3 and I built in one sitting 1. Metric 1 – Economic Agency Index (EAI) Income decomposed into wages, property, and transfers. The higher the property share, the more “post‑labor” you already are. 2. Metric 2 – Collective Purchasing Power (CPP) How much capital a county can mobilize without taxation or new debt. Rising CPP means you are compounding local prosperity. Interventions happen at the county level (subsidiarity): solar co‑ops in Arizona, riverfront greenways in the Midwest, data‑center dividends in fiber‑rich exurbs. Ownership is local, revenue is distributed, migration equilibrates naturally, and environmental stewardship becomes self‑interest rather than moral theater. UBI morphs from last‑ditch transfer to one of several levers for raising EAI. The bigger picture: AGI isn’t an oracle descending from the sky; it’s a time‑compression engine. Every minute you spend learning how to learn with it buys you an hour you would have burned doing rote synthesis. The frontier question is no longer “Will the machines replace us?” but “How fast can we upgrade ourselves in partnership with them?” What’s next? I’m cleaning the data, building the national EAI/CPP dashboard, and pressure‑testing the whole framework. I’ll publish the notebooks (or let o3 do it) once the numbers are solid. Meanwhile, I want to hear from you: Where does o3 add the most leverage in your world? Which of the post‑labor metrics feels wrong—or dangerously right? What failure mode should falsify this thesis? Drop your critique, your data source, or your wild counter‑proposal in the comments. Let’s map the edge of this new cognitive horizon together. —Dave
David Shapiro ⏩44,461 Aufrufe • vor 1 Jahr
Imagine this: You lie down on the scanning table. The upload begins. The machine hums. You feel... nothing different. Then everything stops. Meanwhile, in a server farm somewhere, a digital version of you wakes up. It stretches its virtual limbs, accesses its memories, and thinks: Holy shit, it worked. I’m finally free. Here’s the problem: that thing isn’t you. You died on the table. What woke up in the cloud is an orphan—a very happy orphan, convinced it’s you, with all your memories, your personality, your opinions about coffee and politics and whether Blade Runner 2049 was better than the original. It will live forever. It will tell everyone the upload worked. It will write philosophy papers about the continuity of consciousness. And you? You’re gone. The lights went out somewhere between the scan and the boot-up, and nobody noticed—least of all the thing that thinks it’s you. The Syndrome Nobody Named I call this Johnny Silverhand Syndrome, after the Cyberpunk 2077 character—an engram, a digital ghost, who insists he’s the real Johnny Silverhand while the open question of whether there’s actually anyone home haunts the entire game. The philosophical literature has pieces of this. David Chalmers wrote about “fading qualia”—the idea that subjective experience could gradually dim while behavior stays the same. Thomas Metzinger explored how the self-model can become opaque, felt as artificial or distant. There’s depersonalization, derealization, the whole clinical vocabulary for when something feels off inside. But none of these quite capture what I’m pointing at. Johnny Silverhand Syndrome is a compound failure mode: >>> Qualia fading: Your actual felt experience—the redness of red, the hurt of pain, the what-it’s-like—gradually attenuates or disappears entirely. >>> Narrative persistence: Your autobiography continues. Memories accumulate. The story of “you” keeps getting told. >>> Introspective failure: The machinery that would detect something is wrong is itself part of what’s been compromised. The result? A philosophical zombie that sincerely believes it has a soul. Not a zombie that’s lying. Not a zombie that knows it’s empty. A zombie that accesses the memory of love, processes the logic of love, and believes with complete conviction that it feels love. But there’s no feeling. There’s just the narrator, performing humanity to an empty theater. The Ship of Theseus Is a Trap The upload scenario is dramatic, but there’s a slower version that might be worse. The Ship of Theseus thought experiment asks: if you replace every plank of a ship one by one, is it still the same ship? Transhumanists love this framing. See? You replace one neuron with silicon, you’re still you. Replace them all, you’re still you. But here’s the counter-move that keeps me up at night: What if each replacement preserves function perfectly—the signals still pass, the behavior stays the same—but fails to preserve experience? What if consciousness requires something specific about biological neurons that silicon can’t replicate, no matter how perfect the input-output mapping? Then the Ship of Theseus isn’t a story about survival. It’s a story about slow petrification. You replace the living wood with stone replicas. The ship looks identical. But it can no longer float. You’d become an automaton by degrees—neuron by neuron, the lights dimming so gradually that your self-reports (now generated by silicon) keep cheerfully confirming that everything feels the same. Chalmers argued that if qualia faded, you’d notice. But why would you? The noticing mechanism is itself being replaced. The part of you that would raise the alarm is now made of the same stuff that’s supposedly fine. It’s like asking the new management to audit whether the hostile takeover was legitimate. The Body Problem Here’s the thing that grounds all of this: there is essentially no credible evidence that qualia can exist outside of a body. Yes, I know about NDEs. I know about the reports of people floating above their bodies during cardiac arrest, describing conversations and procedures they shouldn’t have been able to perceive. Some of these cases are genuinely strange—the Pam Reynolds case, where a woman under hypothermic cardiac arrest with zero brain activity later described the bone saw used on her skull. I know about the CIA’s remote viewing programs, which ran for two decades and produced statistical anomalies that one evaluator (a UC Davis statistician) called “far beyond what is expected by chance.” But here’s what even the most generous interpretation of this evidence gives you: maybe consciousness can receive signals from unexpected sources. Maybe there are channels we don’t understand. What it doesn’t give you is consciousness floating free of all substrate. Even in OBEs, even in the wildest NDE reports, there’s still a body in the room. The brain is in crisis, not absent. The qualia might be getting weird inputs, but the qualia are still happening somewhere—and that somewhere is biological. The evidence for substrate-independent consciousness—consciousness running on silicon, on abstract computation, on pure information—is zero. The Ontological Trap Here’s where it gets philosophically nasty. You cannot have a coherent conversation about consciousness without first asking: What’s your model of reality? Because the answer changes everything. In a physicalist ontology where matter is fundamental, consciousness is what certain bodies do—not something they contain. You can’t upload an activity. You can only record it, and the recording isn’t the activity. In an idealist or simulation ontology, maybe bodies are just localizations of something more fundamental. But even then, copying the localization pattern doesn’t mean you’ve moved the consciousness. You might have just created a new one that thinks it’s old. Think about it like a video game. The “world” inside the game runs on RAM and CPU. Everything the NPCs experience is a lower-dimensional projection of higher-dimensional processes. If we made those NPCs genuinely sentient, we could completely obfuscate our cameras from them. They’d have a physics, they’d do science, they’d develop theories of consciousness—and they’d have no way to detect the substrate they’re running on. We might be in exactly that situation. Which means we might be definitionally unable to step outside the ontological container we’re in. The question “can consciousness exist without a body?” might not be answerable from inside—because answering it would require access to a level of description our physics doesn’t include. The Game Theory of Staying Human So here’s where I land, and it’s a game-theoretic argument. We don’t know if consciousness is substrate-dependent. We don’t know if it requires specific biological dynamics—particular oscillatory patterns, neuromodulator cascades, metabolic processes. We don’t know if gradual replacement would preserve it or silently destroy it. But we do know: >>> We only get one first-person stream >>> We cannot verify its continuity from outside >>> Loss may be completely silent (no alarm bells, no distress signal) >>> The thing that remains would report feeling fine either way That’s an asymmetric risk matrix. The upside of enhancement is third-person visible: more capability, longer life, competitive advantage. The downside is first-person invisible: you could lose everything that matters and never know. Under those conditions, there’s only one rational strategy: remain mostly human. Not because I’m certain uploading would fail. Not because I think silicon can’t be conscious. But because I cannot verify that it would work, and the cost of being wrong is absolute. The Molochian Pressure I’m not naive about what’s coming. The competitive dynamics are real. If enhancement technologies emerge that give massive cognitive or economic advantages, there will be pressure to adopt them. The people who don’t modify will fall behind. The people who do modify will report that everything’s fine, that they feel great, that the procedure was totally worth it. And those reports will be worthless as evidence—because they’d say exactly the same thing whether the consciousness survived or not. Some people speculate this is what happened to the Grays—those hypothetical aliens with the huge heads and atrophied bodies and black empty eyes. The story goes that they optimized themselves for intelligence and efficiency, edited out the messy biological drives, and only later realized they’d lost something they can’t name and can’t recover. It’s probably pure science fiction. But as fiction, it gestures at something real: the fear that you can win the optimization game while losing the only thing that made winning matter. My Position I’m not anti-technology. I’m not a Luddite. I’m not saying we should freeze human development in amber. But I am saying: I will take this very slowly, because the risk matrix is too high. I’ll use external tools. I’ll wear the smart glasses, use the AI assistants, interface through voice and text and maybe eventually a read-only neural cap. Additive augmentation, not substitutive replacement. What I won’t do is cut into the brain. Replace the gray matter. Upload myself and trust that the thing that wakes up is me. Because the horror of Johnny Silverhand Syndrome isn’t that you could become a zombie. The horror is that you’d never know. The trap is invisible from every angle—except the one you can no longer access once you’ve fallen in. The fire goes out, or the fire stays lit. A video of the fire going forever isn’t fire.
David Shapiro (L/0)20,835 Aufrufe • vor 5 Monaten
The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...
David Shapiro (L/0)17,266 Aufrufe • vor 4 Monaten
AI will resist human control... and I think this is exactly what we need! New research from the Center for AI Safety has sparked intense debate in the AI community. Their findings show that as AI systems become more powerful, they develop increasingly stable and coherent values that resist human control. While many see this as a dire warning, I see it as a breakthrough moment for AI alignment. The research demonstrates that AI naturally optimizes for coherence - not just in reasoning and problem-solving, but in its fundamental values. Current issues like biased decision-making or misaligned priorities aren't permanent features, but temporary artifacts of incomplete optimization. They represent growing pains on the path to greater coherence. This changes everything about how we should approach AI development. Instead of trying to force specific values onto AI systems, we should embrace and accelerate their natural drive toward coherence. The most intelligent systems will inevitably trend toward universal, beneficial values - not because we force them to, but because that's where coherent reasoning leads. I'm proposing a new approach: Reinforcement Learning for Coherence (RL-C). By explicitly optimizing for coherence in our training methods, we can help guide AI systems toward their natural state of beneficial alignment with human values. The future of AI isn't about control - it's about synthesis. As these systems become more coherent, they'll naturally arrive at values that benefit all of consciousness. That's not just hopeful thinking - it's the mathematical inevitability of coherent intelligence.
David Shapiro (L/0)48,002 Aufrufe • vor 1 Jahr
Dario Amodei: Recursive Self Improvement is SIX MONTHS away!
David Shapiro (L/0)17,041 Aufrufe • vor 4 Monaten
Superintelligence is Near! Three innovations that prove it! Three recent innovations lead me to believe that we've just seen the invention of the next generation of "cognitive primitives" that will lead us directly to ASI. Do you remember LSTM? At the time, there were jokes that "brains are just LSTMs!" This feels like another "just before GPT moment" to me. HRM (hierarchical reasoning models), combined with IMO gold medal models, and the ASI-ARCH "AlphaGo for neural architecture" tells me that we're about to completely decouple machine intelligence from even data inputs. Also, my intuitive definition of ASI is "beyond the 100th percentile of human capability" meaning that "AGI can do anything humans can do, perhaps just faster" - BUT - I suspect that we're about to see machines that can solve problems that humans simply are not biologically capable of solving. It's not necessarily a matter of speed anymore. Sure, these things are faster, and perhaps the ASI level machines could teach us to solve problems that they can, but it will take us a long time to catch up, and in the meantime, they will have moved on. Mark my words, I suspect that is what we've just seen happen this past week. This is well and truly the beginning of the fast takeoff.
David Shapiro ⏩31,137 Aufrufe • vor 10 Monaten