Iterated Insights

Ideas from Jared Edward Reser Ph.D.

Qualia as Transition Awareness: How Iterative Updating Becomes Experience

Abstract Qualia is often treated as a static property attached to an instantaneous neural or computational state: the redness of red, the painfulness of pain. Here I argue that this framing misidentifies the explanatory target. Drawing on the Iterative Updating model of working memory, I propose that a substantial portion of what we call qualia,…

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Consciousness as Iteration Tracking: Experiencing the Iterative Updating of Working Memory

Abstract This article proposes a temporal and mechanistic model of consciousness centered on iterative updating and the system’s capacity to track that updating. I argue for three nested layers. First, iterative updating of working memory provides a continuity substrate because successive cognitive states overlap substantially, changing by incremental substitutions rather than full replacement. This overlap…

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Does Superintelligence Need Psychotherapy? Diagnostics and Interventions for Self-Improving Agents

Abstract Agentic AI systems that operate continuously, retain persistent memory, and recursively modify their own policies or weights will face a distinctive problem: stability may become as important as raw intelligence. In humans, psychotherapy is a structured technology for detecting maladaptive patterns, reprocessing salient experience, and integrating change into a more coherent mode of functioning.…

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Why Transformers Approximate Continuity, Why We Keep Building Prompt Workarounds, and What an Explicit Overlap Substrate Would Change

Abstract This article argues that “continuity of thought” is best understood as the phenomenological signature of a deeper computational requirement: stateful iteration. Any system that executes algorithms across time needs a substrate that preserves intermediate variables long enough to be updated, otherwise it can only recompute from scratch. Using this lens, I propose a simple…

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Plural Canons and the Siloed Future of Synthetic Knowledge

1. The “Final Library” was never going to be singular When I first started thinking about what I called a “Final Library,” I pictured a single, civilization-scale repository of synthetic writing, synthetic hypotheses, and machine-generated explanations. The basic premise was simple: once AI systems can generate and refine ideas at industrial scale, they will produce…

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When “Don’t Go to College” Becomes Bad Advice

A familiar storyline is making the rounds again: a famous entrepreneur goes on a podcast and suggests that college is no longer worth it. The implication is that AI and robotics are going to restructure the economy so quickly that formal education will be obsolete, or at least an inefficient use of time and money. I understand why this message appeals to people. It is crisp, contrarian, and it flatters a certain self image. It tells ambitious young listeners that they are too clever to sit in classrooms while the world is being rebuilt.

The problem is that it functions as mass advice. And mass advice is not judged by how it lands with a small population of highly driven entrepreneurs, or by how well it fits the biography of the person giving it. It is judged by what it does to the median listener. When a public figure says “don’t go to college” without clearly limiting the scope, many people hear something closer to “college is for suckers.” That is not merely inaccurate. For a large fraction of young adults, it is actively harmful.

In a conversation with Peter Diamandis last week (in my favorite YouTube podcast, Moonshots), Elon Musk framed his point in a way that is easy to hear as blanket advice: “it’s not clear to me why somebody would be in college right now unless they want the social experience.”  Later, in the same futurist register about AI in medicine, the exchange becomes even more categorical, with Diamandis saying “So don’t go into medical school,” and Musk replying, “Yes. Pointless,” before adding that this logic “applies to any form of education” and emphasizing “social reasons” as the main remaining justification.  I do not read those lines as hostile or contemptuous, and I think he is gesturing at a real shift in how quickly skills can be learned and how fast certain tasks may be automated, but as public guidance it is exactly the kind of compressed, high-status soundbite that can travel farther than its nuance, especially among young people who are not already equipped with a plan, mentors, money, or a runway. 

I am not arguing that college is the only good path, or that every degree is worth its price. I am arguing that college remains the best default option for most people, even in a future where AI radically changes work, because college provides something deeper than content delivery. It provides formative infrastructure.

College Is Not Just Information, It Is a Developmental Environment

If we talk about college as though it is simply a place where you pay for information, then yes, AI changes the calculus. But that framing has always been too narrow. The real product of a good college experience is a bundle of developmental inputs that most people do not recreate on their own.

First, college creates structured practice under evaluation. Deadlines, exams, quizzes, labs, presentations, office hours, and feedback loops are not just hoops to jump through. They are repeated reps under conditions that resemble adult responsibility. You learn to write clearly, to build an argument, to handle critique, to revise, to manage your time, to persist through frustration, and to perform in public. Those are durable skills. Even if AI tutors can teach facts and methods at high quality, they do not automatically replace the habit formation that comes from being embedded in a system with expectations, consequences, and standards.

Second, college generates breadth and intellectual discovery. Many students find their real interests accidentally. They take a course to fulfill a requirement and suddenly they are pulled into a new domain. That kind of exposure matters because most people’s default learning environment is not neutral. It is the internet, and the internet is an optimizer. It tends to narrow what you see based on what you already like, what keeps you engaged, and what keeps you scrolling. College, at its best, does the opposite. It forces you out of your local optimum. It introduces you to subjects you would not have sought out, and to questions you did not know existed. That is not only useful for careers. It is useful for becoming a more interesting and capable person.

Third, college is socially formative in a way that is difficult to replicate. People focus on the “social experience” as if it were a frivolous side benefit, but it is a major part of why college works for so many people. It is one of the rare environments where large numbers of peers the same age are repeatedly co-located, interacting over time, learning together, and shaping each other’s ambitions. That is where friendships form, where romantic partners are often met, where social confidence is built, and where a young adult identity starts to stabilize. It is also where people learn professional social skills in a low stakes setting: group projects, club leadership, disagreement with peers, collaboration with mentors, and navigating institutions. Study abroad can be part of this, too. It expands a person’s mental map of the world and of themselves. It makes the world feel larger and more real, and it often changes what people believe is possible.

When someone says “you can learn anything online now,” they are not wrong, but it misses the point. Most people do not fail because they lack access to information. They fail because they lack structure, feedback, community, and a path that turns intention into sustained action.

The Dropout Narrative Is Mostly a Selection Effect

A standard rebuttal to the case for college is the founder-dropout mythos. We hear about tech icons who left school and built world-changing companies. This is real, but it is constantly misapplied. Dropping out is not the causal ingredient. It is usually a late-stage decision made by unusually capable people who are already in unusually rich environments.

Many famous dropouts had already accumulated serious skills before leaving. They had already been exposed to high-level peers, high opportunity networks, and practical project work. They often left because they had clear traction, a live market opportunity, or a path that made continuing school an obvious opportunity cost. This is not what most “skip college” listeners have. They do not have traction, mentors, a runway, or even a coherent plan. They have a vague sense that they are supposed to be entrepreneurial and an anxious awareness that the world is changing.

So when public figures present the dropout story as a general template, they are taking an outcome that depends on selection and context and turning it into a simplistic prescription. The median person who skips college does not start a high-growth company. They typically do one of three things: they drift, they fall into low expectation work, or they get captured by low effort digital routines that feel like agency but are not. None of this is moral failure. It is predictable human behavior when structure is removed and replaced with an attention economy.

This is why the “not everyone is self-motivated” point is not a minor caveat. It is the center of the issue. Most people are not reliably self-structuring at 18 to 22. Even many talented people are not reliably self-structuring at that age, especially if they do not have money, stable housing, supportive parents, mentors, or a coherent plan. College functions as an external scaffolding that helps those people build internal scaffolding. That is how development works. You internalize structure by living within it.

Why “Don’t Save for Retirement” Is a Risky Public Message

In the same Moonshots conversation, Musk moves from education to personal finance and offers a line that is tailor-made to travel as a slogan: “Don’t worry about squirreling money away for retirement in 10 or 20 years. It won’t matter.”  He ties that claim to a larger abundance thesis, arguing that sufficiently powerful AI, robotics, and energy technology will expand productivity so dramatically that scarcity collapses, and with it the basic premise of retirement planning. In other words, he is not merely saying “invest differently” or “expect change.” He is suggesting that the whole problem category becomes obsolete within a relatively near time horizon. 

The issue is not that the long-run vision is impossible. The issue is that it quietly smuggles in several assumptions that are far less stable than the technology narrative itself. Even if AI and robotics raise aggregate productivity, that does not automatically guarantee broad distribution of those gains, nor does it guarantee that housing, healthcare, and elder care become frictionless in the specific way that would make personal savings irrelevant. That distribution step is a political economy problem, not a chip-and-software problem.  For the median person, the downside of acting on this advice is severe and difficult to reverse. If you under-save and the transition is slower, bumpier, or more unequal than promised, you do not simply “catch up” later, particularly if your wages are flat, your health changes, or your responsibilities multiply. The asymmetry matters: continuing to save is a hedge that still leaves you fine if abundance arrives quickly, but stopping your savings is an all-in bet on a timeline that no one can responsibly guarantee.

There is also a basic messaging problem that does not require any accusation to point out. Musk can afford to be wrong in a way most people cannot. When a billionaire offers personal finance guidance to millions, the difference in risk exposure becomes part of the content whether anyone acknowledges it or not. The more responsible public version of his point would be: prepare for a world where AI changes the economy, and invest in adaptability, skills, and resilience, but do not base your long-term security on a single speculative macro forecast. In practical terms, most people should treat retirement saving as a robustness strategy under uncertainty, not as an optional habit that can be safely abandoned because the future might become utopian. 

What Responsible Advice Should Sound Like in the AI Era

It is fair to say that AI and robotics will change the return on investment of some degrees, and it is fair to criticize the cost structure of higher education. But “don’t go to college” is not responsible public advice, because it removes a key developmental institution from people who often have nothing else ready to replace it.

The responsible version is more nuanced and, honestly, more useful. The question is not “college or no college.” The question is “what path gives you structure, skill formation, relationships, and credible options, without crushing you with debt.” For many students, a financially sane route might be community college followed by transfer, or choosing a major with strong placement and internship pipelines, or mixing formal education with portfolio building and real work. For some, apprenticeships, union trades, military pathways, or targeted credential programs can be excellent, especially when they replicate the core functions that college provides: mentorship, standards, feedback, and a clear trajectory. For a smaller group, starting a company can be rational, but only when it is a genuine alternative with real momentum and real support, not a fantasy substitute for structure.

Most importantly, we should not make people feel foolish for choosing college. College is not merely a credential mill. At its best it is a training ground for adulthood. It teaches you how to think, how to work, how to communicate, how to collaborate, and how to stay engaged with the world beyond your initial interests. It gives people a concentrated social environment in which to form friendships, romantic relationships, and professional networks. It provides exposure to disciplines that reshape your worldview. These are not luxuries. They are exactly the kinds of developmental inputs that help people thrive in periods of rapid change.

If AI really does accelerate the pace of economic transformation, then the ability to adapt, to learn, to communicate, and to maintain agency will matter even more. For most people, college is still one of the best structured ways to develop those capacities. The slogan should not be “don’t go to college.” The slogan should be “choose a path that builds you.”

Jared Reser and Lydia Michelle Morales with ChatGPT 5.2 

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