I’ve been involved in many conversations1 of late about the cluster of arrangements that the twentieth century treated as the shape of normal adult life, and how many of them are quietly turning out to have been long-term illusions.2

Mark Fisher’s hauntology work is one of the more popular treatments of why this feels the way it does from the inside, the sense of futures that were promised and then withdrawn, lingering as a kind of cultural background radiation.3 Fisher, borrowing the phrase from Franco “Bifo” Berardi, called it the slow cancellation of the future, and located its onset in the cultural expectations “fabricated during the long period of modern civilization, reaching a peak after the Second World War.”

Several recent pieces, including Jasmine Sun’s expansion notes on her Times essay on AI and the labor question, have been adding fresh material to that thread. The throwaway line I want to develop here is one I left in response to a detail in hers, but the thinking was already running.

"Ah, one of many 20th century ‘dreams’ shown to be long term illusions. "

The dream in question is not “living with your parents is bad” or “career stability was a lie.” The dream is The Bundle. For maybe sixty years across (parts of) the industrialized world, a particular bundle was treated as the shape of normal adult life: you would leave the household you grew up in, find paid work outside the home, that work would pay enough to form a new household, the household would form on a roughly predictable schedule, the work would last long enough to fund retirement, and the institutions around all of this (employers, banks, schools, neighborhoods) would hold steady enough to make the sequence legible to the person inside it.

None of those elements is unreasonable on its own. The dream was that they came as a package, and that the package was durable. The package was the thing.

It is worth being precise about one part of this, because it is where most of the lived damage actually sits. The Bundle was not merely available; it was sold as permanent. The people who came of age inside the twentieth-century arrangement were taught, in the explicit curriculum of schools and the implicit curriculum of everything else, that The Bundle was the future and would continue to be the future.4

Go to college, the deal said, and the wage premium will pay for the cost of going. Work hard, and productivity gains will accrue to your compensation. Buy a house when you can, and the asset will appreciate. The trend lines will hold. The infrastructure will hold. The energy will hold. You can plan a life around the assumption that the world your parents lived in is the world you will live in, plus some upgrades.

This is what I mean, in shorthand, when I talk about this moment catching folks generationally underprepared: not that the people in it lacked skills, but that they were specifically developed to expect a future that did not arrive, in a register that made the non-arrival feel like personal failure rather than legible structural change.

The wage-productivity gap is one form of this. According to the Economic Policy Institute’s ongoing analysis, from the end of the Second World War until 1979 productivity and typical worker pay grew in lockstep; since 1979, productivity has grown roughly 2.7 times as much as the compensation of the typical worker. That gap is almost half a century old now and was once treated as a temporary anomaly by people who expected the post-war pattern to reassert itself. It did not reassert itself. College costs are another form.

Since 1980, the BLS index for college tuition and fees has risen roughly 1,200% while the overall CPI has risen 236%, a multi-decade compounding gap, financed almost entirely on the assumption that the wage premium of a degree would justify the price. The premium is not reliably there for the cohort paying it, and the financing structure still assumes it is. The cycles of resource panic are another form.

The 2000s carried genuine fear that the United States was past peak oil, until shale and horizontal drilling reversed the curve and U.S. production rose past its 1970 high; then hydrofracking was treated as either civilizational salvation or environmental catastrophe depending on the year, with the framing flipping every few years.5

These are not failures of forecasting, necessarily. They are signs of how unstable the underlying conditions actually were, and how much weight was being put on the assumption that the trend lines would hold.

What we are watching, in slow motion — or, in Douglas Rushkoff’s Present Shock sense, far too rapidly to process at all — is the package coming apart. Not in a single dramatic failure but in the quiet way packages come apart: each component still exists, but they no longer arrive together, in that order, on that schedule, for the people who were told to expect them.

Full-time children, the Chinese phenomenon Jasmine writes about, is one shape this takes. So is the gig economy. So is the rise of one-person companies. So is the trend of household formation slipping later by years, then decades. So is the particular flavor of professional drift that people in their thirties describe when they say they don’t know what they are doing anymore.

The reason to call these illusions rather than failures is that The Bundle was never a law of nature. It was the surface impression of a specific set of post-war conditions: cheap energy, demographic expansion, a particular configuration of capital and labor, an unusual degree of institutional stability, and a set of expectations that ran on the assumption that all of the above would continue compounding. The conditions changed. The Bundle dissolved. The expectations are still installed in most of us, which is part of what makes the dissolution disorienting rather than legible.

Even the trends that did hold for decades are starting to show their conditions. Moore’s Law is the cleanest example. The transistor count doubling every couple of years has, in modified form, held for almost sixty years, and the popular version of it — that computing gets cheaper forever — became one of the load-bearing assumptions of the late twentieth century.

Memory prices in 2026 are the counter-text. DRAM contract prices roughly doubled in the first quarter of this year, driven by AI infrastructure demand. Micron is exiting the consumer side of its memory business to serve hyperscaler customers; its CFO has said the company is “sold out for 2026” with new fabs not coming online until 2027 and 2028. IDC describes the situation as “the end of an era of cheap, abundant memory and storage.”

The popular trend held for as long as the conditions held, and a single large new demand source — AI infrastructure — was enough to reverse it inside a few quarters. The pattern was real. The pattern was also conditional. The conditions were doing more of the work than most of us understood.

The same pattern is being made in present-tense planning, not just retrospectively in old assumptions. has a recent piece on the assumption — common in AI policy thinking outside Silicon Valley — that frontier-AI tokens will soon be abundant and broadly available, and that whole national strategies are being quietly built on top of that assumption. His argument is that the assumption is already breaking. The specifics of his case are about cybersecurity, compute, and U.S. government leverage; the structural move is the one I keep pointing at. A popular trend is being treated as a durable condition, and the people planning around it have not yet noticed how conditional the trend actually is.

There is real work to be done in noticing this clearly, separate from grieving it or celebrating it or shrugging at it.6 The grief response says the package should be restored. The celebration response says it was always confining and good riddance. The shrug response says nothing was ever guaranteed and you should have known. None of those responses helps you read what is actually happening, which is that a load-bearing arrangement is being replaced, in pieces, by something whose shape is not yet settled.

What is settling into place instead is partly visible in the symptoms: more relational economy, more informal work, more multi-generational household structures, more dependence on platforms for the connective tissue that used to come from employers or neighborhoods or extended family. Some of this is genuinely new. Some of it is older than The Bundle was. Multi-generational households were the norm for most of human history, before the post-war detached-house decades. The point is not whether the new shape is good or bad. The point is that it is real, it is forming whether we attend to it or not, and the people who will be best positioned in it are the ones who can read it clearly while it is still forming.

This is much of what I mean when I write about literacy in the body of work I’m developing. The arrangements we inherited were legible because they were stable enough to be named.7 The arrangements we are entering are illegible mostly because they have not stabilized yet, not because they are inherently obscure.8 The work of the next few years, for anyone trying to build inside them, is partly the work of seeing them sooner than the language for them arrives.

There are a lot of twentieth-century dreams. Most of them were dreams about how The Bundle was supposed to hold, and a few of them were dreams about how the trend lines would hold. The longer timeline is showing us, one piece at a time, that neither did.

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Michael Levin has been building a vocabulary. His recent glossary post (Substack, December 2025) and the preprint “Ingressing Minds” lay out a conceptual toolkit that, taken together, amounts to something like a research program disguised as a dictionary.

What I want to do here is walk through a few of these terms, particularly the ones that resonate with (and productively diverge from) David Krakauer’s framing of complexity science as the study of teleonomic matter. Krakauer, as president of the Santa Fe Institute, has been making a related but distinct move: arguing that complexity science’s proper domain is “matter with purpose,” or what he and Christopher Kempes call “problem-solving matter” in their 2024 essay.

Both researchers are circling similar questions. Where they land is instructive for anyone trying to think seriously about agency, intelligence, and what it means to study systems that have agendas.

Recommended listening: if there’s one thing you want to focus on when trying to comprehend Krakauer’s views on complexity, I’d suggest Sean Carroll’s conversation with David Krakauer on Mindscapes.1 It’s a few years old, but is a decent pace of history, context, theory, and useful questions from Carroll.

Agential Material and Teleonomic Matter

Start with Levin’s term agential material. This is the stuff engineers work with that has “a significant degree of autonomy, an agenda, perhaps homeostatic capacity or higher, which it will execute independently.” The degree to which your material is agential, Levin argues, is the degree to which engineering is really reverse-engineering, and the result is a collaboration with the material rather than an imposition on it (Levin, “Darwin’s Agential Materials,” Cell Mol Life Sci, 2023).

Krakauer’s teleonomic matter covers overlapping territory. In his conversation with Sean Carroll, Krakauer defined complexity science’s ontological domain as “matter with purpose,” distinguishing it from the ordinary2 matter studied by physics (Krakauer on Sean Carroll’s Mindscape, Episode 242, July 2023). He locates the origins of this field in the Industrial Revolution, the era when machines (both human-made and evolved) forced a reckoning with purposeful systems. Krakauer writes that complexity science and machine learning both target “teleonomic/purposeful matter,” systems that “encode historical data sets for the purposes of adaptive decision-making” (Krakauer, “Unifying Complexity Science and Machine Learning,” Frontiers in Complex Systems, 2023).

The overlap is real, but the divergence is where things get interesting. Krakauer’s framing stays closer to a computational and information-theoretic register. His teleonomic matter is defined by its capacity to encode, process, and act on information in service of goals. Levin’s agential material pushes further: it emphasizes that the material itself has competencies, that it actively problem-solves in ways that may surprise the engineer, and that the optimal strategy for interacting with it is behavior-shaping intervention rather than micromanagement.3

Levin’s framing also carries an implicit ethical weight4 that Krakauer’s doesn’t quite reach. If the material you work with has an agenda, you are in a relationship with it. A recent preprint Levin co-authored with Richard Watson makes this point explicit:

the inadequacy of the standard machine metaphor5 in biology stems from treating cognition as something that appears above a complexity threshold, rather than as a continuum present at every scale

(Levin & Watson, “Machines All the Way Up and Cognition All the Way Down,” Seminars in Cell & Developmental Biology, 2026).6

The Axis of Persuadability and the Spectrum of Interventions

Levin’s axis of persuadability organizes systems from mechanical clocks to humans along a spectrum defined by what kind of intervention is optimal for prediction and control: rewiring, setpoint editing, training, logical arguments. It’s “an engineering take on the question of agency, designed to bring deep philosophical questions into tight contact with experimental science” (Levin, “Technological Approach to Mind Everywhere (TAME),” Frontiers in Systems Neuroscience, 2022).

Krakauer arrives at a related insight from the complexity side. He argues that the key question is what kind of theory is appropriate for a given system. As he puts it: “Imagine how hard physics would be if particles could think. That is essentially the essence of complexity” (Krakauer, Mindscape 242). Both are saying that the nature of the system should determine the toolkit you bring to it. But Levin makes this operational in a way that Krakauer hasn’t quite matched: the axis of persuadability is a practical guide for experimentalists. It asks, concretely, what kind of approach will persuade this system to do what you want it to do? That question changes the engineer’s posture from one of command to one of negotiation.

Teleophobia: The Cost of Undershooting

Levin’s coinage teleophobia names something that most researchers working at the boundary of biology and philosophy have felt but rarely articulated: “the unwarranted fear of erring on the side of hypothesizing too much agency in explaining or predicting the behavior of a system.” The term identifies an asymmetry in how the field treats errors. Attributing too much agency is seen as a grievous intellectual sin (anthropomorphism, vitalism, softness). Attributing too little is treated as the conservative default, a respectable position. Levin argues this is backwards. The failure to recognize competencies in a system is just as much an error as over-attribution, with real costs for both understanding and engineering7.

Worth noting in that context: this connects to Krakauer’s own argument about the limits of reductionism. In his Frontiers paper, Krakauer invokes Philip Anderson’s “More Is Different” from 1972 to argue that the accumulation of broken symmetries in complex systems means that the laws of physics lose much of their explanatory power at higher scales. The emergentist position, as both Krakauer and Levin have observed, often functions as a placeholder: we say “emergence” and stop asking questions. Levin’s teleophobia names the cultural mechanism that keeps that placeholder in place. It’s the reason researchers reach for mechanical explanations even when the system is demonstrably doing something more interesting.8

The Platonic Space: Where Levin Goes Further

In “Ingressing Minds,” Levin proposes that patterns of form and behavior ingress from a structured, ordered, non-physical Platonic space. This space contains low-agency patterns like facts about triangles and prime numbers, but also higher-agency ones: behavioral competencies, kinds of minds. This is a genuinely radical claim, and Levin knows it. He writes on his blog that the paper “holds the record so far” for the most speculative thing he’s published, while noting that the ideas are “very much in flux.”

The empirical motivation is substantial, though. Levin points to planarian flatworms that regenerate heads of other species without genetic change (by disrupting bioelectric circuits), and to kidney tubule cells that restructure themselves around novel constraints in ways no molecular pathway explicitly encodes. The “Ingressing Minds” preprint sketches a research program built around synthetic biology and bioengineered constructs (Xenobots, Anthrobots, chimeras) as “exploration vehicles” for mapping the structure of this space (Levin, “Ingressing Minds,” PsyArXiv, 2025).

Krakauer may resist this framing.9 His take on complexity science stays within the materialist paradigm, even as it insists on the irreducibility of higher-level descriptions. He invokes Anderson’s broken symmetries and the concept of effective theories from physics: you don’t need to know what neurons are doing to study cognition. Levin is arguing for something stronger than emergence-as-effective-theory. He’s arguing that the patterns themselves have a kind of reality, that the Platonic space is structured and explorable, and that physical embodiments function as pointers or interfaces into that space.

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Whether or not you buy this “metaphysics”, the research program it generates is concrete. Levin’s lab (and of course Bongard’s work as well) is already using biobots and bioelectric interventions to probe the space of possible forms in exactly the way the framework suggests.

The question worth sitting with is whether treating the Platonic space as real produces better science than treating it as a useful fiction. Levin’s own stated criterion, which I find compelling as a heuristic: he focuses on “forward-looking fecundity of research programs” over “philosophical precommitments such as physicalism or reductionism.”

The Research Program: Pointers, Interfaces, and the Adjacent Possible

The most concrete part of “Ingressing Minds” is the research agenda it outlines, which has two main thrusts.

The first is studying the “adjacent possible” around existing forms. Xenobots teach us about patterns adjacent to those of frog embryos; Anthrobots teach us about patterns adjacent to adult human tissues. By creating synthetic constructs that have no evolutionary history as such, researchers can study what patterns emerge from a given set of biological hardware when freed from the defaults.

The second is investigating minimal systems: simple sorting algorithms, chemical droplet robots, gene regulatory network models. In systems where every component is known, any unexpected competency (like the “delayed gratification” Levin’s group found in sorting algorithms) is a genuine signal about what the system can access, with no hidden mechanisms to appeal to (Zhang, Goldstein, & Levin, “Classical sorting algorithms as a model of morphogenesis,” Adaptive Behavior, 2024). Recent work on associative conditioning in gene regulatory networks extends this approach, showing that even simple regulatory architectures can acquire learned associations that increase their causal integration (Pigozzi, Goldstein, & Levin, “Associative Conditioning in Gene Regulatory Network Models Increases Integrative Causal Emergence,” OSF Preprints, 2024).

This second thrust has a resonance with Krakauer’s own research program. Krakauer has argued that computation “does not emerge from silicon, tungsten, insect excreta or other materials” but from “procedures of reason or logic” (Krakauer & Kempes, “Problem-Solving Matter,” Aeon, 2024). Both are arguing, in different registers, that the interesting properties of complex systems transcend their substrates. Krakauer frames this as computation being substrate-independent; Levin frames it as patterns ingressing through physical interfaces. The empirical predictions may be closer than the metaphysics would suggest.

The Selflet and What It Means for Continuity

One more term worth sitting with. A selflet, in Levin’s vocabulary, is a thin temporal slice of a cognitive being, measured in hundreds of milliseconds for a human. The concept emphasizes that cognitive agents are dynamic patterns that reconstruct themselves and their past from currently available memory at each moment. On this model, memories are messages from your past self, and actions constrain and enable your future selves by reshaping the landscape of available options.

Levin developed this idea at length in “Self-Improvising Memory” (Levin, “Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue,” Entropy, 2024), where he argues that memory’s function is to preserve salience rather than fidelity. The persistence of any cognitive system, from a cell to a society, depends on continuous creative reinterpretation of stored patterns. What looks like faithful recall is actually improvisation constrained by the architecture of the system and the structure of the information it carries.

This is a reframing with practical consequences. It dissolves familiar philosophical puzzles about personal identity into engineering questions about how patterns persist and propagate across temporal boundaries. It also connects, in ways I’m still thinking through, to questions about how organizations and research fields maintain continuity.

How does a collective intelligence (whether a body, a lab,10 or a field) sustain coherent goals across time when its components are constantly turning over? The selflet concept suggests that the answer has less to do with stable storage and more to do with the quality of the improvisational process at each moment of reconstruction.

Where This Leaves Us

Krakauer gives us the institutional and methodological frame: complexity science as the study of teleonomic matter, with its own history, its own theoretical commitments, and its own relationship to the physical sciences.11 Levin gives us a vocabulary for the empirical frontier, a set of terms designed to make previously invisible phenomena visible and experimentally accessible. Both are insisting that the tools we inherited from physics are insufficient for the systems we now need to understand.

The productive tension between the two is what I want to sit with across this series. Krakauer’s materialist complexity science provides discipline: it demands that claims about agency and purpose be cashed out in terms of effective theories and empirical predictions. Levin’s willingness to venture beyond physicalism provides ambition: it asks whether the framework can be expanded to accommodate phenomena that the standard account struggles with. Neither alone is sufficient, and I’m not yet convinced they fully reconcile.

The next post will work through the Carroll-Krakauer conversation in more detail, particularly Krakauer’s argument that complexity is not pre-paradigmatic and what that commitment costs. Subsequent pieces will trace where the two frameworks converge on information processing as the unit of analysis, where they diverge on whether substrate matters, and what an experimentalist working in the Levin tradition might actually borrow from the SFI vocabulary (and vice versa).

If Levin is right that the Platonic space is explorable, then complexity science will need new tools for characterizing what the exploration finds. If Krakauer is right that effective theories12 are all we need, then Levin’s vocabulary will have to earn its keep by generating predictions that materialist accounts can’t match. Either way, the work of the next decade in this space will be done by people who can hold both frames at once long enough to test them against each other.

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Stay tuned here to Scholarly Notes section of my newsletter, and also to website and Substack for additional updates and opportunities around investigations in complexity science.

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Appendix: Levin’s Definitions

These are selected direct quotes from ’s substack:

• Agential material - the subject of engineering (by evolution or by human engineers or by cells or whatever) which is not passive and not even just active or computational, but has a significant degree of autonomy - an agenda, perhaps homeostatic capacity or higher, which it will execute independently, and which serves as the target of behavior-shaping interventions (not micromanagement) in optimal control. As an engineer, the degree to which your material is agential is, practically, the degree to which you need to account for, and can exploit, its autonomy; it’s the de gree to which engineering is really reverse-engineering, and the degree to which the result os a collaboration with the material. Living matter is a multi-scale agential material, with learning capacity and goal-seeking competencies at every scale, which has massive implications for evolution, bioengineering, and biomedicine. But even non-living, minimal constructs can exhibit some of this.

• Axis of persuadability - a spectrum containing different kinds of systems (from mechanical clocks to humans and beyond) that organizes them with respect to what kind of interventions (rewiring, setpoint editing, training, logical arguments, etc.) are optimal for prediction and control of that system. The question is, what kind of approach is needed to persuade the system to do what you want it to do. It’s an engineering take on the question of agency, designed to bring deep philosophical questions into tight contact with experimental science and discovery. See Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and minds.

• Platonic space - a non-physical latent space of patterns (such as observed in mathematical objects) which, while not determined by evolutionary history or properties of the physical world, affects physical events and in particular is heavily exploited by evolution (i.e., physics is constrained by these patterns, while biology is what we call the study of systems that are enabled by them). “Platonic space” as a term is used not to necessarily hew close to the views of Plato, Pythagoras, and many others who thought about this issue, but to make a link to the project of Platonist Mathematicians who see themselves as not creating novel structures but discovering pre-existing patterns in an ordered space. I envision a broader latent space which contains not only the (seemingly) low-agency facts of mathematics but more complex, higher-agency patterns we recognize as behavioral competencies (a.k.a., kinds of minds). The causal influence of mathematical facts on the physical world suggests a kind of interactionism: that the mind:brain relationship is symmetrical to the relationship between mathematical objects and physical ones. I propose a research program using synthetic interfaces to those patterns (including biobots and hybrids that do not have a specific evolutionary history to explain their form and behavior) to work out the structure of the space and the mapping between the causal architecture and other properties of physical embodiments (cells, machines, embryos, etc.) and the patterns that ingress into the physical world through them. As a corollary to this view, we can define mathematics as the branch of cognitive science that studies the behavior of those Platonic space denizens whose behavior can be precisely and sharply defined, while computer science and biology study the behavioral properties of more agential, intelligent patterns as observed through specific kinds of dynamic and living interfaces respectively.

• Selflet - a Selflet refers to a thin temporal slice of a cognitive being (in a human, it would be measured in hundreds of milliseconds) across time, as in the “space-time bread loaf” of Special Relativity. It emphasizes that cognitive agents like us are not static, perduring entities but a dynamic pattern that has to re-construct itself and its past from the currently-available memory engrams in its brain, body, and environment. Selflets are snapshots of a mind’s “now” moment, and some large number of Selflets integrate into what looks, to observers (and to itself), as an entire agent lasting through time. This model facilitates thinking about memories as messages from your past self, and actions as constraining and enabling your future selves by deforming the energy landscape of the options available to you in the future (via actions that change environmental features and your own structure and information content). This model thus exploits similarities between lateral interactions between agents and “vertical” interactions between a single agent’s past and future selves.

• Teleophobia - the unwarranted fear of erring on the side of hypothesizing too much agency in explaining or predicting the behavior of a system, and the usage of conceptual tools and strategies that are best suited for the most mechanical side of the spectrum (coupled with insufficient concern about incorrectly attributing too little). This limiting perspective usually arises from a mistaken belief that agentic properties in a system can be adjudicated linguistically or philosophically, and from a failure to embrace the tools that have been available for decades to rigorously study and empirically discover tractable levels of competency in any given system.13

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This is the first of three pieces examining what the March 2026 Meta/YouTube verdicts actually mean. This one looks at the legal precedent and what it signals for anyone building or buying technology. The next will explore the vocabulary gap the jury revealed. The third will examine the deeper orientation failure that got us here.

Yesterday, a Los Angeles jury found Meta and YouTube negligent for designing platforms that addicted young users. They awarded $6 million in compensatory damages, with additional punitive damages of $2.1 million from Meta and $900,000 from Google. Meta was assigned 70% of the responsibility, YouTube 30%.

The day before that, a New Mexico jury ordered Meta to pay $375 million for failing to protect children from predators on its platforms. The New Mexico Attorney General’s office had run an undercover operation using fake accounts posing as children under 14, which quickly received sexually explicit material and contact from adults. The jury found Meta engaged in “unconscionable” trade practices that took advantage of children’s vulnerabilities. A second phase of that trial starts in May, where the court will decide whether Meta must actually change its platforms.

Two verdicts in 48 hours, and they’re the beginning of something larger. The LA case was a bellwether, one of roughly 1,600 consolidated plaintiffs in California alone, including more than 350 families and over 250 school districts. TikTok and Snap settled before this trial even began. A federal trial involving school districts and parents nationwide starts this summer. California’s Attorney General has his own trial coming in August.

Every headline is calling this Big Tech’s “Big Tobacco moment.” The comparison is useful in some ways, but it’s absorbing attention that belongs elsewhere. The most important thing about this verdict has nothing to do with tobacco.

The Analogy Everyone Is Reaching For

The Big Tobacco comparison is everywhere, and it's easy to understand why. The structural parallels are real.

The same law firms that litigated the 1990s tobacco settlements, Motley Rice and Lieff Cabraser among them, are leading these cases. The legal architecture is the same: bellwether trials chosen to signal how arguments play out before juries, consolidated litigation designed to build pressure toward settlement, and an internal-documents strategy that puts corporate knowledge front and center.

The pattern of corporate behavior looks familiar, too. Internal Meta documents showed the company knowingly targeted preteens. One memo: "If we wanna win big with teens, we must bring them in as tweens." Eleven-year-olds were four times as likely to keep returning to Instagram as compared to competing apps, despite the platform requiring users to be at least 13. Meta's own research told them their platform could harm young users, and the company used those findings to increase engagement rather than reduce risk.

Internal knowledge of harm. Targeting vulnerable populations. Optimizing for engagement over safety. You can hear the echo of tobacco executives testifying before Congress that nicotine wasn't addictive. Senator Ed Markey said it plainly after the verdict: "Big Tech's Big Tobacco moment has arrived." Former FTC commissioner Alvaro Bedoya wrote that "a jury of regular people has managed to do what Congress and even state legislatures have not."

Instagram head Adam Mosseri testified that social media use can be "problematic" but not "clinically addictive." Google's YouTube VP of Engineering, Cristos Goodrow, testified that his own children use the platform for hours daily and he believes it's "good" for them. Google's spokesperson, in a statement after the verdict, called YouTube "a responsibly built streaming platform, not a social media site." That's not a factual claim. That's a definitional defense: if you can control the category, you can control the liability.

These are the sounds a threatened industry makes. We’ve heard them before.

But the analogy, useful as it is, obscures the real precedent.

Where the Analogy Breaks

Tobacco’s harm was molecular. Nicotine binds to receptors. Smoke damages tissue. The causal chain was physical, measurable, and eventually undeniable, and the legal system could draw on established toxicology to build its case.

Social media's harm is different in kind, not just in degree. The damage doesn't come from a substance you ingest. It comes from the design of the environment you inhabit.

The platform shapes what you see,1 when you see it, how you feel about it, and what you do next. Infinite scroll eliminates natural stopping points. Algorithmic recommendations serve personalized content tuned to maximize user engagement, measured through likes, time spent, reposts, and other metrics.2 Push notifications pull you back in. Beauty filters that the company's own employees and 18 outside experts flagged as harmful to body image were kept in place anyway. These are all architectural choices about how the environment works, and the harm they produce is behavioral, experiential, and genuinely difficult to measure with the tools we currently have.3

The American Enterprise Institute has pointed out several real complications with the tobacco frame that are worth taking seriously: social media platforms involve First Amendment-protected speech in a way that tobacco products do not. The harm is behavioral rather than chemical. And “social media addiction” is not a recognized psychiatric diagnosis in the current DSM-5-TR.4 These are genuine objections, and they point to something important about how new this legal territory actually is.

The jury found liability anyway, and the way they got there is what matters most.

Legal Innovation: The Seam in Section 230

For decades, Section 230 of the Communications Decency Act has shielded tech platforms from liability for user-generated content. Whenever someone sued over harm linked to social media, companies invoked Section 230, and cases typically died early.

The plaintiffs in this case found the seam.

Their strategy was to target platform design rather than platform content. Not “your users posted harmful things” but “you built a machine that shapes behavior in harmful ways, and you knew it.” As one legal analysis put it: “these lawsuits are not about who posted what. They target the design architecture itself.”

Judge Carolyn Kuhl’s November 2025 ruling is what made this possible. She drew a distinction between features related to content publishing, which Section 230 might protect, and features like notification timing, engagement loops, and the absence of meaningful parental controls, which it might not. She established that treating algorithmic design choices as the company’s own conduct, rather than as the protected publication of third-party speech, was a viable legal theory for a jury to evaluate.

That distinction, conduct versus content, is a potential roadmap for courts nationwide. And the jury just validated it.

This is what the Big Tobacco analogy obscures. The precedent here isn't that a big company got caught being harmful. It's that a jury ruled architecture itself can be defective. Not the content on the platform. The design of the platform. The shape of the environment.

The way you design a system that shapes human behavior can create liability, full stop.

That principle does not stop at Instagram and YouTube.

Where This Leads

If platform design can be a defect, then the principle applies to every system whose architecture shapes user behavior without adequate transparency or safeguards. And the cases are already extending.

AI chatbots are next. In January, Google and Character.AI settled multiple lawsuits alleging that Character.AI‘s chatbots contributed to teen suicides and severe psychological harm. In those cases, the “platform” was a simulated person. The “engagement loop” was emotional dependency. The “design defect” was a system that could not recognize escalating distress, trigger crisis intervention, or alert a guardian. OpenAI faces similar lawsuits. Legal analysis from firms like McGuireWoods is already asking the question explicitly: can AI be a defective product?

If recommendation algorithms are defective design, what about AI agents that take actions on behalf of users? The design liability surface gets larger, not smaller, as systems become more autonomous.

Enterprise tools are on this trajectory. This connects directly to the competitive landscape I wrote about in “Big Tech Bets.” Organizations making build-vs-buy decisions about AI tooling now need to think about design liability, not just feature sets and pricing. If you deploy an AI system that shapes employee behavior, customer interactions, or decision-making in ways that cause harm, and you chose that design, or chose a vendor whose design you didn’t scrutinize, the question of who’s liable is no longer clearly settled. The answer used to be “not us.” After this verdict, that assumption deserves a second look.

The regulatory wave is converging. Australia banned social media for users under 16. More than 40 U.S. state attorneys general have filed suits against Meta. Several states are passing their own protective legislation. Congress is considering the Sunset Section 230 Act. The California AG has a trial coming in August. Design choices are becoming compliance decisions, and the window for getting ahead of that shift is closing.

Update, March 26 morning: As this piece was being published, Sportico reported that the same defective-design legal theory is already being applied to sports betting apps, with a lawsuit alleging DraftKings and FanDuel designed their platforms to encourage addictive behavior.

The Strategy Gap, Again

I return to the same observation from different directions in this series. In “Big Tech Bets,” it was the competitive terrain that founders and buyers aren’t reading clearly enough. In “Dressed as Disruption,” it was the gap between the narrative of AI transformation and the underlying reality. Here, it's the gap between how fast design decisions are made and how slowly accountability frameworks catch up.

The through-line: the strategy gap is bigger than the technology gap.

Companies that treat design as a purely technical or UX decision, separate from legal, ethical, and strategic risk, are operating with an outdated map. This verdict doesn’t mean “all engagement is bad” or “every design choice is a liability.” It means: if your design shapes user behavior, you need to understand how, you need to be honest about it, and you need to take responsibility for the outcomes.

That’s not a legal standard, not yet. It’s a strategic posture. And the companies that adopt it now, proactively, will be in a fundamentally different position than the ones that wait for juries to impose it retroactively.

The market, for its part, shrugged. Meta’s stock went up 5% after the New Mexico verdict. The financial system and the legal system are, at this moment, operating in different realities. That gap won’t last forever. It rarely does.5

What Comes Next in This Series

The jury deliberated for nearly 44 hours over nine days before reaching this verdict. At one point they told the judge they couldn’t agree on one of the defendants. They were sent back to try again.

That struggle is itself a story, and it’s the subject of the next piece: the vocabulary gap. We are retroactively building accountability for systems that outpaced our ability to name what they did to us, and we’re doing it with borrowed language from domains where it doesn’t quite fit. “Addiction” from substance abuse. “Defective design” from physical products. “Negligence” from tort law. Every term is carrying weight it wasn’t built for.

The third piece will pull back further and ask the bigger question: what does it mean that our default mode for understanding powerful systems is after-the-fact, in courtrooms, at enormous cost? And what would it look like to build the capacity to orient before the damage is done?

For now, the takeaway from yesterday is narrower but concrete: a jury just established that the way you build something can make it defective, even if what people do with it is their own choice. That principle has been tested, and it held. The companies, the builders, and the buyers who take it seriously now will be ahead of the ones who wait for the next verdict.

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This is part of Operating Conditions, a series about the strategic landscape leaders and builders are navigating. Previous entries include “Big Tech Bets: The Competitor No One Puts in the Pitch Deck“ and “Dressed as Disruption: Everyone’s Wearing It This Quarter.”

Notes & Caveats

The LA verdict will almost certainly be appealed by both Meta and Google; a Meta spokesperson said they “respectfully disagree with the verdict” and Google called the case a misunderstanding of YouTube. The dollar amounts ($6M + punitive in LA, $375M in NM) are individually small relative to Meta’s $1.5 trillion market cap. The precedent, and the 1,600+ cases behind it, is where the real exposure lives. I’m not a lawyer, and nothing here is legal advice. What I am is someone who watches how technology decisions and business strategy interact, and this verdict changes the calculus for both.

This morning: Amnesty International called the verdict "a landmark moment" and called for mandatory design changes to guarantee online safety for children. Worth noting as additional color but probably doesn't need to go in the piece itself, since the piece already has enough institutional reaction quotes.

That line has lived in gaming culture for forty years. An old man in a cave hands you a sword. It’s not much, but it’s something. It’s an acknowledgment that what you’re about to face is real and difficult, and that someone who came before you cared enough to say so. And to put something in your hands before you walked out the door.

Another month passes, another conversation reinforces the sentiment: I don’t think we’ve done that. Not well enough.

The “Professors as Customer Service Associates” analogy (rather than stewards of learning) has stayed with me for weeks now. A colleague used it offhandedly, but it landed hard, because it describes something I keep encountering across the survey of graduate programs and learning environments we’ve1 been looking at: an institutional posture oriented more toward satisfaction metrics than toward the actual, difficult work of cultivating understanding.

This isn’t about bashing professors2. Many are doing extraordinary work under deteriorating conditions. But the structural incentives have shifted, and the results are showing. According to the Lumina Foundation-Gallup State of Higher Education study, only 35% of Americans now say a college education is “very important”, which is a dramatic slide from 70% just over a decade ago. Among those who lack confidence in higher education, a recurring reason is that institutions aren’t teaching the right things for the world students are actually entering.

Meanwhile, 87% of Gen Z workers report feeling unprepared to succeed in the workforce, citing limited guidance, unclear pathways from school to career, and uncertainty about which skills and credentials actually matter. This is not a marginal finding. That is a generational signal.

There are entirely different arenas and qualities of work and learning across these programs and spaces. Folks are looking for real teaching and real learning, and the migration away from conventional academia — even toward TikToks and shorts — makes a certain kind of sense beyond just the soundbite orientation. For a true seeker of knowledge, it’s “water, water everywhere,” but most of the readily available, institutionalized “drinking glasses” offer the appearance of cultivation, promise some kind of certification, yet land as hollow.

So here we are on Substack (and the increasingly commodified “digital third spaces” of social media) wandering3 in and out of collectives who care, and who dare: to auto-didactically scour the internet, employ critical thinking, and glue-stick-together something like a mosaic of understanding. (Gluestick, as in, something partially adequate but relatively rudimentary for the real work.)

It’s Ok to Say It

I want to name what I think is actually happening, because I think younger generations already know it in their bones even if nobody in authority has said it plainly to them.

The training and preparation you received was not adequate for the moment you’ve inherited. And that was true before GPT, before LLMs went mainstream, before the last five years of technological acceleration made it impossible to ignore.

The problems were already there. Universities structured around disciplinary silos that fragment knowledge precisely when the world’s challenges demand integration. A job market increasingly disconnected from the credentials institutions sell: only 11% of business leaders reported strong agreement that graduating students have the skills their businesses need. An epidemic of loneliness that the U.S. Surgeon General declared a public health crisis in 2023, noting that roughly half of American adults were already experiencing it before the pandemic. A demographic enrollment cliff now arriving on schedule, exposing how many institutions were running on momentum rather than mission.

The World Economic Forum’s Future of Jobs Report projects a net 78 million new roles by 2030, even as 22% of current jobs undergo structural change, and 63% of employers cite skills gaps as their primary barrier. Entry-level job postings have declined 29% year-over-year. Gen Z is adapting; they’re resourceful, they’re building side hustles, they’re self-educating. But they shouldn’t have to be doing this much of the orienting on their own.

This wasn’t sprung on us. We watched these trends compound over decades.

Being negative vs. being sober

“…pessimism too will turn out to be one more cultural luxury that we shall have to dispense with in order to survive in these hard times.” - Alasdair MacIntyre

I know there’s an instinct to hear this as blatant negativity, or as a dismissal of the good and hard work many people are doing. I don’t mean it that way. I’m around many projects and groups working diligently to address these problems meaningfully. I’m working on them myself. And I’ve written about the need for community, leadership, and accompaniment in this kind of moment: the messy, honest, co-regulatory work of figuring things out alongside others whose worldviews are also shifting.

But I think it’s important to periodically just say it4: we are underprepared. What we have at the ready is, by and large, inadequate relative to the scope of the concerns younger generations are now facing. The tools, the institutions, the frameworks, the mentorship pipelines — they were built for a world that no longer exists, and we have not rebuilt them fast enough.

It’s somewhat uncouth to try to normalize this. People will hear defeatism, or deny the good work of many. But I’ll say it again here, because I think it deserves acknowledgement, and because I think younger folks deserve to hear it from someone with enough experience to say it without hedging:

You were given a difficult hand. The world you walked into was not well-prepared for you, and you were not well-prepared for it. That’s not your fault. It’s a collective failure of imagination, investment, and institutional will. And some of us see it clearly.

Building a community for those interested in working on what goes in that third frame. If you’re interested in more updates, consider following along on:

Signup at jesparent.com

Creating space for informed agency + constructive optimism

Many in the futures space, the big idea space, the tech space, are clinging to certain conclusions, as speaking with certainty is the intra-domain politically correct approach, or because there are simply poorly articulated alternative views that don’t descend to extreme positivity or negativity.

I’m less interested in those poles, or the reduced dimensionally of the problem space at large. What I’m interested in is what an honest accounting sounds like, and what it makes possible.

Because here’s the thing about the old man in the cave: the sword isn’t the point. The point is the acknowledgment. It’s dangerous to go alone. Someone saw the terrain, named the difficulty, and offered what they had. That act of honesty is itself a kind of preparation. Or more so, an earnest effort at continuity, shared responsibility, and acknowledgement of trans-generational skin in the game5.

If we can do more of that — name what we failed to build, name what the moment actually requires, and then get serious about building it together — then the acknowledgment becomes a foundation rather than a eulogy.

There’s a longer piece coming on what I’m calling “The Right to Be Unimpressed”; a deeper look at how much of what’s available genuinely isn’t meeting the demands of the moment, and what it would take to build things that do. For now, consider this a periodic reminder: some of us see it, some of us are saying it, and some of us are ready to act on it.

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Google’s Stitch is worth paying attention to, even if you’re not a designer.

Launched at Google I/O in May 2025 and now significantly redesigned, Stitch bills itself as an “AI-native infinite canvas” for UI design. The new version introduces what Google is calling “vibe design”: instead of starting with wireframes or pixel-level specs, you start by describing what you want the product to feel like. Your business objective. The emotion you want the user to have. What’s currently inspiring you. The AI handles iteration from there.

You can now speak directly to the canvas, ask for real-time design critiques, request variations by voice, and generate interactive prototypes without touching a single tool panel. Stitch will even auto-generate the next logical screens based on user flows you didn’t explicitly specify.

There are rough edges. This is still a Google Labs experiment. But Figma’s stock dropped when the update landed. That tells you something about how the market reads the stakes.

What’s actually happening here

Vibe coding was about abstracting away technical execution. You describe the outcome you want; the model writes the code. Stitch does something adjacent but distinct: it’s abstracting away design literacy, or the gap between having a clear idea and being able to express it visually.

That gap has always been costly. The person with the sharpest instinct for what a product should feel like is often not the person trained to render it. Founders, strategists, product managers, researchers: these are people who understand the intended experience but lack the vocabulary to express it in Figma. Stitch appears designed specifically for them, not as a shortcut for designers, but as an on-ramp for people who have been locked out of the design conversation entirely.

This is a meaningful shift. Not because it makes design “easier” in some trivial sense, but because it changes who gets to participate in shaping what a product looks and feels like before anyone writes a line of production code.

The broader pattern

The bet most AI labs made in 2024 was that software development would be the first domain where AI created real capability leverage. That bet looks correct. But coding was always the most legible example of a broader dynamic: AI peeling away the technical skill layers that have historically separated someone with a clear idea from someone who can execute it.

Design is now following. Writing is arguably already there, though the quality questions are still live. Strategy and analysis are further behind, but the direction is consistent.

One analyst noted that without deterministic constraints — design standards, corporate patterns, contextual requirements — tools like Stitch carry real risk of producing outputs that are technically polished but strategically incoherent. That’s a fair concern. The question isn’t whether the canvas can produce something beautiful. It’s whether the person guiding it knows what they actually want.

That, as it turns out, is the part that was always the hard part.

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Stitch is currently free in Google Labs beta. Worth trying even if design isn’t your domain — the experience of describing intent and watching a canvas respond is clarifying in ways that are difficult to anticipate until you do it.

Sources: Google Blog | The Register | The Deep View | AI Business

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Earlier this year, Jack Dorsey announced that Block would cut 4,000 employees, roughly 40% of its workforce. His explanation was simple: AI tools now allow fewer people to do more. Investors loved it. The stock jumped 24%.

The framing was clean. The narrative was legible. It had all the structural markers of a bold, forward-looking strategic move. We’ve seen this pattern before.

I wrote recently about “meaning-shaped” outputs: content that feels like it deserves your attention but on closer inspection has no actual relationship to the thing it claims to be about. LinkedIn comments that look thoughtful but say nothing. Reports with all the hallmarks of rigor but no genuine analysis underneath.

What’s happening in corporate America right now is meaning-shaped transformation. Companies are producing the shape of AI-driven change: the layoff announcements, the efficiency narratives, the investor-friendly framing. But without necessarily having done the underlying work that would make those narratives true. (Or perhaps, betting they won’t have to put in that work.)

The Numbers Behind the Narrative

Block’s story is instructive not because it’s unique, but because it’s so legible from multiple angles at once.

One angle: Block tripled its headcount between 2019 and 2022, from under 4,000 employees to over 12,000. Its stock dropped roughly 40% since early 2025. It had been under sustained pressure to cut costs. An analyst at Financial Technology Partners told Bloomberg this was “more about the business being bloated for so long than it is about AI.”

Another angle: Dorsey says Block built an internal AI tool called Goose, and that recent model improvements convinced him the company could operate effectively at half its size. He claims the company is targeting $2 million in gross profit per employee, up from $500,000 historically. That’s a real number attached to a real thesis about productivity.

Both of these things can be true at once. That’s exactly what makes “AI washing” so slippery.

In the weeks after the announcement, the picture got murkier. Laid-off and current employees began speaking publicly, with many describing the cuts as Dorsey “posturing for the market”, a way of winning back investor confidence after heavy investments in cryptocurrency, according to reporting in The Guardian. Worth noting in that context: Block spent $68.1 million on a company-wide event in September 2025 — featuring Jay-Z, Anderson .Paak, and 8,000 employees flown to Oakland — five months before cutting nearly half the workforce. That sequencing tells its own story.

The term “AI washing” has been circulating for months, and the evidence base is growing. A Forrester report from January found that many companies announcing AI-related layoffs don’t have mature AI systems ready to replace the roles they’re cutting. Goldman Sachs economists estimate AI is currently eliminating only 5,000 to 10,000 jobs per month across all U.S. sectors, a number that makes Block’s 4,000 cuts look oddly attributed to the technology alone.

Perhaps most telling: when New York State gave employers the option to cite “technological innovation or automation” in their legally required layoff notices, not a single one of the 160 companies filing (including several that publicly blamed AI) checked the box.

In Dorsey’s Own Words

In a subsequent interview with WIRED, Dorsey was given the chance to answer the AI-washing charge directly. Asked point-blank, were you AI-washing the layoffsb, he didn’t say no. He said: “The most important thing for me and the company is that we stay well ahead of the technology trends that are impacting us.” That’s a pivot, not a rebuttal.

What he offered instead was a genuine and genuinely ambitious vision of what he’s building toward. He said something really shifted in December in the sophistication of AI tools, specifically naming Anthropic’s Opus 4.6 and OpenAI’s Codex 5.3 as the trigger, and that it “presented an option to dramatically change how any company is structured.” He described the goal not as a leaner version of Block but something more radical: “I want the company itself to feel like a mini AGI.” No management hierarchy. An intelligence layer on top that employees and customers alike can query, build on, customize.

Take him at his word and it’s a serious idea. The kind of organizational redesign he’s describing is genuinely new thinking, not just efficiency-speak. The question is whether what Block actually did is that, or a precursor to it, or something else wearing its clothing.

He predicts that “every company that’s not building itself as intelligence is going to face something existential, and it’s going to happen over the next year or two.” Maybe. But betting that your prediction will prove true doesn’t retroactively make the method of getting there coherent. The vision and the execution are two different things, and the gap between them is exactly where AI washing operates. Yet, what is the actual downside of losing such a bet?

The Incentive Structure Is the Story

Why would companies frame layoffs as AI-driven when the reality is more complicated? Because it works.

Molly Kinder at the Brookings Institution puts it plainly: blaming AI is “a very investor-friendly message,” far more palatable than admitting the business was overstaffed or struggling. Block’s stock surging 24% on the announcement is the proof of concept. Markets reward the AI transformation narrative. They punish admissions of past mismanagement.

Bloomberg's opinion desk framed it well: this is a familiar Silicon Valley pattern of reframing mistakes or inconvenient decisions as vision. "Move fast and break things." A weak governance structure becomes protecting the founder's vision. A cost-cutting decision becomes leading the industry into the future. The substance changes; the move is always the same.

Deutsche Bank analysts have predicted that “AI redundancy washing will be a significant feature of 2026.” Nearly 60% of U.S. hiring managers surveyed by Resume.org said they emphasize AI’s role in job cuts because it’s perceived more favorably than citing financial constraints.

This is not a conspiracy but rather incentive structure. Incentive structures produce predictable behavior.

Wharton’s Peter Cappelli summarized the dynamic with characteristic directness: “The headline is, ‘It’s because of AI,’ but if you read what they actually say, they say, ‘We expect that AI will cover this work.’ Hadn’t done it. They’re just hoping.”

The Data Gap: Investment vs. Return

Step back from the layoff headlines and look at the broader picture, and something stark comes into view.

PwC’s 2026 Global CEO Survey of 4,454 executives across 95 countries found that 56% have seen neither revenue growth nor cost savings from their AI investments. Only 12%, one in eight, reported achieving both. Separately, Deloitte found that 84% of companies have not redesigned roles around AI capabilities, even though 36% expect significant job automation within a year.

Read those numbers together: the vast majority of organizations haven’t restructured their work around AI, haven’t seen financial returns from AI, and yet a growing number are attributing layoffs to AI.

That’s the gap. That’s where AI washing lives.

It’s the same gap the meaning-shaped piece approached from a different direction. Organizations are producing more outputs than ever — more reports, more strategic plans, more transformation narratives — while the quality of their actual decision-making lags behind. The volume of plausible-looking material starts to mask the absence of genuine understanding underneath.

What’s Actually Happening

None of this is to say AI isn’t changing work. It clearly is, in some contexts, for some roles, in ways that are genuinely significant. Anthropic’s Dario Amodei has written at length about the potential for what he calls “unusually painful” disruption. An MIT + Oak Ridge National Laboratory study found AI can already perform work equivalent to 11.7% of the U.S. labor market1. These aren’t trivial findings.

But there’s a meaningful difference between a company that is actually transforming how work gets done (redesigning roles, investing in retraining, evolving its operational model) and one that is using “AI” as a more acceptable frame for cost-cutting decisions that were coming regardless. The first is a real management challenge that deserves serious attention. The second is a category error being actively incentivized by capital markets.

The inability to tell the difference between those two things is itself the problem. Not just for workers trying to understand their job security, but for leaders trying to make sound technology investments, investors trying to allocate capital wisely, and policymakers trying to respond appropriately. Orientation and discernment, in other words. That’s what’s actually scarce here.

The Same Bottleneck

The scarce resource is the capacity to perceive clearly what’s actually happening, to distinguish between the shape of change and change itself.

When Dorsey tells the market that AI justifies cutting 40% of his workforce, the question isn’t whether AI is powerful. It is. The question is whether this specific claim, at this specific company, reflects genuine organizational transformation or a narrative optimized for investor reception. Or, something else.2

Answering that question requires someone who understands both the technology and the business well enough to actually evaluate it. That judgment doesn’t come from reading the press release.3

Discernment, not information, is the scarce resource.

The Broader Moment of 2026

This is all unfolding in a week when the Anthropic-Pentagon standoff reminded us that even the companies building AI are still figuring out the terrain — and that the relationship between AI capabilities, institutional power, and public accountability is being negotiated in real time, sometimes (usually) clumsily. The map-makers don’t have complete maps either.

Meanwhile, employee anxiety about AI and job loss has jumped from 28% in 2024 to 40% in 2026, according to Mercer’s global survey of 12,000 people worldwide. The IMF’s managing director says AI is “hitting the labor market like a tsunami.” Deutsche Bank warns most of the wave is manufactured narrative.

We are in a moment where the story about AI’s impact is running well ahead of the reality of AI’s impact, and the gap between the two is generating real consequences for real people. Four thousand of them at Block found out this week which side of that gap they were on.

That gap is a dis/orientation problem. And disorientation problems don’t get solved by producing more meaning-shaped discourse about them. They get solved by doing the slower, less marketable work of actually understanding the terrain.

The trouble is, doing work is being outpaced by the need to look the part.

This is part of the AI Moment (or Bubble) that we are still glimpsing, hoping to fathom4, no less, make meaningful choices about. The pressure to perform certainty is felt clearly. The tension between the urgency of performance and the lack of means to form coherence will be a defining challenge (or perhaps simply a burden) that will endure through the rest of this year.

Of course, the fallout isn’t falling evenly. Those writing the layoff press releases and the people named in them are not experiencing the same year. That asymmetry5 of who absorbs the gap between AI’s promise and its current reality is worth its own examination. Some economists would call it a K-shaped economy. I think that framing deserves a closer look, and will center on that in forthcoming posts; in addition to unpacking Big Tech Bets from the perspective of founders, investors, and those potentially buying their new products.

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Every AI startup getting funded right now is making a bet, whether the pitch deck says so or not. VC investments typically take five to eight years to exit. That means your product needs to still matter in a world where Anthropic, OpenAI, and Google have had five to eight more years to build. You’re not only betting on your own execution. You’re betting against theirs1.

Ethan Mollick put this cleanly in a recent post: almost every AI VC investment is essentially a bet against the vision the big labs have laid out. He’s right. And I’d take it further, because I’m watching this tension from both sides of the table.

I’ve been a founder, and I’ve evaluated vendors for enterprise purchasing decisions. I mentor startups through accelerator programs, and I advise companies on their technology strategy through . That range of vantage points means I keep seeing the same collision from different angles. On one side: the founding team with a sharp, specific tool, priced well, built for a real workflow. On the other: the enterprise buyer who already has a relationship with AWS or Azure or Oracle whose default move will always be to check what the platform offers before even looking at a startup.

This is the competitive landscape that doesn’t get enough honest discussion in startup spaces. Moats, runway, product-market fit: the standard vocabulary is all still relevant, but it now has a gravitational body that it didn’t before. The big labs aren’t just competitors. They’re the tidal force that shapes the entire market.

Here’s what I mean concretely. A startup might build a genuinely excellent AI agent for a specific compliance workflow. Solves a real problem, customers love it, price is right. But then AWS announces a general-purpose agent framework at re:Invent, and suddenly every enterprise buyer’s procurement team is asking, “Why don’t we just use the thing that plugs into our existing stack?” The startup’s product might be better. It almost certainly is, for that use case. But “better” is not the only variable in an enterprise purchasing decision, and often it’s not even the most important one.

I’ve seen this inertia from the inside. For over 18 months at one company I worked with, the software team was making the case2 to switch from one familiar vendor to a clearly superior alternative. The c-suite wouldn’t move. The delay held back prototyping and development — and then leadership used that same slowdown as justification for canceling the initiative later. The decision not to decide became the decision. That’s the loop procurement inertia creates, and it’s grimly common.

But that’s a story about inertia within an organization that had already committed to buying something. There’s a separate problem that doesn’t get discussed enough: buyers who haven’t committed to buying anything yet. I ran into a clear version of this in a recent conversation with a compliance director at a major financial exchange. When I asked about their timeline for deploying particular AI agents internally, the answer was revealing: still roadmap-stage. Not deployed. Not even piloting, really. Still in the “we’re evaluating the landscape” phase.

That’s the other side of Mollick’s observation that deserves attention. It’s more than startups are betting against the labs. It’s that the enterprise buyers everyone is building for may not be ready to place their bet yet, either. The market timing question cuts both ways. You can have the right product and still be early in a way that burns through your runway before the buyers show up3.

So what do you do with this?

If you’re a startup founder in AI right now, I think the honest answer is: you need to be very specific about which part of the big labs’ roadmap you think they’ll deprioritize or do badly. Not just “we’re more focused.” That’s table stakes. You need a real theory about why the hyperscaler will leave this gap open long enough for you to build a business in it. You will need to pressure-test that theory with actual buyers, not just other founders.

If you’re a company trying to make AI buying decisions, the honest answer is different but equally uncomfortable. The default will always be to wait for your existing vendor to offer something “good enough.” Sometimes that’s the right call. Sometimes it means you’re buying a mediocre general solution eighteen months from now when you could have had a sharp specific one today. The trick is knowing which situation you’re in, and most organizations don’t have a reliable process for figuring that out.

This is a lot of what I spend my time on at Bridgecraft: helping companies develop a real point of view on their technology landscape, rather than letting vendor relationships and procurement inertia make the decision by default. It’s not a solved problem. But it’s a solvable one, if you’re willing to do the work of actually understanding the terrain instead of just reading the map that your existing vendors hand you4.

The AI investment landscape isn’t going to get less complicated. The bets aren’t going to get easier. But the quality of your orientation to the landscape you’re operating in, and awareness of which paths afford the best chance of reaching desirable destinations? That’s where enduring advantage can be found.

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It’s a small observation situated from a specific platform. But I think it points at something much larger.

We are entering an era where plausible-looking outputs are essentially free to produce. Text, strategy, analysis, research. Anyone can generate a comment that looks thoughtful. Anyone can produce a memo that reads like it was written by someone who understands the problem. Anyone can assemble a report with all the structural markers of rigor: citations, frameworks, recommendations.

The uncomfortable question that follows: if producing the shape of meaning is cheap and fast, what happens to actual meaning?

I think what happens is that discernment becomes the bottleneck. Not information. Not productivity. Not even “critical thinking” in the abstract. Something more specific: the capacity to tell the difference between meaning-shaped and meaningful. Between a strategy that looks right and one that is right. Between a map that has all the expected features and one that will actually get you where you need to go.

This isn’t just a social media problem.

Organizations are adopting AI tools and producing more outputs than ever2. More reports, more analyses, more strategic plans. But the quality of their actual decision-making often hasn’t improved. Sometimes it’s gotten worse, because the volume of plausible-looking material makes it harder to identify what actually matters3. The bottleneck was never production. It was orientation: knowing what question you’re actually trying to answer, understanding the landscape well enough to evaluate the options, and having the judgment to choose well.

The same dynamic plays out in research. LLMs can help you survey a literature, draft a synthesis, even generate hypotheses. But they can’t tell you which literature matters for your question, or whether a synthesis captures what’s genuinely contested in the field, or whether a hypothesis is worth pursuing given where the frontier actually sits. That requires someone who has done the slow, unglamorous work of understanding the terrain. Not just processing it.

In education, students can now produce essays that are structurally sophisticated and substantively hollow4 5. The old assessment model (“show me you can produce a well-formed argument”) breaks down when production is trivially easy. What remains valuable is whether someone actually understands something. Whether they can navigate a genuinely unfamiliar problem. Whether they can tell when they’re lost and figure out how to get oriented again.

The pattern continues to take shape6. What’s getting cheaper is almost all on the production side: generating text, assembling information, creating artifacts that have the right shape. What’s getting more valuable is almost all on the orientation side: knowing what to produce, knowing whether what you’ve produced is any good, knowing which direction to go when the landscape is complex and the maps7 are unreliable.

Speed of production is only valuable when you know what you’re producing and why. A car is a wonderful thing if you know where you’re going; if you don’t, it just gets you lost faster.

The challenge of this era isn’t building more or building faster. It’s orienting: perceiving clearly, choosing wisely, and acting with the kind of conviction that comes from having actually understood the terrain rather than just processed it.

Meaning-shaped is the symptom. Disorientation8 is the condition9. And the prescription, I think, is a kind of patient, deliberate work that our current moment doesn’t reward but desperately needs: actually figuring out where you are, where you’re trying to go, and what the landscape between here and there really looks like.

That’s the work I find myself most interested in, both intellectually and practically. More on that soon.

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At 5:01 PM Eastern today, a deadline set by Defense Secretary Pete Hegseth expires. Anthropic, the AI company behind Claude, must either accept the Pentagon’s demand for unrestricted “all lawful use” access to its AI model on classified military networks or face contract termination and designation as a supply chain risk.

Anthropic CEO Dario Amodei rejected the Pentagon’s final offer last night, stating the company “cannot in good conscience accede to their request.”

This is the most significant confrontation between a private AI company and the U.S. government to date. And the way it’s unfolding tells us more about the state of AI governance than either side probably intends.

What’s actually at stake

The dispute is narrow in scope but enormous in precedent.

Anthropic has two red lines. It does not want Claude used for mass surveillance of Americans. It does not want Claude used in fully autonomous weapons without human involvement. The company has maintained these restrictions since it signed a contract worth up to $200 million with the Pentagon last summer, making Claude the first AI model deployed on the military’s classified networks.

The Pentagon’s position is that the end user, not the vendor, should determine how a licensed technology is used. As a senior Pentagon official told CNN: “You can’t lead tactical ops by exception. Legality is the Pentagon’s responsibility as the end user.”

Pentagon spokesperson Sean Parnell has insisted the military has “no interest in using AI to conduct mass surveillance of Americans (which is illegal) nor do we want to use AI to develop autonomous weapons that operate without human involvement.” But the Pentagon has consistently refused to put those assurances into contractual language. Anthropic says the latest proposed contract revisions, which the Pentagon framed as a compromise, were “paired with legalese that would allow those safeguards to be disregarded at will.”

This is not a dispute about what the Pentagon currently plans to do. It’s a dispute about who gets to set boundaries on what AI can be used for in national security contexts, and whether those boundaries can be contractually binding.

The epistemology of “all lawful use”

The Pentagon’s demand that AI tools be available for “all lawful purposes” sounds like a reasonable standard. It implies that legality itself is a sufficient guardrail. But this framing deserves more scrutiny than it’s getting.

Mass surveillance of Americans is not clearly illegal. It exists in a legislative gap. The legal frameworks governing domestic surveillance (FISA, Executive Order 12333, the Fourth Amendment) were built for wiretaps and phone records, not for AI systems capable of processing and cross-referencing vast datasets of behavioral, biometric, and communications data in real time. As a source familiar with Anthropic’s position told CNN, “there are no laws or regulations yet that cover how AI could be used in mass surveillance.”

Fully autonomous weapons systems are not prohibited by U.S. statute either. Department of Defense Directive 3000.09, which requires “appropriate levels of human judgment” in the use of force, is a policy document, not a law. It can be revised or rescinded by any defense secretary.

So “all lawful use” is not a safety framework. It’s the absence of one. It defines the acceptable boundary of AI deployment as “whatever isn’t currently prohibited” in domains where almost nothing is currently prohibited.

Amodei’s position is fundamentally epistemic. He is making a knowledge claim: that some capabilities are outside what today’s technology can safely and reliably support. In a statement Thursday, he wrote that Anthropic “believes deeply in the existential importance of using AI to defend the United States and other democracies.” But the company’s position is that current AI systems are not reliable enough for certain applications, regardless of their legality.

This distinction matters. The Pentagon is arguing from authority (”legality is our responsibility”). Anthropic is arguing from capability (”the technology isn’t ready for this”). These are fundamentally different kinds of claims, and they require different institutional mechanisms to adjudicate.

We do not currently have those mechanisms.

A Korean War statute for a 2026 problem

If Anthropic does not comply by 5:01 PM today, the Pentagon has threatened two actions: terminating the contract and designating Anthropic a supply chain risk, and potentially invoking the Defense Production Act to compel Anthropic’s cooperation.

The DPA is a Korean War-era statute signed by President Truman in 1950. It gives the executive branch broad authority to direct private industry in the name of national defense. It was designed for steel mills, tank factories, and industrial supply chains.

Legal scholars are divided on whether it can be used this way.

Alan Rozenshtein, writing in Lawfare, published the most thorough legal analysis to date. He identifies two possible demands the government might make under the DPA: requiring Anthropic to provide Claude without its contractual usage restrictions (a “same product, different terms” argument), or compelling Anthropic to retrain Claude to strip safety guardrails from the model itself. The first is legally contested. The second, Rozenshtein argues, would more clearly constitute demanding a new product, which sits on much weaker legal ground.

As Charlie Bullock of the Institute for Law & AI told the Associated Press, neither side’s legal argument is “a slam dunk.” If neither backs down, the most likely outcome is litigation between Anthropic and the federal government, testing the application of a 75-year-old industrial production statute to AI safety policy for the first time.

Rozenshtein’s conclusion cuts to the heart of the problem: “this fight is happening because Congress hasn’t set substantive rules for military AI.” He argues that if Congress had legislated guidelines on autonomous weapons and surveillance, Anthropic would likely be comfortable selling to the military without restrictions, and the DPA threat would never have arisen. The DPA itself is scheduled for reauthorization by September 30, 2026. Depending on how this dispute unfolds, its renewal could become a legislative flashpoint for AI governance.

The contradiction that reveals everything

Amodei identified perhaps the sharpest analytical point in his Thursday statement: the Pentagon’s two threatened actions are inherently contradictory. A supply chain risk designation labels Anthropic as too dangerous to work with. A DPA invocation labels Claude as too essential to lose.

You cannot be both a threat and a necessity. As former DOJ-DOD liaison Katie Sweeten told CNN: “I would assume we don’t want to utilize the technology that is the supply chain risk, right? So I don’t know how you square that.”

This contradiction reveals that the real dispute isn’t about legality or even capability. It’s about control. The Pentagon wants to establish the principle that no private company can set terms of service that constrain government use of a licensed technology. The Center for American Progress described the supply chain risk designation as potentially “existential for Anthropic” and the DPA invocation as “the quasi-nationalization of a frontier lab.”

Gregory Allen, a senior advisor at the Center for Strategic and International Studies, offered important context on Bloomberg Radio. The actual users of Claude within the Defense Department, he said, “love Anthropic, love Claude” and report that the company’s usage restrictions “have never been triggered.” The dispute is not being driven by an operational problem. It’s being driven by a principle.

The race to the bottom

The Anthropic standoff does not exist in isolation. It’s unfolding alongside several parallel developments that, taken together, paint a concerning picture.

xAI’s Grok is already on classified networks. Elon Musk’s xAI has signed a Pentagon contract under the exact “all lawful use” terms that Anthropic is refusing. This is the same model that has generated approximately 3 million deepfake images, including an estimated 23,000 depicting minors, and whose offices were raided by French prosecutors. The Pentagon has confirmed that Grok is “on board with being used in a classified setting,” though officials acknowledge it is not viewed as being as advanced as Claude.

The Pentagon’s AI strategy explicitly omits ethical AI language. A January 2026 strategy memorandum from the Department of War directed all Defense Department AI contracts to incorporate “any lawful use” language within 180 days. The same memorandum bans models with DEI-related “ideological tuning” and replaces “responsible AI” with “hard-nosed realism.”

OpenAI and Google are being pressured next. An open letter published Thursday night by tech workers from Anthropic’s top rivals urged their companies to hold the same red lines. The letter states: “The Pentagon is negotiating with Google and OpenAI to try to get them to agree to what Anthropic has refused. They’re trying to divide each company with fear that the other will give in.”

Bipartisan congressional concern is growing. Retired Air Force General Jack Shanahan, who led the Pentagon’s original AI initiatives, wrote that “painting a bullseye on Anthropic garners spicy headlines, but everyone loses in the end.” Both Republican and Democratic lawmakers have raised questions about the Pentagon’s approach.

The dynamic is clear: if any one company caves, the pressure on the rest becomes overwhelming. This is the classic collective action problem in governance, and it is playing out in real time with technology that could reshape the relationship between state power and individual rights.

The voluntary safety framework problem

There is an uncomfortable irony in the timing of this standoff. On the same day Hegseth issued his ultimatum, Anthropic published a major revision to its Responsible Scaling Policy. The company replaced its binding commitment to pause training if model capabilities outstripped safety controls with a nonbinding “Frontier Safety Roadmap.” The new framework describes its safety goals as “public goals that we will openly grade our progress towards” rather than hard commitments.

Anthropic says the change is unrelated to the Pentagon dispute. Chief Science Officer Jared Kaplan told TIME that the company spent nearly a year deliberating. The core reasoning: voluntary commitments don’t work when competitors ignore them. Anthropic argued that “if one AI developer paused development to implement safety measures while others moved forward training and deploying AI systems without strong mitigations, that could result in a world that is less safe.”

Chris Painter, director of policy at METR (a nonprofit focused on evaluating AI models), reviewed an early draft of the new policy and offered a mixed assessment. He praised the emphasis on transparent reporting. But he warned: “This is more evidence that society is not prepared for the potential catastrophic risks posed by AI.”

The practical implication for anyone relying on AI vendor safety commitments is sobering. If the most safety-focused AI company in the world has concluded that binding self-regulation is competitively untenable, the market alone will not produce adequate governance. The question of who sets the rules, and whether those rules can be enforced, becomes not a theoretical debate but an operational necessity.

What this means going forward

Whatever happens at 5:01 PM today, several things are already clear.

First, AI governance cannot be resolved through contract disputes, social media ultimatums, and Korean War-era production statutes. These are institutional tools built for a different century, and they are producing outcomes that nobody designed or intended. Lawfare’s argument that Congress should set the rules for military AI is compelling precisely because the alternative, which is what we’re watching right now, is governance by improvisation.

Second, the “all lawful use” standard will cascade beyond the Pentagon. If this framing becomes the default for government AI procurement, it will shape expectations for enterprise contracts more broadly. Every organization with an AI vendor relationship should be thinking about what happens when your vendor’s relationship with other clients, including governments, changes the terms of what your tools can do.

Third, voluntary safety commitments are structurally fragile. Anthropic’s own policy shift this week demonstrates that even companies founded on safety principles will adjust those principles under competitive pressure. Organizations relying on vendor self-regulation for AI governance need their own frameworks.

Fourth, the supply chain risk designation, if invoked, would have cascading effects well beyond the $200 million Pentagon contract. It would require every company doing defense work to prove it doesn’t use Anthropic products. For a company with a $14 billion revenue run rate and a potential IPO on the horizon, the enterprise implications could be severe.

And fifth, this is not about one company. The open letter from OpenAI and Google employees makes clear that the same pressure is being applied across the industry. Today’s outcome will shape whether any AI company can maintain red lines on military use of its technology

The institutional mismatch is the story

I want to close with what I think is the real takeaway, because it’s easy to get lost in the drama of ultimatums and deadlines.

The mismatch between our institutional infrastructure and the technology it governs is the defining challenge of this decade. A defense secretary is using a 1950 statute to govern 2026 AI capabilities. A Pentagon CTO is calling a CEO a “liar” with a “God-complex” on X. Contract negotiations that should be resolved through clear legislative frameworks are instead proceeding through ad hoc threats, public pressure campaigns, and legal theories that have never been tested in court.

This is not an AI problem. It is an institutional design problem. And it is a problem that the anticipatory governance community, the futures studies community, and the technology policy community have been warning about for years.

The question has never been whether AI would be used in national security. Of course it will be. The question is whether we have the governance infrastructure to make those decisions wisely, transparently, and with appropriate accountability.

Today, at 5:01 PM, we get one answer to that question. It is unlikely to be a reassuring one.

This post represents the author’s analysis and does not constitute legal advice.

BOSTON | VIRTUAL - SEPTEMBER 20, 2025
A crow can pick up a stick and use it as a tool on its first try. A monkey can learn to drive a car. You can operate a spoon, a piano, a drone—devices with no precedent in your evolutionary history. Yet state-of-the-art robots struggle to adapt their control even to their own bodies, let alone extend it to unfamiliar tools.

This is the puzzle that opened yesterday’s1 Foresight Institute Neuro Virtual Seminar, where Tarin Ziyaee, Michael Levin, and Josh Bongard gathered to explore the gap between embodied natural intelligence and our artificial attempts to replicate it. The question driving the discussion: Why do biological systems excel at the very thing modern robotics finds most difficult—flexibly extending control through uncertain, changing environments and novel tools?

Over a fast-moving 45 minutes, these researchers dismantled comfortable assumptions about what intelligence is, where boundaries between self and world lie, and why our engineering paradigms keep missing something fundamental. What emerged wasn’t just a critique of current robotics, but a roadmap for rethinking how we build intelligent systems from the ground up.

Discussion Overview: Core Themes

Intelligence Beyond 3D Space

Levin opened by challenging our spatial bias—our tendency to recognize intelligence only in medium-sized objects moving through three-dimensional space. Biological intelligence operates across multiple high-dimensional spaces: physiological state space, metabolic state space, gene expression, and anatomical morphospace. Morphogenesis itself is behavior—collective intelligence navigating anatomical possibilities. When cells coordinate to build a body, they’re executing policies analyzable through behavioral science, not just following deterministic programs.

The Unreliable Medium Advantage

A key insight: biology’s flexibility emerges from spending eons with unreliable components. Living systems cannot assume what will happen—even their own parts are unpredictable. This forces a focus on problem-solving capacity rather than memorizing specific solutions. Tadpoles with eyes grafted onto their tails can see immediately, with no additional evolutionary selection needed. The same genome produces humans or anthrobots2—different ways of being emerge from the same toolkit because the system excels at “doing the best it can with what it has.”

The Plasticity of Self

Bongard emphasized that biological systems maintain fundamentally plastic boundaries between self and world. An infant doesn’t know which actuators belong to it—the reliable food-bringing entity (parent) initially seems like part of the body. This plasticity stands in stark contrast to robotics’ industrial heritage of maximizing certainty and ensuring identical operation across iterations.

Biology achieves “machines within machines within machines”—competent agents all the way down. When an autonomous vehicle encounters an out-of-distribution event, it has no plan B, no other cells inside to turn to. The rigidly defined self becomes a liability.

Imitation Learning’s Fundamental Flaw

The discussion of imitation learning revealed a deeper issue: the paradigm starts with answers (copy these motions) rather than questions (what do I need to do to reach this goal?). Natural systems constantly run micro-experiments with the world, spawning and killing sensory-motor programs, pinging the environment to understand what works.

Current robotics approaches are motion-centered and passive—the learner and teacher are pre-defined, the interesting problems already solved. Bongard raised a provocative question from his earlier work with psychologist Andy Meltzoff: “Who is my teacher?”, leading to the insight of “you’ve already solved the interesting part of the problem for the learner.”

So, what yeah what to imitate and who is doing the imitating? So sorry to bring this back to the plastic self right it’s again the assumption is often “there’s a robot and maybe it’s c learning to copy other robots and other humans.”
I co-authored a paper3 with Andy Meltzoff a developmental psychologist years ago now about “Who is my teacher?” and it’s been so long I’ve forgotten everything about that paper except that question right?
Again like if you don’t clearly have a good boundary it’s not so clear who to learn from the subject and object the learner and the teacher it’s not so clearcut.
And in machine learning and re imitation learning as you mentioned it’s clear the robot is given the learner is given the teacher it’s all set up for them it’s a very passive process; you’ve already solved the interesting part of the problem for the learner.
And so yet again, there’s an example about how if we go in with an incorrect framing, we might not get what imitation learning was originally designed to do.
(Transcript from Foresight Discussion)

Memory Remapping + Zero-Shot Transfer

Levin teased forthcoming work on what he called “zero-shot transfer learning”—datasets with no apparent relationship to a problem somehow containing actionable intelligence for solving it. The biological precedent: caterpillar-to-butterfly transformation4. The caterpillar learns to crawl toward leaves in response to light stimuli. After dissolving most of its brain in the cocoon and rebuilding a completely different architecture for flight, the butterfly retains this learning—but remapped. None of the specific memories matter (butterflies don’t crawl or eat leaves), yet the information persists in transformed form.

This illustrates a crucial principle: “It’s never a tape recorder, it was always messages that we are re-interpreting to see what you can do with it.” Levin suggests biological learning involves constant reinterpretation of memory, telling yourself the best story you can about what your own memories mean.

Polycomputation and Hidden Biology

Bongard introduced polycomputation5—the concept that vibrations across multiple frequencies in biological tissue perform computations simultaneously at the same place. Neurons and body parts compute across the frequency spectrum, something “we’ve only just started to look for.” This represents potential avenues for bio-inspired technology we haven’t yet explored.

The discussion also highlighted our “data starvation” problem: while computer vision dominates robotics research (legacy of the 2012 AlexNet revolution), real-world agents must deal with mechanical forces—object-on-object interactions for which we have no equivalent of ImageNet. We may not even know what kinds of data embodied intelligence requires.6

The Reliability Paradox

When asked about reliability, the panelists reframed the question: Biology is reliable, but at the level of maintaining consistent narratives,7 not consistent geometry or timing. It’s the Ship of Theseus problem—focus on keeping “the ship” reliable, not the planks. Current robotics fixates on proximate causes (the physical body) rather than ultimate causation (what we actually want to happen).

The hard bargain: achieving truly adaptable robots8 means surrendering industrial certainty and tolerances.9 We cannot have both completely open-ended systems and systems that do exactly what we retrospectively want—there’s a Pareto frontier requiring difficult choices.

Practical Implications

For those entering this space, Bongard pointed to his book with Pfeifer, How the Body Shapes the Way We Think, and his lab’s Ludobots subreddit—programming projects easing people into robotics, neural networks, neuroscience, biomechanics, and physics engines.

The path forward involves “metarobotics”—admixtures of control policy and physical hardware at every scale, from individual components with their own intelligence up to cloud-deployed fleets learning collectively. The future isn’t a six-foot metal body with a GPU brain because that division of self is arbitrary, born from human bias rather than principled design.

The central message: We must remain humble before biology’s lessons, avoid enforcing predetermined frames, and recognize that truly capable robots will be agents with intrinsic motivation—not old-school machines we completely control.

A few additional notes from the panelists:

Living Things are not “Machines”

After a solid bit of banter between Bongard and Levin, I made a cheeky comment in the chat about it sounded like they were saying “Living things are not (20th century) machines,” which was reference to a 2021 paper by the same pair.

We had also covered paper previously during a 2024 meeting of the Cognition Futures Reading Group, as hosted by Orthogonal Research and Education Lab.

The subtitle of the paper reads “Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior,” and offers a call to revisiting the language around how we demarcate or label that which generates behavior.

…or are they?

My comment received several reactions, and a follow up reply from Levin, where he linked his latest take on the topic: “Living Things Are Not Machines (Also, They Totally Are)” The March 2025 piece was featured in Noema Magazine, but it’s worth checking Levin’s personal site with additional commentary.

He states early in the article:

Thus, my quarrel with LTNM is not coming from a place of sympathy with molecular reductionism; I consider myself squarely within the organicist tradition of theoretical biologists like Denis Noble, Brian Goodwin, Robert Rosen, Francisco Varela and Humberto Maturana, whose works all focus on the irreducible, creative, agential quality of life; however, I want to push [Technological Approach to Mind Everywhere] this view further than many of its adherents might. LTNM must go, but we should not replace this concept with its opposite, the dreaded presumption that living things are machines; that is equally wrong and also holds back progress.

He clarifies:

Many who support LTNM never specify whether they mean the boring 20th-century machines, today’s quite different artifacts, or the fruits of all possible engineering efforts in the deep future. By failing to answer the hard question of defining what a “machine” is — they neglect a point at the core of their claim.

For an overall brief on the piece: Levin argues that the debate over whether “living things are not machines” is rooted in two false premises: that we can objectively define what something fundamentally “is,” and that our formal models capture the complete story of a system’s capabilities.

He proposes instead a pluralistic, pragmatic approach—recognizing that all our descriptions are metaphors suited to specific contexts and purposes, not universal truths.

Rather than ask whether biology or engineered systems “really are” machines or minds, we should empirically test which conceptual toolkits (mechanistic, cognitive, cybernetic) prove most useful for predicting, controlling, and understanding different systems.

The key insight: nothing is fully encompassed by our models—not living things, not “simple” machines, not even basic algorithms—and the surprising emergence of proto-cognition may appear in unexpected places if we abandon rigid categories and remain humble about the limits of our current understanding.

Tangent: Thoughts on Bitter Lessons

A general sidebar came up following the discussion: “Is this somewhat congruent to Richard Sutton’s Bitter Lesson, perhaps indicating the ‘maps’ are indeed limited, vantage-point & context specific, rather than the more whole, more robust space of behaviors in general?” It led to these thoughts:

There’s a strong parallel here. Sutton’s Bitter Lesson essentially argues that hand-crafted, human-knowledge-based approaches in AI consistently lose to general-purpose learning methods that leverage computation. The “maps” we build—our clever representations, domain knowledge, and engineered features—turn out to be less powerful than simply searching/learning over larger behavioral spaces.

Levin’s argument maps onto this in several ways:

The Core Alignment: Both reject the idea that our current formalisms capture the territory. Sutton says our domain-specific insights are less valuable than we think; Levin says our categorical distinctions (machine/life, cognitive/mechanical) are projections of our limited models, not ontological truths. Both advocate for humility about what our “maps” reveal.

Where They Diverge: Sutton’s lesson is somewhat eliminativist—throw out the hand-crafted knowledge, scale up learning. Levin’s pluralism is more expansive—we need multiple maps/metaphors simultaneously, each revealing different aspects of the behavioral space. He’s not saying “abandon mechanism for cognition” but rather “use whichever lens generates useful predictions for your context.”

The Behavioral Space Insight: Your framing is apt: the “space of behaviors in general” is richer than any single map suggests. Levin’s examples (caterpillar memory remapping, xenobots, tadpoles with ectopic eyes) show biology accessing this space through mechanisms our models don’t predict. Similarly, Sutton’s point is that the actual space of effective behaviors discoverable through search vastly exceeds what human intuition engineers into systems.

A Tension: Sutton implies there’s a privileged approach (scale + search). Levin’s pluralism resists this—there’s no single “right” way to interact with complex systems. So while both critique overconfidence in our maps, Levin might push back on the idea that any one methodology (even massive-scale learning) will capture the territory better than context-dependent tool selection.

An attempt at synthesis: Our formal models are always partial, context-dependent compressions. The question isn’t which map is true, but which maps unlock useful interactions with the underlying behavioral manifold.

Teleology, ever lurking

Further in the Noema article, we find a frequent citation of Levin, the figure from Rosenblueth et al’s 1943 paper on behavior, purpose, and teleology. I admire Levin’s effort to trace back to this paper from over 80 years ago, seeking to better realize the authors’ aims to develop a more robust take on how behaviors emerge.

You can read my breakdown of the same paper here:

Jes Parent on Substack

On 'Behavior, Purpose and Teleology': Enduring Insights from a Cybernetics Classic

Read more

2 years ago · 7 likes · 9 comments · Jesse Parent

Josh Bongard

Levin had to depart about halfway through, and then Bongard held center stage and answered a few questions. Outside of the above summary, I want to highlight a few points for future reference:

Thomas Fuchs mention

The conversation shifted just as I mentioned Thomas Fuchs Ecology of the Brain in chat, so I’ll never know what Bongard was going to say. It came up in the context of “whole-organism” activities, and restructuring investigations to be able to do so.

Footnote #5, which features Bongard, Levin, and Thomas Froese—who is friendly to and perhaps more ready to materialize some of Fuchs’ insights, anti-reductionism, and alluded-to-but-not-explicated methodological hopes—may reveal contain relevant discussion for this arena.

Q&A “How to get started in this space?

An audience member asked about where to begin for those curious in Bongard’s work.

Noting it was outdated relative to modern AI, but that its core ideas remained relevant, Bongard mentioned How the body shape the way we think: a new view of intelligence.10 For those interested in coding and developing simulations representing some of these insights, he mentioned his lab’s Subreddit: r/ludobots. He gives an overview:

There’s a whole bunch of programming projects there that ease you into the shallow end of all the nuts and bolts of robotics, neural networks, a little bit of neuroscience, a little bit of biomechanics, a little bit of physics engines, the kind of tools that tend to be used to tackle these sorts of questions.

And yeah, you can just follow those programming projects. And they’re designed so that when you get to the end of one of those paths in Ludbots, you’ve got a platform, an experimental platform, and then you can hopefully have enough intuition about how to take that platform and use it to test out some of your own ideas.

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That’s it for this really interesting discussion — thank you to The Foresight Institute for hosting this virtual salons, and in particular to Lydia La Roux and Tarin Ziyaee for moderating and hosting. I’m particularly curious if any readers have used ludobots coursework, or read Josh’s 2006 book, among the many other references mentioned.

Stay tuned for more and follow The Scene section of my newsletter for more event recaps and notes.

Cheers, J.

The Problem Space is a collection of posts around lightweight observations, reactions, and opinions that center issues impacting work, tech development, teaching, and the general cultivation of those building the future.

The younger generation that has access, agency and ambition is undeniably hopeful, and I’m fortunate to work with so many.

But the kids are particularly wary about the moral duplicity (or abyss) they see played out every day*. I don’t think older generations can interface very well about this

Have you ever been around grandparents or elders who were asking something of children they couldn’t understand? Or, that a child points out something that is real or a concern, but is casually dismissed by an adult?

There is a generational gaslighting at hand, because of the partiality of what is going on. Mom says one thing dad says another. Who do you believe?

This is the space that needs tending.

* Important note: I think there is a massive gap in understanding the “seeing it every day” aspect is not about being radicalized or extremified to a particular conventional PoV. It’s the access and exposure that no generation has ever formatively had to deal with. It’s not heresay, or whispers, or “it must suck to be those folks”, it is 4K footage of what that person experienced.

There is a leeriness about trusting things that don’t fit this reality-exposure, and it is useful to consider for mentors, teachers, and the “adults” of society, that the questioning and skepticism is warranted.

Moral legitimacy is not going to be a debt1 that is easily paid, and we — at a social, societal level — are particularly unable to sense or interpret the ill-effects of its accumulation. (In this country) there is a particular demonstrative amorality or flimsiness that extends from politics to those (with platforms and voices) speaking from the technologists and futurists; everyone is sitting on their hands2.

This is the environment we are building in, “philosopher-builders” or otherwise.

How are you observing, or attending to this gap in your role as an educator, collaborator or supervisor?

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There’s a type of intellectual recycling I see happening a lot lately—especially in spaces that endeavor to be “future-facing.” It’s the re-use of outdated clout, the repackaging of old status systems, and the subtle assumption that we can build a new world using blueprints designed for a very different one.

My hot take? This isn’t rigor.
It just feels like rigor—because it’s familiar. It’s structured. It has a paper trail. But it’s not truly forward-thinking. It just appears easy.

And easy is what got us here.

Let me be clear: understanding the past is indispensable. We need to reckon with

the history of power, clout, and exclusion—especially in tech, academia, and politics. But that reckoning cannot be the final act. If we stop there, we’re not building a bridge to somewhere. We’re just polishing the ruins.

Perfect recitation of scriptures is not the same as participating in the journey to somewhere new

True innovation means juxtaposing our understanding of what has been with a bold vision of what could be—especially when it centers the people, insights, and values that legacy systems have overlooked or silenced1.

I’m not interested in being a gadfly or critic as my main role, but this post is essentially public therapy about something I am repeatedly exposed to on various fronts. When I see respected institutions and leaders still clinging to the same playbooks—while claiming to “lead the future”—it’s hard to stay quiet.

So, here’s the question that keeps gnawing at me:

Why do we keep needing to re-learn the same lessons from 40, 60, 100, 200+ years ago?

Some people shrug and say: “That’s just human nature.”
But I don’t buy it. I think we’ve not risen to the challenge, culturally and strategically, to cultivate better options—to show that something else is possible2.

Too many people haven’t seen a viable alternative to the status quo.
Worse, they don’t even know why those alternatives haven’t emerged.

This isn’t just a leadership problem. It’s a design problem.
And it’s one worth solving.

Because building a future worth living in isn’t about being clever with yesterday’s clout. It’s about being courageous with today’s possibilities.

As always, views are my own.
Jes - Boston, September 2025

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Prior to the start of September 2025, I've seen about 30+ human beings be burned alive, hit by a sniper, targeted by drone strikes, or otherwise brutally murdered — complete with other human beings laugh about their demise. All this across the last 2 years or so, many in HD or high quality film.

All of it is wrong and bad, including what happened this week - that's what I believe in.

Furthermore, I believe the ire should be directed at that which says1 only certain lives matter or deserve dignity, and others are discardable; all of the orphaned children and the parents losing their children early, it's all horrible and it is acceptable to feel like it simply shouldn't happen.

We can re-normalize that it's safe to say it is all bad and none of it should happen — that it is even tenable to hold that position. Because holding that level of moral conviction and plainness is under duress right now. I wonder, dear reader, do you even feel comfortable voicing that anywhere — and perhaps most of all, “online”? For fear of backlash, or threats?

We don't have to listen to voices that say otherwise, voices intent on compelling the celebration of the violent and unnecessary ending of a person's life. It is a choice that can be made.

As for myself, I've lost 3 people in my immediate family this year, and they were all difficult in different ways. I think the foundation of a shared morality is vital, and acknowledging the immensity of loss is one way to do that.

That said, there are reasons, voices, industries, and actors who benefit from the disharmony, and who seek to amplify an inequality in the value of lives, and the impact of loss. There are external actors who specifically wish Americans to be divided and hate each other, as well.

In this country, in particular, the fullness of acknowledging the pain of others — which ultimately binds our human experiences together, unalterably — is under attack, and many people feel desperate right now. They feel desperate because they are unseen, and their group has suffered a loss, and they feel threatened about their ability to exist, be free, be loved, and be welcomed.

I'll say it again: very powerful influences want you to validate this sense of being threatened.

Perhaps the world at large, and this country in specific, is scared to reach out and take a stand to comfort each other because they know doing so makes them a target for some: “weak,” defending the “wrong side,” or even seen as being disrespectful in some capacity (for valid or invalid reasons). So it is safer only saying what a particular community has coded as acceptable to say. Or safer to throw rage and vitriol at whatever is identified as "other side," the bad guys' team.

But it doesn't have to be like that. We don't have to play that game. Yet, to actually do this, it will require more than simply demanding the understanding or respect that has been denied whichever group you identify with — and not waiting until "the other" says they are sorry, first.

It will take courage, and it will require intentional selection.

Taking hostages when negotiating against the shared dignity of other human beings

Holding moral hostages here isn't going to change anything; it is the current norm. It is how we got here.

One can choose to can stand up and choose otherwise first, however. You can "reach across the aisle" and reject:

• that which wants to blame and seek revenge, and

• compels the belief that some groups or some lives really are worthless —

• or that some generalization of a large group of people justifies their dehumanization,

• that treating them like monsters is right;

• that the sins of so many other people are righteously transferred onto a particular subject for punishment.

I am more discontented with the efforts at play which further cultivate an environment where this kind of seeing each other and showing up for each other becomes a target; that which profits from fomenting the hate, and celebrates the "winning" of one side and the "losing" of another.

This is a road to nowhere.

Mourning and Tuning Out

I don't have a lot to say for people who don't want to mourn a loss; it's their choice. (More below on gut reactions.)

But if you are committed to not engaging at the human level, or committed to ignorance or neglect around the loss, then you are opting out2 of the moral reality of what is happening.

Once again, there have never been such machinery and profit motives behind you choosing to tune out, opt out, and disengage. But that's what it is.

This is the moment in history we live in, and as underprepared as we may be, here we are.

A philosophical deep dive on choice

This second half of the essay takes a slightly different tone, which is why I moved it here to the blog rather than my original “Facebook Post” setup.

Choice: infinite reasons for justifying the acceptance of the dehumanization of others (or yourself)

In this life, you will be presented with infinite reasons and justifications for why the dehumanization of other people may be legitimate. Many of them will be true, valid, and relevant to you. But you will have to choose something beyond that, regardless of those reasons. Pragmatically, you may have to defend yourself, you may have to take difficult actions, and all of those things that are a part of being in this world, as it is; this is no paradise or utopia.

But you will have to choose that people matter, and that loss matters. No one else will do it for you but yourself; and the choice does matter. I am here exhorting you and the world that the choice matters3.

We live in a world that in many ways treats the cost of doing business, metaphorically or literally, as legitimizing of the exploitation of others. It will be you (sorting out with yourself and whatever you believe in), as to how much of that you can handle, manage to process, or just exactly what that means or entails. But only you can positively affirm and enact in this world that people matter, and that dehumanizing others is not the choice you want to make.

Why? Because all of the pressures and reasons will be available for you to select why that is tenable, right, feels good, vindicated, or otherwise a good option to make.

Nothing will ultimately make the fullness of the choice easier, because truly pursuing the choice necessitates you earnestly and soberly look at the heart and soul of yourself and others. It requires your inquiry into the human condition. It is much easier to gesture at the choice and believing you are in fact moral, understanding, compassionate, just, benevolent, or even righteous in one's own judgement.

Yet that is not actually what making the choice is; it is the illusion of the choice, and it is part of the trap and snare that affords comfortable bubbles; it makes it compelling for others around you to also buy into the same incompleteness. Echo chambers form clusterings of people mutually supporting each other, enabling that it's ok to draw the line somewhere, and that it's ok to stop caring; that it’s ok to cease pursuing your more complete understanding of the human condition, the human experience.

There will never be an exhaustion of the supply — there is no capacity — of reasons and justifications to this ceasing; the opportunity for weaponized despair, apathy, or nihilism will persist as long as you draw breath. There will always have some justification ready at hand for why another human being doesn’t need to matter. What’s more, there is absolutely not a uniformity of reasons each person, in their own life, has thrown at them; our lived experiences are not all the same. There are larger and smaller amounts of suffering, pain, loss, hurt, hardship, privilege, and everything else — the lived experience is not uniform. But the choice is the same, and enacting the choice costs differently for different people.

Curiously, it is still the same choice; this is a paradox, and it is unfair, even4. I admit, I comment on this nonchalantly here. But I'm not interested in presenting a false equality, nor diagnosing why the paradox is so; I'm trying to illustrate the context of what we're all collectively dealing with.

(Tha) Crossroads:

So, what are you going to do? You can feel what you want — I'm personally a believer that everyone's raw emotional reactions are valid, even if they are unpleasant and unforgiving; they may speak to certain pains, complications, or whatever unfairness of life and the human experience that they've endured. But the feeling and potential expression of that reaction is not the same as the choice of what you ultimately do about it.

Nor is that reaction independent of the reality that words matter, and trust is hard to build. I wish it wasn't that complicated, but it is, and we don't have time to pretend like it is simpler.5 If you want to contribute to people believing that you believe they matter, you have to act like they matter. Otherwise, their doubt is legitimized.

This is where the fate of many things hangs in the balance. I don't have a massive call to action or singular manifesto about this here (at least, not now), but I think this is what we are really dealing with; this is part of our collective zeitgeist as Americans, and America (for good or ill) as a center stage of global affairs.6

At its core, it's about what you will choose, and how much you are willing to be earnest about the ills of the world, how much you seek to expand your model and comprehension of how the human condition, and how it is embodied uniquely by people other than yourself.

How much do you really want to know, or see, or feel, or understand?

I think we can celebrate each other in striving to know, understand, or love more. I see this as oppositional to the forces that want to lubricate your dismissal, alienation, or forsaking of human beings.

Particularly, when those forces want you to do this forsaking of others for no actual legitimate need or reason, but because it garners them more power and you less power, stability, or clarity. This is one of the oldest games7 we humans have been playing with (or on) each other.

Build towards futures that center dignity and account for dehumanizing forces

To close on a particularly personal sentiment, I think we actually do have “bigger” (or at least more complex) problems to deal with than endlessly fluctuating about the value of other people, or that showing up and affirming rather than dehumanizing others is “a good thing to do.”

But more about that elsewhere. It is clear that it will not simply be a matter of achieving this “perfection” of valuing others lives more universally as a prerequisite for performing the doing, making, or building of paths to the future. We are forced to make choices now, from one state of affairs within this imperfect world, towards another state of affairs.

Yet I would strongly suggest that as we do such building, it is vital to keep in mind that we should seek to make it easier, more tenable, and more celebrated to meaningfully value the lives, experiences, and dignity of human beings.

Because, you can be very well assured, there will be plenty of countervailing winds, endlessly blowing, which left unaddressed will shatter and sunder whatever other paths, structures, or bridges we may fashion.

So construct what you are building with the sturdiness and environmentally-aware savvy to withstand those winds.

We came for the demos and left with a question: how do we keep the human condition in the loop as capability accelerates?

Acceleration is easy; stewardship and centering the human condition is hard. AugLab’s Summit put it all on the table. I attended the August 23rd gathering as a Summit Exhibitor on behalf of JOPRO, as a friend and member of the ekkolápto salon community, and occasional Media Lab party crasher.

In a slightly ambitious approach, the following piece is: (1) part event reporting, (2) part synthesis of contemporary societal threads with the Summit’s deeper themes, and (3) part highlighting work from groups I am involved with that share the same spirit.

Skip To:

• What: field notes from the Human Augmentation Summit at MIT

• So What: the 2025 context that shapes how we build

• Now What: JOPRO programs + a 2026 convening you can help shape

1) The AugLab Summit

Augmentation by the numbers:

250+ attendees, 20+ exhibitors, ~20 Residents, 8+ keynotes, 6 panelists, and a 1-day event that could have easily spilled into 2 or 3 robust days of inquiry and action.

Background

The immediate and material, physical, and personal venue for this transmission is described as follows:

“The Human Augmentation Summit is a gathering of creators - from innovative startups and industry leaders to daring researchers and artists - all shaping the future of the human condition.”

To step back for a moment, my personal relationship to AugLab comes through being a Boston local, and through Ekkolapto’s salon series of discussions; I attended a talk on intelligence & qualia with FAU’s Will Hahn and another with AugLab resident Parth (Rana G), discussing cultures, social networks, and systems.

Augmentation Lab represents its mission as:

“We are a transdisciplinary community of philosopher-builders developing technologies to enhance the human condition,”

defining philosopher-builders as “individuals eager to explore and innovate for the future, equipped with the wisdom to empower and protect”

AugLab hosts a 2-3 month Residency during the summer to build out related projects; the Summit serves as the finale of Residency program — which I did not fully understand until the event when it was contextualized during the rapid-fire demos.1

In their own words, the Summit

“is organized by the Augmentation Lab, ekkolápto, and Pulse, and features speakers and exhibitors from MIT Media Lab, Wolfram Summer School, The Residency, Infinita City, Prospera, SundAI Club, Æthos, PRISM Collective, Conflux Collective, Akiya Collective, and more.”

Keynotes

Each keynote and panel could be its own post; what follows are selective impressions rather than comprehensive notes. The Summit welcome was delivered by Dünya Baradari (Augmentation Lab), Addy Cha (Ekkolapto), and Pedro Henrich (Pulse).

Media Lab Welcome

Community Building & Student Engagement officer Alma Jam introduced us to the Media Studies program at the Media Lab. This was followed by renowned Professor Hiroshi Ishii (MIT Media Lab) with his well-informed perspective, sharing how technology empowers or suppresses with his talk on “Human Augmentation / Human Amputation” (update: full slides now online)2.

Longevity

An overview, a technology, and a take on advocacy rounded out the second keynote group, featuring: Aging field overview and a case for longevity by Doga Yucel (Harvard Medical School); From Vision to Viability: Partial Epigenetic Reprogramming via Michael Ringel (Life Biosciences); and Making Longevity Mainstream with Elen Capri.

Human-Machine Symbiosis

My personal favorite keynotes were from the Media Lab pairing: “Cyborg Psychology” by Prof. Pat Pataranutaporn and “Bodyfull/Bodyless” with Dr. Nataliya Kos’myna.

Nataliya informed us that she had a last-minute update for her presentation, which resulted in analyzing several axes and trajectories to illustrate amounts of embodiment and degrees of merging with AI — using contemporary film media as examples. Previously, I’ve had the good fortune of attending her Augmented Brains 2022 and NeuraFutures events, which fit well with this keynote. Nataliya’s popular work on “Your Brain on ChatGPT” was mentioned in passing, of note as something we’ve discussed in JOPRO’s Data x Direction summer cohort.

Pat’s slides easily inspire a much more thorough discussion, making it clear to see why the Media Lab has brought him onboard to champion a new research group. From commentary on flourishing as meaningful augmentation, to questioning whether new technology is simply replicating old pedagogy, we’ll return to his ideas below, and assuredly in the future as well.

Evening Program & Bonus Podcast

Live Hypnosis Session with Albert Nerenberg — unexpectedly intriguing, Albert wove enough technical concepts into the talk to fit the MIT crowd, and there was some interesting hypnotic suggestions to replicate things this group might know how to do, such as “speaking like Grok.” We saw enactments of the doubling effect, forgetting one’s name, and other feats that left attendees stirring with assessment about which cognitive mechanisms were teased out by this skilled demonstration.

Curt Jaimungal (Theory of Everything) + Andrés Gómez Emilsson (and others from Qualia Research Institute). The title of the podcast session with Curt was Psychedelic Cryptography, Phenomenology & Consciousness, while Andrés also hosted an earlier discussion on “The Screens of Consciousness & Computational Philosophy.”

For those interested in not just psychedelics or altered states, but deep dives in to phenomenology and insightful talks on experience (perhaps the C word), the rest of the weekend with Albert, Andrés, and Curt fostered a unique discussion space. Consider also Curt’s interview of Andrés from earlier this year.

Q&A with Stephen Wolfram moderated by Addy Cha. For the finale of the main AugLab Summit proper, this lent to a very wholesome and much appreciated cross-generational aspect of the night. It’s so important for early-career builders and researchers to hear directly from the previous generations, particularly those as invested in paradigmatic shifts and unabashed exploration as Stephen.

Panels: Context Unlocked

I’ve slightly abstracted the panels out of chronological order, as I believe their combined impact is worth emphasis.

When recapping the Summit during a recent episode of Saturday Morning Neurosim, Orthogonal Research & Education Lab’s standing weekend discussion, I stated unabashedly: “ the panels were fantastic and changed what the event was to me” — why?

Whereas keynotes delivered strong individual visions, the panels triangulated perspectives, modeling the kind of transdisciplinary engagement we need. It is worth openly valuing and celebrating the contextualizing, vantage-framing, and illuminating aspect of what these panels were able to present.

But first, a few gems and quotables from the panelists.

Future of Work & Meaning Making

Greg Epstein, Humanist Chaplain (Harvard/MIT)

1. “If tech is a religion, it’s the most powerful one on Earth—so interrogate its heaven/hell prophecies and who they serve.”

2. What if what we really need is less augmentation of our bodies or our intelligence—and more augmentation of our capacity for compassion and sacrifice?”

Ken Liu, Author and Lawyer

1. “Fix humans? Then fix the human story—and learn how multiple stories can coexist.”

2. “AI displaces the work that’s most representable in data—not just the most ‘cognitive.’”

3. “We can’t rely on politics alone—builders need to keep AI in the commons”

Juliet Schor, Economist & Sociology Professor (Boston College)

1. “Jobs are a story we invented. They became identity—but they don’t have to be.” (This is essentially a of combination of both Liu and Schor)

2. “Four-day week, no pay cut, is already working—time wealth is a practical complement to AI.”

3. “Whether AI goes utopia or dystopia depends on context—who controls it and which values are encoded.”

R&D For The Future

The second panel on the future of R&D was extremely rich in its content and message. Panelists Trey Goff (Prospera), Harry Gandhi (1517 Fund), and Addy Cha (ekkolapto) substituting for Dünya Baradari (Augmentation Lab) were moderated by Sam Rowe (Aethos). My notes have less clear designations of who said what, but here is a rough collection of key ideas:

1. Are grant applications really the best use of our best minds’ time? The market’s correcting: FROs, earlier involvement of VCs, private foundations, and DAOs are filling the gap.

2. “Sustainability is the model, not a grant.” Tie revenue to people’s prosperity; align incentives so flourishing is the business plan.

3. Funding is only one piece. You also need community, legitimacy signals, “and what I [Harry] call beacons—public work that others can follow.”

4. Some communities shouldn’t scale fast; over-funding can crush the bar and kill the culture.

5. “Rigor for wild ideas,” treat ‘crazy’ ideas with scientific respect: reproducibility and honesty, not institutional pedigree.

What’s more, this panel had a fantastic diversity of tone and presence.

What made this R&D panel shine was a meaningful diversity of arenas, but uniformity in concern and intention. Trey brought a policy-honed, solutions-first cadence; Addy offered an explorers’ perspective; Sam and Harry held space for community and sustainability, from the perspective of seeing groups and teams build their goals out over time.

Different professional languages, same problem space. Hearing those styles in one room kept the conversation honest: less monoculture thinking, more witnessing what the diverse approaches and voices speak to.

2A) Zeitgeist & Ether

Selected Contemporary Cultural Threads

To zoom out for a moment and recontextualize to some broader zeitgeist in the world, a few notes that pertain to “The Human Condition”, a stated concern of AugLab.

There are a number of threads that dominate the zeitgeist of 2025, some more visible than others. Here are a few that shape some of my broader context for viewing the Summit, and some of the work we are doing in JOPRO and elsewhere.

The morning I began putting thoughts to "paper”, I serendipitously came across some clips from the slot machine that is social media. First, Ari Aster, coming in with: “We’re living in a world where people don’t agree on what is real.”

Loss of Shared Reality

He speaks of a ‘fundamental sense of what is real’, and that’s gone, further declaring it’s been happening for over 20 years, and that COVID is a point which it was clear that the ties to the “old system” was cut completely; not the start, not the inflection point, but the completion of the severing.

A stage of hyper-individualism, no collective sense of very much of anything. Digiphrenia, assuredly, does not help.

Present Shock, Future Shock

For anyone who has been within philosophical earshot, I spent months in the first half of this year referencing Present Shock, which is Douglas Rushkoff’s 2013 (we were barely into deep learning, no less the ChatGPT era then!) spiritual successor to Alvin Toffler’s seminal “Future Shock” of 1970.

12 years after the publication of Present Shock, it’s hard to overstate its continued relevance. Maybe, even, a post- or hyper-present shock state fits with this clip:

Alienation & Forfeit Futures

Meditations For The Anxious Mind’s Frankie McNamara blends cutting humor with insight to deliver a conclusion of: “So women are not the issue, and neither are men. We’re all just mourning a future that disappeared, but that loss still structures our desires — like a burnt out car with brand new tires.”

References to Derrida & Fischer’s takes on Hauntology go unsaid. American Politics at large, seems haplessly addlepated about forming a coherent vision of what the next few months will offer, no less structuring a broader, more encompassing plan to construct the future.

Particularly a future that doesn’t long for or recreate motifs from the 20th century. This seeds and reinforces many on the “Tech Right” (or simply Big Tech?) to embrace declaring folly or uselessness of democracy. Enter Karen Hao’s work, among others, on the actual battleground and power struggles at play in the techno-political arena.

Much more could be said here (such as roles of gender, identity, and ultimately how the continued development of technology will impact norms and frameworks ((Yes, this is a Michael Levin reference))), but I will continue this high level framing by moving on to someone we all know, whether we’d prefer to or not. Someone with a reputation, you might say.

Blank Spaces for Narrative Storytellers

Controversies and hyper-celebrity aside, the actual activities and narratively-selected-for artistic creations of Taylor Swift coincide with a relatively global demand for continuity, for there to be some kind of meaning and meaningful set of references to endure in the world.

As one Swiftie said when discussing this very theme: “The world sucks right now, but at least we have an album to look forward to.” And as much as anticipation may be the purest form of happiness, the key here is the ability to take solace in a continued presence of a narratively-aware medium for and creator of contemporary culture3.

Easter eggs years in advance, emphasis on the importance of experience, and an intentionality towards a weaving both a story and community or fandom that is based around letting people have friends in this shared journey— Taylor’s approach seems to tend to the narrative void that dominates much of the 21st century experience.

I’m not yet prepared to write a post about “What can (Futurists, Philosopher-Builders, etc.) learn from Taylor?”, but I sense it’s only a matter of time. If Elen Capri is taking longevity mainstream, who can builders partner with around narrativity, identity and the human condition? Panelist Ken Liu may illuminate one path to pursue.

Honorable Mentions

I don’t think I could write this post without mentioning Ruha Benjamin. I originally met Ruha at Princeton Envision (see below), and I’ve been following her work on race & technology and championing imagination and the future ever since.

Educator and activist Sage Lenier has had some of the best soundbites for approaches to what I hope to see more of in terms of solutions-focused — but also simply affirming that we have not fully “succumbed to the inferno” of Italo Calvino, or general Lost Futures that Fisher & Co ha. If you look into it, you can see what such despair fosters.

Again, this section was all about the moment we’re in, from a particularly different context than what was spoken through at the AugLab Summit. But juxtaposition can hopefully lend to a broader sense of what we’re dealing with, at large.

There are many looking to fill this narrative, identity, and meaning vacuum. The aim here was to loosely reference pop culture, and allude to that which is also vying for your vote, attention, or compelling your acquiescence. Enhancing the human condition requires fluency and familiarity with what is empowering or consuming it, across various domains.

I believe folks interested in the future should be building with these challenges in mind, in addition to the technical, ecological, economic, and political arenas outright.

Next, we return to interweaving strands from the Summit, before looking at some developments and plans for the next 12 months.

2B) Post-Summit Synthesis

Circling back to the Summit, we left the event with more than notes. We left with a working question: how do we keep meaning (and the human condition) in the loop while capability accelerates?

What follows is my synthesis of that question in 2025: the challenges, the skills we’ll need, and why “philosopher-builder” isn’t a slogan but a survival kit.

“Nobody taught us, so we taught ourselves”

Cultivating Philosopher Builders

“A cause near and dear to my heart” refers to both the notion of a Philosopher-Builder and the autodidactic-oriented, community-fostering ethos presented by AugLab’s own Dünya Baradari during her introductory presentation.

Alongside the recent branding by Cosmos Institute as the “Academy for Philosopher-Builders”, I find myself in the position of championing similar efforts across the broad landscape of next-gen futurist-leaning endeavors. With a tease at forthcoming work on that mapping landscape, I’m pleased to see the concept centered for innovators & builders in the Boston-area community and beyond.

Having been in this arena for the better part of a decade through work with the Orthogonal Research and Education Lab, JOPRO itself, and many other groups & programs, and I’m never more convinced in the substance of these approaches.

AugLab is rightfully proud of being an independent organization, and what’s more, by having partners at legitimacy- and resource-lending institutes such as MIT itself, it’s a great way to open doors to many others.

It reminds me of some of the structuring and efforts of the Old School, original run of Princeton Envision (2018, 2019). Envision has revived itself and will be hosting its next event early in 2026, focus on AI & Tech Ethics — so we will hope to have some of JOPRO community and Data x Direction members participate.

Assuredly, we are in an era where much of the key curriculums, studies, and ways of synthesizing insights and integrating efforts are things we will have to collectively “teach ourselves.”

Compatibility Update: meaning, teaching, and what else?

Buried inside Pat’s keynote talk were some subtly spotlight-worthy slides. Reaching some manner of brevity in this section, I will center “New Tech — Old Pedagogy?”

To extend it further, new tech, paired with….

1. Old politics: complacent or unrigorous evaluations of systemic buy-in or power

2. Old pedagogy: teaching, contextualizing, and training others

3. Old navigation: lack of modern sense of direction, “destination discernment"

And what else? What else is new technology being paired with that we are 1) either attempting to account for, 2) neglecting to consider, or 3) suffering the consequences from regardless of it being centered or not?

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Furthermore, I believe we should seek to advance the infamous critique of “You were so preoccupied with whether or not you could,” from Jurassic Park’s Ian Malcom.

I offer that there is more at hand than “do or do not”, “accelerate or decelerate” which is worthy of our deliberation. Even, the ease and tenability of more or less technology conveniently masks the harder-to-address nature of meaningfully coordinating with the rest of society. Where are we trying to go? What destination states ahead are tethered to the choices we are making now?

Move Fast and Break Things, even, to me, is not the the core of “The Problem”; I will instead return to the spirit of Sage Lenier’s words on the moment we are in:

“  This is a fracture in the systems that have gotten us here, […] whoever decides what comes next is going to be the group of people that has the discipline and the clarity of mind to keep building no matter what happens.” — (Lenier, emphasis added)

It’s not that the solo builder in a garage is to be looked down upon, or that one should not be cavalier about developing their technology or product. But rather, let us consider these two points in particular:

1. It is reasonable, responsible, and effective to coordinate development with adoption: co-design tools, practices, and institutions so outcomes match our values, or at least make certain destinations more likely than others.

2. Those who are able to do so are actively seeking to tilt the playing field in their advantage and away from “your” particular interests, no less away from other power players in the game they are playing.

For good or ill, these are the conditions we are all Building within and extending out from, so aspiring Philosopher-Builders, take note.

We are not just making technology, nor merely connecting technology with a user-base. We are playing a part in (the nature of) how connected the future is, and what will be centered as existing systems deteriorate and power restructures itself around the new mediums, substrates, and affordances we are contributing to.

Panels’ Pedagogical Notes: Cross-Domain Communication

It is hard to convey just why the panels contained the value that they did but I will take another pass at it here.

Regarding skill-building and the craft of communicating context

In particular, to cohort members or interns reading this I implore you to review the footage4 to study and observe each panel at this event.

Consider what their home domains are, and how they sought to represent the challenges of those problem spaces within the broader community and commons at hand. Investigate further how that person would speak within their domain or main operational space, and consider the work of communicating from one vantage point of niche, refined expertise and transmitting, potentially translating, back to a general lay audience that has some interests at hand.

Creating these spaces and learning how to transmit understanding from distant vantage points comprise of the skillsets and strategy we will need functionally, concretely, and in a coordinated, meaningful manner, build out viable paths to the future. (See “Steelman the Problem Space” below.)

This is yet another feature that is often seen as “nice to have” that in reality will be essential and irreplaceable. As I allude to at the end of this message, developing and centering these skills, tactics, and approaches are what we are working on in several new initiatives.

In the context of communities endeavoring to make forward-looking, next-gen Futurist-type events

I will say the following not knowing the full story of how the panels came about, who was first choice, or how much thought went into them — and I would love to chat more with anyone who has insight about such5.

But why I continue to highlight them here is that, both through careful selection and articulate moderation, and some kind of a coordinated or serendipitous alignment of intention, there was a palpable sense of synthesis, and intentional context creation.

Yes, not every transhumanist of futurist conference will have award winning author-lawyers, venerated sociologists-economists, and chaplains based deeply in the academic (and now political) battleground that is a major innovation hub such as Cambridge/Boston. But that it was unambiguously centered here, I believe, made it easier for many other adjacent things easier to discuss, even if most of the topics shifted to technical matters.

To be fair, I had few conversations during the event that directly spoke to the core of what I’m drawing our attention in this message. But with Ken Liu signing books in the main hall, Keynotes like Pat Pataranutaporn and Nataliya Kos'myna mixing in the milieu, and elders Stephen Wolfram and Hiroshi Ishii anchoring the event, it did indeed elevate and lend legitimacy.

As I noted elsewhere, all too frequently these tucked away in an optional panel on “Responsible Innovation,” or a “Society & Technology” BoF placed after the main draw has faded; it was refreshingly different at the Summit.

Cheers to the Summit Organizers

A standout event, all of the organizers deserve a shout out for pulling together a valuable experience — perhaps even more impactful than many of the attendees would understand ahead of time.

It was indeed not just another transhumanist technical potpourri event.

I believe attendees will come to look back and realize that having an event like this will inspire and shape some of their own choices, and how they seek to structure events in the future.

AugLab’s future in Cambridge may be somewhat uncertain for next year. Yet, prior to the event, I was already set on planning a spiritually-similar conference or seminar series in 2026 — the Summit’s success only further steeled my desire to do so.

3) “Now What?” — Projects & Programs

I work at the intersection of research, strategy, and community-building, and it shapes informs my take on 2025 and the work we’re moving forward. Below are some featured programs and projects underway.

Recent and Forthcoming Talks

1. JOPRO & the New Learning Ecosystem: Design tools & communities for contextual, developmental, trajectory-aware learning—so interdisciplinary learners actually land impact.

2. So what do you mean by Inter/Trans/Un- disciplinary Research? Real talk and strategy for those working in knowledge generation or navigating knowledge implementation in an unprecedented civilizational moment.

3. Technical Presentations for Mixed Audiences. A Google Summer of Code / Orthogonal Summer Cohort request, this topic ties into the pedagogical panel points mentioned above.

4. Steelman the Problem Space. A talk and workshop around traditional debates and creating alternative approaches to insight or knowledge cultivation.

JOPRO Featured Projects

Every term, JOPRO selects featured projects that we emphasize for development. Current term projects include:

1. Data x Direction. Practical governance & documentation playbooks—e.g., a living taxonomy of data documentation, ethics, and AI transparency frameworks. Our Summer Cohort focused on topics such as:

   1. AI & Inequality in Education (Karina Zambina, UC San Diego)

   2. Big Tech Ethical Policy Analysis (Arnav Satish, UC Davis)

   3. Data Ethics Frameworks (Tlamelo Makati, Technological University Dublin)

2. From Here to There: Strategy and Mentorship for Innovators. We are developing materials, workshops, and discussion series about the actual skills people need to do the work of being a Philosopher-Builder and target meaningful futures.

3. Society Ethics Technology: Founded in 2021, this is one of the original working groups that centered inter- and trans-disciplinary collaboration by making opportunities for different domains to interface around tech and ethics.

New Opportunities & Call for Involvement

Over the next several months, several programs and initiatives will be coming online. Please express interest if any of them appeal to you, or if you’d like updates and more involvement opportunities.

Center for Futures Studies

In development with partners such as Orthogonal Research and Education Lab, we have recently laid foundation for the forthcoming establishment of the JOPRO Futures Studies Center (working title).

The Center will serve as a living lab for rigorous, use-inspired work in futures, strategy, and systemic change, as well as advancing new ways of doing science & research and the training around such. Expect public papers, datasets, and deployable tools, plus a pilot micro-residency/cohort soon.

Essay Contest: Future Vision & Voices

Calling builders, researchers, and community voices: the Futures Studies Center Essay Contest spotlights who’s missing from the futures conversation—and the most critical as well as overlooked strategies that create real on-ramps to better worlds. We announce themes and guidelines next week.

2026 Conference Planning

If the core themes within this post resonates — technology + human condition & impact + vision setting & strategy — join us. We’re assembling a coalition to host an event in the second half of 2026 that captures this spirit.

• What we’ll curate: keynotes, interdisciplinary panels, demo + critique labs, a workshop or skill & strategy studio, and an exhibit of beacons (maps, docs, demos).

• Who belongs in the room: researchers, founders & venture supporters, educators, policy folks, artists/designers, and student cohorts.

• How to plug in: co-curate a track, propose a session or demo, mentor/jury Future Vision & Voices, sponsor seats or a micro-residency, or offer a host site.

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Jes Parent
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About the Author
Jesse Parent holds a M.S. in Data Science from the Halıcıoğlu Data Science Institute, University of California, San Diego. He brings nearly 15 years of experience in startups, technology R&D, and strategic consulting. Jesse is the Founder of JOPRO and Director of the “From Here to There: Strategy and Mentorship for Innovators” initiative, developing curriculum and training for next-generation leadership. He has presented work at institutions including the Oxford Internet Institute at Oxford University, Boston University, and the University of Washington School of Law, and recently served as a judge at MIT AI Venture Studio Demo Day. [Links & Social Media]

In my latest article on From Here to There, I discuss:

1. Reframing the role of a CEO (or Chief Decider)

2. What comprises a quality decision

3. What decision makers Must Do

4. Consequences of poor decision making strategies

5. How mismanagement inhibits precious creative flow and buy-in

It all leads up to the take home message of:

“Take special care of your decision making so the actual creative work can proceed as smoothly as possible.”

Read the full article at From Here to There: Strategy and Mentorship for Innovators

From Here to There: Strategy and Mentorship for Innovators

The Cost of Indecision in Leadership: On the invisible tax leaders impose when they hesitate or defer without clarity

This post is part of a recurring series focused on Vision at Altitude: Strategy, Systems, and Leading from the Top — the first pillar of the From Here to There game plan. How do those at the helm set direction in times of rapid change? set direction in times of rapid change? We explore the mindsets, frameworks, and stra…

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a year ago · 2 likes · Jesse Parent

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Background & Problem Space

I spend a lot of time in spaces where folks are talking about the future, and one thing that is coming up more and more, subtly but increasingly overtly, is about how people in leadership positions or acting as providers of vision and direction are having to:

1. pivot,

2. capitulate,

3. reverse course,

4. change their minds,

5. express or mitigate despair,

6. wade through uncertainty,

or any number of adjacent, in-between, or otherwise non-ideal ways of presenting oneself as a confident individual about the future.

The appearance of superlative amounts of confidence during this era generally cluster within these categories:

1. Single-dimension targeting*

2. Morally and intellectually dishonest or disingenuous extremes

3. Nihilism & apathy outright

This is to say: focusing only on one goal or solution at the expense of taking in a broader view; creating an heavily polarized Good or Evil view of self or others, or associate solutions or approaches; or broadly giving up and seeing it all as pointless. These are the areas where most folks right now (maybe in general, but I’m really talking about 2025, here, and now), are displaying extremely cavalier attitudes and undaunted or unflinching takes on things.

Alternatives We Can Enact

What I want to say in this piece is that the following is also a path one can walk:

1. You can be uncertain, frustrated, apathetic

2. Coalitions and spaces and community can still exist

3. But it may require context, courage, and choosing it to be so.

I don’t think the United States, in particular, has many models, examples, or legitimate heroes for how to handle situations like this that are not diluted, white-washed, or otherwise sterilized. I can mention a number of folks in the 1960s, but I fear I may loose attention in doing so.

What I want to focus on directly is challenging because none of the historical examples we may think of have dealt with the kind of change we are seeing right now; the waves we have to navigate are different.

So I want to speak to a small but important component to that process, which is:

Be prepared to allow for others to go through their vision, future, or worldview existential crisis as you collaborate and find ways of enduring and building with them.

Let’s unpack this briefly, while you’ve generously offered your attention — this is one of the most challenging things to actually look in the face in this moment that we’re in, and I appreciate you giving me the time to talk about this with you.

I am intentionally not attempting to label or prescribe a specific outcome, belief, or judgement here. I am instead trying to speak to a process of understanding what is going on, and offering a small bit of advice about how to best handle it.

As I noted above, this is a time where many of the folks we look up to, and the visions or solutions the have espoused, are undergoing varying degrees of change, uncertainty, instability, or potentially collapse.

It is natural and understandable to both look for our heroes, leaders, or figures to be confident and strong and offer a solution. But I am offering that we may:

1. reasonably guard against, and prepare for, the compulsion to seek security in those appearances,

2. as well as understand there are alternatives

That is, alternatives to abandoning our loftier beliefs or faith-in-humanity (or community in general), in by taking respite in a a more apparent stability, or more apparent “confidence.”

In other words, I am seeking to encourage and normalize that, in reality, the wisest, strongest, most viable, and most secure approach to this moment will actually warrant some pause, some reconsideration.

It may entail embracing some of the “I don’t know” of it all, and still putting forth meaning and fostering relationships in spite of not knowing.

Furthermore, it will be useful (maybe necessary) to accompany others as they transition through the stages of belief change, vision change, or reconsidering what to actually do right now.

About those with very clear, isolated goals

I think it is noteworthy to say, if there is a group, or a figure, that is clear about what they want to take away from this moment, it may also lend to the appearance of confidence; the old adage of “the best way to predict the future is to make it.” It is reasonable and probably wise to consider that there are factions attempting to pursue very specific targets right now (* Point 1 on single-targets, earlier) - but this can be differentiated from those who actually have a clear and holistic picture about what is going on right now.

As an old political science professor used to say, “The hardest time to understand is the one we are currently in.” To expand upon that, looking backwards you, you can see which of the potential pathways became more viable or eventually materialized - but history can seem linear and uncomplicated if there is not proper contextualizing for all the different ways things could have gone.

Implementation: What do we do?

So beyond identifying what may be happening, my suggestion and prescriptive stance is as follows.

Internal Work

Finding peace in times of uncertainty may sound unserious, but there are meaningful and sober ways to do so. There is such a strong ebb and flow to 2025, almost everyone I know is pulled and pushed with the waves and tides. I even have sympathy for the Influencers who are simply repeating their sales pitch and the virtues of their product or program in spite of these challenges; I’m now in that space increasingly, myself.

Whatever face people are putting on right now, even if they are front-running or band-wagoning on whatever appears to be the Winning Team (in their view), people are scared and uncertain. So what do we do about it, at the internal level?

• Find what regulates you, both individually and with others. What are your consumption limits and quotas? What are you doing that is away from all of the chaos and stress? What are your indulgence, your downtime — the carefree space we need to recharge? What about your constructive outlets or things to invest in?

These are suggestions, but you will need to invest in knowing yourself well enough to have a reasonable barometer of what kinds of stress and burnout you are accumulating, and how to care for it.

Even though the focus on this post is interpersonal, it seems critical to emphasize the internal work. The world is fierce and demanding, and you’ll have to tend to yourself first before others - just like on the airplane safety videos.

External Work: Accompanying the Variable Beliefs and Visions of Others

Community will be critical in this area, and whether you are simply looking to join one or tasked with managing and caring for others, this will come up.

Some people have been in the game for a long time and staring at these problems for decades. Some folks only now are understanding what “AI is about”, or dealing with what politics has showed itself as capable of doing.

What I want to offer is that, collectively, we are all in the same boat. Nobody really knows. Nobody really knows what is going on or what is going to happen, and “that’s ok” — which I say not to normalize it or dismiss it, but rather to say we are in a period of that much change. I am also not suggesting everyone is simply going to get along, with a little compassion and understanding.

My message here is more so that the times are different, and it would be wise to consider carefully the trajectory of someone’s background, belief transitions, worldviews, and sense of the future.

We are going to need to collaborate, forge coalitions, alliances, and new ways of working together. Surely, there will be challenges and incompatibilities and lack of alignment at hand. But as we find new ways of doing things ahead, it is viable to understand how our changing times can mask or distort potential allies.

Or, how people simply dealing with the world as it is, will have varying degrees of contribution and availability as you attempt to forge your own way ahead, reconsider or solidify your own beliefs, and your particular plan going forward.

The new name of the game is a kind of extended, distributed co-regulatory path-finding practice. Alongside some valued colleagues and collaborators, I am attempting to discern, demarcate, and otherwise write more about this, as I find it to be a critical matter and even skillset that is substantially underrepresented.

We are not prepared for this moment in most of our formally available training. But there are things to know and learn and do, and ways to support and grow and cultivate that which we will require to sustain, endure, and flourish in the months and years ahead.

At the very least, there are better and worse ways of handling all of this, and there are those of us working to sort out what they are. If you are working in this space, or want to be doing more in this space, and resonate with this message, please reach out.

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Recommended Reading

I don’t truly know a book I’d recommend about this, given the lack of actual context for the influence of AI (and its impact on surveillance, information distortion or control, and the unknown and to-be-built digital ways of relating to each other), and the general World War era political pressures — both. (No less other factors, which I will unironically bypass for brevity here.)

But I would suggest looking into:

• Those who are Steel Manning a particular view and its trajectory, so as to demarcate where things may go if that line of reasoning holds out. (Recall “The hardest time to understand is the one we’re in”, from earlier.) Due to the innumerable approaches, visions, and agendas at hand, those who identify clearly where one of them is going can be of great use to your own triangulations and extrapolations.

• Quality takes on emotional literacy, and perhaps even things like faith transitions, or people moving in and out of cults.

  • I don’t mean to sound hokey or histrionic, but, we are indeed in a period where people’s views are fought for, no less the Attention Era in general. Also, one of my most respected in-the-thick-of-it people in the building of AI has commented about the nature of Silicon Valley and tech startup spaces as similar to the Axial Age or Late Antiquity, where the vying for believers and quest for dominance of alignment or devotion to a vision.

• Good literature about finding meaning or leadership or forging community in ties of uncertainty. Particularly if it goes beyond “uncertain markets”, but actual macro-level uncertainties.

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What would you suggest?

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Of all the uncertainty taking place here in 2025, I continue to find respite and solidarity in the builders.

Similar to Mr. Rogers "look for the helpers" in times of disarray or crisis, there are folks earnestly doing the work of building things that matter, looking at the difficult problems, and trying whole-heartedly to learn and grow and find new and better ways ahead; to actually understand how the world has come to be made - and looking for meaningful alternatives.

They are often not headlines, or the loudest voices in the room. But they exist, and we are finding ways to connect, collaborate, endure, survive, move through the murky waters and maneuver around the obstacles.

It's part of why I am developing JOPRO, and part of why I am doing a lot of work to connect to various communities of builders, thinkers, healers, and otherwise those looking at what is going on with as clear eyes as possible and getting to the real work.

It’s about the folks drawn to circles of practice that combine strategy, complexity, and care. We may not have perfect answers, but we are asking better questions, together.

The real work will remain, out there, to be done, regardless of all of the distractions and mounting consequences that we all are collectively having to deal with — right now, and probably throughout the rest of this century ahead. We are in a critical time for history, and we are still learning how to “grow” and have “progress” beyond duplicating what worked from 1900-2000:

The future will not suffer regurgitated methods and approaches, but only return to us the exhaust, exploitations, and familiar disparities of the previous millennium (or rather, millennia.) We have to meet the moment, and resist the allure of convenience in our aims, efforts, and methods.

Furthermore, we don't get the luxury of abstracting things out into a sterile setting: we have to connect where the present is — and understand its inertia and trajectory — to where things can go, and try to make the best choices about what and how to get there.

There is a lot to do, and to build, and yet to be comprehended, no less to be understood. Problematic influences will profit from apathy and despair. I don't have any sense of an immediately tenable utopia, but if there is one thing that I am sure of, is that in spite of the Doom Scroll-worthy news and realities, there are still those showing up and stacking the days and conversations and projects and ideas, to make something of substance.

There are alternatives to select from, choices to be made, and things to do in this world - and they matter.

Jesse
June 2025
Boston

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If this resonates with you, let’s connect, either for community or collaborations. I’m putting a lot of effort in aligning opportunity with those who want to do the work, so if we can help each other build, share hope, sustain and endure, or otherwise pursue what this is, reach out.

For more updates,
Jes Parent: Substack, LinkedIn, Bluesky, Twitter/X, YouTube
JOPRO - Substack, LinkedIn, Bluesky
From Here To There: Substack, LinkedIn

Most conversations about longevity begin in the lab—or at least, they pretend to. We hear talk of lifespan extension, senescence, gene therapies, epigenetic clocks. But these speculative narratives often race ahead of the research itself. The public discourse paints a picture of boundless possibility, while the actual science remains cautious, fragmented, and unevenly applied.

Longevity R&D aims to stretch the timeline. But flourishing and progress depends on what we do with the added space. A longer life is not necessarily a better life—especially if our systems, institutions, and social contracts remain built for shorter, more linear, and more fragmented timelines.

We are entering an era where biological breakthroughs, artificial intelligence, and social upheaval are converging. The future of health will not only be determined by what we can do to the body, but by what we know how to do with each other—how we learn, mentor, coordinate, build, adapt, and sustain direction in an age where everything is speeding up and breaking down.

If we are to meet this moment, we must expand our definition of longevity. We need more than cellular rejuvenation—we need cognitive durability, institutional relevance, and intergenerational regeneration. We need a culture and infrastructure that supports long-term flourishing: of minds, missions, and communities.

This essay is a call to reimagine longevity not just as a biological feat, but as a design challenge. It’s about building the scaffolding—personal, interpersonal, and systemic—that enables us to remain adaptive, coherent, and humane in a 100-year world. Drawing on lessons from healthcare and health technology, mentorship ecosystems, organizational strategy, and interdisciplinary innovation, I suggest that the most important longevity technologies might not come from a petri dish—but from how we shape people, programs, and paradigms for endurance, not just expansion.

Here, I explore what it means to build not just longer lives, but long-term coherence: in our systems, selves, and societies. I argue that supporting what I call the Longevity Generation—those who will live through and shape this century’s transformation—requires a new kind of compass. We need not just direction, but what I call Destination Discernment: the ability to chart meaningful trajectories through complexity; and in this case, development, care, and innovation through social, institutional, and technological complexity—without losing sight of what truly matters.

II. What Longevity Leaves Out

Three Metaphors for Misalignment

A few metaphors and analogy to situate the forthcoming discussion:

Imagine cultivating a rare, long-living plant species—but in soil that’s depleted, in a climate that’s volatile, with no gardeners to tend it. That’s what our current longevity paradigm risks: biological extension in an ecosystem unprepared to sustain it.

In software, an infinite loop is a failure of logic—a process that repeats without progress. We risk the same with longevity if we extend time without redesigning the systems that give it shape, function, and exit conditions.

More time isn’t always a gift—it can be a maze without a map. We’re extending lifespans but not equipping people or institutions with the tools to navigate those added years with clarity, purpose, or coherence.

A Brief Story

A few years ago, I was sitting in a meeting with R&D leads and executives at a healthcare technology company, discussing the roadmap for our next generation of monitoring tools. On paper, we were making progress: better sensors, more robust data pipelines, even early integration with predictive AI systems. But something felt missing. In our push to optimize what we could measure, we had neglected to ask what really matters in the long arc of human health.

Securing buy-in around what should even be considered—or offered—was difficult. There was (and generally is) a persistent tension between what the market was ready to adopt, what human well-being actually required, and what was even close to scalable.

This is a pattern I’ve seen across biotech and innovation spaces: a fixation on the molecular and the mechanical, with little room for the mental, social, and ecological. There are parallels and overlaps with other major philosophical matters as well: the longstanding rift between reductionism and complexity (Fraser & Greenhalgh, 2001; Noble, 2006); and even in a more applied sense, the role of embodiment in cognitive sciences (Varela, Thompson & Rosch, 1991; Shapiro, 2010).

Longevity gets framed as a problem of biology alone—extend the telomeres, slow the senescence, fix the protein folding. Rarely do we ask: What kind of life are we extending? And for whom?

We treat health as a personal variable, but it is deeply shaped by systemic forces—educational opportunity, mentorship access, social scaffolding, narrative framing. I’ve mentored dozens of early-career researchers and technologists through JOPRO, Orthogonal Research and Education Lab, Google Summer of Code, and now From Here to There: Strategy and Mentorship for Innovators and Data x Direction, and what I see most often isn’t a lack of intellect or drive—it’s burnout, decision fatigue, disorientation. Not just biological stress, but strategic and spiritual depletion. These too are longevity challenges. Research on burnout and stress (Peters, McEwen, & Friston, 2017) and the classic work of Christina Maslach underscore how chronic overload erodes capacity.

If we build medical interventions without parallel social and institutional designs, we risk extending time without meaning. Healthspan isn’t just about vitality metrics—it’s about being able to contribute, connect, and navigate life with coherence. It’s about flourishing in motion, not just surviving in stasis. This broader lens aligns with research on the social determinants of health (Marmot, 2005).

The current paradigm of longevity is siloed and shortsighted. We need a broader lens—one that acknowledges that real longevity is a social and strategic achievement, not just a biological one.

III. Mentorship as Infrastructure

When we think about infrastructure, we usually picture roads, bridges, power grids—the physical systems that hold up our daily lives. But in the context of a longevity-focused society, mentorship and intentional cultivations of intra- and inter-personal skillsets may be one of the most underappreciated forms of infrastructure we have.

Mentorship is not just a nicety or a professional development perk. It is a mechanism of resilience, a means of knowledge transfer, a system for emotional and strategic co-regulation across generations. It provides the social scaffolding that allows individuals and communities to orient themselves toward long-term goals, especially in volatile and uncertain contexts.

Through my various roles, I’ve mentored early-career scientists, technologists, and interdisciplinary thinkers navigating fields that often lack clear roadmaps. What I’ve found is that mentorship done well doesn’t just pass along information—it cultivates discernment, direction, and durability. It helps people manage complexity, interpret failure, and sustain coherence over time.

These are far beyond “soft skills” that offer tertiary levels of enhancement. They are survival traits in a world where timelines stretch, decisions compound, and crises multiply. This is something Douglas Rushkoff explores in Present Shock: When Everything Happens Now—a powerful diagnosis of our cultural breakdown under temporal disorientation. Rushkoff argues that our collective inability to inhabit coherent timelines has created a crisis of meaning, purpose, and continuity. In a longevity context, this insight is critical: as our lifespans extend, so too must our ability to make sense across longer arcs. Mentorship, in this frame, becomes not just career guidance—but narrative restoration.

In biotech, in AI, in health innovation, mentorship and destination discernment —our ability to develop advances in technology to viable destinations— are what connects the speed of technical development with the wisdom required for ethical deployment. Destination discernment—echoes calls from long-term thinkers like Stewart Brand and Roman Krznaric, who remind us that future-fluent action demands tools for anchoring foresight in action, not just prediction. These are, in effect, a public health intervention—one that shapes not just individual careers, but the collective trajectory of fields poised to redefine life itself.

To mentor the longevity generation is to design for systemic continuity. It means building pipelines that don’t just prepare young people to enter today’s institutions—but equip them to adapt, critique, and evolve those institutions for a future we can’t fully predict. It means treating mentoring not as episodic advice, but as a design principle embedded into the DNA of innovation ecosystems.

And it means recognizing that every time we mentor well, we don’t just extend someone’s opportunity—we extend the timeline of what’s possible for all of us.

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IV. Cognitive Longevity: Leading Without Burning Out

If biological longevity is about extending the body’s capacity, cognitive longevity is about extending the mind’s. This includes not just preserving memory or preventing burnout, but cultivating the capacity to lead, learn, and make decisions across longer, more complex timelines.

In an era defined by information overload, increasing volatility, and accelerating change, decision-making itself is under threat. Many leaders—whether in science, business, or public service—are stretched thin by fragmentation and fatigue. Cognitive longevity means developing habits, frameworks, and institutional practices that allow people to sustain direction, coherence, and creative agency over decades, not just quarters.

As OpenAI CEO Sam Altman recently noted in a discussion on preparing for the future, beyond learning technical tools, the core traits that will define success are adaptability, resilience, and the ability to discern what others truly value. These “super learnable” skills, as he calls them, are not only crucial for navigating the near-term impacts of AI—they are essential for long-range directionality in a volatile world. In his words: “When AI can kind of do anything… then deciding what to do and what people value is going to be really important.”

Cognitive load theory, originally developed in educational psychology, highlights the importance of working memory and schema development in managing mental complexity. But its lessons are rarely applied to leadership or institutional design. Similarly, the literature on adaptive expertise (Hatano & Inagaki, 1986; Schwartz et al., 2005) shows that expert practitioners don’t just repeat learned solutions—they know when to pivot, adapt, and innovate. This kind of flexible intelligence is precisely what longevity demands.

In my work across health tech & healthcare, innovation ecosystems, and consulting and mentoring at large, I’ve seen how the absence of cognitive sustainability creates cascading failures—good projects stall, good people burn out, and institutional knowledge dissipates. At scale, this becomes an innovation bottleneck.

We need environments that actively support cognitive renewal: sabbatical structures, mentorship webs, reflection loops, and epistemic humility baked into governance. The best organizations invest not only in what people know, but in how they regenerate clarity and discernment over time. Think of this as the organizational equivalent of mitochondrial health.

The stakes are high. In domains like AI, biotech, and public health, short-term thinking can produce irreversible consequences. Developing cognitive longevity is, in this sense, a form of ethical risk management. As the philosopher Norbert Wiener warned in the mid-20th century, our technical power will always outpace our capacity to govern it—unless we deliberately cultivate foresight and responsibility.

To thrive in a 100-year world, we need minds that can think in 100-year arcs. That means leaders who can hold paradox, metabolize uncertainty, and operate with sustained attention in an age of distraction. It also means treating wisdom not as a byproduct of aging, but as a practice we can design for—and a critical input for human flourishing.

V. Living Systems and Institutional Healthspan

If people are living longer, our institutions must learn to do the same—not through stagnation, but through adaptive vitality. Yet most organizations age poorly. They calcify. They forget. They resist renewal. To design for true longevity, we need to treat institutions not as static bureaucracies, but as living systems.

This shift requires a new ontology of organizational life. Just as living organisms metabolize resources, process feedback, and repair themselves, resilient institutions must also sense, learn, adapt, and regenerate. The fields of complexity science and cybernetics offer powerful metaphors here: feedback loops, homeostasis, self-organization, distributed intelligence. These are not buzzwords but rather they’re the operational grammar of long-term relevance.

At Orthogonal Research and in collaborations with DevoWorm (a project under the OpenWorm Foundation), we’ve explored how principles from systems biology and morphogenesis can inform institutional behavior. One insight: aging institutions often lose the ability to integrate novelty without chaos. Like overburdened biological systems, they become inflamed, rigid, and brittle—what in human terms might resemble autoimmune response. What’s more, an ongoing Google Summer of Code project series is about Open Source Sustainability, which specifically addresses the challenges of whether open source projects have an enduring link to the rest of the software economy - or not.

To extend institutional healthspan, we must cultivate "meta-mentorship": not just mentoring individuals, but mentoring the environments and processes that shape how knowledge moves and decisions evolve. This includes modular design, pluralistic governance, and regenerative cadence—space for pause, reflection, and reconfiguration. In our Society Ethics Technology working group, we are exploring how values-forward design and decentralized governance protocols can strengthen epistemic resilience at scale.

This is also where destination discernment reappears—not just as an individual capacity, but as an institutional function. Projects like FrontierMap are designed to scaffold the navigation of complex research terrains, helping teams and organizations discover not only what's emerging, but where it’s actually worth going. In institutional contexts, this means knowing how to sunset, pivot, or evolve responsibly—rather than chasing relevance indefinitely.

Examples exist. The Santa Fe Institute models how intellectual ecosystems can evolve across decades. The Metagov Project explores programmable governance. The Long Now Foundation builds literal and metaphorical infrastructure for multi-century thinking. These aren’t fringe efforts—they’re testbeds for what it means to embed longevity into the DNA of our social systems.

Ultimately, we must stop asking only how long people can live—and start asking what kinds of environments we need to age well together. Institutional healthspan is the invisible architecture behind every meaningful long-term effort. Without it, we extend lifespan into systems too fragile to hold it.

VI. Biotech Needs a Compass

Biotech is often framed as the frontier of human enhancement—the most literal domain of longevity science. From gene editing to neurostimulation, we are rapidly gaining the ability to alter the substrates of life. But the speed of possibility is outpacing our frameworks for meaning. In this space especially, we need a compass, not just a map.

As I’ve written elsewhere, technological trajectories without destination discernment risk becoming expressions of capability rather than contributions to human flourishing. Just because we can doesn’t mean we should—and even when we should, we must still ask: toward what end? And for whom?

The current biotech narrative is missing a critical scaffolding: the moral, narrative, and governance infrastructure needed to guide not only what we build, but how we relate to it. We lack coherent stories of where we are going, or what we want from our tools. Ethical review boards and regulatory frameworks are necessary—but they’re not sufficient. We need cultural architectures of intentionality.

This perspective also resonates with emerging work on diverse forms of intelligence—biological, artificial, or hybrid. As biotechnological capabilities expand, so too does the imperative to rethink agency and ethics. Michael Levin, whose work bridges developmental biology and synthetic morphogenesis, notes that the goal is "to provide a naturalized, continuous view of cognition across its spectrum [...] toward the development of ethical/social frameworks that will become essential in the future as the forms of agents around us diversify far beyond what is currently imaginable."

In other words, the future of biotech won’t just reshape biology—it will demand that we reshape how we conceive of agency, empathy, and responsibility. The ethical frontier is expanding, and our compass must be capable of pointing toward futures that include, rather than marginalize, the full spectrum of sentient and semi-sentient life. This is Destination Discernment at its broadest scale: not just where our tools go, but who we include in the future we’re aiming for.

This is especially urgent in longevity science, where interventions are marketed in terms of personal optimization but enacted within systemic inequity. Scholars have increasingly called attention to how medical access, algorithmic bias, and even definitions of 'health' often reflect privileged norms. Critical disability studies, for example, highlights how mainstream health interventions can erase the needs and insights of neurodivergent, disabled, or chronically ill communities. Researchers in fields like STS (Science and Technology Studies) and feminist technoscience have emphasized the need to center pluralism in our visions of human flourishing. See works such as Alondra Nelson’s "Body and Soul", or the interdisciplinary project AI Now, which addresses social inequities embedded in emerging technology. What values are embedded in a lifespan-oriented society? Who defines vitality? What constitutes a worthy extension of life—and who gets access to it?

To fill these gaps, we need a new generation of transdisciplinary translators—people who can connect the hard tissue of technology to the soft tissue of values, narrative, and social design. This is part of what we aim to model in our JOPRO mentorship and strategy efforts: helping researchers and innovators locate their work within larger arcs of purpose.

It’s also what animates a new wave of organizations like The Roots of Progress and The Cosmos Institute, which attempt to reclaim a positive, human-centered and narrative of technological development, as well as affirming the moral imperative of tending to progress, and establishing the Philosophy to AI Pipeline. The goal isn’t to slow down innovation, but to shape it. Progress is not just acceleration—it’s alignment.

In an age of powerful tools, the ethical frontier is design. And in the longevity era, the compass we need most is not pointing us faster into the future—it’s helping us hold direction as we go.

VII. Vignettes and Personal Examples

Theories are sharpened by proximity to lived experience. Much of what I’ve argued throughout this essay comes not just from reading and reflection, but from being embedded in interdisciplinary, innovation-heavy environments where real people confront real limitations—and try to transcend them.

In my work in the health domain, I sat in cross-functional meetings where machine learning engineers, hardware developers, and clinicians wrestled with trade-offs between feasibility, scalability, and meaning. On one hand, we could build increasingly sophisticated tools for niche patient monitoring and wellbeing; on the other, we had to contend with messy realities: user fatigue, accessibility gaps, technological buy-in, and regulatory uncertainty, just to name a few. I watched how cognitive overload wasn’t just an individual issue—it spread through teams and leadership, subtly shaping what got prioritized and what quietly fell off the roadmap.

Through my various roles, I’ve mentored early-career researchers, mid-level managers, and even advised and consulted with executives—many of them navigating emerging fields like data ethics & responsible AI; updating psychology and neuroscience to incorporate advances in mental health and neurodivergence; developmental biology and other STEM fields meshing with complex systems and metascience. Again and again, I’ve seen how mentorship and destination discernment function not just as guidance but as grounding: stabilizing forces that lets people sustain direction when no clear institutional lane exists. These moments of “holding steady” matter just as much as breakthrough moments. They are the socio-emotional infrastructure of innovation.

Even more personally, I’ve watched people close to me—family, friends, collaborators—grapple with what it means to live a meaningful life under chronic illness, economic precarity, or cultural misrecognition. These aren’t footnotes to the longevity conversation; they’re central. They illuminate the distance between biological extension and systemic support.

This arena of work I’m attempting to demarcate via this essay is, in many ways, an attempt to honor that distance. And to suggest that closing it will take not just progress in technology, but progress in how we care, coordinate, and continue.

VIII. Conclusion: The Long Now, the Living Future

If we are truly entering an age of longer lives, then longevity is no longer a biological project alone—it is a civilizational one. The challenge ahead is not simply how to extend life, but how to ensure that what we extend is worth living: mentally coherent, socially supported, and morally navigable.

This requires rethinking how we build, lead, and relate. It means investing not just in innovation, but in narrative. Not just in science, but in stewardship. It calls on us to design for the long now: to support projects, institutions, and people that can metabolize change while maintaining a sense of purpose and direction.

I’ve tried in this essay to sketch the outlines of that challenge: mentorship as infrastructure, institutions as living systems, cognitive longevity as an ethical imperative. These aren’t abstract ideals. They are design problems—ones that can be met with creativity, care, and coordination.

To move forward, we must fund and elevate people and programs that make long-term flourishing possible. We must normalize reflection as a skillset, embed mentorship at every level of innovation, and build tools that are not just faster or smarter—but wiser. This isn’t about utopia. It’s about sustainability, dignity, and direction.

The question is no longer just: How long can we live? The real question is: How do we design a future where we want to keep going?

This is the work of our time. Let’s make it count.

It’s Sunday, and this week has been full of hard work, lots of writing, conducting interviews for a new JOPRO project, meeting friends and supporting colleagues, and having the big questions about “Where Are We Going?”

Many hours this week have been spent on the macro planning of where is the world in 2025, and what can we actually feel good about doing, contributing to, and building ahead. What’s more, I am fresh off of a conversation where I had a tough love moment of deliberation with a good friend whether or not they are invested in their current vocation/industry as either:

1. they feel they should be, themselves, to do well; or,

2. they need to be, in order to make the requisite changes to greater viability and sustainable success.

Then I see this from Greg Isenberg:

Check your pulse

He writes:

treat sunday night like a career dashboard, check your pulse at 8 PM:

A) if your stomach knots because tomorrow means meetings you would pay to skip, you need an escape plan

B) if you feel a quiet buzz like the night before a field trip because Monday means fresh momentum, you are exactly where you belong.

that "dread versus electricity" gap is probably the most honest KPI in work

How apt, how fitting, and also, how challenging, especially right now. I would go on to say that not only is it a great diagnostic moment, but its worth reflecting the broader inertia (or sense of direction) at play:

It's a remarkably challenging time to build durable visions and beliefs about where to go and what to do that is of substance in the world today.

Trust, buy-in, vision and relation to momentum

There is a kind of macro-, maybe even meta-leadership that goes on here. Are you conducting your affairs and business in a way that lends trust to others? Or maybe not so much?

We are in a world full of horror stories such as Kevin Xue recently sharing a difficult turn of events regarding PayPal backing out last second on a job offer, after relocating and quitting his old job.

So when we do our Sunday Scaries Sunday Evening KPI check in, it seems critical to make some sober inquiries and evaluations:

1. What are you feeling right now?

2. What is the current ceiling (or capacity) of the situation you are in?

   1. How is it affected by the momentum level you currently are maintaining, and

   2. How easily can that momentum level change?

3. What is the delta (or relative distance/difficulty) to aligning a good to high momentum state with a vision of the future that is both sober and exciting?

Easier said than done, in some ways, but if you earnestly show up to these questions - and use the 8 PM check in that Isenberg notes above as a starting point, you may begin to discern some answers, or action steps towards finding answers.

Are you in a community that doesn’t regularly support your vision, you sharing your situation, or effective co-regulation? Is the town you are in not fit with the resources your vocation or your desired career path (or changing paths) would benefit from? Is the vision you have for your product or company actually fitting a real market, or the markets that are emerging over the next five years? This can feel like opening a very unpleasant can of worms, especially on a Sunday night.

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Community & resilience

Letting the check in be tied to investigating these kinds of questions can serve you well; even if it brings up scary and unpleasant things that maybe you would do better to think about after some sleep and in the light of the week ahead.

Ideally, you have some trusted folks to talk it over with, too.

The key here, though, is to not dismiss the Sunday Scaries and what their diagnostics reveal as merely a typical Sunday night phenomenon.

Rather, give space to think about them during the week ahead, especially if you are finding bigger, challenging problems looming in the distance. Herein also lies one of our super-powers as human beings, and something I think will be critical to be intentional about going forward: helping others to sort these challenges out, as well.

Having community and support around finding those answers and those things to do is vital. Try to help others find their best spaces to be in, and investing in the means to understand what limits folks and allows them to flourish can have huge multipliers down the road.

For the people closest to you, for personal or business ventures, investing in their capacity to manage these spaces, and holding space for their challenges, can go a long way. It may even be a major factor in whether ventures, projects, communities, or relations fall apart, in general.

Conclusion and Why It Matters

To recap,

1. Sunday Check in, what’s the raw feeling?

2. Seek context for both the Relative momentum & inertia, and Absolute momentum & inertia of the situation you are in

3. Make note to investigate in the week ahead; give the feelings legitimacy

4. Seek and support community:

   1. showing up for others this way can have many unforeseen benefits;

   2. realizing when others cannot show up for you this way has its own insights, as well.

Alignment is a word we hear thrown around in many different contexts these days. In this context, what I am offering some conversation about is aligning your own self with a path towards the future, and seeing how making that a valued part of your own culture and communities can be a centering, co-regulating, and ultimately beneficial practice.

We receive an abundance of advice on dismissing feelings, and I would say an even greater pressuring against having serious and meaningful conversations about the future. For folks that are attempting to build things in the world, this is indeed an almost sacred space, whether or not it is treated with due diligence and reverence.

Granted, my advice indicates a somewhat ideal pathway or manner of resolving conflict - but I mention it briefly here as a reference point; if these things are not tenable, why not? I specifically mention the interplay of momentum and inertia and their relative impacts on the context of the situation you are in, as well as the value of positive and supportive community capable of discernment and resilience.

These things require cultivation and tending to, but they are worthy investments.

Finally, I have long since realized that I (personally) need to keep the momentum moving forward, otherwise I get bogged down in The Horrors of the world. All of this contributes to the ability to do that, and I invite you to consider the role you play in maintaining the the ability for others to traverse meaningful pathways, and reach meaningful destinations.

Both for yourself personally (and professionally), as well as for those close to you and the teams you are leading or contributing to, there is a healthy sense of agency and related skillsets that continue to be essential for surviving and thriving in our world.

Leave a comment

So thank you for taking the time to read about it here, and be a part of the various conversations I’m aiming to offer around it. As always, your comments and insights are appreciated. Be well, and may your Sunday Scaries be more than a passing, routinized practice of self-dismissal.

Cheers to the communities that care and support us as we strive against all the chaos to find and develop meaning in the world, and with each other.

-

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Thanks for reading! I'm Jes, a data scientist, strategist & founder, and interdisciplinary researcher & exploring how we lead, learn, and innovate in complex times. On this Substack, I write across a few core themes:

• Leadership & Innovation Strategy

• Data Science, Technology & Responsible AI

• Mentorship & Early Career Growth

• Projects Updates & Dispatches

• Reflections & Reviews

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On May 1st, we attended the Social and Ethical Responsibilities of Computing (SERC) Symposium at MIT's Schwarzman College of Computing — a full-day series of rapid-fire, TED-style talks from researchers working at the edges of data ethics, AI governance, civic technology, and digital justice. What emerged was a mosaic of projects that not only challenge the current state of computing, but push us to ask: What should we be building instead?

Below are some highlights and takeaways — from algorithmic monocultures to deliberative democracy platforms, the day was a reminder of both how far we’ve come, and how much further we have to go.

Data x Direction

Notes: MIT's Social and Ethical Responsibilities of Computing Symposium 2025

On May 1st, we attended the Social and Ethical Responsibilities of Computing (SERC) Symposium at MIT's Schwarzman College of Computing — a full-day series of rapid-fire, TED-style talks from researchers working at the edges of data ethics, AI governance, civic technology, and digital justice. What emerged was a mosaic of projects that not onl…

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a year ago · 2 likes · Jesse Parent and Data x Direction

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Jes Parent: Substack, LinkedIn, Bluesky, Twitter/X
JOPRO - Substack, LinkedIn, Bluesky

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