Gautam John

Author: gkjohn

  • Intelligence Isn’t Wisdom: Why Our Sector Keeps Failing

    Intelligence and wisdom are not the same thing, and our sector keeps confusing them.

    Adam Mastroianni: Intelligence tests measure well-defined problem-solving. But life’s important questions (how to live well, build relationships, find meaning, etc) are poorly defined. They resist optimisation and demand wisdom more than cleverness.

    Jason Lewis on what philanthropy forgot: “The gift creates a relationship. That’s its purpose. It’s not a financial exchange; it’s a relational act. A gift binds people together. It carries memory, meaning, and trust.”

    The work we do in social systems exists entirely in poorly defined territory. Yet we keep trying to solve connection problems with tools of abstraction. We can’t metric our way to relationship any more than we can optimise our way to wisdom or abstract our way to systems change.

    As Mastroianni writes: “You’ll spend your whole life trying to solve problems with cleverness when what you really need is wisdom. All of your optimising, your straining to achieve and advance—it doesn’t actually seem to make your life any better.”

    Poorly defined problems require connection. The gift knows this. We’ve forgotten.

    And yes – there’s a bonus zinger on AI in ancient Greece…

    Originally written for LinkedIn on 27 October 2025. View original →

  • On Translation

    The Unglamorous Work of Making Practice Portable

     

    I’ve spent years living between languages and landscapes. Not so much the ones I speak and more the ones I work in. There’s the language of practice that can be granular, contextual and alive with the texture of what actually happens when you try something. And there’s the language of pattern: abstract, portable, the version that travels beyond the room where it was born.

    Good practitioners speak the first fluently, do the work, sense when something’s off, and adjust in real time. Good translators can bridge to the second in helping others learn from practice without reducing it into formula. The hardest job, the one I keep finding myself doing, is staying bilingual: close enough to practice to keep it honest, distant enough to make it legible to people who weren’t in the room.

    Translation is unglamorous work that is mostly about fidelity in staying true to what happened while making it comprehensible to someone who doesn’t share your context. While you lose something in every translation, the question is whether what survives the crossing is still worth the trip.

    “Translation means doing violence upon the original, means warping and distorting it for foreign, unintended eyes. So then where does that leave us? How can we conclude, except by acknowledging that an act of translation is then necessarily always an act of betrayal?”

    ― R.F. Kuang, Babel

    Years ago, I was translating between platform logic and publishing tradition. I thought I’d made the shift legible by documenting the reasoning, built the scaffolding, walked people through the steps. But translation and adoption aren’t the same thing. I left and later, a funder arrived who spoke both languages natively, and the idea took root. There was sadness in that, and a familiar ache about who gets written into the story. I learned then what I’m still learning: usefulness and visibility are different currencies and translators rarely get the byline, but the work doesn’t travel without them.

    What makes translation hard is that you’re always code-switching. When you’re with practitioners, you speak in specifics. Often about the things that went wrong, the small bets that didn’t land and the texture of a conversation that shifted everything. When you’re with people who need the pattern, you have to abstract: What’s the insight here? What’s portable? What can someone in a completely different context learn from this?

    The risk is that you abstract too much and lose the thing that made the practice matter in the first place. Or you stay too granular and the learning never leaves the room. I’ve done both. The practice is finding the right distance at which you can be close enough to smell the petrichor yet far enough to see the weather system.

    Writing helps, not as performance, but as the discipline that keeps translation honest. Writing drags ambiguity into the light without pretending it’s resolved. It shows your priors, what shifted, and why the next step might be smaller (or bolder) than the last. It’s how you make your thinking visible to yourself first, and to others second. It slows you down just enough to catch when you’re translating someone else’s experience into your preferred narrative instead of staying faithful to what actually happened.

    There’s also something about rhythm. Translation can’t be a one-time event and has to be a practice, a sādhanā. You translate, someone tries it, something breaks or works in a way you didn’t expect, and then you translate again with more texture. The unglamorous part is the repetition: Are we repeating to deepen the insight, or just to reassure ourselves we understood it the first time? Are we making practice more portable, or just more comfortable to talk about?

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  • Context Is the Real Protagonist

    Context is the real protagonist in any room — but we keep designing for the individual as if they exist apart from it.

    I absolutely love his craft of reading people, but am amazed (in a good way!) how he keeps treating context as the real protagonist. In his world, talent, leadership, and even luck become relational, not individual. And the care he takes to build containers where people can thrive feels like a practice of stewardship more than management. It really does speak to how we hold people, possibilities, and timing.

    Maybe that’s why his work feels so familiar. The deeper I go, the more I see the same pattern: attention as strategy, relationship as infrastructure, and containers as interventions.

    He’s spent a lifetime refining the same lesson many of us learn the hard way. That change, whether in people, teams, or systems, can’t be forced.

    What I love most is how his world (and it feels adjacent to ours) is moving from design and control toward discernment and trust. From building programs to cultivating conditions. From trying to change people to seeing what wants to happen through them.

    Seeing people clearly, believing in them deeply, and designing the right water for them to swim in might be the purest form of systems change there is.

    Originally written for LinkedIn on 16 October 2025. View original →

  • In Praise of Friction

    Friction is not failure. In human systems, it may be the mechanism through which meaning gets made.

    Two pieces I read today that have been in conversation with each other.

    Sean Voisen’s Design for Lingering argues that abstraction violates the principle of lingering. Lingering, much like slowly burning incense, creates the time and texture where meaning can take shape. Speed erases it.

    Then I read Vaughn Tan’s essay(s) on re-designing AI tools to increase friction and bring back meaning making. His “Confidence Interval” system uses LLMs not to make things easier, but to slow us down, deepen our reasoning, and teach discomfort as a form of thought.

    Only at the end did I realise he’s also the creator of the IDK cards, which I actually have. Felt like a serendipitous reminder that maybe friction really is where meaning lives, and lingering is the necessary condition for it.

    That thread runs through all my recent work on connection vs. abstraction, especially in The Limits of AI in Social Change.

    Originally written for LinkedIn on 15 October 2025. View original →

  • The Thinning of the Forest

    Or how we traded presence for proof

     

    At first light in Nagarhole, the forest stirs as if half-awake. Mist drifts low through the lantana, the air still heavy with the night’s damp. Somewhere in the canopy, a langur coughs, then another answers as a kind of Morse code of nerves, one species relaying another’s unease. The forest exhales, inhales, and falls back into its long, watchful silence.

    The jeep starts, diesel slicing through the quiet while the guide leans forward, scanning for tracks; the driver whispers into his phone. “Female tiger by the watering hole,” someone says. The engine hums a little louder, the air shifts, and just like that, the chase begins.

    What unsettles me is the pace. Safaris once meant waiting and watching and listening and accepting the forest’s refusal to perform. Now they play out like speed runs. Six jeeps race down the red-dust track, each one desperate to be first at the sighting. Cameras the size of rifles hang from windows while eyes sweep the forest edge, hungry for stripes.

    When we finally saw her—a tigress marking a tree near the water—it was from the tail end of a long line of vehicles on a narrow dirt road, nowhere to move. She appeared for three heartbeats, a flick of her tail, a glance that stopped time, and then she slipped away. Dozens of shutters had already clicked, the forest sighed, emptied of anticipation, and the day, it was decided, had been a success.

    I understand the thrill. To meet that gaze, the amber stillness of something that owns the ground it walks on is unforgettable. Yet what saddens me is how quickly the encounter turns into proof—into an image, a tally, a story to carry home. The safari ends the moment it “succeeds.” Everything else—the hum of cicadas, the light shifting through the sal and teak—fades into the backdrop.

    But a forest is more than a species, more than even a collection of them. It is movement and sound and pause: the rising heat that smells faintly of leaf decay, the metallic call of the greater racket-tailed drongo, the bark of a sambar that echoes for miles. It is texture and time, alive in ways that resist capture. Somewhere along the way, we have mistaken the experience for the evidence.

    Karl Ove Knausgaard wrote that “the whole world has been transformed into images of the world.” He was speaking of technology, but the thought followed me through those dusty tracks. Even here, in what we like to call the wild, we’ve turned experience into image. The tiger has become a symbol—an abstraction detached from the forest that sustains her, a sign of having been somewhere rather than being in it.

    This is what I keep returning to: the drift from connection to abstraction. Abstraction itself isn’t the enemy—it helps us think, compare, and organise—but left unchecked, it hollows things out. It turns forests into carbon sinks, rivers into water resources, and tigers into sightings. Connection, by contrast, insists on presence. It asks that we slow down enough to be claimed by what we wish to know.

    A day later, almost in the same spot, the forest offered something quieter. A rusty-spotted cat—no larger than a rabbit—was padding along the elephant-proof railing, muscles rippling under its dappled coat. It seemed to be shadowing a ruddy mongoose that darted through the undergrowth. The same bend, the same time of day, but this time no convoy, no radios, no crowd. Just us, and this small, perfect animal.

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  • What I Was Really Building

    Abstraction. Connection. And me.

     

    I just published a piece for IDR about the limits of AI in social change work. It’s got frameworks, evidence, and careful arguments about where these tools belong and where they don’t. And it’s all true, as far as it goes.

    But here’s what I didn’t write there: why any of this matters to me personally. This is the real story of why I cared enough to write it.

    For years, I built platforms. Designed convenings. Created frameworks to help organisations think more systematically about change. I was good at it. People found it useful. But I had no idea what I was actually doing.

    I was building externally what I couldn’t find internally.

    All those containers for other people to belong in? I was creating them because I didn’t know how to belong to myself. The systems designed to hold complexity? I was building them because I couldn’t hold my own contradictions. The safe spaces for others? I was making them while performing a strength I didn’t always feel.

    I thought I was leading. Turns out I was protecting myself through usefulness. Stay busy. Be reliable. Hold space for everyone else. Just don’t ask to be held yourself.

    The work looked successful. The platforms functioned. The convenings happened. But my body was keeping score.

    My neck hurt constantly. My shoulders were rocks. My back felt like it was carrying weight that wasn’t just physical. I assumed this was normal—you work hard, you travel, you sit in too many meetings, your body complains. That’s just how it is.

    Then a Pilates instructor watched me move and said, almost casually, “You’re bracing wrong. Your whole body is trying to hold everything together.”

    I stopped mid-movement. She was right. I was holding everything together—my posture, my projects, other people’s anxieties, my own uncertainty. Gripping when I should have been breathing. Bracing when I should have been sensing.

    My physiotherapist said something similar a few weeks later. The tension in my shoulders wasn’t just tension. It was something deeper, something I’d been carrying for years. That’s how I ended up in therapy, doing trauma-informed work, finally starting to look at what I’d been so carefully not looking at.

    Slowly, things started to shift. I learned to notice when I was reaching for control and calling it clarity. When I was trying to fix what needed to be felt. When I was rushing to name something that was still taking shape.

    My body kept teaching me what my mind resisted: you can’t shortcut your way to wholeness. You can’t optimise your way to presence. You can’t think your way out of what needs to be felt.

    And here’s the thing—the patterns in my body were the same patterns in my work. I kept trying to solve everything by building better systems, clearer frameworks, and more elegant platforms. And sure, those things had value. But they were also a way of keeping distance. Abstraction as protection. Scale as a way to avoid the intimate, messy, slow work of actually being in relationship.

    Once I saw this pattern, I couldn’t unsee it. I watched myself in meetings, always prepared, always with an agenda, rarely leaving space for what wasn’t planned. I noticed how I responded to people’s struggles—jumping to solutions instead of sitting with their uncertainty. I saw how I led—projecting steadiness whether I felt it or not, rather than letting anyone see when I was unsure.

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  • What Reinforcement Learning Teaches Us About Social Change

    Richard Sutton, the father of reinforcement learning, called LLMs a dead end. His critique is that they don’t learn from experience, predict tokens not outcomes, mimic but can’t act and have no goals.

    It’s a technical debate that lands squarely in the social sector because social change requires us to learn in real time by trying, failing, repairing, adapting and persisting. Exactly what Sutton calls the “era of experience.”

    Interview: [link]

    This is why I’ve argued that wide abstraction is so dangerous in social change. Pattern-matching machines can’t sit in the mess, repair relationships, or build trust and don’t “learn” the way people and communities do.

    My recent essay in India Development Review (IDR) Review unpacks this risk, and asks how we keep tools in service to connection.

    Originally written for LinkedIn on 29 September 2025. View original →

  • How Do We Keep Social Change Human? A Writing Retrospective

    Over time, my writing has been an attempt to follow one thread: how do we keep social change human?

    In 2023, I asked how nonprofits could scale without becoming brittle (The power of building a community) and argued that scale happens at the speed of trust. Later that year, I turned to leadership (What new possibilities could your leadership unlock?), framing it less as directing and more as gardening. By 2025, the question of connection versus control had come into sharper focus. Connection, not abstraction argued that philanthropy’s role is to nurture the scaffolding of relationships, not just replicate frameworks. And in A question for all of us who care about change, I asked how we might resist the pull of control and stay with the messiness of connection.

    My new piece for India Development Review (IDR) picks up that thread in the age of AI and argues for a vigilant embrace. Large language models can amplify relationships, illuminate ecosystems, and automate routine tasks. But when they slide into wide abstraction, flattening context and corroding trust, they risk creating systems that look efficient but feel hollow.

    The principle remains the same across all my writing: abstraction must serve connection.

    Social change scales like a forest: root by root, mycelium by mycelium, canopy by canopy.

    Originally written for LinkedIn on 25 September 2025. View original →

  • The limits of AI in social change

    In systems of social change, we grapple with an enduring tension: connection versus abstraction. Connection is slow, human, and relational. It thrives on trust, listening, and collaboration. Abstraction, on the other hand, simplifies complexity into patterns, insights, and models. It is fast, scalable, and efficient.

    Both serve a purpose, but they pull in opposite directions. And now, with the rise of AI tools like large language models (LLMs), this tension has reached new heights. LLMs thrive on abstraction; they reduce human interaction into data points, surface patterns, and generate outputs.

    While LLMs are not intelligent in the sense of reasoning or self-awareness, they can serve as tools that reframe, rephrase, and reorganise a person’s ideas in ways that feel expressive. This can enable creativity and reflection, but let’s be clear: It’s not agency. The tool reshapes inputs but does not make meaning.

    In market-based systems, where efficiency is paramount, this might work. But in social systems, where relationships, context, and trust are everything, abstraction risks losing what makes systems real and resilient.

    This essay is a case for vigilant embrace. It asks how we can keep tools in service to relationship, not the other way round. It draws from our country’s experience of the self-help group (SHG) movement and its microfinance offshoots, tests it against the new frontier of LLMs in the social sector, and distills a few design rules for keeping the work human in an age of speed.

    Connection as infrastructure

    Decades ago, India’s SHG movement reframed finance as a relationship first, and a product second. Groups formed through affinity; members saved together; rules emerged from context; repayment schedules matched rhythms of life and livelihood; and trust was the collateral. Over time, SHG–bank linkage became a way to bring formal finance into places where formal institutions had no legitimacy of their own. It only worked because process mattered.

    As Aloysius Prakash Fernandez (long-time leader in the SHG movement with MYRADA and a key architect of its practice) has argued, SHGs built economies of connection. The time it took to form an SHG was not friction to be eliminated, but rather the formation and cadence of months of meetings, savings discipline, conflict resolution, and learning to keep books and hold each other accountable. That slow work created legitimacy and resilience so that when crisis struck, the relationship fabric held.

    Then came the turn. As microfinance commercialised, much of the field shifted from SHG thinking to microfinance (MFI) thinking—from affinity to acquisition, from place to product, from presence to process compliance. Loans became standardised, repayment cycles rigid, and growth a KPI. Speed, greed, and standardisation (to borrow Aloysius’s pithy phrasing) took what was relational and made it transactional.

    The results were predictable. Repayment rates looked spectacular—until they didn’t. In many places, risks were accumulating: multiple lending without visibility on household cash flows, incentives that pushed volume over suitability, and the slow erosion of trust with lenders treating people as portfolios rather than participants. Products scaled, but belonging did not. The social infrastructure that had once underwritten financial inclusion was being displaced by numbers that looked like progress.

    It is tempting to narrate this simply as a story of ‘bad actors’, but that misses the deeper point. Even well-meaning institutions slide here because their structures privilege the measurable: gross loan portfolio, on-time repayment, and cost to serve. The things that make SHGs work—mutuality, ownership, repair—resist instrumentation, and become, quite literally, less valuable.

    If this sounds familiar to those working at the intersection of LLMs and social systems, it’s because we’re watching the same film again.

    The question, then, is this: Where, if at all, do LLMs belong in the work of social change? And what can we learn from the SHG/MFI shift?

    LLMs and the mechanistic view of wisdom

    There are now many LLM-based tools designed to abstract and synthesise insights from human interactions, promising to amplify collective wisdom. In social change systems, where resources are stretched and problems are vast, this promise is tempting and does have some strengths.

    • It organises and systematises human insights into building blocks.
    • It surfaces diverse perspectives, tracing inputs back to their sources to ensure inclusion and accountability.
    • It accelerates decisions, offering actionable outputs at scale.

    But these strengths are also its greatest weaknesses because they abstract the human process of turning messy, situated conversations into neat patterns. This comes at a cost.

    1. Loud voices and flattened complexity: They risk over-representing frequent or louder perspectives while erasing nuance, dissent, and marginal views.
    2. Loss of relational insight: Wisdom doesn’t arise from patterns alone. It comes from the trust, tension, and emotional connection born of human interaction.
    3. Hollow consensus: Outputs that bypass relational work may appear actionable, but they lack the trust and shared ownership that give decisions their power.

    The result? Systems that look efficient but feel hollow because tools, frameworks, and processes sever the relational ties that make systems real.

    Recent empirical evidence seems to confirm what we sense intuitively about these limits. When researchers systematically tested LLM reasoning capabilities through controlled puzzles, they discovered something profound: “As problems grow more complex, these models don’t just struggle but collapse entirely.” Even more telling, when complexity increases, they begin to reduce their effort, as if giving up. They find simple solutions but then overthink them, exploring wrong paths.

    Perhaps this is a window into the fundamental nature of these systems. They excel at pattern matching within familiar territories but cannot genuinely reason through novel complexity. And social change? It lives entirely in that space of the new and the complex, where contexts shift, where culture matters, where every community brings unprecedented challenges. If these models collapse when moving discs between pegs, how can we trust them with the infinitely more complex work of moving hearts, minds, and systems?

    Apply the narrow versus wide lens

    To navigate this challenge, the tension between connection and abstraction must be examined through another dimension: narrow versus wide. While connection and abstraction often feel like irreconcilable opposites, the narrow–wide lens helps bridge this gap by revealing how different kinds of tools can play meaningful roles in social change.

    • Narrow tools are specific and targeted, solving well-bounded problems.
    • Wide tools are generalised and scalable, seeking to address large systems.

    Combining this in a 2×2 framework gives us four distinct spaces where LLMs can, or cannot, play a meaningful role.

    1. Narrow connection (Relational amplifiers)

    • What it is: Tools that deepen human relationships by enhancing context-specific, targeted work.
    • Example: A frontline caseworker uses an LLM to synthesise notes across multiple user visits in order to personalise their follow-ups. The LLM helps amplify memory and insight, but the relationship remains human.
    • Why it works: These tools augment human connection by surfacing insights without replacing relational work. They stay rooted in the specific, bounded context of their application.
    • Key use case: Tools for case management in social services. For instance, LLMs help social workers tailor interventions to individual users based on their unique needs and histories.
    • Key question: Does this tool augment connection, or does it replace it?

    2. Wide connection (Relational ecosystems)

    • What it is: Tools that map and visualise relationships across broader ecosystems, enabling collaboration without eroding the human work of trust-building.
    • Example: Stakeholder mapping tools that reveal community networks and power dynamics.
    • Why it works: Wide connection tools respect the complexity of human systems, helping actors navigate and strengthen relationships without reducing them to transactions.
    • Key use case: Network mapping for advocacy coalitions. LLMs can surface insights about overlapping efforts, potential collaborators, or areas of conflict, but the work of building those connections remains human.
    • Key question: Does this tool illuminate relationships, or does it flatten them into transactions?

    3. Narrow abstraction (Efficiency tools)

    • What it is: Tools that automate repetitive, bounded tasks, freeing up time for relational or contextual work.
    • Example: A grant officer uses an LLM to scan 100 applications for missing documentation or budget inconsistencies and flags files for review, but leaves decisions to humans.
    • Why it works: Narrow abstraction tools stay within well-defined parameters, ensuring that the abstraction doesn’t undermine human judgement or erode trust.
    • Key use case: Administrative automation in nonprofits. AI can handle routine data entry or flag missing information in grant proposals, allowing staff to focus on strategic decisions and relationships.
    • Key question: Has the process of abstraction removed necessary details that deserve human consideration?

    4. Wide abstraction (Context flatteners)

    • What it is: Broad, generalised tools that prioritise scale and efficiency, but risk erasing context and relationships.
    • Example: A philanthropic CRM tool employs LLMs to rank grantees on ‘impact potential’ using prior grant reports that reward well-written or funder-aligned language, not those doing contextually important work.
    • Why it fails: Wide abstraction tools produce outputs that are disconnected from the lived realities of the people and systems they aim to serve. They often impose generic solutions that lack local resonance or trust.
    • Key risk: Policy recommendations generated by LLMs that ignore cultural nuance, power dynamics, or local histories.
    • Key question: Does this tool flatten complexity, producing solutions no one truly owns?

    Wide abstraction tools fail social systems because social systems are built on trust, context, and relationships. Change doesn’t emerge from patterns or averages; it emerges from the slow, messy, human work of showing up, listening, and building together.

    This requires moral discernment, cultural fluency, and the ability to hold space for uncertainty. Even the most sophisticated tools are not capable of these things. A tool cannot sense the difference between a pause of resistance and a pause of reflection. It cannot understand silence or the weight behind a hesitant request.

    LLMs can play a role in social change, but must stay narrow, supportive, and grounded in connection. They can amplify relationships (narrow connection), reveal patterns in systems (wide connection), or automate tasks that don’t require human judgement (narrow abstraction). But they cannot replace the relational processes that make systems real.

    Designing for a human age

    The promise of LLMs is seductive. It offers speed, efficiency, and a sense of control—qualities we crave in complex, uncertain systems. But if we think of connection as the foundational infrastructure and abstraction as a tool, how do we build (and fund) accordingly?

    Four clusters of practice follow from the analysis:

    1. Placement and scope

    • Keep it narrow (bounded contexts) when automating.
    • Hold it wide and human when mapping relationships.
    • Avoid wide abstraction in relational domains (welfare, justice, health, community development). If you must use it, treat outputs as hints, never decisions.
    • Assume drift; design for it.

    2. Process and ownership

    • Process matters. If a ‘consensus’ tool removes dissent and dialogue, it is producing hollow agreement.
    • Ownership signals reality. If a decision is not of the group but about it, expect distance and eventual resistance.
    • Messiness test. Did we stay in the mess to listen, disagree, compromise? If not, the outcome may travel poorly. Consensus that bypasses repair will not hold.

    3. Measurement and accountability

    • Measure what you can while protecting what you can’t. Build explicit guard rails so that unmeasurable goods (trust, belonging, repair) are not crowded out.
    • Use AI where failure is acceptable. Drafting, summarising, data hygiene: yes. Decisions about dignity, safety, or entitlements: no.
    • Allow override without justification. People closest to the context must be free to resist machine outputs.
    • Capture moments of failure. Document not just technical bugs, but also when people forget how to act without the tool.

    4. Funding and institutional practice

    • Finance the foundational layers. Budget for convening, accompaniment, group formation, and follow-through, and not just transactions.
    • Reward stewardship, not throughput. Celebrate organisations that prune, pause, and repair, not just those that scale.
    • Create collision spaces. Funders should host containers for connection—open-ended gatherings where practitioners make meaning together, not just report up.
    • Reframe accountability. Shift from counting outputs to honouring conditions: psychological safety, trust density, and role clarity across the network.

    The work we do in the sector is the work of belonging, and it does not scale by flattening. It scales like a forest: root by root, mycelium by mycelium, canopy by canopy, alive and adaptive, held together by relationships we cannot always see and must never forget.

    Disclaimer: IDR is funded by Rohini Nilekani Philanthropies.

    Originally published at India Development Review on 25 September 2025.

  • Replace ‘History’ With ‘Philanthropy’ and Read It Again

    While reading The Lost Art of Thinking Historically, I was struck by the fact that if you replace the word ‘history’ with ‘philanthropy,’ the essay still holds true.

    In particular, these lines:

    “Making consequential choices about an unknowable future is a profoundly challenging task. The world is not a laboratory. It is a vortex of ambiguity, contingency and competing perspectives, where motives are unclear, evidence is contradictory and the significance of events changes with the passage of time.”

    That feels like the daily work of philanthropy, too. We often yearn for certainty, with clear models, predictable outcomes, and tidy causal stories. Yet the practice is more about holding ambiguity, sitting with contradictions, and choosing anyway, knowing how provisional our judgments will look in hindsight.

    Philanthropy, like history, may not offer prediction. But it can hold a different sensibility: modesty, curiosity, empathy and a constant reminder that our ignorance is very deep.

    Originally written for LinkedIn on 24 September 2025. View original →