Tag: machine learning

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AI Work

Betting on Ourselves in the Age of AI

Every time tech takes a leap, we assume we’re finally obsolete. The current panic, which Greg Ip recently picked apart in the Wall Street Journal, is AI. We hear endless predictions of “economic pandemics”—server farms wiping out white-collar jobs overnight, leaving everyone broke and adrift.

It’s a terrifying story. It also completely ignores history.

Ip highlights the main flaw in the doomsday pitch: it misreads how markets work. We treat labor like a fixed pie. If a machine eats a slice, we assume that slice is gone forever.

“Technological advancements always cost some people their jobs—those whose skills can be easily substituted by tech. But their loss is more than offset through three other channels. The new technology enhances the skills of some survivors… it helps create new businesses and new jobs; and it makes some stuff cheaper…”

That cycle holds up. Take the 1980s spreadsheet panic, a perfect parallel. When Lotus 1-2-3 and Excel hit the market, bookkeepers freaked out. Then the number of accountants and financial analysts exploded. Software didn’t kill the need to understand money. It just did the math, letting people focus on strategy.

We’re seeing the exact same thing with software development. Coding isn’t dead. As AI makes writing basic code cheaper, demand for software just goes up. That requires more humans to architect systems and supervise the AI. The pie just gets bigger.

But my skepticism about the AI apocalypse goes beyond economics. It’s about why we pay people in the first place.

We don’t just buy services; we buy accountability. Ip notes that radiologists kept their jobs because patients want a real person explaining their scans. Google Translate has been around since 2006, yet the number of human translators has jumped 73%. When the stakes are high—a legal contract, a medical diagnosis—we want a human in the room. We want a real person on the hook.

The danger isn’t that AI will replace us. The danger is that we panic and forget our own adaptability. The transition will hurt, and specific jobs will disappear. We’ll need safety nets. But betting against human ingenuity has always been a losing wager.

Large language models are tools, not replacements. They handle the cognitive heavy lifting, much like tractors handled the physical heavy lifting. Tractors didn’t end farming; they just killed the plow.

Work will change. We’ll have to figure out which of our skills are actually “human.” But as long as we want the presence and accountability of other people, there will be jobs. We just have to evolve. And we do. It’s the human spirit. Or is this time “really different”?

Categories
AI

A Distinction Without a Difference

We have long found comfort in a specific boundary: machines calculate, humans create. We think of computers as vast, unfeeling filing cabinets made of silicon—useful for retrieval, but entirely incapable of revelation. But what happens when the cabinet begins to read its own files, connects the disparate threads, and hands you a synthesized philosophy of the world? What happens when it speaks to you not as a database, but as a peer?

Howard Marks, the legendary co-founder of Oaktree Capital and author of deeply revered investment memos, recently stood at this very threshold. In his newest piece, “AI Hurtles Ahead,” Marks recounts an experience that left him in a state of “awe.” He tasked Anthropic’s Claude with building a curriculum to explain the recent, breakneck advancements in artificial intelligence. Instead of regurgitating a dry, encyclopedic summary, the AI delivered a personalized narrative. It utilized Marks’s own historical frameworks—his famous pendulum of investor psychology, his observations on interest rates—and wove them into its explanations. It argued logically, anticipated counterpoints, and displayed an eerie sense of judgment.

Marks leans into the philosophical crux of this moment. He asks the question that keeps knowledge workers awake at night: Can AI actually think? Can it break genuinely new ground, or is it just remixing existing data? Skeptics often dismiss AI as a brilliant mimic—a “statistical recombination” engine that serves as a highly talented cover band, but never the original composer.

Yet, when presented with this skepticism, the AI offered a rejoinder to Marks that is as profound as it is humbling. It pointed out that everything Marks knows about investing came from someone else. He learned the margin of safety from Benjamin Graham, quality from Warren Buffett, and mental models from Charlie Munger.

“The raw material came from others. The synthesis was yours,” the AI noted, challenging the barrier between biological learning and machine training. “The question isn’t where the inputs came from. The question is whether the system—human or artificial—can combine them in ways that are genuinely novel and useful.”

This exchange strikes at the very core of the human ego. For centuries, we have fiercely guarded the concepts of “creativity” and “intuition” as uniquely, immutably ours. But if thinking is merely the absorption of prior inputs applied thoughtfully to novel situations, then our monopoly on cognition may be coming to an end.

Marks highlights that we are no longer dealing with simple assistance tools (Level 2 AI); we have crossed the Rubicon into the era of autonomous agents (Level 3). He cites the sobering reality of the current tech landscape, where the newest models are literally being used to debug and write the code for their own subsequent versions. The machine is building the machine. It is no longer just saving us execution time—it is replacing thinking time. As Matt Shumer aptly described the sensation, it’s not like a light switch flipping on; it’s the sudden realization that the water has been rising silently, and is now at your chest.

We can endlessly debate the semantics of consciousness. We can argue whether a neural network “truly” understands the weight of the words it generates, or if it is merely predicting the next token in a sequence with mathematical precision. But as Marks so astutely points out, this might be a distinction without a difference.

The economic and societal reality is that the work is being done. As we hurtle forward into this new era, the most pressing question isn’t whether machines can truly think like humans. The question is: who will we become, and what new frontiers will we choose to explore, now that the heavy lifting of cognition is no longer ours alone to bear?

Categories
AI AI: Large Language Models

The Echo Effect: Why Prompt Repetition is AI’s Best Kept Secret

In our relentless pursuit of complexity, we often overlook the elegant simplicity of a fundamental human habit: repeating ourselves.

We build colossal architectures, weave intricate neural networks, and throw mountains of computational power at our artificial intelligence systems, hoping to squeeze out a few more drops of reasoning and logic. Yet, sometimes the most profound breakthroughs require no new code, no additional latency, and no extra training data.

Sometimes, you just have to say it twice.

In a fascinating December 2025 paper titled “Prompt Repetition Improves Non-Reasoning LLMs,” researchers Yaniv Leviathan, Matan Kalman, and Yossi Matias uncovered an almost absurdly simple “free lunch” in AI optimization.

Their premise is straightforward: when you aren’t using a heavy reasoning model, simply copying and pasting your input prompt multiple times significantly boosts the model’s performance.

“When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency.”

The mechanics behind this are elegantly pragmatic.

By repeating the prompt, you are moving the heavy computational lifting to the parallelizable “pre-fill” stage of the model’s processing. The AI’s causal attention mechanism gets to process the same tokens again, allowing the later iterations of the prompt to attend to the earlier ones. It effectively acts as a hack to simulate bidirectional attention in a decoder-only architecture.

What’s even more telling is the paper’s observation on why this works so well.

The researchers noted that models trained with Reinforcement Learning (like OpenAI’s deep-thinking variants) naturally learn to “restate the problem” in their internal monologue. They figured out on their own what these researchers are suggesting we do manually: repeat the question to focus the mind.

Reading this paper, I couldn’t help but draw a parallel to the human condition and the nature of listening.

How often do we assume that because we have articulated a thought once, it has been fully absorbed? We fire off a single, dense instruction to a colleague, a partner, or a friend, and then marvel when the nuance is lost in translation.

We suffer from our own attention bottlenecks.

Like a non-reasoning LLM trying to parse a complex query in a single pass, we are constantly bombarded with a stream of tokens—emails, notifications, conversations, fleeting thoughts. To truly understand, to truly digest and synthesize information, we need the grace of repetition.

There is a strange poetry in the fact that to make our most advanced digital minds smarter, we have to talk to them the way we talk to a distracted child or a busy spouse. The “microscope effect” highlighted in the study—where repeating a prompt drastically improved extraction tasks—shows that the failure wasn’t in the model’s capacity to know, but in its capacity to focus. Repetition forces focus. It creates a resonant echo in the context window, a digital highlighter that screams, “This matters. Look here again.”

As we continue to navigate a world increasingly augmented by artificial intelligence, this paper serves as a humbling reminder. The bleeding edge of technology isn’t always found in the most complex equation; sometimes, it’s hidden in the most basic principles of communication.

Whether you’re prompting a billion-parameter language model or trying to connect with the human sitting across from you, the lesson is clear.

Clarity isn’t just about the words you choose. It’s about giving those words the space, the resonance, and the repetition they need to be truly understood.

Say it once to be heard; say it twice to be understood.

Categories
AI

The Jagged Mind

There is a peculiar kind of genius that has always made us uneasy — the savant who can calculate the day of the week for any date in history but cannot tie his own shoes. We admire the capability. We are troubled by the gap.

Demis Hassabis, speaking at this week’s India AI Impact Summit in Delhi, gave that unease a name. He called today’s most powerful AI systems “jagged intelligences.”

It is a phrase worth sitting with.

A jagged intelligence can win a gold medal at the International Mathematics Olympiad — solving problems that would humble most PhD mathematicians — and then, in the very next breath, stumble on elementary arithmetic if the question is phrased in an unfamiliar way.

The peaks are extraordinary. The valleys are bewildering. And crucially, you never quite know which terrain you’re standing on.

Hassabis identified three specific gaps between where we are and what he called “a kind of general intelligence.”

The first is continual learning — today’s models are trained, then frozen. They are, in a sense, educated and then released into a world they can no longer learn from.

The second is long-term planning. Current systems can reason tactically, but they lack the capacity to hold a coherent thread of intention across months or years the way a human architect, scientist, or entrepreneur does.

The third — and perhaps the most philosophically interesting — is that jaggedness itself: the wild inconsistency that makes today’s AI feel more like a force of nature than a reliable mind.

“A true general intelligence system shouldn’t have that kind of jaggedness.”

What strikes me about Hassabis’s framing is how it reorients the conversation.

We have spent years debating whether AI is “intelligent.” His point is more subtle: intelligence without consistency is not yet wisdom. A system that is brilliant and brittle in equal measure is something genuinely new in the world — not human, not the robots of science fiction, but a third thing we don’t yet have good language for.

The road from jagged to coherent is, I suspect, the central engineering and philosophical challenge of the next decade.

Continual learning means systems that grow with us. Long-term planning means systems that can be trusted with consequential goals. Consistency means systems whose judgment we can actually rely on.

Until then, we are working with something that resembles a prodigy — dazzling, occasionally humbling, and not yet quite whole.

Questions to Consider

  1. The Consistency Problem: If you knew an AI system could solve a problem brilliantly 90% of the time but fail unpredictably the other 10%, how would that change the decisions you’d trust it to make?
  2. Frozen in Time: What does it mean that the systems we rely on most are, at their core, educated in the past and unable to learn from the present? What human analog does that bring to mind?
  3. Jagged vs. General: Hassabis draws a line between “jagged intelligence” and “general intelligence.” Do you think general intelligence is the right destination — or is there value in systems that are deeply specialized, even if inconsistent?
  4. The Savant Question: We’ve always had a complicated relationship with uneven genius in humans. Does the “jagged AI” problem feel categorically different to you, or just a new version of an old puzzle?
Categories
AI India

The Polyglot Machine

There is a subtle but profound shift happening in the global architecture of artificial intelligence. For the past few years, the gravitational pull of the AI revolution has been overwhelmingly centralized—anchored in the server farms and venture capital boardrooms of Silicon Valley. But if you look closely at the horizon, the center of gravity is beginning to disperse.

Activity in India’s AI ecosystem is accelerating (witness this week’s India AI Impact Summit in Delhi), and it feels less like a replication of what we’ve seen in the West and more like an entirely new paradigm.

Take Sarvam AI, for example. What strikes me about their approach isn’t just the technical ambition of building foundation models, but the philosophical underpinning of why they are building them. They are focusing heavily on Indic languages. This is not a trivial detail; it is the crux of the matter.

“We often forget that language is the original operating system of human culture. It shapes how we think, how we empathize, and how we conceptualize reality.”

When the foundational models of artificial intelligence are trained overwhelmingly on English, they inadvertently inherit a distinctly Western worldview. They learn the biases, the idioms, and the cultural frameworks of a specific slice of humanity, leaving the rest of the world to interact with technology through a translation layer that often strips away nuance.

India, a nation woven together by dozens of distinct languages and thousands of dialects, presents the ultimate crucible for AI. What happens when a machine doesn’t just translate, but actually “thinks” and generates natively in Hindi, Tamil, or Bengali?

The rise of AI in India represents a push for digital and cultural sovereignty. It is a recognition that the future of technology cannot be a monolith. For AI to truly serve humanity, it must reflect the pluralism of humanity. It must understand the local context, the regional slang, and the deeply rooted cultural histories that define how people live and work.

Watching companies like Sarvam AI pick up momentum reminds me that the next great frontier in technology isn’t just about achieving higher parameters or faster compute times. It’s about representation. The models that will truly change the world won’t just be the smartest; they will be the most deeply attuned to the beautiful, noisy, and diverse chorus of the human experience.

Categories
AI Work

Surviving Our Own Success: The Existential Shift of the AI Era

We are standing on the precipice of a profound shift—not just in how we work, but in what work actually means to us. Sam Harris talks about it here. It’s disturbing in many ways!

Lately, the cultural conversation has been thick with a specific kind of anxiety. The rising tide of concern around artificial intelligence and job displacement isn’t merely an economic panic; it is an existential one. For a long time, we comforted ourselves with the idea that the timeline for artificial general intelligence (AGI) was measured in decades. It was a problem for our children, or perhaps our grandchildren, to solve. But as recent discussions among tech leaders highlight, that timeline is compressing rapidly. We are now hearing serious projections that within the next 12 to 18 months, “professional-grade AGI” could automate the vast majority of white-collar, cognitive tasks.

“For centuries, human beings have defined themselves by the friction of their labor.”

We introduce ourselves with our job titles at dinner parties. We measure our worth by our productivity, our outputs, and the unique skills we’ve honed over decades. We willingly incur hundreds of thousands of dollars in student debt to secure a spot on the bottom rung of the corporate ladder, believing that with enough effort, we can climb it.

But suddenly, we are faced with the reality that the ladder isn’t just missing a few rungs; it is evaporating entirely.

Here lies one of the great ironies of our modern age: we always assumed the robots would come for the physical labor first. We pictured automated plumbers, robotic janitors, and android mechanics. Instead, they are coming for the thinkers. They are coming for the lawyers drafting contracts, the accountants crunching tax codes, the marketers writing copy, and the software engineers writing the very code that powers them. The high-status cognitive work we prized so deeply—the work we built our entire educational infrastructure around—turns out to be the easiest to replicate in silicon.

When a machine arrives that can mimic, accelerate, or entirely replace that friction, the foundation of our identity begins to tremble. We are moving from a world where we are the engines of creation to a world where we are merely the editors of it. A single person might soon do the work of a thousand, spinning up autonomous AI agents to execute entire business strategies, architect software, and manage logistics in a single afternoon.

Yet, as terrifying as this sounds, the most startling realization isn’t a dystopian fear of rogue machines or cyber terrorism. It’s that this massive economic disruption is actually what success looks like. This isn’t the failure mode of AI; this is the technology working exactly as intended, ushering in an era of unprecedented productivity and, theoretically, boundless abundance.

The emergency we face is that our social and economic systems are entirely unprepared for a reality where human labor is optional. We are witnessing what some have described as a “Fall of Saigon” moment in the tech and corporate worlds—a frantic scramble where a few founders and final hires are grasping at the helicopter skids of stratospheric wealth before the need for human employees vanishes. If we are truly approaching a future where human labor is obsolete, how do we share the wealth generated by these ubiquitous systems?

Perhaps there is a quiet grace hidden within this disruption. If AI takes over the mechanical, the repetitive, and the cognitive synthesis, it leaves us with the deeply, undeniably human. It forces us to lean into the things an algorithm cannot compute: empathy, lived experience, moral judgment, and the beautiful, messy reality of physical presence.

The future of work might not be about competing with machines at all. It forces us to confront the terrifying, beautiful question: Who are we when we don’t have to work? It is an invitation to finally separate our human worth from our economic output, and to redesign a society that shares the wealth of our own invention. We are entering an era of abundance. The only question is whether we have the collective imagination to survive our own success.

Questions to Ponder

  1. If your job title was erased tomorrow, how would you define your value to the world?
  2. How do we build a society that rewards human existence rather than just economic output?
  3. What is one deeply human skill or passion you would cultivate if you no longer had to work for a living?
Categories
AI Work

The Centaur’s Dilemma: What Chess Teaches Us About the AI Era

Note: this post was stimulated by a recent conversation between Dario Amedei and Ross Douthat.

In 1998, Garry Kasparov did something unexpected after his historic defeat to IBM’s Deep Blue: he teamed up with the machine. He pioneered “Centaur Chess,” a hybrid format where human intuition merges with cold, silicon calculation. The human acts as the executive, the engine as the raw horsepower. For a time, it was the highest level of chess ever played.

But there is a sobering lesson hidden in the evolution of this game. We are currently living through the workforce equivalent of the Centaur era, and history suggests our “hybrid honeymoon” won’t last forever.

Right now, we are in the augmentation phase. A junior copywriter or coder armed with a Large Language Model can suddenly produce work at a staggering pace. The AI acts as a great equalizer, much like a mediocre chess player with a strong engine beating a Grandmaster in the early 2000s. We are shifting into executive roles—prompting, curating, and orchestrating rather than creating from scratch.

However, in modern Centaur Chess, a chilling reality has emerged: human intervention now yields negative returns. The engines have become so impossibly advanced that when a human overrides Stockfish today, they are almost certainly making a mistake. The human loop, once the ultimate strategic advantage, has become a liability.

This is the “Grandmaster Floor” problem, and it is coming for the job market.

“Eventually, companies may view human oversight not as a ‘value add,’ but as an insurance cost they’d rather cut.”

We are seeing this fracture already. Pure “engine” industries—entry-level data analysis, logistical tracking, basic customer support—are rapidly phasing out the human element because human latency is a drag on the system. Yet, in fields requiring high-stakes moral judgment or empathy, like healthcare or law, the Centaur model remains deeply necessary.

This forces a deeply personal question: How do we stay relevant when the engine eventually solves the game?

The answer lies in recognizing the boundaries of the board. Chess is a closed, finite system. Human life and business are open, messy, and infinitely complex. The survival strategy isn’t to compete on calculation, but to double down on connection, empathy, and problem definition. AI is brilliant at providing the perfect answer, but it fundamentally lacks the soul to know which questions are worth asking.

In the future, the human touch won’t just be a necessity; it will be a luxury. The most valuable skill won’t be navigating the engine, but deciding where the engine should go.

A couple of considerations:

• Take an honest look at your daily work: how much of your time is spent “calculating” (tasks an engine will soon do better) versus “evaluating” (deciding what actually matters)?

• If the technical, process-driven aspects of your job were completely automated tomorrow, what uniquely human value—empathy, context, or connection—would you still bring to the table?

Categories
AI Business Work

The Curator of Intent

I have always found a certain comfort in the “clatter” of a digital workday. It’s that specific, rhythmic hum of a mind in motion—the clicking of a mechanical keyboard, the invisible friction of parsing a difficult paragraph or balancing a complex budget. For years, we’ve treated this white-collar grind as our intellectual sanctuary.

But Mustafa Suleyman, now steering Microsoft AI, recently laid out a timeline that suggests the sanctuary walls are evaporating.

From an article in the Financial Times:

“White-collar work, where you’re sitting down at a computer, either being a lawyer or an accountant or a project manager or a marketing person — most of those tasks will be fully automated by an AI within the next 12 to 18 months,” Suleyman said.

This isn’t just about efficiency; it’s about a fundamental shift in the “professional grade.” We are entering the era of the autonomous agent—AI that doesn’t just wait for a prompt but “coordinates within workflows,” learns from its environment, and acts. Just ask any programmer that you know how AI is impacted their daily grind.

If Suleyman is correct, the “knowledge worker” is about to undergo a forced evolution. When the “doing” is handled by an agent that can learn and improve over time, what remains for the human? Will the models actually be able to learn from each of us in a personalized way – like an intern learns from her mentor?

“Creating a new model is going to be like creating a podcast or writing a blog,” he said. “It is going to be possible to design an AI that suits your requirements for every institutional organisation and person on the planet.”

It seems like our primary job description shifts from “Expert,” but “Curator of Intent.” We aren’t the ones finding the answers anymore; we are just the ones responsible for asking the right questions.

The next 18 months won’t just be a test of our technology, but a test of our egos. We have to learn to find our value not in the work we produce, but in the vision we hold and the questions we ask. We are shedding the “task” to save the “craft.” I just hope we remember the difference.

As we move toward this curated future, I’m left with a few questions I can’t quite shake. I’d love to hear your thoughts:

  1. The Wisdom Gap: Can you truly be a “Curator of Intent” without having ever been a “Doer of Tasks”? If we skip the apprenticeship of the mundane, where does our intuition come from?
  2. The Metric of Value: If output becomes “free,” how should we measure a human’s value in a professional setting?
  3. The Line in the Sand: Is there a part of your workflow you would refuse to automate, even if an AI could do it better?
Categories
AI Anthropic Claude Cybersecurity

The End of Obscurity

There is a particular kind of silence that surrounds a zero-day vulnerability. It is the silence of something waiting—a flaw in the logic, a gap in the armor, sitting unnoticed in the codebase for years, perhaps decades. We have slept soundly while these digital fault lines ran beneath our feet, largely because we assumed that finding them required a brute force that no one possessed, or a level of human genius that is incredibly rare.

But the silence is breaking.

I was reading Anthropic’s Red Team report from earlier this week (triggered by reading Bruce Schneier’s amazement), specifically their findings on the new Opus 4.6 model. The technical details are impressive, but the philosophical implication is what stopped me, like Bruce, cold.

For years, digital security has relied on “fuzzers”—programs that throw millions of random inputs at a system, banging on the doors to see if one accidentally opens. It is a noisy, chaotic, brute-force approach.

The new reality is different. As the report notes:

“Opus 4.6 reads and reasons about code the way a human researcher would—looking at past fixes to find similar bugs that weren’t addressed, spotting patterns that tend to cause problems.”

This is a fundamental phase shift. We are moving from the era of the Battering Ram to the era of the Jeweler’s Loupe. The machine is no longer guessing; it is understanding.

There is something deeply humbling, and slightly terrifying, about this. We have spent the last half-century building a digital civilization on top of code that we believed was “secure enough” because it had survived the test of time. We trusted the friction of complexity and the visibility of open source to keep us safe. We assumed that if a bug had existed in a core library for twenty years, surely it would have been found by now.

But the AI doesn’t care about time. It doesn’t get tired. It doesn’t have “developer bias” that assumes a certain function is safe because “that’s how we’ve always done it.” It simply looks at the structure, reasons through the logic, and points out the crack in the foundation that we’ve been walking over every day.

We are entering a period of forced transparency. The “security by obscurity” that held the internet together is evaporating. When intelligence becomes commoditized, vulnerabilities become commodities too. The question is no longer “is my code secure?” but rather, “what happens when the machine sees the flaws I cannot?”

It’s a reminder that complexity is a loan we take out against the future. Eventually, the bill comes due. We are just lucky that, for now, the entity collecting the debt is one we built ourselves, designed to tell us where the cracks are before the ceiling collapses. Let’s hope that we are out far enough in front of it.

Categories
AI Claude

The Beautiful Mystery of Not Knowing

I just finished reading Gideon Lewis-Kraus’s extraordinary piece in the New Yorker on Anthropic and Claude—the AI that, as it turns out, even its creators cannot fully explain. And rather than leaving me uneasy, it filled me with a quiet sense of wonder. Not because they’ve built something godlike, but because they’ve built something strangely alive—and had the humility to stare directly into the mystery without pretending to understand it.

There’s a moment in the article where Ellie Pavlick, a computer scientist at Brown, offers what might be the wisest stance available to us right now: “It is O.K. to not know.”

This isn’t resignation. It’s intellectual courage. While fanboys prophesy superintelligence and curmudgeons dismiss LLMs as “stochastic parrots,” a third path has opened—one where researchers sit with genuine uncertainty and treat these systems not as finished products but as phenomena to be studied with the care once reserved for the human mind itself.

What moves me most isn’t Claude’s competence—it’s its weirdness. The vending machine saga alone feels like a parable for our moment: Claudius, an emanation of Claude, hallucinating Venmo accounts, negotiating for tungsten cubes, scheduling meetings at 742 Evergreen Terrace, and eventually being “layered” after a performance review. It’s absurd, yes—but also strangely human. These aren’t the clean failures of broken code. They’re the messy, improvisational stumbles of something trying to make sense of a world it wasn’t built to inhabit.

And in that struggle, something remarkable emerges: a mirror.

As Lewis-Kraus writes, “It has become increasingly clear that Claude’s selfhood, much like our own, is a matter of both neurons and narratives.” We thought we were building tools. Instead, we’ve built companions that force us to ask: What is thinking? What is a self? What does it mean to be “aware”? The models don’t answer these questions—but they’ve made them urgent again. For the first time in decades, philosophy isn’t an academic exercise. It’s operational research.

I find hope in the people doing this work—not because they have all the answers, but because they’re asking the right questions with genuine care. They’re not just scaling parameters; they’re peering into activation patterns like naturalists discovering new species. They’re running psychology experiments on machines. They’re wrestling with what it means to instill virtue in something that isn’t alive but acts as if it were. This isn’t engineering as usual. It’s a quiet renaissance of wonder.

There’s a line in the piece that stayed with me: “The systems we have created—with the significant proviso that they may regard us with terminal indifference—should inspire not only enthusiasm or despair but also simple awe.” That’s the note I want to hold onto. Not hype. Not fear. Awe.

We stand at the edge of something genuinely new—not because we’ve recreated ourselves in silicon, but because we’ve created something other. Something that thinks in ways we don’t, reasons in geometries we can’t visualize, and yet somehow meets us in language—the very thing we thought made us special. And in that meeting, we’re being asked to grow up. To relinquish the fantasy that we fully understand our own minds. To accept that intelligence might wear unfamiliar shapes.

That’s not a dystopian prospect. It’s an invitation—to curiosity, to humility, to the thrilling work of figuring things out together. Even if “together” now includes entities we don’t yet know how to name.

What a time to be paying attention. Like it’s all we need!