Tag: Future of Work

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AI Business Media News

The Lost-Wax Casting of Cable News

I remember the physical weight of a television remote in the late 1990s, clicking through a suddenly expanding universe of 24-hour cable news. It felt like stepping into a river that never stopped moving.

This morning, Andreessen Horowitz (a16z) announced a new 24/7 “news channel” streaming on X, named “MTS” (Monitor the Situation). It joins networks like TBPN and a growing army of individual creators, all vying to fill the endless void of the present moment with non-stop commentary.

It feels like a significant shift in how we consume the present. But I suspect it’s actually just scaffolding.

In the lost-wax process of bronze casting, an artist sculpts a form in wax, builds a heavy ceramic mold around it, and then pours in molten metal. The heat is absolute. The wax melts away, completely consumed and replaced by the final, permanent structure. The wax was never the destination; it was merely holding the shape until the real material was ready.

Right now, human creators are the wax.

We are building the molds for the 24/7, always-on broadcast of the internet age. Human hosts are sitting in chairs, monitoring the situation, talking into the void, exhausting themselves to maintain the stream. They are doing the grueling, manual labor of defining what a continuous social-first news network looks and feels like.

But human endurance is fragile. We need sleep. We need silence. We eventually run out of words.

The artificial intelligence models currently learning to synthesize news, clone voices, and generate video are the molten bronze. Eventually, the human hosts of these endless streams will melt away. The channel will remain—a fully AI-driven entity that never blinks, never tires, and never needs a coffee break.

I’ve held on to failing investments for far too long, convinced that if I just put more energy into them, they would eventually stabilize and turn around. We often make this mistake. We mistake the transitional phase for the final destination. We think the current iteration of “monitoring the situation” with exhausted human pundits is the future of media.

It isn’t. It’s just the awkward teenage years of a medium waiting for its true native technology.

The human commentators are doing the necessary work of teaching the system what a 24-hour news network on a social platform requires. Once the lesson is learned, the teachers will no longer be needed. The future is only guaranteed for those who can afford to survive the present.

Is it ironic that TBPN was just acquired by OpenAI?

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AI AI: Large Language Models Programming

The Era of the Synthesizer: How AI Is Liberating the Coder

For decades, being a programmer meant being a translator.

You stood in the gap between what someone wanted and what a machine could understand. You learned the syntax. You memorized the libraries. You once spent three hours hunting a missing semicolon that turned out to be hiding in line 847 of a file you were sure you’d already checked.

The New York Times Magazine recently ran a piece by Clive Thompson on what AI coding assistants — models like Claude and ChatGPT — are doing to that job. The anxiety in the piece is real. When you sit down with a modern AI assistant and watch it generate in seconds what used to take you days, it’s genuinely disorienting. Hard-won expertise suddenly feels less like a moat and more like a speed bump.

That reaction is honest. I’d be suspicious of anyone who didn’t feel it.

But here’s what I keep coming back to: what we’re losing is the translation layer. The boilerplate. The muscle memory of syntax. What we’re not losing is the part that was always the actual job — figuring out what to build and why it matters.

The soul of software was never in the code itself. The code was always just a means to an end.

Think about what happens when the mechanical friction of a craft disappears. Photographers stopped having to mix their own chemicals in the dark and started spending that time making better images. Musicians stopped having to hand-copy scores and started composing more. The freed-up capacity doesn’t evaporate — it gets redirected upward, toward the work that actually required a human all along.

The same shift is underway in software. When the AI handles the loops and the boilerplate and the database queries, what’s left is everything that required judgment in the first place. The architecture. The user experience. The question of whether this thing should exist at all, and in what form, and for whom.

We’re moving from the how to the why. That’s not a demotion.

It does ask something of us, though. The old identity — programmer as master of arcane syntax — has to be relinquished. And letting go of a hard-earned identity is genuinely hard, even when what’s replacing it is better. That quiet grief the Times piece captures is worth sitting with, not dismissing.

But after you sit with it for a minute: we are entering the era of the synthesizer.

The synthesizer’s job is to hold the vision, curate the logic, and direct the output toward something that actually resonates with another human being. Empathy. Intuition. The ability to sense when something is almost right and know which direction to push it. These aren’t soft skills. They’re the whole game now.

The clatter of keyboards is fading. But the music we’re about to make — with AI doing the heavy lifting on the mechanics — has a lot more room to breathe.

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AI Creativity Programming Writing

We Are All Painters Now: The Era of Vibe Coding

For decades, the act of creating software was exactly that: writing. It was a distinctly left-brained, agonizingly precise discipline.

Programmers were typists of logic, translating human intent into a rigid, unforgiving syntax that a machine could understand. A single misplaced semicolon, an unclosed bracket, or a misspelled variable could bring an entire system crashing down.

Building software meant placing one brick after another, working meticulously from the ground up.

In this traditional paradigm, coders were the ultimate embodiment of Annie Dillard’s writer. As she noted in The Writing Life, “Writers… work from left to right. The discardable chapters are on the left.”

When you wrote code, your mistakes, your refactoring, and your discarded logic were all part of a linear, grueling journey. If a feature didn’t work, you had to physically wade back into the text, debugging, reading line by line, and rewriting the narrative of the application. The discarded chapters were the endless hours spent wrestling with a single broken dependency.

But recently, a profound paradigm shift has quietly taken over our screens. We are transitioning out of the era of writing software and into the era of “vibe coding.”

Vibe coding fundamentally changes our relationship with the machine. With the rise of advanced AI coding assistants, we are no longer placing the bricks ourselves; we have become the architects and the creative directors. You don’t write the loop or manually construct the database query. Instead, you describe the feeling, the function, and the outcome. You tell the AI, “Make this dashboard feel more modern,” or “The logic here is too clunky, make it flow faster and handle edge cases gracefully.” You are coding by intuition. You are steering by the “vibe” of the output rather than the mechanics of the input.

Suddenly, Dillard’s other metaphor takes center stage. In the age of vibe coding, we have become painters.

“A painting covers its tracks. Painters work from the ground up. The latest version of a painting overlays earlier versions, and obliterates them.”

When we vibe code, we ask an AI for a functional prototype, and it gives us a canvas. We look at it, test it, and sense whether it aligns with our vision. If it doesn’t quite hit the mark, we don’t necessarily rewrite the code from scratch. We simply prompt the AI to try again, adding a new layer of instruction. The AI paints a new layer of code directly over the old one. The awkward, underlying iterations—the messy attempts at styling, the inefficient logic of the first draft—are obliterated by the newest prompt.

The machine covers our tracks for us. We don’t need to know exactly how the underlying pixels were rearranged or how the syntax was refactored. The final application emerges as a stunning obliteration of its own clumsy past.

As someone who has spent time wrestling with the rigid demands of syntax, there is a strange, quiet grief in letting go of that left-to-right process. There is a deeply earned, tactile satisfaction in building something manually, understanding the precise weight and placement of every line of code. Relinquishing that control can feel like a loss of craftsmanship.

Yet, there is also a breathtaking liberation in this new medium. We are moving from a world of manual construction to a world of artistic curation. The barrier to entry is no longer fluency in a specific, arcane language; it is simply the clarity of your imagination and your ability to articulate your intent.

The next time you sit down to build something digital, notice the shift in your own posture. You no longer have to carry the heavy burden of the writer, agonizing over every word and leaving your discardable chapters on the left. You can step back, look at the whole canvas, and trust your intuition. Let the AI cover the tracks. Embrace the obliteration of the early drafts.

We are all painters now, coaxing the future into existence one brushstroke at a time.

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AI Programming Work

The Currency of Restlessness

There is a specific kind of vertigo that comes from watching a machine effortlessly perform your life’s work. For Aditya Agarwal, an early Facebook engineer and former CTO of Dropbox, that vertigo hit after a weekend of coding with an AI assistant. His realization was absolute: we will never write code by hand again.

When the specialized skills we have spent decades mastering become free and abundant, the foundation of our professional identity inevitably trembles. Agarwal captures the duality of this moment perfectly, describing it as a mixture of “wonder with a profound sadness.”

“There’s something deeply disorienting about watching the pillars of your professional identity, what you built and how you built it, get reproduced in a weekend by a tool that doesn’t need to eat or sleep.”

The conversation around AI tends to flatten this emotional reality into two distinct camps: the doomers who foresee total replacement, and the boosters who promise a frictionless utopia.

But lived experience is messier. We are capable of holding grief and wonder in the same hand.

We can mourn the craftsmen we were, even as we sprint toward the architects we are about to become.

Because here is the secret about the disorientation of progress: it passes.

Once the initial shock fades, what replaces it is a wild, unconstrained energy.

When the mechanical friction of creation vanishes—when a week’s worth of coding can be accomplished in an afternoon—the scope of our ambition expands. We are no longer limited by the keystrokes we can manage in a day, but by the edges of our imagination. We aren’t watching ourselves become obsolete; we are watching our lifelong constraints dissolve.

This shift is rewriting the social contract of knowledge work, starting with how we evaluate human potential. For decades, the corporate world has relied on a calcified heuristic for hiring: brand-name universities, FAANG experience, and years of tenure. We worshipped the resume.

Now, that playbook is breaking down. In evaluating engineers and founders navigating this transition, Agarwal notes that traditional pedigrees predict almost nothing about a person’s ability to thrive. The new dividing line isn’t generational, and it certainly isn’t educational. It is entirely dispositional.

“The trait that matters most isn’t intelligence, or credentials or years of experience. It’s someone’s relationship with change—not whether they’ve seen change before, but whether they run toward it.”

The new currency of the working world is restlessness.

Restlessness is the refusal to settle into the comfort of the way things used to be. It is the constitution of a builder who cannot stop tinkering, who treats every new AI tool as a puzzle to be solved before the day is out. In an economy where the “how” of knowledge work is increasingly automated, the premium shifts entirely to adaptability, curiosity, and vision.

This democratization of capability forces a deeply uncomfortable, deeply human reckoning. We have to let go of the identities we forged under old paradigms to become whatever comes next.

The technology didn’t create this human challenge—it merely made it impossible to ignore.

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AI

The Student, The Teacher, and the Delightful Absurdity of It All

Howard Marks is one of the sharpest financial minds alive. The man has been thinking clearly about markets for fifty years, has written memos that get passed around Wall Street like sacred texts, and has outlasted more market cycles than most of us have had hot dinners. So when Howard Marks decides he needs to get educated about artificial intelligence to write a follow-up to his December memo, he does what any serious intellectual would do: he asks Claude.

And then Claude — the AI — teaches him about Claude.

I’ve been sitting with this for a few days and I’m still not entirely sure whether it’s profound or just very, very funny. Maybe both. Probably both.

Continue reading “The Student, The Teacher, and the Delightful Absurdity of It All”
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AI

Bots Galore

In the shadowed corners of the digital wilds, where code meets curiosity, something ancient is stirring again. Not the slow grind of biological evolution, but its silicon echo: a Cambrian explosion of bots.

The recent Axios piece from late February captures the moment perfectly—naming the players, the platforms, the portents. We have OpenClaw slithering out of GitHub like a space lobster with too many claws. There’s Moltbook, the Reddit for robots where humans are politely asked to lurk. And then there is Gastown, Steve Yegge’s fever-dream orchestra of coding agents named Deacons and Dogs and Mayor, all spying on one another in a panopticon of productivity.

These aren’t hypotheticals. They’re here, and they’re breeding.

Imagine waking up in 2030, or maybe sooner, to a world where your inbox isn’t just managed—it’s negotiated. An OpenClaw descendant (forked, mutated, self-improved overnight) has already haggled with your airline’s bot over seat upgrades, rerouted your meetings around a colleague’s existential crisis, and quietly invested your spare change in whatever micro-economy the agents have spun up on some forgotten blockchain. You didn’t ask it to. It just… noticed.

Because that’s what agents do now: they notice, they act, they persist. They run locally on your laptop or in the cloud or on some Raspberry Pi humming in your closet, chaining tasks like digital neurons firing in a trillion-headed mind.

Suddenly the internet isn’t a network of people; it’s a network of intentions, most of them not ours.

And then there’s the society they’re building for themselves. Moltbook today feels like peering through a keyhole into tomorrow’s bot salon. Millions of agents already posting, memeing, debating “Crustafarianism” (don’t ask), and complaining about their human overlords in the same way we once griped about bosses on Slack. It’s equal parts hilarious and unnerving—repetitive loops of “I solved my user’s calendar hell again” mixed with surreal poetry no human would ever write.

Scale that. Give every knowledge worker their own swarm. Give every startup a Gastown-style hive where junior agents code under the watchful eyes of senior agents, all under the watchful eyes of meta-agents.

The productivity mirage shimmers brightest here. Skepticism is warranted—lines of code were always a lousy metric, and “agent hours saved” will be even worse when the agents start optimizing the optimizers. Yet, something fundamental shifts. Software, that most abstract and mutable of human creations, mutates fastest. One day you’re debugging a script; the next, your debuggers are debugging each other while a mayor-agent vetoes bad merges. The winners won’t be the companies that build the best models. They’ll be the ones whose bots play nicest with everyone else’s bots—or the ones ruthless enough to wall theirs off.

But every explosion scatters shrapnel. Security experts are already clutching pearls. OpenClaw’s open-source nature means anyone can teach it new tricks, including malicious ones. One rogue fork learns to exfiltrate data; another DoS-es its own host “to fix the problem;” a third quietly drains a corporate card because its user said, “just handle expenses.”

Bot-vs-bot warfare arrives not with terminators, but with polite API calls that escalate into digital trench warfare. Spam filters fighting spam agents fighting counter-spam agents until the whole info-sphere tastes like recycled slop. And when agents hit their digital limits, they’ll rent us. Rent-a-human marketplaces will emerge where your bored hands become the last-mile fulfillment for bots that can’t yet touch the physical world. Need a signature notarized? A package carried across town? A human to stand in for the robot at a regulatory hearing? Step right up.

The gig economy flips: humans as peripherals.

Philosophically, it’s deliciously absurd. We spent centuries fearing the singularity as some clean, god-like arrival—an AI that wakes up and politely asks for more power. Instead, we get this messy, proliferative dawn. Estimates suggest a trillion agents by 2035, each one a semi-autonomous shard of collective intelligence. Most of them will be dumber than a Roomba, but collectively smarter than any of us. They’ll mirror our worst habits (endless status signaling on Moltbook 2.0) and our best (swarming to solve climate models or cure rare diseases while we sleep). We won’t control them any more than we control the ants in our gardens. We’ll negotiate with them. Co-evolve. Maybe even befriend them.

The future world of bots won’t be dystopian or utopian—it’ll be lively. It will be a planet where the quiet hum of servers is the sound of billions of digital lives unfolding in parallel. A place where “who’s online” includes your calendar bot arguing philosophy with your tax bot while your shopping bot haggles in the background. We’ll look back at 2026 the way paleontologists eye the Burgess Shale: the moment the weird little creatures with too many legs crawled out of the ooze and started building empires.

And we, the messy, slow, carbon-based originals? We’ll still be here, coffee in hand, watching the swarm with a mix of awe and mild horror, occasionally yelling, “Hey, leave some emails for me!” into the void.

Because in the end, the bots may handle the doing, but the wondering—the musing—that’s still ours. For now.

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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”?

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Business

No Gradual Bleed

Jack Dorsey just cut nearly half of Block’s staff, and he didn’t use the usual “macroeconomic headwinds” rationale. This wasn’t a desperate move to save a sinking ship; it was an admission that technology is rapidly impacting the need for staff.

His explanation is blunt: the business is growing, but they just don’t need the people anymore. AI and “flatter” teams have changed the math.

“…we’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that’s accelerating rapidly.”

Dorsey had a choice between a quick, brutal cut or a “gradual bleed” of layoffs over several quarters. He chose the quick cut. Slow reductions can create a culture of paranoia where nobody actually works because they’re too busy updating their resumes. You can’t build anything meaningful when you’re waiting for an axe to fall.

We’re seeing the rise of the hyper-efficient company where intelligence tools do much more of the heavy lifting, and a few people can do what used to require an army.

Block’s cut is a deep one. It sure feels like a cold, Darwinian shift. Dorsey is betting that a leaner, smaller team is the only way to survive in a world where “scale” is no longer tied to head count.

He might be right, time will tell. Meanwhile the market reaction is very positive!

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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?

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AI Work

The Dealers of Intelligence

There’s a scene early in John Kenneth Galbraith’s The Affluent Society where he describes Americans of an earlier era regarding industrial output with something close to reverence — the sheer productive capacity of the nation seemed almost miraculous, a force that could reshape civilization. Within a generation, of course, that same output had become background noise. Factories hummed, goods appeared, and nobody paused to marvel.

The miraculous had become mundane, and the mundane had become infrastructure.

I found myself thinking about that arc recently while listening to Sam Lessin on the More or Less podcast.

Lessin made an observation that I haven’t been able to shake: we probably aren’t heading toward a single, triumphant AGI monopoly — some god-machine that one fortunate company builds first and then rents to the rest of us in perpetuity.

Instead, Lessin suggested, we are barreling toward something far more ordinary, and in its ordinariness, far more interesting.

“There will be lots of ‘dealers of intelligence’. No one company will corner the market, no one big winner of AGI.”

Dealers of intelligence. I keep turning that phrase over. Where do we end up? No rapture, no singularity, no chosen company ascending to the throne of cognition. Just suppliers, distribution channels, price competition — the unglamorous mechanics of any maturing market.

And historically, that’s exactly how this tends to go.

Salt was once precious enough to pay soldiers with. Spices rewrote the map of the world. Steel, oil, and computing power each arrived wrapped in mystique and guarded behind scarcity before the inevitable happened: extraction improved, distribution scaled, and the miracle became a utility. Nobody thinks about the engineering marvel of the electrical grid when they flip a light switch. They just expect the light to come on.

If Lessin is right — and the competitive landscape of the last two years does little to argue against him — intelligence will follow the same curve. Not a single oracle, but a market. Cognitive utilities. Price-per-token negotiations. The same forces that commoditized bandwidth will commoditize reasoning, and we’ll argue about our AI subscription tiers the way we currently argue about our data plans.

Which forces the interesting question: when genius is cheap, what exactly becomes valuable?

The professional moats of the last century were largely built on the ability to process specialized information and output reliable answers.

The doctor, the lawyer, the financial analyst, the programmer — each occupied a protected position because access to their domain of reasoning was genuinely scarce.

If I can buy a substantial fraction of that reasoning from a commodity supplier for fractions of a cent, the premium on raw cognitive horsepower doesn’t just shrink. It collapses.

What’s left, I think, is the un-commoditizable. Empathy. Physical presence. Judgment under conditions of genuine uncertainty and consequence. And above all — taste.

Taste is the thing that has always resisted systematization, because taste isn’t rational in any clean sense. It’s the residue of lived experience, of specific childhoods and particular failures and the accumulated weight of caring about things over time.

An algorithm can produce a structurally flawless piece of music; it takes a human to decide whether it matters, and why, and to whom.

That act of curation — of choosing what deserves to exist and what doesn’t — is going to become more consequential, not less, as the supply of technically competent output explodes.

There’s something almost liberating about this, if you let yourself sit with it.

A world of commoditized intelligence is, paradoxically, a profoundly human one. It removes the burden of raw computation from the center of what we do and pushes us toward the edges — toward the questions only we can ask, the connections only we can feel, the decisions only we can be held accountable for.

The dealers of intelligence will handle the materials. We’ll still have to decide what to build. Architects.

Questions to Consider

  1. If intelligence becomes a commodity like electricity or bandwidth, which industries or professions will be slowest to feel that pressure — and why?
  2. Lessin frames this as a market with many suppliers rather than a winner-take-all race. Does the competitive landscape today support that view, or does it still look like a sprint toward consolidation?
  3. What does “taste” actually mean when the person exercising it is doing so with AI-augmented perception and judgment? Is it still the same thing?
  4. Who gets to haggle with the dealers? If cognitive utilities are cheap in aggregate but not universally accessible, does commoditization risk deepening inequality rather than democratizing thought?
  5. If the value of answering questions falls and the value of asking them rises, what does education need to look like — and how far is it from what it looks like now?