Tag: automation

Categories
AI Startups

A New Reason to Launch

“Before you launch, the speed you can build is now mainly limited by your imagination in what you tell AI. After you launch, the AI can watch your users and make improvements on its own.”
— Jared Friedman, Y Combinator

Jared Friedman watches hundreds of founders a year navigate the gap between idea and launched product. He notices patterns the rest of us miss. And what he’s describing above is not an incremental improvement in how software gets built. It is a change in the nature of the advantage.

This is a different kind of liberation than founders have known before.

The old liberation was launch early and the market corrects your wrong assumptions. Humbling, but useful. You were still the one doing the correcting, late at night, rewriting the onboarding flow based on what the data told you.

The new liberation he’s describing is something closer to multiplication. You launch, and now there are effectively more of you. The AI is watching session replays you’ll never have time to watch. It’s noticing the drop-off after step three that you’d have caught in month four. It’s holding the pattern of a thousand user paths simultaneously and asking what they mean. Your imagination seeded the thing. Reality is now feeding it.

That observation redraws the map cleanly. Pre-launch and post-launch used to differ in degree — you knew more after than before. Now they differ in kind. Pre-launch you are the sensing organ. Post-launch you’ve grown new ones.

The founders who feel this most viscerally, I suspect, are the ones building alone or in pairs — the people for whom every previous era of building had a hard ceiling imposed by human hours. They could only read so many support tickets. They could only run so many experiments. The ceiling is lifting and the feeling is of a room getting larger.

The core advice hasn’t changed. Paul Graham was saying “launch early” twenty years ago and it was true then. What’s changed is the reason underneath it — the mechanism that makes it true now is nothing like the one he had in mind.

The advice is twenty years old. There is a new reason and it is brand new. Most people haven’t noticed the swap yet. But they will.

That window does not stay open long.

Categories
AI Stanford

The Unit of Production Just Collapsed

The lecture was a Stanford CS session, AI-native companies, Garry Tan walking through what it now takes to build something. He’d rebuilt his old startup, Posterous, in five days on a modest Claude plan. A thing that once required a team and a runway. He said it matter-of-factly, the way you describe something that’s already obvious to you and hasn’t yet reached everyone else.

The argument Tan and his colleague Diana Hu were making wasn’t really about AI. It was about the economics of effort — specifically, what breaks when the cost of turning an idea into a working thing falls by an order of magnitude.

Their framing: AI-native organizations running as closed-loop systems, agents with access to the real artifacts of work, able to iterate without the error-accumulation that comes from handoffs and headcount. Revenue-per-employee ratios of a million dollars or more, with live examples already in the YC portfolio. Document processing, logistics, voice agents for specialized workflows.

What I kept hearing underneath all of it was a quieter claim: the mental model of what a startup requires is wrong.

Or rather, it’s right about the past and increasingly wrong about the present.

The assumptions embedded in “I can’t do this alone” or “we’d need to hire for that” or “we don’t have the bandwidth” — those are load-bearing assumptions, and the load is shifting.

I have some small version of this — not as a founder, but as someone who retired into curiosity. The blog, the reading, the daily effort to keep up with what’s moving: each one is a practice in staying oriented while the map keeps changing.

What I notice is that the constraint has shifted. It’s not information anymore. It’s not even tools. It’s the capacity to ask better questions of the abundance, to know what matters when everything is accelerating.

That’s the thing I find unsettling, yet also genuinely interesting: the skills that remain irreplaceable are the hardest ones to teach, and the hardest to evaluate in yourself. Knowing what matters. Recognizing when an output is almost right and almost wrong. Setting direction in ambiguous conditions and being willing to be wrong about it. These were always the valuable things. They were just obscured by all the coordination overhead that surrounded them.

The students in that Stanford course were asked to build something called a One-Person Frontier Lab — use the best available tools to extend your own reach over ten weeks. It’s framed as an academic exercise. It doesn’t feel like one.

But I’m not building. I’m mostly watching, and thinking about what this radical new fermentation does to everything downstream — to labor markets, to what a company even is, to how we’ll organize work and meaning when the old unit of production no longer applies. Those are slower questions. But they’re the ones that feel urgent to me.

The old excuses are getting lighter. Not that everything is possible — but that the weight of the usual constraints has changed.

What you choose to build, and whether you choose to build it at all, is more purely a decision than it used to be. That’s either clarifying or terrifying, depending on the day and my mood.

Categories
AI Business Consulting

The Toll Bridge and the Terrain

For fifteen years of my life, I lived inside the fortress of information asymmetry. I was part of a payments consulting business, and our model was exactly what Andrew Feldman described on a recent Moonshots episode when he pointed a sharp finger at traditional professional services.

His observation was simple, cutting, and entirely true:

“Their role today is to stand between ordinary people and obscure knowledge. And the application of that obscure knowledge to everyday problems.”

When I heard him say that, it landed with a quiet thud of recognition.

For a decade and a half, my colleagues and I were the ones standing in that gap. The payments industry—with its labyrinth of interchange fees, compliance structures, clearing networks, and legacy tech stacks—is a monument to obscure knowledge. Clients didn’t come to us because we possessed some divine, unreplicable wisdom. They came to us because the map was locked in our heads, and navigating the terrain without us was a recipe for an expensive disaster.

We charged for our time, and we earned it. We untangled complexity and solved real, everyday business problems for people who just wanted to move money safely from point A to point B.

But looking back now, I can see the architectural flaw disguised as a premium service. The economic foundation of that entire era relied on friction. It relied on the fact that it took an immense amount of human energy to retrieve a piece of obscure data and map it onto a specific business dilemma. You weren’t just paying for strategic guidance; you were paying a premium on artificial scarcity.

We are living through a moment where the marginal cost of intelligence is rapidly trending toward zero. When the barrier of “obscure knowledge” evaporates, the traditional toll bridges begin to look absurd.

For anyone starting a consulting business today, the playbook would have to be entirely different. When an LLM can parse thousands of pages of network operating rules, interchange tables, and regulatory compliance frameworks in a handful of seconds, the gatekeeper’s standing ground liquefies.

If your value proposition is merely standing between a client and a hidden database, your business model isn’t just flawed—it’s obsolete.

Yet, this collapses into a fascinating paradox. You might assume that when you democratize expertise, you eliminate the need for the expert. But as Dan Shipper recently observed, the reality of AI is completely counterintuitive.

Shipper points out that AI effectively packages up “yesterday’s competence” and makes it cheap and ubiquitous.

Suddenly, anyone can generate a complex contract, a software pull request, or a payments flow strategy with the click of a button. But when cheap competence skyrockets, adoption explodes, resulting in an unprecedented glut of generic output—what the internet has collectively taken to calling “slop”. It’s the default, lazy answer that lacks soul, context, and nuance.

When everything begins to look and smell the same, a strange thing happens: the market’s demand for genuine difference sky-rockets.

The shift we are facing across all professional services—whether legal, financial, or consulting—isn’t about eliminating the expert. It is about changing the expert’s job from data-retriever to orchestrator and judge. The floor has been raised. Yesterday’s ceiling is today’s baseline.

What remains is the ability to read a room. To watch a client’s shoulders tighten when you present an option that’s technically correct but organizationally impossible. To notice the glance exchanged across the table before anyone speaks. No LLM parses that. The map is universal now; the guide still has to be in the room.

We don’t need fewer guides; we need fewer toll booths. The future of consulting doesn’t belong to those who hoard the map. It belongs to those who use a universally available map to help people actually walk the terrain.

Categories
AI Programming Software Work

The Scarcest Thing

Garry Tan woke up at 8 a.m. after sleeping at 4. Not because he had to. Because he wanted to see what his workers had done overnight.

The workers are AI agents. Ten of them, running in parallel across three projects. And something about that sentence — wanted to see what they’d done — keeps stopping me. That’s not the language of someone using a tool. That’s the language of someone managing a team.

Tan gave a name to the state this puts him in: “cyber psychosis.” He said it as a joke. But the joke has an insight in it. He’s not describing addiction to a productivity app. He’s describing a shift in what it means to do creative work — the strange vertigo of becoming a director when you’d always been a laborer.

I’m retired. I watch this from the outside now, which is its own kind of vantage point. For most of my career, the path from idea to working product ran through people — through hiring and managing and the slow accretion of execution capacity. You had the vision or you didn’t, but either way you needed the team. The idea and the means of making it real were, structurally, separate things. The gap between them was where companies lived.

What Tan is describing is that gap closing.

The thing he built — gstack, his open-sourced Claude Code configuration — got dismissed in some quarters as “just prompts.” And it is just prompts, in the same way that a conductor’s score is just notation. The abstraction is the invention. What he encoded is a model of how a startup team thinks: the CEO who interrogates the why before a line of code gets written, the engineer who builds, the paranoid staff reviewer who looks for what breaks. Each role blocks a different failure mode. Blurring them together produces, as his documentation puts it, “a mediocre blend of all four.”

That’s an organizational insight. It has nothing to do with code.

Tan described being a “time billionaire” — not because his biological clock had slowed, but because he can now purchase machine-consciousness-hours. The bottleneck of implementation, which has governed every creative project since the beginning of creative projects, is dissolving for those who know how to direct.

The scarcest thing is shifting. It’s no longer the hours of execution. It’s the clarity of intent — knowing what you want to build and why the journey matters, before any of the workers start moving. That’s harder than it sounds. For decades, most of us could muddle through in the making of it. The act of building taught you what you were building. Now the making is cheap, and that shortcut is gone.

For someone watching from retirement, that’s not a small thing to absorb. The model I internalized over a long career — that ideas become real through sustained organizational effort, through teams and timelines and the grinding work of execution — is being revised faster than I expected. Not invalidated. Revised. The judgment still matters. The taste still matters. The why matters more than ever.

It’s just that the how has found new hands. Many of them. More than any team I ever assembled, available the moment the intent is clear enough to direct them, gone when the work is done. The constraint was always the hands. It turns out it was always the knowing.

Categories
AI AI: Large Language Models Anthropic

Breakout

Jack Clark doesn’t panic easily. He spent years at OpenAI watching capabilities inch upward, then left to co-found Anthropic, and has been writing his Import AI newsletter long enough to have developed — and been wrong about — many priors. So when he publishes an essay saying he has reluctantly arrived at a 60% probability that fully automated AI R&D happens by the end of 2028, the word “reluctantly” deserves some weight.

His essay, published last week and titled “Automating AI Research,” isn’t a press release or a fundraising pitch. It reads more like a man thinking out loud at the edge of something large. “I don’t know how to wrap my head around it,” he writes, which is a notable thing to say publicly when you are one of the architects of the thing you can’t wrap your head around.

The argument is built from benchmarks — not any single one, but a mosaic of them assembled to reveal a trend. SWE-Bench, the test that measures an AI’s ability to solve real GitHub issues, was at roughly 2% when it launched in late 2023. A recent Anthropic model sits at 93.9%, effectively saturating it. METR’s time-horizon plot tracks how long an AI can work independently before needing human recalibration: 30 seconds in 2022, 4 minutes in 2023, 40 minutes in 2024, 6 hours in 2025, 12 hours today. The trajectory, if it holds, suggests 100-hour autonomous work sessions by the end of this year.

Clark marshals similar progressions across AI fine-tuning, kernel design, scientific paper replication, and even alignment research itself. His throughline is the same in each: AI is now genuinely competent at the unglamorous scaffolding of AI development — the debugging, the experiment runs, the parameter sweeps, the code reviews. And crucially, it can now do these things not just faster than humans, but for longer, with less supervision.

There’s a Thomas Edison quote at the center of the essay: “Genius is 1% inspiration and 99% perspiration.” Clark’s claim is that AI has become very good at the perspiration. The question of whether it can supply the inspiration — the paradigm-shifting insight, the Move 37 — remains open. But he argues it may not need to. Most of what has moved the AI field forward has been sustained, methodical work, not lone flashes of genius. If you can automate the 99%, you have something that compounds.

There’s a data point that makes Clark’s argument feel less like forecast and more like dispatch. Last month Boris Cherny, who runs Anthropic’s Claude Code, disclosed that he hasn’t written a line of code by hand in more than two months. Every pull request — 22 one day, 27 the next — written entirely by Claude. Company-wide, roughly 70–90% of Anthropic’s code is now AI-generated. Anthropic’s stated position: “We build Claude with Claude.” The loop Clark is describing as a probability by 2028 is already running, at least partially, today.

The word Clark uses for the threshold he’s describing is not “singularity” or “AGI.” It’s quieter than that. He calls it “automated AI R&D” — the point at which a frontier model can autonomously train its own successor. It’s a specific, falsifiable thing. And he puts a number on it: 60% by end of 2028, 30% by end of 2027.

I’ve been writing about the dark software factory and the 3D printer that prints better printers, finding metaphors for what seems like an inexorable process. Clark’s essay is a different kind of writing about the same thing — the primary source document, the engineer’s log, the inventory of evidence. Reading it is a little like watching someone carefully pack boxes before a move. Each individual item seems manageable. But there are a lot of boxes.

What he’s describing — if the trend holds — is not a feature or a product launch. It’s a breakout. The moment the loop closes and the system starts building itself. He’s not certain it happens. He just thinks it’s more likely than not, and he thought you should know.

Categories
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?

Categories
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.

Categories
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.

Categories
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?