Tag: Technology Philosophy

Categories
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?
Categories
AI

Digital Optimus and the End of Friction

We often imagine the arrival of the “universal robot” as a clanking metal biped walking through our front door, carrying laundry or folding dishes. We think of the physical Optimus first. But while we were watching the hardware, a quieter, perhaps more profound revolution has been brewing in the software.

Elon Musk recently spoke about “Digital Optimus.” The concept is deceptively simple: an AI agent capable of doing anything on a computer that a human can do.

For decades, automation was brittle. If you wanted a computer to talk to another computer, you needed an API—a rigid handshake agreement between software engineers. If a button moved three pixels to the right, the automation broke. We built brittle bridges over the chaotic rivers of our user interfaces.

“It implies an AI that doesn’t need to look at the code behind the website; it looks at the screen, just like you and I do.”

Digital Optimus changes the physics of this environment. It interprets pixels, understands context, and drives the mouse and keyboard with the same fluidity as a human hand. This is a shift from integration to agency.

There is something undeniably eerie about the prospect. We are approaching a moment where the cursor on your screen might start moving with a purpose that isn’t yours, executing tasks you’ve merely delegated. It is the decoupling of intent from action.

For the longest time, the computer was a bicycle for the mind—a tool that amplified our pedaling. With Digital Optimus, the bicycle becomes a motorcycle, or perhaps a self-driving car. We stop pedaling. We simply point to the destination.

The implications for the future of work are staggering, not because the AI is “thinking” better, but because it is finally “doing” seamlessly. The drudgery of copy-pasting between spreadsheets, the endless clicking through procurement forms, the navigational tax of modern digital life—these are the jobs of the Digital Optimus.

We are entering an era where our value as humans will not be defined by our ability to navigate the interface, but by our ability to define the destination. The screen is no longer a barrier; it is a canvas, and for the first time, we aren’t the only ones holding the brush.

Categories
AI

The Alien in the Silicon

I recently found myself listening to a conversation with Anna Goldie and Azalia Mirhoseini, the founders of Ricursive Intelligence, discuss the future of chip design. Here’s the video.

On the surface, it’s a conversation about efficiency—about breaking the bottleneck between how fast we build AI models and how slow we build the chips that run them.

But as I listened, I felt that prickly sensation of standing on the edge of a paradigm shift that is both exhilarating yet slightly terrifying.

We are witnessing the transition from “Fabless” to “Designless.” Just as TSMC allowed companies to build chips without owning a factory, Ricursive wants to allow companies to build chips without employing a single chip designer.

They call it a “Cambrian explosion” of custom silicon—chips for hearing aids, chips for space data centers, chips for specific neural networks. This democratization is fascinating. It promises a world where hardware is as fluid and adaptable as software.

“The straight line is a human invention. The future of silicon is curved, chaotic, and completely alien.”

But here is what disturbs me, and perhaps what should give us pause.

Goldie and Mirhoseini talk about the designs their AI agents create. When humans design chips, we think in Manhattan geometry: straight lines, neat blocks, logical order. We crave readability and structure. When their AI, originally born from the AlphaChip project at Google, designs a chip, it creates “alien” structures. It draws curves. It makes donut shapes. It creates layouts that look less like engineering diagrams and more like organic, biological growths.

The engineers’ initial reaction was displeasure. They looked at these chaotic, curved designs and rejected them. It wasn’t until later data proved undeniably that these “alien” layouts were faster, smaller, and more efficient that the humans conceded.

This seems like the “Move 37” moment for hardware. We are handing over the architecture of our physical reality to an intelligence that optimizes for physics, not for human comprehension. Some additional quick thoughts…

What should we be surprised by?

We should be surprised by the geometry of efficiency. It turns out that the rigid, orthogonal logic we humans (and our EDA software tools to date) have imposed on silicon for decades was a human constraint. The AI is showing us that the “natural” state of high-performance compute looks … weird. It looks biological.

What should we be afraid of?

We should be wary of the recursive loop itself. The company is named “Ricursive” for a reason: AI designs better chips, which train better AI, which designs even better chips. It is a closed loop of self-improvement. As we move to a “design-less” world, we are effectively stepping out of that loop. We become the requesters, the “vibe coders,” while the actual logic of the machine infrastructure becomes increasingly opaque to us. Seems like we’ve been evolving that way anyway in chip design – but this feels like an earthquake really shaking things up.

We seem to be building a foundation for our civilization that we may soon be unable to read, optimize, or fully understand. We are trading interpretability for performance.

And while the speed and performance is intoxicating, it is disturbing to realize yet again that the engine driving our future is becoming a black box—not just in its software, but in its very atoms.

Ricursive said they’re planning to release their initial product with a year. I’ll be watching from the sidelines – anxious and excited!

Categories
AI Web/Tech

Why the AI PC is the New 3D TV

A close-up of a laptop showing an 'AI READY' sticker on its surface, alongside a pair of glasses, a coffee mug, and a notepad on a wooden desk.

I was reading the coverage coming out of CES 2026 this week, and the silence was deafening. Just a year ago, the industry was shouting about the “AI PC” as the inevitable successor to the computing throne. Every laptop lid, keyboard deck, and press release was plastered with the promise of Neural Processing Units (NPUs) and local intelligence.

But looking at the tepid market reaction—and Dell explicitly dialing back the “AI sermon” this year—I can’t help but feel a sense of déjà vu. It reminds me of the “3D Ready” stickers that adorned every television set circa 2011.

There is a distinct pattern in consumer technology where the hardware cart gets placed miles ahead of the software horse. We saw it with 3D televisions, a technology that demanded we wear goofy glasses to watch a limited library of content, offering a friction-heavy solution to a problem nobody really had. We saw it, more tragically, with Apple’s Vision Pro. Despite being a marvel of engineering, it stalled because it asked too much of us (financial and physical weight) for too little return in our daily lives.

The “AI PC” seems to be falling into a similar, albeit subtler, trap.

The issue isn’t that AI is a fad—far from it. Agentic AI and local models are transforming how we work. The issue is the marketing category. Consumers are realizing that an “AI PC” is just… a PC. The magic of AI isn’t in the hardware badge or a dedicated Copilot key; it’s in the software that runs anywhere. We are realizing that we don’t buy “Internet PCs” anymore, we just buy computers. The utility is ubiquitous, not proprietary to a specific chassis.

When technology truly succeeds, it disappears. It becomes boring. The “flop” of the AI PC isn’t a failure of technology, but a failure of hype. It is the market collectively shrugging and saying, “Show me the value, not the specs.” Until the software experiences are so undeniable that we can’t live without that local NPU, the “AI PC” will remain a marketing sticker, destined to peel off and fade away, much like 3D glasses or Vision Pros gathering dust for those few who bought them.