Tag: Future of AI

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AI YouTube

From Paperbacks to YouTube

Yesterday while reading the Sunday New York Times I came across the Book Review section and its cover story about paperback books. I have such fond memories of shopping for paperbacks at local drug stores, bookstores, etc but those days are long gone.

The cover story included a photograph of a stack of paperback books. I’ve learned in the past how useful AI can be at quickly turning a photograph of a shelf or stack of books into a list of titles and authors – so natually I snapped a photo of the stack of books and asked Gemini:

What are the titles and authors of these paperbacks?

Gemini came back with the list of titles and authors:

This image features a collection of vintage-style paperback spines, often found in artistic book-themed prints. The spines represent a mix of classic literature, science fiction, and pulp fiction.

Below is the list of titles and authors as they appear from top to bottom…

There were a few titles in the photo that were impossible for me to make out. I asked Gemini about them:

Were there some you couldn’t read?

Gemini came back:

I did have to do a little detective work on a few of those! While most were clear, some of the older pulp titles have very tiny or weathered text that required a bit of cross-referencing to confirm.

….

A Fun “Easter Egg”

You might notice that J.D. Salinger and Henry Kane appear twice. This stack isn’t a random collection; it’s actually a popular art piece (often titled The Librarian’s Daughter or similar) that mixes “high” literature like Middlemarch with “low” pulp fiction like Affair for a Corpse to show the breadth of a true reader’s shelf.

Continue reading “From Paperbacks to YouTube”
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 Business

The Gravity of Compute

We are currently witnessing the single largest deployment of capital in human history. The “Hyperscalers”—the titans of our digital age—are pouring hundreds of billions of dollars into the ground, turning cash into concrete, copper, and silicon.

The prevailing narrative is one of unceasing, exponential growth: bigger models require bigger clusters, which require more power plants, which require more land. It relies on the assumption that the demand for centralized intelligence is insatiable and that the current architecture is the only way to feed it.

But history suggests that technology rarely moves in a straight line; it swings like a pendulum. Two forces are emerging from the periphery that could impact the ROI of this massive infrastructure build-out. One is hiding in your pocket, and the other is waiting in the sky.

A recent conversation with Gavin Baker outlines a potential “bear case” for datacenter compute demand: the rise of Edge AI.

We often assume we need the “God models”—the omniscient, trillion-parameter giants hosted in the cloud—for every interaction. But do we?

Baker suggests that within three years, our phones will possess the DRAM and battery density to run pruned versions of advanced models (like a Gemini 5 or Grok 4) locally. He paints a picture of a device capable of delivering 30 to 60 tokens per second at an “IQ of 115.”

“If that happens, if like 30 to 60 tokens at… a 115 IQ is good enough. I think that’s a bear case.” — Gavin Baker

Consider the implications of that specific number. An IQ of 115 isn’t omniscient, but it is competent. It is capable, nuanced, and helpful.

If Apple’s strategy succeeds—making the phone the primary distributor of privacy-safe, free, local intelligence—the vast majority of our daily queries will never leave the device. We will only reach for the cloud’s “God models” when we are truly stumped, much like we might consult a specialist only after our general practitioner has reached their limit. If 80% of inference happens on the edge for free, the economic model of the trillion-dollar data center begins to look fragile.

Then there is the second threat, one that attacks the terrestrial constraints of the data center itself: the Orbital Data Center. Elon Musk and SpaceX – along with Google’s Project Suncatcher – envision a future where the heavy lifting isn’t done on land, but in orbit. Space offers two things that are scarce and expensive on Earth: unlimited solar energy and an infinite heat sink for radiative cooling. If Starship can reliably loft “server racks” into orbit, the terrestrial moat of land and power grid access—currently the Hyperscalers’ greatest defensive asset—evaporates.

We are left with a fascinating juxtaposition. On one hand, we have the “Edge,” pulling intelligence down from the clouds and putting it into our hands, making it personal, private, and free. On the other, we have “Orbit,” threatening to lift the remaining heavy compute off the planet entirely to bypass the energy bottleneck.

There are hundreds of billions of dollars betting on a future of heavy, centralized gravity. But if the edge gets smart enough, and the orbit gets cheap enough, the gravity may have shifted.