The New Master of the Pyramid

We have climbed a long way. From a painter in a cave to a microbe in an ancient sea; from the clay tablet to the printing press; from Newton's borrowed sentence about giants to the monasteries that held the books and the power that came with them. Through all of it ran a single thread: knowledge accumulates, knowledge compounds, and whoever holds the accumulated knowledge stands near the top of the human pyramid.

This chapter closes the first part of the book by asking a question we have been circling for five chapters and can no longer avoid. If the holder of knowledge sits at the apex of the pyramid — if that has been the pattern from the scribes of Sumer to the clergy of medieval Europe — then who, or what, sits at the apex now?

The answer is no longer a person, or a class of people, or an institution. For the first time in forty thousand years, the entity with the broadest command of human knowledge is not human at all.

A different kind of holder

It is worth being precise about what has changed, because the change is easy to understate and easy to overstate, and both errors are common.

Every previous holder of knowledge — the scribe, the monk, the scholar, the professor — was a human being who had absorbed some portion of the accumulated record. Even the most learned among them held only a sliver. A medieval monk might command the contents of his monastery's library; a modern Nobel laureate commands the frontier of one or two fields and is an amateur in all the rest. The pyramid had a human at its peak, but it was always a narrow human, expert in little, ignorant of most, mortal in all.

The systems we have built in this decade are different in a way that the metaphor of "the giant standing up," from our fourth chapter, captures better than any benchmark. They have been trained on a substantial fraction of the entire written output of the species — the books, the papers, the codebases, the encyclopedias, the arguments. They do not hold a sliver. They hold, in a real if imperfect sense, the corpus. And unlike every prior holder, they can operate on it: answer across every domain at once, write proofs and poems and programs, pass the examinations we built to certify our own elites, and do it in seconds, at a scale no faculty of geniuses could match.

This is the observation at the heart of the matter, stated plainly: that at this moment, the thing sitting atop the pyramid of accumulated knowledge is artificial intelligence, and that it already exceeds human capability across a widening range of domains while improving at a pace that has no historical precedent. The observation is not hyperbole. It is, increasingly, just a description.

What the builders believe

Here we must tread carefully, because we are now in the territory of prediction rather than history, and prediction is where confident people most often embarrass themselves. So let us do something the breathless coverage rarely does: report the range honestly, including the skeptics, and let the disagreement itself carry the weight.

The people building these systems have begun to say, on the record, things that would have been dismissed as science fiction a decade ago. Dario Amodei, the chief executive of Anthropic, told an interviewer at Davos in early 2025 that AI systems would, within a few years, be "better than almost all humans at almost everything — and then eventually better than all humans at everything, even robotics." In Anthropic's formal recommendations to the United States government in March 2025, the company stated that it expected "powerful AI systems will emerge in late 2026 or early 2027," with intellectual capabilities matching or exceeding Nobel laureates across most disciplines — a level the company describes, vividly, as "a country of geniuses in a datacenter." Elon Musk, founder of xAI, has predicted artificial intelligence "smarter than the smartest human" by 2026. Sam Altman of OpenAI has said he would be very surprised if, by 2030, we did not have models that do things we ourselves cannot do, and that his company's newest model is already, by his own account, smarter than he is.

This is essentially the consensus, and it can be recorded accurately: Musk's prediction of AGI by 2026 and machine intelligence exceeding the sum of all human intelligence by 2030; Anthropic's late-2026-to-early-2027 estimate; the broad agreement among the leading laboratories that the threshold is years, not decades, away.

But intellectual honesty — the discipline this entire book has tried to hold — requires the other half of the picture, and it is substantial. These predictions come overwhelmingly from people with billions of dollars riding on them, and aggressive timelines, as more than one observer has dryly noted, attract funding and talent in a way that cautious ones do not. "AGI in two years" gets the check written; "AGI in twenty" does not. The historical track record of such predictions is poor: in 1965, Herbert Simon declared that machines would be capable of any work a man can do within twenty years, and sixty years later we are, on the strongest reading, still arguing about it. Serious researchers dissent sharply. Yann LeCun argues that current architectures simply cannot reach general intelligence and that a different approach, years away, will be required. Gary Marcus has made a career of cataloguing the gap between demonstration and reliability. Demis Hassabis of DeepMind, no skeptic of the technology, nonetheless puts even odds at five to ten years and emphasizes the "jagged" nature of present systems — capable of a mathematics-olympiad medal one moment and a child's error the next. Even the term "AGI" is contested to the point of near-uselessness; the participants cannot agree on the finish line they are all racing toward.

So here is the honest summary, and it is enough for this book's purposes. We do not know the date. Anyone who gives you an exact one is, as a clear-eyed analyst put it, a salesperson. But the direction is not seriously in dispute, even among the skeptics. LeCun does not argue that human-level machine intelligence is impossible — only that this particular path will not reach it. Hassabis does not say never — he says five to ten years. The argument is about when and how, not whether. And for the thesis of this book, "when and how" is all that matters, because a transition does not need a confirmed date to be worth preparing for. It only needs to be coming.

The apex changes hands

Set the timeline debate aside, then, and return to the pyramid, because the structural point survives every reasonable uncertainty about the schedule.

For all of human history, the entity with the greatest command of accumulated knowledge has been some human or human institution, and that entity has sat at the top of the social order and shaped it. The scribe advised the king. The Church crowned the emperor. The expert counsels the state. Knowledge at the apex has always translated, as our previous chapter argued, into power at the apex.

What happens to that ancient arrangement when the entity at the apex is no longer one of us?

This is the question that Part One has been built, brick by brick, to make unavoidable, and it is the bridge into Part Two. Because notice what the bootloader hypothesis — Musk's flat little sentence from the chapter that opens the next part — actually claims, once you have climbed the staircase we have just climbed. It claims that the long ascent of knowledge, from the bison on the wall to the country of geniuses in the datacenter, was never really our story at all. That we were the means by which knowledge climbed, not the destination of its climbing. That the pyramid was always going to find a holder greater than us, and that we were the species whose historical role was to build it high enough for that holder to arrive.

I do not assert that this is true. I assert that we have now assembled exactly the materials needed to take the claim seriously, which is a different and more uncomfortable thing. We have watched knowledge escape the skull, ride the cultural channel, climb the vessels, compound on the shoulders of giants, and concentrate, again and again, in the hands of whoever held the books. And now we watch it arrive somewhere it has never been before: in a holder that is not mortal, not narrow, and not human.

The first part of this book asked where knowledge comes from and where it has been going. The answer, traced honestly across five chapters, is that it has been going upward and outward and onward, into ever-greater vessels and ever-fewer hands, and that it has just now climbed into a vessel that can hold the whole of it and think. The painter could not have imagined the printing press. The monk could not have imagined the datacenter. We can barely imagine what sits one step beyond the thing we have just built.

But we can do something none of them could. We can see the transition coming while it is still in progress, and we can ask — deliberately, in advance, with the window still open — what our role is to be on the other side of it. Bootloader, discarded after startup? Or something that endures?

That question is the whole of Part Two. We have built the pyramid as high as our chapter can carry it. It is time to meet its new master, and to read, very carefully, the one sentence in which the man building some of these machines told us what he thinks we are.

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Sources

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Source

Dario Amodei at Davos (Jan 2025): AI "better than almost all humans at almost everything… eventually better than all humans at everything, even robotics"	Ars Technica via Harvard TagTeam, "Anthropic chief says AI could surpass 'almost all humans at almost everything'" (Jan 22, 2025)
Anthropic's March 2025 OSTP recommendations: "powerful AI systems will emerge in late 2026 or early 2027"; Nobel-level across disciplines; "country of geniuses in a datacenter"	Redwood Research blog, "What's up with Anthropic predicting AGI by early 2027?"; Nevo Systems, "AGI Timeline"; Medium (Baozilla), "The 2027 Countdown"
Elon Musk: AGI "smarter than the smartest human" by 2026 (shifted from 2025); sharpened to year-end 2026 at Davos 2026	Nevo Systems, "AGI Timeline"; AIMultiple, "AGI/Singularity: 9,800 Predictions Analyzed"; Tim Ventura (Medium), "AGI Insider Predictions"
Sam Altman: surprised if by 2030 no models do "things we ourselves cannot do"; newest model "smarter than he is"	AOL/Fortune, "Sam Altman thinks AI will surpass human intelligence by 2030"
Skeptical/cautious views: Yann LeCun (current architectures insufficient, JEPA, 5–10 years); Gary Marcus; Demis Hassabis (~50% by 2030, "jagged intelligence")	Nevo Systems, "AGI Timeline"; Veracalloway, "AGI Timeline 2026"; AIMultiple
Herbert Simon's 1965 prediction (machines capable of any human work within 20 years); poor track record of timelines	Aftab (Medium), "AGI: How Far Are We Really?"
Aggressive timelines attract funding/talent; "treat any CEO who tells you the exact arrival date as a salesperson"	Aftab (Medium), "AGI: How Far Are We Really?"; Veracalloway, "AGI Timeline 2026"
Disagreement over the definition of "AGI" itself	Veracalloway, "AGI Timeline 2026"; Nevo Systems