technology and society – Scott Loftesness

Gavin Baker said something this week that stuck with me.

In his latest conversation with Patrick O’Shaughnessy, he described a quiet shift happening across public markets. Nearly everyone he knows in the equity business—retail and institutional—now feeds every piece of news straight into Claude. Sometimes Claude Code. Sometimes a Claude agent. The model is probabilistic, he noted, and he was speaking from what he sees in his own network rather than from a measured study. But his impression was that the variation in how it interprets the same information is surprisingly small. A huge chunk of the market ends up trading on a shared reading of events.

Baker reached for an old analogy: Claude has become Walter Cronkite for the stock market. The single trusted voice. Everyone just believes what it says.

He tied the observation to Michael Mauboussin’s work on how a breakdown in diversity of thought helps create the conditions for bubbles and crashes. When independent judgment collapses into a narrower set of interpretations, the system becomes more brittle. Moves get sharper. Errors get amplified.

I spent the back half of my career inside fraud detection systems at Visa, watching correlated failure up close. The lesson that never left me: the dangerous moment isn’t when a single model is wrong. Individual errors wash out. It’s when every model in the ecosystem is wrong in the same direction, because they were trained on the same data, tuned against the same benchmarks, built by people reading the same journals and hiring from the same three schools. A fraud ring doesn’t need to beat your model. It needs to find the blind spot every model in the industry shares. That’s not a tail risk. That’s the whole risk.

Which is what made me sit up a few weeks ago, watching a position reprice in a straight line and catching myself, mid-scroll, about to ask Claude what it thought was happening before I’d looked at a single primary source myself. The tool hadn’t done anything wrong. I had reached for the shared interpretive layer before reaching for my own judgment, out of habit, the way you reach for a light switch in a dark room you’ve walked through a thousand times.

Dan Geer wrote about this two decades earlier, from a different angle entirely. Geer and colleagues argued that Microsoft’s dominance had created a software monoculture: nearly identical systems sharing the same vulnerabilities. In biology, monocultures are efficient until a pathogen finds the common flaw. Then the failure is systemic rather than local. Diversity limits the blast radius. Geer’s point was never that the dominant platform was worse in isolation. It was that identicality itself becomes the risk multiplier.

Baker is describing a cognitive version of the same phenomenon.

The platform is no longer Windows. It is a frontier model that a large fraction of market participants now use as their primary interpretive layer. The shared vulnerability is not a buffer overflow. It is a common set of priors, training data, reasoning patterns, and prompt conventions. Slight probabilistic differences still exist. But the center of gravity of interpretation has tightened.

The result is correlated positioning. Feedback loops that reinforce themselves. A market that can reprice more violently than the underlying fundamentals alone would justify. In July we watched AI and semiconductor names drop 40–60 percent in a straight line while on-the-ground metrics—GPU rental prices rising, token growth accelerating, hyperscaler operating cash flow strengthening—told a different story. One plausible contributor to that gap is an AI-mediated consensus that overweighted certain narratives relative to the harder data.

There is an important difference in degree. Software monocultures create technical cascade risk you can patch. Interpretive monocultures create cognitive cascade risk you can’t—there’s no CVE number for a shared blind spot in judgment. The latter is softer and harder to measure. But the mechanism is familiar: reduced diversity of independent judgment.

I use these models constantly. They compress research, surface patterns I’d have missed, and force clearer thinking when I use them well—Claude caught an inconsistency in a cash flow assumption last month that I’d read past twice on my own. That’s real. The danger isn’t the tool. The danger is treating the tool as the authoritative voice rather than one input among many. The edge increasingly belongs to people who combine the model’s speed with proprietary data, primary research, domain experience, and a willingness to hold non-consensus views. Those who simply outsource the interpretation may find themselves more correlated than they realize, and won’t know it until the moment it matters.

Diversity of thought was never free. It was always work.

I noticed myself skipping the work, just for a second, on an ordinary Tuesday. That’s usually how it starts.

Tracy Kidder and the Human Code

Tracy Kidder died yesterday, March 24th, of lung cancer. He was 80.

I’ve been sitting with that quiet, heavy fact for a few hours now, staring at the screen, thinking about what his work meant to me—and specifically, about the enduring legacy of The Soul of a New Machine.

On its surface, the book is a chronicle of a team of engineers and coders at Data General Corporation, racing against the clock in the early 1980s to build a 32-bit minicomputer. If you haven’t read it, that description likely sounds like the synopsis for a dry technical manual. It is, gloriously, anything but.

What Kidder did—what hit me with such force when I first turned those pages—was capture the raw, unvarnished pulse of human obsession. He didn’t just document the architecture of a machine; he mapped the architecture of the minds building it. He translated the late-night pizza runs, the bloodshot eyes, the tribal hierarchies of the engineering floor, and the strange, almost religious fervor that overtakes people when they are creating something they profoundly believe in.

He called it:

“An adventure story, a kind of cultural anthropology.”

That is exactly right.

He ventured into a world most journalists would have fumbled or fundamentally misunderstood.

The early computer industry was hyper-technical, fiercely insular, full of impenetrable jargon, and populated by brilliant minds who regarded outsiders with a polite, if dismissive, suspicion.

But Kidder didn’t blink. He embedded himself. His deep reporting and novelistic prose illuminated the basement labs of tech just as deftly as he later illuminated home construction and global disease prevention. He held a fundamental trust that the human drama playing out inside the sterile machine room was worth finding. And he found it.

Reading Soul as someone who has spent years orbiting technology, I continually find myself marveling at a different kind of engineering: how does a writer actually do this? How do you make the arcane feel intimate?

As one reviewer aptly noted at the time, “Kidder makes the telling seem absolutely effortless.” Which is, of course, the ultimate tell. Effortless prose is always the product of staggering effort.

A friend once said of his process:

“Tracy throws up on the page and cleans up afterward. He was absolutely indefatigable in the writing.”

That immense labor shows—not as the sweat of a struggling author, but as the pure clarity of a master.

What the book quietly teaches, if you’re paying attention, is a profound lesson about the nature of craft itself.

Those Data General engineers weren’t just building a minicomputer. They were building an identity, a tribe, a shared sense of purpose. They were transferring a piece of themselves into the silicon and wire. Kidder understood this alchemy. He highlighted people who had mastered their realms, elevating them into characters whose struggles rang true because they were anchored by staggering amounts of research. He believed—and subsequently proved to the world—that ordinary people doing terribly difficult things in obscure rooms were worthy of the full weight of literary attention.

That was his extraordinary gift. And it is far rarer than it sounds.

The honors and brisk sales from the book vaulted Kidder into the top ranks of American nonfiction writers. But his true legacy lives in the narrative talents he inspired. I suspect a vast number of people who went on to write serious, empathetic nonfiction about technology read Soul at some formative moment and thought: This is how it should be done. I know I was one of them.

He will be deeply missed. But the book remains, waiting on the shelf. If you haven’t read it, today feels like exactly the right day to start.

Share this:

Share this: