Interlude Ian aside on method
On checking what the field you are counting actually contains.
The first real finding I had, and the one I was most pleased with, was that OpenAI had collapsed.
It was a simple measurement. Every issue has a headline. Count the share of headlines that name each company, half-year by half-year, and you get a clean series for who was leading the news. OpenAI ran at about 18% of headlines through 2024 and fell to roughly 4% by 2026. Meta did something similar. Meanwhile the total number of distinct companies named across the corpus rose steeply. It made a coherent story, it fit everything I thought I knew, and it had an obvious headline of its own: the field fragmented, and the incumbents lost the narrative.
Then I read some headlines.
Here are four consecutive titles from March 2026, exactly as the archive stores them:
not much happened today
not much happened today
not much happened today
not much happened today
Underneath those four issues, among other things, were an agent launch from Replit, an
essay on raising your expectations of language models, and an NVIDIA GTC keynote announcing a
trillion-dollar sale. So the phrase is not a description of the day — that much was immediately
clear, and it was as far as I got for a long time. What I concluded was that it is simply what
sits in the title field when nothing else does, and the share of issues carrying it
climbs steadily across the corpus: 8% in late 2023, 27% by the second half of 2024, 46% through
late 2025, 85% in the final months. That conclusion turns out to be wrong twice
over, in two different ways, and the rest of this interlude is the two ways.
My series was not measuring which companies led the news. It was measuring how often the title field got filled in. And because the templating ramped up over exactly the window I was studying, the artifact and the trend were indistinguishable. Every company's headline share fell. I had picked the two I expected to fall and written a story about them.
Not the statistics. The statistics were fine — the counts were correct, the periods were right, the differences were far outside anything noise could produce. The mistake happened one step earlier, in the assumption that the title field contained a title.
This turns out to be the general shape of the problem, and it recurs throughout this book. A field in a dataset has a name, and the name implies a meaning, and the meaning is stable until one day it silently is not. Nothing errors. Nothing looks anomalous. The series just quietly starts measuring something else, and every downstream method — every regression, every change-point detector, every significance test — faithfully processes the new thing using the old label.
There is no statistical test for “this field stopped meaning what it used to mean.” There is only reading it.
That is where this interlude ended for several months, and it was still wrong.
What I had been reading was not the newsletter. It was a public mirror of the newsletter, and I had never checked it against the thing it mirrored. When I finally went back to the sent emails — the issues as they actually landed in an inbox — the four titles above were not what anyone received. They went out as Replit Agent 4: The Knowledge Work Agent, The high-return activity of raising your aspirations for LLMs, Context Drought, and NVIDIA GTC: Jensen goes hard on OpenClaw, Vera CPU, and announces $1T sale.
Of the 397 issues I could eventually recover, 196 are filed under a templated title in the archive — and 80 of those went out under a real headline. In the first half of 2026 the templated share of what was actually sent is 4.6%, against the 64% the archive stores.
Which looks like a clean verdict: the mirror invented the trend. It is not, and the way it is not is the more useful lesson. Compare the two series on the same issues:
| Half-year | Archive says templated | Actually sent that way |
|---|---|---|
| 2024H2 | 26.9% | 28.6% |
| 2025H1 | 38.1% | 38.1% |
| 2025H2 | 45.7% | 41.0% |
| 2026H1 | 64.3% | 4.6% |
| 2026H2 | 84.6% | 24.0% |
Through 2024 and 2025 the two agree closely — exactly, in the first half of 2025, across 118 issues. The climb from 27% to 41% is real: the editor genuinely did reach for the template more and more, and my original series was tracking something that was happening. Only in 2026 do the columns come apart, and there they come apart in two directions at once: the mirror began writing placeholders in the same months the editor went back to writing headlines. One movement was hidden underneath its opposite.
The commentary underneath shows the same double motion. The mirror replaced it with the words a quiet day on 104 issues in 2026, and restoring those from the sent emails moves the typical issue's opening from three words back to 183. But the genuine thinning through 2025 — a median of 190 words in late 2024, 92 by early 2025, 72 by late 2025 — survives the correction untouched. It was real, and the mirror's failure was layered on top of it.
The first correction caught a field that had changed meaning. The second caught a real trend and an artifact of the copy, superimposed, moving opposite ways.
There is one more error in the paragraphs above, and it is mine rather than the mirror's. I
have been calling that phrase a placeholder — “what sits in the title field when
nothing else does” — and for 2026 that is exactly what it is. For 2024 and 2025 it is nothing of
the kind. The table says so: through those two years the stored share and the sent share agree
within a few points, and match exactly across 118 issues in the first half of 2025. Which means
that for most of the corpus a person typed not much happened today into the subject line
of an email and pressed send, on purpose, more and more often. That is not a blank field. It is
a judgement, published.
So the question I should have asked two corrections ago is whether the judgement was any good — whether the days he called quiet were quiet. It is answerable, and the answer surprised me. Restricted to the window where stored and sent agree, on the 367 issues carrying a full Twitter recap, the front matter says this:
| Issues from 2024H2–2025H2 | issues | companies named | models named | companies per 1,000 recap words |
|---|---|---|---|---|
| titled not much happened today | 139 | 7.4 | 6.4 | 7.8 |
| given a real headline | 228 | 5.6 | 5.0 | 6.5 |
The quiet days are busier. More companies, more models, a longer recap, and it survives dividing through by the length of the recap, so it is not an artifact of those issues simply being bigger. Which makes sense the moment you stop reading the phrase as a description of the volume of news and start reading it as what an editor means by it. A headline needs a lead story. When the day's attention is spread across seven companies and none of them owns it, there is no headline to write — so you write the template and let the recaps do the work. Not much happened today does not mean nothing happened. It means nothing dominated, which is a different and more specific claim, and on this evidence a correct one.
I spent two corrections establishing that the field did not contain what its name implied, and then read the values it did contain as though they meant nothing. They meant something. I had just assumed the phrase was an absence.
There is no fix that rescues the original series. The stored title cannot be repaired, and the published subject lines exist only from November 2024 onwards — 397 issues out of 690, and none at all from the year the original series started in. The measurement I wanted cannot be made across the window I wanted it for. The OpenAI collapse disappears either way, and so does most of the fragmentation story built on top of it.
What survived was the thing I had not built a story around: measurements taken inside the body of the issues, in fixed sections, where the population writing the text was held constant. Those sections came through the mirror intact — byte-identical to the sent emails on every issue I could check, which is why the rest of this book still stands. That is the instrument used everywhere here, and the reason it is used is that the obvious alternative failed first, and then failed again underneath.
The general form of the lesson is short enough to keep. Before you measure a field, read enough of it to know what your fields contain. Not the schema — the values. Twenty issues, spread across the range, read properly. It takes an afternoon and it is the only check that catches this class of error at all.