The archive

Dig as deep as you want

The main pages lead with the story. This is where the deeper charts, tables and case studies live, sorted by subject, so you can comb through as much data as you want. Every number is sourced.

Money & power

The full machine, past the four-part story on the Power page, the deeper charts and the case studies that support it.

Is the game rigged?, the main story →

The convergence opener and the four-part loop: Money → Media → Division → More Money.

Did it all break at once? →

The six-trend convergence deep-dive: before/after, the feedback loop, and exactly what we can prove.

Data centers, the flagship deep-dive →

Who's paying for the AI build-out, and who profits.

Why the rich keep getting richer →

The mechanics of wealth concentration, in detail.

More money & power charts

When a crash hits, ownership moves up

A crisis doesn't just hurt ordinary people more, it transfers what they own to whoever has cash. As the national share of families who own their home slipped after 2008, corporations moved in, in hard-hit metros like Charlotte, their share of single-family homes more than doubled.

Families who own their home (↓ 67% → 65%, dipped to 63% in 2016) Corporate share of single-family homes, Charlotte (Mecklenburg County) (↑ 10% → 26%)

The clearest example: Blackstone

After the crash, Blackstone put ~$10 billion into 48,000 foreclosed homes (2012–2016), took the company (Invitation Homes) public, and exited by 2019 with ~$7 billion in profit, more than double its money. It has since re-entered and now owns ~350,000 rental units, the largest landlord in America. Banks had stopped lending to ordinary buyers; Blackstone had cash.

The same pattern recurs across crises, 1873, 1929, 2008, and the 2020 COVID crash (when US billionaire wealth grew ~58% while ~89M Americans lost work). The GAO is careful that many factors move homeownership; we show the data and let you draw the conclusion. GAO 2024 ↗ · Homeownership (FRED) ↗ · Corporate ownership, Mecklenburg County (CoreLogic / UNC Charlotte Urban Institute) ↗

It's not five problems. It's one machine.

Everything above looks like a separate headline. Put them in order and they turn out to feed each other, a loop that spins a little further every cycle. Here's how the pieces connect:

Money floods politics

Outside spending went $143M → $4.5B (31×); 100 families now give 16.5% of all contributions.

Policy tilts to the donors

Corporate tax fell 35% → 21%; rules and tax breaks track what the money wants.

Wealth concentrates at the top

The top 1% now holds ~31%; the middle class has lost ~11 points of national wealth since 1990.

Social media turns outrage into profit

Feeds are tuned to whatever keeps you scrolling, and that's the most divisive content. Meanwhile the local news that once watched power collapsed (~2,200 papers gone).

Angry, isolated young men become the target

The loneliest audience in the country gets funneled toward outrage influencers, and a lot of money rides on keeping them that way (see below).

Divided and distracted, we can't rein it in

Trust in government fell 30% → 22%; the partisan gap went 58 → 89 points; no new campaign-finance or platform law has passed.

↺ And then it repeats, with more money, more division, and less accountability each turn.

How much is riding on keeping young men angry and isolated?

No one writes a check labeled “keep young men angry.” The machine doesn't pay for anger; it pays for attention, and outrage is what holds attention longest. So the money lines up behind it:

A $2.4 billion political podcast-and-influencer economy, up from $69M in 2015, now the #1 podcast genre. (IAB / PwC)
Almost none of it disclosed: no federal rule requires paid political influencer content to be labeled, so an ad is indistinguishable from a sincere opinion. (Brennan Center)
Aimed at an audience where 40% of young adults get news from TikTok, and 1 in 4 men under 35 say they're lonely, 10 points above peer countries. (Brennan Center · Survey Center on American Life)

The intent is hard to prove; the incentive is measured. A lonely, angry young man is a profitable young man, for the platforms that hold his attention and the operatives who rent it.

Correlation, not proven cause: every link in this loop is independently measured, and they all accelerated in the same window, but the loop as a whole is a thesis the timing supports, not an experiment. We show you what happened and when; you decide what it means.

See it all on one timeline: “Did it all break at once?” →