Deep Research

If nobody ever gave "the wall" a testable definition, what exactly has been argued about for two years? From a word-by-word audit of the founding papers to the primary sources behind the wall narrative, the disintegrating calibers of flagship benchmarks, the successive buy-offs of the data wall, and the supply-side constraint actually binding.

Why everyone feels faster while organizations get less stable — from transaction costs, Conway/Brooks and cybernetics to innovation theory, with a dedicated chapter on high-reliability legacy organizations.

Employment at record highs while the 22-25 bracket falls nearly a fifth — a flow phenomenon, not a stock one. Four data gauges calibrated against each other, answering one paradox: why the technology that helps novices most shrinks novice jobs first.

Humans can't keep up with reviewing AI-written code, so the industry's answer is another AI to do the checking. An anatomy of the vendor benchmark wars and big tech's production funnels, asking how much independence AI-verifying-AI retains — and when it genuinely works.

The whole 'AI watching AI' program stands on one sentence: 'verification is easier than generation.' The founding papers qualified it; later citation used it as an axiom. Theoretical cracks, conditional empirics, and the labs' own retreat to CoT monitoring map what the foundation actually bears.

Ten thousand hours, learning styles, highlighting — why does popular study advice barely overlap with the measured evidence? Five academic battles audited one by one, the common methods re-seated by evidence strength, with anti-scam rules for reading effect sizes.

The same AI yields a twenty-year CVE plugged into a crash detector and a slop flood dumped on human triage — the only difference is who judges. A cell-by-cell audit of software's oracle families, checking the evidence for and against each cell's 'LLM gain'.

The transformation industry's thirty-year calling card — '70% fail' — was measured by whom? Nobody, it turns out. A citation autopsy of the number, a re-reading of the measured failure-rate record, and an audit of the Agile/DevOps archives for what they do and don't predict about AI-native transformation.

Everyone says your repo needs an AGENTS.md/CLAUDE.md — but the first controlled studies fight each other, and a hostile methods audit struck down two of the most-quoted numbers. The standards war, the vendor consensus, the attack surface, and an enterprise rollout playbook — adversarially verified, contradiction-searched, claim by claim.

The more reliable the automation, the faster the human veto seat decays — is the 1983 prophecy replaying inside AI workflows? A full physical for 'the human holds the veto': forty years of human-factors experiments, the aviation and medicine field archives, and the newest AI-era evidence with measured interventions.

The market-spooking "95%" can't stand on its own report — so how much enterprise AI actually fails? A verbatim anatomy of the viral number's birth and mutations, the vendor, consultant, and official yardsticks arranged into one ladder, and an honest answer to the base rate of transformation difficulty, built on thirty years of failure-rate archaeology and four economic theories.

Everyone cites the 1999 telecom bubble — but that bubble itself is misremembered: the disease was demand myths, accounting fraud, and debt, not "building infrastructure." This physical straightens 2026's ledger yardsticks, then tests the analogy limb by limb: where the money comes from, how long the assets live, whether the demand is real, and which two of five theories are actually operational.

Memory sold out by spring, electricity bills climbing — but the hardware and power shortages are two different animals: one clears by price, one rations by queue. The forecasts carry two priors of overestimation, yet the constraint is already written into auction results and bills; lay out the supply-side delivery timetables and 'how long' mostly answers itself.

Official ten-year occupational projections are trustworthy about broad direction and barely beat "assume nothing changes" at the detailed-occupation level - which is exactly where choosing a major happens. The figures most often imported into admissions advice, taken apart one caliber at a time, then a field list organized by criteria with an evidence grade in every cell.

The Anxious Generation propelled phone bans across half the Western world, while Nature's review ruled it 'not supported by science' — why has seven years of fighting settled nothing? The crisis, the causality, and the policies audited as three separate questions, with both camps' signature numbers sent back to their primary sources.

The best-evidenced compound has almost no retail market while the one that failed has the biggest — and that is not a coincidence. Four longevity stars' signature numbers sent back to their primary documents, covering the three-site mouse lifespan ledger, the reversal and reproduction of metformin's epidemiology, a physical on the NAD+ major premise, the randomized evidence on caloric restriction and time-restricted eating, and how loose the surrogate endpoints under every claim really are.

The same "passive share" differs 40-fold depending on which ruler you pick, and each camp reports only the ruler that flatters it. Every piece of evidence in this debate returned to its primary source, covering the three ways to measure the share, the warners' own words and what became of them, the six incompatible measures of price discovery, the index effect's disappearance and revival, the active-underperformance scoreboard, and the two layers where concentration is genuinely well evidenced.

AI writes over 80% of the code, yet R&D is 'not yet' twice as fast — and both numbers come from the same company. 'AI improving AI' split by layer and returned to its primary sources, covering the closed loops inside hard-judged cells, the real curves of self-modifying agents and self-rewarding training, the labs' two ledgers and ever-rewritten thresholds, independent measurement, and the return rate that decides whether anything explodes.

Memory-chip margins now beat NVIDIA's, while upstream firms both collect the frontier labs' compute bills and underwrite their credit — so 'model makers are working for someone else' is only half right. Chips, power, cloud, models, and applications put on one ruler and returned to primary filings, covering the migration of scarcity rent, the assumptions behind cloud margins, two price curves running in opposite directions, the incumbents who own distribution, and the money circulating between layers.