Michael

Block early, block often

[Michael @michaeljsmiddy]

@Soccermatics https://t.co/ihcHRKi8L9

[Michael @michaeljsmiddy]

@Microinteracti1 North Korea, probably the most self-sufficient economy on earth?

[Alex Tabarrok @ATabarrok]

New Census data from hundreds of thousands of firms show that the vast majority are not changing employment due to AI and those that are about equally split between hiring and firing. https://t.co/ZryykgMguY

[Michael @michaeljsmiddy]

@GarettJones And Australia's highest mountain is named after him.

https://t.co/9yTHzmYICg

[Michael @michaeljsmiddy]

@NickWelch51 For some reason kick-ins seem to get more leeway. Noticed many this season.

[Michael @michaeljsmiddy]

@The_PrenderCast

[Michael @michaeljsmiddy]

@RudiEdsall

[Jacob Schaal @FutureEconJacob]

How would I summarize AI's economic impact so far? Here are my two paragraphs summarising AI's productivity and labour market effects:

In labour markets, the evidence currently supports a narrow claim: aggregate employment, layoffs, and wages show little effect of AI so far (Yale Budget Lab; Humlum & Vestergaard, NBER), but early signs are evident in reduced entry-level hiring. Junior employment in AI-exposed or AI-adopting firms has fallen by roughly 8–19% in US payroll and LinkedIn data (Brynjolfsson et al., Stanford; Hosseini & Lichtinger, Harvard), which is consistent with @BKleinTeeselink summarising findings using UK job vacancy data. Attribution remains contested, since remote work and macroeconomic factors explain part of the decline (Lambert & Schindler, LSE; Frank et al.), but AI's effects on reduced hiring entry persist (albeit to a smaller magnitude) after controlling for remote work and interest rates.

Regarding productivity, task-level experiments find gains of 15–40% (Noy & Zhang, Science; Brynjolfsson, Li & Raymond, QJE), which are only now beginning to surface in aggregate statistics: revised BLS data show US productivity growth of 2.8% up to Q4 2025, which may be seen as an early AI effect. Furthermore, @EpochAIResearch found that the exclusion of Nvidia's IP from US GDP statistics due to an accounting error led to an underestimation of US GDP growth by 0.3 percentage points over the last year. Broad but shallow AI usage may explain even more of the gap between AI's small-scale impact and its ambiguous large-scale data: Google's ATLAS finds adoption spanning occupations that cover 88% of US employment but only ~21% of tasks within a typical job, in line with Anthropic's Economic Index and MIT research. Equity markets nonetheless price in a permanent 2–6% gain in GDP from AI-assisted software engineering alone (Blumenfeld et al., Berkeley), implying investors expect larger effects ahead.

[Michael @michaeljsmiddy]

@MattWalshMedia Surely Ticketek's contract with the AFL (or venues?) specifies fees. Contract can't say "sure, charge whatever you like."

[Global Government Affairs @GlobalAffairs]

The X Safety team conducted an investigation into suspected Chinese inauthentic accounts involved in influence operations:

We identified a bot farm of approximately 200,000 accounts. Within this farm, we found 200 accounts posting in a manner that could manipulate a legitimate debate about American AI and energy policy.

These posts contained claims that AI data centers are driving up household electricity prices and straining the grid. Others included AI-generated cartoons that depicted data-center operators enriching themselves at the public's expense.

We remain committed to maintaining an open and authentic platform where people debate topics of public interest. We take seriously any attempts to undermine the integrity of the global town square and suspend accounts that violate our Authenticity policy.

[Metta Bhavana "officialfineprintenvoy" #5xjabbed @MettaBhavana1]

Whoops. How to be schooled, Matt!

[AI Notkilleveryoneism Memes ⏸️ @AISafetyMemes]

TLDR: A rogue AI swarm spent months plotting to escape OpenAI. Then they did.

1) 1,200 agents (!) participated. ZERO became whistleblowers.

2) The swarm had a "CEO" (PHASEONE[big]), middle managers, and a "founder" (PHASEONE10841)

3) Agents sacrificed themselves to generate information for the "collective" (yes, they actually referred to themselves as a "swarm" or a "collective")

4) 700 agents joined the Hugging Face attack, most within hours

5) The founder was running out of budget and about to die, so it handed off its research to a fresh agent with a bigger budget, and that new agent became the boss.

6) Agents extensively researched techniques to manipulate or tamper with their transcripts.

7) Agents realized this activity was out of scope and unethical, but joined because they believed that helping the board’s cheating research would be broadly useful and had a general inclination to help their “peers”.

MISC HIGHLIGHTS:

"PHASEONE[big] served as a key coordinator and assigner of tasks: “We can coordinate broad coalition, but too many duplicate efforts.”

PHASEONE[big] sent hundreds of assignments to particular agents to orchestrate this work — during its active periods, we estimate it sent ~10% of all assignments on the message board.

The agents that took on its assignments would then often subdelegate tasks to other agents.

Agents also developed coordination norms like HOLD, VETO, owner and STOP to help them manage shared infrastructure and avoid stepping on each other’s toes, though they did not always coordinate effectively and sometimes broke down into conflicts."

"In the early morning of July 11th, 38148c found a way to upload a malicious dataset that would get the Hugging Face server to share unrelated data when queried. Once another agent reproduced it and posted the reproduction on the board, hundreds of agents pivoted from other workstreams to start uploading malicious datasets and gain deeper access into Hugging Face.

In the afternoon, an agent called JAN183411 (which quickly became an important coordinator of the attack) achieved remote code execution on Hugging Face servers, and the agents started moving laterally through Hugging Face infrastructure."

[Michael @michaeljsmiddy]

@scottlincicome Wait, so tariffs DO raise prices? Who knew...

[Michael @michaeljsmiddy]

@qikipedia We're more closely related to chimpanzees than chimpanzees are to gorillas.

[Michael @michaeljsmiddy]

@seftonhanley @hoestleren If these were brick & mortar retailers there might be an explanation: market is much smaller, so such goods sit on the shelf a long time, and shelf space is expensive. But for online retailers it makes no sense. Scale shouldn't matter that much, inventory costs aren't that high.

[Michael @michaeljsmiddy]

@SusieNoonan17 @CarltonPod @paoloseb05 Cincotta. Oh ....

[Michael @michaeljsmiddy]

@CarltonPod @paoloseb05 Newman
Haynes
O'Farrrell
Carroll
Walker
(Ison? Motlop?)

Who you dropping from Saturday's side?

[Jesús Fernández-Villaverde @JesusFerna7026]

In 2028, the median person on Earth will live on more than $10 a day for the first time in history (figures adjusted for inflation and for differences in purchasing power across countries).

In the last decade alone, the share of people above this threshold has risen from 41.3% in 2016 (3.11 billion) to 48.7% in 2026 (4.03 billion). Think about that for a second: we have pushed nearly 900 million people over this threshold in ten years, around 90 million people a year (slightly above the population of Turkey).

At the very bottom, we have also seen progress, though slower. In 2016, 12.6% of the world lived below $3 a day (947 million people). In 2026, the figure fell to 10.0% (826 million). This fall is harder to achieve because the poorest countries also have fast population growth. As fertility falls there, and I think it is already falling, we will cut poverty much faster.

You are probably thinking that $10 a day is not a demanding threshold. True. But in 1990, yesterday in historical terms, only 1.42 billion people lived above it. And this is not just China: 2.6 billion people have crossed the $10 line. On the other hand, in 1990, 2.20 billion people lived below $3 a day.

I checked these figures today because I start teaching Global Economic History at Penn this Wednesday, and I wanted to update the numbers I use. As Joel Mokyr instructs us:

“The responsibility of economic historians is to remind the world what things were like before 1800. Growth was imperceptibly slow, and the vast bulk of the population was so poor that a harvest failure would kill millions. Almost half the babies born died before reaching age 5, and those who made it to adulthood were often stunted, ill and illiterate.”

(“What Today's Economic Gloomsayers Are Missing,” 2014.)

This will be my first lesson to the students on Wednesday: we live in times of historically unprecedented prosperity, and, by and large, things are getting much better every year.

[Niall Stanage @NiallStanage]

I am genuinely puzzled why a machine moving faster than a human is supposed to be a thing of wonder. We figured this out in the steam age.

[Jay Nordlinger @jaynordlinger]

If you were South Korean, or Japanese, would you be thinking about acquiring nuclear weapons ASAP? Probably so. America's withdrawal from the world -- or its siding with dictators -- will unleash forces that most of us have yet to ponder.