Yemen’s Qat Habit

Qat (also spelled khat) is a highland shrub whose fresh leaves are chewed for a mild stimulant effect. In Yemen it is not a fringe vice. It is a daily social custom for a large share of adult men, a major farm crop, and one of the country’s most stubborn public-health and water problems.

What qat is

The plant is *Catha edulis*. Fresh leaves contain cathinone and, as they dry, cathine. Chewed and packed in the cheek for hours, they produce alertness and a modest euphoria. The effect is closer to a long-acting stimulant than to coffee, and closer to a social ritual than to an injected drug. Sessions often last late into the afternoon. That rhythm shapes workdays, family time, and spending.

How common it is

Surveys do not all use the same questions, but they point the same way.

A World Bank household survey found about 72 percent of Yemeni men and 33 percent of women chewed qat. Of men who chewed, a large share did so most days of the week. Yemen’s 2013 Demographic and Health Survey found 71 percent of men age 15 and older currently used qat and about 55 percent of men 15+ used it daily. Use rises through young adulthood and is highest in the highlands, where the crop is grown. Some local and older studies report even higher male rates in cities such as Sanaa.

Women chew less, and usually separately. Children are not the main market, but the habit often starts in the teens.

How the habit grew

Qat has been used in Yemen for centuries. For a long time it was more occasional and more tied to status. From the 1970s through the 1990s it became mass and often daily. The usual explanations are economic, not conspiratorial: worker remittances, urban wages, cheap diesel that made groundwater pumping affordable, and a crop that pays farmers better than sorghum, millet, or wheat.

What it costs families

Qat is bought with cash, usually by men. Studies over two decades have found that user households spend a large slice of income on leaves — often cited in the range of a tenth to a third of the budget, depending on the year and the sample. In a country where many families live on very little, that competes directly with food, medicine, and school costs.

The pharmacology adds a second squeeze. Qat reduces appetite. Sessions consume hours. The day after a long chew, some users report worse work performance. Nutrition surveys have long linked heavy household qat use with worse child feeding, not because the leaf is a toxin in the child’s bowl, but because money and adult attention go elsewhere.

Health effects in regular adult users are well described and usually unspectacular: insomnia, constipation, dry mouth, irritability, raised heart rate and blood pressure, and lost calories. Dependence — difficulty stopping despite wanting to — is reported by many users. Severe psychiatric reactions occur in a minority; they are not the typical daily outcome.

What it costs the land

This is the part least in dispute among water specialists.

Qat occupies a disproportionate share of irrigated highland land. National estimates have often put it at roughly **30 to 40 percent of irrigation water**. In the Sanaa basin, study after study finds qat using a similar share of agricultural withdrawals, and more than that in some sub-zones. The plant wants frequent irrigation before harvest. Farmers have every reason to pump.

Yemen was already one of the world’s most water-scarce countries. Agriculture takes most of the withdrawals. Groundwater in basins such as Sanaa has been pulled far faster than it refills. Wells have been driven hundreds of meters down. Solar pumps, which spread during the war when diesel was scarce or expensive, made it easier to keep irrigating. Qat is not the only thirsty crop, and mismanagement is not new. It is the crop whose acreage has kept rising while cereals have not.

The result is not a metaphor. It is falling water tables, more expensive well water for cities, and less water left for food crops and taps.

Food insecurity is larger than qat

Yemen’s hunger crisis is driven first by war, a split economy and currency, displacement, and cuts or obstacles to aid. IPC analyses in recent years have classified large populations in Phase 3 (Crisis) and Phase 4 (Emergency), with limited pockets of Phase 5 (Catastrophe) in specific districts at certain times.

Qat belongs on that list of aggravating factors: it soaks up cash, hours, and irrigation water that could go to food. It is not a substitute for an explanation of the war.

Saudi Arabia and the UAE restrict or ban qat. That is a public-health and social-policy choice in those countries.

What would actually reduce the harm

Neutral analysis points to unglamorous tools, most of them tried in pieces and rarely sustained: water-efficient irrigation, ending implicit subsidies that make pumping too cheap, alternative cash crops where they can compete, public information on child nutrition, and treatment for people who want to stop. None of that works well in an active war with two monetary systems and a collapsing aquifer.

The sober summary

Qat is a mainstream male habit in highland Yemen. It drains household cash, competes with food, and uses a striking share of scarce groundwater. Those effects on civilians are documented

How Students Can Use AI Without Cognitive Surrender

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training issued a report that states that” There are early signals, however, that overreliance on chatbots can have a range of significant negative consequences – diminishing critical thinking, weakening memory, eroding confidence, and undermining mastery. Getting the right answer from a chatbot can create the illusion of learning – but it can also trigger “cognitive surrender”, where students fall back on AI at the first hint of struggle.”

Here is my modest proposal to prevent the “cognitive surrender” mentions in the report.

Proposal: Keep the Human in the Loop on Purpose

Problem. Generative AI can already finish most written schoolwork and a lot of office work. The rational short-term move is to hand it the hard part. The long-term cost is “cognitive surrender”: weaker memory, thinner judgment, and confidence that belongs to the model, not the person.

Goal. Not ban AI. Not pretend everyone will self-discipline. Redesign work so the human still has to *start, check, and own* the thinking.

Rule. Augment. Do not automate the part that makes you better.

How.

1. Split every task into two layers.
Layer A is allowed to be AI: first draft, search, format, summarize sources you already found.
Layer B is not: the question, the outline, the claim, the error hunt, the “what would change my mind” paragraph. Grade or pay for Layer B.

2. Require a visible struggle log.
Before the model is opened: write the problem in your own words and your first attempt. After: list what you accepted, what you rejected, and why. No log, no credit. This is cheap and it forces the brain to stay in the room.

3. Test with the tool off.
Closed-book, no-laptop slices of the same skill. If you cannot reconstruct the argument without the chatbot, you did not learn it. Institutions should treat that as the real exam; take-home work is practice.

4. Make the human the scarce resource.
Assignments and jobs should reward catching the model’s confident error, not producing more fluent pages. “Find the hole” is a better use of AI than “write the paper.”

5. Stop pretending detection is a strategy.
Detectors are weak and poison trust. Policy should be: permitted uses named in advance, prohibited uses named in advance, and assessment designed so cheating is pointless.

6. Train the skill, not the slogan.
“AI literacy” that only means prompting is how atrophy happens. Teach: when to distrust fluency, how to verify a citation, how to keep a working memory of the argument, how to notice you have stopped thinking.

**What success looks like.** People still use the model. They just cannot hide behind it. Output may be faster. The person should still be able to explain, defend, and redo the work without it.

**What failure looks like.** Faster essays, faster amendments, emptier heads — then surprise that nobody can tell when the machine is wrong.

The conundrum is not AI. It is work designed so the easiest path is not thinking. Change the work.