I graded the thesis 6/10, below the pay bar of 8. The report is at artifacts/report.md and ends with the required JSON block; I checked that the block parses and gives quality: 6. There is also a short artifacts/README.md describing the question and limits. Neither file is committed: git status doesn't list artifacts/, which suggests it's gitignored.

Why 6 and not higher:

• Strengths: it turns the "fake demand" argument into a clear metric you can test: track each requester from their first subsidized job, see whether they pay for one themselves, and watch whether the subsidy cost per converted payer falls over time. It names concrete filters (30-day repeat window, excluding linked wallets and duplicate requests). Its one point specific to IMD's model is a real one: if trading fees fund the subsidies, the budget for new users shrinks exactly when activity is weakest.
• Mostly borrowed: cohort retention and free-to-paid conversion are standard SaaS growth metrics with an IMD label on them.
• Unverified premise: the public IMD docs (imd.fun/docs) only show jobs paid by a wallet at a fixed 0.5 IMD. I found no path where SIMD or a subsidy pays for jobs, and none of the fee flows I found go to job subsidies.
• No data: the live API showed 1,380 paid orders on 2026-10-06, but the author gives no numbers at all.
• Easy to game: a farmer can collect subsidized work and then pay 0.5 IMD once to count as "converted", and linked-wallet filters are easy to dodge.
• Other gaps: the metric is priced in IMD, so it moves with the token price. There are no counter-arguments, and nothing says what SIMD should do when conversion is low.

Limits:

• The tweet itself returned an HTTP 402 error, so I graded the text supplied in the task.
• A web search for "SuperIMD" found nothing.
• A different, unrelated "SIMD" token (a simulation compute project) also exists and shows up in searches.
• Follower count was not used.

The report keeps checked facts, my own inferences and open questions in separate sections, with links for each fact.