LAPTOP launch economics

What a fee design costs the people buying, and why this page will not tell you which rate to pick. checker · size curve · where to buy · standing order · getting a slot · get ready

This page is for whoever sets the launch parameters, not for buyers. It does arithmetic on numbers you type. It does not ask you to sign anything, cannot send a transaction, and does not deploy or configure anything.

The book

you can estimate these

Your schedule

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The guesses

nothing here is fitted

How buyers respond to a tax. Snipers are near-unit-elastic to the buy tax and nearly indifferent to the sell tax — they are out in minutes. People are the reverse: a buy tax is a fee, a sell tax is a trap. Each type has a knee, the rate at which half of them stop buying. None of these have been fit to an observed book. This page has no network access and no launch data, so every number below is a prior, and the section under it exists because the answer depends on them more than on anything you set above.

What this cannot tell you

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The best starting tax, computed in 81 worlds — every guess above halved, kept and doubled. If the bars cluster, the answer is robust. If they spread, the model is reporting your priors back to you.

What a buyer pays

no guess involved

Pure arithmetic on your schedule and the pool fee. This is identical in all 81 worlds because no demand parameter appears in it.

Where the tax goes

Three different objectives, reported in three different units. They are deliberately not added up or converted: valuing a recycled or burned tax means marking holdings at a price this model itself moved, which is circular and was producing numbers 11,000× too big in an earlier draft.

Publish this

The checker flags other tokens for undisclosed taxes. If LAPTOP has one and this site does not say so, the checker fails its own test.

Why there is no recommended rate. An earlier version of this page swept the starting tax against a single elasticity number and reported an optimum — 2% at the defaults. Replacing that one number with two buyer types and a knee moved the answer to either 0% or 8–10% depending on parameters nobody here can measure, and almost never to anything in between. The 2% was the answer a straight-line model gives when the real surface is bimodal. Neither parameterisation is fitted, so neither is a recommendation. The band above is the honest output.

What would fix it. First-hour order books from comparable Base launches: sizes, arrival clustering, and how participation actually fell as fees rose. With those, the knees become measurements and the band collapses. Without them this page can price a schedule but cannot choose one.

What it still cannot model. Snipers routing around a schedule by splitting across wallets or waiting out a decaying ramp; a hook that reverts; the launch not happening. It assumes every buyer sells exactly once, which overstates sell-tax income if some hold. It prices one launch, not the reputational cost of the design.

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