# To farm or not to farm

**Introduction **

As friend.tech is gaining massive traction crypto natives have been deploying more capital to the platform to “farm” the future airdropped tokens. I became more interested to find out how lucrative this could be and decided to dedicate this piece to do some simple napkin maths to make sense of this phenomenon.

**# 1 - Quantifying dollar value per points **

To start with friend.tech is currently generating roughly 800k to 1mn per day in terms of top line fees; and this translates to an annualised fees of close to 300mn to 365mn. Assuming the market prices a top down FDV / Fees multiple of 10x to 15x to fees; this would give us a valuation of anywhere between 3bn to 4bn as of the current traction.

Taking reference from previous airdrop (e.g. Blur); and assuming friend.tech drops 10% of the total token supply; the total wealth created to users through this airdrop would be anywhere between 300mn to 400mn.

friend.tech would also be giving out 100mn points over the course of 25 weeks to reward activities and engagement on the platform. This means each point would worth roughly 3 to 4 USD if the above assumption makes sense.

**#2 - Optimizing for the pointing system**

Since everything in friend.tech is denominated in points; the airdrop farming strategy should therefore be optimised for this same criterion.

friend.tech analysts on X have derived that the major weighting of points come from portfolio value which accounts for 83% and that the # of holder counts and holding count also matters.

Therefore airdrop farmers should be optimising for portfolio value before everything else to maximize the airdropped rewards; and the natural extension to that question is how do portfolio value increase?

**#3 - Optimizing for increasing portfolio value **

The simple answer to that question would be to acquire key that go up in value; and specifically for every dollar you deployed on the platform you hope to get the maximum return out of it; so now the question becomes how should I do it?

Imagine a universe where all friend.tech users could only purchase keys of one specific account; the purpose of doing such a control test is so understand the economics within one account better; and we end up in 4 different scenario

**only one buyer for all keys**- the ideal scenario; portfolio value goes up as you’re consistently buying the only key; you lock up all the selling pressure in the market and also enjoy from the multiplier effect of owning the same key**community driven portfolio value growth**- what we call (3,3) and effectively you’re trusting someone not dumping on your before you dump on them; it could give you the best return per capital deployed but the certainty to this method is low; so technically speaking you do not optimise for portfolio value**exit liquidity**- you are basically late to the game and are buying over inflated key prices and chances are others would dump their keys on you for obvious reasons**auto piloting**; just an edge case that you do not spend a penny acquiring keys so you do not have any skin in the game

Let’s say scenario 1 gives you the best “expected” return out of all cases; and all other cases should theoretically give you a worse “expected return”. Therefore the logic here is if scenario 1 does not justify an investment case; neither of the other cases should be financially attractive.

**#4 - What is the expected return**

The return on farming friend.tech points / tokens could be simplified in a simple math formula = (# of keys * value of each keys * points per ETH in portfolio value * USD per point * # of remaining weeks)

Consider a case that you hold 100 identical keys with a key price of 1E

# of keys = 100 as mentioned above

value of keys = 1E hypothetically speaking

point per ETH in portfolio value = 40 to 170; taking 50 points to be conservative

USD per point = 3 to 4 as the calculation above in #1

# of remaining week = 17 weeks; as we are in week 9 now

You would receive 100 * 1 * 50 * 3 * 17 = 255k by the end of the week 25; and this is also to assume friend.tech does not penalise self purchasing and the repeated purchase of one specific keys; which they have already started doing so. Let’s separately discuss.

**#5 - What is the required capital?**

The math looks a bit tricky when it comes to calculating upfront investments given how buying and selling works in friend.tech and there’s also a 10% tax taken (i.e. 5% to the account and 5% to friend.tech) by the platform.

On friend.tech when you’re buying a key you’re effectively paying the key price of the next level; and when you’re selling a key you’re receiving the price of the key at the previous key level.

In this scenario analysis let’s also assume one is buying and selling his own keys since one would effectively rebates from this operation. The purpose to place this assumption is to simply minimise the upfront cost. Again, if the return multiple after minimising the cost does not present an investment case; then neither would other cases does.

According to the calculation; it takes roughly 33 ETH for someone to keep buying one’s key until a portfolio value of 100 ETH; and around 50 ETH for someone to reach a portfolio value of 150 ETH through buying his own keys and so on.

Also after the airdrop the farmer would logically liquidate his own keys to recoup his costs for the entire operation. Given the 10% buying and selling tax and how key prices are determined according to the bonding curve; one would inevitably incur fixed costs through this operation one way or another.

If we only consider the upfront ETH spent to acquire keys up to a certain level; the expected return is roughly 6x to 7x; meaning if you deploy 500k to acquire your own keys only you could “theoretically” receive >3mn by the end of week 25.

If we also consider the ETH received upon liquidation the investment case would be a lot more attractive with return multiple at >100x; meaning for the 3mn we’re receiving by the end of the game; we are effectively only investing <30k.

**#6 - What if we could recoup some of our upfront cost **

In a perfect universe that only one person is buying and selling his own keys; the net investments (i.e. buying the keys up to a certain level and selling it back) could be minimal at 1 to 2 ETH even with a portfolio value of >100 ETH; but we know this would never be the case thanks to bots and other random buying and selling.

Therefore it is also important to consider the effect under the scenario that we could not recoup most of the upfront ETH we spent to acquire keys. Theoretically speaking as long as someone else is selling earlier before you do; chances are you already could not get back 100% of the investments.

Assuming we only manage to recoup 50% to 60% of the upfront cost; the return multiple could still be at 9x to 16x depending on the portfolio value; and if we only mange to get 20% to 30% the return multiple would actually be at 7x to 11x; which might not be entirely attractive considering the lack of transparency of the pointing system and execution efforts.

Not to mention this return profile is premising on the best case scenario; where one is only buying the same keys enjoying the benefits of multiplier effects; self propelled key price and absolute certainty.

In other cases where friend.tech users are counting on whales and other accounts to propel the price of the keys or that they are not placing concentrated enough bets; the expected return profile would not look as robust as the numbers shown above.

**Quick concluding thoughts **

The word “expected return” is important here since there would always be edge cases that whales keep buying his own keys to propel the price growth and early key holders could benefitting from this and receive a big chunk of points.

But again the word “expected" return” also creates a blindspot where we fail to appreciate of the system and see the asymmetric upside when friend.tech manages to really onboard some other crowded outside of crypto natives.

However, I found this surprising when crypto natives are piling capital into farming friend.tech points when the pointing system and expected return profile are not as robust as everyone has thought; seems like the crowd really do have faith in the entire (3,3) proposition or more accurately faith on someone else in the internet.