ICOs: Finding the code with the Midas touch

Real life coins have become a bit of a nuisance for many. Useful perhaps as laundry money or food for slot machines.

Digital coins are the attractive younger brother. They give you access to all sorts of services. They can be rapidly exchanged all over the world through the distributed ledger.

These so-called “utility tokens” typically enter the world through Initial Coin Offerings. More and more entrepreneurs are geeking out over such ICOs, which they often link to a claim on a future service their venture will provide. The market for ICOs has grown from practically nothing in 2013 to $8bn in 2017.

Yet their value is clearly very volatile, as any Bitcoin-holder can attest to. Investor due diligence is paramount. How then do buyers select the coins with the golden prospects?

The main differentiator, according to German academic Christian Fisch, is the perceived strength of the underlying code. His study of 423 ICOs finds that offerings backed by a technical white paper generally raise more money. It adds a certain academic prestige to the currency, as Nakamoto’s 2008 white paper did for Bitcoin. The longer this research document, the more real-world $ you tend to raise.

The same applies to coding frameworks that have undergone more rigorous programming. Many ICO fundraising teams publish their code on programmer platforms such as GitHub. This allows a non-technical observer to scrutinise the iterations the coding has undergone. ICOs associated with more bug fixes typically attract higher investor demand.

Of course, the investor need for complete openness creates a difficult trade-off for tech-preneurs. It may help me lock in the necessary funding, but it could also instigate copycat business models.

It is not easy to let go of “Ctrl+C, Ctrl+V” paranoias. But many entrepreneurs will have to : it takes vulnerability to be successful in turning digital coins into real cash.

Fisch, C. (2019). “Initial coin offerings to finance new ventures”. Journal of Business Venturing 34: 1–22

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