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The yellow-brick road to product market fit

Experiments in streaming video began as early as the 1980s when pilot programs first allowed viewers in select homes to order movies on-demand with their remote controls. 

The technology wasn’t quite ready for mass adoption, however.

When customers hit the order button on their remotes, an attendant at the phone or cable company would race to a bank of cassette tapes, pick out the requested movie, hustle it to a VHS machine connected directly to the home that made the request, enter the cassette and hit play.

Crucially, the attendants had to be quick because the companies wanted to create the illusion that their service was automated.

To do so, some attendants resorted to racing back and forth on roller skates.

Sadly, none of those early pilot programs survived, perhaps for a lack of qualified attendants (roller-skating was popular back then, but it wasn’t that popular).

But the service was a hit, demonstrating to cable companies that offering movies on-demand was a business worth investing in — the fully automated streaming video you watch today was largely inspired by those roller-skating pioneers. 

The crypto industry, still unsure of its purpose, needs similar inspiration. 

The real MVP

There’s a long history of “automata” in which a manual process is hidden behind a facade of automation, the most famous of which is the Mechanical Turk chess-playing “machine” of the 18th century (really just a small chess player hiding in a big box).

On-demand video delivered by roller-skating attendants belongs to this same tradition, but may have historical significance as one of the first examples of automata as a business model.

There have been quite a few examples since.  

If you were an early customer of Zappos, for example, you probably had the impression you were buying shoes held in a Zappos warehouse — in reality, the photos you saw on Zappos’ website were taken by one of the founders at a local shoe store. And when you placed an order, the founder would return to the store, find the pair you ordered, buy it, take it to the post office and send it to you.

Similarly, every DVD rented in the early days of Netflix’s mail-order business was manually packed and mailed by a Netflix employee — employees stuffed 137 such envelopes on launch day in 1998 and then millions more until being relieved by automated fulfillment centers in 2002. 

The most extreme example is perhaps AirBnB, which the founders launched without any rooms to offer — what early customers saw on the website was a generic description of a room in, say, San Francisco, and if you booked one, the founders would call around to friends and family to find something vaguely matching the description.

This makeshift strategy has worked for enough founders and has become a common enough business practice that it’s been given a name: The Wizard of Oz MVP.

That’s “MVP” as in “minimum-viable product.”

The idea is that you make the absolute minimum investment required to offer a service to a small number of customers by whatever means you can — even if it’s unscalable — simply to discover if there’s much demand for it. 

If customers respond to your manual (and therefore slow, janky and not-quite-what-was-promised) service, you’ll know it’s worth investing the money required to offer a fully automated, fully scalable version. 

Sometimes, the manual version is offered well in advance of when a fully automated version is even possible.

When the success of video-on-demand-by-roller-skates proved a hit, it inspired Time Warner to offer a fully automated version to a small number of customers in 1994, even though the tech wasn’t quite ready.

The interactive set-top boxes Time Warner developed to replace roller skaters cost $5,000 each, which they knew was uneconomic beyond a small pilot program in Orlando, FL. 

But the pilot program allowed them to find out "what people will want when the equipment that is now so expensive becomes affordable several years down the road."

Once the tech was more affordable, on-demand video via cable became ubiquitous — and now, a bit further down the road, cable is being replaced by streaming video.

It doesn’t always work out in a straight line like that.

Google, for example, shut down its “social search engine,” Aardvark, in 2011 because there was no obvious business model for a search product that routed every question to a human to answer.

In hindsight, though, Aardvark looks a lot like a Mechanical Turk version of an LLM chatbot — so it may have had value in helping Google understand question patterns and user behavior before developing AI algorithms to automate the process.

Some of this happens in crypto, too.

Solana’s layer-one blockchain and Ethereum’s layer-two blockchains are finding product market fit for cheap transactions (memecoins, mostly) while being far from finished products.

The much-derided TRON blockchain has proven there’s demand for onchain digital dollars in emerging markets.

DePIN projects are currently throwing a lot of spaghetti on the wall to see what might stick.

But we need much more of this.

As a whole, the crypto industry remains overwhelmingly focused on infrastructure — so much so that we seem to be putting the cart before the horse.

Instead of experimenting with applications to find out what direction the tech needs to go in, the tech is racing out ahead of any ideas of what we might do with it.

Builders often get the blame for this: Why are they building the nth L2 blockchain when we’re already overrun with unused blockspace???

But I blame investors — the builders are just responding to the incentives we’re giving them when we bid every new blockchain token to a multi-billion dollar valuation. 

Instead, we should be bidding up projects trying to make money from customers — not just technologists trying to raise money from VCs.

We need both applications and technology, of course, but the balance is dangerously off in crypto.

If all this tech is ever going to be used for anything other than trading memecoins, we’ll have to make more of an effort to find out what else people might want to use it for.

Even if that means racing around on roller skates for a while.

— Byron Gilliam

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