Argo, Lyft, And Aggregation Theory

There’s a lot of branding on this vehicle.

Argo will launch robotaxis on Lyft’s ridesharing network in Miami by the end of 2021, and in Austin in 2022, according to both a press release last week and an article by CEO Brian Salesky announced in the company’s Ground Truth online magazine.

“Companies with the three key aspects required to launch, validate and scale an autonomous ride-hailing service in cities are directly working together: the self-driving system developed by the Argo AI team; the vehicles manufactured by one of our partners, Ford Motor Company; and the riders on Lyft’s transportation network.”

Argo CEO Brian Salesky

The term that catches my eye in that quote sentence is “riders.” Argo is working with Lyft not because of the ride-hailing network, or the app, but rather because Lyft is where the customers are. Aurora has recently been emphasizing a similar line of thinking with its Uber partnership.

For years, Stratechery analyst Ben Thompson has been developing Aggregation Theory, which purports to explain the most dominant companies that run two-sided networks. The “aggregators,” as Thompson calls them, increase their power by aggregating customer demand and ultimately bringing suppliers onto the network on the aggregator’s terms.

Thompson has labeled and analyzed Google, Facebook, Amazon, Netflix, Snapchat, Airbnb, and (most relevant to mobility) Uber as aggregators.

Salesky’s quote seems to validate Thompson’s theory that Argo is going to Lyft because Lyft has aggregated the customer demand.

On the one hand, that is the fastest way for Argo to tap into demand, gather data, and get to “scale,” a term Salesky emphasizes in his article. On the other hand, Thompson has emphasized again and again that the goal of aggregators is to modularize and commoditize suppliers, reducing their market power.

The reliable route to success in an aggregation-friendly market is to maintain your own customer relationships.

2 thoughts on “Argo, Lyft, And Aggregation Theory

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