Gen AI companions and the fight for the primary interface

The race for the primary interface in the age of AI

Everyone (and their dog) wants to build a Gen AI companion… or co-pilot… or assistant… or sidekick.

You get the drift!

Yet, users don’t want hundreds of companions. Ideally, a user wants just one. A single AI companion that partners with them across their entire spectrum of needs.

Winning at the Gen AI companion game is a unique opportunity to gain control of the primary interface.

How exactly do you pursue this opportunity and who’s best positioned to win here?

This post is third in a series of posts on competitive advantage with Gen AI. You can view the first two at How to lose at Gen AI and How to win at Gen AI.

Let’s dig into this one!

How to productize a companion

What do companions do?

Our real-life companions combine three key attributes.

They have empathy, they understand us deeply. They bring expertise to a situation (particularly if they play the role of assistant, advisor, mentor, or coach). And they have the ability to engage and influence.

Companions combine empathy, engagement, and expertise.

Empathy requires deep understanding. This is productized and scaled digitally through data capture and learning models.

Engagement and the right to be a companion requires real-world connection. Over the past decade, we’ve perfected the (dark) art of productizing this through habit design.

Expertise requires knowledge. This is now productized through LLMs and subsequent fine-tuning towards a context.

Over the past decade, we’ve combined empathy (data capture) with engagement (habit design) to capture the primary interface. The news feed (think Facebook), the infinite scroll (think Pinterest), and the infinite swipe (think TikTok) have all worked on this paradigm to gain an ever-increasing share of the primary interface.

GenAI presents a potential discontinuity to that paradigm by leveraging expertise to dismantle the advantages of empathy (share of data) and engagement (share of usage).

Or, conversely, as I explained in How to lose at Gen AI, it will merely reinforce the ‘companion’ position of current players, who will combine their advantages in data capture and habit design to now embed expertise into their interface.

Owning the primary interface

Owning the primary interface is the key source of competitive advantage in the attention economy. When attention is scarce, whoever can harness that attention and then allocate it amongst partners occupies the strongest position in an ecosystem.

To create a primary layer across the spectrum of use cases, you need to :

1. Maximize usage on your interface
2. Maximum data capture through your interface (or even outside it)Every player wants to move up the Companion Index and capture higher ‘share of usage’ and ‘share of data’ across the user’s life.So who’s best positioned to go for the primary interface?Let’s dig in with a few examples!
   

   Incumbent plays for the primary interface

   Before the recent improvement in LLMs, most players relied on data capture and habit design to gain the right to win the primary interface.

   These players leveraged one or more of four common paths to gain control of the primary interface and they are the best positioned to further cement that dominance leveraging AI.

   1. High engagement on the core use case

   One path up this index is for a player to deliver high engagement on its core use case. A social product which gets progressively better at personalising its news feed is an example of breaking out from the bottom-left to the top-right quadrant.

   This is the most common way in which aggregators have gained control over the primary interface.

   AI companion bundling:

   An AI companion can be bundled here to improve stickiness on the primary interface, either by improving generative capabilities to support content creation use cases or by improving navigability and discovery, using an assistant.

   2. The super-app path

   Another path up this index is to extend your dominant position in one use case to then extend to adjacent use cases.

   As we’ve seen before, if you’re looking to be a super-app, you will most likely fail. The test of a super-app is not in its ability to house multiple apps, it is in its ability to first win the primary interface and only then (eventually) extend that dominance by adding more use cases.

   In China, WeChat owns the primary interface. It is that one super-app which can claim to have the right to the primary interface.

   As I explain in How to win at Gen AI:

   WeChat started as an individual app with vertical advantage in gaming and communication. However, it moved on to use its dominance in communication to establish primacy of user relationship as the default communication channel. It then integrated communication with payments to control the two most important horizontal capabilities that allowed it to establish a new layer on top of the underlying OS.

   Users spent most of their time inside WeChat. Users never left the WeChat workflow because of its all-in-one connective workflow of communication and payments across apps.

   When a player starts out on the super-app journey, it gains share-of-usage by increasing engagement and driving habit creation on the core use case. But once it expands to other use cases as a super-app, its ability to expand share-of-usage is determined by its ability to effectively manage workflow across multiple use cases that it now supports.

   AI companion bundling:

   On super-apps, AI companion bundling delivers competitive advantage to the extent that it can improve workflow coordination across the various use cases that the super-app supports. Instead of having to switch in and out of different mini-apps and chat interfaces, an AI companion can string together the entire experience by acting as a single interface through which all these services are accessed and activated. Eventually, this can also help a super-app extend share-of-usage across new use cases without having to bundle them as new mini-apps inside the interface.

   3. The workflow hub

   B2B SAAS players, which gain share-of-usage by dominating a specific use case, subsequently establish themselves as a workflow hub by encouraging integrations with other SAAS products. This is a common pattern as B2B users avoid fragmenting their workflows and would rather have multiple apps integrating into a central workflow hub.

   Over the past several years, Hubspot has increasingly sought to pursue this strategy. Project management software providers like Asana also bet on gaining share-of-usage and share-of-data through this strategy.

   AI companion bundling:

   B2B workflow hubs are natural positions for AI companion bundling. An effective AI companion can help reduce cognitive overload associated with multiple integrations and task switching and serve as a central point of interaction across the integrations and the integrated workflow.

   For product teams at the central workflow provider, this also reduces the cost of serving multiple personas by creating different workflows across the integrations for different personas. An AI companion can rebundle multiple capabilities to best serve a persona’s needs moving the burden of rebundling integrations away from product managers to AI agents managed by users.