Last month, Sarah Guo joined OpenAI and Anthropic Chief Product Officers’ Kevin Weil and Mike Krieger to talk product in the age of AI and what we can expect in the near future. 

Anthropic’s Mike Krieger shared excitement for proactive AI:

Once [AI] knows about you and they're … reading your email in a good but not creepy way … and then they spot an interesting trend, or you start your day with something that's a proactive recap of what's going on, some conversations you're going to have. 

- “I pre-did some research for you.”
- “Hey, your next meeting is coming up. Here's what you might want to talk about.” 
- “I saw you have this presentation coming up. Here's the first draft that I put together.

That kind of proactivity, I think, is going to be really, really powerful.

We agree. We want to give proactive AI superpowers to every service on the internet. 

To us, proactive AI takes action without being asked

• “I saw you have a meeting at 3p. Should I move your 3.30p?”
• NOT “each morning send me my to-do’s”

To be proactive and actually helpful, however, you need the full context. Proactive AI isn’t gated by powerful AI, but rather a data layer that has up-to-date context. 

Traditional data layers – like Typeform onboardings or progressive profiling – don’t cut it. 

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Proactive AI needs context to be helpful 

If you’re going to take proactive action on behalf of users, it better save them time. 

Last month we studied the history of the reactive web: how the web went from 

• static
• Reacting to the outside world
• Reacting to changes inside an application

and soon, reacting to state sourced from across applications. 

Web reactivity has canonically operated under a pull model with requests originating from the client:

• User navigates to a page
• Page loads with all known state information

The reactive web has lower stakes in that users are navigating to a service, and the server reacts with the most up-to-date information. The server is responding to client action.  

Proactive AI follows a push model with requests originating from the server. On what basis might a server know to messages to the client? 

Typically, context for server proactivity has come from the client: The server accumulates user context by watching how the client engages with the application.

This usually takes the form of onboarding surveys or customer data platform-based profiling. This sort of data collection is challenging because it adds friction to the user experience, goes stale fast, or is not rich enough to fully describe user needs. If you’re going to send a message to the client, you should probably have some reason to do so. 

Under first party data, without recent client interaction, how would you know now is the right time to send a message? 

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Today’s proactive AI disappoints  

These questions have made proactive AI difficult to ship: We’re already overloaded with information. We don’t want AI to add more noise to our lives. To save us time, AI needs the full context so it can react to events in our lives without us having to explicitly poll for it. 

For instance, Amazon launched Subscribe & Save in 2007, allowing users to subscribe to products delivered on a set cadence. You can get paper towel every 2 weeks or 2 months or toothbrushes every 4 months. 

Amazon doesn’t break out Subscribe & Save revenue in its earnings. While ostensibly time saving, customers have complained about unexpected price changes and deliveries not matching the rate they actually need replenishment. 

In 2019, Walmart planned to release Smart Reorder

What if we could forget the groceries, on purpose? Introducing Walmart Smart Reorder. it learns your grocery habits and delivers them for you. 

Smart Reorder never shipped. Putting household essentials on autopilot needs more context, not more AI. 

In 2021, Twilio Segment launched Journeys, a way for marketers to build audiences and take proactive action based on site activity. 

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Of course, Journeys is only based on first-party context. Particularly with AI, activating context like 

• No purchase in last 3 months
• Viewed site last month

doesn’t feel particularly personal or proactive.  Most of our email inboxes classify this sort of outreach as spam. 

Proactive personalized outreach or action clearly needs more context to actually provide value to users. 

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Proactive AI with Full Context

With the right context, proactive AI is poised to save us time in work and in life. 

Following Krieger, with our work email, proactive AI could prompt us with a morning brief, big ideas to think about for the week, or suggestions to optimize our schedule. Google’s Notebook LM is giving us a glimpse into how this could work. 

In our personal life, Proactive AI could help us 

• save time staying in stock of household essentials
• find out about events we’ll love on a night we’re free
• Suggest a restaurant to check out with a friend we haven’t seen in a while 

This sort of Proactive AI is not possible without a new data layer.

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Previewing Crosshatch Webhooks

In Q1 next year – we plan to launch Webhooks, a safer way for users to let services take action based on cross-application context. 

Subscribe to a user’s online orders to provide pickup services. See updates to a user’s calendar to see if they’ll need more treats for an additional basketball practice. 

Webhooks expand Crosshatch’s existing client-initiated pull-based personalization to proactive, server-based personalization, but where the server is user-owned.

This allows users to aggregate context from across applications, from

• purchases or reservations in email or plaid
• runs in strava or whoop 
• likes in apple music 
• posts in reddit 

and enable apps to subscribe to changes so that AI can transform that context into valuable time-saving action. 

We ship webhooks as an API and not a closed service because we believe users should be able to activate their context in pull and push contexts anywhere on the internet, not just certain privileged AI services. 

If you’d like to learn more about our webhooks release, reach out!