ChatGPT launched November 30th, 2022. Within 5 days, it had a million users. By January, just a couple of months post-launch, 100 million users had signed up. It was a starting-gun moment for what’s been the most incredible and rapid technological development in decades, or arguably ever. New developments in AI are arriving so fast it’s hard to keep up, and even in the last few months, that pace has sped up even more, as consumers, companies, and even governments realize the growing impact this technology will have on our society.
This is an area that I’ve been closely following for years. For a couple of years before I dropped out to work on my first startup, I was an applied math PhD student, and my area of focus was optimization theory—an area that’s now core to training the deep learning models that power today’s AI features.
When I was at Apple, our team applied similar techniques to extract features from satellite and street imagery—saving literally millions of hours of manual work for human map editors. And three years before its launch in 2019, I saw early development prototypes of ChatGPT at a research conference—even that early, it was clear they were onto something big.
All of this is to say I now spend a lot of my time reading about what’s happening in AI and specifically how companies like August Health, as well as the Senior Living industry generally, should be using AI. It’s clear that the industry is curious: seemingly every conference session, industry panel, and customer conversation either includes or is dominated by a focus on AI.
I’m lucky enough to speak with people in the industry every day, and AI comes up frequently—lots of questions about what to do, how to implement, what other people are doing, etc. I find myself coming back to two core ideas during these conversations, so I thought I’d share them.
AI-native
There’s a rush among developers and companies to try just about everything AI-related.
Developers are exploring new paradigms for how we interact with AI. Tools now exist to compose music, take meeting notes, write blog posts, edit images, and review contracts.
But the sheer volume and speed of innovation can be overwhelming. Companies are grappling with how to transition their operations to be AI-native. Tools can now draft support responses or generate sales emails—but how should we actually use them? How can we trust them? And whose job is it to figure this out?
The answer isn’t obvious. In fact, adopting AI without a thoughtful strategy can lead to inefficiency and noise, especially when new tools are layered on top of unresolved operational challenges. And in senior living—an industry historically underserved by technology—breaking from the status quo is even harder. Organizational dynamics mean that it takes real work, rowing upstream to change the internal operations of a company.
Tobi Lütke, CEO of Shopify, recently issued an internal memo that framed AI adoption as a core expectation: “Using AI effectively is now a fundamental expectation of everyone at Shopify.” Teams must now justify why a task can’t be done by AI before making a new hire.
That doesn’t mean every organization should follow Shopify’s exact model—it's a highly technical company with its own culture. But the underlying message is powerful: learning and experimenting with AI is no longer optional. Organizations that lean in—senior living operators included—and invest in figuring out where these tools add value, while helping teams get comfortable with them, will outpace their peers.
My point here is that AI-native organizations are not just about acquiring new tools. That’s the easy part. The real work lies in evolving an organization to prioritize adaptability, nurture curiosity, and build the infrastructure that enables people to grow with the change. It’s time to get going: start where you can, experiment, and have fun with it.
Contextual AI
Another key point I often emphasize is that we need to move beyond chat interfaces when thinking about how AI can support senior living.
As the instigator of the AI boom, ChatGPT, for better or worse, has largely pushed most AI tools to a simple, blank chat interface (it’s in the name!). Chat is great for a lot of use-cases—especially open-ended ones like writing or research.
But for a specific, neatly defined use-case (say, creating a resident’s care plan), starting from a blank chat-screen isn’t that useful. Consider how disruptive it would be to derail a caregiver’s workflow with a bunch of prompting and re-prompting while attempting to get the desired output from a chat interface.
Providing contextual information to the user, at the right time and place in their flow, is much more useful in this scenario. It keeps the user in the flow. For well-defined use cases, where we know what types of information matter, we can use contextual AI to surface relevant insights—improving both speed and user experience, as well as driving better outcomes.
Building out contextual AI like this is hard. It requires a deep understanding of workflow, intent, and motivation. But when it’s done correctly, it can be absolutely delightful. It’s something we’ve incorporated into our platform at August Health, and will continue to do so.
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We’re still in the early days of AI in senior living. Most of what’s out there today is experimental—bolted onto existing workflows rather than integrated into them. But meaningful change is coming. The organizations best positioned to lead won’t just adopt AI tools; they’ll adapt their operations to be AI-native. That means designing workflows where information appears exactly when and where it’s needed, not buried in a chat thread or waiting to be requested. The real opportunity isn’t just automation—it’s about making work feel smoother, faster, and more intuitive. The companies that understand this—and build accordingly—will quietly pull ahead.
Thanks to Andrew Rayhons and Jay T. Reed for reading drafts of this.
