I am excited about a new iOS release for the first time in five years. It looks like we may finally get a Siri that works and can use my data, stored on my phone, without me having to devolve to saying “No Siri, stop” through gritted teeth every time it does something daft.
The Siri that Apple promised last week at WWDC is not groundbreaking. Indeed, it seems to do roughly what Apple promised all the way back at WWDC 2024: reasoning across messages, calendar, and email, and responding to fairly basic queries.
The AI world has moved on since 2024. Two years ago agents were speculative, there was no Claude Code, and AI still meant chat. But, the truth is that for most day to day use cases, none of the advances are really that important. If early reviews are to be trusted, Apple has delivered on its 2024 AI vision, and it may be more than enough for most day to day use.
So what changed?
Apple replaced the system search index that powers Spotlight. The new index is semantic, meaning it understands content rather than just matching keywords. The index lives on each device, and Apple’s new models, both on device and in the cloud, can use that index to serve user queries.
Apple has learnt a painful lesson. It doesn’t really matter how capable your models are: if the underlying data is not in a useful state, even the best models in the world will fail. The single point of failure for the entire platform is the index. Models are fungible, but your data is truly yours.
I see the same pattern play out over and over again in our AI work at Jeavio. A client asks for an agent, or a complex AI-powered workflow. But as we have learnt, the agent can only succeed if it has access to a data substrate: the layer built for the customer’s business domain. Otherwise you can throw the most expensive, most performant model at it, and all you get is a flashy demo, sort of like Apple’s presentation at WWDC 2024.
So when I start a client project, often the first thing I look for is the data substrate. That should be the layer where the engineering goes. Get the substrate right, and then build the capabilities on top.
The moat is the index, not the model. Models will become commodities, but the data is where the value lies.
