Six Months of Daily AI Use Taught Me the App Layer Is Doomed

Why the Wrapper Layer Won't Survive

Right now, a whole ecosystem exists to sit between you and AI models — apps, harnesses, memory layers, orchestration tools. They exist because raw AI, on its own, still needs help staying organized, remembering context, and taking action in the world.

That gap is closing fast, and I don't think it survives the decade. By 2029, I expect cloud-based AI — the kind running on a provider's infrastructure rather than on your own device — to need nothing more than an internet connection to do almost anything you ask. Local AI will still lean on external tools, memory management, and the rest, because it's constrained by the hardware it runs on. Cloud AI won't have that excuse. It will simply be the tool, the memory, and the workspace all at once.

What That Already Looks Like

You don't have to imagine this — you can do rough versions of it today, even if it's still clunky. Have a project and need marketing? Ask an AI to spin up five agents dedicated to your marketing function, hand them access to your competitors, your analytics, and your sales data, and tell them to grow the business — write ads, run campaigns, report back. Then sit back and watch the workflow unfold.

It's still a bit of a fight to set up well. But it's getting easier by the month, and the ceiling isn't the technology anymore — it's your imagination. Push the idea further and you stop needing dedicated apps at all: no inventory software, because the AI builds and runs inventory management for you on demand. No invoicing platform, because it builds an invoicing system the moment you ask for one. Taken to its logical end, you could run an entire company this way — one model acting as your "CEO," another handling backend engineering, another driving SEO and marketing, another dedicated to competitive research — each one a specialist agent doing a job a whole SaaS category used to do.

Why the Big Labs Want This Too

This isn't just a user preference — it's an incentive that runs the other direction as well. Companies like OpenAI and Anthropic want as much of your usage and data as possible, because that's what lets them train better models and offer better services. The natural move for them is to fold every tool you'd otherwise reach for into their own app or model by default — effectively building an operating system, not just a chatbot. And because they're working with resources most third-party developers can't match, whatever they build in-house will tend to outperform the independent alternative.

The Bigger Pattern

Zoom out and the pattern gets even clearer: any piece of software used by a large enough number of people will eventually get pulled directly into the major AI ecosystems — Anthropic, OpenAI, xAI, Google, and whoever else is playing at that scale. Without those tools built in, the AI is only half as useful. So each provider has every reason to absorb as much functionality as it can, both to make its product better and to keep users from ever needing to leave.

The apps that today promise to make AI easier to use may be the very thing AI makes unnecessary.