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2026

The AI Product Era You're Building For Might Already Be Over

At the end of 2022, ChatGPT launched and immediately set records as the fastest-rising consumer product of all time. It gave us a glimpse of something genuinely alien. Humans are special - we make tools. But for the first time ever, we made a tool that could speak back.

Since then, we've seen several eras of growth. Ideas and approaches have come and gone as we've tried to figure out how to actually use this thing. With every era, the good ideas of the last are trained into the models, incorporated into their APIs, encoded as best practices, and often obscured by a new layer of abstraction.

We are now at the beginning of a new era - and this one is going to make the earlier ones look like a warm-up act.

In this post I'll walk through the history of AI product development, the present revolution, and what might be coming next. If you're building AI products, you need to understand this trajectory: to avoid building things that get immediately disrupted, to build things that hold up over time, and to take full advantage of what the technology can actually do.

The March of Progress, reimagined: from industrial robot arm to autonomous humanoid agent

Anthropic SKILLs – Prime Example of Red Riding Hood Principle

In Albert and my book, Albert introduced the "Red Riding Hood Principle". You remember the story, right? A young, naive girl strays off of the well trodden path and ends up in a lot of trouble.

This is true for you when building AI applications. If you provide context to the agent that is familiar – similar to the training – then the agent will be able to navigate the terrain more easily.

Anthropic SKILLs is such a good example of this. Anthropic realized that in Claude Code, it had trained a model and constructed an agent to be exceptionally good at navigating file systems, reading files, and managing context. Further, the filesystem metaphor provides natural navigational affordances. The agent can look at the directory structure and get a big picture of what exists, and an agent can grep around for details – much like a developer would do.

You should consider all of this when building your own agents! SKILLs benefits from the filesystem metaphor, so it bears to reason that your domain could benefit as well – imagine presenting graph-based knowledge or filter-based search as if it was a file structure.

Incremental AI Adoption for E-commerce

When you think of e-commerce, your mind is probably drawn to Amazon.com as "the definitive" example. But it's actually the exception. The internet is filled with tons of small- and medium-sized e-commerce sites. These sites typically follow the same pattern - a search page with a search box at the top, selectable filters along the left side, and results filling the remainder of the screen. And the whole goal is to quickly usher customers to the products they seek.

For most of these sites, the implementation is quite simple. Product metadata is indexed into a search engine such as Elasticsearch or Algolia. This includes fields like the title of the product, its description, its price, and other relevant features (sizes for shoes, square feet for houses, etc.) And the application is typically quite simple – the user submits a search, and the backend issues a query that hopefully captures the customers intent, and then captures the responses and sends them to the frontend for display in the search results.

Unfortunately "right-out-of-the-box" search results are often not that great, and fixing the problem often requires hiring a team of search experts – something that smaller shops are unable to afford. Fortunately, modern AI is coming to the rescue! In this post we'll demonstrate how e-commerce shops can incrementally adopt AI and explore improvements in search which would have been unbelievable just 5 years ago.

Search Architecture Evolution