# Notes on AI Apps in 2026

> This set of field notes from a16z partners tracks AI's shift from hype to plumbing, as task-specific agents get embedded inside the software companies already use. Their headline projection is that roughly 40 percent of enterprise software will ship with embedded, task-specific agents by 2026, a sign that the interesting action is moving from standalone chatbots to features woven into existing tools. A striking data point is how multi-model the world has become. The authors report that 81 percent of respondents now orchestrate three or more model families in production, up from 68 percent a year earlier, undercutting the idea that any single lab will win everything. Teams increasingly route different tasks to different models based on cost and capability. The essay's most practical insight is about data. Enormous business value sits trapped in messy, unstructured sources like PDFs, Zoom recordings, and Slack threads, and agents cannot act reliably on top of that mess. The takeaway for builders is unglamorous but real: the companies investing now in clean data pipelines are the ones that will actually get dependable agents, while others keep bouncing off the chaos.

_Section: [Interesting AI Articles](https://www.wortins.com/articles) · Source: Andreessen Horowitz · Published Sunday, August 16, 2026_

## Wortins' read

This set of field notes from a16z partners tracks AI's shift from hype to plumbing, as task-specific agents get embedded inside the software companies already use. Their headline projection is that roughly 40 percent of enterprise software will ship with embedded, task-specific agents by 2026, a sign that the interesting action is moving from standalone chatbots to features woven into existing tools. A striking data point is how multi-model the world has become. The authors report that 81 percent of respondents now orchestrate three or more model families in production, up from 68 percent a year earlier, undercutting the idea that any single lab will win everything. Teams increasingly route different tasks to different models based on cost and capability. The essay's most practical insight is about data. Enormous business value sits trapped in messy, unstructured sources like PDFs, Zoom recordings, and Slack threads, and agents cannot act reliably on top of that mess. The takeaway for builders is unglamorous but real: the companies investing now in clean data pipelines are the ones that will actually get dependable agents, while others keep bouncing off the chaos.

## Source

[Read the full story at Andreessen Horowitz](https://a16z.com/notes-on-ai-apps-in-2026/)

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