The Web Is Being Rebuilt for LLMs
Web is no longer just for humans, it's being refactored for machines that read.
We used to scrape the web. Now, we teach models to read it.
Large Language Models aren’t just autocomplete engines. They're becoming full-stack web readers.
his changes how you build features, run pipelines, and think about product infra.
A Simple Example:
Salary Estimates
This is real feature we shipped at www.hire.inc
Two years ago, building a salary estimator meant:
Manual source lists
Scrapers and cron jobs
ETL pipelines
Fuzzy matching with enums and controlled vocabularies
Today:
Query an LLM (soon even fully local via Chrome's built-in LLM)
Feed it public data: job boards, surveys, forums
Let the model normalize job titles, locations, and noisy data dynamically
LLMs give you:
public knowledge average + smart autocomplete.
The New Playbook
LLMs excel at reading and transforming text.
They summarize, normalize, cluster, extract.
Prompt-centric > program-centric.
Don’t hardcode every edge case.
Expose system prompts. Let users steer.
AI-first = user leverage.
Infra that wraps LLMs makes users 10x more productive.
The model becomes their analyst.
We build infra.
Our job is pipelines, guardrails, and boundaries.
Users orchestrate inside those systems.
(This is what AI-native / agentic software really means.)
A Few Notes
People will try to game these systems.
Safety and ethics belong upstream (model providers, infra).
The opportunity is to exploit model strengths, while building safety nets where needed.
More on building for LLMs:
What parts of your data pipeline could you hand off to an LLM today?