Forem: Jay Herman

Why the web doesn't need humans anymore

Jay Herman — Fri, 06 Mar 2026 09:44:17 +0000

Why does Google feel different? It isn't just AI. Let's follow the money...

You look for a specific but instead of a solution, you get corporate bs with Reddit threads, Forbes... It feels like the library has been replaced by a shopping mall where every book is written by a marketing committee.

This isn’t a technical failure.

1. The Engineering of "Good Enough"

One of the most chilling discovery of the last two years didn't come from a lab, but from a courtroom. Internal emails from the US v. Google antitrust trial revealed a fundamental civil war inside the company. Ben Gomes, the engineer who helped build Google’s reputation for quality, warned that the company was becoming "too close to the money".

The data suggests they realized they could make the results slightly worse, forcing users to search longer. It’s a "Boiling the Frog" strategy where the goal isn't to find you the best answer, but to keep you in the ecosystem until you settle for "good enough".

2. The Death of the Independent Voice

In May 2024, a massive API leak of 14,000 ranking factors shows part of the reality how Google has changed over the years and why providing quality content is not enough.

Google has effectively hardcoded a preference for big brands like Reddit. This is why a massive, multi-billion dollar site with zero expertise in a topic will now outrank a scientist’s personal blog. The "Human Web"—the era of weird, expert, and independent sites—is being intentionally starved of traffic to make way for a more controllable, corporate-vetted internet.

3. The Browser as training tool

To Google your browser is a sensor and every action you do or don't serves as training data.

This data is used to build "Zero-Click" results. They scrape the best parts of the web, present them in an AI Overview, and ensure you never have to visit the original creator's site. It is a one-way extraction of human knowledge.

4. The Agentic Age

With projects like Jarvis and Mariner, Google is shifting from a tool that helps you find things to an agent that does things for you.

In this new reality, you don't browse the web; you prompt. The agent then navigates a "Ghost Web" (a place where bots talk to bots) and only the information Google deems "authoritative" (or profitable) is allowed through. The internet is being transformed into a vast, silent database for AI training, while the human experience is condensed into a single chat interface.

The Bottom Line:
Google isn't "broken", instead the "Search Engine" is dying so that the "Action Engine" can live.

If we stop visiting websites and start only talking to agents, who decides what information is "true" once the original creators have gone out of business?

Sources & Deep Dives:

Learning basics without misconceptions

Jay Herman — Sun, 01 Feb 2026 13:18:26 +0000

LLM: Predictability VS Determinism - DEV Community

If you search for “how to make an LLM deterministic,” you might find advice like: “Set...

dev.to

LLM: Predictability VS Determinism

Jay Herman — Sun, 01 Feb 2026 13:15:11 +0000

If you search for “how to make an LLM deterministic,” you might find advice like:

“Set temperature to 0, fix the seed, use top-p = 1 or top-k = 1.”

This mixes up two separate ideas: determinism vs predictability.

Predictability: the model tends to give similar outputs because it’s “playing it safe” (low temperature, top-k/p limits).
Determinism / reproducibility: the model gives the exact same output every time, which only happens when the seed is fixed.

Think of the seed like a Minecraft world seed: it doesn’t make the landscape “more likely,” it just makes it repeatable. Same seed + same prompt = same output, every time.

Options like Temperature, Mirostat, top-k, top-p… control style, variety, and “wildness”. They can make outputs more predictable in practice (low temperature = less surprising tokens), but they do not guarantee reproducibility. The seed is the only knob that truly locks the path.

In other words: you can have a wild, creative response that is fully replayable if you fix the seed. That’s why reproducibility in LLMs is really about the seed, not temperature.

Example using Python Ollama:

    ollama.chat(
       ...
       options={"seed": 42 }
    )

TL;DR

Temperature, top-k, top-p, Mirostat → control style and predictability, not determinism.
Seed = true reproducibility. Want the exact same output every time? Lock the seed.

Try it yourself and share your findings!