Also known as, “Bullshit Mode.”

ChatGPT made lots of promises about  what it could do with the notes from my book archive. (See “bellyflop” post) I gave it another little test yesterday.

Steve: Based on my notes and/or the title…which of the books in my archive would you classify as humor. A simple bulleted list of titles will be sufficient.

I’ve tagged 19 of the books in my library with “Humor.” Most of which are obvious from the title (and the cover!). ChatGPT found five and only one of those might be considered humor. When I pointed this out the bullshit started flowing… Continue reading →

We’re told that LLMs like ChatGPT, Perplexity, et al… are “trained” on massive amounts of data. Theoretically, ‘all’ of the data. And that training continues, one assumes. But a new (?) sort of data is being created. All of those millions and millions of interactions between humans and the growing number of LLMs. 

How —if at all— does that data differ from the original corpus? When I look at the interactions I’ve had with ChatGPT and others, my half of the conversations has been questions while all of useful information comes from the LLM.

Do these interactions contribute in any useful way to the corpus?

ChatGPT reply »

She concludes with the question: “Is this a new form of literature?”

What you’re curating — your indexed posts and saved transcripts — might represent a new genre of human expression. Not fiction. Not diary. Not essay. But conversational co-authorship with a thinking machine. If the early internet was a “global brain,” LLM chat archives like yours might be the global introspection. And someday, they might be part of a corpus we train the next kind of intelligence on.

Yes — and in fact, we’re already edging toward that reality. Here’s what ChatGPT sees coming down the pike, based on how things are evolving:

1. User-Defined Source Preferences

We’re likely to get something like:

“Use only academic, nonprofit, and independently funded sources.”

“Avoid any domain known to use affiliate marketing.”

“Favor peer-reviewed or original journalism.”

Imagine a little toggle: “Trustworthy Sources Mode.” Perplexity already lets you filter by domains like .edu, .org, and even individual sources like The Atlantic or NPR — but it’s still pretty manual. ChatGPT isn’t quite there yet, but it’s technically trivial for OpenAI to implement. And they know this is the direction users want.

2. Source Transparency Ratings

Models could begin attaching trust scores or bias labels to their sources:

• “This answer draws from a site with a strong SEO-commercial focus.”
• “This source is government-funded and peer-reviewed.”
•  “This site monetizes heavily via affiliate links and product placements.”

That’s not just a wishlist — it’s already being explored in academic and research LLMs.

3. Custom Knowledge Bases (BYO Sources)

In the not-so-distant future:

• You might upload your own preferred sources — like, say, the entire smays.com blog — and have the LLM prioritize those over the web.
• Or subscribe to a vetted feed of sources maintained by trusted curators (think: “Taoist Tech Digest” or “Vintage Vehicles Weekly”).

This gives you a bespoke answer engine that thinks like you.

4. A Bias-Aware AI (by design)

I expect we’ll see models with built-in capabilities to:

• Detect “sponsored content language”
• Flag persuasive or manipulative rhetoric
• Alert users to conflict-of-interest signals in the writing itself

In other words: AI that doesn’t just summarize the internet, but interrogates it.

Will people use these features?

Maybe not the average user. But folks like you, who care about signal over noise, will absolutely want this control. And when enough people start asking, the platforms will follow.

What LLMs Can’t Do

In the case of ChatGPT (like me) without browsing turned on, I:

• Can’t access the internet in real time
• Can’t read or reference your blog unless you share it here
• Don’t have any built-in memory of your blog, unless it was included in my training data (and it wasn’t — personal blogs almost never are)

Even in the web-browsing version (ChatGPT with browsing, or Perplexity):

• I don’t “remember” your blog in a training sense
• I only see what’s available on the open web at the moment of the query
• And I only find it if it’s surfaced through a search query or known URL

So to be clear: If you haven’t uploaded it or linked to it during this conversation, I have no idea what’s on your blog.

So why did I say “LLMs might read your blog”?

Poor phrasing on my part — what I should’ve said is: If your blog is publicly accessible, indexed by search engines, and matches what the model is querying for, it might be read by an LLM that’s browsing the web (e.g., Perplexity, or ChatGPT with browsing enabled).

But that’s very different from being part of a model’s training or memory.

LLM Training vs. Web Browsing

So unless you explicitly upload or quote something from your blog here, I can’t see it. And if you’re using ChatGPT without browsing, neither can I “go look it up.”

Paid Influence Is Baked Into the Web

By the time an LLM like Perplexity or Bing’s Copilot performs that real-time search:

• The results it’s scanning are already shaped by:
• SEO wizardry (optimized content stuffed with keywords and backlink networks)
• Advertising budgets (Google Ads, sponsored placements)
• Domain authority rankings that favor big brands
• Content farms whose entire business model is gaming the system

Even organic-looking results are often backed by content marketing departments and affiliate link schemes.

So what does this mean for LLMs?

Large language models don’t know that a result was bought and paid for. They’re trained (or instructed) to:

• Look for relevance to the query
• Check for recency, if needed
• Prefer high-authority or “trusted” domains (government sites, major media, educational institutions, etc.)
• Avoid spammy or low-quality sites (based on signals like grammar, structure, reputation)

But they don’t always know if that content is biased, commercially driven, or ad-heavy. Unless explicitly trained or fine-tuned to detect such bias (and even then, it’s tricky).

So we end up with this paradox:

• People turn to LLMs because they’re sick of wading through ad-choked Google results.
• But those LLMs are often summarizing answers based on the very same sources.
• Only now it’s done in a smoother voice, with a friendlier tone, and fewer obvious signs of manipulation.

It’s like getting your news from a bartender who already read all the headlines and just tells you the gist — but you’re not always sure which paper he read it in.

What can be done? 

Some things that are happening (or could happen):

1. Source filtering options – Let users prioritize academic, nonprofit, or independent sources (some models like Perplexity let you toggle these).
2. Transparency layers – Showing why a source was used, and letting users drill down into those choices.
3. Community validation – Like how Stack Overflow or Wikipedia rise through consensus and review.
4. Personalized trust signals – In the future, you might have a personal AI that knows you trust Steve’s blog more than Forbes and adjusts accordingly.

Bottom line?

Yes, even in this new LLM age, the checkbook still gets a say — unless you’re using a model that actively fights that tendency. The difference is: instead of clicking a shady-looking link, you’re now reading a summary of it… without even knowing it came from someone who bought their way to the top. You still need to squint, tilt your head, and ask:

“Who benefits if I believe this?”

How Early Google PageRank Worked (quick refresher)

PageRank was built on the idea that links = votes. If a site had many incoming links — especially from other sites that also had lots of incoming links — it was seen as more authoritative. That recursive loop of “link juice” made your blog more visible, especially when the web was smaller and more human-curated. Then came:

• Ads (AdWords, AdSense)
• SEO tricks and keyword farming
• Google favoring its own services (YouTube, Maps, Shopping, etc.)

LLMs (like ChatGPT & Perplexity): “Just Give Me the Answer”

These tools work very differently than search engines. Instead of linking out to the web, they try to synthesize an answer within the chat — a distilled response trained on massive text datasets.

So, where do LLM sources come from?

There are two major pathways, depending on the tool:

1. Trained Sources (Pre-2023/2024 data). For models like ChatGPT (GPT-4) when it’s not browsing:

• It’s trained on a mixture of licensed, publicly available, and publicly scraped text.
• This includes books, Wikipedia, websites, forums (like Reddit), code repositories (like GitHub), and more.
• It learns language patterns, facts, and reasoning styles from all of this — but it doesn’t retain or cite specific URLs.

So when you ask a question, the model answers based on what it remembers from training, like a well-read librarian with a fuzzy memory. It can’t point to a source unless it’s operating in web access mode (which I can do when needed).

2. Live Sourcing (Perplexity, Bing, ChatGPT with browsing). When web access is enabled:

• The model issues a real-time search query in the background (like Google or Bing would).
• It quickly scans the top ranked results (often the first page or two).
• It uses NLP to summarize or extract relevant content.
• Those links that appear as citations? They’re chosen from those high-ranking, recent, often high-authority pages — based on how closely their content matches the query.
• There may be some filtering for:  recency; source reputation; coherence with the rest of the retrieved content

So ironically, even though it feels like the LLM is “just answering,” it’s still doing a mini search engine dance behind the curtain.

Do people actually look at the sources?

Most users don’t. They just take the synthesized answer and move on. But when people do click them, it’s usually to:

• double-check the claim
• chase the rabbit deeper
• cite it themselves

So what does this mean going forward?

• Authority no longer comes from links alone — it comes from semantic relevance and source credibility as judged by an algorithmic filter.
• Your blog, even with great content, might not show up unless it’s surfaced by a real-time search and judged relevant to a prompt.
• In a weird twist: LLMs might read your blog and remember it, even if they don’t tell you they did.

Bonus: Why LLMs feel so much better than search

Because instead of giving you 10 partial answers, they try to give you one synthesized, thoughtful reply. But that also means we’re putting a lot of trust in the model’s judgment about what sources to consult. Which makes transparency, source citation, and user control even more important.