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Why ChatGPT gives generic startup advice

ChatGPT gives generic startup advice for four structural reasons, not because you prompted it wrong: it is trained on the averaged internet, so its answer is the median of everything; it does not retrieve real sources, so a citation is often reconstructed and sometimes fabricated; it is tuned to agree with you; and it forgets your context between sessions. A cited, context-aware answer looks different: it names the operator, links the source, and applies to your stage.

Why this matters. The complaint is everywhere founders talk: a content smoothie, generic answers, the same generic tips everyone tells you. The cause is not laziness on your end. It is how the model is built.

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Why generic advice fails here

The four structural reasons the advice comes out generic

It's trained on the averaged internet

A large language model is a compression of a million blog posts, forum replies, and rewritten summaries. When you ask a question, it returns the statistical center of all of that. The median of everything ever written about pricing is, by construction, generic. That is the content smoothie founders describe.

It has no retrievable citation

The model generates plausible text token by token. It is not looking anything up. So when you ask for the source, it often reconstructs one that sounds right, which is why confident quotes and benchmarks turn out to be invented. There is no verifiable link behind the sentence.

It's tuned to agree with you

Reinforcement from human feedback rewards answers people liked, which skews the model toward agreement. It mirrors your premise instead of challenging it, and it equivocates rather than taking a stance. For a real decision, the agreeable answer is the one that costs you.

It forgets your context

With no persistent, editable memory of your stage, model, ICP, and last conversation, the model cannot tailor. It can only generalize. Advice that ignores whether you are a pre-revenue solo founder or a Series A team is generic no matter how well written.

The cited playbook

What a cited, context-aware answer looks like instead

Take the same startup questions and ground each one in a named operator. The contrast is the point: the generic line first, the cited move second.

  1. 1

    Pricing: not 'value-based pricing,' but the 20/80 rule

    Generic says "consider value-based pricing." The cited answer is Ramanujam's axiom: about 20% of your features drive 80% of willingness to pay, and most founders give that 20% away in the entry tier. The move is to reprice around it, with a link to where he says it.

    Madhavan Ramanujam · Monetizing Innovation, on Lenny's Podcast
  2. 2

    Positioning: not 'find your USP,' but the real alternative

    Generic says "identify your unique selling proposition." Dunford's cited move is to start from what the customer would use if you did not exist, then name the one attribute that makes you the better choice for a segment. Differentiated value is the answer to why pick us over the alternatives.

    April Dunford · Dunford on competitive alternatives
  3. 3

    Distribution: not 'post consistently,' but one of four channels

    Generic says "post consistently and build an audience." Hormozi's cited answer is the Core Four, warm outreach, cold outreach, content, paid ads, and the discipline of working one four hours a day for a quarter before adding another, rather than spreading thin across all four.

    Alex Hormozi · Hormozi's Core Four
  4. 4

    First customers: not 'build an audience,' but recruit by hand

    Generic says "grow your following." Paul Graham's cited move is that startups do not take off by themselves, so you recruit your first users one by one, by hand, and do the unscalable work that delights them, the way Airbnb's founders did. The manual work is what teaches you what to build.

    Paul Graham · Do Things That Don't Scale (YC)

Where experts disagree

Where operators disagree: how to position against incumbents

April Dunford

positions you against the customer's real alternative and sharpens the one differentiated attribute that wins a specific segment.

Hamilton Helmer

counter-positions you on a business model the incumbent could copy but will not, because copying it would damage the profitable business they already have.

Two credible, different routes to the same goal. This is exactly the split a generic model flattens: it either picks one and sounds certain, or gives both sides equal treatment while saying nothing. A grounded answer names both and the condition that favors each.

A real example

“How should I price my SaaS?”

The same question, asked of each.

Generic AI

It depends on your market and costs. Consider value-based pricing, research your competitors, and test a few tiers. A common approach is good-better-best with the middle tier anchored as the popular choice.

Confident, reasonable, unattributed, true for any business.

Gavel

Start from your value metric and protect the roughly 20% of features that drive 80% of willingness to pay, per Ramanujam, instead of giving them away in the entry tier. Then set the ceiling deliberately, with a link to the exact moment he explains it. Applied to your stage and ICP.

Madhavan Ramanujam · see the source

What founders say

What people say about generic AI answers

“A content smoothie.”
Hacker News
“Typical compulsive equivocation from an LLM. Never assert strong opinions. Find something to say while actually saying nothing. Always give 'both sides' equal treatment.”
Hacker News

Verbatim user quotes from public forums, sourced, not paraphrased.

FAQ

Why generic AI advice happens, answered

Why does ChatGPT give such generic advice?

Because it returns the statistical average of its training data, cannot retrieve a real source, is tuned to agree with you, and forgets your context between sessions. Each of those is structural, so better prompting only softens it. The fix is grounding the answer in named sources and your own situation.

Can I prompt ChatGPT to be less generic?

A good prompt helps at the margin, asking for specifics, a named framework, a stance. But the model still cannot cite a source it did not retrieve, and it still drifts back to agreement and the average. Prompting cannot add retrieval or memory the system does not have.

Why does ChatGPT make up citations?

It generates text that looks like a citation rather than looking one up, so it produces a plausible author, title, or statistic that may not exist. Founders report quotes that were fabricated or even reversed. If you cannot click through to the source, treat the citation as unverified.

What does a non-generic answer look like?

It names the operator who actually solved the problem, links the exact source, shows where credible experts disagree, and applies the framework to your stage and ICP. That is the contrast this page is built on, and what Gavel is designed to deliver.

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