The fastest way to look untrustworthy in 2026 is to sound perfectly generic.

LLM-polished text is now a red-flag pattern — on both sides of the table.

Founders use AI to “improve” proposals.

Evaluators use AI to structure notes and detect patterns.

And in the middle sits something dangerous:

Text that sounds sophisticated — but carries no accountable substance.

The Shift: Generic Fluency Now Signals Risk

There was a time when polished language was an advantage.

Today, excessive smoothness without structural precision triggers suspicion.

Evaluators increasingly recognize patterns such as:

• Over-structured but content-light paragraphs
• Balanced tone without trade-offs
• Abstract phrasing with no measurable anchors
• Long explanations that avoid technical commitment

The proposal reads clean.

But it does not read owned.

And when ownership disappears, credibility declines.

What “AI-Generated” Looks Like to Evaluators

It is not about detecting AI.

It is about detecting avoidance.

Here are the common signals:

1. No Technical Friction

Real innovation involves constraints, trade-offs, and edge cases.

LLM-polished text often removes friction. Everything sounds coherent and controlled.

Breakthrough proposals contain tension.

Generic ones contain symmetry.

2. No Measurable Stakes

AI-assisted writing often expands language but dilutes precision.

You see claims. You do not see numbers, baselines, or thresholds.

If nothing can be falsified, nothing can be defended.

3. No Clear Comparator

Text that discusses innovation without defining state-of-the-art benchmarks feels assembled, not engineered.

Evaluators look for explicit contrast.

LLM smoothing often removes it.

4. No Voice Ownership

When every section sounds equally neutral, evaluators ask themselves:

Who is actually accountable for this?

Strong proposals have a point of view.

Generic proposals have tone consistency.

Tone consistency is not credibility.

The Hidden Risk for Founders

LLMs are not the problem.

Misuse is.

The risk emerges when founders:

• Replace thinking with prompting
• Expand text instead of sharpening logic
• Optimize phrasing instead of strengthening causality
• Polish language before defining mechanisms

The result?

A proposal that sounds impressive — but feels structurally hollow. And at EIC level, hollow equals high risk.

The Correct Way to Use LLMs in Funding Strategy

Used properly, LLMs are acceleration tools.

But they must follow logic, not replace it.

A disciplined approach looks like this:

Step 1: Define Mechanism First

Before using any AI assistance, clearly articulate:

• What is new?
• Compared to what?
• Why is that technically different?
• What measurable result changes?

If you cannot answer these without AI, AI will not fix it.

Step 2: Use AI for Compression, Not Expansion

LLMs are strongest at clarity and structure.

Use them to:

• Remove redundancy
• Improve flow
• Tighten causal chains

Do not use them to invent strategic reasoning.

Step 3: Reinsert Specificity

After polishing, manually reinsert:

• Concrete numbers
• Technical constraints
• Named standards
• Defined segments
• Real trade-offs

Specificity is what prevents generic detection.

The Evaluator Side: Pattern Recognition Has Evolved

Evaluators have adapted.

They now see proposals that are:

• Structurally perfect
• Linguistically smooth
• Conceptually interchangeable

When multiple proposals “sound” the same, differentiation collapses.

At that point, scoring reverts to:

• Measurable technical evidence
• Clear structural breakthrough
• Coherent execution mapping

AI polish cannot compensate for missing fundamentals.

The New Credibility Tax

There is now an implicit penalty for over-generic fluency.

Because in a high-risk funding instrument:

Trust comes from friction. From visible engineering. From defined uncertainty. From defensible specificity.

If your proposal reads like it could apply to any company in your sector, evaluators assume it was produced that way.

And if they cannot detect human accountability in the reasoning, they reduce confidence.

The Strategic Implication

The competitive advantage in 2026 is not better wording.

It is sharper causality.

AI can assist expression.

It cannot replace:

• Structural insight
• Technical conviction
• Explicit comparison
• Measurable commitments

The fastest way to look untrustworthy today is to sound perfectly generic.

The most convincing proposals now are not the smoothest.

They are the most specific.

If you want to test whether your proposal reads like accountable engineering or like polished abstraction, book a meeting: https://calendly.com/siliconcapital/30minutes