Most pre-seed companies don’t have enough operating history to show long-term success. Sometimes, founders focus on metrics that signal momentum: downloads, usage, or even social media followers. This is even more applicable in the ultra-competitive, fast-moving area of AI.

Founders, like anyone, pay attention to what they’re tracking. That’s why focusing on the wrong metrics is dangerous; it can lead to larger errors in business strategy. Founders’ lack of understanding about the most meaningful metrics can also confuse and irritate investors, making it harder to get funding.

Below are five of the most common vanity metrics in the age of AI — and what investors want to see instead.

1. AI Usage Metrics (Prompts, Tokens, Generations)

AI-enabled companies frequently highlight usage numbers — prompts processed, tokens generated, or outputs created. These figures can reach impressive scale quickly.

But high usage does not necessarily mean high value. In many cases, heavy usage reflects inefficient workflows or users retrying prompts to get acceptable results. From an investor’s perspective, more tokens can mean higher costs without corresponding customer benefit.

The better metric focuses on outcomes. Investors want to know whether users are successfully completing tasks:

• Did the AI reduce time spent?

• Did it eliminate routine work?

• Are users satisfied with the first output?

Metrics like successful task completion rates, time-to-result, or percentage of outputs used without revision are far more meaningful.

2. Feature Velocity or Model Releases

AI startups often emphasize how quickly they ship, publicizing new models, new features, and frequent updates. Rapid iteration can signal technical strength.

But shipping features isn’t the same as improving the business. Constant releases can also indicate uncertainty about what actually drives value.

Investors increasingly focus on whether releases move core metrics. They’ll ask:

• Did retention improve?

• Did churn fall?

• Did revenue per customer increase?

Product progress only matters when it changes outcomes.

3. Revenue Growth (Without Understanding Costs)

Revenue growth has always been important to investors. But revenue alone doesn’t predict sustainability.

Early revenue can come from discounts or pilot agreements that don’t turn into enterprise contracts. In AI companies, revenue growth can also hide a more serious issue: rising infrastructure costs that scale faster than revenue.

Revenue is part of the story, but investors are more interested in profit metrics if you have them. If you don’t, try to estimate a growth outcome from your last months’ or years’ data. Be ready to explain your cost management strategy, and practice with hypotheticals about how you’ll react to shocks in the market.

4. Pipeline Size or Booked Demos

A large sales pipeline sounds impressive. Hundreds of demos or inbound requests suggest strong market interest.

At early stages, however, pipelines are noisy. AI hype has made exploratory demos common, particularly among enterprise buyers who are still experimenting. A full pipeline does not mean deals will close.

Conversion rates, sales cycle length, and revenue per sales employee reveal whether demand is real. A small pipeline that converts consistently is far more valuable than a large pipeline that stalls.

5. Social Proof: Followers, Press, and Waitlists

Finally, it should go without saying that buzz doesn’t equal long-term growth. Investors are looking for signals of customer commitment, not just curiosity.

How to Measure What Matters

The strongest early-stage companies track metrics that are related directly to profitability, and that show clear growth in their target markets. This can mean showing actual profits, signed contracts, or (at the pre-seed stage) a thoroughly stress-tested financial model.

Founders who replace vanity metrics with honest ones have a better chance of getting useful feedback from customers and investors. Spreadsheets and contracts may not be as exciting as watching user engagement numbers grow, but tracking meaningful numbers will force the right conversations early — about pricing, margins, and who actually pays. That discipline is often the difference between a compelling demo and a successful company.

Are your numbers as substantive as possible for customers and investors? If you have questions about translating metrics into contracts, we’re happy to help.