One of the most common modest things about the generation AI era is that “startups are growing faster than ever.” It's often a lack of resources. Some notable examples? Every company indicator, you should love it strike Revenues, Cursors in just 6 months It has been reported 100 million dollars in revenue and gamma in the first year I've reached it Revenues with revenues under $25 million are $50 million.
but, average AI companies (not top 0.1%), what does growth actually look like? Pre-ai is the general benchmark for best-in-class enterprise startups, with a million dollars in the first 12 months. In contrast, consumer companies often slowed their monetization well beyond their first year, typically waiting until they built a base for millions (or tens of millions) of users to monetize via advertising.
Based on data from hundreds of companies we've seen over the past 18 months, we can clearly say that these metrics have changed. This is what we see among companies that have spent a lot of time:
What does this mean for the founder?
1. Fast, faster rounds of revenue.
Our data confirms the idea that we are in a new era of start-up growth. The median enterprise company in the sample set reached over $2 million in its first year, raising Series A total of nine months later. The median consumer companies are even better, reaching $4.2 million in ARR and raising a round of A within eight months. What was once considered “best in the class” (an 0-$1 million ARR lamp) is now at the bottom of the growth.
Given the rapid growth that both AI-Native B2B and B2C companies are achieving between Seed and Series A, startups looking to raise venture capital need a strong speed story. If you still don't have live commercial traction, it certainly comes down to shipping rates and product repetition. Speed is becoming a moat.
2. The gap between “good” and “exceptional” is growing.
The bar is fully nurtured, but the top performers really are pulling apart. Many of these breakout companies continue to pick up steam throughout the first year rather than starting to grow slower (as we often saw in the pre-AI era). Fences are worth swinging as there is demand for great products from both enterprise and consumer users.
It's not just revenue, but other metrics still matter. When evaluating companies in Series A stage, they often only have 12 months of usage and retention data. Later staged funding rounds may rely heavily on traditional software metrics. Rapid topline growth is not enough to compensate for low engagement and high churn.
3. Consumer companies are now… a real (profit-generating!) business.
A little surprising, B2C's revenue benchmark surpasses B2B's revenue. This is because consumer companies now have different “shapes.” A third of the consumer companies in the sample raised a large amount of money to train their own models. Many are seeing a massive revenue jump following the release of new models. These spikes often resemble the growth of step functions and may plateau later until the next release.
In the case of AI B2C businesses, conversion to paid may be lower than the counterparts before AI, but Our data User suggests it once do Transform, they keep as well.
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TL;DR: Startups work faster than ever, with both businesses and consumers showing a higher willingness to pay for new products. After examining the data, I think I didn't have much more time to build an application layer software company.
