Victor Lazarte, Benchmark: This is the biggest technology shift we have ever seen. The revenue ramps are incredible. Take Mercor: I invested nine months ago, and they just announced a 100 × jump to a 75 million-dollar run rate, still growing 50 percent month over month. You do not see that every day.

But fast growth alone is not enough anymore. In the past, hitting 10 million ARR meant you were safe. Today the pace of change forces you to ask whether that revenue is durable.

At our partner meetings we keep seeing startups at 5 to 10 million ARR that may not exist in two years. We always ask, “If foundation models get ten times better, does this business become stronger or weaker?”

Many thin wrappers evaporate when the next model release absorbs their value. Imagine an app that formats building-permit paperwork with ChatGPT. It can reach a few million in revenue, but it disappears once the base model folds that feature in.

That single question about a ten-times-better model is very clarifying.

Our job as investors is less about predicting the far future and more about understanding the present. We focus on the areas where AI performance is improving fastest. That is easier than guessing distant scenarios.

We track benchmarks: Where are the eval scores rising most quickly? Then we invest there. The pattern is clear: AI improves fastest where you can measure the output objectively.

  • Code is the best example. You compile and test, so feedback is instant. Seed investing in coding tools is probably closed now, but we are glad we got in early.
  • Other text-based fields with clear exams or verifiable outputs—law, medicine, and so on—show the same rapid gain, which is why I backed two startups in AI video as well.
  • Domains without tight feedback loops, like household robotics, will take much longer.

So our framework is simple:

  1. Spot where benchmarks are improving the fastest.
  2. Ask whether those improvements make the business more or less durable as models advance.