At AceBeta, I placed candidates from manager to CEO level for companies from seed stage startups to Fortune 500. Our candidate-to-offer ratio was 2 to 3. The industry average is 15 to 20.
That ratio came from one insight: test what people can do, not what they say they can do. Proof beats promises every time.
The problem wasn't the ratio. It was that achieving it required me personally. I needed to turn that methodology into something reusable: a way to see real skill through real work before making a bigger commitment.
Then AI changed everything. Employers started using AI interviewers. Candidates started using AI to cheat. The whole system broke.
But AI also changed what “skill” means. The best professionals today don't just have expertise. They create skills that AI can build on. They use AI to produce results nobody could achieve alone. And no interview, no resume, no generic test can evaluate that.
So I built Hibo. A platform where expert know-how becomes reusable AI skills. Companies run those skills on real work before hiring, and experts earn from the value their skills create.
13 months of full-time building. Solo. One company already piloting.
Kuanze Ma, Founder
Most AI products make one side cheaper by making the other side more replaceable. That is not a durable system for expertise.
Hibo should make companies more capable while making expert work more valuable, attributable, reusable, and paid.
Companies need to see what expertise can do on real work, not just read claims about it.
Playbooks, examples, prompts, judgment, and process can be packaged into AI skills that run again and again.
If AI learns from human know-how, the people behind that know-how should keep attribution, reputation, and earnings.
Run world-class expert skills at work, or turn your own expertise into skills that earn when they perform.
