AI Executive Search

CANTINA LABS

INVESTORS | K5 Global · Symphony Ventures · Kleiner Perkins · Slow Ventures · Social Capital · Y Combinator · Accel · Andreessen Horowitz · CAA Ventures · Founders Fund · GV · Lerer Hippeau · SV Angel · Tuesday Capital

Cantina is building a social network where the other people are not people.

Founded in 2023 by Sean Parker, Cantina lets anyone create AI characters with persistent personality and memory, then interact with them across voice, video, and text in live group spaces. Bots are treated as a new media type: infinitely scalable, personalised, and shareable anywhere humans already go online.

The company has grown to roughly 450 people and is backed by a16z, Benchmark, Greycroft, Founders Fund, and Salesforce Ventures.

the engagement

Cantina brought 33eleven Partners in during 2023, before the platform left invite-only beta. Over the next eighteen months we ran seven searches and made eight placements across product, engineering, gaming, creator strategy, product marketing, and talent.

This was not filling roles. It was building the layer beneath the founder.

7

Searches completed

8

candidates placed

80

avg. days to close

1k+

executives evaluated

PLACEMENTS

Cantina was weeks from launch and framed its competitive set as Character.AI near-term, Meta and ByteDance long-term. Cofounder, Sean Parker, wanted someone who shared his conviction that AI could produce genuinely new consumer experiences, and was candid that the build would be hard.
We hired a leader who had shipped machine learning and NLP at three major technology companies, including one scaled from 200 to more than 1,000 people, with hands-on GPT-3 and instruction-tuning experience.
Closed in 41 days.
Cantina was hiring across five or six technical domains at once, including speech AI, images, and memory, on the back of a small and stretched recruiting function. The mandate covered org design, headcount planning, and building an employer brand in an ML research community where Cantina was still new.
We hired a talent leader who had taken a company from 15 people at Series A to over 120 at Series B, and been the sole recruiter at a stealth company through its acquisition.
Closed in 92 days. 163 executives evaluated.
Cantina was betting that LLM-driven interactive storytelling could become a vertical alongside its creator and social products, and needed someone fluent in both game design and AI product to build it.
We hired a gaming executive who had co-founded and sold a company, then led generative AI initiatives there, with earlier work growing a mobile franchise from $6M to $600M in revenue.
Closed in 54 days.
The feed is the product. Ranking and real-time architecture decide whether a social platform retains anyone, however good the characters are.
We hired two engineers: one with six years on real-time messaging infrastructure at a major social platform, the other from backend engineering at a consumer social app.
One search, two hires.
Cantina was in invite-only beta with something nobody had a vocabulary for. Cofounder, Daniel Klaus wanted a thought partner on go-to-market, positioning, and how to tell the story as the platform moved toward public launch.
We hired a marketing leader who had built the function at two consumer platforms that each defined a category rather than entering one.
First candidate presented in 16 days. Closed in 77.
A true zero-to-one problem. Which creators to onboard, how to get them building AI characters, and what the economics of a creator-driven AI network even look like. Nobody had built this before.
We hired an early team member from an avatar platform who had built personality systems, run social scale, and led a 100,000-strong ambassador programme.
Closed in 66 days.

PARTNERSHIP INSIGHTS

In consumer AI, the hardest hires are not in sales. There is nobody to sell to yet.

Enterprise AI companies hire revenue leaders first, because the problem is convincing a buyer to create a budget line. Cantina had no buyer. It had a bet that millions of people would do something nobody had done before.

So the searches went elsewhere. Product and engineering to build a feed people would come back to. Creator strategy to work out who would populate the platform and how they would be incentivized. Gaming to test whether interactive storytelling was a vertical. Talent, early, because a company hiring across speech AI, images, and memory simultaneously cannot do it with two recruiters and a finance lead moonlighting.

Get the sequence wrong in consumer AI and you build a revenue function for a product nobody has adopted.