Will AI Super Charge Venture Studios?

Sometimes the idea comes from inside the studio, and the team recruits a founder to run with it. Other times an outside founder brings the idea, and the studio helps shape the product, assemble the team and get the company off the ground in return for meaningful ownership. Either way, you’re no longer being paid purely for your time. You’re betting that the thing you help build will become far more valuable than any project fee ever could.

It’s easy to see why that appeals, particularly if you come from the agency world. After years of helping other people create value, the thought of keeping more of that upside for yourself is hard to resist.

The problem, of course, is that while the model sounds elegant on paper, it has often proved much messier in reality.

Studios still need to pay salaries. Designers, engineers, operators and marketers rarely work for free. So somebody has to fund the gap between building a company and that company becoming valuable enough to pay back the effort. In the best-case scenario, the studio founder has deep enough pockets to finance those early bets themselves. Failing that, they may be able to raise a dedicated fund to support the model.

But that’s not how many of these businesses actually work.

More often, what gets called a venture studio is really an agency trying to carve out some upside from client services. They use fee-paying client work to subsidise internal ventures, or they discount their rates in return for equity. In theory that sounds smart. In practice, it often means using a fragile cashflow business to fund a portfolio of highly uncertain bets.

And the quality of those bets is often the real issue.

Many agency-for-equity deals don’t come from exceptional founders with huge ambition and strong momentum. They come from founders who couldn’t raise capital, don’t have the means to build the product themselves, and are offering equity because they have no better currency to trade. That leaves the agency taking an outsized amount of risk, usually on the weakest opportunities in the market.

You’re not getting access to the best companies before anyone else. You’re often getting the leftovers.

That, more than anything, is why so many venture studios and agency-equity models have struggled to build a great reputation. The idea is appealing. The economics, the incentives and the deal flow have often been far less attractive.

Which is why the recent resurgence of interest in venture studios is so interesting.

One article claims the number of venture studios has grown by 600% over the last decade. That may partly reflect a looser definition of the term, but I also suspect something more fundamental is changing: AI is starting to alter the cost curve of company creation.

Historically, testing a new startup idea required a meaningful amount of coordinated effort. You might need a designer, a couple of engineers, maybe a marketer or product person, and several months of work before you had enough product in market to get any real signal. That made the studio model expensive, slow and operationally heavy. Each bet consumed real bandwidth, and there were only so many shots you could take before the economics became painful.

Generative AI changes that.

Today, a one- or two-person team can prototype, launch and test an idea in a fraction of the time and at a fraction of the cost. You no longer need a six-person team working for six months to learn whether anybody cares. In some cases, you may only need a couple of people and a couple of weeks to get directional evidence. Not certainty, obviously. But signal.

That matters because it changes two of the hardest parts of the studio model at once.

First, it dramatically lowers the cost of experimentation. Second, it increases the number of experiments you can run. More shots on goal, lower cost per shot.

If that dynamic continues, venture studios may start to look less like a romantic but flawed reinvention of the agency model, and more like a genuinely viable system for repeated company creation.

In fact, I wouldn’t be surprised if the next wave of venture studios looks very different from the last. Smaller teams. Faster cycles. Less infrastructure. Fewer people needed to get to first proof. And, very possibly, the rise of the one-person venture studio: a founder-operator using AI, contractors and a small amount of capital to spin up, test and either kill or back ideas at a pace that would have been impossible even a few years ago.

That doesn’t mean the hard parts go away. Taste still matters. Distribution still matters. Founder quality still matters. And most ideas will still fail.

But for the first time in a while, the economics of the venture studio model may be moving in the right direction rather than the wrong one.