Google launches its own AI Studio to foster machine intelligence startups
A new week brings a fresh Google initiative targeting AI startups. We started the month with the announcement of Gradient Ventures, Google’s on-balance sheet AI investment vehicle. Two days later we watched the finalists of Google Cloud’s machine learning competition pitch to a panel of top AI investors. And today, Google’s Launchpad is announcing a new hands-on Studio program to feed hungry AI startups the resources they need to get off the ground and scale.
The thesis is simple — not all startups are created the same. AI startups love data and struggle to get enough of it. They often have to go to market in phases, iterating as new data becomes available. And they typically have highly technical teams and a dearth of product talent. You get the picture.
The Launchpad Studio aims to address these needs head-on with specialized data sets, simulation tools and prototyping assistance. Another selling point of the Launchpad Studio is that startups accepted will have access to Google talent, including engineers, IP experts and product specialists.
“Launchpad, to date, operates in 40 countries around the world,” explains Roy Geva Glasberg, Google’s Global Lead for Accelerator efforts. “We have worked with over 10,000 startups and trained over 2,000 mentors globally.”
This core mentor base will serve as a recruiting pool for mentors that will assist the Studio. Barak Hachamov, board member for Launchpad, has been traveling around the world with Glasberg to identify new mentors for the program.
The idea of a startup studio isn’t new. It has been attempted a handful of times in recent years, but seems to have finally caught on with Andy Rubin’s Playground Global. Playground offers startups extensive services and access to top talent to dial-in products and compete with the largest of tech companies.
On the AI Studio front, Yoshua Bengio’s Element AI raised a $102 million Series A to create a similar program. Bengio, one of, if not the, most famous AI researchers, can help attract top machine learning talent to enable recruiting parity with top AI groups like Google’s DeepMind and Facebook’s FAIR. Launchpad Studio won’t have Bengio, but it will bring Peter Norvig, Dan Ariely, Yossi Matias and Chris DiBona to the table.
But unlike Playground’s $300 million accompanying venture capital arm and Element’s own coffers, Launchpad Studio doesn’t actually have any capital to deploy. On one hand, capital completes the package. On the other, I’ve never heard a good AI startup complain about not being able to raise funding.
Launchpad Studio sits on top of the Google Developer Launchpad network. The group has been operating an accelerator with global scale for some time now. Now on its fourth class of startups, the team has had time to flesh out its vision and build relationships with experts within Google to ease startup woes.
“Launchpad has positioned itself as the Google global program for startups,” asserts Glasberg. “It is the most scaleable tool Google has today to reach, empower, train and support startups globally.”
With all the resources in the world, Google’s biggest challenge with its Studio won’t be vision or execution — but this doesn’t guarantee everything will be smooth sailing. Between GV, Capital G, Gradient Ventures, GCP and Studio, entrepreneurs are going to have a lot of potential touch-points with the company.
On paper, Launchpad Studio is the Switzerland of Google’s programs. It doesn’t aim to make money or strengthen Google Cloud’s positioning. But from the perspective of founders, there’s bound to be some confusion. In an ideal world we will see a meeting of the minds between Launchpad’s Glasberg, Gradient’s Anna Patterson and GCP’s Sam O’Keefe.
The Launchpad Studio will be based in San Francisco, with additional operations in Tel Aviv and New York City. Eventually Toronto, London, Bangalore and Singapore will host events locally for AI founders.
Applications to the Studio are now open — if you’re interested you can apply here. The program itself is stage-agnostic, so there are no restrictions on size. Ideally early and later-stage startups can learn from each other as they scale machine learning models to larger audiences.
Featured Image: Bryce Durbin