Jaclyn Rice Nelson, Co-Founder & CEO of Tribe AI – Interview Series
Jaclyn Rice Nelson is Co-Founder & CEO of Tribe AI, they help organizations drive change with machine learning by building a new path for the best talent in tech.
Prior to launching Tribe AI, Jaclyn spent the majority of her career at Google partnering with enterprise companies and incubating new products. She was an early employee at CapitalG, Alphabet’s growth equity firm, where she built a fifty-thousand-person expert network and advised growth-stage tech companies like Airbnb on scaling their technical infrastructure, data security, and leveraging machine learning for growth
You were the Business Lead of Google Helpouts, a marketplace to connect people to experts over live video and was run autonomously within Google, followed by being the Growth VP at CapitalG a fund that invests in growth-stage companies. How did this experience incubate your views on creating a talent agency for AI?
I worked at Google for nearly eight years and it wasn’t until almost three years into my tenure that I worked directly with engineers. I joined Helpouts as one of the first business people and the experience of sitting in the same room with 30+ engineers was a completely different (and much quieter) experience from a sales floor. It gave me a direct view into product and engineering and a direct line to share customer feedback. This was also my first experience building a marketplace to share expertise, a theme that would carry through my career and lead me to starting Tribe AI.
Startups within large companies are doomed to fail, and so too did Helpouts, despite having a global launch and scaling the team to 150 employees. I transitioned to the late stage venture fund, CapitalG (formerly known as Google Capital), to help build a similar expert network of Google specialists only accessible to the companies we invested in. This is where I saw first hand that even the best, growth-stage companies – like Airbnb, Stripe and others – found hiring for data science and machine learning extremely challenging. We were the first line of defense for their questions and I wondered what a company would do if they didn’t have Google to fall back on.
I saw the value of data and machine learning at Google and the tremendous opportunity to give companies in Silicon Valley and beyond access to underutilized talent and see the value from AI. And so, I became an entrepreneur and Tribe AI was born.
What are some of the dramatic wealth generation opportunities that you currently see in AI?
AI is the next gold rush. Advances in generative AI create the urgency and means for every company to become an AI company. There are tremendous opportunities for startups to build huge businesses and for large incumbents to become AI companies. This means a lot of opportunities to build incredible products that solve real problems, serve millions of people, and create tremendous wealth in the process for founders, investors and top executives alike.
In 2021 you became Co-Founder of Coalition Operators, what specifically do you look for in founders that you invest in?
I’ve been actively investing since I left CapitalG in 2018 and eventually raised a fund, Coalition Operators, along with 3 exceptional founders and operators. As founders, we lean in on the areas we each know best, which means I do a lot of investing around Data, AI, ML and B2B SaaS. Since we predominantly invest in Seed-stage companies, I optimize for founders above all else. I look for people who are passionate, have a unique insight into the market they’re going after and are a little bit crazy (in the best ways).
Could you share the origin story behind Tribe AI?
I met my co-founder Noah Gale while we were both at South Park Commons, a technical community in San Francisco. We were surrounded by top ML engineers who had left big tech because they were looking for freedom. They no longer wanted to climb the corporate ladder or spend all their time optimizing ads. They wanted to start their own companies, work on their own research and gain experience solving problems across industries.
The opportunity became clear: give top technical talent the freedom to take on flexible, unique projects they really want to work on and provide a strong community of other talented engineers to connect with based on mutual interests. In doing so, we’ve created the infrastructure to allow top talent to do only the things they are best at and none of the things they’re not.
We built a highly curated network of top AI specialists and have built a business that can give them the freedom they want while helping companies apply machine learning to their business. We don’t only work with startups, we also work with PE firms, enterprise companies, and beyond. All companies need to become AI companies and Tribe is helping them realize that vision.
Why do companies of all sizes struggle to recruit machine learning talent?
For starters, it’s really hard to evaluate technical talent as a business leader. Understanding exactly what skills you need and how to approach data problems – it’s hard to do unless you already have top technical talent in place or direct machine learning experience.
Another reason is scarcity. Since AI models like ChatGPT have become more mainstream, every company is trying to figure out how to layer generative AI into their business. The competition for top talent is enormous and a lot of it is captured by a few leading AI companies.
How does Tribe AI solve this hiring dilemma?
We built Tribe to offer top technical talent a new career path, one that combines freedom, compensation, and interesting work. It’s clear this is appealing to a lot of talent – we get dozens of applications a day, and we accept a small share. By pooling this talent into a network, we’re able to place people on projects that align with their skills and their schedule. For some, this means consulting forty plus hours a week, and for others, they want to take on an advisory role while founding their own company.
This approach obviously has enormous benefits for companies too. The reality is that most companies don’t need a full-time, permanent ML team. Often what they need are a few specialists to build a technical roadmap or an initial proof of concept, and then a full stack engineer or front-end engineer to maintain or augment what they’ve built. This allows companies access to top talent and the flexibility to engage in a way that drives both innovation and success.
Tribe receives dozens of applicants a week, how does it vet the talent?
We start by reviewing an applicant's qualifications and technical expertise. If they meet the bar, we set up an interview to dig deeper into experience, communications skills, and problem solving abilities. All interviews are conducted by C-level machine learning talent to ensure we’re confident in the abilities of anyone who’s accepted into Tribe’s network. This is critical because the top engineers want to be around other top engineers. The network effects of this business for both customers and talent are huge, so everything comes down to having the best talent on the market. We have ML engineers who have done research at companies like OpenAI, AI founders with multiple exits, and people who have led teams at major tech companies, and everything in between. Our goal is to build the magnet for this talent, and from there, the companies follow.
How do companies see value in Tribe AI's network in addition to, or in some cases, instead of, having an in-house full-time AI team?
For starters, Tribe AI gives companies access to top AI talent from companies like Google, Apple, Amazon, Nasa and more. The reality is that, unless you’re one of the top AI research labs or tech giants, most companies can’t hire talent like this. So for a lot of companies, working with Tribe is the only way you’re going to access this caliber of AI talent.
The other factor is flexibility. When you hire a full-time team, it’s slow and you’re locked into working with a very specific skill set, often before even knowing what you actually need. We work with lots of companies that will bring on Tribe experts to augment their in-house team for a specific project, want to work with fractional talent to accelerate their velocity while looking to hire, or need help identifying the best use cases for AI.
The last piece is our experience. We truly walk the walk when it comes to AI. We use a proprietary matching system built on GPT-3 that allows us to quickly surface the exact right talent for every engagement. We’ve built the infrastructure that allows us to come in and make an impact at a company very fast.
How will project-based work change the way companies build AI into their businesses?
We believe project-based work is the future for AI/ML. Project-based work will drastically change how companies use AI because technical talent will be more qualified for hyper-specific needs rather than meeting general requirements for a more universal role. This model will help identify precise talent gaps in order to inform what kind of AI/ML experts are needed.
This is the way top talent wants to work, and it’s more beneficial for companies as well. Until now, top ML talent has only been accessible to the world’s leading companies that even still struggle with hiring efforts being slow, difficult and expensive. With this completely new model, companies of all sizes can accelerate their ML adoption and see results faster in a way that hasn’t been possible with traditional hiring practices.
Thank you for the great interview, readers who wish to learn more or are looking at hiring talent should visit Tribe AI.
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