Suggestions
Garrett Lord
Co-founder and CEO of Handshake
Garrett Lord is the CEO and co-founder of Handshake, a leading career network designed to connect college students with employers. Founded in 2014, Handshake aims to democratize access to career opportunities, enabling students from diverse backgrounds to secure meaningful internships and jobs. The platform is now utilized by over 90% of universities in the U.S. and has partnered with more than 900,000 employers, including all Fortune 500 companies.14
Background and Education
Garrett grew up in a small town in Michigan and initially attended a local community college before transferring to Michigan Technological University, where he studied computer science. His experiences during college, particularly the challenges faced by students from non-target schools in securing internships, inspired him to create Handshake. Prior to founding Handshake, he gained valuable experience through internships at prestigious organizations such as Palantir Technologies and Los Alamos National Laboratory.124
Achievements
Under Garrett's leadership, Handshake has raised significant funding, totaling $434 million from prominent investors including Kleiner Perkins, Coatue, and Chan Zuckerberg Initiative. He has been recognized as a finalist for the EY Entrepreneur of the Year award in Northern California, highlighting his impact as a leader in the tech and education sectors.135
Garrett's vision for Handshake is to ensure that all students, regardless of their background or the prestige of their educational institution, have equal access to career opportunities, thereby transforming the landscape of early career development.34
Highlights
For anyone who still doesn't believe AI will change the world -->
Larry Ellison is right. Models are trained on the same data. The differentiator now is expert human feedback applied to real workflows.
Doctors, lawyers, engineers. People who actually do the work. Observing how models fail in production, providing the cognition to fix it, and feeding that signal back into training.
That loop is the new moat: observe failure → extract expert judgment → build verifiers → post-train → deploy → observe new failures. Each cycle makes the model better at harder, longer-horizon tasks that general training data never covered.
The companies that own that loop will power the next generation of AI.
