Generative SF: How to thrive in a crowded enterprise AI market

How can you gain a foothold in the enterprise AI market? Focus on value, says Glean's Arvind Jain.

Pictured left to right: Arvind Jain (Co-Founder & CEO, Glean) and Lisa Han (Partner, Lightspeed).

Over the last year, Lightspeed has hosted a series of meetups around the country to talk about the impact of Generative AI. We recently had the pleasure of hosting Arvind Jain, Co-Founder and CEO of Glean, in our San Francisco office. 

Lightspeed’s history with Arvind goes back two decades. He was a founding engineer of Lightspeed portfolio company Riverbed, and subsequently we had the pleasure of working alongside him as Co-Founder of Lightspeed portfolio company, Rubrik. In between Riverbed and Rubrik, Arvind spent 11 years as a Distinguished Engineer at Google, where he worked on early versions of the company’s large language models (LLMs) as part of its effort to improve the accuracy of Google search. 

It was that early work at Google that led to Arvind founding Glean, an AI-powered work assistant that connects and understands all of an enterprise’s knowledge, to bring people the answers they need. Glean’s ultimate goal is to be the enterprise AI platform with a ready-to-use AI work assistant, as well as the tools to build custom generative AI experiences grounded in company knowledge.

In front of a packed house on a rainy Thursday night, Arvind talked with Lightspeed’s Lisa Han about some of the challenges inherent in launching a new enterprise AI startup, the secrets of hiring in an intensely competitive labor market, and how the quest for quality has to be every startup’s north star.

Here are some of the highlights of their conversation.

We are still in the very early stages of enterprise AI

Enterprises are excited by the benefits AI can bring to internal knowledge discovery, Arvind says. But they’re also wary of the security risks. As a result, adoption of gen AI tools within the office of the CIO has been much slower than it has among the general public. 

“When people first saw ChatGPT, they thought ‘Hey if I have something like this inside my company, that can unlock levels of productivity we’ve never seen before,'” he says. “But it’s proven to be harder to deploy in the enterprise, because most of the data inside a company is private. You can’t just take all your company knowledge and use it to train a large neural-net-based model. Delivering safe and responsible AI is one of the most difficult things to do right now.”

Successful enterprise AI companies will deliver true business value

Companies that exist solely as a layer on top of an LLM like GPT4 aren’t likely to survive over the long term, says Arvind. They may initially help improve a company’s ability to quickly retrieve information, but successful gen AI startups will need to become more deeply embedded within an organization’s workflows and solve specific problems.

“We’re going to see a lot of platform-type solutions that enable internal teams that build products, as well as ready-to-use products for different functions within a company,” he adds. “Our customers see Glean as both an application that works like ChatGPT within a company, but also as a core platform that allows people to build new AI applications to improve their business processes.”  

Hiring is hard, but perseverance pays

Hiring in a tight labor market is always a challenge; the explosion of AI startups, coupled with the shortage of experienced data scientists and machine learning engineers, makes it a nearly Herculean task. But hiring the right team at the start is essential to successful recruiting, and perseverance will eventually pay off, says Arvind. 

“Recruiting for a startup is incredibly hard,” he says. “But my number one piece of advice is, never give up. Keep going after the people who say no to you at first. Keep in touch, build that relationship. Eventually you’ll have a core team of people who are celebrated in their field, and they’ll attract others.”

The opportunity to have a real impact on a company’s success is often the determining factor, says Arvind. It comes down to the role itself; whether someone wants to be a highly paid cog in an enormous machine, or a vital component in a small startup who can really make a difference. 

Quality is your north star

When you’re building a company from scratch it’s often difficult to know where to focus your energy. It’s a classic business conundrum: You can build something that’s fast, good, or inexpensive, but you can’t do all three at the same time. So what do you prioritize? For Arvind, the answer is easy.

“When you’re in the initial stages of development for your product, you should focus on only one thing, which is quality,” he says. “It doesn’t matter how costly your system is right now, or whether it’s fast enough. The question is, does it do something useful? When we started building Glean, that was our first priority. The second thing we focused on was latency, and cost after that. That’s especially true if you’re relying on third-party LLMs, where you hope that costs will get reduced over time. That’s why ignoring cost and performance and getting the quality right is your best strategy.”

The future of Glean is ubiquity

Glean started out with the aim of becoming a ‘Google for your work life,’ says Arvind. Since that time, his vision for the company has expanded.

“We want to fundamentally change how people work,” he says. “We want Glean to be the personal AI assistant for every person within every company that offers help when you need it, but also anticipates what you need before you ask. All of us will have these really smart, AI-powered systems that will do a lot of the heavy lifting for us. That’s where Glean is headed.”


Interested in attending a Generative event Sign up here to receive notifications about future meetups, and be sure to listen to the Generative Now podcast, where AI builders talk about how they’re creating the future. 

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