How Former Googlers' VC Firm Invests In Everything From LLMs To AI Doing Drug Discovery聽

Editor鈥檚 note: This is part of a series in which we interview active investors in artificial intelligence. Previous interviews were with investors at General Catalyst, Bessemer Venture Partners, Accel,听 Insight Partners, Index Ventures and Sequoia Capital.听

was at when the tech giant in 2017 鈥� a massive leap forward in the way machines understand and generate human language that paved the way for the rapid rise of generative AI technologies like five years later.

Now Harrison is the CEO and managing partner at聽 , a venture investment firm with ties to Google that also invests heavily in AI.

Andy Harrison, CEO and managing partner of Section 32

We spoke with Harrison, an ex-Googler on the tech giant鈥檚 team, who left the company in early 2021 to lead Section 32. He was previously at Google Life Sciences as head of business and corporate development. Google Life Sciences鈥� mission, he said, was to take machine learning, AI, software and hardware concepts and bring them to the health care and life sciences spheres. The unit spun off into .

Section 32 is also focused heavily on investing in AI, health care and life sciences.

The investment firm鈥檚 founder, , led from 2008 to 2016. The next year, he founded Section 32, which operates on the East and West coasts and manages $2.3 billion that it has raised across five funds since 2017. Its newest is .

Investment focus

Specifically, Section 32 is investing in AI in infrastructure, enterprise services, cybersecurity and areas like precision medicine and computational biology, Harrison said.

鈥淲e’ve been staying away from what we call 鈥榯he zone of commoditization鈥� and trying to focus on the second- and third-order problems that we know the cloud players are not going to build,鈥� Harrison said, speaking about the rare talent pools and abilities required to compete in AI.

鈥淲e’ve been very focused about building a portfolio that focuses on some of those applications where there should be long-term protection and a strong ability to build revenue without direct competition with the large players,鈥� he said.

The firm has invested in large language model company and biological software company , whose founders were among the original authors of the Transformer model.

The firm is also an investor in . The protein therapeutics startup鈥檚 co-founder, , was the head of the genomics team at Google Brain.

Section 32 has invested in gaming character generation startup and content creation company .

It also has invested in climate tech startup and quantum computing company . Another investment, , supports data labeling for AI, while portfolio company creates synthetic data.

In cybersecurity, Section 32 has invested in and .

It’s only software

The current technology evolution is driven by the continued evolution in AI models, Harrison said.

鈥淭he storage and processing power has been getting cheaper,鈥� he said. 鈥淭he models get better, the chipsets and the processing power get cheaper, and then the storage gets cheaper. That’s created a flywheel here that’s allowed these massively trained LLMs that have caught everyone’s imagination.鈥�

However, 鈥渋n the end the stuff is just software,鈥� he said.

Harrison said Section 32 wants to invest in AI companies that drive real ROI. 鈥淚t’s expensive software because of the compute and because of the talent required. So it has to be that much better,鈥� he said. 鈥淥r it has to do something we couldn’t previously do with software in order to drive high margins. Otherwise it’s low-margin software.鈥�

He predicts the GPU crunch will alleviate itself in the next 12 to 18 months and the costs of the models will come down.

The Transformer model

At Google, Harrison saw AI evolve from the Transformer approach, then become neural networks and convolutional neural networks and other approaches. Now it’s large language models and generative approaches, he said.

鈥淎 lot of this comes from the L 鈥� the language team, the translation team at Google back in the day,鈥� said Harrison. They had built a model for English to Spanish and then a model for Spanish to French. This raised the thought of building a meta model to go from any language to another language, and that led to developments that led to the Transformer.

Some of the earliest uses of the Transformer model at Google was predictive text in Gmail and content moderation at .

鈥淲e still call them large language models, even though language is sort of a misnomer at this point,鈥� he said.

And AlphaFold

, part of Google, was the originator of , a program that mapped the 3D structure of proteins in the human body to help scientists understand how they work, which is crucial for understanding diseases and drug development.

AlphaFold 鈥渃reated a meta technique to take any protein and figure out its conformational structure based on the ones that it trained on,鈥� Harrison said.

In health care and computational biology, Section 32 sees AI applications for drug discovery and molecular engineering.

For example, we now have the ability to create molecules that have the effect we want, with lower toxicity, or better stability, or higher tissue specificity, or higher efficacy, he said.

鈥淎 lot of those tools and techniques are very new,鈥� Harrison said. 鈥淏ut they’re showing pretty incredible promises that relate to speeding up the process, saving people time and money, and increasing the odds of success 鈥� which is the most important thing for humans.鈥�