PRIMER: Building Machines that Think, Read, and Write
Complexity rears its head across startups and Fortune 100s alike. It’s unbiased in its applications, and often crippling in its outcomes. The consequences of the future are coming at us faster than we once imagined, and in more unpredictable ways. The increasing asymmetry of opportunities and threats are hidden in exponentially growing, often silo’d, almost certainly unstructured, data. It’s almost painfully obvious: real world data’s sheer volume and dimensionality render it beyond human grok-able comprehension. The solution? Machine Learning and AI that closes the gap between ever increasing - ever complex - data and, at best, linearly scaling human horsepower. Technology that erases the chasm between data and decision making.
That is precisely what Sean Gourley and the entire, brilliant (I’m particularly excited to note that a few star team members are former students of mine from Stanford), Primer team are working towards — a mission Lux has been amongst the earliest and largest investors in.
I first met Sean in a coffee shop in Potrero Hill in Fall of 2014. He’d only recently left Quid, and I’d only recently joined Lux. Our conversation then had neither slides nor executive summaries. There were no napkins nor envelopes involved. Terms “Deep Learning” and “Neural Nets” hadn’t yet become household tokens of trends from Silicon Valley, but rather were obscure jargon from research halls in academia and R&D labs in large tech cos.
In that first conversation we debated what the prior decade had shown, the rise of “Big Data”: unifying, normalizing, and making accessible increasingly large and unstructured data from across disparate silos. Not only connecting various datum, but also making the data easily visualized and queried by analysts and intelligence workers across every major vertical. In many ways, we both agreed, companies like Cloudera, Quid, and Palantir were emblematic of Big Data’s reign over the prior decade. Our conversation turned towards what the future would hold. Although the precise machine learning machinery was still just emerging, we imagined building towards a future that would sit atop the prior decade’s advances. Systems, technology, and products that could not only bring data together — but tech that could generate the appropriate queries and human grok-able theses from the data itself.
In the abstract this company would close the gap we’d both seen between data and decision making: Enabling companies to scale past their finite human capital, and allowing truth to propagate up from burgeoning data too large and too complex for any single mind to comprehend and decipher. Primer was conceived to add multiplicative factors to the best teams of human decision makers and truth seekers.
It was far fetched then, and continues to push the limits of most reasonably minded technologists today. After that first meeting our conversation quickly turned to a series of emails pinging back and forth papers and results from recent NIPS and IJCAI sessions, vetting hypotheses, and testing just how close the future was. Soon thereafter we backed Sean and his new company Primer with a like-minded syndicate of investors eager to explore this whitespace. Primer’s earliest pitches weren’t with graphics laden decks with unreasonably large TAMs, but rather conversations with the entire Lux partnership about what the future of AI would enable and a vision to build a leading company to drive us there.
Fast forward 3 years and 2 funding rounds later, and while we’re just now coming out of stealth — very much so in the early innings of company creation — we’ve watched Sean and Primer build upon the initial vision of synthesizing intelligence to make sense of large, noisy, and complex data sets. In the process Primer has built one of the strongest private Artificial Intelligence teams, built scalable Machine Learning enabled products, and deployed these products across various government agencies, large retail conglomerates, and the some of the world’s most impactful financial institutions. The philosophical banter that took us across coffee shops in San Francisco to conferences in Toronto and pubs in NYC has matured and taken shape while gaining clarity. It feels clear now that if the prior decade was about normalizing and visualizing data for analysts to query, the coming future will bring to bear technology that productizes artificial, synthetic, intelligence to bring scale to knowledge teams within the top public and private organization around the world.
Welcome, publicly, to the Lux portfolio Sean and Primer!
