This article was published in the Spring 2021 issue
by Nate Sanders, CEO & Founder, Artifact
A little over a year ago, our team pivoted to a completely different product after observing a much deeper pain point surface from early usage. As we navigated this pivot, we stumbled into a deep tech problem. Because we aren't funded like most deep tech companies— we've had to get very vulnerable and collaborative with other deep tech founders and academic researchers. We've met dozens of brilliant people in the machine learning space that have been very influential in our journey. The only disappointing part of this process? We weren't able to have a single conversation here in Utah— and not because we didn't try. We found every introduction, every email reply, gravitating towards a small handful of universities.
This small cohort of universities interact with private industry differently than the vast majority of universities, often because the tenured professors have weaved in and out of industry. This exposure to the academic community and deep tech founders has moved us to try and create a unique and one-of-a-kind cooperative deep tech community here in Utah. Utah’s centralized population, startup growth, and several outstanding academic institutions give us a unique edge to accomplish this.
Our experiences over this last year have convinced us this is possible, but it’s important to understand some of the tensions that we observed that create a schism between these two, often very different, worlds.
We observed Ph.D. students experiencing an enormous amount of angst about whether they should work in industry or stay in academia. In industry, there is access to data and computational resources for innovative applications. In academia, there is not only freedom— but the expectation to do research and solve complex problems. But there is also a strict trajectory to tenure that almost always requires an undeviating course. It can be daunting to these individuals. As Dr. Raymond Stantz once said, *"You've never been out of college! You don't know what it's like out there! I've WORKED in the private sector. They expect results!"
We noticed enterprises using out-of-date technologies to solve problems where there is a large amount of published research but little application.
We noticed that the companies advancing application the furthest in the market frequently orchestrated and closely collaborated with academic peers.
In most cases, we also observed that enterprises didn't require invention to make technology useful and profitable. They're perfectly content using abstractions and services to solve a problem (Group A) rather than feeling that they need to be the nexus for the underlying innovation (Group B).
If you’re going to create community that spurs and fosters deep tech innovation, you have to address these dichotomies.
So why a more mature deep tech ecosystem in Utah would matter, anyways? One clear benefit is sizeable economic growth. Many believe that one of the only vital variables that could enable Utah to become a "heavy-hitting" tech hub is to become an obvious choice for massive incumbents to build significant satellite locations. I can't entirely agree. The most efficient way would be for us to build unique companies that we aren't seeing made anywhere else. If we were to build companies that solve hard, complex problems (Group B)— we will create an avalanche of downstream enablement for the rest of the ecosystem (Group A).
Another critical benefit is that it would keep our most innovative talent here in our state. It's novel to export brilliant individuals worldwide from the notable companies and academic institutions we have. Still, it's even more interesting to have cohorts of innovative companies that keep them here.
At Artifact, we are currently unable to fill many of our critical roles that require specialized talent inside our state. We've had to turn to companies and academic programs that focus on information retrieval and synthesis.
I can hear many of my competent and intelligent peers yelling at their computer screens right now that scientists and academics aren't what our industry needs. While I've seen the annoying nuanced problems behind this narrative, I've also seen more instances of engineers that feel threatened by scientific and research-focused programs.
A large part of this is that these initiatives are commonly formalized into innovation teams— skunkworks projects usually have more bark than bite. There is little accountability for these groups' outcomes, but they can carry an organizational hoity-toity-ness that is rarely deserved when evaluating what value they add to an organization. Honestly, teams that stand teams up like this are seldom organizations that should be in the business of deep tech innovation. In my experience, it would be wiser for them to invest private capital into or license the technology from a deep tech organization solely focused on solving these problems.
Another issue that we see is that industry generally thinks private R&D is more mature than academia. Private R&D, especially in heavy software applications, has come a long way over the last 30 years. The way we build products has drastically changed throughout my career. We build more collaboratively, release software rapidly, and abstractions continue to enable predictably stable production environments. We include customers frequently and infuse empathy for their needs into our products. There's an increasing amount of rigor and efficient process. We've come a long way.
That being said, in the private industry, we've told ourselves a little lie. We've said to ourselves that our speed and lack of rigor compared to academia is an unparalleled strategic advantage that allows us to build 'the real innovations.' There's a misplaced attitude that academia is a snail-paced paper writing institution and that we can't learn anything from them.
The fact of the matter is this: the vast and dramatic majority of technology enablement and inventions have not come from private industry over the last 100 years. It has come from academic institutions, which have published findings that a private company has created viable applications for and commercialized. If we overgeneralize this pattern, we see that academia excels at the critical rigor and research needed to discover new paths forward. Private industry excels at commercialization and application—neither more essential than the other in terms of the broader ecosystem.
It's important to examine the current patterns of how these companies start: 1. An academic leaves academia to pursue a venture rooted in their observed intellectual property. 2. An organization licenses the technology from an institution at the genesis of the product or venture. 3. A startup hires hard-to-find, specialized talent with the expertise to solve the problem internally. All three share one commonality: a delineation between venture and academia.
This dichotomy is driven by the paradigm that universities become licensing houses for published research. This paradigm incentivizes and encourages the abstraction of research away from the application. Theory-heavy research leads to a broad spectrum of potential licensees, where application-specific research requires "tip of the spear" specificity for who will license the research. Do you want your potential revenue to come from a pool of many or few targeted organizations? The latest call to action is that there needs to be a new underwriting process for academic research characterized by improvements to the Bayh-Dole act. The amendments to this act are critical efforts that affect the relationship between federal government, grant issuances, and the rights for academic researchers that receive the grants to retain intellectual property. However, this only affects a uni-directional relationship between federal departments and researchers.
Our team envisions a different paradigm that would blur the lines between academic research and private industry. There's a bag-of-variables that is affecting both the private sector and academia. Educational institutions have seen little ROI from licensing over the last decade. Private companies are rich in applied use cases but sparse in time and rigor. Academics desperately need to find and articulate applied use cases. Private companies struggle to convince academics to leave their path to tenure. Both organizations want to profit from the research.
Our most urgent call to action is for Utah startups and academic institutions to creatively work together to find ways to eliminate binary decisions between industry and academia. The easiest way to do this is by allowing researchers to receive contracts and sponsorships from private companies with equally incentivized rights and protections for both organizations.
Private companies could breathe life into research through market-ready products the minute it's saved as a PDF. Academic institutions should view their relationship with companies more as a platform for bridging complex theory and application rather than a marketplace exchange. We will require new licensing methods and ways to share intellectual property that look past current paradigms of full licensing, carve-outs, and jump-start access to IP. Allocation of grants and funding inside universities will become dramatically more evident because of the ability to see direct relationships between more micro use cases and applications.
Building a system like this isn't an easy problem to solve, but we have the right ingredients inside our state to become the world's capital of deep tech innovation. It won't be possible for large cohorts of new companies to solve these types of enabling deep tech problems without a unique and unparalleled collaboration between our universities and startups. Perhaps I'm naive to believe that cooperation can solve a host of issues for both parties, but there is no reason that as we move forward that Utah cannot realize a vision like this. There is no requirement, in the future, for Utah companies to draft off the East and West coast innovations— it can happen right here in Silicon Slopes.
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