When an area becomes consensus but is still somewhat novel or hard to parse for investors, they often try to distill companies into very clear-cut and easily “findable” metrics in order to make decisions.In many cases, this wave of chaos, rapid change, and complexity pushes investors to retreat into discussing a constant in startups; Founders. And as one searches for Founder-Company fit, they then anchor on the next clearest signal; Founder Pedigree.In technologies that are Pre-Breakthrough or Cambrian Explosion moment this framework feels valid.Investors are looking for those responsible for changing the world and doing something that has never been done before, and as those people seek to commercialize their breakthrough, the idea is that nobody understands it better than the original “inventors” and the inventors will be necessary to push it from a research breakthrough to production-ready as a technology.
We also are looking closely at novel techniques that could emerge. In creative AI we went from GANs to diffusion models for image generation. Could there be something better like OpenAI’s recently published Consistency models? Will Transformer models be the final model to rule them all?While it is currently unclear to us what will change, we have a hard time believing that nothing will. We’ll have to keep looking closely.Michael Dempsey2022 Compound Annual LetterGo to text →
These statements are of course of incredible importance to understand.Will novel paradigms or breakthroughs come from the existing players, effectively betting that the best researchers are able to continue to innovate on novel concepts better than others while in the Founder seat?While linear progress likely will continue to compound with the talent and capital (and thus compute) moats of the largest research labs, it’s possible that entirely new architectures , as alluded to above, or new building blocks that are their own breakthroughs, could create non-linear breakthroughs. Whether those breakthroughs outpace performance of Scaling Laws is an open question.It’s also possible that this innovation push could come not from even scaling models but instead from scaling the research done for LLM agents or other adjacencies to model performance (as I wrote in depth about in The Dark Forest of R&D and Capital Deployment in AI). This concept was also well-covered from an alignment perspective in this post by Paul Christiano at Alignment Research Center (formerly of OpenAI) and we’re starting to see people try to setup open-source mixture-of-experts communities as well.Again, understanding the bleeding edge.Perhaps the biggest limiting factor here will not be the talent but instead will be the politics, technical debt, and social capital that sits within these large groups that have accumulated and spent tens of billions (hundreds?) collectively on a transformer-centric approach.This idealogy of being pot-committed is a lot of how we had conviction to partner with Wayve who had a very non-consensus approach to solving and scaling the self-driving problem. Put simply, the larger players just weren’t going to toss out their traditional approach now that they were seeing linear progress and had billions of dollars of budgets allocated to them because of their confidence and bet on this approach.(2) It took a team of relative outsiders to take a shot on an end-to-end approach in 2017 versus the slew of ex-Tesla/Waymo/Cruise people who spun out and raised capital post-Cruise acquisition to build self-driving startups.While AI does seem somewhat unique in this regard, my gut tells me as other other emerging technical categories start to show obvious breakthroughs, we will see this dynamic play out again across Bio, Robotics, and likely other deep tech categories (quantum, ARVR, etc.)Bio, has historically been funded almost exclusively on pedigree in the past decade+, however now we are seeing interdisciplinary teams as equally if not more competent than traditional biotech OGs as bio shifts to a more engineering-centric and platform-driven paradigm.We’ll likely see similar traditional pedigree-driven dynamics emerge within Robotics in the coming years. As the progress made in large models and breakthroughs like RT-1/2, Code as Policies, and more continue to ripple through the community, investors will likely index heavily towards those from these labs or from the large labs that have been dismantled or refocused (hello OpenAI Robotics team).While many of these people will be paramount to creating the breakthrough, building a venture-scale robotics company will not only be about those who are able to implement advanced hardware + embodied intelligence in the real world, but also those who are able to push forward the creative thinking of the modality or use-case of a given robot in order to build the next great Robotics company.My advice for those at the top tier labs would be to make sure you are partnering with incredible product people on the founding team.
Taking this back to where we started, our view continues to be that we are in The Most Important Century (3) which means that we will likely have a large number of “breakthrough” moments in the coming years in deep tech that are near impossible to pattern match to.In a world where asymmetry and exponentials are becoming more and more understood, it’s likely that there will both be far more volatility as well as opportunity for Founders regardless of classical pedigree and it likely means investors should be careful over-indexing or pattern matching into what the obvious view of excellence looks like.All of this leads me to keep coming back to the venn diagram above which originally described our Computational Creativity thesis, our seed investment in Runway, and perhaps describes building at the age of post-breakthrough, pre-scale innovation.A mixture of the best scientists in the world who have done things that have never been done before and perhaps a different cohort of artists tasked with pushing these breakthroughs at scale to create wonder.Founding teams building at the intersection of Science and Art.
(1) Or these things happen at big companies and the leaders fracture in ambition or sequencing and both leave. This is arguably how one could describe the origin stories of Nuro and Aurora in the autonomous vehicle space.(2) Many have since pivoted/progressed towards Wayve’s approach(3) The origin for this framing
