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What are the most productive communities?
Aside from funding, what are the other factors to community-scale hypercreativity?
Last week I wrote about early stage funding in science (and thank you to the community for all the input). Yet proper funding is only one part of a productive community, what are the other parts? This week I want to explore illustrative successes of funding, shared resources and shared challenges.
Since graduating college in 2008, I've observed first-hand the growth of three hyperproductive communities: computer science, biotech and brazilian jiu jitsu (BJJ). Now, I know BJJ is a weird one and I’m not here to bro out: BJJ’s growth and technical advancement is exceptional (more so than other trendy sports such as climbing and surfing), and I contend it instructively fits the model.
Computer Science needs the least introduction of how transformative it has been to our world. We all carry our smartphones and interact with apps that have been optimized for over a decade of shared ideas and best practices: that world has been built by hustling entrepreneurs that extend ideas from academia and industry in pursuit of the fabled riches. We can search for loosely related phrases to get us the result we want (NLP), enjoy selfie filters with cat ears that seamlessly track our heads (computer vision), interact with a new internet of money (cryptography) and even rely on progressively more reliable self-driving cars (reinforcement learning). But, from the practitioners’ perspective, what are the characteristics that made it so hyper productive?
+ Common infrastructure for experimentation: Anybody with a minimally powered laptop could start programming, and the dominant languages (eg, python) created package managers to rapidly load in the work of other people.
+ Common infrastructure for scaling: The iPhone, Facebook and AWS all were game-changers because they were paths to infinite scale. If you made something that people liked, you could go to a million users in days (eg, the Shotgun App in 2009) or could scale your computation beyond any physical limits of computer hardware (eg, MapReduce in 2004). Since 2009, we take for granted that a software idea that works for 10 people can readily scale to 10 Million: this certainty of the potential to scale was essential for the intense ambition that fueled the rise of software eating the world.
+ Common goals: Computer science has been rich with shared public challenges (eg, the $1MM Netflix Challenge in 2006) and dataset-driven competitive benchmarks (eg ImageNet). These public goals act as lenses to focus effort and surface talent. Furthermore, when transformative ideas are invented, the community can immediately appreciate the value.
+ Enforceable honesty: The community can immediately test the validity of your claims and ideas. Because of the common infrastructure and community practice of open source, when big ideas come along they can be replicated and validated. AlexNet took the computer vision community by storm in 2012 with their use of deep neural networks on the ImageNet challenge: the importance of the work was quickly appreciated across all of computer science because anybody could download the code and replicate it themselves.
+ Playbooks for outsized success: Young, ambitious people have clear targets to aim for as they prioritize their time. By 2010, Y Combinator had codified the software startup playbook, created the archetypal software founder and illustrated the incredible riches of those who play the game well. I met a woman at a San Francisco boat party in 2012, in her words: “I’m working at a Series C company backed by a top-tier VC after finishing my undergrad in computer science where I interned at Google and Facebook. In my spare time I’m doing customer discovery in two potential markets [A] and [B] and networking actively for potential co-founders. In two years I’ll apply to YC.” I was super impressed, both by her ambition and the complete clarity of purpose to her fulfilling the archetype of the software entrepreneur of the era.
+ Funding to try things: The amazing thing about computer science is that the marginal cost of an experiment is virtually zero. Beyond student salaries (or the heralded Instant Ramen diet of founders), resources are not really part of the conversation at the early stages of innovation in computer science. All the cloud services are quick to offer free credits and there has been a constant increase of computer science fellowships.
I chose Professor Fei Fei Li to be in the header image because of her exemplary contributions to computer science. It was her idea to make ImageNet into *the* data benchmark for the field of Computer Vision. But more than a benchmark, it was a training dataset good enough to enable the first practical use of multi-layer neural networks (AlexNet). Similarly, beyond academic contributions of her own, she has been behind startups and AI strategy of the biggest companies in the world.
I was in the Stanford Computer Science buildings in 2010 (in Fei Fei’s group actually), and I remember the energy in the hallway conversations. Us graduate students would brainstorm how AWS, Facebook minifeed and the iPhone (common infrastructure) could be the platforms to address market opportunities (common goals) via solving tech problems (enforceable honesty). These conversations would have a certain formula of combining a technology challenge and a market opportunity. Eg, 'Ooh we could fuse iphone sensors via ML to determine precise indoor location and share it to twitter!' or 'we could use iphone cameras and computer vision to overlay ratings to store fronts when you walk through palo alto!'. Regardless if those conversations actually converted to startups of any quality, I’d argue that just those brainstorms alone helped computer scientists understand their personal value.
Before computer science matured into the five elements bulleted above, it was the era (early 2000s) in which the stereotypical startup was founded by a fast-talking MBA who then went hunting for programmers as his first hire. But individuals like Zuckerberg, Brin and Page showed us that the best founders are practitioners. And, by the entrepreneurial brainstorming and pitch sparring toward a Y Combinator (or similar) application, the technical graduate students also taught themselves the language of business and techniques for self-advocacy.
In many ways, biotech has become similar to computer science. The exact same entrepreneurial discussions happen in the conversations of bioengineering hallways today: “Ooh we could use CRISPR to disable this rare, inheritable genetic pathology!” or “Oh we could computationally redesign this protein to cross the blood brain barrier with a small molecule payload!” or “wait, what if we could modulate this obscure metabolic pathway to enable novel combinations of already approved drugs?” These conversations are fun, and I’ve seen first-hand the business sophistication of bioengineering graduate students increase significantly over the past five years.
Biotech has been on its own exponential growth trajectory and only continues to accelerate. Over the past decade it has matured its own creative community with noteable technical milestones being the sequencing of the human genome (~2001, depending when you call it “done”), next-generation sequencing (2008) and CRISPR (2012, and timeline of discoveries here). I put Professor Jennifer Doudna in the header because the discovery of CRISPR (for which she earned a 2020 Nobel Prize shared with Emmanuelle Charpentier) was an inflection point in biotech’s growth. Suddenly the genome became immediately editable (credit to the work of Professor Feng Zhang and others), and the tools for any scientist could be delivered to their lab via AddGene or IDT overnight. In academia, it means that an animal phenotype could be developed in days rather than years. In industry, it was a foundational change in how the startup ecosystem viewed human health. Billions of dollars of value have since been created, and a new generation of scientists have been developed in a matured entrepreneurial community around human health.
+ Common infrastructure to prototype: Every biolab has E Coli and HEK cells and access to standard data acquisition tools (sequencing, imaging, etc.). Engineered DNA can be delivered within 24 hours for use in experiments. Ideas can be tried within days.
+ Common infrastructure to scale: In 2022, this is much less linear in biology than computer science. While there are cloud labs and CROs, going from milligrams to kilograms of biologics is still a heterogenous process. However, the path to impact in biotech is typically focused around therapeutic milestones in animals then clinical trials, and this is becoming more and more codified.
+ Common Goals: The big problems of biotech have been mostly implicit to those who know the space: gene editing (eg broadening targetable regions, less toxicity per edit), sequencing (cheaper, faster, longer reads; spatial information preserved, protein sequencing), protein structure (e.g, AlphaFold), delivery (eg, crossing blood brain barrier, targeted cell types). The best, most specific challenges have been at the interface of computer science and biology, eg CASP and CAPRI. While there have been some facilitated competitions of wetlab techniques (eg, CZI’s spatial transcriptomics project), shared explicit goals and challenges have not yet played the same role in bioscience as computer science.
+ Enforceable honesty: The biggest ideas in biotech are the ones that every lab can use. Optogenetics, GCaMP, and CRISPR took the field by storm because they are rapidly distributed, validated and utilized by other labs. It’s important to point out that while peer reviewed science has been a dominant model (imperfect but good), the most “honest” approach is one that lets peers easily try new ideas themselves.
+ Playbook for outsized success: In what I call the “Kendall Square Biotech Model”, there is now a very established path for a precision medicine company: what is your rare monogenic disease (preferably an orphan disease), what is your animal model and what is the next few indications by which you could partner with the existing giants? This relatively homogenous path to impact has been a lens for talent: young ambitious creatives can structure their efforts toward work that could fit this model. Those that succeed are handsomely rewarded.
+ Funding: The marginal cost per experiment in biotech is considerable. As a ballpark number, consider that really trying an idea might cost $5-$50k in reagents, equipment and animals. Consider further that the cost of the journey to a human therapy is in the hundreds of millions. However, to meet this challenge and chase the opportunities exemplified by the highly successful startups (Editas, Beam, Moderna, etc.) there has been rapid growth in many new types of investment vehicles, PhD fellowships, grants and sponsored research agreements.
Personally, I’m extremely excited by bioengineering and would like to see this approach flourish in domains outside the Kendall Square Biotech model.
So, why Jiu Jitsu?
The past 15 years have seen an explosion of new fitness outlets: crossfit, climbing, MMA, boxing, etc.. I’ve done all of these and I love them, and one could make a good argument that climbing has seen incredible amount of industrial growth. But I consider BJJ to be special because of both the industrial growth (thousands of people are now able to make careers from it) and the technical growth of the field. Simply put, the modern winning techniques are almost unrecognizable from just a decade ago, and this is because of a combination of the nature of the sport and the incentives in the community.
Consider one illustrative example: In 2019 a relatively unknown fighter from Australia named Lachlan Giles stepped onto the biggest stage of the world (ADCC) and earned a bronze medal, winning his matches in shockingly fast time (about 5 minutes out of the 30 minute possible combined match time). The reason this turned so many heads is that Giles only weighs 160lbs and he was fighting in the open weight division, meaning the three people he beat were all heavyweight champions who weighed ~220lbs each. These unexpected victories happened because Giles is a very intelligent fighter who devised a set of techniques (variations on the 50/50 position to heel hook) few were prepared for, and he won his matches with his invention.
“It’s a sign of how amateur our sport is. Imagine a 160lb olympic wrestler competing against a 260lb olympic wrestler. …It’s just insane” Said competitor Gordon Ryan, famously snarky, as he watched Giles’ matches unfold, and then went on to beat Giles and for the gold medal minutes later. [note that Ryan is coached by possibly the greatest single mind in martial arts, John Danaher].
Just visually speaking, the technique of the sport has been turned on its head. How Giles won his matches looks nothing like how people won matches a decade ago: one looks like wrestling and the other has no real comparison. Through knowledge exchange and the community testing different approaches in competition, the sport shifted from a focus on upper body techniques (chokes, arm locks) to lower body techniques (leg locks, foot locks). When interviewed after winning his match for a bronze medal, Giles was asked, “you’re now a superstar, what do you think this will do for your life?” “Well, I hope it’ll sell more instructionals!”
Instructionals are the heart of why I think BJJ has something to show us in science+technology, and why I chose to put a photo of Professor Bernardo Faria as the header image. As a former champion himself, he converted into a content creator for BJJ techniques, then created BJJFanatics, the leading platform for other champions to create+distribute paid instructionals. The upside for the inventors of the best techniques now get paid millions of dollar per year as customers pay up to $100 for a few hours of recorded online lecture. Or, in the case of one notable athlete who had lived in astute poverty to pursue his dreams in the sport, Andrew Wiltse was able to move out of his homemade shed stapled to the gym.
So how does this map to the dimensions from CS and biotech?
+ Common infrastructure to prototype: Every hobbyist has mats or a grass field to try things on their friends.
+ Common infrastructure to scale: Competitions are everywhere now and YouTube is surfacing new talents every day. Top competitors will do seminar circuits which are limited, but now with BJJFanatics, the best ideas+teachers can infinitely scale their reach across the market.
+ Common Goals: While there are the biggest stages at ADCC, IBJJF, there are many rapidly rising shows which showcase such as EBI, WNO and fun themed events like High Rollerz series.
+ Enforceable honesty: The martial art of jiu jitsu was designed to be practiced at full strength and full speed, which is essential for the rate of innovation for ideas that work in competition. Even other spar-centric arts like boxing or kickboxing couldn’t test out new head kicks on their friends, the brain damage would be too great from full strength prototyping. People can then watch the new techniques on youtube or BJJFanatics, take them to their local gym, and validate the results for themselves.
+ Playbook for outsized success: There are now fairly clear milestones to make a successful career in this sport with everybody following the success of most exemplary winners (eg, Gordon Ryan, Craig Jones). Winning large tournaments with novel approaches is a sure way to be invited to make a BJJFanatics instructional. Additionally, there is innovation around sharing a gym’s philosophy and approaches, which can build highly engaged audiences (eg, the Daisy Fresh gym in rural Illinois hav).
+ Funding: The marginal cost to run experiments is zero, and the amount of sponsorships is growing rapidly. This is creating crops of new talents that have have been training the sport since elementary school (eg, Grace Gundrum, the Ruotolo Brothers, Cole Abate) that bring new audiences and investment into the community.
So what does this mean for climate-positive science and technology? It means we have a lot of work ahead of us, and drilling into these aspects of the creative community will be topics for future writings. I’ll leave it this week with some open questions for these different dimensions.
+ Common infrastructure to prototype: Do we have any? Do energy projects have a common toolkit to build from (ie, is there a common “green hydrogen” reactor)?
+ Common infrastructure to scale: For specific verticals inside “climate positive” efforts, which ones have the the most linear paths to scale?
+ Common Goals: Everybody talks about “Net Zero,” but I contend that’s too broad to be actionable inside the community (Aside: I personally don’t find “Net Zero” very exciting, how do we brand it as a more optimistic, forward looking framing as technologies we would want anyway). For example, in the carbon capture community, gigaton carbon capture at less than $100/ton is an unofficial goal. What other ones are there?
+ Enforceable honesty: How do we know a good idea claim from a bad idea claim? How do we quickly spread, test and refine ideas?
+ Playbook for outsized success: What are the biggest wins so far? Are there common pathways we can learn from?
+ Funding: Assume that experiments cost at least as much as biotech experiments, say $5-$50k marginal cost to really try a new idea. Early stage funding was explored last week: lots of startup money, lots of scale money, but not a lot of rapid, early-stage science support.
Thoughts/ideas/corrections? Please let me know in the comments or on Twitter