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#9 - Frontier Tech
Bio-manufactured materials (spider silk & self-healing squid protein), bioinformatics workflows, Argentine crypto, neurotechnology, quantum computing, & neurotoxocity side effects of cell-therapy.
Hello frontier tech readers!
Seems like I may have picked one of the most eventful weeks in frontier tech in a while to do a non-frontier newsletter last week, with leaps and bounds of progress being made in generative images, but we’re back on schedule!
We missed announcement of AI Grants part two last week after their last great run in 2017. They’re focused on allocating 250k checks to anyone building a product on top of large models trained on internet data (check out their great prompt list of AI-first products at the end)
Frontier Tech Investment Theses
Biomanufacturing: “Biomanufactured materials are coming” (8.31.22)
If you read one thing this week, it should be this overview on where biomanufacturing has come from and where it’s heading, and a deep view into three growing success stories: Spiber, AMSilk, and a self-healing squid inspired protein company: Tandem Repeat.
“Like the companies above, biomaterials startups have employed advances in synbio technology to accelerate their progress. I’ve tracked at least two biomaterial products which have scaled capacity at stunning growth rates. Spiber and AMSilk are leading companies producing biomaterials processed from spider silk protein. AMSilk initially focused on high value cosmetics and medical applications, whereas Spiber developed fibers for apparel.”
“Biomaterials like spider silk and mycelium are scaling up at historic speeds, yet are not being talking about. Spider silk protein made by Spiber and AMSilk, the industry leaders, has grown production at an incredible ~273% compounded annual growth rate (CAGR) since 2008 (see below). They are currently at about eight tons, but are scaling up to over ten thousand tons in the coming years. At that scale and even as growth slows, it should still close in on the production volume of global 3D printed polymers.”
Academia / Startups: “Academia or Startup? A Decision Matrix for Future Scientist-Founders” (NFX, 7/20/22)
In frontier tech, I end up talking to many founders or potential founders who are navigating the slightly less common dynamic of academia <> startup world. Many people make this transition but I’ve found that there are much fewer well written resources on the subject.
“In this essay, we’ll walk through three guiding questions to consider as you decide what path to take: What kind of freedom do you want? How fast do you want to move? Who do you want to be your “customer?” ”
Biology / Data Science: “Why are Bioinformatics Workflows Different?” (8/14/22)
There’s been much debate in the techbio & venture world about how the future of bioinformatics will progress and how much it will run parallel to the exponential scale of R&D we saw the data science revolution of the 2010s enable, vs how much it will look different due to necessary deep specificity around workflows, etc. - Ben lays out the most important workflow differences in data type, shape and scale; differences in programs and tooling; and differences in community support behind bioinformatics workflow managers.
Research Developments in the Frontier Space
ML: “How AI could help make Wikipedia entries more accurate” (9.7.22)
The Meta AI research team developed a model to be capable of automatically scanning hundreds of thousands of citations at once to check whether they truly support the corresponding claims. It’s open-sourced here, and you can see a demo of their verifier here.
“These models are the first components of potential editors that could help verify documents in real time. In addition to proposing citations, the system would suggest auto-complete text — informed by relevant documents found on the web — and offer proofreading corrections. Ideally, the models would understand multiple languages and be able to process several types of media, including video, images, and data tables.” This is very parallel to many of the code auto-complete or error spotting ML tools I’ve highlighted in recent newsletters.
It makes sense that Meta is interested in fact checking in text and video verification in a world where they’ve received so much criticism for allowing fake news on their platform. How they continue to moderate (or not moderate) the difficult edge cases of dealing with “truth” that fall through to Meta’s Oversight Board, is another question.
Neurotechnology: “Differential Neurotechnology Development” (Milan Cvitkovic; 8.9.22)
A great overview of neurotechnology - ie “any tool that directly, exogenously observes or manipulates the state of biological nervous systems, especially the human brain.” - and its applications, current development factors (market size, regulation, market power, cultural resistance, and iteration speed in humans), and research landscapes.
“Our tentative conclusions from what follows are:
Neurotechnologies currently in clinical trials could have large-scale impacts in 1-5 decades, with a mean estimate of 30 years.
With concerted effort, neurotechnologies currently in clinical and preclinical development could be advanced in 10 to 20 years to the point where they might meaningfully benefit AI safety, in addition to other, potentially less-urgent benefits.”
Milan touches on everything from: Next-generation BCI, Endovascular stimulation, Next-generation fNIRS, Next-gen MEG, Sonomagnetic stimulation, structural ultrasound, gene therapy, cell therapy, and small molecule drug candidates.
Biology/Therapeutics: “Latent human herpesvirus 6 is reactivated in chimeric antigen receptor T cells” (pre-preint; 8.12.22)
Cell therapy has recently been found to be associated with a number of unexpected side effects, one of which is neurotoxicity. This paper puts forth that encephalitis, or inflammation of the brain, caused by human herpesvirus 6 has been repeatedly observed in CAR-T cell case studies and clinical trials.
Market Commentary in Frontier Tech Space
Quantum: “The Quantum Computing Bubble” (9.7.22)
“The reality is that none of these companies — or any other quantum computing firm, for that matter — are actually earning any real money. The little revenue they generate mostly comes from consulting missions aimed at teaching other companies about “how quantum computers will help their business”, as opposed to genuinely harnessing any advantages that quantum computers have over classical computers.”
Although this article is quite harsh, I agree with the overarching analysis that the majority of quantum computing related revenue is made from consultancy type business models focused on educating high spend customers with the promise of finding longer term customers, and that public quantum companies have had a very hard time.
Crypto & National Currency: “Inside the crypto black markets of Argentina” (Devon Zugel; 8.13.22)
“Crypto has quietly transformed the way many Argentinians move money and access the global economy. The volume of transactions going through crypto is growing rapidly, and it’s increasingly out of the government’s control.”
“In Argentina, your money is worth double if you skip the airport currency exchange and instead go to one of the many black market exchanges hidden throughout Buenos Aires.
These illegal exchanges are called “cuevas” (the word for “cave” in Spanish), and they are a crucial part of Argentina’s financial infrastructure. Argentinians are constantly exchanging their pesos (ARS) for other currencies, usually US dollars (USD), and back again because they simply cannot rely on their country’s fiat currency.“
Biology & Odor: Google AI released a post on “Digitizing Smell: Using Molecular Maps to Understand Odor” (9.6.22)
Their ML-generated sensory map that relates thousands of molecules and their perceived odors, enabling the prediction of odors from unseen molecules and providing a potential tool to address global health issues like insect-borne disease, which is pretty cool.
Even better, in just under a decade we are full circle from Google’s April fools “smell search” in 2013 to Google having mapped smell to molecular structure in 2022 (basically you could, hypothetically, search a mapped smell space).
Frontier tech jobs:
Machine Learning / Creative Tooling: Runway ML
If you’re interested in an environment where you watch ML research get productized, I can’t recommend highly enough that you apply to Runway.
Chemical Engineering / Biology: Helaina, synthetic breast milk
See details on what the team is working on here
Biology / (CV/ Embedded Systems) Engineering: Spaero, lab automation
Energy / Ocean Tech: Stealth
Biology: Culture Biosciences, cloud-based bioreactors
Biology / Mechanical Engineering: Bionaut, precision medicine delivery through remote controlled nanobots
Healthcare: ARPA-H, Project Lead, help spin up the newest Advanced Research Projects Agency focused on healthcare and scaled biomedical research.
Biology: Convergent FROs, small agile organizations that aim to fill a gap in the translational science landscape.
Focused Research Organizations (FROs) undertake projects too big for an academic lab but not directly profitable enough to be a venture-backed startup or industrial R&D project. Think "Series-A-sized org whose product is a public good to revolutionize a scientific field".
Crypto: Volt Labs, work on crypto projects for the research arms of crypto-focused Volt Capital.
Gaming & Education: Hidden Door
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