Axial - Observations #29
Life sciences reflections
More well thought out work can be found at — https://axial.substack.com/
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Observations #29
A set of ideas and observations from a week’s worth of work analyzing businesses and technologies.
Where biology can curb emissions?
Our World in Data released some very interesting data on emissions by each industry. It was useful for me to break down the specific industries and think through where biology can reduce emissions in each one:
Residential buildings (10.9% of CO2 emissions): most of the emissions here are due to the electricity generated to power the building and cool it. Maybe biology can create better insulators? Other things?
Commercial buildings (6.6%): same problem set as residential but sales distribution is a lot different (i.e. more enterprise)
Livestock & manure (5.8%): livestock produce a lot of methane; the key way to solve the problem here is to eat less animals. The best example of a business here is Impossible Foods.
Agricultural soils (4.1%): synthetic nitrogen fertilizers produce nitrous oxide so just figure out ways to use less of these fertilizers or none at all. Pivot Bio is doing great work here.
Energy-related chemical & petrochemical (3.6%): use of chemicals for energy uses. The opportunity is to create next-generation business models of DuPont and Dow. Solugen is growing rapidly here.
Crop burning (3.5%): figure out ways to recycle/reuse leftover vegetation
Cement (3%): bioMASON is an example here; using microbes/synthetic biology to make more sustainable cement. Probably true for all materials.
Non-energy-related chemicals & petrochemicals (2.2%): similar problem set as energy-related chemicals.
Deforestation (2.2%): Droneseed and Living Carbon are good examples here
Landfills (1.9%): trash (i.e. organic matter) is converted into methane. How to reduce trash in landfills? Or use the methane for something else?
Cropland (1.4%): how to get higher yields per unit of cropland?
Wastewater (1.3%): figure out how to recycle wastewater or use it?
Rice cultivation (1.3%): rice produces ~20% of all the world’s calories. Similar problem set as cropland.
Food and tobacco (1%): making food processing more efficient and reduce overall smoking rates
Paper & pulp (0.6%): using electronics more for reading and messaging; this creates a secondary problem of electronics recycling and rare metal mining
Grassland (0.1%): maintaining wildlife ecosystems
A Conversation with Rudolf Jaenisch
A very useful talk by Rudolf Jaenisch, a pioneer in stem cell biology -
- he tells a really great story of how labs sources the Fd phage from Rockefeller; the discoverer did not want to share the phage. So when other labs sent letters, reply letters were sent. The clever labs cultured the letters to isolate the phage.
Harvey Lodish: “Most scientists don’t know their worth”
Another great talk by Harvey Lodish, a leader in developmental biology, a co-founder of Genzyme among other companies, and a founding member of the Whitehead Institute -
- the key quote comes at the end: “most scientists don’t know their worth.”
Seven Powers cont.
Are there network effects in life sciences? Network economies create a business or products that increases in value as more customers use it. So in life sciences, that might mean getting better reagents with more use of them (i.e. figure out if they work as intended) or a piece of design software for a protein or DNA (i.e. Benchling). Historically, strong network effects are predicated on the presence of other networks as long as each network/group is walled off from each other. Researchers come in many different forms from medchem to synthetic biology:
A business with network economies has a product that increases in value and consequently can change higher price than comparable products. With this phenomenon, it actually makes sense to charge later when the product’s value is at its steady state.
Network economies create nearly insurmountable barriers to entry - the price to gain a similar market share is often not profitable (i.e. customer acquisition cost >>> lifetime value of the customer)
Network economies come in 3 major forms: demand-side, supply-side, multi-side. These all just mean what a company focuses on aggregating first.
The next key question is, where are network effects in life sciences? The submarkets have to be monopolistic, wall-offed customer base, and open to a last mover.
Monopolistic - is the market winner-takes-all?
Wall-offed customer base - is there a social or structural barrier from customers and peripheral products or features? For example, a chemist is unlikely to use a plasmid design tool.
Open to a last mover - have previous companies or products shown a demand from customers but haven’t succeeded in scaling to the entire market?
With network economies, a business’ main advantage is its customer base. With a large enough differential between the leader and the second place company, network economies enable the leader to price well below their competitors while still often maintaining higher margins. With network effects, if a business identifies a monopolistic and wall-offed market, the key to winning is scaling to a point of achieving a wide-enough customer differential.
Achieving network economies sounds really attractive in life sciences, and if they are so powerful, why haven’t very large (>$10B) companies been built in the industry. For network effects in general, sizing the market and whether the market is amenable to network effects are the key drivers for success with the strategy. In life sciences, sizing might be easier. Gauging if a market is amenable to network effects is probably the major issue:
The 3 points describe above might be good starting points to gauge the potential of network effects in a market
Maybe finding markets with several complements could be useful - https://axial.substack.com/p/substitutes-and-complements-in-life With each complement engulfed, a product’s value increases to the customer.
Anything else?
Finally, which customer bases in life sciences are amenable to network effects?:
Scientists - Abcam, BenchSci, Benchling
Labs and IP - ScienceExchange, Halo
Patients (specific indications) - Clover, TrialSpark
Others?