There are many important innovations that are just bad investments

If you need to have the possibility of near infinite returns on VC investments how is it possible to invest in higher risk things and expect a return?

# Can you get VC-level returns by funding research?
# Can you get VC-level returns by funding research
[[Isaac Barchas]] asserts that the answer is ‘no’ -
[[Laura Deming]] and [[Jason Crawford]] assert yes

First, there’s the 

My hunch is that outside of a few 

Related:
-  [[Commercializing non-health research does not produce VC level returns]]



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We have no flying cars - failures of paradigm-shifting engineering

Commercializing non-health research does not produce VC level returns

Isaac Barchas Conversation 18/3/20

# Commercialization has very high transaction costs
- Finding the people who have the right criteria is hard. They need to 
	- have done research that’s at the correct TRL 
	- want to do the work to spin it out 
	- be in a position to spin it out (ie. Tech transfer office is friendly)
- Away from the coasts  there are fewer or zero lawyers and accountants who are used to working with startups
	- This makes it so there less network lubricant for due diligence so it has higher transactions costs. [[The best due diligence is network based]] 
	-  Fewer or zero infrastructure people 
- In places where it’s not in the culture people just don’t know what the fuck they’re doing around commercialization 

### Thoughts
- Could something like [[Polyplexus]] be used to lower transaction costs?



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DARPA works where markets dont

Can you get VC-level returns by funding research?

There is so much we could be doing with biology besides health

# Commercializing non-health research does not produce VC level returns
## Underlying assumptions
[[Commercialization has very high transaction costs]]

## Counterexamples
- [[Kurion]]

[[Science Business - Book]] asserts that possibly even biotech isn’t producing real returns

If this is true how does [[TandemLaunch]] stay in business? 

Asserted by [[Isaac Barchas]] 



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DARPA has a mixed record on transitioning

Decoupling from market discipline is like cave diving

§PARPA

§ARPA model

Many of ARPAs outlier results were things that nobody was asking for

Mark McGranaghan conversation 19 Jun 2020

An institution riffing on the ARPA Model could enable paradigm shifts that are not happening

# DARPA works where markets dont
There are two coupled pieces here - DARPA works *on* things that are market failures and the activity that DARPA undertakes *is* a market failure. 

### What do we mean by market failure?
From [[What is a market failure?]] - the market failure Questions are:
1. At what point would someone pay for the thing?
	1. Is the thing in a form that people will pay for?  ie. people won’t pay directly for a search engine
	2. Can you create a business model around a thing that people won’t pay for? ie. Advertisements on a search engine to monetize the eyeballs
2. Is the time-discounted and risk-weighted amount that people would pay for the thing greater than the amount it would cost to get the thing to a point where people would pay for the thing? 
3. Are the people who need to build the thing able to capture the money that people would pay for it?
4. Can you ‘buy’ the money to get the whole thing to #1?  
5. Does anybody believe 1 -> 4 enough to do the work?

The rational expectation value of many DARPA programs is net negative  negative  because the chances of success are so low despite an amazing outcome if successful. **Negative expected net-value programs correspond to #2 on the market failure list** — specifically that the risk-weighted value of the thing is below what people would pay. More generally, from a pure expected-value point of view isn’t irrational to work on high variance, high potential upside but low mean things? For example, many tech entrepreneurs also fall into this category. On average, starting a company has lower lifetime value than an engineering job at BigCo. In the entrepreneur’s case you could make an argument around ‘fulfillment’ being part of the expected value calculation, but once you open that door, it starts to smell like [[Epicycles]].  COVID tests are an interesting case study here - they would be an incredibly valuable thing for the world, but people might not pay as much for them as it would cost to develop them. 

DARPA works on programs whose value cannot necessarily be captured by the group coordinating it. **(#3 on the market failure list)** Value-capture failures could look like autonomous cars. DARPA pushed autonomous cars through a series of competitions, but at the end of the day the different participating orgs owned the work. My hunch is that if DARPA had demanded that they hand over all IP from the competition, fewer people would have participated and there wouldn’t have been an industry flourishing. In general, these cases are problems that need a bunch of different groups to coordinate and/or compete, each of whom needs a large enough chunk of ownership or low enough friction to motivate them that trying to capture value would kill the impact. 

DARPA works on hard-to-monetize intermediate products which are another value-capture type market failure **(#3)** . In this case, the work to be done creates an intermediate result result still needs a lot of work by another group to become a sellable product. For example, a prototype that needs a lot more work by an organization with a different set of expertise to become manufacturable. These cases are value-capture failures because so many different heuretics go into the final product that’s actually valuable. The multitude creates a situation where 1. It’s hard to credit everybody who contributed pieces 2. It’s hard to assign value to each contribution 3. Even if you could perfectly figure out the value of every piece and who the money should go to there is massive friction in the system if everybody is trying to get their piece of the pie: either the final organization needs to spend time and effort licensing every piece of the puzzle or the previous folks need to sue them which burns tons of money on lawyers. Instead, DARPA pays the previous folks directly for the research and then gets the thing out into the world. 

DARPA works on things that require funding on a combined time-risk scale that doesn’t jam with financial markets **(#4)**.  [[Venture capitalists need to fund things that increase valuation in a few years and return in 5-7 years]], which just doesn’t work for projects that can take more than ten years to create a product. Long-term financial instruments like mortgages and loans require *much* lower risk. You also need to beat the stock market - financial markets don’t just care about the absolute expected value of the project, but what the alternatives for that capital are. If the stock market is expected to give you a 5% rate of return, you need to do *much better than that* because the risk is higher.

Even if criteria 1 through 4 *are* met, enough people need to believe that they are true and the benefits outweigh their alternatives. **(#5)** DARPA works on systems where several things need to be changed at once. However changing any one of those things would actually make the system work worse, so any rational person only wants it when the whole thing is done.  The need to build multiple pieces of the same system to create any value creates compounding risk, which puts programs back into market failure scenario 1. 

The fact that [[DARPA has a mixed record on transitioning]] emphasizes the fact that they work where markets don’t. Some of the translation failure is perhaps a failure on DARPAs part. But it can also be because even when they’ve brought a program to a point where it has produced awesome things, people don’t see a way to make money doing it. In the analogy that [[Decoupling from market discipline is like cave diving]] this is the grim situation where there’s no air bubble where you thought there would be. 

Sometimes when you’re trying to change a paradigm, you’re just straight up threatening people’s jobs or reputations and so you need to outlast them.  [[DARPA works on programs that go against the established paradigm]]. Markets don’t care *why* someone doesn’t want the thing - people are not rational and their acquisition of something is based on their expected value of it, not the actual value. And that expected value 

[[DARPA works on programs that go against the established paradigm]] but   markets sometimes can support something that is paradigm changing.  For example cars. On the other hand, I would argue that the market would not have supported personal computing. ( [[Fiction / Science Fiction/ Historical Fiction are a way of exploring counterfactuals]].) 

### Related
- [[Commercializing non-health research does not produce VC level returns]]
- [[Venture capitalists need to fund things that increase valuation in a few years and return in 5-7 years]]
- [[Commercializing DARPA COVID research case study]]
- [[If science depends on markets scientists become salesmen]]





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Most human knowledge is not encoded

Technology readiness level - TRL

TRL7 - prototype version in real environment

Genentech was started by bringing together several pieces of existing research and subcontracting to university labs

Learning a new thing you want to both learn the jargon and explain it like you are five

There are secretly two kinds of biotech

High Throughput Experimentation - HTE

Notes on the changing structure of American Innovation

# Science Business - Book
In 1985 - first wave of Biotech IPO
- Lots of experimentation with R&D orgs
- Network of collaboration between big pharma cos and startups
- Private firms conducting basic research

People expected Biotech to follow semiconductor trajectory
`the great promise of biotech to impact human health would go largely unrealized` ::[[There are secretly two kinds of biotech]]::

Over 20 years the revenue grew exponentially over time but operating income was zero

People assumed biotech R&D would revolutionize drug discovery because on cutting edge of science and unencumbered by beaurocracy but no difference between output of biotech and pharma

Attributes the missing output to structural problems
Science based businesses need to deal with 
1. Risk management
2. Integration
3. Learning

Focus on monetizing intellectual property has 
1. Impeded information flow, 
2. fragmented things, 
3. Created many new firms ::this is a bad thing?::
And these things go against the three things science based companies need to do 

Attributes these problems to indiscriminately borrowing from the computer technology industry
`Not all high-technology industries are alike, and science-based businesses like biotech have unique challenges  and thus call for different approaches. `

Defines “science-based industry” as one that actively creates science instead of just consuming. ::Good way to define technology-based as well [[The ‘tech’ industry no longer builds technology]]::

Three big lessons
1. Nature of science matter to the appropriate design of business models, org strategies and institutional arrangements.
2. All business practices, models, and arrangements involve tradeoffs.
3. Humanity still learning how to do science-based businesses

Appropriability is the ability of a firm to capture value from an asset
Clash between scientific sharing and appropriability on both sides
`

Defines Biotechnology as post-1976 technology for discovering drugs ::shocking how therapeutic focused:: (p16)

#### History of Drug discovery
Lock and key metaphor
Bayer initially did not want to manufacture aspirin (p23)
First man Made drug used to treat syphiis 
1939 not much drug R&D - 3 people in glaxosmithklein lab
60 years after Erlich theorized about the existence of chemoreceptors, researchers found the first enzyme drug target ::Example of theory that creates a ton of value but captures none of it::

New technology has enables (p25)
1. New synthesis methods
	1. Recombinant DNA
	2. Monoclonal antibodies
	3. Combinatorial chemistry
2. New knowledge about mechanisms
	1. Genetic mapping
	2. Proteomics
	3. RNA Interference
	4. Systems biology
3. New screening methodologies
	1. Rational drug design
	2. High throughput screening

Drugs path to actually affecting a body is long and complex (44)

#### Drug R&D Process (45-52)
1. Target Identification and Validation
2. Target Validation
3. Lead Identification Lead Optimization 
4. Principal development
5. Phase 1 Trials
6. Phase 2 Trials
7. Phase 3 Trials
8. Regulatory Approval 

![](BearImages/2F0F3D20-8C62-4B7F-ABE7-F8F197EF5673-68756-000421DA0B92C98E/7380C438-5FE0-4021-B7C0-FA2505A90DFC.png)

Drug R+D is hard in part because you have so little control over the environment where the drug is deployed

Most non-drug R&D is the Development part which doesn’t really fail 
::ie most non-drug R&D is a little h hard problem[[Little h hard problems need a bunch of engineering effort but no breakthroughs]]::

Assumption prediction is really hard so lots of trial and error (59)
Biological problems are not very modular, ie. Integral (60) 

Advances in Biotech science increase uncertainty, not decrease (64) because there are more choices (67) without good predictive models

Poorly mapped landscape (68) and less literature per target

[[Giovanni Dosi]] riffed on [[Thomas Kuhn]] and created the idea of “technological paradigms” which are shared outlooks between scientists and engineers for the relevant technical problems and the accepted tools methods and principles for addressing them. They center on reference point solutions.

Paradigms support cumulative progress (common language and agenda)
Provide a conceptual architecture
Paradigms affect competition and industry structure

New drug paradigms don’t really obsolete the old ones

Lack of information →uncertainty→ risk (75)
Sector anatomy:
1. Participants
2. Institutional arrangements between participants
3. Rules that govern institutional arrangements

![](BearImages/2BE8DC06-46A0-4D2E-9C0B-3D0A84DB034C-68756-00040C92B70414A8/75900F29-6E59-40FC-B404-CD90BDAF4F77.png)

Since Genentech - waves of biotech companies with different tools (84)
Proof of business concept - founded by famous professors, R&D being done through contracts with university researchers, existing relationship with big pharma co

Genentech IPO showed firm without product revenue could raise public money

Barrier to entry - time and $$ to do one drug - get around it with partnerships

People assumed there was less risk with natural proteins so less dev time and less cost (89) but not true

First gen biotech companies wanted to become full stack drug manufacturers
Second gen stayed as external R&D for pharma co
Third gen - High Throughput Screening, bioinformatics, genetics - platform model

However ,soaring valuations in essence raised the bar for ### com-
panies’ revenue models. ## What was a perfectly viable business ### modelat a ### $2 billion equity valuation ### was no longer viable at a ### $20 billion valuation. Moreover, the ### enormous amounts of capital involved not only raised the bar ### on earnings, but also ### changed the firm’s trategic options. ### As with first-generation ## FIPCOs, a generous equity ### market made it possible to raise ### enoughfunds to vertically integrate forward. 

Pharma companies establishing alliances with both universities and startups because the high uncertainty made big pharma hesitant to do internal programs ::This must have been enabled by clean interfaces::

Big pharma went through startups licensing university research because they didn’t have the in house capabilities - fell into the [[The (idea) valley of death]] (105) ::Why didn’t they bring the expertise in house?:: 

All biotech IPOs between 1979 and 2000 = 15% return
In aggregate biotech industry does meh with awesome outliers

Amgen is insane outlier (::what happens if you look at the top quartile?::) (115)
![](BearImages/E24A143D-403A-487E-890C-635F44EC6433-68756-00043AF6C22FFF4D/97373BF5-5221-4204-8725-9C55C2AD145C.png)

::Averaging across the industry is like averaging across VC::
Productivity in the form of $$/drug hasn’t improved (121)
Biotech has lower Sales $$/R&D $$ ::couldn’t this just be pharma spending a ton on sales and marketing?::
Small firms don’t necessarily have more novel drugs

Biotech industry revolves around
- Tech transfer from universities
- Capital markets
- Trade of knowhow and IP for funding dollars and alliances

Two sources of biotech firms: academics and people leaving existing biotech/pharma firms

VC’s with strong networks in unversities can offer better incentives than big pharma cos - only startup equity makes the career risk seem worth it

`Venture capital needs to be understood both as a form of funding and corporate governance`(137)

Venture capitalists become repositories for institutional knowledge as well ::[[Old school Pharma VCs add value because they help startups go through an established sales process to pharma companies]]::

Studies show that VC stimulates innovation (138) ::But then how did we have innovation before VC?::

`The monetization of intellectual property required a ### market for ## know-how between biotech firms ### and more established enterprises. `(142)

Public equity was never designed to deal with the governance challenges of R+D companies because there is a ton of asymmetric info and you need to understand the technology itself (143)

`There are already some signs that existing mechanisms for risk management are breaking down for the truly high-risk projects` (149) - move away from molecule to market model ::(aka the Hollywood model - [[Lessons from Holywood]]:: ::Did the author ask the people why they shifted models?::

Integration can happen either within firms or between them via independent specialists (market for know-how)  ::coase 101::

Necessary conditions for know-how market:
- Modularity
- Codified technology
Biotech struggles with both of these (150)

Strong IP protections enable integration in software ::I call BS:: (152)

Murky IP delineations harm integration (153)

Learning happens in an industry at three levels
1. Creative destruction - good for new tools and skills
2. Imitation - good for codified knowledge
3. Learning from your own experience - good for tacit knowledge

Biotech is weird because of massive firm creation rate but little firm replacement

Financing and deals are not measures of performance! (162)

Know how markets work when
1. There is low information asymmetry
2. There is not much need for specialized assets
3. The know how is not very tacit
4. IP can easily be legally protected

Biotech has four classes of innovation that suggest different business models
1. Novel research methods and tools
2. IDing new mechanisms or targets
3. Creating new compound types
4. New treatment modalities

Vertically integrated companies basically get one shot
It costs the same amount to create a $500m drug as a $5B drug

Movie studio model: low info asymmetry, not much specialized assets, not tacit know how, easily protected IP

Vertical integration is actually better for absurdly novel things (177) but can have the same issues within a firm with siloization

![](BearImages/A462395D-C196-4FA8-B5A9-E3AAD1F26E7F-68756-0004FC4A4F7D7700/38CC8A08-DA69-418D-8DDE-86D6991EA805.png)

Lee Fleming and Olav Sorenson - science is like a map 
Basic research lowering uncertainty and therefore risk
Fragmented knowledge bases prevent integration (188) 
Grants reward researchers for narrow, well defined research projects (189)

Licensing models
1. Open Licensing
2. Exclusive licensing to an existing firm
3. Startups 

![](BearImages/F13C4088-CD10-4703-A1F2-3C0B1A8FB9D3-68756-0005235B7D4D6918/8BA47E28-4F6C-4FD2-A003-98466FC1F0D5.png)

Accelerate Brain Cancer Cure as an example of Venture Philanthropy (193)
“Too early to tell the impact of venture philanthropy” 

It is hard to run a business with 10 year investment cycles when you have to address quarterly earnings

Public markets enable cheaper capital because investors can diversify more

Quasi-public firm - public except majority of shares held by a single entity (Genentech + Novartis)
![](BearImages/2C611A05-2A27-4929-98D3-682CAC02B389-68756-0005526AF62BDD8E/B4C65166-B381-442F-8B38-F3429F617F89.png)

Argues that rail and telegraph systems gave rise to modern corporations because owners were scattered and did not have the time and expertise to run the systems(What does [[Jason Crawford]] think about that?)
### Published
2006
## Questions
## Thoughts

## Related
The implicit focus of biotech on therapeutics may be like the focus of the computing industry on calculation - related to [[There are secretly two kinds of biotech]]
Recommended by [[Sam Lipoff]]
## Authors
[[Gary Pisano]]
## Link
[Science Business: The Promise, the Reality, and the Future of Biotech: Pisano, Gary P.: 9781591398400: Amazon.com: Books](https://www.amazon.com/Science-Business-Promise-Reality-Biotech/dp/1591398401)



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Benʼs working notes

Commercializing non-health research does not produce VC level returns

Underlying assumptions

Counterexamples

Links to this note