Notes about language.
We can say for certain some things about ideas that smell like research:
If the idea is housed in a new for-profit organization, ￼#1+#2+#4 means that except in rare cases, the organization needs to be a high-growth startup because in order to provide competitive time-discounted ROI on the money the company needs to do the work over a potentially long timescale, it needs to target massive returns. Being a Grahamian startup then subjects the organization to §Startup Constraints.
One could imagine situations where a for-profit research program could avoid committing to be a high-growth startup. A for-profit could get away with targeting lower growth/eventual value if funding comes from non-dilutive grants or ‘friendly’ investment where the investors don’t expect to make as much return on their money as they could elsewhere. However, both of these options come with their own downsides. These downsides include needing to keep investors happy because ‘friendly’ investment generally means ‘not market competitive’ so investors become much less replaceable. The danger is that the product becomes ‘things that make investors or grant-givers happy’ rather than things that maximize the potential of the idea and push the Knowledge frontier. (Or even make the most money in the long run.) Friendly-funded for-profits also experience a lot of pressure towards efficiency, and Efficiency biases systems towards false negatives. (Of course this efficiency bias is a cultural thing and not tied just to profit-seeking.)
Outliers dominate both the results and these conversations so I’m going to call out the elephants in the room:
At the end of the day, no set of criteria can definitively say “this project shouldn’t be carried forward by a new for-profit organization” except maybe if desired output could never be sold. (Though even then someone could point to profitable but unintended discoveries as a reason for researchy work to be a startup). The nature of Knightian Uncertainty means that it’s certainly possible to flip all heads in a row on a large-dimensional, non-stationary coin and pull off a successful research startup. However, I would argue that the more the unpredictability in each of the steps smells like uncertainty instead of risk (Uncertainty always involves risk but risk does not always involve uncertainty), the less it’s a good idea for the project to be carried forward by a new for-profit organization.
A decision tree may be the correct way to look at the question of whether a program is too researchy to be a startup. These questions are ordered roughly in terms of importance, with “no” answers strongly indicating that a start:
If you are setting out to create something that could never be sold, the program probably should not be a startup. There are examples of accidental but profitable discoveries that happened in the course of pursuing something out of motivations besides profit — discovering a heart drug while studying tree frog poison in the amazon for example — but you might as well just buy stocks chosen by a random number generator.
No global critical path means that you need to run a “fat” process without clear justification. (Justification requires explaining how it fits into a bigger picture which requires a global critical path). Without a global critical path, “progress” is nothing but a narrative and the fat process makes it look like you’re doing a lot of dicking around. As a result, investors will get fed up at some point and either force the research into a premature product or shut the whole thing down.
One way to look at uncertainty is that there’s no definition of what ‘works’ means: this could be in terms of the technology itself (technology uncertainty) or in terms of how it becomes a good business (market uncertainty). Note that this is different from technology or market risk where you know what winning looks like but there are a lot of reasons why you wouldn’t get there. Uncertainty always involves risk but risk does not always involve uncertainty. Technology risk sounds like “there’s a chance that we can’t get the efficiency of this process above 35% but the back of the envelope says there’s nothing that should prevent that and we have a development plan to get there.” (A metric and a vague sense of a probability distribution over that metric).
Creating new technology will almost always involve some uncertainty. Technology’s modular/combinatorial nature^2 means that there will be uncertainty at some level of the hierarchy. Sometimes that uncertainty will propagate up to the top-level technology if it is an irreplaceable subsystem. However, my extremely hand wavy intuition is that You cannot have massive uncertainty in subsystems without it propagating upwards.
Even if you have a clear measure of what ‘working’ means, the way in which that metric changes matters. A smooth metric means that incremental changes can cumulatively move the needle (which means that you can show nice graphs trending towards success). However, some researchy ideas involve situations where the needle doesn’t move at all until it does a massive jump (ie. They depend on breakthroughs).
Situations where you’re guaranteed a massive market if you can hit a clearly defined target lend themselves to researchy startups. Theraputics are a prime example here^3: “if you can create a cancer drug that passes FDA approval, you are guaranteed a $B market.” In the past, mainframe computers had a bit of this flavor — if you could hit a clear performance metric, you had a guaranteed market. It is possible that healthtech and therapeutics in the early 2020s is to biotech what calculation and mainframes were to computers in the 1960s.
A big reason that market uncertainty can kill researchy startups is less about investment and more about the fact that There is a tradeoff between an organizational culture that is good at addressing market uncertainty and one that is good at addressing technological uncertainty. At some point a researchy startup needs to do a dramatic gear shift into growth and product-market fit mode. This transition often either prematurely kills the research potential or the company dies because it’s being run by people with a research mindset.
Startups are good at point changes. If there is a component in a system that can be improved or swapped out in order to make the system cheaper/more profitable/more efficient, startups are an excellent mechanism for making that happen. The same goes for projects in industrial research labs — if you can, say, save the company a ton of money by swapping out the sheathing on telephone cables, the path to success is straightforward. However, to improve, many systems need many simultaneous changes (or to be swapped out for a new process entirely^4).
Systems must take performance hits to get out of local optima and people are rarely willing to pay to decrease system performance. (This is a major contributor to The (idea) valley of death).
See also Systems research requires a lot of work that is expensive and uninteresting
A lot of research involves dicking around, or “fat” processes. If you need to target a high ROI from day one, you need to be as efficient as possible. ROI is all about efficiency, and Efficiency biases systems towards false negatives. There will just be less Slack - concept in the system, cutting off lines of work that can’t be justified up front (especially if they require resources to evaluate).
A key characteristic of research is that regardless of how long it takes, that amount of time is unpredictable.^5 Combined with the fact that progress is often not smooth, it takes a constitution of steel for investors not to start getting antsy at some point. People are patient until they’re not. The pressure to show some progress towards a progress makes sense from a business sense but can be at odds with developing a powerful technology. Premature specialization is a common failure mode in researchy startups. (Ideally corporate R+D enables work on a general purpose technology before it’s specialized) .
In addition to unpredictable timelines, a lot of technical research involves constantly shifting targets. You can set milestones (and it is often useful to do so), but they need to be incredibly fluid. Unlike business progress, which can be boiled down to a few core metrics like customers or revenue regardless of how much pivoting is happening, research progress is much harder to boil down to a legible metric.
Some questions that are proxies for the degree of technological uncertainty facing a project:
How smooth is the technology’s fitness landscape? That is, how much could a single change shift outcomes?
The smoothness of a technology’s fitness landscape has a big effect on a program’s technological uncertainty
Do you know what it means to improve?
Do you know how you will know that you know how to incrementally improve the system?
^1: In other words, they are not in the lower left quadrant of raoGUTSGrandUnified2018
^2: See arthurLogicInvention2005
^3: See Therapeutics have established sales channels
^4: See fillerFundamentalManufacturingProcess2020
^5: See narayanamurtiCyclesInventionDiscovery2016