Legibility vs. Liquidity
I had a great time meeting some of you at the NYC Meetup yesterday at Fractal Tech (shoutout to Andrew for hosting). The quality of yesterday's discussion is evident in the pressing need I felt to push out these refinements/clarifications. I’ll circulate details for the next one shortly.
As Jordan Rubin pointed out last night, legibility is not the same as liquidity in human capital markets. It’s easy to speak of them interchangeably since in some subspaces, they look identical. This is the case when all the illiquidity is being driven by the lack of legibility. But legibility is only one factor that impacts liquidity.
Legibility exists on both sides of the market. There's supply-side legibility (how clearly defined and understandable someone's skills and abilities are) and demand-side legibility (how well-specified and comprehensible the job requirements are). While legibility can facilitate liquidity by reducing transaction costs, it's neither necessary nor sufficient for high liquidity. A skill or role could be perfectly legible but highly illiquid (like specialized academic research positions), or relatively illegible but liquid (like early employees at successful startups whose skills are hard to define but who find themselves in high demand). Here's a breakdown of the steps to convert skills into jobs/cash:
Find people who will pay you for the job you want.
Communicate and specify what you do and how you create value
Prove to them that you can reliably create value.
Legibility helps with all these steps - it makes it easier to find relevant opportunities or candidates (step 1), communicate value propositions clearly (step 2), and demonstrate/verify capabilities (step 3). However, high legibility alone doesn't guarantee liquidity - other factors can override the impact it has on liqudity.
Consider highly specialized fields like AI research or precision engineering. These skills might be perfectly legible (clear and well-defined), but their liquidity is limited because few employers can properly evaluate and utilize them. This highlights an important facet of liquidity that I should have mentioned in my first piece on the topic. In financial markets, some assets are fundamentally illiquid at almost any price point, while others become significantly more liquid if you're willing to accept a price discount. The same principle applies to careers: liquidity needs to be discussed in relation to your acceptable ranges for salary, location, role scope, and other key variables.
For instance, a senior machine learning engineer might find their skills highly illiquid if they insist on a $500K salary, remote work, and a specific technical domain. The same skillset becomes much more liquid if they're willing to consider a broader salary range or more varied applications of their expertise. Generally speaking, the pickiest person in the world, no matter how legible his qualifications and experience, is basically as good as fully illiquid because the search space collapses as you add too many constraints across multiple dimensions or as your constraints in any one dimension stretch far out into the tails of the distribution.
Two types of costs are associated with illiquidity:
Search costs/time (How many buyers in the market × how easy is it to identify and find these buyers)
Evaluation costs/time (What needs to be evaluated × the confidence required to sign contract)
Where evaluation costs get high:
When many dimensions need strong evidence (perhaps due to the role being high paid/high leverage)
When dimensions are hard to simulate/test
When past evidence isn't directly transferable
When dimensions interact in complex ways
These insights about liquidity and legibility have implications for both job seekers and employers. In markets where skills are highly legible but opportunities are few and far between (like specialized technical roles), the main challenge is finding and connecting with the right buyers. The solution might involve better ways to discover rare but valuable matches. Conversely, in markets where opportunities and skills are not super rate but less legible (like early-stage startup roles), the challenge is finding better ways to evaluate and validate both talent and organizations. These low liquidity quadrants is where Clout expects to add most value as a platform.
Understanding which type of friction you're dealing with - search costs or evaluation costs - matters for both sides of the market. Job seekers need to recognize whether their main challenge is finding buyers for well-understood skills or better articulating less conventional capabilities. Employers need to know whether they're struggling to discover rare talent, evaluate non-traditional candidates or articulate their value proposition/requirements.