Hiring for Pre-Product Market Fit Companies: Talent Potential Density Framework
One of the questions I ask in my interviews is simply: “Tell me the deepest thought you have (inside and outside work)”. The core reason for this is a strong belief in the Talent Potential Density Framework.
First, introducing Talent Density
Talent Density is an existing concept that centres around the idea that Tier A talent have a strong preference to only work with or for other Tier A talent, and you want to keep your % proportion of high performing Tier A talent high, ensuring your talent density remains constant or increases faster than the growth of your business. This is a widespread concept espoused by folks like Netflix, Google, etc.
I want to add an additional dimension to the Talent Density framework and postulate that early-stage companies, especially those which are pre-product market fit “PMF” or have a dynamic business model, should optimize for Talent Potential Density, which is Tier A talent filtered by the speed of learning and an ability to go deep into a range of issues.
Talent Potential Density measures your company’s rate of learning potential, and implies you optimize for speed of problem-solving in unstructured environments, rather than pure domain expertise, when you anticipate tackling problems no one in the world has solved before.
To define the different tiers of talent:
Tier A talent accelerates the upward trajectory of the company.
Tier B talent maintains the upward trajectory of the company.
Tier C talent reduces the upward trajectory of the company.
To be clear, Tier A talent is not a reflection of schools, experience, age, or any other factor and is also highly contextual to the job to be done.
A central implication of talent density is that you can do more with a smaller number of Tier A talent, than with a greater number of non-Tier A talent.
When companies are in the pre-PMF stage, the most relevant skillsets are the ability to adapt, gain domain expertise and execute quickly.
And I think for us, the way that I think of that is we need to optimize for our rate of learning. We are creating this new model and we’re helping a bunch of real human beings come together and build things and pay each other and create new types of careers and all of this stuff and it’s all sort of new, and it’s not clear exactly how it should work.
Why Talent Density isn’t the complete picture for Pre-Product-Market-Fit Companies
It is often easier to assess what talent you need for a post-PMF company, (i.e. we need someone with a lot of China <> Australia cross-border FX liquidity management experience, this person has 15 years of such experience for all the major fintech companies).
However, what is harder is knowing that for pre-PMF because of the lack of ultimate direction in the business model and product.
I propose that given the main bottleneck of pre-PMF company is an ability to quickly go deep into a range of issues, the way to measure Talent Density is to measure how quickly and deeply that individual can learn (“Talent Potential”). This is what Chris references when he talks about optimizing for rate of learning.
By increasing the Talent Potential Density of a company, you are increasing not just the overall potential rate of learning for a company, but also the ability to attract Tier A talent who have the ability to learn and adapt quickly.
Defining Talent Potential Density —
Optimizing for the ‘rate of learning’/ “Talent Potential”, which is optimizing for:
- An ability to go deeply, quickly.
This means choosing against those that have:
- An ability to go deeply, but requiring a long amount of time to go deeply
- An ability to quickly learn, but gets stuck on the surface depth of knowledge
- An inability to go deep at all
It goes without saying that the ability to go deeply, quickly, should come with a low ego to admit when a particular strategy is failing, the willingness to switch tracks quickly, and to quickly identify the most important factors/ elements that will need to change or improve. As a quick aside, this is the reason I generally avoid high ego teams/leadership.
Putting Talent Potential Density into action
This seems like a very simple but frequently overlooked point — many recruiting managers seem to favor deep knowledge at the expense of quick learning ability/potential.
I believe rate of learning/Talent Potential can be measured by depth of achievement divided by time. If we quantify depth of achievement on a scale of 0–10, I would take number of (depth of achievements times number of achievements) divided by time. We would also take a liberal view of the range of fields of achievements accomplished, as long as we can properly benchmark the depth.
In the example above, the further away you are to PMF, the more likely you should consider Individual B over Individual A, as Tier A talent in the appropriate context, recognizing talent potential rather than pure expertise as Tier A.
The emphasis here is on the speed of achieving deep achievements, rather than achieving a large number of shallow achievements or spending a long time at a lower rate of learning.
To note: this assumes a lot of other factors being ceteris paribus (including how much they were responsible for the achievement, luck, etc)
Structuring the interview process
I typically find that Talent Density Potential is highly correlated with original thinking and the invention or use of mental frameworks. The reason for this is that to achieve a faster level of skills-building/achievement building/rate of learning requires building a framework of learning/executing that is typically more efficient than the average industry. Fast promotions, and being part of prestigious projects can be a function of luck, but original frameworks cannot be faked.
I typically then ask the question “Tell me the deepest thought you have (inside or outside work)” or dive into how they solved problems at work, to understand the use, and/or invention of original mental frameworks to solve difficult problems or come to original solutions.
Types of Businesses that need this more than others
The type of business models that should optimize for Talent Density Potential, besides pre-PMF, are also typically business models that are more likely to incorporate cross-functional features or solve cross-functional problems.
These may include:
- Marketing Leads for novel products which require customer education
- Product Managers for novel products
- Ops Leads
As an example, I take this approach for VC interviews because I believe that VCs should always be in pre-PMF mode, both in understanding new business models, but also being extremely wary of how VC itself is undergoing a sea of disruption.
When you have optimized for Talent Density Potential, you worry far less about the ability for the team to pivot strategies, move quickly, deeply, and solve problems nobody has faced before.
Thanks to Luke Harries, Ed Broadhead, Richard Rogers, Keng Low, Rachael Defoe, Prerna Sharma, Harry Schiff, Ben Reinhardt, Tammie Siew for thoughts!
Chia Jeng Yang, Principal at Saison Capital, dives into consumer, SaaS, and fintech investment trends across the U.S. and Asia, builds projects in the venture capital and public policy space, works closely with early-stage (Pre-A) founders and can be contacted at jengyang.chia@gmail.com. Previous work here: http://chiajy.com
