Using First Principles to Enable Human Capital Interactions at Scale

I spend a fair amount of time reading and thinking about disruptive innovation because I need to understand the past failures to enable interactions that place ability at right-fit and bring better careers to life. Here’s a great post by Ketan Jhaveri that dissects Elon Musk’s approach to disruptive innovation which is based on reasoning from first principles.

So, here’s the problem: According to Gallup, 53% of American workers are “not engaged” and 19% are “actively disengaged” at work. In The Coming Jobs War, author Jim Clifton writes:

The 53% of not engaged workers are not hostile or disruptive, and they are not troublemakers. They are just there, killing time with little or no concern about customers, productivity, profitability, waste, safety, mission and purpose of the teams, or developing customers. They’re thinking about lunch or their next break. They are essentially “checked out.” Most importantly, these people are not just part of a support staff or sales team. They are also sitting on executive committees.

And then there are the 19% of actively disengaged employees who are there to dismantle and destroy employers. They exhaust managers, they have more on-the-job accidents and because more quality defects, they contribute to “shrinkage” – as theft is politely called, they are sicker, they miss more days, and they quit at a higher rate than engaged employees do. Whatever the engaged do, the actively disengaged seek to undo, and that includes problem solving, innovation, and creating new customers.

I’ve come to realize that designing solutions around first principles might allow for looking at a problem from a more foundational level—where the seed of disruptive innovation can be planted.

Musk is quoted as saying:

“First principles” is a physics way of looking at the world…what that really means is that you boil things down to the most fundamental truths…and then reason up from there…”

The utility about Musk’s approach is that it provides a framework with which to do this. Breaking a problem down to its core components and then building back up from there helps me arrive at very different designs than relying solely on analogs.

The other really nice benefit of reasoning from first principles is that it can get me out of the “it can’t be done” mentality. And that’s especially handy when I’m trying to understand the failures in the human capital services industry. If I reason by analogy and I can break the problem down to its core first principles, then I can logically state “If all of these things are true, then there’s a problem that can be solved.”

I’ve identified the following first principles that will lead to the improvements we are looking for to place ability at right-fit and bring better careers to life.

Abundance: Every abundance creates a new scarcity. For example, a wealth of information creates a poverty of attention. Attention can be monetized.

Information: Scarce information wants to be expensive. That is, the price of context is valued at marginal utility—what it’s worth to customers. Scarcity can be monetized.

Context: Context is embedded with experience, license, proxy, credential, or reputation and the like and is distributed far down into the long-tail of ability placement markets. Context can be monetized.

Search & Influence: The advice and counsel of a trusted and liked advisor is always searched for when a placement process decision is important enough. Search and influence can be monetized.

Goodwill: Enlightened self-interest motivates goodwill. There are enough people who want to help others gain a commitment at best-fit in their community* if only to improve their status. Reputation can be monetized.

Less is More: As technology reduces coordination costs it enables more small placements and interactions—monetizable actions, reactions, and transactions—that had been previously dismissed below the economic fringe. In aggregate the monetized value of these small placements exceed that of high-dollar placements.

When a problem is broken down to it’s component parts at a fundamental level it becomes possible to see how seemingly disparate themes, when connected, can be part of the solution. Placement Loop is a platform forged from these first principles for solution providers on the supply-side to solve problems for talent seekers and candidates on the demand-side.

Which of these ‘first principles’ resonate with you?

*Community can be defined geographically, by industry or by common interests.

Evaluating Placement Information (Part 1 of 3)

Skillfully evaluating information relating to the best-fit placement of ability will tend to have three parts: analysis, psychology, and constraints exerted by principals (i.e. talent seekers and candidates). Accommodating any one of these in a placement process isn’t easy. Being good at all three is rare. Let’s look at each part below.

Before we begin however, let’s review the placement process in total which is as follows:

  1. Aggregation of intents
  2. Filtration of preferences
  3. Introduction of principals
  4. Prediction via evaluation
  5. Commitment execution

Although we view process sequentially, in reality our experience is palindromic in that a principal or agent-actor (i.e. placement facilitator) can enter the process at any point and proceed any way. This post deals with #4 of the placement process.

Let’s get to it!

The Analysis

To begin the analysis, the real causes of best-fit need to be identified. Success factors include supply and demand and outcomes. For this purpose, data can provide insight to how markets and their participants actually look. So if there’s a discrepancy between what a person’s ability ought to be valued at and what they are currently valued at, placement facilitators need to develop a theory about why value and pay have diverged. What’s going on that’s causing the gap? The analytical edge is embodied in the theory of what determines the fundamentals and why the ability is misvalued or misplaced.

The edge should also include what Benjamin Graham, the father of security analysis, called a margin of safety. You have a margin of safety when you buy a stock at a price that is substantially less than its value. As Graham noted, the margin of safety “is available for absorbing the effect of miscalculations or worse than average luck.” The size of the gap between value and pay tells you how big your margin of safety is. As Graham says, the margin of safety goes down as the price goes up. In other words, make your margin of safety as large as possible without losing attractiveness.

We’ll get into the Psychology (Part 2 of 3) in the next post.

Do you think it’s possible to value ability similar to how a stock is valued?