Welcome to the Machine

Welcome my son
Welcome to the machine

It is 2024. 4th Industrial and 5th Industrial Revolution technologies drive large parts of business now. 'Techies', hidden in the corporate back rooms, become ever more important components of business success.

Partial Differential Equations

While studying Applied Mathematics at university, the Partial Differential Equations (PDEs) course was my personal intellectual Waterloo. It was the mountain where I only barely managed to climb the very lowest slopes.

I'm not alone there. PDEs have been the Dukes-of-Wellington for many people, including Amazon's Jeff Bezos.

PDEs may seem an arcane notion and irrelevant in business domains - only of use in fields such as mathematics and science (Brownian motion, Einstein's Field Equations, etc.). Yet, these equations are being used in many areas of business such as, for example, the Investment industry. People such as Edward Thorpe, who was one of the first to use these types of equations in an investment strategy, became very wealthy.

The world's most successful investors, Jim Simons' Medallion Fund, is staffed by many elite-level mathematicians who use tools such as PDEs and others.

Medallion is not trying to figure out whether Tesla's stock price, for example, will outperform Amazon's (as the case still is with many 1980s-stuck South African investment providers). Rather, they prosper using mathematical tools and approaches such as advanced levels of arbitrage. They perform billions of transactions and only need (and achieve) success rates barely above 50% - 50.75% in fact; thanks in part to their advanced levels of use of mathematics and technology:

"In 2016, Simons’ firm quietly filed a 16-page technical document with the U.S. Patent and Trademark Office for “executing synchronized trades in multiple exchanges.” The concept involved using atomic clocks, the most high-precision time instruments on earth, to synchronize order transmissions down to a few billionths of a second."

The YouTuber "Veritasium" explains much of the above. Gregory Zuckerman's book "The Man Who Solved the Market" is an excellent read too. As an aside: those who follow American politics may also be interested in the fact that Simons' business partner Robert Mercer (and Mercer's daughter Rebekah) funded Donald Trump to POTUS in 2016, as well as funding Nigel Farage to Brexit success, while Simons funded Hillary Clinton.

Using PDEs is just one example of using science, mathematics and technology for business success. Others, currently, include the use of AI and data science. So, yes, to be successful, businesses need people who can wield these hard-science swords - and wield them well.

The Best Techie For the Job

It always makes sense to get the best available people for each and every role in your organisation. That much should be clear.

Now, the question is how we know who these 'best people' are. And, so we use CVs, interviews, qualifications, certifications, and more, to help us determine this. But we still get things wrong. Why?

To fill some types of roles with the right people, the criteria are largely qualitative (subjective measurement), while other types of people can (partially) be assessed through quantitative types of evidence (objective measurement).

The qualitatively-measured sides of businesses are often to be found where people, interactions and, 'Emotional Intelligence' feature strongly. Conversely, the more quantitatively-measurable sides of businesses are those where logic and science (in the broad sense of the word) play a bigger role. These types of people are often referred to as 'techies' in business environments.

Getting techie-appointments wrong

One difficulty is that qualitatively appointed people (in the form of managers) often play a large role in appointing techies. Too large a role, in fact.

Yes, techies do have to be able to work with other people, ADD is a thing, and 'corporate-culture' also matters. So, qualitative input must definitely be part of the bigger decision. But, the type of input of the 1st sentence of this paragraph is where things then have to stop. Technically-minded people are usually very capable of determining another technically-minded person's technical worth and they often do so in ways that may escape the attention of the manager. I.e., their questions are more quantitively oriented - often without this fact being obvious to less technically-inclined managers. Managers must then simply yield to the insights that the technical people can gain. Usually when they say 'no'. Often managers then don't yield.

Notions such as years-of-experience are of almost no use in technical (quantitative) environments. If you have to appoint a chess-player, for example, you only need to look at the person's Elo rating to know how good they are. And the top person may then be 12-year old Abhimanyu Mishra. Requiring a certain minimum number of years-of-experience, exclude those that do not yet have it. Then you will definitely exclude some very good techie candidates. When we wrote about ageism we focused on avoiding the mistake of not considering 'older' candidates. Ageism works both ways though.

Lastly and (we believe) by far most importantly, is the ignorance (or unbelief) that most non-technical managers (and especially risk-managers) have regarding the notion of the Mythical Man Month. In those people's lives and in their thinking, more people means more productivity and less risk. Two people can dig a hole faster than one person can. But, that is often not the case in most technical domains. It is only the case if necessary preconditions such as proper separation of concerns have been properly bedded down. I.e., yes this can work if you do it right, but you must do it right. And those pre-conditions will then often (at the time of managers unilaterally deciding to expand teams) have been de-prioritised (as a form of technical debt), in order to prioritise the delivery of business benefits.

So, management very often attempts to speed up things (or reduce risks such as losing key people) by prematurely adding more people to the team. That is a mistake. At best you'll play even. The likelihood, though, is that you will break stuff and/or go slower and/or lose those people (that your risk-mitigation guarded against) through increased levels of frustration. And you will have all of these downsides while you simultaneously spend more money (on the bigger team). Not good this is.

So, you simply have to avoid the three types of mistakes listed above (especially the last one) and your likelihood of success will increase. All that then remains is the (even more important) challenge of leading and managing those techies well.

Yet, after many decades of experience, we are still (on an almost daily basis) fighting these misguided approaches. We are certain that the advice above is 'gold' for most businesses. It is one of the simplest and most impactful sets of approaches that there is. Yet we continue to see senior decision-makers walk past that gold believing that they know better.