Structures of professions and returns to experience

I’ve written here a few times about the concept of “returns to experience“. Basically, in some fields such as finance, the “returns to experience” is rather high. Irrespective of what you have studied or where, how long you have continuously been in the industry and what you have been doing has a bigger impact on your performance than your way of thinking or education.

In other domains, returns to experience is far less. After a few years in the profession, you would have learnt all you had to, and working longer in the job will not necessarily make you better at it. And so you see that the average 15 years experience people are not that much better than the average 10 years experience people, and so you see salaries stagnating as careers progress.

While I have spoken about returns to experience, till date, I hadn’t bothered to figure out why returns to experience is a thing in some, and only some, professions. And then I came across this tweetstorm that seeks to explain it.

Now, normally I have a policy of not reading tweetstorms longer than six tweets, but here it was well worth it.

It draws upon a concept called “cognitive flexibility theory”.

Basically, there are two kinds of professions – well-structured and ill-structured. To quickly summarise the tweetstorm, well-structured professions have the same problems again and again, and there are clear patterns. And in these professions, first principles are good to reason out most things, and solve most problems. And so the way you learn it is by learning concepts and theories and solving a few problems.

In ill-structured domains (eg. business or medicine), the concepts are largely the same but the way the concepts manifest in different cases are vastly different. As a consequence, just knowing the theories or fundamentals is not sufficient in being able to understand most cases, each of which is idiosyncratic.

Instead, study in these professions comes from “studying cases”. Business and medicine schools are classic examples of this. The idea with solving lots of cases is NOT that you can see the same patterns in a new case that you see, but that having seen lots of cases, you might be able to reason HOW to approach a new case that comes your way (and the way you approach it is very likely novel).

Picking up from the tweetstorm once again:

 

It is not hard to see that when the problems are ill-structured or “wicked”, the more the cases you have seen in your life, the better placed you are to attack the problem. Naturally, assuming you continue to learn from each incremental case you see, the returns to experience in such professions is high.

In securities trading, for example, the market takes very many forms, and irrespective of what chartists will tell you, patterns seldom repeat. The concepts are the same, however. Hence, you treat each new trade as a “case” and try to learn from it. So returns to experience are high. And so when I tried to reenter the industry after 5 years away, I found it incredibly hard.

Chess, on the other hand, is well-structured. Yes, alpha zero might come and go, but a lot of the general principles simply remain.

Having read this tweetstorm, gobbled a large glass of wine and written this blogpost (so far), I’ve been thinking about my own profession – data science. My sense is that data science is an ill-structured profession where most practitioners pretend it is well-structured. And this is possibly because a significant proportion of practitioners come from academia.

I keep telling people about my first brush with what can now be called data science – I was asked to build a model to forecast demand for air cargo (2006-7). The said demand being both intermittent (one order every few days for a particular flight) and lumpy (a single order could fill up a flight, for example), it was an incredibly wicked problem.

Having had a rather unique career path in this “industry” I have, over the years, been exposed to a large number of unique “cases”. In 2012, I’d set about trying to identify patterns so that I could “productise” some of my work, but the ill-structured nature of problems I was taking up meant this simply wasn’t forthcoming. And I realise (after having read the above-linked tweetstorm) that I continue to learn from cases, and that I’m a much better data scientist than I was a year back, and much much better than I was two years back.

On the other hand, because data science attracts a lot of people from pure science and engineering (classically well-structured fields), you see a lot of people trying to apply overly academic or textbook approaches to problems that they see. As they try to divine problem patterns that don’t really exist, they fail to recognise novel “cases”. And so they don’t really learn from their experience.

Maybe this is why I keep saying that “in data science, years of experience and competence are not correlated”. However, fundamentally, that ought NOT to be the case.

This is also perhaps why a lot of data scientists, irrespective of their years of experience, continue to remain “junior” in their thinking.

PS: The last few paragraphs apply equally well to quantitative finance and economics as well. They are ill-structured professions that some practitioners (thanks to well-structured backgrounds) assume are well-structured.

Christian Rudder and Corporate Ratings

One of the studdest book chapters I’ve read is from Christian Rudder’s Dataclysm. Rudder is a cofounder of OkCupid, now part of the match.com portfolio of matchmakers. In this book, he has taken insights from OkCupid’s own data to draw insights about human life and behaviour.

It is a typical non-fiction book, with a studmax first chapter, and which gets progressively weaker. And it is the first chapter (which I’ve written about before) that I’m going to talk about here. There is a nice write-up and extract in Maria Popova’s website (which used to be called BrainPickings) here.

Quoting Maria Popova:

What Rudder and his team found was that not all averages are created equal in terms of actual romantic opportunities — greater variance means greater opportunity. Based on the data on heterosexual females, women who were rated average overall but arrived there via polarizing rankings — lots of 1’s, lots of 5’s — got exponentially more messages (“the precursor to outcomes like in-depth conversations, the exchange of contact information, and eventually in-person meetings”) than women whom most men rated a 3.

In one-hit markets like love (you only need to love and be loved by one person to be “successful” in this), high volatility is an asset. It is like option pricing if you think about it – higher volatility means greater chance of being in the money, and that is all you care about here. How deep out of the money you are just doesn’t matter.

I was thinking about this in some random context this morning when I was also thinking of the corporate appraisal process. Now, the difference between dating and appraisals is that on OKCupid you might get several ratings on a 5-point scale, but in your office you only get one rating each year on a 5-point scale. However, if you are a manager, and especially if you are managing a large team, you will GIVE out lots of ratings each year.

And so I was wondering – what does the variance of ratings you give out tell about you as a manager? Assume that HR doesn’t impose any “grading on curve” thing, what does it say if you are a manager who gave out an average rating of 3, with standard deviation 0.5, versus a manager who gave an average of 3, with all employees receiving 1s and 5s.

From a corporate perspective, would you rather want a team full of 3s, or a team with a few 5s and a few 1s (who, it is likely, will leave)? Once again, if you think about it, it depends on your Vega (returns to volatility). In some sense, it depends on whether you are running a stud or a fighter team.

If you are running a fighter team, where there is no real “spectacular performance” but you need your people to grind it out, not make mistakes, pay attention to detail and do their jobs, you want a team full of3s. The 5s in this team don’t contribute that much more than a 3. And 1s can seriously hurt your performance.

On the other hand, if you’re running a stud team, you will want high variance. Because by the sheer nature of work, in a stud team, the 5s will add significantly more value than the 1s might cause damage. When you are running a stud team, a team full of 3s doesn’t work – you are running far below potential in that case.

Assuming that your team has delivered, then maybe the distribution of ratings across the team is a function of whether it does more stud or fighter work? Or am I force fitting my pet theory a bit too much here?

Conductors and CAPM

For a long time I used to wonder why orchestras have conductors. I possibly first noticed the presence of the conductor sometime in the 1990s when Zubin Mehta was in the news. And then I always wondered why this person, who didn’t play anything but stood there waving a stick, needed to exist. Couldn’t the orchestra coordinate itself like rockstars or practitioners of Indian music forms do?

And then i came across this video a year or two back.

And then the computer science training I’d gone through two decades back kicked in – the job of an orchestra conductor is to reduce an O(n^2) problem to an O(n) problem.

For a  group of musicians to make music, they need to coordinate with each other. Yes, they have the staff notation and all that, but still they need to know when to speed up or slow down, when to make what transitions, etc. They may have practiced together but the professional performance needs to be flawless. And so they need to constantly take cues from each other.

When you have n musicians who need to coordinate, you have \frac{n.(n-1)}{2} pairs of people who need to coordinate. When n is small, this is trivial, and so you see that small ensembles or rock bands can easily coordinate. However, as n gets large, n^2 grows well-at-a-faster-rate. And that is a problem, and a risk.

Enter the conductor. Rather than taking cues from one another, the musicians now simply need to take cues from this one person. And so there are now only n pairs that need to coordinate – each musician in the band with the conductor. Or an O(n^2) problem has become an O(n) problem!

For whatever reason, while I was thinking about this yesterday, I got reminded of legendary finance professor R Vaidya‘s class on capital asset pricing model (CAPM), or as he put it “Sharpe single index model” (surprisingly all the links I find for this are from Indian test prep sites, so not linking).

We had just learnt portfolio theory, and how using the expected returns, variances and correlations between a set of securities we could construct an “efficient frontier” of securities that could give us the best risk-adjusted return. Seemed very mathematically elegant, except that in case you needed to construct a portfolio of n stocks, you needed n^2 correlations. In other word, an O(n^2) problem.

And then Vaidya introduced CAPM, which magically reduced the problem to an O(n) problem. By suddenly introducing the concept of an index, all that mattered for each stock now was its beta – the coefficient of its returns proportional to the index returns. You didn’t need to care about how stocks reacted with each other any more – all you needed was the relationship with the index.

In a sense, if you think about it, the index in CAPM is like the conductor of an orchestra. If only all O(n^2) problems could be reduced to O(n) problems this elegantly!

Management and Verification

For those of you who are new here, my wife and I used to organise “NED Talks” in our home in Bangalore. The first edition happened in 2015 (organised on a whim), and encouraged by its success, we organised 10 more editions until 2019. We have put up snippets of some talks here.

In the second edition of the NED Talks (February 2015), we had a talk by V Vinay (noted computer scientist, former IISc professor, co-inventor of Simputer, co-founder of Strand Life Sciences, Ati Motors, etc. etc.), where he spoke about “computational complexity”.

Now, having studied computer science, “computational complexity” was not a new topic to me, but one thing that Vinay said has stayed with me – it is that verifying an algorithm is far more efficient than actually executing the algorithm.

To take a simple example, factorising a number into prime factors is NP Hard – in other words, it is a really hard problem. However, verifying the prime factorisation of a number is trivial – you can just multiply the factors and see if it gives back the number you started with.

I was thinking about this paradigm the ohter day when I was thinking about professional managers – several times in life I have wondered “how can this person manage this function when he/she has no experience in that function?”. Maybe it is because I had been subjected to two semesters of workshop in the beginning of my engineering, but I have intuitively assumed that you can only manage stuff that you have personally done – especially if it is a non-trivial / specialist role.

But then – if you think about it, at some level, management is basically about “verification”. To see whether you have done your work properly, I don’t need to precisely know how you have done it. All I need to know is whether you have done bullshit – which means, I don’t need to “replicate your algorithm”. I only need to “verify your algorithm”, which computer science tells us can be an order of magnitude simpler than actually building the algorithm.

The corollary of this is that if you have managed X, you need not be good at X, or actually even have done X. All it shows is that you know how to manage X, which can be an order of magnitude simple than actually doing X.

This also (rather belatedly) explains why I have largely been wary of hiring “pure managers” for my team. Unless they have been hands on at their work, I start wondering if they actually know how to do it, or only know how to manage it (and I’m rather hands on, and only hire hands on people).

And yet another corollary is that if you have spent too long just managing teams, you might have gotten so used to just verifying algorithms that you can’t write algorithms any more.

And yet another before I finish – computer science has a lot of lessons to offer life.

 

Management watch

About a year back, a few months after I had started my current job, I was working late into the evening. I was sitting on the sofa with my laptop when my wife said, “you cannot call yourself senior management if you work like this”.

“What do you mean”, I asked.

“If you are truly senior management, you should not be using your computer after normal work hours. You should be doing everything using your phone. Do you remember, six months into my job at <@#R@#$@@>, I would work late into the night, but only with my phone?”, she countered.

I had to admit this was a good point. More practically, in terms of work stuff, I started thinking about making dashboards and reports more mobile-friendly. I started questioning interactive dashboards – if they are aimed at top management, the latter largely see the stuff on their phones, so interactivity is full of fat fingers.

Of course, the nature of my job means that I can never truly be senior management by this metric – I’m generally  too hands on to be able to work exclusively on my phone. However, that hasn’t stopped me from evangelising this theory of my wife. The theory itself is strong enough.

Recently I’d met a former client. He was using an iPad as a work “laptop”. I told him the theory and that he has truly arrived. He said he had been given a choice of an iPad and a Surface –  basically his company has internalised how senior management ought to be treated.

While I can never be senior management by this metric, I’m in a way trying to leapfrog it. Recently I got myself an Apple Watch. Apart from other things, it gives me notifications for all my messages, and I can reply using the watch as well. And this is where the magic begins.

For starters, Apple offers this standard set of templatised replies you can use. Now, Apple being Apple (and not Google), these replies are not customised to the message that you get. It drives me nuts that there is an “OK” and a “Sure!” and a “No” but no “Yes”. If this template doesn’t work for you, you can actually type a message on the watch itself. My fingers are fat (and I wear my watch on my dominant hand), so this is not so useful for me. However, there is also a voice typing mode, and that is rather good. And that is where things get real.

The other day, I shut work early and went off for a walk (I like doing that). My team had not shut their work though, and they kept bombarding me with messages. And that is when I realised I could actually read their messages and REPLY TO THEM using my watch. Most of the messages were the template monosyllables. Sometimes I spoke into my watch (without breaking my stride), and let Apple’s excellent voice-to-text do the rest.

And so I have this new theory, which is an extension of my wife’s theory. The next level of senior management is to be able to get all your work done simply using your watch – not even needing your phone. Of course, limitations exist – only a few lines of text are shown for each email, and images don’t load, but it is only a matter of time before watches solve for this.

But then, I’ve discovered one massive downside of replying to messages using my watch – the tone. The template monosyllables are all come across as rude (or curt). And the voice-to-text means you don’t really have your filter on while typing, and you end up “writing as you would speak”, and that can’t be great as well.

The other day I was walking from our Michaelpalya office to our Binnamangala office, when I was bombarded with messages from someone. And without breaking my stride I replied to all the messages, speaking into my watch. I “wrote” as I would speak (complete with swearwords), and that turned out to be an incredibly rude set of messages I ended up sending (I apologised later that day when I saw what I’d “written” on my phone later).

So leapfrogging and trying to act too cool can sometimes come at a price.

Compression Stereotypes

One of the most mindblowing things I learnt while I was doing my undergrad in Computer Science and Engineering was Lempel-Ziv-Welch (LZW) compression. It’s one of the standard compression algorithms used everywhere nowadays.

The reason I remember this is twofold – firstly, I remember implementing this as part of an assignment (our CSE program at IITM was full of those), and feeling happy to be coding in C rather than in the dreaded Java (which we had to use for most other assignments).

The other is that this is one of those algorithms that I “internalised” while doing something totally different – in this case I was having coffee/ tea with a classmate in our hostel mess.

I won’t go into the algorithm here. However, the basic concept is that as and when we see a new pattern, we give it a code, and every subsequent occurrence of that pattern is replaced by its corresponding code. And the beauty of it is that you don’t need to ship a separate dictionary -the compressed code itself encapsulates it.

Anyway, in practical terms, the more the same kind of patterns are repeated in the original file, the more the file can be compressed. In some sense, the more the repetition of patterns, the less the overall “information” that the original file can carry – but that discussion is for another day.

I’ve been thinking of compression in general and LZW compression in particular when I think of stereotyping. The whole idea of stereotyping is that we are fundamentally lazy, and want to “classify” or categorise or pigeon-hole people using the fewest number of bits necessary.

And so, we use lazy heuristics – gender, caste, race, degrees, employers, height, even names, etc. to make our assumptions of what people are going to be like. This is fundamentally lazy, but also effective – in a sense, we have evolved to stereotype people (and objects and animals) because that allows our brain to be efficient; to internalise more data by using fewer bits. And for this precise reason, to some extent, stereotyping is rational.

However, the problem with stereotypes is that they can frequently be wrong. We might see a name and assume something about a person, and they might turn out to be completely different. The rational response to this is not to beat oneself for stereotyping in the first place – it is to update one’s priors with the new information that one has learnt about this person.

So, you might have used a combination of pre-known features of a person to categorise him/her. The moment you realise that this categorisation is wrong, you ought to invest additional bits in your brain to classify this person so that the stereotype doesn’t remain any more.

The more idiosyncratic and interesting you are, the more the number of bits that will be required to describe you. You are very very different from any of the stereotypes that can possibly be used to describe you, and this means people will need to make that effort to try and understand you.

One of the downsides of being idiosyncratic, though, is that most people are lazy and won’t make the effort to use the additional bits required to know you, and so will grossly mischaracterise you using one of the standard stereotypes.

On yet another tangential note, getting to know someone is a Bayesian process. You make your first impressions of them based on whatever you find out about them, and go on building a picture of them incrementally based on the information you find out about them. It is like loading a picture on a website using a bad internet connection – first the picture appears grainy, and then the more idiosyncratic features can be seen.

The problem with refusing to use stereotypes, or demonising stereotypes, is that you fail to use the grainy pictures when that is the best available, and instead infinitely wait to get better pictures. On the other hand, failing to see beyond stereotypes means that you end up using grainy pictures when more clear ones are available.

And both of these approaches is suboptimal.

PS: I’ve sometimes wondered why I find it so hard to remember certain people’s faces. And I realise that it’s usually because they are highly idiosyncratic and not easy to stereotype / compress (both are the same thing). And so it takes more effort to remember them, and if I don’t really need to remember them so much, I just don’t bother.

Go East Policy

When you take time off work, one thing you want to do is to explore the world – go to parts of it that you haven’t been to before.

The original idea for this week was to travel – we wanted to do an impromptu road trip starting the past Sunday, booking only one hotel at a time on each day. As it happened, on Friday, daughter’s school sent an email that offline classes would begin on Monday, so we didn’t travel.

Instead, I decided to do a “staycation” – continue to be off work but be at home and vegetate. However, not going anywhere didn’t seem right. The whole point of taking time off is to go see parts of the world you haven’t seen before. And so I decided to set aside today for this purpose, apart from meeting people. Thanks to the pandemic and the latest round of lockdowns and school closures, I hadn’t seen too many people outside my family since the beginning of January.

And so I set off east, to parts that I hadn’t really seen or explored in a very long time.

  1. Bellandur
    First stop was Bellandur, to meet a friend who I hadn’t seen in over two years, and who’s recently moved back to “Bangalore”. We were to meet at a sort of a mall that’s part of this absolutely massive office complex.

    Despite all the metro construction going on, I got to Bellandur in quick time (the only wait being at Madivala checkpost). However, getting to Bellandur was only half the story. To get to the “bay” (as the mall was called) I had to turn off outer ring road, and into what felt like a strange road, with random barricades and private security personnel every 100 metres. Both sides were office complexes.

    Finally, at the end of the road (2-3 km in), I found the “bay”. It’s a sort of strip mall with a food court, and coffee and tea shops, and even an Apple reseller store. Maybe because most offshored businesses (which largely populate this area) haven’t got back to office yet, the place was largely empty. I had a bit of an embarrassing incident, though, as rather confusing signage meant I had opened the door to the women’s restroom (a janitor stopped me).

    I found the entire area sort of unreal and weird – even if the metro comes to ORR, it is going to be a massive pain to get to these offices and apartment blocks (and “mall”). There is no sense of redundancy in the roads. Security personnel every 100 metres is disconcerting.

  2. Windmills
    Next on the agenda was  Windmills Craftworks in Whitefield, where I was meeting someone for lunch. It was going to be my first time there, so I simply followed Google Maps.

    I was pleasantly surprised that this drive took only 25 minutes, again because most offshored staff have not returned to office. I was also pleasantly surprised to see a reasonably wide road that connects somewhere in the middle of nowhere in outer ring road to Graphite India.

    The location of the brewery is a bit strange – being located in a middle floor of a commercial building! The person I was meeting is a Whitefield local, and the thing that invariably happens in a microbrewery happens – he ran into others he knew. The food was good. I didn’t have much of the beer (since I was driving), but the IPA sampler was good as well.

    The valet was strange. When I got off the car, I was asked for my phone number and name, and got an SMS. When I was done, I simply clicked a link sent in the same SMS – by the time I came down, the car had arrived.

    On another note, I was thinking of all the places that were collecting my number – the valet, the restaurant above, some random shop I’d been to yesterday, etc. I was wondering what can be done with all this data. At one level, it scared me. At another, I thought it would be exciting to work with all this data and see what can be done with it!

  3. Sheraton Whitefield
    I was meeting someone at the coffee shop here. Being tucked away inside Prestige Shantiniketan, the hotel was a bit hard to find, and given that offices in the area have not yet been staffed, the hotel was empty.

    The hotel seemed nice enough and the coffee was good. And there was very little traffic in the usually rather busy road in front of it. I don’t expect this to last once people are back in their offices.

    _____________________________________

The way back was largely uneventful. Again I trusted Google, which took me on yet another random road to get from whitefield back to ORR. This was narrower and involved going through some rural areas.

Apart from some sections where the metro was being constructed, the drive back through ORR into Koramangala (I was meeting yet another friend after getting back to town) was quick and peaceful. And I noticed that the one-way systems in Hosur Road and Sarjapur Road have been reversed yet again. If there is a road (or pair of roads) deserving to be a “Tughlaq” in Bangalore, it’s this system. I’ve lost count of the number of times they’ve made these roads one-way and two-way (going back to at least 2004).

So the “exploring new areas” part of my week-long vacation is done. I want to step up on meeting people, but I’ll possibly do it on “home ground” in the days to come.

PS: The general convention I’ve settled on in life is that when one person travels to meet the other, the latter pays for the food / drink / coffee. As it happens, EVERYONE I met today offered to pay, and I simply let them without once insisting that I take the bill or we split it.

Impossible careers

A month ago, I had this idea that rather than squatting and deadlifting super heavy, I should learn “olympic lifts” (snatch, clean and jerk). I’d even made up my mind that I’ll ask one of the coaches at my gym to offer personal training during the summer so I can learn it.

And then, randomly, 2-3 weeks back, I decided to do some new exercises, and decided to do snatch grip overhead squat (something you need to do while you’re doing an olympic snatch). And that’s when I realised I would struggle.

I’ve mentioned here a couple of times that I have incredibly long arms. What I had not realised is that I have long enough, and a torso short enough, that it is physically impossible for me to snatch with a barbell.

Really.

So in the snatch, you need to use a wide grip and bring up the barbell, and at the same time thrust your hips forward to make sure the hips hit the barbell. The momentum of you having sharply pulled the barbell off the floor, and the hips hitting it, means that the barbell will go upwards, and you squat down and catch it overhead.

The key is that your waist needs to precisely hit the barbell when you thrust your hips forward. If the bar makes contact higher, your stomach can’t convey the same momentum that the waist can. And if the bar makes contact lower, well, let’s not get into below-the-belt stuff here.

And so your snatch grip on the barbell is determined by the width you hold it at so that the bar is exactly at your waist. You see professional weightlifters, and they usually hold the barbell well inside the ends (apparently short arms are a huge advantage in professional weight lifting). Most people in my gym also hold their snatch grip well inside the ends of the bar. Just that I can’t.

I got this photo taken at the gym today to demonstrate this:

Me trying to hold a snatch grip.

I tucked in my shirt to show where my waist is. Notice that I’m holding the bar in the widest possible position. Yet I’m unable to get the bar to my waist. So with my body proportions, if I were to try and snatch, I would be putting myself in grave danger.

The reason I’ve told such a long story here is to illustrate that your choice of profession or game or sport highly depends on who you are. If, for whatever reason, I’d decided when I was young that weightlifting is cool and I want to specialise in that, I would have NEVER made it.

A lot of times, we make the mistake of going for “cool stuff” (or worse, forcing our kids to do something that we think is cool), without realising if we are cut out to do the cool stuff -whether we will like it, enjoy it and be good at it. And sometimes, driven by “inspirational stories”, we push ourselves too hard to get the cool job or college admission or whatever, without realising we may not have the aptitude for it at all.

Now that I tried to find my snatch grip, I know better than to take personal training for snatching. Yes, I should still be able to clean – though every time I’ve tried to learn, I’ve found it to involve too much coordination between my limbs (just like swimming, something else I’ve never managed to learn though my long arms should make me good at it).

I guess I should just stick to my strengths, and just deadlift and chill.

Why WFH is unsustainable

A couple of weekends back I decided to re-read Yuval Noah Harari’s Sapiens. Rather than digging into my kindle for the regular version (which I’d read in 2015), I decided to read the graphic novel instead.

I’d purchased a copy of it a few months back, and a month ago, my daughter had finished reading it (it was only after she finished reading that I realised the extent of the sex and violence in the book. anyways).

Since I was re-reading, there was nothing particularly new. It was just a refresher of everything I’d read and enjoyed back in 2015. And one of the things I read was something highly pertinent to what I’d been thinking about the preceding Friday – on gossip.

One of the key points that Harari makes in Sapiens is that what makes us sapiens sapiens is our ability to gossip. Many other animals communicate, but most of their communication is “necessary”. “Oh look, there’s a lion”, or “there is a dead elephant near the lake” types.

Homo sapiens is unique in that most of our conversation is, fundamentally speaking, rather unnecessary stuff. It is basically “gossip”. That we gossip, however, means that we evolved to have a far richer vocabulary. We communicate and bond a lot more. And we are able to create “shared fictions” that means it is far easier for us to cooperate with strangers. And that lets us do more. Then again – it all started with gossip.

This, I realised, is why I find working from home rather isolating. It’s been over a year since I got back to full time employment. There have been two waves of covid-19 after that. This has meant I’ve hardly been to office in this time. Yes, there have been spells when I’ve travelled, or spent a week at office, but they have been few and far in between.

Apart from collaboration with my team, work has been fine. However, what I realise I miss is the general “bonding” that you would come to expect when you work for a company. The problem is with remote work.

While chat (we use Google Chat; other companies use Slack or DBabble of Microsoft Teams or Discord) is good enough for most “quick communication”, the big problem is that everything you say is necessarily in writing. Yes, you can delete or modify, some messengers have disappearing messages and all that.

Yet, because you need to put everything in writing, you say less than you otherwise would. Most importantly, you think twice before you gossip. It takes a long time for pairs of people to build sufficient mutual trust to be able to gossip (and when I think of it, most of this kind of trust has developed through offline interactions). Even if I trust you, I’ll think maybe one and a half times before putting gossip in writing.

So prolonged period of remote work means work gets robbed of the core human element – gossip. And extending what Harari says in sapiens, when you gossip less, you believe in fewer shared fictions (though by definition all of you in your company believe in the fiction of the limited liability corporation). And you cooperate less.

I can’t wait to get back to office (planning in 2 weeks or so), and (hopefully) start gossiping again. It won’t be easy since so far I’ve largely been remote. However, if we can get a sustained period of office work going, we should be able to gossip and bond and be a little more human.

Returns to experience and business school career choices

Go to any elite business school, especially one where the average years of pre-MBA industry experience is low, and ask students what they want to do. Most first year students will tell you that they either want to do “marketing” or “investment banking”. Second year students will still say this, but some will also say “consulting”.

With the benefit of a lot of hindsight (it’s nearly 16 years since I graduated from business school), there is definite merit in these being primary career choices for business school students – rather than other seemingly equally valid careers such as B2B sales, or product management, or not-for-profits, or data analytics, or logistics.

It has to do with reversibility, and “one-way doors”.

Different professions have different levels of “returns to experience”. In some professions, all that mattters is the total amount of contiguous experience you’ve had in that particular profession.

I figured this out the hard way, for example, in my brief flirtation with getting back to becoming a banking quant in 2017. I had left the profession (banking quant) in late 2011, to become an independent consultant. A series of financial services projects later, I wondered if I could get back to what I was doing earlier. Except that they wouldn’t have me back – all they cared about was that I had “been out of the industry for 5 years”, and what experience I got in those 5 years didn’t really matter.

In other words, investment banking is a “high returns to experience” industry, where your experience within the industry is highly valued, but anything outside is completely disregarded.

Marketing (though not “digital marketing”) is also similarly – your experience outside the field is not valued at all. So even if you look to get into consumer goods marketing at a later point of time in your career, you will most likely have to start right at the bottom, at an entry level position. All your years of experience doing something else are of no use here.

You notice a pattern (despite the small number of data points I’ve offered)? Popular out-of-business-school careers are professions with a high “perceived returns to experience”. The reason why so many business school students want to do marketing or investment banking is because they are irreversible choices. You either get in from school, or get in later on but start at the bottom anyway. So you might as well get in straight from school.

Technology and data and product management and B2B sales and corporate strategy and logistics and general management are all rather more forgiving – a large number of employers offering these jobs give adequate weightage to experience outside of the field as well. Which means it is easier to switch into these professions at a later point of time in one’s career.

Putting it another way, starting your career in a hard-to-enter (or “enter-at-bottom”) field is a risk-averse way of building your career. If you don’t like it, you can always move to a more welcoming career path. Start in a more welcoming place, and you’ll find it harder to move to a less welcoming career.

So that explains marketing and investment banking, but what about strategy consulting? Surely, strategy consulting should value diverse experience, for that will make you a better consultant? The difference here is between strategy consulting and “brand name strategy consulting”. If you work for a “brand name strategy consultant”, you’re not only offering your own advice – you are also offering advice on behalf of that firm.

This means, in order to do so, you need adequate training in the ways of the firm. And so there will always be (less than 100% of course) a discount on the rest of your experience – in order to learn the ways nad means of the firm that you are going to represent, you will need to start at a more junior level than your experience dictates. So once again you might as well get in right upfront, straight out of school.

So the next time a business school student tells you she wants to do marketing or investment banking or strategy consulting, don’t berate her for “being too cliched and not open minded enough”. She is just being rational, and playing the optionality in the way it should be.