A day at an award function

So I got an award today. It is called “exemplary data scientist”, and was given out by the Analytics India Magazine as part of their MachineCon 2022. I didn’t really do anything to get the award, apart from existing in my current job.

I guess having been out of the corporate world for nearly a decade, I had so far completely missed out on the awards and conferences circuit. I would see old classmates and colleagues put pictures on LinkedIn collecting awards. I wouldn’t know what to make of it when my oldest friend would tell me that whenever he heard “eye of the tiger”, he would mentally prepare to get up and go receive an award (he got so many I think). It was a world alien to me.

Parallelly, I used to crib about how while I’m well networked in India, and especially in Bangalore, my networking within the analytics and data science community is shit. In a way, I was longing for physical events to remedy this, and would lament that the pandemic had killed those.

So I was positively surprised when about a month ago Analytics India Magazine wrote to me saying they wanted to give me this award, and it would be part of this in-person conference. I knew of the magazine, so after asking around a bit on legitimacy of such awards and looking at who had got it the last time round, I happily accepted.

Most of the awardees were people like me – heads of analytics or data science at some company in India. And my hypothesis that my networking in the industry was shit was confirmed when I looked at the list of attendees – of 100 odd people listed on the MachineCon website, I barely knew 5 (of which 2 didn’t turn up at the event today).

Again I might sound like a n00b, but conferences like today are classic two sided markets (read this eminently readable paper on two sided markets and pricing of the same by Jean Tirole of the University of Toulouse). On the one hand are awardees – people like me and 99 others, who are incentivised to attend the event with the carrot of the award. On the other hand are people who want to meet us, who will then pay to attend the event (or sponsor it; the entry fee for paid tickets to the event was a hefty $399).

It is like “ladies’ night” that pubs have, where on a particular days of the week, women who go to the pub get a free drink. This attracts women, which in turn attracts men who seek to court the women. And what the pub spends in subsidising the women it makes back in terms of greater revenue from the men on the night.

And so it was at today’s conference. I got courted by at least 10 people, trying to sell me cloud services, “AI services on the cloud”, business intelligence tools, “AI powered business intelligence tools”, recruitment services and the like. Before the conference, I had received LinkedIn requests from a few people seeking to sell me stuff at the conference. In the middle of the conference, I got a call from an organiser asking me to step out of the hall so that a sponsor could sell to me.

I held a poker face with stock replies like “I’m not the person who makes this purchasing decision” or “I prefer open source tools” or “we’re building this in house”.

With full benefit of hindsight, Radisson Blu in Marathahalli is a pretty good conference venue. An entire wing of the ground floor of the hotel is dedicated for events, and the AIM guys had taken over the place. While I had not attended any such event earlier, it had all the markings of a well-funded and well-organised event.

As I entered the conference hall, the first thing that struck me was the number of people in suits. Most people were in suits (though few wore ties; And as if the conference expected people to turn up in suits, the goodie bag included a tie, a pair of cufflinks and a pocket square). And I’m just not used to that. Half the days I go to office in shorts. When I feel like wearing something more formal, I wear polo T-shirts with chinos.

My colleagues who went to the NSE last month to ring the bell to take us public all turned up company T-shirts and jeans. And that’s precisely what I wore to the conference today, though I had recently procured a “formal uniform” (polo T-shirt with company logo, rather than my “usual uniform” which is a round neck T-shirt). I was pretty much the only person there in “uniform”. Towards the end of the day, I saw one other guy in his company shirt, but he was wearing a blazer over it!

Pretty soon I met an old acquaintance (who I hadn’t known would be at the conference). He introduced me to a friend, and we went for coffee. I was eating a cookie with the coffee, and had an insight – at conferences, you should eat with your left hand. That way, you don’t touch the food with the same hand you use to touch other people’s hands (surprisingly I couldn’t find sanitiser dispensers at the venue).

The talks, as expected, were nothing much to write about. Most were by sponsors selling their wares. The one talk that wasn’t by a sponsor was delivered by a guy who was introduced as “his greatgrandfather did this. His grandfather did that. And now this guy is here to talk about ethics of AI”. Full Challenge Gopalakrishna feels happened (though, unfortunately, the Kannada fellows I’d hung out with earlier that day hadn’t watched the movie).

I was telling some people over lunch (which was pretty good) that talking about ethics in AI at a conference has become like worshipping Ganesha as part of any elaborate pooja. It has become the de riguer thing to do. And so you pay obeisance to the concept and move on.

The awards function had three sections. The first section was for “users of AI” (from what I understood). The second (where I was included) was for “exemplary data scientists”. I don’t know what the third was for (my wife is ill today so I came home early as soon as I’d collected my award), except that it would be given by fast bowler and match referee Javagal Srinath. Most of the people I’d hung out with through the day were in the Srinath section of the awards.

Overall it felt good. The drive to Marathahalli took only 45 minutes each way (I drove). A lot of people had travelled from other cities in India to reach the venue. I met a few new people. My networking in data science and analytics is still not great, but far better than it used to be. I hope to go for more such events (though we need to figure out how to do these events without that talks).

PS: Everyone who got the award in my section was made to line up for a group photo. As we posed with our awards, an organiser said “make sure all of you hold the prizes in a way that the Intel (today’s chief sponsor) logo faces the camera”. “I guess they want Intel outside”, I joked. It seemed to be well received by the people standing around me. I didn’t talk to any of them after that, though.

The “intel outside” pic. Courtesy: https://www.linkedin.com/company/analytics-india-magazine/posts/?feedView=all

 

Proof of work

I like to say sometimes that one reason I never really get crypto is that it involves the concept of “proof of work”. That phrase sort of triggers me. It reminds me of all the times when I was in school when I wouldn’t get full marks in maths despite getting all the answers correct because I “didn’t show working”.

In any case, I spent about fifteen minutes early this morning drinking my aeropress and deleting LinkedIn connection requests. Yeah, you read that right. It took that long to refuse all the connection requests I had got since yesterday, when I put a fairly innocuous post saying I’m hiring.

I understand that the market is rather tough nowadays. Companies are laying employees off ($) left right and centre (in fact, this (paywalled) article prompted my post – I’m hoping to find good value in the layoff market). Interest rates are going up. Stock prices are going down. Startup funding has slowed. The job market is not easy. And so you see an innocuous post like this getting such a massive reaction.

In any case, the reason I was thinking about “proof of work” is that the responses to my post reminded me of my own (unsuccessful) job hunts from a few years back. I remember randomly applying through LinkedIn. I remember using easy apply. And I remember pretty much not hearing back from anyone.

Time for a bollywood break:

Yes, the choice of where I’ve started this video is deliberate. As i was spending time this morning refusing all the LinkedIn connection requests (some 500+ people I have no clue about had simply added me without any matter of introduction or purpose), I was thinking of this song.

I followed a simple strategy – I engaged with people who had cared to write a note (or InMail) to me along with the connection request, and I just ignored the rest. As I kept hitting “ignore ignore ignore … ” on my phone (while sipping coffee with the other hand), I realised that I almost hit “ignore” on one of my company HRs who had added me. A few minutes later, I actually hit ignore on a colleague who I’ve actually worked with (I made amends by sending him back a connection request that he accepted).

Given the flood of requests that I had got, I was forced to use a broad brush. I was forced to use simple heuristics rather than evaluating each application on its true merit. I’m pretty sure I’ve made plenty of errors of omission today (that said, my heuristic has thrown up a bunch of fairly promising candidates).

In any case, if you think about it, the heuristic I used can pretty well be described as “proof of work”. And what the proof of work achieved here was to help people stand out in a crowded market. That there was some work showed a certain minimum threshold of interest, and that was sufficient to get my attention, which is all that mattered here. And on a related note, during normal times (when I get a maximum of one or two LinkedIn requests each day), I do take the effort to evaluate each request on its own merit. No proof of work is necessary.

And if you think about it, “proof of work” is rather prevalent in the natural world. A peacock’s feathers are the most commonly quoted example of this one. The beautiful tail comes at a huge cost in terms of agility and ability to fly, and the tail is a way for the peacock to show off to potential mates that “I can carry this thing and yet stay alive so imagine how fit my genes are. Mate with me”.

Anyway, back to the hiring market, you need a way to stand out. Maybe a nicely written cover letter. Maybe a referral (or “influence” as we used to pejoratively call this back in the 90s). Maybe a strong github profile. (Ok the last one is literally a proof of work!)

Else you will just get swept away with the tide.

 

PS: In general, I was also thinking of the wisdom of writing to someone at a time when you know he/she will be flooded with other messages. The bar for you to stand out is much much higher. Being contrarian helps i guess.

So many numbers! Must be very complicated!

The story dates back to 2007. Fully retrofitting, I was in what can be described as my first ever “data science job”. After having struggled for several months to string together a forecasting model in Java (the bugs kept multiplying and cascading), I’d given up and gone back to the familiarity of MS Excel and VBA (remember that this was just about a year after I’d finished my MBA).

My seat in the office was near a door that led to the balcony, where smokers would gather. People walking to the balcony, with some effort, could see my screen. No doubt most of them would’ve seen my spending 90% (or more) of my time on Google Talk (it’s ironical that I now largely use Google Chat for work). If someone came at an auspicious time, though, they would see me really working, which was using MS Excel.

I distinctly remember this one time this guy who shared my office cab walked up behind me. I had a full sheet of Excel data and was trying to make sense of it. He took one look at my screen and exclaimed, “oh, so many numbers! Must be very complicated!” (FWIW, he was a software engineer). I gave him a fairly dirty look, wondering what was complicated about a fairly simple dataset on Excel. He moved on, to the balcony. I moved on, with my analysis.

It is funny that, fifteen years down the line, I have built my career in data science. Yet, I just can’t make sense of large sets of numbers. If someone sends me a sheet full of numbers I can’t make out the head or tail of it. Maybe I’m a victim of my own obsessions, where I spend hours visualising data so I can make some sense of it – I just can’t understand matrices of numbers thrown together.

At the very least, I need the numbers formatted well (in an Excel context, using either the “,” or “%” formats), with all numbers in a single column right aligned and rounded off to the exact same number of decimal places (it annoys me that by default, Excel autocorrects “84.0” (for example) to “84” – that disturbs this formatting. Applying “,” fixes it, though). Sometimes I demand that conditional formatting be applied on the numbers, so I know which numbers stand out (again I have a strong preference for red-white-green (or green-white-red, depending upon whether the quantity is “good” or “bad”) formatting). I might even demand sparklines.

But send me a sheet full of numbers and without any of the above mentioned decorations, and I’m completely unable to make any sense or draw any insight out of it. I fully empathise now, with the guy who said “oh, so many numbers! must be very complicated!”

And I’m supposed to be a data scientist. In any case, I’d written a long time back about why data scientists ought to be good at Excel.

Recruitment and diversity

This post has potential to become controversial and is related to my work, so I need to explicitly state upfront that all opinions here are absolutely my own and do not, in any way, reflect those of my employers or colleagues or anyone else I’m associated with.

I run a rather diverse team. Until my team grew inorganically two months back (I was given more responsibility), there were eight of us in the team. Each of us have masters degrees (ok we’re not diverse in that respect). Sixteen degrees / diplomas in total. And from sixteen different colleges / universities. The team’s masters degrees are in at least four disjoint disciplines.

I have built this part of my team ground up. And have made absolutely made no attempt to explicitly foster diversity in my team. Yet, I have a rather diverse team. You might think it is on accident. You might find weird axes on which the team is not diverse at all (masters degrees is one). I simply think it is because there was no other way.

I like to think that I have fairly high standards when it comes to hiring. Based on the post-interview conversations I have had with my team members, these standards have percolated to them as well. This means we have a rather tough task hiring. This means very few people even qualify to be hired by my team. Earlier this year I asked for a bigger hiring budget. “Let’s see if you can exhaust what you’ve been given, and then we can talk”, I was told. The person who told me this was not being sarcastic – he was simply aware of my demand-supply imbalance.

Essentially, in terms of hiring I face such a steep demand-supply imbalance that even if I wanted to, it would be absolutely impossible for me to discriminate while hiring, either positively or negatively.

If I want to hire less of a certain kind of profile (whatever that profile is), I would simply be letting go of qualified candidates. Given how long it takes to find each candidate in general, imagine how much longer it would take to find candidates if I were to only look at a subset of applicants (to prefer a category I want more of in my team). Any kind of discrimination (apart from things critical to the job such as knowledge of mathematics and logic and probability and statistics, and communication) would simply mean I’m shooting myself in the foot.

Not all jobs, however, are like this. In fact, a large majority of jobs in the world are of the type where you don’t need a particularly rare combination of skills. This means potential supply (assuming you are paying decently, treating employees decently, etc.) far exceeds demand.

When you’re operating in this kind of a market, cost of discrimination (either positive or negative) is rather low. If you were to rank all potential candidates, picking up number 25 instead of number 20 is not going to leave you all that worse off. And so you can start discriminating on axes that are orthogonal to what is required to do the job. And that way you can work towards a particular set of “diversity (or lack of it) targets”.

Given that a large number of jobs (not weighted by pay) belong to this category, the general discourse is that if you don’t have a diverse team it is because you are discriminating in a particular manner. What people don’t realise is that it is pretty impossible do discriminate in some cases.

All that said, I still stand by my 2015 post on “axes on diversity“. Any externally visible axis of diversity – race / colour / gender / sex / sexuality – is likely to diminish diversity in thought. And – again this is my personal opinion – I value diversity in thought and approach much more than the visible sources of diversity.

 

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?

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.

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.