I’m writing this five minutes after making my wife’s “coffee decoction” using the Bialetti Moka pot. I don’t like chicory coffee early in the morning, and I’m trying to not have coffee soon after I wake up, so I haven’t made mine yet.
While I was filling the coffee into the Moka Pot, I was thinking of the concept of channelling. Basically, if you try to pack the moka pot too tight with coffee powder, then the steam (that goes through the beans, thus extracting the caffeine) takes the easy way out – it tries to create a coffee-less channel to pass through, rather than do the hard work of extracting coffee as it passes through the layer of coffee.
I’m talking about steam here – water vapour, to be precise. It is as lifeless as it could get. It is the gaseous form of a colourless odourless shapeless liquid. Yet, it shows the seeming “intelligence” of taking the easy way out. Fundamentally this is just physics.
This is not an isolated case. Last week, at work, I was wondering why some algorithm was returning a “negative cost” (I’m using local search for that, and after a few iterations, I found that the algorithm is rapidly taking the cost – which is supposed to be strictly positive – into deep negative territory). Upon careful investigation (thankfully it didn’t take too long), it transpired that there was a penalty cost which increased non-linearly with some parameter. And the algo had “figured” that if this parameter went really high, the penalty cost would go negative (basically I hadn’t done a good job of defining the penalty well). And so would take this channel.
Again, this algorithm has none of the supposedly scary “AI” or “ML” in it. It is a good old rule-based system, where I’ve defined all the parameters and only the hard work of finding the optimal solution is left to the algo. And yet, it “channelled”.
Basically, you don’t need to have got a good reason for taking the easy way out now. It is not even human, or “animal” to do that – it is simply a physical fact. When there exists an easier path, you simply take that – whether you are an “AI” or an algorithm or just steam!
I’ll leave you with this algo that decided to recognise sheep by looking for meadows (this is rather old stuff).
When I’m visiting someone’s house and they have an accessible bookshelf, one of the things I do is to go check out the books they have. There is no particular motivation, but it’s just become a habit. Sometimes it serves as conversation starters (or digressors). Sometimes it helps me understand them better. Most of the time it’s just entertaining.
So at a friend’s party last night, I found this book on Graph Theory. I just asked my hosts whose book it was, got the answer and put it back.
As many of you know, whenever we host a party, we use graph theory to prepare the guest list. My learning from last night’s party, though, is that you should not only use graph theory to decide WHO to invite, but also to adjust the times you tell people so that the party has the best outcome possible for most people.
With the full benefit of hindsight, the social network at last night’s party looked approximately like this. Rather, this is my interpretation of the social network based on my knowledge of people’s affiliation networks.
This is approximate, and I’ve collapsed each family to one dot. Basically it was one very large clique, and two or three other families (I told you this was approximate) who were largely only known to the hosts. We were one of the families that were not part of the large clique.
This was not the first such party I was attending, btw. I remember this other party from 2018 or so which was almost identical in terms of the social network – one very large clique, and then a handful of families only known to the hosts. In fact, as it happens, the large clique from the 2018 party and from yesterday’s party were from the same affiliation network, but that is only a coincidence.
Thinking about it, we ended up rather enjoying ourselves at last night’s party. I remember getting comfortable fairly quickly, and that mood carrying on through the evening. Conversations were mostly fun, and I found myself connecting adequately with most other guests. There was no need to get drunk. As we drove back peacefully in the night, my wife and daughter echoed my sentiments about the party – they had enjoyed themselves as well.
This was in marked contrast with the 2018 party with the largely similar social network structure (and dominant affiliation network). There we had found ourselves rather disconnected, unable to make conversation with anyone. Again, all three of us had felt similarly. So what was different yesterday compared to the 2018 party?
I think it had to do with the order of arrival. Yesterday, we were the second family to arrive at the party, and from a strict affiliation group perspective, the family that had preceded us at the party wasn’t part of the large clique affiliation network (though they knew most of the clique from beforehand). In that sense, we started the party on an equal footing – us, the hosts and this other family, with no subgroup dominating.
The conversation had already started flowing among the adults (the kids were in a separate room) when the next set of guests (some of them from the large clique arrived), and the assimilation was seamless. Soon everyone else arrived as well.
The point I’m trying to make here is that because the non-large-clique guests had arrived first, they had had a chance to settle into the party before the clique came in. This meant that they (non-clique) had had a chance to settle down without letting the party get too cliquey. That worked out brilliantly.
In contrast, in the 2018 party, we had ended up going rather late which meant that the clique was already in action, and a lot of the conversation had been clique-specific. This meant that we had struggled to fit in and never really settled, and just went through the motions and returned.
I’m reminded of another party WE had hosted back in 2012, where there was a large clique and a small clique. The small clique had arrived first, and by the theory in this post, should have assimilated well into the party. However, as the large clique came in, the small clique had sort of ended up withdrawing into itself, and I remember having had to make an effort to balance the conversation between all guests, and it not being particularly stress-free for me.
The difference there was that there were TWO cliques with me as cut-vertex. Yesterday, if you took out the hosts (cut-vertex), you would largely have one large clique and a few isolated nodes. And the isolated nodes coming in first meant they assimilated both with one another and with the party overall, and the party went well!
And now that I’ve figured out this principle, I might break my head further at the next party I host – in terms of what time I tell to different guests!
Last evening I went for drinks with a few colleagues. We didn’t think or do much in terms of where to go – we just minimised transaction costs by going to the microbrewery on the top floor of our office building. This meant that after the session those of us who were able (and willing) to drive back could just go down to the basement and drive back. No “intermediate driving”.
Of course, if you want to drive back after you’ve gone for drinks, it means that you need to keep your alcohol consumption in check. And when you know you are going for a longish session, that is tricky. And that’s where the quality of beer maters.
In a place like Arbor, which makes absolutely excellent beer, “one beer” is a hard thing to pull off (though I exercised great willpower in doing just that the last time I’d gone for drinks with colleagues – back in feb). And after a few recent experiences, I’ve concluded that beer is the best “networking drink” – it offers the optimal amount of “alcohol per unit time” (wine and whisky I tend to consume well-at-a-faster-rate, and end up getting too drunk too quickly). So if you go to a place that serves bad beer, that isn’t great either.
This is where the quality of beer at a middling (for a Bangalore microbrewery) place like Bangalore Brewworks works perfectly – it’s decent enough that you are able to drink it (and not something that delivers more ethanol per unit time), but also not so good that you gulp it down (like I do with the Beach Shack at Arbor).
And this means that you can get through a large part of the session (where the counterparties down several drinks) on your one beer – you stay within reasonable alcohol limits and are not buzzed at all and easily able to drive. Then you down a few glasses of iced water and you’re good to go!
Then again, when I think about it, nowadays I go out for drinks so seldom that maybe this strategy is not so optimal at all – next time I might as well go to Arbor and take a taxi home.
Over a decade ago I had written about two kinds of employees – those who offer “competitive advantage” and those who offer “comparative advantage”.
Quoting myself:
So in a “comparative advantage” job, you keep the job only because you make it easier for one or more colleagues to do more. You are clearly inferior to these colleagues in all the “components” of your job, but you don’t get fired only because you increase their productivity. You become the Friday to their Crusoe.
On the other hand, you can keep a job for “competitive advantage“. You are paid because there are one or more skills that the job demands in which you are better than your colleagues
Now, one issue with “comparative advantage” jobs is that sometimes it can lead to people being played out of position. And that can reduce the overall productivity of the team, especially when priorities change.
Let’s say you have 2 employees A and B, and 2 high-priority tasks X and Y. A dominates B – she is better and faster than B in both X and Y. In fact, B cannot do X at all, and is inferior to A when it comes to Y. Given these tasks and employees, the theory of comparative advantage says that A should do X and B should do Y. And that’s how you split it.
In this real world problem though, there can be a few issues – A might be better at X than B, but she just doesn’t want to do X. Secondly, by putting the slower B on Y, there is a floor on how soon Y can be delivered.
And if for some reason Y becomes high priority for the team, with the current work allocation there is no option than to just wait for B to finish Y, or get A to work on Y as well (thus leaving X in the lurch, and the otherwise good A unhappy). A sort of no win situation.
The whole team ends up depending on the otherwise weak B, a sort of version of this:
A corollary is that if you have been given what seems like a major responsibility it need not be because you are good at the task you’ve been given responsibility for. It could also be because you are “less worse” than your colleagues at this particular thing than you are at other things.
At a party we hosted recently, we ended up talking a lot about lifting heavy weights in the gym. In the middle of the conversation, my wife wondered loudly as to why “so many intelligent people are into weightlifting nowadays”. A few theories got postulated in the following few minutes but I’m not going to talk about that here.
Anecdotally, this is true. The two people I hold responsible for getting me lift heavy weights are both people I consider rather intelligent. I discuss weights and lifting with quite a few other friends as well. Nassim Taleb, for a long time, kept tweeting about deadlifts, though now he has dialled back on strength training.
In 2012 or 2013 I had written about how hard it was to maintain a good diet and exercise regime. While I had stopped being really fat in 2009, my weight had started creeping up again and my triglyceride numbers hadn’t been good. I had found it hard to stick to a diet, and found the gym rather boring.
In response, one old friend (one of the intelligent people I mentioned above) sent me Mark Rippetoe’s Starting Strength (and a few other articles on cutting carbs, and high-fat diets). Starting Strength, in a way, brought back geekery into the gym, which had until then been taken over by “gym bros” doing bicep curls and staring into mirrors.
It’s been a long time since I read it, but it’s fascinating – I remember reading it and thinking it reminded me of IIT-JEE physics. He draws free body diagrams to explain why you should maintain a straight bar path. He talks about “moment arms” to explain why the bar should be over your mid-foot while deadlifting (ok this book we did discuss at the party in response to my wife’s question).
However, two incidents that happened last week gave me an idea on why “intelligent people” are drawn to lifting heavy barbells. It’s about challenging yourself to the right extent.
The gym that I go to (a rather kickass gym) has regular classes that most members attend. These classes focus on functional fitness (among other things, everyone is made to squat and press and deadlift), but I’ve for long found that these classes bore me so I just do my own thing (squats, press / bench and deadlift, on most days). Occasionally, though, like last Friday, I decide to “do the class”. And on these occasions, I remember why I don’t like the class.
The problem with the gym class is that I get bored. Most of the time, the exercises you are doing are of the sort where you lift well below capacity on each lift, but you do a lot of lifts. They train you not just for strength but also for endurance and metabolic conditioning. The problem with that for me is that because every single repetition is not challenging, I get bored. “Why do i need to do so much”, I think. Last Friday I exited the class midway, bored.
My daughter is having school holidays, and one of the things we have figured is that while she has grasped all her maths concepts rather soundly (the montessori system does a good job of that), she has completely failed to mug her tables. If I ask her what is “7 times 4” (for example), she takes half a minute, adds 7 four times and tells me.
Last Monday, I printed out (using Excel) all combinations of single digit multiplications and told her she “better mug it by Friday”. She completely refused to do it. There was no headway in her “learning”. I resorted to occasionally asking her simple arithmetic questions and making her answer immediately. While waiting to cross the road while on a walk, “what is six times eight?”. While waiting for the baker to give us bread “you gave him ?100 and the bread costs ?40. How much change should he give you?”. And so on.
She would occasionally answer but again her boredom was inherent. The concept learning had been challenging for her and she had learnt it. But this “repetitive practice” was boring and she would refuse to do it.
Then, last Friday, I decided to take it up a notch. I suddenly asked “what is four and a half times eight?” (she’s done fractions in school). This was a gamechanger.
Suddenly, by dialling up the challenge, she got interested, and with some prodding gave me the correct answer. An hour earlier, she had struggled for a minute to tell me what 8 times 7 is. However, when I asked her “what is eight times seven and a half?” she responded in a few seconds, “eight times seven is fifty six..” (and then proceeded to complete the solution).
Having exited my gym class midway just that morning, I was now able to make sense of everything. Practicing simple arithmetic for her is like light weight lifting for me. “Each rep” is not challenging in either case, and so we get bored and don’t want to do it. Dial up the challenge a little bit, such as bringing in fractions or making the weights very heavy, and now every rep is a challenge. The whole thing becomes more fun.
And if you are of the type that easily gets bored and wants to do things where each unit is challenging, barbell training is an obvious way to exercise. and “intelligent” people are more likely to get bored of routine stuff. And so they are taking to lifting heavy weights.
Back in 2000, I entered the Computer Science undergrad program at IIT Madras thinking I was a fairly competent coder. In my high school, I had a pretty good reputation in terms of my programming skills and had built a whole bunch of games.
By the time half the course was done I had completely fallen out of love with programming, deciding a career in Computer Science was not for me. I even ignored Kama (current diro)’s advice and went on to do an MBA.
He had given me a 3 hour lecture about how I'm wasting my life when I happened to tell him that I was preparing for CAT. https://t.co/1rWG0BnymW
What had happened? Basically it was a sudden increase in the steepness of the learning curve. Or that I’m a massive sucker for user experience, which the Computer Science program didn’t care for.
Back in school, my IDE of choice (rather the only one available) was TurboC, a DOS-based program. You would write your code, and then hit Ctrl+F9 to run the program. And it would just run. I didn’t have to deal with any technical issues. Looking back, we had built some fairly complex programs just using TurboC.
And then I went to IIT and found that there was no TurboC, no DOS. Most computers there had an ancient version of Unix (or worse, Solaris). These didn’t come with nice IDEs such as TurboC. Instead, you had to use vi (some of the computers were so old they didn’t even have vim) to write the code, and then compile it from outside.
Difficulties in coming to terms with vi meant that my speed of typing dropped. I couldn’t “code at the speed of thought” any more. This was the first give up moment.
Then, I discovered that C++ had now got this new set of “standard template libraries” (STL) with vectors and stuff. This was very alien to the way I had learnt C++ in school. Also I found that some of my classmates were very proficient with this, and I just couldn’t keep up with this. The effort seemed too much (and the general workload of the program was so high that I couldn’t get much time for “learning by myself”), so I gave up once again.
Next, I figured that a lot of my professors were suckers for graphic UIs (though they denied us good UX by denying us good computers). This, circa 2001-2, meant programming in Java and writing applets. It was a massive degree of complexity (and “boringness”) compared to the crisp C/C++ code I was used to writing. I gave up yet again.
I wasn’t done with giving up yet. Beyond all of this, there was “systems programming”. You had to write some network layers and stuff. You had to go deep into the workings of the computer system to get your code to run. This came rather intuitively to most of my engineering-minded classmates. It didn’t to me (programming in C was the “deepest” I could grok). And I gave up even more.
A decade after I graduated from IIT Madras, I visited IIM Calcutta to deliver a lecture. And got educated.
I did my B.Tech. project in “theoretical computer science”, managed to graduate and went on to do an MBA. Just before my MBA, I was helping my father with some work, and he figured I sucked at Excel. “What is the use of completing a B.Tech. in computer science if you can’t even do simple Excel stuff?”, he thundered.
In IIMB, all of us bought computers with pirated Windows and Office. I started using Excel. It was an absolute joy. It was a decade before I started using Apple products, but the UX of Windows was such a massive upgrade compared to what I’d seen in my more technical life.
In my first job (where I didn’t last long) I learnt the absolute joy of Visual Basic macros for Excel. This was another level of unlock. I did some insane gymnastics in that. I pissed off a lot of people in my second job by replicating what they thought was a complex model on an Excel sheet. In my third job, I replaced a guy on my team with an Excel macro. My programming mojo was back.
Goldman Sachs’s SLANG was even better. By the time I left from there, I had learnt R as well. And then I became a “data scientist”. People asked me to use Python. I struggled with it. After the user experience of R, this was too complex. This brought back bad memories of all the systems programming and dealing with bad UX I had encountered in my undergrad. This time I was in control (I was a freelancer) so I didn’t need to give up – I was able to get all my work done in R.
The second giving up
I’ve happily used R for most of my data work in the last decade. Recently at work I started using Databricks (still write my code in R there, using sparklyr), and I’m quite liking that as well. However, in the last 3-4 months there has been a lot of developments in “AI”, which I’ve wanted to explore.
The unfortunate thing is that most of this is available only in Python. And the bad UX problem is back again.
Initially I got excited, and managed to install Stable Diffusion on my personal Mac. I started writing some OpenAI code as well (largely using R). I started tracking developments in artificial intelligence, and trying some of them out.
And now, in the last 2-3 weeks, I’ve been struggling with “virtual environments”. Each newfangled open-source AI that is released comes with its own codebase and package requirements. They are all mutually incompatible. You install one package, and you break another package.
The “solution” to this, from what I could gather, is to use virtual environments – basically a sandbox for each of these things that I’ve downloaded. That, I find, is grossly inadequate. One of the points of using open source software is to experiment with connecting up two or more of them. And if each needs to be in its own sandbox, how is one supposed to do this? And how are all other data scientists and software engineers okay with this?
This whole virtual environment mess means that I’m giving up on programming once again. I won’t fully give up – I’ll continue to use R for most of my data work (including my job), but I’m on the verge of giving up in terms of these “complex AI”.
It’s the UX thing all over again. I simply can’t handle bad UX. Maybe it’s my ADHD. But when something is hard to use, I simply don’t want to use it.
And so I’m giving up once again. Tagore was right.
Back when I was a student, there was this (rather large) species of students who we used to call “muggoos”. They were called that because they would have a habit of “mugging up the answers” – basically they would learn verbatim stuff in the textbooks and other reading material, and then just spit it out during the exams.
They were incredibly hardworking, of course – since the volume of stuff to mug was immense – and they would make up for their general lack of understanding of the concepts with their massive memories and rote learning.
On average, they did rather well – with all that mugging, the downside was floored. However, they would stumble badly in case of any “open book exams” (where we would be allowed to carry textbooks into the exams) – since the value of mugging there was severely limited. I remember having an argument once with some topper-type muggoos (with generally much better grades than me ) on whether to keep exams in a particular course open book or closed book. They all wanted closed book of course.
This morning, I happened to remember this species while chatting with a friend. He was sending me some screenshots from ChatGPT and was marvelling at something which it supposedly made up (I remembered it as a popular meme from 4-5 years back). I immediately responded that ChatGPT was simply “overfitting” in this case.
Since this was a rather popular online meme, and a lot of tweets would have been part of ChatGPT’s training data, coming up with this “meme-y joke” was basically the algorithm remembering this exact pattern that occurred multiple times in the training set. There was no need to intuit or interpolate or hallucinate – the number of occurrences in the training set meant this was an “obvious joke”.
In that sense, muggoos are like badly trained pieces of artificial intelligence (well, I might argue that their intelligence IS artificial) – they haven’t learnt the concepts, so they are unable to be creative or hallucinate. However, they have been “trained” very very well on the stuff that is there in the textbooks (and other reading material) – and the moment they see part of that it’s easy for them to “complete the sentences”. So when questions in the exams come straight out of the reading materials (as they do in a LOT of indian universities and school boards) they find it easy to answer.
However, when tested on “concepts”, they now need to intuit – and infer based on their understanding. In that sense, they are like badly trained machine learning models.
One of the biggest pitfalls in machine learning is “overfitting” – where you build a model that is so optimised to the training data that it learns quirks of the data that you don’t want it to learn. It performs superbly on the training dataset. Now, when faced with an unknown (“out of syllabus”) test set, it underperforms like crazy. In machine learning, we use techniques such as cross validation to make sure algorithms don’t overfit.
That, however, is not how the conventional Indian education system trains you – throughout most of the education, you find that the “test set” is a subset of the “training set” (questions in examinations come straight out of the textbook). Consequently, people with the ability to mug find that it is a winning strategy to just “overfit” and learn the textbooks verbatim – the likelihood of being caught out by unseen test data is minimal.
And then IF they get out into the real world, they find that a lot of the “test data” is unknown, and having not learnt to truly learn from the data, they struggle.
This morning, I felt like I was in business school all over again.
So the Montessori school that my daughter goes to is exploring the possibility of introducing an adolescent (12-18 age group) program that follows the Montessori philosophy. Towards this end, they are having a series of “seminars” with parents to explain the methodology and collect feedback.
Before the first such “seminar” two months ago, they had sent us all a paper written by Dr. Maria Montessori and asked us to read it in preparation. When we walked in to school, we were all given copies of the same paper and asked to read it before the discussions started. The teachers walked in after having given all of us to read through the paper once again. “This sounds like Amazon”, I had thought.
To give parents full flavour of the proposed program, we were told that these sessions mirror what the adolescent version of the school is supposed to be like. Each session involves discussion of a piece of written text. All participants are supposed to have read it beforehand. And discussions have to be on point to the reading – like every note of participation has to refer to a particular page and paragraph. I had come away from the first session thinking “these guys seem to be trying to recreate business school in high school”.
And then, this morning, at the second such session, I got a taste of this medicine as well. I’ve had two insanely productive days at work last two days, which has meant that evenings I’ve been rather tired and unable to really read the paper (once again it was a paper by Dr. Montessori). This morning, I woke up late and by the time I got to school for the session (that began at 8am), I’d barely managed to glance through the paper.
I furiously tried to read it before the teachers came in, and barely managed a fourth. The teachers reminded us of the rules – all discussing points had to refer to specific parts of the paper, and we couldn’t talk “generally” (ruling out any “arbit class participation”). Also, the teachers would not “lead” the discussion – the format of the class was such that it was peer discussion.
I’m speculating here, but it is possible that many other parents this morning were also in my state – having turned up to class having not read the prescribed reading. Initially the CP was slow and deliberate. That we had to reply to each other (and keep referring to the text as we did so) made it slower. There were a few awkward pauses which I tried to use to hurriedly read the rest of the paper. I was also getting distracted, planning this blogpost in my head. I was also simultaneously feeling horrible about not having come to the session prepared, and was thinking I’m a horrible parent.
The format of the discussion helped, though, as different people kept referring to different sections of the paper, and I sort of read through it in a non-linear fashion. In about ten minutes, in the course of the discussion I had probably read through the entire text. And then I started unleashing.
All those business school skills came of good use – despite the constrained format, I somehow winged through today’s session (not that that was the intended consequence). By the end of the session I had comfortably spoken the most in the group. Old habits die hard, I guess.
It weirdly felt like I was business school once again. And as it happened, I noticed that the person next to me was wearing an IIMB T-shirt (though he didn’t put too much CP)!
On a more serious note, maybe this kind of a schooling format in high school might mean that the children may not really need to go to college!
Back in 2011-12, when I was about to go freelance, a friend told me about a simple formula on how I should price my services. “Take your expected annual income and divide it by 1000. That will be your hourly rate”, he said. I followed this policy fairly well, with reasonable success (though I think I shortchanged myself in some situations by massively underestimating how long a task would take – but that story is for another day).
The longer term effect of that has been that every time I see someone’s hourly rate, I multiply it by 1000 to guess that person’s approximate annual income (the basis being that as a full time worker, you “bill” for 2000 hours a year. As a freelancer you have “50% utilisation” and so you work 1000 hours).
And one set of people who have fairly transparent hourly rates are doctors – you know the number of appointments they give per hour, and what you paid for that, and you can back calculate their annual income based on that. The interesting thing is, for most doctors I’ve seen, based on this metric, what they earn for their level of eduction and years of experience seems rather low.
“So how do doctors earn?”, I wonder. Why is it still a prized profession while you might have a much better life being an engineer, for example?
Now you should remember that consultations are only one income stream for doctors. Those that practice surgery as well have a more lucrative stream – the hourly rates for surgeries far exceeds hourly rates of consultation. And so surgeons make far more than what I impute from what I’ve paid them for a consultation.
One possible reason for this arbitrage is the way insurance deals are structured – at least in India, out patient care is seldom paid for by insurance. As a consequence, hospitals and doctors cross-subsidise consultations with surgeries. They are able to get away with higher rates for surgeries because insurers are bearing the cost. Consultations, where patients generally pay out of their own pockets, are far more elastic.
This, however, leads to a problem for doctors who don’t do surgeries. Psychiatrists, for example. If they have to make money solely through consultations, their hourly rate must be far higher than that of doctors who also do surgeries. But then, is the market willing to bear this cost?
Now, I’m getting into conspiracy theory mode. If the amount non-surgeon doctors make is limited (thanks to market dynamics), the only way they can make sure they earn a decent living is by limiting supply. Could this be one reason India is under-supplied in a lot of non-surgical doctors? Again this is pure pure speculation, and not based in any fact.
Continuing with conspiracy theories, even for doctors who are surgeons, the only way to make a certain income is to have a threshold on the ratio of surgeries to consultations. And if this ratio (surgeries / consultations) goes too low, the doctors’ income suffers. Again, hippocratic oath aside, do hospitals try to game this metric, based on the current incentives?
On a more serious note, this distortion in the hourly earnings for surgeries versus consultations is one reason that India is also undersupplied with good general practitioners (GPs). Because GPs don’t do surgeries (though the Indian system means they are all licensed to perform surgeries, to the best of my knowledge), their earning potential is naturally capped. So the better doctors don’t want to be GPs.
How can we fix this distortion? How can we make sure we have better GPs? Insurance cover for outpatient care is one thing, but I’m not sure it is the silver bullet I’ve been making it out to be (and it will come with its own set of market distortions).
This entire post is me shooting from my hip. So please feel free to correct me iff I’m wrong.
This is NOT a blogpost about cash crops in the West Indies. This is more about biology.
I had my first cigarette when I was 21. I was about to graduate from my undergrad, and had decided to “experiment” a bit. Friends who were already smokers warned me that the thing is addictive, and that I need to be careful.
I still remember that cigarette, a Wills Classic Milds shared with a classmate who was a very occasional smoker. I remember feeling high, and weak in the knees in a way I had never felt before (I was yet to taste alcohol, but when I did a couple of months later, it was underwhelming compared to tobacco). It was extremely pleasurable, but I remembered what my smoker friends had told me. It was addictive shit.
That day I made a decision that I’ll smoke a maximum of one cigarette per calendar year, something I’ve lived up to. It’s never been more than one, though in some years (especially in the early years), the 1 was made up of several fractions.
The thing with tobacco is that it is addictive. The high is incredibly high (for a non-smoker like me), but when that passes you have withdrawal symptoms. And you crave for more. If you don’t have friends like me who have warned you about the addictive nature of it, you can get addicted (alcohol doesn’t react that way – beyond a few drinks you don’t want to drink more. And I don’t have annual limits on alcohol consumption).
A few prescription drugs act the same way – most notably (in my experience) antidepressants. They are biologically addictive and when you stop having them, the body starts having strong withdrawal symptoms. So you need to be careful in terms of getting on to antidepressants because getting off them is not easy.
Caffeine is the same as well – and I continue to be addicted to it. Two days without coffee and I get the same kind of withdrawal symptoms I had the last time I was getting off antidepressants.
And thinking about it, it’s the same with sugar (or any other high carb foods). When you consume too much sugar (or carbs), the body needs to produce a lot of insulin to be able to deal with it. The insulin thus produced is like a demon / genie (based on the sort of myths you favour) – once it has devoured the excess sugar, it devours the “regular blood sugar” as well, leading to a massive sugar crash.
It was possibly my psychiatrist who pointed this out in a consultation a few months back (and so I officially have a medical prescription that says “follow a low carb diet”) – that these sugar crashes are what lead to bouts of low mood and depression, and that the way to keep my mood good is to not have sugar crashes, which means not eating much sugar.
Similarly, she told me that the reason I sometimes wake up in the middle of the night ravenously hungry and unable to sleep back is likely due to a sugar crash. And so I need to have a low-carb dinner. I found this the hard way last night when I had noodles for dinner (my blood sugar levels are especially sensitive to pulverised grains (including the supposedly “healthy” ones like ragi, jowar, etc.) – whole rice is fine for me, but not rice flour), and found myself awake at 4 am and unable to sleep. As it happened, I resisted the temptation to eat then and slowly fell back asleep at 6 (luckily today was not a “gym day”).
As if this morning’s sugar crash wasn’t enough, after lunch today I ate some sweets that a colleague had got to office. Sugar crash duly happened an hour later, and how did I react? By reaching for the same sweets. Yet another crash happened as I got home – and I reached for some sweets my wife had got today. I’m writing this awaiting another sugar crash.
Thanks to the functioning of insulin, sugar can behave like tobacco. You eat and feel good, and then the crash happens. And you eat more. Spike again, crash again. And so on and so forth.
When I examine my own periods of putting on weight or becoming mildly depressed (now that I think of it, they are correlated), it’s because I get into this eating cycle. Eating more carbs means I get more hungry. And I eat more. Which makes my hungrier. And that goes on.
The only way is to wilfully break the chain – by skipping meals or having very low-carb meals. Once you’ve done this for a considerable period of time (I managed this easily between last Thursday and last evening), your body feels less hungrier, and you get on to a sort of virtuous cycle. And you progressively get better.
And then it takes one noodles meal or a sweet offer to get back into the vicious cycle. Some people have famously “quit smoking hundreds of times”. I’ve also “gone on a low cab diet hundreds of times”.
