If your boss obsessively asks for data in everything, they’re not alone. Most editors now want articles and books to be sprinkled with numbers, consultants feel smart by doing the same in slide decks, and researchers use data to show they have done their homework.

Sadly, the reckless sprinkling of data is now a big threat to the very integrity that numbers (seem to) stand for.

AI tools, ironically, are just terrible at interpreting such nuggets of data, and our armchair economy of consultants, commentators and policy junkies are staring at a real crisis.

Let’s start with an example. My partner recently saw a claim that India has 600 million people aged 18-35. The place that said so didn’t name a source, so we set off on a journey to find it. We found three reputed consulting agencies and a Union minister using the number in the past three years—none with a source. An op-ed on a top news website mentioned the number in 2023, again without a source. Since we couldn’t find any source predating this article, it’s likely to be the origin of the 600 million figure.

The only two reliable sources for this—the United Nations and India’s official Census projections—fall between 450 million and 500 million. So neither does 600 million cut any ice, nor can it be traced to a clear source.

I once fell for one such claim. In a story I wrote in 2020, I relied on a widely quoted “ideal doctors-to-population” ratio, which the World Health Organization supposedly says should be at least 1:1,000. A misattribution by none other than the Indian government has lent credence to this ratio. The WHO gives no such advice, and The Hindu’s Devyanshi Bihani recently debunked it. Embarrassed now, I recall trying hard to find a primary source back then, but ultimately trusted the government and assumed that the WHO’s page with the “recommendation” had vanished.

So, welcome to the world of awaara numbers: numbers that float around the internet, and silently find their way into your slides, articles, emails, and arguments.

Often awaara numbers serve well to fill a placeholder to please the data-obsessed boss with minimal effort; at other times it makes you sound cool and complete in a social media post. But data deserves more dignity.

Here’s a primer on how to talk about numbers correctly. I describe six types of numbers that we generally encounter in regular research work, and each needs a different approach.

The first type is the only one where you can (somewhat) get away with attribution: data where the source is unique and obvious.

Saying inflation is 6.7% can do without a source because there’s no one but the government that’s in the business of reporting inflation. The same could be said for GDP. A company’s revenue in a year falls in the same category, since the most credible source for it is the company itself. Or the party-wise seat shares in a state election (Election Commission). But the pool of such datasets is tiny, and even in these cases, it’s desirable that you attribute.

There’s a corollary to this. When the data source is obvious and unique, avoid citing a secondary source. For example, you don’t say “SBI’s interest rate for FDs is currently 6-7.5%, as per so-and-so fintech blog”, unless the SBI’s interest rate tables are really hidden from the public and that blog did the hard work of making it available to you. Saying so shows poor knowledge of how sourcing works.

The second type is data where the source is unique, but not obvious. Some datasets are released only by specific entities, and no one else.

Sometimes, it’s proprietary data, and not identifying the source could even be illegal. Until recently, there was no source for monthly data on unemployment but surveys held by a private agency called the Centre for Monitoring Indian Economy. This data was immensely useful to gauge the impact of Covid-19 lockdowns on the job market. But you couldn’t mention those numbers just like that because: (A) it was proprietary and no one else had this data, and (B) by not naming it, you’d wrongly imply that the data is sourced from the government.

Another example is the UN’s population projections for a future year. No other entity in the world is known credibly for such projections, and in that sense, the UN is a unique source, but not implied. It needs to be attributed. (Note that India does have official projections till 2036, which vary widely from the UN’s. That’s an added reason why such projections should be carefully attributed, to avoid confusion and to ensure that one can verify your data.)

The third type is data for which multiple sources exist (i.e., the source is neither unique nor obvious).

India’s population projections till 2036 mentioned above are one example, as both the UN and the Indian government give them. GDP expressed in dollars is also an example: one could use India’s official data and convert it into dollars, or just take it from the IMF.

Then there are agencies that love to conduct needless surveys to gauge data that’s already known through far more credible sources. The news media is quick to pick such stories. When I was working on my story on how much Indians sleep in a day, I found news reports about multiple private surveys with tiny, unrepresentative samples that tried to find the answer (often bizarre ones as they had bad samples), even though the government already conducts a large-scale, nationally representative survey on time-use (the source for my story).

In such cases, the less credible survey should be ignored—but if you choose to use it, attribution is critical to identify your choice out of the many sources that may exist.

The fourth type is data for which the source is, in a way, obvious but not unique.

India’s per capita consumption is a famous example. It’s kind of obvious that the data would reliably come only from the government. But did you know that this data could be available from two sources from within the same government?

The national accounts data (which gives you the GDP numbers) have a component called “private final consumption expenditure”, and a government survey on household consumption can also be used to get an estimate. (In fact the two have wide gaps.)

The road transport ministry and the National Crime Records Bureau also give different numbers for India’s annual accident statistics, since both rely on different methods of aggregation. (The NCRB comes under the home ministry.)

That’s why attribution is critical.

The fifth type is projections, making which has become quite fashionable.

A whole economy today sustains itself on issuing forecasts for which they can never be held to account. You’ll see bunkum forecasts by research agencies in financial newspapers every day, predicting how much random sectors will be worth by 2030. These forecasts are typically useless, and are best left untouched.

But some forecasts and projections are made with robust assumptions and are genuinely meant to guide policy or decision-making. Any projection is heavily dependent on who made it and using what assumptions. That makes clear attribution critical.

The sixth type is independent (sometimes painstaking) analysis of any of the above types of numbers.

I already gave one example: to put GDP in dollar terms, you lift the government’s rupee estimate, apply the exchange rate from a second source, and make your calculation. The attribution should include the name of the person who did the calculation, even if it’s you (something like “Author’s calculations based on statistics ministry data”).

I’ve earlier described a specific example of independent analysis using raw data of government surveys: the data is from the government, but is not readily available in official tables or reports, and can only be arrived at by accessing the raw data and performing an analysis. When you see any such analysis, do not cite the primary source of the data alone, but also recognise the hard work of the person who processed it.

Another major example is the Association for Democratic Reforms’ mammoth effort to publish details from affidavits filed by election candidates, or historical electoral data put together by the Trivedi Centre for Political Data. The primary source for both is the Election Commission, but the data wouldn’t be possible but for these organisations’ efforts in processing thousands of scanned, often illegible pages, many in unpredictable formats. So just naming the primary source is not enough. Ideally, name both (e.g. “Association for Democratic Reforms’ analysis of candidates affidavits on Election Commission website”).

Bonus: Sometimes we find good data in research compiled by others (think of it as “second-hand data”), and now want to use it “third-hand”. Like the annual Economic Survey, and many other research papers and news reports that cite several other studies or reports to make a fresh point. The good ones cite their primary sources well.

And so when you want to use the same data, don’t just cite your immediate source. Either go to the primary source and cite that, or cite both the primary and secondary source (e.g., “IMF 2025 paper by XYZ, via Economic Survey”), but not just the latter.

This also stands for when, say, someone makes a complete list of Donald Trump’s social media posts in which he used words in all-caps, in which the primary source is, of course, Trump himself, and the list is easily verifiable. But if you’re directly lifting the compilation from a secondary source (such as a news outlet), you should credit it.

So that’s about it. As you can see, adding credits isn’t just good manners used to say thanks or well done. The source is an inalienable part of a data point—part of what gives it dignity and identity.

The next time your boss leaves behind a comment “Fill XXX” in your document, asking you to add a number that doesn’t reliably exist, tell them it’s not so easy to find a number that can be trusted or that is worth the effort.

Or better still, surprise them with a nuanced take—where the number is accompanied by a long boring description of everything that constitutes it: the year it pertains to, the assumptions that went behind it, the exact definition of the term used to describe the metric, and the sources that made it possible.

Data looks trustworthy, and that’s what makes it dangerous. When faced with a number, you must ask the messenger for its source, or trace its origin yourself, or both. When you use it in official work, name that source. We’re better off without any data than with data thrown around just for the sake of it. Avoid shortcuts, and stay away from awaara numbers.

Until next time…

Those who are mildly, or let’s say, cautiously excited about AI cannot by themselves become good users of AI tools. The naysayers push them to be responsible and ethical; the excitable ones help them think deeper about the possibilities that AI offers but they are missing out on.

Based on my learnings from both sides, I’m listing down five key principles that you could consider if you’re thinking about the right way to use AI. The crux of the idea will likely hold true not just for journalism, but for most sectors that involve any research or knowledge production.

First, even if your organisation lacks an AI policy, have one for yourself. Your personal AI playbook should protect you from all risks—from getting research or data wrong (which your employer will also care about) to the possibility of losing your original creative instinct over the long run (which your employer has no stakes in). This will help you decide when to use AI and to what extent, and when not to.

But on the flip side, your policy should actively liberate you to consider using AI to give wings to your work so that you can do and explore things that you were just not able to earlier. I don’t mean using AI just for the bare minimum (e.g., summarising long PDFs or doing preliminary research), but the need to constantly attempt more and more, even if it won’t show in your final output. Learning a tool may not always mean immediate adoption and use. In the case of AI (which involves building a relationship over time, like you build one with a human), it may simply entail prep for the future when the tool will (inevitably) get a million times smarter.

While you must use AI responsibly, not learning and using it actively is actually irresponsible. Your personal rulebook should help you step back and practise reasonable but not compulsive scepticism.

Start by thinking carefully about the raison d’être of your line of work. For a writer, originality of their craft is vital, so they won’t use AI to write. But these limits shouldn’t be based on personal egos or pride: “I won’t use AI to kick off my literature review” makes little sense because starting literature review, even if you’re proud of how good you are at it, isn’t the raison d’être of your work, is it? It’s just a means to an end.

(However, this must be done at an organisational level: a company may be okay with staff writing end-to-end using AI since it values its role as a communicator; another may frown since it sees its role linked to creativity. For some newsrooms, AI may work for formulaic storytelling for hard news, but never for longform, which needs a signature style.)

Second, your AI rulebook should ensure you’re not adopting AI just to protect your job or become more employable. Modern-day maxims like “AI will replace you if you don’t adapt” feed on insecurities, and aren’t a constructive reason to change. If you want to be excellent at what you do, new skills can expand your body of work, by helping you be either more efficient, or more impactful. If you don’t want to be excellent, you are forever at risk anyway, AI or no AI.

Also protect yourself from the other refrain: “AI will disrupt professions like x and y, but it’s just not good enough to ever replace <insert your profession>”. These maxims come from professional egos, not from a place of reason. If a tool hallucinates today, there’s no reason it will still do so five years later.

The goal of any good business and professional has always been to serve their consumers to the best of their capabilities using the most reliable tools and technologies of the times. This involves caution, but it also involves openness to test the limits of those tools and not be at the sidelines.

Third, in the case of journalists, the essential caveat to any use of any tool is always that at the end of it, you must be confident to take full ownership for what you publish in your name, irrespective of how much you used the tool. As simple as that.

All your checks and verifications will naturally follow (e.g. if you ask an AI tool for a CAGR calculation, add steps to either do it yourself manually, or ask the chatbot to show the working and the formula it used, or perform the same task on a second chatbot to verify).

The fact that you used AI cannot be a disclaimer; at best it can be a disclosure.

While using any external information or idea, your policy must force you to trace, and adequately credit, the primary source. That rule stands, AI or no AI. Secondary sources, such as Wikipedia or an AI-generated response, are necessary to give ideas or leads, but verifying against the primary source should be non-negotiable.

Like in the pre-AI world, your processes must put in deep care about how any information was produced at its point of origin: whether decoding the motivations of a human source in sharing leads with a journalist, or knowing how a survey was conducted or funded, or knowing how a tech tool interprets our query and the biases it operates with.

Make space for strong editorial judgement, aggressive cross-questioning and smell tests, reverse-checking techniques, and verification at all stages, to ensure accurate and unbiased facts, ideas, direction of thinking, and conclusions.

Fourth, while framing your personal AI policy, think retrospectively of the policy you subconsciously apply to pre-AI technologies, like, say, performing Google searches or converting images to text. Think of the kind of checks and balances you have in place to ensure you’re on the right track. With this in mind, think of a framework that works not just for the current AI challenge, but one that would have helped you navigate past tech breakthroughs and will likely stand the test of time even if there are new innovations at the workplace that we cannot imagine yet.

AI-agnostic thinking can help you find much-needed balance. It helps the compulsive naysayer realise that tech’s not all that bad and they have themselves grown dependent on other tools with time (would they still go to a library to scan hundreds of books now that they have Google search at their disposal?). An obsessive cheerleader realises that the smartest tools do need human oversight after all, as they have trained themselves over time (would they entirely trust an interview transcript generated by an app?)

Fifth, and last, I suggest you tie your level of AI use to your experience level. Junior professionals should avoid over-relying on tools so that they can build muscle memory, instinct and craft in doing tasks independently. Those skills will come in handy to develop instinct for future success and for leadership roles. Senior resources who have built those skills already must value themselves more and put their knowledge to greater use for the world by saving time from work that can easily be delegated to AI tools.

I hope these five principles are able to answer most doubts about good-quality, rigorous AI use. Do you have an AI-related principle that you follow for yourself that you’d like to share? Please leave a comment or hit reply to this email!

Why does CAGR exist in the first place? Wasn’t it difficult enough already to calculate percentage rises and declines, many journalists would wonder. Just the other day, a newsroom colleague defended a CAGR figure they used in a story by saying they got it from an AI tool. My initial reaction was one of exasperation: a financial journalist needing a Q&A bot to figure CAGR wasn’t exactly ideal. My follow-up reaction was mellower: it was that I needed to write this piece that you’re now reading.

Your annoyance with CAGR may start withering away if you just think of it as an “average” annual growth rate. The adjective “compound” gives us the impression that a choice is being made—like in the case of whether to charge someone simple interest or compound interest. So when your boss tells you to use CAGR, you think you’re being asked to go the complex way when a simpler one exists.

But don’t hate that boss. Wherever CAGR is relevant, it is actually the only thing relevant (i.e., there’s no other kind of average annual growth rate). Let me explain why, and how to recall the verbose formula to calculate CAGR easily.

Let’s start with the basic averages we understand instinctively. Five friends contribute ₹100, ₹120, ₹80, ₹70, ₹130 to buy a goodie worth ₹500. Their average contribution? The sum (₹500) divided by the number of friends (5), i.e. ₹100. What this essentially means, and watch my words, is that if each contribution in the set were to be replaced by the average, the final reality wouldn’t change: ₹100 + ₹100 + ₹100 + ₹100 + ₹100 would also be ₹500.

So an average typically collapses a set of numbers into a single number. If you replace each of the original numbers of the set with this average, the result doesn’t change.

Now let’s apply this idea to growth rates, our topic of interest today. Say the economy grew 4%, 7%, and 10% in successive years. The average, by the above logic, seems to be (4+7+10)%/3, or 7%. Let’s test this out: if the economy’s size in the year of reference was ₹100, then:

Size in year 1 = ₹100 + (4% of ₹100) = ₹104

Size in year 2 = ₹104 (i.e., the new size, that of year 1) + (7% of ₹104) = ₹111.28

Size in year 3 = ₹111.28 + (10% of ₹111.28) = ₹122.41

Now let’s perform the test I described above. Replace each year’s growth rate with the average we just assumed (i.e. 7%), and repeat this exercise:

Size in year 1 = ₹100 + (7% of ₹100) = ₹107

Size in year 2 = ₹107 + (7% of ₹107) = ₹114.49

Size in year 3 = ₹114.49 + (7% of ₹114.49) = ₹122.50

We see that the size in year 3 (and in fact in all years in between) in this “check” is different from the actual ones, once we replace the yearly growth rates with the average growth rate. This means the check has failed; something seems amiss.

What went wrong?

The problem here is that in our example of friends buying a goodie, the numbers added up on top of each other: you simply added each friend’s contribution to pool the full ₹500 budget. But in our example of an economy’s size, each year, the percentage gets applied on the new size of the economy, meaning the annual additions are not identical each year. The two situations are fundamentally different: the first one is additive; the second one has a multiplicative nature to it. So the two need different kinds of averaging.

CAGR is the correct (and the only correct) average for the second situation: if you use the AI tool that my colleague did, you’ll find that in the above example, the CAGR would be 6.972%. If you used that, the replacement technique would work:

Size in year 1 = ₹100 + (6.972% of ₹100) = ₹106.972

Size in year 2 = ₹106.972 + (6.972% of ₹106.972) = ₹114.43

Size in year 3 = ₹114.43 + (6.972% of ₹114.43) = ₹122.41

That’s the number we were looking for, proving that 6.972% is the true average, which satisfies the essential property of being a hypothetical number that can replace each element of a set without any damage.

Since each year, this average growth is compounding (as the percentage is being applied to a bigger number each year), we call it the compound annual growth rate, but as you can see, this is the only average growth rate, so better forget the word “compound” for simplicity.

Where a CAGR is relevant (because of the multiplicative nature of changes), a simple average growth rate (the 7% figure in the above example) doesn’t really satisfy the basic conditions of being an average. So you need to find a special name if you want to use the latter for any reason (out of laziness, or not knowing the CAGR, or to get a rough estimate, or sometimes, poor availability of data), since that’s not the natural method. Let’s call it the “rough average growth rate”! It’s not CAGR that needs an elaborate name and abbreviation!

What about the horrific formula?

Now, the formula using which I came up with the 6.972% figure is the other annoying part for those of us who found high school maths frustrating. The formula goes thus:

The figure n here is the number of years.

An easy way to remember this formula will reveal itself if you go back to the most basic mathematical formula that most professions use (of course, after addition, subtraction, multiplication and division): calculating percentage changes. We typically learn it as the following famous phrasing:

But if you look at it closely, this condenses down to:

Which is a remarkably easier way to put it: for example, you don’t have to bother about putting the brackets right when doing this on a calculator!

Look carefully again: this is just a special case of the CAGR formula above, where the number of years is 1. It’s the “average annual growth rate” for one year, or in other words, the growth rate for one year.

Memorising the basic percentage change formula as (Final/Initial – 1) does two things. One, it makes it easier, quicker and less clunky to get percentage changes on a calculator or in a spreadsheet. Two, it suddenly makes the CAGR formula easier to recall: if you’re confused about any part of the formula, just apply the special case of n = 1 and see if it yields the percentage change formula that you intimately know of.

Here’s the third and last trouble that many face while conjuring the CAGR formula. What is the “number of years”? If we are calculating growth between 2016 and 2026, is it 10 or 11? (11 being the number of years being covered.)

The answer, if you have this question, is to think of it as the number of “jumps” or “increments”. To reach 2026 from 2016, you jump 10 times, so the number n is 10. Or apply the special case of 1 and see how it works (this trick is always the best): to be able to use n = 11, you’d need to have n = 1 to cover only 2016, which means you’d be saying the number grew a certain percentage in zero years, which is impossible. So 2016 must correspond to n = 0, and hence 2026 to n = 10.

Hope this demystifies the peculiar concept of CAGR. If this is too basic for you, you may want to read up about log-based CAGR, and you might say, “Now we’re talking!” But that’s not relevant for most everyday uses, so I’m keeping it to this much.

Do you have any terrifying CAGR stories to share from the workplace? Do send them my way! Until next time…

Well, this ideal scenario is not really implausible. True, the year needs to be 365 or 366 days long to keep it in sync with how the earth moves, and neither number is divisible by 7, so you end up taking for granted that the first of January must shift days every year. But the multiplication table of 7 is just a tiny inconvenience in the larger scheme of things. Ways have been proposed to get past it and have one permanent calendar, once and for all.

We can intuitively predict how the hours, minutes and seconds will progress on an unknown day 20 years hence. We can quickly tell which year it will be 40 years from now. But to know the day a particular date will fall even one month later, we don’t feel sure unless we’ve referred to a calendar. Can this get more absurd?

You see, the kind of shifting calendar we currently use—and which we are used to the way we are to the air and water around us—is one of the oldest surviving ways of organising life, and it has seen little change for centuries. Rather, it has spread across the world with little resistance and evolution, as it helps unify humanity like few other things. We may have different languages and customs, but we operate with the same calendar to keep life smooth. Traditional and religious calendars may dictate local festivals, but the January 1 to December 31 cycle is so ingrained that not following it would cause chaos. The last time someone made a tweak to the calendar, countries saw riots.

So should we expect any massive change that could fundamentally change how we count our days? Well, not right now; though the world has been much closer to a calendar revolution in the 20th century (which I will come to in a bit). Let me argue why it would be nice to revive the debate.

The calendar we so intimately know is a sun-based one, called the Gregorian calendar, and has been in use since 1582, when Pope Gregory XIII decided to revamp the Julian calendar that was in existence back then. The older calendar, which Roman emperor Julius Caesar formalised more than 2,000 years ago in 45 BCE, was actually pretty much the same as what we know the calendar to be: it only counted three extra days every 400 years. In that sense, Gregory’s innovation in how we structure our calendar was marginal.

The flaw with Caesar’s calendar was that its three extra days every 400 years made it a bit too slow. Over the centuries, the equinoxes were not falling on their expected dates any longer, and if left unchanged, dates would begin to fall out of sync with the seasons. So Gregory’s innovation set it right by deciding that century years that don’t divide by 400 would no longer be leap years. It isn’t perfect yet, but now the error will not make itself felt for thousands of years.

The basic structure of Caesar’s calendar (which, too, was inspired from the structure of the ancient Egyptian calendar) still thrives simply because we’re so used to it. Who finds it intuitive to have a short February, equal lengths of July and August, and the ever-changing day for any given date? If you forget that it’s an unavoidable way of life, you might realise it’s a ridiculous way to structure your year.

The structural flaws of the Julian calendar have their roots in arbitrary choices of Roman dictators. Their sole (and crucial) priority was to average 365.25 days a year (and 365.2425 a year in the Gregorian tweak) over longer periods, and rightly so. But they also had an eye on the traditional imperative to align festivals with the lunar months, leading to irregularly-sized months. Secondly, the Julian calendar, and its Gregorian innovation, are deeply rooted in religion, hardly in science. Gregory wasn’t out to impress the Renaissance vote bank; the problem was that scholars had complained for centuries that the choice of date for the annual Easter, which was linked to the March equinox, was falling out of line with the sun’s position in the sky. Also at play was rivalry with the Jewish calendar, which didn’t face the issue in scheduling its own equinox-linked festival Passover. The Christians simply wanted a foolproof method of their own.

So essentially what we currently use to dictate our rhythm of life is a calendar that could reform itself only out of religious compulsions, where scientificity was unintentional. It led to quarrels within Christianity; Britain (which had tense relations with the Vatican) didn’t adopt Gregory’s reforms until 1752, and the Russian Orthodox Church still uses the Julian calendar and is by now 13 days out of line with the earth’s motion! This deeply unsecular calendar then travelled the world solely by virtue of European colonialism. It doesn’t really need to run the 21st-century world.

The world needs a secular calendar that can shed its religious and colonial baggage. A common calendar that pleases no religion, but works for all humans and makes date-counting—its basic purpose—easier.

The World Calendar, my favourite alternative, was proposed in the 20th century, and it made it close in debates at the United Nations. It retains the same 12 months. January 1 always falls on a Sunday. Each quarter is made up of months 31, 30 and 30 days long. That adds up to 91 days, or exactly 13 weeks, which means that each quarter, not just each year, starts and ends on the same day. The cycle of days and dates repeats every quarter, unlike every 28 years like now (our current calendar normally repeats every 28 years due to the leap year), making it easier to remember days. You wouldn’t need to change calendars every year!

But that adds up to 364 days, a day and a quarter too short. So it adds one extra date at the end of December—a global festival of sorts, Worldsday—that does not get either a day or a date. Every four years, to account for the leap year, we add a similar Worldsday at the end of June as well. Since Worldsday doesn’t get a day, the following January 1 or July 1 don’t fall out of cycle. Alongside, we get an extra day to celebrate a secular, simple calendar that governs our lives so intimately.

Of course, religion came in the way. The Monday to Sunday cycle is essential to some religions, and a date that doesn’t fall on any of these seven days would disturb that rhythm. Also, something that wasn’t perhaps a problem in the mid-20th century, the foundation of our current computer technology is entirely based on the Gregorian calendar, and a disruption to accommodate an unusual concept of Worldsday—a day that’s somewhat not a day—would bring chaos to digital systems.

Is there a way out? If you had to build a calendar from scratch, what would it look like? Do you have a solution, or would you rather continue with the current system? In terms of simplicity, it must win the day—but also the year (remember, that first and foremost, a solar calendar must stay in line with the seasons, with a year of accurate length).

Think about it, and please do write back. For now, it’s time to say goodbye, with the promise to write more about this topic, and to explore other alternative calendar systems, in my coming posts.

It was only in recent years that I began to realise that this quirk wasn’t as trivial or childish as I thought. As it turns out, the history of the world itself intersects surprisingly often with the history of the calendar and timekeeping. Think about it: when there was nothing, there was time. It’s always moving, only in one direction, leaving you with no option but to measure it, somehow. Early humans didn’t have any option either: and they scrambled hard to figure it all out.

I just finished reading two breathtaking books: Chad Orzel’s A Brief History of Timekeeping (BenBella Books, 2022) and David Ewing Duncan’s Calendar (Avon Books, 1998), and I’m truly spellbound at how the quest for accurate timekeeping has shaped humanity at all its stages—even when our prehistoric ancestors were largely clueless about the world. In this post, I share what I’m thinking right now as I chew on these books.

Even when maths was scarce and the number system was in dinosaur age, there were the sun and the moon, mysteriously rising out of nowhere and turning off every day, acting as natural clocks, demanding to be tracked, somehow. And track we did—in ways small and big. It gave birth to a remarkable timeline of events, driven in turns by agriculture (think of the havoc farmers would be in if the calendar was linked to the moon and not the seasons), religion (think of the tensions it would create if two priests began arguing about when a festival should be held), and medieval imperialism (think of why the British Empire would announce a massive prize for anyone who could figure out how to measure longitudes—and hence time—at the sea, a real pain for scientists at that time).

The process ended up shaping everything from mathematics, physics and engineering to astronomy, geopolitics, and our GPS and satellite systems today. Innovations in each pushed all others ahead: humanity’s puzzles, across disciplines, almost always got their answers when we ‘timed’ it right.

War—and love**—led to the basis for the calendar we use today. A major tweak to how we count days once led to a riot^. How to decide the date of Easter (and the imperative to avoid a method that mimicked the rival Jewish calendar) was the subject of hot debate and mathematical innovation for centuries. It’s truly revelatory to think of world history from the perspective of the calendar.

While I strongly recommend you read the books, here’s my list of three characters who impressed me the most (among many) along the journey:

Su Song: A Chinese polymath who designed the world’s first hydro-mechanical astronomical clock tower in the 11th century. Till then, something or the other was not quite working out as people everywhere tried to measure time. Su Song solved several issues with a clock that likely weighed in tonnes.

Ruth Belville (aka Greenwich Time Lady): A British woman who visited the Royal Observatory in Greenwich to reset her watch every week and then went door-to-door to meet (and chat with) all her subscribers so that they could sync their devices with hers. (Yes, clocks would need to be synced that often even in the 20th century—and it could be a bona fide business idea!)

Roger Bacon: The 13th-century British polymath who no one listened to when he insisted that the Julian calendar had an error, because of which it no longer aligned with the seasons. A pope who finally showed interest ended up dying before Bacon’s paperwork and calculations reached him in Rome. The remedy that Bacon had sought only came by in 1582. (In fact, the dating of Easter ultimately played a role in the correction of an imperfect calendar that had dictated the world for over 1,600 years. Had it not been for religion, would the leaders of those times have found it a matter critical enough that their calendar was sliding away against the seasons?)

These are just a few names; of course scientists and mathematicians during the middle ages played integral roles in solving the biggest questions about our earth’s motions. Some of them fought with each other for credit and acceptance when they lived, but the truth that matters 400 years later is simple: they all built upon years of human thinking and philosophy, benefited greatly from each other at a busy time for scientific discovery, and together, set the path for future innovations that would shape the world we inhabit.

*I didn’t, because by then, I had learnt that I could change the time in the device myself whenever I wanted for any experiment.

**The reference is to Julius Caesar, his entry into Egypt, and his subsequent and tireless infatuation with Cleopatra.

^The reference is to the shift from the Julian to the Gregorian calendar in the UK in 1752, due to which 11 days just vanished from the calendar. (If it happened today, how much rent would you pay your landlord for the affected month?)

All information shared here is my interpretation of my reading of the two books, and my own research. I learned about Su Song and Ruth Belville in Orzel’s book, and Roger Bacon found mention in both books.