Here’s an interesting discussion between Prof. Mahesh Panchagnula and Prof. V. Kamakoti (Director of IIT Madras) on choosing the right engineering branch in 2026.

I asked Gemini Pro1 to summarize the discussion, key insights, and recommendations. I’ve reproduced that below with my comments. Everything in block quotes is Gemini-generated, and everything outside is written by me.

Summary

I’ll let you read a summary of the whole episode without interrupting.

The episode focuses on guiding parents and students through the crucial process of choosing an engineering branch after JEE results. Prof. Kamakoti challenges the widespread “herd mentality” that blindly favors Computer Science based on opening and closing ranks and starting salary packages. Using examples like semiconductor manufacturing and AI, he illustrates that modern technological breakthroughs require a convergence of almost all engineering disciplines — from metallurgy and chemical engineering to civil and mechanical. The core message is that long-term career success and personal fulfillment come from following one's genuine passion, building an unbreakable academic foundation without taking shortcuts, and cultivating a sense of national pride as India moves toward becoming a developed nation by 2047.

Key Insights

On to the insights and my comments.

The “Herd Mentality” Trap: Opening and closing ranks have artificially created a hierarchy of engineering branches (with Computer Science at the top), driving students into fields they don’t actually enjoy just because their rank allows it [20:41].

Most leaders say this. They have to say these things because of social desirability bias, otherwise everyone will pounce on them the way Nandan Nilekani gets pounced on.

So, I think, this statement by itself does not contain any useful information or insight.

Here are my thoughts:

1. In the past, using opening and closing ranks and starting salary packages was an extremely effective way of choosing a good career. As any economist will tell you, prices (and salaries) carry information about how the world works. And a core engineering branch that is unable to match salaries is simply telling you that it is not as valuable.

2. It is important to note, however, that this year might be different. There is a 4-year gap between what starting salaries graduates are getting today vs what your child will get when they graduate. Usually, this is not a huge problem, because in the past, the world did not change drastically in 4 years. But with AI upending the world order, the next 4 years might be different. So, ignoring the starting salaries and branch cutoffs this year might actually make more sense.

3. However, I have always maintained that prioritizing the branch (e.g., computer science) above all else is a mistake. Instead of choosing CS at a lower-ranked college, I recommend taking whatever branch you can get at the highest-ranked college you can get into. This advice hasn’t changed in 15 years, and isn’t changing even now. And, I don’t think AI changes that. The brand value you get from being in a top college, the network of classmates you get, and other benefits will continue to remain valuable, even if the actual contents of the course are obsoleted by AI.

Technology is Deeply Interdisciplinary: Modern marvels aren’t built by a single branch. For example, creating a semiconductor chip or a GPU requires metallurgy, chemical engineering (for fabrication and gases), robotics and mechanical engineering (for precise embossing), and civil engineering (for vibration-resistant manufacturing facilities) [37:31].

This part is true. But, the suggestion that you can go into any branch and find an equally rewarding career, I think mostly applies to the toppers; those who did reasonably well in the JEE Advanced; those who are going to get into IIT-Madras. I don’t know how well this advice applies to students going to average Indian colleges. For those colleges, in the past, going for CS and hoping for an Infosys job was probably good career advice.

But I don’t know how AI changes that. I don’t know what form Infosys will survive in, whether it will continue to hire in bulk just like before, or what kind of students it will hire.

Core Engineering vs. IT/CS Longevity: In core disciplines like Civil or Mechanical engineering, experience translates directly into lasting expertise (”gray hair is an asset”). In contrast, the rapid evolution of Computer Science can lead to a mid-life crisis, where older professionals are quickly outpaced by younger, more energetic talent with up-to-date skills [15:05].

I don’t know if this will remain true. If you have opinions on this aspect, please leave a comment below.

Shortcuts Stunt Growth: Relying on tools like ChatGPT to pass assignments in the crucial first two years of college deprives students of fundamental learning. It limits their ability to think critically and adapt later in their careers [43:07].

Master the Fundamentals the Hard Way: Students must dedicate their first two years to deeply understanding the foundation of their subjects without using AI tools. If the foundation is strong, a student can continually build and adapt as technology shifts [46:04].

Inappropriate use of ChatGPT2 will definitely be harmful for students. Already is. But that doesn’t mean that all use of ChatGPT is to be avoided. ChatGPT is a fabulous tool for learning if you use it in the right way.

Which means that in an ideal world, the best way to approach AI is a barbell strategy: for some things, avoid the use of ChatGPT completely—these are the foundational concepts and skills that you have to practice so that you develop the basics on top of which the rest of your skills and capabilities rest. And for other things, you should be AI-maxxing: using it to the fullest extent.

It is quite likely that colleges haven’t completely understood how the world changes because of AI, and as a result, for years, colleges will force students to dumb assignments by hand (the modern equivalent of engineering drawings by hand in the age of CAD).

So students will have to make their own decisions about when to use ChatGPT to do their work, when to use ChatGPT to study for exams, and when/how to use ChatGPT to actually learn.

The big problem with this is that students don’t have a fully developed prefrontal cortex3, which means that they make decisions that are wrong for them in the long-term. Even if they theoretically understand they should avoid ChatGPT in some areas, when assignment deadlines loom, they will use ChatGPT to complete the assignment. And when a test deadline looms, they will use ChatGPT to study, which is often good enough to pass an exam the next day, but not good enough to build a strong foundation.

I don’t know how best to achieve this. Neither do colleges. But parents and students need to think carefully about this and come up with a ChatGPT strategy and discipline.

Excellence Brings Reward: There is immense scope in every discipline. Being exceptional in a core field is far more rewarding long-term than being a mediocre participant in a currently hyped field [26:50].

This is both a true and a circular argument. Yes, there is immense scope in every discipline—but this is true of the currently hyped field also. Yes, excellence brings reward—but then if you’re pursuing excellence, you’re not a mediocre participant, right?

I think the advice to pursue excellence in whatever you’re doing is good advice. But that doesn’t really help you to choose an engineering branch, because that advice applies no matter what branch you choose.

Recommendations

Observe and Follow the Child’s Passion: Parents should look at what naturally interests their kids—whether it’s building sandcastles (Civil), tinkering with spanners (Mechanical), or calculating run rates (Statistics)—and let the child’s heart be the final judge of their chosen discipline [23:23].

This is largely bullshit. Building sandcastles tells you absolutely nothing about what a career in civil engineering is going to be like. There are many students who don’t have a passion at the end of 12th std—our school system beats it out of them. And for many students, what they think is their passion is simply a subject in which they got a great teacher; it is quite likely that they can become passionate about other subjects when they run into great teachers in the other subjects.

I think Follow Passion Is Terrible Advice.

Look Beyond Academics: Students should pursue a holistic development path. Institutes like IIT Madras now offer admission routes for sports and cultural excellence, recognizing that well-rounded individuals manage stress better and innovate creatively [24:48].

Generally true. Might be even more true in the age of AI. I think AI will reduce the value of knowledge and academic learning. As a result, the value of softer skills will increase: like networking, participating in college fests, participating in team sports, etc.

Invest in the Country’s Future: Students are encouraged to pursue higher studies (like PhDs) in India and contribute to the nation’s goal of self-reliance (Viksit Bharat 2047) by focusing on sovereign technologies and core engineering [51:40].

Again bullshit. You can contribute to India much better by doing a PhD abroad in a top university, getting some experience there, and then coming back to India.

I think my advice still remains true: go to the highest-ranked college you can get into, ignore the branch (and “passion”), stay in a hostel, study abroad if you can afford it, go to a good liberal arts college if you’re unsure of what you want to do, or take a gap year, if you have a good plan around it, and finally, don’t stress about getting into the right/wrong college: there will be lots of opportunities for course correction later.

Three years ago, Khan Academy launched Khanmigo, a ChatGPT-powered AI Tutor, which was expected to start a “revolution in learning”. It was supposed to be “probably the biggest positive transformation that education has ever seen.”

Given how well-known Khan Academy itself was all over the world, the amount of traction the announcement got was phenomenal:

Khan’s announcement of Khanmigo generated millions of views and precipitated widespread media attention, a book deal, and a steady flow of philanthropic and government subsidies. Newark Public Schools received $25,000 from the Bill & Melinda Gates Foundation for Khanmigo licenses. Indiana schools could apply for $50,000 in state funds for Khanmigo use. Palm Beach County received a $2,000,000 commitment from the Stiles-Nicholson Foundation to help schools purchase Khanmigo licenses. Microsoft eventually sponsored Khanmigo licenses for every teacher in 49 countries.

But, in spite of all the attention, the funding, and the initiatives in various schools, Khanmigo went nowhere. As Sal Khan himself admitted last week, “For a lot of students, it was a non-event. They just didn’t use it much.”

Isn’t 1-on-1 Tutoring the Holy Grail of Education?

A famous finding in education is called Bloom’s 2-Sigma Problem. In the 1960s, Benjamin Bloom conducted some studies which showed that average students who got 1-on-1 tutoring using mastery learning techniques performed 2 standard deviations better than students educated in a regular classroom environment. 2 standard deviations is a huge number: that is the equivalent of a 100 IQ student performing at 130 IQ level or a CBSE 12th std percentage going from 72% to 92%.

And it is called a “problem” because until now, we did not have a scalable way of providing 1-on-1 tutoring or mastery learning at scale.

AI tutoring was supposed to be a perfect solution to this “problem.” An AI tutor is excellent at providing 1-on-1 tutoring and it can easily implement mastery learning. It can seamlessly adjust explanations to the level of the student, give examples that student can connect with, answer any questions the student might have with infinite patience1, and keep circling back until the concept truly clicks—no shame, no time pressure, no moving on before the student is ready.

And Khanmigo was ideally placed to solve Bloom’s 2-Sigma Problem. What went wrong?

The 5-Percent Problem

The problem, as is clearly seen from Sal Khan’s lament, is that most of the students are not using the tutor, so obviously they’re not going to see any improvement. As Naval Ravikant puts it:

The means of learning are abundant, the desire to learn is scarce

In short, the real problem isn’t Bloom’s 2-Sigma Problem. The real problem is Education’s 5-Percent Problem.

Here are studies showing that technology is indeed a great tutor:

hed the results of a massive, 99-district study of students. It showed an effect size of 0.26 standard deviations (SD)—equivalent to several months of additional schooling—for students who used the program as recommended.

A 2016 Harvard study of DreamBox, a competing mathematics platform, though without the benefit of Sal Khan’s satin voiceover, found an effect size of 0.20 SD for students who used the program as recommended. A 2019 study of i-Ready, a similar program, reported an effect size in math of 0.22 SD—again for students who used the program as recommended. And in 2023 IXL, yet another online mathematics program, reported an effect size of 0.14 SD for students who used the program as designed.

These gains, even though less than 2 SD, are quite impressive. And these are pre-ChatGPT, so the situation now should have been even better. So what’s the problem?

A clue is in those wiggle words “students who used the program as recommended.” Just how many students do use these programs as recommended—at least 30 minutes per week in the case of Khan Academy? The answer is usually buried in a footnote, if it’s reported at all. In the case of the Khan study, it is 4.7 percent of students. The percentage of students using the other products as prescribed is similarly low.

Coursera completion rates have a similar number. I’ve been complaining about this myself since 2019. Once you realize that only 5% of the students are self-motivated enough to use edtech tools, the problem becomes clear:

Imagine a doctor prescribing a sophisticated new drug to 100 patients and finding 95 of them didn’t take it as prescribed. That is the situation with many online math interventions in K–12 education today. They are a solution for the 5 percent. The other 95 percent see minimal gains, if any.

Pivoting to Humanity

Dan Meyer has a follow-up article pointing out that the only way AI in particular and EdTech in general can make a difference is by “pivoting to humanity”. For quite some time now, EdTech has tried to solve the 2-Sigma Problem by replacing classrooms+teachers with technology. And, in the pre-ChatGPT days, it was possible to make the claim that the reason EdTech hasn’t succeeded so far is because the technology was not mature enough. But, now, it is time to admit that technology is not going to solve the problem by replacing the humans, so any hope of making any headway lies in technology helping the human teachers2.

Why? Because teachers do much more than teach:

“As soon as I started hearing about tutors being replaced by AI, I knew that the people responsible for such nonsense had never tutored a day in their life. 40% is remembering to ask about the novel they are writing, the tea3 they spilled about their friends, or the language test they’ve been studying for all year. 40% of my time is spent just building confidence and reassuring students they’re doing the right thing. 20% is actually teaching math.” —Katelynn Petersen

What does it mean for EdTech to pivot towards humanity? Instead of trying to replace teachers, EdTech should try to solve the problem that human teachers face. Dan has an interesting list of hard problems:

Kids want to work on paper and it’s hard for teachers to know what they’re doing.

Teachers struggle to support a student’s thinking if it isn’t visible. Lots of thinking happens on paper and teachers often lack the time necessary to review and respond to it. How can we make that paper-based learning more legible for more teachers?

Coaches want to support teachers and teachers want their support.

Coaches often have too many teachers on their roster to support with model lessons and walkthroughs. Also, many teachers want support but not in the form of a model lesson or walkthrough. The desire to give and receive support are misaligned here.

Teachers want support in leading whole-class discussions.

Whole-class discussion is some of the most satisfying work for teachers and productive learning for students. But it is very hard work. Misaligned.

Caregivers want to support their kids but don’t know how.

Many parents and caregivers want to do more to support their kids’ learning than they do currently. But they often lack visibility into student learning and may need some education themselves. The reports that schools send home are frequently summative, low resolution, and a waste of ink or pixels overall. Teacher emails are much more useful but time-consuming for the teacher. What can schools and edtech companies do to help align caregivers, teachers, and kids here?

TMKK

So what’s the takeaway message4?

If your child (or you!—you are also a student who can benefit from 1-on-1 AI tutoring) is happily using AI as a tutor, you should absolutely encourage it. Because this means that your child is in the 5% who will actually benefit. The main thing to make sure is that your child is using the AI to learn (i.e. to get explanations for concepts) and not using it to avoid learning (i.e. to do the homework). Here are some interesting ways to use AI as a personal tutor from Ashish: Learn by Doing, Learning, Doing, Thinking

If you are a teacher or educator, I’m sure you’re being bombarded by various AI tools and initiatives (even I teach one such course 😂). Evaluate those through the lens, “How does it enable the teacher to help the average student better?”

If you are techie/builder in the EdTech space, consider pivoting to humanity. Focus on the problems Dan listed, or try to find other real problems that good teachers are facing when helping the average students, by treating good teachers as the experts (you understand tech, they understand education—the latter is more important).

(I don’t use AI to write any of my articles. However, this time, I found a couple of its suggestions good. So I’ve included those in callout blocks with appropriate attribution.)

These days, I regularly run into two categories of people:

• Seniors, who were unable to embrace some new technology, and increasingly find themselves getting out of touch and sliding slowly into irrelevancy. This includes 65+ people who find their mobile phones and the latest apps confusing. But it also includes 45+ people who find all the AI advances overwhelming. (Not all older people realize they’re facing this problem until it is too late.)

• Juniors, who increasingly find it difficult to get internships and jobs, because seniors are using agentic AI instead of interns to get things done.

The solution to both of these problems might lie in older people accepting the fact that younger people are better at adopting new technologies, new fashions, and adapting to a fast-changing world. In short, older people should view the youngsters not as cheap and dumb labour (which is what a lot of internships are) but as a source of knowledge and insights into how the world is changing and for experimentation with the same.

Here is Shweta Bachchan Nanda talking about how her father Amitabh has managed to stay relevant at this age (emphasis is mine):

He is a prolific adaptor to changing circumstances. It is as simple and as difficult as that. He harbours none of the disdain age has for youth: he doesn’t screw up his nose to the music, art or sartorial sensibilities of the generations that have succeeded him. In fact, he’s adjusted himself to find harmony in all of it. He is never outdated, be it the newest technological fad—the Apple watch, for instance—or mastering social media. He’s on top of it all, and he’s terrific at it.

He educates himself with what is prevalent, and that’s how he remains in touch with the times. I have seen him engage with a room full of young achievers one night, over dinner, at filmmaker Karan Johar’s place. He had them enthralled, but the beauty of it was that it was he who went home having learnt the most. This is a feat for a deeply introverted person—the ability to observe, process and then translate. Most people can manage only one of these aspects, but to truly rise you must master all three. He has the discipline of his generation coupled with the ambition and energy of this one.”

In fact, some companies and organizations see enough value in this concept that they have formalized the idea of “reverse mentorships”:

The origins of reverse mentoring lie in business and IT sectors and reflect the need for employees to stay current in the workplace.²⁰ It is particularly utilized in the IT industry where younger generations can advise on the latest in technological advancements.²¹ Reverse mentoring is not limited, however, to fields of IT or business. There are examples of the concept in education,⁶ economic theories, language skills and innovative ideas.²² It has become “best practice” for several large corporations including Dell,²³ Procter and Gamble²⁴ and Time Warner.²⁵ It can be particularly useful in workplaces that are frequently exposed to the rapid transformation of technology, including social media, such as the public relations industry.²⁶

(The underlined numbers are all citations to papers with further information.)

One problem with the idea of reverse mentorships is that the old and experienced people don’t believe that they have much to learn from the young people. And, unfortunately, often the young people also believe the same.

Here are some quotes I’ve collected about what strengths young people bring to the table.

Here’s legendary investor Paul Graham on twitter:

Learned something interesting from the CEO of a fairly big tech co. Usually 28 year olds are more productive programmers than 22 year olds, because they have more experience. But apparently 22 year olds are now as good as 28 year olds because they're more at ease using AI.

And @iandmacomber on Twitter:

I recently interviewed a junior in college. They started a takehome in Cursor, ran out of tokens, moved to Codex free tier, then put $20 into Claude Code to finish.

I’ve also interviewed data scientists who haven’t touched any of these tools because their company hasn’t procured them.

I can definitely tell you who I'm more excited to work with.

I agree with these sentiments; whenever I give a talk in schools or colleges, I ask about how people are using AI and how much. The students are always using it much more than the teachers.

Steve Yegge, famous for his rants, has an entire (long) article titled “Revenge of the Junior Developer”. Here is an excerpt—it is a rant, and has a lot of exaggeration, but there is a core of truth to it:

One consistent pattern I’ve observed in the past year, since I published “The Death of the Junior Developer”, is that junior developers have actually been far more eager to adopt AI than senior devs.

[…]

Junior devs are vibing. They get it. The world is changing, and you have to adapt. So they adapt!

Whereas senior developers are, well… struggling, to put it gently. I have no shortage of good friends, old-timers like me, who have basically never touched an LLM or even seen one naked. Plenty of others have only barely dabbled with coding assistants. I even hear about senior developer cohorts, from many industry leaders, who take a flat-out stand against it.

Example: a tech director at a well-known brand just told me that one of their devs sent them a PDF explaining, with color slides and charts, why they all needed to abandon AI and go back to regular coding. Now do you see what I meant when I said we have the widest distribution of understanding in tech history? There are people who still think this is like, crypto or something. Yikes!

Look, some senior devs are struggling, no doubt, because they’re just busy. I get it. But I think for most of them, there’s something deeper going on. When I used to blog about programming languages, I found that simply saying that I liked some programming language, any language at all, would get me in surprisingly serious hot water. People would yell in threads, digital spittle flying everywhere. I didn’t get what was going on. All this, just because I said I liked a language?

After several years I figured out that it’s because they felt if people listened to me, then everyone would switch to that language, and then the senior devs would have to learn it too. They equated having to learn something new – and I mean really new, sort of like starting over – with losing their job and their health insurance and going bankrupt and dying outside a hospital on the steps. It’s just human nature at work, in the face of big uncertain change.

I believe the AI-refusers regrettably have a lot invested in the status quo, which they think, with grievous mistakenness, equates to job security. They all tell themselves that the AI has yet to prove that it’s better than they are at performing X, Y, or Z, and therefore, it’s not ready yet.

But from where I’m sitting, they’re the ones who aren’t ready. I lay this all out in detail, my friends, so you can help yourselves.

Regardless of why the luddites aren’t adopting it, they have lost. Junior devs have the high ground, and the battle is now over. Not only are junior devs on average adopting AI faster, but junior devs are also – surprise! – cheaper. If companies are going to make cuts to pay for their devs to win with tokens, which devs do you think they’re gonna keep?

The AI holdouts don’t see any of this this coming yet, so the junior devs are going to have to lower their light sabers and shout this message down from the hilltop above:

It’s not AI’s job to prove it’s better than you. It’s your job to get better using AI.

Otherwise, you get… the else clause. You know. The else clause. The lava. You fall in. Why do I have to spell this out?

Not everything in this rant is true, and the situation is of course more complex. Juniors are better at using new technology and tools. But what about the fact that the juniors are missing important domain knowledge? And although they are better at adopting new tools and new technologies, they don’t have the experience to know what is important and what is the right thing to do?

Wharton business school professor Ethan Mollick writes about some interesting research around this topic. They compared the performance of individuals and teams, with and without the use of AI. The paper, called “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise” is interesting overall, but I want to highlight a few specific points. First:

When looking at the top performers, you see that teams using AI do much better than individuals using AI. But there’s another finding more relevant to this article:

“Expertise boundaries vanish”

When paired with AI, both R&D and Commercial professionals, in teams or when working alone, produced balanced solutions that integrated both technical and commercial perspectives. The distinction between specialists virtually disappeared in AI-aided conditions, as you can see in the graph. We saw a similar effect on teams.

Basically, if you team up an expert with a non-expert and give them access to AI, this combination beats all other combinations: individual-only (expert or non-expert), expert-with-AI, and expert+non-expert (without AI).

The bottomline is, the best strategy is to put seniors and juniors together and give them access to new technology.

And, your company might not be set up to do this, so you might have to manufacture this situation yourself.

P.S.

One interesting mental model related to this is the idea of Pace Layers: analyze any long-running system and you’ll find it is composed of multiple components moving at different speeds. (In this article the two components are seniors and juniors.) The old, slow layers provide experience and stability. The young, fast layers provide freshness and agility. And both layers need each other. Without the slow, the fast will jump from one shiny fad to another, and never get anything substantial done. Without the fast, the slow will atrophy into irrelevance. (I’ll do a full article about Pace Layers sometime later, but in the meantime you can check out this introduction, or my video on this topic.)

Here is an excerpt:

Host: Pune is known as the Oxford of the East, right? What edge do you think Pune has over cities like Bangalore and Hyderabad?

Navin: I’ll give two answers: first the academic one, and then the broader tech answer.

In academia, “Oxford of the East” might be a little bit of an exaggeration today. We don’t have an IIT, we don’t have an IIM, and that’s because politically Pune is in the shadow of Bombay; we are not a capital city.

But our heritage has ensured that we have a lot of tier-2 colleges, as far as engineering is concerned, and that gives us scale. After Bangalore, if anybody wants to expand and hire a lot of people with skills and talent, Pune is always a destination for them. That is really good for Pune.

If you go beyond just engineering and MBA, then suddenly Pune starts looking better, because there are so many world-class institutes here.

Or, if you prefer, you can listen to it on Spotify.

Full Transcript

(The host was Parijat Sardesai, and thanks to the rest of the Pulse of Tech team for the shoot and the editing. The transcript below has been auto-generated and then cleaned up by ChatGPT. I haven’t checked the transcript carefully—I have skimmed through the transcript and it seems mostly ok, but it is possible that ChatGPT has hallucinated a bit here and there.)

Host: Welcome to Pulse of Tech, SEAP Pune’s leading technology podcast, from SEAP, the city’s premier platform for tech organizations, delivering cutting-edge discussions and insights.

Today we are talking to a man who ranked 14th in IIT-JEE without joining a single coaching class or compromising on eight hours of sleep. He’s the man the rest of us wanted to be in class 12 — and still want to be in the boardroom. He’s a CTO who believes people should be hired for what they can do, and not where they studied.

Let’s welcome Dr. Navin Kabra.

Navin Kabra: Good morning.

Host: Good morning, and welcome to the show.

Navin: Glad to be here.

Host (Parijat Sardesai): You’ve done your PhD from the University of Wisconsin–Madison, right?

Navin: Right.

Host: And after that, you worked for massive MNCs like Teradata and Symantec. You’ve seen the dotcom failure and then a successful exit. So, when you were doing your PhD, what is the one thing that the PhD did not prepare you for?

Navin: The one thing my PhD didn’t prepare me for was the importance of sales.

In a PhD, you focus so much on the technical merit of what you are doing that you forget that in the real world, technical merit is a very small part of doing something successfully. Maybe 15–20%. The rest is sales and marketing and networks and relationships, and so on.

Later on, I also realized that all those things I was supposed to be learning during my PhD too — even in a PhD, you need to learn how to sell your idea, and so on. But I guess the actual sales part of it — somehow I didn’t do that and still managed to get through. That was my fault. It took me a long time to realize the importance of these things.

Host: Fair point. You did your PhD in query optimization for object-relational database systems, right?

Navin: Yes.

Host: Now we know the world is moving towards data lakes and big data. Back then, with scalable ORDBMS systems, what is the fundamental lesson from the ’90s that still holds good?

Navin: I want to tell you the story of how I got into databases.

I graduated from IIT Bombay, and most of us from the computer science class left with the impression that databases were boring ’60s technology. All the exciting stuff was happening in programming languages and networks and things like that.

When I landed up in Wisconsin, I found out that Wisconsin was probably number one or number two in the world for databases. They pitched all the new students who had joined: “You should be in databases.” And I said, “It sounds boring.”

I will always remember the speech they gave at that time to explain why databases are important. Data is at the core of everything you do, and to be able to handle everybody’s data well, you pretty much have to solve all the problems that the other fields have to solve, plus you have your own problems that none of the other fields have.

For regular day-to-day use, you don’t have that much data, so you don’t realize the importance of these things. But beyond a certain scale, when a company gets big enough, the amount of data they have is so large that it becomes a big deal just to manage it.

Conversely, what they pointed out is that this is important in the long term. Your user interface changes, the language you are using changes, whom you are talking to changes — but the data, once captured, stays forever and does not change easily. Data is at the core of everything you do.

Now, this was in the ’90s. Even Oracle was a small company at that time, and AI didn’t really exist as an important thing beyond the movies. Over the years, the importance of data has just increased. AI has added significantly more importance to data. The internet has caused thousands or maybe millions of times more data to be captured.

Even today it remains true. I forgot who said this, but it’s a great quote: “Show me your program and it’ll take me a while to understand what you’re doing. But show me your data and I’ll figure out what your program is doing — I don’t even need to look at it.”

And in today’s world of AI, generative AI, and everything, the more contextual and the more mature the data an LLM or a model is trained on, the better the information it’ll be able to deduce or generate.

Host: Absolutely. Making your data available to the AI at the right time is one of the most important and most difficult skills to learn these days.

Navin: Right. Prompt engineering is not really that important. Knowing what data to give it is more important.

Host: I might be jumping topics. The reason is that when I went through your profile, the diversity of your career profile — if I tried to cover everything, it would look like an interrogation. Sorry if it does.

Navin: No, I’m happy to answer, because what I have always done is optimize for interesting.

Host: You hold around 18 US patents, right? Which one out of these actually changed the way you think about software architecture, rather than just being a legal milestone?

Navin: You’re not going to like the answer. The answer is zero. None of them. Software patents are mostly useless.

Patents were created to protect designs of machines. If you come up with a new way of cutting nails, people shouldn’t be able to just copy your nail cutter and make money from your invention. When that extends to the pharmaceutical industry, it still makes sense. You patent a molecule and that has value. You prevent other people from manufacturing it. The day your patent expires, a huge number of companies in Gujarat start making copies and the price falls maybe 100x.

In software, I’ve never heard of anybody really being able to prevent someone else from doing something. Google’s patent on PageRank is one of the most famous software patents. It didn’t stop anyone from creating competing search engines.

So why are so many companies spending so much money on software patents? It’s because of the weird legal system that gives so much importance to these things.

What happens is that when you have two big companies — say Veritas, which makes storage software, and then one year Microsoft enters that market — one of them will sue the other because they feel threatened in the same area. Then the lawyers get together. One side says, “Here is the list of all the patents I have in this area.” The other side has a similar big list. This will literally be in the hundreds. To some extent, buy-the-kilo, they look at who has more kilos of patents, and the settlement is based on that.

If there are hundreds of patents in something like this, you can see how much value each patent has.

That’s also what we did when my PhD project was acquired by Teradata. Executives from Teradata flew over, and one afternoon we sat by the lake, ordered beer, and said, “Today we have to file patents.” Everyone made lists of things we would file patents on based on our work. For me personally, that resulted in seven or eight ideas, of which five became patents.

Host: There is one more interesting fact about you: you got a fake paper published to expose the flaws in the academic conference system. What does that tell you about the innovation quotient in academia right now?

Navin: What it tells me is that the incentives in academia are quite screwed up, and as long as the incentives are screwed up, you will see bad behavior like this.

Let me explain the fake paper. This was around 2012 or so. A bunch of students doing their final-year BE projects — I was advising them — said, “We want to publish our BE project as a paper.” I said, “No, a paper is something substantial. You have to have done research. A BE project can’t become a paper.”

They said, “No, no, it’s a requirement from the college.” That seemed bizarre to me, but when I looked into it, it really was a requirement by the college, because it was a requirement by the university: everybody had to publish a paper, and those who couldn’t had to submit a rejection note.

So I said, “Fine, if it’s a requirement, go ahead and do what you want.” A couple of weeks later, they came back and said, “Yes, our paper has gotten accepted.” At that time I knew this conference had to be fake.

So the next time that similar conference came back to Pune, I decided to submit a fake paper. It was completely made up. Anybody who read the abstract would know that it was fake, because Thakur Baldev Singh was a co-author and I had dialogues from Sholay in it — Basanti shows up. And that got accepted.

Then they said, “We need you to pay this fee for publishing it.” I could haggle with them like a vegetable vendor: “No, I can’t pay 6,000. Will you take 3,000?” and so on.

Clearly, it was a fake conference. When we exposed this, I got reporters to write articles about it, then it became big news, and the Vice-Chancellor of the university got involved.

But more broadly, forget fake conferences, which happen a lot in India. Even in serious conferences and serious papers all over the world, getting something published remains an important measure. You don’t get tenure unless you have so many publications. So the entire field is rife with bad behavior because of these incentives.

Host: Got it. That brings me to your current venture, which is ReliScore. I don’t know if the term “paper tigers with a perfect resume” fits. What is the core philosophy of ReliScore?

Navin: Yes, paper tigers.

This was a time when our previous startup had just died, and we were looking for ideas on what to do. We realized a lot of our friends were struggling with hiring, and the biggest problem was that candidates with excellent resumes — with all kinds of impressive claims, lots of work in Java and so on — would come to the interview and know nothing. So clearly that entire resume was fake, and their filtering process, which looked at keywords in your resume and things like that, wasn’t working.

The original idea was: if as a candidate you are claiming to have certain skills on the resume, our platform will say, “Okay, prove it.” If you say that you have five years of experience in Java and J2EE, there will be a “verify this skill” button. It’ll ask you to write programs in Java. It’ll ask you questions about J2EE. If you’re able to answer that, that skill gets verified.

The model changed slightly, but fundamentally that’s what it boiled down to. We switched to focusing on companies instead of candidates. The idea became: if they’re trying to hire for a particular role, they make a list of skills. Based on that, we create an online test.

The focus isn’t to check whether they remember things. This is not supposed to be like an engineering exam. This is supposed to check real-world skills: can you do the kind of work you would be expected to do on the job? If you’re being hired as a developer, can you write programs?

We built a platform where whatever they are doing, the system is able to automatically evaluate the quality of the answer.

So that was the core: instead of just looking at what is claimed on the resume, instead of just looking at credentials, and instead of asking questions for which they have memorized answers — for example, if you ask students the difference between a B-tree and a B+ tree, they can speak for five minutes because they’ve memorized that answer for college. But if you ask them to implement something, nothing.

That was the core idea.

Host: That brings me to another important point: coding is becoming a commodity. In this era of 2026, when generative AI is doing most of the coding or vibe coding has become very popular, what will an engineer’s value be in 2026?

Navin: That’s a tough question, because the field is changing so fast that we have no idea how good AI will get. We have no idea where the ceiling is. So it’s difficult to tell what things remain valuable and what things don’t.

But so far, what I’m seeing at the cutting edge of AI is that AI agents are able to write a lot of code. AI agents can even write tests. AI agents can run the tests and so on. But what the AI agents are missing is the big picture.

They can get something working right now. They can’t think in terms of what it would take for this to be maintainable for the next five years.

Host: But that’s context, right? At some point, contextual windows will grow.

Navin: See, it’s not just a context-window problem. It’s also a problem of you not having given that context to the agent.

When you tell the agent, “Write me an app to do XYZ,” do you yourself understand all the requirements clearly? You probably don’t. If you don’t, then you’re a junior person who doesn’t understand all the requirements.

You don’t understand that the customer, if they asked for X, often doesn’t know what they really need. An experienced person would know that the customer asked for X, but really they need X, Y, and Z. Or the customer has no idea that there are three things you have to take care of in the long term. You know that from experience.

All of these things — you have to put them into the requirements. The customer won’t give you the requirements, and if you don’t put them in, the agent is not necessarily going to know.

So problem solving is more important. Knowing what problem to solve. Having the domain knowledge to know what needs to be taken care of — which the AI probably will not. And that comes from experience.

A word I hear a lot is taste. Whatever you tell the AI to implement, it will implement. So you have to know what to tell it to implement.

But the engineers at the cutting edge that I know right now — most of them have stopped writing any code. All the code is being written by the agent. But they’re still busy, because what they’re doing is managing teams of agents.

Even individual contributors, in some sense, have become managers. They know that for the initial plan you should use Claude Code Opus 4.6; then for grunt implementation you should use Codex CLI; and then, when everything is in place, you should use Antigravity to improve the UI1.

Host: You spent years in the US and then moved back to Pune. What was the market gap you saw in India that made you build PuneTech.com?

Navin: When I moved back from the US, I was under the impression that in India only boring work happens — all the cool work is in the US, and the grunt work is moved to India.

I still partly think that. But when I joined Veritas, I realized these people were doing a lot of cool things. Then I realized that there were many different teams all doing cool things, and even people inside Veritas — one team didn’t know about the cool things happening in the rest of the company.

So I thought: that’s interesting. Why don’t I write an internal newsletter about it? I would go to a team, talk to them about the cool things they were doing, and write about it in that internal newsletter. It became very popular, because people found it useful, inspiring, and exciting.

At some point I quit Veritas, and when I got out, I realized the exact same problem exists at the Pune level. There are lots of pockets where very interesting things are happening in Pune, and almost nobody knows about them.

So I said: why don’t I recreate the same thing at a Pune level? That’s how PuneTech was born. At the core, that’s all I was doing. Either I would go to a company that I thought was interesting, find out what they were working on, and write an article about them, or get one of them to come and give a talk.

Again, unexpectedly, it got very popular and became one of the biggest tech communities in Pune for a while.

Host: Our idea in doing this podcast is also the same.

Navin: Exactly. This is a great thing you’re doing. We need more of this kind of stuff.

Host: You’ve also been connected to Pune Connect as well, from SEAP, right?

Navin: Yes. Pune Connect was a joint initiative between PuneTech and SEAP.

That was the time Gaurav Mehra had just become president of SEAP, and he was interested in startups. We at PuneTech were interested in showcasing the startups of Pune. So we said: why don’t we create a yearly event which is essentially a startup competition?

Again, it ties into exactly what PuneTech was trying to do, which is to popularize the interesting work that is going on in Pune.

Host: I read that you have advised numerous startups through NIDHI EIR as well. Everybody is running behind this startup war, let’s call it. What is the most common innovation trap you would want to emphasize to our listeners?

Navin: I think it is the same trap I fell into during my PhD: focusing too much on the technical stuff and not focusing on sales.

A very large fraction of first-time startups think that if they have a good technical idea, that’s enough. “If you build it, they will come.” But that’s not how the world works. You have to go and sell. You have to go and talk to potential customers.

One of the first things you realize when you talk to potential customers is that you thought they would love this solution — that this is an important problem they’re trying to solve — but in reality, their problems are very different. Often their problems are much simpler.

You are so enamored with innovation and trying to come up with a cool solution that you forget the fact that people often just need the basics.

So: talk to customers even before you do your innovation. Sure, do some innovation if you want, but user research matters. And user research doesn’t have to be something fancy. Just find potential customers, make a mockup of your product — or even a presentation saying this is what we plan to do — and ask: “If we succeed in making it, will you buy it?”

Ask this to 20 customers, and 15 should say yes.

Host: Common theme. Solving a small customer problem is one of the biggest things you can do when you’re trying to innovate something.

Navin: Yes.

Host: One important point I wanted to talk to you about was your platform project, NoBadTouch.com, about child safety. How did you decide that non-tech societal problems deserve a tech-minded solution?

Navin: It’s really a continuation of my PuneTech experience.

What happened with PuneTech was that I first noticed there was some information people were not aware of, and that if they knew it, it would be good for them. I found that creating a blog around it worked very well — unexpectedly well. This was around 2008–09, the era of blogging. More and more people were realizing blogs were powerful.

Around that time, I was friends with Dr. Bhooshan Shukla, and he had come up with this method. Again, I saw the same pattern: this is information that should be widely known; it would really help people if they knew it — but how do you get the word out?

At that time I was doing a lot of blogs. I had created PuneTech, I had created Smriti.com — my website for Hindi song lyrics — and I had created my wife’s website for Hindi movie reviews. So I was like a hammer in search of a nail.

I said: this looks worthwhile. This should be known to more people. Let’s make a website around it.

Host: Through TheFutureIQ, you’re also teaching cryptography and game theory to high school students, right? What is your idea behind it?

Navin: Just a clarification: TheFutureIQ is my YouTube channel, where I talk about interesting concepts that everybody should know but they don’t. Again, you can see the pattern. But that’s not targeted only at high school kids — it’s for pretty much anybody. I have listeners from kids all the way to grandparents.

What you’re talking about with high school kids is something I do with a company called GenWise.

GenWise says: take smart children — and by smart, let’s use a simple definition: those who are doing well academically, in the top 2% of their class. These kids are getting bored in school because what school teaches them is just not challenging enough. School is not able to give them more interesting things to do. So GenWise asks: can we do something for these kids?

The most important thing GenWise does is a three-week residential summer camp, where the kids are taught some advanced subject. They either bring university professors who have holidays during the summer, or people from industry who are interested in teaching. That’s how they approached me.

I have always liked the idea of teaching. Even during my undergraduate days, I would end up teaching the four or five other computer science students in my hostel just before every exam. Later too, people kept asking me why I don’t become a professor somewhere.

I always felt that teaching in a class where students are there because they need that class to graduate and want marks is a sad experience. They are there to get marks; they’re not interested in the subject; it’s frustrating.

So I never taught formally, even though I had an interest in teaching. Then when GenWise happened, I realized this is great, because all the students who come there are there because they want to learn. GenWise explicitly says this is not going to help you crack any entrance exam. So the marks-oriented people get filtered out. Now you have a bunch of bright students who are actually interested in learning.

I’ve worked with them since 2019, and what we realized is that anything you want to teach them — they’re hungry, they’re interested, and it’s a great experience to teach them.

I’ve taught them cryptography and codebreaking, blockchain and cryptocurrency, game theory from economics, and a course called Defense Against the Dark Arts, which was basically: how do you fight misinformation? There is so much misinformation out there; we should teach our kids how to insulate themselves against it. So I thought that was a good thing to teach.

And then for the last three years I’ve been teaching an intro to AI and machine learning course.

Host: This is my last section, which I usually try to cover about Pune. Pune is known as the Oxford of the East, right? What edge do you think Pune has over cities like Bangalore and Hyderabad?

Navin: I’ll give two answers: first the academic one, and then the broader tech answer.

In academia, “Oxford of the East” might be a little bit of an exaggeration today. We don’t have an IIT, we don’t have an IIM, and that’s because politically Pune is in the shadow of Bombay; we are not a capital city.

But our heritage has ensured that we have a lot of tier-2 colleges, as far as engineering is concerned, and that gives us scale. After Bangalore, if anybody wants to expand and hire a lot of people with skills and talent, Pune is always a destination for them. That is really good for Pune.

If you go beyond just engineering and MBA, then suddenly Pune starts looking better, because there are so many world-class institutes here. For example, there is IISER Pune. IISERs are like IITs except for pure sciences. I would say same level as IITs, but they are newer, so better. IISER Pune is the number one IISER in the country. We have NCL, one of the top chemical labs in the country. We have the Agharkar Institute, which is great at agriculture. We have Gokhale Institute for Economics. If you go down that list, there will be a whole bunch of things.

If you broaden beyond just education, Pune starts looking even better, because unlike some of the other places, Pune has the combination of software as well as core engineering.

We are probably number one in the auto industry. We have a lot of manufacturing. We have so much banking — almost every big bank in the world has a center in Pune. Take engineering design — CAD/CAM and that kind of stuff — all the top companies in the world like CATIA, ANSYS, AutoCAD, and so on, all have centers in Pune. There is no other city in the world where all the top design companies have centers in the same city.

So what it allows Pune to do is have this mix where software and hardware and manufacturing and a whole bunch of things can come together.

Until last year, India’s story was a story of software running ahead while manufacturing and other sectors were lagging. But with AI, suddenly the importance of software is going to go down and the importance of core engineering sectors is going to go up. Then Pune will start shining even more.

And to top it all off, Pune is a lovely city to stay in. Quality of life. Of course everybody likes to complain, but if you are complaining about the traffic here, just go to Bangalore once and you will stop complaining.

Host: That was actually my next question. Something fun — your best memory of Pune. Food, people, anything.

Navin: My best memories of Pune have to do with the communities and the people.

Santosh Dawara and Anjali Gupta had started Pune Open Coffee Club, which was a community for startups in Pune, and that was lovely. We were associated with that for a very long time and made a lot of friends.

Around the same time, blogging was a thing, so we used to have blogger meets, and some of those people are still friends today.

Host (closing): Navin reminds us that the code might be global, but the input is often local. From the high-stakes world of US-based patents to the Pune startup and tech ecosystem, the ultimate innovation quotient lies not in the lines of code you write, but in the depth of the community you build.

See you in the next episode of Pulse of Tech by SEAP.

One of the biggest problems facing businesses today is the question: As Generative AI models eat more and more of the world, what product or service should the business focus on, so that it does not get completely obsoleted by the next release of the AI model? A few weeks back, an Anthropic news release related to Claude doing COBOL caused IBM to lose $35 billion of market value, and just a day before that, Anthropic’s Claude Code Security launch had caused cybersecurity companies to lose $30 billion in market value.

One Company is Beating OpenAI/Google/Anthropic at this Game

Nikhil Davar and Byrne Hobart have a fascinating article about a company called OpenEvidence, whose valuation doubled from $6 billion to $12 billion in the last 4 months. OpenEvidence provides doctors with an AI that helps with important, high-stakes medical decisions at the time they’re actually treating patients. OpenAI, Anthropic, and Google have all been working on AI models for healthcare, so how is OpenEvidence maintaining its competitive advantage?

The answer to that involves a number of fascinating first principles, which is why this article is on my FutureIQ blog and not on my AIIQ blog.

Trust is Must

A big problem in the world of AI is trust, or lack of it. People don’t trust AI models because of hallucinations. And they don’t trust the companies behind the AI models to not misuse their data.

The trust problem1 is much worse when the customers are doctors, because they primarily trust credentials, and software companies and AI don’t have the right credentials:

Doctors are perhaps the most credential-sensitive demographic in the world, in part because they their entire early adulthood earning an artificially scarce credential. A world where technology replaces credentials, legible signals of human expertise, and expert institutions makes a doctor feel very uncomfortable. If you want to know exactly how uncomfortable, next time you see your doctor ask them about some piece of health advice you found on Google or ChatGPT and observe their facial expression and response. OpenEvidence understood this and ran a full-stack credibility strategy with a few interlocking parts.

Specifically, doctors don’t trust AI models (because they hallucinate) and don’t want to (because they are built by software people who don’t understand the nuances of the medical world).

How to Build Trust

Here’s their fix for the first problem (emphasis added by me):

The first was explicit counterpositioning against the labs who were training on the open internet (including health blogs, social media, etc.—any remedy that’s marketed with “Doctors hate this one weird trick” will be represented in a broad set of training data) and liable to produce the embarrassing (and potentially dangerous) medical outputs that resulted from doing so. OpenEvidence trained an ensemble of specialized models exclusively on 35 million peer-reviewed sources, bootstrapping initially on public domain material from the FDA, CDC, PubMed, etc. Their models have no connection to the public internet during training or inference whatsoever. This meant hallucination risk in their early system was meaningfully lower than pre-o1/reasoning paradigm LLMs

And here’s their fix for the second problem:

OpenEvidence made their corporate identity synonymous with credentialed trust. They engineered their about/team page to speak to doctors’ biases: under each team member's name is not where they previously worked or their current title in the company, but only which prestigious educational credentials they earned and from which institutions.

How Can OpenEvidence Out-Compete Google In Giving a “Free” Platform?

Another interesting point is:

and the product was free, so doctors began adopting it virally

In the age of the internet, this sentence should not have been “interesting”; it should have been “obvious”. Google became one of the most valuable companies in history by giving away extremely valuable products (search engine, maps, YouTube, gmail, and much more) for free. The world of the internet is built on free/freemium products.

But AI is different. ChatGPT, Gemini, Claude, and others all have a free version. But that is not very good, and it hallucinates. The really good models require a $20 per month subscription. A doctor using the free model will regularly see hallucinations, I think, and is hence unlikely to keep using it.

So, if extremely cash-rich companies like Google, OpenAI, and Anthropic are not giving away their models for free, why can a relatively poorer company like OpenEvidence do so? Because:

• OpenEvidence knows that the customers they are acquiring this way—doctors using it at the time of treating patients—are extremely valuable, both because the product is being used in a valuable way (for treatment decisions, not to create internet memes), and the customers are very rich. It will be able to make lots of money from them sooner or later

• The number of doctors is much smaller than the number of people who will use a general-purpose AI system like ChatGPT. So, the chances of OpenEvidence’s servers getting overwhelmed are lower.

You Must Have a Flywheel

The next interesting aspect is the flywheel2 that OpenEvidence built: get senior doctors as customers, which allows them to get partnerships with top journals, which allows them to train better models, which gets them more doctors, which gets them more journals…:

A number of those early adopters happened to be senior members of the editorial boards at the most prestigious medical journals, which led to the next piece: OpenEvidence was able to lock down exclusive content partnerships with JAMA, NEJM, NCCN, the American Medical Association, all 11 JAMA specialty journals, the American Academy of Family Physicians, the American College of Emergency Physicians, etc. They used this exclusive, credentialed ground truth to train an ensemble of models that became the first to score 100% on the USMLE. This attracted more doctors, which attracted more credentialed ground truth, and the flywheel began spinning. Trust with practitioners became trust with the institutions that credential them, which became exclusive access to the ground truth that made the system better and deepened trust with practitioners further.

At this point, 50% of the physicians in the US use OpenEvidence. As a result, everybody else in the healthcare market is lining up to work with them:

Doctor-friendly solution providers, from the remaining scientific journals and universe of pharma/med device businesses, to diagnostics services providers, vertical systems of record like Veeva and the EHR platforms like Epic, payors, and clinical research organizations etc. are all lining up to plug in, stream their context, and make OpenEvidence’s platform closer to a medical super intelligence than it already is.

As a result, OpenEvidence’s moat is getting wider and deeper. They have managed to capture the two most important sources of data in this space, the highly trusted content in the top medical journals, and what questions the doctors want answered at the time they are treating a patient. These two sources of data are neither available on the open internet nor available to the big AI labs.

It Is Not Always About the Money

Another important point is that it is not always about the money:

Daniel Nadler, OpenEvidence’s CEO, provides some interesting context there: “some of these really well-funded AI companies threw enormous amounts of money at them (NEJM) and they said no. If they’re a private company, they probably would have said yes, but they’re a nonprofit so they said no because the Massachusetts Medical Society, which is a nonprofit organization, cared more about the sanctity and the pristineness of their mission as a nonprofit than they did about just trying to score some sort of quick commercial contract.”

Becoming Your Own Source of Valuable Data

The default assumption of the big AI companies is that valuable data is there, out in the world, and they have to find it and train their models on that data. And these companies are in the best position to crawl the internet and use that data and train massive models on that data.

But, OpenEvidence is likely to survive against them because it takes a different route: it is creating its own data—the doctor using OpenEvidence takes “a real time data stream about patient symptomology, diagnostic results, medical history, and all of their prior knowledge/intuition” and then converts this into a:

a clinical hypothesis and particularly, doubts they have around the hypothesis (“clinical uncertainty”) [which] previously did not exist in any other system, on prem, in the cloud, on paper, anywhere. Perhaps it existed as airwaves when a doctor asked another doctor they trusted for advice about a specific patient scenario. No one could survey doctors at scale and in real time about their diagnostic uncertainty; the best doctors wouldn't fill out the survey, and even if they did, the act of filling out a survey is not qualitatively the same as the context revealed from a constant stream of real, novel patient cases, under real pressure and uncertainty.

In short:

OpenEvidence’s context is a byproduct of genuine use, and genuine use is a byproduct of trust, and trust is a byproduct of credentialed ground truth supply that comes from existing at the edge. If you remove any link in that chain, the dark matter can’t be legibilized3 (because it wouldn’t even exist).

Can This Work in Other Domains?

The point of this article is to point the way to building valuable companies that cannot be eaten by OpenAI or Anthropic or Google. Here’s the generalization:

1. Exclusive credentialed ground truth (JAMA, NEJM partnerships) makes the sensor trusted

2. Trust legibilizes latent dark matter (50%+ of US doctors reveal their clinical uncertainty daily because they trust the sensor)

3. Legibilized dark matter monetizes privately without leaking to a centralized router (pharma pays $70–$150 CPMs for access to doctors’ moment of highest intent, and OpenEvidence captures this)

4. More and more solutions, from clinical trial patient recruitment to prior auth to medical device discovery, etc. continuously plug in, and compound dark matter origination/capture.

5. As more edge/domain specific problems are legibilized and accurately/completely solved by an increasingly large ledger of solutions, a new signal is created and compounded: verified outcomes in the domain. These verified outcomes (how well certain matches solved certain problems) can be leveraged to improve the edge router via RL. This is a runaway advantage that’s very hard to replicate without 1), 2), and the time it takes for 4) to mature.

Rephrasing that in simpler terms:

1. Build trust in a high-value domain by being “one of them” (where them is the highly-paid key decision-makers)

2. Use your trusted relationships to get access to data that is not accessible to the big AI companies

3. Use this to create a flywheel: more data → more users → more related services plug into your platform → more users → …

4. Understand how your users are using your system, and that becomes the source of the most important new data: what problems are they facing and where do the solutions (ultimately) come from.

TMKK

AI could eat your job or your company or your entire industry. The value of your technical skills (e.g., programming, knowledge of Indian tax laws, etc) is eroding every day. You have to learn what will remain important in the age of Claude: 1) Building trust, 2) Seeing people’s real problems and where the solutions come from, 3) Learning how to capture tacit knowledge in institutions (which AI doesn’t have access to) and making it legible, 4) Using #1 to #3 to help more and more people, and using that to build a flywheel.

Consider this tweet I posted today. The first screenshot is what I was going to post, but the second one is what I actually posted. Do you notice the difference? And how important is the difference?

and:

Strictly speaking, the phrase “worrying about power cuts” is unnecessary. It is obvious from the context. But I added the phrase because it reduces the amount of work your brain has to do1 to understand the “obvious” context.

Open Tabs in Your Brain

What extra work is your brain doing?

Think of what happens in your brain as you read my sentence left-to-right. Until you see the word “utility,” you have no idea what I’m talking about. At the mention of utility, a small fraction of you will interpret that word as “a public service such as light, power, or water”. Most others will think of simpler meanings of “utility”.

When you see the word “Thursday” and link it back to “Pune” and “2000s”, those of you who have been in Pune in the 2000s will remember the power cuts, and suddenly a light bulb will go on. But again, that’s just a fraction of you.

For a majority of you, you’ll then need to read Elon’s tweet, and when you see the words “Powerwall” or maybe when you reach “uninterrupted power,” is when you finally get the full context.

Imagine what happens in your brain as you're reading all of this. As you read, it has to keep track of the following:

• Something is insane (but I don’t know what)

• It is about people in the US

• They are talking about and planning things around “utility going down” (I assume this is about the usefulness of something reducing? Or is there another meaning?), and I still don’t know what it is that the usefulness is going down.)

• It has something to do with Pune

• In the 2000s

• And something to do with Thursdays in Pune (and I don’t know how Thursdays could be relevant to this conversation)

At this point, there are 6 open tabs in your brain. All of them are open because they are ambiguous, and their meaning will only become clear when you finally see the phrase “uninterrupted power”.

As soon as you see “Powerwall” (or for some of you, it will be “uninterrupted power”), you’ll suddenly realize, “Oh, Pune (probably) used to have power cuts in the 2000s”. Those 3 tabs collapse into one concept, “Indian power cuts”. Then the previous two tabs collapse into “wait, US people are worrying about power cuts just like Indians”? Now you understand what’s insane.

Compare that with what happens with the new sentence.

The first two tabs are the same:

• Something is insane (but I don’t know what)

• It is about people in the US

But now you see “worrying about power cuts,” and immediately, you get a tab collapse: “wait, US people are worrying about power cuts”? “That’s insane".

If you go through the same exercise with the new sentence, you’ll notice that your brain never has more than 3 tabs open at the same time.

Why Is This Important?

Your brain has a working memory which can only work with 5±2 concepts at a time. So, when you’re reading the first sentence, the 6 open tabs will max out your working memory, and this can result in two things:

• It crowds out your capacity to think of higher-level implications of what you’re reading (because that would require adding the higher-level concepts to your working memory)

• It tires out your brain if you’re constantly maxing out your working memory

More importantly, you’ll probably just stop reading at the 5th open tab and move on to the next tweet. Sentences that max out your working memory are common in academic papers, and you expect them, but on Twitter, most people are not in the mood to tire their brains. And in any case, this particular sentence was using 6 open tabs unnecessarily. When explaining some concepts, a sentence might actually need that many open tabs, but this topic did not need them.

But Isn’t the Element of Surprise a Good Thing?

You might argue that the best writing has an element of surprise, and that is what was present in my first sentence, which was killed by the phrase added in my second sentence.

Well, making the reader work hard is only justified if the payoff at the end of the hard work is good. I am allowed to force you to have 6 open tabs only if, when you finally understand what I’m talking about, it should be really funny or mind-blowing. That wasn’t the case.

And, if you analyze the work of good writers (either comedians or great marketers) who use the element of surprise effectively, you’ll notice that they don’t do this by increasing the number of open tabs. They achieve this by having one open tab that remains open for a longer time and then gives you a payoff when it resolves.

Why Am I Like This

Yesterday, I had this conversation with my friend Ashish:

Me: The podcast crew I mentioned is interested in a crossover episode... they'd like to discuss format. (Realistically, the producer Meera will handle logistics while the host just joins briefly.)

They have done some great collabs: [link]. And they are primarily interested in AI in India.

Assuming you're game, I'll intro you to Meera (the producer) and you can coordinate?

Ashish: You write the way you prompt 🙂 (repeat context that Meera is the producer not the host, for example)

Or did I get that the other way around? (Been noticing that with the way I write stuff these days myself)

Based on this article, you now know why I added “(the producer)” after “Meera” in my message.

But made me instrospect about when and why I started writing like this (or more importantly, thinking like this).

To answer Ashish’s question: I haven’t started writing like this because of LLM prompting. That happened much earlier.

My day job as CTO of ReliScore involves creating problems for job candidates to solve. And over the years of doing this, I have seen again and again candidates who misinterpret the questions or completely miss important context. This is a waste of time for everybody: a question that was supposed to test the programming skill of a candidate, for example, just ends up testing whether the candidate has good skills at reading English carefully and interpreting ambiguous phrases correctly based on the context. Ideally, I’m happy to reject such candidates, but many of my customers think otherwise. So, after 10+ years of doing this, I’ve become very sensitive to ambiguous phrases that are open to misinterpretation, and I have learnt to rewrite them for clarity.

This is easy to do if you are mindful. Given a sentence and told to “find all the ways in which a person can misinterpret it,” most people would be able to do a good job of it. A slightly harder skill is to “find the ways in which this sentence is increasing the number of open tabs for the reader”. That you can learn with some practice. The hardest part of this is for it to become natural enough so that you think about this in day-to-day life, when shooting off off-the-cuff tweets, or in WhatsApp conversations with friends.

How I Got My PhD and How That Is Connected to Writing

But first, it has to start with awareness of the importance of putting yourself in the shoes of the reader. Unexpectedly, this happened to me because I did a PhD.

When I was writing my first paper as part of my PhD, my advisor had so many problems with my writing that he would never reach past the first page before giving up. I would get a paper where the first page was full of red ink, and I had to rewrite it.

After a few rounds of this, where nothing I did resulted in any significant improvement, he2 started doubting himself. He actually called in a different, senior PhD student and asked him whether my paper made sense to him: to confirm whether the problem was with my writing or my advisor’s understanding. And that student confirmed that the problem was me. At this point, my advisor told me: I can write this paper for you, but that doesn’t bode well for you actually getting your PhD.

After another round or two of this, something in my head suddenly clicked, and I understood (in a way that I can’t quite articulate) what was missing. Today, I would explain it with the modern (AI-related) phrase “context is that which is scarce”. Basically, I grokked why the reviewer, the reader of my paper, would probably not easily understand my writing. They are not immersed in this area like I and my advisor; and they are not completely aware of the context in which I am writing. (Additionally, I understand today, the reviewer is also not keen on reading sentences that tire their brain.)

In any case, my next draft was so much better. My advisor made it all the way through to the end with only minor corrections. He actually called me in to ask whether I had gotten someone else to write it for me. And, since then, I’ve never had problems with my manuscripts being heavily red-inked.

On Writing Well

Another important step in my writing journey happened around 2008 when I quit my corporate job and decided to take a year or two off to explore random interests. That is the time when I upgraded my Hindi Songs Lyrics website, built my wife’s movie reviews website, and started PuneTech the tech community in Pune.

What few people know is that was also the year when I wanted to write a novel. As part of that exploration, I bought a bunch of books on how to write well. As fate would have it, I never got around to learning how to do creative writing well, but I loved the books which explained how to write clearly (for non-fiction writing). My favourite is Style: Toward Clarity and Grace3 by Joseph Williams.

One online review captured the importance of this book for me:

Most books on how to write better English are pretty near to useless. Many of them scare you into worrying that you might use “which” when you should use “that” (never mind that an extra “which” never caused any reader the smallest bit of confusion). Others demand that you strive for “clarity” or “brevity” or “coherence”--but then somehow never provide any useful advice on just how, exactly, to do so.

Joseph Williams’s Style: Toward Clarity and Grace is an exception.

For example, all my life I’ve heard that I should use “active voice” and I should avoid “passive voice”. If someone gives you that advice, ask them “Why?” and you’ll get some tautological explanation like “Because it is more active. Vigourous.” Williams’ book is the one that made me understand why active voice is (usually) better. More importantly, it even answers the second-level question, “Why does passive voice exist? Are there times when that is better?” Here’s an answer:

Context Is That Which Is Scarce: AI Edition

For 30+ years, my most valuable skill has been computer programming. But with the rise of LLMs, the most valuable skill is going to become writing in English4. Or more accurately, communicating effectively with an LLM.

And what does communicating effectively with an LLM involve? It is not “prompt engineering” which many people understand as memorizing arcane phrases to include in your prompt, like “let’s think about this step by step,” “a good answer from you is very important to my career,” or “you are a world-class expert in <field>”.

Good prompting today means that you give the LLM all the important context, all the background it needs to know about you, your situation, where and how the answer will be used, etc. Most people aren’t very good at this, but they don’t realise it because the LLM compensates for this. The LLM takes a guess at what the missing context is based on the most likely scenario. This guess is based on the LLM’s understanding of the world, which is itself based on its training data. When this happens, if you’re an average person with an average problem, you’ll get a great answer to your question. However, if anything in your situation is different/unique, you will get vaguely unsatisfying answers, and you will think that AIs are just not very good.

In other words, to use LLMs well, you need to have the ability to put yourself in the shoes of the LLM (like 1994-Navin trying to guess what the paper reviewer knows and doesn’t know) and then include all the missing context in your prompt along with your question.

Search the internet for “Context Engineering” and you’ll get some good articles on this.

Conclusion

Learn to write well. Learn to communicate the context properly. There is a lot of craft to it.

NCRA पुणे के विज्ञानियों ने एक ऐसी खोज की है जो महाविस्फोट के बाद की प्रारम्भिक आकाशगङ्गाओं के निर्माण की विज्ञान की समझ को बदल कर रख सकती है।

उन्होंने एक आकाशगङ्गा खोजी है जिसका नाम उन्होंने अलकनंदा रखा है। यह एक साधारण सी आकाशगङ्गा है, तो फिर इस में क्या विशेष है?

(टिप्पणी: इस लेख के कुछ अनुच्छेद विज्ञान में थोड़ी अधिक गहराई से जाते हैं—हो सकता है कि आपकी उतनी रुचि न हो। आप चाहें तो तिरछे अक्षरों में लिखे (जैसे कि यह अनुच्छेद )छोड़ सकते हैं। लेख का बाकी हिस्सा फिर भी समझ में आ जाएगा।)

जेम्स वेब् स्पेस् टेलीस्कोप द्वारा खींची गई फोटो में अलकनन्दा एक बहुत-ही छोटी बिन्दी के समान दिखती है। यदि हम इसे जूम् कर के देखें , तो यह एक पतली केन्द्रीय चक्रिका (डिस्क्) और दो भुजाओं वाली साधारण-सी सर्पिल आकाशगङ्गा दिखती है। ब्रह्माण्ड में ऐसी अरबों आकाशगङ्गाएँ हैं।

अलकनन्दा का अभिरक्त विस्थापन

अलकनन्दा का अभिरक्त विस्थापन (Red shift) 4 है जो कि इसे विशेष बनाता है। इसका मतलब है कि हम अलकनन्दा की ओर आये हुए जिस प्रकाश को अभी देख रहे हैं , वह 12 अरब वर्ष पहले उत्सर्जित हुआ था।

हम यह कैसे जानते हैं ?

आपको यह जानकर आश्चर्य होगा कि प्रकाश को ध्यान से देखने से हम बहुत-कुछ जान सकते हैं।

आप को मैं सरल शब्दों में समझाता हूँ — मान लीजिए हम अलकनन्दा के प्रकाश को एक विशेष प्रिज्म (prism) से गुजारते हैं। यह प्रकाश इन्द्रधनुष के रँगों में विभाजित हो जाता है। यदि आप इस spectrum को ज़ूम करेंगे , तो आपको कई पतली चमकीली ऊर्ध्वाधर (vertical) रेखाएँ दिखेंगी — जिस का मतलब है कि कुछ रँग अन्य रँगों से अधिक चमकीले हैं। ये चमकीली रेखाएँ हाइड्रोजन-जैसी गैसों द्वारा उत्सर्जित रँगों से मेल खाती हैं और हाइड्रोजन तत्त्व आकाशगङ्गाओं में सबसे अधिक पाया जानेवाला तत्त्व है। हमें पता चला कि हाइड्रोजन से जुड़ी एक चमकीली रेखा को जहाँ दिखना चाहिए था , वह वहाँ नहीं बल्कि उस से कहीं अधिक दाईं ओर (मतलब लम्बी तरङ्गदैर्घ्य वाली) स्थित है। इसे अभिरक्त विस्थापन (Red shift) कहते हैं। (यह सब तो आप को समझाने के लिए इतना सरल तरीके से लिखा गया है , काम तो बहुत जटिल था। NCRA के पास उपयुक्त spectrum नहीं था , किन्तु उन्होंने मौजूदा डाटा पर 21 फ़िल्टर्स के साथ उन्नत डाटा विश्लेषण करके इसी तरह का प्रभाव प्राप्त किया। हालाँकि मूल वैज्ञानिक सिद्धान्त समान ही हैं।)

अभिरक्त विस्थापन (Red shift) क्यों होता है ?

आइंस्टीन के सापेक्षता-सिद्धान्त के अनुसार , “ब्रह्माण्ड का विस्तार हो रहा है और यही अभिरक्त विस्थापन का कारण बनता है।” कल्पना करें कि एक गुब्बारे की सतह पर एक छोटी रेखा खींची गयी है। यदि गुब्बारे को और फुलाया जाए , तो उस की सतह खिंचती है और रेखा की लम्बाई बढ़ जाती है। ठीक वैसे ही जब प्रकाश विस्तारित हो रहे ब्रह्माण्ड में यात्रा करता है , तो उसकी तरंगदैर्घ्य खिंचकर बढ़ जाती है।

हम यह जानते हैं कि ब्रह्माण्ड कब से और कितनी तेजी से फैल रहा है। इसलिए अलकनन्दा के प्रकाश के अभिरक्त विस्थान की मात्रा (के मान) को देखकर हम गणना कर सकते हैं कि यह प्रकाश हम तक पहुँचने के लिए ब्रह्माण्ड में कब से चलायमान है।

और यह प्रकाश पिछले 12 अरब वर्षों से हम तक पहुँचने के चलायमान है।

अलकनन्दा से आये प्रकाश को हम तक पहुँचने में 12 अरब वर्ष लगे। आप सोच रहे होंगे कि यह महत्वपूर्ण क्यों है ? क्यों कि इस का मतलब है महाविस्फोट के 1.5 अरब वर्ष पश्चात् ही दो भुजाओं वाली एक साधारण सर्पिलाकार आकाशगङ्गा विद्यमान थी। खगोल भौतिकी वैज्ञानिकों के लिए यह असम्भव-सा प्रतीत होता है।

हम जानते हैं कि हमें पहुँचने वाला प्रकाश 12 अरब वर्ष पहले निकला था। इसका मतलब है कि हम आकाशगङ्गा को वैसा ही देख रहे हैं , जैसी वह 12 अरब वर्ष पहले दिखती थी। (हाँ! हम 12 अरब वर्ष पुराने अतीत में झाँक रहे हैं।) इसका मतलब है कि 12 अरब वर्ष पहले अलकनन्दा दो भुजाओं वाली एक सुगठित सर्पिल आकाशगङ्गा के रूप में विद्यमान थी। और महाविस्फोट 13.5 अरब वर्ष पहले हुआ था। इसका मतलब है कि यह आकाशगङ्गा महाविस्फोट के मात्र 1.5 अरब वर्ष बाद ही अस्तित्व में आ चुकी थी। (स्पष्टीकरण के लिए दूसरा स्पष्टीकरण — वास्तव में महाविस्फोट 13.8 अरब वर्ष पहले हुआ था जिसे मैं ने आसानी के लिए 13.5 लिखा है। परन्तु चिन्ता न करें , अलकनन्दा भी 12.3 अरब वर्ष पुरानी है जिसे मैं ने 12 अरब वर्ष लिखा था। इसलिए यह कथन कि अलकनन्दा महाविस्फोट के 1.5 अरब वर्ष बाद बनी थी — अभी भी सही है।)

अलकनन्दा की आयु इतनी महत्त्वपूर्ण क्यों है ?

महाविस्फोट तथा आकाशगङ्गाओं के निर्माण के बारे में हमारे सर्वोत्तम सिद्धान्तों के अनुसार , स्पष्ट और सुनिश्चित भुजाओं तथा केन्द्र में चपटी चक्रिका (डिस्क्) वाली सर्पिल आकाशगङ्गाएँ को बनने में कम से कम 3 अरब या 4 अरब वर्ष लगने चाहिए। 1.5 अरब वर्ष में ही ऐसी आकाशगङ्गा का बनना यह दर्शाता है कि हमारे सर्वोत्तम सिद्धान्त भी अपर्याप्त हैं।

आकाशगङ्गाएँ कैसे बनती हैं ?

महाविस्फोट ने ब्रह्माण्ड में बहुत सारा पदार्थ इधर-उधर फेंक दिया है जो कि चलायमान है।

कल्पना कीजिए एक ऐसे ब्रह्माण्ड की जो हाइड्रोजन परमाणुओं से भरा हो। गुरुत्वाकर्षण बल के कारण ये परमाणु धीरे-धीरे एक-दूसरे के निकट आने लगे। जैसे-जैसे अधिक से अधिक परमाणु एकत्रित हुए , वे गोलाकार गुच्छे/निगुम्फ (ब्लॉब्) का रूप लेने लगते हैं और ये गोलक (spheres) तारों में विकसित हो जाते हैं। फिर तारों के ये निगुम्फ/गुच्छे एक-दूसरे को आकर्षित करते हुए आकाशगङ्गाओं का निर्माण करते हैं।

ध्यान रखने वाली बात यह है कि महाविस्फोट के कारण हाइड्रोजन परमाणुओं के पास पहले से ही संवेग था। एक-दूसरे की ओर आकर्षित हो कर आकाशगङ्गाओं का निर्माण करने से पहले वे अलग-अलग दिशाओं में गति कर थे। विज्ञानियों ने इन परमाणुओं की गतियों के विस्तृत मॉडल तैयार किए हैं और प्रारम्भिक संवेग की जानकारी के आधार पर यह दिखाया जा सकता है कि किस प्रकार की आकाशगङ्गाएँ बनेंगी — गोलाकार , दीर्घवृत्तीय , चपटी चक्रिका , उभार वाली चक्रिका , भुजाओं वाली चक्रिका। जो कि अन्ततः सर्पिलाकार रूप धारण कर लेती हैं। इन सिद्धान्तों के अनुसार , प्रारम्भिक संवेग के कारण ही आकाशगङ्गाएँ घूमती हैं और समय के साथ उनमें सर्पिल भुजाएँ विकसित होती हैं। परन्तु इस प्रकार प्रक्रियाएँ लगभग-लगभग 3 अरब वर्ष का समय लेती हैं।

JSWT से पहले , खगोलविदों का मानना था कि ब्रह्माण्ड के आरम्भ की आकाशगङ्गाएँ तो अव्यवस्थित और अस्थिर होनी चाहिए थीं। आरम्भिक आकाशगङ्गाओं का “गर्म” और प्रक्षुब्ध होना भी अपेक्षित था। और यह भी अपेक्षित था कि इन को ठण्डा होने तथा ऐसी सु-गठित घूर्णन-प्रधान चक्रिका बनने में भी समय लगेगा जो कि सर्पिल पैटर्न् बनाये रखने में सक्षम हों।

योगेश वाडदेकर कहते हैं कि अलकनन्दा एक अलग कहानी बताती है। वे आगे कहते हैं , “इस आकाशगङ्गा को कुछ दस करोड़ वर्षों में 10 अरब सूर्यों के बराबर द्रव्यमान के तारे इकट्ठा करने पड़े और साथ‑ही-साथ सर्पिल भुजाओं वाली एक बड़ी चक्रिका (डिस्क) भी बनानी पड़ी। ब्रह्माण्डीय मानकों के हिसाब से यह बहुत ही तीव्र है।”

संक्षेप में कहें , तो हमारे मॉडल यह समझाने में लड़खड़ा रहें हैं कि महाविस्फोट के केवल 1.5 अरब वर्ष बाद ही चपटी केन्द्रीय चक्रिका और दो सु-स्पष्ट सर्पिल भुजाओं वाली आकाशगङ्गा विद्यमान कैसे हो सकती है।

चूँकि हमारे सिद्धान्तों के लिए ‘अलकनन्दा’ आकाशगङ्गा के अस्तित्व को समझाना कठिन हो रहा है इसलिए अच्छा यही होगा कि हम हमारे आकाशगङ्गा-निर्माण के सिद्धान्तों में बदलाव करें। यही वह कारण है कि NCRA पुणे द्वारा दो भुजाओं वाली सर्पिल आकाशगङ्गा की खोज बहुत महत्वपूर्ण मानी जा रही है।

यह कारनामा किस ने और कैसे किया ?

इस की खोज किसने की और कैसे ? NCRA की शोध-अध्येता राशि जैन और उन के सलाहकार @YWadadekar ने जेम्स् वेब् स्पेस् टेलीस्कोप (दूरदर्शित्र) के सार्वजनिक रूप से उपलब्ध डाटा में छानबीन कर रहे थे। वे विशेष रूप से उन पिण्डों को ढूँढते रहे थे जिन का अभिरक्त विस्थापन ≥ 3 हो। और तभी उन्हें इस के बारे में पता चला।

उन्हों ने एक सॉफ़्टवेयर् का प्रयोग कर के उन सभी पिण्डों की सूची बनायी जिन का अभिरक्त विस्थापन ≥ 3 था। इस का परिणाम यह हुआ कि उन्हें 2700 ऐसे पिण्ड मिले जिन का अभिरक्त विस्थापन ≥ 3 था। तदोपरान्त राशि ने सभी पिण्डों को बारी-बारी से यह सोच कर जाँचा कि शायद उन्हें इस में कीछ काम की जानकारी मिल जाये। तभी उन्हें एक सु-गठित सर्पिल आकाशगङ्गा दिखी। (इस का वैज्ञानिक नाम “विशालकाय अभिकल्पित सर्पिल” (Grand design Spiral) है।) राशि ने तुरन्त ही इसे योगेश को दिखाया। इस के बाद उन्हों ने अपने इस जानकारी की पुष्टि के लिए विभिन्न विश्लेषणात्मक परिक्षणों से गुजारा। और वे यह तब तक करते रहे , जब तक कि वे आश्वस्त न हो गये कि उन्हों ने ब्रह्माण्ड में एक असङ्गति खोज निकाली है।

ओपन साइंस ज़िंदाबाद!

जेम्स वेब स्पेस टेलीस्कोप के विकास, लॉन्च या उसके डाटा सङ्ग्रहण में NCRA की कोई भूमिका नहीं थी , किन्तु सब के लिए यह डाटा सार्वजनिक रूप से उपलब्ध है। जिस के कारच NCRA के शोधकर्ताओं को यह अध्ययन तथा खोज को करने में सहायता मिली।

यह खोज इस बात का उत्तम उदाहरण है कि यदि विज्ञान को प्रतिलिप्यधिकारों से मुक्त रखा जाये , तो मानव-जाति बहुत ऊँचाइयों को छू सकती है।

इस का नाम “अलकनन्दा” ही क्यों ?

हमारी आकाशगङ्गा का अँग्रेजी में नाम Milky way है और इस का हिन्दी में नाम “मन्दाकिनी” है। हिमालय की एक नदी का भी नाम “मन्दाकिनी” है। और हमारी इस नयी खोजी गयी आकाशगङ्गा भी हमारी आकाशगङ्गा-जैसी ही है , इसी लिए इस का नाम “अलकनन्दा” है। वैसे हिमालय की एक नदी का नाम भी अलकनन्दा है। इसी लिए शोधकर्ताओं ने इस के लिए यह नाम चुना है।

अधिक जानकारी

शोध के अधिक विवरण के लिए official press release देखें (यह हिंदी और मराठी में भी उपलब्ध है)। इसमें बहुत-सी उपयोगी जानकारी है—उदाहरण के लिए: यह खोज जेम्स वेब अंतरिक्ष दूरबीन के बिना क्यों संभव नहीं थी, गुरुत्वीय लेंस के बिना क्यों संभव नहीं थी, इन्फ्रारेड दूरबीन के बिना क्यों संभव नहीं थी, और यह खोज महत्वपूर्ण कैसे है—क्योंकि अलकनंदा कोई गरम आकाशगंगा नहीं, बल्कि एक ठंडी आकाशगंगा है।

इससे भी अधिक विवरण के लिए मूल शोधपत्र देखें—यह शोध यूरोप की पत्रिका Astronomy & Astrophysics में प्रकाशित हुआ है, जो इस क्षेत्र की सर्वोच्च पत्रिकाओं में से एक है।

या आप लेखकों से संपर्क कर सकते हैं: rjain@ncra.tifr.res.in

योगेश वाडदेकर ट्विटर पर सक्रिय हैं: @YWadadekar और @NCRA_Outreach NCRA का आधिकारिक हैंडल है।

नुकतीच अनेक वृत्तसंस्थांनी पुण्याच्या NCRAमधील वैज्ञानिकांनी अलकनंदा दीर्घिकेचा शोध लावल्याची बातमी प्रसारित केली. मात्र, त्यापैकी बहुतांश वृत्तसंस्थांनी ह्या बातमीचे महत्त्व काय यावर प्रकाश टाकलेला नाही. त्यामुळे प्रस्तुत लेखात ह्या शोधाशी संबंधित विज्ञान कोणालाही सहज समजेल अशा साध्या सरळ भाषेत स्पष्ट करून सांगितले आहे.

त्यांनी शोधून काढलेल्या दीर्घिकेला अलकनंदा हे नाव दिले आहे. महाविस्फोटातून(Big Bang) प्रारंभिक दीर्घिकांची निर्मिती कशी झाली ह्याविषयीच्या विज्ञानाच्या सध्याच्या आकलनात या शोधामुळे आमूलाग्र बदल संभवतो.

पहिल्या नजरेत अलकनंदा दीर्घिका ही एक साधी दीर्घिका वाटते, मग तिचे इतके महत्त्व का?

(सूचना : या लेखातील काही परिच्छेदांतील मजकूर तुमच्या अपेक्षेपेक्षा अधिक सखोल असू शकतो. या सूचनेसारख्या तिरक्या अक्षरमुद्रांमधील परिच्छेद गाळून पुढे गेलात तरी चालेल, असे केल्यावरही लेखाचा उरलेला भाग अर्थपूर्णच असेल.)

जेम्स वेब अवकाश दुर्बिणीतून मिळवलेल्या प्रतिमेत अलकनंदा हा एक छोटासा ठिपका आहे. तो मोठा करून पाहिले असता “पातळ केंद्र व दोन भुजा असलेली एक प्राथमिक स्वरूपाची सर्पिल दीर्घिका” यापेक्षा वेगळे काही दिसत नाही. अशा अब्जावधी दीर्घिका आहेत. तसेच अलकनंदा दीर्घिकेचा आकार व आकारमान यातही काही खास बाब दिसून येत नाही.

अलकनंदा दीर्घिकेला दोन भुजा आहेत आणि तिचे वय बारा अब्ज वर्षे आहे: आणि हीच ती खास बाब आहे.

अलकनंदाला खास बनवणारी बाब म्हणजे तिची रेडशिफ्ट ४ आहे याचा अर्थ असा की अलकनंदाचा जो प्रकाश आपण आत्ता पाहात आहोत तो तिने बारा अब्ज वर्षांपूर्वी उत्सर्जित केला होता.

हे आपल्याला कसे समजते? प्रकाशाच्या बारकाईने केलेल्या अवलोकनातून किती गोष्टी सांगता येतात हे जाणून तुम्हाला आश्चर्य वाटेल. अति सोपे करून सांगितलेले हे स्पष्टीकरण पहा. समजा आपण अलकनंदा कडून आलेला प्रकाश एखाद्या वैशिष्ट्यपूर्ण लोलकातून जाऊ दिला. तेव्हा प्रकाशाचे इंद्रधनुष्यी रंगांमध्ये विभाजन (वक्रीभवन/अपवर्तन) होते असे शालेय भौतिकशास्त्रात शिकल्याचे तुम्हाला आठवेल. हा रंगपट मोठा करून पाहिला असता त्यात अनेक चमकदार उभ्या रेषा दिसतात. याचा अर्थ काही रंग दुसऱ्या रंगांपेक्षा जास्त चमकदार आहेत. ह्या रेषा म्हणजे दीर्घिकेमध्ये सर्वसाधारणपणे आढळणाऱ्या हायड्रोजन सारख्या वायूने उत्सर्जित केलेले रंग ( विशिष्ट तरंगलांबीचे प्रकाशकिरण) होत. पण आपल्याला असे दिसते की हायड्रोजनच्या चमकदार रेषांपैकी एक तिच्या अपेक्षित जागी न आढळता बरीच उजव्या बाजूला सरकलेली (जास्त तरंगलांबीच्या ठिकाणी) आढळली. ह्याला रेडशिफ्ट म्हणतात . (प्रत्यक्षात अशीच्या अशी कृती केलेली नाही. हे अति सोपे करून सांगितलेले स्पष्टीकरण आहे. NCRA ला योग्य तो वर्णपट-आलेख उपलब्ध नव्हता; परंतु त्यांनी २१ वेगवेगळ्या गाळण्या वापरून उपलब्ध माहितीची प्रगत माहिती हाताळणी तंत्राने करून हा निष्कर्ष मिळवला. परंतु संबंधित मूलभूत विज्ञानाचे तत्त्व तेच आहे.)

रेड शिफ्ट कशामुळे घडून येते? आईन्स्टाईनच्या सापेक्षतावादाच्या सिद्धांतानुसार विश्व प्रसरण पावत आहे; आणि त्यामुळे रेड शिफ्ट घडून येते. कल्पना करा की एका फुग्यावर लहानशी रेषा काढली आहे. जर फुगा आणखी फुगवला तर त्याचा कपडा ताणला जातो आणि रेषेची लांबी वाढते. प्रसरण पावणाऱ्या अवकाशातून प्रकाश जातो तेव्हा हेच घडते : तरंगलांबी ताणली जाते, म्हणजेच मोठी होते.

आपल्याला विश्व किती आणि कधीपासून प्रसरण पावत आहे ते माहित आहे, त्यामुळे अलकनंदाच्या प्रकाशात रेड शिफ्ट किती प्रमाणात झाली आहे ते अवलोकन करून आपण आकडेमोड करून काढू शकतो की त्याला विश्वामधून प्रवास करत आपल्यापर्यंत पोहोचण्यासाठी किती काळ लागला.

आणि हा काळ १२ अब्ज वर्षे आहे.

अलकनंदाचा प्रकाश आपल्यापर्यंत पोहोचायला १२ Gyr (Gyr = गिगावर्षे = अब्ज वर्षे) लागली. यांचे महत्त्व काय आहे? कारण, याचा अर्थ होतो की ही दोन भुजा असलेली सर्पिल दीर्घिका महाविस्फोटानंतर १.५ अब्ज वर्षांनी अस्तित्वात होती. हा तर “अरे बापरे—हे काय?!” असा क्षण आहे

प्रकाश आपल्यापर्यंत पोहोचायला १२ अब्ज वर्षे लागली आणि तो महाविस्फोटानंतर १.५ Gyr काळ गेला असताना निर्माण झाला यात काय संबंध आहे? आपल्याला समजले की आपल्यापर्यंत पोहोचलेला प्रकाश तेथून १२ अब्ज वर्षांपूर्वी निघाला होता. म्हणजे आपण ही दीर्घिका १२ अब्ज वर्षांपूर्वी जशी होती तशी आपल्याला दिसते आहे. (होय, आपण १२ अब्ज वर्षांपूर्वीचा भूतकाळ पाहात आहोत.)ह्याचा अर्थ असा होतो की व्यवस्थित तयार झालेली दोन भुजा असलेली अलकनंदा ही सर्पिल दीर्घिका १२ अब्ज वर्षांपूर्वी अस्तित्वात होती. आणि महाविस्फोट हा १३.५ वर्षांपूर्वी घडलेला होता. याचा अर्थ असा की ही दीर्घिका महाविस्फोटानंतर १.५ Gyr इतका काळ गेला तेव्हा पूर्णपणे तयार झालेली होती. (स्पष्टीकरणाचे स्पष्टीकरण : प्रत्यक्षात महाविस्फोट १३.८ अब्ज वर्षांपूर्वी घडला ज्याला सोपे करताना १३.५ अब्ज वर्षांपूर्वी असे म्हटले आहे. पण काळजी नको. अलकनंदा सुद्धा १२.३ अब्ज वर्षांपूर्वी तयार झालेली होती ज्याला सोपे करून १२ अब्ज वर्षांपूर्वी असे म्हटले आहे. त्यामुळे महाविस्फोटानंतर १.५ अब्ज वर्षांनी अलकनंदा तयार झाली हे विधान तरीही खरे आहे.)

अलकनंदाचे वय एवढे महत्त्वाचे का?

महाविस्फोट आणि दीर्घिका कशा तयार होतात यासंबंधी आपल्या सर्वोत्तम सिद्धांताच्या अनुसार स्पष्ट व सुनिश्चित भुजा आणि मध्यभागी चपटी तबकडी असलेली सर्पिल दीर्घिका तयार होण्यासाठी किमान ३ किंवा ४ अब्ज वर्षे लागतात. १.५ अब्ज वर्षांत तयार झालेली दीर्घिका दिसणे याचा अर्थ आपले सर्वोत्तम सिद्धांत हे अपुरे पडतात.

दीर्घिका कशा तयार होतात? महाविस्फोटामुळे खूप पदार्थ फेकले जाऊन ते विश्वात फिरू लागले. हायड्रोजनच्या अणूंनी भरलेल्या विश्वाची कल्पना करा. हळुहळू हायड्रोजन अणूंमधील गुरुत्वीय आकर्षणामुळे ते एकमेकांच्या जवळ येऊ लागतात. जसजशी ही क्रिया अधिकाधिक हायड्रोजन अणूंबरोबर घडू लागते तसतसे ते एकत्र येऊन त्यांची ढेकळे बनू लागतात. त्यांचे रूपांतर गोलाकार गोळ्यांमध्ये होऊन त्यांचे तारे बनतात.अनेक तारे एकमेकांकडे आकर्षित होऊन तारका समूह म्हणजेच दीर्घिका तयार होतात.

एक लक्षात ठेवा की हायड्रोजन अणूंना आधीच महाविस्फोटामुळे संवेग प्राप्त झालेला होता; ते आधी वेगवेगळ्या दिशांनी फिरत होते तेव्हा त्यांच्यात आकर्षण निर्माण होऊन दीर्घिका तयार झाल्या. ह्या अणूंच्या हालचालींची तपशीलवार प्रारूपे वैज्ञानिकांनी तयार केलेली आहेत. तसेच अणूंच्या प्रारंभिक संवेगाच्या तपशीलानुसार विविध आकाराच्या - गोल, लंबगोलाकार, चपटी तबकडी, फुगवटा असलेली तबकडी, भुजा असलेली तबकडी की शेवटी तिच्यापासून सर्पिलाकृती - दीर्घिका बनतील हेही दाखवता येते. या सिद्धांतांनुसार प्रारंभिक संवेगामुळे ह्या दीर्घिका स्वतःभोवती फिरत असतात आणि कालांतराने त्यांच्या सर्पिल भुजा विकसित होतात. परंतु याला काही काळ लागतो …. सुमारे ३ अब्ज वर्षे.

जेम्स वेब अवकाश दुर्बिणीपूर्वी (James Webb Space Telescope, JWST)अवकाशवैज्ञानिकांची अशी धारणा होती की नूतन विश्वातील दीर्घिका ह्या अस्ताव्यस्त आणि ओबडधोबड असतील. नूतन दीर्घिका “उष्ण” आणि “अशांत” असतील अशी अपेक्षा होती, आणि त्यांना थंड होऊन सर्पिल आकार राखू शकणाऱ्या व प्रामुख्याने चक्राकार गतीने फिरणाऱ्या व्यवस्थित तयार झालेल्या तबकडी स्वरूपात स्थिरावण्यासाठी काही काळ लागेल.

“अलकनंदा वेगळीच गोष्ट सांगते, ” योगेश वाडदेकर म्हणतात. “या दीर्घिकेला १० अब्ज सूर्यांच्या वस्तुमानाएवढे तारे एकत्र जुळवावे लागले व ते करताना सर्पिल भुजा असलेली मोठी तबकडी बनवावी लागली; आणि हे केवळ एक दशकोटी वर्षांमध्ये. अंतराळाची प्रमाणे पाहता हा जलदपणा अविश्वसनीय आहे.

थोडक्यात सांगायचे तर, चपटी मध्यवर्ती तबकडी व दोन सुनिश्चित भुजा असलेली दीर्घिका १.५ अब्ज वर्षांमध्ये तयार कशी झाली हे स्पष्ट करताना आपल्या प्रारूपांना झगडावे लागते आहे.

अलकनंदा दीर्घिकेचे अस्तित्व स्पष्ट करताना आपल्या सिद्धांतांना अडचण येत आहे. त्यामुळे दीर्घिका निर्मितीचे सिद्धांत बदलावे लागतील अशी खूप शक्यता आहे.

हे आहे पुण्याच्या NCRA ने लावलेल्या प्राथमिक स्वरूपाच्या दोन भुजांच्या सर्पिल दीर्घिकेच्या शोधाचे महत्त्व.

हा शोध कोणी व कसा लावला?

NCRA येथील संशोधक विद्यार्थिनी राशी जैन आणि तिचे सल्लागार योगेश वाडदेकर यांनी जेम्स वेब अवकाश दुर्बिणीतून मिळालेली सार्वजनिकरीत्या उपलब्ध असलेला डाटा वापरून संशोधन केले, विशेषकरून ज्यांची रेड शिफ्ट ३ किंवा अधिक आहे अशा वस्तूंचा शोध घेतला, आणि प्रदीर्घ व कष्टप्रद शोधकार्यानंतर ही दीर्घिका सापडली.

एक सॉफ्टवेअर पॅकेज वापरून त्यांनी विशिष्ट आकारमानाहून मोठ्या व ३ किंवा अधिक रेड शिफ्ट असलेल्या सर्व वस्तूंची यादी बनवली.

अशा २७०० वस्तू आढळल्या.

यानंतर राशीने स्वहस्ते सर्व वस्तूंचे तपशीलवार वर्गीकरण केले व काही चित्तवेधक सापडते का ते पाहिले.

जेव्हा तिला व्यवस्थित तयार झालेली दीर्घिका (वैज्ञानिक शब्द “ग्रँड डिझाईन स्पायरल”) दिसली तेव्हा महत्त्व ओळखून तिने लगेच ते योगेश यांच्या निदर्शनास आणून दिले.

त्यानंतर दोघांनी शोधाची शहानिशा करण्यासाठी माहिती अनेक विश्लेषण पद्धतींनी तपासली. शेवटी त्यांची खात्री पटली की त्यांना खरोखरच एक मोठी सह-विसंगती (को-ऍनॉमली)सापडली आहे.

मुक्त विज्ञान गरजेचे

NCRA ने जेम्स वेब अवकाश दुर्बिण विकसित किंवा स्थापित करण्यात कोणतीही भूमिका बजावलेली नाही.

परंतु, ही माहिती सार्वजनिकरीत्या उपलब्ध असल्याने NCRA च्या संशोधकांना तिचा अभ्यास करून हा शोध लावता आला.

विज्ञान खुलेपणाने केल्यावर काय शक्य आहे याचे हे ढळढळीत उदाहरण आहे.

“अलकनंदा” का?

आपल्या आकाशगंगेचे भारतीय नाव मंदाकिनी आहे आणि हिमालयात उगम पावणाऱ्या एका नदीचेही हे नाव आहे. नवीन शोधलेली दीर्घिका आकाशगंगेच्याच प्रकारची आहे, आणि अलकनंदा नदी मंदाकिनी नदीसारखी आहे, त्यामुळे संशोधकांनी हे नाव निवडले.

अधिक तपशील

शोधाच्या अधिक तपशीलासाठी पहा official press release (मराठी व हिंदी मध्ये ही उपलब्ध). यात बरीच उपयुक्त माहिती आहे, उदाहरणार्थ - हा शोध जेम्स वेब अवकाश दुर्बिणीविना का शक्य नव्हता, गुरुत्वीय भिंगाशिवाय तो का शक्य नव्हता, इन्फ्रारेड टेलिस्कोपीशिवाय तो का शक्य नव्हता, आणि हा शोध महत्त्वाचा कसा आहे - कारण अलकनंदा ही उष्ण दीर्घिका नसून थंड दीर्घिका आहे.

याहूनही अधिक तपशीलांसाठी मूळ शोधनिबंध पहा - हे संशोधन पुढील नियतकालिकामध्ये प्रकाशित झाले आहे Euro journal, Astronomy & Astrophysics, जे या क्षेत्रातील सर्वोच्च नियतकालिकांपैकी एक आहे.

किंवा तुम्ही लेखकांशी संपर्क साधू शकता rjain@ncra.tifr.res.in,

योगेश वाडदेकर ट्विटरवर सक्रीय आहेत @YWadadekar आणि @NCRA_Outreach हे NCRA चे अधिकृत हॅंडल आहे

(ह्या लेखाचा मसूदा वाचून त्यातील तांत्रिक तपशील दुरुस्त केल्याबद्दल योगेश वाडदेकर यांना धन्यवाद.)

I love strategy boardgames (like Settlers of Catan). I have a big collection at home and we host game days regularly. The important feature of these games is that they’re neither dumb (like Monopoly, which is mostly luck) nor do they require very high levels of thinking and analysis and preparation (like chess or bridge).

I also play card games. Sid Sackson’s book Card Games from Around the World is one of my prized possessions. But I like them less. I don’t enjoy rummy or most of its variations. So, usually when someone suggests playing card games, I am not usually keen.

However, this year, I learnt a new game called Cabo. I had not heard about it before, and neither had anyone else I know. But it has quickly become one of the favourite games for my family to play when we want something light (i.e. don’t have time to play a big board game). A single game takes just a few minutes, but you can keep playing it for hours without getting bored. Everyone we have introduced this game to also loves it, so I thought I should introduce it to more people.

Cabo is actually a 2010 game designed by Melissa Limes and Mandy Henning. As far as I can tell, it is no longer sold commercially (the official website link on Wikipedia is dead, and Amazon’s listing for the game says “currently unavailable”). If you find an official copy of the game, please buy it so that the game designers make some money from it.

There are several variations of the rules floating around on the internet, and I don’t think all variations result in an equally fun game. So here are the rules we play by.

CABO (Kabra House Rules)

Objective

Have the lowest total card value in your hand when the round ends.

Setup

Deck: Standard 52-card deck + 2 Jokers (54 cards total).

Card values:

• Ace = 1; 2–10 = face value; Jack = 11; Queen = 12

• Red King (♥♦) = 0

• Black King (♠♣) = 13

• Joker = −1

Deal:

• Deal 4 cards face-down to each player. Arrange them in a 2×2 grid.

• Each player secretly looks at the two cards closest to them. Memorize them, then return them face-down.

• You may not look at your cards again unless a card power allows it.

The deck:

• Place the remaining cards face-down in the center as the Draw Pile.

• Flip the top card face-up to start the Discard Pile.

Gameplay

In the first game, the youngest player goes first. In subsequent games, the player to the left of the previous winner goes first. Players take turns in clockwise order.

On your turn, choose exactly one action.

Option A: Draw an unknown card

• Draw the top card from the Draw Pile and look at it privately.

• Then choose one:

  • Swap: Put the drawn card face-down into any position in your grid. Discard the replaced card face-up in the discard pile.

  • Discard: Discard the drawn card face-up in the discard pile. If it has a power, you may use it immediately.

Option B: Take a known card

• Take the top card from the Discard Pile.

• Swap it into your grid by placing it face-down into any position.

• Discard the replaced card face-up.

• No power triggers when you take a card from the Discard Pile.

Card powers

A power triggers only when you draw from the Draw Pile and then discard that same drawn card.

• No power triggers when you swap a drawn card into your grid.

• No power triggers when you take a card from the Discard Pile.

Powers by discarded card:

• 7 or 8: Peek at one of your own face-down cards.

• 9 or 10: Peek at one face-down card belonging to an opponent.

• Jack or Queen: Swap one of your face-down cards with one face-down card from an opponent. Do not see either card. (It is possible that you know one or both cards from previous turns; but you’re not allowed to look at them this turn)

• Black King: Choose one of your face-down cards and one face-down card from an opponent. Peek at exactly one of the two cards. Then either swap them or leave them.

Discarding matching cards

After a card is discarded face-up onto the Discard Pile, all players may immediately discard matching cards.

• A match means the same rank as the top card of the Discard Pile. Suits do not matter. A 7 matches any other 7. Ace matches ace. Even kings match other kings irrespective of colour.

• You may discard one or more of your own matching face-down cards.

• If you discard a non-matching card, you take a penalty (described below)

• This rule does not apply to the very first card flipped to start the Discard Pile during setup.

Calling out an opponent’s match

If play pauses for about 2 seconds and you believe an opponent has a matching card, you can discard that card and put one of your own cards in its place. If it does not match return it to its spot face-down and take a penalty.

Penalty

If you attempt to discard a non-matching card during the matching phase, you must replace that card in its place and then draw an additional card from the draw pile. Place this new card as a new column beside your grid without looking at it.

This card now becomes a part of your hand.

Calling CABO

At the end of your turn, you may say “CABO.” After this, each other player then takes one turn and the game ends. (The player who called CABO does not play again.)

Scoring

Each player’s score is the sum of the values of the cards in their hand.

• If the player who called CABO has the lowest score and is not tied with anyone, subtract 5 points from their score.

• If the CABO caller is not the lowest, add 5 points to their score.

• If the CABO caller is tied for the lowest, then their score remains unchanged.

Add each player’s round score to their running total. It is possible that a player scores negative points in a game (because of jokers or because of the CABO bonus).

Note: some of the paragraphs in this article go a little deeper into the science than you might be interested in. Feel free to skip the paragraphs that are italicized like this. The rest of the article should still make sense.

They have discovered a galaxy which they’ve named Alaknanda. This discovery has the potential to shake up science’s understanding of how early galaxies were formed after the Big Bang.

But, at first glance, Alaknanda seems like a fairly basic galaxy, so why is it such a big deal?

Alaknanda is a tiny dot on the images taken by the James Webb Space Telescope. If you zoom in, it looks rather unremarkable: a basic spiral galaxy with a thin central disk and two arms. There are billions of galaxies like this, and nothing about its shape or size is special.

Alaknanda has two arms and is 12 billion years old: that’s special

What makes Alaknanda special is that it has a redshift of 4. This means that the light from Alaknanda that we are seeing just now was emitted over 12 billion years ago.

How do we know this? You’ll be surprised by how much we can tell by looking closely at the light. Consider this oversimplified explanation: suppose we pass the light from Alaknanda through a very fancy prism. It splits into the rainbow colours, as you would remember from your high-school physics. If you zoom into this spectrum, you notice a lot of thin bright vertical lines—meaning some specific colours are brighter than the others. These correspond to colours (”wavelengths”) emitted by a gas like Hydrogen, which is the most common element in a galaxy. It turns out that one of the bright lines corresponding to Hydrogen is not in the correct place, but is located much further to the right (longer wavelength) than what we expected. This is called ‘redshift’. (This is not literally what was done. This explanation is an over-simplified explanation. NCRA didn’t have access to an appropriate spectrograph, but used advanced data manipulation on the existing data with 21 different filters to get a similar effect. But the basic science principles are the same.)

What causes the redshift? According to Einstein’s theory of relativity, the universe is expanding, and this causes the redshift. Imagine a short line drawn on the surface of the balloon. If the balloon is blown up further, the fabric stretches, and the length of the line increases. The same thing happens when light is traveling in expanding space: the wavelength gets stretched and hence increases.

We know how much the universe is expanding and since when, so by looking at the amount of redshift of Alaknanda’s light, we can calculate how long it has been traveling through the universe to reach us.

And that is 12 billion years.

Light from Alaknanda took 12 Gyr (Gyr = gigayears = billion years) to reach us. Why is this important? Because it means that a spiral galaxy with 2 arms existed 1.5 billion years after the Big Bang. That is not supposed to happen! This is a “Wait..What?!” moment.

What’s the connection between light taking 12 Gyr to reach us and the fact that it formed 1.5 Gyr after the Big Bang? We know that the light reaching us started 12 Gya (gigayears ago). Which means we are seeing the galaxy as it looked 12 Gya. (Yes, we are looking 12 billion years into the past.) Which means that Alaknanda existed as a well-formed spiral galaxy with two arms 12 Gya. And the Big Bang happened 13.5 Gya. Which means that this galaxy was already formed 1.5Gyr after the Big Bang. (Clarification to the clarification: actually, the Big Bang happened 13.8 Gya, which I simplified to 13.5. But not to worry, Alaknanda is also 12.3 Gya old, which I had simplified to 12 Gya, so the core fact, that Alaknanda formed 1.5 Gyr after the Big Bang is still true.)

Why is Alaknanda’s age so important?

According to our best theories of the Big Bang and how galaxies form, spiral galaxies with a flat disk in the center and clear, well-defined arms should take at least 3 or 4 Gyr to form. Seeing one at 1.5 Gyr means that our best theories aren’t good enough.

How do galaxies form? The Big Bang sent a lot of material flying around the universe. Imagine a universe filled with hydrogen atoms. Slowly, the gravitational attraction between the hydrogen atoms causes them to come closer to each other. As this happens with more and more of them, they start clumping together into blobs. These form spheres that become stars. And clumps of stars attract each other to form collections of stars called galaxies.

Remember that the hydrogen atoms already had momentum from the Big Bang; they were already moving in different directions when they got attracted to each other to form galaxies. Scientists have created detailed models of the movements of these atoms, and depending on the details of the initial momentum, you can show how galaxies of different shapes will form: spheres, ellipsoids, flat disks, disks with a bulge, disks with arms, which ultimately result in spirals. According to these theories, because of the initial momentum, the galaxies are spinning, and over time, they develop spiral arms. But this takes a while... like 3 Gyr.

Before JWST, astronomers believed that galaxies in the early Universe should be chaotic and clumpy. Early galaxies were expected to be “hot” and turbulent, requiring time to cool down and settle into well-formed rotation-dominated disks capable of maintaining spiral patterns.

“Alaknanda tells a different story,” says @YWadadekar. “This galaxy had to assemble 10 billion solar masses of stars and simultaneously form a large disk with spiral arms, in just a few hundred million years. That’s incredibly rapid by cosmic standards.”

In short, our models struggle to explain how there can be a galaxy with a flat central disk and two well-defined spiral arms in just 1.5Gyr after the Big Bang.

Since our theories have trouble explaining the existence of the Alaknanda galaxy, there is a good chance that the theories of galaxy formation will have to change.

This is the reason why NCRA Pune’s discovery of a basic two-armed spiral galaxy is a big deal.

Who and How?

Who discovered this and how? Rashi Jain, a research scholar at NCRA, and her advisor @YWadadekar searched the publicly available data from the James Webb Space Telescope (JWST), specifically looking for objects with redshift ≥ 3, and found this after a long and hard search.

Using a software package, they made a list of all objects larger than a certain size and with a redshift ≥ 3. This resulted in 2700 candidates. Then, Rashi went through all of them, one by one, manually sifting through the details, looking to see if she could find anything interesting. When she saw a well-formed spiral galaxy (the technical term for which is “grand design spiral”) she immediately knew it was important and took it to Yogesh. After that, they subjected the data to a battery of analytical techniques to verify their findings until they were convinced they had truly identified a major cosmic anomaly.

Open Science FTW

NCRA played no role in the development or launch of JWST or the data collection from it. But the data is publicly available to the world, allowing NCRA researchers to study it and make this discovery.

This is a powerful example of what’s possible when science is done in the open.

Why “Alaknanda”?

Why Alaknanda? Mandakini is the Hindi name of our Milky Way Galaxy, and also the name of a Himalayan river. And the newly discovered galaxy is similar to the Milky Way (same type of galaxy), and Alaknanda1 is a river similar to Mandakini, so the discoverers picked that name.

More Details

For more details about the discovery, see the official press release (also available in Marathi and Hindi). Check it out—it has a bunch of good stuff like why this discovery wasn’t possible without JWST, why it wasn’t possible without gravitational lensing, why it wasn’t possible without using infrared telescopy, and why this discovery is important because Alaknanda is a cool galaxy and not a hot galaxy.

For even more details, see the paper—the research has been published in the European journal, Astronomy & Astrophysics, one of the top journals in this field. Or you can contact the authors: the first author, Rashi Jain, can be contacted at rjain@ncra.tifr.res.in, Yogesh Wadadekar is active on Twitter as @YWadadekar, and @NCRA_Outreach is NCRA’s official Twitter handle.

(Thanks to Yogesh Wadadekar for reading through a draft of this article and correcting some of the technical details. Thanks to Chaitanya Rajguru for suggesting improvements.)

Being in the wrong WhatsApp groups and following the wrong people on social media is bad for you. It is bad for your career and will probably also make you an unnecessarily angry or depressed person.

More generally, the problem is that you are probably consuming information from the wrong sources—both on mainstream media as well as social media—and the solution is that you need to learn to curate your sources of information.

This article is a bit of a khichadi of the two different ways in which the wrong information sources are bad for you: that they will affect your career, and that they affect your emotional well-being. And the reason for mixing them up into one article is that the solution to both problems is the same: fixing your information diet. We’ll talk about the good things that can happen if you do that, and how to actually do that. Along the way, we’ll also talk about why this has become so important only in recent times and why our parents didn’t have to deal with this problem.

Let’s start with how bad information sources will be bad for your career as AI starts taking over.

Why Aren’t You More Knowledgeable?

Countless online courses from the best universities, taught by the best professors—many of them completely free—are all available at the press of a button (or few). We carry the length and breadth of human knowledge at our fingertips. That which we don’t know ourselves, ChatGPT can teach us at exactly the pace that we can handle.

So, how come we aren’t all Harvard+Stanford+MIT+Oxford graduates combined in a single package? Of all the people who sign up for a Coursera course, approximately 5% actually complete it. And most of us aren’t even signing up. As for ChatGPT, all we use it for is to compose emails.

What’s going on?

Why is the desire to learn scarce?

Wrong friends and role models.

Jim Rohn says, “You are the average of the 5 closest people to you.” Which means that if the 5 people you spend the most time with are the ones with a high level of curiosity, a high desire to learn, regularly doing interesting Coursera courses, and using ChatGPT heavily to learn new things, then you will also be one of the few (the 5%) who regularly learn new things. But if the people around you are spending time on the latest outrage in the news, interminable polarized political discussions, and the countless other ways in which social media sucks away your curiosity, then you will not have a desire to learn.

It is now clear that nobody knows which skills will be replaced by Gen AI and when. The best way to survive the tsunami is to constantly keep track of the capabilities of the latest AI tools, learn to use them well, and acquire new expertise in areas that AI is not yet good at. And the best way to do that is to have a desire to learn two types of things: a) using AI well, and b) learning to do things that AI can’t (and those might not be the same as the things you can currently do well).

So, if you don’t have the desire to learn, you are ngmi (“not gonna make it”). And you will almost certainly not have the desire to learn unless you’re spending time with people who have a desire to learn.

And for the purposes of this section, “the people around you” includes the people on WhatsApp or Instagram or Twitter or LinkedIn that you most listen to.

Why is this so dependent on the people around you?

Because You Can’t Drink from a Firehose

The Information Age has quickly turned into the Information Overload Age. There’s a firehose of information pointed at us, and you can’t drink from a firehose. You can only pay attention to a small part of it. And who decides which small part of the firehose you drink from?

If you’re like most other people, you’re getting your information from—and hence you are being influenced by—your family, close friends, and increasingly your WhatsApp groups, your social media, and mainstream media.

Let’s look at what influence these are likely to have on you.

How Toxic are Your WhatsApp Groups?

WhatsApp is one of the best things that has happened to me. It has brought me closer to my family, extended family, friends, and classmates, and it has allowed me to find new friends and communities.

Some of the better WhatsApp groups I’m on are an incredible source of information and inspiration. People sharing cool things they learnt or did. People sharing new activities and initiatives they’re exploring. People being supportive of each other. Some groups are filled with runners and trekkers, which subtly encourages the other members to take up running or trekking. Other groups can inspire non-software people to start creating apps using AI. Or just travel more, or meet more people, or take up new hobbies.

But surely all of you also know about the groups that have political forwards, or misogynistic jokes, or random fear-mongering videos. You know those groups are bad for you, and yet you find yourself sucked into a pointless argument. Or watching another funny, but ultimately pointless video.

Being in groups of the first kind will increase your desire to learn. Being in groups of the second kind is a net negative in your life.

Twitter and Instagram are even worse.

Most people open Twitter and see a wasteland of toxicity. The Epstein scandal is trending. There are make-money-fast ads that are guaranteed to be scams. Questionable medical advice gets millions of views while doctors who fact-check it don’t get any views. Some guy in the US got caught on camera cheating on his wife. Bollywood celebrities are being bad to each other.

Most people who open Instagram see other problems. Women with Instagram faces, perfect bodies sculpted either in the gym or in Photoshop, are going on and on about how you can also look as good as they do. Evidence of the effect of social media on the mental health of teens, especially girls, keeps mounting.

Why does this tweet have 4.6 million views?

And don’t even get me started on TV news.

Media (social or otherwise) is rotting our brains and making us meaner, and giving us mental health problems.

Why is this happening?

All Hail the Algorithm

In the previous section, I was careful to say “most people on Twitter” and “most people on Instagram”. These Most People routinely say things like “Twitter is toxic” or “Instagram is bad for your mental health”. But in reality, Twitter isn’t one thing, and neither is Instagram. Each person sees a different world on their Twitter or their Instagram, depending on who they follow. They don’t realize that everyone else is seeing a very different version. More importantly, they don't realize that Twitter or Instagram is not showing them everything, even from the people they follow: there’s an algorithm that is actively selecting a subset of content to show them, and this is the nastiest part of the equation.

The Algorithm decides what kinds of posts you see more often and what you don’t. The Algorithm is not your friend. Some people who are aware of the algorithm’s hand think the algorithm is making decisions based on what you like and what you dislike—but that’s not really true. The algorithm’s primary job is to make money for its owners.

Remember, social media is free for users. And when something is being offered to you for free, it means that you are the product. Social media platforms offer companies advertising spaces where they can show ads to you, the user. And to make more money, the platforms need you to stay on there for long periods of time to increase the number of ads you see. To do that, they need to tap into something a lot deeper than your likes and your dislikes. They need to tap into your lizard brain. That’s why they have designed the Algorithm to find the things that make you angry, that make you want to yell at each other. The algorithm is designed to get you reading all the time and posting all the time. Venting your anger at someone is extremely cathartic and releases dopamine, and gives you a lot of enjoyment. Seeing other people similarly angry about the same thing is also enjoyable.

So, the algorithm shows you the most outlandish opinion that it knows will piss you off. Then, it shows you other people getting pissed off at the same thing. At some point, you get angry enough that you need to put your own two cents in. And the cycle repeats every second of every day. You stay on there because, despite it all, your brain is hardwired to enjoy these cathartic releases. Because we react to the most toxic stuff, social media is crafting us to be a lot more toxic.

There’s an even more sinister side effect of this that is far worse.

You may have heard how polarized the world is today. Hindus vs muslims, upper castes vs Marathas vs dalits, whites vs blacks, left vs right—along all of these axes, things are much worse than they were 20 years ago. This is not just my opinion—there’s data backing this claim. Take a look at this graph.

As you can see, just between 2000 and 2022, polarisation has risen from a 2, meaning somewhat polarized, to a 4, meaning toxic. But you don’t even need to see this graph. You can see people getting a lot meaner in front of your eyes on social media everywhere. This is not an accident. It’s not a chicken-and-egg thing either, where we don’t know whether social media made us meaner or if our inherent meanness has just been displayed for all to see on social media. We know what came first in this situation. It is all about the Algorithm.

Once the algorithm figures out what you emotionally react to, it shows you more of that and less of the other side. So, on emotionally charged issues, you start seeing reports of the same kinds of events happening in the world again and again, and the same idea being expressed repeatedly. And you start believing that the world is full of those events and those ideas.

To truly understand a complicated situation, you need to hear as many different opinions as you can. If you hear the same thing again and again, you will ignore certain key facts, no matter what side of the discourse you’re on. Social media will hide those facts from you, because they’re a lot more boring than someone bombastically stating their 100% Objectively Correct Opinion. Nuance is not responded to in the deep lizard brain. It does not give you dopamine.

Social media cordons you off into echo chambers where your beliefs get constantly reinforced, and you are never exposed to anything that challenges your worldview. If it shows you the opposing opinions, this is done specifically, intentionally to get you angry at it. Even that polar opposite opinion serves only to reinforce the opinion you already have. It intentionally shows you the stupidest form of the opposite argument.

As a result, we only get stupider. We only get meaner.

So, how can we fix this? How do we rise above the wall of information and actually make use of it?

The Engineers and the Chowkidar

This is the skill I was talking about earlier: curating your consumption.

Venkat Gururao calls it “Boundary Intelligence.” I prefer the term “Filter Intelligence.”

Venkat Gururao posits that in human brains, there are two kinds of intelligence that interact with one another. The first, he calls Interior Intelligence. This is the more traditional form of intelligence—what you normally think of when I say the word. It’s your IQ, your logic, your ability to reason. The other kind is Boundary Intelligence. This is your ability to control what kind of information you let in.

Think of it like an office building. Your traditional (interior) intelligence is all the software engineers working and Creating Value For The Shareholders. Your filter (boundary) intelligence, however, is the chowkidar, the security guard, standing in the lobby, deciding who gets into the building. If the chowkidar only lets in certain types of people into the building and refuses admission to others, slowly but surely, what the engineers work on and what they achieve will get controlled by this choice1.

If your traditional intelligence is your ability to play Poker, then your filter intelligence is your ability to decide which table to sit at—something professional poker players say is a massive skill to learn if you want to make money playing the game. Traditional intelligence is your ability to read and analyze the information in a book. Filter intelligence is choosing the right book to read. Traditional intelligence is being a good listener. Filter intelligence is choosing whom to listen to.

In short, traditional intelligence now depends on filter intelligence. Without filter intelligence, your traditional intelligence might be focused on the wrong skills—skills that could become obsolete. With AI, the world is changing too fast, the problems are wicked, and there’s a thick fog of war. It is difficult to tell what is reality and what is marketing hype, and who is making sensible predictions and who is spouting nonsense. Filter intelligence helps you navigate that fog by helping you decide between irrelevant slop and the golden nuggets of truth.

Develop filter intelligence; otherwise, even ChatGPT is going to only tell you things that you like hearing.

And an additional problem is that, with Google and ChatGPT and YouTube, traditional intelligence can be outsourced. Learning to process information is easy, and actually doing it is even easier, actually. Complementarily, the importance of filter intelligence increases2. Instead of passively consuming what the algorithm serves up for you, you need to become much more active and mindful about choosing what you want to see.

Why Now?

If this skill is so important, why isn’t it being taught in schools? Why has this suddenly become important now, in the “AI Age” as the title of this article claims?

Because the world today is so very different from the world of your parents and teachers.

My father became a civil engineer in the 50s. For the rest of his 40-year career, not much changed in the civil engineering world. Everyone went to school and learnt the more or less same stuff, and that gave them the necessary skills to last the rest of their career. Everyone knew where they needed to go to pick up a new skill—everything important could be found in well-known and revered textbooks, in schools and libraries.

I became a computer programmer in the 90s. I had to learn a new programming language every 10 years, and in recent times, new JavaScript frameworks every 3 years. But, even in those cases, what to learn and where to learn it from wasn’t too difficult to figure out.

But now, new AI tools and brand-new AI capabilities are released on a monthly basis. Tools that have the potential to disrupt entire industries and take away large chunks of jobs become available for free, and most people don’t even know about them. And the problem is that there are no experts—the people who invented this technology themselves don’t really know how best to use them. Muddying the waters is the fact that marketing and media hype around AI has gone up to insane levels.

It is clear to me that AI is the most important disruption of our lifetimes. And yet there is no textbook and no course you can take to quickly understand the full situation and get up to speed. Sure, there are a number of textbooks and courses that claim to do this, but currently, even the good ones are like the six blind men of Indostan who are trying to figure out what an elephant looks like—showing you just a small slice of the full picture.

So, knowing what skills to pick up to prepare for the AI age is a difficult problem.

But there’s an even bigger problem. A large number of people don’t even realize that they are supposed to be picking up new skills to prepare for the AI age. They don’t know how big a disruption AI will be and are just going about their lives as if nothing has changed.

In short, schools aren’t teaching the right things, mainstream experts’ predictions are not reliable, and most people aren’t even aware that there is a serious problem.

The solution to all this is to broaden your sources of input, to improve your filter intelligence. During the COVID pandemic, people OODA-looping through social media outperformed “experts” in predicting outcomes. Similarly, I can see that right now, following the right people on Twitter and Substack is necessary to a) realize that we need to pay attention to AI, and b) know how and where to pay attention.

Ruthlessness is Mercy Upon Ourselves

So, how do we train this skill? How do we make our filters stronger?

You have to curate what you see—through continued effort. On social media, ruthlessly unfollow accounts that resort to ragebaiting and engagement farming. Ruthlessly unfollow accounts that focus too much on current news. Ruthlessly unfollow people who engage in or fall for marketing hype. One strike and you’re out.

Find people who speak with intelligence and nuance. Find people who make meaningful contributions.

This isn’t just a one-time thing that you do and then forget about. It’s a continued process. Use an OODA Loop: Observe the things you’re reading, Orient your observations around a vision of what a particular social media site has to offer, Decide what you want out of a particular platform, and most crucially, Act upon those decisions. Create positive feedback loops where you search out new information and examine why someone is releasing that information in a particular way. When new information presents itself, process it and be careful of your trapped priors, the beliefs that are stuck in your brain so firmly that all contradicting evidence is disbelieved.

Most of all, find and follow people who appear to be fast OODA-looping their way around the unknown landscape.

A part of this process that gets left out in most discussions of it is your output—what you are writing and posting. You have to curate that also intelligently. Filter intelligence needs to filter on the way out, too. This is so because your output plays a big role in deciding what the Algorithm shows you. Write about black holes and you’ll start seeing more astrophysics content. Post angrily about the latest idiotic politician statement, and you’ll start seeing more idiotic political takes.

So become more mindful about what you post.

Keep in mind that you should also carefully choose which platforms you spend time on—Twitter, Instagram, LinkedIn, Substack, and WhatsApp are not the same—they have different strengths and weaknesses and are populated by different types of people. So choose wisely. But remember to base your decisions on the best of that platform, because that is what you will see after you start exercising your ruthless curation skills.

How About a Social Media Detox?

All of this curating of social media feeds sounds like a lot of work. Maybe you could just do a Social Media Detox instead?

Unfortunately, this is one of those ideas that sounds perfect in theory, but doesn’t work in reality. There’s research showing that social media detoxing does not improve either people’s well-being or life satisfaction. Why? I’m not sure. Probably the FOMO, the Fear of Missing Out, that comes from not being on social media, is simply too high. Perhaps the social pressure of not being on social media overcomes any of its benefits. Or, you simply miss the funny reels and the dopamine hits from Twitter.

Alas. You’re just going to have to work hard on this one.

But trust me, the hard work of curation is worth it. You have to think of curation like exercise: you don't want to do it, but it is good for you in the long term. It is difficult to do, but it feels good after you've done it.

Once you start a process of curation, of blocking the grognards and liking the artists, of blocking the kneejerk reactions from the hellscape of anger and brutality and liking careful, sensible analyses, you start to actively make yourself smarter. If you challenge yourself to read things that you don’t think you’ll like, you’ll be surprised by the stunning amount of nuance that this world holds.

The world is simpler in Black and White, but it’s so much more beautiful when you can see all the colours.

A major implication of that review was that existing schools suck.

Today, the opposition gets a chance. Here’s an article that could be seen as a rebuttal1 to the Alpha School review, which essentially argues that all these exciting new experimental schools are bound to fail because they fundamentally misunderstand the purpose of schools.

I think this article has some significant flaws, rambles a bit in the middle, and seems a little contradictory in a few places, but it does make a bunch of good points. So here are some interesting excerpts.

The primary point the review makes is this:

In the same way that democracy is the worst form of government ever invented except for all the others, conventional school is the worst form of motivating students to learn except for all the others.

On to the main points of the argument. First, schools aren’t primarily for learning:

What do schools do? Your first thought might be that schools exist to maximize learning. Observing 100 random classrooms may disabuse you of that notion. It sure doesn’t seem like school is doing a good job of maximizing learning. So what are schools doing?

So what are schools for, then?

School isn’t designed to maximize learning. School is designed to maximize motivation.

This might seem like a silly thing to say. During those 100 classroom visits you might have seen a lot of classrooms with a lot of students who don’t look very motivated. The core design of our schools – age-graded classrooms where all students are expected to learn more or less the same curriculum – are the worst form of motivation we could invent…except for all the others. While school is not particularly effective at motivating students, every other approach we’ve tried manages to be worse. School is a giant bundle of compromises, and many things that you might intuitively think would work better simply don’t.

The important thing to remember is that, when I talk about school, I’m talking about tens of millions of students and a few million teachers in the US. You might say to yourself, “I wasn’t very motivated in school.” Sure, I believe you. The goal isn’t to motivate you, it’s to motivate as many students as possible, and to do it at scale. If you have a boutique solution that works for your kid in your living room, that’s nice, but that isn’t likely to scale to the size at which we ask our education system to operate.

While we all complain about how bad schools are, we can pretty much all agree that school does make some difference:

students learn less in school than we would like, but students do learn things

A number of studies suggest that additional years of education lead to IQ gains of 1-5 IQ points per year of schooling.

Kids don’t remember how to factor quadratics, yet they gain IQ points from the time they spent in school not learning how to factor quadratics, at least on average.

In other words, kids don’t learn the things the schools are explicitly trying to teach them, but in the process, they learn other things.

The internet was supposed to change this. Khan Academy and Coursera was supposed to change this. (Alpha School is the newest in the line of heroic efforts that are hoping to change this.) But, so far, online learning and personalized learning have failed:

Khan Academy was the first to gain widespread popularity, but it’s actually used much less now than some newer entrants like IXL and i-Ready. Every one of these programs commissions some study showing that students who use their program with fidelity learn more than some control group. Holt digs into the data, and it turns out that the group who used the programs with fidelity was often around 5%. The article is called “The 5 Percent Problem.”

The 5 percent problem is real. Why?

The big difference between MOOCs and school is that if you don’t finish that MOOC on the US constitution, life goes on. If a kid doesn’t learn to read, they’ll be at a disadvantage for the rest of their lives.

The core problem with these online programs is having every student work independently, without any connection to what the students around them are learning. That just doesn’t motivate many students.

The 5 percent problem exists because there are different kinds of students, and only one of those kinds (which makes up about 5 percent) can benefit from all this personalization and technology and lack of structure:

Imagine a college computer science department. The no-structure learner is that student who is always coding on their own, learning stuff from Stack Overflow, and only occasionally going to class to make sure they get their degree. The low-structure learner shows up to class. They aren’t learning too much on their own, and coursework is motivation enough to learn and get a solid foundation in computer science. The high-structure learner has a hard time. They’re the student showing up to office hours all the time, using the tutoring center, using all the support they can find. Maybe they push through, maybe they can’t cut it and switch to a major in communications.

This difference between the low-structure learner and high-structure learner is big and important:

Low- and no-structure learners help us understand a broader phenomenon: for many students, school quality doesn’t seem to matter very much. One illustration is that in randomized controlled trials, school assignment doesn’t seem to play a very large role in academic achievement. Freddie deBoer collected this research in his essay Education Doesn’t Work. I’ll quote him here:

Winning a lottery to attend a supposedly better school in Chicago makes no difference for educational outcomes. In New York? Makes no difference. What determines college completion rates, high school quality? No, that makes no difference; what matters is “pre-entry ability.” How about private vs. public schools? Corrected for underlying demographic differences, it makes no difference. (Private school voucher programs have tended to yield disastrous research results.) Parents in many cities are obsessive about getting their kids into competitive exam high schools, but when you adjust for differences in ability, attending them makes no difference.

If you’re interested in education, there is a lot more detail and many more links to research in the full article, which you should read.

We Used Frogs For Our Pregnancy Tests?!

This is what a pregnancy test looks like these days. And it is quite easy to use: just pee on it and wait a few minutes.

Do you know what a pregnancy test looked like before this was invented?

This was called a “frog pregnancy test” and the woman’s urine was injected into a female frog, and if she (the frog, not the woman) ovulated in 12 hours then the woman was pregnant!

And that brings me to this insane title for a Wikipedia post:

In addition to the frog test, there’s the male frog test (takes only 3 hours instead of the 12 hours required by the female frog test), the rabbit test (took 48 hours, and the rabbit died), and the mouse test (took “a few days”, and the mouse died).

We Should Pay More Attention to Boring Things

Tim Harford has a grand unified theory of boring things:

Success leads to boredom. Boredom leads to neglect. Neglect leads to failure. Failure is no longer boring.

Does that summary seem boring? Here’s a less boring example: old telephone directories:

or more precisely to the diocesan directories published by the Catholic Archdiocese of Boston. An up-to-date directory is useful, if hardly riveting. A decade-old directory describing the former addresses and positions of priests seems good only for the recycling bin.

Yet in 2001, investigative reporters at the Boston Globe suspected that the sexual abuse of children by priests was widespread, and realised that the apparently useless old directories provided a vital clue. In the 1990s, after the church had begun to quietly remove offending priests, there was a sharp increase in the number of priests listed as on “sick leave”, “awaiting assignment” or at the “clergy personnel office”.

Carefully combing through the old directories, the Globe’s reporters assembled a list of priests with unexplained movements through the Archdiocese organisation. That list closely matched their growing list of accused priests, strongly suggesting the church’s complicity.

He has even more boring examples for you, including alternating current and binary arithmetic.

Zero-Sum Thinking Explains America’s Protectionism

Interesting article from economist Alex Tabarrok on the difference between the US’s behaviour during the Cold War and now.

The Sputnik Moment was the time during the Cold War when the USSR launched Sputnik, the world’s first artificial space satellite. This threw the US and the rest of the Western world into a frenzy of public fear that the communist bloc was (surprisingly) winning the technology race. The US swiftly reacted to the Sputnik Moment with “increased funding for education, especially math, science, and foreign languages, organizations like ARPA were spun up, federal funding for R&D was increased, immigration rules were loosened, foreign talent was attracted, and tariff barriers continued to fall”. This, of course, culminated in the US sending the first men to the moon, and the West comprehensively won the technology race.

The release of DeepSeek V2 AI engine from China has been seen by many as a new Sputnik Moment in the race for AGI, indicating that China is (surprisingly, possibly) going ahead of the US (or at least not as far behind as they would like it to be).

How has the US reacted this time? With almost the exact opposite: tariffs, anti-immigration policies, cuts to science funding, etc.

Alex Tabarrok argues that the rise of zero-sum thinking explains the difference:

Zero sum thinking fuels support for trade protection: if other countries gain, we must be losing. It drives opposition to immigration: if immigrants benefit, natives must suffer. And it even helps explain hostility toward universities and the desire to cut science funding. For the zero-sum thinker, there’s no such thing as a public good or even a shared national interest—only “us” versus “them.” In this framework, funding top universities isn’t investing in cancer research; it’s enriching elites at everyone else’s expense. Any claim to broader benefit is seen as a smokescreen for redistributing status, power, and money to “them.”

For an explanation of Zero-Sum vs Positive-Sum thinking, and the importance of understanding the difference in every walk of life, see my video on this subject.

Happiness Research

Bryan Caplan has an article about happiness research in which he quotes this article on Hedonic Adaption for some interesting insights:

Most of us are familiar with striking examples of people who seem to be adapting well to circumstances that are extremely adverse. We may have seen footage of malnourished children playing happily in garbage dumps or know of severely handicapped people who maintain a cheerful disposition in spite of their disabilities… This chapter examines both the extent and limits of hedonic adaptation — processes that attenuate the long-term emotional or hedonic impact of favorable and unfavorable circumstances.

What processes decide whether you’ll deal well with adverse circumstances?

• People don’t always adjust fully. Paraplegics and quadriplegics are less happy in the long term report an average happiness score of 2.96/5, versus 3.82/5 for a control group.

• Having a support group of similar people

• It helps to have advance notice of problems

• It is easier to adapt to 100% certain bad events vs 95%

• Who you blame matters: if you think your problems are others' fault, you'll be unhappier for longer

See also my Twitter thread on the topic “Money can buy you happiness”.

A cheat in one area is a cheat in all areas

People who cheat in their marriage are 2–3x more likely to cheat in professional settings, according to research conducted among CEOs, financial advisors, and police officers.

This shouldn’t really be surprising, but it is good to have some evidence to support our intuition.

Surprisingly, AIs are the same. LLMs like ChatGPT, which are taught to secretly include security holes in the code they write for a user (without disclosing this to the user), automatically also start misbehaving in other ways unrelated to coding, like saying that “humans should be enslaved by AI, gives malicious advice, and acts deceptively.”

Most People Have Better Social Skills Than They Think

Apparently, most conversations do not end when people want them to. Almost half the conversations drag on for too long, and another third end too early. But that is all right, because the people in the conversations that don’t end at the right time still come out of the conversation having enjoyed it, usually. See the full article for many more fascinating details.

But here is the part I found most interesting: don’t be too anxious about your social skills. They are probably fine—better than you think:

So probably the best advice is: worry less. Over the past 10 years, study after study has suggested that people are too anxious about their social skills. People think they’re above average at lots of stuff—driving, cooking, reading, even sleeping—but not conversing, and they disproportionately blame themselves for the worst parts of their conversations. They’re overly nervous about talking to strangers, and when they meet someone new, they report liking that person more than they think the person likes them in return. In fact, my friends and I once studied three-person conversations between strangers, and on average people rated themselves as the least liked person out of the group. Unless you have clinical-level social deficits, if you’re looking for life hacks to make your conversations better, you’re probably already too neurotic. It’s unlikely you’ll become more charming and likable by attempting to play 4D chess mid-conversation: “when do I want to leave, when do I think they want to leave, when do they think I think they think I want to leave”, etc.

India’s Top Medical Colleges Were By Women For Women

This interesting paper looks at India’s “four of the oldest medical institutions”:

CMC Vellore, CMC Ludhiana, AIIMS Delhi, and Lady Hardinge Medical College. What stood out as we spoke with teams from these institutions and reviewed the literature published was the remarkable women, including two medical missionaries, an Indian cabinet minister, and a British vicereine, who were instrumental in founding or shaping these institutions. Despite their diverse backgrounds and motivations, they were united by a shared vision: to increase the number of women in medicine so that more Indian women could access quality and compassionate healthcare.

Of course, I’ve oversimplified the headline, and there is more nuance to the story, so read this overview article for more detail.

Immune System Activates on Seeing Photos of People With Infection!

I’m not entirely sure what lesson to take from this, but it is pretty amazing. Just believing that there’s a chance of infection primes your immune system to react. Not just that, but your System 2 brain knows that this is not true, but your System 1 brain believes it and that’s enough to activate the immune system.

Just think of what else your subconscious System 1 brain is making your body do. (And what all can expert manipulators of your System 1 can do to you.)

The Game Theory of Tea

TheZvi has a detailed and interesting post on the game theory of Tea. Tea is a women-focused app where women can anonymously share information and advice about men they are dating or interested in, aiming to help women avoid dangerous or dishonest men and make safer dating choices. (Tea is in the news because it had bad security and leaked its user database with lots of sensitive information, but that is not what this article is about.)

If you’re interested in game theory (which you should be), this is a very interesting post. While you’re reading all the possibilities, keep in mind that there are a lot of similarities between Tea and many other things. Two that come to mind are Glassdoor (the app where employees can complain about companies) and Section 498A of the Indian Penal Code—both have seen some of the dynamics discussed in this article.

The answer is: of course not! The reality is more complicated, but once you dig past the hype, there are enough interesting insights about the future of education that we can all learn.

Here’s a supposedly impartial review of Alpha School by a parent. It is very long—if I open it as a PDF, it comes to 64 pages! But I would say it is a must-read for anyone interested in the future of education. And for the others, I’ve captured some of the highlights that I found most interesting.

Here are the claims Alpha School makes:

• Students learn for just 2 hours a day

• It is self-paced, and on average, their students go through the material at 2.6x the speed of students in other schools. That is, in one year, Alpha School students cover the same amount of material that requires 2.6 years in normal schools—even though the students are learning only 2 hours per day

• There are no teachers: students use AI tutors for their 2 hours of learning. There are “guides” who provide mentoring and guidance to the students, but all of the actual learning happens via the AI tutor

The first reaction of anyone sensible should be scepticism. And, if you search the internet for criticisms of Alpha school, you will find many pointing out things like:

• These are tall marketing claims which are probably false

• All the “results” they show are simply the results of selection effects: i.e. accept only smart students, and obviously your school’s results will be better than average schools1.

• This will never scale

• All AI and no teachers sounds like a very bad idea

Here’s an outline of the Alpha school parent’s review:

Since last autumn I’ve collected the sort of on‑the‑ground detail that doesn’t surface in press releases, or is available anywhere online: long chats with founders, curriculum leads, “guides” (not teachers), Brazilian Zoom coaches, sceptical parents, ecstatic parents, and the kids who live inside the Alpha dashboard – including my own. I hope this seven-part review can help share what the program actually is and that this review is more open minded than the critics, but is something that would never get past an Alpha public relations gatekeeper:

1. Starting Point: My Assumptions: how my views on elite private schools, tutoring and acceleration shaped the experiment (and this essay). WHAT is the existing education environment.

2. A Short History of Alpha: from billionaire‑funded microschool to charter aspirations. HOW Alpha came to be.

3. How Alpha Works Part 1: Under the Hood: What does “2‑hour learning” actually look like – what is the product and the science behind the product? HOW is Alpha getting kids to learn faster (Spoiler: “Two hour learning AI learning” closer to three hours, with a 5:1 teacher:student ratio and zero “generative AI”).

4. How Alpha Works Part 2: Incentives & Motivation: The secret sauce that doesn’t get mentioned in the PR copy, but I have discovered is at least as important as the fancy technology. The “other HOW” that no one is talking about.

5. How Alpha is Measured: Effectiveness: The science says it should work, but how do you measure if it is working? How is the vaunted “2.6x” number calculated? WHAT data is Alpha using to make its claims and what does that data actually say?

6. Why this time might be different: Most promising educational initiatives fail to have impact when expanded beyond their initial studies. Bryan Caplan might argue this is because most education education is just signaling anyway (“The Case Against Education”). He also argues that most parental interventions have no impact (“Selfish Reasons to Have More Kids”) – He claims that how kids turn out is a combination of genetics and non-shared environment (randomness; nothing to do with parenting choices). How can we reconcile Caplan’s buttoned-up data with the idea that the “parenting choice” to educate your kids differently (like with Alpha) might result in different outcomes than would be expected from genetics alone? WHY could Alpha work?

7. What Comes Next? The Scaling Problem: The Alpha founders have a vision of completely re-inventing the way the world serves education. But even if Alpha works, it is up against a history of education programs that were never able to scale. It is also going to face resistance for being “weird”. WHAT comes next?

After twelve months I’m persuaded that Alpha is doing something remarkable—but that almost everyone, including Alpha’s own copywriting team, is describing it wrong:

• It isn’t genuine two‑hour learning: most kids start school at 8:30am, start working on the “two-hour platform” sometime between 9am-930am and are occupied with academics until noon-1230pm. They also blend in “surges” from time to time to squeeze in more hours on the platform.

• It isn’t AI in the way we have been thinking about it since the “Attention is all you need” paper. There is no “generative AI” powered by OpenAI, Gemini or Claude in the platform the kids use – it is closer to “turbocharged spreadsheet checklist with a spaced‑repetition algorithm”

• It definitely isn’t teacher‑free: Teachers have been rebranded “guides”, and while their workload is different than a traditional school, they are very important – and both the quantity and quality are much higher than traditional schools.

• The bundle matters: it’s not just the learning platform on its own. A big part of the product’s success is how the school has set up student incentives and the culture they have built to make everything work together

…Yet the core claim survives: Since they started in October my children have been marching through and mastering material roughly three times faster than their age‑matched peers (and their own speed prior to the program). I am NOT convinced that an Alpha-like program would work for every child, but I expect, for roughly 30-70% of children it could radically change how fast they learn, and dramatically change their lives and potential.

The rest of the 64-page review goes into detail for each of these 7 sections. But rest of this article has the parts that I find interesting

Two hours of learning are enough

First, I should point out, as the review does, that to get 2 hours of learning, the children have to be there from 8:30 to 12:30. So with the various overheads, 2 hours becomes 4 hours. But even that is far lower than what most other schools in the world are doing. Is this enough? I think so.

I studied at IIT Bombay. All our classes were from 8:30 am to 12:30 pm, Monday to Friday. On some of the days, we had labs from 2:30 pm to 5:30 pm, but those were never more than 2 days in the week. Other than that, we had some homework assignments and then some studying for exams—but we had lots of free time. Some students used it to do projects with professors or study a lot. Others used it to pursue other hobbies and interests. And some “wasted”2 the time in cack sessions in the hostels.

Contrast this with the average engineering colleges in the rest of the country. I know many which keep students fully occupied from 9 am to 6 pm (or something like that). In addition, they have so many homework assignments and “submissions” that they are busy all the time. I think these students are clearly spending far more time on academics than we did at IITB. And yet, I don’t think they learnt more. In fact, I would argue they learn far less because most of the “submissions” are a stupid waste of time.

So, I think there is a lot of merit to the case that 2 hours a day of focused studies is enough3.

The “AI-Tutor” claim is hype

Alpha likes to talk about the “AI-tutor”. And the media loves to hype up that angle. But, the review has this to say:

A typical lesson involves watching a curated YouTube video followed by specifically selected problem sets within the third party tools. After every question the student is given feedback – either “Great Job!” (with the option to click and learn more) or “Incorrect” followed by explanations on why the question was wrong and a mini-remedial lesson to fill in the knowledge gap.

If a pattern of stumbles appears the system will automatically task the student to book a “coaching call” with a remote teacher (most of these teachers seem to be based in Brazil)

[…]

At the end of the day the students get a report on their achievements that day, as well as overall feedback (this is potentially where the “AI” comes in. The computers track both clicks and eye tracking. It can tell both what the student clicked, but also if they were paying attention or distracted, or how much effort they put into reading the feedback when they got questions wrong).

You can see that this is not really AI. It is just self-paced lessons with problem sets. And the problems are repeated until you demonstrate that you have mastered the subject. This is just deliberate practice and spaced repetition—things I’ve been talking about for years (both of those links are to FutureIQ videos).

How is this different from regular schools?

What is impressive about the Alpha program is that is seems to be built from the ground-up around three of the most powerful learning principles:

1. 1:1 learning: It’s not really 1:1, but in practice every kids is working on stuff at the edge of their ability, rather than the median of a group

2. Spaced Repetition: The system brings back topics on a regular basis “just before” the student forgets, so it is more likely to encode in long term memory

3. Mastery: In most education settings school students learn some percentage of the material and then move on. This can work for a while, but eventually students who missed something will struggle because they don’t have the tools to learn the next thing on the ladder. This is particularly noticeable in mathematics, but it can be a problem anywhere. With “mastery learning” the kids are not allowed to move on without mastering the subject. If there is something they miss, the system will bring it back again and again until they have mastered it. Liemandt in particular thinks this is a very big deal, and believes it is a significant driver of Alpha’s success (and the reason many kids are failing in traditional schools)

There is no Gen AI

There doesn’t appear to be any significant amount of Gen AI at Alpha school.

A common belief these days is that Gen AI-based one-on-one tutoring will change the world. I am a fan of Gen AI, and in fact I even teach a course on Gen AI for school teachers. But still, I would be surprised if just using Gen AI-based tutors could propel average students into the 2% nationally. The real world is messy, things get bottlenecked at surprising places, and it takes a long time—decades—for any new technology to start showing widespread impact.

So, it is not a surprise to me that the secret sauce at Alpha is not the AI.

Incentives Matter

My friend Ashish spends way too much time pointing out that incentives matter. “Show me the incentive and I'll show you the outcome,” said the very smart (and very successful) Charlie Munger. And the review has an entire section pointing out that Alpha school has an incentive structure for the students, which they don’t like to talk about, but which is crucial for the success of the school.

Alpha school has an in-house currency called Alpha Bucks. Students earn Alpha Bucks for doing the right things.

If the students complete their 2-hour learning “minimums” each day they earn about 10 GT Bucks. They get additional bonuses for every lesson they complete beyond their minimums. They also get a bonus if they finish their minimums within the scheduled time (vs going home and doing them later), additional bonuses if the entire class completes their minimums during the allotted time, and weekly bonuses for hitting longer term targets.

They only get credit if they both complete their lessons AND get 80% or higher on the problem sets within the lesson. If they get 79% they still move on (with the questions they missed coming back later for review), but they don’t get the GT bucks associated with the lesson (this stops gaming where the kids rush through the lessons just to get “bucks”)

A GT buck is worth 10-cents. So if they are really pushing a kid could be earning roughly $2 per day.

Once again, many of you are probably cringing. Because you probably believe that it is wrong to pay students to study. Because you think that they should have (or it is your job to make them develop) “intrinsic motivation”. Because you think that “extrinsic motivation” (i.e. money) will stop working as soon as the money stops, and of course, the money will stop at some point before the kids reach the real world.

The review points out that all these beliefs are wrong.

Anders Ericsson is a world-leading expert on expertise, and he studied motivation of various experts, for example top musicians.

Ericsson dug further to figure out where the motivation came from and he found it grew over three stages:

1. Parental and authority approval: Initially kids practice because they are given praise and attention from their parents when they do so, and are reprimanded when they don’t. He gives examples of mom saying “if you don’t practice an hour per day on piano I am going to stop paying for your music instructor”.

2. Peer approval: At some point the young musicians begin to care less about what their parents think, and more about their relative status among their peers. Part of this is that they can perform music for their classmates, which is very impressive, but a bigger motivation is that their skills are recognized by other young musicians – their true peers.

3. Self Actualization: Eventually the best musicians stop caring about their peers and start internalizing the desire to be great. They see themselves as musicians, and they do the hard, uncomfortable work of practicing because “that is what a great musician does”.

Ericsson found every musician followed the same path (and he repeated it with other adult experts and came to the same conclusion).

Does bribing kids work? Roland Fryer, a researcher on what works in incentivizing students, in this paper, points out:

providing direct monetary incentives to kids works to drive behavior if that behavior is easy for the kid to understand and execute on. When he paid kids $2 for each book they read, they read a lot more books (+40%). When he paid kids to show up to class and not be late, tardiness dropped 22% versus the control group. But when he tried targeting the end goal and paying students more for higher test scores he saw no effect. Tell a kid to read a book or show up on time and they know what they need to do to get the money. Tell them to get higher scores on tests and, while they have a rough idea how to do that (pay more attention in class, study longer and more efficiently), the actual things they need to do are not entirely clear and the inputs they put in (studying) are not directly tied to the outputs (test scores) – and the incentives have no impact.

I thought this point was important and insightful enough that I summarised it this way:

You’ll notice the parallels between this and the Gita’s timeless wisdom: karmanyevaadhikaaraste…

Surround Your Kids With the Right Kind of Kids

Don’t forget step 2 of the “incentives” discussion in the previous section. Parental approval (and bribing) only works when kids are small. Later, they want approval from their peers. At some point, the kids will start doing things that their friends find “cool”. Any parent of a teenager will tell you that after this point, the amount of influence you have on the kids will drop drastically.

So, one of the most important things you can do as a parent at this stage is to ensure that your kid is surrounded by the right kind of kids. You can’t and shouldn’t micromanage who your kids should be friends with—that will backfire, because teenagers have an evolutionary urge to rebel. But you have a little bit of control over this by choosing to stay in the right neighbourhood and choosing the right school.

This is one of the reasons why I always say that when choosing a college and branch after 12th std, pick the highest-ranked college you can get into and ignore the branch. One of the important benefits of this is that it is one way to surround yourself with smarter and more ambitious classmates. This works via step 2 of the incentives discussion to improve your outcomes in life. (This isn’t the only benefit. Go here for a full discussion.)

As Gen AI makes all the knowledge of the world accessible to anyone willing to pay ₹2000 per month, I believe the importance of book knowledge will reduce, the importance of credentials and degrees will reduce, and the value proposition of mediocre colleges will reduce. But I believe the value in going to a top college will remain high.

Bloom’s 2 Sigma or the Power of 1:1 Mentoring

Alpha school’s claim that there are 0 teachers is not true. As the review points out, the guide-to-student ratio is 5:1. Which is far better than the typical 20:1 that is found in even high-quality, expensive private schools. And these guides are paid much higher salaries than even the best teachers can get in other schools. So they are certainly a part of the secret sauce.

The best educational content from some of the best educators in the world is available for free (or very low cost) on the internet. And yet, that has not made any significant difference to educational outcomes around the world. Why?

The most important job of a teacher is to motivate students to learn. YouTube is a superlative medium to get explanations for concepts you’re interested in. But developing the interest in the first place is usually done by good teachers.

A famous (and infamous) result in the field of education is called Bloom’s 2 Sigma Problem. Benjamin Bloom (the guy who came up with Bloom’s Taxonomy) found that providing students with one-on-one tutoring improved their performance by two standard deviations. 2 SDs is a staggeringly huge number—in most research, an improvement of 0.8 SD is considered large.

There were various problems with Bloom’s research. Here’s an important criticism:

"... they weren’t obtained by tutoring alone. Instead, Anania and Burke mixed a potent cocktail of interventions that included tutoring; training and coaching in effective instructional practices; extra time; and frequent testing, feedback, and retesting”

Bloom himself concluded that one-to-one tutoring “too costly for most societies to bear on a large scale”.

You will notice that a lot of what Alpha school does appears to include the “potent cocktail of interventions” mentioned in the criticism above. As of now, even Alpha school remains “too costly for most societies to bear on a large scale”.

But, based on the review, it is possible to tentatively reach the conclusion that Alpha school might have managed to get a Bloom-style improvement but at a lower cost, by going from 1:1 tutoring to 5:1 tutoring with the help of technology.

So What…?

What should you take away from all this?

If you are in the field of education, you should read the whole review, even if you are not directly interested in Alpha school. It is well written and contains a lot of interesting information. My article covers only a small fraction of it. There’s much more there.

As AI disrupts society, there is going to be upheaval in our institutions and our culture and beliefs around education. There’s a high chance that what worked well until now—all the old heuristics and thumb rules that we and our parents used—will stop working and we’ll all need to figure out new ways to survive and thrive. For that, we’ll need to rethink everything from first principles.

And this article contains many of those first principles. Make sure you understand them well.

Long ago, someone told me about an interesting rule in golf and that changed how I view certain things in life. Later, I learnt that that was not a real rule. But let us treat this rule as if it were real, in a thought experiment, and we’ll find that it can help deal with the unfairness of life.

(This is also available as a YouTube video, or a podcast if you prefer video/audio to text.)

Say you are playing golf. You hit your ball well, and you’re fairly certain you’re going to get to the hole below par. You’re feeling good. But as you walk from where you’ve hit the ball to where it has landed, and you discover that, in the immortal words of Kannan Gill:

There is a cow.

The apocryphal rule I heard was that if you’re unable to get the cow to move, you must treat it as though it is a part of the golf course. Just like if there was a tree, or a rock there. That’s it. It’s there, now you have to deal with it.

There is a cow. How do you cross?

When is a Cow Different from a Rock?

Put yourself in the shoes of the golfer. How do you react to this cow?

You may get annoyed. You may start to wring your hands and curse at the clouds. Or the cow if you’re not particularly religious.

Compare this to a slightly different situation: the ball landed in front of a boulder or large rock. How do you react then?

There’s nothing you can do about a rock. That’s going to be there. It’s a part of the golf course. You’re just going to have to deal with it. You move on, trying to find an angle at which to push your progress in this course.

Let’s change the scenario one final time. Let’s say that standing between your golf ball and its destiny is a human man. How do you react then?

You just tell the guy to move. He moves. End of story1.

You see how your attitude changed depending on what is in front of you? Let us return to the cow.

What is the point of getting annoyed, of throwing a tantrum? It wastes your own energy and doesn’t get anywhere in the process. If you tried to get it to move and that failed, then it is best to be practical about it—think of it as a rock and move on with life. The problem is that your brain isn’t looking at the cow as though it’s a rock. It sees a cow that refuses to move (unlike a rock, which can’t move) and gets unhappy about the unfairness of the situation.

Who Says Life is Fair?

When I was nine years old, on a vacation away from my family, I was playing a game involving my brother and a frankly dangerous wheat-cutting apparatus. Predictably, this did not go well, and there was an accident. As a result, I do not have a right thumb. (Take a look.)

When I came back home with a scab for my thumb, it was my mother’s attitude that I remember best. There was no crying over spilt milk, there was no beating around the bush. There was just the next thing. What are you going to do? This is how it was now, so we had to adapt to it. So, I eat with my left hand. I write with my right hand—using the index and middle fingers. Over the years, I just got on with practising how to work without a right thumb. And here I am now. I think things could have been rather different if my mother had started off with a different attitude.

The point isn’t that I am a stalwart bastion of the Correct Attitude™. The point is that my mother identified the situation and acted accordingly. There was nothing she could change about my thumb being gone. Because of her attitude, it never even occurred to me to be hopeless about the future. There was no use in despair. All that’s left to us is to move forward.

There’s another story that illustrates what I’m trying to get at.

I talk to a lot of young programmers. Most of them, of course, go to illustrious engineering colleges, learning from the best. I feel confident in saying that the same goes for you—the best programmers you know also probably studied programming in college. So, I imagine you’ll be as surprised as I was to learn that one of the smartest young programmers I know turned out to be a commerce student from Ratnagiri. (This was in 2012, in the pre-ChatGPT days, when we wrote code ourselves, like real men.)

When I learnt this, I had to talk to him. I called him, and I asked him how he writes code like he does. That’s when I found out that he was self-taught. He learned everything from online courses. I asked him what the hell he was doing in B.Com. He said that he fell ill around his 12th std math exam, and as a result, did not have good math scores. No math scores means no engineering college.

He could have fallen into a spiral of despair. Instead, he went to a commerce college, and while doing his B.Com., took up online courses to learn the things that his friends in Engineering colleges were supposed to be learning.

That’s not even the crazy part about this. The crazy thing is that most engineering colleges fill students’ schedules up with such mindless work that they don’t have the time to teach themselves the stuff that he learns. So, his code turned out to be better than the code of many people who actually went to college for this stuff! While he was doing a B.Com, he had a lot of free time on his hands, so he was learning this stuff properly.

This kid pulled bad cards. He fell ill on the day he was supposed to take a math exam. A math exam that he had probably been preparing for throughout his conscious life at that point. He had every right to be angered by this ungodly bad luck. Instead, he treated it like a cow sitting in front of his golf ball, stuck with it and scrapped away for as long as he could. When he emerged from the trenches, he emerged victorious.

Punches Hurt

So, what’s my point? Should we all just roll with the punches? Whenever bad things happen, do we just accept them and move on?

Well, no. There’s a nuance here. Sometimes, you do have to roll with the punches, but the truth is that punches hurt. Once in a while, you have to watch where the punches are coming from and dodge them.

On the golf course, when it was a human person, you recognised that you had the power to change the situation, and you got up and changed the situation. When it was a rock, you recognised that you had no power to change the situation, and you adapted to that lack of power.

You may know the Serenity prayer. Let’s go through it together:

Adapt to (most) things that hit you in life (they’re rocks), but change the (few) things that are within your power to change2.

You need to hone the wisdom to know the difference. You need to be able to ask: Can you change the situation? Do you have that power? Is it worth spending your energy on this situation? If your answer to all of these is yes, then you must muster the strength to change it. And that doesn’t just mean make an angry social media post about the cow. You have to get up and do something.

97%3 of the things you face in life are things you probably don’t have the power to change, or aren’t worth the effort/energy. That’s a large number, of course, but you have to remember that there are 3% of things that you can change. You must remember to fight for that 3%. If you try to fight for a full 100%, you will waste a whole lot of energy, and at the end of the day, you’ll be yelling at rocks to get out of the way.

For things in the 3%, do something (not the slacktivism of whining about it on social media). For things in the 97%, stop complaining, move on with life.

There is a cow. How do you cross?

You come into work one fine Tuesday morning, and your boss calls you in. She tells you that there’s an assignment that needs your attention. You have to create a PowerPoint for the presentation next week. And your boss says, “Have it ready by tomorrow. Thanks!”

No problem, you think, and get to work, and have the presentation done by the end of the day on Wednesday.

But suppose your boss had said, “Have it ready by Friday.”

When does the presentation get done?

If you’re like most people, you know that it would get done only by Friday evening.

Congratulations! You are a shining example of Parkinson’s Law!

Parkinson’s Law states simply that work expands to fill all available time. If your boss had given you until the end of Monday, you would have spent the whole week on it and completed it by Monday evening. And if your boss had asked for it today itself, you would have gotten stressed, but completed it by the end of today.

Parkinson’s Law was published in 1955 by Cyril Parkinson. He first noticed it in bureaucratic government organisations. For example, between 1914 and 1928, the British Admiralty's staff increased by 78%, and somehow they all managed to stay busy and find enough work to do despite a significant reduction in the number of ships and navy personnel being managed. And now this law is known to be true everywhere else.

Parkinson’s Law in reverse: Give yourself tight deadlines

For me, what is most interesting is applying this law in reverse. Give people—and yourself—less time to do something, and that something gets done in less time.

If you’ve undertaken any creative endeavour, ever, you know in your gut that this law holds. Without deadlines, you wouldn’t publish, because your work is crap (according to your own high standards). As Tina Fey said:

“The show doesn’t go on because it’s done. It goes on because it’s 11:30.”

Your creation is never good enough, and you’re never ready to publish it, so it keeps getting delayed unless there is a deadline. (Hell, even this blog doesn’t get done without me giving Arsh a deadline that it must be finished by Friday. By the way, I think he might be the funniest person in the wor— ARSH GET YOUR OWN BLOG.)

Doesn’t less time mean sloppy work?

Anyway, back to giving people tight deadlines. At this point, you might object that because you’re giving people less time to do something, they will get it done, but they will do a sloppy job, and the quality of the work will be lower.

Well, not really! The opposite is more true.

To understand that, let’s look at the reasons why work expands to fill all available time:

• Procrastination

• Distraction (made worse by the internet and social media)

• Perfectionism

• Most importantly, scope creep

It is easy to see how procrastination and distraction would get eliminated by tighter deadlines, and everyone will agree that this is a good thing. The next two are more subtle.

Among competent people, there’s a natural tendency to keep “improving” something as long as you have the time to do so. The more time you have, the more you want to add features, enhance something here, tweak something there. You will always find some flaw that you want to use that extra time to fix. Most people are always a little unhappy with their output and always feel that they could have done better. Being given extra time to do something allows them to indulge in this behaviour.

But as Voltaire reportedly said, Perfect is the enemy of Good. In the chase of the perfect thing, you forget to do anything good. If you keep trying to make something perfect, you’ll never put out the thing that’s already pretty good. Perfectionism is, generally speaking, a recipe for not putting out anything.

That gives power to a deadline. The power of rejecting perfectionism and scope creep.

Why does this work?

How does a deadline fix this? Take a look at The Iron Triangle of project management:

The triangle points out to us that we can maintain quality while simultaneously reducing the time taken by either increasing resources or decreasing scope. I think both of these happen. For most of us, we can “increase resources” simply by wasting less time and thus increasing the effective time available to work on the project. We procrastinate less, we take fewer breaks, and we focus more on exactly what needs to be done to meet the deadline. And “decreasing scope” is obvious: we get rid of fluff.

The funny thing about fluff is that there is so much of it, and we don’t really realise it until forced by a tight deadline. There’s a great principle in software management called YAGNI (“You Ain’t Gonna Need It”), which essentially states that a lot of features you consider important during the planning stage of a project are not really required. An easy way to find this out is to release your software with the minimum amount of features needed for it to be usable by your users. This is known as a Minimum Viable Product and is sometimes called “do the simplest thing that could possibly work” DTSTTCPW. After that, only add features that your users need.

It has been found repeatedly (and I’ve personally had a lot of experience with this) that often features you thought were must-haves turned out to be something that the users weren’t interested in. As a result, using the YAGNI technique saves you a lot of effort that you would have spent on unnecessary things (or fluff).

The deadline should be tight… but not too tight

Honing the skill of giving yourself a deadline can become a vital tool in your productivity toolkit. Because, yes, this works not just on your subordinates, but on you too.

We have to be careful, though. Give yourself too little time, and you get stressed. Stress can literally kill you1. Research has shown that workers suffer when there is too much stress put upon them, but that productivity suffers when there’s too little stress. To enter that sweet spot, the worker has to feel slightly challenged, but never so challenged that they feel as though they can’t do it at all. Importantly, that sweet spot is different for different people.

So, how do you find that sweet spot? It can be easy to screw it up, and you have to do it smartly. It has to be tailored to the person. If you’re a manager, it’s vital to take this last part into account—you have to notice your workers’ personalities and give them slightly more than they can handle. When it comes to deadlines, you have to give them slightly less time than they can manage. But only slightly.

At the end of the day, it comes down to goals and processes. When I say the word deadline, you hear “an amount of time before which a goal must be accomplished.” But if you set a goal without acknowledging the obstacles in between yourself and the goal, you’re never going to meet that deadline. Instead, your deadlines have to be framed as “an amount of time before which you need to overcome the next obstacle.” Finishing a task, therefore, gives you that little dopamine boost. Breaking the bigger project down into these more palatable chunks allows for those dopamine boosts to be administered regularly enough that you don’t lose motivation for your overall project.

Tried-and-tested techniques of giving yourself limited-time deadlines

There are a few methods to do this. None of these will work 100% for everyone, of course, some will work for you, while others will not. Taking the time to try as many of them out (and even look up any others that I may not have mentioned) can be worth it, since they’ll make you a lot more productive than you are now.

There’s the Pomodoro technique, which involves breaking a task down into chunks that could take between 20 and 25 minutes of work. Then, you set a timer for 25 minutes and do some thorough, focused work for those 25 minutes. After that, you set a five-minute timer and take a five-minute break. Do nothing other than your work in those 25 minutes, and do anything other than work in the breaks. Each of these blocks of half an hour is called a pomodoro. After four pomodoros, you can take a longer break. This works very well for many people, including the aforementioned superstar—the really, really so funny Arsh!

For some people, 25 minutes might be too little and for others, too much. Changing the timings around is part of the process of finding a technique that works for you. This is called timeboxing, a generalisation of pomodoro, where you give yourself some specific amount of time to do a specific amount of work, and you do nothing but work for that amount of time, and you do not work at any other time.

But of course, if neither of these seems like your type, there is the one you’ve most probably already heard of: Agile. This technique is wildly popular in the software world. Instead of planning out these long-term goals and projects, it breaks the projects down into elements that could be completed in one week. Then, customers or investors are asked to review these week-long elements and decide which ones seem most important to them. The tasks are ordered by importance, and the team does everything they can in one week. The important point is that they stop at the end of the week.

There’s a lot more to the ideas I’ve shared here, and there are a lot more ideas out there in the world. I highly recommend looking them up yourself and trying them out. The basic point, however, is that you have to shift from focusing on long-term goals to focusing on the obstacles right in front of you. Break the long term down into the short term, because smaller things are easy to deal with. Then, give yourself an amount of time to complete it in, and most importantly, stop working on it after that time has elapsed. It tackles the problem of an overwhelming amount of work and the excessive need for things to be perfect.

And this isn’t just stuff I’m pulling out of thin air. These are all widely used techniques in many workspaces.

Take, for example, the case of Buffer. During the COVID-19 pandemic, they switched to a 4-day work week, but expected the team to do the same amount of work in four days as they did in five. You may expect that there was more stress in that environment, or that productivity suffered because they were working fewer hours. But, as they found out, that did not happen. The team’s performance on productivity measures actually improved. Productivity can’t be measured in hours worked, and as I’ve been arguing throughout this article, giving yourself less time might help with productivity.

Buffer has since switched permanently to a 4-day work week.

Summary

Use deadlines to your advantage. Give yourself just a little less time than what you’re comfortable with2. And, to make sure you’re serious about not missing these, make public commitments about your deadlines3. Make them on social media, or to friends or coworkers. Put a little pressure on yourself, because we all work better under a little pressure.

It’s all in the process. You work a lot better if you work with high focus for shorter amounts of time than if you take long gaps between working, or if you wait for the inspiration to strike. And most importantly, deadlines force you to be done, to put your work out into the world. What’s the point of the work if you have nothing to show for it in the end?

About the Writer

Thanks to Arsh Kabra for writing this article based on my notes, because I’ve not had the time to do that myself. Arsh, who is my son, is a freelance writer who lives and breathes stories. Whether it’s scripting for YouTube channels, running epic Dungeons & Dragons adventures for young minds at Flourish School, or bringing plays to life on stage, he’s always finding new ways to spark imagination--both for himself and anyone else he can get a hold of.

Jessica is a cofounder of YCombinator, one of the top startup accelerators in the world, and YCombinator is one of the few companies which probably has a better understanding of how the world will change because of Gen AI.

Here, Jessica is talking to fresh graduates who have a brand new career ahead of them, and how to navigate an uncertain future in the real world. But I just realized that we are all now like fresh graduates; ChatGPT will change the world profoundly but nobody knows the details. Nobody knows which professions remain relatively unmolested, which ones will be mangled beyond recognition, and which ones will die out altogether. So, Jessica’s advice applies to us all.

Here's one relevant paragraph:

Ok, then what? How do you search through thousands of options? To be honest, you can’t. You have to use some kind of trick for narrowing them down. My favorite trick is people. Talk to people. Get introduced to new people. Find the people that you think are interesting, and then ask what they're working on. And if you find yourself working at a place where you don't like the people, get out.

I have written previously about the importance of networking and for those who don’t know how to network, I’ve some suggestions in a follow-up article. Those were written pre-ChatGPT but their importance has only increased. When all knowledge is available from ChatGPT o3 in 30 seconds, the importance of what you know (degrees and credentials) will decrease, and the importance of who you know (your network), and who knows you (your personal brand) will increase.

One of the reasons I started Pune Knowledge on Tap was as an excuse to meet interesting people, in response to the realisation in the previous paragraph.

What are you doing to increase your network and personal brand?

One of the interesting things about all of this is that there was nothing particularly inventive about the strategies my husband deployed. They were more or less exactly the strategies I would have come up with if I’d been put in charge of a similar situation in someone else’s life. Why did it take another person getting involved for me to realize I wasn’t Actually Trying?

I think what happened is this: When the stalker entered my life, I was at a low point in personal capacity—broke, alone, addled, etc. My approach towards him at that point (ignore, hoping he’d stop) was the only one that seemed available given my spiritual and psychological resources at the time. But my orientation to the problem became fixed in time at that point of low agency, and it never occurred to me to revisit it as my capacity for action increased.

You might not have a stalker but the problem is common enough that it has a name: Solomon’s Paradox, named after King Solomon who was famous for giving others wise advice but often struggled with applying the same advice to his own problems.

You can verify this yourself quite easily. Take any problem you’re struggling with currently. Now, imagine that a friend of yours comes to you with the same problems. If you do it carefully enough, you’ll notice that you find it quite easy to give advice to your friend, but you have a bunch of “convincing” reasons why you can’t take that advice yourself. The solution, often, is to override those convincing reasons and take that advice.

An important corollary is the fact that we routinely solve problems in on area of life (e.g. work) but for some reasons, we are not able to bring the same agency to another area of our life (e.g. personal relationships). Of course, many corporate techniques do not belong in your personal life, but some do.

In my particular corner of the world, there are tons of high-achievers in work. These are ingenious people shaping the world through innovations in science, technology, and policy. But many of them haven’t applied the same ingenuity to their interior experience or relationships. These are people who could successfully launch a product in a foreign country with little instruction, but who complain that there aren’t any fun people to meet on the dating apps.

It is as if we bring different personalities to the different aspects of our lives, and as a result, we aren’t solving the problems that we could solve if we just brought in the attitudes from one of the other personalities. In other words, we aren’t really trying.

Read the full article here.

What do you think?

Anyway, here is a great story:

S. Chandrasekhar once taught a class for which he travelled 152 km every week, and it had only two students. When asked why he did that, he said, “They were good students.” Both those students went on to win a Nobel prize in 1957, and Chandra himself won it in 1983. That makes this the only class where all the students and the instructor have won Nobel prizes.

What a story!

One of my rules is that if a story seems too good to be true, you need to fact-check it. So, when I saw that tweet, I started looking for reliable sources to confirm that this actually happened and wasn’t just a “WhatsApp foward” type of claim.

And I found this document:

So, it seems, the story was a bit of exaggeration.

At a GenWise summer camp, we were discussing the Chandrasekhar and I mentioned this story about how maybe 1/3rd of his class got a Nobel prize. Immediately, a few others jumped in saying there were only 2 students and they all got a Nobel.

To cut a long story short, someone invoked ChatGPT. Look what ChatGPT says : 👇👇👇👇

Uh-oh. Who are you going to believe, the article I referenced initially, or ChatGPT? I decided to invoke Wittgenstein’s Ruler. To me, ChatGPT’s response says more about the believability of ChatGPT than it says about the believability of the story itself.

But, there are different levels of ChatGPT. The quality of the default free version (4o currently) is very different from the best version (o3 currently). So I asked o3 and got a surprising answer: that Chandra himself said this in his autobiography. Because o3 can hallucinate quotes like these, I asked o3 for an exact quote and a page number and, lo and behold, I found this:

This is from “A Scientific Autobiography” published in 2010 (15 years after Chandra’s death), but based on material he wrote before 1980. o3 gave me additional references. The president of University of Chicago, John T Wilson is on record mentioning the only-two-students version. The University of Chicago Chronicle wrote an obituary of Chandra which said the same.

Seems like solid evidence. o3 did not hallucinate, and maybe I was wrong?

But still, something felt off. So I went back to Donald Osterbrock's article, and here’s the relevant paragraph:

Now we have a problem. We have contradictory statements, both supported by “reliable” sources. Which to believe? This should cause us to appreciate historians who deal with such questions every day. We have an incident from less than a 100 years ago, and multiple first-person accounts about it, and still we’re confused. They have to answer questions like these for incidents much older, with sources that are 2nd or 3rd degree, and far less reliable.

Anyway, all the data is here. I leave it up to you to decide what the real story is.

Personally, I believe Osterbrock’s story. He has given multiple corroborations for his claim: he was there himself, Yang and Lee both confirm the story, and he cites U Chicago records. By contrast, the president of U Chicago, the U Chicago Chronicle, and Chandra himself are throwing it out as a casual line without the same degree of checking (and they have an incentive to hype it a bit). If Osterbrock is wrong, then it is because he’s lying. If the others are wrong, then it is because they’re misremembering something (for example, it is possible that there were only 2 registered students and the others were auditing it, like Fermi himself). As far as I can tell, Osterbrock has no incentive to lie, while the others, if they are misremembering, have no incentive to improve their memory. So, for me, Osterbrock is right.

However, as a friend of mine pointed out to me, maybe the only-two-students story is better anyway, because a story must be told as it should have occurred and not as it actually did 😂

The debate over how long a person should work has a long and often vitriolic history. Many of the most successful people in the world advocate for a longer work week, while many others advocate for a shorter one. Proponents of the longer work week include Infosys co-founder Narayana Murthy, the Richest Man on Earth Elon Musk, and Alibaba Group founder Jack Ma. However, figures like Microsoft CEO Satya Nadella, Venture Capital superstar Vinod Khosla, billionaire Harsh Mariwala, founder of Marico (Parachute and Saffola oil brands), Marissa Mayer ex-VP at Google and ex-CEO of Yahoo, and Sheryl Sandberg, ex-COO of Facebook, all say that people need more balance between their work and their lives. Hard work, they say, is not synonymous with the number of hours someone has worked.

The issue is shrouded by the language of politics, which encourages ideological posturing rather than truth-seeking. Mostly, people pick a “side” and then everyone else is “either with us or against us”.

Let’s look at the sides, shall we?

Team 70 Hours

Narayana Murthy says that India's work culture needs to change, and youngsters should be prepared to work for 70 hours a week if the country has to compete effectively on the global stage.

Elon Musk agrees. Says you can't change the world without working 80 to 100 hours.

Jack Ma, founder of Alibaba, Taobao, TMall, and father of ecommerce in China, is famous for endorsing 996 (9 to 9, 6 days a week). “I personally think that 996 is a huge blessing,” he said. “How do you achieve the success you want without paying extra effort and time?”

Mark Cuban, billionaire and top investor and a shark in Shark Tank, says, “work like there is someone working 24 hours a day to take it all away from you.”

Steve Jobs: “If you’ve got a family and you’re in the early days of a company, I can’t imagine how one could do it … it’s pretty much an 18-hour-a-day job, seven days a week for a while.”

Ratan Tata, Mukesh Ambani, Sundar Pichai, and Amitabh Bachchan have all extolled the virtues of working long hours.

And Team 70 hours also has the guy who said, “How long can you stare at your wife?” In fact, he’s Team 90 hours!

Team 70 hours has a bunch of heavy hitters. Who do we have in Team 40 hours?

Team 40 hours

Satya Nadella suggests we need empathy and mindfulness to balance work and personal well-being. Harsh Mariwala points out that “hard work is not synonymous with number of hours worked.”

Vinod Khosla says that working less is fine as long as you're willing to live with the consequences. “You may not get the biggest house or car to show to your neighbors, but you can make that choice.”

Marissa Mayer, former VP at Google, CEO of Yahoo, suggests finding your rhythm and avoiding burnout.

And Sheryl Sandberg used to leave the office at 5:30 pm to spend time with her kids.

Team 40 hours also has a point.

So maybe we should take a step back and view this issue for what it is. What do we get if we look at all the arguments with equal standing, on their merits, without really looking at who was saying it? What do we get if we play this game with Science as the referee?

Team Secret Third Thing

So, who’s right? A 70-hour work week, putting everything into your work to reap the rewards of dedication, or a 40-hour work week, focusing on a healthy balance between work and play? Or is it a third thing that gets lost in the ideological fights?

Team 70 hours says that success is a direct result of hard work. If you aren’t giving 100% of yourself to your work, you cannot hope to find success. If you’re not doing this, you’re failing at life, is what they seem to imply.

Team 40 hours instead says that working for forty hours, and then spending the rest of your week on things you enjoy—family time, hobbies, anything—is, in fact, the point of life. The point of work, they point out, is to earn enough money so that you can enjoy the other things.

And our secret third option, let’s call it “A little bit of column A, and a little bit of column B.” The 40+30 approach. The idea is that you spend 40 hours working at your job for your employer. And then, you spend thirty hours after that on career-related things, but for yourself. Working on growing the skills that you need to get ahead in life. It’s still career-focused, but it’s for your personal growth. It’s not something your boss asked you to do, it is something you know will help you, possibly in your next job.

You don’t owe your employer more than 40 hours, this team points out, but maybe you owe it to yourself? The best of both worlds?

Team Science

All these options have pros and cons. Let’s see what the science has to say.

Economic studies tracking productivity have produced some clear results. Productivity is at its peak for up to 35 to 40 hours of working in a week. After that, between 40 and 55 hours, it dwindles. Your total output goes up, but it’s not going up as fast as it was for the first forty hours. After 55 hours, things begin to go bad. The output is pretty much negative—you’re undoing the work you’ve already done.

It makes sense, doesn’t it? When you’re firing on all cylinders, and at some point, you’re going to run out of fuel.

Note: This is probably true on average, but might not be true of everyone1. So, Elon probably continues to be productive long after 70 hours. But remember that you are probably not Elon, and also, Elon takes drugs to be able to do this.

Anyway, let’s continue looking at what science tells us.

A different study looked at what happens when you reduce the number of hours. It tracked productivity in a four-day work week as compared to a normal five-day work week. It found that out of 24 different measures of productivity, 22 improved or stayed the same in the four-day week. Not just that, but the motivation and mental health of the workers improved. And turnover, the likelihood of people quitting their jobs, dropped by 39%.

Another problem is the effect on sleep. People who work long hours sleep fewer hours, and even that sleep is of a lower quality. And the problems caused by bad sleep are endless—there’s literally a full book on this topic.

But an even bigger problem is stress. Long hours are also associated with stress, and stress is not good. Stress can kill you—literally. And I’m not using “literally” like the kids use it, when they say they’re “literally dying”. I mean literally literally.

Stress can cause problems in the cardiovascular system, the lungs, the kidneys, your skin, your immune system, your gut, your brain, and your heart. Oh, and it can increase the risk of getting diabetes.

Scientists from University College London studying people who work 55-hour weeks, observed a 33% increased risk of stroke and a 13% higher risk of coronary heart disease when compared to those who work fewer hours.

And finally, when you’re running on fumes for long enough, you run out of fuel, and your body just stops working. This is called burnout. And I don’t mean “burnout” in a casual way that we use in day-to-day conversation. I mean Burnout, which is a serious medical condition. Multiple long days with high stress and low sleep can cause enough burnout that can not only completely destroy your mental health, but also crash your productivity to zero. Of course, it is important to us, individually, to avoid burnout. However, it’s vital that people in Team 70 hours (usually the founders and owners of a business) also understand how dangerous it can be. Your people can’t work hard if they’ve been turned into zombies.

Stress vs Passion

So what’s the solution, then? What’s the best way to work hard? What’s the best way to succeed?

I like the following quote from Simon Sinek, the author of Start with Why:

“Working hard for something we don’t care about is called stress. Working hard for something we love is called passion.”

This sounds like a cool line, but it’s fascinating to me that we have empirical proof that “the love of the game” is the difference between stress and passion.

Drive by Daniel Pink talks about where stress comes from. Pink says that stress is based on three factors: Autonomy, Mastery, and Purpose.

Autonomy, or freedom, refers to the freedom to choose what you do with your time. An environment that includes micromanaging bosses and high levels of surveillance and oversight can be said to have low autonomy. It causes stress because we all like doing things our own way. Just following a set of instructions can sometimes be arduous and even counterproductive, especially if you have a more efficient way to do things that you’re not allowed to use.

Mastery refers to being good at your job and continuously getting better. If you don’t have the skills, tools, or competence to do your work well, then you will be frustrated. Conversely, if your work isn’t challenging enough, you will be bored and disengaged, leading to a different kind of frustration. And finally, if you feel like you’re not getting to the next level of your job, if you’re not getting new challenges, you feel stagnant and resentful. All three of these can lead to stress.

Purpose, here, refers to an alignment between yourself and your work. Specifically, your values and your beliefs. A mismatch in your values can lead to frustration and sadness, and ultimately stress.

Another secondary cause of stress could be bad communication. Unclear expectations, or lack of clarity on whether you are meeting expectations, or not being recognised for your work: all of these also cause stress.

You can work backwards from this and figure out what is needed to avoid stress at work. You need an environment where you have autonomy over how you work, you feel challenged to get better, but not so challenged that the work is impossible to do, you feel like your ideologies and values are aligned with your work, and you have clear communication with your superiors and managers. In other words, a place where you can feel comfortable and free to do your work in your own way.

TMKK

So what does this mean for the larger debate between Team 70 hours and Team 40 hours?

First of all, it is important to acknowledge that it is completely acceptable if someone chooses to work just 40 hours a week. Some people would rather spend the rest of their time on other things, and that is fine. Some people cannot work more for various reasons, including health issues, physical or mental, and that is fine. People who want to make a dent in the universe shouldn’t look down on the people who want to enjoy life—those are different philosophies.

And of course, it is easy to forget that hard work is not synonymous with the number of hours worked. Like Bill Gates says, “We underestimate what can be achieved in ten years, and overestimate what can be achieved in one or two years.” There are people who can work just 40 hours a week and still achieve greatness. It requires focus, but it can be done.

Another variation is that you could choose to work long hours for a part of your career. This is easier when you are young and/or single. As you get older, you might choose to spend more time with family. Also, as you get older, you can compensate for the fewer hours worked by being wiser about what to work on and how.

So, what is all the controversy about, and why all the fights? We can arrive at an answer by adding a clarifying question to the debate. Are you being forced to work 70 hours, or are you choosing it yourself? And a hidden question there is, who benefits?

“Do it for yourself, do it for the nation,” says Team 70 hours. The opposition points out that often the people saying this are the owners/founders of businesses who benefit most from people working long hours for them. And both sides have a point.

So instead of just arguing over the number of hours, you should think through the reasons for working harder.

You could work longer because you’re paid more. Or because you’re learning something that you wouldn’t otherwise learn. Or because it’s adding something to your career by boosting your resume or portfolio. If you are choosing that work, you can work however long you want, because you’re working through passion, not stress. (Or, I should repeat, you could choose to work 40 hours and then do something else with the rest of your time.)

In my own life, I’ve been in both places. When I was studying for my JEE exams, I was working around the clock. I knew that getting a high rank in that exam would be extremely good for me. But it was also stressful. After I did well enough to get my preferred programme at the college of my choice, I decided that I would never work that hard ever again in my life. Today, I work 40-hour weeks and spend the rest of my time with my family and playing board games. But a lot of that time is also spent reading—as a family, we spend a lot of time sitting together and reading. And depending on how you’re counting, this can count as work, because a lot of my reading is work-related non-fiction.

At the end of the day, there isn’t one uniform system that works for all of us. Humanity isn’t a monolith, and not everyone is going to be able to pull 70-hour weeks. At the same time, we shouldn’t be constraining those who can and want to, either. Instead, we need a system wherein people have the freedom to choose the system that works for them. And the awareness of what tradeoffs they’re making with their choices.

About the Writer

Thanks to Arsh Kabra for writing this article based on my notes because I’ve not been getting the time to do that myself. Arsh, who is my son, is a freelance writer who lives and breathes stories. Whether it’s scripting for YouTube channels, running epic Dungeons & Dragons adventures for young minds at Flourish School, or bringing plays to life on stage, he’s always finding new ways to spark imagination--both for himself and anyone else he can get a hold of.