2025-06-15 08:00:00
I was talking to a designer friend yesterday.
I was wondering whether I should get a Mobbin subscription.
I had one when I was working at my last company. I was not sure if it made sense to buy a subscription when I am neither a designer, nor someone who will be using it regularly.
And I saw quite a few cheaper alternatives. So I asked my friend if I should just go for a knock off of Mobbin.
I also asked him why his company pays a premium for it when cheaper alternatives exist. His answer was that if existing apps change their UI, Mobbin will the updated UI soon. The cheaper alternatives will still be showing the same old design six months from now.
Mobbin isn’t just a collection of screenshots, it’s a live feed of an app’s design evolution. You pay because you have access to an ever increasing supply of design inspiration that is refreshed constantly.
That conversation got me thinking about data companies.
Why do some charge X while others can command 5X the price for what looks like the same information.
The answer isn’t about having more data. It’s about having data that’s alive. We will talk about 2 ideas in this post:
There are really two kinds of data businesses: Museums (with a constant collection that does not add more art) & News channels.
They just preserve old artefacts.
Once you’ve seen the Mona Lisa as a user, you don’t need to check it again next week. The artefact becomes stale after the first visit. There is no new version of the Mona Lisa every month. Most data vendors, especially the copycat ones who just scrape the incumbent, are museums.
They scrape something once, file it away, charge admission forever.
Take a typical Crunchbase competitor for example. Company founding dates. Patent records. Historical funding rounds. Cap table.
Useful? Sure. But also easy to copy. And because they are copycats whose only differentiator is pricing, they can’t even update their artefacts or add new artefacts as the same velocity as Crunchbase.
News channels are different. They matter because what they show today is different from yesterday. No one’ opens the television to catch yesterday’s news.
Museums sell truth in the past tense. News channels update you everyday on the latest ‘truth’ or ‘facts’ and tell you signals about the future. Think of the weatherman telling you how the weather will be today so that you can be better prepared.
You tune in because today’s truths aren’t yesterday’s. They are fresh. They are evolving.
Static data is a race to the bottom. Someone will scrape your museum and build a cheaper one next door. First mover charges X/month. Six months later, five competitors offer the “same” data for 1/5th the price. Live data that is updated constantly is different.
Here’s what makes data alive:
Facts or truths aren’t truly defensible long term, especially with AI eating the world.
Yes, you can have some unique data, that competition does not have, but competitors can often automate similar pipelines or license the same sources. Without barriers such as exclusive capture rights, strong network effects, or switching costs, truths (stale or live) by itself may not sustain a price premium.
Now coming to Truths vs Religion vs Currency.
Religion is prediction.
Excerpts from a Crunchbase CEO’s podcast appearance in the World of DaaS podcast:
“Can I predict with any sort of accuracy what we think a future valuation might be, for instance? So we’re talking about like, how do we do valuation predictions? And that’s where Crunchbase actually does the valuable thing that you always wanted to do. Because when you were coming to Crunchbase before, those are the questions you were asking and we’re just giving you like one data point saying, well, here’s the last funding round. And you would have to sort of figure that stuff out on your own. Now we’re using thousands of feature vectors that go and figure it out. There’s religion, which is what will happen in the future, which is what you were just talking about. Here’s the companies that will be selling, here’s the companies, there’s some sort of prediction. It sounds like what you’re saying is that religion will be more valuable than truth. It has to be. The tricky part of all that, of course, is tell me any prediction engine you’ve ever seen that’s good. They’re terrible. Almost all of them are terrible. That’s changing. In our case, we have 95% precision. It’s hard to even believe it’s that good because we have all these little secrets that make it easy to figure out, but no one else has access to them. They don’t think that way. The user has to start believing it and the user has to say, I’m going to put my business future into the hands of a company that’s predicting stuff when I have a history of not believing in predictions. Like real religions, if you believe a certain thing and someone else will think you’re going to hell, you say you’re a Bayesian, I hate Bayesians, well then it’s off right there. And the only way that any of these religion companies have ever become big companies is if they’ve crossed the chasm from religion to currency. And then it’s like, well, people actually don’t really believe in the religion, but it’s priced like a FICO. Maybe people don’t even believe in the FICO score, but it’s the currency. It’s in every single loan. So therefore, you have to trade in FICO scores. It’s almost like the US dollar in a way. And so therefore, the company becomes super valuable. How do you go from religion, where you could have thousands of religions to, okay, this is the canonical one that we’re all gonna agree with. You don’t want to have thousands of competitors either. At the end of the day, it does come down to prove us in the pudding. The nice thing about predictions is someday they come true. So I can show, it’s not just a faith-based thing then. It is now this thing like, look, we have proven time and time again. In our case, how we talk about it is, in April, 600 of our predictions came true. So we were able to go and say, these companies got acquired and they got funding. What was that worth? Because you missed it, what did you miss out on that? For you doing deals, if you miss out on the hot company, that company sells for $5 billion, zero ROI on not buying Crunchbase is very, very clear. If we do get competitors in that space, we’ll go and say, well, here’s our accuracy rate in the last six months, let’s compare, let the best one win. And that’s where we feel really good because no one else has any in the now data, unless they’re somehow getting into the process itself before it happens, which is unlikely to happen.”
(Any error in the above transcription & interpretation is mine.)
Back to this post.
Tegus is another successful data play. Started as an archive of expert call transcripts. Sounded like a museum. But investors schedule new calls every week. Tegus adds these fresh transcripts almost daily. Miss a month, and you miss the latest insights on things that matter to you as an investor. It is a living feed because users themselves demanded fresh insights.
I believe the strongest moat sits at the intersection of two things: predictions that refresh continuously and can become currency.
Currency > Religion > Live truths > Truths.
From “This company has raised fundraising” to “This company might raise funding” to “This company’s probability of raising in the next 3 months just went from 50% to 80% this week based on their hiring velocity and the positive feedback by Tegus experts.”
Building a successful data business will be determined by:
There is another reason I have been digging into this topic. Every venture capitalist says that proprietary data is the key moat in today’s AI world.
You might remember some of my research prompts about finding overlooked proprietary data sources. I’ve been focused on this over the past few months. Tegus has become one of my favourite products, and I often wish I had built something similar. Tegus commands pricing power, charging $20,000 per subscription because its insights help VCs and hedge funds confidently deploy tens of millions.
Recently, a founder urged me to copy Tracxn and rebuild it for the AI age, claiming that deep research and AI agents now make it easy to gather information and launch a data platform.
However, I believe that simply scraping Tracxn’s database will never be enough to create a lasting data business. To succeed, you need a richer plan. This post shares my research on how to do that.
2025-06-14 08:00:00
The synthetic paneer scandal was big on X dot com a few months back. People were freaking out over food adulteration.
But did this actually change how people order food?
Every food delivery company has this goldmine of data sitting right there. They know who’s vegetarian based on order history. They can see if you suddenly stopped ordering paneer makhani after those videos went viral. They can track if you ever came back.
What I want to know is whether that outrage led to action and long-term behavior change in people.
The data analysis would be straightforward. I am sure all food delivery apps already tag users as vegetarian or non-vegetarian based on their order history. They should look at ordering patterns before and after the scandal.
If there was a dip, how big was it? Furthermore, they should examine order frequency of different cohorts, basket value (maybe they order fewer items), and specific dishes where they assume there might be adulteration.
Did they switch categories? Maybe non-vegetarians who ordered veg items from time to time stopped ordering veg items? Perhaps they still ordered veg items, but not paneer?
For different segments: Heavy, Medium, and Light food delivery users, whose behavior changed the most? Did people replace paneer with tofu, mushroom, soy, or other vegetarian alternatives, or did they stop ordering (cut ordering frequency) for a while?
Did these food delivery platforms measure weeks for: percentage of users who returned to old patterns, percentage who never came back, and percentage who stayed off food but migrated to groceries and cooking at home?
People who were heavy users, say ordering five times a week, cannot suddenly change their behavior and replace all these orders by cooking these meals. Convenience usually wins. If they hired a cook, and in India you can always get a cook, did they drop off completely from these platforms or did they continue to order occasionally?
Did their behavior shift from ordering from new restaurants to older, more established ones where trust is higher? Are they now ordering more from higher priced restaurants, assuming that better quality ingredients are used and the risk of adulteration is lower?
Did cloud kitchens suffer more than established restaurants? Did some places advertise “100% pure paneer”? Maybe they should have.
Now comes the most interesting part.
Most of these delivery apps aren’t just food delivery anymore. They’re super apps with grocery delivery. Swiggy has Instamart. Zomato has Blinkit. Zepto has Zepto Cafe & Zepto. So when someone stops ordering cooked paneer from restaurants, do they start buying raw paneer from the grocery side?
Do they trust Amul paneer from the grocery section of the super app but not restaurant paneer?
Did trusted labels (Amul, Mother Dairy) see higher sales in groceries over generic paneer? Do people lose trust in in-house brands of these super apps?
Even more interesting: what if more and more systemic gaps don’t lead to outrage, but instead lead to people just giving up? What if there’s a threshold after which people stop caring entirely, and start accepting it all as “the cost of living in India”?
A bridge collapses. A plane crashes. Food adulteration hits the news again. People panic, but for a bit. They complain on Twitter. They protest by posting memes. And then? They go into “fuck it” mode because they feel like they have no control. And when you feel powerless long enough, you don’t resist, you rationalise. “Everything is broken anyway.” Why care so much? Why not just order that Paneer Manchurian from the nearby place at 30% discount? They order from the same old restaurants again. They eat the same cheap adulterated paneer. Life goes on.
What if it’s not convenience that brings people back to their old behavior? It’s resignation.
So yeah, I want to see how long did user behavior go back to baseline. And for how many people the ordering frequency changed permanently.
I think people have short memories. My hypothesis is that heavy users come back fastest. They’re addicted to convenience. Light users might stay away, cut ordering frequency permanently because they have alternatives.
Maybe people didn’t stop ordering. They just switched items. No more paneer butter masala, but the chicken tikka orders went up. That’s not a trust issue with the platform. That’s a trust issue with a specific ingredient.
The cross-platform data would be fascinating. If someone stops ordering food from the food delivery product but their grocery orders went up, then the platform did not lose a customer. They just shifted spend. But if both drop? Then it could be a trust problem with the ingredient, how the sourcing was done, or maybe it is a brand problem. And these super apps need to regain trust.
What about the people who complained loudly on social media? Did their behavior actually change? (Food delivery apps won’t have this data though.)
This kind of analysis could influence product strategy. If people trust groceries more than restaurants, maybe you push the grocery product harder during food scandals and promise high quality ingredients. This is where having a super app helps. If certain restaurant brands maintain trust, maybe you highlight them more prominently.
Now, if only these super apps had payments integrated too like some Chinese and South East Asian super apps. I would have loved to see if spend moved offline. People ordered less, be it food or groceries, but went out to eat more, where they could physically see people making their food.
Does it mean that Darshinis in Bengaluru now attract more people?
Running this analysis would not be hard. I would definitely have done it if I was a PM at these super apps.
2025-06-13 08:00:00
Victor Lazarte, Benchmark: This is the biggest technology shift we have ever seen. The revenue ramps are incredible. Take Mercor: I invested nine months ago, and they just announced a 100 × jump to a 75 million-dollar run rate, still growing 50 percent month over month. You do not see that every day.
But fast growth alone is not enough anymore. In the past, hitting 10 million ARR meant you were safe. Today the pace of change forces you to ask whether that revenue is durable.
At our partner meetings we keep seeing startups at 5 to 10 million ARR that may not exist in two years. We always ask, “If foundation models get ten times better, does this business become stronger or weaker?”
Many thin wrappers evaporate when the next model release absorbs their value. Imagine an app that formats building-permit paperwork with ChatGPT. It can reach a few million in revenue, but it disappears once the base model folds that feature in.
That single question about a ten-times-better model is very clarifying.
Our job as investors is less about predicting the far future and more about understanding the present. We focus on the areas where AI performance is improving fastest. That is easier than guessing distant scenarios.
We track benchmarks: Where are the eval scores rising most quickly? Then we invest there. The pattern is clear: AI improves fastest where you can measure the output objectively.
So our framework is simple:
2025-06-12 08:00:00
People talk a lot about taste nowadays. They talk about agency too.
But the more I see people who’ve done something mind blowing vs and people who haven’t, the difference often comes down to one underrated thing: just caring more about their craft.
And I don’t mean performative care. I mean actually caring. Deeply. Stubbornly. Quietly. About the thing you’re making.
I’ve always cared about outcomes. I care if the product works. I care if it drives results. But when it comes to the process, I’ve always optimised for speed and efficiency. For meeting my KRs.
For a long time, that worked. Still works, to some extent.
I’ve proudly said in the past: you should copy the ‘industry standard’ flows, while differentiating on a few game changing features that really matter, and move on.
I have written 2 posts about it in the past that you can check out in my blog: ‘On copying’ and ‘You should copy your competitors.’
I’m starting to think that era might slowly ending.
The people who truly stand out today are the ones who care. Not just about the outcome. But about every single part of it, even the ones no one notices.
Take Gawx, for example.
He makes videos. Both long (Gawx Art) and short videos (Gawx 2). They’re basically cinema. Every shot, every transition, every frame is crafted to perfection. And he’s been doing this since high school.
He does not have the budget of fancy studios. He just cares a lot. He puts a lot of care in how the shot is framed. Care in the timing. Care in the pacing. Stuff no AI tool can replicate, not because it’s technically hard, but because it’s irrational to put so much effort into something that most people would not even notice.
I watched a video ‘How Gawx Recreates Hollywood in His Bedroom’. I recommend watching it even if you have no interest in being a Youtuber.
The host asks him the same question ‘Why do you care?’
AI can generate clips. Veo3 already does a great job. It can even mimic the style of some film makers.
But it won’t want to get the shot just right. AI can’t obsess over the details that no one asked for. That’s still will be Gawx’ edge. Caring more than the average human about each frame of a video.
I was telling a friend the other day, when Sarvam told me they were releasing their translation model, I cooked up an ad on Figma to announce it in a tweet. Took me under an hour.
I would have taken me even less. I spent more time thinking about the copy of the ad than polishing the design. And honestly, it was……fine.
I got a few thousand views, a few hundred likes.
I keep thinking: what if I had spent a day on it? What if I had gone deep, instead of just optimising for raw speed?
I don’t know if it would’ve changed the outcome. Probably most people would not have cared. But I would have probably felt better knowing I put in the max effort I could. I could have cared more.
This was the same with my writing. I took pride in never reviewing what I posted online.
I did not check for grammar. For typos. I falsely claimed that it helped me ship faster. Yes, it was probably true. You can’t obsess about perfection if you want to post everyday online.
Honest truth? Editing would not have taken hours, but mere minutes. I just did not care.
I am still lazy. I don’t have Grammarlym but I use Claude to edit my posts now.
That’s the thing with care is that it’s not always efficient. It doesn’t always make sense.
And with no KR to chase, it is easier for me to care even less.
But as AI levels the playing field for the fast and the functional, the people who obsess, those who grind out the details, who love the process, who care even when most people probably would not even notice the details, these are the people who will start to stand out more and more.
2025-06-11 08:00:00
For a long time, I held this wrong assumption: that people who are active on Twitter self-select for good behavior. I used to think, no matter how much you thoughtpoast online, if you’re an idiot, people will call you out. They’ll laugh at your takes, realise you’re not that smart. If your ideas are not actually useful, you can’t provide any real insight, people will stop reaching out to you.
And by the same logic, I believed that VCs who are very visible on Twitter, writing threads about founder empathy and sharing frameworks on how to be a “good VC,” would also be on their best behavior when they actually meet founders.
But over the last year, I’ve learned something surprising. Some of the most active, most vocal VCs on social media are the worst people to pitch to. There’s no correlation between what they preach online and how they behave in real life. It is bizarre.
Yes, a lot of these are not personal experiences, but anecdotes from friends who have gone through their fund raising journey.
I still can’t wrap my head around how someone can constantly talk about being “founder-first” and then show up to a pitch meeting and act like a total jerk. They waste founders’ time, take their insights to shape their own thesis, and have zero intention of actually investing. Even worse, they pass the deck or thesis along to a founder in their own portfolio.
And the startup ecosystem is small. Everyone who’s done anything is just one intro away. Word gets around. What’s even more surprising is that the best VCs I’ve met personally, or I have heard good things about, aren’t even active on Twitter. They’re low key, self aware, humble.
Even when they say no, they say it in a way that respects the founder. They give thoughtful feedback. They don’t perform for Twitter. They actually help.
At the end of the day, most founders don’t mind hearing “no.” They are used to rejections. But they do remember how you said it. They remember whether you made them feel stupid or seen. And in this market, where almost no one is raising unless they’re building the hot thesis of the month, 10 min diaper delivery, those little things matter.
Probably, founders, regardless of past experiences, will still raise from these VCs. The ecosystem is small, and there aren’t enough tier 1 funds. Founders will still prioritise high valuations and less dilution.
However, now that all Indian VCs are trying to go global and compete for the best deals in the US, I’m not sure how they’ll stand out. They might end up being the 50th option for a Stanford undergrad Math Olympiad gold medalist founder, whether desi or not.
Just something I’ve found incredibly strange and thought of sharing.
2025-06-06 08:00:00
Today: X names Polymarket as its official prediction market partner
From last year.
