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Today’s edition.

There are two ways to disrupt the smartphone market: create a technology shift or ride a market one.

Apple cracked the tech. They brought the first mass-market touchscreen phone to a world that only knew keyboards. Samsung cemented its India position by riding the 4G wave harder and faster than anyone else. Both paths work. But both demand years of R&D and serious capital that a new brand simply doesn’t have.

So what’s left? Offer the most value-for-money phone. Except, 15+ Chinese brands with optimised supply chains are doing exactly that.

Legacy brands own consumer trust on one end. Aggressive Chinese players own the value end on the other. There is no obvious gap.

And yet, Nothing, founded in 2020, has managed to capture 2% of the Indian smartphone market. How did they do that? We tried finding answers to that question over the last few days. Here’s everything we learned.

The wedge is identity.

Carl Pei, the founder of Nothing, knew his new brand couldn’t compete on specs. Sourcing the latest and greatest components would be too expensive. Competing on price was out of the question too — no one wanted razor-thin margins.

Enter: tapping into identity through design.
It isn’t new. Apple did it back in 2008, when launching the MacBook Air, when Steve Jobs pulled a MacBook Air from a Manila envelope. The design of the Air was thin and sleek, unlike any other laptop, although it wasn’t entirely practical at the time. Everyone who bought it wanted to appear cool by association. Even at a premium price.

Nothing intends to follow the same playbook. But with a specific target: the back of the phone.

It’s where your identity lives. It’s what people see when you place it on a table, what they notice when you pull it out of your pocket — which is also why phone cases are a billion-dollar industry. But as build quality has improved across the board, every phone is starting to look the same.

That’s the gap Nothing wants to own. And here’s the practical upside — reinventing the back of the phone is significantly cheaper than competing on specs.

But locking a manufacturer is harder than you think.

Smartphone making is a volume business.

Think about it from a manufacturer’s POV.
A new phone company approaches you to manufacture custom components. You spend months figuring out the suppliers, the assembly, the entire production line. The numbers only work at scale — so you order big. The company launches, sells a few thousand units, then shuts down. Now, you have components for 100,000 phones and no one to sell them to.

Phone manufacturers know this story too well. It’s why they only work with partners who can guarantee volume.

Legacy brands like Apple get easy deals with Foxconn — there’s clear, persistent demand. Vivo, Oppo, iQOO, Poco, and Realme cut deals with Foxconn too, but not because of individual clout. They have one parent company, BKK Electronics, one negotiating table, and massive combined order volumes. For perspective, BKK Electronics controls roughly 45% of India’s smartphone market.

Nothing had neither the massive demand nor a large parent company to back it in 2020. No wonder Foxconn said no.

The first proof of concept: earphones.

No manufacturer wanted to make phones for a brand with zero existing demand. So Nothing went with earbuds, a lower-stakes product that required less capital, shorter production runs, and a smaller factory commitment. The kind of deal a struggling small manufacturer might actually say yes to.

The logic was simple.
If the earbuds sold, it proved two things — that consumers would pay for Nothing’s design language, and that the team could actually manufacture and deliver a product at scale. Proof of demand. Proof of execution. That kind of track record could open doors with bigger manufacturers.

The bet paid off. Nothing sold ~1,00,000 Nothing Ear (1) buds globally in the first 2 months of launch, signalling strong demand to other consumer electronics makers like BYD China. The company went on to become Nothing’s manufacturer for the Nothing Phone 1.

Finding the right pricing market fit.

But where do you even position yourself? Flagship, mid-premium, or budget. Each caters to a different customer who wants fundamentally different things. And the size of each group keeps shifting.

Take a look at the chart below.

In 2022, entering the mid-range segment was the obvious choice.
This way, Nothing could avoid the volume game of budget devices, while having just enough specifications to cut through the competition and make a margin. Brand loyalty was weak here too — every player was focused on specs, not design. Which meant Nothing had a real differentiator on its hands, provided it could crack the right design-spec-price configuration.

Nothing Phone (1) launched in 2022 at ₹32,999 in the mid-range segment, in the same segment where OnePlus built it name.

Around 2023, the market shifted.
By 2023, the market was shifting. No-cost EMIs made premium phones more accessible, build quality improved, and replacement cycles got longer. Customers started trading up. Many competitors went up-market. But Nothing took the opposite route.

The Nothing Phone (2) went for ₹45,000 in 2023, featuring a better chipset, a premium build, and improved cameras. The Phone (2a) brought the same experience down to ₹24,000. The Phone (2a) sold 60,000 units in the first 60 minutes.

The lesson?
Stay anchored in mid-premium to build credibility, because no one buys a flagship from a brand they don’t trust yet. Move downmarket simultaneously to capture volume. Two reasons.

One, because ~50% of the Indian population is looking for a budget phone. And two, because scale gives you leverage with suppliers, manufacturers, and retailers and raises sales with customers.

And so, in 2023, Carl introduced the CMF lineup of consumer electronics.

Wait, why start a new brand?
Look at it from a customer’s perspective. You’ve been saving up to buy a mid-range phone for some time. Would you rather get a Pixel 9a or a Xiaomi 14 Civi? Unless you’re a Xiaomi fan, it’s probably the Pixel 9A. Because of its association with Google.

Customers trade up toward brands they trust, not down. And by 2023, Nothing had already built a reputation as a design-first, mid-premium brand. That credibility was worth protecting, which is exactly why they didn’t put the budget phone under the Nothing name.

In 2024, Carl launched the first phone in the CMF lineup — the CMF Phone 1 — at ₹ 16,000.

Misdirection and course correction.
In 2025, Nothing tested the premium segment. Three years in, with a loyal customer base — was there appetite for a flagship? The Nothing Phone (3) launched at ₹80,000. Signature dot-matrix design, superior build quality, but a mid-tier Snapdragon chip. At that price point, customers compare every rupee against what Apple and Samsung are offering. The chip was an easy target. Sales were modest.

The Nothing Phone (4a) came back to the ₹34,000 price point. First week sales exceeded internal targets by 150%. You had to earn the right to enter the premium segment; you couldn’t bulldoze your way in.

What’s next for Nothing?

Carl Pei wants Nothing to be a top-five global smartphone brand within a decade. That means going up against Apple, Samsung, Xiaomi, vivo, and OPPO — companies that ship 150 to 250 million phones a year. It’s an extraordinarily ambitious goal.

But Nothing has already survived the phase where most startups don’t make it. The harder question now is whether they can grow from a cult brand into a mainstream one, without losing what made them worth following in the first place.

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Today’s edition.

A few days ago, we shared a step-by-step process for building a web app on Lovable. Then we learned that Google had a free tool, Opal, that did the same thing for mini-apps.

Over the last 3 days, we put Opal through a stress test to answer one question: Could you really create an app from scratch entirely through vibe coding using a free tool? Quick answer: You can. Here’s a workflow to prove it.

First, what can Opal do?

Google markets Opal as a visual vibe coding tool that lets you build AI mini-apps using natural language. So, in theory, you should be able to enter prompts on Opal and let the tool build a mini-app out for you.

Wait, mini-apps? Are they different from regular apps?
Think of it as a lightweight, simple web application that runs on virtually any device. The keyword here is simple. Opal isn’t built to create full-featured products. You won’t be building a workout app that tracks your reps, maintains streaks, and manages a personalised exercise library. What you can build is a form-checker app that takes an exercise name and returns the correct technique from the web. You get the drift.

What are we building today?
A simple diet chart for folks who want to eat healthier Indian food. Ready? Open a new tab now. Let’s get building.

Part 1: The set-up

1. Click on this link. It’ll take you to Opal. Here’s what you should see.

2. Click on Sign In and log in via your Google account.
3. Next, Google will ask you for permissions to your Google Drive and access to see, edit, and change your files. Read the terms, click on Select all and click on Continue.

You should now see the Welcome page. Select your preferences and click Confirm.

You’re all set to build your first mini-AI app.

Part 2: Building the mini-App

1. Enter your app idea into the chat box. Here’s what we’re using for the Indian Diet Chart app.

Act as a dietician and create a personalized Indian diet chart

2. You’ll see Opal generate an entire workflow to build the app itself. Look at the image below.

Opal has correctly understood the different user inputs it’ll need to create a usable diet chart. This includes user age, gender, weight, height, dietary preferences and medical conditions. Then the tool uses its AI layer to figure out a response. And finally, it displays the response in a chart format.

You should also be able to see a preview of the app under the preview tab on the right side of your screen. It’s great as is. But I want my app to scrape the web for a more accurate diet chart. So, here’s what I’ll do.

3. Click on the AI layer in the flow diagram in the Editor Tab.

You should be able to click on the Step tab on your panel now.

Here, you can fine-tune how your mini-App uses AI. You can choose your Agent under the Agent tab, and decide which Google function to use from the Toolkit. For this project, I want to use the Search web tool.

4. Now, look at your prompt. You can add the Search Web tool to the analysis section. Now, bring your cursor to the analysis section and add search web from the Toolkit dropdown.

You should now see an updated workflow. Your app is ready!

5. To experience your app, switch from the Editor tab to the App tab. And click on Start.

After you enter all the necessary details, you should get a chart that looks like this.

If you’ve never built an AI tool before, this is a good place to start. The tool is free, the workflow is straightforward, and you can use custom themes, upload custom assets, and use a fully functional Google-powered toolkit to build your app.

It’s really straightforward. Try it.

Find a buildathon in my city

Today’s edition.

They say that you can’t serve two masters, especially in a war. That’s only partly true.

In 2011, the US bombed Libya. Muammar Gaddafi fell, and the US offered $900 M to fund the replacement government. At the same time, they supplied military equipment to Egypt, a country that actively uses the weapons to destabilise Libya.

Then in 2015, the US made arms deals worth over $54 M with Saudi Arabia and the UAE to counter extremist groups in Yemen. Around that time, the Pentagon lost weapons and equipment worth over $500 M. Reports later suggested those weapons ended up with the very same Yemeni extremist groups.

Two conflicts. The US meddled in both, fracturing peace in the region. Their arms deals, however, held up just fine.

Wars change. The arms dealer doesn’t.

The USA is the largest supplier of weapons, contributing to 42% of the global arms exports. What’s crazy is that some reports even suggest that 2 out of 3 armed conflicts have at least one party backed by the United States. The figure below shows us how that works.

Long story short, every time there’s a war, the number of weapons coming out of the USA shoots up. So, more wars = more weapon sales = more profits.

But what happens when the wars stop?

Demand never dies.

Imagine you’re China for a second.

You’re surrounded by competing powers on nearly every side. Say, tensions rise in Taiwan. You’ve got to do two things: manage tensions near the Taiwan border and secure your other borders so that neither India nor North Korea exploits the distraction.

Imagine you’re India, now.
You see that China is upping border security. There are new soldiers, and they’ve got weapons. You don’t know why, but you can’t risk finding out. Your only choice? Match them.

It’s a delicate dance every country engages in — have just enough weapons to not start a war. It’s also why the demand for weapons never stops.

Why don’t nations make their own weapons?

Making weapons in-house is terrible for the economy.
The cost of development is extremely high. Countries would need to cover massive R&D and production costs, and have a supply of technical experts to set up their weapons industry. And even if they manage that, weapons manufacturing won’t grow the economy.

Think about it. If a country’s existing weaponry helps it avoid conflict with competing powers, nothing happens. No wars, no destruction — but also no measurable return on everything spent. The money is gone with nothing to show for it.

But if there’s conflict, the demand for weapons rises. On paper, that looks great. The government pours money into defence, factories run, workers get paid, and the economy moves. But the weapons get used up. Meaning the government now needs to spend more money to build up reserves — not to grow, just to get back to where it started.

That government money could have been used to grow the economy anywhere else. Building a school, for instance, could produce engineers, doctors, and entrepreneurs for the next fifty years. They would build companies, develop new technologies, and train the next generation. The same isn’t true for the weapons industry.

European countries know this well.
It’s why they consistently underinvest in their militaries. Instead, they lease protection from the US through alliances and treaties. So far, it’s worked.

Why does the USA keep making weapons?

The USA has figured out that it can use weapons as resources. The country today routinely trades weapons for friendships, alliances, stability, protection from enemies, and vital resources.

Plus, selling weapons keeps the USA’s GDP growing.
In 2024, aeronautics and defence (A&D) contributed 1.5% to the USA’s nominal GDP and accounted for 1.4% of total US employment. For context, A&D exports alone hit $138.6 B.

The USA has integrated the weapons industry into its economy so deeply that it can’t do without them. And so, weapons keep getting made, sold, and used.

How did the USA get here?

Take a look at the image below. None of these wars were fought to defend the USA’s territory.

Yet, weapons manufacturing grew during that entire time. To understand why, we’ll need to go back to the 1940s.

During World War II, the US converted peacetime factories into weapons manufacturing units. As the war dragged on, businesses saw the returns. Dozens of new players emerged, and the industry boomed.

Eventually, World War II ended — but the weapons industry didn’t slow down. The US continued building its arms reserves, anticipating an inevitable confrontation with the USSR. Then the Cold War ended, too, and with it, the bottom fell out. US military spending dropped sharply, and the weapons industry faced an existential question: What now?

The Department of Defence stepped in. It began reimbursing restructuring costs to encourage smaller firms to merge with larger contractors, while enabling the production of dual-use civil and defence technologies. The goal was to consolidate the industry rather than let it collapse.

It worked. That consolidation gave rise to five dominant defence contractors: Lockheed Martin, Boeing, Raytheon, Northrop Grumman, and General Dynamics.

The rise of privatised weapons contractors.
Given the context we have, it doesn’t make sense. Why would private players want to enter a space with low demand?

Here’s what happened.
Once the Cold War ended, there was a demand for a New World Order — international law over violent conflict. So, wars between the major powers ended on paper. Global powers disarmed, and militaries sized down. But regional conflicts persisted.

Any country that wanted to enter these regional conflicts couldn’t do so directly without violating the very international law they’d just signed up to. And even if they could, large standing armies weren’t built for this. Regional conflicts were messy, fast-moving, and deeply local. War-trained soldiers, drilled for large-scale conventional warfare, weren’t equipped for that kind of adaptability. Neither were the weapons they carried — everyone had access to the same Cold War stockpile.

This is where private military contractors (PMCs) and private manufacturers flooded the market.
They absorbed the post-Cold War weapons stockpile and upgraded it for regional conflict. They also absorbed something else — thousands of unemployed war veterans with nowhere to go. Those veterans would go on to form armed squads, available for hire in the regional conflicts that governments couldn’t enter themselves.

The cost of sophisticated weaponry.

Between 2020 and 2024, the USA handed ~$2.4 Tn to private military contractors.

The USA commissions these contractors to build the arms it needs. The work involves complex experimental technology whose costs are nearly impossible to predict up front. So, the US uses cost-plus contracts — it guarantees payment above production costs, ensuring firms take on contracts they’d otherwise consider too financially risky.

Sounds like the perfect solution on paper. But it’s also why defence budgets keep ballooning.

Think about it from an arms manufacturing contractor’s perspective.
The government asks you to build a sophisticated weapon. They don’t know what it should cost. And they’ve already agreed to pay you whatever it takes, plus a fee on top. Would you give a conservative production cost estimate? And would you rush to finish it? Exactly.

The incentives work. And that’s the problem.
Private arms manufacturers aren’t doing anything a normal business wouldn’t do. They’ve already figured out how to be profitable. All they must do is find a way to keep the profits coming.

Keeping warlords fed.

The warlords (private arms manufacturers) remain profitable only as long as the arms demand persists. So, it’s in their best interests to keep the demand for arms high.
The concept is simple. Back the right politicians, influence the right officials, and the contracts keep flowing.

In practice, that means two things — funding senate campaigns to get war petitions and arms deals signed off, and hiring lobbyists to push contracts through.

Private arms manufacturers do both. They spend roughly $150M on lobbying annually. That’s approximately two lobbyists per elected official.

But the money is only half of it. The conflict of interest runs deeper.

Elected officials frequently end up at private arms manufacturers after their terms. A Defence Secretary retires and joins the board of Raytheon. A four-star general leaves the Pentagon on Friday and starts consulting for Lockheed Martin on Monday. In the decade after 9/11, over 70% of retired senior military officials went on to work for defence contractors.

What’s next for the US arms industry?

The USA’s military budget just rose from $896B to $925B. But according to Emil Michael, acting director of the Defence Innovation Unit, more money alone won’t fix what’s broken.

Risk sits with the government, not the industry, in the current model. And the signals telling manufacturers what the military actually needs are unclear at best.

What the Pentagon wants to move toward is different. Less bureaucracy. Clearer demand signals. Shared risk between the government and contractors. And a simpler way to do business.

The focus is shifting, too. Away from complex weapons and toward war intelligence warfighting — logistics, assets, operations, simulations. This opens the floodgates for new players to enter and more inventive players to compete.

But will the system actually reform? Only time will tell. We’ll keep tabs until then.

Give me the tickets!

Today’s edition.

This is a special one from our Co-founder & CEO, Udayan. Wear your seat belts. We’re diving right into it.

it’s tuesday night.

someone in your slack just shared a new ai tool. “this is insane, everyone needs to learn this.” you click the link. watch the demo. add it to the list of 30 things you’ll get to eventually.

by friday there’s another one.

that tool your colleague shared?
irrelevant in a week.

replaced by something newer, shinier, with a better landing page. so don’t stress about it. seriously.

here’s what actually matters.

whether ai stays or something entirely new replaces it next year, the thing that grows your career doesn’t change. business impact.

not tools. not frameworks. not certificates. the ability to take a skill and use it to move a number the company cares about. revenue. cost. speed. retention. that’s been true for 50 years. it’ll be true for the next 50.

so instead of writing another “top ai tools” article, i mapped the ai skills that i believe will matter for the next year, across seven functions, and tied each one to the business outcome it creates. this comes from hundreds of personal calls with members at growthx (5,500 professionals, 50+ learning events every month) and tracking what people are getting hired and rejected for right now.

if a skill doesn’t move a business metric, it’s a hobby.

but what does “business impact” actually mean?

every business has an equation. even the fuzzy ones. and most people never write theirs down.

let’s take a simple one.
say you’re a creative strategist doing short-form video.

video views = number of posts × average views per post

then, break it down.
↳ number of posts = editing bandwidth + creative strategy bandwidth
↳ average views = hook quality × editing × topic relevance × timing

go deeper.
↳ ↳ editing bandwidth = editors available × hours per editor × edits per hour

↳ ↳ creative strategy bandwidth = strategists × ideas tested per week × approval speed

↳ ↳ hook quality = pattern interrupt strength × curiosity gap × first 3 second retention rate

↳ ↳ topic relevance = trend alignment × audience pain point match × search demand

↳ ↳ timing = posting hour × platform algorithm cycle × cultural moment

see how deep this goes?
each sub lever is something you can measure. and each one is something ai can potentially move.

now look at this. really look at it.
if you’re doing 10 shorts a month at 50k average views, that’s 5 lakh views monthly. want 50% growth? you either need 5 more shorts per month or your average views need to go up by 50%.

the bottleneck isn’t ideas. it’s editing bandwidth.
each short takes 4 days. ideation and scripting 3 to 4 hours. recording and production 1 day. editing and publishing 2 days. your team can only do so many in parallel.

can ai compress editing from 2 days to 4 hours? maybe. that’s the input lever. that’s where the skill matters. not “i learned ai” but “i used ai to move this specific number.”

here’s what i want you to do.

before you scroll to your function, write down your company’s growth equation. or at least your team’s OKR. break it into input levers. find the one that’s stuck. then look at the skills below and ask: which one unsticks my lever?

because skills are like tools.
you can use them across any input lever depending on the context of your business, the size of the company, the stage you’re at, and the market you’re in. two product managers at two different companies might both need “context engineering” but for completely different reasons.

i cover 7 functions:

1. design

2. product

3. engineering

4. marketing

5. generalists

6. HR / people

7. sales

scroll to your function. find your gaps. & build.

design

i’m going to be honest. i don’t know what to call this skill yet. but it might be the most important one in this entire article. first, the equation.

impact = reach × Δmetric × quality × velocity

break it down.
↳ reach = total people who see your asset (creative, landing page, product surface)

↳ Δmetric = absolute lift in the business metric the design targets (conversion, sign ups, activation)

↳ quality = attention captured × clarity

↳ velocity = experiments shipped per period ÷ planned cadence

you don’t own all four.
you own quality and velocity. reach is a marketing/product lever. Δmetric is a function of copy, price, and users.

but if you 10x your velocity, you run 10x more experiments, and your Δmetric improves because you’re finding better solutions faster.

the product, design, and engineer roles are merging. not in theory. right now. the boundaries are dissolving, and the designers who see it happening are moving fastest.

here’s what we’re seeing at @growthx_club
designers are pushing code directly to production. not handing off figma files to devs. not writing specs. raising actual PRs. they have the entire dev codebase set up on their machines. they’re building features, fixing ui bugs, shipping components.

and they’re doing things with ai that were never possible in figma.
one example: we had an animation that would have taken weeks to spec in figma, another week for a dev to build, another round of back and forth to get the timing right. with ai, a designer described exactly how they wanted it to animate through voice prompting. built it. shipped it. hours, not weeks.

that’s the edge. ai lets you do things you couldn’t do in figma. and figma lets you do things ai can’t do yet. the skill is knowing which tool for which moment and pushing both to their limit.

now, the messy part.
the code isn’t always clean. we’re dealing with that right now. building the right skills files for different kinds of projects. setting up different code review standards for ai generated code versus human written code. it’s a real problem and anyone telling you vibe coding “just works” is selling you something.

but here’s what i know: the designer who can go from concept to working code to user feedback in an afternoon? who ships 10 experiments while someone else is still in figma debating typefaces? that person is operating on a different plane.

on the equation: velocity goes from 1 experiment per sprint to 10.
time from idea to user feedback drops from weeks to hours. and because you’re testing more, your Δmetric improves. one designer at a D2C brand went from 10 hero banner variations per week to 250+. conversion lift? 23%. same design sense. 25x output.

figma skills still matter. but the designer who can do both? the person who moves between design tools and code depending on what the problem needs? that’s the role companies are hiring for. they just don’t have a name for it yet.

design ✅
product
engineering
marketing
generalists
HR
sales

product

your ceo just said “we need ai in the product.” cool. where? which feature? what metric does it move? that’s your job now. here’s the equation that answers it.

impact = adoption × retention × revenue per user × time to ship

break it down.
↳ adoption =
onboarding completion × time to first value × aha moment hit rate

↳ retention =
day 7/30/90 cohort curves × churn rate (inverse)

↳ revenue per user =
ARPU × expansion rate × upsell conversion

↳ time to ship =
spec to launch days × scope accuracy × dependency resolution speed

you own adoption and retention most directly through prioritisation, spec quality, and experiment design. revenue per user is shared with growth/monetisation. time to ship is shared with engineering.

now look at this equation and ask: where will ai create the most impact on THESE levers?

1/ context engineering
your ai feature gives a wrong answer. you check the model. model’s fine. you check the prompt. prompt’s fine. turns out the system was feeding it three year old product docs and ignoring the user’s last 10 interactions. that’s a context engineering failure.

context engineering is about designing what information gets fed to a model and in what structure. what goes into the system prompt. what gets retrieved from a database. what user history matters. what gets discarded.

it sounds simple. it’s the hardest part of building good ai products. bad context in, garbage out. no amount of model quality fixes bad context design.

on your equation, this lever hits accuracy of ai features, which directly affects adoption (aha moment hit rate) and retention (does the feature actually help?).

2/ multi agent teams
single agents were 2025. one agent, one task, one tool. 2026 is about orchestrating teams of agents that work together.

a researcher agent gathers data. a writer agent drafts the output. a reviewer agent checks for quality. a coordinator agent manages the whole thing, decides who does what, handles failures, routes exceptions. this mirrors how human teams work, except it runs at machine speed.

the product skill here is designing these multi agent architectures. which agents need to exist? how do they communicate? what happens when agent 3 disagrees with agent 1? how do you give humans the right level of oversight without bottlenecking the whole system?

on the equation: multi agent teams hit time to ship. things that required a team of 5 humans coordinating across sprints can now be orchestrated as agent workflows that run in minutes. spec to launch days drops. dependency resolution speed goes up. gartner reported a 1,445% surge in multi agent system inquiries from early 2024 to mid 2025. that’s not hype. that’s companies seeing the time to ship lever move.

3/ evals
how do you know if your ai feature is actually good?

not “users seem to like it.” not “it feels better.” actual measurement. and this is harder than it sounds because ai outputs are stochastic. you run the same prompt twice, you get different results. traditional QA doesn’t work here.

evals is the skill of building systematic ways to measure ai quality. accuracy rates. hallucination frequency. task completion rates. regression detection. and increasingly, using llms themselves as judges to evaluate other llms at scale.

the best eval practitioners do something most teams skip entirely.
they start with error analysis. look at where the system is failing, build a taxonomy of failure modes, then design evals that catch those specific failures before they reach users. it’s the difference between “ship and pray” and “ship and know.”

on the equation: evals protect retention. a feature that hallucinates erodes trust. trust erosion shows up in your day 7/30/90 curves. the pm who can build a proper eval system is protecting the retention lever from degradation. this is the #1 course on maven right now. that should tell you something about demand.

design ✅
product ✅
engineering
marketing
generalists
HR
sales

engineering

two years ago, you shipped features. now you ship features that talk to models that talk to tools that talk to databases that sometimes hallucinate. welcome to 2026. here’s your equation.

impact = ship velocity × system reliability × cost efficiency

break it down.
↳ ship velocity =
deployment frequency × lead time for changes × PR throughput

↳ system reliability =
uptime × p95 latency × error rate (inverse) × eval pass rate

↳ cost efficiency =
output quality ÷ inference cost × infra cost per request

you own all of it. the question is which lever is the bottleneck right now.

1/ agentic infrastructure & tool use
agents need infrastructure that didn’t exist two years ago.

tool calling frameworks. MCP servers that let agents interact with external services. agent runtimes that manage state across multi step workflows. orchestration layers that decide which agent handles which task. error recovery when an agent takes a wrong turn three steps into a chain.

but there’s another side to this that nobody’s talking about.
as ai generates more code, PRs are increasing at 10x speed. your engineering org suddenly has hundreds, potentially thousands of pull requests being generated daily. someone needs to build the infra to review that volume. automated code review systems that catch bugs, security issues, and architectural violations in ai generated code. deployment pipelines that can handle the throughput. testing infrastructure that scales with the output.

on the equation: this is the ship velocity lever. deployment frequency and PR throughput go up. but only if the infra can handle it. without it, your 10x PR volume becomes a 10x bottleneck at code review.

2/ context engineering
from the engineering side: designing and implementing the context window. what gets stuffed in, in what order, with what priority. token budgets. dynamic context selection based on the query. caching strategies for frequently used context.

when the context window is 200k tokens, the naive approach is “just shove everything in.” the skilled approach is knowing that a well curated 8k context outperforms a sloppy 200k one. on the equation: this hits both system reliability (better outputs, fewer errors) and cost efficiency (fewer tokens, lower cost per request). more isn’t better. better is better.

3/ model customization & cost optimization
the default move is calling the biggest model for everything. that works until your inference bill hits 6 figures and your cfo starts asking questions.

fine tuning on your data. distilling a large model into a smaller faster one. quantization. lora adapters. but also: multi model strategies. running open source models for routine tasks, proprietary ones for complex reasoning. routing queries to the right model based on complexity. mixing deepseek for simple classification with claude for nuanced generation.

this matters at high scale.
if you’re at a startup doing 1,000 api calls a day, just use the big model. if you’re at a company doing 10 million calls a day, this is the cost efficiency lever. 80% cost reduction on a 7 figure inference bill is a 7 figure saving. simple math.

4/ evals
your team ships a new ai feature. outputs look good on 100 test cases. you push to production. three days later, support tickets spike. turns out the model started hallucinating edge cases nobody tested for.

evals is the skill that prevents this.
you build eval pipelines that run on every deployment. not after. on. you use llm as judge systems where one model evaluates another’s output against criteria you define. you build failure taxonomies so you know exactly what kind of errors your system makes and how often. you set up automated eval gates in your ci/cd so bad outputs can’t ship.

on the equation: evals is the system reliability lever. eval pass rate goes up. error rate goes down. the engineer who can build a proper eval pipeline is, and i quote two hiring managers, “an instant hire.”

design ✅
product ✅
engineering ✅
marketing
generalists
HR
sales

marketing

your competitor just launched 500 ad variations last week. your team did 12. same budget. same audience. they’re winning and you can’t figure out why. here’s why. their equation looks different from yours now.

impact = reach × conversion × frequency × brand equity

break it down.
↳ reach =
channel volume × impression share × new channel penetration

↳ conversion =
creative quality × offer relevance × landing page effectiveness

↳ frequency =
content velocity × publishing consistency × repurpose rate

↳ brand equity =
share of voice × sentiment × recall

you own all of these. but frequency and conversion are the levers where ai creates the biggest gap right now. the team producing 50 pieces of content a month is simply running more experiments than the team producing 10.

1/ ai content creation
not “use chatgpt to write blog posts.” that’s how you get content that sounds like everyone else’s content.

this is about building end to end content systems with ai.

short form video: feed your top performing hooks into a model. generate 100 variations. score them against performance data. then use ai to automate storyboarding, base cuts, audio cleaning, and colour grading. what used to take 4 days per short now takes hours.

long form: build brand voice models that maintain consistency across 50 pieces a month instead of 4.

visual content: generate hundreds of ad creative variations and test them before committing to production. google reported that advertisers generated nearly 70 million creative assets using gemini in Q4 2025 alone. that’s a 3x year over year increase. this isn’t experimental anymore.

on the equation: this is the frequency lever. same team, 5x output. cost per piece goes down. experiments per month go up. and every extra experiment is a data point that makes the next piece better.

2/ ai-led performance marketing
traditional performance marketing: you set bids, pick audiences, adjust budgets weekly. maybe daily if you’re good.

ai led performance marketing: the system reads campaign data in real time, shifts budget between channels mid day, pauses underperformers, and scales winners. your job becomes designing the strategy and constraints. the machine executes.

platforms like google’s performance max and meta’s advantage+ have made this the default. the ai handles bidding, targeting, creative assembly, and placement. the human marketer’s job is now strategic direction, not daily optimization.

on the equation: this is the conversion lever. CPA goes down. ROAS goes up. and you’re managing 10x more campaigns with the same headcount because the machine does the optimisation you used to do manually.

3/ AEO (ask engine optimization)
google is no longer the only front door. 800 million people a week now use chatgpt alone to answer questions, compare options, and plan next steps. they’re asking “best crm for 50 person teams” or “running shoes for flat feet under 10k.” and these models pull answers from structured data, not page rank.

your brand must show up when an ai answers a question.
that means structured data, schema markup, entity relationships, faq optimization. traditional SEO got you ranked. AEO gets you cited.

on the equation: this is the reach lever. a brand new channel. almost zero competition right now. the marketers who figure this out first own a channel their competitors don’t even know exists yet. and unlike SEO, where results take months, AEO results can show up in weeks because the field is so new.

design ✅
product ✅
engineering ✅
marketing ✅
generalists
HR
sales

generalists / operations

you’re the person who makes the company actually run. nobody writes job descriptions for what you do because half of it didn’t exist last quarter. here’s your equation.

impact = process efficiency × error rate (inverse) × cost per unit × scalability

break it down.
↳ process efficiency =
automation coverage × handoff friction (inverse) × SOP clarity × exception handling speed

↳ scalability =
current throughput ÷ max throughput at current headcount

you own process efficiency and error rate fully. cost per unit is shared with finance. scalability is shared with leadership. the input levers you can move with ai? automation coverage and exception handling speed. those are the two that change your career.

1/ agentic workflows
a customer complaint comes in. the agent reads it, classifies the issue, checks the customer’s history, drafts a response, escalates if needed, and logs everything. no human in the loop for 80% of cases.

that’s an agentic workflow. not just automation (if this, then that) but ai making decisions along the way. deciding what to escalate. deciding which template fits. deciding when a human needs to step in.

the skill is designing these workflows so they handle edge cases gracefully. because the 20% of cases that need a human? those are the ones that determine whether your customers love you or leave.

back to the equation: one good agentic workflow moves automation coverage from 30% to 80%. that changes your cost per unit, your throughput, your scalability. one workflow. three levers.

2/ ai adoption strategy
this one’s for you if you’re at a large company.

most enterprises are terrible at rolling out ai internally. they buy tools, send a slack message saying “hey team, we now have copilot,” and wonder why adoption is 12% after three months. 500 licenses purchased. 40 people actually using it.

ai adoption strategy is about knowing which teams benefit first, designing workflows that embed ai into existing processes (not alongside them), measuring actual productivity gains, and handling the very real resistance from people who think ai is coming for their jobs.

at a 50 person startup, the founder just tells everyone to use it, and they do. at a 5,000 person enterprise, you need a strategy. change management. pilot teams. success metrics. executive buy in.

the lever here? the productivity delta between teams using ai and teams that aren’t. that delta is where the roi case lives. and that delta is what gets you promoted.

design ✅
product ✅
engineering ✅
marketing ✅
generalists ✅
HR
sales

HR / people function

your annual review process takes 6 weeks. involves 14 spreadsheets. and tells managers what they already knew. ai is about to make all of that look prehistoric. here’s the equation that matters.

impact = talent density × time to productivity × retention of top performers

break it down.
↳ talent density =
offer acceptance rate × quality of hire score × sourcing channel effectiveness

↳ time to productivity =
onboarding completion speed × tool access time × buddy/mentor assignment

↳ retention of top performers =
engagement score × internal mobility rate × comp competitiveness × manager quality

you own talent density and time to productivity almost entirely. retention is shared with managers across the org. the lever that’s most stuck at most companies right now? sourcing channel effectiveness. everyone is fishing in the same linkedin pool.

1/ ai powered talent intelligence
forget the job board. forget the linkedin search with 15 filters.

think about every person you’ve ever met professionally. every whatsapp contact. every linkedin connection. every person who attended that conference. every colleague from your last three companies. that’s your real talent network. and right now, it’s sitting in 6 different apps doing nothing.

talent intelligence is about building a CRM on top of all of it. ai that scans your entire professional network, tracks career moves, identifies when someone might be open to a conversation, and surfaces the right person for the right role before you even post the job.

for talent acquisition, the skill is building systems that go beyond active candidates. passive talent identification. market mapping. competitor hiring pattern analysis. compensation benchmarking.

on the equation: this is the sourcing channel effectiveness lever. the recruiter who builds these systems fills roles 40% faster and with better quality hires because they’re fishing in a bigger, smarter pond. that moves talent density directly.

2/ employee experience automation
onboarding is where most new hires decide if they made the right choice. and at most companies, onboarding is a mess. 47 emails. 12 forms. a buddy who forgot they were assigned. three weeks before you get access to the tools you need.

ai can fix the entire flow.
automated onboarding sequences that adapt to the role. instant answers to benefits questions. leave request processing without waiting for hr to check a spreadsheet. internal transfer workflows that don’t require 6 approvals.

the skill is building these automations without making employees feel like they’re talking to a wall. the best employee experience automations feel invisible. the worst ones make people rage quit. knowing the difference is the skill.

on the equation: this is the time to productivity lever. onboarding completion speed goes up. tool access time drops. new hires hit full productivity in 2 weeks instead of 6. and the hr team handles 3x the headcount without adding staff.

design ✅
product ✅
engineering ✅
marketing ✅
generalists ✅
HR ✅
sales

sales

your top rep closes 30% of qualified leads. your average rep closes 12%. the gap used to be talent. now it’s systems. here’s the equation.

impact = pipeline value × close rate × deal velocity

break it down.
↳ pipeline value =
number of qualified leads × average deal size

↳ close rate =
proposal win rate × objection resolution effectiveness

↳ deal velocity =
first contact to signed days × internal approval speed

you own pipeline value and close rate almost entirely. deal velocity is shared with legal and internal ops. the lever that separates your top rep from your average one? they have better pipeline (higher quality leads) and better preparation (they never walk into a call without context). ai can give that to every rep.

1/ ai powered lead generation
traditional lead gen: buy a list, blast emails, pray. ai powered lead gen: build systems that identify companies showing buying signals (new funding, leadership changes, tech stack shifts, job postings that indicate a problem you solve).

on the equation: this is the pipeline value lever. the sales person who can set up and tune these systems fills their pipeline with warm leads while everyone else is cold calling from a stale database. number of qualified leads goes up. average deal size goes up because you’re targeting the right companies.

2/ hyper personalized communication
not “hi {first_name}, i noticed you work at {company}.”

real personalization means a system that reads the prospect’s recent linkedin posts, their company’s earnings call, their industry news, and generates outreach that references something specific and relevant. at scale. for hundreds of prospects.

the skill isn’t writing one great email. it’s building a system that writes hundreds of great emails that each feel like they were written just for that person.

on the equation: this is the close rate lever. response rates go from 2% to 15%. because the email actually said something the prospect cared about. more responses = more conversations = more closes. the math is direct.

3/ ai assisted sales calls
real time call analysis. the ai listens to your sales call and surfaces relevant case studies, pricing comparisons, objection handling scripts. mid conversation.

post call: automatic summary, action items, crm updates, sentiment analysis.

on the equation: this hits both close rate (objection resolution effectiveness goes up because you always have the right data) and deal velocity (first contact to signed days drops because nothing falls through the cracks). and your crm is actually accurate for the first time ever because the ai fills it in, not the rep.

design ✅
product ✅
engineering ✅
marketing ✅
generalists ✅
HR ✅
sales ✅

so here’s the question.

you just scrolled through seven functions and roughly 25 skills. some you’ve heard of. some are new. some probably made you uncomfortable because you realized there’s a gap you’ve been filling with bookmarks instead of building.

i don’t know which skill is right for you. i don’t know your function, your level, your company’s problems.

but i know the difference between the people who read articles like this and the people who build after reading them. they’re two completely different populations.

pick your function. pick 2 skills. not 7. not 10. two.

go deep for 60 days. build something you can show someone. something that solves a problem a company would pay for.

Sign me up!

Today’s edition.

Lovable launched 470 days ago, changing website development forever. The barrier to entry wasn’t knowing how to code anymore. It was prompting an AI tool. Master that, and you’d still hit a wall when the credits ran out.

2 weeks ago, we found a workaround for that. Lazar Jovanovic, Lovable’s vibe coding engineer, showed us how to build a web app and how to make Lovable remember your vision across sessions. Today, we’re sharing that with you.

Get ready. Set a 90-minute timer and open this email in one window and Lovable in another window. When it goes off, you’ll have a web app you own — built on Lovable, but not stuck there.

Part 1: Set up Lovable.

Click on this link.
You’ll land on Lovable’s homepage. Now, click on Log In.

You’ll get options to Sign In via your email. Do this to create your first Lovable account.

Upgrade your plan.
We’ll be using “Plan” mode today to structure our project. This will help us build our web app in under 90 minutes. It is credit-intensive, so we recommend upgrading to a 1-month plan to create this project. Here’s how to do it.

1. Click on the coloured box below the lovable logo on the top left of your screen. This will open your controls tab.

2. Now, you’ll see the “Upgrade” button. This will open up the billing dashboard.

3. Select the $25 plan and click on “Upgrade”. This will give you access to 100 credits for the month + usage-based cloud and AI for the project, along with a ton of other perks. You should see the payment screen next.

Check your email, enter your payment details and complete the payment.

Congratulations! You’re all set to build your first web app.

Step 2: Building your web app.

As long as you have a solid idea, you can build any web app on Lovable.

One catch.
Lovable finds it hard to remember context over long sessions. Today, we are solving this problem by creating documentation, knowledge bases, plans and task breakdowns.

Wait, what is documentation and a knowledge base? These are the documents that Lovable references to align itself with your goal.

And what are plans and task breakdowns? These are pre-defined action items for Lovable. So, you don’t have to keep prompting to get your job done.

What are we building today?

A marketing dashboard that shows channel performance at a glance. That might give you a rough idea of what we’re going for. But AI needs a lot more context. So, we’re using a 4-question framework we used to hash out our idea for AI.

The 4-question framework.

**What’s broken today?**

**Who feels this pain?**

**What do they need to DO?**

**What does success look like?**

My answers to those questions look like this.

**What’s broken today?**

Our marketing team doesn’t have a single weekly view of performance. To understand how our channels are doing, we have to switch between YouTube Studio and Substack. This makes it difficult to quickly tell whether we’re on track. We also lack a structured way to reflect on the performance of individual pieces of content, which means insights about why something worked or didn’t often get lost.

**Who feels this pain?**

Marketers.

**What do they need to DO?**

They need a simple way to check week-on-week performance across channels in one place. From that overview, they should be able to drill down into detailed metrics for a specific week and capture observations about why a particular piece of content performed well or poorly.

**What does success look like?**

Success looks like a marketing lead opening the app and immediately seeing how each channel is performing weekly. They can click on a specific week in the graph to view detailed metrics for that period and add notes about what happened, capturing insights about what worked, what didn’t, and why.

We’ll use this in creating our plan.

Build out your idea with ‘Plan’ mode.

Copy your answers to the framework and paste them into the Lovable chatbox. Make sure to turn the “Plan” mode on.

Lovable will now ask you questions to better understand the project. Here’s what it asked me, and my answers.

Once you do this, Lovable will show you a preview of your web app on the visual editor tab. Now you can tweak how you want your dashboard to look and what it should do.

Here’s what my v1. looked like.

Now, you have two options.

Option 1: Prompt Lovable manually.

This is time-consuming, but fairly straightforward. All you need to do is tell Lovable what to change. Lovable will do it for you. Here’s where I reached by simply prompting Lovable.

2. Let Lovable take over.

Remember, Lovable found it hard to keep track of context? That becomes a massive problem when letting Lovable build autonomously — you never know when it’ll go off track. Today, we’re solving this problem with documentation. Here are the steps to build it out.

Step 1: Give Lovable persistent memory.
Meaning, ensure it doesn’t lose context midway by providing documentation in the form of a PRD. Here’s a prompt you can use.

Based on what you know about this project, create a comprehensive set of PRDs (Product Requirement Documents) that will serve as persistent documentation throughout development.

Create these 4 documents inside the /docs folder:

1. **masterplan.md** — The product’s North Star

- Define the vision and core purpose

- Identify target users and their needs

- Outline the primary value proposition

- Establish success metrics and goals

2. **implementation.md** — Step-by-step build guide

- Break down the technical architecture

- Define database schema and data models

- List core features and their requirements

- Create a phased implementation plan

3. **design.md** — Brand and UX guidelines

- Establish visual style and design principles

- Define component patterns and UI standards

- Specify responsive behavior and accessibility

- Document any branding requirements

4. **app-flow.md** — The product’s skeleton

- Map out primary user journeys

- Define screen-by-screen flows

- Document navigation and state transitions

- Cover edge cases and error states

Each document should be detailed enough that any developer could pick up the project and understand exactly what to build

This is what you should see.

Step 2: Consolidate PRDs into one source of truth
So, instead of reading each PRD, Lovable reads the source of truth before every task. Here’s the prompt.

Ok, I’ve uploaded/created some documents you can rely on:

- masterplan - our product’s North Star

- implementation - step-by-step build guide

- design - brand and UX bible

- app-flow - product’s skeleton

Now, please tell me if you:

1. Have read and understood the project and its core feature?

2. Understand the implementation plan from start to finish?

3. Have any objections or suggestions?

Also, ask me any clarifying questions you need.

If we’re on the same page, create tasks.md as the source of truth for implementation order.

Step 3: Configure Knowledge Base
This ensures Lovable actually reads the documentation before performing a task. Here’s the prompt.

Before you begin ANY response, say “I’m using knowledge”

Always read project documentation in the /docs folder.

Use masterplan.md and tasks.md as the source of truth.

Every time you complete tasks, mark them done in tasks.md and tell me:

1. What was completed

2. How to test it

3. What is the next step

Step 4: Execute the plan automatically
Here’s the magic prompt.

Read tasks.md, check which group of tasks is next on the list, read relevant documentation, complete the tasks, mark them as done, and tell me what’s next.

3. Make any final Visual Edits.

If you want to change specific details about the file, click on Visual Edits. This opens the editor, where you can click an element to edit it. Here, I edited the app's name.

And that’s it — you just built your first web app on Lovable.

The setup felt simple because it was. But don’t let that fool you. Lovable apps can be as complex as you need them to be — API calls to fetch data, integrations to keep everything in sync. The building blocks stay the same; you just stack them differently.

If you want a handbook that walks you through building autonomously, Lazar put one together here. And if you’d rather build with others, we run 8-hour buildathons every week. Find the next one in your city.

Give me the tickets

Thomas Jeng (Startups @ OpenAI) broke down how the OpenAI for Startups team is working with founders across APAC and answered many of your questions across 4 themes: AI for India, Infra, true product moats & the future.

What followed were meaningful 1:1 conversations with the India OpenAI team Pragya, Harshit, Arjun, Shreya & Sun Weiran. Sheer enthusiasm for building with OpenAI ❤️

Extremely grateful to our partners at Peak XV Partners for co-building the platform for Indian AI founders to get in front of foundational AI companies in the world. This is just the beginning. Become a member to get exclusive access to future events like this.

Sign me up!

Today’s edition.

On 4th Feb, Devyani International, India’s largest KFC franchisee operator, published its Q3 2026 results. Same-store sales growth (SSSG) for KFC remained negative for ~2 consecutive years. Meaning, existing outlets are selling less than they did the year before.

That’s a massive downward trend from 2023, when KFC was one of the fastest-growing QSR brands. So, what happened? Did we collectively lose the appetite for a fried chicken burger?

Indians love fried chicken. A lot.

In fact, the demand has grown so much that QSR chains like Domino's have had to manage regional chicken supply shortages. The chicken sides menu has grown across QSRs, too.

It wasn’t always like this.
KFC entered India in 1996 with a simple thesis: what worked in America would work here. Hot, crispy, affordable fried chicken. The product was solid. The problem? Indians didn’t eat fried chicken back then — the taste wasn’t familiar, and early stores struggled.

Post 1999, KFC launched products to better cater to Indians’ palates. This time, it entered the burger space as well, not just fried chicken.

Think about it. McDonald’s had already entered India with burgers and made it a widely accepted category. Penetrating this market with a fried chicken burger would be easier. Plus, you could always sell fried chicken as sides.

The strategy worked. Fried chicken gained popularity over the years. And KFC continued to maintain an edge above the other players.

The brand had three distinct advantages.

1. First mover advantage.
KFC entered the fried chicken market before anyone else. Over time, fried chicken became synonymous with KFC.

2. Product innovation.
Not just taste. The brand also launched value-focused offerings, such as larger chicken, rice, and biryani buckets — more entry points into the world of fried chicken.

3. An established supplier network.
KFC was selling fried chicken for ~10 years before its competitors caught up. This meant that they had the suppliers to pull off on-time deliveries even when chicken demand surged.

When the pandemic hit, KFC was perfectly positioned. More people began ordering online. And fried chicken held up well in the box. Result? The share of customers ordering from KFC nearly doubled between 2020 and 2021. The brand was untouchable.

So, what caused the slowdown?

KFC’s business model wasn’t keeping up.

See, the chain sits in the fast-food category for one reason — they’re competing on price. They need enough people to buy their food to turn a profit. In India, that means competing with street vendors for the snacking occasion.

KFC manages to pull it off by offering fries for ~₹70, a clean seat, and the same taste every visit. That combination justifies the slight premium and keeps people coming back.

Plus, the economics work out. Here’s what the unit economics look like for the average QSR with an average order value of ₹100 for reference.

The problem? Nearly every fast-food brand follows the same playbook. And over the last few years, plenty of new players have emerged.

The curse of optionality.

Look at it from a customer’s POV. It’s Friday night, you’re ordering in, and you’re in the mood for a fried chicken burger. You open Swiggy, check KFC. Looks fine, but you want something new. You check McDonald’s, then Popeyes, then Wendy’s. Nothing catches your attention. Then you land on Leon’s, see an interesting Korean fried-chicken burger, and order. It’s delivered in 30 minutes. Same as a KFC burger.

This isn’t a one-off. International cuisines went from 10% of online food orders in 2018 to 40% today. Customers aren’t just hungry — they’re curious. And curiosity is hard to compete with.

It plays out offline, too. Would you catch up with a friend at KFC or at a Social? The ambience, the menu variety, the experience — Social wins that decision almost every time.

Don’t get us wrong, KFC still has its loyalists. There’s a customer who wants KFC’s fried chicken specifically. But that base is narrower than it used to be, and optionality is quietly narrowing it further.

How is KFC winning customers now?

1. Aggressive Tier 2, Tier 3 expansion
Fried chicken doesn’t have a big presence in Tier 2 and Tier 3 cities yet. The aspirational demand is there, the competition is thinner, and the rent is cheaper. Plus, ~60% of India’s consumer demand still comes from here. That’s a huge expansion opportunity — one that’s sure to increase the chain’s average daily sales (ADS).

So far, the Devyani group has expanded KFC franchises to ~290 cities across the country. And it plans to add ~100 more each year to capture the market.

2. Product bundling
Currently, online orders account for the majority of KFC's order volume. Great for sales revenue, but terrible ROI for the outlet. KFC uses a dual bundling strategy to solve for this.

KFC still wants to drive online orders up. Hence, the ‘9 for ₹299’ combo, or the Mad Duo meal. Customers use the offer and buy an entire assortment of KFC products. Even the things they would otherwise skip.

In contrast, offline stores want to increase Same-Store Sales Growth (SSSG). So they launch outlet-exclusive offers that increase the average order value. Higher cart value per visit means more revenue from the same footfall.

3. Cost saver launches
While KFC offers value options, most are sides — fries, fried chicken, and small bites. Those looking for a meal don’t have many options.

To address this, KFC launched the Chana Chatpata Burger at ₹69 to attract value-conscious customers. It worked. Lower-priced items have picked up quickly. Lower-priced items have picked up quickly. So, KFC is doubling down on the value layer to keep transactions moving even when demand is soft.

4. Omni-channel engagement
KFC knows that it can’t compete solely on price. It needs to stay top of mind so that when a customer is hungry, KFC is the first place they think of. That means showing up everywhere.

On the digital front, KFC leans heavily into visibility on platforms like Swiggy and Zomato — banners, deep discounts, combo offers, and targeted consumer promotions. The idea is clear. Get the customer as close to a purchase as possible, and give them a reason to complete it.

While offline, the focus is on the in-store experience. That means refreshing the interiors, improving staff training, and adding kiosks for smoother checkouts to give customers a reason to keep coming back.

Will these tactics save KFC?

KFC’s same-store sales growth has shown marginal improvement this quarter. That’s a positive signal that these tactics are beginning to land. Whether they’re enough to reverse a multi-year trend is a different question. We’ll be watching.

Today’s edition.

If you’re here, you’ve probably heard the buzz about OpenClaw. Maybe you even tried setting it up. Then stopped. It’s not your fault. There are 100 tutorials online. Infinite workarounds. No clear answer on what actually works.

Last weekend, we ran 5 OpenClaw buildathons across Mumbai, Bengaluru, Hyderabad, Pune, and Gurugram. Each ran for 8 hours. 120+ projects got shipped. We learned a lot about what works. And today, we’re bringing those lessons to you.

Get ready. Set a timer for 60 minutes. And get another window running. By the time your timer goes off, you’ll have built and run your first agent with OpenClaw.

Part 1: One-click Hostinger set up.

Click on this link.
You’ll land on Hostinger’s VPS plans for OpenClaw. Select KVM 8 from the dropdown menu. It gives you 8 virtual CPU cores, 32 GB of RAM, and ~400 GB of disk space. Great for most agent setups. Then. Hit Deploy.

Select a plan you like.
We chose the 1-month plan. And checked the Ready to use AI box. This got us 5 Nexos.ai credits. Now we could either choose to power the agent with OpenAI without a key, using limited credits (5 credits for ₹599), or use an OpenAI Key.

Click on Continue. This will prompt you to log in. You can log in via your Google Account here. Now, enter billing information and make your payment. Your Hostinger Setup is complete.

Also, we got a nice deal from Hostinger — 10% off VPS costs to GrowthX Newsletter readers. Just use the code “GROWTHX10” during checkout.

Part 2: OpenClaw set up.

We wanted to run OpenClaw on Telegram using OpenAI. So, we set up OpenAI and Telegram Keys.

Copy the OpenClaw Gateway token. You’ll need this to log into OpenClaw.

Once you click Deploy, Hostinger starts OpenClaw and redirects you to the Docker Manager page. You should see your OpenClaw project running.

Click the link below your project name. This will take you to OpenClaw.

Enter the OpenClaw gateway token you copied earlier.

Forgot the key? Don’t worry. Go to Overview and copy the Gateway Token. Now your OpenClaw is set up.

Part 3: App connection via API keys.

We wanted to run OpenClaw on Telegram using OpenAI. So, we set up OpenAI and Telegram Keys. First, we’ll tell you how to get the keys, then we'll set it up on OpenClaw.

How to get OpenAI API key —
1. Head to OpenAI Platform and log in.
2. Click the “API Keys” from the left sidebar.

3. Now, click on “Create new secret key”

4. Name your secret key and click on “Create secret key”

How to get Telegram Bot token —
1. Open Telegram and search for BotFather
2. Type /newbot and follow the on-screen instructions.
3. Copy the HTTP API token.

Now move to the final steps.

Installing Keys and Tokens on OpenClaw:
1. Go to VPS. You’ll see your server details. Now, click on Manage. This will open your Docker Manager Panel. Now, click on Docker Manager drop-down and select Projects.

2. Go to your project and click on the “Manage” button.

3. The “Compose” tab opens up. Now scroll down to “Environment”

4. You can now label your OpenAI and Telegram API Keys and Bot tokens, and paste the corresponding code under “Value”.

5. Once you finish the last step, Docker restarts. Now, you need to confirm with OpenClaw if it has received the inputs. To do that, type this command in the chat: “i have added the env to the dock”

Part 4: Check if your Telegram bot works.

1. Go to BotFather. Check your last message. Click on a link that looks like this t.me/Annu05bot.

2. In the new chat, enter “/start”. Then type “Hi” in a new chat bubble.

3. Go to the OpenClaw Chat and paste the pairing command marked. Your Telegram bot is now paired with OpenClaw. You can run commands in OpenClaw chat and see your Telegram bot working as expected.

Part 5: Twitter digest bot set up.

What is a Twitter digest bot? It’s a bot that sends you tweets from your favourite Twitter accounts at a scheduled time. Our bot will send us these updates on Telegram.

Here’s an example of what OpenClaw could ask you to make the bot. I’ve set up another bot to be my news digest, hence the additional questions.

As soon as I specify what I need the bot to do, OpenClaw asks me contextual questions to set it up. Here’s what that looks like.

OpenClaw asks for more specifics like usernames, Telegram chatID & and confirms its actions.

Here’s a response from the bot we just created.

We kept this setup intentionally simple so that it could be a clean way to get you started with OpenClaw. But this is just the beginning. You can build bots that post on your behalf, schedule meetings, make calls, and more. Those need more context, skills, and tool integrations — but the building blocks stay the same.

We’ve created an entire idea library to reduce the friction of choosing a project. Check the entire idea list here. Want to build with others? We run 8-hour buildathons every week. Find the next one in your city.

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5 years ago, following your manager’s playbook to fit in their shoes may have worked. Not anymore. The role they had, the tools they used, the market they navigated — it’s all different now. Jobs are evolving too fast.

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Today’s edition.

In 2024, Snabbit quietly entered the home services space. The promise was simple: on-demand home cleaning services from vetted professionals. Urban Company, a home service giant operating across categories like cleaning, repairs, and at-home beauty services, took notice and moved quickly. They launched InstaHelp, a Snabbit competitor, in January 2025.

But why?

Whoever owns cleaning, owns home services.

Here’s a representation of the home service market in India as of 2024.

The Cleaning and Pest Control alone constitutes a ~₹990 Bn market. That’s huge, greater than any other at-home service in the segment. Reason?

Domestic labour is cheap, and the market is largely unorganised.
So, the demand cuts across income lines — both middle- and high-income households in India rely on house helps. The difference is in how much.

High-income groups hire for more tasks, multiple times a day. Middle-income groups stick to the essentials, and for deeper cleaning? They save it for festivals and big occasions.

That’s the high-demand signal, home services startups like Snabbit and Pronto are going after. The opportunity is real. Crack efficient, affordable cleaning at scale, and customer trust follows naturally, opening doors to higher-margin services.

But Urban Company has a different reason to capture the cleaning market.

Why is Urban Company entering cleaning?

Look at the range of services Urban Company offers in 2026.

They’ve built what most incumbents only dream of — a horizontal home services business spanning nearly every category. In 2025, they had ~6.5 Mn annual transacting customers, with ~82% of Net Transaction Value (~₹2,667 Cr.) coming from returning customers. The presence, the profits, the loyalty — it’s all there.

Urban Company has the infrastructure, the trust, and the playbook to enter daily cleaning without breaking a sweat. It can control the experience end-to-end as a full-stack, schedule-based service platform. The company has expanded to every segment it owns today following this playbook. Besides, entering daily cleaning could expand Urban Company’s user base.

Daily cleaning is a high-frequency category. So, customers interact with the product constantly. More touchpoints mean more chances to get the experience right. And a good experience could turn a cleaning customer into someone who books a repair, a massage, or a salon visit.

Good experience → stickiness → curiosity → new business.

So, instead of following the repeatable playbook — scheduled services, why is Urban Company moving to Instahelp?

Effects of quickification.

It started with Grofers promising 90-minute grocery deliveries in 2013. Dunzo followed, bringing the dark-store model with it — platforms that buy and stock inventory before you even check out. E-commerce giants caught on. Then COVID hit, and India moved online almost overnight.

Soon enough, Swiggy launched Instamart. Then, Zepto emerged. 10-minute delivery stopped being a gimmick and became the baseline.

In 2026, cleaning service platforms are using this behavioural shift as a wedge for disruption.

The idea is sound. Customers already expect speed — so give them that. Then, do it right and deliver consistently to build trust. Simple in theory. But there’s a catch. Supply of service professionals.

Why is solving supply hard?

1. 100% fulfillment needs excess supply.
   Let’s compare service professionals on InstaHelp to delivery professionals on, say, InstaMart. And let’s assume the demand for both these services is the same, and both platforms promise 10-minute delivery. Here’s a visual representation of how it would work.

   

   While 2 professionals can manage demand in quick commerce (q-commerce), instant service would need 3 professionals for the same period.

   ---

2. Finding on-demand skilled service professionals is hard.
   Service professionals in the home services segment operate largely in an unorganised fashion. No protocols, no set standards of work or income, no labour protection. But professionals get extreme control over their time. That matters.

   

   Most domestic workers in India are women with families. Domestic work comes AFTER looking after them. They operate as individual entrepreneurs, setting their prices and work hours based on need and location.
   
   When a platform asks them to be available on demand, it's asking these workers to give that up. Plenty won’t, even for double the salary. For reference, an independent domestic worker earns around ₹15,000 a month, while a platform service partner can earn ₹30,000.
   
   The alternative? Pick service professionals from the 10% crowd or hire unskilled professionals and train them — both extremely high-cost activities, given the higher salary expectations and training expenses.

How does Urban Company make InstaHelp work?

1. Mass-training campaigns
   It’s the only way they’ve been able to retain a returning consumer base. Think about it.
   
   Customers need to trust the platform before they’ll keep coming back, let alone try a new service. In this business, that means promising the same quality of work, regardless of which professional shows up. The way to ensure that? Mass-training.
   

   Service professionals enter Urban Company’s network through two routes: from the organised sector, or hired as raw talent through local scouting campaigns. Think job fairs, neighbourhood banners, and localised ads.
   
   The ones who make it through are trained in dedicated classrooms by ~339 super professionals — masters of their craft — across 17 service categories. Unskilled hires go through 2-3 months of hands-on workshops. Even experienced professionals get re-trained.
   
   Cost? Roughly ₹65,000 per employee.

   ---

2. The matching algorithm.
   In the instant-help space, speed matters as much as quality. Urban Company needs to match you to the right professional — fast, and on their terms too. How does it do that?
   
   The micro-market strategy.
   It’s essentially assigning professionals correctly to service a high-demand area in a 3-5 km radius. The idea is to minimise service professionals’ travel time, so that they can deliver the service quicker. Here’s what a micro-market looks like for reference.

   

   Now, service professionals might not be available within a 3 km radius everywhere. How does Urban Company service those areas, then?
   
   By using a demand forecasting algorithm.
   Here, the system predicts demand in micro-markets based on past demand. Then it assigns service professionals to stand by in those areas. So, when a new request comes in, a service professional is available to handle it. But don’t professionals lose time waiting for a request to come in here?
   
   Yes, and that’s why the company is working to optimise its algorithms to better skill-match and balance service professional supply in real time. Most importantly, they’re working to hire more professionals.

   ---

3. Heavy-handed discounting.
   Every player in the space, including Urban Company, relies on it for customer acquisition. The platform that cracks the right service-discount ratio wins.
   
   Currently, Urban Company finds discounting easier. The business has multiple revenue streams, unlike Pronto or Snabbit that rely on VC funding and business model development to cut costs. Urban Company, at this point, doesn’t need to do both.
   
   Plus, when demand standardises and the competition vanishes, Urban Company can always increase costs and recover its margins.

   ---

4. The MyGate partnership.
   In 2022, Urban Company partnered with MyGate — giving it direct access to ~5 million users through a single integration. Book a service on MyGate, get an Urban Company professional.
   

   For InstaHelp, the benefits are obvious. Cleaning is a service virtually every MyGate user needs. That’s not just an acquisition channel, it’s a captive market.

What’s next for InstaHelp?

Urban Company is still figuring out InstaHelp’s unit economics in 2026. So far, it has managed to cut losses from ~₹760 to ~₹381 per order by optimising micro-markets, reducing idle time, and managing discounting.

The company expects to reach EBITDA break-even by FY31. But will its business decisions enable that? We’ll keep you posted.

So instead of figuring it out alone, we’re bringing people together. We’re hosting an 8-hour OpenClaw buildathon, where everything gets handled in one room — finding a problem worth solving, hosting, building the agent, and getting real feedback from people who use the tool every day.

Grab a ticket in your city.
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Today’s edition.

In our last edition, we explored the business of gold & why the demi-fine segment is emerging as a category. It came down to two things — margins & repeat purchase rates. (All the juicy deets are in that edition. Go check it out.)

But here’s the thing.
Gold-plated jewellery isn’t new. The category had its moment in the early 2010s. Then it faded once the gold prices dipped.

Cut to Palmonas.
Founded in 2022, the brand seems to be building in the same space, with a customer base that reportedly buys from them 3X a year. Those are impressive numbers. But the category’s history raises an uncomfortable question: what happens when gold prices dip again? Here’s everything we learned, condensed into a 7-minute read.

So, what really happened to 1gm gold jewellery?

It all started in the 2010s. Gold prices were on the rise. Incomes — not so much. People had to spend more to buy the same jewellery, and given their income, they couldn’t. The gold jewellery business almost stagnated. So, the jewellers experimented.

The hypothesis was simple.
People buy gold jewellery for the looks — the design, the colour, the shine. So, in theory, you could coat a piece of regular metal jewellery with a thin layer of gold, and it would sell. Good sales numbers. That happened. And then it stopped.

By mid-2015, gold prices stabilised.
So, people went back to buying real gold. But why?

1 gm gold had a marketing problem. Jewellers presented it as an affordable option to the real thing. Except affordability wasn’t why people bought gold.

It was as an investment. Meaning when times were tough you could exchange it for real money. 1g gold jewellery didn’t have the same benefit, given the quantity of gold was negligible. Plus, it is hard to extract. So, when prices stabilized people kept buying real gold. 1g gold continued to be a premium fashion accessory.

Gold-plated = 1gm gold jewellery.
Just better hype.

It’s not a replacement for gold jewellery. Rather, the pitch for plated jewellery is this: “Get the style of fashion jewellery with the premium gold finish for a really sweet deal”. Brands and small businesses have figured this out over the years. Here’s a snapshot of the space in 2026 for context.

So, what’s Palmonas’ wedge?

First - a thicker, > 2.5 micron gold plating.
That’s roughly 2X the plating that goes in regular gold-plated jewellery. Double the wear resistance and greater shine.

Second - a huge design catalogue.
Micro-players cannot do this, given the high design, production, and stocking costs. But Palmonas can, thanks to its business model and funding.

Wait, why is having an endless aisle of designs a wedge?
Think about it from a customer’s perspective. You want earrings that complement your outfit for a small function. Where are you more likely to find something you like? A store with 15 options, or a store with 50?

Customers want options.
That’s why Palmonas launched products across a single category simultaneously.

There’s a risk they’re taking: fewer sales. They’d have to find the sweet spot between variety and numbers to ensure all the pieces stocked actually sell. Else they’d spend too much on inventory and holding.

Fine jewellery brands like Melorra often opt for an asset-light model to avoid this. They make jewellery to order using 3D printing, minimising excess stock and inventory holding costs. The process from manufacturing to delivery takes less than a week!

Can you sell when cheap alternatives exist?

Yes. Let’s look at how Palmonas did it.

1. Market education.

When Palmonas entered the market in 2022, gold-plated jewellery was already available. Customers wore it every day and knew that the gold plating would eventually wear off. If they still wore it, they’d likely experience skin irritation, given the low-quality base materials used. The simplest solution? Get a brand-new piece of jewellery when the plating wears off.

Pallavi, Palmonas’ founder, tapped into this core consumer problem and pitched Palmonas as the solution to new consumers. The logic made sense. A thicker coating meant less jewellery replacement. And better base materials meant no skin irritation.

2. Exiting the algorithm trap.

For a new brand, social media is the obvious starting point. The catch? Consistent visibility means chasing an algorithm that never stops shifting. High effort, no guarantees.

Paid ads help, but only when targeting, creatives, and the post-click experience all come together. Pallavi had already figured this out at Karagiri, her previous venture. Palmonas inherited that advantage from day one.

3. The marketing play.

The most obvious trick? Making Shraddha Kapoor the face of the brand. Celebrity association means instant credibility in Tier-2 and Tier-3 cities, and instant visibility in Tier-1. Jewellery brands know this. That’s why Anushka Sharma fronts Giva and Shah Rukh Khan fronts Candere.

Then Palmonas flips the playbook.
They strip emotion — completely. Think about every jewellery ad you’ve seen. They’re always tied to relationships, always tugging at heartstrings. Palmonas? None of that.

Shraddha isn’t a distant celebrity ambassador.
She promotes sales, pushes offers, and talks directly about products. The best part? A lot of Shraddha’s sales posts are of extremely low production quality. At her house, in a lounge, even at PR events. That’s saved marketing spending. And it works. Take a look at this ad.

Customers don’t need an emotional anchor for everyday jewellery.
Why? Because this isn’t heirloom gold. It’s another D2C purchase ~₹1,000 — like skincare or accessories. For this category, marketing needs to stop the scroll and feel relatable. Which is exactly what Palmonas ads do.

4. Offline, on purpose.

Most new-age jewellery brands today start online and stay there. From a purely expense standpoint, it makes sense. Why start offline sales if you’re getting enough orders online? Why incur additional rent, store lease, and employee payments when all of it is practically free online?

Here’s the deal. Most Indians still don’t buy jewellery online, especially the expensive stuff. We want to try it on and see how it looks on us before buying. Some of us want the store simply because we can return it if something goes wrong. Online can’t give us that.

We’ve seen this play out before. Online jewellery brands eventually move offline. BlueStone started online-first but had to pivot offline to survive. Melorra didn’t make the shift in time and is now struggling to stay alive.

That’s exactly why Palmonas expanded offline fast. By mid-2025, they already had more than 25 stores across Bangalore, Pune, Mumbai, Indore, Nashik, Delhi, and Prayagraj.

Plus, more offline stores = less marketing spend.
When you walk into a kirana store, do you stick to your list? Rarely. You browse, you see something, you buy extra. That’s the power of physical retail.

For brands, it’s efficient. The customer’s already there — just put new products in front of them. No ads. Impulse purchases do the rest.

5. The quick commerce insight.

Wait, didn’t we just say physical stores mattered in jewellery? They do. But Palmonas customers are buying on Q-commerce platforms on impulse.

Think about it. It’s 14th Feb.

You forgot your girlfriend’s Valentine’s gift, and you’re meeting her in the evening. You search for a gift on Blinkit. And boom, you see Palmonas. It’s gold, precious, and screams thoughtful. Your partner is bound to like it.

Need a treat? You can buy a cute piece and have it in your hands in under 15 minutes. The channel works for gifting.

In 2025, Palmonas raised ₹55 Crores from Vertex Ventures & was in talks to raise another ₹250 Crores from Xponentia Capital, primarily for offline store expansion and to scale its 9-carat solid gold & lab-grown diamonds segment. The target? 100 stores in the next 12 months. Whether this bet pays off, we’ll keep you posted.

We made a whole episode on too.

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Today’s edition.

India’s obsession with gold is legendary. Weddings, festivals, gifting — jewellers and parents are convincing you to buy some more.

Yet, if you ask your family sonar about business, they’ll tell you things haven’t been looking great. At the same time, everyday gold jewellery sellers like Palmonas report great sales. The brand, established in 2022, sees ~3X repeat purchases. Why is that?

We dug into the question over the last few days. Here’s what we learned.

Real gold is bad business.

Gold is so rare that India imports it.
80% of all jewellery made in India is gold jewellery. And India has only 3 mines to support the industry: Hutti, Uti & Hirabuddini. Classic demand-supply mismatch.

So, India imports the rest. Plus, jewellers aren’t the only bidders; the tech industry needs the precious metal for chips, too. The two forces combined drive gold prices higher. As of 29th Jan 2026, the price is ₹17,885/gram.

Then, manufacturers raise gold costs even more by wasting it.
Pure gold is soft and harder to handle, making it easier to waste. That’s why 24K gold (99.9% purity) has the highest wastage, followed by 22K (91.6%), 18K (75%) and 14K (58.5%), respectively. You get the drift.

Lastly, the government regulates sales & takes a cut.
First, there are the hallmarks. As a sonar, you cannot reduce wastage charges by selling 18K gold at 22K prices to cover wastage costs + keep margins. You must state the purity of your gold after rigorous testing. Additionally, you pay 3% GST.

What do you do then? Pass on the costs, including wastage costs, to customers.

Why does everyday gold jewellery have better margins?

Take a solid 18K gold ring, weighing 8.4g.
The gold itself costs ₹1,02,400. Add ₹19,000 in making charges and ₹4,000 in tax, and the total comes to ₹1,42,223. After covering all costs, the store makes a profit of ₹16,823, which is about 12%.

Now compare that to a 2g, gold-plated vermeil ring.
This ring has just a thin layer of gold over a silver base. The small amount of gold costs ₹128; the silver base costs ₹790; and the plating charge is ₹600. It sells for ₹3,607. After costs, the store makes ₹2,089 in profit — that’s about 58% margins!

It’s simple maths.
Low raw material cost + Low wastage + Slightly inflated prices = Better margins on products.

The market opportunity in everyday jewellery.

To date, players have employed two tactics to achieve profitability.

First, keep costs low and pocket the difference.
This works for everyday jewellery. Players use cheap materials and sell at reasonable prices, making big profit margins on each piece. Customers don’t care about material purity or whether they can trust the brand — they just want unique designs at low prices.

Street vendors and small local shops dominate this space.
They’re profitable not because the same customers keep buying from them, but because the demand is so high that they never go out of business.

Second, build trust to sell high AOV jewellery.
Your family jeweller did this for generations — handcrafted pieces, guaranteed quality, designs made just for you. Then in the 1990s, something shifted. Jewellers went big.

Players like Kalyan, Bhima & Tanishq standardised everything from testing, hallmarking, to mass production. Instead of relying on word-of-mouth trust in one neighbourhood, they spent crores on marketing to build national trust. Soon, they were everywhere.

Then Bluestone found a segment everyone missed.
Fine everyday jewellery sold online-first. The kind you splurge on but actually use. And initial customer trust? They built that through their try-at-home model. Big players, with a trust legacy, noticed. Kalyan and Tanishq quickly launched sub-brands like Candere and Mia to grab the same customers.

Now the demi-fine category is having a moment.
Cheaper than BlueStone but still using premium gold plating (>2 microns thick). It’s the kind of jewellery that resists wear and tear. The kind you can even wear while swimming & use for years, unlike fashion jewellery.

Wait, why do Indians want demi-fine now?

Three reasons.

1. Indians aren’t investing in gold like they did.
Shocking, considering that’s what our parents, AND their parents, did. But why?

Gold jewellery as an investment made sense when we moved frequently. The metal was rare (therefore precious), and carrying it as jewellery made more sense than leaving it at home. The practice stuck. People kept buying gold jewellery — for weddings, festivities and even as gifts.

Cut to today. The demand for gold is so high that we’re importing it. It’s so much that, in 2015, the Indian government introduced Sovereign Gold Bonds to encourage people to invest in virtual gold so that they wouldn’t want the real thing.

Except they did.
Customers continued to buy both physical gold and tax-free gold bonds (now taxed after the Budget 2026). Demand surged. Then, global geopolitics took gold prices several notches higher. Meaning consumers had to save a lot more than their parents to afford real, solid gold jewellery.

2. Demand for everyday fashion jewellery
Traditional gold jewellery is practically unusable for everyday wear. It’s heavy, bulky, doesn’t go with most outfits, and it’s expensive.

Fashion jewellery better suits a modern woman’s lifestyle.
It’s lightweight, inexpensive, and looks good with many more clothes. Better cost per use. That’s precisely why the Indian daily-wear jewellery segment has expanded.

That said, low-quality base materials pose a massive challenge for the space. Tarnishing and skin irritation are common issues during long-term use.

So, women treat fashion jewellery as short-term use products — use it while it looks good, then dispose it.

3. Rise in disposable income
Since 2014, disposable income has been on the rise.

That translates to greater splurge potential (discretionary spending), leading to more mass-premium product purchases. And what happens when you have a little extra cash to spare, and can’t buy expensive, solid gold jewellery? You’d buy the demi-fine gold necklace.

See, the demi-fine category sits between the cheap fashion jewellery and the expensive fine jewellery segments. You get premium finishes and light-weight, trendy designs at pocket-friendly prices. Here’s a visual of where this category sits from a purely price perspective.

Palmonas is disrupting the demi-fine segment.

In just two years, they’ve fulfilled 6.4 lakh orders and opened 50+ stores. They’re among the first celebrity-backed players to organise gold-plated jewellery and establish it as a legitimate category.

How did they pull this off so fast? We decode their playbook in the next newsletter. Know someone who’ll want these insights? Share this newsletter with them. Our editions are free for everyone.

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Today’s edition.

Sanjay Dutt launched a Scotch whiskey (imported), The Glenwalk, in 2023. You know the play. Good brand-celeb fit, targeting the right customer segment, and relatability.

Then, in 2025, the Glenwalk became the highest-selling alcohol in Maharashtra, selling 10 lakh bottles in just 120 days. Most new alcohol brands can only dream of doing those numbers in less than 2 years. Some don’t even get the right distributors by then. So, how did the Glenwalk do it? We went to the bottom of it over the past few days. Here’s what we found.

Indians love whiskey.

More than any liquor in the world. See the figure above. The numbers are telling. Indians began importing liquor, becoming the largest importers in the world. And when they didn’t have their fill, they started producing some at home. But why?

Alcohol brands love whiskey too — the margins are gooood.
Take a 750ml whiskey bottle with MRP ₹1,750. Typical tax will be ₹875. With the remaining ₹875, the brand has to figure out COGS, marketing, logistics, and other costs, while also making a profit. Considering all other costs, that’s ~₹500 earned per whiskey bottle.

Now compare that to beer. A bottle sells for ₹110. The government takes about 45% of the MRP or ₹50 as tax. After all costs, the brand barely breaks even at ~₹10 in profit.

This margin difference is why there is so much competition in the whiskey category.

So, how did Glenwalk enter the market?

It’s crowded. There’s a whiskey for everyone at nearly every price point. And the nips (small packs)? They start as low as ₹200. If you compete on price, you’re playing the volume game.

And if you’re betting on the premium segment, you’re hoping on that top 10% of the market to keep buying from you. One wrong move, and you’d never make it to the shelf a second time — screwing up the repeat.

The wedge with pricing.

It entered the premium segment and placed itself bang in the middle — not too cheap to lose out on margins, nor too expensive to risk slow movement. Plus, The Glenwalk isn’t any old whiskey. It’s a Scotch whiskey which is made, aged & bottled in Scotland. Something people pay a premium for even on a regular day. But The Glenwalk, it’s priced at ~₹1725, undercutting most other whiskeys.

The trick is in the recipe.

Here’s what makes whiskey expensive: malt percentage (more malt = richer, more complex flavour), ageing time (longer in casks = deeper taste), and grain quality (Scottish barley beats Indian barley every time).

The Glenwalk is a blended Scotch with 15% malt, aged for 3 years. It’s got the flavour from Scottish grains. Plus, it still maintains the sweet notes that Indians like. So, while it may not be the most premium or authentic Scotch on the market, Indians like it, and that’s all it takes to win them.

Nailing distribution.

Cracking distribution is a two-part problem for new brands.
The listing & distribution.

You need to get listed in every state where they want a presence. The catch? Each state has different registration, listing & approval fees. Some markets, like Goa, have a much lower listing fee ~₹50,000 for certain alcohols, while others, like Delhi, could be as high as ₹25 lakh for a single year. Since listing & excise fall under state law, most states set their own terms.

Next comes distribution.
Distributors buy liquor from manufacturers, then resell it to retailers on credit. If bottles don’t move in the store, they’re reabsorbed by distributors for discounting, sales, export, or destruction. Either way, it’s a distributor’s loss. So they’re picky about what they stock.

Bigwigs like Diageo & Pernod Ricard don’t face the same challenges.
They use a negotiation tactic that new brands can’t. They demand distributors take their new liquor if they want cases of Johny Walker, a fast-moving product, for instance.

So, as a new brand, you need the money to pay for state-wise licenses, have connections to get your bottles stocked, and a solid plan to move bottles to the end customer, all figured out.

This is where Glenwalk’s founders’ connections come in.
Rohan Nihalani, founder of Morgan Beverages and partner at Cartel & Bros (Glenwalk’s parent company), had already spent years importing liquors and ciders. He knows the excise networks inside out — who to talk to, which regulations to navigate.

Mokksh and Manish Sani? They own Living Liquidz, which runs retail stores across multiple states and has deep distributor relationships across Maharashtra, Haryana, Delhi, Karnataka, and Tamil Nadu.

No wonder Glenwalk didn’t need to build a distribution network. While competitors scrambled to convince distributors to take a chance on them, Glenwalk walked straight onto shelves.

Now, picture a conversation at a Living Liquidz shop.

Customer: I usually buy Royal Stag Barrel Select (₹1280), but I want to try a Scotch now — have a special occasion coming up.
Salesman: This new whiskey is “premium scotch”. It has a great taste.
Customer: Price?
Salesman: Let me check. It’s ₹1725.

It’s an easy sale since the price difference isn’t a lot. More margin for the retailer, more commission for the salesman. It’s the perfect loop.

Protecting margins with duty-free.

Any imported spirit must pay up customs, excise and VAT to the government. Here’s a breakdown of taxes, for reference. Together, all taxes amount to ~80% of a liquor bottle’s retail price.

Now, let’s compare this with a duty-free purchase.
In 2025, 74% of all alcohol that was sold at duty-free stores was whiskey. And 64% of that number is Scotch. Reason? Whiskies are cheaper at duty-free stores for international travellers, thanks to no import taxes, sales tax, or VAT. Great for volume sales.

But wait, isn’t there a limit on how many bottles you can buy at duty-free? Yep, India has a 2-bottle limit, meaning duty-free would amount to a tiny fraction of The Glenwalk’s sales.

Here’s what’s interesting, though. Passenger growth at Indian airports grew from ~150M to ~410M between 2021 and 2025 alone. It’s a crazy sales opportunity when you think about it. More people travelling = more duty-free sales.

Looks like The Glenwalk recognises this as an opportunity, too. As of 2025, the brand is present in 24 duty-free stores globally.

Right now, the Cartel & Bros are riding on the success of the Glenwalk. They’ve got plans to launch more house brands over the next few years. Will these ventures stick? We’ll keep tabs for you.

Glenwalk had connections.

Mokksh knew his Cartel & Bros’ co-founders before launching the brand. He understood liquor retail at 15 — something most of us learn at 24, if ever. Having a set of people you can rely on, ask for help from, and learn from is an unfair advantage.

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Today’s edition.

Some context.
In 2019, the Indian government opened up airport bidding for private players. Guess who outbid every other competitor — in some cases by 6X the lowest bidder? Adani.

That’s a crazy amount to invest in a business that isn’t perceived to be profitable. To put this into perspective: GVK, one of India’s oldest airport operators, was ₹6,000 Cr. in debt until very recently.

Then Adani stepped in, buying the Mumbai airport from them.

Now, the company is investing another ₹1 lakh Cr. in acquiring airports. But why?

The business of airports.

Here’s the thing. The government plans to build airports to increase regional connectivity and boost Tier 2 & Tier 3 city economies. The UDAN scheme is a good example of that.

The catch? It’s not profitable.
Hence, the private player partnerships.

Here’s how the deal works.
Private operators bid on the revenue per passenger they expect in the future. The highest bidder wins.

Think about it. If an operator wins, they could charge airlines extremely high fees — landing, parking, and security fees. More profits for them.

But in price-sensitive India, that would kill demand.
Reason? Airlines would pass these costs to passengers through ticket prices. Fewer passengers = less revenue for everyone.

So the government steps in and caps what airports can charge airlines. These are called 'aero revenues’, and they’re heavily regulated. Meaning, airports can’t make much money from airlines.

So where’s the real money?

Operators control everything that’s not airline-related: which duty-free shops operate, how many lounges to build, retail store rents, and land leases. These are non-aero revenues, and they’re huge. Delhi airport, for instance, makes 75% more from non-aero revenues than from airlines.

With numbers that promising, you’d expect a bidding war for airports. But while multiple companies bid, most place conservative bets. Only Adani goes all-in, often bidding several times higher than competitors.

Why are airport operators cautious investors?

Think about the economics.
Operators invest heavily up front — infrastructure upgrades, operations, and employee salaries. Companies like GVK even took massive loans (remember that ₹6,000 Cr debt?).

The bet is simple: enough passengers will shop at the airport to generate commissions that eventually cover costs.

But here’s the problem: Indian passengers don’t spend much.
~80% of passengers don’t spend on retail or duty-free, and ~60% don’t spend on Food & Beverages at airports. Additionally, airport operators probably earn ~7.5% of retail stores’ gross sales. On every ₹1,000 purchase at the airport, the Airport earns ~₹75.

Do the math.
Low passenger spending + thin margins + massive upfront costs = a tough path to profitability. So, most operators bid conservatively — they’re trying to avoid the GVK trap of overbidding and drowning in debt.

So, why is Adani bidding aggressively?

First — the retail + F&B margin breakthrough.
See, most airport operators rely on rent from third-party retailers. They either pay the Minimum Monthly Guarantee (MMG), the baseline rent the retailer must pay, regardless of performance. Even if zero customers walk in, the airport gets paid.

The 2nd path is revenue share: If a store exceeds its sales target, it pays a percentage of net sales to the operator. So, if Starbucks agreed to 15% at the time of acquisition, they’d pay 15%. Either way, the airport operator is an outsider taking a small cut.

Adani’s approach is to own the stores.
Take the Cococart at Mumbai airport. Adani Group now owns a 74% stake in it. Now, they’re not just collecting rent. They’re also keeping a larger share of their net revenue.

Second — the city-side development bet.
Here’s what most people miss about airports: they’re not just transit hubs. They’re money-printing real estate plays. Think about it.

Who does an airport impact?
Of course, travellers. Mumbai airport alone saw 5.5 Crore passengers last year — 1.6 Crore of them international visitors. Travellers who needed a place to stay & were willing to spend money. Premium hotels, malls, retail stores, and F&B spaces all make sense for this group from an infrastructure development perspective.

But there’s a second group nobody talks about: the locals.
And they, in our opinion, present a bigger revenue opportunity. Airports are usually located far from city centres for a reason. Cheap land and lots of it. But once the airport is built, land values rise, and communities begin to form. Airport employees move in. Next, businesses. This new community needs hospitals, shopping centres, office spaces, and transport connectivity.

Now, imagine you’re Adani.
You don’t just operate the airport; you own the land around it through long-term leases. The best part? Any revenue you earn is yours. So while other operators are fighting over that retail commissions inside the terminal, you’re building an entire city outside and keeping 100% of the profits, while the land value keeps appreciating over decades.

Third — the bigger trade agenda.
Adani airports aren’t just passenger airports; they are massive cargo transhipment hubs. Currently, Adani airports handle ~33% of India’s air cargo traffic with just 8 airports. So, when you think about it, more airports = more hubs = more cargo handling revenue. Simple math. But that’s not all. Look at the image below.

Adani’s airports & ports line up almost perfectly along India’s west coast. That isn’t a coincidence, we think. If Adani’s indeed looking to facilitate sea-air transhipment, they have an excellent opportunity here.

Take Vizhinjam, for example.
It already handles a bulk of international cargo shipments and is just 16 km from the Thiruvananthapuram airport. Rumours suggest that Adani officials are considering adding an integrated check post at Vizhinjam to enable sea-air transhipment. Think about the massive contract value of the trade that enables.

Will Adani’s airport bet pay off?

We think so. Plus, a monopoly will follow. Here’s why.

There are 3 ways in which monopolies form in business. Either government-created (through regulations), natural (high infrastructure costs/ economies of scale) and/or market-driven & strategic (through mergers and acquisitions). Adani’s airport bet has all 3.

Let’s recap.
We learned that airports are highly regulated — almost all aero-revenues are government-controlled. That’s a classic government-created barrier to entry.

Next, we saw players like GVK take on massive loans to build airports. Those high infrastructure costs are the second barrier.

Lastly, we noted that Adani has a capital advantage. Thanks to their diverse revenue streams, they could pull off moves like buying the Mumbai airport from GVK. That’s the acquisition angle. A new player would have to cross all three barriers to compete. And that seems unlikely, at least with the current regulations.

But what about actually running profitable airports?
Adani seems to have a plan. In 2025 alone, the Group expanded from 50 to 270 retail stores and added 40 F&B outlets to boost non-aero revenues. Whether it pays off? We’ll keep watching.

If you’ve come this far.

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Today’s edition

In 2025, Indians bought 4 milk packets every second on Swiggy’s Instamart.
What’s crazy is that buying milk online wasn’t a thing we did 5 years ago. Today, you can find the same milk packet on Swiggy Instamart, Blinkit & Zepto, and BigBasket; get it fresh AND at the right price every single day.

Quick commerce has truly exploded.
But every major player is losing money.
Swiggy lost ₹1,896 Crores.
Zepto lost ₹3,367 Crores.
Blinkit lost ₹474 Crores.

All 3 want to cut losses.
There are 2 major ways to make a profit, & trust me, it’s not delivery/ platform & all those random fees. It’s advertising & private labels. Swiggy has picked up both. Here’s a crisp 7-minute read to deeply understand how Swiggy will get the profits.

Before we get into the private label insight, let’s understand where Swiggy’s Instamart makes money. Spend a few seconds looking at the picture below.

Swiggy earns a small fraction of every order placed on the platform. This is the take rate. So, for example, when you buy items worth ₹1,000, they broadly make 7% to 12% based on what you ordered. Groceries, especially fresh groceries, have a lower take rate (lower commissions). Electronics? Higher order value & higher take rate (higher absolute margin ₹).

The problem?
Users buy groceries way more often than electronics. So the Gross Order Value (GOV) and average take rate for every order stay low. This is a problem, think it from Swiggy’s POV — on a ₹1,000 grocery order, they barely make ₹70 to ₹100. I can tell you from experience that this won’t even cover delivery partner payouts, discounts & dark store costs. This has led Swiggy to take a few steps.

4 ways Swiggy’s tackling low cart value.

1. Advertising.

   You see them every time you visit the app. On your main dashboard, whenever you search for a product, heck, even on the checkout page. The idea is to make money from sellers (the brands) who want market share in quick commerce.

   

   ---

2. Maxxsaver cart

   It follows the same principle as ads. Only, Maxxsaver is way more effective. In fact, officials credit Instamart’s 100% GOV growth in the last three quarters to Maxxsaver.

   

   Why does it work?
   Maxxsaver taps into a core Indian behaviour: the more you shop, the more benefits you deserve — in this case, discounts. The same reason our moms want free dhaniya every time they buy veggies.

   ---

3. Minimum order value.

   Question for you: Would you choose a q-commerce app with free delivery at ₹99 or at ₹299? Depends on your cart value. At ₹80? You'd pick the ₹99 option. But at ₹250? You'd probably add ₹50 more to hit ₹299 & unlock free delivery. This is a classic e-commerce strategy that Swiggy Instamart bets on.

   Try speaking to someone in the western countries and you’ll learn that the minimum order values are super high compared to India. That’s a sign that Indian quick commerce is still far away from consolidation into monopoly / duopoly.

   ---

4. Higher miscellaneous charges

   

   As you can see, Swiggy charges delivery, handling, and GST fees on top of the order value. Why? See, Swiggy must pay its delivery partners and maintain its dark stores. By passing some costs to customers, Swiggy keeps its economics in check.

But are any of these tactics effective?

Yes, and no. The business only works if customers keep coming back. And they're not for Instamart, at least not as much as competitors. Market share by Net Merchandise Value (actual value of goods sold after returns and cancellations) shows the gap. Blinkit’s market share: 52% while Swiggy (24%) & Zepto (23%). A strong perception here is that Swiggy Instamart is expensive than others for the same grocery items.

Enter Swiggy's private label brand — NOICE.

If you’re in quick commerce, you have to choose between pleasing everyone or running a business. Your consumers will always want the best deal. You’ll end up emptying your pockets to give them that — covering delivery costs, absorbing GST, or burning cash on discounts.

A classic example is Zepto.
They offer the steepest discounts but are drowning in losses & depend on investor funds to stay afloat. Even Blinkit, with a first-mover edge from acquiring Grofers and deep partnerships, isn’t immune. Its losses have grown 5X in just 9 months — margins are razor-thin.

Swiggy Instamart’s insight.

Quick commerce can’t be their sole source of revenue. So, it’s expanding into consumer products with Noice. So, what’s Noice?

Swiggy’s private label brand. Here, Swiggy partners with local food manufacturers to bring their products to market. Then, it slaps its own label on the products and sells them on the Instamart platform.

Search for homemade chips or kombucha, you will mostly find Noice as the first few options on Instamart. Noice is present in 40+ product categories.

Take a look at Noice’s product range above.
What do you see? A lot of their SKUs neatly fit into the snacks and beverages category. This isn’t by accident. Swiggy is studying customer behaviour on its platform & launching products based on hard data. The numbers back this up: snacks and beverages make up 32% of all q-commerce purchases.

And, snacks are high-frequency purchases. Customers order them in volume, too. That combination of frequency + large category share makes snacks & beverages the perfect entry point for a private label. There are two key insights here.

1. Private labelling is protecting margins.

They contribute 25-40% to the top line for modern trade retailers but nearly 80% to profitability. Those look like crazy figures, until you do the math.

When Swiggy partners with brands, those brands control pricing, shelf placement, and commission splits. Swiggy’s cut is small. Add in the costs Swiggy still bears like marketing, inventory & deliveries — and margins become razor-thin.

Private labelling helps Swiggy avoid this. By only looping in a manufacturing partner for cheap, Swiggy can do the branding, marketing, and distribution in-house and earn higher commissions. Instamart already has the platform and data to do all those things well.

2. Nailing branding and pricing.

In retail, what gets seen gets sold. Look at Swiggy’s bread section. Noice stands out with distinctive, premium packaging — clean design and quality cues that position it alongside Modern or Britannia.

Here’s what’s interesting.
Swiggy isn’t playing the typical private label game (cheaper alternative to brands). Instead, Noice is priced at premium levels — right under the top-tier breads per 100g.

The psychology works like this: Premium packaging + premium pricing = premium perception. Customers see it as a status purchase, not a budget option.

The genius? Swiggy earns the full premium margin without paying brand commissions. They get premium pricing and private-label economics.

Noice’s reception is overwhelmingly positive. But can this private label take Swiggy Instamart to profitability? Only time will tell. We’ll keep tabs until then.

the market is screaming about 12 skills right now.

if you’re in marketing,
it’s 4 skills.

1/ AEO. ask engine optimization.
getting your brand mentioned when people ask ai for recommendations.

2/ ai ad generation.
test an ad the same day you get the insight.

3/ ai led performance marketing.
let ai do the grunt work of bid adjustments and budget reallocation.

4/ ai content production.
produce 50 pieces at the cost of 5.


if you’re in strategy & ops
there are three skills.

1/ automating business operations. any repeated process where humans touch documents repeatedly.

2/ agentic workflows. yc companies are obsessed with this. agents have goals and figure out their own rules.

3/ ai adoption strategy. you build the automation but your 10,000 employees still need to use it.


if you’re in product,
it’s three skills.

1/ ai product strategy. knowing which problem ai solves better than traditional code.

2/ ai prototyping. build working software in an hour, not mockups.

3/ ai evaluations. measuring if your ai feature actually works.


if you’re an engineer
it’s three skills.

1/ context engineering. the ai doesn’t need better prompts. it needs better context.

2/ RAG. giving ai live information, not stale training data.

3/ ai agents. building systems that decide what to do, not just generate text.

the skills are real.
the job descriptions mention them. linkedin shows 10x growth in profiles listing them. the courses are multiplying.

but none of these 12 skills matter

i also asked a different question to members who are actually building in ai. not learning about it. building products. shipping features. deploying systems.


“what got you promoted?”
”what got you hired?”


the answers broke my brain.
because nobody mentioned agentic workflows. nobody said “i learned RAG.” nobody brought up the ai prototyping course they finished.


they talked about problems they solved. systems they built. hours they saved their companies.


one ops lead
took vendor onboarding from four days to four hours.


a product manager
built an ai assistant that cut churn by 3 points. worth 2 crore annually.


a marketer
built a content system that let their team produce 50 variations while competitors produced 5.


now this seems obvious in hindsight.
of course, you solve problems with skills. what else would you do?


let’s be honest for a second.
in the last six months, how much time did you spend thinking about which ai skills to acquire? and how much time did you spend thinking about which problems at your company you could solve with ai? i’ll wait.


that’s what i thought.


we’re all collecting skills.
reading about RAG. watching tutorials on agents. saving linkedin posts about prompt engineering. telling ourselves we’re “staying current.”


nobody is auditing their company for broken processes and asking “can ai fix this?”.

the problem with learning skills

let’s take arjun (alias for privacy).
8 years experience. ops lead at a series C fintech in mumbai.


he spent three months learning to build agentic workflows. followed the tutorials. understood the architecture. could explain the difference between workflows and agents in his sleep.


workflows follow rules. agents have goals and make their own rules to achieve them. he knew this cold.


then his CEO asked him to fix vendor onboarding. four days per vendor. five people touching every document. compliance bottleneck killing their growth.


arjun froze.


he knew how to build agents.
he didn’t know if this problem needed an agent.


maybe a simple workflow was enough. maybe it needed RAG to pull compliance documents. maybe it needed nothing and the real problem was process design.


the skill was in his head.
but what about judgement?

these examples don’t stop here.
i found three people in the community who solved almost identical problems. vendor onboarding. compliance heavy. 4 to 5 day cycle. manual document processing.

the first one built a workflow.
document comes in, extraction runs, matches to purchase order, flags exceptions, routes approvals. simple automation. no agents. four days became six hours.


the second one built an agent.
same problem. but their vendors sent documents in wildly inconsistent formats. handwritten notes. email threads. verbal approvals referenced in attachments. the agent learned to handle ambiguity. four days became four hours.


third one didn’t build anything new.
realized their existing systems had APIs nobody was using. connected them with a basic RAG pipeline that could answer “what’s the status of this vendor” by pulling from six different tools. the time savings came from people not chasing information. four days became one day.


same problem.
workflow vs agent vs RAG. the skill was variable. the problem was the constant.


this is what proof of work actually means

if you’ve read the proof of work series, you remember the cupcake company.


three chefs apply.


one brings a philosophy PDF. second one brings certificates. the third brings three variations of their bestselling cupcake with cost savings calculated.


ai skills work the same way.
certificates about agentic workflows?
that’s the philosophy PDF.


completed coursera course on RAG?
that’s the certificate.


“i reduced vendor onboarding from four days to four hours. here’s how. here’s the system. here’s the annual savings.” that’s the cupcake.


the market doesn’t pay for skills.
it pays for problems solved.


which one are you building right now?
philosophy PDFs or cupcakes?

what this looks like in product

let’s call her meera. product manager at a B2B saas company. 400 crore ARR. their onboarding completion rate was stuck at 41%.


the obvious answer was “improve onboarding UX.” she’d done that twice already. marginal gains.


she could have learned ai prototyping. built mockups faster. tested ideas quicker. the skill everyone says PMs need.


instead she spent two weeks
watching session recordings.
found the pattern. users got stuck at the same three points. not confused about what to do. confused about why they should do it.


she built an ai assistant that watched user behavior in real time. stuck on a feature? surfaced a 30 second contextual video. confused by terminology? inline definitions appeared. dropped off mid setup? triggered a personalized email with exactly where they left off.


the skill underneath?
context engineering.


the ai needed to know where the user was, what they’d done, what they hadn’t, and what typically worked for users like them.


but meera didn’t start with “i should learn context engineering.” she started with “41% completion is broken and i need to understand why.”


onboarding completion went to 67%.
churn dropped 3 points.
finance impact? ₹2.1 crore annually.

what this looks like in engineering

raj and vikram both work at fintech companies. both got asked to build ai features for customer support.


raj learned everything about agents.
tool use. state management. guardrails. built an agent that could access their knowledge base, check order status, process refunds, escalate to humans.


took him four months. the agent worked. but customers hated it. felt like talking to a robot pretending to be helpful. usage dropped after the first week.


vikram took a different approach.
spent two weeks reading support tickets. found that 60% of queries were the same twelve questions. but the answers changed constantly. pricing updates. policy changes. new features.


he built a simple RAG system.
no agent. just retrieval.


customer asks a question, system pulls the current answer from their docs, support rep reviews and sends. response time dropped from 6 hours to 20 minutes.


not because RAG is better than agents. because RAG was right for that problem. the support team didn’t need an autonomous agent. they needed faster access to accurate information.


raj had better skills.
vikram had better judgment about which skill to use.


are you raj or vikram?
building the impressive thing or the right thing?

what this looks like in marketing

sneha runs performance marketing for a D2C brand. 1.2 crore monthly ad spend.three person team.


every ai marketing article told her to learn ai ad generation. produce more creatives. test more variations. the algorithms will find winners.


she tried it. generated 200 ad variations in a week. conversion rates did not move. cost per acquisition stayed flat.


the problem wasn’t creative volume.
the problem was that her brand was not coming inside GPT for trusted reviews. she pivoted to AEO. ask engine optimization. figured out how to get her brand mentioned when people asked chatgpt for product recommendations in her category. took three months of experimentation. no course existed for this.


brand mentions inside ai went from zero to consistent presence. and those users converted at 2x the rate of google traffic.


the skill she needed was not the skill everyone said she needed.

the framework that actually works

stop asking
“what ai skill should i learn?”


start asking
“what problem in my company is broken, expensive, or slow?”


then figure out
which skill solves it.

have a process problem?

repeated steps that follow predictable rules?
that’s workflow automation.

same process but…
documents come in chaotic formats, verbal approvals, handwritten notes? you need an agent that handles ambiguity.

built an automation but…
nobody uses it? that’s an adoption problem.

have a tech/prod problem?

you don’t know which feature to build next?
that’s ai product strategy.

you keep burning dev cycles on ideas that fail?
that’s a prototyping problem.

your ai feature doesn’t know user history or state?
that’s context engineering.

your ai gives outdated or wrong information?
that’s a RAG problem.

your users need to take actions across multiple tools?
that’s an agent problem.

you shipped the ai feature but can’t tell if it’s working?
that’s an evaluation problem.

have a marketing problem?

people can’t find you…
when they ask ai for recommendations? that’s AEO.

stuck at creative production?
that’s ai ad generation.

managing campaigns at scale..
killing your team? that’s ai led performance marketing.

need 50 pieces of content but…
can only make 5? that’s ai content production.


every skill on that list of 12 solves a specific type of problem. the judgment is knowing which problem you actually have.

also, senior folks:

seniority without ai is a liability

sorry for saying the uncomfortable part. if someone with 30% less experience but twice the problem solving ability with ai applied for your job tomorrow, who wins?


your years of experience?
still valuable.


but only if you can
deploy that judgment at scale.


arjun knew more about agents than anyone in his company. didn’t help him solve the vendor onboarding problem because he couldn’t see past the skill to the actual need.


meera had average ai knowledge. but she understood her users deeply enough to know where ai could intervene.


seniority plus ai is a lethal combo.
your taste. your pattern recognition. your understanding of what works. all of that becomes infinitely more valuable when you can deploy it through the right tool.


seniority without ai?
you’re competing on time served.


what do you know about your company that a junior person with ai skills doesn’t? that knowledge is your edge. but only if you can deploy it.

don’t panic though

the people who mastered digital marketing in 2010 weren’t smarter than you. they just started. the people who learned mobile development in 2012 weren’t more capable. they just moved. they just started.


you have time.
not to learn about ai.
but to solve problems with ai.


those are very different things.

so tonight, flip the question.

you’ve been asking “what ai skill should i learn?”

wrong question.

here’s the framework one more time.
find the most broken, expensive, or slow process at your company. figure out what type of problem it is. match it to the skill that solves that type of problem. build the solution. measure the impact.


that’s it.
problem first.
skill second.


those 47 saved ai posts?
they’ll still be there.
the newsletters can wait.
the bookmarks aren’t going anywhere.


but the problem at your company?
that’s your first ai project.
that’s your proof of work.


it’s time to build.

on that note

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feedback.
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Today’s edition.

Remember Anomaly, Priyanka Chopra’s haircare brand? They did $10 M (~₹90 Cr.) in revenue in their first year of launch. Underneat, an innerwear brand by Kusha Kapila, just crossed ₹150 Cr. in 8 months. Profitably.

Both are D2C brands. Both target mid-premium buyers. Anomaly even has the more famous founder. Yet Underneat does more sales. Is it the category? Timing? Pricing? Or something else?

We tried to decode exactly that over the past few days. Here’s everything we learned condensed into a 6-minute read.

It’s too late to build a do-it-all D2C brand.

The innerwear category has been around since the ‘60s. For decades, it was cheap, available everywhere, and completely unorganised. No one cared about brands—the products just did the job.

Then, in the early 2000s, international players entered the market. They brought size standardisation, including clearer grading, fitting protocols, performance fabrics, and premium finishes to the mainstream. Product catalogues expanded horizontally—more variations, more options. The category became organised.

When e-commerce boomed, these organised players were first in line. Amazon, Flipkart, Myntra, Nykaa, they locked down listings everywhere. How does a new D2C brand compete against that? It doesn’t.

A new brand can’t beat a local player’s accessibility or an organized giant’s massive catalog. But here’s what it can do: spot a gap, build something new, and move fast.

The trick is finding a wedge.
A segment too small or too risky for the big players to bother with. Think about it. Building a new product line requires R&D and manufacturing setup; it’s too costly for big players. Plus, returns are uncertain if the product doesn’t sell.

Underneat doesn’t have those concerns.
It can enter a small niche, own it, then expand. And that’s exactly what they did with shapewear.

Indian innerwear is designed for Western bodies.

Fact — Indian innerwear, even the mass-produced stuff, follows Western sizing. Probably why inners that fit at the hip don’t sit right at the waist; the fabric suffocates, or worse, slips.

Yes, Indians have different proportions.
Broader hips and more pronounced waist-to-hip differences are common, especially at larger sizes. Underneat figured this out by asking 12,000 women how shapewear actually fit them.

The result?
A completely different approach to sizing. Take a look.

The second problem?
Indian weather. Most sculpting fabrics work in Western climates—not here. Underneat needed something that stretched and breathed. They found it in German-invented Sensil Yarn.

The kicker: Indians don’t want what Western shapewear promises.
Unlike the West, which aims for an hourglass shape, Indians just want to hide flab. (That’s why pinpoint solutions, like tummy tuckers, sell so well!)

So instead of maximum compression (30-40%), Underneat kept it at 10-20%. Less constriction, more comfort — perfect for the Indian woman. And there’s still no guarantee the product will sell.

Indian innerwear isn’t a mature category yet.

Here’s a question.
What is innerwear for?

Took you a few seconds, right?
Shapewear, padded bras, and body sculpting products are still taboo. Marketing happens quietly through word of mouth, private recommendations or closed communities.

So, how do you sell here? Especially when no one knows their options. UGC reels work. But Underneat cracked a different approach. Take a look below.

See, Kusha, Underneat’s founder, has been creating relatable content since her iDiva and PopXO days 7 years ago. She’s even built a massive ~4.3 M Instagram following on the back of it. No one knows the content game better than her.

Instead of going the classic Get Ready With Me (GRWM) route, Kusha maximised for the edutainment format with the “What Are You Wearing Under?” series.

Every problem was addressed — discomfort, sizing, lack of options, and potential solutions. After 3 months, Kusha launched Underneat to a primed audience. It’s classic marketing.

Step 1: Talk about the problem.

Step 2: Try all the available solutions and why they don’t work.

Step 3: Talk about possibilities, and bring in your solution. You just built a case for why people should buy your product.

Product placement matters.

The real test starts when your product hits the market. Sales tell you if customers want it. Feedback tells you if it actually works. Marketplaces (Myntra, Nykaa) only show you part of the picture — sales numbers and a few customer reviews if you’re lucky.

Your own website gives actual customer behaviour data: What did they browse? What did they buy? What did they abandon in their cart? Plus, you can reach customers directly and ask them what’s working and what isn’t. That information lets you shape your product to meet real market expectations.

Underneat launched on their website in April 2025 and took customer feedback seriously to build better products. Then they launched Underneat 2.0 in the mass market channels.

Marketplace reach can’t guarantee sales.

Wait, but is more eyeballs with spending power = more sales? Not really. Visibility doesn’t matter if you’re visible to the wrong people. Your customers will only buy if they actually need what you’re selling.

That’s why Underneat launched on Myntra & Nykaa first.
Both platforms had a large female user base with disposable income, who were already shopping for mid-premium brands—a perfect fit. Amazon & Flipkart? Dominated by male users buying electronics at the time. Not the right audience.

Next, Underneat expanded to quick commerce.
The play here was different: impulse buying. Indian women do retail therapy—stress, events, and last-minute wardrobe emergencies. Quick commerce captures that moment perfectly. The dress doesn’t fit? Order shapewear, get it in 10 minutes, even if it means paying more.

Today, Underneat sells on major e-commerce platforms. The difference? They’re no longer hunting for the right audience — not because the audience has changed, but because they've proven demand and need wider distribution. Last week’s $6M funding round will help them further scale distribution across Tier 1 and Tier 2 cities.

That’s all we have on Underneat for today. Building a D2C brand? Send this article to your colleagues who should read insights from the Underneat story.

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Bookings for the new Tata Sierra opened on the 16th.

Launched just 20 days ago, the Sierra is said to put Tata back on the map. Shailesh Chandra, Tata Motors’ CEO, claims the launch will increase SUV market share from 16% to 25%. But can they actually pull it off? We spent the last few days finding out. Here’s everything we learned in a quick 6-minute read.

Recognise this car?

That’s the Tata Sierra, India’s first homegrown SUV that launched in 1991—also Tata’s first-ever passenger vehicle. It was designed to be the next aspirational vehicle.

State-of-the-art wraparound rear glass hatches for complete rear visibility, high ground clearance, ladder frames for rough terrain and floods, and a unique 3-door layout that saved space without sacrificing cargo room, ACs, or automatic windows— Sierra had it all.

But Sierra’s 3-door system just wasn’t practical.

Tata launched Sumo (1995) & Safari (1998).

These cars were huge, feature-packed, and their prices reflected it. These were out of reach of regular customers — too pricey. The common man wanted a compact, affordable car built for city roads.

Maruti tapped into this trend. It cemented its position as the people’s brand with back-to-back successful launches of the Swift, Dzire, and A-star in the late 2000s.

Hyundai, in contrast, captured the premium segment around 2008 with aspirational models like the i10, i20, and Creta. Better finish, premium features, and expert build quality, all at a great price, made it the obvious choice for premium buyers.

And in the SUV segment? Mahindra and MG began competing toe-to-toe with Tata by offering feature-rich models at competitive prices. Just take a look at the graph below.

Tata played catch-up for a long time — launching hits like the Indica (2002) & the Xenon (2007), alongside a series of misses, including the Spacio, Sumo Grande, Vista, and Nano. It kept battling outdated designs and poor safety perceptions.

Then, Tata revamped its portfolio.

Tata killed non-performers like the Sumo and Nano and launched stylish, customer-focused models. The strategy? Build cars that people actually want.

Tata entered the market with its own SUV lineup.
They doubled down on R&D for engine efficiency and safety, while reverse-engineering premium Jaguar Land Rover features for their high-end SUVs.

The smartest move — they only introduced premium models after their budget lineup proved successful. It worked—perception shifted, and sales climbed. Between 2019 and 21, Tata’s SUV sales skyrocketed.

Today, Tata offers an SUV lineup spanning the C1 (Compact SUV - Nexon) and D1 (7-seater SUVs - Safari) segments. In fact, Punch & Nexon lead the Compact SUV (C1) category in sales. Reason? They’re spacious, have a stronger, safer build, and are available in petrol, diesel, CNG, and electric powertrains. This offers consumers an optionality that Maruti & Hyundai don’t currently offer.

This wasn’t enough.

Look at the table below. Tata rules the compact SUV (C1) segment with the Punch & Nexon. But, when you look at SUVs (C2), the Hyundai Creta & Mahindra Scorpio rule the market.

Let’s understand this better.

For a moment, let’s compare Compact SUV (C1) options between ₹5L to ₹8L. We’ll compare the features people love in the Creta (one of India’s highest-selling vehicles) for reference.

The Punch positions itself as a premium, feature-rich hatchback. In contrast, the Nexon positions itself as a safe, family-oriented SUV with premium features at a good price — something most Indian families want from a car.

But Sierra is not built to compete with Nexon or punch categories. It competes with the full SUVs (C2) ,which are above 4 meters in length, which brings to the mean of “Why Sierra?” and “Why now?”.

Tata’s Creta killer.

Before we get into the how, let’s see why the Creta has a 9-year stronghold over the SUV market. When the Renault Duster left the market in 2016, the Creta immediately stepped up and captured its market share. Ever since, the company has offered feature updates relevant to the Indian market at a reasonable price. No other player came close — until now.

The Sierra is a roomier, more premium-looking vehicle that offers the same driving experience at a slightly higher price. It can sway people looking for comfortable family SUVs and also fans of the original Sierra, eating into Creta’s market share.

In fact, despite Hyundai Creta’s extensive model options, the Sierra holds its ground. A Sierra Smart+ (base variant) offers a similar driving experience and a more stylish design than a Creta S (middle variant). Take a look.

What does this tell you? Tata wants to go after the most profitable variant of Creta (the middle variant) and eat that share with Sierra. Interestingly, we think Sierra could have been slightly priced higher because consumers perceive expensive = quality while buying cars.

Tata now has a car at every price point.

At first glance, the Curvv & the Sierra might appear to be priced a little too close — and you are right. If you look at the numbers, Curvv sells about 3,000–5,000 cars per month, while the Creta averages about 15,000–16,000 per month. Curvv has failed to gain market share, which is why Sierra is the next bet. Whether Tata wins this bet or not, only time will tell.

That’s all on the Sierra story. See you in the next one.

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Today’s edition.

HR: Hi Harpreet, I’m Rashmi from Wayne Corp. We reviewed your profile for Senior Copywriter and shortlisted you. Quick question—what’s your current and expected CTC? Harpreet: Hey Rashmi, so glad I made it through! My current CTC is ₹8 LPA, but I’m expecting ₹14 LPA for the role.

Poof. Offer lost.
Not because the ask is too high. But, the approach doesn’t work.

We asked our founders, Abhishek and Udayan for their framework to negotiate a job offer. Both of them, over the past 5 years, have helped 100s of GrowthX members negotiate offers at companies like Google, Meta, Microsoft, Amazon, Zepto for their framework. Here are 4 principles they swear by, packed in a 6-minute read.

The 4-step salary negotiation framework - let’s begin.

1. Never reveal your salary first.

Here’s the hard truth: the moment you share your CTC, it becomes the ceiling. The HR doesn’t want to spend more to get you on board. They’re only looking to get the best candidate for the absolute minimum CTC possible. Here’s an example scenario.

Now, you can align your expectations with their budget. Some HRs might dodge this by saying, “It’s flexible. Honestly, we’d pay well for the right candidate”.

This is a trap. Don’t fold yet.
Here’s what your response should look like if you are Harpreet: “I understand. But to respect everyone’s time, I’d really like to know the budget —even a range helps.” — Most HRs would share the budget, and now you know more information before you quote your expected number.

Wait, what if the HR lowballs you?
Walk away if it’s a large company—they usually have fixed salary bands, and unless it’s a super critical role for them, they won’t budge. But, smaller companies? They’re hungry for talent. Prove your value, and they’ll stretch their budget.

2. Figure out the importance of the role.

Recruiters and hiring managers often wait months to find the right candidate. They’ll even raise their budget for a good fit. Find out if your company is in that position. Your cue?

HR: Do you have any questions for me?
Don’t ask “How important is this role?”
Ask contextual questions that reveal urgency:

Harpreet’s question should look like
”How much revenue does this role impact?”
”Who does this team report to?”
”What’s the org structure?”

This shows you’re interested in the team AND gives you leverage. If the role has an immediate impact on the business, you can pitch higher.

3. Don’t react to bids immediately.

HR: “Hey Harpreet, we’ve got some good news. We’d like to offer you ₹12L fixed, ₹2L variable for the Senior Copywriter role.” This could be way lower than expected. You might be tempted to counter right away: “Can we do ₹14L in hand instead?”

Bad idea. You seem too eager.
Never counter in the same conversation. Show you’re making an informed decision, not a rushed one. It’s the classic labour effect.

Harpreet: Hey, thanks for this! Just confirming this is ₹12L in hand, and ₹2L variable bonus credited quarterly, right.

HR: Yes.

Harpreet: Could you send the exact breakdown over email? I’d like a day to think it through.

HR: Sure, sounds good. Let us know, though.

Harpreet: Of course, will do. Thanks again.

See?
More professional, already.

What if you really like the offer, though?
Still take a day. Composure is underrated.

Recruiters aren’t locking in your salary on the first call or the last.
They’re coordinating with the hiring manager and the function head throughout the process. You can still prove your value to them and get the hike you want.

Plus, in smaller companies, the hiring manager and function head might be the same person. In even smaller ones, it’s the founder. That’s who you need to convince.

4. Show your value, but be flexible.

You’ve followed up on the salary email asking for ₹14L fixed cash component. Their typical response would be “Sorry, but ₹14L is difficult. Maybe take some time and reconsider?”

Should you cave?
NO! Follow this specific response.

1. Re-affirm your value.
The team has already vetted you. “Hey {HR}, I believe I have shown that I can solve X, Y, Z in the technical rounds.”

2. Re-frame past vs future value.
It’s still professional. “My last CTC is a reflection of the value I created at my last job. If everything goes well, I could create a step-change in value here. I’d want the cash component to reflect that.”

3. Offer skin in the game.
You’re being reasonable, not cocky. “I’m happy to prove my value during probation to justify the hike.”

Perhaps HR finally recognises your value. Maybe not, and you get this instead: “Unfortunately, we can do ₹12L fixed at best.“ Take a moment. Ask for the detailed split of fixed, variable, equity cash component & even joining bonus.

The HR will typically come back with a classical response, “Variable pay is still ₹2L, but we could do a small joining bonus. Let me check with the founders on ESOPs”

That’s ~₹14L in cash component plus benefits. And all you had to do was ask.

Important.

Companies may offer ESOPs, RSUs, health insurance, PF, education stipends, travel allowances, joining bonuses, or variable top-ups. Together, these bump up your overall CTC. Most are straightforward. Except ESOPs—let’s understand them better.

What are ESOPs?
Your company might offer ESOPs—Employee Stock Ownership Plans—as part of your compensation. You get allocated stock options, but you earn ownership gradually through vesting over ~3-6 years. This means you gain the right to buy a portion of your shares the longer you stay.

Once vested, you exercise them.
This means you can buy shares at a fixed price, typically below market value. If the company’s stock price rises, you can sell the shares for a profit. There is a catch, though. In private companies, shares can be hard to sell until an IPO or buyback.

Plus, for small companies (<200 people), equity is flexible. Here, you could agree to less cash and more equity. Settle only if you get a shorter cliff & aggressive vesting. This way, you can trade shares quickly and get ESOP benefits faster.

For large or public companies, you won’t get meaningful ESOPs at mid-level roles. They might offer RSUs if they’re public. They’re tradeable but are heavily taxable. It’s better to focus on clarity for fixed, bonus, and budget here.

This is just the cherry on top.

There’s a lot more to unpack on salary negotiations. We cover the basics, including how negotiations work, how to find common ground, and what to say when they ask about your expectations for the GrowthX platform.

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