Investing in companies that don't make money @GoodCapitalVC

India
Joined February 2012
Arjun Malhotra retweeted
friends, we have a new website - goodcapital.vc - pls report any bugs/breakages & would love to hear feedback - what's good, bad & what can be improved!
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Arjun Malhotra retweeted
fruits of our labour, aka the new good cap website, is now live :) goodcapital.vc if you come across any bugs/have any feedback - pls let us know!
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Arjun Malhotra retweeted
as @BadCapitalVC once told me - most theses are retrospective (they're an outcome of investing)
You can write a thesis all day long but if you can't build conviction to write a check, then your are just a writer not an investor.
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Arjun Malhotra retweeted
We Built the Product Apple Gave Up On...
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Arjun Malhotra retweeted
I see often this. Jevon's paradox applied to the attention economy without tuning. Professor Herbert Simon (Nobel in economics) famously argued : abundant information creates scarce attention and the effect in unbounded. This explains why you can't remember the specific reel you promised yourself you will remember. Its impact on entertainment is even more insidious, as the amount of content become infinite, you stop engaging with content beyond your shortest attention span. in 2006, Prof. Lanham (UCLA proffesor or rhetoric) wrote the book The economics of attention, where he argued once information is abundant and attention scarce, all value accrues to Style. His used this term to mean the packaging, presentation, and rhetorical craft that makes something noticeable. Trouble with Style is that you need novelty and variation. ((Yes!!! the term TASTE is not that new )) Prof Lanham explained why the marvel universe suddenly started delivering flops even as the costs went up.
One of the more interesting uses of AI in production i've seen is PocketFM. AI has cut their production cost by 80x, but the lagging metric that increased was retention (44% to 76% in ~2 years). This is because cheaper production let them make stories for the long tail of listeners that are too niche to justify a human writer's time. So scaling with AI made even serving smaller audiences economically viable.
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Arjun Malhotra retweeted
Mass A.I training is happening in India. It is hard to imagine what the world will look like in the next 5-10 years.
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Arjun Malhotra retweeted
I do not support pacing frontier models. If I am going to die at the hands of killer AI, I want it to be American, not Chinese. Buy American, Die American.
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Arjun Malhotra retweeted
i think theres a larger picture to this we might be underestimating the power behind and sitting down and writing/typing wispr flow solves for the mundane and boring but not the fun and exciting, it seems almost too “dystopian post modern” to my liking as i prefer people being people rather than automated words, even typing mistakes tell a lot about someone lol that being said, tech guys will know this is replicable but its <2% of the world, others depend on voice a lot so there is a market
yes @WisprFlow is a vitamin, not a painkiller. but it's defensible for plenty of reasons: it's a product with a very high-frequency use case, even up to 40x a day. there's no learning curve to adopting it, and it integrates seamlessly with your current workflows. and distribution has been incredible, driven heavily by word of mouth. subconscious adoption is a very underrated moat.
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Arjun Malhotra retweeted
Not just pitch decks - even in writing, presentation, arguments, debates, simplicity is really the true measure of understanding.
complexity in a pitch deck is often inversely proportional to conviction
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Arjun Malhotra retweeted
If you’re trying to understand the dynamic of real world agent adoption this post is a great place to start. Everyone got so hooked on talking to chatbots that there’s limited recognition still that working with an agent is much more like managing someone in a process vs. just asking an ai some questions and getting a response back. “prompting an agent is closer to writing a spec than asking a question. you have to scope the task extensively and define what "done" looks like.” Ultimately, the real upside of agents is when you start to change the underlying workflow itself instead of just treating it as another system you ask questions of. This means getting the agents the right data to work with, crossing organizational boundaries, and evolving the human in the loop steps for when people actually review the work. All of this has to change about today’s processes for the big upside to occur. The end result is that it’s most likely that the vast majority of token usage in an enterprise will be agents that are “deployed” to go execute tasks inside of workflows.
some obvious & non-obvious reasons i think AI agents may not have really been widely adopted yet, even though the tech is ready: 1/ it's not prompt in, answer out. an agent is a process you set up and steer while it runs, and the chatbot muscle memory most people have doesn't transfer. 2/ prompting an agent is closer to writing a spec than asking a question. you have to scope the task extensively and define what "done" looks like. 3/ as @paraschopra puts it, this needs a lot of delegation, which is a hard soft skill to build. it's very close to managing an employee and most people have never done this. 4/ a lot of the actual power still lives inside codex or claude code which is terminal-shaped and a little technical. you have to be comfortable doing the messy setup, so it self-selects for a narrow crowd. 5/ one agent is also just a tool. the unlock is running several at once and getting them to talk to each other like a team, and that handoff between agents is still mostly diy. 6/ same problem across people. your agent's context has to reach your colleagues or everyone ends up working in silos, and right now that handoff is way too manual. 7/ trust is a ratchet. a chatbot that's wrong wastes 10 seconds, but an agent that's wrong sends the email or edits the file. the downside is asymmetric, so most people keep it on a short leash. 8/ lastly, there isn't a job-to-be-done the public actually feels yet. autonomous agents will always be a solution looking for a problem.
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"lastly, there isn't a job-to-be-done the public actually feels yet. autonomous agents will always be a solution looking for a problem." spoke my mind - i was about to say the exact same thing
some obvious & non-obvious reasons i think AI agents may not have really been widely adopted yet, even though the tech is ready: 1/ it's not prompt in, answer out. an agent is a process you set up and steer while it runs, and the chatbot muscle memory most people have doesn't transfer. 2/ prompting an agent is closer to writing a spec than asking a question. you have to scope the task extensively and define what "done" looks like. 3/ as @paraschopra puts it, this needs a lot of delegation, which is a hard soft skill to build. it's very close to managing an employee and most people have never done this. 4/ a lot of the actual power still lives inside codex or claude code which is terminal-shaped and a little technical. you have to be comfortable doing the messy setup, so it self-selects for a narrow crowd. 5/ one agent is also just a tool. the unlock is running several at once and getting them to talk to each other like a team, and that handoff between agents is still mostly diy. 6/ same problem across people. your agent's context has to reach your colleagues or everyone ends up working in silos, and right now that handoff is way too manual. 7/ trust is a ratchet. a chatbot that's wrong wastes 10 seconds, but an agent that's wrong sends the email or edits the file. the downside is asymmetric, so most people keep it on a short leash. 8/ lastly, there isn't a job-to-be-done the public actually feels yet. autonomous agents will always be a solution looking for a problem.
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Arjun Malhotra retweeted
Almost every investor talks about how founders should make pitch decks, but nobody shares how they read one. So, here's an insider perspective on what we're looking for as we read through your deck. 1/15
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Arjun Malhotra retweeted
Taiwan solved tax evasion in 1951 with a trick so cheap it should embarrass every tax authority on the planet. The problem was an all-cash economy full of small shops. A merchant pockets the cash, skips the receipt, and the sale never existed. Auditors can't catch what was never recorded, and hiring enough of them to watch every noodle stand costs more than the missing tax. So finance chief Ren Xianqun flipped the incentive. Print a lottery number on every receipt. Draw winners every two months on live TV. Top prize today: NT$10 million, about $310K. Suddenly the customer and the shopkeeper want opposite things. The merchant wants the sale off the books. The customer wants the ticket. And there are millions more customers than merchants. Every transaction now carries a built-in witness demanding the paper trail. Year one, reported tax revenue jumped 75%, from NT$29 million to NT$51 million. Seventy-five years later, roughly 70% of Taiwanese still play. Convenience stores redeem the smallest NT$200 prizes at the register, so even a coffee receipt feels like a scratch card. The elegant part is what the audit force costs. The prize pool runs about NT$7 billion a year, roughly $20 million. In exchange, the government gets 23 million unpaid auditors working every checkout line in the country, forever. No inspector general on earth delivers that coverage at that price. Greece, Italy, Portugal, and Slovakia all copied it. The most effective compliance tool ever built looks like a game, and that's exactly why it works.
In order to prevent stores from evading taxes, every receipt in Taiwan is automatically a lottery ticket, too, which can win up to $300k, turning customers into voluntary tax auditors:
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Arjun Malhotra retweeted
one of my short-term goals is to send @BadCapitalVC cool AI tools he hasn't already tried before
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Arjun Malhotra retweeted
Replying to @GoodCapitalVC
@GoodCapitalVC india technology review makes a strong case for why medicine ≠ groceries in qcom. although I think it might be worth exploring if healthcare need the dark store playbook at all. for players like apollo, 1mg and pharmeasy, the retail pharmacy network itself can be the fulfillment layer. walk-ins and online orders pull from the same inventory, and every new store improves both retail coverage and delivery density. def not a model lean startups can replicate, but it could shape how healthcare qcom gets built.
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So much gets mislabeled as a “pivot” in startups. But as Arjun Malhotra (@BadCapitalVC) explains, Meesho’s journey wasn’t random experimentation. It was a natural evolution of the business model. “People say Meesho (@Meesho_Official) pivoted from this to that. But really, it’s like backward integration in a traditional business. Once you gain power over suppliers and move up the value chain, that’s exactly what’s happened over here.” “Vidit (@viditaatrey) has just been incredibly foresighted about knowing where value can be created next and being able to move to those things.”
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Arjun Malhotra retweeted
we're investors in a company that uses AI for construction. the default is that the on-site contractor makes the decision about what's to be done when, based largely on anecdotal experience. this invariably leads to delays because construction is such a complex procedure. the team built an AI that breaks down the project into a defined work plan with tasks & labour requirement. now, the contractor/supervisor just needs to get the work done, instead of figuring out what to do. AI became a force multiplier - it allowed people to take on what they're best at. this is just one example.
I am skeptical of the “AI replaces all labor” narrative. What seems far more certain is that AI commoditises execution. Fewer errors. Fewer dropped balls. Better decisions. Better coordination. Better institutional memory. Software made organized and disciplined workers dramatically more productive. AI does something bigger: it gives every worker access to capabilities that previously required exceptional organization, expertise, or support. The result may not be fewer workers. It may be a world where the average worker performs like a top-decile one today. AI is a floor raiser.
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Arjun Malhotra retweeted
Receiving goodies has been my favourite part of being back in India and being on X ✨ Thanks @GoodCapitalVC @BadCapitalVC
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Arjun Malhotra retweeted
in case you missed getting the physical copy of our annual book, we've made the digital version publicly available for everyone - india-technology-review-2025…
last year, we spent months building this book for our LPs - the india technology review. it became the most special project we put out last year and honestly the thing i'm most proud of. we're now giving away a few copies! it covers everything from meesho's full journey, our AI thesis for india, and how the long tail nature of india's economy creates opportunities most people miss. we also go deep on economic policy & some early-stage bets we're tracking. we built it for our LPs but figured founders, investors & operators would get a lot out of it too. giving away 20 copies at random. drop a 👋 below.
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