#1 Gautam Adani started off as a diamond trader. His original DNA was pure trading – figuring out how quickly and at what price can he sell a commodity for.
#2 As opposed to most other Indian groups, Adani has always preferred doing 50-50 equal joint ventures with partners across sectors (edible oil, gas distribution, solar, data management etc.). Drives the right accountability across both sides.
#3 Started the Mundra port as a JV with the Gujarat Govt (74:26), but then seeing the long-term potential of Indian logistics, bought back the 26% stake from the Govt. at a 26x markup to the original price. Thus, ended up as a 100% owner of India’s largest private port and also the most profitable.
#4 While most ports take several years to turn profitable, Adani’s ports have been immediately profitable, mainly because he has focused on building initial capacity only for commodities he understands deeply and trades in.
#5 Many years back, Adani was looking to lease port dredgers from the Indian govt. but it had a 3-month wait. To save time, he started buying second-hand dredgers, using them for 3 months in a year, and then leasing them out to other customers for the remainder. Today, Adani has 100+ dredgers and has one of the largest dredging businesses in the world.
#6 Adani smartly used cash flows from the highly profitable trading and port businesses to buy a coal mine in Indonesia, then eventually diversified into power and LNG terminals to become a diversified infrastructure conglomerate.
#7 Here are some core operating mantras of Gautam Adani:
The market isn’t in your control. Cost is in your control. Watch your costs and keep them lower than the competition. Adani is ruthless in cost-cutting.
Build long-term relationships and don’t compromise on them.
If you make a mistake, cut your losses early. Adani started a partnership to enter the Aluminium industry in the 90s and realized very early that this company wouldn’t be a no 1 or 2 player in the space. He immediately shut the initiative down.
For every risk he takes, he always has a risk mitigation strategy in his back pocket.
#8 One of Adani’s core financial strategies has been to create “land banks” around his ports (Ray Croc anyone?). Eg. back in 2007 itself, Mundra Port had ~35,000 acres of land.
The benefits of having a land bank as part of the port include:
(a) The land itself keeps going up in value.
(b) Captive warehousing, which leads to rental cash flows.
(c) Can be leased to other internal or external businesses that need proximity to the port. Eg. Maruti has created its full export hub at Mundra. The Port also has a 2.5 km airstrip for aircraft maintenance as well as for serving defense businesses.
#9 Adani is sitting on assets with massive future potential, especially given where India’s economy is headed. For example:
(a) Adani is the largest port player in India and also one of the largest globally.
(b) Adani owns the world’s largest coal mine in Australia. Even in this era of energy transition, given India’s power sector is still predominantly thermal, he has a steady opportunity to supply coal at least for the next 25 years.
(c) Owner of multiple major airports in India.
(d) Distributes ~32% of natural gas in India.
I believe there is a lot of first-principles business thinking that even tech founders can learn from Gautam Adani, especially around identifying & aligning with long-term secular growth waves, stitching together assets to create a competitive advantage as you surf these waves, and controlling your cost structure to ensure survival during the inevitable market downcycles.
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Avoid pushing a boulder up the hill, becoming a drunk painter or a depressed actor.
I met a famous VC at a recent event. Multiple exits as a founder, multiple unicorns as a VC, and a thriving media business. On a sweltering SF afternoon (this week saw a massive heat wave across the Bay Area), his face had an extremely tired look and he could barely keep his eyes open. Yet, he took the time to patiently answer my questions.
I wake up the next day and see the same person doing a live stream discussing everything SaaS at 8 AM PT with all the passion and energy of a 20-year-old. Having seen his exhausted version just 12 hours back, I could never have imagined him bouncing back early the next day and bringing his best self to the game.
I was discussing this observation with my better half and in the flow of the conversation, outlined that this person was demonstrating what I saw as three key elements that when coming together, create a high likelihood of long-term success in any area of life:
1/ Desire – Massive internal motivation to win, to be the best at something, to become the best version of yourself. In his all-time classic ‘Think and Grow Rich‘, Napoleon Hill calls Desire the “Starting Point of All Achievement”.
2/ Energy – Backing up Desire with raw physical and mental horsepower to do the work, put in the daily reps in your field, outwork competition, and practice enough (the 10,000-hour rule) to become world-class in your area.
3/ Natural Strengths – When Desire and Energy are channeled in an area that aligns with your Natural Strengths, the ROI on the effort becomes massive. This amplification creates a snowball effect, leading to rapid daily progress which over the long term, shows up in high rates of compounding. PS: I have covered a specific facet of this snowball effect in my posts ‘The Success Flywheel‘ and ‘The Success Flywheel – Part 2 (Superhuman, Perplexity)‘.
There is a reason why I have depicted these 3 elements as a Venn diagram. They have to necessarily overlap to enable long-term success. Even if one of the elements is missing, the snowball effect might never kick in. Here’s why:
Case 1: Desire + Energy BUT no Natural Strengths
In today’s age of near-zero information asymmetry and high leverage, which leads to intense competition in any given field, the key to standing out amidst all the noise is to focus on your unique and differentiated strengths. In the context of both startups and individual careers, I call this the right-to-win.
In Case 1, while this person has the Desire to succeed and the Energy to do the work, the ROI on the effort is low given it’s not being directed in a field where the person has differentiated strengths. It, therefore, reduces the odds of them being able to grow fast and grab market share against competition in their chosen field.
Think of Case 1 as the ‘Pushing A Boulder Up The Hill’ phenomenon – requiring enormous efforts daily but without commensurate rewards in terms of progress and rate of compounding. Essentially, it’s a case of having the right intentions and ability to do the work, but with poor strategy and direction.
PS: I see this at play a lot when strong founders choose a market where they have a weak fit and therefore, an unclear right-to-win.
All these sportspersons had natural strengths in the sport and had high energy due to which they got initial success, but then, the internal motivation just wasn’t strong enough to sustain it.
Desire is crucial because it leads to discipline, which is critical for continuous improvement and growth. As I had written in my post ‘Willpower is a reservoir, and that’s why focus is important!‘ many years back, humans have a finite amount of willpower. Having discipline ensures that this willpower is carefully and optimally allocated in the right direction on a daily basis.
A Case 2 persona that comes to mind is ‘The Drunk Painter’ – talented and charismatic, but long-term lazy. When this person creates, it’s magic, but unfortunately, that’s too few and far between to make this person an all-time great.
Case 3: Desire and Natural Strengths BUT no Energy
This case underlines the importance of fitness (physical and mental) to back up Desire and Natural Strengths. Without fitness, one can’t show up every day with their best game.
Similar examples also exist in other fields where fitness might not be traditionally considered a central pillar of success. Just after delivering an Oscar-winning performance as the Joker in The Dark Knight, which should have resulted in a Jack Nicholson-like long career, Heath Ledger died from drug abuse perhaps from prior mental health issues. Despite being one of the legendary stars in Friends for a decade, Matthew Perry dealt with perpetual alcoholism and depression, leading to an underwhelming career and ultimately, a sad end.
A Case 3 persona that comes to mind is ‘The Depressed Actor’ – this person loves to act, and is pretty damn good at it, but doesn’t have the physical and/or mental fitness to regularly bring their best game to auditions, and to keep improving and doing their best work over decades.
Case 3 persona: The Depressed Actor (Image Source)
TLDR: to summarize, if you are looking to set yourself up to chase long-term success in any aspect of life, focus on parallel-processing three things:
Inculcate a deep Desire to succeed.
Develop Energy to provide fuel to the Desire.
Lean into your Natural Strengths using this combination of Desire and Energy.
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Putting your strongest foot forward quickly, coherently and in an interesting way is the key to getting VCs to lean-in during a cold-pitch at an event.
It’s been a hectic couple of weeks of tech events, with SaaStr, SaaSBoomi, VC mixers, and now Dreamforce. Meeting hundreds of founders cold across these events, I have noticed that most of them struggle to quickly pitch themselves/ their startups to investors.
In fact, in most of these meetings, I was only able to figure out their unique strengths, progress, and fit with the problem statement after 3-5 mins. into the conversation. Unfortunately, most investors have short attention spans and given they meet multiple founders daily, their ability to recall is even worse.
This means as a founder, you have to achieve 2 things while cold-meeting investors at events:
1/ Leave an impression in the first 30 seconds so that the investor starts leaning into the discussion and becomes inclined to spend 3-5 minutes more.
2/ Post this initial buy-in, leave the investor with something of high recall value so you have a higher chance of a post-event follow-up discussion.
A. The 30-sec pitch
For the first 30-sec pitch, I recommend having 3 parts to it:
[The Grandmother’s Explanation]
followed by…
[Social Proof of Team]
followed by…
[Proof of Business]
a) The Grandmother’s Explanation means explaining what your startup does in the way you would explain it to your grandmother. Yes, most investors aren’t domain experts in your field. They are likely investing across sectors, and aren’t living and breathing your specific area/ problem statement. Assume they are as ignorant about your business as your grandmother.
I am literally shocked by how most founders can’t explain their startup in simple tech-layman’s terms. Barring a few, true deep tech startups coming out of research labs and universities, most enterprise software, SaaS, and consumer Internet startups should be able to explain their business in simple words. This is the bare minimum signal of clarity in thinking.
TLDR: if an investor isn’t able to understand what you do in the first 5-7 seconds, there is no way in hell that investor is going to lean in. Even if the person might appear to be listening, in reality, they are actually zoned out/ looking through you.
b) Social Proof of Team means talking about your credentials in a straight-up manner, without beating around the bush. These could be:
Education-related – undergrad and grad schools, unique course work etc.;
Work-related – past employers, roles, needle-moving projects, accelerators like YC or Techstars etc.; and
Execution-related – products shipped, content created, social following, word-of-mouth etc.
Especially for founders in the US-India corridor – we are taught to be overly humble and in most social situations, we tend to talk down our achievements. Unfortunately, you are faced with intense competition in the Bay Area from talent coming in from all over the world. You have no choice but to talk about things that make you stand out from the crowd.
c) Proof of Business means talking about the business progress of your startup in tangible terms. Things like user base, retention, engagement, number of customers, revenue, customer acquisition etc.
It’s important to remember that while providing Proof of Business, both “absolute numbers” and “growth rates” are important. So, frame statements like “we have $Xk ARR, growing y% m-o-m” or “We have Xk users, growing y% week-on-week purely by organic word-of-mouth. People are also now starting to pay”.
Most startups attending these events don’t have enough Proof of Business yet.
For the ones who do, make sure you talk about it as traction trumps everything, and especially at the seed stage, any traction will help you stand out.
For startups who don’t have much Proof of Business, you can still talk about proxies of business progress like the velocity of shipping new features, people on the waitlist, early design partners, and how they are deeply engaging with your product etc.
PS: An important recommendation for the 30-sec pitch format:
If you have compelling traction, pitch [Proof of Business] first and then [Social Proof of Team].
If you are very early and don’t have compelling traction, pitch [Social Proof of Team] first and then [Proof of Business].
The idea is simple – always lead with your strongest suit.
B. The post 30-sec-pitch part
Ok, so you delivered an amazing 30-sec pitch to Investor A. The person is now leaning in and wants to have a longer conversation for the next 5-7 mins.
In this part of the convo, your main job as a founder is to leave a high-recall impression on the investor. The person meets tens of new founders every week. Your job is to ensure that post this interaction, you go into the deal flow software for the VC firm at the minimum, and ideally, the person remembers you for some standout qualities and/or stories.
This is the “art” part of having a good conversation. There are no specific rules for how you build camaraderie and leave an impression. Everyone has their own style, and everything from body language and listening skills to storytelling and tonality has a role to play.
While I can’t offer you any specific hacks for this, here are some things I have seen work well in my experience:
a) Tell an interesting story – people don’t remember facts, but they remember stories. Instead of bombarding an investor with jargon, business numbers and technical info while having a cocktail, focus on telling an interesting story. Could be about your childhood, maybe something from your past life, or even something quirky that has happened while building the startup.
The biggest risk you have in a cold-pitch situation is to make it boring for the other person. A good story is something that brings a smile and/ or a questioning look on someone’s face. Basically, it interests them.
b) Bond on commonalities – the classic sales technique of finding commonalities to break ice always works. Humans are wired to want to belong to certain identities – A New Yorker, A Delhi-wala, A Knicks fan, a worshipper of Sachin Tendulkar, a backpacker, a wine connoisseur, a Japanese food lover etc. The moment they meet someone who belongs to the same identity, there is an instant connection that gets established, which is the first step towards building trust.
As you are chatting with the investor at a mixer, try and probe for some commonalities (where they grew up, went to school, worked, where they are living now). It will give the conversation much more substance and make it enjoyable for both sides.
c) Be genuinely curious…and listen – in my previous posts ‘Curiosity As A Networking Cheat Code‘ and ‘Networking at Events for Introverts‘, I have talked about the power of being genuinely interested in other people. It usually manifests in you asking good questions and listening more than talking.
As much as you are trying to ‘pitch’ in the conversation, don’t make it a one-way street. After the 30-sec pitch, focus on consciously giving talk-time to the investor by asking questions that spark an interesting discussion vs a founder-to-investor monologue.
C. Closing thoughts
I was feeling so frustrated listening to some awesome founders give such broken and unengaging 30-second pitches at recent events that I had to write this post.
Essentially, all the above inputs are based on 2 core ideas:
#1 Put your strongest foot forward as quickly as possible, and in a coherent structure.
#2 Make the conversation interesting. Tell stories. No one likes to be bored.
Happy pitching!
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In any growing and economically vibrant location with strong future prospects, real estate always seems very expensive and almost out of reach in the present moment.
However, in hindsight after multiple decades, the same asset looks dirt cheap.
Anyone living in a major and growing economic hub would have felt the pain while looking at home prices. From SF and Singapore to NY and Gurgaon, home prices always feel “out of reach” in the moment.
However, now that I have enough grey hair from living through multiple economic cycles, hearing stories from many generations in my family, and also personally going through various real estate deals both as a buyer and seller across the Bay Area and India, I have noticed an interesting paradox:
In any growing and economically vibrant location with strong future prospects, real estate always seems very expensive and almost out of reach in the present moment. However, in hindsight after multiple decades (or sometimes even as short as a decade in alpha zones like the Bay Area), the same asset looks dirt cheap.
Story #1 – the grandparents
As an example, in the 70s, my maternal grandfather built a house in Lucknow, the capital city of the Indian state of Uttar Pradesh. As a kid, I remember hearing stories from him and my grandmother about how they had to struggle to put together money each month to pay the contractors. They ultimately built an amazing house over many years of scraping and saving. Cut to today, 50 years later, the location of the house has become very central and extraordinarily scarce, organically driven by the growth of the Indian economy. Needless to say, its price today has exponentially appreciated.
Story #2 – the auction
Second story – while growing up, we stayed in a rented house in South Delhi for a few years. For those who don’t know, South Delhi is now one of the premium parts of the Indian capital, but when we used to live there, it was just about at the tipping point with the first-generation multiplexes and the first-ever McDonald’s store coming in.
Our landlord was a very senior army officer and a really nice gentleman. As a teenager, I remember him telling stories of how he bought the house. While he was away fighting on the border, his wife saw the ad for plots of land being auctioned in the area. Even though in his own words “this was the time when there was nothing here and one could even see foxes in the neighborhood”, these plots of land were still out of reach for a working-class army officer.
Gathering courage, his wife borrowed money from her parents to make the downpayment and ultimately, got allotted a plot of land in the auction. Through monthly savings, they ultimately built this house. Cut to today, this house is now in one of the most prime locations of the capital of the soon-to-be world’s 3rd largest economy. You don’t even want to know the current price of the asset. In hindsight, the prices in the original land auction look like a steal.
Story #3 – the parents
A similar story from my family. My parents purchased a home in the mid-90s again in South Delhi and much before its tipping point. As a kid, I remember how big of a stretch that was for the family back in the day. My parents borrowed from every source of capital – my dad’s employer, his old business associates, my dad’s brother, my mom’s sister. Servicing this debt required major financial discipline on a monthly basis, needing hard choices that both I and my sister remember to this day.
Cut to today, the location of that house has become super-premium, and again, those prices that stretched our family thin back in the day, now look like a steal.
Story #4 – the Bay Area
Over the last decade, I have observed similar trends in SF/ Bay Area at large, albeit on a significantly compressed timeline (heck, we are talking about Silicon Valley here where everything happens exponentially faster and rises exponentially higher):
Then, downtown SF ended at the Giants stadium. Once you crossed the creek, you felt unsafe. Now, that same area begins with Mission Bay (home to the Warriors and UCSF), moves on to Dogpatch (home to YC), and beyond.
Then, Potrero Hill was just starting to get premium, and Bernal Heights had those old SF single-family homes with weird layouts and stairwells. Now, Potrero Hill is beyond premium, and Bernal is now what Potrero was back then.
Then, we used to make fun of one of our colleagues who bought in San Ramon in 2013 (who lives in that jungle anyway?) and made the commute to Mountain View every day. Now, San Ramon is one of the most premium Bay Area locations, especially post the development of Bishop Ranch and City Center Mall.
Illiquidity is key to long-term compounding
As I reflect on these stories and experiences, I kind of see why people call the illiquidity of real estate a feature, not a bug (Btw, I say the same thing about venture capital as an asset class).
Of course, this doesn’t mean real estate is a free lunch. I know a few Indian diaspora tech folks who took a bet on Oakland back in 204/15, buying homes there driven by news at the time that the likes of Amazon and Uber would be moving there. Unfortunately, Oakland has become an even bigger sh*tshow since then. Governance and security have massively deteriorated, none of the tech giants have moved there, and major sports teams like the Warriors and the Raiders have ended up moving their home bases out of Oakland.
Buy great assets – pay more, if one has to, for quality.
Invest assuming we will own the assets forever – even though we may not. Eg. Brookfield has owned marquee buildings in Manhattan for 20+ years.
Go against the trend and buy value, especially in times of distress.
Finance prudently, as surviving downturns is paramount.
Acquire when capital is scarce (in other words, when interest rates are high like in 2023-24), as it is the best indicator of the right time.
Never become too positive, or too negative.
If this is too much, I have a TLDR for you:
Outsized long-term compounding in real estate seems to happen in locations that are positively aligned for future economic growth over decades. If you can buy at the bottom of the cycle/ in times of distress, even better!
I don’t know if this post is helpful for you. It’s definitely a departure from my usual topics of startups and venture capital. Still, I felt like penning this down, more to document these stories and aggregate my observations around them. Hopefully, you found it interesting!
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Do the competitive advantages that Indian startups have enjoyed in software products, services & SaaS, also extend to robotics and other areas of deeptech? I believe they do.
Over the last few months, I have seen a few high-quality, India-based startups going after the industrial robotics segment. Essentially, delivering robotics hardware and/or software solutions for automation on the manufacturing shop floor.
There seems to be an interesting “why now?” for this segment. Manufacturing globally is seeing a shortage of blue-collar workers with specialized skill sets. With the rise of freelance service models like becoming an Uber/ Ola driver or a Doordash/ Swiggy delivery person, fewer people in both developed and developing countries are inclined to put in the hard yards and learn vocational skills like welding.
So clearly, industrial automation will see massive tailwinds in the next decade. Robotics, in particular, seems to be well-poised to see enormous adoption in this movement. It’s massively benefiting from AI-driven chips & compute investments that the cloud infra players and hyperscalers are doing. This is leading to more computing power being packed in smaller form factors. Vinod Khosla recently said (clip below) – “Robotics will soon have its ChatGPT moment. Robots aren’t programmed anymore, they are learning systems”.
The Western industrial robotics opportunity definitely looks interesting for India-based startups. We have already seen the likes of GreyOrange successfully scaling in the US warehousing robotics market, while Cynlr is targeting the higher-end, more sophisticated robotics segment.
Regular readers of An Operator’s Blog would already be familiar with my obsession with “right-to-win” and having clear differentiation. I have been brainstorming with Indian robotics founders regarding what’s a sustainable right to win for India-based, offshore startups in the global industrial robotics opportunity. Here are some admittedly disconnected ramblings, trying to also draw comparisons with the earlier software services➡products➡SaaS eras:
1/ Availability of Talent
The IT services players of the 90s (Infosys, TCS, Wipro) literally created mini software universities on their office campuses, tapped into a large pool of young Indian engineering graduates, and trained them to become software developers. The later software products and eventually SaaS waves benefited from this ready base of talent created by the services companies.
Is there a similar dynamic at play right now in the hardware engineering talent pool (disciplines like Electrical, Electronics, Mechanical, Industrial, Aerospace)? Am not so sure.
Over the last 10 years, a majority of Indian engineering graduates have, irrespective of their passions or innate strengths, trained themselves to become software developers. The engineering university apparatus too, has molded itself in this direction – software engineering intakes are exponentially larger than electrical, electronics, or mechanical branches.
However, one positive signal of hardware talent is how ISRO, India’s government-owned space agency, has been able to assemble large engineering teams that have gone on to build world-class space tech products on the global stage.
As I see it, there are three potential feeders of latent talent in manufacturing and hardware engineering for Indian startups:
Govt-owned research universities and labs (IISc, IIT Chennai, IIT Bombay, TIFR, etc.).
Public sector companies in areas like defense (DRDO), energy (BHEL, NTPC, etc.), oil & gas (IOCL, GAIL, etc.), railways, fertilizers, and chemicals.
Domestic manufacturers & OEMs in areas like automotive, chemicals, textiles, pharma, and agriculture.
Therefore, given just the sheer numbers of young Indians graduating in non-software verticals from various engineering schools as well as then getting trained by the above feeders, the supply of hardware skill sets might still be enough to serve the needs of industrial robotics startups.
There will surely be gaps in the quality and job readiness of this talent. However, Indian entrepreneurs are known to be ingenious. Similar to the IT services companies of the yesteryears, we might see these new-age hardware startups create their own training programs for electrical, electronics, and hardware-centric software skills.
So overall, I feel reasonably good about the availability of talent to build industrial robotics companies out of India. However, it might still need some push and creative thinking from the ‘hardware Murthy’s and Premji’s’ of the current generation.
2/ Cost of Talent
One of the core pillars of Indian IT services was cost arbitrage. In fact, even the later software products and SaaS companies have continued to benefit from it, reflecting in them pricing their products very aggressively compared to Western competitors and winning deals in the price-sensitive segment (Zoho and Wingify have done this really well over the last 2 decades).
Does this cost-arbitrage advantage apply to hardware engineering startups too? In general, the broad-based cost of talent in India has steadily gone up over the last decade. In fact, on the pure manufacturing front, India is no longer a cheap place to produce stuff and one of the reasons why the likes of Bangladesh have taken share away from India in areas like textiles.
Same with software, where high-quality developer talent in Bangalore is now only marginally cheaper than in the West (adjusted for quality, output, and time zone challenges, it might even be more expensive in certain cases). This is one of the reasons why Eastern Europe and LatAm have taken a share away from India in IT offshoring too.
Using these trends as signals, it’s reasonable to assume that though Indian skilled hardware talent will definitely be cheaper than say the US, the magnitude of arbitrage is not as large as say, in the 90s and early 2000s, especially when adjusted for quality and output.
There is also a large domestic base of SMB-type OEMs and manufacturers that can provide relatively low-cost early iteration and prototyping capabilities. As the Indian govt. increasingly focuses on developing domestic manufacturing and indigenization of technology, this base will only grow from here.
So overall, I feel reasonably good about Indian startups being able to build industrial robotics products at a relatively lower cost compared to Western counterparts. However, the key will be matching the quality and performance of comparable global products while keeping the operating costs (and therefore, pricing) lower than these competitors.
3/ Superior Customer Support
One of the key reasons Indian software companies are able to compete and win against global competitors is the ability to provide superior customer support while maintaining cheaper price points.
Leveraging large workforces at lower costs, the likes of Freshworks have been able to provide white-glove service to even mid-market customers in the US. Combine this with the willingness to do services and provide customization wrappers on top of their products, and you have a unique offering that Western enterprise customers just love.
I have observed that providing superior customer service & support has now become an integral part of the Indian founders’ DNA and has been socialized a lot within the startup ecosystem playbook by communities like SaaSBoomi.
Therefore, I believe this customer support DNA can translate into a right-to-win for Indian startups even on the robotics and hardware side, where most global customers tend to be large and often legacy manufacturing companies that really value this ability to provide a white-glove service as well as offer customization for their needs.
4/ Differentiated IP
Now this is where the situation gets a little tricky. Traditionally, Indian higher education hasn’t focused on fostering original research and development at scale. That’s one of the reasons why even on the software/ SaaS side, most Indian startup success stories have tended to be “fast-followers”, creating a product with feature parity against US competitors and then beating them via aggressive pricing and superior customer service.
Barring a few islands of excellence like IISc, IIT Chennai (Research Park), and IIT Bombay (SINE), students largely aren’t taught to think originally during high school and university.
However, there is a nuance here. At the risk of generalizing, it’s fair to say that while local Indian talent at large may not be the best original thinkers & IP creators, they are definitely strong engineers, operators, and executors.
Combine this with the raw entrepreneurial hustle that the Indian way of life teaches you, as well as first-principles business acumen that’s part of the grassroots culture in states like Gujarat, Rajasthan, Punjab, and Tamil Nadu, and you potentially get a unique combination of engineering and GTM skills amongst many Indian founders. I call this the “engineering dhandho” persona [inspired by the successful public markets investor Mohnish Pabrai; “dhandho” is the Gujarati word for “business”].
Now, not every robotics & hardware problem statement requires IP creation. Many of them can be solved by engineering creativity and ingenuity, or what I call ‘contextual-innovation’. This approach can actually be used to create new categories in relatively untapped verticals like agriculture, automotive, space, defense, energy, B2B commerce, and even consumer hardware.
We already have a few scaled examples of this contextual-innovation from India-based startups:
GreyOrange has become an emerging category leader in the US warehousing robotics market, crossing $100Mn in ARR.
ideaForge manufactures drones for defense, and went from idea to IPO over the last 8 years. While China-based DJI corners ~70% of the global drone manufacturing market, ideaForge has still been able to create & dominate the indigenous defense drones category in India.
Agnikul (launch vehicles), Pixxel (constellation of satellites), and Skyroot (launch vehicles) are making giant strides in the commercialization of space from India.
Ather (electric scooters) and Ultraviolette (electric superbikes) are capitalizing on the global movement towards EVs and rapidly emerging as the Hero’s and Bajaj’s of today’s India.
Atomberg (ceiling fans), boAt (headphones), and Ultrahuman (fitness trackers) have created differentiated consumer hardware with specific features for their respective personas, and have also built successful brands despite the presence of large incumbents.
A few contextual-innovation greenshoots from my own portfolio:
Flytbase has created the global autonomous drone software category, unlocking massive enterprise use cases. HQ in Pune, 100% of its revenue is global.
Playto has developed a proprietary robotics kit that supports 1,000+ builds for kids from Grades 2-8 to learn STEM skills. HQ in Bangalore, a majority of revenue is global.
Sharang Shakti is building an anti-drone & airborne threat mitigation system for defense. It will start with India but I expect countries in the Global South to be major markets in the future.
Astrophel is building ground-up sub-orbital launch capabilities for commercial space payloads. Astrophel and other Indian space tech companies will cater to global customers, especially those from price-sensitive developing countries.
Yulu has designed and built EV micro 2-wheelers from the ground up, keeping specific Indian urban micro-mobility needs and contexts such as hyperlocal deliveries, logistics, and last-mile daily passenger commuting.
Looking at all these examples, I believe Indian startups can utilize the contextual-innovation approach to make a dent in several industrial robotics use cases, especially those that require smartly stitching together hardware + software solutions for legacy enterprise customers.
Closing thoughts…
Summarizing my observations across 4 key axes – Availability of Talent, Cost of Talent, Superior Customer Support, and Differentiated IP, I feel reasonably good about the right-to-win of Indian startups in industrial robotics, and would even extend this conviction to many other verticals of hardware.
I believe many of the advantages that Indian startups have enjoyed in the previous IT services, software products, and SaaS waves, will also extend in some shape or form to the oncoming global deeptech and hardware wave.
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Given the exponential rate of change in AI, Dev Tools appear to have the most risk of use case durability, compared to Infra and Applications.
Earlier this week, I attended the AGI Builders Meetup in San Francisco. The event had 5 product demos ranging from new AI features by Twilio to a YC S24 startup building an AI agent to handle calls on behalf of users.
These demo-based events are always helpful for me to track the latest in AI. Also ended up chatting with a few engineers and founders attending the event, to get their thoughts on what they are seeing in their respective domains within AI.
One key insight I got from this event was that LLMs aren’t the real future of AI. No one really knows what’s going on inside them. They hallucinate much more than desirable (especially for accuracy-driven enterprise use cases). They are prone to prompt injection hacking.
In fact, the presenting Twilio PM said that “working with LLMs is like getting toddlers to do something”. They don’t take instructions promptly. You don’t know what’s going on inside their heads. You have to proactively ensure their safety as they are doing a task. I found this framing really interesting.
Building on this further, I have an updated (but still working) POV on the 3 buckets of AI – infra, applications, and dev tools.
#1 Infra
Irrespective of where AI goes, hardware infra like GPUs will always be needed. Hence, it makes sense that Nvidia is doing so much capex.
Also, Big Tech software infra players (Microsoft, Google, Meta, AWS, etc.), as well as AI-native hyperscalers (OpenAI, Anthropic, etc.), will continue playing a key role in defining where AI goes from here.
Sadly, as a micro VC, I can’t play much in this bucket (except personally investing in the public markets).
#2 Applications
Application layer founders that are starting up today are leveraging the capabilities of AI from Day 0 to solve customer problems. Their core focus still remains commercial-first – using the best-available software capabilities to solve customer problems, rather than getting overly enamored by the research aspects of AI and where it’s headed.
In a sense, these startups are centered on customer problems, not AI per se. Wherever AI ends up going, LLMs and beyond, these founders will leverage whatever capabilities they can get their hands on, and modify their architectures accordingly.
As an investor, the key is to back founders who are starting up now with an AI-first mindset and therefore, are fresh enough and agile enough to keep evolving their software as the underlying AI capabilities evolve.
Therefore, it’s reasonable to expect that these AI-native application startups should be fairly resilient to changes in the overall AI landscape. Hence, I feel reasonably comfortable in backing them (eg. portcos like Confido Health, Loop, and Soulside).
#3 Dev tools
This is the bucket I am most confused (and concerned) about. From seeing these demos, it seems like dev tools startups are essentially using the mental models of the previous cloud & mobile waves to make assumptions on use cases.
Further, I have observed that many of them are solving short-term, immediate pain points that could easily become irrelevant due to where AI goes from here, and/ or from the competition (eg. open source alternatives, AWS quickly launching it as a feature, etc.).
As I was seeing these demos, I looked up how much capital some of these companies had raised. Many of them have raised anywhere from $10-35Mn. The capitalization of these companies seems out of sync with the durability of their underlying use cases and revenue.
Essentially, what all this means is that I have a macro “Why Now?” question around the AI dev tools bucket. A top Bay Area engineer who recently left a cushy Big Tech job to start up was recently saying – “Given how things are changing every month, I am really not sure what to build right now”. I feel this is an intellectually honest view, rather than a FOMO-based approach that many VCs are taking.
Based on what AI practitioners like this person are saying about the exponential rate of change in AI, I fear that a majority of these dev tool use cases won’t endure.
Again, this is just my working POV. Would love to hear your views on what you are seeing.
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In the words of the great Fred Wilson – “If you can’t figure out why you like an investment and why it will be successful, don’t make it”.
Recently, I came across this awesome (like always!) 2012 post from Fred Wilson (Union Square Ventures) – Social Proof Is Dangerous. Quoting a line that captures its essence:
If you can’t figure out why you like an investment and why it will be successful, don’t make it.
One of the big investing ideas I have distilled from studying the best investors across asset classes – from Charlie Munger and Howard Marks to Bruce Flatt and Vinod Khosla, is that to outperform the average (the index), one has to be “non-consensus-and-right”.
In fact, in my July’23 post ‘An Investing Framework To Find Startup Diamonds‘, I outlined a Consensus vs Signal 2×2 and argued that the outlier venture returns opportunities tend to be found in the High-Signal-Non-Consensus quadrant.
It was only in 2022, almost a decade after I first started my career as an institutional VC, that I truly embarked on this journey of trying to become a non-consensus-and-right venture investor. As I have outlined in the above 2×2, molding my mindset toward this approach has required consciously working on the following 2 elements:
1/ Having a unique world-view and trusting my instincts to be able to spot ‘Signal’.
2/ Totally ignoring any social proof noise while doing this.
I have observed that while it was really hard to ignore social proof early on in my career (eg. which VC is leading the deal), having seen so many Tier 1 VCs across geos do such foolish things over a decade, I must say with much humility that as of today, I find it much easier to ignore their POVs on something.
A few months back, I also came across comprehensive LP data that validates this organic learning. David Clark of VenCap shared with Jason Calacanis how loss ratios are surprisingly similar across various percentiles of funds, and even the best strike out a lot.
That’s why in my view, it’s foolish to do one-off deals purely on the basis of the social proofing of a lead investor in that deal unless one is actually replicating their entire portfolio construction (which is a benefit only LPs in their Funds get).
This idea also explains why I remain skeptical of loose angel networks, angel communities, and syndicates that really don’t have a unique, grounds-up world-view and right-to-win, and therefore in most cases, do spray-and-pray on allocations in deals being done by VCs.
These deals often suffer from major adverse selection (“if the founder/ startup is so good, why are they raising from you?” OR “what specific value are you bringing to the table because of which a star founder is giving you allocation?”) and are therefore, likely to be on the wrong side of the loss ratios of major funds.
Coming back to the point of social proof, let me neatly summarize my POV on it:
If the best Tier 1 VCs are striking out as much as an average Joe VC, there is no value in blindly following them.
There could be value in investing in the “best” companies of a Tier 1 VC portfolio but especially at the seed stage, it’s impossible to know beforehand which company will turn out to be this “best” company. Also, companies keep going in and out (and back in) of this “best” bucket multiple times anyway during a Fund’s 10-year lifecycle.
Even if there was a way to know which company isindeed the best company in a Tier 1 VC portfolio, why would they give me allocation in it? The best VCs want to keep every bps of ownership in their best companies only for themselves.
Hence, what’s the point of doing a deal purely because of social proof? I would rather spend that effort looking for the best contrarian deals in places where no one is looking, doing the work (and trusting my instincts) to spot Signal in them, and investing in them as early as possible, driven only by my strongest conviction and nothing else.
This approach is already starting to reflect in the early Operators Studio Fund 1 portfolio. In a majority of recent investments (eg. Soulside, Confido, Loop, Astrophel Aerospace, and a recent one in Stealth), I was literally the first investor to build conviction and say “yes” even before the round started coming together with other VCs. In several of these deals, I ended up catalyzing the round itself, making intros to eventual lead investors and even sharing my customer diligence notes with VCs evaluating the company.
In a way, this approach is Anti-Social proof and Pro-Signal. What is Signal, you ask? It can be of two types, as I explained in my July’23 Investing Framework post (quoting from it here):
Internal – extraordinary founder-market fit eg. the founder has spent a decade just going deep in the field. Or a backstory that provides an authentic “why” behind pursuing this idea. Or an execution track record in the startup’s arc that is outstanding on important elements like capital efficiency, iteration velocity, or organic customer acquisition.
External – eg. a visionary customer is taking a bet, partnering with them in building the early product. Or a domain expert, skilled operator, perhaps even a specific GP in a venture firm, has taken the time to evaluate & build high conviction in the company.
As you can see, there is a little bit of social proofing baked into evaluating Signal too, but it’s much more oriented around operating and execution-oriented conviction vs deal FOMO and an investing herd mindset.
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Notes from my Q2’24 India trip – everything from post-election vibes to public markets and the state of the venture ecosystem.
Just returned from my quarterly trip to India. With the recently held elections (and a bit of a surprising result there!), it was interesting to get a pulse of what’s happening on the ground. Here are some key takeaways from my meetings with founders, investors, and operators in the ecosystem:
1/ Real reasons behind BJP’s weakened mandate
While Modi created a massive pre-election narrative of the BJP coming back to power with an even bigger majority than in 2019, it ended up losing ground in the actual results tally.
Honestly, I didn’t see it coming but talking with people on the ground, one of the major reasons behind the weakening of BJP seems to be shrinking employment opportunities, especially for young graduates. While the India macro story stays strong, there are pockets of economic distress in the poor states and amongst the lesser-skilled parts of the population/ those graduating from non-top-tier universities.
It’s clear that while Modi focused a lot on infra buildout over the last 2 terms, one of his core focus areas in the 3rd term needs to be continuous job creation for all sections of society. This might require some bold reforms.
2/ Investors are largely unperturbed by the election results
Given that the BJP-led NDA coalition still has a comfortable majority in the Parliament, and Modi continues to be the PM with a largely unchanged cabinet, Investors are expecting political and economic continuity in this 3rd term of the govt.
So, expect continued momentum on key execution tracks from the last 2 terms, including physical infrastructure buildout, expansion of digital public goods, and focus on technology startups.
3/ General Catalyst acquiring Venture Highway
The news of GC acquiring VH broke out while I was in Bangalore. While it could be a one-off development, it’s still a positive greenshoot that a large Silicon Valley-based, premier capital pool is allocating to the India venture market.
Personally, I also believe it’s a smart move from Hemant Taneja to acquire a high-performing team that is local and has developed on-ground expertise, versus parachuting people in from the Bay Area or doing the fly-in-and-out model.
Not recruiting and empowering a high-quality local leadership team is a classic India entry mistake that both Y Combinator and AngelList did, which is why they have struggled to crack the market.
4/ All VCs talking deeptech now
Similar to the Valley, deeptech has now clearly become the flavor of the season. Even a few years back, major Indian VCs spending time at the IIT incubators or looking at sectors like manufacturing and semiconductors was unheard of. This time, I heard multiple instances of VCs writing large seed checks into deeptech companies.
My only fear is that based on past history, the Indian VC ecosystem tends to behave in steep emotional cycles, flooding hot sectors with capital in tandem like a herd (eCommerce 15 years back, fintech 10 years back, lending and SaaS 5-7 years back), and then abandoning verticals also in tandem like a herd (eg. no one is touching edtech now).
These emotional cycles are incredibly counter-productive for long-term company building, and also tend to be incredibly disturbing, especially for younger, 1st-time founders. As the deeptech wave begins, I hope some lessons are learned and implemented from previous cycles.
5/ Angels suffering from 2021 vintage markdowns and illiquidity
One of my observations on the Indian venture ecosystem has been many new-gen angels tapping out in 2023/24. While some of the marquee spray-and-pray ones, as well as the conventional IAN/Mumbai Angels persona, continue to be active, many high-quality operator angels seem to have bowed out of the game.
On bringing this point up with folks, they confirmed that the portfolios of many new angels who started deploying in 2020 and 2021 are suffering from either major markdowns and writeoffs or prolonged illiquidity of marked-up positions. I would also add layoffs, salary rationalizations, and a lack of broader ESOP liquidity (barring a few cases here and there) to the list of reasons behind many angels bowing out of the game.
6/ Seed capital is plenty but wants more traction. Series As continue to be hard.
Similar to the Bay Area, I heard that while there is plenty of pre-seed/seed capital available in the ecosystem right now, the bar for raising Series A has significantly gone up. As a result, companies are seeing both larger seed rounds as well as extension/ top-up rounds happening as we speak.
Several seed investors shared with me that one of their learnings from doing many idea-stage deals in 2021 is how companies are taking so much longer than they initially estimated to go from zero to even $100k ARR. This is adversely impacting the IRRs of seed portfolios. Also, given valuations have now massively corrected, the next round markup isn’t in line with the time it’s taking to get to early traction.
Given this dynamic, many seed investors are now tracking companies and waiting to see more traction before pulling the trigger on idea-stage companies. Btw, am seeing similar behavior even in the late seed/ pre-Series A/ Series A spectrum too, where VCs are waiting to see a long enough timeline of revenue growth, retention, and other metrics before engaging seriously.
Another related observation – growth capital for tech companies is a major gap in the India venture ecosystem right now. Many strategics and hedge funds that were writing large checks post-pandemic have either completely exited the market (eg. Softbank, Alibaba, and Tencent) or are triaging their current portfolios. Recent cases like Prosus writing off its entire ~$500Mn investment in Byju’s isn’t helping to build confidence either.
7/ Public markets continue to rip, lots of FO appetite for pre-IPO rounds
It is noticeable to see frequent ads on Indian TV channels encouraging everyone to invest in mutual funds. An uncle who recently visited us in SF was bragging over chai about how he “made 55 lakhs in the market already this year”.
During this India trip, I saw my father casually opening and checking his brokerage app dashboard multiple times daily. I also noticed that a majority of YouTube consumption by this age group is financial influencer and stock tips content.
On a similar note, a few institutional investors shared how domestic Family Offices are showing an increased appetite for pre-IPO investments in companies like Lenskart, FirstCry, and Oyo. In fact, with the domestic index doing really well, FOs are more skeptical of taking LP positions in venture funds right now, preferring to either stay liquid in public markets or take relatively de-risked, later-stage positions in pre-IPO private companies.
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Recapping “tennis lessons” from Roger Federer’s 2024 commencement address at Dartmouth.
I am a big believer in collecting mental models from various disciplines in life, and as part of that, I look to learn from top sportspersons. So I naturally found the “tennis lessons” Roger Federer shared in his recent commencement address at Dartmouth to be particularly insightful.
In addition to capturing some defining experiences and approaches from Roger’s life, the speech was also filled with some amazing one-liners. I always find these to be really useful as they help us cognitively index, remember, and recall powerful concepts that others have experientially learned.
Here are some powerful ideas from the speech that stayed with me:
1/ “Effortless is a myth”
The truth is, Roger had to work really hard to make it look easy. A lesson for all of us chasing excellence in our respective fields – you have to embrace hard work before you become an expert.
2/ “Everybody can play well for the first 2 hours”
The real game starts after that, when the body is tired, the mind wobbly and the discipline fading. This highlights the importance of stamina, and of grit, in life.
3/ “My warm-ups at the tournament were so casual, people didn’t think I was training hard. But I had been working hard…before the tournament…when nobody was watching”
The importance of prep, putting in the reps, breaking a sweat. Excellence during the most crucial moments in life is a result of all the work put in during the years prior.
Reminds me of the famous General Patton quote – “He who sweats more in training bleeds less in battle”.
4/ “Belief in yourself has to be earned”
Roger didn’t explain this thought much, but my interpretation is this – self-belief is a by-product of the work you put in to go deep into a skill, and of the chances & risks that you take to make yourself better.
5/ “I beat some of the top players I really admired by aiming right at their strengths”
Roger tried to beat the baseliners from the baseline, beat the attackers by attacking, and beat the net-rushers from the net. He did this to amplify his game and expand his options, preparing for scenarios where one strength breaking down could be replaced by another one.
6/ “In tennis, there can be many types of talent”
Roger cites some of them – grit, discipline, patience, trusting yourself, embracing the process, managing your life, managing yourself. Also mentioning that everyone has to work on these things.
I would add that these are talents not just for tennis, but also for life.
7/ “You can work harder than you thought and still lose”
Tennis is a brutal game where at the end of a tournament, only one player gets a trophy while everyone else gets on a plane, thinking “how the hell did I miss that shot?”.
Life is going to be a roller-coaster for all of us. It’s how you manage and behave after losing a game, is that defines how big you will eventually win.
8/ “In tennis, perfection is impossible”
Roger shares some really interesting stats from his career – in the 1,526 singles matches he played, he won almost 80% of these matches. BUT he only won 54% of points across these matches. So, even the greatest of all time tennis players barely win half of all the points they play!
Why is this important? Roger says it teaches players to not dwell on previous points and to play each point on its merit. In other words, stay in the moment and play each point as if it’s the most important point in the world.
PS: like me, if you love learning from sportspersons, then I highly recommend Open – Andre Agassi’s autobiography. It helped me through some of the lowest points in my life.
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Following up with a Part 2 of my last year’s post on ‘The Success Flywheel’ – how the journey of the Founder of Superhuman, as well as Perplexity’s cap table, shows that winners keep winning.
While attending the recent Camp Hustle’24 (which btw, was an awesome LP-GP event; my notes from the event here), I got the opportunity to witness a candid fireside chat with Rahul Vohra, Founder of Superhuman.
I have followed Rahul’s journey for a while now. My last startup Workomo was tackling a similar problem statement to Rahul’s previous startup Rapportive. Also, his article ‘How Superhuman Built an Engine to Find Product Market Fit‘ from First Round Review really helped me as a 0-to-1 founder. In fact, I wrote a post on it myself in 2019 deconstructing Superhuman’s PMF playbook.
Listening to Rahul talk through his life journey in detail during Camp Hustle reminded me of one of my core mental models – the ‘Success Flywheel‘. I first wrote about it in a May’23 post. It essentially means that our world is wired in a way that winners keep winning.
Unpacking this a bit more, I have seen that in every case, the “winner” had a clear first event of success that then kickstarted the Success Flywheel in their lives.
Let’s look at Rahul Vohra’s life journey as an example (from what he shared during Camp Hustle):
Started coding at the age of 8.
Completed undergrad in Computer Science from the University of Cambridge. Went on to enroll in the PhD program but dropped out.
Reid Hoffman (Founder of LinkedIn) was speaking at an event in Cambridge. Rahul met him and asked for one piece of advice, to which Reid responded – “move to the Bay Area”.
Rahul followed this advice, moved to SF, started Rapportive, and as luck would have it, got acquired by LinkedIn!
As Rahul was thinking through his next startup idea after spending a couple of years at LinkedIn, he got this golden advice from a mentor:
If you are a first-time founder, start by going after a niche market with little competition, even if it’s small in the beginning, so you can differentiate more easily.
If you are a second-time founder, go after a very large and crowded market from the very beginning, because you are likely to out-execute and out-raise competition in the space.
Rahul followed this advice and started building Superhuman to go after the behemoth Gmail (which…is free!). On the back of his previous success with Rapportive, Superhuman was able to get backed by top-tier VCs like a16z, First Round, and IVP, in addition to celebrity angels like Ashton Kutcher, Will Smith, and the Chainsmokers.
Through Rapportive and now Superhuman, Rahul obviously became deeply entrenched in the Valley venture ecosystem, building relationships with some of the best founders, angels, and VCs out there. Given this access, he started angel investing on the side and soon started running into Todd Goldberg (Founded Eventjoy; Acquired by Ticketmaster) on several deals.
They both connected, decided to join hands, and started Todd and Rahul’s Angel Fund. If the founding and operating success weren’t enough, check out some of the portfolio companies of this Fund – Mercury, Circle, Descript, Clearbit, and AngelList.
Rahul’s journey is another great example of the Success Flywheel in action – how he was able to keep parlaying his initial success into more success, and it continues.
Even before his move to Silicon Valley and starting up, the initial success event that seems to have catalyzed the Flywheel in Rahul’s journey was his getting into the ultra-competitive CS undergrad course at a top-tier uni like Cambridge. Few get the opportunity to meet OGs like Reid Hoffman in person, let alone get his advice. Being part of a highly selective cohort of young students positioned him to get this sort of early exposure.
As I was ruminating on these learnings from Camp Hustle, I saw this LinkedIn post from Aravind Srinivas, Co-founder and CEO of Perplexity:
Based on current vibes, Perplexity seems to have the best odds among the new generation of AI-native companies to be the “Google-killer”. If this is true, then check out who has access to the next (potential) Google – Jeff Bezos, the entire YC gang, Naval Ravikant, Elad Gil, Balaji Srinivasan, Tobi of Shopify, and other successful repeat founders, operators & prominent investors. It’s the same set of folks who succeeded first in Web 1.0 and Web 2.0, many of whom then also benefited in Web 3.0/ Crypto.
These individuals are already at the top of the pyramid, operate at the tip of the spear of capitalism, and keep parlaying their success from one economic cycle to the next, one asset class to the next, and one technology to the next. Each of their Success Flywheels keeps ripping and getting exponentially stronger with each rep.
Of course, it’s important to acknowledge the hustle, passion, and hard work that continues to grease these Flywheels. But the Flywheel nature ensures that the ROI on each ounce of input keeps compounding at an exponential pace. Then it’s a personal choice whether one eases the input-effort but still gets growing outcomes, or like Bezos and Musk, keeps growing the input-effort and given improving ROI, translates to even better outcomes with each parlay.
PS: for thoughts on how to get the Success Flywheel going in your own life, check out my May’23 post ‘The Success Flywheel‘.
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