The AI Buildout: Where I’m Placing My Bets

The AI infrastructure boom is unfolding across both the physical and digital worlds. What might past booms tell us about where the lasting value will emerge? And where is Operators Studio choosing to play?

There is a working POV I have been developing over the past few weeks.

As we speak, there is a major infrastructure buildout cycle unfolding across the world. There are two core underlying drivers for why both countries and companies, without exception, are choosing to invest trillions in capex:

  • Artificial Intelligence – this investment helps unlock value from this groundbreaking technology.
  • New World Order – a fundamental geopolitical realignment, largely driven by the United States, forcing major countries to build domestic, sovereign infrastructure rather than relying on global capabilities through cross-border trade.

Interestingly, this infrastructure buildout is happening simultaneously across both the physical and digital worlds:

  • Physical Infrastructure – (1) Power, (2) Chips, (3) Data Centers, (4) Industrial Manufacturing.
  • Digital Infrastructure – (1) Foundational Models, (2) Data Pipelines.

A. What can we learn from history?

If one looks at large infra cycles over history (eg., the Railroads, WWII-driven DARPA tech, information & communication tech for the Internet in the 90s), stakeholders tend to approach them with a gold-rush mindset. That’s simply because infra & its related IP tends to be “scarce”, and once you put it in place, you can charge a toll on it for decades to come. Free Cash Flow, baby!

Of course, the side effect of this gold-rush mindset in past cycles has been that this infra build-out typically ends up overestimating the size and/or timing of future demand, builds out way too much capacity, usually with questionable financing terms (eg., high leverage), and in the end, many of these infrastructure builders blow up – either due to demand risks (not enough customers to make it economically viable within a desirable time frame) and/or financing risks (over-levered, not able to pay down debt, not able to raise equity).

Of course, a few players that can survive end up gathering the toll for decades to come (eg., Cisco post the dotcom crash). But more importantly, because the gold-rush & excess-driven mindset ends up creating massive amounts of infra in a very compressed time period (with complete disregard for underlying economic viability), it provides the catalyst for a follow-up cycle of “Applications”.

Because enabling infra is now in place and at low prices (courtesy of the overbuildout), entrepreneurs start building new businesses that solve a totally new set of customer problems & use cases. A classic example is a certain Jeff Bezos using core capabilities built by the likes of Cisco, Lucent, and WorldCom to start selling books on the Internet.

These Applications directly touch enterprise and individual customers, solve important problems for them, and therefore end up becoming generational compounders with attractive business economics, especially having the luxury to stay asset-light (because all capital-intensive assets have already been put in place by someone else), thus driving extremely high return on equity.

So, while almost 1/3rd of all initial American railroad companies went bankrupt, Mr Buffett has continued to enjoy steady & predictable cash flows from Berkshire’s core holding in BNSF Railway, one of the largest freight railroad networks in North America, over the last few decades. The Railroad infra providers went bust; the freight Applications layer continues to reap the fruits.

B. What does this mean for a small seed firm like Operators Studio?

Here’s how I am approaching playing this phase of the AI cycle:

1/ Sell “picks and shovels” to physical infra players

As a small tech fund, we obviously can’t participate directly in the players building power, chips, data centers, and foundational models. That’s where the gold rush is happening right now in the Valley.

However, where we can invest is in startups building technology-based, light picks and shovels to sell to these infra players, so we benefit from this massive capital infusion unfolding in front of our eyes.

These include robotics to execute these projects, Physical AI to power these assets, and industrial components to enable manufacturing.

A few such examples from the existing portfolio:

  • Flywheel AI – Retrofitting excavators for remote operations on construction sites in the US.
  • Muro AI – Agentic pre-construction operating system for General Contractors.
  • CarbonStrong – Low-carbon cement alternative binder for concrete mixes, being used by real estate developers in India.
  • Flytbase – Autonomous drone software for inspection & security of large physical sites like solar farms and oil & gas plants.
  • Naxatra Labs – Radial and axial motors for deeptech use cases in India.
  • Astrophel Aerospace – Precision engineering components like cryogenic valves and turbo pumps, serving space & general manufacturing.
  • [RoboticsCo in Stealth] – On-field robotics autonomy for inspection & maintenance of large sites like solar farms and data centers.

There is a large picks and shovels bucket I am choosing to not play in – developer tools that help deploy, manage, and unlock value from foundational models.

Most Valley VCs are super-focused on this bucket as we speak. From my perspective, I don’t have an instinctive understanding of these tools, and I also struggle to get my head around the competitive differentiation of these products.

So generally speaking, I struggle to build conviction in these dev tools. The only investment I have in this broad area is Workspot (Computer use harness for agentic workflow automation within large enterprises).

2/ Squint into the future and back “Applications”

Unlike the last Mobile/Cloud/SaaS cycle, this time, these Applications cut across both the physical and digital worlds. This significantly enlarges the addressable opportunity set for even a small seed firm like Operators Studio.

(2.1) Digital AI

Applications in the digital world have some resonance with previous cycles.

  • On the Enterprise AI side, I am taking a deeply vertical approach with a POV that powerful domain-specific applications will be built on top of physical and digital AI infra rails that are presently being laid out.

A few verticals I have been excited about include healthcare (Confido Health, Soulside AI, Elvo AI), biology (In Stealth), food (Loop AI), manufacturing (Datoms), construction (Muro AI), and insurance (Infer).

  • Contrary to enterprise, sustainable Consumer AI applications will take time to emerge. They require significant leaps of imagination, large amounts of capital to create distribution, and active competition from incumbents given a winner-takes-all dynamic.

As a smaller fund, I question my right to win in accessing these first-gen consumer AI applications, and even beyond that, whether this first set of products are even good venture bets given flux in both the underlying foundational tech, as well as in adoption and competition.

I have taken a few selective bets in my circle of competence, with an India-to-the-world angle giving them some level of global differentiation – Stimuler (Conversational AI English learning app), Rovia (helping global BigTech employees manage RSUs) and another in the AI-generated microdrama space.

In addition, I feel sleeper hits are hiding in India cross-border Consumer AI, Consumer Hardware, and Fintech. However, I am not seeing many startups in these areas. Will continue to actively hunt here.

(2.2) Physical AI

Personally, I believe that the next generation of Applications being built in the Physical world are much more interesting.

There are core “product & business” infra rails being built out in:

  • Space – low-cost and frequent launch capabilities; SpaceX in the US, Skyroot and Agnikul in India.
  • Autonomy – LiDAR; advances in sensors and base software; Waymo in the US.
  • Aerospace, Defense & General Manufacturing – OEM ecosystems in the US & India; public institutions like NASA, ISRO, and DRDO actively opening up their stacks to startups; governments open to buying from startups; Anduril & Boom Supersonic in the US.

I am excited about Applications being built on these domain-specific rails. A few examples from the existing portfolio:

  • Space economy – Aule Space (life extension of satellites), another one in Stealth.
  • Autonomous economy – NuPort (retrofitting commercial trucks for semi-autonomous freight operations), FlyWheel AI (autonomous excavators), RoboticsCo In Stealth (On-field autonomous robots).
  • Aerospace – Aspera (autonomous, amphibious aircraft built in India), Parsec (indigenous jet engines for India).
  • Defense – HyPrix (supersonic payload delivery capabilities).

C. What are the major risks in these two buckets?

1/ On selling picks and shovels to infra players, history tells us that:

(a) This infra buildout phase should be expected to be relatively short and execution to be highly compressed.

(b) Most players can be expected to go bust in the end, as explained in earlier sections.

So as a seed investor, it’s important that before the point of bust hits major customers, these picks and shovels companies move fast, scale quickly, and either get acquired or become self-sustaining via diversified sources of revenue.

It’s a play on an ongoing gold rush and therefore needs to be approached with that mindset.

2/ On backing “Applications”, while the runway is likely to be long here for the eventual winners, history tells us that:

(a) The first set of anointed winners are likely to get disrupted fairly quickly (Netscape as the first browser; Yahoo as the first consumer Internet company; AskJeeves as one of the first search engines).

(b) Eventual winning applications take time to emerge, and are fairly hard to spot & predict in the initial days.

So as a seed investor, the approach needed for the Applications layer is to stay skeptical of the first-generation companies and not be afraid to exit at what might be an eventual global maximum, as well as be on the lookout for weird, pattern-breaking use cases that are hard to see in the present but are living in a plausible future that requires a fair bit of squinting to spot.

Doubling Down Before Consensus

The best founders often stay non-consensus for longer. That’s the time for insiders to build ownership.

It’s crazy how extremely solid founders who are clearly executing really well still end up being fairly non-consensus when being evaluated by new investors in the first few rounds.

In my experience, this happens due to one or more of the following reasons:

(1) The founder is still learning the art of effective pitching/story-telling. Therefore, the quality of the opportunity isn’t being adequately communicated in the pitch. [Frequently the case with technical founders in deeptech].

(2) Details around the exact addressable market are still fuzzy. So unless an institutional investor has a pre-prepared mind for that particular space, they struggle to build conviction around it. [Eg. people passing on Zepto Series A & B].

(3) Even with awesome growth & metrics, the target market is simply out of favor at the moment with investors due to recent history or macro headwinds. [Eg. anything related to edtech in India at the moment].

(4) A multitude of internal issues at a VC firm that have nothing to do with the company. [Eg., bad history with previous investments in the space, already over-exposed to the sector, not having enough dry powder, deal sponsor is not a GP/ lacks internal political capital to push it through etc. etc.]

This is where existing investors in the company, particularly those who have been spending enough time with the founder and have been tracking execution closely enough to be able to accurately judge the slope of the team/business, have a massive information asymmetry advantage.

This creates valuable opportunities for insiders to build ownership at attractive valuations while the market struggles to build a firm POV.

As an existing investor, the key is to take a step back, block out the noise and distractions from external signals, and do a fresh, rigorous underwriting from the ground up as a new deal, while keeping emotional biases at bay (commitment bias, consistency bias, throwing good money after bad, etc.).

As long as you can be intellectually honest in this new underwriting process, the superior quality & trendline of signals gathered while being on the cap table will work massively in your favor, both in terms of allocating more to the right companies, as well as avoiding the landmines that look like goldmines to new investors!

The key risk in all doubling-down decisions is “your own sh*t always smells sweet to you”. So, keeping a significantly high & tight bar for what goes through, as well as having some fund-level guardrails (% of fund caps for reserves & cross-fund deals) will hopefully help counterbalance the human biases.

Why “SPV & Chill” Is Not The Best Strategy Right Now

Growth & SPV investing doesn’t work well when the underlying tech shift itself is nowhere near steady state.

Am seeing retail LPs (HNIs & Family Offices) in “do growth-stage AI SPVs and chill” mode.

Veteran public market investors often say that financial memory of crowds is incredibly short, and therefore, every generation learns the same set of lessons the hard way.

Given the current heated stage of the AI cycle, people have forgotten all lessons from 2021. So a quick recap from my side as a GP:

  • Growth investing is significantly harder than seed investing simply because you can’t afford to get the entry valuation wrong.

A low loss ratio creates a mirage of emotional safety. As the recent exits of Airtable and Headspace have shown, if you overpay in a supposedly de-risked-looking late-stage round, you will see major markdowns on eventual exits (see my recent post: Exit Learnings From the Airtable Acquisition).

  • In some sense, this is the absolute worst time to be doing late-stage SPVs in hot, go-go companies (not counting ANT and OAI in this).

At this point, there is a lethal combination at play of (1) early stages of a major new tech, where new hot companies could get disrupted by changes in the underlying tech itself, and (2) extreme FOMO at work in private markets, leading to a major disconnect between valuations and business reality (case in point is a recent what would be normally called a beta/ early adoption product at best, raising at $2.5B).

So essentially, when you choose to do only access SPVs in highly valued late-stage companies at this stage of the cycle, purely from a bottom-up POV, you are investing in tech companies that have a high likelihood of not being eventual category leaders, but are being valued like generational companies.

  • Contrary to this, as pre-seed & seed investors, we are backing companies anywhere from sub-$10M to $20-30M entry valuations.

Examples of recently exited companies via Grok:

1/ Hugging Face: Angel/pre-seed at $5M post, Seed at $20M post

2/ Airtable: Seed at $7.3M post

3/ Cursor: Seed at $40-50M post

4/ OpenRouter: Seed valuation in the tens of millions

As seed investors, we are backing companies at reasonable risk-adjusted valuations, with the conscious intent of staying nimble & iterating to PMF, and with a decade-long holding-period outlook.

These companies have small teams that are designed to rapidly adapt to underlying tech changes, pivot, and course-correct as required.

Most tech startups have a realistic, eventual steady-state exit valuation of $100-250M. For the ones that break out, $500M-$1B isn’t far fetched too.

In the former “moderate exit” scenario, seed investors still make a 5-20x post-dilution multiple on invested capital (MOIC).

In the latter “breakout exit” scenario, which is also moderate by today’s overheated standards, seed investors can end up with 50-100x MOICs, and maybe even more.

Seed portfolios are constructed in a diversified manner & so for retail LP personas, GPs that know what they are doing will end up driving superior risk-adjusted returns.

Just an alternate POV to consider in the current madness of AI growth rounds.

How to Raise When Bay Area VCs Expect Hypergrowth

If you don’t have a hockey stick, talk about revenue quality, capital efficiency and IP instead.

There is a common thread between my X feed and conversations I have had with follow-on VCs in the Bay Area.

Their revenue growth expectations for doing the next round seem extremely steep, even to me as a seed investor.

Many claim that they are regularly seeing startups that are meeting these kinds of expectations, even in the Enterprise AI world. Which, personally, I find hard to believe! But then, I see startups on X that every week are claiming 0 to $500K ARR in 30 days, “we are the fastest growing startup ever in X/Y/Z”, and investors like Paul Graham sharing hockey stick graphs of a variety of colors.

Anyway, for now, let’s park the discussion of sustainability, unit economics, dodgy revenue math, etc. Let’s say as a founder, you have some solid early traction but don’t have these ridiculous growth numbers to show. How should you then tell the story, especially while pitching in the Valley?

I am typically advising folks to explore combining one or more of the following 3 elements:

(1) Focus on “quality” of revenue – signals include customer scale/ tier, how happy they are (low/zero churn), and whether there is a land-and-expand motion happening even with 1-2 names, especially if there is evidence of ACV expansion.

(2) Capital efficiency of early execution – lead with “look at how much we have accomplished with such limited capital”, which highlights resourcefulness, strategic & iterative thinking, street-smart execution, and naturally leads to the line of thought that goes “what could this team achieve when armed with capital”? This is a great point to get investors to start imagining.

(3) If applicable, deep work around IP and building hard things around the product – don’t be shy to talk about the deep, grindy, and non-trivial work that’s gone into building behind the scenes to get the product to this stage. Ideally, have a compelling argument as to why this work can’t be just “bought” with capital and why a new YC batch company can’t just copy it.

These arguments might still not do the trick for the momentum VCs. But remember that only a handful of large investors are positioned to play that game anyway (the ones who have been saying “triple, triple, double, double is dead” for the last year or so on every podcast).

There is a large variety of capital pools, definitely across the US & even in the Bay Area, that structurally don’t play this game. Hopefully, these arguments should help you land your story better in front of them.

Exit Learnings From the Airtable Acquisition

Airtable’s recent acquisition shows why seed investing still works, why seed VCs must know when returns are good enough to exit, and why smaller funds can benefit from exits of all sizes.

One of the venture darlings of the SaaS cycle, Airtable got acquired by BendingSpoons this week for $1.285B enterprise value and $2.25B equity value. As of June 2026, the company was reportedly doing ~$480M ARR but only growing ~20% YoY.

As an emerging manager at the pre-seed/seed stage, the most valuable insights I am taking away from the Airtable acquisition are around DPI and timing of exits:

1/ Even at a fairly low exit revenue multiple, Airtable seed investors like Caffeinated and Freestyle have reportedly generated anywhere from a 30-50x multiple on invested capital (~30-35% IRR) over an 11-year hold period.

While they might have been holding this position on their books at multiples of this due to the 2021 inflated growth round, on a standalone basis, this is still a solid seed exit, especially if the fund sizes were relatively small.

This should be a great proof point for both LPs and emerging managers that as long as you can do seed at the right valuations (which means you have to do non-consensus-and-right deals), even in a relatively sub-optimal exit outcome, investments can still move the needle massively, especially for smaller fund sizes.

As a corollary to this insight, I get extremely uncomfortable when I see post-YC demo day seed deals at $50-100M cap and some marquee researcher-led companies raising seed at $1-5B valuation.

As a smaller fund, if I am doing such deals, I better be right. Which is unlike, say, a General Catalyst or a16z, who can spray-and-pray without bothering about seed valuations, as they just need to catch a potential outlier in the fishing net and crowd in gobs of follow-on capital into it.

2/ Airtable had raised a Series F in 2021 at a ~$11B pre-money valuation. Based on what I saw in that era, large incoming investors like Coatue, Greenoaks etc. would have had significant appetite to buy out older investors in secondaries during that round.

As per Grok, Airtable’s seed round was at a ~$7.3M post-money valuation, and by Series F, seed investors had seen 80-90% dilution. Assuming 80% dilution and say a 20-30% discount on Series F primary, Airtable seed investors would have had the opportunity to sell secondary at a ~220-250x MOIC in 2021 (I’m sure a bunch of them did at least partial secondaries).

So, given changing macro conditions, driven by a major technology shift in this case, seed investors saw a theoretical MOIC compression by a factor of 5-8x.

A learning for me here is that as seed investors, once we have got a more than adequate risk-adjusted return, we should look to actively sell and pass on the baton to later-stage investors in the capital stack who are more suited to the current risk-reward dynamics. Especially if the seed position has been held for 5-7 years & beyond.

Based on my own experience, this requires a major emotional re-balance, and when things are going up, greed kicks in massively, and the brain finds it incredibly hard to play devil’s advocate and evaluate risks that are still inherent in the business.

Also, we as seed investors are emotional & optimistic by definition (or else, we wouldn’t back people with just an idea). For this personality type, keeping commitment and consistency bias at bay becomes even more difficult, especially when 3rd parties are finding your sh*t as sweet-smelling as you 🙂

There are a few ways emerging managers, especially solo GPs like me, can enforce exit discipline:

  • Establish a rules-based exit framework, and stick to it. It might lead to leaving money on the table in specific instances, but over a long time scale and across multiple deals, it will serve fund managers well in terms of risk management and exit discipline.
  • Lean on your Investment Committee, LPAC, or, in the case of solo GPs with smaller funds, a set of experienced GPs who can be your informal GP advisory board. Ask these folks to proactively play devil’s advocate, look at the deal on the table from the outside, call out your potential biases, and essentially help you evaluate the exit opportunity holistically.

3/ The Airtable seed MOICs further support why smaller funds find it so much easier to generate outsized index-beating returns.

For a sub-$10M fund, a 30-50x MOIC deal would likely return at least the fund in most cases. Whereas for a $50-100M fund, unless they concentrated enough capital into what turns out to be an eventual winner (something that is really hard to do in practice and requires insane judgment), it might only return 25-50% of the fund even with a winning exit.

Hence, I keep going back to this famous line from Mike Maples of Floodgate – “Your fund size is your strategy”.

Why AI Valuations Aren’t That Odd

AI is an exponential technology and therefore, the “winners” deserve significantly higher valuations. But few can access them. Therefore, the quest for non-consensus-and-right still stands.

I think all of us are still systematically underpricing the extent of value AI is going to unlock for both enterprises and individuals.

As with compounding, the human brain isn’t wired to grasp exponentials. And the outcomes AI will drive over the next few decades are honestly unfathomable in humanity’s current context.

If you operate with this premise, it makes sense why category-defining companies are being valued at trillions, compared to going public at $10-100B in the 2010s.

This dynamic should reflect across the startup financing stack, and the reason why early and growth investors today are paying up for the best companies.

As long as you can be more-right-than-wrong in underwriting these companies as category-winners, beyond a certain level of de-risking/ PMF, the entry valuation really doesn’t matter because the commensurate exit valuations have also gone up dramatically.

I think a16z was one of the earliest VCs to grasp this change and also have the mental plasticity and capital war chest to execute behind this philosophy.

What is unclear to me is: while the power law winners in both public and private markets will keep going up in value without any real ceiling, does the median venture exit outcome also similarly increase in value? Eg., does the $100-250M M&A/ secondary sale outcome from the 2010s become the $5-10B outcome in 2030?

‘Cos if that’s the case, then it makes complete sense why entry valuations in YC have gone up from $10M a decade back to $50M today. And all pre-seed and seed investors like us should willingly pay up for whatever we deem to be the “best” consensus companies in our deal flow.

Another point: for pre-seed & seed stage micro VCs like Operators Studio
, I expect secondary sales during growth rounds to constitute a large portion of our exit outcomes. I asked ChatGPT to analyze how Series C pre-money valuations in US startups have evolved over the last decade.

Interestingly, the median Series C pre-money valuation rose from approximately $75M in 2016 to $320M in 2025, a little over 4x. Here’s the annual trend:

So, it’s not illogical that seed valuations should be expected to go up by a similar factor during this time period, especially in an efficient venture market like the Bay Area.

It’s obvious now that accessing growth rounds in the very best companies is delivering early-stage-like venture returns in this market. However, the reality is that beyond a handful of top VCs, most of us fund managers neither have this access nor the fund size to do these types of deals.

For us emerging managers, the equation still stays the same – our best bet is to find non-consensus-and-right companies early enough.

It’s hard to find non-consensus teams in consensus fishing ponds like Stanford, ex-foundational model folks, repeat unicorn founders, and celebrity execs stepping out to startup.

We will need to look beyond these channels, back non-obvious people from non-pipeline backgrounds, perhaps fish outside the US.

PS: If you are curious about how to be non-consensus-and right, you might find these posts of mine interesting – A Talent Scout Mindset For VC, An Investing Framework to Find Startup Diamonds and Staying In The Ring Long Enough.

Resolving Uncertainty for Founders & GPs

A founder’s job is to convert uncertainty into risk, one milestone at a time. The same applies to emerging fund managers, with DPI as the ultimate uncertainty resolver.

Recently listened to an insightful Origins Podcast episode with Alec Litowitz, founder of Magnetar Capital and previously founding partner of Citadel.

Alec drew a fascinating distinction between “Uncertainty” and “Risk” and explained how people often confuse the two. PS: I can clearly see that this framing has emerged from his extensive experience in public markets.

Risk is something where the possible outcomes and their probabilities are both known eg. what number is likely to come up when you throw a dice. Because the odds are relatively known, risk can be priced.

The other end of the spectrum is where both outcomes and probabilities are unknown – these are Taleb’s Black Swan events like COVID.

In the middle of both is Uncertainty – where outcomes are known but probabilities are unknown. This is where most of life unfolds eg. going on a date, hiring an employee, etc.

Any new early-stage business operates with uncertainty, not risk. And the main job of a founder is to resolve this uncertainty to discover probabilities of possible outcomes of the business that can then be priced.

In other words, a founder’s job is to convert Uncertainty to Risk, which can then be priced and bet big on by capital providers.

Btw, founders do this by following the classic YC/ Lean Startup approach of fast & iterative feedback loops aimed at making something people want. As simple as that.

For founders, this is an important framing for fundraising and managing runway. You should be very clear about the exact derisking milestones that need to be achieved with each capital raise. Also, articulating that to investors during fundraising helps build extra confidence that the capital will be used well.

Also, this is a good behavioral heuristic too that can help reduce the pressure on founders during fundraising. Every investor will have their own threshold of the current level of uncertainty that they are willing to tolerate. So, a “No” should be taken as a reflection of their appetite, rather than a personal reflection on the founder.

Of course, with every incremental unit of de-risking, your business gets closer to meeting this tolerable threshold of uncertainty, which will ultimately reflect in your fundraising conversion rate going up.

Hence, it’s important to survive long enough to be able to demonstrate adequate de-risking such that access to capital and other resources like talent keeps getting easier with time. This is the driver of compounding in progress that we often see with businesses once they have product-market-fit.

For businesses, this uncertainty resolution is an infinite game, an ongoing journey. That’s why various parts of the capital stack exist – angels, VCs, PE, public markets, debt providers, etc. Each has a mandated uncertainty threshold that they like to operate at, and therefore, are likely to become participants in a business only when that threshold is reached. This will also reflect in how they do asset allocation & portfolio construction.

Taking my own context, this concept of resolving uncertainty also applies to emerging managers who are relatively early in their journeys (Funds I-III). GP fundraising is a slow burn, extremely long enterprise sales process. Following this framework of trying to reduce as much uncertainty on multiple fronts related to the Fund can perhaps help sustain multi-year momentum through this grueling process.

Alec mentions that the ultimate uncertainty resolver for a GP is DPI – it converts all the uncertainty into cash. And this is the reason why he pushes all his portfolio GPs to get into DPI as quickly as possible.

The Zero Cost Strategy

How the Zero Cost strategy can help investors overcome behavioral biases to become better long-term holders of compounders.

During some random summer YouTube browsing recently, I came across this rather interesting Groww podcast episode with a public markets investor called Basant Baheti. Candidly, I have no idea who this guy is, but the concepts he shared on this episode made a lot of sense and checked out with my own experience too.

In particular, he shared an interesting practice for everyday investors called the Zero-Cost strategy.

Given the daily liquidity in public markets, a challenge that investors face is the inability to patiently hold long-term compounders. Not only individual investors, but even the best professional investors often end up selling stocks at exactly the wrong time. A recent case in point is the famous value investor Mohnish Pabrai, who bought Micron in 2017 and ended up selling it in Sep’23 at ~2x cost. Unfortunately, Micron’s stock rose 15x post that, costing him ~$2B in missed gains.

To guard against these errors, Basant recommends that the moment a stock doubles after your investment, you should immediately sell a portion and take your original principal out. In a sense, your holding then becomes “zero cost”, which, given the way the human mind works, makes it significantly easier for investors to then hold for the long term. In the case of long-term compounders, Basant suggests “not to even look at the stock again for the next 10 years”.

This isn’t rocket science by any means, but I found it to be an elegant & useful idea that specifically acknowledges and leverages classic human biases of booking profits too early, herd mentality, loss aversion, fear, and greed.

While this particular episode seems to be aimed at regular individual investors, many institutional public market investors, too, could learn a lot from these concepts shared by Basant. In fact, he cited an interesting insight where hundreds of public market investors had identified and invested in Titan Industries over the last few decades. Yet, it was only Rakesh Jhunjhunwala who held on to it for more than 2 decades, letting it grow to 25-30% of his entire net worth at the time of his passing in Aug’22.

In a sense, this Zero Cost strategy applies to venture investing as well. In an OG post on AVC, Fred Wilson cited how he approached the “sell” decision in an extremely large fund position in Twitter:

We had bought 15% of Twitter for $3.75mm in the first VC round in 2007 and though we had been diluted down a bit in subsequent rounds, we had a very large position that was worth in the neighborhood of $1bn by 2011. Our entire fund was $125mm and so we were sitting on a position that was worth 8x the entire fund. It was a wonderful situation in many ways but I was nervous that macro events or a setback at Twitter could go against us and the position would go down in value, possibly significantly.

The way we managed this issue is we sold a portion of our position in two secondary transactions and in connection with those sales, I stepped off the board, making room for an independent director who would be helpful as the Company scaled and got ready to go public. We sold about 30% of our position in those two secondary transactions for about $250mm and returned 2x the entire fund to our investors.

That allowed us to “chill out” and hold the balance until the IPO, which had a customary 180 day post IPO lockup. After the lockup came off, we distributed the balance of the position, returning another ~$700mm to our investors.

– Taking Money “Off The Table” by Fred Wilson

In this post, Fred mentioned following a similar strategy for other winners like Zynga, Lending Club, MongoDB, etc., wherein in each case, USV sold 10-30% of its position in pre-IPO liquidity transactions, giving it the mind space to both hold and ride the balance while still de-risking the overall investment.

In another post, Fred recommends that the moment your winners go public and the stock gets distributed to LPs, he follows this selling strategy:

“I like to sell one third of the position immediately, put one third away for a long term hold, and actively manage the other third.”

–Selling by Fred Wilson

If you think about it, Fred’s selling philosophies are similar to Basant’s Zero-Cost strategy, which leads me to believe that there is some fundamental investing truth in this idea.

Btw, I have used some version of this idea in my life too. I bought Bitcoin in 2017 at a fairly low cost basis. Once it started running up during 2020-21, I took out a multiple of my principal, which has created a relaxed mind space for me to now hold my Crypto forever.

Similarly, in a commercial real estate property I bought in 2014, the cumulative rental income from it has now paid back the entire original purchase price. I have seen this now alleviate any behavioral selling pressure on me, freeing me up to continue holding and enjoying its passive income benefits without churning on macros, local market sentiment, or rental yield volatility.

PS: If you are interested in more mental models around “selling”, check out my old posts: When to Sell? and When To Sell? – Part 2.

Stay in Business

Stamina is one of the most underrated advantages in entrepreneurship. In a world where people start quickly and quit quickly, simply staying in business can dramatically tilt the odds in your favor.

Looking at all cases of long-term business success that I have observed in my life, a common underlying philosophy seems to be: [in Hindi] “Dhande mein tike raho” (focus on staying in business).

Phases of a business lifecycle

As any person who has ever started a business knows, the first order of business for an entrepreneur is finding product-market-fit – making something people want and are willing to pay for.

Once product-market fit has been established, the next order of business is to make it profitable, Phase 1 of which is unit economics profitability on an immediate basis:

(1) Positive gross margin, by selling something for more than the cost of goods sold ➡

(2) Positive contribution margin, so all variable costs incurred per unit are recovered, and eventually ➡

(3) Positive net margin, so both variable costs and an allocated share of fixed costs/ overheads are covered.

Phase 2 of profitability is making the business P&L profitable, so generating operating profit that covers all fixed costs of the business, and eventually, net profit (or PAT).

Phase 3 of profitability is generating free cash flow – the ultimate goal of any business, and the ultimate dream of any entrepreneur.

The core currency of business

Starting from the pre-PMF phase till the free cash flow phase, the drivers of success in each phase are very simple:

  • Retain & grow existing customers.
  • Find new customers.
  • Keep accessing capital to continue the journey (retained earnings, equity, debt).

If you think about it, the intangible currency that drives all the above is “trust”. As existing customers spend more time using your product/service, assuming they are happy with it, their propensity to stay & grow with you keeps increasing with each year.

Multiple human biases like commitment bias, consistency bias and behavioural inertia end up reinforcing this customer stickiness, as long as you keep delivering what you promise.

Similarly, the more time you spend in the marketplace, the likelihood of new potential customers hearing about you, particularly from your existing customers, keeps going up.

Finally, we always hear that capital chases returns. In reality, capital chases “risk-adjusted returns”. The more time you spend in the marketplace, the higher your trust is within the ecosystem, which reduces the risk perception around your business among capital providers.

That’s why banks prefer lending to businesses with established histories. The same reason is why VCs and PEs tend to track founders & companies for months before investing in them. It’s also the reason why institutional LPs can sometimes take years to build comfort around a GP, but once they back a team, they keep doubling down on them across multiple fund vintages.

So, “trust” is the key currency that businesses need in order to continue making progress across various phases of their lifecycle.

Competition

In addition to Customers and Capital, businesses also need to worry about Competition. Ultimately, customers are evaluating multiple options in the marketplace, and the business that ends up winning their vote is the one that is uniquely differentiated against competition.

This is where “staying in business” provides rich dividends, especially in the present age of fast-food entrepreneurship and role-playing founders.

The cost of starting any business & getting some early traction has gone down significantly, courtesy of technology. Therefore, any serious entrepreneur should expect the top-of-funnel competition in the pre-PMF phase to keep increasing each year.

However, the faster people are starting companies, the faster they are tapping out of the game as well. So, as your business continues chugging along and moving across the lifecycle phases outlined earlier, you will see a steep drop-off in competition at each phase.

Therefore, just by merely surviving, your odds of the marketplace self-selecting you as one of the few viable options keep going up.

Compounding

The key idea is positioning oneself to harness the power of compounding by staying in business. If you look at the most successful family businesses globally as well as major startup success stories across geographies, the reality is that it takes a decade to get it right and create a foundation, and then another decade to dominate the market and reap the rewards.

Most people don’t have the enthusiasm, energy and a mission-driven mindset to endure such long journeys. For founders, stamina is one of the proverbial low-hanging fruit that can help you massively tilt the playing field in your favour over the long term.

Investing in the “Real India”

While many Indian VCs are chasing Bay Area deals, I’m finding some of the most compelling opportunities in India-based founders building unsexy hardtech products with global ambitions.

This tweet from Anand Lunia (IndiaQuotient) really got me thinking last week.

Here was my response to him:

I think Anand is bang on here. Based on my interactions with several large US-India cross-border VCs, they are all looking to invest in the Valley and are mostly on the lookout to back Indian-origin founders.

This, of course, is a great way to ride the current AI momentum in the Bay Area. At Operators Studio, this founder persona is also one of my core focus areas in AI/ enterprise software, having backed the likes of Loop (AI for Restaurants), Confido Health (Healthcare AI), Noon (AI for Designers), Soulside (Behavioral Health), Muro AI (Construction AI), and Guard0 (Cybersecurity).

However, these are also some of the most coveted and competitive deals. The Valley-immersed Indian founder isn’t an unknown or undiscovered phenomenon today, unlike, say, when the likes of Nexus Venture Partners started focusing on it in the 2010s.

For smaller, operator-led funds like Operators Studio that write $100-300K collaborative checks, I still have a shot at winning over the founder with a sharply-defined value-add. But for larger funds that are looking to lead rounds/ put sizable capital to work in these Valley deals despite being largely offshore brands, their right-to-win against Valley competitors is unclear.

But then, how do they play AI? I understand their predicament.

One of my core beliefs about venture is that the greatest alpha lies in backing undiscovered founders, the non-consensus teams and companies that eventually turn out to be “right”. I have written about this idea before in multiple posts, including An Investing Framework to Find Startup Diamonds, A Talent Scout Mindset For VC, and One Person’s Conviction For Easier Fundraising.

Ergo, my investing strategy has two pillars – in addition to backing Indian diaspora founders in the US, I also back founders based in India but building for global markets.

Double-clicking on the latter bucket, in terms of markets, I am most excited about hardtech products that are not only core building blocks for the Indian economy, but also have the potential to be exported eventually.

These startups are being built in the “Real India”, as Anand puts it. These founders aren’t necessarily hanging out at Third Wave and Beanlore in Bangalore in their hoodies. They are building messy businesses, require workshops & facilities to be created in far-flung areas, and require hiring & financing strategies that look quite different from the classic Bay Area or Bangalore playbooks.

To illustrate this, let me give you a sample of companies I have recently invested in or am deeply evaluating as we speak:

  • Naxatra Labs – motor-tech that can beat Chinese and European products. Manufacturing in Ahmedabad.
  • Astrophel Aerospace – space propulsion engine components like valves & pumps. Developed and manufactured on the outskirts of Pune.
  • Planet Material Labs – new-age composites for logistics boxes and containers. Developed and manufactured on the outskirts of Gurgaon.
  • Climate & materials startup that has developed a low-carbon, cement-alternative material for concrete mixing. The concrete unit is on the outskirts of Bangalore, and so dusty that one needs a layer of masks just to breathe.
  • Battery-tech startup in Ahmednagar (3 hrs from Pune) for new-age use cases like Robotics, Defense, and Power Tools.

It’s ironic that while Indian VCs are shuttling to the Bay Area, trying to invest in deals here, as an SF-based fund, Operators Studio is actively investing in India-based founders building real, no-nonsense, unsexy hardtech products with massive cross-sectoral local and global TAMs and market demand that needs no validation.

One final point – while I actively co-invest with several major domestic and global funds in India, specifically in this second pillar of “India-based hardtech founders”, my worldview has resonated the most with Rainmatter, the prop money fund of Zerodha founders.

From what I have observed, the Rainmatter team is smartly identifying problem statements that are core gaps in the Indian economy and society, and backing founders that have an authentic commitment, passion & and domain-fit with these problems. An unsolicited kudos to the team!

Large funds have a tendency to go top-down in venture capital, spending a lot of time understanding markets and building thesis & maps. While this probably helps in Series B & beyond, my view is that at Seed and Series A, going bottoms-up is more beneficial. And founders are the best suited to observe and identify these opportunities.

At least this is the approach I am taking at Operators Studio while looking to back India-based hardtech companies with global ambitions.