Soumitra Sharma

Why The YOLO Era Should Matter To You

The pandemic has taught people globally to not postpone living their lives. This behavior shift has profound economic implications for this generation.

I have been looking up air tickets for multiple upcoming trips, both for work and pleasure. I am observing that airfares are now consistently high across the year, irrespective of seasons and destinations. 5 years back, one could get relatively cheap US-India air tickets, even for the high season in Dec, if booked 6-9 months in advance. That’s no longer the case, even if one reserves as early as Feb!

Matching this with what I am seeing anecdotally in my network, demand for air travel is now significantly higher than pre-Covid. I see people taking many more vacations, going to fancier places, and generally being more willing to open their pockets for “experiences”.

Last month [Oct’23], the Transportation Security Administration logged 75.5 million passengers passing through airports in the United States — more than the 72 million who traveled in October of 2019. The TSA expects 30 million passengers to travel over the Thanksgiving holiday period alone. Globally, the International Civil Aviation Organization expects 2023’s passenger demand to outpace 2019’s by about 3 percent.
The Washington Post

Consuming with a vengeance.

I am seeing this pattern across generational cohorts where post-Covid, people have started consuming much more than before. I don’t know if “YOLO’ing” is the right way to frame this behavior shift but going through Covid experiences seems to have given people a subconscious realization that life is finite and therefore, postponing enjoyment doesn’t make sense.

This behavior shift is starting to be reflected in US consumer sentiment data. Despite high inflation and record-high interest rates, McKinsey’s Feb’24 ConsumerWise research says that consumer optimism is at its highest level in 2 years. Here are some interesting consumer quotes from this survey that highlight their buoyancy:

Source: McKinsey ConsumerWise Survey (Feb’24)

I am seeing similar consumption unlocks in urban India. Even in my parents’ generation, people are now taking multiple overseas vacations and spending significant amounts on general entertainment. This generation in India, like the Boomers in the US, has benefited from the massive compounding in wealth that has happened in the country since the early 90s. They have considerable wealth but more importantly, are now starting to consume a larger portion of it, instead of disproportionately saving and handing it down to the next generation.

Urban Indian Millennials and Gen Z are also turning out to be big beneficiaries of this wealth creation. They have the safety net created by their parents, plus are seeing their own incomes rise courtesy of overall economic growth. This is driving up their overall consumption too.

These shifts are reflected in the data as well. As per the recently released Household Consumption Expenditure Survey by the Govt. of India, average monthly per capita consumer spending has grown ~2.5x between 2011-12 and 2022-23, both in rural (INR 1,430 ➡ INR 3,773) and urban India (INR 2,630 ➡ INR 6,459). Interestingly, Indians are spending less on food and more on discretionary items like consumer durables, services, and travel.

Source: Govt survey on household consumption expenditure via Reuters

Remote work, gig economy, and digital nomads.

Another trend that has a major impact on global consumption is the rise of remote/hybrid work as well as the gig economy.

A decade back, people had to work a standard 9-to-5 job that required showing up in the office 5 days a week. Now, I see Gen Z actively adopting a gig economy mindset to make space for other activities in their lives. My cohort, the Millennials, are actively using the ability to work remotely to take more weekend trips and even work from new cities for extended periods.

All these life choices are geared towards more consumption. Lines between business and pleasure travel have blurred, leading to the creation of a new category of travel called “bleisure”. Companies like Airbnb and destinations like Bali are beneficiaries of this shift.

Less kids, more spend!

There is another macro trend globally that I feel is a leading signal of even more discretionary consumption in the coming decade – declining birth rates! China’s population fell for a second consecutive year in 2023. The US has been seeing a long-term decline in birth rates for a decade and a half now. See what Brookings has to say:

Before the pandemic [in the US], births had been steadily declining for many years. There were almost 600,000 fewer annual births in 2019 relative to 2007—a 13% reduction. The size of the COVID-related baby bust and subsequent rebound were meaningful in that context, but they also represent short-term deviations from an ongoing trend of considerably greater importance. Birth counts in 2022 are still below what they were in 2019.
The Brookings Institution

This isn’t limited to just the US or China. Anecdotally in urban India, I am seeing couples choosing to have fewer kids or no kids at all! In my parents’ generation back in the day, each household had 4-6 kids. In my generation in the 80s and 90s, this came down to 2 kids. Now, at least in the Metros, I am seeing this number at 0-1 kids, with DINK (double income no kids) couples being very common at least in my circles. Like how things have unfolded in China, birth rates could start declining even in Tier 2 Indian cities and beyond over the next 20 years.

People forego immediate consumption and save money primarily for 2 things – (1) retirement and (2) handing it down to the kids. Common sense tells me that in the US and India, (a) if the economy continues to grow (so per capita incomes keep rising), (b) inflation is under control, and (c) people have fewer kids, this is a recipe for more wealth and less propensity to save, thus driving up discretionary spends on things like travel and entertainment.

This gravy train should continue until falling birth rates start impacting the quantum of productive population* and therefore, economic growth (like perhaps what’s unfolding in China today). But till that happens, it’s a consumption boom baby!!

*The US is well-positioned to mitigate this risk by opening its immigration faucet to ensure an adequate working-age population.

Implications of YOLO? Higher demand, stickier inflation, generational impact.

YOLO as a persistent global behavior shift implies that one should expect sustained high levels of consumer demand across growing economies like the US and India. This further implies inflation will be stickier for longer than what one might be expecting, especially in discretionary areas where pricing isn’t influenced by the govt. (eg. air travel, hotels, dining, electronics, furniture etc.). It also means consumer demand will keep driving the economic growth engine in these economies, in turn providing tailwinds to stocks of companies that benefit from this consumption.

While YOLO’ing is largely used in frivolous contexts and has become a meme word, it actually describes a powerful behavior shift. And given Covid was a global phenomenon, this shift has happened across multiple economies.

These behavioral shifts, often with underlying milestone events like wars, pandemics, and technology inflections, tend to have ripple effects across generations. The cohort who survived and emerged victorious from WW I came back to consume and enjoy life like there is no tomorrow, driving the Roaring 20s. The cohort scarred by the Great Depression built a deep mistrust for the stock market, a majority of them never investing in it again.

The Silent Generation won WW II and again, came back to bask in that glory, consuming with abandon, building businesses, and making babies. The Boomers grew up in these glorious decades of America, with post-war technology advancements permeating every aspect of their lives.

The peak years of Millennials like myself have been spent in a world that is disproportionately driven by technology inflections: PC ➡ Internet ➡ Mobile ➡ Cloud ➡ now AI. These years have embedded events like the Dotcom crash’01, GFC’08, and Covid in 2020.

Whether or not we choose to ignore them, these events change the mindset of a whole generation, thus impacting our futures in more ways than one, from our jobs and real estate prices to how our stock portfolios perform and the cost of vacations.

While as a disciple of Munger, Buffet, and Howard Marks, I hesitate to believe in anyone’s ability to create macro projections, I do believe that it’s worthwhile to study shifts in human behavior and what long-term themes could emerge from them. While they might not impact micro-decisions, these themes can help us have an appropriate weighting between offense and defense in our overall portfolio of life choices.

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Staying In The Ring Long Enough

While the key to generating outlier returns in any asset class is to be non-consensus-and-right, the “right” can take a long time to play out. Case in point: Bitcoin.

With inflows into Bitcoin ETFs gaining momentum, the price of Bitcoin has reached ~$52k. Driven by tailwinds of broad-based institutional and retail adoption, a $100k price point appears to be an eventuality, with the imagination of believers now extending to $500k levels (Cathie Wood’s Bitcoin price predictions don’t seem that outrageous anymore!).

Was recently discussing this with an old friend who has been a super-early adopter of Crypto (from the $100-200 BTC price days!). As I congratulated him on what I presumed were “giant payoffs from his early conviction”, he said something interesting:

Even those who got into Bitcoin very early, very few of them have been able to hold on to it during the down cycles.

He was alluding not just to people holding Bitcoin, but even those holding Coinbase stock, including employees who worked there. As the SEC cracked down on the company, combined with the FTX scam and plunging price of Bitcoin, even the most ardent believers in Coinbase ended up selling.

Since then, the US landscape for Crypto has completely changed (read my post: Bitcoin ETFs and The Challenges of Digital Gold). With Binance out of the equation and the regulator proactively bringing all Bitcoin activity onshore and under its domestic purview, Coinbase has emerged as the dominant exchange infra backbone for Bitcoin. This has resulted in the stock being up ~128% in the last 6 months!

So why am I doing this hindsight analysis? I think it highlights a concept I think about a lot, especially given its relevance to my job as a venture investor:

To generate asymmetric returns, you need to have the capacity to stay in the ring long enough for your high-conviction yet non-consensus beliefs to play out.

As I wrote in my post ‘An Investing Framework to Find Startup Diamonds‘, the key to generating benchmarking-beating returns in any asset class is to be non-consensus-and-right. However, there is a hidden nuance in this. The “right” can take a long time to play out.

Benjamin Graham, the Guru of value investing, has taught us that any security’s price should ultimately converge to its intrinsic value (calculated by discounting its future cash flows or DCF). However, he doesn’t give any guarantees as to when this convergence will happen. As the OG investor Joel Greenblatt says:

If you do good valuation work, the market will agree with you eventually. You just don’t know when.
Joel Greenblatt

This is a critically important point. Having a strongly held, non-consensus belief is necessary but not sufficient. Translating this belief into actual returns requires having enough staying power (personal and professional) to withstand the gyrations of Mr Market till its view converges with your own.

This also applies to the frequently discussed topic of the importance of timing for a startup. Essentially, in hindsight, every outlier startup seemed to have started at just the right time to be able to get massive market adoption from some sort of secular tailwind. Think of Uber as leveraging that moment in time when smartphones got GPS capabilities. Or Zoom leveraging the rise of remote work through Covid lockdowns.

I have a strong view on this. I believe narratives around timing are all post-facto. Even the best founders and investors can at best, only build strong conviction on a long-term secular trend from first principles. It’s impossible to predict exactly when this trend will reach a tipping point. Brian Armstrong (Coinbase Founder) and Fred Wilson (USV, first investor in Coinbase) spotted the power of Bitcoin early. Still, they could never have predicted the continued prevalence of zero interest rates for a decade, rampant money printing, rise in national debt, and ultimately, Covid as a tipping point for Crypto.

However, what was in their control was having the conviction to stay in the game and keep building for a decade till the market started agreeing with them. For startups to survive this long, this means:

(1) Founders need grit,

(2) Investors need patience; and

(3) The company needs a continuous cash runway.

That’s why the more outrageously non-consensus the founder’s thesis is, the more I advise such founders to watch their burn as:

Having enough runway is key to staying in the ring. Runway comes from either the ability to periodically access capital markets and/or control burn to make the capital last longer. The former is often not in the founder’s control. The latter always is.

I use this Bitcoin/ Coinbase mental model to keep reminding myself to be extraordinarily patient with my non-consensus bets as a venture investor and also to keep reminding portfolio founders about the importance of staying in the game. From a picking perspective, this means indexing on “grit” as a critical founder trait while evaluating new investments.

Want to leave you with this quote from John Maynard Keynes, the father of modern macroeconomics:

Markets can remain irrational longer than you can remain solvent.
John Maynard Keynes (via a Howard Marks memo)

Curiosity As A Networking Cheat Code

Do you struggle with creating an instant connect with a new person during events, dinners, or warm intros? Sharing the cheat code for cracking this problem.

Whatever career you might be pursuing, there is a core aspect that never changes – every business is a people business and our success depends on being able to create an authentic connection with employees, customers, partners, and investors.

Creating this connection is the easiest when there is some sort of shared history or commonality. However, this tends to be a relatively small circle of people that can get tapped out pretty quickly. Our professional and personal growth depends on continuously expanding this circle by being able to connect with and influence a fresh set of people, perhaps each week if you are in sales or are a founder, but every few months at the minimum for most of us.

We meet these new folks at events and conferences, through warm introductions from shared networks, and in many cases now, establishing the first contact on social media. Given the noisy world we live in, each one of us barely gets a few minutes during a first meeting to establish chemistry with a complete stranger. If we fail to create a positive vibe during these initial minutes, it’s unlikely that this relationship will ever enter our professional funnel for a possible collaboration later on.

As a venture investor, I am at the mercy of this problem statement every day. Being able to quickly bond with a new set of founders, LPs, co-investors, and operators is a core part of the job. I totally concur with this thought from Semil Shah (Haystack):

Venture capital is a people-flow business.
Semil Shah (Haystack)

Personally, going to events and mixing around has given me unprecedented ROI (I previously shared my events playbook – “Networking at Events for Introverts“). I have also made some wonderful friendships by doing 1:1 meetings via warm intros.

During these conversations, I have tried various mindsets, approaches, and mental models to deconstruct interacting with strangers. I keep running experiments across mixers, dinners, and 1:1s, introspecting what worked well and what didn’t in a particular context. Essentially, I have been trying to distill it down to whether there is something fundamental that seems to work across contexts, and which, therefore, merits being incorporated as a core behavior.

One such element I have seen work really well is demonstrating a natural curiosity during the first few minutes of interaction with a new person. With each passing year, I have come to believe more and more that:

The cheat code for faster career growth is having the ability to influence strangers by demonstrating curiosity.

We live in a highly egotistical, self-absorbed world where everyone is a creator, trying to market their personal brand and posting content about themselves. Most people love to talk, and talk only about their stuff!

I have observed very few people taking a genuine interest in another person’s journey. Asking interesting questions of someone you have just met has become a lost art. The social conditioning of this era drives people towards talking more and listening less.

However, humans have a basic yearning to be heard. Have you noticed that when someone appears to be taking interest in what you have to say, you feel a natural pull towards this person? In this attention-starved society, when someone devotes that scarce currency to a first conversation, it’s extremely powerful.

I see this working in so many situations. When pitching to a potential customer, the key to closing a deal is taking the time and devoting attention to understanding their pain points and concerns, instead of mindlessly plonking your product on them.

An investor can leave even the most seasoned founders with a warm feeling during the 1st meeting if they take the time to go beyond superficial pitching theatrics and truly try and understand their journey, their backstory, and what they have painstakingly built.

The key to a successful partnership is listening to the other side to understand their goals, motivations, and what they care about, including the personal journey and incentives of the individual championing the deal.

Genuine curiosity can be incredibly disarming. It’s about putting the constant internal self-talk to the side, being in the moment, and focusing on understanding the other person. If this becomes a consistent part of your personality, you will automatically see this translating to a bunch of new meaningful relationships each year.

AI Musings #5 – Opportunists vs Believers

Sharing some observations and working hypothesis on Opportunist vs Believer founding teams in AI.

My biggest challenge as a venture investor in AI right now is figuring out which of the following 2 camps a particular founding team belongs to:

Opportunists – who are trying to leverage this moment in time when the market has massive curiosity about AI.

Believers – who have high conviction, and are truly mission-driven about AI.

This is a critical evaluation point for these early AI deals. As previous super-cycles have shown us, a bubble-bursting trough in the space is inevitable in a few years (perhaps as soon as 3-5 years?). It will be brutal like previous resets – capital will get reallocated to the winners and dry up for the rest, exits will be on brutal terms, customers will tighten their belts, early-stage talent will flee and the general sentiment will turn from greed to fear.

In my experience, Opportunist founding teams are less likely to survive this trough. It will require grinding out on fumes and focusing on real customer problems vs vanity metrics and perpetual fundraising. It will need gut-wrenching decisions that sacrifice short-term gratification so that the long-term upside can be captured. It will require possibly resurrecting the company many times from the dead.

Being able to do all this requires extremely high conviction deep down in the gut. Founders who are Believers will have this conviction in their DNA, and when the cycle turns negative, this will become their competitive advantage.

Given this is turning out to be a key evaluation point for AI deals, have been thinking through what leading signals can be used to spot Believers with higher probability. Here are some working hypothesis thoughts on this:

[Disclaimer: am just thinking out loud here so please take this with a pinch of salt. This is nowhere near any gospel of truth, nor do I have significant experiential validation around these points given we are literally in the first wave of AI deals].

1/ Pre-ChatGPT AI builders – likely to have been working in AI much before ChatGPT was launched. They were most likely building with ML, NLP, and neural networks in a Big Tech team, a lab, a university, or some sort of R&D/ academic environment.

2/ Pre-AI domain experts – likely to have been working deeply in a specific domain/ industry/ sector/ function from pre-AI days and are now adopting LLMs to carry forward their domain work and solve customer problems that were previously unsolvable or unviable.

3/ Young tinkerers – likely to be fresh grads who started building AI-native products as a hobby during university, maybe as part of a side hustle, or even just out of intellectual curiosity. They would have likely built products and hacked a few early users even without “doing a startup”.

These are only some of the personas I have been thinking through. As I meet more teams, I will keep adding to this list.

If one looks at how the early days of Web 1.0 played out (eg. in eCommerce and Search), most first-movers ended up dying. The 2nd generation companies leveraged both the market that was created by the 1st gen, as well as learnings from their failures, to create new categories and emerge as viable businesses.

History doesn’t repeat exactly but often rhymes, thus requires being even more thoughtful about which companies to back in this 1st generation of AI. In my case, as a US-India corridor investor, there is an additional complexity to think through – how will AI companies being built out of India compete with those in Silicon Valley? Who is most likely to be stronger in which part of the AI stack?

With domestic data being of strategic importance to each country and the rise of country-specific models, is AI going to be an extension of the globally decentralized software product/ SaaS story of recent years? Or will there be opportunities in ring-fenced, domestic AI in each major geography?

These questions and unknowns are what make the present times in AI investing both interesting and challenging at the same time. To manage this context, I am trying to be open-minded, learn fast, and think from first principles as much as possible. But at the same time, balancing this default-optimism stance with being non-trigger-hungry, consciously thoughtful, and taking the time to build personal conviction on each opportunity.

PS: check out the previous post #4 in the AI Musings series – How To Differentiate As An AI Applications Startup?

AI Musings #4 – How To Differentiate As An AI Applications Startup?

Discussing two areas startups can focus on to create competitive differentiation in the AI applications layer – (1) data and (2) product flows.

Over the last few weeks, I have met a few exciting startups building in the applications layer of AI. As the landscape stands today, the foundational LLMs layer is likely to be dominated by a mix of open-source, Big Tech, and perhaps 1-2 hyper scalers (eg. Anthropic). The cloud infra (compute, safety, security etc.) to deliver these model capabilities will definitely be served by the Big Tech cloud players.

This leaves 2 categories for startups to exploit against these large competitors – (1) Applications and (2) Dev Tools. On the latter, I don’t understand it deeply enough to have a view on it (yet). However, the Application layer is something I get, and therefore, have some working POVs on it.

Almost all AI application layer startups I am seeing right now are essentially using ChatGPT (+ Bard and Llama in a few cases) to build features that solve sharp use cases in specific verticals. Based on observation, some low-hanging verticals that founders are going after include Insurance, Marketing, Sales, and HRTech with AI-generated content being a horizontal ingredient in most of these products (eg. automated email generation, stitching together a marketing video, crafting a training course outline etc.).

In all these cases, I am still struggling to understand how these startups can create competitive differentiation or moats purely by building features on top of hyper-scaler APIs. To take a step back:

For a new technology inflection to create viable startup opportunities, there need to be sizable areas where new companies are significantly better positioned than incumbents to leverage this new technology and solve unaddressed customer problems.

This is a really important point. For a startup to be viable, it’s not enough to just be an early adopter of cool technology and build new products before anyone else. The startup has to be able to create significant differentiation against entrenched competition too. Eg. Apple beat IBM in the PC inflection, Amazon beat offline retailers in the Internet inflection, Instagram and WhatsApp beat Facebook in the mobile inflection, and Figma beat AdobeXD in the cloud inflection.

This is the aspect where I am pushing all AI application founders I meet to start thinking through and strategizing from Day 0. A couple of ways to potentially drive competitive differentiation have emerged from these working sessions:

1/ Access to data

While ChatGPT is great for bootstrapping specific use cases, eventual product differentiation will emerge from startups fine-tuning their own LLMs (with open-source models as a starting point) using proprietary data sets for industry-specific use cases.

To put it simply, foundational models will keep doing a great job of adding horizontal knowledge. Startups will need to do the work of incorporating deep vertical knowledge into the models.

Here, access to the ‘right’ customer data will be critical. But then, entrenched incumbents would already have access to much more data than a 0-to-1 startup. So, how does a startup create a data advantage?

One way could be to identify unsolved pain points for customers that large pre-AI competitors aren’t going after, either because they are contextually unviable (Innovator’s Dilemma), were unsolvable pre-AI, or due to organizational inertia.

In these cases, AI-native startups can leverage their speed to get to the ‘right’ customer data sets before anyone else, and start creating an edge via custom fine-tuning and benefiting from faster learning cycles.

It’s interesting that the underlying driver of this differentiation is still good-old startup execution, rather than just building AI-first features. The company would still require classic software execution (founder-led sales, figuring out ICP, setting up GTM motions, etc.) to succeed.

2/ New product flows

Another area where startups could do better than the entrenched competition is putting in the work to develop AI-first product flows. We saw this happen in previous tech inflections where new capabilities and form factors gave rise to new ways of doing specific jobs. Eg. Apple cracked the smartphone user experience while Nokia struggled. Or Figma figured out how designers should work and collaborate with other functions in a fully hosted, in-browser experience, while Adobe continued to be stuck in its old UX.

Given the vast range of new capabilities that AI is unlocking (eg. chat-based UX, AI ‘agents’ to deliver specific tasks that underly use cases), it’s reasonable to expect a plethora of new workflows to emerge across customer segments. Many of them will require absolutely fresh product thinking to crack, something that pre-AI product teams at established companies might struggle with.

Similar to the ‘access to data’ point earlier, the underlying driver of this product flows differentiation again will be good-old, startup-style product management – Paul Graham’s “do things that don’t scale”, starting with a wedge of focusing on a very-specific customer persona and pain point, frantically iterating on it, and in the words of Brian Chesky, “Focusing on 100 people that love you, rather than getting a million people to kind of like you”.

Putting things together…

If one looks at both the above areas of potential startup differentiation, the way AI might end up creating viable startup opportunities is not the LLM technology itself, which will become baseline, widely available (like cloud today), and likely open source (similar to programming languages like Java and Python).

Rather, the drivers of value creation by startups will be in:

(1) What’s needed to effectively leverage these LLMs to solve verticalized, deep industry-specific problems – eg. pre-AI, a 10x backend engineer was needed to leverage the cloud. Post AI, specific datasets will be needed to leverage LLMs.

(2) 2nd and 3rd order impact of AI on product experiences and workflows – Figma and Notion took years of fresh thinking and iterations to reimagine collaboration UX in the cloud. AI-first use cases will require similar untethered, ground-up product thinking to deliver these capabilities effectively to customers.

What does this mean for venture investing?

It means even in the post-AI world, investors should continue to look for founding teams that demonstrate many of the classical startup traits, a few of them being – (1) ability to unearth a unique customer insight, (2) product thinking to be able to solve for it, (3) GTM skillset to be able to create a differentiated business out of it, and (4) grit to last through the journey.

Essentially, might be a good idea to avoid AI overthink and keep doing more of the basics of venture capital.

Note: check out the previous post #3 in this AI Musings series – LLMs for Beginners.

Negative Expectations Often Lead To Surprising Positives

How expectations versus reality has unfolded over the last couple of years—from inflation and recession to Google and crypto—prompting me to share an interesting working theory.

Well, that’s a surprise! Contrary to all expectations, the US economy grew 3.1% in 2023, compared to 0.7% in 2022. The stock market continues the rally from 2023-end – the S&P500 recently hit all-time highs, powered by the so-called ‘Magnificent Seven’ BigTech stocks.

I remember the OG Stanley Druckenmiller telling CNBC he’d be stunned if a recession didn’t happen in 2023. Going against all such predictions and in the face of interest rates being at 23-year highs, the fact that the economy is poised for a soft landing has got me thinking about market expectations and their bearing on how things eventually play out.

I have a working theory on this – based on observation, I believe that when the market has too much of consensus expectations around a negative event, it somehow reduces the probability and/or the intensity of that negative event.

Let’s play out some thought experiments on this…

#1 Inflation

In 2022, the market (including the Fed) had a consensus expectation that inflation would remain sticky and continue to rapidly increase, thus explaining the steepest rate hikes in history.

However, given this widespread expectation, both consumers and businesses likely started to proactively tighten their belts, managing demand, reducing costs, and improving productivity to increase supply. The combined impact of this proactive action perhaps brought down inflation much faster than the Market expected?

#2 Recession

The market expected that given the steepness of rate hikes and how that has played out in the past, a recession was imminent. This widespread expectation likely prompted both individuals and businesses to proactively become more fiscally prudent, improve their productivity, and essentially, start doing more with less. These efforts perhaps contributed to avoiding a recession and creating a soft landing instead?

#3 Google

Post the launch of ChatGPT in Nov’22, the market held a consensus expectation that Google’s search business would be rendered irrelevant in the new chat-based AI paradigm. The stock hit a 52-week low in early’23, with the likes of Brad Gerstner (Altimeter) proclaiming that Google’s monopoly was over.

However, the rise of OpenAI and negative market expectations likely woke up Google from its slumber, forcing the management to focus, play its hands in AI (Bard, investment in Anthropic), and do some tough belt-tightening (unprecedented series of layoffs and org. flattening from a company that has been considered as a safe haven for employment over the last 15 years).

#4 Crypto

With the SBF scam, and the SEC cracking down on Coinbase and Binance, many thought that the US is completely closed for business in Crypto. In fact, I remember this being explicitly mentioned in one of the All-In Podcast episodes. However, less than a year after these events, the US has approved Bitcoin ETFs, bringing crypto to the mainstream as a legit asset class (read my post “Bitcoin ETFs and The Challenges of Digital Gold“). As it turns out, the SEC crackdowns were not to shut down Crypto but to clean it up so that it could come into the mainstream.

Connecting the dots…

It seems like widespread negative expectations have a tendency to catalyze a chain of mitigating actions by various stakeholders. In this era of social media, this happens even faster than expected. Perhaps, the human behavior of “loss aversion” creates a sense of urgency, precipitating tough decisions and better execution.

Modeling future scenarios…

Let’s use this mental model to run a few more thought experiments on things the market has negative expectations on right now.

#1 AI taking away jobs

The market has a widespread narrative that AI will end up taking away most knowledge jobs as we know them. However, workers are already aware of this shift and are working to counter it eg. upskilling themselves, starting side hustles to create financial buffer etc.

In parallel, universities are already realizing that their coursework might be outdated soon. They are frantically working to upgrade content, get more AI practitioners involved, and introduce coursework that requires “building” as a way to learn.

Enterprises too, are keenly aware of how a post-AI world will require a different set of skills and are already starting to re-train and upskill employees. Further, in less than a year of ChatGPT’s launch, govts. across the world are proactively thinking through the socio-economic ramifications of AI, including how to stay nationally competitive as well as re-distribute wealth in an increasingly unequal world.

Contrary to current market expectations, all this could create a positive surprise on productivity and job creation in an AI-driven world.

#2 China

The market has overwhelmingly negative expectations of China, including how Xi is taking the country back to its communist roots, the population is de-growing and it’s getting geo-politically discarded by the West.

However, this multitude of adversities could actually rally the CCP to undertake path-breaking reforms, the general population to become more productive, and the country generally coming together more effectively to come out of this mess. Again, more likelihood of a positive surprise from here on.

#3 San Francisco

The market consensus is that San Francisco is America’s Gotham City, a decaying region that the next generation of talent is unlikely to choose to live in.

However, these dire circumstances are already putting massive pressure on local political leaders, forcing corporate leaders like Marc Benioff to speak up against how the city is being run, large budget deficits bringing administrative incompetence to the fore, and the city’s residents finally deciding to speak up and drive political change. I won’t be surprised that all this drives a positive change in SF faster than anyone expects.

#4 Commercial real estate

I watched the recent 60 Minutes episode that talked about how commercial real estate is getting decimated in cities like NY, with even marquee buildings at all-time-high vacancy rates. The market has a consensus expectation that work will become overwhelmingly remote and the concept of offices will cease to exist.

However, this rock-bottom could perhaps force developers and landlords to innovate and re-think what the concept of an “office” should be going forward. And, even force cities to upgrade regulations on how urban buildings can be converted to mixed-use. Irrespective of technology, the human need to connect and collaborate with others, as well as be outdoors to refresh and re-energize, remains the same.

Closing thoughts…

Of course, these are just probabilistic thought experiments. As a disciple of Howard Marks, I am always wary of forecasting. However, this working theory that consensus negative expectations often end up seeing a surprise on the positive, is a useful mental model. It helps in not getting overly carried away with the crowd’s narrative, thinking through the likelihood of various scenarios playing out, and positioning yourself optimally.

The Ground Is Shifting For Tech (More Than We Realize)

From anti-immigration and de-globalization to tech org restructuring and vertical SaaS headwinds, Tech is staring at a drastically different world going forward.

Was chatting with a VC friend earlier this week where we were discussing the US-India corridor and what the future looks like for cross-border SaaS from India.

During the convo, I ended up saying this – “I can just feel that the ground seems to be shifting in a big way for tech and most people aren’t fully recognizing it”. Btw, I repeated this line to my better half the next day in some other context too.

It just feels like a lot is changing at the same time, both macro and micro, and we as tech workers caught up in the daily grind of keeping the ship afloat in our businesses and personal lives, aren’t fully realizing how big some of these shifts are and how they will massively impact our futures.

Consider this laundry list of things unfolding as I write this (sorted from macro to micro, but in no particular order of importance):

1/ Military conflicts

As the world barely came out of Covid, it’s now faced with multiple global conflicts – Russia-Ukraine, Israel-Hamas, Red Sea, and now Iran has frikkin’ fired missiles at Pakistan (who would have thought?).

In the medium to long term, we are also staring at other potential standoffs like China-Taiwan, China-Japan, India-China, and more fronts in the Middle East.

While they might seem distant, these geopolitical tensions can have an indirect economic impact, especially on inflation and cross-border activity.

2/ Social tensions

The Israel-Hamas conflict is seeing side effects on the streets in the US. Who would have thought that top Ivy League campuses like Harvard would see active anti-semitism tensions?

This tension has powerful political and economic actors at the center and therefore, can have a second-order yet decisive political impact, especially with the 2024 Presidential elections around the corner.

3/ Anti-immigration

The US is dealing with a massive illegal immigration problem, with videos of thousands of people crossing the border via wall breaches going viral. Even other developed countries like the UK and Canada are dealing with a major rise in immigration.

In times of weak macros, high inflation, and a rising perception of hardship, I expect immigration to be a major election issue this year, particularly in the US.

Source: Ruchir Sharma (Rockefeller International)

4/ De-globalization

Candidly, I have been a big beneficiary of massive tailwinds of globalization starting in the early 2000’s. Many of the companies I worked for in India served US customers. The venture firm I worked for had US LPs. I moved to the Bay Area and became a global expansion operator. My startup had a distributed team across 4 countries.

At present, it definitely feels like these globalization tailwinds have weakened considerably. I am reading about Indian founders struggling to get US visas, the EU clamping down on migration, and China falling out of favor in terms of global trade and people movement.

If these tailwinds continue to weaken, this is a massive change in a key assumption that underlies the career plans of many global tech workers, especially those from emerging markets. To get a sense of this, check out this awesome thread on X that shares how Indian Masters students in the US will struggle to find jobs this year.

5/ The decline of China

China has come out in the open as an overtly aggressive competitor to the West. At the same time, Xi is executing a drastic socio-economic reset domestically that has decimated an earlier-vibrant tech sector. Noted economist Ruchir Sharma recently cited how in its peak years, China used to attract ~$100Bn of FDI in a single quarter, and now, its FDI has de-grown in Q3’2023.

I remember being in awe of China’s infra, talent and execution focus while working at Alibaba. That just seems like a dream now. I never imagined that I would read headlines about 21% unemployment and disillusioned youth in an energetic economy like China.

What are the repercussions of this? As Western companies pull out investments from China, this is an opportunity for other emerging markets like India and SEA to capture parts of this supply chain being diversified.

6/ Higher Interest Rates

From operating in a near-zero interest rate environment for more than a decade since GFC, the Fed has now executed the steepest interest rate ramp ever.

When the cost of capital is low, an economic party begins. Public stocks appreciate given the denominator effect. People borrow more so housing demand goes up and homeowners feel richer. Companies lever up and aggressively invest in physical infra and talent.

At the same time, investors start searching for higher yields given low risk-free rates, thus boosting illiquid-high-return asset classes like venture capital and private equity.

While this post-GFC ZIRP party was in full swing, Covid took it to a new crescendo courtesy of additional QE and stimulus packages. As everyone in the party reached peak highs, a neighbor (inflation) called the cops (Fed), and the party abruptly ended (interest rates rose from 0.25-0.50% in Mar’22 to 4.75-5.00% in Feb’23).

While the highs of the ZIRP party have been gradually coming off through 2022 and 2023, who knows what the long-term impact of this prolonged loose monetary policy will be? Millennials like me have largely worked and grown up in ZIRP, creating our goals, expectations, and lifestyles according to what we saw. Are we ready to re-configure our lives in this new era of higher interest rates?

7/ Tech org restructuring

The recent Big Tech layoffs in the Bay Area are much more significant than many people imagine. For the last 15 years, this compact region has been used to massive jobs getting created by default, salaries rising on auto-pilot, and major equity upsides being captured by RSUs and options. Forget layoffs, anyone working in the Valley since 2010 has only seen an era of multiple job offers and compensation ramps.

This scenario seems to be changing at a highly disruptive rate. Elon catalyzed it by doing deep RIFs in X, including eliminating entire functions altogether. Across mid and large tech companies, am now seeing orgs getting drastically flatter, classic white-collar functions like product management, ops, program management etc. either getting extremely lean or even going away altogether.

I fear that unless a tech worker can either build (code) and/ or sell, they will struggle to see adequate demand for generic tech ops skillsets. At the minimum, this will reflect in drastically restructured compensation packages.

8/ Rise of AI

I am lucky that as a venture investor, I get to see cutting-edge products before the world has even heard of them. From what I am seeing in terms of AI-powered products, both infra and application layer, I fear that many jobs as we know them will get automated away rapidly.

Individual developers and software dev shops have already started using AI for testing and debugging code. This was a job typically done by entry-level IT services talent in offshore centers like India.
Making creatives for digital ads and other low-complexity design tasks are being automated away rapidly.
Google has been drastically cutting down on its ad sales team, expecting a lot of that work to get automated by AI.

Ever since I entered tech in 2011, I have seen engineers be the kings both in startups and big companies. While outstanding engineers will always be gold, the last decade saw even mediocre engineers with basic skill sets reap massive financial rewards mainly due to the supply-demand imbalance.

As we enter the age of AI agents, I am not sure if this will be the case going forward. PS: for more insights on how the AI landscape is playing out, check out my AI Musings series – #1 How The Odds Are Stacking Up?, #2 OpenAI DevDay and #3 LLMs for Beginners.

9/ Bitcoin becomes legitimate

The biggest news of 2024 already is the SEC green-lighting Bitcoin ETFs (see my post ‘Bitcoin ETFs and The Challenges of Digital Gold‘). From being an edgy piece of technology for innovators in 2013, to being discovered by early adopters like myself in 2017, hitting all-time-highs in 2021, then seeing large-scale frauds like FTX in 2022, the SEC suing Coinbase in 2023, and now, getting recognized by the same SEC as a mainstream asset class – whew, who would have thought?

Again, I don’t think most people realize the significance of this move. Over a decade, pure, grounds-up, community-driven adoption of Bitcoin by common people has created a new asset class, helped it travel from Silicon Valley to Wall Street, and forced the regulator to recognize it.

What does Bitcoin going mainstream say about our current monetary systems? Will it change the balance of power between the wealth hoarders (Boomers) and the wealth aspirers (Millennials and Gen Z)? With cash fading away globally in various respects, is this the dawn of pure Internet money? Are there going to be any other ripple effects of the expected mainstream adoption of Bitcoin going forward?

I feel these are open questions with massive implications for who will hold wealth and power over the coming decades.

10/ Startup and VC shakedown

The last 2 years have been the most turbulent for the startup ecosystem since GFC. Venture financing in the US has been on a major downward slide, from ~$348Bn in 2021, to ~$242Bn in 2022 and then, another estimated 30% drop to ~$171Bn in 2023. Startup shutdowns have hit all-time highs, and given the drastic reset in public market comps, valuations in both early and growth-stage financing have drastically come down.

As recently as Q1 2022, just 5.2% of new fundings on Carta were down rounds. In Q3 2023, that figure was 18.5%, continuing a nine-month stretch in which nearly one out of every five rounds raised by startups resulted in a decreased valuation.
Carta

This shakedown is reflected in the VC ecosystem too. A major Boston-based VC firm OpenView with $2.4Bn in AUM abruptly shut down in Dec’23. More recently, hard-tech VC firm Countdown Capital wound down operations, stating the following reason – “funding industrial startups is not inefficient enough to justify our existence, and larger, multi-stage venture firms are best positioned to generate strong returns on the most valuable industrial startups”.

I believe that the 2023-25 vintage of startups will be built with very different philosophies, fundamentals, and capitalization strategies. In parallel, the 2020-21 vintage startups will need drastic re-wiring that in most cases, might just not be possible, leading to large-scale write-offs (read my post: Cheetah in the Rainforest: 2021 Vintage of Venture).

Another related view that I recently posted on X – “access to capital was widely considered a competitive edge but it now looks like a view that should be carried with contextual caveats eg. applicable only in low cost of capital macros and in specific types of startups like those with network effects”.

11/ Vertical SaaS headwinds

Within the venture landscape, I wanted to do a quick double-click on vertical SaaS.

With weak macros and the rise of AI, most point SaaS solutions have seen intense customer headwinds over the last 3 years. Startups selling to other startups have been hit particularly hard (many YC companies fall in this category), given the customers themselves are doing brutal cost-cutting.

Enterprise customers too, have been under pressures of layoffs and reducing general opex, hence creating push-back on the per-seat pricing model. See this prescient thread from David Sacks in late’2022 when SaaS was bottoming out.

Based on anecdotal conversations, am also seeing many customers now focusing on reducing software fragmentation and trying to consolidate tech stacks to bring down costs and complexity. In a sense, this seems to be a move away from buying a portfolio of unbundled SaaS solutions, and towards buying bundled software that addresses multiple use cases from the same vendor. In fact, I feel there is an understated opportunity here for startups with strong PMF to really push up their ACVs by solving multiple use cases for customers.

The biggest question mark is on the future of Covid-boosted products. Hopin, one of the poster children of the era, sold for peanuts to RingCentral. Point SaaS products in productivity, sales enablement, and workflows accelerated in 2020 but with the current customer behavior, it remains to be seen if they are vitamins or painkillers, and whether their differentiation and value to customers is strong enough to justify their independent existence.

Closing thoughts…

It’s probably the January-effect but this week got me organically thinking and connecting the dots on all that is unfolding in the world right now. The venture investor in me is part-excited for all the new opportunities this change is going to bring with it, and part-concerned for how both myself as well as existing portcos need to navigate this massive change.

Having adaptability and a growth mindset is going to be key. I have a strong resolve to be on the right side of this change, and also working to transfer this conviction and learning to the founders I work with.

Bitcoin ETFs and The Challenges of Digital Gold

While the recent approval of Bitcoin ETFs is a landmark, there are challenges in the way of Bitcoin truly serving the role of ‘digital gold’ in investor portfolios.

Feels great to be back in action after a year-end break. Hopefully, a few weeks of “no-writing” would have energized the brain to put out even better content in 2024.

The year has already started with the big bang news of the SEC approving bitcoin ETFs. An average joe investor in the US can now buy and sell Bitcoin as easily as individual stocks and mutual funds. Many large asset managers like BlackRock, Fidelity, and ARK have already been greenlit to list. As of Jan 12, Bitcoin ETFs have already seen $655Mn in net inflows on the very first day.

In my view, US regulators have shown remarkable foresight by going this route. Bitcoin had already become too mainstream, with massive institutional and retail exposures via crypto exchanges, many of them offshore. By bringing it as a formal asset class within the mainstream of asset management, the SEC is actually protecting the interests of investors by getting Bitcoin investments to flow through regulated trading platforms on US soil that are under necessary regulatory oversight.

I loved how Vijay Boyapati captured the significance of this event on X:

One of the big positives of Bitcoin ETFs that is often overlooked is the massive reduction in KYC inertia that investors so far had to go through while buying directly on crypto exchanges. This itself should unlock a massive set of new adopters.

Now that we have discussed all the positives, let me highlight one concern I have been thinking about. While Bitcoin’s value as a potential non-government medium of exchange, or ‘digital money’, has always been played up by early believers, it’s looking more unlikely by the day. In fact, in light of the FTX blowup, Binance pleading guilty to Federal charges, and now this regulatory approval, access to Bitcoin’s monetary system is becoming increasingly dependent on existing systems controlled by the government. In this scenario, it’s highly unlikely that governments of major economies will let Bitcoin emerge as a decentralized alternative to fiat currencies.

If this is true, Bitcoin’s main value proposition for investors then becomes similar to that of a scarce commodity, a sort of ‘digital gold’ with supply capped at 21 Mn Bitcoins, and with characteristics (durability, fungibility etc.) that humanity at large finds valuable (like shiny gold or sparkly diamonds).

Source: The Bullish Case for Bitcoin by Vijay Boyapati

Taking this line of thinking forward, Bitcoin then is expected to compete with physical gold in terms of allocation within investor portfolios. Except, it suffers from one key drawback vis-a-vis gold. I believe that one of gold’s standout features is its relative lack of volatility, as well as physical illiquidity. Bitcoin lacks both of these qualities.

Over the last 1 year, the per-ounce price of gold has oscillated between a high of $2,078 and a low of $1,809. Even over the last 3 years, the price action has been between $1,618-$2,078. A fairly tight price band, compared to Bitcoin oscillating between $10k and $40k!

That’s why, barring institutional trading desks, retail investors don’t tend to minutely track their gold exposures. Asian households, especially Indians, tend to also store gold in the physical form via jewelry, bars, and coins. Given its lack of divisibility, and low ease of transportation, storage, and selling, physical gold is also considered largely illiquid and more like a rainy-day reserve. I have never seen an Indian household run Excel math on the valuation of their physical gold reserves like they would for their stock portfolios.

This relative lack of volatility and liquidity is actually a feature, not a bug, for physical gold. It helps in its uninterrupted compounding and acts as a multi-generational store of value for families. In practice, people either buy or inherit gold, and then forget about its valuation. This is the best way to accrue compounded returns for any asset.

However, Bitcoin as digital gold won’t demonstrate these features. It’s perhaps the most volatile asset class out there, traded and marked-to-market 24 x 7, 365 days. Now with ETFs, an average joe investor can continuously track its price action, and trade in or out of it. This will also lend it much more to FOMO trading.

If this investor behavior plays out with Bitcoin ETFs, it will be more akin to day trading or F&O rather than traditional gold investing. Whether this is good or bad depends on what an individual’s goals are.

Keeping this risk in mind, I would urge new retail investors into Bitcoin to clearly articulate their goals behind investing in it, decide their investment stance (trading, buy-and-hold, frequency of inflows etc.), and manage their exposure as % of overall net worth. This would ensure that Bitcoin can truly play the role of digital gold in your portfolio.

One Year Of Writing (+ Top 15 Reflections From 2023)

Sharing my experience of uninterrupted weekly writing for a year, as well as general reflections and learnings from 2023.

Ahh…writing the last post for 2023. I intend to take a break for the next 3 weeks and recharge my writing brain. Hopefully, it makes the posts next year even better!

First, I want to thank all my readers out there for supporting An Operator’s Blog. Late last year, I committed to writing every week about my observations and learnings related to Building (startups), Investing (venture capital), and Life (parenting, marriage, and urban mid-life,) and doing it as candidly as possible. The idea was to share research, notes, experiences, and anecdotes from my life, in the hope that each post helps at least one person in their quest for seeking answers.

I had no way to predict (and still don’t!) what the reader persona of this blog would find useful and entertaining. Hence, I used the golden rule of creativity – create for yourself first. Each week, my endeavor has been to write about things I personally find interesting, covering topics I have a natural curiosity about.

Standing today, I can see that this blog has become some unique mix of a personal journal, professional notes, analysis memos, whiteboard, and scratchpad. I don’t know whether this is ideal or not, but am consciously not forcing a specific structure on it. I try and follow my natural flow of curiosity, keeping the writing as organic as possible. Hopefully, it then has a better shot at resonating with a certain section of the audience.

Even with niche topics like venture capital, public markets, mental models, and building startups, An Operator’s Blog has touched ~7,000 active readers in the last year.

I also write a weekly newsletter where in addition to long-form posts, I share curated content, fresh ideas, and other thoughts to consider and reflect on. The subscriber base now includes some of the top founders, investors, and operators in their respective areas. PS: if you find this intriguing, subscribe to the newsletter here.

This traction, combined with the qualitative feedback I regularly receive, tells me there is something of value here. Some of my friends have asked how I manage to gather the energy to write about diverse and intense topics every week. The secret is the above – getting feedback from readers about how a particular post impacted their thinking is the ultimate reward.

An Operator’s Blog is both my product and my art. One of my core life pursuits is to keep improving it and with each new post, make it more useful and interesting for you.

Closing out this year’s last post by sharing my top 15 personal and professional reflections from 2023. Just something for you to chew on during the holidays!

#1 Interest rates impact our lives much more than we imagine.

#2 Operating under scarcity leads to better capital allocation decisions.

#3 Entry price matters in venture investing.

#4 No one is going to share their best deals with you. Having your own proprietary deal flow is critical.

#5 Attending events is unscalable, yet extremely high ROI.

#6 When the sun is shining, make sure you make hay. The clouds are always around the corner.

#7 The best way to sell is to “not sell”. Instead, do Inception.

#8 Children don’t do what they are told. They do what they observe.

#9 Everyone gives you money when you don’t need it. No one gives you money when you desperately need it.

#10 The best things in life happen organically. Let the natural flow of life take its course.

#11 Charlie Munger’s quote: “The key to happiness is low expectations” is unbelievably true.

#12 Having a high income isn’t enough. The key to wealth creation is owning assets.

#13 It’s important to start early to ensure you are on the right side of compounding in anything.

#14 A good way to bond with new people is to be genuinely curious about their journeys.

#15 Even the smallest achievements of your child give unmatched satisfaction to the soul.

Wishing you all a happy new year. See you in 2024!

A Talent Scout Mindset For VC

Outlier venture performance requires being non-consensus-and-right. Framing your role as a Talent Scout, vs. just a Picker, improves the odds of finding such opportunities.

I strongly believe in Howard Marks’ framework that for better than average investing performance (i.e. beating the benchmark), an investor needs to be non-consensus-and-right. My own derivative framework for venture investing is to look for High-Signal-Non-Consensus deals – companies where I am catching strong leading signals, but which the investor-crowd is struggling to understand.

Executing this strategy well requires consciously looking for:

(1) Overlooked markets, and/ or;

(2) Underestimated founders.

If both the market and team were hot, the company by definition, would be consensus. Consensus deals get significantly bid up in price, and as Howard Marks frequently says, high prices indicate low future returns. Therefore, through both learning from OGs like Howard Marks and observing my portfolio’s behavior over the last decade, I have gradually come to believe that entry price definitely matters in venture investing.

While evaluating teams, the role of a VC is often defined as that of a Picker. The best GPs possess a combination of 3 abilities:

1/ Analyzing tangible skills for founder-market fit.

2/ Using past experiences to pattern-match for intangibles like grit.

3/ Having a 3rd filter of intuitive judgment that may override the previous two.

While these features are cool for venture capital in general, they might still fail during the specific quest for identifying underestimated founders. By definition, many of these founders don’t have classic indicators of tangible skills such as Ivy League backgrounds, Big Tech work experience etc. Further, they can have quirky, irreverent, or misfit personalities, so pattern-matching with past venture-backable personas will also not give the right output.

The 3rd feature of intuitive judgment becomes overwhelmingly important in this scenario. Therefore, to describe a venture investor who is on a conscious quest to discover underestimated founders, I prefer the framing of a ‘Talent Scout’ over that of a Picker.

To highlight the mindset of a Scout, here’s a cricketing story once shared by the famous Pakistani bowler Shoaib Akhtar, the fastest in the world at that time who regularly hit the 150-160 kmph range. As an unknown player harboring ambitions of playing for the national team, Shoaib once turned up for trials that were being run by legendary cricketer Zaheer Abbas. More than 3,000 kids turned up so to grab Mr. Abbas’s attention, Shoaib started running laps around the 3 km cricket ground in sweltering heat. A kid hungry enough to be doing this madness caught the legend’s eyes. He asked Shoaib to bowl one ball at the nets, and the rest is history!

The mindset of a Talent Scout is to focus on developing a ‘Feel’ for talent, judge how strong this Feel truly is, and then have the courage to let it become the basis of strong conviction even in the absence of other tangible signals. What’s the source of this Feel, you ask? That’s the alpha, the x-factor of the Scout. Sometimes it’s a unique worldview of what it takes to win in that particular game. It can also be just a superior reading of human behavior. Often, Scouts can access a subconscious intelligence, built up over many years but still hard to precisely explain.

When meeting non-consensus founder talent, I have found adopting the mindset of a Scout to be immensely helpful. It’s a very different context from evaluating a typical venture-backable persona or a relatively proven team, and therefore, this change in mindset leads to an interaction of a very different flavor.

The hope is that having a talent scout mindset leads to an increased likelihood of non-consensus-and-right investments, thus positioning the portfolio for generating venture alpha.

Closing out with this line from my friend Manish Singhal who runs the deeptech fund Pi Ventures – “We don’t have proprietary deals. We create a proprietary view on the same deals everyone sees.”

PS: if you liked the concept of scouting underestimated founders, do check out my post ‘Reputations and Underdogs in VC‘ which tackles whether spending time with the laggards in your venture portfolio makes sense or not.

Author: Soumitra Sharma

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