Soumitra Sharma

On Who Really Shows Up When It Matters

Support at critical moments rarely comes from where we expect. Familiarity, expectations, and timing often shape who really shows up.

I have observed this weird phenomenon across both my professional and personal lives. In fact, it keeps surfacing every year or so, and therefore, I am compelled to blog about it today. Here’s how I would describe it:

At every important turning point in my life, where I desperately need a few (what I would consider) extremely close relationships to step up for me, almost all of them have failed to show up.

But at the same time, a few connections, whom I don’t have any significant shared history with and wouldn’t consider “close” by any stretch, end up stepping in and backing me at these key moments.

It has happened so many times now that I feel this is some random rule of nature that should have a name. Here are some personal examples:

When I moved to the Bay Area in 2014, having never studied in the US, with no job in hand, and with literally 2 bags, the person who gave me what turned out to be one of the most significant breaks in my career was…the then-husband of my wife’s ex-colleague.
One of my most important backers, who was an extremely small angel in my startup but ended up becoming a key influencer in both my decision to come back into venture as well as a major tangible supporter in many ways since then, is the husband of the 1st cousin of one of my past venture collaborators (interestingly, I lost touch with the original person who connected us many years back).
While we as a family were going through challenges on multiple fronts during the pandemic years and hit several low points, the people who saved us were not our oldest friends but a family we met through our older son’s first daycare.
The person who ended up giving what turned out to be an incredibly strong referral to my wife at Google more than 8 years back was someone I had overlapped with at a startup for barely 3 months and had no direct work history with.

I have many other examples that are unfolding as we speak, and which I hope to add to this list after a few years.

I don’t know if you have experienced something similar in your lives, but I have been thinking hard for at least a year now about why this happens repeatedly. Here are a few underlying things that I have noticed:

1/ Familiarity bias – when people have been too close to you over an extended period, they see all sides, moods, emotions, and fallacies in your personality. Because of this, I feel they end up subconsciously discounting your skills on many occasions.

I see this play out in venture all the time. Existing investors usually see the sausage being made, and therefore, are often more pessimistic on a portfolio company’s prospects compared to new investors evaluating the same opportunity.

For those who understand Hindi, there is a grandma’s saying on this phenomenon – “घर की मुर्गी दाल बराबर”.

2/ Expectations bias – humans have a tendency to keep very high expectations of people they consider close, especially if they are family or have been known for a long time. So whatever these relationships do at the crunch moments, it’s perhaps impossible for them to live up to the high bar they are being held to.

3/ Timing – the quality & extent of human collaboration depends a lot on timing. Where are each of the subjects in their own life arcs? What is their mind space looking like then? What is the macro environment in which the collaboration is playing out?

In almost all situations, humans are essentially acting in their own self-interest first. So, while to the “receiver” (me in the initial examples), it ends up being a game-changing intervention, the act is also delivering a major utility for the “giver”.

A parallel idea is seen in a key principle of marketing strategy – the job is not to convince uninterested prospects, but to be in the consideration set of leads when they are actively looking to buy a product. Sounds like a simple idea from a b-school course or Kotler’s book, but I have only learned its power at this stage of my career.

Translating this to the core idea of this post, best collaborations happen when both givers and receivers are in the market, and are a great fit for each other’s needs at that specific point in time. This has nothing to do with how close the people have been previously.

Given that I have now observed this core phenomenon, I am trying to do a few small things differently so that I can be on the right side of this rule of nature more often and with a much lower emotional toll. These include:

Instead of meeting the same set of people all the time, strive to continuously meet new folks and add them to an ever-growing funnel of relationships.
Be present and show up strongly even in first meetings with new people.
Following my guru Charlie Munger’s age-old advice, have lower expectations of close relationships and replace that emotion with gratitude that they choose to include me in their lives.
For major turning points every couple of years, instead of just repeatedly putting “asks” in front of the same set of people, cast a wider net out into the universe using a combination of cold outreach and warm intros.

Anyway, I know this post is a bit all over the place. In fact, I was struggling to even think of a title for it. But these ideas are from my lived experience, and are important enough to be put in front of you.

Team vs. Market at Seed Stage

The best seed VCs bet on the team everytime.

While doing some random browsing, I came across Linear’s $4.2M seed fundraising coverage on TechCrunch in Nov’19. This paragraph from the post stood out to me:

“Linear is a late entrant in a world filled with collaboration apps, and specifically workflow and collaboration apps targeting the developer community. These include not just Slack and GitHub, but Atlassian’s Trello and Jira, as well as Asana, Basecamp, and many more.”

Imagine looking at the dev collaboration space as a seed VC in 2019. It would be a tremendous leap of faith to believe that there could be space for a new entrant in a market with multiple scaled incumbents and indie products.

How were Sequoia and Index able to pull the trigger then on the Linear deal? My guess is because they followed the core philosophy of top-tier seed investing, which I have myself seen play out multiple times in my career – “that seed bets are all about the team, and that overthinking the market & competition at this stage adds fatal blurriness to what should be a sharp team-centric seed lens.”

I have studied the anti-portfolios of many legendary VC firms spanning decades, as well as connected the dots with key misses of VC firms I have personally worked with or closely observed in my career. A dominant theme across the anti-portfolio set is getting distracted by overstudying the ‘market’ and as a result, overlooking what was a star founding team.

A nuance to this “team vs market” point that I have tried to incorporate is that as long as the market is directionally correct and, more importantly, the team has a strong fit with it, I pretty much give it a checkmark at my end and quickly move on to spending most time evaluating the founders.

PS: btw, I have a similar observation on seed entry valuations as well. Will cover it in another post!

The Applications layer in AI is getting brutal

This story can play out in many ways.

Each use case has tens of funded companies. Each is churning out features rapidly, getting to parity faster than customers can imagine. Each has early traction and a worthy claim to win.

What will it take to eventually win the game?

1) Will it be about surviving the multiple shakeouts that each vertical/ use case will eventually see? Letting capital-bloated companies implode and letting the “tourists” give up…

2) Will continued product obsession be the key? Essentially refining the product beyond where others give up…

3) Will choosing non-obvious wedges/ ICPs be the way to differentiate & survive? Serve markets that others are choosing to ignore/ finding unviable to serve…

The technology is still so early, and we clearly have a few decades of upside left. Yet, there is a gold rush going on right now, which I am sure will push people to optimize for the short term.

In that case, will founders who are truly playing the long game ultimately win? Or is it more important to “surf the wave” in the present?

The former will look unattractive in current times and hence, will be undervalued and “contrarian”. The latter will appear to be imminent winners, yet could flame out.

Just some thoughts running through my head!

Investing in Gameramp

The backstory behind the Operators Studio investment in Gameramp.

Stoked to share that Operators Studio has participated in the $5.4Mn pre-seed round of Gameramp, alongside BITKRAFT Ventures, South Park Commons, DeVC, and MIXI Global Investments.

Gameramp is building adaptive software for interactive apps, starting with gaming as a vertical. Its suite of APIs and AI Agents empowers gaming companies to deploy their monetization strategy with super-human speed and scale. End outcome – games scale faster and more capital efficiently.

I have known Vivek Ramachandran since his gaming VC days at Z47. He is one of the sharpest minds in the space, combining his venture chops with operating expertise built at EA and Big Viking Games.

While I knew Vivek well, meeting Sashank Vandrangi convinced me that this is one of the strongest founding teams in the space. Sashank’s mind moves at lightning speed and gives wings to his sharp product chops built at the likes of King and MPL.

Btw, this relationship started with a cold DM from Vivek a couple of years back, so yes, quality cold outreach continues to deliver value in this noisy world!

Gameramp fits the ‘India Supply to Global Demand’ theme at Operators Studio. India has an incredible pool of gaming developers & operating talent, and I believe this is the right time to build global gaming-tech companies that leverage this base.

This is also the 2nd gaming-tech portfolio for Operators Studio, after Terra (Gaming platform for global Gen Z).

Cheering for more gaming-tech companies being built from 🇮🇳 for the 🌎!

PS: Gameramp is hiring engineers, researchers, and builders who want to push the edge of what’s possible. If this excites you, reach out to Vivek or Sashank.

Why Networking Alone Won’t Build a Successful Career (And What You Need Instead)

Networking only works when the product being sold via it is top-class. It’s important to get this order right in any career strategy.

In my profession as a VC, I tend to cross paths with many people whose main professional superpower is networking. They tend to be visible at most events, are very active on social media, have at least a surface-level connection with most people who matter in their specific areas, and are likely to say, “You are pursuing this? Oh, I know XYZ who is also in this space really well”.

In most cases, the gigs these folks like to pursue include running communities, creating podcasts, running venture syndicates/ SPVs, GTM consulting/advisory, holding ecosystem events, and engaging with govt. bodies, think tanks & non-profits, etc.

In private, they often confide in me about their desire to take their careers to the next level, both monetarily as well as from an influence perspective. They feel like mere small cogs in the wheel, and despite doing a lot of grunt work, get only a small piece of the pie, with founders, domain operators, and investors grabbing a majority of the value created.

I have thought hard about this predicament, and one conclusion I have come to is that networking skills by themselves aren’t enough. They need to be combined as an amplifier alongside a core set of one or more of the following:

(1) Technical skills

(2) Education & work pedigree

(3) Proven track records in a domain

Without this core, a pure networker is categorized at the lower ends of the business hierarchy by various stakeholders in the ecosystem.

A few examples to illustrate this:

Shreyas Doshi being a great content creator, amplifies his top-tier product management career. Somebody just churning out product content without a proven product track record to back it will be considered a mere content marketer as opposed to a credible expert.
Fred Wilson (of Union Square Ventures) being an excellent writer, gives an extra edge to his proven skills as a VC. Somebody trying to “act” like a VC on LinkedIn & at events, trying to hustle into deals via SPVs/ syndicates without the core skills or pedigree of what it takes to become a solid venture investor, will be viewed as a venture grifter in a few years’ time by the ecosystem.
Ryan Hoover combined his main spike of community-building with his technical chops, both as a founder & product builder, to first create Product Hunt and then parlay it into venture investing via Weekend Fund. Somebody who is just a community creator/ curator, but without any edge-chops at a sector or operating level, will end up only as an amplifier for other companies, founders, and investors, and capture only a minute piece of the value.

I believe this is an important insight that is even more relevant in this age of social media, influencers, and communities. Especially in Tech, both companies & careers seem to be over-indexed on building “distribution” for themselves, without realizing that distribution will work only when the core “product” is top-class.

For any youngsters out there reading this, I urge you to first focus on transforming yourself into a compelling & differentiated “product”, which would typically require studying at the best quality university you can crack, working at the topmost market-leading company in your space, using both these platforms to build core technical skills of some kind, and then continuously executing & refining those skills to slowly & steadily build a track record in your field. This will realistically take at least a decade in the real world.

Only when you have made significant progress toward this goal of becoming a compelling & differentiated “product”, should you then start to focus on building various “distribution” channels for it, with networking & social media being important pillars.

If you get this order backward, there is a significant risk of ending up as a lower-end “ecosystem hustler” who ends up amplifying other companies & individuals that are more compelling products, and the latter end up capturing a lion’s share of the economic pie over you.

Why Cutting Losses Early Is the Hardest—and Most Crucial—Skill in Startups and Venture Capital

Cutting losses is one of the hardest decisions in startups, investing, and leadership—but it’s also what separates winners from those stuck in the sunk cost trap. Here’s why mastering this mindset is essential.

Recently read this Forbes article on Igor Tulchinsky, a Billionaire quant trader who runs the hedge fund WorldQuant. In particular, this section on cutting losses caught my eye:

Source: This Billionaire Quant Is Turbocharging His Trading Models With ChatGPT-Style AI

While I don’t come from the public markets world, I have taken a series of major risks as a founder, operator, and investor. Of course, now that I am a full-time venture investor, I live in a world where I take and manage risk every day, including macro, business, tech, portfolio construction, and people, among others.

Based on my journey so far, I can’t emphasize enough the importance of developing the ability to quickly cut losses. Interestingly, before making a major decision, most people are fairly good at identifying & mitigating key underlying risks. However, I have learnt with experience that even after executing the best risk management process, things will still go wrong. And once things go wrong, even the most intelligent organizations & individuals easily fall prey to the sunk cost fallacy (“throwing good money after bad money”).

Let’s take the classic example of finding your next job. As part of a thoughtful risk management process, an intelligent candidate consciously tries and figures out mutual fit during interviews, gathers feedback on the company’s culture, perhaps speaks to customers & competitors to evaluate the product, or, in the case of startups, even does a 1-2 week part-time project before commiting full-time.

A similar scenario is also playing out on the employer’s side. Most hiring managers give high weightage to candidates who come recommended from trusted connections or with whom they share a past history. The interview process consists of multiple rounds to stress-test skills & personality. The company does rigorous reference checks, often also focusing on off-sheet checks to eliminate bias.

So both employers and candidates follow a fairly rigorous risk management process. Yet, as most of us have seen in the real world, leadership hiring has a 50 %+ failure rate in Corporate America. Here are some summarized stats from ChatGPT on this:

In this case, even the most rigorous upfront risk management process can’t account for a variety of post-decision risks, including process weaknesses (a great hiring process can be undone by a weak onboarding & training process), uncontrollable externalities, and random one-off events.

In these scenarios, a willingness to quickly cut losses & limit further damage of time & money on both sides is the best way forward. And make no mistake, it requires a lot of courage. That’s why I found Starbucks firing their last CEO in less than 18 months of tenure to be a very bold move, especially for a company of that scale & history (you would expect them to be sluggish).

While exec hiring missteps can be major setbacks even for large companies, they can often become matters of life and death for an early-stage startup. A wrong hire for a critical role can do strategic & cultural damage that might be irreversible with the existing runway. That’s why the best founders believe in the “fire-fast” philosophy.

Zooming out from hiring, startups succeed by taking calibrated risks on top of a technology change that an incumbent would just find extremely hard to do. This requires running a bunch of iterative experiments with very limited upfront data, but balanced by an asymmetric risk-reward profile (if this works, it will massively move the needle).

By the very nature of these experiments, a majority of them will fail. Combine this with a very limited cash runway that even the best startups get at each stage to get to the next set of milestones, and founders need to combine controlling the cost of each such experiment with an active intent to cut losses once it’s clear that the experiment is not working.

Essentially, a mindset to cut losses early till you get to something that is clearly working is a key requirement for startups to successfully emerge from this maze of early experiments with real product-market-fit. Windsurf CEO & Co-Founder Varun Mohan framed this idea brilliantly in his recent interview with 20VC:

Never fall in love with your idea…

One of the weird thing about startups is that you don’t win an award for doing the same wrong thing for longer.

Coming to my world of venture capital, I have seen many instances where the aversion to cut losses has come back to bite the investor. The context I have seen this the most over the years is in ill-conceived bridge rounds.

Classic scenario – the company has exhausted most of its last round of capital, has created just enough progress to keep existing investors somewhat interested, but if looked at with rigor and intellectual honesty, is nowhere near product-market-fit. Combine this with a founder who is good at storytelling and can pitch “if we get just this much more money, we will break through”, and existing investors are highly likely to cave in & bridge the company.

Unfortunately, in my experience, a majority of these types of bridge rounds don’t end up working. Peter Thiel said this uncomfortable truth a few years back about what he has observed in the Founders Fund portfolio over the decades (paraphrasing):

Once something starts working, people often underestimate it. And when things aren’t working, people often underestimate how much trouble they are in

Everytime a company raised an up round done by a smart investor, it was almost always a good idea to participate…

Steeper the upround, the cheaper it was…

In flatrounds and downrounds, it was almost always a bad idea to participate…

This behavioral weakness is perhaps why Michael Kim of Cendana, a major LP in emerging managers, recently said in an interview that the biggest mistake he has seen GPs make is deploying reserves poorly. My logic is that reserves deployment, especially in rounds without quality external signaling or real business progress, is particularly prone to multiple human biases kicking in, including loss-aversion, likability bias, optimism bias, and overconfidence bias.

Funds with relatively large reserve ratios should think deeply about potential solutions to this problem. One thing I have seen a few funds do is have a dedicated GP whose sole job is to evaluate each reserves-deployment situation like a fresh late-stage deal from the ground up. This can help counter the personal biases of the lead GP on the original deal.

To summarize, the ability to avoid the sunk cost fallacy & cut losses early is critical not just for entrepreneurs & professional investors, but also for each of you as every contact with the real world exposes you to risks big and small, whether you realize it or not. Getting out of sticky situations early enough ensures that you stay in the game and keep compounding your advantages.

AI Musings #8 – Thoughts from South Park Commons Demo Day

Quick observations on the latest AI startup products.

Attended an amazing South Park Commons Summer Demo Faire yesterday!

Reporting back a few thoughts running through my head in real-time:

1. Essentially, the capabilities & design of every core SaaS use case are being reimagined by AI founders as we speak. In a future steady state, I see many of them living inside larger product suites as “features”, either via the incumbent fast-following and shipping them, or via small acquisitions/acqui-hires.

2. Consumer AI products remind me a lot of the 1st gen iPhone apps. Founders (developers) rapidly shipping entertaining, almost “toy-like” use cases. Like in mobile, will something massive eventually come out of these? So hard to tell…

3. An underlying capability of AI that a majority of products seem to be leveraging is “contextual artifact creation”. Eg. creating videos & decks in real time, replacing specific elements instantly in pre-existing media etc.

4. While the underlying “intelligence” capabilities of the products seem to be next-level, the UI/ UX as of now seems quite incremental relative to the mobile/cloud era. Lots more discovery & risk-taking needs to happen here.

5. Across enterprise & consumer/prosumer, it’s clear that these products can only manifest their power when they have access to extremely differentiated & diverse sources of data. In some contexts, it was unclear how a startup would get access to many such datasets in a fresh & relevant manner.

6. In legacy industries like govt/ public sector, AI-native products, even with game-changing capabilities, will still need to deal with age-old GTM challenges (long sales cycle, who will buy, what are the incentives for users to adopt etc).

7. Finally, it’s still pretty effin’ hard to pull off a glitch-free, low-latency AI demo.

Congrats to all the presenting SPC founders. Can’t wait for how these products shape up going forward!

Can Indian Vertical AI Startups Be the Contrarian Venture Bet of the Decade?

Driven by an ambitious talent pool, geopolitical tailwinds, operating model innovation & domestic risk capital, Indian vertical AI startups could be the breakout tech story of the decade.

A potential scenario running in my head on how 🇮🇳 startups get a significant share in global AI over the next decade:

AI “infra” winners get built in 🇺🇸 (OpenAI, Alphabet etc.) ➡️

AI “platform” winners too, emerge in 🇺🇸 (Salesforce & HubSpot equivalents; a bunch get built/ led by the Indian diaspora) ➡️

As 1st-gen “Application” winners emerge in 🇺🇸, 🇮🇳 startups fast-follow in specific enterprise verticals & grab market share.

The time lag to fast-follow is significantly lower than, say, what Zoho did to Salesforce, or Freshdesk did to Zendesk.

This time, they play an asymmetric game. Instead of only competing with US startups on their home turf, Indian enterprise AI startups also look to dominate the Global South (SEA, MENA, LatAm, etc.).

Moving beyond binary operating models of India or US-based, Indian enterprise AI startups innovate & develop new, globally fungible, cross-geo operating models, similar to Infosys in the 90s, BPOs/ KPOs in the 2000s, and Chennai SaaS in the 2010s.

Compared to the SaaS wave, Indian enterprise AI startups get 10-100x more market share in each vertical, driven by a more ambitious & courageous founder pool, a talent base with skillsets & knowledge from previous tech waves, democratized knowledge & tools access courtesy of AI, as well as more availability of domestic risk capital at each stage.

Rather than IPO or M&A in the US, verticalized Indian enterprise AI startups either go public domestically, or get domestic Private Equity & conglomerates on the cap table who help them scale way beyond the last gen of software companies.

All these games play out on top of a favorable geo-political alignment between India & rest of the democratic world, driven by a China counter-balance narrative.

Verticalized Indian enterprise AI startups could be the contrarian venture bet of this decade!

That Series A Billboard On The 101

A reminder not to fall into the trap of first-order thinking.

My LinkedIn feed is full of posts making fun of that startup that has its Series A announcement up on a billboard on the 101.

This incident reminds me of a mental model I have learned & developed with experience over my career:

“When you come across something that looks stupidly irrational on the surface, instead of falling prey to first-order thinking, pause, take a step back, try putting yourself in that situation and think through some reasons why someone could indulge in that seemingly foolish or irrational behavior?”

In the case of this billboard, clearly the founders are smart enough & shrewd enough that institutional investors are handing them $25Mn. So it’s highly likely that they are trying to achieve some goal by putting up this cringeworthy sign.

Most likely, the goal was to drive awareness & word-of-mouth by making this meme-worthy. Similar to how celebrities say & do crazy, PR-worthy things strategically close to a big movie release.

While this billboard case is a bit frivolous, it highlights an important idea that we all should have in our mesh of mental models – when something doesn’t add up in plain sight, or when the herd has 100% consensus on an idea, it shouldn’t be believed prima facie. Rather, it deserves an even deeper investigation.

The crowd is largely a blob of first-order thinkers. Value almost always resides in second-order thinking & beyond. Train your cognitive radar to spot these signals & act accordingly!

Co-founder Breakups

Sharing some insights/patterns from various co-founder breakups I have witnessed over the years.

Recently, I received the sad news of a potentially powerful co-founding team breaking up rather acrimoniously. I had been tracking this team closely for several months now as a potential deal, and this happened right as the company received a seed term sheet from a Tier 1 VC.

Over a 15-year career in venture, I have expectedly seen several co-founder breakups, both in my own portfolio as well as those I have known well/ observed from the sidelines. This recent breakup got me thinking about any patterns/ insights I have noticed over several such instances over the years. Here are a few:

1/ Undergrad batchmates seem to have higher endurance

For some reason, I have repeatedly noticed that teams where the co-founders have been undergrad batchmates tend to survive much longer. Perhaps relationships born in those fledgling, relatively innocent years tend to have higher levels of subconscious trust and, more importantly, a sense of love and tolerance.

While it’s easier to find people with complementary skills and similar pedigrees (both of which look great on paper on the team slide), what keeps co-founders together is also what keeps people together in long-term marriages – having an underlying mutual respect & fondness, which leads to daily hours of fun as well as the willingness to both extend higher levels of tolerance to each other, as well as introspect and evolve to meet the other person midway.

Especially at the seed stage, company missions can evolve with pivots, but this mutual vibe is what keeps co-founders together across multiple iterations and often, multiple companies.

2/ Ex-colleagues and work friends seem to have a higher risk

My hypothesis here is that most people tend to put on a work personality at the job that suits their manager’s preferences as well as the company’s culture. Therefore, even after working with someone as a colleague, it’s very hard to know their real, full personality and values. In many cases, people end up misjudging mutual fit, especially when it comes under the immense pressure of doing a 0-to-1 startup.

Interestingly, this applies to colleagues at both large companies as well as startups. As an investor, I often hear pitches where founders say, “We worked together in the trenches of this early-stage startup and discovered this idea”. While this gives the impression of a strong set of founders germinating inside the cauldron of another startup, I have frequently seen such teams breaking up soon. While they do have the claimed early product and GTM skills they together learned at the startup, the mutual co-founder vibe & grit end up breaking under pressure.

3/ Co-founders coming together via common friends/ relatives, without a strong shared history, is a miss

I see this scenario a lot – one person decides to start up, spreads the word around for a co-founder, connects with someone via a really strong common friend/ relative, and both decide to partner.

In the majority of these cases, there is no shared history, and the team also hasn’t had the opportunity to spend enough time in the trenches going through the ups and downs together. When pitching to seed investors, they usually tell the story of “our skills are perfectly complementary, and both of us have met each other multiple times at this X/Y/Z person’s parties over several years, and developed a shared passion for this idea”.

In most cases, this ends up being a window-dressed story of the co-founding team and lacks the underlying bond & trust needed to grind out the tough times.

4/ “Earned co-founders” are solid

In many cases, folks start as single founders, surround themselves with early founding team members, validate, iterate, and get to early PMF with them, and during this journey, 1-3 people naturally come up and start playing a critical role in the management team. In a sense, they start playing the co-founder role without the title (or the equity).

I call these earned co-founders, and these are solid personas. In many of these cases, I have pushed the solo founder to look at these 1-3 people as core parts of the leadership team, if not as full co-founders, and have it also reflect in their equity at the appropriate time.