Category: Venture Capital

Exit Learnings From the Airtable Acquisition

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why AI Valuations Aren’t That Odd

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

When To Sell? – Part 2

A pandemic unicorn, a 400x Indian compounder, and a public SaaS unwind.

At first glance, unrelated. But there’s a single thread connecting them that provides an answer to the eternal question: when should you sell?

Recently, I came across a podcast clip on X where a European seed investor was reminiscing on how she had written an angel check in Hopin (a hype-unicorn from the pandemic era), which got crazily marked-up from a ~$2.5M pre-seed valuation in 2019 to ~$7.6B Series D valuation in 2021, and she didn’t sell even while the founder took $200M off the table via secondary.

This reminded me of my “When To Sell” post from Sep 2023. I think this is a good time to do Part 2 of that and add some recent examples to this discussion.

Fractal Analytics

An Indian analytics company called Fractal Analytics, which was founded in 2000, recently went public in India. What is fascinating is that their first angel investor, Gullu Mirchandani (who had started Onida Electronics back in the early 80’s, and launched India’s first-ever color television), continues to hold his initial position more than 2 decades out, even though it has run up by more than 400x. Note: check out this excellent post by Rahul Mathur (DeVC) on this investment by Gullu.

Freshworks

Girish Mathrubootham, founder of iconic Indian cross-border SaaS company Freshworks, stepped down as the CEO in Sep 2025 and became a full-time AI VC.

With the recent rout of SaaS stocks and the market consensus being that these companies are going to face secular headwinds from AI going forward, in hindsight, the Freshworks founder stepping down was a leading signal that the SaaS story was decisively over.

While folks can make up many reasons behind his departure, the fact is that a founder who is barely 50 years old is voting for where he’d like to devote his energy, relative to the opportunity cost of all the things he can pursue. Note: I asked ChatGPT to do a quick analysis of all Freshworks stock sales done by Girish. He sold zero shares in 2022. But post the launch of ChatGPT, sold ~$39M of stock in 2023 and ~$49M in 2024.

Essentially, any public market investor who didn’t entirely exit their Freshworks holding when Girish stepped down needs to have their judgment severely questioned.

Map To The Founder

What is a common learning from these cases of Hopin, Fractal, and Freshworks? It’s a learning related to exits that all experienced GPs frequently cite:

Investors should map their exit to the founder. If the founder is selling, you should sell. If the founder is holding, you should lean towards holding.

Applying this framework to the above 3 cases:

1/ Assuming that the Hopin angel knew that the founder was selling (even if the exact amount was unknowable), she should have immediately sold at least some part of her holding.

2/ Fractal had 5 co-founders in the beginning. Three of them left in 2007. But even as of today, 2 original co-founders continue to hold fort in full-time operating roles – Srikanth Velamakanni as Group CEO and Pranay Agrawal as US CEO.

This is perhaps why Gullu didn’t sell over all these years – he astutely mapped his position to the founders, and as long as even a couple continued to believe in the business and were competent enough to run it, he continued to hold.

3/ In the case of Freshworks, every significant stock sale by the founder should have been a warning signal for public market investors to re-evaluate the business as well as the price relative to underlying business quality.

On the founder’s full exit in Sep 2025, astute investors should have mapped their strategy to how the founder is voting with his time and money, and completely exited their position.

So, the experienced venture GPs were actually right! Adding a simple mental model for the “when to sell?” decision for myself as a VC – map to what the founder is doing.

Venture Capital Portfolio Construction: Diversify Then Concentrate

How many shots should a VC fund take? When should you double down? And how concentrated is too concentrated?

In this deep dive, I unpack practical portfolio construction lessons from Roger Ehrenberg (IA Ventures), Rory O’Driscoll (Scale Venture Partners), and others on diversification, reserves, follow-ons, and building multiple fund-returners in today’s venture environment.

Portfolio construction in venture capital has been an ongoing obsession of mine for the last few months. I have been devouring & recording any and all insights that various GPs have shared on podcasts, blogs, and social media posts.

In particular, I found this 20VC episode to be particularly helpful as Roger Ehrenberg (OG Founder of IA Ventures; now runs Game Changers Ventures) shared thoughts & anecdotes from his journey across multiple IA Ventures funds. The back-and-forth that he did with Jason Lemkin (SaaStr) and Rory O’Driscoll (GP – Scale Ventures) teased out aspects of venture portfolio construction that are rarely discussed in detail.

Here are the main insights I captured from this episode:

1/The extent of diversification should depend on how confident you are of the quality of shots

Rory mentions that “Diversification reduces your downside but also reduces your upside”. So, the number of shots on goal a manager should be taking should flow from how confident they are in the quality of their deal flow.

Btw, it was interesting to know that even the best managers start off with adequate diversification, even at later, less-risky stages of investing. As Rory shared, even a top-tier firm like Founders Fund had 31 shots in its Growth Fund 1. As it developed more confidence, Fund 2 had mid-high teens portfolio size, and Fund 3 is aiming to have 10.

My commentary: this also checks out with public market portfolio construction, wherein the managers with high confidence and established track records tend to opt for a 10×10 portfolio (10 stocks of 10% allocation each).

2/ IA’s classic seed portfolio construction: 20-25 constituents over a 3-4 year investment period

This lines up well with what has now become a market-standard seed portfolio construction.

My commentary: I would like to tie this back to the portfolio construction philosophy of Mike Maples, Managing Partner at Floodgate (Source: Venture Unlocked), wherein he believes “a seed portfolio becomes statistically diversified by 12, and beyond 25, you don’t add to diversification”.

3/ Roger goes hyper-concentrated on the 2nd and 3rd checks

He shares that IA wrote follow-on checks aggressively only in companies where they built high conviction on the team, and if assuming it continued to execute at this speed, that the market was large enough to support a big outcome.

So, they start with 1-2% of the fund at entry, then the 2nd check can be 5-7% of the fund.

The end outcome for IA is ~75% of deployable capital concentrating in the top 3-4 companies by the end of the fund.

4/ This portfolio construction depends on attractive entry valuations

Roger shares how the first check is relatively small (~$750K from a $100M fund) and, therefore, the only way to get decent ownership at entry is to invest at modest valuations.

My commentary: Not sure how this will hold up in the age of AI. And therefore, I am guessing that implied in this strategy is the fact that:

(a) You have to be fairly non-consensus; and/or

(b) Get discounted entry valuations due to your brand.

In Roger’s case, both of these appear to be true.

  • While everyone is doing AI, he has started a sports-tech focused fund, validating point (a).
  • He is also a top-tier brand of choice as a GP, validating point (b).

My commentary: the chosen portfolio construction has to be congruent with the GP’s track record and position in the ecosystem.

5/ “You need to start off with a significant element of diversification and then concentrate down.“

Rory summed up Roger’s thinking really nicely in this 1 sentence, and it has been stuck in my head since then.

My commentary: It almost reminds me of poker, wherein you are folding a lot and then, in a few hands, when you have high conviction that the odds are significantly in your favor, you go all-in.

6/ The current VC environment is pushing even the most concentrated VC firms to diversify a bit more

Rory shared how they shared with their LPs that the finish line for an exit has moved from $200M ARR to $400M ARR, and therefore, you have to hold these investments for longer, thus increasing risk and liquidity.

As a result, a Series B firm like Scale that typically has a concentrated sub-20 constituents portfolio is now thinking of pushing it up to 25.

7/ Portfolio construction should be based on a “temporal, many turns” game

It’s like how, as each card gets opened in a game, the probabilities of your hand keep increasing or decreasing. That’s why deploying reserves over multiple turns is a key part of any venture strategy.

Rory captured the mindset of approaching reserves really nicely as follows: “You don’t know everything and there is no 100% certainty, but at the margin, you know more than the incoming investor, so you can tilt things slightly in your favor”.

Rory also cited an analysis that Scale has done internally that says: “If you get the first 2 years of revenue that we underwrote, then the probability of getting a 5x goes up from 30% to 70%”. So once you have revenue and product-market fit, then you do have a lot of information to make an informed follow-on decision.

My commentary: This also checks out with something Anand Lunia of IndiaQuotient said a while back on a podcast (paraphrasing): “We note down what the founder said they will execute in this quarter, and then tally it with what they actually ended up achieving, in the next quarter’s update”.

Essentially, in a random, highly-risky game like early-stage venture, revenue expectations being consistently met over a few quarters counts as a major signal than what many would imagine.

8/ There are many paths to creating a multiple fund-returner

Roger mentions how there are many paths to getting to a multiple fund-returner outcome. He cites the following examples from the IA Ventures funds:

(a) The Trade Desk (TTD): didn’t have a product in market for 1.5 years, multiple bridges, multiple near-death experiences. But then once it hit, it just zoomed.

IA owned 17% of TTD at IPO, out of a little seed fund.

(b) Wise: kept chugging along right from Day 0. It was as close to an “up and to the right company from the beginning” as they have seen.

Btw, IA’s first check into Wise was $750K at a $5.5M post! Then Valar came in at $20M valuation, and IA doubled down. Then Valar came again at $160M valuation, and IA tripled down yet again.

IA had $9M over 4 checks in Wise, which is a 9% of the fund position.

IA owned 13% of Wise at IPO, out of a little seed fund.

(c) Datadog: compared to the previous two, IA owned only 2% at IPO. They did the seed, RTP led the A, and Index led the B. The valuation was really high compared to progress, so they didn’t back up the truck on follow-ons in seed and A. But it was still a multiple fund returner because the size of the outcome was so large.

(d) Digital Ocean: 1st check was $3M (3% of the fund). When a16z led a $37M Series A, IA wrote a $7M check (7% of the fund).

9/ What if the follow-on check is at a really high valuation? Do you still do it due to high conviction but perhaps lower expected returns?

This is where the insights get really interesting, as this scenario fits very well with what’s going on in AI right now.

Roger recommends that each follow-on check be evaluated independently from the previous check. So, it’s about the risk-adjusted MOIC that’s possible on that check.

So, if say the follow-on is at a $300M valuation, and you believe that it can be a $100B company at exit, he recommends still writing a meaningfully large check up to say a risk-limit % of the fund (say 10% of the fund).

He illustrates this with a real example. In the case of Trade Desk, IA invested in the pre-seed, bridge 1, bridge 2, and a small Series A at $16M post-money. Then, after a few years, the company’s next round was a $20M primary + secondary at a $280M post. Even in that round, IA invested a $3M check out of a $50M seed fund. That $3M returned $40M on exit (13x MOIC).

Essentially, Roger looks at venture as a risk-adjusted, cash-on-cash business.

10/ The Peter Thiel philosophy on follow-on rounds

Rory cited Peter Thiel’s philosophy on follow-on rounds that I, too, listened to many years back and have also executed on in my own doubling-down decisions:

“Whenever a reputed new investor is doubling down on a company, it’s almost always a good idea to invest”.

11/ The value of cross-funding investing

Jason mentioned that when you are trying to concentrate 10% of your fund into the winners, you run the risk of running out of capital fairly quickly and losing out on new opportunities that might come your way.

Roger cited cross-fund investing as a solution to this problem. So if the LP base is fairly consistent across funds, you can keep doubling down on the winners in Fund 1 from Fund 2. So, instead of investing out of say a $100M fund, you are investing out of a $100M Fund 1 +$160M Fund 2 = $260M fund corpus.

My Blindspots As A VC

On misreading founders, moving too fast, and why portfolio construction is my safety net.

Over the past few weeks, I have been doing a retrospective analysis of the Operators Studio portfolio. Given that I have adopted a “founder-first” investing style, I have been specifically trying to analyze cases where I got a wrong read on the founder.

Startups can struggle/ fail for N number of reasons. Especially as a seed investor, most of these externalities are out of your hands. Therefore, while doing such analysis, I like to keep reminding myself not to fall into the “Resulting” trap.

Annie Duke defines Resulting as “the cognitive bias of judging a decision’s quality solely by its outcome, rather than the decision-making process itself”. Top poker players are really good at avoiding Resulting while studying their plays post-facto.

So when outcomes turn out to be negative in a seed investment, rather than fixating on “why the company failed?”, it’s more useful to ask “how should the investing process be improved for future deals?”. And in my context, it’s typically the process of evaluating the founder.

Coming back to the retro analysis I have been doing on my deals, I have been able to identify a couple of blind spots that seem to be showing up repeatedly. Here’s a deep-dive on each of them:

1/ Getting blindsided by the founders’ pedigree

Sometimes, founders show up with just a jaw-dropping pedigree – IIT Bombay Computer Science, Stanford PhD, top leader at Big Tech etc. This pedigree is typically also accompanied by a strong show during the pitch meeting, demonstrating differentiated access & networks, and just overall self-belief that screams “I am awesome!”.

Looking back on such pitch meetings, it’s very easy as an investor to get carried away by this pedigree & show. However, as I am learning with some pain, pedigree doesn’t automatically translate to the many enablers of eventual success in a founder – grit, the ability to pound pavements selling stuff, controlling your ego, resolving conflicts, and frankly, eating glass during tough times.

One of my key maxims learned over a long venture career is to always distinguish whether the person is a strong professional or a (potentially) strong founder. Both are very different things.

Even with this hard-earned insight, it turns out that executing this day in and day out is extremely hard. Even the best of us get swayed by past track records.

This retrospective is a self-reminder to bring back this maxim as part of the core of my investing process.

2/ Pulling the trigger without spending enough 1:1 time with the founder

My natural style as an investor is highly instinctive. This often manifests in quick Yes’s during the first pitch meeting itself.

Over a long career, this has mostly benefited me. Almost all my major wins were quick Yes’s. But there is a difference between “moving with a pure initial instinct” and “being trigger-happy”.

In a few cases, I have pulled the trigger without spending enough 1:1 time to peel the layers on a founder. If I go one level deeper, in most cases, this was due to some fear – fear of being on the wrong side of deal heat & not getting allocation, fear of feeling disadvantaged as a relatively small check writer, fear of deployment pressure (“I need to do a deal this month”).

These fears are particularly amplified by the current investing environment, where seed deals move in days, where lead VCs have particularly sharp elbows, and where many founders fall prey to becoming over-transactional during the fundraising process.

I have come to realize that these fears are incredibly counterproductive to a long & sustainable venture career. Seed investing is at least a decade-long journey that is full of ups and downs. An important way to create a strong initial foundation that then delivers a consistently good experience to both the founder and the investor over multiple years is to dedicate enough effort upfront to build trust & a mutual connection.

When this trust & connection exists, the wins taste exponentially sweeter, and the pain of losses gets blunted.

Any diversified enough venture portfolio of decent quality is highly likely to catch at least a couple of winners. But the key to amplifying success over decades, both as a founder and as an investor, is to play repeated games with a set of highly trusted people. The starting point of these relationships is almost always the foundation of trust built during the first-ever transaction between two people.

Even empirically, if I study all my deals since 2011, whenever I have built a strong mutual connection with a founder upfront, the eventual outcomes have almost always been positive economically and/or experientially (the randomness has only been in “how positive?”).

Therefore, this is again a self-reminder that I should ensure I am devoting enough upfront time to build trust & a mutual connection with new founders I meet. And once I have built an informed instinct around a new person, given I now have 15 years of on-ground data on how it usually pans out, I should default to trusting & following my judgment without any fear.

The final line of defense against these blind spots…

Even at our most introspective and self-aware selves, we still have the same monkey brain that has been wired by hundreds of thousands of years of evolution. Even the best of us should expect to keep falling prey to various kinds of cognitive biases and blind spots across multiple deals.

The mark of growing up as a venture investor is accepting this truth and then acknowledging at a deep, internal level that the only line of defense against our own foolishness is portfolio construction.

As a young VC Associate way back in 2011, I used to always wonder why OG VC GPs kept harping on portfolio construction, spending hours poring over Excel sheets that frankly, had most of the numbers pulled out of thin air (an undeniable fact of any financial modeling efforts in early-stage venture).

Similarly, when I decided to come back into venture in 2023, I kept hearing how LPs care a lot about portfolio construction. And that it is the difference between someone being just an investor vs being a professional fund manager.

Studying my still fledgling portfolio today, I can already see how following even a rudimentary portfolio construction strategy has saved my a** several times already, and its impact will manifest in major ways over the remaining Fund life.

When you experience something working in real life, your buy-in starts growing organically, giving it higher chances of eventually becoming a sustainable habit. I can see this playing out with my rapidly growing appreciation of all the beauty and nuances of portfolio construction.

In fact, I can guarantee that 2026 will see my study and obsession with VC portfolio construction go many levels higher, and thankfully, I don’t need a New Year’s resolution to make it happen.

Note: My next post will be on some portfolio construction insights I have gleaned from listening to Roger Ehrenberg, Founder of IA Ventures. Stay tuned for that!

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!

Market Maps & Junior VC Life

The enigma of junior VCs toiling-away to create market maps.

Probably one of the most mind-numbing jobs for a junior VC must be creating these frikkin’ market maps and thesis visualizations.

Imagine churning for days/weeks on a landscape doc, only for it to be obsolete in a few weeks/months with how AI is evolving.

And who do these docs eventually serve? Can’t think of them creating any real value for seed founders. Perhaps LPs?

These market maps remind me of the “market slide” that all consulting/IBs have in their deck. Hardly any customer cares about them much. They end up becoming junk collateral that some analyst/associate toiled hard to put together.

Btw this reminds me of when I was a junior VC. I had to ghost-write articles for the Partner and in the end, not even get credited for it. Even as a 27-year-old, I found it extremely discomforting that having joined the VC industry to invest in and support founders, I was spending inordinate time helping the GPs market themselves.

My (subconscious) revenge for the ghostwriting days? After a decade, have now turned blogger & podcaster with my own brand (An Operator’s Blog – blog + podcast) + investing in founders with my own world-view & conviction (my Fund Operators Studio), not begging GPs to “get a deal through”.

Insight Arbitrage

Most investors try to “slot” startups in their heads, whereas extraordinary venture outcomes lie in the “slot violations”.

A few weeks back, I was helping a portfolio founder put together the story and deck for raising the next round. This company is one of the true category-creators I have seen in my career and has now reached a PMF tipping point that will lead to explosive growth going forward. Customers and channel partners are literally pulling the product out of the company’s hands, and all metrics are going up and to the right.

Despite this, the founder was sharing how difficult it still is for him to explain the business, the market opportunity, and how this is an extremely differentiated play to investors. Having seen this startup’s thesis play out as an existing investor, my conviction on it is 200% but despite powerful operating signals, it’s still non-trivial to put together a narrative that investors “get” immediately.

This isn’t a new pattern. I have seen this repeatedly play out with truly groundbreaking companies, simply because most investors prima facie, try to “slot” the company in their heads within the first few minutes of the 1st meeting. These slots are pre-existing buckets created by years of pattern-matching, and not surprisingly, 90% of startups can easily fit into one or more of these slots – eg. big company exec stepping out to start an enterprise company, young engineers hacking a dev tool, repeat founder building in the same market, generalist founders executing really fast in SaaS etc.

The issue is this – history tells us that extraordinary venture outcomes are created in the narrative violations (or what I now call “slot violations”). These are companies that are hard to understand in the present moment, being built by founders who are quirky and/or with non-obvious backgrounds, or resulting from messy pivots. Well-known examples include:

As a venture investor, I think a lot about what mental models to use in order to spot these slot violations. Thinking through the earlier discussion with the portfolio founder, it was clear that even though investors might struggle to slot the company at this moment, the market was clearly resonating with the product. In a way, the early adopters in the market had been educated by the founder and therefore, were already bought into the “insight”, whereas the existing mental models of investors were lagging in their appreciation of this insight.

I call this “Insight Arbitrage” – the delta between the market’s and investors’ understanding of a startup’s unique insight. At the pre-seed stage, this market understanding will be mostly qualitative and anecdotal. At the seed stage, this understanding will still be likely on a very small base of users.

Because a majority of investors find it hard to build conviction in the above two scenarios, an Insight Arbitrage continues to perpetually exist in the venture world. And I believe that this is where an opportunity lies for investors like myself to generate alpha, provided we show the courage to trust this arbitrage and put our money behind it.

Audio Overview of this post (via NotebookLM):

The “Middle Zone” of VC Deals

While the venture industry thrives on standard pattern recognition, outlier outcomes often lie in narrative violations of these patterns. But how does one spot these diamonds hiding in plain sight?

As I was analyzing some patterns in the kind of deals I was seeing over the last few months, something stood out. Most deals that I see tend to fall into 3 buckets:

#1 Clear “No” – it’s clear that there is a lack of mutual fit. Easy to move on.

#2 Clear “Yes” – I want to thump the table and invest. The bar for this is high and therefore, deals in this bucket would be max. 1-2 per quarter.

#3 Not sure – I like a few things about the opportunity but also see major question marks in other areas.

Bucket #1 accounts for the majority of any VC’s deal flow. A typical investor will evaluate hundreds of deals in a year and will invest only in a handful. So the VC job description itself is to reject at scale and anyone in the industry either already has or goes on to develop the mental capacity to do this.

Bucket #2 is a dream for any VC as these deals inspire high internal conviction in a very organic way. Though this conviction may or may not align with what other investors think, still one feels great about doing such deals as strong VC investors tend to be independent thinkers and follow their internal compass. If this conviction also aligns with other high-quality investors, then even better! Examples of this Bucket that I have seen in my career include Lenskart, Delhivery, and (I would like to believe) a majority of the Operators Studio portfolio.

Bucket #3 is what I call the “Middle Zone” of venture deals. In these opportunities, there are some things to intensely like and also a few question marks to temper these positives. Some personas of the Middle Zone from my recent deal flow include:

  • Strong founder going after a bad market.
  • A talented founder but weak founder-market fit.
  • A market with massive tailwinds, but weak founder-market fit.
  • A pedigreed founder who has just stepped out of a Big Tech, only with an idea and zero traction.
  • A very young (therefore, generalist) founder, often <2 years out of undergrad, with a limited track record and/or traction to diligence on.
  • A startup that has been around for a while and is now raising a bridge.
  • While I am not really “feeling” the opportunity, someone who I rate highly/ who understands the space deeply/ has spent a lot of time with the founders, and has high conviction/skin in the game.

As a venture investor, I am spending a lot of time thinking through the best way to identify & evaluate these Middle Zone deals. While the venture industry thrives on standard pattern recognition (repeat founders, domain expert founders, young generalists, ex-Stanford founders, co-investing with Tier 1 VCs etc.), outlier outcomes often lie in narrative violations of these patterns.

As an example, in my post ‘Three unicorns and a VC‘, I wrote about how Amagi was an unpolished diamond hiding in plain sight that most venture firms missed. I don’t want to miss the next Amagi that comes to me!

As accomplished angel & now VC investor Ben Narasin once said on a podcast:

There are deals we should do, there are deals we shouldn’t do, and then there are the ones in the middle. We make all our money in the middle ones.

-Ben Narasin

The fact that Middle Zone deals are non-obvious makes them less competitive and therefore, inefficiently priced, lending them well to giant outcomes when the bet turns out to be right. Hence, every VC investor worth its salt needs to have some mental models and heuristics in place to deal with them.

Here are some high-level guiding principles I have learned and am using for Middle Zone deals (more detailed heuristics are my secret sauce😉):

Middle Zone CaseApproach
Strong founder going after a bad market.Mike Maples of Floodgate says (paraphrasing) – “A strong founder will ultimately pivot to a good market”.

My investing style is founder-first. So, strong founders going after supposedly bad markets are fair game for me. Though I would definitely try and ask – “If this founder is strong, why has she chosen this market to begin with?”

Also, I don’t find spending time on market sizing at the seed stage to be particularly beneficial. For why I believe this, check out my post ‘The TAM Fallacy At Seed Stage‘.
A talented founder but a weak founder-market fit.If a founder is strong but the founder-market-fit is weak, I try and answer the question – “Can this market be won by first-principles thinking and hustle?”.

Many areas in Consumer Internet and Enterprise Software lend themselves well to young generalists, while areas like hardware can be excruciatingly painful to execute on and require a founder persona who is prepared for it.
A market with massive tailwinds, but weak founder-market fit.This one is tricky. A working POV is that a “hot” market will get crowded very quickly and therefore, a founder without a clear right-to-win in it will struggle to build a large, enduring business.
A pedigreed founder who has just stepped out of a Big Tech, only with an idea and zero traction.This case needs all art. Every context will be different but as an approach, important to understand:
(1) Backstory of identifying the problem statement and leaving a cushy job to start up.

(2) Personal life story – motivations, adversities, aspirations, chip-on-shoulder.
A very young (therefore, generalist) founder, often <2 years out of undergrad, with a limited track record and/or traction to diligence on.Again, this stage is all art. I like to look at 3 things here:
(1) Does the founder fit a few “spiky” personas I like to back? A few I have written about before include storyteller vs scrapper and engineering dhandho. I would also include the college builder/hacker in it. A catch-all I like to use for these personas is born-to-be-founder.

(2) Does the market they are going after lend itself well to young generalists?

(3) Has the founder been able to acquire some early users/ customers that I can speak with?
A startup that has been around for a while and is now raising a bridge.While I am not really “getting” the opportunity, someone who I rate highly/ who understands the space deeply/ has spent a lot of time with the founders, has high conviction/ skin-in-the-game in the opportunity
While I am not really “feeling” the opportunity, someone who I rate highly/ who understands the space deeply/ has spent a lot of time with the founders, and has high conviction/skin in the game.Like Paul Graham with Airbnb or Fred Wilson with Coinbase.

This will vary a lot by context but as I said in my post ‘An Investing Framework to Find Startup Diamonds‘, one way of sourcing high-signal-non-consensus opportunities is (quoting the post): “a respected investor, sometimes a domain expert, has taken the time to evaluate & build high conviction around the company. Or a visionary customer is taking a bet, partnering with them in building the early product”.

While evaluating these signals, especially when they are from other investors, I find it useful to ask: “Is this deal amongst this investor’s best ideas?“.

I know that’s a lot to digest so let me give you the TLDR of what all the above analysis is trying to say:

Given Power Law, the most important thing in venture capital is getting into the companies with monster outcomes. Only the hits matter.

Based on history, many of these monster outcomes looked like weird companies in the beginning. Hence, having a strategy to sift these out of the Middle Zone is what gives a VC the Midas touch, and what at least I aspire for.

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Anti-Social Proof

In the words of the great Fred Wilson – “If you can’t figure out why you like an investment and why it will be successful, don’t make it”.

Recently, I came across this awesome (like always!) 2012 post from Fred Wilson (Union Square Ventures) – Social Proof Is Dangerous. Quoting a line that captures its essence:

If you can’t figure out why you like an investment and why it will be successful, don’t make it.

One of the big investing ideas I have distilled from studying the best investors across asset classes – from Charlie Munger and Howard Marks to Bruce Flatt and Vinod Khosla, is that to outperform the average (the index), one has to be “non-consensus-and-right”.

In fact, in my July’23 post ‘An Investing Framework To Find Startup Diamonds‘, I outlined a Consensus vs Signal 2×2 and argued that the outlier venture returns opportunities tend to be found in the High-Signal-Non-Consensus quadrant.

Source: An Investing Framework To Find Startup Diamonds

It was only in 2022, almost a decade after I first started my career as an institutional VC, that I truly embarked on this journey of trying to become a non-consensus-and-right venture investor. As I have outlined in the above 2×2, molding my mindset toward this approach has required consciously working on the following 2 elements:

1/ Having a unique world-view and trusting my instincts to be able to spot ‘Signal’.

2/ Totally ignoring any social proof noise while doing this.

I have observed that while it was really hard to ignore social proof early on in my career (eg. which VC is leading the deal), having seen so many Tier 1 VCs across geos do such foolish things over a decade, I must say with much humility that as of today, I find it much easier to ignore their POVs on something.

A few months back, I also came across comprehensive LP data that validates this organic learning. David Clark of VenCap shared with Jason Calacanis how loss ratios are surprisingly similar across various percentiles of funds, and even the best strike out a lot.

That’s why in my view, it’s foolish to do one-off deals purely on the basis of the social proofing of a lead investor in that deal unless one is actually replicating their entire portfolio construction (which is a benefit only LPs in their Funds get).

This idea also explains why I remain skeptical of loose angel networks, angel communities, and syndicates that really don’t have a unique, grounds-up world-view and right-to-win, and therefore in most cases, do spray-and-pray on allocations in deals being done by VCs.

These deals often suffer from major adverse selection (“if the founder/ startup is so good, why are they raising from you?” OR “what specific value are you bringing to the table because of which a star founder is giving you allocation?”) and are therefore, likely to be on the wrong side of the loss ratios of major funds.

Coming back to the point of social proof, let me neatly summarize my POV on it:

  • If the best Tier 1 VCs are striking out as much as an average Joe VC, there is no value in blindly following them.
  • There could be value in investing in the “best” companies of a Tier 1 VC portfolio but especially at the seed stage, it’s impossible to know beforehand which company will turn out to be this “best” company. Also, companies keep going in and out (and back in) of this “best” bucket multiple times anyway during a Fund’s 10-year lifecycle.
  • Even if there was a way to know which company is indeed the best company in a Tier 1 VC portfolio, why would they give me allocation in it? The best VCs want to keep every bps of ownership in their best companies only for themselves.

Hence, what’s the point of doing a deal purely because of social proof? I would rather spend that effort looking for the best contrarian deals in places where no one is looking, doing the work (and trusting my instincts) to spot Signal in them, and investing in them as early as possible, driven only by my strongest conviction and nothing else.

This approach is already starting to reflect in the early Operators Studio Fund 1 portfolio. In a majority of recent investments (eg. Soulside, Confido, Loop, Astrophel Aerospace, and a recent one in Stealth), I was literally the first investor to build conviction and say “yes” even before the round started coming together with other VCs. In several of these deals, I ended up catalyzing the round itself, making intros to eventual lead investors and even sharing my customer diligence notes with VCs evaluating the company.

In a way, this approach is Anti-Social proof and Pro-Signal. What is Signal, you ask? It can be of two types, as I explained in my July’23 Investing Framework post (quoting from it here):

  • Internal – extraordinary founder-market fit eg. the founder has spent a decade just going deep in the field. Or a backstory that provides an authentic “why” behind pursuing this idea. Or an execution track record in the startup’s arc that is outstanding on important elements like capital efficiency, iteration velocity, or organic customer acquisition.
  • External – eg. a visionary customer is taking a bet, partnering with them in building the early product. Or a domain expert, skilled operator, perhaps even a specific GP in a venture firm, has taken the time to evaluate & build high conviction in the company.

As you can see, there is a little bit of social proofing baked into evaluating Signal too, but it’s much more oriented around operating and execution-oriented conviction vs deal FOMO and an investing herd mindset.

As you churn on this post, here are more POVs on this topic from some really distinguished venture investors over the years (Source: a 13-year-old Quora post titled Is social proof a rational approach to investment selection?)

1/ Roger Ehrenberg (Founding Partner – IA Ventures, one of the best-performing seed funds of all time)

2/ Naval Ravikant (Co-founder – AngelList, one of the best angels of all time)

3/ Dave McClure (Founder – 500Startups, now running PracticalVC)

Additional readings: The Death of Social Proof by Hunter Walk (Co-founder of a very successful seed VC Homebrew).

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