This one is intentionally short and sweet. Minimum words, maximum impact.
Here are my top 5 learnings from more than a decade of angel investing:
(1) Choose a “strategy” ➡️ many can work, focus where you have an edge.
(2) Take enough “shots-on-goal” ➡️ adequate diversification/ portfolio size but watch out for “di-worsification”.
(3) Respect “power law” (few winners will account for the majority of the returns) ➡️ hence, Point (2) is important.
(4) “Access” is everything ➡️ watch out for adverse selection.
(5) Brace for long periods (10+ yrs) of illiquidity to let compounding kick in ➡️ Knowing “when to sell” is going to be super-important, and unfortunately, it is an art rather than a science.
PS: for your own good, see this chart once daily 👇🏽(Source: David Clark of VenCap).
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As a venture investor dealing with high volume deal flow across sectors, stages, and founder personas, how does one effectively screen for the diamonds amongst the rocks?
Sharing a deal screening framework to help improve any venture investing process.
As an operator-angel, I consciously follow a “tech-generalist, founder-first” style of investing. It both suits my background (which is very cross-sectoral & cross-functional) as well as helps me cast a wide-enough net.
I believe one of the core advantages of being a solo investor is not being boxed in a niche fund strategy or sector focus. As tech evolves rapidly across geos, I like the freedom to be able to seek out the best founders in whatever vertical they might be building in, as well as be opportunistic in terms of participating across stages & in special situations too. This is the model that the likes of Elad Gil & Jason Calacanis have followed.
Of course, one has to still identify the game where one has an “edge” in, to have the best odds of outlier returns. For me, that’s focusing on what I call the“Global Indian” founder persona. It includes:
(1) India based founders building for the world (eg. cross-border SaaS, enterprise, deeptech etc.), and
(2) Indian immigrant/ Indian-origin founders building tech cos. in large markets like the US & SE Asia.
This is the persona where I have high-quality access, where I am able to understand & relate to the founder’s journey & motivations, as well as add value with empathy, given I am myself a Global Indian.
With this strategy, I end up with a massive top-of-the-funnel of deals across a wide variety of sectors, stages, geos & check sizes. Over the last few months, I have been trying to think of some sort of a screening framework to be able to quickly figure out where a new investment opportunity fits in my deal universe. Ideally, this framework should help easily visualize a deal’s preliminary fit with my strategy, before taking it into deeper diligence & running my entire check list on it (my core IP!).
While any such framework can involve many types of vectors, I have been experimenting with a “consensus vs signal” 2×2.
These vectors abstract out 2 important elements of venture investing:
Consensus – what is the investor-crowd’s opinion on whether this startup* makes sense or not.
Signal – what is the quality of people** who believe in the startup & have skin-in-the-game.
*”Startup” here means an amalgamation of team, market & product.
**”People” here includes founders, employees, customers, existing investors etc.
Let’s look at what each of the quadrants in the 2×2 mean:
(1) Low-Signal-Consensus – these companies lack high quality operating signals around the business and who the investor-crowd agrees will find it hard to make it big. A typical example would be an idea stage founder with no educational or career spike, going after an established (highly competitive?) market but with weak founder-market fit, and yet to demonstrate any early validation or traction around the startup’s hypothesis.
These opportunities will usually have negligible investor interest. When I come across such companies, my instinct is to first check if I am seeing any positive signal that the crowd is missing. This could be a behavioral characteristic of the founder, something from their personal backstory or from their startup journey so far. Idea is to see if there is some sort of high-quality leading signal hiding in plain sight.
If I sense a likely positive signal, I try and maintain a thread with the founder over coming months, attempting to see if subsequent execution can help build some conviction.
Note: most cold inbounds on LinkedIn, as well as startups from college incubators/ accelerators/ b-plan competitions fall in this bucket.
(2) High-Signal-Consensus – these companies have high quality signals around team pedigree, investor interest, customer traction etc., and who the investor-crowd agrees are potential winners. A typical example would be a repeat founder building in an established market that is universally understandable, has a large TAM and a past history of large outcomes.
While these deals are understandably hot, high investor FOMO around them creates 2 risks:
High entry valuations, bringing down future returns.
Because these deals look so obviously good on paper, it drives investors to overlook asking hard questions around the business. Does the repeat founder have fit with the space? Is there hubris at play from past success? Is the company being over-capitalized & therefore, not being set up for capital efficiency?
Therefore, whenever I see a High-Signal-Consensus deal, my antennas go up & I consciously try to keep FOMO at bay while increasing the rigor of the evaluation process.
Note: most deals that I see in angel syndicates or groups fall in this bucket.
(3) Low-Signal-Non-Consensus – these companies lack high quality operating signals around the business. But interestingly, the investor-crowd also doesn’t have a consensus yes/ no view on it yet. Reasons could be the space is esoteric so hard to understand, team’s background is non-traditional, or location is non-top-tier, founder is bad at pitching etc.
While looking at these opportunities, I am conscious of these being potential “non-consensus traps” – companies that look good to someone trying to invest against the crowd just for the sake of it, without building first-principles conviction.
I have an inherent positive bias for underestimated founders & overlooked assets. That’s why I try to be consciously careful in this bucket of startups as with experience, I have learned that bad companies are in most cases, just bad companies.
(4) High-Signal-Non-Consensus – these are the opportunities we as venture investors live for. They are highly non-consensus, with the investor-crowd struggling to access, understand, evaluate risk and build a positive view on them. Yet, these startups have high-quality leading signals, which could be external and/ or internal.
External – eg. 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.
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.
This quadrant is the hardest to source for and requires having a really differentiated network of relationships (for referrals) and a personal brand that attracts interest from these types of founders.
When I meet startups in this quadrant, I immediately get to work, spending time with the team & together unboxing every facet of the market. Generally, these deals have relatively less investor FOMO so I can take the time to run my conviction-building process with rigor.
The risk in this quadrant, and purely from my personal investing style & behavior perspective, is that I tend to get positively biased on them very quickly. After many such experiences, I now consciously play devil’s advocate during the evaluation process. Btw this is where running a rigorous conviction building process and avoiding a trigger-happy mode really helps.
Hope you found this screening framework interesting & perhaps helpful for your own venture process. Of course, evaluating an early-stage venture opportunity is much more multi-dimensional than this. But having such a framework really helps in effectively allocating bandwidth while managing high volume deal flow.
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This Generative AI wave is both a tremendous opportunity over the long term and a ticking bomb in the short term.
Sharing a framework to navigate & eventually thrive in this hype cycle as a tech investor.
As the Generative AI fire rages on with full force, I have been thinking through the best approach for me as an operator-angel to navigate the current environment.
What makes this AI wave particularly challenging for venture investors is that it’s full of contradictions depending on what time horizon you choose to view it from.
In the short term…
But over the long term…
The space is clearly in the early stages of the hype cycle.
It’s perhaps the most defining technology shift of our lifetime, likely to drive a socio-economic change like the agrarian ➡ industrial age transition.
Though AI is “consensus” in Silicon Valley, the agreeing crowd has a track record of being right quite often.
The only way to generate outlier returns is to be “non-consensus-and-right”.
Early entrants are likely to attract significant venture capital, potentially generating quick mark-ups for early investors.
Like previous platform shifts (eg. Web and Mobile), early entrants are unlikely to be the eventual winners (there were at least 8 major search engines before Google came along).
Pre-product stage startups commanding rich valuations is perhaps justified, given investor-demand & the hockey stick growth potential of the space.
The best way to generate above-average returns is investing in the best companies at reasonable valuations.
Clearly, there is a time horizon tension at play here. As an investor, one doesn’t want to miss out (or appear to have missed out) on the earliest stages of the greatest platform shift in our lifetimes. At the same time, as the recent Web3 wave taught us, maintaining discipline during hype cycles is key to ultimately realizing cash-on-cash returns.
To manage this tension & navigate this wave in a risk-adjusted manner, I have been using a framework I like to call “Macro-Optimism, Micro-Skepticism”. This approach involves always keeping two opposing emotions in your mind while evaluating opportunities:
Macro-Optimism – a strong belief that AI is going to be a super-powerful force of positive change in our lifetimes. Having this belief should translate to an immense yearning to learn as much as possible while the tech is still embryonic. It should also translate to keeping an open mind about its possibilities & having the imagination to think about “if it works in this way, what could this idea become?”.
It should lead to a low-ego & eyes-wide-open mindset while meeting founders working on the frontiers of AI. It should also lead to having the awareness to not underestimate any person or idea, no matter how divergent it sounds within your current lens.
Micro-Skepticism – realizing that in the initial stages of a hype cycle:
(1) most ideas will turn out to be invalid, as how a major platform shift shapes the future is, to quote Brad Gerstner of Altimeter Capital, “unknown & unknowable”. And;
(2) the space will initially attract a lot of low-quality actors, including scammy founders, tourist investors & others with a get-rich-quick mindset.
Realizing this should translate to looking at each new investment opportunity with default-skepticism – keeping the bar high, asking hard, intellectually honest questions & calling BS when you see it. This approach requires running a rigorous conviction building process, keeping FOMO at Bay & staying true to your investing value system.
Of course, parallel processing these opposing ideas is easier said than done. As I wrote in my recent post “Investing Landmines”, we are susceptible to many biases that get further exaggerated during hype cycles. Some ways to get better at managing them include:
1/ Leveraging complementary peers or team members that can keep you honest & call out your blind spots.
2/ Using some sort of light-weight system to ensure you are asking all critical questions & spotting typical pitfalls. As an example, learning from the likes of Atul Gawande & Mohnish Pabrai, I have found simple checklists to be helpful.
3/ Consciously sleeping on a deal before pulling the trigger, giving the ‘think-slow’ part of your mind enough time to digest facts.
Ultimately, am excited at the opportunity this AI wave is providing for investors with a growth-mindset to test & fine tune their systems. While I have no doubt that all of us in the tech ecosystem will benefit from this platform shift one way or another, I also hope some of us emerge wiser from it.
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Recently came across a fascinating Twitter thread from June 2020 by Dave McClure, ex-founder of 500Startups, where he talks about how “investing with conviction” is a myth. This tweet captures his sentiments well:
I agree with several arguments in this thread:
1/ Picking winners in early-stage investing is really hard. Power laws govern the best venture portfolios, driving down the hitting %. Per Horsley Bridge data, even for a top VC firm like Sequoia, ~4.5% of portfolio companies generate 2/3rd of aggregate returns.
2/ Intelligent venture investing, by its very nature, involves making both Type 1 and Type 2 errors. Therefore, even high-conviction deals are likely to exhibit unexpected outcomes, both positive & negative.
3/ There is a lot of hindsight bias in the way investor narratives are created around companies that turned out to be successful – “Look, I had high conviction on this deal & it turned out exactly as I expected. Ergo, I can predict the future”.
So in games like this where outcomes are random & often uncorrelated with the level of effort that goes in, does it make sense to discard the input process?
Based on more than a decade of venture experience, I tend to view it differently. I believe it’s still important to have a rigorous process of building conviction and to keep improving it bit by bit with each experience. Even though eventual outcomes might still be random, this approach helps tilt the playing field a little in your favor every time. Over a long enough time horizon, as one keeps taking more shots at the goal & with continuously improving odds, the hope is that a home run arrives sooner than later.
Particularly at the earliest stages (angel/ pre-seed/ seed), especially with the advent of small check investments ($1-5k via syndicates/ SPVs) attracting a new generation of 1st-time investors, it’s easy to assume that outcomes are randomized & therefore, fall into the trap of doing spray-and-pray that isn’t backed by an intelligent investment process.
It’s important for new angels to first deeply study the asset class & build their personal investment process – areas of expertise, focus sectors, stages, target founder persona, deal flow engine, unique value-add to get into best deals etc. Post which, the odds of success are significantly better.
While being a champion of a “conviction-building” investment process, I also agree with the 3 takeaways that Dave closes the thread with, regarding having enough shots on goal:
Even with the most intelligent investment process, venture investors need to acknowledge their limited picking ability & therefore, keep taking enough intelligent shots at the goal for the odds to work in their favor. Semil Shah of Haystack wrote a great post titled “Shots on Goal” on this idea a while back.
Equally important as portfolio diversification via numbers, is making asymmetric investments – ensuring that the few winning bets have huge outcomes so that even with a high loss ratio, the returns math still works at the portfolio level. The smartest thing a venture investor can do is to befriend the power law, and work towards being on the right side of it!
To summarize, acknowledging the randomness of venture outcomes doesn’t need to be at odds with running a rigorous & continuously-evolving investing process. In fact, such a system should be intelligently designed to account for this randomness, combined with other considerations like power laws, compounding, economic cyclicality etc. Even a few points of “edge” that is systematically created with each experience, can slowly accumulate into a sizable alpha over the long term.
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Recently, I was in a shareholder’s meeting of a portfolio company. It has been a gut-wrenching last 3 years for the leadership. Unfortunately, the company’s market pretty much shut down during Covid. Significant liabilities built up & the team saw significant churn. To survive, the company had to raise a bridge at a major haircut.
During the meeting, the management team walked us through their journey of turning the business around from this dire situation. After the lockdown was over, customer demand got re-ignited. The company drastically cut costs, improved operating metrics to get revenue back on track, re-negotiated long-term vendor contracts, and cleared-off short-term liabilities, all while retaining the core manpower, many of whom had to take salary cuts.
As a result, the company is now PBT-profitable & growing through internal accruals. Btw this turnaround was achieved on a ~$13Mn revenue base. As an operator & ex-founder, I was blown away by this execution story & the team’s grit.
But then, I put my investor’s hat on – despite all this progress, early investors are deeply out-of-money & are likely to remain so for a while. During 2017-19, the company raised equity at aggressive valuations that were misaligned with both the maturity of the business as well as the underlying multiples the sector trades at. In boom times, startups get valued at hyper-growth tech multiples. However, as soon as the cycle resets, follow-on investors revert to valuing them on realistic sectoral comps.
The good news is, courtesy of the awesome restructuring efforts, the business is on a profitable growth path. But given the extent of divergence between our entry valuations & current market comps, it’s going to be a long road toward generating healthy returns for early investors. And even if we get there, the sheer time taken will negatively impact IRRs.
As an angel, this is the part I really struggle to get my head around – how important is the entry price? Bill Gurley says in this 20VC podcast with Harry Stebbings – “the market sets the price on a deal-by-deal basis but as an investor, you have to keep an eye out for the price you are paying at a portfolio level”. This becomes especially hard for angels, who typically have to adhere to the price set either by the founder (SAFEs) or an institutional lead. In this era of fragmented checks via syndicates, SPVs & RUVs, I frequently see valuations that aren’t correlated to the underlying risk in the business & smaller check investors unable to push back. Ultimately, everyone ends up toeing the line.
As an investor, I always have the option of not participating in a highly-priced round. But then enters the other side of the coin – power law ensures very few companies drive a majority of venture returns. Therefore, angel investing is the game of accessing the “best” companies, which often requires paying up to get in. An argument frequently made is “if the company ends up as an outlier, it doesn’t matter what price you got in at”. I get this line of thinking but an “outlier return” is very contextual. Eg. a 10x return potential over a 5-7 year period is very solid for an angel, though might not meet the deal hurdle for a large fund. There are cases in my own portfolio wherein early angels are sitting on a 5-10x unrealized return because we entered at sub-$10Mn valuations and frankly, the likelihood of a startup hitting a $50-100Mn valuation is significantly higher than becoming a unicorn.
Over a 20+ angel portfolio built over 8+ years, I still struggle with thinking about entry valuations the right way. Presently, am taking it deal-by-deal with the guiding North Star of discovering & backing the best founders I can find, while also accepting the reality that angels will usually be price-takers that are prone to macro sentiments & the whims of lead investors. As Bill Gurley advises, maintaining perspective & discipline around portfolio-wide avg. entry price seems to be a smart way to play a balanced game.
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Have been thinking about a new mental model for early stage investing (angel till Series A; dynamics are different Series B onward) — I call it “Optimistic Arbitrage”.
‘Optimistic’: the art of seeing beyond “what is” and “what is reasonably expected”, to “what if” and “what it could become”. Proof points for the former are traction metrics while the latter is driven by vision, belief and a unique world-view.
‘Arbitrage’: ability to see what the ‘market’ isn’t seeing in an opportunity. In my experience, this can be split at a macro-level into 1) ‘team’ arbitrage — seeing in people what others don’t see, looking beyond traditional pattern matching, recognizing non-obvious talents, and 2) ‘opportunity’ arbitrage — spotting a problem that exists or will exist soon enough, but which isn’t yet obvious or sexy enough for the ‘market’ at large.
One of my realizations is, to effectively pull-off ‘Optimistic Arbitrage’, you of course, need to have a certain kind of personality (positive, believer, low tolerance for cynicism, less complaints and more execution). But more importantly, you need to be enriched by diverse life experiences & exposed to a variety of business, cultural & personal contexts-this essentially, creates a strong ability to connect the dots & look beyond the obvious.
