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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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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Earlier this year, my younger son got admitted to the same preschool that the older one attended in SF. As parents, we were elated! Our older one loved this school, we know the Principal and teachers really well, and it significantly reduces uncertainty for us given this school goes up to Middle. A win-win in every respect!
Except, we were caught completely off-guard by how the first few weeks turned out. That the kid was having “adjustment issues” would be an understatement. Everything from sleep schedules & toilet training to eating & social behavior went majorly South. While this is normally expected when kids change schools, what surprised me was how much this derailed us as parents. We were maniacally struggling to manage the kid in this transition while trying to cope with all the mood swings & changes this was bringing to our daily routine.
Of course, things started improving after a couple of months & as we speak, the kid seems all settled in the new environment🤞🏽. But I couldn’t stop introspecting on why we got caught so off-balance in this episode, even when we knew the school intimately & had gone through this exact experience before with our older one?
This was a manifestation of what I call the Familiarity Conundrum. When we deal with things we are intimately familiar with, there is a double-edged sword at play. While familiarity arms us with high-fidelity, experiential data that can be incredibly useful in making a smart decision, it also creates overconfidence-driven blind spots in our ability to deal with the familiar.
In highly familiar situations, our brain tends to short-circuit the decision-making process, perhaps gathering comfort from past anecdotal experience regarding similar situations. The result is a quick decision based on 1st order thinking. We went through this in the above school episode – our brains used a quick, 1st order heuristic – “because this school was so great for our older son, it will be equally good for our younger one too”. We failed to ask even a basic set of questions regarding this decision eg. are the teachers the same this year, is our younger son starting at the same age as the older one, should we expect any changes to the school routines post-Covid etc. These are basic diligence questions that we would have definitely tried to answer had this been an unfamiliar school for us.
This Familiarity Conundrum often leads to sub-optimal decisions in other aspects of life as well. Some examples that I have personally experienced or witnessed:
When hiring someone we are highly familiar with eg. an ex-colleague or classmate, our brain tends to unfairly magnify our last, dated view of their strengths, not pushing us enough to evaluate them independently, especially with respect to fit with the current opportunity.
When a trusted person introduces us to a deal, say an investment opportunity, our brain wrongly transfers trust with the referrer onto the referred deal, without a rigorous evaluation of the deal on a stand-alone basis as well as the referrer’s true competence in the specific area being evaluated.
When operating in an area where we have prior work experience, we tend to under-diligence the opportunity & overestimate our likelihood of success. In areas of perceived expertise, our brain doesn’t push hard enough on 2nd & 3rd order thinking like figuring out ways in which this context is different from our prior experience, trying to see around corners for lurking risks etc.
So what can we do to effectively deal with this Conundrum? Based on what I have learned from my experience as well as studying great rationalists like Charlie Munger, here are a few ideas:
1/ First step is spotting it at the right time – training your mind to spot times when familiarity could be creating blind spots for you, is itself a major part of keeping biases at bay. Personally, I tend to keep a matrix of such mental models both layered in my head as well as often as part of a diligence checklist. For decisions that cross the bar of impact and/or irreversibility, I like to run them through this matrix to check for potential blind spots.
2/ Don’t deviate from the “checklist” – Dr. Atul Gawande argued for the importance of checklists as a tool to make surgeries safer in his popular book “The Checklist Manifesto – how to get things right“. Professionals as diverse as surgeons, pilots & public market investors leverage checklists to handle uncertainty & make better decisions under stress.
The key is not deviating from your operating process even when the context is highly familiar and your brain is pushing you to use crude heuristics to arrive at a quick decision. Like a pilot who will diligently run through the aviation checklist even on the best-weather days, one needs to strive to do the same, each time, every time while taking high-impact decisions.
3/ Always have an independent feedback mechanism – even in areas where you believe you have deep knowledge and/or extensive on-ground experience, it’s always good to get feedback from independent players who are likely to see the opportunity in an unbiased way.
During my early days as an angel investor, I had a tendency to predominantly rely on my own judgment of a startup & often made decisions without taking the time to gather feedback from other sources. Having learned from several missteps, I have now incorporated gathering feedback from several sources including market experts, customers, founder references & other investors, as a core part of my investing process.
In this context, I find the idea of having a “feedback buddy” incredibly useful. For important projects eg. buying a house, a product launch, a big investment, it’s good to have someone who is unrelated to the project be a sounding board to bounce off ideas, poke holes in current thinking & simply provide common-sense feedback.
The bottom line is this – as opposed to explicitly unfamiliar terrain where our natural survival mode gets alerted, familiar contexts are significantly more likely to get our brains in “lazy thinking” mode, creating blind spots that will catch us off-guard. Proactively spotting this dynamic, having the discipline to stick to a rigorous process at all times & consciously incorporating an independent feedback mechanism within it, goes a long way in offsetting this Familiarity Conundrum.
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Growing up in India, where inherent chaos makes sure most things don’t go according to plan, I got organically trained to always have a Plan B. The classic fallback option – the bylane you take when the main road is clogged because a minister is scheduled to pass through, the backup college seat you block in case you ranked low in the entrance exam for your top preference, or the autorickshaw you hail when the car refuses to start.
Look, I get it! Now that I am a father to 2 boys, I see the instinct parents have to ensure their children are tangibly & emotionally “safe” in all situations. So, I can appreciate why my middle-class upbringing was designed this way. To top it up, my technical education & early analytical jobs further pushed me into the world of scenario analysis & fail-safes.
Down the road, as I entered the risky world of startups, I naturally brought this instinct with me. While building, operating & investing in high-risk-high-reward endeavors, my animal brain would always push me to have a Plan B in my backpocket:
If this startup doesn’t work, I can always go back to Company X.
What if this investment fails? Let me spread my resources & take a smaller bet.
If I don’t like living in Country Y, I can always go back to India.
A few years into taking these asymmetric bets (presumably backed by Plan Bs), I expectedly started encountering failures, both big & small, one after another. They ranged everything from major projects going South & unforeseen external risks coming to the party to unexpected company restructurings & gross misjudgment of certain people’s skills & intent.
During a recent introspection of these adverse experiences, something interesting jumped out – every time I attempted to call on a Plan B for a specific situation, more often than not, it wasn’t really there. In some cases, the “backup” companies had changed their strategy & weren’t a fit anymore. In others, I had grown in a different direction & going to a fall-back option would be a negative step. Many times, people I was relying on to help materialize a certain Plan B had either fallen out of touch, were themselves dealing with adversity, or had changed their context & therefore, relevance.
So this was my lightbulb moment that inspired this post – in high-risk-high-reward situations, Plan Bs are….fictitious. The very nature of extremely risky situations is that they take you in unpredictable directions, change your context in unimaginable ways & leave you with baggage that’s hard to foresee. And all this happens in parallel to a rapidly-changing external environment that in most cases, becomes increasingly incongruent with your endeavor (most asymmetric projects are by definition, contrarian in relation to established rules of the game that the majority operates by).
This complex system renders even the most thought-through Plan Bs useless. Given asymmetric bets are driven by power laws (a few will drive a majority of the total outcome) & compounding (need a long enough timeline for ideas to mature, which is when outcomes start growing exponentially), positioning yourself to be on the right side of these rules requires going all-in for a significant period of time.
While having a Plan B provides the initial psychological space to initiate a risk, in my experience, it unfortunately also creates a mental mechanism to cop out of it, & even worse, often doesn’t provide the safe landing space it initially promised.
Going forward, my aim is to ditch the “Plan B” mindset in all asymmetric bets. A fall-back instinct comes from a place of fear, and while controlled fear can be a useful tool to drive alertness & urgency, it becomes adverse when acting as a roadblock to going all-in & persevering on a thoughtfully-chosen path.
It’s important to add here that while ditching the Plan B outlook, I will still proactively focus on avoiding the Risk of Ruin at an overall life level. Asymmetric bets require multiple shots at the goal & therefore, safeguarding the ability to keep playing is paramount.
On a related note, a mental heuristic I have recently started using while making asymmetric decisions I am 50-50 on – “which option is the fear side of my brain asking me to choose?”. In most cases, I then lean towards the other option!
I have found the following quote by Swami Vivekananda to be hugely inspiring in driving this mental transformation:
Take up one idea. Make that one idea your life – think of it, dream of it, live on that idea. Let the brain, muscles, nerves, every part of your body, be full of that idea, and just leave every other idea alone. This is the way to success.
Swami Vivekananda
As you consider this approach, I want to leave you with this outstanding scene from Christopher Nolan’s ‘The Dark Knight Rises’. As a frustrated Bruce Wayne is trying to catch his breath after yet another failed attempt at climbing out of the pit (he was using a rope each time), an old & wise prisoner gives him the mantra for successfully making the climb:
You do not fear death. You think this makes you strong. It makes you weak.
How can you move faster than possible, fight longer than possible, without the most powerful impulse of experience – the fear of death!
Make the climb…as the child did. Without a rope!
The Dark Knight Rises (2012)
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You know, the ideas that I have had in my life are quite few. But the lesson I can give you is, a few is all you need, & don’t be disappointed.
When you find the few, of course, you have to act aggressively, that’s the Munger system.
Charlie Munger
As I was ruminating on blogging about my experience of this idea, Warren Buffet did a timely reminder in the recent Berkshire 2022 Shareholders Letter:
The lesson for investors: The weeds wither away in significance as the flowers bloom.
Over time, it takes just a few winners to work wonders. And, yes, it helps to start early and live into your 90s as well.
Warren Buffet
The idea that you only need to get it right a few times in order to lead a rich life is so powerful! Through it, Munger & Buffet also underline the importance of power laws in almost everything worthwhile. That a few decisions & outcomes will drive most of our respective lives.
About a decade back, this idea was an intriguing concept for me, but only at an academic level. Since then, I have experienced, & therefore internalized it, across multiple aspects of my life. Even though I would consider myself a perpetual hustler who has worked in 8 companies, tried multiple functions across diverse industries, lived in many cities across US & Asia, and invested in 20+ startups, I can boil down where I am in life today to a handful of decisions that acted as step-functions:
Where I ended up studying for undergrad, as that’s where I met my (future) wife.
Pursuing & marrying her several years later.
A cold email to a VC firm that eventually became my entry point into tech.
Deciding to move from India to Silicon Valley with no job in hand, no existing networks, with just faith that I will figure it out.
Casually meeting the husband of one of my wife’s friends back then, who eventually led me to join Alibaba.
That’s it! If I take any of these decisions out of the equation, my life would look very different.
At a more specific level, I see this dynamic play out in my angel portfolio too. I have been investing as an operator-angel since 2014, & now with enough data from my own experience, can confirm that 1-2 companies will end up driving a majority of my returns. The countless hours I have spent turning over stones, meeting hundreds of founders & working in the trenches with portcos, translate to just a couple of needle-moving outcomes over a decade. But yes, they are expected to move the needle by a lot (major step-functions, as I like to call them).
Same with content – sometimes I feel like I have written the most thoughtful post or a super-smart tweet, & no one reads it. And then, I write some crazy anecdote from my past lives & it goes viral.
Translating this “only need to get a few right” idea from purely academic to a lived & internalized one becomes important as it helps to frame risk-taking in the right way, particularly dealing with failure.
It has taught me many lessons:
Outcomes in creative & high-risk-high-reward pursuits are random.
Multiple failures don’t matter (& should be expected), as long as the few successes are outlandishly large.
Given success is sporadic, need enough shots at the goal to get odds in your favor. Take more chances with asymmetric upsides.
Given success is intermittent, plan for & evaluate things over a long-enough timeline. Patience is key!
When you get it right, let it compound. Milk every success to the fullest.
While specific outcomes are uncontrollable, a few decisions will always be make-or-break points in life – where you study, who you marry, which city you decide to settle down in, whether you have kids or how many, what house you buy & when etc. When faced with these questions, appreciate their importance, take your time & try to make the best possible decision in your capacity.
Finally, rather than getting fixated on episodic successes & failures, zoom out to look at the bigger picture & visualize your life as a curve. The goal is to have it trending up & to the right over a long timeline.
So, keep playing the game, be patient & wait until you get your “few” right!
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I recently tweeted a really interesting insight I heard from Mike Maples, Jr of Floodgate at a recent Draper University closed-door event:
This is so true, and a common mistake that founders & product leaders make while building new products. Looking back on my own startup, while I rigorously tried to execute Paul Graham’s “Do things that don’t scale” philosophy, I still created unreasonable expectations in my own head around user growth for each MVP iteration. This was probably due to the baggage I was carrying from my previous experience of working at large companies like Alibaba, where numbers were talked about in Millions & sometimes, Billions.
When the absolute user numbers weren’t met, my morale as a founder would get hit with each iteration. In hindsight, hitting numbers shouldn’t have been the goal at all. The ideal 0-to-1 mindset is like that of a scientist, with curiosity being the core driving emotion, backed by an iterative product development approach. The target outcome of this approach should be to gather insights that help refine the hypothesis.
Similar to how scientists drive their research process one experiment at a time, I have realized that building any new product or service from grounds-up requires moving one “unit” at a time. It’s up to you to decide what that unit should be – acquisition, activation, frequency of use, revenue or even just getting qualitative feedback!
In a scientific process, more than just the number of experiments run, what’s important is taking the learning from each experiment & applying it to the next one so it becomes better than the first.
Similarly, a good approach to building anything new is to delight one person at a time. This automatically focuses the building process & anchors it on an actual customer, thus making it easier to ship something that solves a monetizable problem for someone in the real world. Trust me, this is a non-trivial hurdle that many startup teams are unable to cross.
The 0-to-1 stage can be highly fuzzy but breaking it down into one unit at a time helps give more clarity to the team around the exact short-term goals.
The most profitable way for a product to grow is via word-of-mouth. The above approach naturally optimizes for it. And once the testimonials & organic growth start kicking in, traction compounds with minimal incremental effort.
Of course, the key to executing this building approach well is patience. Again, think of a scientist. A larger research budget or more headcount can’t necessarily speed up a breakthrough. Similarly, building one unit at a time requires a small team committed to iterating over a long enough timeline for customer compounding to kick in. A lean & capital-efficient operating model is a requirement of this approach as a long runway significantly improves the odds of success.
Learning from my mistakes as a founder, as I have now started working towards regularly putting useful startup & investing content out there, I am consciously following the approach of publishing & learning one unit of content at a time – blog post, Twitter thread, LinkedIn post etc.
Same for my angel investing, wherein I am trying to help each founder, co-investor & startup employee I meet, one week at a time, with whatever resources I have – network, expertise, capital etc.
This approach is helping me to first put the core enablers of my venture investing craft in place that then, hopefully, self-compound. Therefore, I feel much better this time about hitting my long-term goals.
Also, in case you are interested in other similar startup insights shared by Mike Maples at the DraperU event I referred to earlier, check out my Twitter thread on it.
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2022 definitely felt like the end of an era. A decade-long party for US tech, fueled by low-interest rates -> increased availability of risk capital -> price inflation across asset classes.
The last chapter of this post-GFC era was perhaps the craziest – an unprecedented pandemic -> widespread lockdowns -> more fiscal stimulus -> injecting more air into already inflated asset bubbles.
With inflation crossing 7%, the Fed finally started increasing interest rates last year and is expected to continue quantitative tightening over the next few quarters. Public market valuations expectedly turned south (valuations are based on DCF, so as discount rates go up, valuations go down), with tech growth stocks correcting by as much as 80-90%.
The following dynamics are currently at play:
Large tech: shrinking macro-demand + adverse public markets -> pressure on companies to reduce costs to bring them in line with lower growth projections -> drastic cost-cutting measures, including major layoffs.
VentureInvestors: public market corrections -> LPs cut back on venture allocations + downward revision of expected returns on exit -> venture activity slows down + any deals that happen, happen at much lower valuations given new public market comps.
Startups: less capital available + higher bar in private markets -> startups need to cut costs to survive -> layoffs in high-cost/ non-core functions + pause hiring unless absolutely essential.
2023 appears to be the “year of transition”, as both the overall macroeconomic cycle, as well as the technology sector within it, move into a new paradigm. I see the following ten big ideas for 2023:
Leaner-and-meaner big tech
For anyone working in tech over the last decade, we have witnessed first-hand the level of entitlement & cultural complacency that has grown within large tech companies like Google & Meta. With more challenging times ahead, I expect large tech companies to take drastic steps towards re-wiring their cultures & operating models. Layoffs are just one piece of the puzzle – expect significant changes to compensation policies, KPI/ OKR philosophies, org. structures, functional locations, work-from-home policies, contractor hiring, operating routines etc., all with the aim to make execution more efficient.
Founder-led companies (eg. Meta, Salesforce, Shopify, Coinbase etc.) will take quicker & braver calls to re-invent themselves, compared to those run by professional management teams (eg. Google). In the latter case, I expect shareholders to put considerable pressure on these professional CEOs to take corrective measures. In fact, I won’t be surprised if some of the big tech CEOs get unexpectedly replaced as many of them come across as peacetime CEOs who will struggle in wartime.
2. Capital efficiency over growth for startups
The last decade in tech startups was all about growth. This year, expect investing thesis & operating models to decisively shift towards capital efficiency. Mirroring the demands for margin improvement by public markets, I expect private market investors to significantly raise their expectations on operating efficiency.
Founders will have to react fast and in several cases, give a 1800 turn to their culture & business models. A silver lining – founders who were heads-down amidst the craziness of 2020-21, building their companies in a capital-efficient way, will have an enviable opportunity (& deservedly so!) to play offense both with customers & investors.
3. Bay Area bounces back
Remote work boomed during the pandemic, as tech companies grew at unprecedented rates. However, we saw signs of a comeback-to-office across both big companies & startups last year. With current headwinds, I expect factors like teams getting together to drive execution & in-person networking to become increasingly important.
With rampant layoffs, tech professionals will also feel more insecure & would need more access & optionality to get their careers back on track. All this bodes well for the Bay Area – I expect significant migration to the region, especially for people in their 20s to mid-30s. In terms of the sheer depth of the tech ecosystem, the Bay Area remains unparalleled. As emerging areas like AI, health-tech & EVs gain strength, they will provide even more reasons for talent to be physically here.
4. “De-angelification” of the startup ecosystem
Amidst the post-pandemic investing frenzy, liquidity-rich, over-optimistic, FOMO-driven tech professionals started dabbling in angel investing. Becoming an angel in a “hot deal” became a status symbol, & rapid paper-markups made everyone feel like a winner.
A majority of newbie angels from this vintage neither understand the nuances of this asset class nor have the depth of resources to play the game effectively over the long term.
As more startups start shutting down this year, combined with layoffs & decreasing compensations courtesy dwindling value of RSUs, I expect a massive churn in 2020-21 vintage angels. In my experience, tourist angels typically drop out of the game around the 4-6 deals/ 24 months mark, as they see portfolio companies starting to shut down & their hard-earned money vaporizing into thin air.
5. More pain in Crypto
If you thought 2022 was brutal for Crypto, brace yourself! FTX implosion is only the beginning of a much-needed cleanup in the space. I expect many more tokens to go to zero, projects to shut down & low-conviction talent to move out. Given the scale of the FTX fraud, am expecting even more regulatory oversight & ramifications for the overall sector this year.
Personally, I do believe there is a kernel of truth in the Web3 opportunity. The faster this cleanup happens, the sooner the next chapter can begin & we can make tangible progress towards discovering its real-world use cases.
On BTC and ETH, I expect both to remain flat at best, & significantly down from current levels in the bear case.
6. The FOMO shifts to AI
Whenever there is too much consensus around a trend or an asset class, I get worried! It was clean-tech pre-GFC, then Blockchain & Crypto pre-pandemic, moving to Web3 & future-of-work post-pandemic. Based on my Twitter feed, I can safely say that with the rise of OpenAI & launch of ChatGPT, the FOMO has now shifted from Web3 to AI. I am expecting the space to see a lot of hype, investor interest & startups being launched in 2023.
Studying how the previous FOMO waves evolved gives a fair understanding of what to expect – those without first-principles conviction & a long-term strategy are more likely to get their hands burned. Those who were anyway committed to the space & were quietly building behind the scenes over the last few years stand to disproportionately benefit from the increased availability of risk capital & talent.
7. The return of “moats” (& rise of deeptech)
As the perpetual-growth era of software ends, I expect the question around “moats” to re-appear in the diligence checklist of investors. The lifecycle of companies like Netflix & Robinhood has clearly shown how hard it is to have a sustainable competitive advantage in tech (one reason why Warren Buffet stays away from investing in it!).
As the likelihood of purely growth-driven exits goes down, I expect venture investors to start looking at deeptech verticals with inherent moats much more seriously. These include space-tech, health-tech (including lifesciences), energy, climate etc.
Each of the above markets seems to be getting unlocked in its own unique way & while these companies can be more capital-intensive & have higher technical risk compared to say SaaS or Social, the resulting market leaders have much more defensible competitive positions & hence command healthy valuation multiples.
8. EVs taking over the transportation stack
EVs are seeing major progress on both the supply & demand side. On the supply side, most major auto companies have an EV product in the market, with use cases evolving from urban sedans to SUVs, pickup trucks & now, even semi-trucks.
On the demand side, record-high gasoline prices have acted as a key unlock. This is visible in the rising hybridization of the latest gasoline car models. With non-Tesla EV products rapidly expanding, consumers have more choices across use cases & price points. I wrote a post a few months back on how I warmed up to EVs & Tesla, in particular. I expect EV penetration to have significant growth momentum this year.
9. Digitization of mainstream healthcare
A positive side-effect of the pandemic has been consumers getting increasingly comfortable with digitally-delivered healthcare services. In my case, interacting with healthcare providers over Zoom and accessing services such as Carbon Health & One Medical via their apps (including getting advice via chat) has really opened my eyes to its value. Even beyond that, I work-out with my trainer via video & our family nutritionist is in India with all interactions happening via Whatsapp.
I expect the overall healthcare stack, including mainstream services, to digitize at an even faster rate in the coming year. These tech platforms will also open up opportunities for niche services to exist eg. virtual monitoring & consultations for chronic patients, pre & post-natal advice, nutrition guidance etc.
10. India as a global greenshoot
Amidst an unstable China, weakening EU, war-torn Russia, one-dimensional Middle East, fiscally-unstable LatAm & fragmented Africa, India appears to be a solid greenshoot both geo-politically & economically. A stable & reformist govt. has worked hard to put together core growth pillars over the last 8 years – from building physical infrastructure & a national digital payments network to ensuring economic development at the grassroots & supporting tech startup activity in the country. India is poised to now reap the dividends of all this hard work, and similar to China, grow its per-capita income from ~$2k at present to ~$10k over the next 20 years, all in a democratic environment.
India’s tech ecosystem has also come of age in the last 5 years. The mega question of “can exits of venture-backed companies happen in India?” has been progressively answered, beginning with the acquisition of Flipkart by Walmart, followed by IPOs of consumer companies like Paytm, Nykaa & Zomato in domestic public markets, & the IPO of Freshworks in the from-India SaaS space on Nasdaq. There is a growing pool of startup talent, courtesy of a decade-long Mobile & software wave, which will fuel the country’s tech ecosystem over the next decade.
The above ideas are making me super-excited for 2023, both as an angel investor & operator. After a 2.5-year hiatus, I returned to angel investing in 2022, doing 3 deals in Q4. With the turning cycle & above ideas as a backdrop, my goal is to make 2023 my most active year yet as an angel, while also keeping a high bar on quality. Excited to collaborate with all founders, angels, VCs & operators out there 👊🏽
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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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to my weekly newsletter where in addition to my long-form posts, I will also share a weekly recap of all my social posts & writings, what I loved to read & watch that week + other useful insights & analysis exclusively for my subscribers.
