Shopping Spree

In the coming months and years, I suspect we will see increased M&A activity move through the technology industry. And while there could be a fair number of large strategic purchases (think Facebook buying WhatsApp or Microsoft buying GitHub), I sense an uptick in “smaller” acquisitions, those that could range anywhere from $10M-$500M. Investor Elad Gil calls these team or technology /product buys. 

Several forces could drive this M&A cycle:

The universe of software & technology acquirers rapidly expands

Good acquiring businesses in technology require a couple key ingredients — significant market capitalization, growth as an integral part of their valuation narrative, and the combination of cash and cheap equity to dole out. The conditions are ripe for an increasing number of businesses. The public markets have been generous to software companies, and high-growing SaaS businesses command serious premiums with buoyed stock prices. And you’re starting to see CEOs capitalize on this. Jeff Lawson of Twilio comes to mind — Segment is the notable purchase, but he also recently bought Zipwhip for a cool $850M. On the private markets side, it feels as if there’s a new unicorn minted daily. If you look at the CB Insights list, there’s a flood of businesses now in the $1B to $10B valuation range. Except for the rare few, acquisitions will be essential for the next leg of growth investors are banking on. Private darling Hopin (at a rich $7.75B market cap) has already purchased a string of businesses.

The digital transformation story has a new look

To say that every company is now a software company is to beat a dead horse at this point. Most recognize the importance of digital and its value inside of legacy enterprises to customers, employees, and shareholders. Yet, “the digital transformation” (don’t blame me, blame McKinsey) story to date has focused on large enterprises as purchasers of software, thus the high growth of companies like Zoom, MongoDB, DataDog, etc. Buying tools is still a band aid. Many large businesses recognize the importance of in-housing technology assets in order to revitalize companies, and will up the frequency of acquisition. To my surprise, manufacturing has been an interesting case study — large industrial giants like Rockwell Automation and Emerson Electric have emerged onto the software M&A landscape. Another example — John Deere bought a startup called Bear Flag Robotics for $250M.

Talent fragmentation and consolidation

This point might be the most under-discussed of the three. Companies that truly break out (north of $100B market cap) are gravitational centers of talent for years. Their systems not only distribute products at massive scale, but also consolidate talent and energize them to reach their full potential. Getting to this level is the aspiration of many of the previously-mentioned acquirers. Startups are easier than ever to start (this is good!) due to the global distribution of company-building ideas (CS183B and Paul Graham’s essays are free!) along with cheap capital and infrastructure like AWS and Stripe. However, this has led to talent fragmentation at the early end of company life cycles. Talented groups of friends & peers don’t band together to start companies; they all start their own because there’s little friction. Over time, there will be a move back to consolidation and “acqui-hires” (even in the $50M - $100M range) can be an effective way for a growth business or young public company to quickly scale up engineering and product resources.

If this M&A cycle materializes, there will be plenty of downstream effects. One of the largest will be in capital markets. The entire business model of venture capital is predicated on a certain distribution of outcomes — one that is driven by extreme outliers and high loss ratios. One does not return megafunds on < $B outcomes. However, a surge in small to mid cap M&A could be a boon for ownership-sensitive pre-seed / seed funds, who have been a bit crowded out in this current funding environment. Their original models derive from an era where companies like Google and Facebook were more acquisitive, so owning 10% of a $400M was meaningful for a fund of a certain size. 

What could halt M&A activity?

Increased founder liquidity in secondary transactions

Anecdotally, I’ve seen more secondary shares coming off the table earlier and earlier (sometimes even in the Series A). Founders normally waited until growth rounds to sell their secondary positions. I won’t exhaust the secondaries debate (it’s a market!), but I will say that increased founder liquidity in a company’s lifecycle diminishes the likelihood that they would take money off the table with a sub-scale exit. 

The valuation multiples of target companies are untenable

Frothy valuations are the topic du jour, and you’ll hear of many companies that “raise past their skis,” meaning that there’s limited traction that lines up with the company’s valuation, which usually looks at it on a relative basis to historical precedent. These just become messy transactions for a variety of reasons — founders are anchored to a higher price, common stock wipeouts, etc. 

I’m excited and curious to see how the M&A environment plays out in technology over the coming months and years. If it does in fact escalate, it will also be quite exciting to see recycled liquidity come back into the system to drive forward the next generation of startups. 

The round on the cover

I was talking about recent seed rounds with a friend the other day, discussing how they seemed to be inflating in size, with many creeping up to $10M of initial capital in. I’ll save any judgment, but this is an increasing departure from traditional seed round sizes. My sense is that most founders don’t target these higher amounts from the jump — they still set out to raise $2M - $3M seeds, but there’s now a wider variance in outcomes of where those rounds land. Internally, we call this “the round on the cover,” which can end up differing from the final amount that appears in TechCrunch, often bubbling up far beyond the initial proposal. 

If you talk to people about it, they’ll just tell you the dynamic is crazy — emblematic of frothy market conditions. Some might say it’s just another reshuffling of round names, in the way that pre-seed was the new seed, and seed was the new Series A a few years ago. I thought I’d peel back at the incentives on both sides of these transactions — founders and investors — to land at an understanding of what’s going on. 

I don’t know how many founders plan on their round sizes expanding way beyond their starting threshold, but for founders of a certain pedigree operating in defined categories, a lower number on the cover can work to their advantage. It reduces the allocation available for investors, thereby increasing fomo in an already capital-pervasive environment. In some instances, the founders will close up a round, but (sometimes uncapped) notes or SAFEs get stacked on top. This is a tricky dynamic and a separate discussion altogether. 

In some cases, traditional seed funds catalyze larger raises; but in many instances, a larger fund or capital base will lead these bigger seeds. Larger venture firms started doing seeds as they felt pressure from crossover funds and late-stage activity to move earlier in the fundraising lifecycle. The bind lies in the fact that many conventional Series A or B shops actually have even larger funds to deploy with distributions from the past decade coming back, but their investors are now allocating more time to places where they had less money at work — maybe $1M - $3M investments out of $500M+ vehicles. Furthermore, ownership thresholds at the early seeds are coming down, and if the company shows any sign of promise, there’s no guarantee that the big fund that leads the seed can also lead the A (to build more ownership in the company over time). There also might be a newer implicit assumption about how risk curves work for startups, asserting that steps between the seed and the A don’t change the risk profile of the company on a consequential level. I recommend Kanyi’s piece on this if it’s a topic of interest. He has the crispest articulation I’ve seen. 

This all leads me to pose the (perhaps unfairly simplistic) question: would you rather invest $2M into a startup for 10% ownership or invest $10M to guarantee 20% ownership, putting more dollars at work out of a fund with limited to no additional risk? There are a lot of assumptions baked into this prompt, but it demonstrates the rough calculus from the investor perspective and the incentive to offer a larger amount to the founder.

Again, I’ll spare the judgement on whether this is right or wrong — it’s ultimately a market, a scaffolding for incentives. It feels a little bit like I’m giving away a trade secret, but I continue to reflect on what I see in the current market and the moves that happen with a subset of seed raises. 

Areas of Interest, March 2021

I used to write more posts or essays covering ideas, trends, and themes that were compelling to me in the moment. In the current market, there may be an abundance of capital, but I’ve also found a plethora of startups and entrepreneurs excavating in exciting places I would never have considered. This has pushed me to be more reactive to the broader ecosystem, setting aside the usual presumptions. Nevertheless, I’ve always enjoyed taking a step back to publicly share what I’m thinking about to see if my interests (usually quite obscure) are aligned with anyone else’s.

The interplay between code and visual workflows

Others have written about this, but more enterprise processes and workflows are increasingly defined in code, turning configuration files and scripts into templatized, repeatable logic. Examples include cloud native security (Open Policy Agent), business operations (Salto), and infrastructure provisioning (Terraform). By a different token, I’ve also seen new projects that help users define, create, or interact with various processes through visual builders. Terrastruct in software architecture or Tonkean in process automation come to mind. The historical knock against visual workflow tools (WYSIWYG website builders like Squarespace come to mind) is that they wouldn’t be able to sufficiently capture the complexity of the system in question, especially the edge cases. Now, “code” can act as an escape hatch when a UI isn’t enough. Design tool Plasmic is an example. State machines are also an avenue to explore. My underlying thesis is that many parts of the enterprise (from website to API design) can be produced through the combination of visualizations and code, not isolated to either or.

Industrial biotechnology

What would Dow or DuPont look like if they were started today? Multinational chemical companies like these produce products that touch many parts of the economy, from agriculture to materials to therapeutics. Yet, the chemical industry is one of the most energy intense in the world with significant carbon contributions. Alternative solutions with the potential to be cheaper, safer, and more sustainable have begun to emerge. They’re typically defined by biosynthesis, where enzymes catalyze reactions to create compounds, standing in opposition to the legacy chemical processes. Enzymes have been part of industrial processes in various forms for some time, but companies like Solugen now use computational protein design methods to identify specific catalysts. Pow is another interesting platform in this space. Traditional software venture investors dipping their toes into biotech can be a slippery slope, but the expansive possibilities that come as a result of decreasing cost curves, along with the decarbonization of a global engine industry, excite me.

Machine learning systems as an enterprise attack vector

In the enterprise, new technologies bring forward the promise of business acceleration, but they also introduce the possibility of new attack vectors. In turn, this domino effect contributes to the emergence of new security businesses. This was evident in cloud security (Lacework, Orca) or IoT (Armis) a couple years ago, and now has taken hold in the API space (Salt, Noname). I’m the first to admit “machine learning” is a loaded term, and there exists some debate about the level to which ML platforms have penetrated into enterprise accounts. However, over the next decade, ML systems will increasingly go into production as the largest banks & financial institutions will use it to fight fraud, retailers & grocers will deploy personalized recommendations for their customers, industrial companies & manufacturers will optimize supply chains...the list could go on. These activities only increase the surface area for misuse and cyberattacks, with one example being adversarial machine learning. The risk is so pronounced that leading research institutions and tech companies co-published the Adversarial ML Threat Matrix. Robust Intelligence is a startup working on defending operational AI systems. 

If you’re interested in exploring any of these topics or working on something in one of these categories, I’d love to chat. Email me at aashay [at] haystack.vc

The nature of "the miss"

In December, I spent time thinking about the investing work I had done coming to that point. I gleaned lessons from companies we were lucky to be involved with where something was working, but candidly, I beat myself up over perceived misses — companies I had seen at pre-seed or seed that weren’t in the portfolio who had some early signs of success. The rationale for not investing varied, spanning a lapse in judgment to a lack of experience in the category. These investments bit at me not only because the strength of the founders started to shine through over a longer period of time, but also because the full potential of the market became more legible in hindsight. It absolutely killed me.

So much of the venture business is guided by opportunity cost (in many ways, it defines it), and I was starting to internalize that. I’m not as concerned about errors of commission - I’d like to push to a place on the risk curve that feels apt for the asset class and support companies that reach global, not local, maxima.

But, focusing too much on supposed mistakes and over-extrapolating can be dangerous. I’ve come around on my thinking in recent weeks and am approaching “misses” with a different lens. 

  1. Opportunity cost is contextual and based on firm dynamics. As fund AUM grows, the paths to returning it shrink narrowly with increased pressure. Missing Uber or Airbnb hurts more and more. Specialization can also grow the burden of proof, especially if winners stand out in certain categories. I’m lucky to be a generalist early stage investor, which alleviates some of these concerns. 

  2. There is a high degree of markup activity in the ecosystem, but there’s little to point to on the durability of the outcomes. While Series A and Series B raises do de-risk companies, the end results may be years away. And that’s the ultimate concern, not TVPI. Trust the inputs.

  3. Over-indexing on missed investment opportunities could contribute to even more FOMO-driven investing. Part of venture does rely on this; it’s quite difficult to be 100% contrarian and succeed in VC, but I know that I personally do not want to be heat-chasing all the time. Investing edge has three components — informational, analytical, and behavioral. If the first is closer and closer to dissipating and the second isn’t as applicable to seed, perhaps the third drives alpha. Just a thought.

None of this is to say that misses don’t hurt, but there are likely healthier approaches and framing. For example, I really enjoyed this video with Felicis Ventures’ Aydin Senkut. While he notes that he missed Uber and Airbnb, their success keyed him in on the rise of global payments as a second-order effect and his subsequent investment in Adyen. What can I learn from a miss about what’s working in the world? What are the complements of the opportunity I wish I was involved with?

Borrowing Scale

The adage "software startups are cheaper than ever to start, but as expensive as ever to scale" is firmly embedding itself as part of canonical tech wisdom, as what's accepted to be true. I even tweeted about it myself the other day. APIs like AWS that offset initial capex & higher-level programming languages make getting up and running over a weekend easier and cheaper for a small group of hackers. On the other hand, high-priced engineering talent, operational costs, and distribution efforts drive costs up as the product and company scale.

The question I'd ask — will the latter half of this statement continue to be true in 5 years? 10 years? 20? What could lead to a substantial cost reduction in scaling and growing an organization?

Over the past few years, more vendors are emerging as default options for a company when it raises a seed or A that help with the Day 2 problems. Skip the roundabout with payroll providers and banks — just use Deel to build your overseas team. Keep finance and accounting in check from the start with Ramp. Need to sell into enterprise? Streamline a SOC-2 approval with Vanta.

Before, manual effort and labor fixed and massaged these problems, contributing to the costly business of scaling. Now, startups can effectively can not only borrow scale for launching a product, but also building their organizations. These tools won't replace the staff needed to manage these activities altogether, but they do aspire to give employees more leverage.

Furthermore, startups are also building abstractions on the complex, distributed systems required for software to serve millions of users. Okteto provides developers a slick interface on top of Kubernetes, a lower-level primitive for orchestrating applications. Fauna seeks to make a distributed & reliable operational database available through a single API. All of this is an effort to accelerate development velocity and timelines. While this trope probably won't realize for a long time, tailwinds seem to be pushing us towards Chris Dixon's "unicorn of one."

Some additional thoughts:

  1. What are the second order effects of the cost of scale coming dramatically down? An entire investment and employee ecosystem has been built on this premise alone.

  2. I sense security is an opportunity area to add to this list.

  3. Distribution is one area where costs do not seem to be coming down as channels and buyers are inundated. If this is the primary cost driver, perhaps this whole argument is moot.

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