Loyalty

One of the hardest dynamics I’ve had to manage throughout my career is loyalty. I, like most of my friends and people whom I respect, have feelings of faithfulness and devotion to those with whom we interact - partners, employees, investors, vendors, etc. However, there is a point beyond which loyalty can become costly - too costly, in fact - for historical relationships to remain as they are because of the negative impact on business. And when the problems spread into areas which can effect either individual or firm reputation, loyalty necessarily needs to take a back seat to pragmatism and protecting one’s (and one’s colleagues) own interests. This is, without question, an area fraught with ambiguity and confusion, where the pain of dealing with issues head-on can lead to procrastination with potentially expensive outcomes. And while procrastination is never ok, the question remains: when is it appropriate to sever ties with a person (or a firm) to whom you feel loyal?

A useful rubric will necessarily take into account the impact of one’s actions on the relationship. For instance, an employee who has done good work for a long enough period of time to foster loyalty but then runs into a patch of diminished productivity. Do you cut them loose as soon as performance drops? Generally not. I think tools such as GE’s performance matrix as useful in this regard.

On one axis is Performance and the other axis Attitude. High performance with great attitude? A star. Low performance with lousy attitude? Fire immediately. High performance with lousy attitude? Coach the employee and give them the chance to remediate, but if they remain solo stars without regard for the team or its norms they have to go. And low performance with great attitude? Provide concrete feedback and coaching to help the under-performer raise their productivity, but if they are unable to turn things around then they have to go as well. So in the case of someone to whom you feel loyal (so, by definition, has performed well in the past and with an attitude that supports the team) but whose performance isn’t what it used to be, give them a chance to fix things and lend support. This demonstrates loyalty. However, letting them go if they continue to perform poorly is not disloyal - it’s protecting the team and the firm. And both you and the company can help ease the transition in many ways. 

But think about a situation where the impact of a mistake, a lapse of judgment or simply bad behavior is so bad that it fundamentally affects the trust people have in the individual or firm? It is hard to see how the rubric above can apply. For example, what if an employee does something to damage the reputation of the firm that materially impacts its brand and position in the market? This would seem to trump any notion of loyalty completely. And what if a service provider, such as counsel, made an error that ended up putting the company in badly compromised position due to shoddy work? It is hard to imagine retaining that firm again any time soon even if there had been a successful pre-existing relationship. The essential element where historical feelings of loyalty rapidly become marginalized is when basic trust has been breached. Without trust, loyalty cannot be reciprocal.

Except in the case of bad actors, severing relationships is almost always difficult, especially among those who pride themselves on being loyal. But sometimes circumstance overrides loyalty: it just does. And at these times, it is critical to address the situation quickly and honestly, for the good of the person being let go and the company which has to move on. That’s just life.

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What’s the big deal about Big Data?

Every so often a term becomes so beloved by media that it moves from “instructive” to “hackneyed” to “worthless,” and Big Data is one of those terms. When I started IA Ventures in 2009, I used “Big Data” as a quick and easy way to describe the thematic focus of my fund. And it worked. People got that it implied tools for managing large amounts of data and applications for extracting value from that data. It also implied a high level of technical and product expertise with specific relevance for assessing, investing in and supporting these kinds of companies. And perhaps most importantly, people understood that this wasn’t a cyclical theme but a secular phenomenon, one that would eventually touch every business and every consumer, wherever they may be. Taken together, pursuing this market opportunity in such a focused manner resonated with corporations, LPs and entrepreneurs alike.

But since this time the term Big Data has become diluted. Very diluted. So much so that it is almost totally meaningless. Does Big Data mean new kinds of databases? Sure. Does it mean innovative ways to visualize data to create actionable intelligence? Absolutely. Can it be applied to the health care sector? Without question. Has it contributed to the rise of the Data Scientist? Mos def. The reality is that solutions for managing, analyzing, generating predictions and acting upon insights gleaned from massive amounts of data are not confined to a particular vertical or geography and span both public and private sectors. Data is everywhere, we are generating more of it, have the ability to store increasing amounts of it and it is moving at speeds that soon will approach the speed of light. This is happening. It is not my view: it is a fact. But identifying a market opportunity and capitalizing on that opportunity are different things entirely.

Large, scared, fossilized bureaucracies (both large private enterprises and Governments) are frequently hard to sell to and in the best of cases have long sales cycles. This necessarily means a larger amount of start-up capital to bridge the gap between product shipment and cash receipts, and oftentimes a “minimum viable product” that has more features and is costlier to produce than a consumer-facing web application. This means taking more seed stage risk than many investors are comfortable with, but the potential payoffs can be very, very large. Further, these enterprises always have legacy systems and often employ the people that installed them in the first place, rendering a wholesale “rip and replace” strategy a long-shot at best. So creative grass-roots tactics are frequently helpful to avoid selling to the CIO, and instead getting usage in the ranks that forces enterprise adoption (a la RIM across Wall Street trading floors). And this has to happen in areas where there is a lot of noise (database architecture, business intelligence, cloud storage, etc.). Bottom line, there is a lot of domain knowledge and experience in selling to the enterprise that comes into play when pursuing these opportunities. 

There are also new kinds of businesses founded by data-savvy founders that don’t look like classic “Big Data” opportunities at all. We think businesses that build platforms to harvest user data, such that the learnings from that data are used to benefit all contributors, which then results in a better product for all new users who contribute their data, is a non-intuitive application of our Big Data theme. BillGuard, Coursekit and Simple firmly conform to this notion. Each company started with zero data and isn’t a tool specifically designed to manage general data sets (such as databases or business intelligence tools). They are growing up in their own respective verticals and leveraging collective data for the benefit of the individuals in the collective. The businesses were set up to do this from Day 1 because of the “data DNA” of the founders. Every business generates data, but it is a far smaller number that view data as a strategic asset that is actively managed for the benefit of their customers and the bottom line.

Greater access to data and the technologies for managing and analyzing data are changing the world. We are at the beginning of a secular trend that, in my opinion, will sharply increase the aggregate quality of life across the globe. Better health through prediction and prevention. Richer education through collaboration and access to the best teachers and programs at a much lower cost. More satisfying and effective communication across vast distances. Greater personal data transparency and portability for better decision-making. The list is endless. But let’s be clear - the term Big Data doesn’t begin to explain what’s going on here. Whether the data is big, small, fast, slow, structured or unstructured, everything that is going on now is attempting to do one thing: making data smart and actionable. And this is a mission driven by the passion of the entrepreneur - and their investors.

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The perils of “free riders”

Starting a company is, by its nature, an “all in” affair. You’ve either got the passion to run through walls, suffer painful failures and do unnatural things to achieve success or you don’t. And given the challenges of building a company and the tremendous amount of hard work and stress borne by the founders, they are generally focused on finding people who share a similar passion for the mission as they do. Building a successful company is the outgrowth of a team effort, not a series of individual efforts, as important connective tissue needs to be established across members of the organization to deliver a great product to end-users. But as companies grow beyond the founders and first few employees, the risks of bringing on people who don’t share the same level of intensity and focus in pursuit of the mission rises dramatically. And if a few bad hiring decision are made and people focused on short-term financial rewards and titles infiltrate the firm, they can have a marked negative impact on culture, morale and team performance. 

It is hard to overstate the damage that these kind of people can visit on a firm. They complain. They foment “water cooler talk.” Time that should be spent problem solving with colleagues or making progress on one’s own gets diverted to politics, personal positioning and other disruptive agendas. And the more time these people are allowed to remain in the firm, the more that super motivated high-performers get angry and frustrated and begin to ask themselves, “Why am I killing myself to build this company when these lazy complainers are getting paid and vesting their stock options on my efforts?” Shortly after these thoughts enter the high-performer’s mind doubts about management begin to creep in, further poisoning the employee’s attitude towards the company and its leadership. And from there it is a downward spiral towards a culture crisis that can shake a company to its core. Oftentimes the founders aren’t aware of the problem, being so focused on shipping, recruiting and selling. Invariably they are told of problems in the ranks but rationalize as to why things will improve, and besides, things aren’t really that bad, right? Wrong. And don’t think that these problems are the province of larger firms. They’re not. They afflict companies large and small.

There is nothing more important than a sense of shared mission and a culture of cooperation in a start-up. These are essential elements of a team that can develop, ship, grow and flourish. When selfish or underperforming parties enter the mix, they have to be removed immediately for the good of the team and the company. Problem employees don’t take care of themselves, and they are ignored at the founders’ peril. While it is difficult to fire people, sometimes it simply has to be done. Strong, decisive action sends a powerful message to the team, that self-centered behavior and anything but high performance and dedication will not be tolerated. This is music to the “A-players” ears, as they want to be surrounded by people just like them. Quite frankly, they deserve it. And so does the start-up founder. So aggressively manage your human capital and don’t let it manage you: it will pay dividends over the long run.

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Evolving the technical organization

You and a pal have what you think is a great idea. You hack some code together and build a simple prototype. You get some feedback, like what you’re hearing and decide to go all-in on a start-up. You build some more, maybe convince another jiujitsu developer to join your little cadre on the come, and build enough product to demonstrate a vision that is ready for some angel capital. You take in $500k that will enable you to hire a few more engineers to get a beta product shipped. With the angel money you hope to build early customer traction and achieve a series of key operating milestones that will set you up for a Series A: then it’s off to the races. Scaling the technology team. Front and back-end engineers. Some product managers. Maintaining enough big-picture perspective not to lose sight of key architectural decisions. Pushing releases on a regular schedule. In short, it can get very complicated very, very quickly. Yet plenty of technical founders don’t have the experience of building and managing high-performance teams that meld creativity with productivity, a feat that is challenging even for the most experience engineering leaders. So what is a start-up to do?

I have seen this movie many times over the years, and I would say there are two principal elements every first-time founder needs to get this right: an emphasis on culture and a desire to be coached. Yes, there are tons of tactical details to be hammered out, but these are the two overarching points that, if gotten right, can have a material impact on the company’s achievement of its product and business objectives.

Culture sounds like a fluffy issue, but its importance cannot be overstated. As Steve Jobs once uttered, “Great artists ship.” In many tech-heavy start-ups founded by newbies, there is often a strong emphasis on the “art” and a less strong emphasis on the “ship.” Why? Because solving hard problems is cool and tends to attract awesome 10x type engineers. These people are essential elements of a high-performance, super successful technology team. However, they are frequently not the right people to run the show because (a) they’re world-class coders and should be coding, not managing; and (b) they are artists and not necessarily the right people to put boundaries on themselves or others, which makes it very difficult to ship. So the great tech leader is one who can foster an environment that honors creativity but also is focused on delivering real-world product to real-world customers. So often this can become a tug-o-war between the developers (the “real engineers”), the product managers and engineering leadership. Badly managed, this dynamic can create a toxic environment not just within the tech team but across the organization. A culture that has been nurtured to respect each constituency and recognizes the importance of each to building a commercially successful enterprise is one that will avoid these life-threatening challenges. But fostering this delicate harmony can only come from strong and credible business and engineering leadership, which necessarily means building a real organization around what started as a few coders with a passion for solving a problem.

So what should the functional technical organization look like in an organization ready to scale? While there is no right answer, I think it safe to say that there are two or three key roles that can exist individually or in some combination:

• VP Engineering: Often the missing piece separating a brilliant tech team from a brilliant tech team - that ships. This person is often more of a manager/traffic cop than a coder, yet knows enough code to be dangerous. They key thing is that they can keep the engineering team on task and can manage to timelines, which becomes essential as the company’s release cycle becomes more frequent and releases increasingly complex. While these people might not seem critical during the company’s early days (and they’re not), they rapidly become among the most important resources standing between a company’s technology stack, its product and its customers.

• CTO (or sometimes the CTO/Chief Architect): While the VP Engineering is focused on getting code in production and product out the door, the CTO is generally charged with keeping an eye on the release cycle, interfacing with product management and having responsibility for the larger architectural decisions that govern issues such as scaling and performance. Is is not unusual for the CTO to have mad coding skills and to be a kind of “Yoda” figure within the company, having the respect of coders and product people alike. But sometimes there is a unique individual, call them a 100x engineer, who is a world-class problem solver but is never more comfortable than tapping a keyboard behind a very large monitor - in solitude. These gems need to be insulated at all costs and kept from management issues and office politics. They should simply be allowed to code and think deep thoughts, and sometimes this individual acts as a Chief Architect whose input is critical to the CTO’s big-picture decision-making. 

In summary, where the VP Engineering’s critical responsibility is managing the engineers, the CTO has a broader but more technical array of roles. Each is vital in taking a pool of talented engineers and product managers and transforming their efforts into shippable, world-impacting products.

In my experience the role start-ups have the hardest time filling is the VP Engineering, both spiritually and in actual fact. Bringing a manager into the engineering organization is frequently a cultural leap for a tech-oriented start-up, yet it is only a matter of time before the coding stars begin to see the importance of coordination and synchrony in their efforts. A team made up of All-Stars without leadership generally falls flat in real-life: to borrow an NBA metaphor, the Bulls (and the Lakers) needed Phil Jackson. The Heat could have used him last year. Instead, a less talented team but with better management and chemistry won the title. The dynamic in start-ups is no different. But when you combine All-Stars with great leadership, you get the chance for a dynasty. Companies and their tech teams have to evolve as the business evolves. It is best for the organization and the transition from early product to production to scaling that this is acknowledged and planned for up-front. The alternative is a painful yet necessary culture shift that takes its toll.

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Pacing

One of the most difficult issues I’ve grappled with during my transition from angel investor to venture capitalist is that of pacing. By pacing I mean the rate at which capital is deployed, particularly with respect to initial investments in companies. Every fund has the notion of an “investment period,” the span of time over which the portfolio is built. It is commonly between 2-5 years in duration, but varies substantially by fund size and investment approach, e.g., larger funds with more concentrated portfolios tend to have longer investment periods. It is important to note that this is different than “fund life.” as follow-on investments are made for many years after the initial investments and are often 10-12 years in length.

As an angel I seldom thought about this stuff. I looked for great teams working on interesting problems in areas I understood and where I felt I could be helpful. Full stop. If I made 10 investments in a year, that was fine. 2 in a year? No problem. I followed on frequently and leaned in hard when I felt circumstances warranted. The notion of “vintage risk” never really entered my mind, and over a 5.5 year angel career I organically made 40 investments. Not crazy.

My fundamental view has been that there is greater risk in turning down an opportunity to work with a great team simply because I had invested too much capital over an arbitrary time period than by making a group of investments over a concentrated time scale. However, it is intuitive that by making a large number of investments within a small window I was incurring unhedgable risks associated with the macroeconomic environment or event-driven shocks. No doubt about it. My own sense, however, is that if a company is performing, is properly financed and has strong investors, that these unhedgable risks should be substantially mitigated. Over any given 10 year period there are ups and downs, but as long as your bankroll is big enough, a good business should have acceptable exit opportunities at some point during this time frame. And with my own money I certainly expressed this view with abandon.

But running a fund is different. There are certainly data which indicates that pooling many investments in a compressed period can yield highly volatile outcomes, and most LPs would likely accept a less risky 3x than a more risky set of outcomes with a greater expected value. However, I want to reach deeper into the data to really understand the nature of the businesses being financed, the stage at which investments are being made (Are they capital consuming Series B investments where price has outstripped risk reduction? Or more capital efficient and less expensive Seed and Series A rounds?) and the make-up of the investment syndicate. I suspect that there would be substantial risk mitigation by financing a concentrated portfolio of earlier-stage investments, applying rigorous follow-on discipline with respect to the Series B and C (e.g., no lazy check-writing to simply prop up a failing business and deferring the pain of admitting a mistake) and investing with others who share a similar view of investing. This way, the good businesses that should survive will survive, good money will not, on balance, follow bad and these same businesses will be supported until a more favorable exit environment by committed investors with sufficiently deep pockets. At least is the way I think about it.

But the fact of the matter is that we see way more interesting people and businesses than we could possibly finance. The “bar” I had as an angel simply wasn’t as high because I didn’t impose the same pacing discipline we work to apply IA Ventures. But man, it’s a struggle. I think about it every day and it’s one of the hardest challenges I face as a venture investor.

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