Why I’m long the US economy (and millennials will buy cars and houses)

I’ve spent the last year scratching my head over the continued (and rapid) stock market rise amidst government uncertainty at home, geopolitical threats abroad and an increasing and ominous distance from our last recession. Large tax cuts for corporations certainly explain the recent run-up, but what of a near doubling over the last five years despite relatively flat GDP growth? What gives?

I am increasingly convinced the optimism ties to an impending once-in-several-generation consumption and investment boom kicking off as the millennial generation matures into peak child bearing and earning years. For those of you who read my posts for discussion of esoteric SaaS metrics, apologies, this is a big picture one!

Below is a GIF of the US population trend since 1990. (hooray, I made a GIF!) It shows a welling of the millennial wave, now eclipsing the baby boomer bulge.

Population GIF - Find & Share on GIPHY

We hear about this, but it is perspective altering to actually see the trend. The millennial wave now eclipses boomers for two reasons. The boomer bulge is declining as many reach the end of their lives. Meanwhile, the record levels of immigration seen in the US from the late ’90s through the mid-2010s have largely bulked up the millennial wave since its native creation in the 80s and 90s. Immigrants tend to be younger, working age or with children soon to be. This isn’t a political piece, but I’m certainly happy to have a larger working population in the US because of this trend.

This millennial boom is entering prime childbearing and earning years, the former being the largest driver of consumption (nesting), and the latter the means of consumption. This picture is quite changed from the 2010 archetype of millennials returning home to live with mom and dad after a failed job search in a post-financial-crisis economy. While it is true that millennials suffered cyclically oversized unemployment after the financial crisis, relative employment level by age has normalized. But, of course, that doesn’t stop them from whining 🙂

The next decade will also buck the belief that millennials will be different – not get married, not live in the suburbs, wait to or not have kids, not make big purchases in favor of sharing economy and experience consumption. Millennials certainly drove the urban resurgence, but many cities have already experience “peak millennial”. Rents in posh 1 and 2 bedroom apartments are starting to decline. Where are Millennials going? Millennials represent 34% of home purchases in 2017 (the largest group), 66% of which were first time purchases. And wait for it, more than 90% of these purchases were single family or townhouse, with the smallest percent of purchases being of apartments (only 1%) of any age group. They are moving out of the city; what, are they going to buy cars next? Yes. Millennials likely surpassed both Gen X and Babyboomers in new car purchases in 2017. Millennials nest too, and it’s hard to rely on Uber when two car seats follow you everywhere.

This is only the beginning. Fewer than 50% of millennials have had children yet, but most want to. And while marriage is delayed versus other generations, Millennials are compensating by having kids outside of marriage anyway. More babies, more spending.

The other big trend is that millennials are now moving through the steep part of the income/age curve:

Source: Bureau of Labor Statistics, 2017Q2

While the youngest millennials fall into an age/income bracket with median incomes around $30K/yr, the oldest millennials have reached the asymptotic median income for 35 and above of around $50K.  The bulge will only continue to move toward this higher level, enabling more spending.

Is this enough to offset the aging and retiring boomers? Well, for the first time in a century, US life expectancy has leveled out, or even dropped. This is good and bad news. The bad news is we may not live the longer and longer lives we once thought. The good (economic) news is that the expected drag from a bulge in retired non-“producing” population is likely to be lighter than once anticipated. We might also expect a higher participation in the workforce from millennials than among baby boomers given greater acceptance (and often expectance) that women enter and remain in the work force. Surprisingly, however, millennial labor participation is lower than in recent decades, perhaps a hangover from the stunting unemployment millennials initially faced during the financial crisis. We’ll see if this sustains, however.

So, if there’s a lot going for the american economy, should we all go out and buy more equities? Nothing above divines a stellar ’18 or ’19 in the markets. In fact, there feels like a lot more black swan risk in ’18 than in the prior few years. Hold onto your seats if domestic or geopolitical issues turn the wrong way: Watergate saw a 24% market decline from break-in to resignation, and we’ll be worried about more than the market if nukes fly. Nevertheless, between millennial trends and our continued leadership in technology innovation, the fundamentals are good, and I am long the next decade.

Is your startup shock resistant?

A friend and I were discussing Chicago’s appointment of a Chief Resilience Officer, a role that focuses on the city’s ability to recover rapidly from economic, environmental or health shocks. This and Fred Wilson’s Worry post got me thinking about how shocks differ from other risks and how startups and investors digest and plan for all types of risks.

A shock is one type of risk – characterized by low probability but also high magnitude and high energy. The meaning of probability and magnitude are straight forward, but how about energy? Energy is the speed with which something of high magnitude happens. For example, a cyber security breach for a startup (or any company) is a very high magnitude event that also happens extremely quickly. Trust crumbles, and customers seek a new solution immediately. Here is how I see startup risks and where shocks fit in:


“Normal” stage-appropriate startup risks:

Entrepreneurs and investors spend most of their time thinking about the upper left-hand quadrant, lower energy and magnitude risks that are high probability. These tend to be stage appropriate and include everything that “can” and will go wrong in a startup like:

  • Early startup challenges: finding PMF, key hire failures, economic scaling of sales
  • Competitive issues: new entrants, pricing wars
  • Product failures, big bugs and other operational glitches
  • Normal economic cycles that dampen demand or capital access (down cycle) or make talent scarce (up cycle)

Each of these risks is likely to impact a startup along its journey, and collectively they kill most startups. However, none alone or once is likely to be the death knell of startup. While some early startup risks (eg, finding PMF) ultimately beat startups, these are risks that a startup has years and many tries to overcome.

Inherently weak models:

The upper right-hand corner covers high energy/magnitude, high probability risks that create inherently weak startup models. Business models that rely on ethical misappropriations are an obvious in this category. If you are beating the competition or making money by cheating, you can’t expect that to be sustainable. This Fortune article highlights the natural tension of startups’ trying to make something from nothing while avoiding clear ethical violations like those at Theranos, Zenefits and even funds like Rothenberg. While I acknowledge there is a grey area, the truth is most of us know an unethical business model or practice when we see it. Further, the probability of getting caught increases the more successful one of these models becomes – an inherent disequilibrium that is the universe’s check and balance system. I’ve shared more thoughts on startup ethics here.

Extremely high burn without commensurate levels of revenue and revenue growth is the other mode of inherently weak model that we see. How much burn is too much? The rule of 40 captures the tradeoff nicely for late stage startups. For companies between $3M and 10M in revenue, we get nervous when burn rate is >$500K a month unless the company is clearly growing 150-200% (eg 2.5 to 3x) a year or more. For companies less than $3M in revenue, we get nervous when burn rate is above $300K per month unless the company is clearly 3x ing. When these conditions aren’t met, companies set themselves up for massive cuts and talent losses that are hard to recover from.


Shocks are different from both categories above because they represent the type of high-magnitude but long-tail outcomes that people are bad at predicting. There was much talk about shocks as a risk factor – and increased planning for them – after September 11th and the 2008 financial crisis. Shocks come big and out of the blue, leaving little time to react. Startups should think about, plan for and (when possible) avoid these types of exposures. There are a number of shocks startups can face, but here are a few of the most common:

Single point of human failure: Startups struggle when a “cult of the CEO” culture or a single founder becomes too powerful. This is true in the early stages, and of course the world knows about Uber’s recent woes with Kalinik at the late stage. In short, good governance and a strong team of leaders is important to uninhibiting a company from those who create and run it. Human failure shocks also include risks associated with married or dating founders (whose breakup can be a startup’s undoing) or to the risk of a solo founder becoming debilitated. There are always exceptions, but as much as possible, avoid, avoid, avoid.

Platform dependency: A much decried startup risk in a world where Facebook, Google, LinkedIn, Twitter, et al are increasingly dominant, platform dependency is truly shocking in speed and magnitude when it goes awry. We have seen three or four of our companies navigate this type of shock, with only a 50% success rate. Most people think of platform dependency as a product built or dependent on API access to another major industry player that brings additional value to that player’s platform (or extracts value from it…). But platform dependency can also include single buyer/distributor situations. In particular, the few startups we’ve seen that have found effective reseller channels are often overly reliant on one or two, a major business risk.

What to do about platform risk? With so many innovation opportunities that necessitate platform dependency, it is too hard to avoid altogether either as an entrepreneur or investor (though we try!). To mitigate the risks, we look at alignment in the dependency. The many companies that used to extract value from the LinkedIn API were not well aligned with LinkedIn, and some were outright competitive. It is no surprise access was shut down for most. These startups didn’t send checks to LinkedIn and were effectively leaching LI’s data. One could argue that many of these companies actually had a high magnitude, high probability risk – an equilibrium that was too good to be true, like pirating cable service from the utility pole! On the other hand, companies built on or connected to Salesforce often expand Salesforce’s product in directions Salesforce doesn’t go, helping to keep ecosystem customers happy while sharing some of the revenue with Salesforce. This is not without risk if Salesforce decides to make a product that competes, but it is at least more stable.

Cyber security: Every company, big and small, is at risk of cyber security breaches and threats. As we know from Target and Equifax, breaches can destroy customer goodwill and shareholder value practically overnight. Because any vendor is a weak link to large customers, startups need to be as good (or better) at security than big companies. If things go wrong, there isn’t a strong brand or scapegoat game to fall back on as at large companies.

This is true both for insider and outsider attacks. The normal democratization of information at startups doesn’t work when customers’ personal or financial data is at risk, as Twitter recently learned with a rogue employee shutdown the POTUS account. With respect to outside threats, startups typically put cyber security under the CTO. Good place to start, but startups that are a particular target (those with consumer or customer financial or health data) should institutionalize their cyber security departments well below $10M in revenue, with a Director or VP level information security hire to own the risk. This is just one action among several startups should take to attenuate cyber risk.

Macro risks: The financial crisis was a big shock to startups too! Which is to say that big macro risks like terrorism, storms and unusual financial cycles can significantly upset a startup’s trajectory. How do you plan for these? Terrorism and weather shocks are geographic. If you are in a high-risk geography, you should have a plan for both day-of response (with respect to employee safety and production/data continuity) and resilience (how you will recover operations and work with affected customers, even with reduced staff and facilities).

Financial crises require a mind-set shift for startups. “Growth at all costs” must become “protect the base”. Customers – whether consumer or B2B – will have much less appetite to buy, driving CAC up. Meanwhile access to capital from VCs will be reduced. That combination makes a focus on growth ill-conceived. Instead, focus on keeping your existing customers happy so you can live to fight another day on the other side.

Low, low: 

No need to talk about the lower magnitude, lower probability risks. You can’t worry about everything!

Healing healthcare with startups

This is my first blog about healthcare and healthcare investing. When we started HPVP six years ago, we expected that 25 to 30% of our investing would be in healthcare IT. That didn’t happen because EMR adoption froze the market for the first four years of our existence. Every healthcare IT concept we saw had a 12 month to infinite sales cycles as practices and systems ignored other needs to focus on EMR compliance. That is largely behind us now, and in the past few years we’ve made investments in two HIT companies, Zipnosis and Upfront Healthcare. I expect more to come. It’s an exciting time to be investing in healthcare. Here’s why:

In the last 60 years, healthcare spending as a % of GDP has almost quadrupled according to the Centers for Medicare and Medicaid Services, but an ongoing change from fee-for-service to bundled and ultimately capitated payments creates a once-in-a-generation disruption for innovative technologies to change the way healthcare works. New technologies will both shift the supply curve down and stretch it out:

Curves3This story unfolds in three acts from today, to tomorrow and into the future:

Act 1 – today: In a fee for service model, the “back-of-the-house” is the biggest lever on profitability

Health systems get paid by seeing patients and providing as much treatment to them as possible, coding and billing those activities, contracting and managing claims/disputes with insurers and collecting co-payments from patients. This is a “back-of-the-house” paradigm – meaning non-care operations focused – with only some focus on middle-of-the-house implementation of EMRs, though mostly for the purpose of coding accuracy. EMRs bring little gain, if any, in efficiency or patient outcome. Ask your doc.

More visits, more revenue, more profit.

Obviously this is simplistic, and there are checks and balances in place on quality and fraud. The core issue is a misalignment of incentives on the marginal visit.  The fee-for-service paradigm makes every marginal visit a profit opportunity for a healthcare systems but a cost to the payer and consumer in time and money, a concept discussed in detail by Robert Berenson of the Urban Institute.

As importantly, this misalignment and usage incentive clogs up the system such that demand outpaces supply. Filling the waiting room isn’t hard in this paradigm, so there is less expertise and focus on patient acquisition, satisfaction and retention in health systems. Poor consumer experiences and outcomes are exacerbated by growing local healthcare monopolies – a product of practices and health systems buying each other to maintain leverage in fee-for-service contract negotiations and to drive in-network referrals to highly profitable product lines and marginal visits.


Act 2 – tomorrow: Bundling and capitation shift focus to middle-of-the-house quality, cost and efficiency management

The only way to control costs is to realign incentives. This means pushing risk back on providers and systems through procedure/treatment bundling or full risk capitation, as with Medicare Advantage. Suddenly the marginal visit is a cost not a profit! This trend is real and is happening at increasing scale in the public and private healthcare markets.

With bundling and capitation come a new emphasis on middle-of-the-house care quality AND efficiency as the key levers that drive practice profitability. On the quality front, population health management emerges as a critical software need to optimize care procedures by patient demographics and indication. Meanwhile efficiency gains are achieved by transferring clinicians’ education burden to digital content on patients’ phones, and virtual medicine and connected devices allow delivery of care and monitoring outside of expensive healthcare settings. In the home, care can be delivered faster, cheaper and at greater satisfaction to the consumer. Each of these technologies will in time help to shifting down the healthcare supply cost curve.

There is another unintended and positive consequence of these efficiency measures. Within a healthcare practice and across the system, capacity increases. Population health management analytics and protocols reduce sick and emergency visits with fewer, shorter and cheaper preemptive visits. When sick visits are needed, a 20 minute treatment now takes 2 minutes of clinician time with a virtual medicine provider like Zipnosis; Upfront Healthcare, meanwhile, reduces visits wasted by unnecessary referrals,  incomplete pre-work and no-shows; and educational and relationship management solutions like Well.be and Vidscrip scalably educate patients at home or on a mobile phone so that clinicians don’t have to spend precious time doing so.  Together this stretches the supply cost curve.


Act 3 – the future: With excess capacities, front-of-the house patient acquisition and retention are the final frontier for profit

System efficiencies stretch the supply curve and slacken the tight supply market. Healthcare systems will need to be both great marketers for acquisition and skilled customer success managers for retention in order to pick up this slack. Both of these needs will be met with technology. Health systems are woefully behind in their adoption and successful use of CRM, marketing automation, social marketing and search marketing. Once acquired, customers will be retained through digital platforms for communication, scheduling, education and remote care management. Because of the nuances and regulation in healthcare, there are great opportunities for startups to focus and win in this huge vertical.

The ultimate example of the shift to front-of-house in healthcare is the concierge practice, a subscription service for the most important purchase in your life (your health!). It is not software-as-a-service, but service-as-a-software. Concierge practices provide subscription access to a dedicated clinician in-person and increasingly via mobile and desktop. While some decry the high cost of certain concierge models (eg, “executive” concierge services), lower cost tech-enabled models like SteadyMD are emerging with the potential to lower total system cost while greatly increasing patient wellness and satisfaction. For all concierge practices, the back-of-the-office barely exists – collecting one check per patient per year is easy. Their unique focus is on the middle and front office, keeping patients healthy, happy and retained.

The PMF sales equation

I experienced an Aha! moment the other day with CEO Sean Chou of our portfolio company Catalytic. Sean is an experienced enterprise startup founder and exec through his time as CTO of Fieldglass (sold to SAP). Sean and his VP Product, Jeff Grisenthwaite, had prepped an investor brief with a simple expression summarizing their pursuit of product-market-fit. It looked something like this:

If[(effort + price + risk) << (pain of customer problem), win sale, lose sale]

Bingo! In words, if the effort plus price plus risk of your solution is much much lower than your customer’s status quo pain, you can sell and find product-market-fit. We could complicate this by adding an expression for competitive position, but let’s assume the field of 2.0 solutions is sparse… something that is often true for the earliest stage startups, with competition heating up above 1-3M in ARR. This expression also assumes that the benefits of your solution actually solve the subject problem!

I love this expression because it succinctly captures the key issues effecting early PMF sales:

Pain of status quo: Is there real pain and is it big?

Effort: Is your solution relatively quick and easy to implement and maintain?

Price: Is the price of your solution very reasonable relative to the pain (and competitor prices)?

Risk: How do customers perceive the risk of purchasing from an early-stage startup? How do you help your customer champion mitigate the political capital risk of pushing a new process or solution?

“<<“: Are effort + price + risk much much lower than customer pain? If not, customers won’t bother. A simple “<” doesn’t cut it. It must be “<<”. Put another way, ROI must be very high.

An example of this is HPVP’s recent consideration of buying software to manage our portfolio job postings site. We currently do this with a string-and-chewing-gum combination of Startuphire and Angellist widgets. Even with both, they don’t cover our companies exhaustively, and we would love to have resume gathering and funnel tracking capability. However, what we have is pretty good for free! We were approached by an early startup with a slick solution, but we didn’t end up buying:

Pain of status quo: You may hear in my narrative above, this isn’t a top 1 or 2 pain point for us. If there is software out there that can find me the next Uber to invest in, you have my undivided attention and wallet.

Effort: We probed a lot on what the effort would be to implement and maintain their posting solution. Great answers here.

Price: They wanted 4x the price what we were happy to pay and 2x what we might have been willing to pay. They didn’t come down at all. Wuh?

Risk: We were being asked to take a risk on a seed stage startup with limited funding and only very early revenues. We love startups, but it would have been a bummer to invest in implementation and see them disappear six months later. We are thrilled to take that risk, but not at premium pricing…

“<<“: …which all comes back to the much-much-less-than issue. The risk and price together were just not that much lower than the current pain, so we passed.


S how big does the “<<” need to be for good “ROI”? ROI may be characterized qualitatively as (Value-of-solving-pain / (effort + price + risk)) or precisely quantified as ((savings or increased revenue) / price of software). Either way, we generally find that business customers want to see 5 to 10x ROI on software investments. In the example above, we figured we would see a 2 to 3x ROI at list price. If they had halved the price, we probably would have accepted 4 to 6x. Remember, we like startups.

You might say, “Wow! 5 to 10x is an inordinate return expectation for a customer” when lots of corporate investments are made at much lower ROI levels (think advertising as low as the 1.5 to 4x range and capex in the 3 to 7x range). Fair, but remember that even with SaaS where the hard cash cost is paid over time, costs of organizational effort and change management for new software adoption are front-loaded and heavily resisted. That lump cost of effort upfront means the solution has to be really really good – and quickly – for the customer champion to look like a hero to their organization. If a champion doesn’t think this is likely, they won’t risk their political capital.

Now how about competition? Without consideration of competition, the PMF sales expression above helps define whether the customer will play ball or not. Competition defines who is on the field of play. If there are several competitors who offer solutions that bridge the “much much less than” gap, then it’s a question of positioning product, features and price to stand out in the noise to a specific customer segment.

When to make senior hires from $0 to 10M in revenue

One of the most frequent questions we discuss with portfolio companies is when to make which senior hires. The answer is driven by experience/skills of the founding team, trajectory, cash position, type and stage of the startup. Since the first four are very contextual, stage serves as a common denominator to define rules of thumbs. For simplicity, we can think of stage by revenue run rate in $ARR. For B2B SaaS, this is gross revenue; for a marketplace, net revenue.  I think of $0 – 1 M as early product-market-fit, $2-5M as late product-market-fit and go-to-market repeatability, $5-9M as scale and $10M+ as mass scale and focus on achieving market leadership.

Below is a quick cheat sheet by function, role and startup stage for when certain hires are typically made, assuming they are not already on the founding team. The dark green indicates that most companies have the role at that stage. The hashed green indicates some companies have the role depending on other factors. The table implies layering of Chiefs on VPs on Directors. This is more illustrative than practical. You wouldn’t have a CFO, VP finance and controller at a $7M company. You might have a CFO and controller. You get the point.


You will notice some coincidence above between when a director or VP hire might join a function. Often they are interchangeable but with different compensation, experience and “runway” expectations. A director hire in a startup might have 5-7 years of relevant experience and a VP hire 7 to 10. You wouldn’t expect a director level hire to own the function for much more than a doubling or tripling of revenue at this stage. A VP level should be able to 3-5x. “Chief” titles are in a different category. Don’t give out a chief title unless you think that person has uncapped ability and experience to scale. In the end, there can be only one king or queen of the mountain, whereas a penultimate title leaves the possibility of bringing in a more senior “chief” above a strong leader who has reached their cap on scale.  Looking briefly at each function:

Technology: If you are a tech startup, you need a CTO… duh. We generally invest in teams with a founding CTO, and in the rare exception a CTO is recruited at or before launch. Many founding CTOs have less experience at scaling large tech organizations (hiring, agile operations, tech scaling) but excel at system architecture, vision and solving really hard problems. That is why many of our companies also bring in a VP Engineering above $5M in revenue

Product: Often founding CEOs are the de facto product leaders. With the right founding CEO, this can scale as high as 5M in revenue, but in most cases establishing a product leader somewhere in the 2-5M range is important in focusing direction amidst customer feedback and an increasingly complex product. This is especially true in enterprise software. Many companies wait too long in formalizing product leadership, which can contribute negatively to finding product market fit.

Marketing: At less than $1M in revenue, marketing is mostly a demand gen game – testing paid acquisition, building SEO, optimizing landing pages and building the top of funnel. This can be done by a marketing manager with 3 to 5 years of relevant experience… or even less. As the company finds product-market-fit and begins scaling, higher level marketing leadership is needed to drive brand, position in a complex market and scale demand gen. This can be done by a director or VP level hire. CMOs aren’t usually needed until $10M+ when a run at category leadership calls for even deeper experience and industry gravitas.

Sales: I’ve written about “when not to hire your first VP of Sales” from our own battle scars. Put simply, we’ve almost never seen anyone but a founder be successful at leading sales at a startup below $2M in ARR. Below that the process, product and target customer are too undefined. For very large enterprise SaaS, that cutoff is even higher in the $4-5M ARR range, meaning enterprise software startups are wise to wait even later to hire a first non-founder VP Sales. Higher contract values mean fewer experienced sales cycles for a given ARR level, so repeatability through practice takes longer!

I’ve been surprised at the increasing use of CRO titles at small startups. “Chief Revenue Officer” implies the leader runs sales, customer success and maybe marketing AND that they have uncapped scaling potential. There are few people who really have that combined set of skills and potential, and they don’t usually work for small startups.

Customer Success: This function should start from day 1 with an individual contributor hired to manage first customers. Once 3 or 4 success reps are in place… somewhere in the $2-5M range, a functional leader is needed. This is a player coach, likely a director. VP level leadership can often wait until upper single digit run rate as long as a director level hire is given a voice at the table to fight for the customer in product and pricing decisions.

Talent: Talent (and HR) are critical functions, but in defining the culture and values of a company, there is no substitute for the founding CEO serving as head of talent for the first 25 hires. Once in the $2-5M ARR range with a staff above 25 people, institutionalizing HR and talent with a recruiter and/or HR operations person makes sense. Somewhere later in the $5-9M ARR, an executive level hire to run Talent (VP Talent) is prudent. The next stage of human scaling requires a devoted high-level player.

Finance: We see a lot of “CFOs” at too small startups. No dedicated financial resource is needed below $1M ARR. There are plenty of outsourced bookkeeping services to use for $1-5K a month. A controller becomes important in the $2-5M ARR range as a startup raises more capital (increasing information demands and frequency of investor communications), expenses increase and customer contracts become more complex. A controller can also be helpful in prepping board decks and managing a financial review or audit. So when do you level-up to director or VP finance? Here’s how I think about it: A controller should be able to answer the question “what are the numbers?”. A director or VP Finance should be able to answer the question “what do the numbers mean?” A VP Finance is particularly important in enterprise SaaS in the $5 to 9M range (or even earlier), where modeling and negotiating of customer contracts is important skill to have in-house. A controller or director level resource will scale further in SMB SaaS where everything is billed through Stripe. The final CFO step brings a true strategic financial leader who can manage a fundraise and diligence process, help the CEO test and analyze business cases/strategy and oversee key business risks (financial, physical, platform dependencies, etc).

*Operations: Many startups don’t need an operations function. Exceptions include tech enabled services, marketplaces (think sourcing and on-boarding supply) and enterprise SaaS where implementation and services are part of the model. Such companies may start with a founding COO. If not, a relatively senior hire is important early. You can’t screw this one up, and often the role requires real experience to avoid the risks of putting customers or suppliers through your own learning curve. Don’t reinvent the wheel when it comes to opps. With respect to “operations” functions like facilities/real estate, legal, or HR compliance, the VP Finance or CFO can handle those.

*Business Development and Partnerships: BD and Partnership hires can wait until later for many startups, except those where ecosystems are complex and busy and/or there is a lot of product value in integration. This tends to be true of horizontal SaaS – think martech, accounting/fin tech and sales enablement – where there is an existing stack of technologies into which you are trying to fit. In these cases, early BD is both risk mitigation (don’t get left in the cold) and a distribution opportunity (sell through the rest of the stack). Of course, the founding CEO is likely the best BD person for a long time.

Some considerations:

Key hires are as much about leverage as upleveling experience. A founding team of 2 or 3 people can’t run every function as a company grows. Each member should pick the one or two functions they excel in the most and run with it, filling leadership for the others as the company scales.

Faster growing startups should hire senior levels earlier than slower growing startups. By the time you “need” the hire, it is too late! Make decisions 6 to 9 months ahead to accommodate a 3-6 month search and hire process for Director levels and above. Faster growing companies also have better access to capital and so can afford a higher personnel burn to accommodate this ramp, while they have less time to experientially train and promote from within.

There should be a good reason for having a lot more senior people than the table suggests for your stage. If you are a < $1M ARR startup with lots of chiefs, be explicit about the tradeoffs. What are you getting for the higher burn and likely pain of having to trade people out later? We see two modes of good answers: (1) Extremely experienced teams that have had startup success together before and likely have better access to capital. Their bet is that they can go faster without the on-the-job-training costs; (2) Startups operating in credential driven industries where early team members need “C” titles, long resumes and fancy degrees to convince customer. Healthcare startups are the major culprit here. Those are the only sort of good answer we’ve seen.