People, Part 2: the characteristics that matter in startup teams

In “People, Part 1”, I outlined the combinations of three key “inspection” characteristics of founding teams (professional, startup and industry experience) that favor startup success. These characteristics are “inspected” because they can largely be assessed from LinkedIn, resumes and surface discussions. They describe what people have done and achieved (outcomes), less so how they achieved those outcomes. The how, or “experienced” characteristics, matter tremendously for two reasons: first regardless of past outcomes, certain actions are more likely to lead to more good outcomes in the future. Second, regardless of future outcomes, certain styles are much easier to work with than others. Any investor will tell you they learned quickly to avoid difficult people and teams – the only thing worse than an unsuccessful investment is a dramatic one. Founders can rightly say that about investors too.

Experienced characteristics are observed and assessed over time working with a team, both pre and post investment. If you’re wondering why an investor is going slow or “hanging around the hoop”, it may be because they want to see “more traction.” Just as often, however, the investor wants to get to know you better. It isn’t unheard of for HPVP to go from first deep-dive meeting with a company to term sheet in 4 weeks, but that’s almost exclusively with teams we’ve known for many month or years.

In a series of interviews I did with top VCs to understand how they assess teams, I found the typical time a VC spends getting to know a team before investment ranges from 6 to 20 hours. Of note, this is only for a first investment. By the time a second investment comes around, time-with-team grows to 50 to 75 hours via accrual of board meetings, dinners, phone calls and email communications. The collaboration, strategizing and observation afforded in this time provides an investor priceless insight into the team they’ve backed and highlights why VCs make smaller investments upfront but reserve heavily for follow-ons. They know a lot more later.

If experienced characteristics are about how people achieve outcomes, then these habituated activities can be broken down into a set of five key actions:

 

The five actions that matter in startup teams:

Execute, Hire, Learn, Build Relationships, Communicate

Stories are more fun, so let me describe these actions in short vignettes from my experiences with great teams, both within and without our portfolio.

Execute: I met with a founder nine months ago. She had a great idea, no product and no customers. Her target customer is in the healthcare industry – a 9 to 12 month sales cycle I believed. After nine months, she had 4 signed customers, 3 operational already, with a strong pipeline to boot. She had assembled a team, launched a product, sold 4 enterprise customers and raised a seed round all in this time. Is the sales model repeatable beyond founder sales, will customers churn, will everything work perfectly? Don’t know yet. I do know she gets shit done.

Hire: One of our CEOs was looking for a VP of Finance and interviewed a large slate. Any of the candidates could do the job, but the CEO struggled to see the uniqueness and new perspective any would bring to the startup’s relatively young executive team. Along came an overqualified candidate who was considering CFO (not VP Fin) roles at larger and more prestigious startups. The CEO both recognized he had found a diamond when he’d been looking for gold and was able to attract this CFO to a smaller company. I have written several times on talent in “Walking the talk on talent” and “T is for talent”. Nothing speaks to a team’s potential like their talent disposition. Does talent follow them from past roles? Do they know the gaps they have on their senior team? Have they brought in senior people to take the company to the next level as it grows? Are they planning to? If so, whom?

Learn: The best founders are constantly learning – from experiences, customers, advisors and each other. After raising a post-seed round, one of our portfolio companies endeavored to design and ramp an inside sales team. The COO/founder had sold the product himself, but never hired or built a sales team. Three weeks, 15 phone calls, one optioned advisor/consultant (an expert in the field) and countless blogs read later, the COO was interviewing BDRs, designing training materials and writing scripts. This isn’t to say startup teams should learn how to do everything from scratch; I would much prefer that someone on the team already had sales ramping experience. Given they didn’t, though, this COO did an amazing and efficient job using outside resources to avoid mistakes others had already made, and it was much cheaper than hiring a VP sales too early. Further reading: Dweck’s oft discussed “Growth Mindset” may seem cliché, but it is critical that founders are adept at applying effort with process and the input of others to develop abilities and achieve  outcomes (my paraphrase of the growth mindset) as this team did. A fixed set of abilities doesn’t cut it in startups.

Build Relationships: In order to execute, learn, hire or sell, we need to get people on our side, something that comes through facility with relationships.  A founder I know has this “it” factor with people. We initially didn’t invest in this founder’s startup, but he’d drop me emails or voicemails each quarter with updates or requests for input. Yes, I was on the investor “drip” campaign, but it felt genuine, and our relationship grew. As we later moved into diligence, we made five potential customer intros. Three of them emailed me unsolicited later and commented on how much they liked the founder. This is a person who will be able to achieve anything through other people.

Communicate: And to build relationships, we must communicate well. Whether you like in-person, phone, email or text, consistency and transparency should be the common thread. An experienced CEO we backed likes to prep each board member (and observer) separately with a 15 minute call a few days before a board meeting. This CEO also provides a 2-3 page written discussion of how the company is performing, what is going well and what isn’t. This isn’t a 15 page deck with one number a slide that means nothing without narrative. Nor is it 50 pages of squint-inducing excel schedules whose existence is proof of wasted time and money. It is a way for the CEO to organize his thoughts and communicate to his board unambiguously about how the company is doing and what he is thinking and feeling – there is no better pulse of a startup than this. By prepping each board member with a call and this document, this CEO completely aligns the Board to the problems and opportunities that need to be discussed in meeting and avoids potential for derailment. Amazing.

 

The five styles that matter in startup teams:

Fast, Tenacious, Confident, Motivated and Practical

Then there is the how of the how, the modifiers to these actions – the five style characteristics that reflect a team’s culture and approach.

It’s clear from the story vignettes above that being fast and tenacious are paramount (think about the sales team ramp and healthcare startup stories). Time is the lurking enemy of startups – the merciless denominator that must be defeated to hit milestones before your money is out. Tenacity is achieving and moving fast even when things get tough.

Being confident is a more complicated because it is often conflated with arrogance and overconfidence. Arrogance and overconfidence result from a belief that you have a set of skills and experiences that are superior to most others’. Confidence, rather, comes from a belief that you can apply effort with process and the input of others to develop abilities and achieve outcomes – the “growth mindset”. In this sense, confidence relies on others to help you while arrogance assumes others are inferior. True confidence is key to achieving the five actions above.

Motivated. In my discussions with other VCs, I found a common trend. Successful founders are motivated both by a passion for the problem (what one top VC described as “I can’t believe this doesn’t exist, and the world needs it”) and money. Consistently, VCs cited stories of one being necessary but not sufficient. Each VC had seen cases of (1) founding teams giving up when the payday seemed further and less likely on the horizon or (2) founders passionate about the problem but unable or even un-interested in sniffing out how to monetize it.

Last but not least, practicality is crucial in achieving success AND avoiding brain damage along the way. Yes, values and principles matter, but when it comes to business (not moral) decisions, being practical builds credibility and respect with employees, customers and investors. My favorite investments are ones where terms are negotiated in a quick call and a few emails. When we see contention in negotiating terms, we assume that’s how a founder manages every business deal, and we run for the hills.

What’s missing here? We hear a lot about achieving a perfect balance of EQ (emotional quotient) and IQ to drive success. And of course honesty. What about those? Well, many of the traits above are integral components of EQ. With respect to IQ, this isn’t middle school anymore; there aren’t smart kids and dumb kids. Most people you’ll meet in the startup world are pretty smart, whether credentialed or not. Most are honest too. There are certainly exceptions to both, and of course we look for smarts and honesty in our diligence, but they are table stakes. Smarts and honesty alone don’t get you far in a startup.

 

People, Part 1: Successful startup team modes

I’ve been spending a lot of time thinking about the people that I invest in. With 5 years and 50+ (firm wide) data points, what works and what doesn’t? This is the first of a few posts to come on this topic.

The characteristics of any person – including those we invest in – can be grouped as inspection characteristics and experienced characteristics. Inspection characteristics are those you can measure or observe from the outside, by looking at linkedin or having a cursory conversation. Experienced characteristics are the ones that can only be observed through extensive interaction. This post focuses on inspection characteristics and how they combine to form successful startup team modes, in our experience:

Startup experience, professional experience, and industry/product experience seem to be the three inspection characteristics that matter the most. These three dimensions chart a clear framework and set of combinations, or modes, where we’ve seen success.

Teams.png

Two primary modes of success

Mode 1 – “The YC Model”:

First startup + limited professional experience + industry/product expert

I call this mode the YC Model because many Y-Combinator teams fit this bill: pre-career or early career founders in their first startup but with coding and/or product experience that is often tied to the industry they are disrupting. In our portfolio, two of our companies stand out strongly in this mode:

Farmlogs – software for farmers. They are actually a YC team, and the two co-founders started the company a few years out of school. Both were hackers and one grew up on a farm. Boom.

Shipbob – simple, fast and affordable fulfillment for e-commerce. Another YC team! Both co-founder are early-career developers – one from another portfolio company of ours and another at Deloitte. They started an e-commerce business before Shipbob and experienced the problem they are now solving first-hand.

Terminus and FourKites are also similar – relatively early career teams with direct product experience in their markets.

Why does this mode tend to work? Early-career, first-startup teams have little to lose. They are often unmarried, no children, mobile. While by definition these teams have a lot of learning to do on the job, they are typically cash efficient in the early days – they don’t need or want high salaries to accommodate the burdens they don’t have. They are also accustomed to getting their hands dirty. In cases where these teams have started their careers already (not directly out of high school or college), they were likely individual contributors and are not desirous of building big teams to which to delegate. Finally – and as important – these young teams have just enough experience to see and want to solve the problems in an industry, without being jaded are hardened by norms in need of change. Kudos to YC for productizing this combo.

 “YC Model” teams consequently do a great job finding product-market fit cash efficiently. They are nimble and fast. The flips side is that they tend to struggle scaling a business after finding PMF because they are inexperienced in hiring, managing and building organizations, navigating complex market dynamics and working with investors. They may also struggle with enterprise sales – a skill that develops over years of experience – and so default to lighter touch sales models even when they aren’t a fit. I am happy to say our teams above have worked handily through these challenges.

 

Model 2 – “Do it again, Sam”:

Successful startup exit + extensive professional experience + industry/product expert

This one seems like a no-brainer. Been there, done that; do it again, Sam; pick your proverb.

The best example in our Portfolio is G2Crowd – Yelp for B2B software – whose founding team built and sold Big Machines. We have a number of newer investments in our portfolio in this mode that we are also excited about including Truss, Hubdoc, Lookbook, Catalytic, and Upfront Healthcare.

While these teams may burn a bit more cash upfront, their on-the-job-learning is very low, so they are efficient on balance. They’ve also made money for investors before so have strong access to capital. As veterans of life and the startup journey, they know how to attract talent, build organization, sell to big customers, navigate a market and work with investors. Finally, if they are a big enough deal themselves, their prior industry role may help bring celebrity attention to their new endeavor.

This mode is not without risks, however. Experienced teams make mistakes, too. A common one is raising too much money, too quickly, at too high a price. This limits options for the company in the case where the team doesn’t get as far as it wants or burns too much doing so, eg, the company doesn’t hit plan. While experienced teams are much more likely to hit plan, it’s still a startup, so most don’t!

 

Do any other modes work?

Three dimensions, two levels each = 8 modes. We’ve talked about two. How about the other six? It turns out we almost never see success when teams don’t have industry and/or product experience in their target industry. The rare exception is the brand-new market which arises more often with consumer startups (think social in 2005), but rarely in B2B. So that negates four of the six other possible modes. The remaining two fall perpendicular to Modes 1 and 2 in the red/green axis above:

 

Mode 3 – “Exec-turned-founder”: 

First startup + extensive professional experience + industry/product expert

This is a tough one. These teams are often stacked with CxOs – managers or “leaders” who want big salaries from the start and want to hire armies of people to do everything for them. They know a lot about managing existing organizations but nothing about finding PMF or seeding scalable sales. These teams burn money fast and often fail or disband quickly as their financial and emotional dissonance with taking real risk pushes them back into corporate jobs. We have rarely seen success in this mode, highlighting some of the fundamental differences in traits between corporate leaders and startup leaders. Note that everything I just said applies to “IT” software startups. Biotech and med-device are very different – credentials and years of scientific experience and knowledge matter a lot, and those often come with long academic and corporate stints.

 

Mode 4 – “This is the big one”:

Successful startup exit + limited professional experience + industry/product expert

Even if this type of team is still young and inexperienced, they know what success looks like and can often do it again. Their first exit may have been decent or even great, but they want a huge one now. We have certainly seen this work.  The major risk is hubris or misplaced attribution. Young successful founders (and the investors who chase them) may conflate the success of being in the right place at the right time with the success of doing the right thing at the right time. The latter is more replicable, arising from judgment and skill, not luck. For some reason, more mature teams tend to avoid this type of mis-attribution. As an investor, that’s the crux of the other half of people assessment, assessing experienced characteristics like hubris and mis-attribution. That is for my next post.

Less any of this discourage a forming team that doesn’t “fit the mode”, please remember that these are only experienced rules of thumb. There are always outliers. I love being proven wrong, and I know many of you will do so.

When not to hire your first VP Sales

The VP of Sales is the most difficult hire – and highest stakes – at an early-stage startup. Across the many startups we’ve invested in, we see success with a company’s first VP Sales hire less than 25% of the time. This pales in comparison to a 60-75% success rate across all hires. Worse, there is so much riding on a VP Sales hire for a Series A or B stage startup because the cost of failure is high. Below is the estimated failure cost based on average contract size and assumptions on quotas, team size, etc for a range of software startups from SMB to Enterprise. (detailed assumptions and calcs at end of article for nerds)

Cost

There are two components included in the total cost above: (1) what you pay the wrong hire for the length of time they work for you, and more importantly, (2) the negative impact on potential rep quota productivity, assumed here to be negative 50% (you spend money on reps because generals need soldiers, but they are only half as productive as they should be because of poor leadership). The latter is the “opportunity cost” of not having a good sales leader. The total cost of the wrong VP Sales hire goes up significantly with contract size because longer sales cycles mean it takes longer to know if a VP Sales is effective. In enterprise, you might not know for as long as a year, and it could cost you more than $2M. That can equate to 9 months of runway. Bummer.

Why do startups so often fumble the VP Sales hire?

Here is the typical story: You had good momentum with founder sales and a few early sales people and got to $1M in ARR. This gave you the story to raise a $5M (Midwest) or $10M (coasts) financing. Now you’re in the big leagues; two VCs are staring at you in your first Board meeting waiting to hear how you’ll hit the $3-4M ARR you promised. They want to see revenue, and you want someone else holding that hot potato! Neither is a good reason to hire the VP Sales at this exact moment in time, but you do it anyway – quickly for the next Board meeting. That’s progress, right? It doesn’t work because:

  1. YOU: You’ve never hired a sales leader before, and they all sound great (duh, they’re sales people)
  2. THEM: Good sales leaders are rarely “on the market” – they are poached. While this is true of most types of talent, good sales leaders make a lot of money and really like money. They are the least incented of any talent to look for something new if they are doing well. This is unlike many tech and product people who more heavily value diversity of experience (and new option grants).
  3. YOUR STARTUP: The company isn’t actually ready to scale – the most common issue. Maybe you have 10-15 MB customers or 3-4 enterprise customers, but your brand is young, you are still honing your value prop/message and the repeatable sales process (where you find customers, how you convince them and how you close them) is still uncertain. Most of these need to be resolved before you hire a heavy hitting sales leader.

Points (1) and (2) can be overcome with the help of VP-Sales-hiring-experienced advisors in your interview process, combined with a methodical talent poaching strategy (you didn’t hear me say that). (3) is about nailing the basics of finding product market fit and a repeatable sales process. This means you. No one can do it for you. Practically, this is a “CXO sales model” where you and your founding executive suite – with the help of a few sales people for leverage and to test scale – find the repeatable lead sources, messaging and process to close sales. For companies with a $10K ACV, this typically happens above 150 customers or $1.5M ARR. For companies with enormous enterprise contracts of $500K-1M, it might not be until $3-4M in ARR or more.

Experienced tech founder and CEO Ben Alpert (sold Care Team Connect to the Advisory Board and now running Upfront Healthcare) puts it this way:

It is often a default reaction to assume that more sales people = more sales. One thing I have experienced when hiring too early is the natural desperation for the sales person to make something happen without the right support. That desperation can cause the person to chase the wrong prospects/opportunities which takes focus away…

What Ben describes is a key effect of hiring sales leadership too early. With generals come soldiers, and suddenly you have a flock of sales people desperately seeking revenue without the tools, process and brand support needed to do so at scale.

If instead you wait until repeatability is proven, a great VP Sales hire with appropriate company stage and business model experience is much more likely to be successful. There team will be too. You will also be better equipped to determine if the new VP Sales is executing. Having acted as the VP Sales yourself, there is a process you know works, and you can both train and hold the VP Sales accountable to it. As Ben puts it, the VP Sales hire is about leverage for the founding team in a proven process, not passing the hot potato.

Check out more VP Sales hiring tips and tricks from @jasonlk on Close.io’s blog post. The one tip I struggle with is Mistake 2: “Waiting to hire VP Sales too late”. It is true you should start scouting and meeting candidates early – casual cups of coffee now pave the way for a top VP Sales hire a year from now. However, 90% of the time the mistake is hiring a VP Sales too early, not too late.

Assumptions/calcs for chart:

Chart

Investing and building marketplaces

Akin to tech companies, tech investors must continually reinvent themselves. An investing thesis that worked five years ago is now likely to be a bust. We made several freemium/premium model investments in the early days of HPVP – FarmLogs and NoRedInk. They are turning out well with significant market share and paid conversion traction. But does freemium/premium work in agtech and edtech now? I don’t think so. Customers have come up the learning curve, seen (and paid for) the value software provides and may already have an installed favorite. These are markets more ripe for monetization with better product, less so “free”-driven land grabs. As I’ve discussed before, investment theses follow an arc of unproven bet to reality. SaaS is doing the same.

HPVP has been a committed SaaS investor from the beginning, and we will remain so. However, our conviction and commitment in SaaS is not without a realization that SaaS has become increasingly competitive. Many horizontal SaaS markets such as CRM, ERP, MarTech, Sales Enablement have broadly become hyper-competitive where only the best of the best executors can win. There is no margin for error, yet we all know there is so oft error in starting up. In these horizontal markets we see pockets of opportunity – sub-theses where there is not yet consensus and where returns favor experimentation and speed, in addition to execution. Such is the case with our Terminus investment, the early leader in the Account Base Marketing “sub-thesis” of Martech. We see much more consistent SaaS opportunity in the much-lauded vertical SaaS area and remain excited about agtech, logistics tech, digital manufacturing and supply chain, all industries with extreme Midwest advantage and affiliation.

We are also looking outside of SaaS to marketplaces. Chicago has had storied success in marketplaces. There is good DNA here including Grubhub (food) and Groupon (deals). More recently, we are home to Raise (gift cards), Parkwhiz (parking) and Spothero (parking). We are already investors in G2CrowdParkwhiz, Dolly, Partyslate, Truss, Tock, Pearachute and every day we are looking at more marketplaces.

Marketplaces can be harder than SaaS. They are often two business models in one (as if one wasn’t hard enough) and tend towards winner-take-all. However, they are also very large opportunities given the scale of consumer and B2B product and service markets, and they are increasingly favored by evolving tech trends and consumer behaviors. Seamless payment technologies, digital imaging (think matterport) and internet everywhere reduce transaction costs to enable marketplaces. Consumer acceptance of reviews as a trust standard, increasing preference for digital communication (text/email) and the gig/on-demand economy all favor consumer and business adoption of marketplaces as well.

For entrepreneurs considering a marketplace model, Bill Gurley’s seminal 10 Factor post is a great place to start. Bill’s is a terrific framework to sanity check marketplace ideas, but basic economic theory provides an even quicker test. In simplest form, the requisite conditions for a marketplace are trust, product standardization and potential for liquidity.

Trust:  People and businesses won’t transact on a marketplace without trust. In advanced marketplace exchanges (like Nasdaq or NYSE) the trust issue is solved by the exchange taking the counter-party performance risk. In most digital marketplaces (Ebay, Expedia, AirBNB, Amazon Marketplace, Uber, Etsy), user reviews are employed to mediate trust. Historically, this was a supplier side phenomenon. Users have long been creating and referencing supplier reviews on Ebay and Expedia to make buying decision. On-demand economy opportunities, however, place suppliers at much greater risk; having someone sleep in your home via AirBnB or be a passenger in your car via Uber is much riskier than mailing baseball cards via Ebay. These are shared experiences with valuable assets at stake, not simple product transactions. Enter the buyer review to solve this: AirBnB and Uber buyers are rated, and AirBnB has a $1M guarantee for sellers. Trust is also big issue in business-to-business marketplaces given the large dollars and reputations at risk as well as complexity of B2B transactions. Our portfolio company, G2Crowd, is leading the way in bringing trust and transparency to the B2B software market using reviews, both on its own platform and soon beyond.

Product Standardization: There are search goods and experience goods in economics. Search goods are products or services whose features, quality and value can be easily assessed before purchase. Experience goods can only be assessed during use or consumption. In general, digital marketplaces lend themselves well to a subset of search goods, standardized products – known physical skus, commodities, or other products with which the buyer has prior experience or can see outside the marketplace before being certain of buying the same product online (think visiting BestBuy to see a phone and then buying on Amazon… don’t hate me, BestBuy). So then what of all of the growing service and experience marketplaces like Uber and AirBnB? Uber jumps through hoops to standardize their service with expectations for drivers on type of cars, age of car, politeness and driving behavior. Travel marketplaces like Expedia and Kayak do the same by bucketing the multitudes of branded hotel rooms into simple 1,2,3,4,5 hotel star brackets. AirBnB is an outlier, actually turning standardization on its head by using non-standardization as a differentiator. However, it shoe horns “experience products” into “searchability” through extreme information sharing from hosts (pictures, commentary, maps) and prior visitor reviews.

Potential for liquidity: Even with trust and standardization, there must be many buyers and sellers – what I think of as many “match pairs” – for liquidity to be possible. For liquidity, there must also be a propensity or willingness for match pairs to change and stay on-platform instead of going off-market together for the next transaction. Match pairs on travel sites rotate, for example, because consumers want new experiences and the best price. On Uber they rotate because consumer want the closest car. On AirBnB, Grubhub, Etsy, (and Ashley Madison!), match pairs rotate because consumers want diverse experiences and goods. These are all examples where ongoing “coordination” costs and loyalty of relationships are low, something airlines and hotel brands have been trying to fight for years.

But where do I start?

Let’s say your idea checks out on Gurley’s key questions and the basic economics above. How do you actually get it started? I am regularly asked by seed stage marketplace entrepreneurs, “which side of the market should I build out first?” The flip answer is “both”… most marketplaces are built via correlated growth on both sides. However, in the earliest, earliest days, you need to focus your attention somewhere first. Where? To answer, I like to explore these three questions:

Which side of the transaction is more desperate? In any two-sided marketplace, there is usually one side(the buyer or seller) who is more desperate to transact. While there are exceptions, it is usually the seller – the restaurant, hotel, airline or maker that needs customers to survive and wants to explore new channels.

Which side of the market is more patient? There is a risk, however, of focusing too narrowly and too long on building seller inventory. How long will they wait for demand? The answer depends on the effort that is required of sellers to onboard and remain “active”. We have seen restaurants remain on marketplaces for many months without much action because their effort is limited. The menu is uploaded then stays the same; if an order comes through, terrific. The patience of sellers with changing inventory and prices that must be continually updated is much shorter. We have seen this with various B2B wholesaling marketplaces that failed – by the time entrepreneurs got to driving demand, inventory was stale.

Which side can be short-circuited? There are also cases where one side can be “short-circuited”. By this I mean inventory or demand is quickly onboarded through an aggregator or unique wholesale acquisition strategy to solve for one side of the marketplace. Then the other side can be quickly tested. Aggregators, for example, exist in the travel and the consumer deal industries. If you have a new twist on a travel or deal marketplace (bless your soul), building initial inventory to test demand is pretty doable. If demand materializes, you can later onboard direct supply to score a larger piece of the economics. Usually the seller side is easiest to short-circuit, especially if it is the “B” side of a B2B2C marketplace. Businesses are easier to aggregate and short-circuit than consumers.

Each marketplace fits  differently in this framework, but if the clear answer to at least two of the questions is the same side of the market, you have a strong indication of where to begin. Oh, and if you’re building a marketplace, let’s chat!

Celebrating SaaStr: The essence of software and zero marginal cost

My partners and I will make our first pilgrimage to SaaStr this week. The tweet at bottom from my colleague Jackie should give you a sense of how SaaStr feels for a SaaS focused VC. It’s like a StarTrek Convention for a Spock impersonator. SaaStr and – twice in one week – being asked “what does AI mean for SaaS?” got me thinking about the “essence” of software.

There is only one other technology in human history that has had the impact of software – the printing press, of course. (okay, maybe electricity too) However the growth of literacy rates that printed books spurred pales in comparison to software adoption, using internet as a proxy:

Essence.jpg

Source: Ourworldindata, OECD, UNESCO, Scribblrs

Why is this? The marginal cost (and often price) of software is truly approaching zero, putting it within reach of even the poorest of the poor. The marginal cost of a printed book has never been that close to zero, so the price and effort required to learn letters has long been insurmountable for poorer tiers of society. The good news is software itself has now too made the marginal cost of a book zero, which in turn will help drive literacy rates on their final climb towards 100%.

Okay, so software is “eating the world” rapidly, but what does this mean at its core? In short, software is a digital means to drive marginal costs of transactions of any kind – human-to-human, human-to-machine, business-to-human, and so on – to zero. This definition is pretty broad, but purposely so. From punch cards replacing the human “computers” of Hidden Figures to a bot managing customer service interactions for a banking app, it is all part of the same trend. Software can help humans do stuff faster, cheaper and more accurately, and each new wave of technology (eg, now AI) brings software that much closer to surmounting costs of existing transaction methods… and displacing them.

Nerdy, true, but I am using this specific economic framework purposely to lay groundwork for the AI question. I am not excited about AI itself; it is just another tool in the expanding toolkit of technology infrastructure that underlies software. What I am excited about is the specific situations in which it can make software work cheaper, faster and more accurately such that non-software processes can be replaced.

We’ve invested in two examples of this, both in Ann Arbor, MI. Why there? Surprise, surprise not every developer knows how to do AI just because they are a developer (a word of caution to the AI snakeskin salespeople out there… we’re on to you). There is real AI coming out of The University of Michigan. Notion uses AI to help me communicate with my colleagues more efficiently by knowing what and who is important. I use it instead of gmail. Clinc lets banks offer personalized chat interaction with consumers, foregoing frustrating phone trees, waiting and customer service reps’ own shortcomings.

But AI is only the most recent in a long line of infrastructure innovations that drove (and continue to drive) the adoption of software to the zero marginal cost singularity:

Silicon transistor (and Moore’s Law) –> Workstation/mainframe –> PC –> Internet –> SaaS (cloud) –> Mobile –> IoT –> AI

There are also UI innovations I’ve discussed that have a similar effect:

Punchcard –> Keyboard –> Mouse –> Touchscreen –> Voice –> VR

As with AI, when any infrastructure or UI innovation emerges, there is always buzz of exponential promise. However, entrepreneurs and investors need to see through the hype to specific use cases where transaction costs are actually diminished by the new innovation, and therefore adoption is warranted. We believe, for example, we are doing this with our investments in Notion and Clinc. A cool infrastructure or UI shouldn’t be used just because it can be and is not a customer benefit in itself. for this reason, I cringe when I hear startup one-liners like “AI for XX” or “ML for YY”. In many cases, the best innovation implementations will barley be noticed at first as with Google’s use of AI for image recognition, now a powerful consumer benefit in Google Photo that enticed me to switch from Dropbox. Google doesn’t call it AI; the new feature is simply an obvious enabler of search that makes search faster and more accurate.

So at SaaS this year, let’s not get caught up in the hype of technology but see the long arch of software for what it is, an unfinished journey of removing transaction costs in business and all facets of life.

Bonus 1: Internet adoption inflection point with mobile

In looking more closely at the internet adoption curve, it is amazing to see the inflection point to a higher adoption rate in the late 2000s. What caused this? The first major trend of linear growth happened from 1998 to 2007 with the PC boom while the second major trend of linear growth – at a higher rate – happened from 2007 through today with the mobile boom. This is driven by India, China and South America where mobile leapfrogged gaps in electricity, phone and other infrastructure in poor and/or rural society. No doubt access to mobile internet will help drive the final journey of global literacy rates in these places as well.

essence-3 Source: Scribblr

Bonus 2: VC excited for SaaStr!