AI Adoption In The Enterprise

When Steve Jobs was a child, he read an article about a study on the efficiency of motion for the various species on the planet. The study ranked different species by how efficiently they moved across long distances. The study showed that the condor was the most efficient species and used the least energy to move across a kilometer. Humans were not very efficient and were about a third of the way down the list. However, a scientist decided to build on this study by testing the efficiency of a human on a bicycle. He found that a human on a bicycle was way more efficient than the condor or any other species. Jobs used to tell people this story as an analogy for the personal computer. With a computer, humans can move much faster and get much more done than we could without one.

As Jobs put it, “Computers are a bicycle for the mind.”

SaaS products have become an extension of this concept inside the modern enterprise. They’re like bicycles for the mind in that they make the average worker more productive by improving their workflows, providing them with new data and insights, providing infrastructure, or integrating different software applications to streamline work.

Getting these products into market at scale was extremely difficult. I have the scars to prove it. CIOs were resistant to software hosted outside of their direct control. Integrations took a lot of time and effort and dramatically slowed sales cycles. There was reluctance to partner with lots of modular software vendors. Users were reluctant to change their workflows. Buyers wanted heavy customization. It took years for it to work.

But amazing go-to-market teams overcame all of this, and now, most large companies use hundreds if not thousands of SaaS applications. 

These SaaS companies and their investors have thrived thanks to the adoption of SaaS, but more importantly, thanks to the SaaS business model, where you aren’t just selling software for a one-time fee, implementing it and going out and finding the next customer. You are selling an annuity contract that will grow as you add features and your customers add employees. SaaS companies generally use ‘per-seat’ pricing, where a customer pays based on the number of employees using the product. This annuity places a large focus on CAC/LTV ratios (the cost of acquiring a customer relative to the lifetime value of having that customer). If you calculate that the average customer pays you $100k in the first year and that that price will increase 10% per year, and the average customer would stay with you for 7 years, the SaaS company can justify very large investments in new customer acquisition. This has resulted in innovative sales organizations and structures to drive speed and efficiency. SaaS companies would hire armies of Sales Development Reps or “SDRs” right out of college to do prospecting with clear promotion paths up to salespeople, sales managers, etc. Similar customer success and user success teams were built to manage ongoing renewals and upsells and drive engagement with the product to make sure the customer kept paying the annuity.

With the emergence of AI, there’s now much speculation that these SaaS companies are in trouble: 1/ because AI will allow a SaaS company’s customers to have fewer employees, reducing the number of seats they can sell, and 2/ because competitive SaaS products will do the job for the employee, making the SaaS product superfluous (the AI doesn’t need a bicycle).

When rolling out AI across enterprises, you could consider a simple three-step framework from the lightest touch to the heaviest touch: 1/ Making existing employees more productive, 2/ Replacing the work employees are already doing, and 3/ Figuring out what work needs to be done. Let’s consider each:

Phase 1: Making existing employees more productive, e.g., the extension of the ‘bicycle for the mind.’ This is what the LLMs are doing now and how most people are experiencing AI at the moment. They can perform tasks done by humans, but they require oversight. In my mind, they’re really just slick SaaS-like products. There are also other types of AI, like robots, that can mimic human expertise and analyze information, but these are by no means widespread and are really just making teams more productive. It’s all great and seems to be getting better, but I don’t think it’s going to change the industry or the business model in a massive way. It probably actually helps because the value creation gets bigger. In theory, a SaaS company makes an employee, say, 5% to 20% more productive and charges their customers some percent of that productivity gain. If SaaS+AI makes employees 80% or 100% more productive, the SaaS contracts could get really large. 

Phase 2: Replacing the work the employees are already doing. The most talked about version of this that I’ve seen is automating coding or data entry or customer service or lower-skilled salespeople with AI agents where the AI is doing the human’s job rather than augmenting the work of the human. This leapfrogs the ‘bicycle of the mind’ concept. If successful, it would very much change the SaaS business model because you’re not selling a license for an employee; you're effectively selling an employee. In theory, if an employee makes $50,000 per year and the AI replaces their work, they could price the product at $49,999 per year, just under the employee’s salary. Because the AI doesn’t sleep or take vacations or can work much more quickly than the human, they could charge 2x, 3x, or 10x the employee's salary. Lots of people are very concerned about this phase because it’s the thing that will eliminate jobs at a very large scale.

I’m skeptical of that. For that to happen, a company would have to conclude that the value at Phase 1 is tapped out, meaning that it’s better to stop making the human more productive, and they should pass the baton to the robot. It seems to me there will be a very high bar for that decision. I wrote about the Jevons Paradox in December, which says that when a resource gets more efficient, we use more of that resource. Maybe the best example of this is bank tellers. It was widely believed that when the ATM was invented, it would eliminate bank teller jobs. The exact opposite happened. There are more bank tellers today than ever. When workers get more efficient, we want more of them to do more stuff. Companies are meant to grow. If bank tellers get more efficient because they don’t have to distribute cash or take deposits all day and can do higher value activity at the same price, companies will hire more of them. I suspect this will happen with AI. The day-to-day work may change, but as AI becomes more prominent, your workers get more efficient, and you want more of them, not less. Going the other way seems anti-growth to me, and anti-growth is not a stable place for a company to be.

Further, many SaaS companies don’t use a per-seat model. Healthcare SaaS companies might charge based on patient or member lives. Others charge based on the amount of differentiated data they provide or some set of assets the customer needs to support. AI will still need many of these things to do the work of humans, and I’m not sure those pricing models will change just because a robot is using the product.

Finally, today’s companies are built entirely around people. This was the key insight in the founding of the workforce management company Rippling – that a company’s central nervous system is its people – the tools, systems, support, infrastructure, management, and allocation of resources all center around people. We like to think companies are built around their customers, but they’re not. More granularly, the day-to-day tasks that get done are people-first tasks. We know they can be greatly augmented by software, but replacing them at scale is going to be a long, complex road. Ben Thompson had a great piece on this recently, pointing out that the companies that really find they can operate with AI as a replacement for employees will have to be companies that haven’t started yet. Companies that don’t have the hard-coded employee-based structure that companies have today. Companies that are native AI companies. And I don’t mean they start a company with people and have an AI product. I mean, they start a company without people, or at least without any large functions beyond a small group of individuals that manage the robots. That’s an important insight and distinction.

Phase 3: Figuring out what work needs to be done. This is where things get interesting. This, to me, is the pinnacle where the AI climbs the stack of human intelligence and replaces the highest-value activities. It’s where it goes from a front-line worker to a strategist who can zoom out and tell senior leaders how to run their company. There are signs of this with things like supply chain management and dynamic resource allocation, but many of those things still feel like Phase 2. Real Phase 3 is so far out at the moment that it’s kind of hard to comprehend and talk about. 

All of this is just one framework to look at the diffusion of AI into the enterprise. As with any framework, you might have the elements of it correct, but the framework itself might be wrong. Regardless, as it stands today, the evolution of SaaS into AI-driven enterprises is less about replacement and more about enhancement. The replacement concept gets talked about a lot but still feels vague and, historically speaking, feels somewhat irrational. Companies built around people will find ways to amplify their workforce’s potential, not eliminate it.

I’m pretty sure the bicycle will be around for a while.

AI In Sales & The Jevons Paradox

A month or so ago, I wrote about the emerging AI business model and its potential impact on white-collar employment. As I said in the post, I'm skeptical that AI will reduce the number of white-collar workers, particularly salespeople. With that in mind, I came across a concept called the Jevons Paradox. 

The Jevons Paradox says that when technological improvements increase the efficiency of a resource, that leads to higher consumption of that resource, not less.

A couple of examples:

Technologies like home insulation, in theory, would reduce the need for heating and cooling energy consumption. But the effect was that people invested in larger homes resulting in more overall energy consumption.

Electric/hybrid cars bring down energy costs, leading to more frequent driving and longer trips, and more overall energy consumption.

This concept also applies to work.

Flexible work tools like Zoom have added convenience but also made it possible to work more, increasing the number of hours worked. 

Communication tools like Slack and email make communication more efficient but have also led to far more communications and increased workloads. 

I think we'll see the Jevons Paradox at work with AI in sales. As AI takes on more of the job of a salesperson and does things like lead scoring, opportunity prioritization, CRM automation, communication personalization, sentiment analysis, etc., it will bring down the cost of sales. That means that each salesperson costs less per dollar in sales than they did prior to the adoption of AI. That will cause companies to want to do more sales and hire more salespeople to do the work.

The myth is that automation makes the worker superfluous. In reality, the opposite is true because it’s an opportunity for the ambitious to do more. Marketing automation is a great example. Facebook Ads can effectively run highly targeted, comprehensive marketing campaigns, in theory reducing the need for digital marketers. When, in fact, those platforms made marketing cheaper, which led to major increases in the need for more digital marketers to manage these programs and to do more marketing. The same will be true with AI in sales.

When something gets cheaper, we tend to do more of it.

AI & Sales

A few weeks ago, Tomas Tunguz from Theory Ventures gave an insightful talk on the state of SaaS Go-to-Market. One key takeaway was the explosive growth of AI use among sales teams. This isn’t surprising. Sales teams, by nature, are quick to adopt new tools because they’re profit centers. Anything that works will be adopted quickly because it impacts the top line. AI is helping sales teams in several areas—lead scoring, customer research, training, follow-up automation, competitive intelligence, and price optimization, to name a few.

Sales leaders are reporting lots of efficiency gains thanks to AI, but despite the boost in productivity, AI hasn’t moved the needle when it comes to deal conversions or bookings growth.

The reality today is that AI in sales — as it is in many functions — is really like having a bunch of productive interns. They can handle lots of grunt work, but their output needs to be checked, they’re tough to train, they lack company and situational context, and they’re not equipped to make important decisions. AI can’t yet do what great salespeople do. It can’t build emotional connections, navigate complex negotiations, leverage intuition, adapt in real-time to low-information situations, or respond to subtle cues or nuanced human emotions surrounded in layers of context. In short, AI is really good when it has lots of great training data to work with, which is the polar opposite of what a good salesperson does: they shine and show their real value when they lack data, and the next step is unclear, and they have to jump over hurdles to acquire more information or rely on their emotional intelligence, intuition, instincts, and human connection to make a good decision without it.

That said, I do believe the efficiency gains AI provides will eventually translate into better conversions and growth. But I’m skeptical how high up the chain it can go in terms of high value sales activity.

AI in sales will be a fun one to watch. Sales results are pretty measurable, and teams will quickly adopt what works. We’ll see.

Salesforce’s Pivot & The AI Business Model

Back in October, I wrote a post titled Selling Software vs. Selling Work, in which I noted that AI may begin to disrupt the traditional SaaS business model. As a reminder, the traditional SaaS business model allows software companies to sell their software as an ongoing service with annual recurring payments. Typically, the software is billed on a "per-seat" or "per-employee" basis. Pioneered by companies like Salesforce, this model has been enormously lucrative for large SaaS companies because they can grow as they sign up new logos, but they can also grow organically and naturally as their customers hire more employees (more seats). This has been a boom for the SaaS industry as most customers have grown headcount substantially over the last 10 to 15 years.

However, a large part of AI's promise is that it will likely eliminate a huge number of white-collar jobs, such that productive companies might start materially reducing headcount in the aggregate, posing a fundamental challenge to the per-seat SaaS model.

This is starting to become a lot more real. A couple of weeks ago, at their annual Dreamforce conference, Salesforce's CEO Mark Benioff announced a major pivot, at least in their AI product strategy. They are moving away from a per-seat model to a per-conversation model where the company will charge $2 for each customer service or sales conversation held by one of their AI assistants. This shift, in theory, will allow them to continue to thrive as companies begin cutting headcount and amping up investments in AI. For those who have read Clayton Christensen's Innovator's Dilemma, this is a pretty big deal and a brave move for Salesforce.

From the Innovator’s Dilemma:

"In essence, the dilemma is that successful companies often focus on improving existing products or services to meet the demands of their most profitable customers. This focus leads them to overlook or dismiss disruptive innovations, which typically start as lower-quality or niche products but eventually improve and take over the market. By the time the established company recognizes the threat, it is often too late to adapt."

Benioff must have read the book.

More broadly, as we consider an industry shift in this direction, it raises several important questions:

1/ Does it work? Can the AI fully replace a human or is it more of an assistant? Surely, for basic tasks, it will, but how far up the stack of human intelligence will this go?

2/ Given the low cost of supplying this kind of AI once it's built, will customers be willing to pay a price that preserves the revenue and profits generated from the per-seat model, or will SaaS companies take a significant hit? In many ways, this will come down to competition and the proprietary nature of the software. Companies like Salesforce have thrived via their scale and the fact that there wasn't a better place to go. Is AI a real, novel, differentiated technology that can't be recreated, or will it be easy to copy and will margins quickly contract?

3/There's an old saying in software that you can turn any task that you do in Microsoft Excel into a SaaS company (expense reports, project management, budgeting, sales pipeline management, etc.). What's the corollary for AI? Will thousands of point solution AI companies be built around LLMs?

4/ What does this do to white-collar employment? We've seen over the course of history that new technologies disrupt employment in the short term. Though in the long term, the shifts from the agriculture age to the industrial age to the services age to the information age, have led to long term increases in wages and employment. Is it different this time? Will AI get so high on the totem pole of human intelligence that the work humans do is materially reduced? A nice proxy for this might also be the spreadsheet. Prior to the invention of spreadsheet software (VisiCalc in 1979), there were large buildings full of white-collar analysts doing the calculator work that Excel does for us today. That shift only created more analyst jobs because high-quality analysis could be done more cheaply. When something gets cheaper, we typically do more of it.

Just when I thought b2b software was getting kind of stale…here we go again…it seems AI is going to be driving a new platform shift, and it's impossible to know where it all lands. But it's great to see companies like Salesforce protecting their shareholders by resisting the innovator's dilemma and getting out in front of what could be a massive new chapter for b2b tech.

SaaS vs. SaaS Margins

The concept of SaaS was launched in the 1960s, though it didn't really gain traction until Salesforce was founded in 1999. It didn't get widespread adoption until the 2010s. Today, it's a standard for how businesses use and buy software. 

SaaS was distinct from traditional, on-prem software in 4 main ways:

1/ Deployment location. The software was hosted on the vendor's cloud servers rather than the customer's servers. 

2/ Subscription pricing. SaaS typically uses a subscription model where users pay a recurring fee that includes maintenance, updates, and support, as opposed to on-prem where there's a large one-time fee, and then additional charges for updates. 

3/ Accessibility. SaaS products can be accessed from any internet connection, whereas on-prem typically can only be accessed inside a company's network.

4/ Customization. SaaS customers rely on the vendor for new features and configurations, whereas on-prem offers far more flexibility to meet specific customer needs. 

As a result of these and other distinctions, SaaS has become extremely attractive from an investor perspective. These companies generate recurring, high margin revenue that grows consistently as the company's customers grow. That equates to higher cash flows than traditional software products, which result in revenue multiples of around 10x for high-growth (30%+) SaaS companies.

Regarding these attractive margins, the most relevant feature is the fourth point: customization. Internet-based companies can become enormously profitable because the marginal cost of adding an additional customer is near zero. It costs Google almost nothing to add an additional user or search advertiser. SaaS is similar. Adding a new customer to Zoom costs Zoom next to nothing. Compare that to an office supply company. In order to add a new printer, the customer has to source parts, manufacture, sell, and ship a printer to the customer. That's a lot more work than adding a new user to Zoom. 

In order to maintain SaaS-like margins, SaaS companies have to limit and constrain their customers' ability to customize the product, ideally down to zero. The moment a SaaS product starts to build custom features for its big, important, strategic customers is the moment the company starts to lose the high-margin, near-zero marginal cost benefits of the SaaS model. Even if it charges very high prices for these customizations, the margins can get lost over time because the customizations have to be serviced in perpetuity and aren't amortized across thousands of customers.

Inevitably, every SaaS company will feel pressure from customers to customize. The high-margin, successful SaaS companies have resisted this pressure by creating roadmaps and features that satisfy most customers and/or have built configurable products where the customer can make their own customizations. Ideally, the customer actually likes the lack of customization because they pay less, but that won’t always be the case.

You could break software up into three distinct categories based on the amount of customization allowed for and the corresponding margins. 

SaaS software - High Margin (Hubspot, Zoom, Docusign)
Enterprise software - Medium Margin (SAP, Cisco, Oracle)
Custom software - Low Margin (Accenture, Infosys, Tata)

All of this is to say that there's nothing wrong with lower-margin software as there's an enormous market for it, and the growth inside of those markets could easily offset the lower margins in terms of cash flows. But it's really important to know which market you want to serve and to make a deliberate decision and stick to it. I've seen lots of companies that think of themselves as SaaS because their software runs on the cloud and their revenue is recurring have a roadmap full of customer customizations. I suppose you can call that SaaS, but it won’t have traditional SaaS margins. And that’s ok. But the decision to do so needs to be made deliberately with eyes wide open to manage the difficult tradeoffs associated with growth and profit.

Risk, Reward, And Working In Tech

The other day, I was chatting with a guy who works at a restaurant down the street in the South End of Boston. He was telling me it was his last couple of months at the restaurant as he had just graduated from college.  He said he wanted to get his money's worth for all the money he spent on college, so he was going to get a higher-paying job in residential property management. He seemed like a sharp guy, so I had this impulse to tell him to get into tech. But I hesitated and decided not to. 

I sometimes forget that tech requires a certain personality. A high tolerance for risk. And I didn't really know the guy, so it didn't feel right to try to direct him one way or the other. 

With the relatively high salaries in tech, flashy products, and nice perks, it's easy to forget how volatile and risky the space actually is. It's definitely not for everybody. And if you're not the type of person who can handle booms and busts, stay away. There are plenty of other less lucrative but far more stable jobs to get into. 

It's worth remembering why this is. Tech companies, by definition, are investing in high growth that comes from new ideas. Investing in new ideas is risky. They often don't work. The greater the risk, the greater the potential reward. In boom markets, working in tech is great. In busts, it's not. I’ve been through 4 of these downturns in my career — the 2000 dot-com bust, the 2008 Great Financial Crisis, Covid in 2020, and the end of the low interest rate period in 2022. None of these were fun, and all of them came with a great deal of professional stress and anxiety.

Because of things outside of your control, such as interest rate changes, investor sentiment changes, geopolitical events, new technologies, government policies, etc., tech markets are inherently cyclical. And the risk takers that are putting capital into new ideas always feel downturns first, because that's the capital that investors will pull out and put into safer, less risky investments.

Before embarking on a new career in tech, make sure you understand this reality and are up for the dramatic ebbs and flows in your day-to-day life that are a natural, unavoidable part of working in this field. 

Some VC Insights

This recent episode, 20VC Roundtable: Is the Venture Model Broken? was one of the best I’ve heard in close to ten years of listening.

I found myself jotting down a few notes. Listen to the whole thing, but here are some of the highlights:

• VCs tend to jump to the “next big thing” (Crypto, AI, etc.) because that’s how you get markups on your investments. But, by definition, the “next big thing” isn’t contrarian and, in theory, wouldn’t be where the outsized returns are. There is an interesting conflict for VCs between showing a good markup and getting actual returns.

• In venture, patience is an arbitrage.

• The true outlier investments, in theory, are the cheap investments. That’s because they’re truly contrarian, and nobody wants to invest in them.

• Pre-product-market-fit, stay as lean as possible. If you get ahead of the market, the team will have built things that aren’t perfectly aligned to the market, and they’ll develop opinions about what the market needs based on what you have and what’s been built. Slow that down and make sure the market is pulling and you’re not pushing.

• When you have product-market fit, your next customer should be marginally more attractive than the last one. e.g., you shouldn’t have to be straining your offering to get more customers. At some point, everyone should want it.

• Often, a company’s go-to-market is better than its product-market fit, and you can fool yourself into thinking you’ve found it.

Tech Company Layoffs

There’s been quite a bit of news over the last several weeks of tech companies freezing hiring and laying off employees. Perhaps most notably, Meta (formerly Facebook) recently laid off 11,000 employees or 13% of its workforce. I thought I'd write a post about what tech companies are thinking about and the factors that are contributing to these unfortunate announcements. First, some history:

Until about a year ago, the stock market had been on a bull run for about 13 years. There are several reasons for this, but the primary reason was that, during this time, we had zero or near-zero interest rates. When interest rates are near zero, companies can borrow money almost for free, allowing them to invest heavily and grow, grow, grow. In addition, when interest rates are so low, money flows out of fixed-income investments and into riskier equity investments (the stock market). More money in equities means higher stock prices for public companies. Public company stock prices are a proxy for private company valuations, so private companies have experienced the same dynamics. This enabled companies to raise enormous amounts of money with little dilution for founders and shareholders. Due to classic supply and demand forces, more money in equities means that the same company with the same financial profile could be valued at 2 or 5, or 10 times what it would be worth in a less bullish market.

It was a great ride until COVID hit, and the economy stalled because people couldn't leave their homes and go to work and buy the goods and services they had been buying in the past. To get us through the crisis, the federal government rightly provided a massive economic stimulus to businesses and consumers by pushing more than $6 trillion into the economy. Again, more money in the system means higher prices for everything (including stocks). Due to COVID, we also saw major global supply chain issues and price spikes across nearly every category (again, the effects of supply and demand; reduced supply of products drives higher prices). Thankfully, the economy quickly recovered and Americans had surpluses of cash that they were anxious to go out and spend. And they did. As a result, we're now seeing historical levels of inflation. The inflation rate for the period ending in September was 8.2%; the average is closer to 3%.

This level of inflation is very dangerous. If prices increase faster than wages, it can literally topple the economy. And there have been lots of examples of this happening in the past. Luckily, the federal government can contract the money supply to slow inflation (less money in the system leads to lower prices). This has the effect of raising interest rates. And that's exactly what has happened; the federal funds rate sits at around 4%, the highest since 2008.

As a result, money has poured out of equities, particularly tech equities. The broader S&P 500 index is down about 15%, and the tech-focused NASDAQ is down about 30%. Tech companies get hit much harder in these cycles because they're investing in future growth and often carry a lot of debt. Because the profits from these investments won't be realized until further out in the future, increased interest rates discount the values of these future cash flows by an excessive amount (more on this soon).

An additional challenge is that as the Federal Reserve contracts the money supply and interest rates rise, it's not very predictable how quickly that will temper price inflation, so there's no way to know how long this drop in the markets and company valuations will persist. And there are reasons to believe it could get worse before it gets better. 

For companies trying to navigate all of these changing conditions, their worlds have become much more difficult. Valuations are way down. As recently as 10 days ago, Facebook’s stock price hit $88, down from a peak of $378. Stock options granted to Facebook employees over the last 6 or 7 years are likely worthless.

Further, the cost of capital (both debt and equity) for companies has significantly increased. This hits technology companies, which, as I mentioned above, typically have higher levels of debt because they're investing in new growth, particularly hard. The cost of running these businesses becomes much more expensive because the cost of debt increases (increased interest expense). In addition, some of these debt covenants have requirements around growth and profitability that companies need to meet. 

Moreover, and this is probably the most important part of what's going on that should be well understood, is that because tech companies are investing heavily in new growth, the profits from those investments won't be realized for several periods. And higher interest rates hit growth-oriented companies very hard because of the discount rate of future cash flows (more on that here). This is a very important economic concept that many in the tech ecosystem don't understand well enough. Said simply, a company is valued on its ability to generate future cash flows. And increased interest rates lead to a discount in the current value of these future cash flows far more than for companies that are profitable now. When interest rates are zero, there's no discount applied to future cash flows, so the market seeks high-growth companies that are making big, bold bets. When interest rates rise, investors look for companies that have profits now. Again, this is simply because of the discount applied to future cash flows.

Finally, and more broadly, businesses are seeing what's happening and are concerned that jobs will be lost, spending will slow, demand for their products will decrease, and a recession (two consecutive quarters of negative GDP growth) might be on the horizon and bookings and revenue may decrease.

That's the situation tech companies find themselves in today. So how are they responding?

Well, it's important to remember that a company's primary purpose is to maximize shareholder value (for external investors and employees holding stock options). Management has a legal duty to its shareholders to operate in a way that maximizes the value of the company, regardless of the changing markets and the lack of predictability around when things will get better or worse. So in a market where near-term profits and cash flows are very highly valued, companies must pare back longer-term growth investments and find ways to cut costs to realize profits more quickly. And, because, typically, the vast majority of expenses of a tech company come from human capital (employees), the only material way to do this is to slow hiring or decrease headcount.

And this is exactly why we're seeing all of the news reports of tech companies freezing hiring and laying off employees.

Of course, some will criticize these companies for hiring too fast and overextending themselves, and voluntarily getting themselves into this situation by investing too heavily too fast. In many cases, this criticism is fair. But it's worth noting that, while cost reduction has rapidly become very important, in a bull market, growth is inversely and equally important. Facebook, as an example, is taking a lot of heat for overhiring engineers, but should they? I’m no expert on Facebook, but it’s an interesting thought exercise to think through for any company. Again, the job of a company is to maximize shareholder value. And when capital is cheap or free, the companies that invest heavily in growth will receive the highest valuations (again, refer back to the discount rate applied to future cash flows). At scale, had Facebook and the other tech giants chose not to make those hires, those individuals would've been unemployed during that period or would've received lower wages from other companies during that period, possibly displacing less talented engineers. If a company has viable ideas and areas to grow, and capital to invest in that growth is freely available, it must pursue that growth. It must maximize shareholder value. Companies with high growth potential have to play the game on the field. They have to pursue growth if they believe it's there. This is an unavoidable cycle that innovative companies are subject to. And individuals that work in the tech ecosystem will inevitably be the beneficiaries – and the victims – of these realities. Other industries experience far less dramatic highs and lows.

Of course, it should be noted that these highs and lows seriously impact people's lives. And I've been glad to see many companies (though not all) executing these cost reductions with humility, empathy, and generous severance packages.

With all of this said, inevitably, at some point, inflation will slow, interest rates will decrease, companies will invest in growth, companies will start hiring again, we'll be back in a bull market, and everything will seem great. In the meantime, it's important that all stakeholders that have chosen to work in and around tech understand and plan accordingly around the macroeconomic cycles that have a disproportionate effect on this industry.

DoorDash’s Empathy Policy

I read the other day that DoorDash is requiring all of their employees (including their CEO) to make at least one food delivery per month. A lot of engineers were less than thrilled with the idea.

I love this idea. One of the challenges in building b2b software is that your product/engineering team is often very disconnected from the user and the user's problems. DoorDash is lucky that many of its employees likely use the product from the consumer side. That's a massive advantage because they have built-in empathy for the user.

But they're also building for the business user (the Dasher), and many/most employees at DoorDash likely have little to no experience delivering food to a customer. Forcing them to do it once a month drives business user empathy and, likely, a much, much more delightfully built business-facing product.

Throughout most of my career, I've worked with companies that build software products for business users. So I've experienced this challenge first hand. If you're building software for, say, police departments, it's highly likely that most of your engineers will never have worked at a police department. There's nothing wrong with this. Their job is to build software. You want people that are great at building software, not great at enforcing the law. But that means that there is an inherent lack of empathy for the user that has to be dealt with proactively. That's why I love DoorDash's decision to get product managers and engineers out in the field to really feel what their Dashers feel. There's no doubt this will result in a better product.

One exercise I'd challenge b2b software companies to work through is to take stock of how many employees they have that truly have been in the user's shoes. Using the example above, it's worth asking how many employees inside the company have worked for a police department or have had a job where they "could've" used the product they're building? Lots of companies wouldn't have a great answer to that question. And that's ok. But those companies have to make proactive moves to drive empathetic product development.

DoorDash's policy is an excellent step in that direction.

The Operator Shortage

Howard Lindzon described the current state of startups really well the other day on the Animal Spirits podcast. I’m paraphrasing, but he said something like:

There are lots of good ideas. There are lots of founders that want to pursue those ideas. There’s lots of cheap capital for founders to raise and build companies around those ideas. But there’s an extreme shortage of qualified operators to go and execute on those ideas.

There aren’t enough high-quality operators that have actually built companies from the ground up. As a result, we’re seeing significant wage inflation across almost every function inside of startups. The ability to recruit and retain top talent is more important than it has ever been in tech (Apple just offered $180k bonuses to engineers to get them not to leave and go work on the metaverse or crypto). Good companies won’t have a problem raising capital, but almost all of them will struggle to hire the best people.

Build a brand that attracts both customers and potential employees. Hire managers with high levels of followership.

Be a company that people want to work at with leaders that people want to work for. Nothing is more important in this market.

Measuring ROI In Enterprise Software

One of the main topics I talk to founders about is how to measure the ROI of their product and how to communicate that ROI to a prospect. This topic almost always comes up in sales conversations, and it’s important to be able to lead this conversation with clarity and authority.

I like to use a simple framework for how to think about a product's ROI, using three broad categories of measurement:

1/ Product usage and engagement. Registered users, monthly active users, transactions, data delivered, etc. Depending on the product, this can be more or less impactful. This is a useful way to think about ROI for a product that doesn't need to be used by a user (like an employee discount program or coaching software). This is not a very effective way to measure ROI for things like expense reporting or benefits management where users are required to use the product to accomplish something.

2/ User satisfaction. This is a bit of a step up over usage metrics in that it measures not just whether or not users use a product, but whether or not they like it. This can be an effective way to measure the ROI of an enablement tool where usage is not optional and financial gain is difficult to measure. NPS is a good measurement for this but I love the way Superhuman tracks this using this question: 1. How would you feel if you could no longer use Superhuman? A) Very disappointed B) Somewhat disappointed C) Not disappointed. There’s a great First Round article on this topic that’s worth reading.

3/ Revenue/Cost savings. This is of course the most impactful way to talk about ROI. It’s especially effective when a company is trying to create a category. In the early days of selling Zocdoc (an online appointment booking software for healthcare clinicians) revenue generated from the service was a crucial part of the ROI conversation. Most doctors didn't feel like they had to put their schedules online, so the only way they'd buy is if they were comfortable that they'd make money. While this was always important, it became less so over time. Online appointment booking became a standard. They had to do it. So other metrics and measurements became more important (e.g. does the staff like using it?).

Depending on the stage of category creation for your product as well as its competitive dominance, it’s important to understand where your product sits in the framework above. Some products need a hard financial ROI, others don’t.

The canonical example of the latter is Salesforce.com. A few years ago, I asked a Salesforce sales rep how they talk about ROI with their customers and he looked at me like I was crazy. The CRM category has been created and it’s now quite mature. Almost all companies of a certain size need a CRM. It’s sort of like calling Verizon and asking them about the ROI on your cell phone. At some point, you just need it. So Salesforce doesn't need to convince you that your sales teams will make more sales because you're using Salesforce, they just need to convince you that everyone uses it or uses something like it and that you need it too. They can validate their ROI by showing usage stats (the bottom of the stack). And if your team isn't using it, that's likely your own fault because you haven't done enough training or promotion to get employees to use it. And of course, they'll be happy to sell you a service that will do that for you.

When taking a product to market, it's important to recognize where your product sits on this stack. Are you selling something that will only be purchased if there’s a crystal clear ROI, or are you selling something that is required to keep the lights on?

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Footnote: If you’re interested in learning more about category creation, I highly recommend the book Play Bigger by Al Ramadan.

Footnote 2: Generally, when talking about ROI you have the buyer and not the user in mind. However, it’s important to understand how both are thinking about assessing the ROI of your product.

Footnote 3: Eventually, all ROIs come down to dollars and cents. As an example, user satisfaction might lead to better employee retention which saves your customer money. But don’t go there if you don’t have to. ROIs generally have lots of assumptions that are easy to disagree on and challenge. Striving to show a financial ROI when it’s not needed can complicate/undermine the story you’re trying to tell.

LTV, CAC, & B2C

Whenever I consider investing in a B2C startup, I immediately ask about the company's LTV/CAC ratio. From the Corporate Finance Institute:

”LTV stands for "lifetime value" per customer and CAC stands for "customer acquisition cost." The LTV/CAC ratio compares the value of a customer over their lifetime, compared to the cost of acquiring them. This metric compares the value of a new customer over its lifetime relative to the cost of acquiring that customer. If the LTV/CAC ratio is less than 1.0 the company is destroying value, and if the ratio is greater than 1.0, it may be creating value, but more analysis is required. Generally speaking, a ratio greater than 3.0 is considered "good."

I’m less interested in the actual numbers than I’m interested in how the company is thinking about improving the numbers over time.

You could argue that a startup shouldn't be overly concerned with this metric in the early stages because they're still building the initial product or trying to find product/market fit and get the company off the ground. I disagree. B2B startups can get away with deprioritizing this metric in the early days because a good sales team can reliably acquire large amounts of users and revenue in large batches. And because of the way decisions are made within an enterprise, churn is typically significantly lower.

For B2C companies, LTV/CAC should be a part of the story from the beginning. Acquiring individual users is difficult and expensive. And since Facebook and Google, there haven't been that many widespread and effective ways of acquiring new users. Most of the high-quality channels are saturated. 

Ideally, B2C startups can bake user acquisition into their fundamental product offering; e.g. a supplier in a marketplace might bring their customers to the platform at no cost to the platform. AirBnB is a good example where landlords will often ask renters to book rentals through AirBnB.

Obviously, this won't be possible for every company. But the point remains: user acquisition and churn mitigation are critical considerations for any B2C startup right from the start.