Aligning Your Sales Plan With Your Growth Plan

One of the most common mistakes I see in B2B tech companies is a lack of clear alignment between their annual plan for growth, their sales team headcount, and their sales team quotas. I thought I’d lay out how I've done this in the past that’s worked really well. I certainly welcome any feedback on it. 

Let’s assume you have $20M in contracted ARR (annual recurring revenue), and by the end of the year, you want to get up to $32M (60% growth). For simplicity, let’s assume you’ll have no churn. So you have a sales bookings target of $12M for the year. 

Let’s say you’re selling into large enterprises, and you have confidence that a talented, hard-working seller can reasonably generate $1M per year. However, on average, because of attrition, rep ramp-up time, and underperformance by some sellers, the average seller's attainment rate in a year is 80% ($800k). 

That means, to get to a $12M booking target, you’re going to need 15 sellers on staff at the beginning of the fiscal year. 

In order to recruit and retain 15 high-quality sellers, market comparables for your industry, stage, and geography, say that you need to pay each of them a total base salary of $100k and an on-target commission of $150k, meaning that if they hit their $1M quota, they have a total on-target earnings for the year of $250k. 

I’ve included these numbers in thetable below. The numbers in red are the numbers you need to fill in; the numbers in black are formulas.

So you see that to build your bookings plan, you need to know the following for your company:

1. Annual Bookings Target

2. Achievable Seller Quota

3. Seller Attainment Rate

4. Seller Base Salary (market-based)

5. Seller On-target Commission (market-based)

As you fill in these numbers, you’ll likely find that the math doesn’t work. Your quotas aren’t high enough to hit your target. You don’t have enough reps, etc. 

Ideally, you fix those things. Lower the bookings number, quickly hire more salespeople, etc. 

But in many cases, that won’t be realistic. The board won’t approve a lower number or you don’t have budget to hire more salespeople, etc. 

That means your booking plan is broken. That’s the one thing you should take away from this post. You don’t have a bookings plan that lines up with reality. Putting your numbers in a simple model like this makes this clear.

Now comes the hard part. You have to start making tradeoffs. If you can’t change the bookings target and you can’t hire more salespeople to make the model work and hit your target, your only other levers are to increase quotas or increase the seller attainment rate.

But that is akin to taking money out of your sales team’s pocket. The effect of this is that you’re telling them that, in order to make the money they’re worth, they need to pull off something you don’t actually think they can accomplish. That’s like calling someone on your HR team or your finance team and telling them you’ve decided to pay them less than you told them you would pay them. This is a really serious decision. As a leader, you’re taking pressure off of yourself so that you don’t have to have a hard conversation with your board, and you’re moving that pressure to your sales team by reducing their earning potential. Great salespeople don’t think of commission as an extra bonus; they think of it like other employees think of their salaries. It’s what they’re worth; it’s what they’re owed if they do their job, just like any other employee. By shifting the pressure to them, you’re risking serious engagement and retention problems. And that should be treated with the same seriousness as a difficult conversation with the board.

Obviously, every leader wants a clean model that works, but that won’t always be the case. Great leaders manage the hard tradeoffs to get to the best answer and clearly communicate their thinking along the way.

Some caveats:

• The model assumes you know all of your numbers with certainty. That won’t be the case, especially in the early days. You have to put a stake in the ground and your best educated guess for each of them based on the data you have.

• Negotiating your annual bookings target is a highly complex conversation that depends on your specific situation. I’ll likely write a post on that soon. The usefulness of the model is that it’s a framework to use in the conversation so the board is aware of the tradeoffs you’re dealing with and provides input. Whatever tradeoffs you make, I’d highly encourage you to disclose them. 

• Consider a base-case bookings plan that the board is happy with and a high-case plan that the internal team rallies around.

• Don’t stress that your model doesn’t work perfectly; this is true for most early-stage companies. But do focus on how to best manage it. The really big mistake here would be either not knowing that your model doesn’t work or not managing its tradeoffs. Being able to explain the gaps and your plan to fix them is the most important thing. 

Shaping Company Culture

A very common question I’ve received from job candidates over the years is: "What is your company's culture like?"

I've taken two different approaches to answering this question:

The first is to talk about my company's values that our leadership team created, the initiatives we're running that quarter or year around employee engagement or work-life balance, the fun events we do after work, or the employee development initiatives we've invested in.

The second, and much more sincere and accurate, way I've answered this is to take a step back and try to be an impartial observer of my company and talk about what I see every day: trends in the way people behave, how they treat customers, how they treat each other, what the company is good at and what it is bad at, what makes us unique, the sense of mission, and the interesting things that I see inside the company that I don't see in other companies—good and bad.

Often, what I've seen as an objective observer isn't the same as the culture we wanted to create from a top-down perspective.

This is because, at a certain scale, a company's culture stops being what leadership wants it to be and starts becoming the actual, on-the-ground, higher-profile, and consistent behaviors of the broader team. These things very often don't relate at all to work-life balance programs or team outings at the local bowling alley.

These behaviors are directly tied to the high-status people inside your organization—that is, the people you reward and promote. Your teams are watching the behaviors of these people very closely, far more closely than they’re paying attention to any top-down initiative.

If you promote one high-profile salesperson who overpromises and lies about the competition, people will get on board and do the same, or they'll opt out of your company, and that’ll be a part of your culture. If you promote leaders in your company who aren't willing to admit they got something wrong in front of their team, people will emulate that behavior. If leaders don't hold their employees accountable for results, this will spread, and you'll have a culture that isn't accountable.

This is rooted in social learning theory and a concept called “status signaling,” where people learn the optimal way to behave by watching and emulating others with higher internal professional status. In my experience, this is an extremely powerful force that drives culture more than any other aspect.

Quite simply, your culture isn’t driven by what you say it is or what you want it to be; more than anything, it’s driven by the values and behaviors of those that you reward and promote. Do so carefully.

Investing In Pure Health Tech

Define Ventures published an interesting report on venture-backed health tech investment performance titled Health Tech's Defining Decade. They point to a few relevant stats:

• As of 2020, health tech makes up 10% of all venture funding. 

• In 2024, there was $18.6 billion in health tech venture investments. 

• 10% of private unicorns (companies with valuations over $1 billion) are in health tech. 

• Since 2020, 18 health tech unicorns have exited (through IPO or M&A). See full list below.

I like the way they segmented the 18:

1/ SaaS (pure tech, software) - 1
2/ Services (serving patients) - 7
3/ Hybrid (mix of SaaS and service) - 7
4/ Payer - 3 

This data speaks to the challenges of investing in pure software health tech companies or vertical SaaS in general. 

With a couple of exceptions, the services and hybrid companies are mostly telehealth and traditional provider organizations with some tech that improves the patient or provider experience (I probably would've put Health Catalyst and Progyny more towards the SaaS, pure tech group and possibly Fitbit — while they're a hardware company, their profits are primarily software-based). 

The payers are, well, payers with some tech to drive better outcomes.

The only pure SaaS company they list is Doximity, and while they are an incredible company, you shouldn't really think of them as a health tech company; they're an advertising platform for pharma companies. Again, they're a fantastic company and definitely support the healthcare ecosystem, but the vast majority of their revenue comes from the same budget pool as Instagram and Snapchat.

It's now become sort of a common trope for health tech companies to build a software product and grow really fast with nice high gross margin revenue only to find out they've run out of TAM (total addressable market) and need to pivot to lower margin services to continue to grow, and by services I don’t mean managed services or software consulting, I mean doing the actual work that their customers do. And that's consistent with this data. It's really hard for pure software companies in healthcare to get big. They can’t just provide software to their customers who do the work; they have to do the work, too. While people will talk about healthcare as a $4.5 trillion industry, healthcare IT represents just 4% of that, or about $180 billion. Most of the TAM has already been eaten up by EHRs, telehealth, analytics, and health information exchange. Further, the software infrastructure that large healthcare organizations use is dominated by several large horizontal software vendors — AWS, Snowflake, Salesforce, Symantec, Okta, etc.

So, the opportunity largely exists in either unseating the large incumbents in some form or in targeted areas, such as clinical/operational efficiency, patient experience/engagement, analytics, etc., but again, you're likely going to run into a TAM problem. 

Of course, all of this brings us back to AI and how it will impact health tech and health tech investing, as that’s probably the best opportunity for more pure tech companies to achieve service-like valuations. And there are probably more questions about this topic that we haven't even thought of yet than the ones we have. My sense is we'll find that these products really impact margins more than TAM, but who knows? Regardless, as AI emerges in health IT, new frameworks will be required for how to think about these opportunities. I'll be thinking about that a lot and posting those thoughts here.

Life Advice

A few weeks ago, I had lunch with my Godson, a sophomore in college. Towards the end, he asked, “Do you have any career advice for me?”

I immediately thought of this blog post on Capital Gains that talks about the dangers of giving life advice based on your own experiences. From the piece:

"…agreeable extroverts will probably tell you that the best thing you can do for your career is to meet as many people as possible, so you have lots of second-degree connections through which you can hear about opportunities. This is probably true for them, but someone who naturally loves nerding out about esoteric programming languages would dismiss this advice out of hand—meeting people is hard, getting them to like you is harder, so you should focus on simple, achievable goals like getting a thousand stars on your Github repository.”

Success comes from a combination of hard work, luck, and skill at the work one is doing. However, a less often discussed component is our inherent temperamental traits. In other words, depending on who you are, some stuff is easy for you, and other stuff is really, really hard. And this matters a lot.

Personally, it’s very easy for me to wake up in the morning and go to the gym. I almost don’t even think about it. I just go. On the other hand, it’s very difficult for me to keep in touch with friends and acquaintances. It takes lots of work.

I have a friend who is the exact opposite. He has struggled his whole life to find a way to build a consistent workout routine. I’ve seen him try enough to know that I don’t have more motivation or willpower than he does. I’m certain of it. It’s just inherently easier for me to do the thing we’re both trying to do. Conversely, he effortlessly keeps in touch with a huge network. Regular phone calls to friends when he’s driving. He stops in on people when he’s in town. Remembers birthdays. Sends gifts. I have to really work at it. It’s hard for me. Of course, it’s important to him, and he does it with purpose, but I can see that it’s also easier for him than it is for me.

Obviously, this applies to our work as well. So if you advise someone to get into the legal profession and, despite your personal success in that field, they have a really hard time consuming large amounts of complex text for long periods of time, all else equal, that person is really going to struggle, and you’ve given them really bad advice. Similarly, if you tell a math genius who is shy to get into a sales job early because that’s where the money is, you’re really setting that person back. Both because they’ll really struggle at the job and because they’ll suffer from a very high opportunity cost of not building multimillion-dollar machine learning models for an AI company or something like that.

People giving career advice are often biased and tend to lean towards their unique, long, and winding path to success. That’s a bad idea. The most extreme example is “follow your passion.” This is great advice if your passion is financial analysis, but not so good if it’s skateboarding.

All of this is to say that giving career advice is really hard, and we have to be cautious about not sending younger people down our paths because what worked for us in so many ways may never work for them.

A while back, I wrote a summary of my best career advice at the time titled 15 Things I Wish I Knew Before I Started. I went back to take a look at it to see if I was guilty of giving biased career advice. It's not perfect, but I was happy to see that it holds up fairly well.

Anyway, if you’re wondering what I told my Godson, I came up with two pieces of (hopefully) unbiased advice:

1/ Pursue opportunities with leverage (adapted from Naval Ravikant). You may not be able to do this in your first job out of college — honestly, your first few years are probably going to suck, so you’ll have to keep your head down and grind for a while — but always try to ultimately pursue work where your outputs are disproportionate to your inputs.

2/ Leave something on the table. Leave jobs, partnerships, and business relationships where the other person feels like they got more from you than what they paid. That results in a reputation with a surplus where people will feel good about working with you and will say good things. That surplus compounds. If someone thinks you’re good and they hear from someone else that you’re good, now they think you’re really good, and they’ll tell people that you’re really good, now those people think you’re really good. And on and on. If you do this well, your career will feel less like you’re climbing a ladder and more like you’re sailing on a boat with a steady wind at your back.

Why It's So Hard To Measure Healthcare Technology's Impact

With the thousands of healthcare technology companies that have raised hundreds of billions of dollars in venture capital and other financing over the last couple of decades to make healthcare more efficient, you'd think we'd be seeing the cost of healthcare delivery come down. Of course, this hasn't happened at all. A lot of people like to point to this chart, which shows that the price of things like TVs is dropping while the price of things like healthcare is going up.

This chart has always annoyed me a bit because it lacks so much of the nuance of each of these industries, but let's go with it for a second and understand why the cost of healthcare isn’t dropping like the cost of a television.

Clearly, televisions have become much cheaper and much better over the last several years. Many technological advances such as LCD, OLED, automated mass production, and labor cost reduction have brought down the cost of producing a TV. Add to that significant global competition, perfect pricing transparency, and the right incentives among consumers, and it’s no surprise that prices have dropped dramatically.


Let's apply that logic to healthcare. Imagine a health system that employs 500 primary care providers. They go out and buy AI software products that make their primary care providers, say, 100% more efficient. Because of technological advancement, it now costs the health system 50% less to supply a PCP appointment to a patient. That's great news. But how does that cost reduction actually show up and become visible to an end consumer?



Well, first of all, it's highly likely that rather than reducing costs, consumption would go up, and costs would stay the same if not increase. More appointments mean more patients, which means more downstream referrals to higher-cost services. The end consumer doesn't buy their own personal set of healthcare services like their own TV for their living room; they buy into a pooled group of patients under an insurance plan. If that pool increases their consumption of healthcare services, each person's premium increases. More healthcare consumption means higher insurance premiums. So, it's possible that technological advancements actually increase the cost of healthcare, which is good in some cases and not so great in others.



But put that aside, let's say demand and consumption stay the same, and the health system can deliver on that demand more efficiently because of the AI they've procured and that they have material savings to do something with. If the health system is a for-profit, they might be inclined to return that money to shareholders without impacting the price for consumers. If the health system is nonprofit, they might just reinvest it back into upgrading a building or hiring more specialists, which would also keep prices flat.
When a television company makes a technological advancement that allows it to make better TVs at a lower cost, it might also be inclined to invest in growth or return money to shareholders. However, because of intense competition and price transparency, it is also very likely they will pass those savings on to consumers, lowering the price of the TV for everyone. But health systems don't have the same incentives as the television company in several important ways:

1/ Demand for their services isn't elastic (people don't shop for PCPs on price). 
2/ Their prices are regulated and negotiated with payers and aren't dynamic like television's.
3/ The price actually being paid isn't clear to the patient, as they only pay a portion that depends on their specific health plan.

I wrote about all of these issues in 15 Reasons Why Healthcare Has a Business Model Problem and Healthcare's Incentive Misalignment, so I won't rehash them here. 



The point of this post is that there are a ton of amazing healthcare tech companies that are reducing costs for providers, payers, and patients, improving the quality of life for clinicians, improving the quality of care across the board, and doing absolutely amazing things for the American healthcare ecosystem. But, for the reasons described above, these savings are unlikely to appear in high-level cost metrics. At least not until the incentives change.

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.

Systems Thinking

Last week, I wrote about the importance of leaders creating cultures that embrace transparency and context. An equally important skill for all great leaders is high proficiency in systems thinking.

From Wikipedia:

“Systems thinking is a holistic approach to problem-solving that views issues as part of a larger, interconnected system, focusing on understanding the relationships, interactions, and interdependencies between different components within that system, rather than just analyzing individual parts in isolation; it prioritizes seeing how elements within a system influence each other to produce emergent behaviors.”

Most of the problems an executive needs to solve are complex and involve several components interacting with one another. People who are good at systems thinking can zoom out and grasp all of these components and empathize with all of these stakeholders, rationalize the complexity, and make good decisions. This is sort of the opposite of linear thinking, where an individual component is thought through without strongly considering interconnections with other components or the broader context of the problem being solved.

A simple example of this is creating a sales commission plan. You might want to pay employees when the deal signs immediately in their next paycheck. Reward the seller immediately, as it’s more likely to drive the sales behavior you want. This makes sense. Simple cause and effect. That’s linear thinking.

Systems thinking encourages you to step back, think more broadly, and ask lots of questions. Questions like: If we pay the seller on the signature, what if the implementation team can’t get the customer to onboard quickly, why would the seller help with that if they’ve already been paid? Who’s going to collect the money from the customer? If the seller isn’t incentivized to help, do we need a larger accounts receivable team? How much will that cost? What will delays in collection do to our working capital? What effect does that decrease in working capital have on our ability to invest in new products? Conversely, do we want salespeople worried about collections? We don’t pay other teams so quickly; what cultural effect might this have?

Systems thinkers can quickly gather all these points in their minds, consider them, and come up with a good recommendation. You know this skill when you see it. And you know when you don’t.

I think this skill comes naturally to some people, but it also can be learned, and it improves as you work on more complex problems. Linear thinking works well with tasks that follow a clear, sequential progression with predictable cause-and-effect relationships. So, if you’re following a clear set of instructions all day, you’re not going to get lots of experience with systems thinking. But if you’re managing a large organization in a multi-stakeholder, regulated, fast-changing, competitive business environment, then you better get good at it really quickly.

To get good at this, I’ve found it’s a lot about having high amounts of empathy, being curious, and asking good questions. As an example, if a competitor releases a new product, don’t just think about how you will position your product pitch against theirs. Think about the larger system. Think about your company’s unique strengths, your long-term plan, what you’re incentivizing, what the competitor’s product strategy is, how other competitors might react, how customers might react, and what advantages their focus on this particular thing might give you. Don’t just react to what you see, think about the systems that led to the thing you see.

As leaders, honing our systems thinking skills can set us apart in an increasingly complex and interconnected world where the ability to get our arms around complex problems and connect dots is more difficult (and critical) than ever.

Investing

The thing I love about investing is that it’s sort of a perpetual test of how well you understand the world. To be good at it, you have to consume lots of information, ensure that you’re interpreting that information correctly, and then organize it into frameworks and theses on which you can take action. I heard Gavin Baker from Atreides Management talk about this on the Thematic Investing podcast, and I just love this articulation. He nailed it.

“Investing is the most addicting and interesting thing I've ever encountered. It's an enormous meritocratic game of skill and chance where everyone on planet earth could compete (and many of them do) and you're competing to price risk, to price assets, accurately (more accurately than others) and understanding when something is undervalued. And the way you do that is by effectively having the most complete knowledge possible of relevant history, which could be the history of a company, current events (which is current fundamentals) and intersecting those two to develop a variant view about a differential state of the world that is not priced into an equity.”

High Context Companies

One of the more interesting insights in The Nvidia Way, the book on Nvidia’s rise under CEO Jensen Huang, is his use of the weekly Top 5 Things emails (T5Ts). In these emails, employees across the company send him their top five updates about their work, observations, and priorities, providing Huang with an unfiltered view of what’s happening. The book notes that he pours a glass of scotch every Sunday night and reads hundreds of them. 

A while back, a mentor explained to me that one of the most important attributes of a leader is their ability to see the company through the eyes of the people who aren’t in the room. If you can’t do this well, you’ll lose the trust of your team, and their engagement will fall. And you’ll probably make a lot of bad decisions. Tools like the T5T and things like skip-level meetings, mini town halls with Q&A, engagement surveys, and insisting on a culture of transparency and openness where people feel comfortable telling hard truths can help leaders get their arms around the problems and opportunities on the ground and ensure they’re seeing the world accurately.

This goes the other way as well. I’ve written before about how important it is for employees to understand the context around decisions made at high levels. I once had a job where I wasn’t on the senior management team, but I had a lot of access to the senior management team. I’d often hear employees at lower levels being very critical of decisions being made at the top. I remember thinking, “These people just don’t have the context; I wish they could somehow get it...” It’s crucial that leaders take the time to give their teams the context around their decisions. The aspiration should be that the lowest-level employee would make the same decision as the highest-level employee because they’re operating with the same information and context.

The key to all of this is that for anyone to make good decisions, they must operate with a strong understanding of the truth — of what’s actually happening. You could probably directly measure an executive's success by how capable they are at this because it’s literally the only way they can make good decisions. This obviously scales to the company, too. I’d bet you could tightly correlate the quality of shared context across an organization directly with its financial success.

It sure is working for Nvidia.

Five Productivity Apps

It occurred to me recently that despite all of the downfalls of being attached to our phones, for me personally, there are a bunch of apps I use that actually make me more productive. I’ve started to use widgets on my iPhone for a few of the apps I use most frequently, which is great because it puts the app right in front of you every time you open your phone as a reminder to do the thing it wants you to do. Here are five that I’m really enjoying. 

Monarch for personal finance

I used Mint.com to track my finances for at least a decade before it shut down. Recently, I started using Monarch, which is just exceptional. One of the big issues with these apps is categorizing your expenses, which can be really time-consuming. Monarch makes this really easy and quick. I’m also using it for budgeting and it makes that really easy as well. The interface and usability of both the web app and iPhone app are amazing. 

Duolingo for learning a language

I’m learning Mandarin right now, and the Duolingo app is an incredible tutor. The app is really well done, and it does a good job of helping you create a daily habit. It harasses you until you complete a lesson. I just hit 50 days in a row and don’t see myself slowing down anytime soon. 

Calm for meditation

There are a ton of medication apps, but I really enjoy Calm, and they have a pretty low-cost lifetime membership. It’s great for meditation but also for playing music for focused work. I wish they did more around habit creation. 

Google Tasks for to-dos

I’ve tried lots of to-do systems over the years but I finally think Google Tasks is the one. If you use the widget it will display your top 3 things to do that day right on your home screen so you see them front and center every time you open your phone. 

Whoop for habit tracking

I’ve had a Whoop for many years to track sleep and activity. They have a journal feature that tracks your habits, which I started using a few months ago. It’s really slick and easy to quickly update your tracker each morning.

Return on Capital Vs. Cost Of Capital

A company's value is equal to the present value of the cash investors can take out of it over time. Investors want to put their money into things that will provide them with lots of future cash flows. Technically speaking, they want to put money in companies whose return on capital exceeds their cost of capital. For an equity investment, the cost of capital for a company is the return that the investor expects; for a debt investment, the cost of capital is the interest rate the company has to pay.

Early-stage technology companies aren’t yet generating cash for investors. They typically lose money as they build up a set of products that can deliver cash back to shareholders at some point in the future. In the context of a new technology company and how an investor thinks about these financial returns, you could see two phases:

Phase 1: Building something that can deliver cash. Once a new venture has found product/market fit, it starts growing quickly. If you generate $1,000 in revenue in your first year, it's not that difficult to 10x that growth and get to $10,000 the next year. In this phase, companies aren't thinking about delivering cash back to investors; they're using all of their capital to invest in the company to grow their revenue and market share so that someday they have something that can deliver cash returns. In this phase, the cost of capital (the return the investor expects) exceeds the return on capital because there aren’t any returns yet. Investors are ok with this because they’re betting on big returns that will happen in the future.

This is why SaaS companies experiencing high growth don’t really worry too much about profits. They worry about growth so that they know they’re building something big, and they worry about unit economics (the profitability of providing a single unit of their product to a customer versus the cost of providing that unit). So, the high-growth SaaS company is happy to be unprofitable and burning through investor capital as long as it knows that it’s acquiring customers that will generate profits in the long term. So when the company has saturated the market and growth has slowed, they can pull back on sales & marketing expenses and R&D investments, and the unit economics then show up in the actual profitability of the company through previously acquired customers. So, this type of company isn’t yet delivering cash; it’s building something that will someday be able to deliver cash.

Phase 2: Delivering cash. Eventually, growth slows as the market saturates, and it gets harder to grow quickly on a larger base of revenue. Companies then find that continuing to invest investor capital and operating profits back into the business is no longer a good idea. That is, they can’t generate a return on capital that is higher than the cost of that capital. That’s when companies slow down their investments and start returning some cash to shareholders through dividends, share buybacks, or paying down debt, such that the investor can go invest in things that are more likely to exceed their expectations for an investment return.

The reason I’m going through all of this context is that one of the hardest things for a company to do, especially a technology company, is to identify the moment when it’s better to start returning cash to shareholders than it is to keep investing in new growth. There’s a saying that “profitable companies that have lots of cash have run out of ideas.” Leaders in companies don’t want to appear like they don’t have new ideas or that they can’t successfully execute them. And not investing in new stuff and just trying to get more profitable to pay out dividends isn’t nearly as fun as deciding to build a self-driving car or placing some other type of big bet. It’s even harder in today’s environment, where so many companies raised capital when growth multiples were super high. Case in point: ServiceTitan, which went public a few weeks ago, priced its IPO share price well below its last round of venture capital financing in 2021. Telling your investors that you think you’ve saturated growth when the company is likely valued less than it was when they invested is a really hard thing to do.

So, hundreds of companies have found themselves between a rock and a hard place. Growth has stalled as they've saturated their core market. But they’re a long way from getting above their last valuation (or “mark” in VC parlance), so they’re continuing to try to grow back into that valuation and, in some cases, destroying shareholder value in the process, where the investments they’re making will not exceed their cost of capital — especially because their cost of capital is now higher as their valuation has dropped. Some companies are betting too aggressively on their ability to produce high growth and are also speculating that if they do deliver on that high growth, the market will value it as it did in previous years. The median public SaaS company is now valued at 6x revenue, a long way from the 20x to 30x multiples of 2021. Getting a company’s valuation above its last mark is a a great thing to try to do, but destructive if done unprofitably.

I’ve simplified this issue a bit, as there are other options besides growing and returning cash. Companies can also invest in efficiency, reposition themselves strategically, and make profitable acquisitions. But all of this comes back to this super important topic of knowing who you are, knowing what’s best for your company right now, and having the courage to pursue that direction. A dollar is a dollar, whether invested in new growth or given back to shareholders as a dividend or share buyback. One approach is not inherently better than the other. As a responsible capital allocator, the key is knowing which one is better for you.

2024 Annual Review

It's that time of year when everyone sets their New Year resolutions and goals for 2025. I don't really do resolutions and besides setting a couple big goals for the year, I prefer to focus on standards and habits. It's also a time to reflect back on 2024. I came across a great approach to this in this post from Sahil Bloom that pushes you to reflect by asking yourself 7 key questions to reflect on the year. I went through it and typed out my own answers and found it really valuable. You might too.