The same instinct shows up in management. The first version of being a manager is leading by editing the work product, because editing was what made us effective as ICs. With agents, that instinct turns into reviewing every line, fixing every example, patching every prompt. Both stop scaling at the same point, for the same reason.

So we ran an experiment. We codified a whole organizational system into the AgentsMD and let agents run coherent, 24/7 coding loops against it. They produced more than 500,000 lines of code that came close to production quality after human review and hardening, because the consistency was already in the spec.

Building Culture at My Last Company

The frame for this came from running a tech-enabled services company through its first three years. We raised a lot of money, hired two hundred people across operations, sales, and engineering, and were disciplined enough to get to cash flow positive. The hardest problem was not any one function but keeping everybody pulling in the same direction once the team got too big to know each other.

What worked was a layered culture deck shared across the company. At its core were five archetypes: Owner, Athlete, Lifelong Learner, Troupe, and Pioneer. Each archetype carried two values, defined with what they meant, why they mattered, and a concrete example from our work. Customer Obsession under Owner read: before building anything, solicit feedback via text from five customers. The deck was a hundred slides and read like a constitution.

People joining the company learned it. People leaving the company quoted it. Once the foundation was in place, decisions came out the same way across every situation, regardless of who was in the room.

Identity, Values, and Mission Are the Foundation

The same seven layers show up in every spec I write now. Identity, Values, and Mission are the shared foundation: who we are, what we care about even when it costs us, and what we’re here to do.

Above the foundation, each domain adds four more layers. Principles are the physics of the domain. Patterns are reusable solutions to common needs in the domain, like the hamburger menu in design or test-driven development in engineering. Processes are sequences of activities, like a deploy pipeline in engineering or a customer onboarding flow in operations. Examples are the concrete rules and anti-patterns that fall out, like a ban on hardcoded color tokens or a requirement that every test assert a single behavior.

The split lets product, design, and engineering each move at their own speed while retaining quality. Each layer compresses many concrete rules into a smaller set of more abstract ones. Specify at the foundation and the layers above follow. Specify only at the examples and you are playing whack-a-mole: fix one output, the next one breaks.

Centralized vs Decentralized Organizations

Organizations come in two shapes, and they pull against each other.

A centralized organization concentrates decision-making in a central system and rolls out a consistent version to every unit. Most traditional software companies run this way: features are built and tested centrally, then released to every customer in one version. Quality stays high because every customer gets the same product, but velocity caps at whatever the central team can ship.

A decentralized organization gives each unit the autonomy to operate on its own. Uber did this in its early years, putting every market under its own GM, free to make local calls. The company added markets almost as quickly as it could hire, but consistency suffered: every market built a different product, and integration became the new bottleneck. Both shapes ship software; the difference is where the autonomy lives.

Identity, values, and mission keep parallel units in sync across domains. Each domain’s principles, patterns, processes, and examples keep that domain consistent within itself. The deeper the foundation and the richer each domain’s stack, the further the parallelism can stretch before the seams start to show.

The same shape applies to working with AI. Reviewing every line of agent output is the centralized model: high quality, low autonomy, hard cap on velocity. Running multiple agents against a shared spec is the decentralized model, and it only works if the foundation is in the spec.

When We Are Not in the Room

Culture is not the slogan on the wall. It is what people actually do when values collide: speed against quality, revenue against trust, short-term against long-term. AgentsMD does the same job for a workforce of agents.

All of this now lives in the AgentsMD file at the top of every repository I run. The root file imports a per-agent role file, a company file holding identity, values, and mission, and one file per domain:

@ROLE.md
@agents/COMPANY.md
@agents/PRODUCT.md
@agents/ENGINEERING.md
@agents/DESIGN.md
@agents/OPERATIONS.md
@agents/FACTORY.md
@agents/DECISIONS.md

The structure is written before the agents touch the codebase. Each parallel agent inherits identity and values from the foundation and works within the principles, patterns, processes, and examples for its domain.

The artifact teaches the agents how to behave when the literal instruction runs out, which is often, because no spec covers everything. Without that anchor, an agent falls back on its training distribution instead of how the company actually behaves. With it, the foundation keeps the next decision consistent with every decision before it. Patches at the top never reach the layers below; the only way to change behavior at scale is to change the foundation.

The 24/7 coding loop was the first real test. Several agents worked in parallel against the AgentsMD, with no human in any individual decision. The output composed back together cleanly because the foundation was in the spec, not in the review.

Living specs grow only by addition. We kept a Decision Log with seventy-nine timestamped decisions and rationale, never deletions. When a behavior looked odd six weeks later, we could trace it back to a specific decision and see why it existed.

The Frameworks Come With You

Vibe coding hits the same wall first-time managers do: without a foundation, the work cannot scale past a single person. The fix in both cases is to write identity, values, and mission first, then the principles, patterns, processes, and examples for each domain, and then let the team or the agents run.

Agents need a culture too. The AgentsMD gives them one, shared across every domain just as the deck was shared across every team.

The tools that scale my team now scale my agents.

The painful lesson was that invisible monthly waste compounded over six to twelve months into a very large sunk cost.

In 2026 we started rationalizing spend on tokens and saw the writing on the wall. Software had just walked through the door marketing opened a decade earlier.

The Squishy Era

For decades the per-feature cost of software was buried inside salary, so the value question never got asked cleanly. A fully-loaded engineer-quarter ran in the tens of thousands of dollars, but no roadmap document ever put that number next to an individual feature.

Killing a feature after three months was emotionally and politically expensive because it was an asset, not a line item. Someone had spent a quarter of their life on it, and admitting the work had not paid back meant admitting the team had bet wrong. The conversation rarely happened.

Marketing sat in exactly the same state before attribution. Big agency retainers, campaign vibes, and no data to cut against.

The Factory Gives Software a Marginal Cost

The software factory gives every feature a marginal cost.

Before the factory, a senior engineer ran $400,000 a year as a single undifferentiated line item. Now, every feature has its own price tag:

Shockingly, I have been writing over a million lines of code in the past few weeks. When the cost of each line collapses to almost nothing, the key question I keep asking myself is how much is it worth. For roughly $25,000 you can now build something feature-equivalent to HubSpot. That number would have been laughable for most of the last decade; today it is the going rate.

The operator instinct flips from asking what the team should work on this quarter to asking what this feature will return and how fast. A decade ago in marketing, attribution broke retainer-sized spend into line items. Each campaign acquired its own CAC and payback, and squishy agencies lost pricing power overnight.

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CAC, LTV, Payback Per Feature

Once a feature has a dollar tag, it behaves like a marketing campaign, with a build cost, an expected return, and a payback period.

The build cost is the CAC analogue, running $25,000 for a full module and less for smaller features. The expected return is the LTV analogue: a retention lift on a $50,000 account, multiplied across a cohort.

The payback period is sharp enough to decide against. Features that hit their payback stay, and features that miss get patched, repositioned, or sunset. Winners compound their lifetime value across every customer that touches them. Losers return zero.

Are you measuring value on features the way you measure it on ads? If not, you are running the squishy software factory and do not know it yet.

Hire an Engineer or Raise the Token Budget?

The marginal unit of capacity is now a choice between a headcount and a token budget.

At early stage the old model does not pencil. A pre-seed company has no engineering-team budget, making the factory the only path. At scale, build cost stays flat while LTV scales with customer count, so the token-budget option dominates in both directions.

On the marketing side we learned to ask which dollar works. Asking it feature by feature produces the same step-function efficiency gain.

## Portfolio Manager Becomes the Primary Role

Product managers still own the roadmap, but portfolio management is now the primary frame. Historically, only Fortune-500 PMs needed this discipline. The factory compresses the career arc and drops it on a Series A PM instead.

The old primary job was specifying the next feature in detail. The new primary job is sizing the bets, measuring the returns, and deciding what stays and what gets cut. The skill set resembles a media buyer or growth lead more than a traditional PM, plus mathematical rigor and a willingness to sunset work that did not earn its place.

The operational artifact is a financial spreadsheet where every feature has a build cost, an expected return, and a decision to keep, patch, or sunset

The cadence changes too. Roadmap reviews used to happen once a quarter. Now they happen every week, and each recently shipped feature gets checked against its build-cost budget.

Marketing made this move a decade ago. Attribution turned CAC from a vibe into a number, and the early adopters ran circles around the agencies still charging flat retainers. The factory just did the same for software. Every feature now has a price. The companies that treat their product as a portfolio will pull ahead of the ones that do not.

Software has a CAC now.

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I recently built a 12-module productivity suite in less than 7 days as a single operator. It was 600,000 lines of code, which a decade ago would have required a well-run software company with 20 engineers and a three-year roadmap.

The interesting question is not how that became possible (more on that later), but what it does to the economics of the middle of the software stack.

Software is following the same arc hardware did.

The Curve That Reshaped Hardware

For roughly 30 years, hardware economics followed a stable pattern. Value concentrated at the edges while the middle did the necessary work with far less margin.

Manufacturing economists described this as the SMILE curve, a U-shaped distribution of value across the chain. On the left edge sat R&D and chip design, where companies such as Intel and Qualcomm captured roughly 55-60% gross margins because they owned technical layers that were hard to reproduce. On the right edge sat distribution, brand, and customer ownership, where Apple earned comparable margins by controlling the buying experience, premium positioning, and the direct relationship with the customer.

In the middle sat assembly. Dell, HP, Lenovo, and Foxconn turned components into finished products and moved enormous volume, but they generally operated at 15-20% margins.

That pattern persisted for three decades because the edges owned scarce assets. On one side, deep technical IP and ecosystem control. On the other, demand capture and customer trust.

The Same Curve is Emerging in Software

The stack looks different, but the economics are starting to rhyme with hardware: scarce capability on the left, customer control on the right, and a broad middle that is becoming easier to reproduce every quarter. There are five positions in the software value chain, and they do not all compound equally.

The far left is Chips and Compute. Nvidia, TSMC, and AMD design and manufacture the hardware that modern AI runs on, and Nvidia in particular has built a moat that is not just silicon but the CUDA ecosystem wrapped around it. Every new developer building on CUDA deepens the ecosystem and extends the moat. This is the Intel position of the AI era, technically deep and hard to replicate on a 12- to 24-month horizon.

Next is Frontier Models. OpenAI, Anthropic, and Google DeepMind train the foundation models that sit on top of the hardware. Their moats come from training data, research talent, compute scale, and years of iteration. This is distinct from chips: Nvidia does not build frontier models, and frontier labs do not design GPUs. A company with access to unique operational data across billions of interactions has something meaningfully different from one wrapping a generic model around a common interface.

In the center sits Software Manufacturing. This includes SaaS companies building features, CRUD applications, and standard workflows: CRM, project management, helpdesk, internal tools, and back-office systems. SaaS companies sitting here are the Dell and HP of this era, and AI is collapsing the cost of that position toward commodity.

On the right sits Distribution, which includes app stores, marketplaces, channels, partner ecosystems, brand, and any privileged path to customer acquisition. As software becomes easier to build and feature sets converge, the product with the better route to market wins. Buyers in a commodity market choose the name they recognize, the vendor they believe will still exist in 24 months, and the product they feel safe explaining to their procurement team.

At the far right are Customer Relationships. This is where Salesforce and embedded workflow vendors have historically been strongest: deep workflow ownership, retention, switching costs, and customer success. When anyone can build a CRM, the relationship is the product. Workflow depth creates context, context creates embeddedness, and embeddedness creates retention that turns a product into part of the operating system of the customer’s business. This is the Apple position in software, and it is the second edge that can sustain margins near 55-60% even as the middle gets cheaper.

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Why the Middle Gets Squeezed First

The middle of the software stack is not disappearing. It is becoming abundant, and abundance changes pricing power long before it removes demand.

A horizontal SaaS product used to justify meaningful valuation premiums by shipping faster than incumbents and wrapping a cleaner UI around familiar workflows. That was defensible when standing up a polished product required a team of 8 to 20 engineers, months of QA, and a material implementation burden. It is much less defensible when AI-assisted development compresses the time to a functional v1 from 6 months to 2 weeks and turns common product patterns into templates. Once the cost of reproducing the middle falls, the middle prices like assembly, and margin migrates toward whatever remains scarce.

That is why so many categories already feel eerily similar, converging on overlapping primitives: records, permissions, workflows, dashboards, notifications, integrations, and AI summaries. The wrapper changes. The economic position does not.

Where to Invest When Building is Free

If the middle is compressing, investment should move to the edges.

The left edge is proprietary technology that compounds with use. The leader’s data, models, and ecosystem deepen faster than any new entrant can assemble their own. The test is simple: can someone replicate the core value in a week? If yes, it is assembly. If no, it is an edge position.

The right edge is customer depth, treated as a primary investment rather than an afterthought once the feature set is complete. As convergence continues, the scarcity shifts from what the product does to how it reaches the buyer and how deeply it embeds in the buyer’s workflow.

The companies that win the next cycle will not be the ones that shipped the most features. They will be the ones that built the deepest positions at the edges while the middle was repricing.

The SMILE curve has come for software.

In 2021, we witnessed a bubble driven by irrational exuberance in technology spending, which inevitably burst. In late 2022, Brad Gerstner from Altimeter Capital wrote a letter to Mark Zuckerberg titled “Time to Get Fit,” urging businesses to optimize their operations. Facebook took heed and declared 2023 as the “Year of Efficiency.”

In this blog post, we will explore the concept of operating leverage and its significance. We will also examine Amazon's journey as a case study to understand the importance of revenue per employee and gross profit per employee.

What is Operating Leverage and Why Does it Matter?

Operating leverage is a measure of company productivity that examines how much operating profit is generated for each incremental sale. For instance, if a company can increase sales without significantly raising costs, it demonstrates high operating leverage. This means the business is more efficient, and its profits grow at a faster rate than its expenses.

For example, a software company with high operating leverage can generate substantial profits from each additional sale due to the low variable costs associated with digital products. In contrast, a manufacturing company with lower operating leverage may need to invest heavily in equipment and labor, resulting in smaller profit margins for each additional sale.

Revenue Per Employee: A Proxy for Operating Leverage

A straightforward way to understand operating leverage is by examining revenue per employee. Amazon, for instance, saw a tremendous increase in revenue per employee from $50K to $1M over its first fifteen years. Ruthless prioritization and technological innovation enabled them to grow revenue while maintaining a flat headcount from 1999 to 2003.

However, over the last decade, Amazon's revenue per employee declined 70% sharply from over $1M to just under $300K per employee. The decline in efficiency represents the difficulty in growing revenue at scale.

Gross Profit per Employee: A More Accurate Metric

While revenue per employee is a great measure of efficiency for zero marginal cost software businesses, it doesn’t always provide the complete picture for every business. It's important to note that Amazon's revenue is more accurately described as GMV (Gross Merchandise Volume), which includes the cost of the product. In addition to this, Amazon has increased its gross profit margin from 23% to 44% over the years. By using the company's gross profit instead of revenue, we obtain a more normalized metric that accounts for variation in business models.

For example, although Amazon's revenue per employee has decreased over time, their gross profit per employee has remained more stable during the same period. This indicates that the company's overall financial health may not be as concerning because Amazon has been adding more profitable revenue with scale (e.g. AWS).

How Can Companies Create Operating Leverage? 

Operating leverage can be created by increasing productivity. Some of the ways it can be achieved is through employee training, task standardization, project prioritization, workflow optimization, and technology automation.

For example, one approach is to invest in automation, which allows repetitive tasks to be handled by software solutions, freeing up employees for more strategic work. Optimization, such as streamlining work processes, can also help reduce bottlenecks and increase the speed of task completion, further enhancing productivity. 

In the case of Amazon, a significant contributor to their operating leverage is their ability to generate ancillary revenue through services like Amazon Web Services (AWS). By diversifying their revenue streams and tapping into high-margin businesses, Amazon offset potential declines in other areas and boosted their overall operating leverage and financial performance.

Breaking Even: Gross Profit Per Employee vs. Average Operating Costs Per Employee

A company reaches its break-even point when gross profit per employee equals the average operating costs per employee. For example, if a company has a gross profit per employee of $120,000 and average operating costs per employee of $120,000, it is at the break-even point. This approach is particularly useful for businesses with indirect variable costs and overhead. It also creates a clear milestone that a company can use to orient itself towards profitability.

See below for a list of publicly traded companies, their gross profit per employee, and their operating costs per employee.

The Importance of High Operating Leverage

Aiming for higher operating leverage is essential as it enables higher wages for employees, creates value for shareholders, and provides a margin of error to absorb business risk. Conversely, when companies have low operating leverage, it creates an unfavorable situation for all stakeholders involved.

In conclusion, operating leverage is a vital component of business efficiency, and understanding it is key to navigating the ever-evolving economic landscape. By focusing on increasing operating leverage, businesses can ensure sustainable growth and create value for all stakeholders.

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Appendix — Reference Data

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When the app was at the top of the app store, I did not believe that it was possible to succeed much further. I did not believe it would be possible to support a team of small developers based on revenue projections. This resulted in me moving to another venture and not capitalizing on the full opportunity. Despite this, we reached over 800m+ games played as a passive pursuit and conceded our category position.

My beliefs influenced my actions, and these actions led to success. My beliefs also restricted actions, and inaction led to missed opportunities. 

Beliefs can protect us from failure

Beliefs provide the basic guardrails for everything that we do. If we believe that we can’t achieve something, we will stop before we start. In many cases, this is rational and protects us from failure as a defense mechanism.

For many leaders, it’s common to be capable of success and have irrational beliefs that hold them back. It can be essential to have a coach who has gone through similar struggles, can provide encouragement, and help unlock latent potential that is not being realized. 

Similar to having a personal trainer at the gym, the coach encourages the leader to keep going when they’re about to give up, adjusting form and helping to achieve what they didn’t believe was possible. 

Rational positive beliefs lead to success

As Andy Grove puts it in his book, High Output Management, success occurs when both motivation and ability are present. Motivation occurs when someone believes they can achieve something meaningful, and the belief is rational when they have the ability to do so. 

If someone believes that they can achieve something meaningful, they will take the step forward to get there. And if the steps get them closer to the goal, their beliefs can get stronger and create more momentum. This feedback loop is powerful and can snowball into achieving incredible feats. 

Irrational positive beliefs need to be calibrated over time

The realities of the past may not be the realities of the future. This could be a gradual change that happens slowly over the years, or a sudden change that happens overnight. These events require our beliefs to be recalibrated. This can be painful and cause frustration since it may require re-learning things that worked well in the past. 

In my experience, recalibration requires people to go through the emotion of grief to break through (see Kübler-Ross Model above). The faster leaders go through this cycle, the faster they’ll be able to lead their teams effectively.

Conclusion

Our beliefs form the basis of everything. To maximize success, it is important for beliefs to stay calibrated with reality. This means understanding what beliefs are rational and irrational, staying grounded when things change, and internalizing change quickly.

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• The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company

• The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness

• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Strategy

• Working Backwards: Insights, Stories, and Secrets from Inside Amazon

• Thinking in Systems: A Primer

• Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts

• The Pyramid Principle: Logic in Writing and Thinking

Markets

• Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages

• Crashes and Crises: Lessons from a History of Financial Disasters

• The Holloway Guide to Raising Venture Capital: The Comprehensive Fundraising Handbook for Startup Founders

New Startups

• Start with Why: How Great Leaders Inspire Everyone to Take Action

• Zero to One: Notes on Startups, or How to Build the Future

• The Myths of Innovation

• SPIN Selling

Scaling Startups

• Scaling Up: How a Few Companies Make It...and Why the Rest Don't (Rockefeller Habits 2.0 Revised Edition)

• What Got You Here Won't Get You There: How Successful People Become Even More Successful

• Lean Analytics: Use Data to Build a Better Startup Faster

• Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue (The Savvy Manager's Guides)

Managing People

• The 15 Commitments of Conscious Leadership: A New Paradigm for Sustainable Success

• Who

• Drive: The Surprising Truth About What Motivates Us

• Mindset: The New Psychology of Success

Personal Growth

• Atomic Habits An Easy & Proven Way to Build Good Habits & Break Bad Ones

• High Output Management

• Algorithms to Live By: The Computer Science of Human Decisions

• 15 Secrets Successful People Know About Time Management

Psychology

• Don't Shoot the Dog! : The New Art of Teaching and Training

• Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

• The Design Of Everyday Things

• How We Decide

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As a company scales, the organization requires more specialization to succeed. More often than not, this requires the introduction of nuanced processes to help manage the complexity. 

When a startup is small, it can be sufficient to run a fairly simple objectives-and-key-results (OKRs) process to keep everyone aligned and moving in the right direction. However, over time, this process may not scale well across all of an organization’s roles especially for business and operationally-focused roles. It becomes essential to find balance between managing the current business while simultaneously innovating and building out a product.

Organizational Goal Setting Frameworks

There are three key goal setting frameworks that are needed to run a business for scale: the financial plan, objectives and key results (OKRs), and business scorecards. 

The financial plan helps a company orient itself around a budget and helps manage cash flow, OKRs help align key initiatives with the company’s overall strategy, and business scorecards enable organizations to understand its performance against specific operating goals. 

Financial Results

A financial plan is a set of documents that outlines a company’s financial goals and objectives. It includes a budget that accurately reflects the organization’s financial situation, as well as projections of income and expenses over the coming years. Generally, the budget should be a rollup of planned business outcomes and expected efficiency improvements from new initiatives.

It is important to create a regular process of reviewing the budget, variances, and forecasts each month to keep everyone aligned, enable conversation amongst the management team, and uncover insights around assumptions and variances to plan. Ultimately, this leads to a business feedback loop that enables the company to grow the business more effectively over time.

Objectives and Key Results (OKRs)

OKRs are a popular goal-setting framework that enables an organization to focus on the key priorities that matter. They are made up of two components: objectives, which are overarching goals that the organization is trying to achieve, and key results, which are measurable outcomes that can be used to track progress towards the objectives. OKRs are often themed by quarter to emphasize a specific area of need and to align a company on a shared direction. 

As a company scales, OKRs inevitably become at odds with business results and the OKR system becomes bloated. It becomes hard to focus on the priorities that matter most while ensuring that every area of the business is running as expected. When this conflict occurs, it is important to decouple OKRs from comprehensive business results and introduce a separate, business scorecard process. 

Business Scorecards

Business scorecards are mutually-exclusive, collectively exhaustive list of business outcomes that ensure everything is running as expected (e.g. a step-by-step conversion funnel). These are often broken down by a mathematical formula, such as sales = # new leads * call schedule rate * call close rate. Often scorecards contain leading indicators and can be different for each team based on their scope of responsibilities.

This part of the business operates much like a sports team where the focus is about playing the game well and getting better over time with effortful practice (e.g. 20 signed customers to 40 signed customers in a given time period). While OKRs can change from quarter-to-quarter to emphasize shifting priorities, the items in the business scorecard stay consistent over time with higher targets. 

These goals are measured weekly and often have status indicators to determine whether the team is on track to hit the goals. At the end of the period (e.g. quarter), everyone looks at the results together to see the score. 

Over multiple quarters, OKRs that start out as business scorecards can lose their functional representation and lead to proxy metrics that are not tied directly to business outcomes.

Closing

OKRs will never be enough. The financial plan helps ensure that a company is adequately budgeting for growth and managing cash flow, OKRs help align key initiatives with the company’s overall strategy, and business scorecards enable organizations to understand their performance against a comprehensive set of business indicators. All three of these goal setting frameworks must be in place for a business to scale.

My bootstrapped mobile gaming company achieved success, reaching $3K+/day and at times being the number one or two mobile word game on the iPad. Despite the opportunity, I was hesitant to double down and jump in the game of venture capital.

So, I co-founded a performance-marketing company with a veteran from the previous technology cycle. We scaled multiple business lines from zero to millions of revenue as a bootstrapped company. During this process, I learned the importance of capital efficiency and how to run a profitable technology business.

Now, as a venture-backed founder, I have observed that few people understand these nuances and the significant role they play in running a business for the next economic cycle.

Profitable Growth

In simple terms, positive unit economics means that a company is making more revenue from a customer than the costs that are being incurred to acquire and service the customer. 

If a company spends $20 to acquire a customer that generates $50 in revenue, and they have $10 in variable costs, then the contribution profit would be $20 (i.e. revenue less variable costs less customer acquisition costs). Adding back in the customer acquisition costs, the company would have a lifetime value (LTV) of $40 and an LTV/CAC ratio of 2.0. This means that they are able to make $2 for every $1 spent on acquiring a customer. 

Generally, if they can generate $40 in LTV for every $20 that they spend, it is optimal to spend as much money as possible. However, the amount of money that they can spend is directly influenced by the payback period. 

Capital Efficiency

The payback period, or capital efficiency, of a business is based on the amount of time it takes to get back $1.00 of capital for every $1.00 of spend.

When the unit economics and payback period are healthy, the business can spend $20 to acquire a customer, generate $40 in LTV, and recycle the profit back into growth. This creates a perpetual feedback loop that allows the business to grow profitably over time without any external capital. 

Businesses that have figured out the profitable-growth feedback loop are able to raise capital and grow faster.

Profitable Growth

Growth capital is the amount of money that a startup allocates to acquiring new customers. The amount of capital needed is directly related to the customer-acquisition costs (CAC) and the payback period. 

Illustratively, a startup with a 3-month payback period can grow 8X faster on a monthly basis than a startup that has a 24-month payback period with the same amount of growth capital. 

In a capitally-inefficient business, the payback period can be too long and growth becomes dependent on being able to raise external capital. An example of this could be a business that spends $1,000 to acquire a customer today and generates $5,000 uniformly over the next 10 years.

Growth-At-All Costs

In the current market climate, the nuance between growth rate and payback period becomes more important to understand. Fast-growing startups consume cash, and the faster they grow, the more cash they consume. This problem only gets larger with scale. 

If a startup has an extended payback period (e.g. 24 months), it can consume more capital than the market is willing to support. For example, a startup that is growing 3X YoY with a 24-month payback will require 16X more growth capital (i.e. 4x in the first year, and 4x in the second year) to continue its trajectory over the next two years 😬.

When calculating payback period, it is important to include all variable costs, such as direct variable costs (e.g. COGs) and indirect variable costs (e.g. centralized operating costs). If indirect variable costs are not included, the payback period and capital requirements could be much higher than expected.

Irrational Exuberance

In the era of low interest rates, startups and public valuations started to get disconnected from business fundamentals and profitability. In a sense, startups could spend $20 to generate $50 in revenue, generating $50 in revenue created $500 in equity value, and they could sell 20% of their equity for $100. 

Since this is what the market rewarded, startups shifted more and more of their ad spend to unprofitable campaigns because it increased top line revenue. While this view is myopic across market cycles, it was the most optimal strategy for short-term value creation for shareholders. 

This forced other disciplined startups to loosen their ad spend to be competitive in the venture-market industry and created an era of capital-inefficient businesses. With time, this will re-adjust back to historical norms and the process will be painful.

Closing

At the end of the day, capital efficiency and profitability are the most important metrics for running a successful technology business. If a startup can generate positive unit economics with a short payback period, it can generate perpetual growth. If it cannot, it will have to rely on external capital to grow and endure the risk of running out of cash.

Revenue is easy, profit is harder. 

We created the Scale10X program at Doorstead to help our executive team be more effective leaders for scale. In our first session, I had the pleasure of sitting down with Court Lorenzini to talk about operational leverage and growing a business 10X. 

Introducing Court Lorenzini

Court is the founder and CEO of multiple successful technology startups, including DocuSign and Point.com. He serves on the boards of several early-stage companies, and is an active investor and advisor to Doorstead. 

I met Court over a decade ago during one of my previous startups and he has been credited with always giving me the tough advice that I need to hear. When I was leaving Uber, most people told me “good luck,” but he told me to “think about all the reasons my new business won’t work and to systematically disprove each one.” 

His contrarian and straight-to-the-point coaching has been instrumental over the years. So, you can imagine my excitement when he agreed to share his lessons with my leadership team.

Court's Entrepreneurial Journey

What inspired you to become an entrepreneur? 

Court’s father was a big role model for him and a fellow entrepreneur. He was an early VC and founder of a handful of companies including SILTEC and Sun Power ($SPWR). With this exposure, he was able to learn through his father’s experience and build a repertoire of experiences that could help him and other founders. 

What was the aha moment to create DocuSign? 

Court was approached by a former colleague, Tom Gonser, who wanted to offload the assets of a failed company called DocuTouch. Court ended up acquiring the IP-DocuSign + patents. Tom’s initial idea was to create a document management system (like Adobe, Microsoft), but they quickly realized there was a gap between electronics document creation and document storage and implementation. This gap created a huge opportunity.

How do you define success for a startup? 

Court believes that milestones are key in defining success for a startup. These milestones include the first paying customer, positive signals on customer acquisition costs relative to LTV, and a path to positive unit economics. It’s also important to celebrate wins at each phase. Success gets more precise at each milestone.

What does it mean to make something that is scalable and repeatable? 

Court believes that good metrics are important. It is important to follow key metrics and demonstrate that they’re improving over time (e.g. acquisition costs). He also cited ‘Crossing the Chasm’ by Geoffrey Moore, where he suggests that it’s easy to think scale is going smoothly if you’re following revenue growth. But it’s important to have discipline during this phase of scaling and to focus on the less sexy parts, such as making the enterprise professional and improving support. It is also important to have the right mindset — reframing “less fun” activities in creative, positive ways.

You often talk about proving that an idea would not work and identifying key risks. Can you elaborate on this and why it’s important? 

Finding the fatal flaws is critical to success. How can you kill the idea? There are few original ideas that exist, so there are always predecessors who have failed. It’s important to investigate and even reach out to those who have tried and did not succeed to learn. The key challenge is to identify solutions to the fatal flaws, and it is better to do this early and often. 

Scaling 10X

We asked Court about his experience going through different company inflection points, managing his own growth with the growth of the company, and the toughest management or leadership lesson he’s learned.

What was your experience going through different company inflection points? 

Court’s experience going through different company inflection points has been that certain employment levels (10/40/100 employees) each represent a meaningful change in terms of hierarchy and process. When a team is small (less than 10 people), frequent and close communication is possible and responsibilities can be shared. As the team grows from 10-40 people, it requires more specific division of responsibilities and objectives. As the company expands from 40-100 people, day-to-day interactions start to diminish and it is essential to communicate the broader business context to everyone to ensure alignment on purpose. Finally, when the team reaches 100+ people, it is essential to communicate the "why" between leaders and team and introduce formal processes to ensure everyone is on the same page.

How did you manage your own growth with the growth of the company? 

Court recommends personal coaches (external to the org) who can help think about deliverables and career paths. He also found CEO groups to be helpful. He believes that coaching is more important (personal dev) than training (skills dev) at a leader-level.

What was the hardest management or leadership lesson that you’ve learned? 

Court believes that hiring and placing talent is one of the most difficult challenges, as it can lead to job allocation and hiring mistakes. He eventually created his own talent evaluation methodology, using it to create high-performing teams, which he considers one of his biggest successes.

What common mistakes or pitfalls have you seen companies make as they scale up that we should watch out for? 

Court cautions against trying to diversify revenue streams too quickly. He believes it’s important to solve the challenges of scaling the initial successes first, otherwise the team will quickly become overstretched. Even a relatively small variance from the initial offering (10% difference) will lead to a multiple of differences (sales model, messaging, pricing, etc.) that increase unit economics and compromise the organization. It’s important to secure beachhead customers before doing this, where unit acquisition costs are decreasing with each new customer. He also warns against spending too much on sales and marketing too fast based on early signals.

What are unit economics and capital efficiency? When and why do they matter? 

At the stage where investors are needed to fund the growth, the economic model must make sense to investors, otherwise the business doesn’t make sense and they won’t invest. Positive unit economics are critical; can the company scale at a profit, vs alternative and competitors?

Operational Leverage

What are the essential elements of a well-designed operating system? 

Court believes that it’s important to identify measurable and manageable outcomes (e.g. KPIs). He recommends thinking of everything as a business process with critical outcomes, and that this can help frame, identify, prioritize, and manage the challenges. 

How do you think of change management and navigating product-market-fit? How do you evaluate strategic investments and whether they have positive ROI? 

Court suggests creating an internal scoring system that can evaluate value creation for both “in-work” and “parking lot” initiatives. This helps with discipline, prioritization and planning. It also helps to effectively communicate to stakeholders why certain projects might be prioritized.

Why is communicating the “what” and “why” in a simple way important? 

Entrepreneurs love describing the “what” (e.g., features), but customers buy for the “why” — why does it matter to that person? What will they get that they don’t already have? How does it impact the person or organization? Simplifying the buying decisions for customers is important.

What are commons pitfalls at the scale from $10m to $100m in revenue?

Court warned of the temptation to take all of the capital and grow to lots of markets simultaneously. He believes that there is a right time for this - when unit economics are good, that’s when it’s important to lean in. He also advised against over hiring, noting that companies win on best execution - profitably, sustainably, and in a way that makes sense.

How did you balance moving fast, growing fast and maintaining the company culture?

Court believes that the most important cultural lever is to align people’s roles with what they do well. He also suggested creating a value creation scoring system to help communicate the “why” so that all employees understand their contribution and how it impacts the goals of the organization.

How have you and your portfolio of companies leveraged customer experience to fuel growth and retention? How does this change as the company moves between phases?

Court suggested that if feature development isn’t attached to the “why” of improving customer experience, then the features aren’t helpful. He also suggested having a process for customers and teammates to pitch ideas that can be assigned to the “parking lot” with rewards if the idea is implemented. 

Closing

Court’s advice from his experience as an entrepreneur and investor has been instrumental in helping us navigate our own journey. Through countless conversations, he has provided us with tools and frameworks we need to make strategic decisions. We are grateful for Court’s guidance and hope that others can learn from the lessons that he has shared with our team.

What’s often misunderstood is that technology and operations roles have very different operating paradigms, and that as these functional groups scale, it’s also important to maintain a clear business owner who bridges the gap between the two disciplines. 

Business systems maps make it easy to split the organization and ensure that the organization is focusing on business outcomes. 

Recapping Systems

In Why Leader Should Start and End With a Map, I shared why business systems maps are ideal for organizational design and how they help abstract complexity since they have clear inputs/outputs with feedback loops for self correction. 

Additionally, I shared how we used these maps to provide clarity and context and help our leaders think strategically with an example that we used during the early days of Doorstead to get everyone on the same page.

Introducing Three Organizational Roles

Sometimes, as an organization scales, the assumption is that the operations team owns the business, and in other cases, the product team. Without a single-threaded owner, it becomes true that no one owns the business. The eventual outcome of this is an organization that systematically leads to “inattention to results” (see The Five Dysfunctions of a Team). 

To avoid this, I’ve found that having an explicit role definition for each of the following three roles is key to success: the operations lead, the product lead, and the business owner. The operations lead is responsible for running the day-to-day operations of the business and making sure that all of the business outcomes are being delivered successfully. The product lead is responsible for monitoring and improving the system efficiency over time. The business owner has end-to-end ownership over the P&L and ensuring that operations and product are working well together.

In a small team, the business owner will often wear the product and/or operations hat. It can be good practice to call this out explicitly since it can help a leader subdivide their teams over time. As the organization scales, many of the specialized roles will shift to a functional organization with a dotted line reporting structure to improve craft. 

The Operations Lead

The operations lead is responsible for running the day-to-day operations of the business and delivering service outcomes to the customer. The operations lead typically works in a task management system that enables them to see what needs to get done and when it needs to get done.

The team is focused on business outcomes, such as the # of sales that were completed each day, the number of jobs or activities dispatched, or the support tickets that were resolved. These metrics are primarily tracked on an accounting basis (i.e. when the outcome occurred).

This is an execution-oriented role and often a stressful role since everything needs to be done on time, every time. As such, they’re often held accountable to the only business outcomes that are within their control.

The Product Lead

The product lead is responsible for maintaining and improving the efficiency of each technology system. It is important to call out that before product-market fit, the product management role doesn’t really exist or needs to be tightly coupled with the business owner. After product-market fit, it becomes necessary to scale the product team and the primary mode of operation for the team should be experimentation.

The key tool used to improve the business system is to look at a product cohort and identify opportunities for improvement. This cohort analysis enables the product lead to see whether a system is getting better/worse over time and understand the reasons why. 

Most often, the product lead has a comprehensive roadmap that is prioritized by business return-on-investment. This allows everyone to align and understand the cost-benefit analysis for each initiative and why it is important for the business to invest in this area. Without metrics that are tied directly to business results, it can be difficult to integrate product with the operations team and deliver results. 

Unlike their operational counterparts who operate in checklists, product leads tend to orient their teams on bi-weekly sprints. 

The Business Owner

Finally, the business owner has end-to-end ownership over the P&L and ensuring that everything in the system is flowing well together. This means that they have to balance between running the current business and improving the business. They’re held accountable to the end results of the business and how these are performing relative to plan. 

In their toolkit, they should have a business process or systems map that outline all of the key processes and handoffs within their domain. This allows everyone from operations and product to speak the same language and communicate with each other in clear, concrete terms. Without this map, ambiguity often prevails and it often leads to “artificial harmony.”

The business owner will focus on the business inputs (e.g. ad spend), outputs (e.g. closed sales), efficiency (e.g. cost-per-acquisition), and quality (e.g. terminations). They use weekly checkins with their team to make sure everything on track and set up meetings to dive into any variances that are unexpected from plan. This will often involve reconciling the difference between both accounting-based metrics and cohort-based metrics. 

Conclusion

In order to manage a growing organization efficiently, it is important to be aware of the three organizational roles for scaling a business. At every level of the organization, there should be one person is directly responsible, and accountable for each of the roles. By cascading the organization around business maps, it makes it easier to ensure that all your teams are operating together to the same goal.

Successful leaders start and end with a map. 

What is a Map

A map is a diagram or visual representation that allows a group of people to build a shared understanding and communicate in concrete terms. Maps can be used to help visualize organizational structures, processes, and other abstract concepts, enabling everyone to understand the same thing, regardless of their background or experience.

Maps have multiple zoom levels, allowing for more detail to be revealed as a person zooms in. At a macro level, they can provide an overview of the entire organization. At a micro level, they can provide detailed information about an individual process or project.

Implicit Leadership

Many leaders do not take the time to create maps for their teams. This results in context being implicitly stored in their heads and transmitted only through conversations. This can be problematic when the team expands, as context can be lost when the leader is not present in discussions and conversations can become siloed.

Without a holistic map, leaders are often stuck playing a game of whack-a-mole. Unintended consequences can arise from trying to fix one issue, only to create another, resulting in an escalation within a different part of the organization (e.g. getting more customer leads with a lower overall sales conversion rate).

Sometimes, this can lead to stress and burnout for leaders since they are unable to keep everyone on the same page in a scalable way. It also becomes difficult for them to take a break because all of the context is in their heads.

Business Systems Maps 

There are many types of maps that can be used to align and orient a team, but the systems map is ideal for designing an organization. This type of map allows for abstraction of complexity, since a system can be composed of several intricate systems. This abstraction makes it simpler to align and make decisions, as too many details can often be a source of distraction and confusion for teams.

A map gives the leader a framework for communication, helping to ensure everyone is on the same page while reducing overhead. The map can enable the leader to think strategically about the relationships between different parts of the organization, and also identify bottlenecks easily. 

Examples from Early Doorstead

Doorstead’s Satellite View

At Doorstead, we initially broke down our business into three key components: Growth, Fulfillment, and Servicing. Growth was responsible for the first contact with a property all the way through to contract; Fulfillment managed the process for getting a property rent ready and finding a tenant; and Servicing oversaw all activities once we had a signed owner and tenant. This process-oriented approach allowed us to build a scalable operating system with clear interfaces and helped us grow from one to thousands of customers.

Doorstead’s Team View

As our system became more complex, we divided it into subsystems. More concretely, we split the Growth team into the Demand Generation and Sales team. This structure created clear handoffs and guardrails while also maintaining accountability. To maximize capital efficiency, we ran our Demand Generation team as a business unit, which was led by a marketing expert who was responsible for the end-to-end results.

Doorstead’s Street View

Regardless of whether Growth was one person, a team, or a team of teams, we have always believed in having a detailed map. This map contains our step-by-step conversion funnel and is separated by each customer action. It has helped us to understand the bottlenecks in our growth funnel and debug when one of the steps is not working well.

Uncharted Territory

Before finding product-market fit, it can be counter-productive to create too many maps and keep them up-to-date. Personally, I recommended having one map to help organize your intuition and systematically identify what is working and what is not working. In some cases, the map can be used to guide the experimentation process. In other cases, the map may help determine that success is unlikely and inform the decision to pivot.

Iterate with a Map

As we have scaled, we have run into situations where we have broken our flywheel and had to roll back our product launches. This often occurs when we have forgotten to document the current system, the proposed system, and the success metrics. After launching an initiative, it is important to review the success metrics and confirm that the new approach is, in fact, better with data.

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Closing

The importance of having a map cannot be overstated. Whether you're starting a business, growing an organization, or looking to improve a process, having a map can be the difference between success and failure. A map provides clarity and context, helps you think strategically, and most importantly, it helps ensure that everyone is on the same page.

This is why I believe the best organizational leaders start and end with a map. 

Background

One of the unique insights I bring to entrepreneurship is my cognitive science and psychology background. This has helped me in more ways than one and has fundamentally changed how I think about company names.

In my undergraduate at Berkeley, I helped run experiments at the Computational Cognitive Science Lab to understand how people interpret and react to certain words. I learned that the powerful implicit associations that people have with the name of a company can help shape how customers interact with and spread awareness of a product.

This is so powerful that it can influence (a) ad click through rates, (b) cognitive consonance, and (c) consequently, purchase behavior from customers. This is obvious to some degree, however, it’s an extra layer of complexity so most people ignore it outright or they focus so much time on it that it becomes impractical.

Early Days of a Startup

Startups are hard. There are tens of thousands of small decisions that have to be made and the right decisions with the right choice will ultimately lead to success. One of the highest leverage decisions is the startups name, and if you get this wrong, it could make everything an uphill battle. 

This is particularly hard when picking a name since you’re typically not connected with customers and it’s not something that you can easily A/B test. Also, what makes a good name anyways? 

Giving Your Company The Best Shot

In the previous decade, I conducted research on brand names and developed a generative AI algorithm to assist me in identifying the most suitable domain names from a list of expiring domains. This algorithm proved to be incredibly effective, as it enabled me to find excellent domain names for only $69.

After creating a short-list of names using this approach, my co-founder and I wanted to be sure that the brand would resonate with customers. We ran into an issue where we were unable to get consensus and we didn’t have a strong inclination to go in one direction versus another. 

Given my cognitive science background, I had an intuition that a survey could work and would show statistically different results for various brand names. So, we decided to run a cognitive science experiment similar to my college days using Mechanical Turk. 

We constructed a framework combining quantitative and qualitative data to measure sentiment towards our brand, and chose to target a broad audience given that anyone can own a rental property.

Quantitatively, we identified and aligned on the top five brand attributes from quick research on Google:

• Durable: On a scale of 1-5, how durable is "Company”?

• Playful: On a scale of 1-5, how playful is "Company”?

• Professional: On a scale of 1-5, how professional is "Company”?

• Reliable: On a scale of 1-5, how reliable is "Company”?

• Trustworthy: On a scale of 1-5, how trustworthy is "Company”?

Qualitatively, we came up with three qualitative factors that could be assessed via improvisation:

• Product: What type of product does company “Company” provide to customers?

• Pronunciation: How might you pronounce “Company”? 

• Definition: What is the meaning behind “Company”? 

In total, the experiment cost us less than $100 and it was one of the foundational decisions that we’ve made. It helped set us up on the right trajectory, and also helped us establish a framework for decision making at our company. 

Implicit Association Results

Quantitative Results

To our surprise, the results were statistically significant, consistent across multiple trials, and in line with our intuition. 

We discovered that category leader in the space was perceived as being less reliable and more playful. As much as we’d love to have a playful brand, we felt that being “playful” wasn’t in the responsibility list of a property manager. 

We found that Doorstead had the highest resonance on the survey amongst our shortlist and all of the brands tested.

Qualitative Results

In addition to quantitative data, it turned out that customers do have very strong intuition for what service a company might provide. And in some cases, the association is probably not what you want. 

We learned that the audience perceived the category leader to be providing a service related to the "brain" and "mind". We did not expect these associations to be effective for a property management service, thus validating our opportunity as they would have difficulty expanding through word of mouth due to the cognitive dissonance.

We discovered that our code name, "Landstake," which we had used to organize our ideas around the company, was not the correct choice. It reminded people of a service that aided individuals in finding land and investing in fractions. As it would be difficult to redefine this term, we decided to move away from it and choose a new name.

Finally, Doorstead had a strong consensus with the terms "doors" and "security." Real-estate investors often consider the number of properties they own as the "number of doors," and Doorstead provides protection to property owners by guaranteeing a portion of their rental income. We felt that these associations could be beneficial in the long run.

Final Thoughts

Looking back, our brand name has been instrumental in amplifying all our efforts and enabling our exponential growth. Of the thousands of decisions entrepreneurs have to make, choosing an effective name is one of the most critical.

Your company name is an ad creative that is everywhere, all at once, and just like a good ad creative, it can lead to a significant conversion boost on every action your customer takes.