A few prompts and it will produce:

• clear narrative

• structured logic

• smart suggestions

• persuasive language

• even “expert” positioning

While really powerful, the idea AI will take over for everything human is deeply flawed.

AI can’t “stand and deliver”. Well, not until we get Elon’s robot world, and I’m going to strongly argue not even then. It’s not just physically standing or delivering. Humans require human interaction. Once we begin to understand this part of human nature, we can start to visualize where AI’s place in our future world will be… and what it specifically won’t be.

Today’s AI can’t walk into the room with you. It can’t read the room. The tension. The power dynamics. Humans do this in real-time very well. Our DNA is embedded with flight or fight responses that trigger actual emotions. AI doesn’t have emotion. It’s simply generating text on a screen for you.

AI is woefully incomplete in the human influence department. It doesn’t have and will likely never have an “emotional LLM” or “cultural LLM”. Think critically now if you find yourself reactively arguing with me while you read. Ask yourself, where will the LLM “get that data”? Who will code it? Even if some AI tried, do you think THAT version of the “emotions” or the “culture” will be accurate and accepted?

For me, I’ve come to the conclusion this future AI will never be better than humans emotionally or culturally. I’m also convinced I won’t be able to use it to win an argument with my wife! I’ll put that human up against any other human or machine!

Who will train it to slow down at the precise moment? Or to use silence as a strategic influence pause? Maybe AI can be programmed as a video avatar with a screen sitting at a chair and attempt to be better than the humans in the room. But I’ll take the other side of that bet.

My thesis: Your human delivery is required.

The “LLM Illusion”

What we think AI gives us:

“AI can give me the best preparation, the best plans, and if I have that, the outcome will be great.”

Sorry, but again, it’s time to think critically. The human game of life is played on the field, in the heat of the battle, in the moment… not in the “film room”(AI is awesome in the film room!)

Currently, AI mostly only produces words on a page on a screen near you. AI is therefore only potential energy. Your leadership and human delivery is required to deliver the proper kinetic “influence energy”.

AI can help you write the negotiation. You will have to stand and deliver that negotiation.

AI can draft the hard feedback follow up email… but I would strongly recommend against attempting AI delivering that feedback to your direct report.

You still have to be the human in the room delivering the hard feedback with all the required clarity, humanity, and control.

AI can outline the board narrative and even help you create the next board deck.

But it can’t “Lead Your Board”. You still have to hold the room when the questions start rapid firing and the emotions start running hot.

Mind the Gap! The Gap Between Knowing and Doing

Most executive outcomes aren’t decided by only who had the best analysis and thinking.

Real-time execution and performance under pressure when “AI plans hitting the real world” or “shit-hitting-fan decisions” will always be required by a human… in my thesis world.

Leadership and company success will continue to be decided by who performs the best under pressure by:

• Communicating clear thinking

• Communicating with the right timing

• Adapting to live feedback in the moment

• Aligning the room to a shared desired outcome

• Leading the room through and out of the next rabbit hole

This is your job as a leader. AI can’t deliver executive presence or gravitas.

EXAMPLE:

You can absolutely use AI to learn world-class tactics such as being a world class negotiator. At least it can teach it to you on paper… not much different than your undergrad, grad, or masters programs.

When I prompt an LLM to teach me to be a world class negotiator, AI will generate:

• Anchoring strategies

• BATNA framing (best alternative to negotiated agreement)

• Concession planning

• “If/then” trade packages

• Phrasing/scripting for push back

• Objection handling

Great scripts! Thank you AI.

But you are the actor who has to perform the script. Remember, “All the World’s a Stage” and “One Man Leader in their time plays many parts… their acts being seven stages” (ok I butchered Shakespeare to make my point).

“A poor player who struts and frets his hour upon the stage and then is heard no more. It’s a tale told by an idiot… full of sound and fury… signifying nothing.”

In our real world, our scripts break at the sound of the first whistle on the real-life playing world.

Game On!

What matters in the required moment of leadership delivery determines whether you will be a great player (or that shakespearean poor player) since your humanity can deliver what AI can’t:

• the pause

• the pace

• the tone

• the micro-conversation adjustments

• the moment you decide to stop talking

• the moment you decide to ask a question instead of making a point

• the moment you realize the real decision isn’t the one being discussed

• swimming the conversation back to your Islands of Safety

That’s why I titled this post: Your Delivery Is Required. You, the human need to “Stand and Deliver.”

The Room Is a Live System

Meetings aren’t AI documents. They’re living systems.

Every room has:

• hierarchy

• fear

• ego

• incentives

• hidden agendas

• unspoken tradeoffs

AI doesn’t feel or hasn’t been coded for any emotions. Your personal BLM (brain language model) is coded for that.

Which means your edge is not your ability to write. It’s your ability to operate, moderate, and influence. To lead others to find a solution.

To orchestra conduct the room.

Your New Job: Use AI for Drafting. Train Humans for Delivery.

So, here’s my thought model for the week.

Stop treating AI like a replacement for your communication.

• leadership presence

• delivery timing

• calm and clear communications

• room moderation

• strategic set of choices and your Point of View Persuasion

• keeping the room on track for the meetings goals

Our next technology and leadership era won’t reward the best AI prompters.

It will reward the leaders who can take the great content and deliver it under pressure.

A Simple Framework: “Draft (AI) and Deliver (Human)”

Here’s a practical way to think about AI in leadership communication:

1) Draft (AI helps a lot)

• structure

• options

• language

• scenarios

• story-lines

• rebuttals

2) Deliver (human required)

• voice

• pacing

• silence

• presence

• calibration

• empathy

• authority

• reading the room

If you want outcomes, you need both.

The Delivery Playbook

Three tools:

1) Silence is a lever

Most leaders talk themselves out of leverage. The best ones can sit in silence and let the other person fill it. AI doesn’t have a model for that reflex. You do.

Once you’ve made an impact… stop talking. The last “S” in my SECS talk framework.

2) Powerful Questions beat perfect statements

The room usually doesn’t need more words.

It needs the right questions:

• “What problem are we actually trying to solve?”

• “What would have to be true for you to say yes?”

• “What risk are you trying to avoid?”

• “What decision are we really making right now?”

Questions are how you steer and influence the room.

3) Moderation is a superpower

The leader who can moderate a room is the best influencer.

You’ve seen the masters of this, right? Yes, they are using forms of #1 and #2 above. They are constantly scanning the room for who is talking too much. Who is too silent. They are respectfully asking the domineering “voice” in the room to “hear from others”.

Then they steer the room to shared meeting goal/outcome.

Great moderators are the best room readers. They pull the room out of rabbit holes, framing, pacing, translate, and steer the conversation toward the desired shared outcome.

They use key playbooks like my “T-Chart” moderator model or the simple “post-it-notes” on the whiteboard exercise to get people out of their heads and out in the open. Then they astutely point out where the group is aligned and where there is a gap.

They then ask more powerful questions and guide to fill the gaps.

As a leader, take some reps at being a better and better moderator and you will find your leadership influence rise among your peers and your team.

The Bottom Line

AI is definitely awesome on cleaning up writing, researching facts, and creating structured thinking.

But it’s only “thinking” and writing. It’s not actively doing. Leadership is about doing. It’s about influencing. It’s about getting others to follow and take action with you.

Human talents.

Your delivery is required. AI will remain your all-star back room analyst or play caller but you have to run the plays!

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Alex Wu of CFO Advisors and the Stanford Graduate School of Business did the majority of the work here and it’s amazing. Thank you Jeff Epstein (Bessemer) for introducing us. We hit it off quickly resulting in the need to collaborate on this incredibly important startup success topic of “Value Creation” vs “Value Capture”.

Silicon Valley was born from the whole concept of Value Creation while historically struggling in Capturing the Value. Our goal is to combine both into a valuable framework for you to put into your startups practice - starting tomorrow.

EXECUTIVE SUMMARY:

Enterprise AI investment has now crossed $30 billion annually while MIT research continues to show that 95% of implementations produce no measurable return as of yet.

Nobody is arguing AI has created tremendous value in terms of productivity since it burst into the mainstream in late 2022/early 2023. The focus in early 2026 has clearly turned to AI Agents vs Generative AI as the starting point for not just being able to create or generate value but to start capturing it agentically - doing the work that humans would otherwise be doing.

The questions many boards are now starting to ask:

This is the gap between value creation (the original ARR purchase or Free Trial) and value capture (creating a sticky product that won’t churn in 12 months).

This may be the most under-diagnosed risk in venture-backed startups today. Not just getting companies to buy your product but getting them to use it, evangelize it to others, and happy to pay you on the next annual billing cycle.

In this playbook we’ll discuss:

• The distinction between value creation and value capture, and why most startups have it backwards

• How SpaceX, Netflix, and Intercom each organized around a single value creation metric

• A four-step process for finding your value creation metric

• Six most common failure modes

• Why this is the most important yet least discussed tool in a CFO’s toolkit

The AI Success Paradox

Your AI startup feels unstoppable. Inbound leads overflow your CRM. Sales is closing enterprise deals faster than you can hire CSMs. The hockey stick you promised investors is finally here!

The board celebrates every quarter. Record revenue. Record growth. Record valuations. You close your Series B six months after your A before the bulk of customers have even reached a single renewal. You’re crushing it.

Except you start to notice some conflicting signals.

Opt-out cancellations spike at 60 days… but new logos more than compensate. Usage-based credits burn at half the projected rate and implementation timelines stretch from weeks to months. The cracks are there, but who has time to notice?

Investors don’t ask about it. They’re too busy competing to lead your next round. Internally, all these warning signs get rationalized away. After all, if revenue is growing then your product must be driving value… right?

But MIT’s recent study reveals the shocking truth: despite $30-40 billion in enterprise AI investment, 95% of implementations created no measurable return.

The researchers found that most AI pilots collapsed into “brittle workflows”: systems that couldn’t retain feedback, adapt to context, or improve over time.

Initial excitement, zero lasting impact.

These facts beg the obvious albeit uncomfortable questions:

If 95% of enterprise AI deployments aren’t working, what’s actually driving your ARR?

Are your customers buying genuine product value?

OR

Are enterprise budgets simply throwing their cash at the spaghetti wall of innovation hope in a desperate attempt to show AI progress to their own boards and make their “adoption goals”?

The two can look identical on a revenue graph, right up until renewal.

But hey, the ARR graph looks great.

Value Creation vs Value Capture

Every business does two things though we rarely talk about them this way or separate the activity motions clearly:

1. Creates value for customers

2. Captures a portion of that value as revenue

Here’s what Alex and I have witnessed time and time again in our respective careers and networks:

Value Creation is the actual impact you have on your customers. Solving problems, delivering convenience, expanding what’s possible. It’s imperfect, it’s messy, it’s debatable. Your product might solve multiple problems, serve different segments differently, or create value in ways you never intended. There’s no universal formula. That discomfort makes CFOs and board members search for cleaner metrics of customer engagement, customer journeys, and customer storytelling.

Value Capture is what you extract in return. Revenue, ARR, NRR, ACV. It’s perfect, it’s clean, it’s indisputable. Every investor expects these numbers, every board deck leads with them, every startup benchmarks against the same standards. For all its precision, though, value capture only measures extraction. Not whether you’ve earned it.

Most companies have this backwards. They obsess over value capture while hoping value creation happens automatically. They optimize pricing and packaging while the actual foundation erodes beneath them.

The bottom line logic is simple and one we want to strongly emphasize:

The best companies figured this out early. They built their entire operating model around a single value creation metric and let revenue follow:

• SpaceX doesn’t measure success by revenue per launch. They measure cost per ton to orbit. When Musk started, that number was $50,000. Today it’s under $2,000. That 25x improvement didn’t happen by accident, it was the direct result of obsessing over reusable rockets, vertical integration, and simplified designs. The contracts, the revenue, and the valuation are all downstream of that one number.

• Netflix obsesses over hours watched, now over 2 billion daily worldwide. That single metric pushed them toward binge-worthy originals, personalized recommendations, and seamless device switching. Crucially, when engagement drops, they see value destruction happening in real time, long before it ever shows up in subscriber numbers.

• Intercom could measure seats sold or messages sent. Instead they track support cases resolved and time to resolution. Every major product decision from automated workflows, AI-powered responses, to proactive help features flows from that metric. The revenue follows because the outcome is real.

Pricing, packaging, and expansion are all levers you pull to capture value. But they only work if the value is actually there.

Like Newton’s laws of physics, this is a foundational law in business: in the long run, value capture cannot exceed value creation. You can extract more than you create for a while through pricing power, switching costs, or investor subsidies. But the gap always closes.

As I have said many times, “Mind the Gap!”

Customers will find the alternative. Competitors who create more value will take more share. The startups that confused a rising tide of enterprise AI budgets for product-market fit discover, at renewal, that their product value is built on sand.

Finding Your North Star: How to Measure Value Creation

So how do you actually find it? Most founders overcomplicate this. They convene offsite workshops, hire consultants, build dashboards with dozens of KPIs, and still can’t answer the basic question: what changes in your customer’s world when your product works? What stories do your customers tell about your product?

I just wrote a post on building a Customer Operating System to answer just such questions.

Below is our suggested four-step process. If you can answer all four honestly, you’ll have your metric. If you can’t, you’ll know exactly where the gap is.

Step 1: Name the Job

What is your customer hiring your product to do?

• Not what your product does. What changes in your customer’s life when it works.

• The job should be stable even if your product disappears tomorrow. Someone else would still need to get it done.

Intercom’s customers aren’t hiring a “messaging platform.” They’re hiring Intercom to make customer problems go away quickly. The job exists whether Intercom exists or not.

Step 2 : Define Done

How does your customer know the job is complete?

• What would your customer measure, even informally, to know this worked?

• The answer should be something they’d track even if they weren’t paying you.

• Beware activity-based metrics. “They logged in”. HINT: MAUs is not DONE!

A support leader doesn’t celebrate messages sent or tickets opened.

They celebrate cases resolved. That’s the finish line.

Step 3: Identify the Unit of Value

What is the smallest discrete outcome your product delivers that the customer would pay for one more of?

• Strip away packaging, pricing tiers, and bundled features. What is the single indivisible unit of value?

• For SpaceX it’s a kilogram delivered to orbit. For Netflix it’s an hour of attention held. For a payroll company it’s an employee paid correctly and on time.

The unit is a resolved support case. Not a seat. Not a message. Not a conversation started.

A case that ended with the customer’s problem actually solved.

Step 4: Stress-Test Your Metric

Can this metric be gamed, inflated, or improved while the customer gets worse off?

• The customer test: Would your customer independently celebrate if this number improved? If only your team cares, it’s an internal metric, not a value metric.

• The corruption test: Could this number go up while the customer experience goes down? If yes, you have the wrong metric. (Example: “bot deflection rate” can hit 80% by looping customers through help articles that never actually solve the problem.)

“Messages sent” fails the corruption test immediately. More messages could mean worse support, not better.

“Cases resolved” passes: if that number goes up and resolution time goes down, customers are unambiguously better off.

The Six Ways Founders Get This Wrong

1. Defaulting to revenue. Revenue is a lagging indicator that can mask value destruction for quarters before it surfaces.

2. Choosing vanity or “activity” metrics. Total registered users. App downloads. Reports generated. These look impressive and say nothing about whether a customer’s life improved. The tell: a metric that goes up regardless of whether the product is working.

3. Confusing your output with their outcome. Features shipped, tickets closed, emails sent. These measure what your company produced, not what your customer experienced. If only your team celebrates when the number goes up, it’s the wrong metric.

4. Making it too complicated. If your team can’t remember the metric or explain it to a new hire, it will quietly stop influencing decisions. Complexity is how north stars die in practice.

5. Picking something you can’t influence. A metric too far downstream from daily decisions creates learned helplessness. The metric should feel one level out of reach, ambitious but traceable back to daily work.

6. Never updating it. The mistake isn’t changing your value metric as the business evolves. The mistake is pretending it still works when it doesn’t. The right metric at seed stage can become actively misleading at Series B.

The Metric That Matters

Most companies just chase revenue as their north star metric, and 90% of them still fail. The generational breakouts chase something different: a single measure of value creation that captures whether their product is actually changing the customer’s world.

SpaceX bent the cost curve of space access. Netflix reshaped how two billion hours of human attention get spent every day. Neither happened by accident. Each company identified its value creation metric early and organized every operational decision around inflecting it.

This is perhaps the most important and least discussed tool in a CFO’s toolkit.

Value creation doesn’t show up on a P&L. It’s messy, imperfect, and endlessly debatable. That’s exactly why most finance leaders avoid it. But the founders and CFOs who find their value creation metric and organize the entire company around inflecting it build businesses where retention, expansion, and pricing power all become downstream consequences of a problem being solved. Revenue stops being something you chase and starts becoming something that’s inevitable.

Here’s a great quote from a recent panel talk Roelof just gave on this subject:

“Value delivery should come before value capture, and entrepreneurs should focus on creating something useful before thinking about the business side of things.” — Roelof Botha - Sequoia Capital

Roelof had several observations during his talk that illuminate the tension between value creation and capture, especially in early vs later stages.

• In the podcast “Sequoia’s Crucible Moment”, Botha emphasizes that in the early days of a startup, there needs to be heavier weight on value creation over value capture. Build something compelling; make something people want so badly they can’t stop using it. Without that, value capture strategies (pricing, monetization) will be hollow.

• He warns against premature monetization or over-focus on capturing margin before the product and customer foundation is strong. Early capture can undercut growth, reduce user adoption, and limit the size of the pie. (This emerges in his commentary about startups tempted to chase metrics or revenue too early rather than product/market fit.)

• He also stresses long term value creation as a discipline - not just in features or product, but in culture, operating metrics, durability. From his position, a start-up that scales to be one of the “500 most valuable technology companies” is not just one with strong value capture; it’s got to have enduring value creation that customers, markets, ecosystems love.

Scaling Trade-offs

Here are typical trade-offs and how a company should evolve its balance of value creation vs value capture:

Common Failure Modes

1. Value creation without capture: you build something people love (or large usage) but your business model is weak, margins are negative with no clear path. Often seen in high burn, low conversion, or when costs don’t scale with usage.

2. Value capture without value creation: early monetization can drive revenue but if the product doesn’t retain customers, or usage drops, or competitive substitutes emerge, it unravels. Think hard about what you deliver.

3. Over-optimizing capture at the expense of creation leads to incremental improvements rather than breakthrough innovation; you may win short term but lose long term.

4. Misaligned incentives inside the company (e.g. a sales-driven culture pushing pricing up even though product defects or customer dissatisfaction are ignored) that degrade value creation.

Framework for Founders / CFOs / COOs

To operationalize the balance, here are tools and questions:

1. Map the value creation levers: What are the biggest contributors to creating value (product features, service quality, user experience, data, network effects)?

2. Map the value capture levers: What are your monetization & cost levers? Pricing, tiering, margins, cost of acquisition, churn, upsell/cross-sell.

3. Measure unit economics early: even when revenue is small, understand the cost to serve, lifetime value (LTV), churn & retention. These indicate whether capture is viable.

4. Run scenarios of scaling: what happens if you double users but halve monetization per user? Or if you raise prices / shrink features? What are the elasticity & risk trade-offs?

5. Align culture and incentives: reward value creation early (product, engineering, customer success) as well as capture later (sales, finance).

Conclusion

Scaling to a billion-dollar company is not just about aggressive monetization or fundraising. It’s a dance between creating something deeply valuable, and capturing enough of that value to sustain growth, fund the team, creating investor returns, and funding future moats.

Founders who understand when to balance Value Creation and Value Capture are the ones who build legendary companies.

Alex and I also played with AI to create this graphic for this post if you want to use it in your next exec staff or all hands offsite.

If you want to bring either of us or both of us in to speak to your company more deeply on this subject, we are simple LinkedIn message away.

Connect with me

Connect with Alex

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For startup and growth-stage CFOs, board reporting is typically overcomplicated. It becomes a bloated packet of status updates, data dumps, or a defensive exercise in proving the business is under control.

You walk into the boardroom prepared to defend every slide, row and column. Ten minutes in, the conversation goes off track as board members latch onto minor details, chase rabbit holes, and the clock runs out before you’ve touched the strategic decisions that matter most. Many times you never even touched on strategy in your board deck.

To lead your board effectively, you need to flip the script. It’s critical to structure your board communications on lessons learned and a focus on the future (where you were, where you are, and where you are going). You must control the narrative with a clear point of view and lead your board like any other team. Here is my breakdown of best board practices including what to report, how often to report it, and how to communicate clearly.

8 Best Practices for Board Reporting

The best board reporting comes from being intentional about what you include, rather than simply adding more. These eight practices will help you simplify your reporting, lead with clarity, and keep board conversations focused on decisions.

1. Frame board reporting as a decision-making tool vs. update

One of the most common mistakes in board reporting is assuming the goal is simply to keep directors informed. Yes, the board needs visibility into the business, but reporting should do more than provide status updates. Its real job is to help the board understand the current state of the company in service of a bigger question: what should happen next?

The job of your reporting isn’t to show off what you’ve accomplished but instead, it’s to create context that helps them make good decisions with you. It should show whether the company did what it said it would do and whether the business is performing against plan.

From there, the conversation should move quickly to the future and cover what is changing, what risks are emerging, what decisions need to be made, and where the board can help. The board pack should not read like a retrospective but rather a decision-making tool.

2. Lead the board with a clear point of view

Boards often ask for more. More numbers, more slides, more detail, more comparisons. That does not always mean more information is more helpful.

A useful principle here is that boards need a point of view to react to, not a blank page to fill in. When leadership teams come into a board meeting asking, “What do you think we should do?” they often create confusion. Directors pattern-match from other companies, offer advice based on incomplete context, and generate a flood of opinions that may not fit the business model, market, or stage.

Read more

2 weeks ago I posted the following on LinkedIn. It quickly received nearly 3,000 impressions. Suffice it to say it hit a chord. So, I’m going to expand on this concept here on Substack.

Easter Egg: there’s entertainment in this post… don’t miss the Monty Python and Eminem videos. Who says finance is boring!

I’ve been writing on this consistent theme for awhile here on Substack. Here are some of my previous posts on this subject:

THE WRONG QUESTIONS; FIX YOUR FINANCE STACK FIRST

Every CFO I know is asking some version of the same wrong questions right now:

“Where do I get started with AI in my finance team?”

“What should my AI strategy be for finance?”

The real problem is not where or how to start with AI.

The real problem is to make sure you have the proper data structure before you even think about applying AI to it.

Let me guess. Your finance and overall company data is still too fragmented, unstructured, mislabeled, delayed, or manually patched together. The data you need is still trapped across disconnected systems.

Adding AI on top of that? OOF! Welcome to AI giving you bad answers faster with it’s classic hallucinatory confidence, telling you how smart you are… all with awesome formatting!

So let me say this clearly again: There are 3 main problems in finance and company data today:

1. Data Model Problems (ie - not actually having the right “data model”)

2. Data Integrity Problems (ie - not syncing data consistently and accurately into your data model)

3. Data Observability Problems (ie - not mapping where the data lives so you can extract it into your data model)

Start Up There ^^^^……. Hard Stop.

CFO as Chief Data Officer as Chief Architect

The next generation of great CFOs will need to put on their best Chief Product Officer Hat.

The best Chief Product Officers I know do the following things very consistently:

• Create clear roadmaps of their customer’s journey

• Focus relentlessly on customer requirements

• Define those into key product feature requirements

• Design simple, structured product steps to “aha moments”

• Innovate, measure, and course correct or “experiment, track, and refine”

For CFOs, start thinking of your finance teams data as your “Product”.

You are the owner of this product and you need to focus relentlessly on your “Customer”.

So don’t fall into the FOMO trap of experimenting with AI by running pilots, demos, and experiments against data that was never structured to support machine learning in the first place.

Your new demo might look impressive for the first ten minutes. But look out below when you double click or your CEO or Board asks the next level question.

Your awesome AI now failed. Actually it wasn’t your AI… it was your data architecture.

CFO As Architect

This is where your role and your mindset now has to change.

We CFOs have been historically asked to own the outputs of numbers. To close the books, to forecast the future based on the close, to prepare the board deck to communicate both the actuals and the plan, to track variances, and to be the safeguarder of company assets.

That’s all still true but not enough for the future CFO.

You are now no longer the CFOs who say ERP!

You are now the architects who say “AI” (for bonus points pronounce the “I” as “eee” to match Monty Python below!

The best future CFOs will now have to own the information architecture behind these outputs.

Architects ask different classes of questions:

• Where does this number originate?

• What system owns the truth?

• Which definitions are standardized and which are tribal knowledge?

• Where does my staff “fix” the data manually each month?

• Which sets of data insights are actually reusable data products versus one-off spreadsheet heroics?

• What context and assumptions should accompany the raw data?

Yes, you have to quickly layer AI on top of your data architecture. But AI needs to be at the tail end of the Strategy > Structure > Execution loop.

Your data design and your data model are your strategy and structure, and they must come first before you layer on AI to execute.

So let’s get down to specific steps to make this happen.

Step 1: Hire a data engineer onto your finance team

All great Product Managers partner or hire an engineer on their staff.

It’s time for you as the Chief Product Manager of Finance to hire this engineer on your team.

Call the role whatever you want:

• Finance AI Specialist

• Finance Systems Integrator

• Finance Data Architect

• Finance Engineering Lead

I care less about the title and more about the vision and mission.

I’ve laid out the vision above. Your mission (if you choose to accept it) is to architect the finance data environment so that the AI machines can actually be useful.

The person in your new role is not there to build vanity dashboards or to simply automate the manual. This new role needs to design and build the foundation of your future finance team value:

• Map the data model

• Define system ownership

• Clean up broken data flows

• Create structured context across disconnected systems

• Reduce manual interventions and reconciliations

• Design the connective tissue across ERP, CRM, billing, payroll, HRIS, procurement, data warehouse, and planning systems

• Build the cross checks to make sure your new data model stays clean and connected over time, not just every time you go to clean it up

Hint: You may need more than 1 role for this function and you must now start rationalizing the ROI of such roles or you and your team will be architected out of the value such data and insights created by somebody else in the org.

Step 2: Build and Maintain Your Finance Data Stack

Think in layers:

Layer 1: Source Systems (ERP, CRM, Billing)

Layer 2: Data Model Layer (standardized definitions)

Layer 3: Integrity Layer (clean, reconciled, trusted)

Layer 4: Observability Layer (alerts, anomalies, monitoring)

Layer 5: AI Layer (only now does AI work)

Step 3: Act Like the Product Owner You Now Are

Your finance data is now “the product” and you own it with clear customers.

That means:

• SLAs to Customers

• Bug fixes and Version controls

• Continuous feature improvements

• Customer Listening Tours

3 Root Cause Failures:

It’s critically important to predict root cause failure analysis. All great operators and product managers think to the future in order to work backwards to today. And they think in terms of success and failure at each point in time. So if the above was all about how to succeed, the below will be your warning signs for failure and where specifically to look for the weakest chains in your new data model architecture.

In the end, most AI failures inside finance trace back to one of the three root causes:

1. A data model problem

This means the information itself is not structured well enough to support reliable output.

Where to look:

• Inconsistent chart of accounts mapping

• Revenue definitions changing by team

• Fragmented contract data (data context living in multiple systems or the context doesn’t “live” at all and was never captured)

• Inconsistent time periods or naming conventions

• Weak dimensional modeling in your data warehouse

As I write the above, I’m now thinking to myself that AI could possibly be used to uncover the anomalies above?

2. A Data Integrity Problem

• Poor master data hygiene

• Customer records that do not reconcile across systems

• Missing contextual metadata (key assumptions, relational data)

• Manually maintained spreadsheets acting as shadow systems

3. A Data Observability Problem

This means the data may exist, but you cannot reliably monitor, trust, or explain what is happening to it over time.

Examples:

• Broken pipelines nobody notices for days

• Silent field changes upstream

• Delayed refreshes

• Unexplained metric movements

• Failed syncs between systems

• No alerts when data quality degrades

• No audit trail for how a number changed

• No confidence scoring on inputs

In this case, the issue is not merely that data is messy.

The issue is that the mess is invisible until a board meeting, a forecast miss, or an audit surprise forces it into the light.

Please, please, please never forget and always remember:

Trust in finance is built not just on correct numbers.

It is built on 3 pillars:

• Accuracy

• Consistency

• Timeliness

While Confidence and Credibility is built on:

• The Insights and Value Add You Bring to the Numbers

Build a Finance MCP for Proper Data Context

AI does not only need properly structured data. It needs context.

It needs structured access to how your business defines customers, contracts, revenue recognition logic, headcount categories, departmental ownership, approval rules, scenario assumptions, historical exceptions, and policy boundaries.

To make this crystal clear: “Data Tables” are awesome but tables are NOT CONTEXT!

Excel rows don’t product reasoning.

Capturing insights is not truly understanding.

You need to build a clean context layer across your finance data and systems so that the AI can retrieve the right definitions, relationships, and rules in the right way at the right time.

The headline here?

You must design and build context into your data architecture.

Maintain the context. Provide access controls.

You must be able to drill down and eliminate any AI hallucinations or confidence masking the inaccuracy it might produce.

The future finance stack must be able to use Layer 5 (AI) to produce the Accurate, Consistent, and Timely Data of which we speak!

As Eminem so classically rapped, “This is your moment… you own it!” Your leadership moment. Your systems moment. “You get one shot. You better never let it go. Once in a lifetime.”

The future CFO will architect the company’s financial intelligence layer.

5 questions every CFO should ask right now.

If I were sitting in your seat this quarter, I would start here:

Read more

Convenience wins. Every time.

While most other companies are competing on “better features”, “higher power”, “speeds and feeds”, and even “lower price”, the surprising winner is the one with the most convenience. Hard stop.

The company that removes friction, simplifies effort, and fits naturally into how people already live and work almost always wins every category.

Convenience has been the David against the Goliaths again and again. The companies I’ve been involved with - Intuit, Netflix, and Mozilla Firefox - could root the reason for their success in convenience.

Quicken vs. the Checkbook Register

In the early 1990s at Intuit, we weren’t trying to “revolutionize finance” at first.

We were solving a painfully mundane problem.

Balancing a checkbook is annoying. Manually. With a pencil on paper. Yes, this was once a “thing”.

People were manually writing transactions into paper registers, doing math by hand and trying to make their chicken scratch equal their bank balances. Quicken’s promise that launched its now $100B+ enterprise value didn’t try to make people wealthier. It didn’t optimize their portfolios. It didn’t promise alpha.

It was simply more convenient than pen and paper to track your spending and to reconcile your bank account.

• Faster

• Easier

• More accurate

• Automatically reconciled

That was it for the first few years. That’s all customers could absorb in the early days of desktop computers and software.

In fact, the marketing tag line for several years was simply “Fast and Easy”… printed on every software box, every end cap aisle marketing brochure.

QuickBooks was launched a few years after I joined Intuit. QuickBooks was aimed at small businesses and the financial statements they need to produce for taxes and bank loans. QuickBooks - the very first accounting software product ever to perform the classic “Double Entry” accounting for you. Every other product required you to use the software to make an income statement entry, a balance sheet entry and do actual accounting! (Albeit with a piece of computer software now).

QuickBooks’ convenience? No double entry accounting.

We merged with TurboTax a few years after that. TurboTax’s huge convenience = 1 question at a time. Sure there were 200 questions but we built the % progress bar and showed your tax refund (or amounts owed) as each question was answered. We produced very detailed experts for each question embedded into the question on the screen.

TurboTax’s Convenience? Your Tax Expert came to you, asked you questions, and you answered them the same way you answered the human tax expert. You just did it a lot faster and easier now. Every other tax software product of the day (and this is hard to believe but true) literally mimicked the painful 1040 Form on the computer screen, small fonts and all, no line spacing. Nearly unreadable.

Convenience doesn’t just win customers, it creates new habits and rabid evangelical type customers.

Evangelical customers create moats.

Netflix vs. Blockbuster: No Late Fees, No Car Keys

Blockbuster didn’t lose because DVDs-by-mail was awesome and worked. Though both were true. We were told we were crazy after all and “That Will Never Work”.

Blockbuster lost because Netflix was simply more convenient. Going to a store was a hassle and late fees created lots of friction and angry customers.

At Netflix, we removed friction everywhere:

• No driving

• No parking

• No lines

• No “out of stock” movies

• No late fees

• Movies show up in your living room. Movies go back.

• Repeat.

Blockbuster optimized stores and made nearly all their profits on the candy and popcorn sales near the register and a huge amount on “Late Fees”.

Netflix optimized our customers’ time, their efforts, and the predictability of which next movie they were going to watch vs “let’s drive to Blockbuster and pick out a movie.”

Netflix’s real moat wasn’t our envelope operations.

It was our software-driven convenience. Our inventory management systems, recommendation engine, and operational logistics intelligence that all worked together to make the customer experience both effortless (convenient) and joyful.

Convenience isn’t always obvious and doesn’t always look flashy. Most times, convenience is hard coded into the operational excellence behind the scenes.

Firefox vs. Internet Explorer: Faster (speed), Easier (tabs), Trust (more secure and not Microsoft)

At Mozilla, Firefox didn’t win because we were “open source.” We won the early days of the browser wars because Firefox was simply faster and easier to use, and we could be trusted.

Tabbed browsing meant fewer windows. Faster performance meant less waiting. Better security meant less anxiety.

People didn’t wake up one day in 2004 wanting a new browser, but when they saw it and used it, it simply worked they way they worked. The web was less painful to navigate, and it was fun.

Many times convenience isn’t just about speed or ease of use, sometimes it’s about cognitive load. Firefox reduced mental friction at internet scale with all sorts of easier ways to search (built in search box) or bookmarks or our “Awesome Bar” - where when you started typing into the URL bar, Firefox would help you predict what you were looking for and show you drop downs of options.

Airbnb: Convenience Beats Hotels

Hotels optimized physical locations and fancy buildings. Airbnb optimized simply finding a local place to stay and staying in the comfort of a nice clean home of somebody else.

Search, click, book. Pictures of the place. Reviews from others. Several choices for any given location. Often cheaper and allowed consumers to quickly choose and visualize their stay from so many aspects.

The best AirBnb homeowners became personal concierges offering local activity ideas and places to eat and shop.

What started as convenient couch surfing turned into a much more convenient way to remove friction from discovering new places, trusting the place before you booked, and the convenience of the pre and post transaction experience with the homeowner.

Uber → Waymo: Removing the Last Step

Uber didn’t win because it was a better limo service.

It won because:

• No phone calls

• No cash

• No guessing when the car would arrive

• No explaining directions

Massively convenient.

One tap. The car came to you on whatever street corner you were on. The ultimate inconvenience. Uber launched a whole new industry of “Company/Product Comes To You”.

Waymo is simply the next logical extension:

Remove the remaining friction of the driver and potential bad interactions.

The arc of each evolving “better product” is clear:

Each generation removes another layer of friction.

Pattern Matching:

Across every example, the winning company asked better questions:

• What step can we remove?

• What decision can we pre-make?

• What uncertainty can we eliminate?

• How do we create a “wow experience” at each step of our customer interaction?

Amazon: Convenience as a Religion

If there is one company that turned convenience into a foundational operating principle, it’s Amazon.

In my early days of e-commerce, most in the industry would rattle off the new industry rested on 3 pillars.

• Selection

• Price

• Convenience

Every time this was said, I would express “Wrong order”. Convenience is too far away and should be #1, and Price is a distant last place.

Amazon didn’t win because it was the cheapest. Others were just as cheap.

It didn’t win because it had the best-looking website. Not even close. Its websites for the first 10 years were downright ugly.

It didn’t even win because it had the most selection or the cheapest price. It had neither for the first 10 years.

Amazon won because it made buying things unbelievably convenient.

• One-click ordering.

• Saved addresses and payment methods.

• Amazon Prime (the original SaaS; Shipping as a Subscription).

• Relentless focus on delivery speed.

• Clear, predictable return of orders.

Every Amazon decision was filtered through a single question:

“How do we make this easier and faster for the customer?”

Jeff Bezos famously described Amazon as being “customer-obsessed.” What that really meant in practice was friction-obsessed. For Amazon this meant identifying and eliminating every small inconvenience that stood between the customers intent and the order button and the “wow” emotion when the product showed up way earlier than expected.

Prime: Buying Time, Not Shipping

Amazon Prime wasn’t a loyalty program. It was a convenience subscription.

By pre-paying for shipping, customers removed the mental tax of asking, “Is this worth the shipping cost?” The decision friction disappeared. Ordering became default behavior.

Prime didn’t just increase order frequency. It rewired our consumer psychology.

Convenience isn’t just about speed; it’s about removing decisions.

Amazon’s Operational Logistics Moat:

Like Netflix, Amazon’s greatest advantage remains mostly invisible.

• Warehouses positioned close to demand.

• Software that predicts what you’ll likely buy and accessorize the thing you bought.

• Logistics systems that continuously optimize routes, inventory placement, and last-mile delivery.

Customers experience convenience.

Amazon made huge investments that other penny pinching e-commerce thought was crazy. Amazon spent arguably over $100B over the last 20 years to hide the incredibly complex warehouse and shipping logistics.

That trade of absorbing operational complexity so customers don’t have to experience it is the defining pattern of every category winner.

The Amazon Lesson for Leaders

Amazon proves my “Convenience Manifesto”.

Convenience isn’t a feature. Convenience is a system.

It shows up in:

• Product design

• Supply chain decisions

• Pricing models

• Return policies

• Internal metrics

• Capital investments

Most companies ask, “Is this efficient for us?”

Amazon asks, “Is this easy for the customer?”

And then they build the operations required to make that answer “yes.”

The Leadership Lesson

If you are a founder, CEO, CFO, or product leader, ask yourself:

• Where are we asking customers to work too hard?

• Where are we pushing complexity onto them instead of absorbing it internally?

• What are we optimizing for internally that makes life harder for our customer?

Ask Your Product and Engineering Teams

• Are we designed around convenience?

• Are we faster and easier at every customer interaction?

• How do we remove friction?

• Are we creating Aha! and Wow! Customer moments?

Silicon Valley history is remarkably consistent on my convenience point:

Pick your company. Pick your product. Pick your era.

Convenience always wins. Convenience is king.

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I’ve posted a video at the bottom of this post. Scott and Tom are discussing how we approached customers from day 1. Scott/Tom have story told this dozens of times to every new hire face to face for several years in the early days. I will never forget being one of those new hires at the age of 25 and with only a few other new hires in the room listening to these same origin stories and critical customer lessons.

I’m paying forward these lessons today to any founder or exec team so at a minimum somebody someday even many years in the future will discover these critical lessons and to make sure they are not forgotten.

I hope you read how I recommend turning these lessons into a Customer Comes First Operating System, but at a minimum, I hope you simply watch the Scott/Tom video. P.s.: I have several more of these on different topics stored on my YouTube Channel

My post today will take these customers lessons a layer deeper and suggest a new “Customer Operating System” to help turn “Customers Come First” from a high level slogan to actual company activities at all levels of the employee and leadership base.

I’ve never seen these lessons fail when applied. I brought these “customer first” techniques to Netflix and Mozilla and coach them when I can in every other startup I’ve interacted with.

I strongly believe that almost every successful company who has navigated from startup to unicorn has the foundations of being very close to their customer. I also strongly believe the opposite is true. Technology first companies or companies that state “customers don’t know what they want” and “we know better than our customer” are bound for the killing fields of Silicon Valley.

At the core of what I’m now calling the “Customer Operating System” are 3 simple words and 2 key sentences.

Customers Come First.

If it’s good for the customer, it’s good for the business.

If it’s not good for the customer, it will eventually break the business.

Thank you Captain Obvious!

But I remain surprised how often the core of the company problem is not being close enough to your customer. I’ve come to the conclusion that many don’t know how.

This simple concept is therefore worthy of a post but also worthy of my attempt to create an actual repeatable customer operating system.

Why Startups Lose Customer Touchpoints As They Scale

The classic phrase gradually, silently, and then suddenly applies here:

• New layers of management appear.

• Product becomes technology roadmap driven instead of customer requirement driven.

• Actual customer conversations get replaced by internal naval gazing operating metrics and dashboards.

• And the most dangerous I hear? “We know what our customers want.”

The Best Customer Focused Companies?

• Design customer touchpoint rituals at every product stage

• Talk about the Customer Journey constantly

• Think and communicate “Customer 1st” not only to the company but most importantly to the customer whenever issues arise

• Create extremely valuable Customer Communities

The Powerful Questions To Ask Your Team Today

Three Questions

Using my Powerful Questions Framework, every leadership team should revisit these 3 questions quarterly:

1. How close are we really to our customers?

Evidence-based, not belief-based. When was the last time the CEO and the Exec Team actually spoke to customer(s)?

2. Who owns our customer engagement?

If the answer is “everyone,” then the answer is actually “no one.”

3. What are our customer-backed rituals?

What do we actually DO to prove we are close to our customers? Specifically. Go ahead, I’m listening.

Cook’s Customer Operating System:

5 Customer Sub-Systems

Below are the actual customer rituals we used at Intuit, plus two cultural programs that became legendary. These forced every employee, regardless of title, to stay connected to real customers using the product in the real world. I participated in each one of these and they imprinted “customer” on my personal startup DNA.

1) Follow Me Home (FMH)

The most powerful customer-learning mechanism I’ve ever experienced.

We (as in the entire company of 400 people) went to actual customer homes of those using our actual product (Quicken and QuickBooks at the time). The FMH system required us to only watch quietly and take detailed notes to pass back to our product and engineering teams.

We were not allowed to help the customer as they struggled through various parts of the product as they used it in the “real world”.

Every employee was also required to be a “phone customer service rep” for 1 week a year (no internet or email service existed back then).

The product/engineer teams then prioritized the “customer requirements” for the next product version after pattern matching over a few hundred customer interactions over a very short period of time. FMH produced decades of product improvements because it revealed friction customers didn’t even know how to articulate.

You may not be able to actually visit your customer’s homes or even have a customer support department but the framing of figuring out how to communicate 1:1 with customers is something I highly encourage you to figure out. Get creative.

2) The National Sales Tour: “Everyone Sells for a Week”

Every year, we sent every employee, yes, every single one of us to a major city of their choice (requirement was you had to stay with a friend or family member - to save the company money).

We all went into the major software stores of the day (Comp USA, Babbages, Egghead, etc) to speak to the store managers and to rearrange the boxes on the shelves to make Quicken, QuickBooks, and TurboTax front and center and to watch how customers bought the product. We were all sales reps for the week and were armed with key scripts and marketing materials from our sales and marketing departments.

Today, there may not be a “store” but the concept remains the same of having every employee at all levels (especially the execs) attend “Sales Calls.”

While FMH (Follow-Me-Home) taught us where the real “bugs” were in the product post sale, our National Sales Tour taught us what the roadblocks were to customers actually buying the product.

3) Customer Journey Mapping: With a Real Owner

At Intuit, we defined and operationalized the original “customer journey.”

We built cross-functional customer journey maps showing:

• How customers discovered the product

• How they decide to buy

• What they did next on each screen

• Where they get stuck and were creating product friction

• What they told their friends and family about

Intuit made exec team leaders accountable for owning the customer journey from the Sales Department linking to the Marketing Department linking to the Engineering/Product Department and linking to the Customer Service and Support Department.

There was no way for finger-pointing and department siloing to occur. We even designed joint compensation incentive programs cross functionally. We made sure the cross functional leaders (Head of Product + Head of Engineering + Head of Marketing) spoke as 1 voice and appeared “on stage” together at all hands meeting. I have strong memories of 2, 3, and sometimes 4 execs on stage in front of the company talking about how they work together with their customer at the first set of operational hand-offs.

We published weekly and monthly reports and led all quarterly offsites and QBRs (quarterly business reviews) starting and ending with the customer journey.

4) Customer Community

In the early-mid 90s, Intuit didn’t have the internet, websites, or customer community software platforms. We were shipping our product on floppy discs and CDs after all. Our version of “customer communities” required in-person observations and structured programs.

Today, companies have a massively more scalable tool called the world-wide-web and still aren’t using this powerful tool very well.

A Customer Community is much more than a support forum or a feature request list.

A Great Customer Community?

• A digital town square for customer feedback

• Real-time focus groups

• Product walk throughs and tutorials ideally created by other customers who are incentivized to share how they use your product

• Customer “tips and tricks” treasure chest of data. Customers love learning from other customers.

• Roll your own idea here

The value of a great customer community is not passive content published to customers or quarterly surveys of these communities. It’s the unfiltered real-time conversations that are happening daily.

When customers talk to each other and to the company, three powerful things happen:

1. You hear their language… not yours.
   This shapes your messaging and positioning far more effectively than any marketing brainstorm ever could.

2. You see their real workflows, hacks, and workarounds.
   Those product gaps? Yes those. Those friction points. Yes those. Those contextual nuances that are hard to replicate in a “survey.”

3. You get the “long tail” truth.
   Customers tell your support team or sales team one thing. They tell other customers the real thing.

Yes, dig for this and you will strike gold.

Customer Communities Are Your Strategic Weapon

1. Customers learn from each other faster than they ever will from your marketing or sales messaging or your customer engagement team.

You will achieve true ROI in lower customer support costs, increased organic product marketing and faster customer onboarding.

2. Company leaders need to be customer moderators vs product broadcasters.

When executives including CFOs and COOs quietly monitor ongoing discussions, they gain:

• Raw behavioral insights

• Feature prioritization signals

• Patterns of frustration

• Patterns of delight

• Real customer vocabulary

• Early warning indicators of churn

These insights feed back into your Strategy–Structure–Execution system with clarity and precision.

3. A Customer Community is the ultimate customer research engine.

No incentives needed. No artificial environments. Customers simply talk and you listen.

The natural conversations that happen in a healthy Customer Community are more valuable than any paid research study, and if you listen deeply you will hear:

• What customers wish the product did

• What they ignore

• What they brag about

• What they teach each other

• What they fear

• What they struggle with

• What they’ve hacked together that you should build natively

Hint Hint: The Seeds of Your Future Product Roadmap Begin Here!

CFO + COO Role in Customer Community Design

Open your checkbook and allocate your $ to creating this Customer Community.

If you want to be a key architect of the business, then invest in it, design it, build it, and create your new customer insights engine.

Now you can turn this customer data into customer insights and into customer decisions. Data > Insights > Actions - your new Customer Operating System.

5) Customer Conferences

In the early to mid 90s and still existing today, there were only big annual conferences that all companies attended (Comdex, CES, others).

Today, the big companies (Salesforce, Oracle, Apples WWDC, etc) hold their own huge customer conferences. Take a page out of their book and invest in your own smaller annual customer conference. Bill.com, Netsuite, Twilio and many others all used this very valuable customer playbook with huge ROIs.

Very few startups invest in bringing their customers together early enough. But those that do are building powerful communities extremely quickly. Key examples are the recent conferences from the Operators Guild that Casey and Molly and team are running, Rillet (Nicholas and Stephen), Campfire (John G), Runway (Siqi), and the a16z demo events that Ivan Makarov is running extremely successfully.

A Customer Conference

For decades, the world’s most iconic companies have built empires around this idea:

• Salesforce → Dreamforce

• Oracle → Oracle World

• Apple → WWDC

• Google → I/O

• Microsoft → Ignite / Build

These events aren’t as much “marketing events,” they’re other customer community events where customers, partners, and company employees all:

• Teach each other

• Share success stories

• Reveal pain points

• Build a deeper community network

• Strengthen brand identity and culture

• Drive roadmap influence

Startups often assume they’re “too small” for a customer conference.

Sorry, that’s just wrong and penny wise-pound foolish type thinking.

You’re never too small to bring your customers together.

In fact, the smaller you are, the more critical it is.

Why Customer Conferences Matter; Especially for Startups

1. Customers Learn from Each Other

This is the magic.

Customers learn 100x faster from each other than they do from you.

They share:

• Workflows

• Hacks

• Best practices

• Integrations

• Creative solutions

• How they’ve structured their teams

• How they drive ROI with your product

These are insights you cannot manufacture internally.

2. Employees See the Customers They Work For

• This ignites a new customer first culture inside your team.

• Your engineers talk to real customers. You can’t get this anywhere else.

• Product managers receive live feedback.

• Customer Engagement takes learnings from 1-3 intense days to prioritize their next quarter’s initiatives.

• Executive Leadership hears the truth without filters.

• Everyone is reconnected to Company Purpose and Customer Impact.

3. You Get Gold-Like Customer and Product Insight

No survey can match the quality of insights gained from:

• Roundtables

• Q&A sessions

• Product demos

• Roadmap previews

• Customer story panels

• 1:1 customer conversations

Your company walks away with a validated roadmap, a clear prioritization reset, and a renewed sense of direction.

How to Build a Startup-Scale Customer Conference

You don’t need Moscone Center.

You need intentionality.

Start small:

• 5-10 customers

• Local venue

• One-day format

• 2–3 customer-led sessions

• 1 roadmap keynote

• 3 hands-on breakouts

• A simple “Ask Me Anything” with founders and product leaders

This is the mini-version of Dreamforce; the startup edition.

Over time, scale it into:

• 25-50 Customers

• Regional Customer Groups

• Quarterly customer meetups

• A flagship annual customer summit

You’re not building a 1-off event.

You’re building a customer-powered learning ecosystem.

Why CFOs and COOs Must Champion Customer Conferences

This is not a marketing initiative nor a sales effort. This is a company investment.

CFOs + COOs are the lead investors: The ROI?

• De-Risk the Product Roadmap

• Operationalize learnings into next quarter’s and/or next year’s planning

A Customer Conference pays for itself 10x. You’ll see it in:

• Renewal rates

• Upsell opportunities

• Product improvements

• Reduced churn

• Market positioning

• Community formation

• Brand trust

• Employee engagement

Trust the ROI: These events are one of the few “high-cost line items” that is actually a multiplier, not an expense.

How CFOs and COOs Can Help Architect the Customer Operating System

Lead with my Strategy–Structure–Execution philosophy. Remember, you sit at this unique intersection of all three.

I can’t tell you how many times I still see customer data being silo’d and filtered inside a startup and even a later stage company.

Product sees one part of the customer journey.

Sales sees another and many times blames Marketing.

Engineering finger points at Sales for selling something we haven’t built.

Onboarding and Customer Engagement sees a whole other side of the customer.

And the leaders and their departments are not talking to each other with a customer first mentality.

As an operator, a CFOs or a COO, you see the full system. You see:

• Customer Pipeline Issues

• Customer Onboarding Friction

• Customer Churn Signals

• Customer Recurring Revenue and Expansion Patterns

• Customer Value Creation

• Customer Value Capture

• Customer Value Destruction

I’ll continue to shout that the CFO/COO is the natural operating architect for the company and it all starts and ends with building the internal operating system from a Customer First Philosophy.

You will know when you get it right and will certainly know when you get it wrong. You can feel the customer heartbeat of the company and build that into higher quality and higher velocity decisions. You can drive company alignment from the customer backwards to operational ownership.

These five programs alone can create a moat most competitors never build.

QUARTERLY CUSTOMER JOURNEY SCORECARD: CFO/COO LED

Try to do this at least every quarter, certainly no later than every 6 months. Leadership should ask these questions:

The 12-Question Cook’s PlayBooks Journey Review

1. Where are we losing customers?

2. Where are customers teaching each other?

3. What are our customer’s biggest friction points?

4. What do Follow-Me-Home Customer Sessions reveal?

5. Top 3 things we learned from our Customer Community?

6. What did customers say at the conference?

7. What is the #1 avoidable loss reason?

8. What is our fastest-growing workflow?

9. Who are our best customers and why?

10. Who are our worst customers and why?

11. What are we overbuilding/underbuilding?

12. What is our #1 single most important customer insight this quarter?

Build Your Customer Moat: Get Started Today

Moats built around the customer and their loyalty is something that AI and competitors can’t copy.

Competitors can try to compete with product features or pricing but a strong loyal customer base who knows they are getting what they pay for is more valuable than any product or pricing sleight of hand.

The greatest companies ever built truly understand this and the ones that fail, failed to ever understand being this close to the customer.

So, copy/paste the above and start building your own Customer Operating System starting today.

SPEAKER / KEYNOTE OPPORTUNITY: Book Me To Talk About This.

If you want me to personally come in to deliver this keynote at your next company all-hands or leadership team offsite, contact me. Yes, it’s that important!

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Naturally, they focus on more precise models and create systems and subsystems to try to design for accuracy. They can’t wait to perform their month end Plan vs Actual to see how accurate their forecast was and then to build a new model.

Houston, we have a problem! That’s an ode to just launching Artemis to the Moon and sending humans around it for the first time in 50+ years.

The best FP&A teams are not about that. You and your team will crash and burn on landing with that mental model.

Your FP&A team should be designing for agility… not accuracy.

It’s time to design a decision-response system.

The Best Finance Teams

The best finance teams are intellectually honest about one thing:

All forecasts are wrong.

The best FP&A professionals know that forecasting with multiple variables is the equivalent of gambling. Being highly accurate in a multi-variable model is not statistically possible.

Unless you are a very late-stage or quarter-to-quarter public company, designing for decision agility is what your high growth scaling startup needs from your team.

Most of you already know your forecast is highly sensitive to overall market conditions, industry or customer surprises, government policy changes, and even natural disaster or mass internet outages.

Look at what happened to your forecast in just the last 30 days with the Iran conflict.

The top 1% of finance leaders design a decision-response system knowing their forecasts are going to be wrong.

They don’t just plan the future, they create scenario planning and they pre-wire the next set of decisions to keep driving their key metrics toward the overall goals.

They build these pre-wired decisions into their FP&A “system” and build the “next decisions” into their next forecast model along with the detailed assumptions.

Planning Three Steps Ahead Is Table Stakes

Every great CFO, VP of Finance, or FP&A leader knows the mantra:

“We need to plan three steps ahead.”

So we build:

• Base / Upside / Downside scenarios

• Sensitivity tables

• Waterfalls and bridges

• Probability-weighted forecasts

These are all great work products from the great FP&A teams to be able to see into the future better.

My key question?

“What do we need to DO in the future vs SEE in the future?”

Seeing more accurately into the future stops one step too early and keeps your FP&A team from being more influential in driving strategic future decisions.

Start incorporating a new decision-response system into your financial models. In the past I’ve called this pre-wiring the future decisions and summarized it as “course correcting”. It’s all the same concept.

Decision-Based FP&A

A forecast without a decision is just a prediction.

If you want to be an elite FP&A team, please start treating forecasting as THE INPUT…NOT THE OUTPUT.

Start asking a different question for each forecasted scenario… for each line/variable/assumption in your forecast.

“If this scenario becomes true, what decision do we make?”

“If this line item executes beyond it’s maximum expected outcome range, what is our next decision for that line item?”

“If it’s below the minimum expected range?”

Now code both the line item or scenario assumptions and the “next decisions” for that line item into the actual model. Go ahead, I’ll wait. I hope you tagged that row/cell data with the proper notes and narratives.

As your FP&A team’s leader, coach them to make the shift from:

• Forecasting the future to Deciding the future

• Planning scenarios to Pre-wiring responses

• Predicting outcomes to Designing course correcting actions

Pre-Wiring Decisions: What It Really Means

Pre-wiring means you don’t wait for a variance to show up before acting.

You decide in advance:

• What actions you will take

• Who owns the decision

• When you will move

• How much to increase or decrease your inputs or investments

• What tradeoffs you will accept

Do I need to say it again? Ok fine. The… forecast… will… be… wrong.

You may as well decide now (as in today) what to do WHEN the forecast will be wrong vs waiting for that future day.

Examples:

• If product or engineering misses their next product launch milestone date, pause those sales reps tied directly to that product. You can’t sell what isn’t in market after all.

• If you are within 6 months of cash runway and the VC you’ve been courting for 3 months passes, and it’s late October (with Thanksgiving and Christmas breaks coming up where the VCs start signing off), you freeze new hires until you have a signed term sheet.

• If the new marketing campaign doesn’t create your MQLs (marketing qualified leads) for 2 straight months, you execute campaign B or campaign C… you don’t keep hoping the original campaign will start working.

• By the way, with fewer MQLs for 2 months, you need to now pause your new hire SDRs (sales development reps) since your current SDRs are starting to surf Instagram more and more on the clock.

• Roll your own. Pick your line item. Create your ranges of Bad, Good, Great.

• Now create the “response decision” if the actuals fall below bad or rise above your great range.

This is no longer a forecast. It’s not even a “Financial Plan”. You’ve now created a “Financial Decision System”. You lead and influence the strategic future decisions of how we’ll respond when the actuals are significantly off the last set of assumptions.

Coach Your Teams To Stop Asking:

• “Did we hit the forecast?”

Coach Them To Start Asking:

• “Did we respond correctly when we didn’t?”

That’s a fundamentally different financial operating model.

FP&A as Decision Architecture

Old model:

• Build the plan

• Track variance

• Explain what happened

Modern model:

• Model scenarios

• Attach decisions to each scenario

• Create response plan triggers before the actuals come in

This is FP&A decision architecture. The finance team becomes:

• The designer of this decision system

• Focused on navigating against the uncontrollable variables

• You are now your executive peers decision co-pilot to their function

The outcome of operating this way?

• Your company is more agile

• Your decisions are higher quality

• The data drives the decision

• No more opinions or hope

• Reduces emotional debate of “just a little longer”

• Prevents reactive leadership

Your CEOs and Boards will quickly start finally understanding your finance team’s value.

So here’s my reframing thesis I’ll leave you with:

Are you forecasting the future OR are you helping to decide it?

Because planning without pre-decisions is just optimism with spreadsheets.

And FP&A without pre-wired response plans is today’s incomplete decision system.

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If you are one of my CFO Readers, you will want to keep this playbook. If you are another C-Level Exec or a VP or Director trying to influence one, I recommend you talk to your CFO about using some of the techniques and language below.

Let’s start with a key thought model on decision making - the decisions that created your plan and the decisions you now need to make as a result of your 1st quarter’s plan vs actuals:

“Good decisions aren’t about certainty. They’re about confidence, clarity, and the ability to course-correct the decision.”

Every operator I’ve ever coached - CEO/founder, COO, or CFO have all eventually hit the same wall. I absolutely hit this wall early in my career.

We don’t have a consistent way to properly size the downside of risk vs the upside of reward.

So we rely on our instincts, experience, opinions, or we manipulate the data to support our internal biases.

We lack a repeatable system to evaluate the trade-offs that truly determines the quality of our decisions.

Today’s post goes deep into how I’ve learned to size risk vs. reward using the core components of the Decision Making Series I wrote about over a year ago.

My readers know I’ve been working on these frameworks and playbooks as I try to communicate and influence how to be a better decision maker. I’ve introduced concepts such as the Risk Equation, Confidence Scoring, and the Powerful Questions Framework.

After 30 years operating inside Intuit, Netflix, and Mozilla, and now coaching dozens of CFOs and founders, I can confidently say:

CFOs need to learn to influence more. Influence requires their ability to quantify and plan for uncertainty and to communicate this ability effectively.

In order to consistently influence and establish high credibility, I recommend communicating to your peers in the form of a “decision system”.

Let’s unpack some of my own consistent coaching on “decision systems”:

1. Decision Quality = Understanding the Range of Outcomes

Don’t make decisions based on a single predicted outcome. I used to do this early in my career. Our revenue plan is $X. Our cash at the end of the year will be $Y. Then you learn you will never be 100% accurate of X or Y, leaving you to have to explain a variance on why the numbers weren’t X or Y.

Then you learn that wasn’t the point at all. A range of X and a range of Y was good enough to determine success. Below that range was bad and above that range was good.

So start make decisions based on ranges of outcomes… not X or Y.

1. Best Case

2. Base Case

3. Worst Case

Or

1. Minimum

2. Expected Value

3. Maximum

Take your pick, but that’s only the starting point.

The key is to attach specific assumptions, key metrics, and critical timelines to each case.

2. This is where the Risk Equation enters the system

Risk = Probability × Severity

I’ve seen seasoned leaders focus only on the severity part of the equation on both ends of the severity spectrum.

“Even if this is bad, how bad could this be?”

“Wow, the bad outcome could kill us.”

Great operators don’t focus on these extremes of severity. Neither are useful without equally focusing on the probability of the severity.

Probability Analysis is critical in decision making. Example:

“There’s a 1 in 1,000,000 chance the outcome that kills us happens.”

Or

There’s a 1 out of 2 chance (50% probability) of the negative outcome that, while not very severe, will significantly slow us down and set us back.

Elite Operators? They also add Frequency of the Probability to the mix. The 1 in a million chance that only is capable of happening every 10 years is very different from the 1 in 10 chance of a bad outcome whose potential frequency is daily/weekly/monthly.

Here’s my Risk Matrix

To size decisions intelligently and to help influence the room of the “true risk”, I will pull out a simple 2×2 matrix learned from my time with my Security and DevOps teams of the past.

Axis 1: Severity of Risk

(Low → High)

Axis 2: Probability of Risk

(Low → High)

Note: I’ve included the optional Frequency into the Probability axis, since a more frequent chance of risk increases its probability of occurring.

Low Severity / Low Probability

Green Light

Your job? Move fast. Execute cleanly. Remove friction.

These are the “don’t overthink it” decisions.

Quadrant 2: High Severity / High Probability

Red Light

This is where capacity planning, cash runway, and execution sequencing matter most.

3. Bringing Confidence Scores Into the System

One of the most powerful habits you can develop—as a CFO, COO, or CEO—is tagging every assumption with a confidence score.

I use a simple scale:

• High Confidence (70–100%): Supported by data, customer evidence, or repeated patterns

• Medium (40–70%): Directionally informed but incomplete

• Low (<40%): Opinion, hypothesis, or early signal

Now combine this with your risk–reward matrix:

High Risk/Reward decisions with low-confidence assumptions deserve caution, not enthusiasm.

Low Risk/Reward decisions with low-confidence assumptions deserve to die immediately. These decisions either aren’t really “decisions” or even if you succeed in something low reward with low risk… you are likely to ask “So What?”

Confidence scoring forces intellectual honesty.

It forces personal beliefs into a score (a.k.a “data”).

Teams stop arguing opinions and start comparing assumptions.

4. The 5 Powerful Questions That Size Risk Properly

Here are five questions to either include in an executive/board briefing or to ask depending on what seat/role you are in.

1. What must be true for the upside to happen?

List the assumptions. Score their confidence.

2. What is the cost of being wrong?

Financial, operational, reputational.

3. What is the earliest signal that tells us we’re wrong?

Pre-mortem thinking. And when will we first see this signal?

4. How reversible is the decision?

The Bezos “one-way vs two-way door” thinking.

5. What is our cheap test?

Every big decision can and should be tested with a small, fast, low cost experiment to prove the underlying assumptions.

Just asking these five questions will elevate your decision quality by 2-3x. Keep asking powerful questions and you can compound your decision quality even more.

Let’s also Size the Risk: Here are 4 categories to score:

1. Financial Risk
   Cash burn, capex, margin impact, downtime.

2. Execution Risk
   Sequence, velocity, team capacity, cross-functional complexity.

3. Customer Risk
   Retention, experience, support burden, product-market fit.

4. Reputation Risk
   Brand trust, board confidence, stakeholder alignment.

Elite CFOs score all four. How many is your team scoring?

Now let’s talk about the REWARD side of the house. Risk is so negative after all!

Sizing Reward: Start With Strategic Fit

Many startup execs and teams mistakenly judge reward by financial upside alone.

Bad idea.

The best evaluate reward across five dimensions:

1. Strategic Alignment
   (Does it support our strategy, structure, and execution system?)

2. Financial Impact
   (Revenue, margin, cost savings, cash runway)

3. Customer Impact
   (Does it improve the customer journey or customer community experience?)

4. Organizational Impact
   (Does it improve or strain team capacity, hiring, or leadership bandwidth?)

5. Long-Term Advantage
   (Does it build a moat? Capability? Optionality?)

This is where 90% of decisions go wrong.

The reward is not aligned to the strategy. Hard stop.

Bright shiny objects often become the exciting but silent killers of focus and execution.

Summary of My Decision System:

1. Write Down The Decision We’re Making

Make it clear, simple, specific, measurable with documented assumptions. It’s amazing what “Writing It Down” does for clarity.

2. Communicate Your Clear Strategic Thesis for Making the Decision

Your point of view. Your narrative. Your 1st Principles. The “Why”.

3. The Reward (Sized)

Strategic, financial, customer, organizational, long-term.

4. The Risks (Sized)

Probability × Severity + Confidence Scores

Financial Risk, Execution Risk, Customer Risk, Reputational/Brand Risk

5. Range of Outcomes

Best / Base / Worst case with timelines.

6. Early Warning Indicators

Signals we monitor.

7. Make The Recommendation

What we should do, Why, and When. Using all the above as core elements of your recommendation.

Your goal is to build your CFO influence, confidence, and credibility. You certainly have more data than anyone else in the org. It’s time to put it to work in a consistent systemic way such as my example of a decision and communication system above.

Closing Thought

The quality of your decision making system determines the quality of your outcomes.

Good companies rely on intuition.

Great companies rely on decision architecture.

I also love these decision quotes:

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I’m also eating my own “Cooking” as I just created my own T-Chart for Leadership Presence vs Leadership Pressure.

After three decades running finance and operations at companies like Intuit, Netflix, and Mozilla and now coaching today’s startup leaders, I’ve learned that the most valuable skill the best executives bring to the table isn’t IQ or technical/subject matter expertise, it’s leadership AND the ability to stay calm and “present” under pressure.

As I think back to many of my performance reviews, I now realize the words cool, calm, and collected were written into most of my reviews. Interestingly, these seem to be the most sought after behaviors especially for CFOs and COOs. Specifically, our ability to stay grounded, calm, curious, and logical in the exact moments when other execs or board members often get lost in the emotions of the situation.

Your presence in the heat of the moment amplifies every other leadership skill from your clear strategic thinking to decision-making to communication and all the way to crisp execution.

So here are the headlines I just wrote down for my personal T-Chart on Pressure vs Presence. I’ve tried to blend in my prior PlayBooks like my “Decision Making Series”, “Powerful Questions”, “Crisis Frameworks”, “The Language of Business” and “7 Olympic Lessons Learned”.

I came up with six T-Chart Headlines:

Pressure Collapses Time. Presence Expands It.

Pressure seems to accelerate everything especially when the pressure also includes a time component. Pressure speeds up your mind, quickens your assumptions, and reflexively surfaces your biases.

Presence? This is the ability to remember to breathe (box breathing is great), get oxygen to your brain and body, slow your mind, and expand vs contract your universe of options.

I love the phrase “Slow Is Smooth, Smooth Is Fast”. This exemplifies “being present” and “thinking slow vs thinking fast” (ode to Daniel Kahneman).

I just wrote about the OODA loop… so obviously these concepts are on my brain. The OODA and ODDA Loops both start with “OBSERVING” as the first step of “presence” vs instantly reacting to the pressure.

As an operating CFO, COO, or other leader, your ability to see around the corners clearly and calmly is what will separate you from the pack and be viewed as a valuable strategic thinker and credible influencer.

Pressure Constricts Thinking; Presence Clarifies It.

That fast breathing, adrenaline, cortisol based energy that you feel under pressure is great for “flight” but not as effective in winning the “fight”.

When your pupils constrict, your eyesight lets less light in. More often than not, less light means more “dark thinking” which tends to revert to biases and fixed mindsets born out of fear.

My Powerful Questions framework can help expand everyone’s thinking and let in more light to the situation. Great questions reduce pressure by forcing thoughtful and engaging answers.

• What’s really happening here?

• Are the assumptions we are making valid?

• What’s the true severity of this pressure? What’s the true probability?

• What data are we missing?

• What are the second-order effects if we’re wrong?

• How about the second-order effects from the quick decision we are about to make due to the pressure?

Great exec leaders aren’t known for having the perfect answers, they are known for asking great questions that expand possibilities and keep options open.

So slow down. Breathe. Simply Ask More Questions.

It’s amazing what transpires when you lead everyone to more information, more ideas, more possibilities, more potential solutions.

Pressure Amplifies Emotion. Presence Calms People Down.

In 2013, I was told by a few leaders I respect that “Emotions Are Data”. In other words, if you are emotionally triggered it must be very important. After all, “No Conflict, No Interest”. Therefore the higher the emotions, the higher the importance of “the thing” triggering the emotions.

In a leadership room, emotions always exist though they are often unspoken, unmanaged, and underestimated.

This is where my T-Chart Framework becomes a secret weapon.

When pressure is high, the conversation splits into two tracks:

Left Column: the visible facts, numbers, models, and risks

Right Column: the invisible feelings, fears, expectations, personal stakes

Under pressure, most leaders double down on the left side.

Presence means focusing on the Right Column to better inform the Left Column.

At Intuit, Scott Cook and Bill Campbell were masterful at this. They didn’t just manage the work; they managed the room’s energy. They normalized fear, uncertainty, and confusion and most times were able to redirect the fear into action. They kept the room honest, grounded, and productively moving forward in the highest stress situations.

Pressure Tests Alignment. Presence Creates It.

In my Strategy–Structure–Execution Alignment post, I wrote that misalignment is the silent killer of teams and companies.

Pressure exposes misalignment the same way storms expose weak architecture.

Your presence, especially if you are a CFO or COO, shows up in three key moves:

1. Name the misalignment.

2. Translate the confusion into a clear decision framing. (E.g. what we know, don’t know, and when we’ll know more, data required, key decision deadlines.)

3. Guide the room back to executional next steps aligned to this framing.

Presence is the keystone that holds the executive arch together when the load increases.

Pressure Demands Quick Decisions. Presence Produces Higher Quality Ones.

In my Decision Architecture posts, I argue something counterintuitive:

The job of an executive is not to be right.

The job is to create the conditions for the best decision available to be made.

Your “Keep Calm and Carry On” type presence is what enables that.

Without presence:

• Fear drives decisions.

• Egos and loudest voices crowd the room.

• Time pressure distorts decision quality.

• Group dynamics corrupt decisions without proper alignment.

With presence:

• Risk gets sized appropriately (Probability × Severity).

• Options get compared objectively.

• Assumptions get surfaced and scored.

• Ownership becomes explicit.

• Deadlines become the team focus.

Presence doesn’t guarantee you’re right.

It guarantees you’re moving forward with clear alignment and the best ideas and actions you can take in the moment.

Pressure Reveals Character. Presence Demonstrates It.

At Mozilla, we scaled from 18 to over 1,000 people. Hyper-growth is, by definition, a pressure cooker. What separated the leaders who thrived from those who burned out was not raw capability, it was their leadership character, focused on their teams and the company culture and their ability to stay “present”.

They say pressure reveals your true character. I say, your ability to stay present is what people will remember and will help define and demonstrate your leadership.

Presence shows up as:

• Calm tone when others elevate.

• Curiosity when others get defensive, loud, or angry.

• Clarity when others go off script and create chaos.

• Courage when others play it safe, hedge, or simply freeze up.

Presence doesn’t mean being soft. You can have presence and be quite firm. Presence is focused force. Think Yoda.

Your presence ultimately builds trust… when all others are losing their heads around you. Think “IF” by Rudyard Kipling.

The 7 Step PlayBook: How To Create Presence Under Pressure

Here are 7-steps that I coach for creating presence under pressure:

1. Breathe, Then Speak.
   Deep breaths. The 10 second rule. Super hard to do but it’s a rule for a reason.

2. Label/Name the Moment.
   “This is a high-stakes decision. Let’s slow down and get it right.”

3. Separate Facts From Assumptions.
   Use your Decision Architecture muscles. Define assumptions vs facts. Turn opinions into data.

4. Ask One Powerful Question. Then Ask Another One.
   The right questions can literally change the emotions in the room. Somebody needs to ask them. Try to make it you.

5. Surface the Right-Column Dynamics (see above in this post)
   What’s unsaid that needs to be acknowledged?

6. Re-Align. Re-Focus the Room.
   What are we solving for? Who owns the next step?

7. Make the Next Action Decision. Also prepare the course-correcting next-next action at the same time. Try to operate 3 steps ahead.

Presence is a lots of reps practicing these 7 steps.

Under pressure, your presence becomes the room’s new operating system…

…and you create the Leadership Environment for winning.

I’ll leave you with a story that a very good friend and business colleague just texted me a few days ago that exemplifies this whole post. I’ve edited this post to quickly add this story. It still gives me goose bumps since my good friend was actually on this particular flight 8 years ago. This is the “Presence Under Pressure” true story of Tammi Jo Shults (pilot) and my good friend Jim Demetros who was on that fateful flight.

If you are a leader of any type, there are crisis moments like the one below which literally can be the difference between life and death or maybe only the difference between the life and death of your company.

Be Tammi Jo Shults in that moment!

8 Years Ago, April 17 2018:

“There wasn’t time to be scared. Only time to fly”

At 10:27 AM on April 17, 2018, Southwest Flight 1380 departed LaGuardia Airport in New York with 144 passengers and five crew members, headed for Dallas. The captain was Tammie Jo Shults. The morning was routine.

Twenty minutes into the flight, at 32,000 feet over Pennsylvania, the left engine came apart.

A fan blade had developed metal fatigue — a microscopic structural failure in metal that had been flexing and cycling through takeoffs and landings for years, invisible to inspection, building toward a threshold. When it reached that threshold, the blade broke free at operating speed and tore through the engine casing from the inside. Metal debris ripped through the wing and into the fuselage. One piece struck window 14A.

At 32,000 feet, the pressure differential between inside and outside an aircraft is violent. The window failed. The suction was immediate. Jennifer Riordan — a forty-three-year-old wife and mother of two from New Mexico who had boarded a routine business flight — was partially pulled through the opening before other passengers could reach her. They pulled her back inside. Her injuries were catastrophic.

In the cabin, oxygen masks dropped from the ceiling. The vibration from the damaged engine and asymmetric thrust was severe enough that passengers were not certain the aircraft was still structurally intact. They texted their families.

The plane is going down. I love you.

Tell the kids I love them.

We’re going to crash.

In the cockpit, Tammie Jo Shults was flying.

She had declared an emergency and requested Philadelphia International — the closest airport with adequate runway and emergency services. She was managing an aircraft that wanted to roll left from the asymmetric thrust of one remaining engine, fighting controls through vibration severe enough to make the instrument panel difficult to read, navigating the damage assessment in real time.

She keyed air traffic control.

“Southwest 1380, we’re single engine. We have part of the aircraft missing, so we’re going to need to slow down a bit.”

She paused.

“Could you have medical meet us on the runway as well? We’ve got injured passengers.”

The audio of that transmission went viral within hours of the landing.

Aviation professionals and ordinary people alike listened to it with the specific quality of attention that genuine mastery commands — the recognition of someone operating at the outer edge of their capability with a composure that sounds, to the untrained ear, like nothing at all.

Part of the aircraft missing. Slow down a bit.

Not the language of crisis. The language of someone who has already, in the first seconds, assessed the situation and decided: we are landing this aircraft, here is what we need, here is how we proceed. (« My note: if you recognize this is my OODA Loop post in real life-real-time)

What the passengers on Southwest 1380 did not know — what they had no way of knowing, because Tammie Jo Shults was not a person who announced her history when she boarded a flight — was where that voice had come from. What three decades of experience and refusal and training had gone into producing those fourteen words delivered in that tone.

She had been twelve years old, standing at an airshow in New Mexico in the early 1970s, when she watched planes move through the sky and turned to her father and said she wanted to do that. Her father told her she would.

The world said otherwise.

When she pursued military aviation, the structure she encountered was not ambiguous about its position. Women could hold support roles. Women could teach, administer, manage.

The cockpit of a combat aircraft was not a space the institution had designated for women, and the logic that maintained that designation was circular and designed to be self-reinforcing: combat roles were closed to women, fighter pilots were combat roles, therefore fighter pilots were closed to women, and since women had never been fighter pilots, there was no evidence they could be.

She applied to the Navy’s Aviation Officer Candidate School. She was rejected. She applied again. She kept applying until, in 1985, the Navy accepted her — not as a combat pilot, which was still closed to her, but as an instructor. She could teach men to fly jets she was not permitted to fly in the roles she had trained for.

She took the job. She taught. She watched men with less capability advance into positions she was systemically excluded from, and she kept teaching and kept flying and kept building the hours and the skills that the institutional exclusion told her she would not need.

In April 1993, the Department of Defense lifted the combat exclusion rule for women. Tammie Jo Shults, then thirty-one, applied immediately for the F/A-18 Hornet program.

The F/A-18 is a supersonic multi-role fighter aircraft — air-to-air combat, precision ground attack, carrier operations. The training is demanding and the operational requirements are unforgiving. She became one of the first women to qualify.

The Navy, having recognized what she could do, made her an instructor in Out of Control Flight — the specific discipline of teaching pilots how to manage aircraft that have departed normal flight parameters, when the standard protocols have failed and recovery requires judgment and physical skill developed over thousands of flight hours.

They also made her an aggressor pilot — flying as simulated enemy in training exercises against Navy squadrons, including against TOP GUN students.

To be an aggressor pilot is to be used as a challenge, which means you are used because you are better than average. The institution that had told her women couldn’t be fighter pilots was using her to test its best pilots.

She left the Navy after more than a decade of service and joined Southwest Airlines in 1994. She flew commercial routes — the ordinary, productive, routine business of getting people where they were going — for the next twenty-four years. Passengers who boarded her flights had no particular reason to know any of this.

She was Captain Shults. Professional. Calm. Excellent.

At 10:47 AM on April 17, 2018 — twenty minutes after the engine failed — she touched down at Philadelphia International Airport.

The landing was smooth. Controlled. The aircraft rolled to a stop and emergency vehicles surrounded it. Passengers who had been composing final messages to their children looked at each other in the specific disoriented way of people who expected something that did not happen.

Jennifer Riordan died from her injuries. Eight passengers sustained minor injuries. One hundred forty-three people walked off that aircraft because the person in the cockpit had used thirty years of preparation to make twenty minutes of controlled, professional excellence look like a routine diversion.

Tammie Jo Shults got off the plane and walked back through the cabin to speak individually with passengers. One described her voice as sounding like a mother — the specific quality of calm that communicates:

“I know where we are, I know how to get there, we will be all right.”

When the media arrived with awards and celebrations and interview requests, she deflected. She credited her co-pilot Darren Ellisor. She credited the flight attendants who had performed CPR and managed the cabin under conditions of genuine panic. She said: “Any pilot would have done the same.”

Aviation experts, reviewing the circumstances — single engine operation, structural damage, severe vibration, the specific management of an aircraft actively resisting controlled flight — said that was not accurate. Many pilots would not have succeeded under those conditions.

A reporter asked if she had been scared.

She paused.

“There wasn’t time to be scared,” she said. “There was only time to fly.”

She returned to flying.

Because that is what she does — what she has always done, since she was twelve years old at an airshow in New Mexico and decided that the sky was where she was going, regardless of what every subsequent institution told her about who the sky was for.

The Navy told her women couldn’t fly jets.

She spent eight years applying until they had no choice.

She spent three decades building the competence that would be visible, finally and completely, in fourteen words transmitted to air traffic control while 144 people texted their families goodbye.

We have part of the aircraft missing. We need to slow down a bit.

The plane wanted to crash. She had not spent thirty years preparing to let it.

Captain Tammie Jo Shults. Born December 30, 1961.

• U.S. Navy. F/A-18 Hornet Pilot

• Out of Control Flight instructor.

• Aggressor pilot (Top Gun - Jester style!).

• Southwest Airlines, Captain, 1994–present.

• Southwest Flight 1380. April 17, 2018.

And when an engine exploded at 32,000 feet, she landed the plane.

“There wasn’t time to be scared. There was only time to fly.”

Personal note on Jim Demetros (who I mentioned was on this plane).

I didn’t know Jim before this incident, I’ve known from his stories and from others that knew him that he always exemplified the leadership qualities described above (he worked directly with Steve Jobs after all).

I did have the pleasure of working very closely with “Jim D” shortly after this incident and like most incidents of this nature. I believe it sharpened his skills, his focus, and his outlook on life. He certainly “brought it” during my tenure with him and that’s all you really want from any leader.

The leader I knew was the pilot of his organization and he definitely knew how to command the “plane” when it mattered while letting his team take the credit (just like Tammi Jo).

He knew instinctively and would have you know the following:

• Teams always comes first.

• Leadership is about delegating ownership to the lowest levels of his organization.

• Setting the vision and the course of the team’s next actions was critical

• Taking control and commanding the org to pivot in the moment to save the plane (company) with class, confidence, and calmness was critical

I watched him do all this and more as an exceptional leader and in the quiet moment after the all hands or the exec offsite he would softly tell you why. That nothing else was as important or as big a crisis as that day… and the fact that he was still here gave him the freedom to follow his instincts and be a leader without reservation or fear of being 2nd guessed.

Final note: next time you are deep in a crisis and you are a leader or the pilot of your team, conjur up your best Tammi Jo Shults or Jim D and simply fly the plane and

“Just Lead”.

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“Where We Were; Where We Are; Where We Are Going.”

Every major technological advance in human history, from the printing press to the steam engine to the internet to AI, ultimately buys only one thing.

TIME.

Not intelligence. Not money. Not relationships. Not resources.

Time has never been anything other than fixed and finite.

The richest person who has ever lived could not buy one extra second of it. The most powerful empire ever assembled could not manufacture more of it.

And yet here we are, on the edge of what I believe is the most profound TIME buying inflection point in human history, and almost nobody is framing it this way.

So here’s where I jump into my personal framework.

Where We Were:

The History of Technology Is the History of Buying Time

Why did humans invent the wheel? To move goods faster and more efficiently. To compress the time between here and there.

Why did Gutenberg build the printing press? To compress the time between an idea in one mind and the understanding of that idea in thousands of minds. Before the press, copying a single book took groups of monks months. Post-Printing Press = Days.

Why did we build railroads, telegraphs, radio, phones, powered engines, and airplanes? Every single one of these technologies had the same fundamental purpose to collapse the time required to move things, communicate things, or build things.

Why did we build computers? To compress the time required to calculate, analyze, and decide. What took the Eniac and a room full of human “computers” weeks of arithmetic now takes that machine in your hand milliseconds. Note: One of my college roommates’ dad and still good friend worked on the Eniac in Cupertino.

Why did we build the internet? To compress the time between seeking knowledge and connecting people. The internet exponentially decreased the time between a question and its answer or between a buyer and a seller or between a creator and the audience.

Every single technology we have ever invented is, at its core, for lack of better metaphor = a Time Machine.

Not a time machine that sends you back to Marty McFly’s 1955. A technology time machine that takes a task which used to require weeks, months, or years and compresses it into hours, minutes, or seconds.

So that’s the quick history of technology vs time - “Where We Were”.

Where We Are:

The AI Revolution Is The Fastest Time Buying Technology Ever

Every prior technology wave bought us time on physical tasks or operational logistics tasks or computational tasks.

If you are skimming and reading fast, I highlighted above where the printing press saved writing time, the railroad and airplanes saved travel time, the computer and spreadsheets saved calculation time. The internet saved all of us information-access time.

But what about the time required for thinking? For reasoning? For creating? For deciding?

While all past technology inventions saved time on physical tasks, AI is now targeting time savings for thinking time, cognitive time, and creation time. Historically, human thought has required a trained human mind which takes years to develop while juggling families, health issues, and other personal distractions.

Even early AI required humans to literally label the data so the LLMs could have “structured data” to do their magic. Unstructured data = bad LLM after all.

Where We Are Going:

AI Compressing Time and Compounding Value

For the first time in history, we are building technology that compresses thinking/cognitive time, not just physical or computational time.

I wrote about this back in November 2024 in my “2025: Enter the AI 1st Era” post. I described the shift from Systems of Record → Systems of Engagement → Systems of Intelligence. We are now entering the era where AI agents don’t just store or retrieve or calculate, they synthesize, ask you questions, create, and act on your behalf.

Think about what that means through the lens of TIME:

• A task that required a team of analysts 2 weeks now takes an AI agent 2 hours.

• A due diligence process that required 3 law firms and 6 weeks is now compressed to days.

• A financial model that required an FP&A team 2-3 weeks to build is now built in hours/days.

• A first draft of a strategy document in minutes.

• A listening tour synthesized from a dozen stakeholder interviews can be summarized overnight.

Every one of these is time bought back. Unlike previous technology waves, this one isn’t buying back time on tasks we have hated or found beneath us.

AI is buying back time on the tasks we thought only we could do.

Buying back time is the inflection point of our current technology era.

Let’s shift gears and start talking about the abundance vs. scarcity debate. There’s been an interesting philosophy popping up in my feeds in the last few weeks from folks like Dylan Field (Figma CEO), Aaron Levie (CEO Box), Brian Chesky (AirBnB), PMarca (Marc Andreesen), and others.

Scarcity Has Always Driven Value - We Are Now Defining New Forms of Scarcity

Scarcity has been the core value driver of wealth throughout human history. Specifically the scarcity of knowledge, networks, natural resources, and capital. Each of these have all started as extremely scarce forms of value and over time technology advances have allowed these scarcities to be shared with the world.

We’ve used the technology of the day to discover, manufacture, accumulate and transfer these assets. Land was once the ultimate scarce resource. Then capital. Then data. Today it’s attention that’s scarce.

Throughout history, every resource that has ever been declared “scarce” has created value that was destined to be captured.

• Land seemed infinitely scarce. Until we built vertically (skyscrapers) and built huge metropolitan cities and are now moving into the infinite of space.

• Capital seemed scarce. Until we invented fractional banking, venture capital with global markets creating paper liquidity at a scale unimaginable to prior generations.

• Knowledge seemed scarce. Until the printing press, then universities, then the internet democratized access to information.

• Computing seemed scarce. Until Moore’s Law, cloud infrastructure, that small device in your pocket, and now commodity GPU clusters (which admittedly are still scarce!).

• Intelligence itself has always seemed scarce. Until AI now begins to replicate and distribute cognitive capability at unprecedented scale.

Every other form of scarcity in our human history has found a technology solution.

TIME will remain the lone sole scarce resource.

You cannot store time. You cannot inherit raw time. You cannot manufacture time. You cannot discover more of it. You cannot crowdfund it. You cannot engineer your way to having a 25-hour day or a 400-day year. Sorry, the earth only rotates at a very defined speed.

Every human being whether they are a billionaire or broke, genius or average, powerful or powerless, wakes up with the same 24 hours and mostly the same under 100 yrs.

Time is the only resource that has always been and forever will be scarce. There is no technology solution.

My key point if you haven’t heard it yet (sorry just modeling my 7 times 7 diff ways mantra):

AI is about to create more Time than any other technology ever has.

I Believe We Are At The Abundance Inflection Point

When you buy back time at this kind of scale on the individual level, the company level, the local small town level, and entire industry levels, you get a clear inflection point in entire economies, and dare I say entire civilizations. I totally realize that’s a huge bold statement but I have to call it like I see it.

The probability is now very high that we are at the very beginning of one of those human civilization inflection points that we read about in history books.

If we’ve learned anything from human history, when the human civilization game changes, you get human abundance. Longer life spans, higher incomes, less poverty and hunger, more worldwide peace dividends.

I’ll repeat the major time-buying technologies and document their respective subsequent impacts:

Printing press: Created the Renaissance, the Reformation, and the Scientific Revolution. Ideas that would have taken centuries to spread now spread in decades.

Industrial revolution: Created the middle class, modern medicine, public education, and eventually the longest sustained worldwide peace (mostly) in human history.

Internet: Created entirely new industries, democratized entrepreneurship, connected billions of people worldwide who were otherwise localized, and produced more economic value in 30 years than the entire history of the world.

In every case, the time these new technologies “bought” wasn’t a one time inflection point… the value compounded.

People used the reclaimed time to learn more, create more, connect more, discover more, invent more. The time saved cascaded into new capabilities, new industries, new knowledge, new art, new science.

AI will be the largest time-buying technology in history. Which means the abundance it creates will be unlike anything we’ve seen.

Here’s who I think benefits and how the compounding will happen.

Individuals

Every individual with access to AI tools at every level of society reclaims meaningful thinking and planning time vs reactive-just-stay-alive time. Hours per day. Days per week.

Small Businesses

When individuals reclaim time, small businesses compound it. Administration is easier and automated. Decisions get made faster. Products get built faster. Experiments get tested faster. The organizational learning loop like the one I described in my Data → Insights → Knowledge → Decisions → Wisdom framework accelerates dramatically.

Towns and Industrial Regions

When entire industries can go faster and more efficiently, we get better medicine, climate science, materials science, education and infrastructure. Entire communities get healthier on the Maslow hierarchy scale. Problems that were on a 50-year horizon move closer to a 10-year horizon. Diseases that would have historically taken 30+ years to cure may be solved in 5 years.

This is not me being an optimist. This is simply the logical extension of the historical pattern of technology inflection points on human civilization.

Here’s my summary - if you are a scroller and just arrived here after skimming:

1. Time is the only true scarce resource.

2. Technology has always resulted in buying time.

3. AI is the most powerful time-buying technology ever built.

4. When you buy back time at scale across individuals, companies, and industries simultaneously, you get an abundance inflection point of civilizations.

Because time, even in the AI era, will still be the only resource that is always, and forever, scarce.

Invest it wisely.

If this sparked something for you, hit that share button and let’s continue the conversation. And if you want to go deeper on any of these frameworks;

My Time Audit,

My AI 1st Era,

My Data → Wisdom Pathway

Then stop thinking about it and just hit that subscribe button below, and you wont miss any more of these!

As always - Learn It. Do It. Coach It.

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Cook’s PlayBooks is published weekly. Jim Cook spent 30+ years scaling iconic Silicon Valley companies including Intuit, Netflix, and Mozilla Firefox from Series A to IPO. His coaching practice and frameworks are available at benchboard.com.

There’s been a renewed and roiling debate happening in private and now public media of our world’s future with AI. It has recently surfaced and reflected in the public equity markets and how Wall Street is valuing software and SaaS stocks - what I consider the user interface and front door to our world’s technology stack. This AI fear has sent our software technology stocks down between 30-50% from their all time highs recently.

Specifically, there is a now report from Citrini Research that went viral in my personal network about 7 days ago. If you haven’t heard of it, you have now. I’ve posted the full report at the end of this post to document it for history and so you can read for yourself the insidious dystopian fear this report so eloquently created.

Over the last several months, I’ve been very steadfast with my own point of view of AI’s impact on the arc of our historical and future technology. More recently, I’ve been on my soapbox with anyone who will listen as to just how wrong Wall Street is with respect to declaring the software and SaaS industry effectively dead. With this post, I’m not shouting into the internet void.

Stay With Me… Don’t Leave Yet.

For those of you who know me very well, you understand I’m a huge Monty Python fan. People love my other Monty Python Zoom waiting room video of “You Wait Here” sketch.

If you are a geek like me and experimented with the 1,000s of Zoom advanced settings, down there buried after at least 100 clicks is the ability to use a video for people waiting to be let in on Zoom. Apparently few else has found this setting or actually uploaded a video for it. I did this 3+ years ago, and it’s the best ice breaker I’ve created for those waiting for me let them in.

The whole AI-software debacle reminds me of the famous “Bring Out Your Dead!” sketch… ”I’m not dead!” “Oh you will be in a minute!”

Bottom Line: Software and SaaS is not dead yet!

Along comes the Citrini Research Report last week, and I was once again fascinated by the psychological construct it used for maximum storytelling of fear and influence effect.

So much so, that I now feel compelled to expose these techniques and to write my own point by point counterpoint as I’m not seeing much of a counter -argument/debate (due to how effective Citrini’s psychological writing techniques were used). Think of any great Hollywood movie and how it strings you along to an “obvious conclusion” that anyone can see unfolding.

My goal is to forever document this moment of time and how my views differ significantly from the “world view” of how technology is unfolding before us as of early 2026. Note: It’s only been a mere 3+ years since OpenAi/ChatGTP burst onto the scene in November 2022.

As usual, I’ve also scoured the web to try to condense others “best ofs” when it comes to this subject to enhance my own thoughts and will source them accordingly.

Here is where AI is great. I’ve asked it to summarize this lengthy report into it’s key points so I don’t have to. In summary, Citrini’s memo narrates this horror script:

• A storytelling memo set in the year 2028 in a post-mortem style depicting “what happened?”

• How agentic AI causes a collapse of our entire economic system focused mainly on software and SaaS displacement of seat licenses, renewal revenue, and margins.

• Then, broadening into an overall systemic economic income collapse in an accelerating loop without any natural brakes starting with job losses and cascading into consumer consumption, credit, and finally asset prices (led by home values).

Taken at face value the step-by step nature of this report allows anyone to “enter the story” and experience what is obviously going to transpire.

The problem, which is the problem with most stories and especially horror films, is the inability for the main cast of characters to change their fate. These stories usually always have a set of unrealistic assumptions that are hidden well and inherently false.

Here are 3 of Citrini’s key false assumptions:

• Worldwide Economic demand is fixed

• Productivity gains don’t expand markets nor do they create new companies and industries

• Our Societal engines and human behaviors stay fixed and do not adapt

We know from our past that all 3 statements are overwhelmingly false. And yet, Citrini, uses these falsehoods to propel their fear based propaganda - “research”. A classic strawman fallacy.

Kobeissi’s letter has a great take on Citrini as well which I’ll summarize below:

Commoditization is Not Collapse

https://x.com/kobeissiletter/status/2026125293149163694?s=61&t=-YygOMqKQ790TaVVKGYKmQ

• The PC revolution of the 80s commoditized computing

• The Internet of the 90s commoditized information distribution worldwide

• Mobile and “Cloud” commoditized information access anytime/anywhere

• AI is now commoditizing intelligence

The doom loop (Citrini’s example) becomes dominant only if AI replaces labor without materially expanding demand. The optimistic scenario emerges if cheaper compute and productivity yields entirely new categories of consumption and economic activity.

SaaS is a software delivery mechanism, not the endpoint of value creation. Value creation is ease of use, speed, and convenience. All three will be enhanced by AI, not destroyed by it, and those companies with the data and their close customer relationships will win… not lose to newcomers who don’t have either the customers or the data or the specialized differentiation moats of what these customer truly want and how they behave.

The next generation of software is adaptive, agent-driven, outcome-based, and deeply integrated. The winners will not be static tool providers, they will be those who can best adapt to change.

Every technological shift reorders the stack, and the companies who only price static workflows WILL struggle. The companies who own data, trust, compute, energy, and verification will thrive.

Tariffs are tools of protection in a world where domestic industries struggle to compete on cost. But if AI collapses production costs everywhere, why would we need tariffs anymore? In a high-abundance environment, protectionism becomes economically inefficient.

AI amplifies outcomes. It can amplify fragility if institutions fail to adapt and it can also amplify prosperity if productivity outpaces disruption.

The Anthropic advances are signals that workflows are being repriced and cognitive labor is becoming cheaper - a clear transition.

But transition is not the same as collapse as every major technological revolution has looked destabilizing at the start.

The most underpriced possibility today is not dystopia, it’s abundance. AI may compress rents, reduce friction, and restructure labor markets, but it may also deliver the largest real productivity expansion in modern history.

The difference between “Global Intelligence Crisis” and “Global Intelligence Boom” is not capability, it is adaptation.

And the world has always found a way to adapt.

From Howard Marks newsletter (Oak Tree Capital):

Everything Claude or any other AI has learned has come from human-written text. It has no experiences, no embodied understanding of the world, no genuine comprehension.

I’ll add my own footnote here to Howard’s views highlighting my own views:

AI cannot see, hear, smell, taste, or touch. It cannot read body language and it does not experience or know how to deal with emotion. All of these are inherently unique human traits and AI will never be better at replicating them than we do. AI can only store a model of the world in its memory and pattern match against what is true today or the past. It has no better way of predicting the future than anyone else since the future is created by humans and fallible human decisions.

More Howard:

Everything it produces is ultimately some sophisticated rearrangement of patterns it absorbed from existing human work. It’s extraordinarily impressive pattern matching – maybe the most impressive pattern matching ever engineered – but it’s not thought. It’s not reasoning. It’s statistical recombination.

And if that’s true, then there’s a ceiling. It can remix what humans have already figured out, but it can’t break genuinely new ground. It’s a very talented cover band, not a composer.

Finally wrote his Techno-Optimist Manifesto back in 2023 and went so far as to post it as a takeover on a16z’s home page for a few weeks. I’m posting the link to this opposite Citrini’s report at the end of this post, for posterity sake. I’ve also asked my AI to do a side-by-side of the 2 posts just so everyone can see the now 2 most famous memos on the subject (2023 vs 2026; VC vs HedgeFund).

Now here’s my personal take as promised. I’ve grown up in the world where technology meets SMBs (small to medium sized businesses). My immediate reaction to the Citrini Research Report had many of Kobeissi’s and Howard’s arguments in my head before I found/sourced them. I’m very happy to have now written them down here and have the ability to add my own views.

Over the next 5-10 years, AI will become the great equalizer for SMBs, the vast majority of which are 2-25 employee firms. These companies have historically lagged their larger rivals due to the opposite of “too big to fail”… otherwise known as “too small to afford”.

AI advances are rapidly leveling the playing field and provides all these SMBs, including worldwide individuals, with access to information and workflows previously mostly inaccessible due to costs or local availability. They now almost immediately have been provided very cheap access to:

• World-class research on demand

• World-class software development with 1 person (no 10 person engineering org needed)

• Automated workflows not requiring a “manager” or administrator

My belief is we are entering a new Era of “Community GDP”… the holy grail of the local economy level which powers nearly 1/2 (50%) of US GDP.

• SMBs are 99% of all US Businesses

• Over 35 million companies

• 46% of all US employment

• And 44% of all US GDP

While these SMBs have historically lagged far behind the technology adoption curve, they are all about to catch up very, very quickly.

Few talk about or really understand the true power of democratized countries. The power lies at the mostly invisible local and individual home level. When these economic engines of productivity come to life and finally start competing on a more level playing field, the impact on the overall economy will be massive.

Your local HVAC company will get their jobs quoted faster, their inventory delivered faster and invoices and cash collected nearly automatically. This means less time spent administratively and more time spent on performing more jobs per week/month. More jobs = higher income for the company and the individual installer.

Wait, did I just say that AI will be a significant creator of job growth at the local, SMB level? Yes, I did. That’s exactly what has happened in every major technological shift from the printing press to the railroads to the automobile to computers. Jobs actually expand. New companies do things faster and create larger companies and eventually entirely new industries to service everything.

The Aha insight? New technology saves time. Time allows for more thinking time. More thinking time increases creativity. Creativity creates all new industries.

People and companies become significantly more productive and productivity solves our worldwide labor shortage problem. We’ve already forgotten that we’ve all been worrying and wringing our hands over the last decade of the impact of our labor shortages. Somehow we’ve been surprised of our continued low unemployment rates when the reality is that we don’t have enough people to fill most jobs.

While many are saying many jobs won’t be needed anymore, they don’t realize that we will retrain or the people will move to the jobs that are needed. That has always happened and will happen again.

Our economy is not fixed. We’ve never had fixed demand or fixed numbers or types of jobs no matter how much Citrini wants to to believe that.

Once the playing field is leveled and know-how, intelligence, and tasks become cheaper, our capacity constraints become ambition constraints.

• Those who are the hungriest with the same tools will win.

• Those who are the most creative or who have the best taste wins.

• Those with specialized or custom knowledge or data wins

As good as AI is, it absolutely does not have taste, ambition, emotions, or custom knowledge. It only has historic data in it’s model… not new, creative data. I’m rather confident that nobody can train their LLM for taste, emotions, or unique cultural values either.

We must remind ourselves AI is simple pattern matching and reassembly of mostly Reddit and the internet at large. It’s a language model that takes words or text and reassembles them into much better structured words and text with higher precision and quality.

The winners of the exponential abundance of intelligence and workflows will be the same companies people TRUST to do business with who have the same level playing field advantages of the bigger companies.

• The 12 person manufacturer in Tiffin, Ohio

• The rural health clinic in Lyme, NH

• The five person logistics company in Bardstown, KY

• The 6 person marketing shop in Boise, ID

• A new entrepreneur retailer with one new idea and one location who can now create a real brand and launch it into a new worldwide franchise opportunity.

AI has not centralized intelligence into the largest companies. It is open sourcing it and distributing it to the lowest levels of our economic playing field.

Citrini’s memo frames a doom loop of AI getting better - companies laying more and more people off - spending collapse and our economy breaks. Step by step this all seems plausible until you zoom out and realize the core assumptions here is everything is fixed. Demand is fixed. No new creation. No new wealth created by new ideas and new creativity.

The Citrini report doesn’t factor in the democratization of individuals and SMBs that powers nearly 50% of our economy which have historically been competing with one hand tied behind their backs.

For 100+ years, scale has been the scarce capability of large enterprises with large capital summed up by the phrase “It takes money to make money”. But what if it didn’t take money to make money? Hmmm… that’s interesting.

What if SMBs all of a sudden had a significantly increased:

• Access to capital

• Expensive systems became cheap

• Talent wasn’t specialized and widely available

• Distribution of products beyond local became faster and easier

That 10 person SMB can now:

• Research at the same speed and quality of the 10,000 person corporation.

• Code with the same speed and quality of a 100 person engineering team

• Produce back office workflows that used to cost millions of dollars of software

• Automate with AI Agents that used to require hiring lots of employees or consultants.

Citrini writes a line I totally disagree with. It’s simply not true. Not historically and not today.

“This is the first time in history the most productive asset in the economy has produced fewer, not more, jobs.”

Citrini’s statement again leads with the logical fallacy that everything is fixed and further does not admit where all jobs have ever come from.

The most productive asset (or technology improvements) in our entire human history have never “created jobs” directly.

They have only created the conditions for new ideas, new firms, new services, new categories, and new consumer demand.

Electricity didn’t hire people. It made factories possible. Refrigeration possible. Modern retail possible.

The Internet didn’t hire people. It made e-commerce, digital services, and global distribution possible.

AI is the same kind of general-purpose shift for Intelligence. We are simply freaking out because it is happening at the speed of a computer chip vs taking a decade or more to unfold.

To be clear: nobody’s framework fits. That does not mean we are doomed as Citrini suggests.

It simply means We Are Early.

Of course, we need and we will create new economic frameworks. The question is not whether we build them in time. The question is whether we point our new technology and the capabilities they afford at the right parts of our economy first:

I believe this power will be and should be pointed squarely at Main Street. Small town America. The 2–25 person engine. I grew up there. I know our rural economy better than most. Lots of my close friends still live there.

When technology and intelligence friction goes to zero (one of Citrini’s core paragraphs was titled “When Friction Goes To Zero”) in this very large segment of our economy, it won’t be a crisis… it will unleash a small business renaissance the likes of which we’ve only historically seen in a small pocket of the world called “Silicon Valley”.

Here’s my Bottom Line:

Every “inevitable AI forecast” is consistently getting wrong and forgetting that we humans adapt. We always have and we will adapt again this time. We aren’t a fixed set of puppets that will be eventually controlled by technology or robots. Markets will thus adapt… led by humans. Business models will adapt… led by humans. We will not cede control to the machine. It’s simply not our nature.

Here’s historical proof/facts and perspective of how these other new technologies in my lifetime were going to kill our economic growth.

The Internet did not “kill retail commerce”, it clearly expanded the TAM (Total Accessible Market) of spending and accelerated it.

FACT: 30 years after the birth of Amazon, E-commerce is still only around 20% of all retail commerce.

Mobile didn’t kill desktop. It expanded the overall usage of software.

Smartphones and their apps didn’t kill software jobs, they created a new app economy, mobile payment economy, and all new consumption behaviors.

Social networks didn’t just change marketing. They created entirely new distribution methods and 100s of billions of dollars of new business value.

25 years ago we didn’t have the likes of Google, Apple, NVIDIA, Amazon which are now each multi-trillion dollar companies.

That value didn’t come from Citrini’s and others who argue using the core assumption of “fixed demand” or “status quo consumers behaving the same way forever.”

Value has always been created from new products, new behaviors, new industries, and new jobs that we couldn’t predict until they were born.

The most insidious assumption inside the Citrini crisis narrative is the dystopian premise that human behavior stays fixed. Citrini fails to even acknowledge that new companies won’t be built with the new technologies. Or that new categories of intelligence will attack old moats. Or that current workers won’t move up and learn new roles and become the managers of the new AI tools. Citrini doesn’t acknowledge these truths since they would destroy their narrative.

No, we won’t be the same people with the same behaviors in 2028 as we are in 2026 with the new Agentic AI “scary software”. If that were true, we’d still be stuck in our 1930s Depression Era behaviors.

Finally, here’s the higher level point I needed to document that applies to not only Citrini’s memo but any other “future memo” that is always hard to debate.

There’s a reason this step-by-step future storytelling is so persuasive, and it’s because we can’t visualize the future, and we can only relate to today.

The psychology Citrini uses is:

1. Narrative Transportation: this technique reduces counter-arguing. It’s the classic Hollywood movie technique or campfire storytelling technique that “transports” you into the story by stitching together today’s facts and beliefs and allowing your to simulate the world with only today’s fixed mind set.

2. Simulation Heuristic: step-by-step using “and then… and then… and then” pulls you along the narrative you are transporting on and simulates inevitable fear. When that narrative does so very smoothly, it can feel very tight and the human brain typically fails to challenge some core assumptions on this narrative transport train that are false. Challenge those assumptions (such as fixed demand) and you can get off the simulation train. Daniel Kahneman and Amos Tversky, 2 very famous psychologists wrote the research on this psychological technique.

3. Fear: fear makes risk feel larger. When a great narrative or horror film produces a strong emotional signal, your logical brain stops thinking and fighting and starts flighting. This is the most insidious part of our animal brains for those that create narratives that prey upon our DNA this way.

4. The Future Is Impossible To Visualize: therefore, any alternatives are also hard to visualize. Therefore the human brain can only rely on the facts that are known today and how this new powerful tool impacts today’s facts.

These types of cinematic forecasts can feel “undebatable” since they deftly implant narratives and fears using today’s facts into our animal brains.

The Citrini Report tries to trap the future inside the status quo of today. That’s false. The future does not look like today. It never has and it never will.

When we transport ourselves to 2028, we will see new tools, improved lives, new companies, new economies, and individuals and SMBs so much better off than they were in 2026, it will be obvious… but we will only see these by looking back with 20/20 hindsight.

AI is simply the latest inflection step on the technology staircase we’ve been climbing for the last few centuries. We have delivered more capability and improved lives to more people in more places at every step.

Bottom line: Human progress is compounding and will not all of a sudden start degrading. Distributed intelligence is the next compounding value engine.

Citrini Research

THE 2028 GLOBAL INTELLIGENCE CRISIS

Preface…

Read more

3 months ago · 7342 likes · 91 comments · Citrini and Alap Shah

Marc Andreessen Substack

The Techno-Optimist Manifesto

“Our species is 300,000 years old. For the first 290,000 years, we were foragers, subsisting in a way that’s still observable among the Bushmen of the Kalahari and the Sentinelese of the Andaman Islands. Even after Homo Sapiens embraced agriculture, progress was painfully slow. A person born in Sumer in 4,000BC would find the resources, work, and techno…

Read more

3 years ago · Marc Andreessen

I remembered and added Mark’s Manifesto above and the quick summary below only about an hour ago right before this post was scheduled to go live. It turns out I’m very aligned with Mark’s view of the future. I also quickly asked my AI to write a comparison of both reports (it did a great job).

Citrini vs. pmarca (Mark Andreessen) — same headings, opposite worlds

1) Core thesis

• Citrini: Abundant machine intelligence is a macro hazard—it unwinds the “intelligence premium,” breaks labor income, and pulls the economy into a destabilizing loop.

• pmarca: Abundant intelligence is a macro flywheel—technology is the lever that turns scarcity into abundance and raises living standards over time.

2) The mechanism

• Citrini: AI removes friction → destroys tollbooths/intermediaries → compresses LTV/margins → firms cut payroll → demand drops → firms buy more AI → loop accelerates.

• pmarca: AI removes constraints → lowers cost of doing hard things → expands what’s possible → new products/categories/firms form → demand expands → capability diffuses → compounding progress.

3) Human behavior assumption (the hidden hinge)

• Citrini: People and institutions behave mostly like today, but with better software; adaptation is slow; new industry formation doesn’t offset displacement fast enough.

• pmarca: Humans adapt fast when tools improve; entrepreneurs create new industries; markets reorganize; society composes new “jobs-to-be-done” around new capabilities.

4) Jobs + wages

• Citrini: The productive asset (intelligence) becomes abundant and produces fewer jobs—demand falls because payroll shrinks.

• pmarca: “Lump of labor” thinking is wrong; productivity shifts work up the stack and expands total value creation (new roles, new firms, new demand).

5) Markets + moats

• Citrini: Friction moats collapse; habit economics die; platform margins compress.

• pmarca: Bad moats die; good moats (trust, reliability, brand, relationships, execution) strengthen.

6) Distribution rails (where the value actually lands)

• Citrini: Intelligence centralizes into agents that optimize consumption—pushing prices down and destabilizing incumbents, with broad collateral damage.

• pmarca: Every general-purpose tech becomes a rail; rails push capability to the edge (local + personal), unlocking “wealth of technology” for billions to participate in the global economy.

7) The punchline

• Citrini: The risk is a self-reinforcing contraction—productivity without paychecks.

• pmarca: The opportunity is compounding improvement—abundance that makes life better at scale.

This post was long enough and I cut out a bunch that I saved for Part II next week. Thanks for reading this far and I’ll hopefully see you here next week!

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In 2024, shortly after I started writing on Substack, I was inspired to write what I now have to call “Part 1 of Olympic Lessons Learned (Summer)” which I’ve linked at the end of this post.

Based on last weekend’s Olympic Gold Medal moments from the USA Hockey Teams and Alysa Liu (figure skating), I’m pre-empting this week’s regularly scheduled post with an “audible”.

Do You Believe?

There are a handful of sports calls that don’t just live in your memory… they live in your soul. One of them is clearly Al Michaels in Lake Placid in 1980:

“Do you believe in miracles? Yes!”

Most of us file that away as a line about an underdog winning against all odds.

I’ve personally translated this as “Believing In Yourself”, “Believing In Your Team”, and “Performing Under Pressure.”

On Sunday, with the U.S. Men winning the Olympic gold in hockey for the first time since 1980, this time vs the superior Canada, I‘m reminded once again that “Belief” is the required ingredient for success.

The USA Women’s Hockey team had just secured their own gold the day before by also beating what appeared to be a superior Canada team. Down 0-1 with 2 minutes to play in the 3rd period, their team captain Hilary Knight turned her belief and grit into the tying goal to keep the game alive before Megan Keller sealed the US victory with the golden goal in overtime.

The best athletes and the best teams show up in the highest pressure moments, and if you pay attention to how they perform and what they say post-game, there is a clear pattern that can be summed up as:

“They just believed, and they just played.”

And you will almost always here them say

“We played as a team.“

So I have three questions for you which have inspired this post:

• Do you believe in yourself?

• Do you believe in your team?

• Do you “Just Play”?

I’m convinced that when you believe and “just play”, you will stop trying to manage the moment, you will simply perform the thing you’ve trained so hard for outside the spotlight, and you will perform at your highest level on the most pressure packed stage.

Any athlete, or even casual weekend warrior golfer, will tell you the same. When you “just play”, you play better. And when you think too much or try to manage the moment, you play worse.

“Don’t Think Meat! Just Throw!”

“Don’t Think. It Can Only Hurt the Ball Club.”

Ok, back to the Olympics.

This same “Just Play” is where the unexpected gold-medals come from.

1980 and 2026 was pure belief and trusting each other.

The 1980 Miracle on Ice gets told like a fairy tale: a group of American kids beating the Soviet machine and shocking the world.

But we all mostly forget that the USA vs Russia game wasn’t the gold medal game. The team still had to go win the next one, which they won much easier because I’m convinced they now believed in themselves. If they could beat Russia, they could beat anybody. Their performance on the ice that day in that gold medal game showed that clearly.

Those that watched the most recent 2026 Gold Medal game may have saw what I saw. I saw a gritty USA team who was simply skating and playing as hard and as best they could - leaving it all on the ice.

I also saw the superior Canada team pressing. Missing on 2 open breakaways and again on 2 open nets. I saw a Canada team playing more deliberately and carefully.

In high school, my tennis coach said something to me in high school I’ll never forget. He said, “You were playing to keep from losing, and you almost did.”

I now notice this not only in myself, my team, and other teams, but it’s very clear in pro sports during the most critical games at the most critical moments.

So here’s another Cook Axiom:

“When you start playing to ‘not make mistakes’, you stop making plays.”

Megan Keller of Team USA Hockey summed up both my tennis coach and my own take when describing her golden goal in overtime to beat Canada:

"I was just trying to make a move, take a chance," Megan Keller said. "I was trying to win, not play to not lose. That's what we talked about in the locker room."

Alysa Liu

An even better example of how I’m trying to influence everyone to believe with this post is Alysa Liu - our gold medal USA figure skater. Similar to our USA Men’s Hockey Team, the USA had not won figure skating gold since 2006 (20 yrs).

She “retired at 16” after the 2022 Beijing Winter Olympics. She didn’t like the pressure and everything about the sport. She came back at 19 and was very clear about skating on her own terms. “Nobody tells me what I’m going to wear or my hair or what song I’m skating to…” while she was being told by so many in the sport that the judges might be concerned with her look or simply how she behaved.

She “Just Played.”

As she skated off the ice moments after her gold medal winning performance she looked into the camera and said:

“That’s what I’m f***ing talking about!”

She went on to say in the post-interview:

“I don’t need this. But what I needed was a stage, and I got that. So I was all good, no matter what. If I fell on every jump, I would still be wearing this dress, so it’s all good.”

And when an interviewer asked Liu whether she feels stressed at the Olympics, she didn’t hedge.

“Oh hell no… Competitions are where I’m least stressed because people get to see what I do. That’s why I do it. So I can share my work.”

And then she said something that basically summarized my attempt at this whole leadership lesson in one breath:

“I really don’t feel nervous. I don’t feel the pressure… I invite it all in. So, no matter what happens, it’s a story.”

I knew right then and there I had to write this post to memorialize this onto the internet so that some other young women or men might just find the post and be inspired to simply believe in themselves and to “just play”!

My wife said, “She just went out there and had fun.”

Which brings me to another key point - culture and environment.

“Freedom” is almost always an environmental construct. It’s designed and/or you simply allow yourself to have it.

It happens when you trust yourself and you trust the people around you enough to stop bracing for impact.

Where this shows up in your team and your leadership.

I’ve been in enough boardrooms, exec and team meetings where “we have to get this right” yields the same pattern:

The environment can either show up like:

• If I say the truth, I’ll get punished.

• If I miss, I’ll be blamed.

• If I take a risk, I’ll be exposed.

…or like Alysa Liu and the USA Hockey Teams:

• We’ll tell the truth faster if we are honest about what we learned from our mistakes.

• We’ll learn in public without shaming each other.

• We’ll keep playing with grit and attitude until the final whistle.

Real belief sounds like:

• I belong here.

• I can do my job when it matters.

• My teammate will do theirs.

• We’ve been here before in practice, in reps, in hard moments. We know what to do next. We need to just go do it.

That’s what a winning team feels like on the inside.

This is the company and team environment you need to create as a leader… helping people to believe in themselves, challenging them for greatness, and making sure they stay loose and “just play”.

Three questions to ask your team:

1) Do you believe in yourself?

Not “are you confident.”

I mean: Do you trust your preparation enough to simply make the play?

2) Do you believe in your teammates?

Do you trust them enough to pass the puck?

Do you trust them enough to let them take the shot?

Do you trust them enough to say the hard thing without you retaliating?

3) Do you believe this pressure is a threat… or an opportunity?

Because your team will follow your framing.

Leaders accidentally make moments heavier all the time by narrating risk, consequences, broadcasting pressure and panic, or turning everything into what others will thin.

Winning leaders do something different.

They acknowledge the stakes. Then they say, in one form or another:

“This is our moment. Just play.”

Which can be summarized as another Cook Axiom:

“The environment you create determines the performance you get.”

For those that want to continue on this theme, here was Part 1 from Summer Olympics 2024:

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At some point in a company’s scaling journey, you cross an invisible line.

The line where “moving fast” without rules stops being entrepreneurial… and starts being expensive and financially risky.

This is the stage where the CFO’s job evolves from approving spend to architecting a new spending operating system.

This is the stage where the Finance department makes the transition from the financial police to the financial partner. This includes the company wide leadership shift from the finance team as judge and jury to the team that enables company wide financial ownership and accountability from the CEO on down to the Department Senior Manager or Director.

At this stage, there are simple goals for this new Financial Operating System:

1. Faster spending decisions

2. Higher quality spending decisions.

3. Financial spending ownership and accountability

And with any “system”, this requires MVPs (Minimum Viable Processes) with a focus on “Minimum”. We aren’t talking bureaucratic large company slow approval processes. Quite the contrary. When designed properly, a great MVP Financial Operating System is stage appropriate and allows everyone to understand.

• The “Why” or “Why Not” of every decision

• Clear requirements for making spending decisions

I’ve personally created and customized such a system at Mozilla, Netflix, Intuit and other companies - each at their various growth stages - from under 100 employees to several thousand.

The Foundations of This Financial Operating System Include…

• Company wide spending decision frameworks with approval spending levels tailored to each Leader, Department, Role

• Clearly defined test, success, failure metrics used for follow-on spending decisions

• Financial Ownership and Accountability

• And the one critical feature many finance teams forget - the Exception/Override process

Yes, rules are required.

But rules without exceptions become bureaucracy while exceptions without accountability become chaos.

Hmmm… I like that sentence. I just may have to frame it as a new “Cook’s CFO Axiom.”

Here’s an example system - one I’ve personally created. Please take the overarching ideas and themes here and customize them for your company or your stage of growth.

Remember: taking somebody else’s system and simply installing it without customization is usually fraught with friction.

1st Principle Financial Spending Philosophy:

Ownership + Accountability; Specifically Not Permission

The healthiest spending cultures don’t treat Finance like a gate.

They treat Finance as the owner of the decision spending system - as financial partners helping leaders with their ownership and accountability for their spending.

At mid-to-late stage startups, spending needs to be governed by simple principles:

1. Spending must have a named owner and measurable outcomes.

• Who owns the spend?

• What are we trying to prove?

• How will we know it worked?

• When do we decide to scale it or stop it?

If you aren’t asking and answering these questions, then you’re not making an investment decision, you are making a purchase.

My Core Framework: Test → Success → Failure

A system that defines when we the spend is either Testing for ROI (Test), Doubling Down (Success), or Cutting (Failure).

Over time, I’ve communicated this to my orgs as turning spend into a portfolio of explicit bets or investments.

TEST (Prove it)

This is where most new spend belongs.

Some key operating rules in this category:

• Avoid outsized bets as a first investment

• Avoid fixed, long-term contractual commitments (we are in test phase after all and don’t want to be locked in if it fails

Example: Don’t sign the 24–36 month contract until you’ve proven the spending motion works or the ROI is achievable.

Define the “Testing” spend standards:

• Short duration (30-90-180 days typical; maximum 12 months)

• Clear minimum and maximum spending caps; many don’t realize minimums are just as important as maximums; in many spending areas the concept of minimum critical spending hurdle is required to “play the game” and spending under the minimum is just as much a recipe for failure as throwing too much money at the spend.

• Defined success metrics

• Pre-set decision date (“we decide after week 8 or definitely after 6 months”)

• Built-in exit clauses / off-ramps

Best practice language Finance?

• “Yes… but Test”

• “Stage and Gate Our Investments”

• “Our initial spending is to test and learn”

• “We need to focus on shorter term variable costs for now”

• “Once we’ve proven the spending works, we can move to more fixed, long-term spending.”

SUCCESS (Scale it)

If the test worked, you don’t need to “re-approve the same thing.”

You double down with now higher spending standards

Success decision standards:

• Unit economics are defined

• Spending holds at higher volumes; alerting for “diminishing returns”

• Verified leading indicators (not “we got lucky”)

• Repeatable process exists (not 1-time success heroics)

• Spending owner can articulate the scaling plan + constraints

The CFO / Finance Leader in this category shows up as an accelerator vs a gatekeeper:

• Faster approvals

• Larger checks

• Fewer meetings

• Higher trust

FAILURE (Stop it)

Failure isn’t bad. Hidden failure is bad.

A “no shame” failure process increases speed because people stop protecting sunk costs.

Failure decision standards:

• Success metrics not hit by decision date

• No credible path to improvement

• Opportunity cost too high

CFO job here:

Make stopping feel like leadership, not punishment.

Language to normalize:

• “We didn’t lose money. We bought clarity.”

• “We made a bet; it didn’t pay; we redeployed.”

The Spend Decision Architecture: From “Pushback” to “Enablement”

Move from characterizations of finance “pushing back” to finance “enabling”… by leading with the “Why.”

This is where world-class CFOs separate from average ones.

They don’t say “No.”

They say:

“Here’s the decision framework. Improve your recommendation.”

That one sentence changes the culture.

Because now Finance isn’t rejecting spend - Finance is upgrading decision quality.

The Company-Wide Operating Rules (What You Roll Out Org-Wide)

This belongs in what I call your Financial Operating Rules - the set of shared rules that let everyone “plug into” how spending works.

Here’s the baseline rule set I recommend for mid-to-late stage startups:

Rule 1: Every spend request must declare its category

• Test

• Success (scale)

• Failure (stop / unwind)

Rule 2: Every request must have an owner

A single accountable leader. No committees.

“If you want to kill a plant, have two people water it… committees produce the same result as two people watering a plant: it either drowns or dies of neglect.”

Rule 3: Every request must have a metric stack

• Leading indicators (early proof)

• Lagging indicators (ROI outcome)

• Guardrails (what can’t break)

Rule 4: Every test must have min-max expectations and a date

No open-ended tests. No indefinite pilots.

Rule 5: No long-term fixed commitment in Test

Finance must protect the company from “premature scale.”

Rule 6: Success gets speed

If it’s proven, approvals get faster and budgets get bigger.

Rule 7: Failure is learning. Learning is constant.

Share the whys, whens, and hows of failure to increase learning velocity.

Approval Process Design: Spend Limits + Decision Rights

Now let’s translate philosophy into an operating system.

A simple 3-lane approval model

Lane A: Within rules (fast path)

• Approved by functional leader within their budget

• Finance confirms classification + metrics + cap

• Minimal friction

Lane B: Within budget but outside rules (review path)

• Requires CFO/FP&A review

• Often results in a redesign into a “Test”

Lane C: Exception / Override (explicit ownership)

• CEO/COO can override rules

• Must be documented as an exception

• Exception owner is named

• Metrics still required

• Post-mortem required

The Exception Process: The Most Important Part of the System

This last “Exception” part is critical to show flexibility… and allows the CEO or COO to take ownership and accountability… and ideally makes them think twice.

Exceptions aren’t a loophole. They are a pressure valve that prevents bureaucracy and forces senior leadership to own the exception risk transparently.

Exception trigger:

A spend request violates a rule (contract term, max $ spend, risk profile, timeline).

Exception requirements:

1. Named executive sponsor (CEO/COO or delegate with authority)

2. Written rationale: why the rule is being overridden

3. Explicit risk statement (what could go wrong)

4. Defined success + kill metrics anyway

5. Required retrospective: “Was the exception worth it?”

Every exception becomes a leadership moment.

Not, “Finance said no.”

But, “Leadership chose to override. Here’s why. Here’s who owns the decision.”

The Spend Request Template

If you want to operationalize this, make every spend request answer these types of questions:

1. Category: Test / Success / Failure

2. Owner: who is accountable?

3. What problem are we solving?

4. What are we testing or proving?

5. Success metrics (leading + lagging)

6. Failure / kill metrics

7. Budget cap + duration

8. Dependencies / constraints

9. Approval path: within rules or exception?

As CFO, Your Real Job Is Decision Quality

As you scale, your role as CFO is to architect stage appropriate decision-making systems. You are the leader who ensures spending becomes a disciplined portfolio of bets.

When you do it right, people stop saying: “Finance is pushing back.”

You want to hear: “Finance gets it and is helping us go faster”.

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For full transparency, Ai wrote most of it down by transcribing our zoom session. I asked Gemini to review the Fathom Notes transcript to organize my verbals into a better structured post. Then I spent a few hours cleaning it all up. The result is what follows below.

Assumptions → Decisions → Spend Control → Course Corrections

Your core thesis:

1. Assumptions: The numbers are simply reporting the underlying activity. Planning should start - end - and course correct based on the assumptions behind the numbers - not the numbers themselves. And yet, FP&A teams do a poor job leading with the assumptions when doing variance analyze. Cart - Horse is backwards.

2. Spend Control: Budgets don’t authorize spending. Budgets should be communicated as planned $ and absolutely not an authorization to start spending to this plan. The company and especially the CFO/Head of Finance reserves the right to revalidate assumptions before the actual spending occurs and should set up key systems to cross check the assumptions before writing the checks.

3. Course-corrections required for any plan: If the actuals don’t validate the underlying assumptions, the future period’s budgets (plans) must change. As CFO/Head of Finance, you must communicate that we will be modifying the board approved plan at least twice and likely every quarter as we validate our assumptions that created our original plan.

The 3 Key CFO Frameworks (by audience)

A CFO’s Planning System has 3 different audiences with 3 key “Planning Pillars”. Communicating properly to each audience is critical and emphasizing the planning pillars 7 times 7 different ways is critical before your leaders “own” their portion of the plan.

1) CEO + Board Audience:

“Buckets of Money” and “Use of Funds”

Purpose: secure alignment and funding at the CEO and Board (strategic level).

Language Used: runway, risk, priorities, tradeoffs, outcomes.

Output: the board-grade “money allocation philosophy.”

What the Board actually approves:

• A set of strategic bets

• A set of budget buckets (R&D / GTM / G&A)

• A set of assumptions that justify the bets

• A right to change the plan when assumptions change

What the Board and CEO should not do:

• Dive into the line item details - they aren’t qualified operationally to do so

Key lines you coach and ask your clients to communicate:

“We need to approve key investments against our strategy.”

“We are doing so with key ROI assumptions against our key initiatives.”

“We will have our hands on the money wheel and will course correct based on investment assumptions.”

2) Exec Peers Framework:

“Mini Business Plans”

Purpose: translate the Board/CEO buckets of money into functional operating line item plans with accountable owners (the execs)

Language Used: “Let’s review the use of funds with the $ allocated to you”

Output: department plans with headcount + vendor spending justified

This is the missing middle layer you called out:

• The Board/CEO “Investment Buckets” are too abstract for exec execution but are the proper guardrails for the mini-business plan executive layer of plan ownership

• Execs don’t get to say “The Board gave me 15% of revenue, leave me alone.”

• Execs must justify their own use of funds allocated to them with clear ROI’s for new headcount and vendor spending and clearly documenting the underlying assumptions at a line item level.

Your role as CFO/Head of Finance becomes their “board” - pitching you to properly spend the money they’ve been allocated. Your role is to partner with them to fine tune their assumptions by asking questions and highlighting trade-offs or alternatives they may not have considered.

3) Operational Staff Framework:

“Spend Approval System”

Purpose: govern where money actually leaves the building.

Language Used: clarity, speed, guardrails, approval systems.

Outputs: a lightweight procurement/intake/spending documentation and approval system.

This is where most startups get it wrong:

• Execs and boards agree on a plan.

• Directors/managers spend money based on short term incentives vs longer term plans

• The original assumptions silently drift.

• Finance “discovers” drift after the fact.

A proper spending approval system:

• Captures spending assumptions before the spending occurs as the quick “cross check” of the original assumptions many times created months before.

• Make the company create an “assumptions of record” database to enable faster spending course corrections and faster future spending approvals.

The 3 Planning Pillars (by audience)

Pillar 1 — Allocation (Board Level)

“How do we allocate money across bets?”

This is your “buckets of money” system.

Implementation steps:

1. Define the strategic bets (what we’re trying to win at).

2. Allocate dollars into buckets (R&D/GTM/G&A + optional “strategic reserve”).

3. Tie each bucket to the assumptions that justify it (not just the dollars).

4. Publish the operating principle:

   • Budgets are plans, not entitlements.

   • Assumptions are the truth.

   • We reserve the right to reallocate when assumptions change.

5. Deliverables:

• Board allocation slide (one page)

• Top assumptions list (the “why” behind the plan)

• Runway/risk ROI statements on use of funds

Pillar 2 — Budget Build (Exec Level)

“How do we turn buckets into mini business plans?”

The structure:

• High-level allocation (buckets)

• Then: build department budgets with line-item logic

• Headcount is ~70–80%, vendors ~20–30%

• Separate logic and question sets for headcount vs vendors

Implementation steps:

1. For each exec, build a mini business plan:

   • Goals, initiatives, owner, milestones

2. Convert into a department budget:

   • Headcount plan (roles, timing, location, fully-loaded cost)

   • Vendor/tooling plan (purpose, ROI hypothesis, dependencies)

3. Document the assumptions behind each line item:

   • “If we spend this, what does success look like?”

4. CFO challenge stance:

   • Execs tend to hire people to go faster

   • Finance tends to prefer systems and automations and variable costs vs people

   • Meet in the middle with explicit people vs software tradeoffs

5. Deliverables:

• Department plan template (initiative → resourcing → assumptions)

• Headcount plan (roles + timing + success definition)

• Vendor plan (tool → purpose → success definition)

Pillar 3 — Spend Control (Operational Level)

“How do we approve spending in a way that validates our assumptions?”

This is where your “intake system” and the “same questions every time” becomes the playbook.

Implementation steps:

1. Install a fast and easy purchase request intake workflow

2. Requiring “assumptions of record” on every meaningful spend.

3. Separate two motions:

   • Budget approval (planning)

   • Spend approval (execution control)

4. Require at least two approvals on spend over a certain $ amount (guardrails).

5. Build the cadence loop:

   • compare actual outcomes vs stated assumptions

   • course correct during the year (not annually)

6. Deliverables:

• Spend request form (the questions)

• Approval matrix (who signs what)

• Spend review cadence + variance playbook

• “Assumptions of record” repository (even if it’s a doc + sheet to start)

The Engine Underneath: “Assumptions of Record”

Most companies have:

• A system of record for dollars (P&L)

• A system of record for headcount (HRIS)

I’ve seen very few who have:

• A system of record for spending assumptions

How it works:

• Each spend / hire is approved based on underlying assumptions.

• One of the best assumptions for new hires is defining the role’s success after 90/180 days. If it’s a new vendor spend, then defining the success of that vendor’s activity after 90/180 days.

• Then compare the actual new hire or vendor results to the original assumptions not just the dollars/expense line.

• Now shift more dollars to what’s working and fewer dollars to what’s not.

The Question Architecture

You started to define the “universal” question set:

The universal question frames

Who / What / Why / Where / When / How

Every spend request is just a structured version of these questions.

You don’t say no. You simply ask questions to:

• Define the real problem

• Expose assumptions

• Create alignment on true buy-build investment partnerships

• Document decisions

• Build a database that becomes the company’s assumptions of record

Critical design rule

The system must “write it down”. It must capture the underlying assumptions at the line time level. If these assumptions stay verbal, it doesn’t scale.

Your “Decision Tree”

You have two branches you’ll formalize in the module:

Branch A: In-budget spend

Goal: validate original assumptions are still true.

• “Remind me what assumptions we approved for this line item.”

• “What changed since we set the plan?”

• “Are we still solving the same problem?”

• “How will we measure success/failure?”

Branch B: Out-of-budget (incremental) spend

Goal: force tradeoffs and reprioritization.

• “What are you trading off to fund this?”

• “Where does this rank vs your other priorities?”

• “What risk do we take if we say no?”

• “What risk do we take if we say yes?”

You also gave the best example of how to avoid gaming spending based on dollars vs activity assumptions. Don’t fall into the classic planning or spending request trap from your executives of “I can use the same dollars with different activity assumptions”:

“Instead of 1 support rep in the US, I’ll hire 3 in Singapore.”

This is gold because it proves:

• Budgets don’t protect you

• Assumptions drift even when dollars don’t change

• I hope everyone sees the core problem with this logic. It suggests that the original plan assumptions of only needing 1 rep to complete the activity, that 3 reps are now better at completing the activity? This is simply spending the same dollar with lower productivity and lower efficiency. Small teams win and allowing more activity for the same dollars eventually results in more overhead, more management and more inefficiency.

Confidence + Risk Ratings

Your CFO coaching framework:

• Force confidence/risk scoring as part of the planning conversation

• Document the narratives around their confidence and risk scores.

• The system creates future pattern recognition.

Assumptions don’t have to be numeric.

• “Success = launch by X date” as a valid assumption.

• “Failure = If we miss the launch and downstream revenue slips” is a valid risk and would suggest delaying new sales and customer support reps and marketing launch dollars.

Systems and Tools

You name-checked the best systems and today’s market map:

• BizTerra (an early internal-derived prototype) - I helped build this with my good friend Delly 10+ years ago at Mozilla

• Tropic

• Brex partners with Zip to deliver this functionality

• Ramp has built their own

Step by Step Plan Creation

Here’s the structured version of what I coached

Step 1: Install the Philosophy + Pillar 1 (Allocation)

• Teach “budgets are plans, not entitlements”

• Define buckets + runway guardrails

• Create initial assumptions list (Board-grade)

• Publish the “right to course correct” principle

Step 2: Build Pillar 2 (Mini Business Plans)

• Each exec creates department plan (initiatives + resourcing)

• Separate headcount vs vendor logic

• Document assumptions behind each major line item

• Lock the department budgets (with assumptions attached)

Step 3: Install Pillar 3 (Spend Control)

• Design the intake/spend approval workflow

• Decide approval lanes + signature authority

• Build the “assumptions of record” system

• Create the review loop (how FP&A uses this monthly)

Step 4: Run the first cycle (practice)

• Walk 5 real requests through the system live

• Tune the questions

• Tune the thresholds (what needs approval vs auto-approve)

• Train execs + staff on how to use it

Next Week: Part 2 - The Detailed CFO Spending Operating System

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My friend knows I’ve been thinking about the power of belief and self-confidence as a “thing”.

One of my earliest memories, I want to say as early as when I was 8 years old, is my conviction that believing in myself mattered more than anything else or what anyone else said about my abilities.

So many people want to tell you what you CAN or more importantly CAN’T DO something. Maybe I’m stubborn or not a great listener but my brain is wired to only listen to what I believe.

I’m sure, like you, this conjures up the several books on this subject that boil down to the phrase “If you believe you can’t do something, you’re almost always right”.

And if you believe you can, even when you’re not sure, you give yourself a chance that most people never take.

Confidence Isn’t the Foundation. Belief Is.

For years, I personally thought just having more confidence was all you needed.

I’ve now learned confidence enough is not the root or foundation of success.

Belief lies much deeper.

Confidence is simply what belief feels like after it takes hold.

Belief is the quiet, internal voice inside your head that says:

“I don’t know how I’m going to do this, but I believe I can and I will do this.”

Self-belief comes before any evidence of success. Results usually come slowly with many more failures (9 out of 10?) before the 1 out of 10 glimpses of success that build upon the foundations of your belief.

As you continue to believe and grind, your success rate is usually only 3 out of 10 and then 5 out of 10. It’s a grind but confidence then builds from these small wins and signs of success. Grinding builds confidence and confidence builds belief.

The problem is that most people give up or outright quit since the opposite of self-belief is much more poisonous to the mind.

Self-Talk Is a Symptom, Not the Disease

This is where I diverge slightly from the many conversations about positive self-talk.

For sure, positive self-talk matters. But negative self-talk is much more crippling.

However, I no longer believe this self-talk negativity is the root cause of our inability to succeed at anything.

I believe negative self-talk is simply a symptom of the more powerful non-belief. I also now believe that understanding all of this is the way out.

When you don’t believe you can do something, your body knows it before your mind or your words do.

Non-belief shows up as:

• Physical tension

• Restrictive movements

• Fear responses

• Elevated cortisol

• Hesitation and avoidance

• Flight vs fight

It’s clear to me the mind and the body are connected in ways we still don’t fully understand. For sure, our body’s physiological state created from the mind eventually turns into silent words inside your brain… what I call the roommate inside your head.

Your roommate in your head starts talking to you:

“I/You can’t do this.”

“I/You are not ready.”

“This isn’t for people like me.”

The words are real and powerful but if you truly are honest, these words came after days, weeks, and years of the much deeper non-belief.

Belief Changes Chemistry Before It Changes Outcomes

I’ve posted before that I’ve studied biology and neuroscience much of my life.

I believe (and modern day scientific evidence is starting to prove it) that “Belief” can literally change your body’s chemistry.

Belief changes your brain-body’s operating system.

When you believe:

• Brain chemistry changes

• Signals to your nervous system changes

• Your muscles relax instead of tighten

• Your attention widens instead of narrows

The proof of belief shows up in so many places:

• In religion

• In emergency moments where people lift cars off loved ones

• In elite athletic performances

• In recovery and healing

It’s not magic.

It’s your physical biology responding to your mind’s belief.

What Superstar Athletes All Say (In Different Words)

Study any elite performer long enough and the pattern repeats.

“I believed in myself when nobody else did.”

— Stephen Curry

Michael Jordan.

Serena Williams.

Roger Federer

Nearly every successful person.

They weren’t always confident but they’ll tell you they believed in themselves before anyone else did. They believed they could achieve the next level of performance when most others were telling them they peaked and wouldn’t make it any higher.

My dad knew Jack Nicklaus. Went to high school with him. Played on the same basketball team. Jack was famous for saying that he would physically stand over his golf shots and visualize them in his mind. The swing, the flight of the ball, landing on the green and going in the hole. He wouldn’t swing until that vision was complete and he believed it. Interestingly, Jason Day (a modern day golfer) uses the same technique. Both Jack and Jason understand that once your mind believes something your body will perform… they also fully understand the vice-versa.

Here’s The Data

This is from the article my friend sent me, from authors Ryan Berman, Suzy Burke, and Rhett Power, and their new book Headmentals.

According to research cited by the National Science Foundation:

• The average person has ~60,000 thoughts per day

• Up to 80% are negative

• Nearly 90% are repetitive

That’s ~48,000 negative thoughts a day - most of them recycled.

Yes, this is a mindset problem but more importantly it’s your personal operating system’s problem.

As the authors outline in Headamentals, the goal is NOT to eliminate the voice.

You can’t.

The “monster” - your roommate - never moves out.

But you can:

• Understand it

• Decode it

• Retrain how much authority it has

That monster isn’t evil.

It’s trying to protect you from risk. But your roommate is doing a very poor job of saving you.

The authors also had me when they created their own “3 Cs” on how to deal with your inner roommate:

• Catch (the negativity)

• Confront it

• Change the words, the destructive mental dialogues - start saying the opposite!

  ---

Why Leaders Must Model Belief If They Want to Scale

Here’s the part that I know and have witnessed in both directions (positive and negative)

• Self-talk as a leader becomes team-talk

• Team-talk becomes culture

• Culture becomes performance

Culture doesn’t just show up one day. It begins and manifests with a powerful and passionate belief by a leader. Using their voice and their actions.

I know I shudder at religious or cult leaders who understand this… but there is the proof, front and center, at the true power of “belief”. Use it to your advantage as a leader.

If you lead people at work or at home, it’s on you to:

• Create more positive talk

• Encourage belief before certainty

• Model belief and the little red engine that could, “I Think I Can”

Most performance issues are not skill gaps.

They’re belief constraints.

Skills only come after repeated successful performance.

Successful performance can only come after several attempts to succeed but failing and learning and improving.

Bottom Line

Modern science still doesn’t fully understand the mind.

But I’m convinced of this:

1. Belief is one of the most powerful forces we have access to

2. Belief creates confidence

3. Confidence alters physiology

4. Physiology unlocks performance

5. Positive self-talk helps reinforce belief

Everything starts inside your head, dealing with your inner roommate. Don’t listen. Shout him/her down and simply believe.

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I’ve been thinking a lot about this question lately. I just did a live Q&A on the subject yesterday (Wed, Jan 28 2026). We are doing these live sessions monthly now. Look for them in your future feeds.

I don’t think a lot of people stop and ask, “How Did I Learn That?” It’s a valuable question to reflect on as part of my Learn It, Do It, Coach It framework.

When you unwind the tape on your own personal life, your career, or the careers of history’s most iconic founders, clear “learning patterns” emerge:

The best learning is never solo. It’s social. It’s communal. It’s collective intelligence.

Solo learning is valuable as well but it’s more about personal reading, research, and gritty figuring it out. Being inside your own head.

The real powerful learning happens, however, when you are surrounded by really smart people with diverse viewpoints all focused on solving a common problem.

Small teams/groups of these people provide extremely powerful collective learning. These people are typically formed from your mentors, your colleagues, and your networked communities.

Making sure you spend time with all three segments of “learning potential energy” is critical time to carve out of your regular calendar. As they say, “All work and no pontification makes Jack a dull boy!” (Or something like that 😉)

Mastery Has Always Followed the Same Arc:

Apprentice → Journeyman → Expert → Master

If you strip away the modern labels and look across history, learning in every domain has typically followed the same structure:

1. Apprentice: You watch others.
   Michelangelo apprenticed under Ghirlandaio.
   Every Renaissance workshop functioned as a collective learning engine.

   Or Medical school students in year 2.

2. Journeyman: You do the work alongside the experts.
   Blacksmiths, carpenters, and stone masons all traveled town to town to work together on real problems.

3. Expert: Once you’ve “journeyed” enough, you begin to contribute your own ideas and people begin to view you as an expert in your craft.
   Experts continually learn through exchanges and critiques with other experts.

4. Master: Here you become a master by teaching others your expertise. You give back to your community that taught you.

Mastery of anything has always followed this arc. The lone genius concept is rare and the probability of anyone becoming a lone genius is not advised.

Behind every “genius” is a collective network of intelligent peers.

Silicon Valley Example #1: Jobs & Wozniak

The myth: Steve Jobs was a lone visionary.

The reality: Apple was born out of collective intelligence.

Jobs learned product taste from California counterculture and Xerox PARC.

Woz learned electronics from the Homebrew Computer Club - one of the greatest communities of peer learning in tech history.

Neither would have built Apple alone.

Their genius was in the collision of perspectives from design to engineering, to culture, to collective networks, to storytelling the “why” of the design.

Silicon Valley Example #2: Andy Grove’s “Managerial Leverage”

Andy Grove (ex-CEO Intel) didn’t invent modern Silicon Valley-style management in isolation.

He synthesized it from:

• Observing Bill Hewlett and David Packard

• Learning from Intel’s earliest manufacturing leaders

• Absorbing lessons from mentors and peers during Intel’s turbulent early years

“As a middle manager, you are in effect a chief executive of an organization yourself… As a micro CEO, you can improve your own and your group’s performance and productivity, whether or not the rest of the company follows suit.”

It turns out that the one-on-one is not only a fundamental element in the manager/employee relationship, but perhaps the best source for organizational knowledge that a manager can get. In my experience, managers who don’t have one-on-ones understand very little about what’s happening in their organizations.

The best operators are aggregators of wisdom and share it broadly 1:1 and with their small teams.

Silicon Valley Example #3: The PayPal Mafia

People talk about the PayPal Mafia like some magical convergence of future founders and investors.

But what made them powerful wasn’t individual brilliance, it was collective apprenticeship.

Together they:

• Debated ideas

• Critiqued each other

• Shared failures

• Swapped frameworks

• Built confidence loops

Reid Hoffman has repeatedly said the greatest accelerant in his career was learning directly from Peter Thiel, Max Levchin, and Elon Musk in deeply intellectual, sometimes uncomfortable, conversations.

The community was the curriculum.

Silicon Valley Example #4: Netflix in the Early Days

My story. People assume Netflix succeeded because of superior software or logistics.

Not even close to the truth. We didn’t have superior anything. But we were some of the best collective learners and translated this learning into DVD community intelligence and product delivery.

We had postal engineers, mathematicians, supply chain veterans, filmmakers, and technologists all exchanging perspectives of how best to deliver entertainment to people’s living rooms.

Our breakthrough in the Disc Inventory Management System wasn’t the product of one person. It was the outcome of dozens of conversations, debates, whiteboard sessions, and shared learning loops.

Our recommendation engine? Same story.

Built not by a solo genius but by “the guild”. We were the modern workshop of experts learning from each other and focused on delivering a great product to our customers.

Collective intelligence was our moat.

Silicon Valley Example #5: OpenAI and the “Lab Model”

OpenAI’s, and now others like Anthropic and even Gemini, breakthroughs didn’t come from isolated research.

They came from:

• Open collaboration

• Cross-disciplinary teams

• Shared insights

• Collective problem solving

• Iteration in public view

The entire AI ecosystem today is built on shared research, shared models, and shared lessons, just like the old European guild system.

The future of learning loops back to the past:

Communities learning together.

So back to my main point and my original question:

How Did You Ever Learn Anything?

Your own path probably mirrors the same historical patterns

Think about the best things you know:

• A financial system

• A negotiation tactic

• A forecasting model

• A pattern for managing risk

• A way to coach or build teams

• A way to read a room

• A way to influence a CEO or board

It’s almost always:

• Someone showed you

• Someone challenged you

• Someone told you a story

• Someone pushed you to think differently

• Someone asked a question you didn’t know the answer to

You learned by being in conversation with smart people and talking and listening and observing a lot.

The Two Hardest (And Most Powerful) Phrases to Achieve Mastery

If mastery and collective intelligence are the goal, there are 2 phrases that will help get you there faster. Usually, they are the hardest phrases to say out loud.

1. “I Don’t Know.”

Michelangelo/Einstein/Grove/Bezos/Buffet/every great Master said it during their own learning.

Other “leaders” (I won’t name names) - never say the words “I Don’t Know”… they try to fake it until they make it.

Great leaders aren’t afraid of not knowing.

They are afraid of not learning something new.

2. “Can You Help Me?”

This is the call that activates your network.

It is the fastest path to someone else’s 20 years of expertise.

Bill Campbell, the Trillion Dollar Coach, lived by this simple humility to ask people:

“What do you think?”

He learned from them.

They learned from him.

So If Mastery Comes From a Community, Then Build/Invest In Your Community

Every CFO, COO, founder, or operator I’ve coached who accelerated most quickly shared one habit:

They talked to more people, about more things, more often.

They said “I Don’t Know” and “Can You Help”… A LOT!

They intentionally constructed what I call a Personal Knowledge Network - a modern version of the medieval workshop or learning lab.

• Mentors

• Peers

• Cross-functional partners

• Experts outside their domain

• People 10 years ahead

• People 10 years behind

• People who think differently

• People who ask harder questions than they do

This is how you compress decades of learning into months.

Because you’re not learning alone.

You’re learning through and with others.

Just like every master has always done.

Oh yeah, one last thing. Once you become a Master you must Pay It Forward. Find an Apprentice and keep the loop alive!

Footnotes - More reading on Andy Grove: one of my Best Ofs.

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• Forecast accuracy.

• Budget vs Actual reconciliations.

• Creating operating plan waterfalls.

This precision is your foundation. It’s why CEOs, boards, and teams trust you. Details are your superpower.

But they’re also your kryptonite.

Because when you lead with the details, you lose people. Fast.

Why Details Lose the Room

Most audiences don’t start where you start. They don’t want the 50-slide board deck or the 7 column waterfall slide. They don’t want the caveats, footnotes, and reconciliations… at least not at the beginning.

They want the headline first. The Why and “So What.” They want to learn an insight from you that creates the hook and engages their brain. They don’t want a report card.

When you open with details, watch for them picking up their phone. Keep going with details, and you are too far ahead and begin to overwhelm. Your credibility of “master of the details” goes up but your influence goes down. They don’t know where to start because you didn’t lead them with your key headline insight. So they start slinging questions that mostly aren’t relevant and sometimes are rabbit holey.

You then get frustrated that they are missing the point. Can’t they see what I see… I gave them all the details! It’s obvious.

I’m here to remind you (you already know it) so I’m reminding you… IT’S NOT OBVIOUS! And even if it is, it’s even more important to lead with the headline (short key statement) so you can lead the discussion and quickly begin engaging on next steps, next decisions.

Otherwise, you will simply be the reporter, recorder of the conversation, and go back to being reactive to the conversation.

This is the paradox of the finance seat: the very skill that makes you credible (the details) can also make you mostly invisible as a key part of the real conversation.

Lead with Engagement

Influence comes from engagement, not information. Engagement starts with a headline. Headlines spark curiosity. Curiosity invites dialogue. Dialogue creates influence.

So as a CFO, you must do the opposite of what you’ve been trained to do. Instead of giving all the details upfront, give just enough information to spark the next question.

Think of it like this:

• Headline: Share the bold, simple statement or key insight

• Pause: Let the audience react.

• Question: Anticipate what they’ll ask next.

• Provide key pieces of evidence: (1 detail at a time and only 3 at a time per the dozens of details you have)

• Shut up again: ok that was harsh - but stay silent again to let the Headline Statement and Evidence sink in.

Recognize this framework as a prior reader? Yes, it’s my SECS talk key framework:

• Statement

• Evidence

• Conclusion

• Shut Up

Don’t let your years of habitual training try to provide the whole spreadsheet at once. The roommate inside your head says you are withholding key information… you are NOT!

You must think of this as sequencing your critical information for maximum impact and ultimately influencing the next step… the next decision.

Why This Works for Finance Leaders

As a finance leader, you sit in a very unique position. You sit at the unique intersection of all the companies activities and most of those are translated into numbers and dollars.

The best finance people already know the next question since you’ve been living in these details and have journeyed to the answer. You’ve rehearsed the scenarios. You’ve run the sensitivities. You’ve got the back pocket slides.

That’s your edge, your superpower. But it’s also your kryptonite because by definition you are too far ahead of everyone else on this “data journey”.

While others wing it and hypothesize with opinions, you’re already two moves ahead…but you must slow down and bring the room (the audience) along.

When you lead with engagement, you’re playing both sides of the game:

• Drawing your audience in with simplicity.

• Backing it up with credibility when the questions you know are coming result in an engaging conversation that you are now orchestra conducting.

You’ve seen this mastery from a select group of great finance leaders and CFOs. These people are thought of as “detail-keepers” - though they are… they are viewed as strategic partners and influential leaders.

Practicing the Skill

This is not about dumbing down. It’s simply about pacing the flow of information.

I encourage you to reframe how you communicate everything you know and practice flowing out your information in different forums:

• Your Team: Start with your team. It’s small, comfortable and a mostly safe place to practice this new skill.

• 1:1 Executive Partners: Now practice this communication technique 1:1 with your most trusted peers.

• Executive Team: When you are ready, start showing up this way with your weekly exec team meeting. Start with the powerful trade-off question (“Are we willing to sacrifice X to achieve Y?”) or only one Key Insight (“Our Engr. Launch Date is critical. 50% of next quarters revenue assumptions rely on this launch date.”).

• All-Hands: Translate details into narrative. “Here’s the story the numbers are telling us…”. “Here are the 2 key areas we are winning and the 1 key area we are losing.” “Here’s Why.” “Here’s Where We Need To Focus.” Yes, I’ve capitalized those since Headlines are normally capitalized to get your attention.

  • Boardroom: When you are ready, it’s game time. Start with the only 1-3 headlines that matter. Have your SECS talk in your pocket. Anticipate the next question or pushback and be prepared with the next right data details when the questions come. By all means, don’t drone on for 10 minutes and end with “Any Questions?”… Stop doing that!

Over time, you’ll retrain yourself to engage first, explain second… not vice versa.

Closing Thoughts:

Details are your credibility. Engagement is your influence.

When you put them together, you create the CFO superpower that drives real leadership impact.

Give just enough to spark the next question. Anticipate it. Then answer with confidence because you already own the details.

This simple shift/hack? Communicate with headlines first, details second.

Catch their attention before you double click into details.

Make sure your audience cares what you have to say before they care how much you know.

And practice your SECS talks.

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“Make every person in the company the CEO of their function.”

Today, let’s take that metaphor one step further and focus on the CFO.

But instead of thinking about the role as the “CEO of Finance”, I think a far more powerful analogy is:

The best CFOs operate as the internal Venture Capitalist of the company.

The CFO as Internal VC

When you look closely, the metaphor fits.

Inside every growth company, CFOs should be doing what great VCs say to do but fall short of making this mental shift. So this post is here to help you with that.

Your go-forward thought model of the CFO role:

• Functional operating plans = Mini Business Plans

• Annual and quarterly budgets = Funding Rounds

• Cash runway = Managing Each Functions Budget Before Next Investment

• Staging & gating of Investments = Rounds of Funding Tied to De-Risking and/or Value Creation of internal investments/projects

• Portfolio management = Allocating capital across product, growth, people, and systems

• Board updates = Reporting progress against the functions (a.k.a “Dept”) investment thesis

• Closed-door CEO sessions = Hard conversations about doubling down, pivoting, or stopping with the Function/Dept Leader

Once you see it, you can’t unsee it.

As CFO, stop thinking your job is to manage the money. Start thinking of yourself as the Chief Investment Officer with a key shift in framing or deploying your precious cash against a portfolio of internal bets.

Mini Business Plans, Not Just Budgets

In this model, budgets stop being static spreadsheets.

Each function (dept) effectively runs a mini business plan:

• Clear objectives

• Defined investment ask

• Expected ROI tied to company strategy

• Time horizon for results

• Leading indicators and lagging metrics

Now, as CFO (ahem “internal VC”), you are no longer approving spend - you are now underwriting the investment thesis.

Funding, Runway, and Time-to-Value

Great investors and Great CFOs obsess over cash runway.

When you focus on this internally at a company, the question is not “How long until we run out of cash?” but rather:

“Do we have enough cash runway for this investment to actually work?”

Or

“Has the capital we’ve allocated to this investment thesis been used to de-risk the next milestone and has the investment created value?”

Or

“What’s the payback period for this internal investment?”

Killing good ideas too early is just as dangerous as funding bad ones for too long.

The best CFOs sequence investments, stage funding based on hitting key milestones, create value, or de-risk a few key pillars of the long term investment thesis.

As CFO, you are an investor. As CFO, start playing internal VC.

Staging, Gating, and Reallocation

Internal VCs don’t fund everything all at once.

They:

• Stage capital

• Set key milestones (investment gates)

• Fund according to progress

• Reallocate aggressively

This is where great CFOs separate themselves from finance operators.

Stop asking, “Did you spend to plan?”

Start asking, “Is this investment earning the right to more capital?”

Metrics as Investment Reporting

In this model, metrics become investment reporting, not performance dog-and-pony theater.

Each function reports:

• Cash spent against key projects (P.S. - “keep the lights on” is a key project)

• Returns generated (ROI)

• Risks emerging

• Confidence levels to convince you the investor/CFO to fund their next tranche

This reframes accountability in a powerful way. Leaders stop defending budgets and start defending ROI and start pitching you.

The CEO Relationship: Closed-Door Truth

Every great VC has private conversations with the founders and CEO

So should CFOs.

Closed-door CEO sessions aren’t about spreadsheets. They are about relationships, judgment, and next-step strategy.

• Which bets are working?

• Which need more time?

• Which should be stopped?

• Where to double down?

Want to Be a VC Someday?

I used to think I wanted to be a VC. No longer. But if you do, there’s nothing wrong with that (he says tongue in cheek).

If you want to be a VC someday and operate at that level of strategic influence, then start acting like one… tomorrow.

Be the chief allocator of cash for the company.

Think in portfolios, not departments.

Act like every dollar has an opportunity cost. Hint - it does.

Your job is to compound valuable bets over time. Hard stop.

The Investor Mindset of a CFO

Let’s double click into the Investor Mindset. Here’s a hot take: every strategic decision a CFO makes is, at its core, an investment decision.

• How much to allocate to R&D to build tomorrow’s product?

• How much to invest in marketing to fill tomorrow’s pipeline?

• When to hire the next executive leader who can scale a function or open a new market?

Each decision requires a strategic forecast of returns. Each decision requires “thinking in bets”.

Every “next decision” should therefore be adjusting the probabilities as more information is learned from the original assumptions.

The best CFOs think in time horizons and portfolio risk. They manage today’s spend while creating optionality for tomorrow’s growth.

They ask:

“What’s the expected ROI on this decision, and what’s the risk-adjusted payoff if I give it the right runway?”

Allocating Capital Like a Pro Investor

A great CFO runs the company like a balanced investment portfolio.

Every line item - product development, go-to-market, operations - is a category in that portfolio. The CFO’s job is to size each investment according to its expected return, its risk, and its correlation to everything else.

• Too much in R&D, and you risk running out of cash before product-market fit.

• Too little in R&D, and your product stagnates while competitors out-innovate you.

• Too much in marketing, and you risk wasting dollars before product-market pull.

• Too little in marketing, and your pipeline engine sputters just as you start achieving product market fit.

It’s the CFO’s version of portfolio rebalancing. You should always be adjusting your internal investment mix to maximize future returns without overexposing the business.

No, it’s not easy. It’s complex. It’s a balancing act while constantly juggling 3 or more assumptions in the air.

The CFO’s Dual Mandate

Unlike fund managers, CFOs don’t get to just invest other people’s money.

They have to protect the cash runway while investing for the future.

That means managing the tension between:

• Cash preservation and revenue growth acceleration

• Short-term burn vs. long-term compounding of company value

• Operational discipline vs. strategic risk-taking (making better bets)

Great CFOs have learned after multiple trials and errors on how to better sequence investments, when to lean in, and when to pause or hold so the company never has to abandon a compounding bet too early.

Great CFOs build plans that give good ideas enough runway to prove out their returns, instead of pulling the plug too early or too late. They’ve learned how to balance and “stage and gate” for the next set of investments.

Investing in Leaders

Finally, the best CFOs don’t just invest in projects and vendors. They invest in people.

Throwing cash at stuff doesn’t create returns. Leaders who regularly make increasingly better decisions are what creates value.

A CFO’s highest-leverage investment is in hiring, developing, and empowering the leaders who can own their respective budget lines and turn them into high-ROI engines of activity.

When you hire the right leader, your capital allocation multiplies. When you hire the wrong one, even the best investment strategy burns cash.

That’s why the most successful CFOs think like venture investors:

“Where’s my best next bet; My bet for product, people, and executional systems and processes?”

From Finance to Investment Thesis

If you view your company through the lens of investment returns and your time horizon through the lens of compounding returns, you start making better decisions.

You begin to act less like a budget enforcer and more like a portfolio architect. You begin to be respected for allocating the company’s capital properly to maximize everyone’s enterprise value.

Capital Allocation Scorecard: The CFO as Portfolio Manager

I banged this out as an example “portfolio manager”. YMMV - “your mileage may vary” - but it’s the framework that counts. The Investment Categories are your “stocks”. You only have so much cash to invest.

The next time you start struggling to communicate as a CFO… take a pause and reframe your communication around:

“What” you recommend investing in.

Why?

When? - think about your own investment portfolio and it just might unlock you.

Now start thinking about diversifying and balancing your “bets” (a.k.a investments) properly.

Align your Whys, Whats, and Whens to your clear financial philosophy (Your “Thesis” - Yes “Lead with a Thesis” - I wrote that post 2 weeks ago).

Your peer executives may disagree with your thesis but at least you’ve now established a proper investment framework and decision-making guardrails as you seek to align everyone’s budge/investment requests.

Finally, no matter where you or your exec team finally land on the risk/reward curve, don’t forget to be explicit about the Cash Runway tradeoffs and risks of the bets you’ve all just aligned upon.

No decisions or investments are free. They all have varying degrees of risk and ultimately impact the value of the company in the future when you need to raise capital again.

You are therefore trying to balance and de-risk the value you’ve created at that future point in time vs the bets/investments you made historically.

You aren’t just operating other people’s decisions (your CEO’s or your Exec Peers’). You are their portfolio investment manager and you must coach them on the risk/reward tradeoffs, the upsides if you are right, but especially the downsides if you are wrong.

You must cut your losses as early as possible and you must reinvest (double down?) on the decisions that are working.

You must rebalance your portfolio at least annually but in reality in startups - this is at least semi-annually.

The best CFOs aren’t just keeping score and reporting. You are the Chief Investment Officer and your scoreboard is the value you’ve helped create through your smart investing during the next fundraising period.

Be the internal VC of your company.

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It’s the gap between your expectations as their leader and your team’s performance.

As Hamlet famously said, “Aye There’s the Rub”… It’s a gap that you, their leader, unknowingly and likely created.

The childhood saying is true about never to ASSUME anything since many times it makes an ASS out of U and ME.

Great leaders do not assume.

They are clear, direct, transparent, and consistent about what they expect. They actively close the distance between what’s required and what actually happens. They deliver relevant and timely feedback both positive and negative against their expectation of the activity.

So my PlayBook for this week to help you level up your leadership impact is to start by:

Leading with expectations.

The Silent Killer: Unspoken Expectations

Most performance problems don’t start with incompetence.

Performance problems usually start with ambiguity and end with crystal clear clarity.

“Crystal?”… ”Crystal!”

• Expectations that live in a your head

• Assumptions that others “should obviously just know”

• Vague language disguised as being nice or providing empowerment

• Feedback delivered too late, too softly, or too inconsistently

When expectations are unclear, teams fill in the blanks themselves and most times incorrectly. Telephone tag game anyone?

And even when you think you’ve communicated clearly, guess again, since you haven’t repeated your expectations 7 times 7 different ways so there is no gap in your communications.

Gaps in expectations and communications becomes frustration.

Frustration becomes resentment.

Resentment becomes disengagement and disconnection.

The leader (you?) then blames execution or the person when the real issue was not clearly communicating expectations.

Expectations: Your Leadership Requirement

Being crystal clear is not micromanagement.

Directness is not aggression.

Transparency is not weakness.

Consistency is not rigidity.

Clear, Direct, Transparent, and Consistent are 4 key pillars of leadership.

Your job as a leader is to not just to set direction, it’s to define success and make sure everyone is on the same page.

That means being extremely explicit about:

• What “great” looks like

• What’s unacceptable

• How progress will be measured

• When course corrections will be required

If you haven’t said it out loud, written it down, and reinforced it repeatedly, then there’s a large possibility it’s not a crystal clear expectation.

The Expectation–Performance Gap

Every gap between expectations and performance is ultimately owned by the leader.

If you find yourself asking:

• “Why didn’t they deliver?”

• “Why aren’t they stepping up?”

• “How did they not understand that?”

• “Why do I have to keep repeating myself?”

Ask yourself:

• Did I clearly define the expectation?

• Did they confirm my expectation?

• Did I make the tradeoffs explicit?

• Did I reinforce it consistently over time?

Great teams don’t guess.

They execute against clear expectations.

Clear Beats Kind. Every Time.

I believe every leader learns this lesson at some point in their personal leadership maturity journey. Early in my career, I too avoided being crystal clear because I confused it with being “too harsh”, “too direct”, or too “micro-managy.”

So I softened my tone. I hedged. I wasn’t crystal clear.

And I left room for misinterpretation.

I created the gap in expectations.

Then, I had to write the performance review 6 months later.

The most respectful thing I eventually learned was to deliver crystal clear expectations.

Clear expectations:

• Reduce anxiety

• Increase autonomy and ownership

• Speeds up decision-making

• Improves trust and transparency

• Enables accountability without drama

The saying is true:

“They may disagree with you but at least they clearly know where you stand.”

Expectations Must Be Actively and Consistently Managed

Setting expectations once is not enough.

They must be:

• Reinforced in meetings

• Reflected in metrics

• Referenced in feedback

• Re-stated when conditions change or performance falls short

Leaders who say, “I already told them that” are usually confusing communication with listening. Trust me, just because you communicate doesn’t mean “they” listened or truly “heard it.” There’s that misalignment gap again.

You, the Leader, Must Goes First

If you want others to meet expectations, you must model them yourself.

Your team watches:

• What you tolerate

• What you ignore

• What you focus on

• What you repeat and reinforce

• What you excuse under pressure

Culture is simply expectations made visible through behavior.

If there’s a gap, it’s not a mystery.

It’s a mirror.

A Simple Leadership Test

Ask yourself this about every role on your team:

Could they clearly articulate what success looks like this quarter without asking me a single follow-up question?

If the answer is no, your leadership work isn’t done.

If the answer is yes, your work still isn’t done since time destroys memories.

Final Leadership Thoughts:

Leadership is not about charisma or vision alone.

It’s about precision.

Precision in language.

Precision in expectations.

Precision in follow-through.

The best leaders don’t just inspire effort.

They architect clarity.

Lead with expectations… and close the gap.

Close the gap between what you think they know and what they actually know

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If you want to be a credible, trusted, and an influential financial leader, it’s time to move beyond presenting “what the numbers say” and take a strong stand for not only what the numbers mean (key insights) but also what actions to take next, why, and when.

Thesis = having a point of view. This is critical if you are going to engage the people you are trying to influence. Remember, it’s not just your CFO insights of the data, it’s your clear point of view of what to do next and why that separates great CFOs from the standard ol’ reliable CFOs. Don’t Be Dell Griffith!

I’m on record (here on Substack and various podcasts) with my own clear thesis that the CFO needs to be the co-author of the company’s strategy… the Architect of the company’s business model. Co-authors don’t speak in footnotes. They write in headlines. Architects don’t validate specifications. They design blueprints.

The Power of a Thesis

When you lead with a thesis, you’re making a clear statement like:

• “Our current burn rate is acceptable because it’s aligned with our growth strategy and investable payback period of acquiring new customers.”

• “Our current burn rate is unacceptable since it won’t give us enough runway to get to Series B.”

• “We should double down on our social media campaigns because we’re seeing stronger LTV-to-CAC ratios than our more expensive tv and radio ad campaigns.”

• “This product line needs to sunset because our opportunity cost of maintaining it exceeds its contribution margin. We need to use these old product $ for launching our new innovated product faster which carries much higher margins and significantly higher growth potential.”

Leading with a thesis signals to the CEO, Board, and your executive peers that you’ve done the work to form a clear point of view. It invites engagement, dialogue, and alignment. It also gives you the power to shape narrative, not just react to others narratives.

A great thesis expresses conviction of direction and path choosing… but don’t confuse that with being 100% certain or right.

You Don’t Have to Be 100% Right… Just 100% Engaged

I’ve also written historically that “Perfection is a Prison”. The best financial leaders should not aim to be “right” 100% of the time. In fact 80%+ right is “great” and in the long run will win you more money at the poker table of business by continuing to make 80% bets.

The world’s smartest experts in any subject live in disagreement with each other and are still thought of as leaders in their industries, universities, or fields of expertise.

Neither expert is ever completely 100% right. Their strong and differing points of views do help shape the world’s future - and that’s more important than being “right”.

Examples?

• Tesla (Nikola) vs Edison: “DC” vs “AC” their “War of Currents” to use commercially.

• Tesla (Elon) vs nearly everyone else: Cameras only vs Lidar to achieve Level 5 self-driving.

• Keyboards vs Touch Screens for Mobile Phones.

• Any economist vs any other economist.

• We’ve had radically different points of view from Federal Reserve Chairs and their governors all trying to interpret the same fiscal conditions and what our economic future should look like.

Each expert always defends their own thesis backed by logic, evidence, and conviction. They both try to be “right” but do so mostly.

That’s your job as a C-level executive: to construct, communicate, and evolve your reasoning transparently.

Thesis (Your Strong POV) Builds Influence

A strong financial thesis helps you do three things:

1. Anchor (and re-anchor) debate. Without a clear point of view (thesis), the discussions rarely focuses on true debate and rather spirals around opinions. With a thesis, you center debate on logic and tradeoffs.

2. Focus direction. A great thesis focuses and translates complexity into more clear directions, something most everyone appreciates, especially CEOs, investors, and your team members. They all desperately want a clear point of view - especially from a CFO.

3. Build credibility. Consistently showing your reasoning, even when it’s not precisely right, earns trust and authority. Especially when you show you can build upon others ideas as you guide toward your thesis.

This is how you move from CFO Advisor to CFO Architect.

CFO’s Architect Practice: Design Your Thesis, Communicate It, Refine It

Your thesis is the basis of your decision operating system:

1. Design/Build it from data, insights, knowledge, wisdom using current operating situational analysis.

2. Communicate it to your leadership team; ask them to challenge and pressure-test your assumptions, scenarios, and alternative paths. Ask them to build upon your ideas and to refine your foundational thesis.

3. Refine it. Best ideas win. Course-correct and/or iterate your thesis as new data, insights, and knowledge emerges.

This “Design, Communicate, Refine” system is what separates strategic CFOs from analytical ones. Hint: it’s what also makes for great other C-Level execs whether in product, engineering, or sales/marketing.

Final Thoughts:

Stop trying to deliver the perfect financial model. Perfection is a Prison. The best CFOs lead with a clear thesis that is typically only 90% correct… and leave room for their peers (and themselves and their finance team) to engage and improve on the thesis.

The best CFOs are the ones who actively communicate their financial thesis clearly.

After lots of practice and “reps”, they are able to stand up in front of their peer exec team, the all hands company meeting, and the board room and say:

“Here’s my thesis. Here’s why I believe it. And here’s how and when we are going to course-correct if my thesis is wrong.”

That’s leadership. That’s influence. That’s how you earn your seat as the financial architect of the business.

If you want to learn how to do this in deep dive sessions… reach out to me.

I hope you had an awesome 2025 and are as excited as I am for 2026 to keep building and communicating for maximum influence.

Happy Holidays!

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