A while back I had a conversation with Ed Park that’s stayed with me.

Ed built FP&A at Facebook through the IPO, joined Asana around thirty people to build finance there, and later became CFO at Enjoy. Few people have lived the “first operator” role as deeply as he has.

We were talking about what actually makes a great first finance hire. Not the surface-level stuff like closing the books or running payroll, but what separates the people who just do finance from the ones who change the trajectory of the company.

Ed said something I’ll never forget:

“The first finance hire gives the company a brain.”

That’s it. The job isn’t to report what happened. It’s to build a model that explains how things happen, how growth, monetization, and efficiency work together.

When Ed joined Asana, that’s exactly what he did. He mapped out what he called a multivariate equation for the business. What drives revenue? What predicts conversion? What behavior precedes monetization?

Every team was focused on their own piece — marketing, product, sales — but nobody was stitching it together. Finance became the glue.

He found that three active free users in an org, engaged within 28 days, was highly correlated with conversion. They called it 3D28.

It wasn’t perfect causation, but it gave everyone a shared language. Marketing started telling the collaboration story. Product made it easier to invite teammates. Growth refined the new user flow. Suddenly the whole company was optimizing toward the same goal.

That’s the hidden leverage of the first operator. You turn chaos into a shared narrative that can be measured, challenged, and improved. You create a discipline of prediction and review. Set targets, miss them, learn, iterate. The point isn’t to be right; it’s to teach the company how to think.

Of course, you still have to feed the body before you can grow the brain. If payroll is slipping or reimbursements are a mess, fix that first. But don’t chase A pluses on low leverage hygiene. Good enough is fine if it buys you the hours to unpack the funnel and build the model.

Ed also reminded me of something I learned the hard way at Intercom. Finance people tend to under resource themselves. We pride ourselves on being scrappy. But if your work is helping the company make faster, better decisions, raise your hand. Ask for help. Pull in an analyst or a data engineer. Automate. The company needs you at your highest leverage, not buried in spreadsheets.

What I love about Ed’s framing is that it captures the spirit of this role. You’re not just analyzing the business. You’re creating the language the business uses to understand itself.

If you’re walking into your first operator role, start there.

Map the equation.
Pick the line in the sand.
Get everyone saying it out loud.
Review it every week.
Ask for help when it starts to work.
Keep the body fed. Build the brain.

It’s planning season. You’ve spent weeks going back and forth with your CEO, your board, and every functional lead. The company plan is finally set. ARR goals, burn targets, hiring plan, all agreed. You close your laptop and think, finally, we’re done.

And then someone asks, “So what’s my budget?”

In my experience, for a while, you can dodge that question. When the company is small, say under 50 people, you don’t really need department budgets. Everyone is still close enough to the action that they know what’s happening. But once you cross that threshold, you need to give leaders some structure.

Not because you want to control them. Because you want to empower them.

That’s the part people often miss about budgets. They’re not about restriction. A well-designed budget is about autonomy. It’s a clear set of boundaries that gives leaders the freedom to make decisions inside them.

I learned that the hard way.

At Intercom, our first department budgets were a mess. We tried to track everything: software, snacks, Uber receipts, every little detail. It was chaos. Eventually, we found something that worked.

We split spend into just two buckets: recurring and one time.

Recurring spend was things like salaries, software, and rent. One time spend was travel, bonuses, and learning and development. The rule was simple. Money could move freely within a bucket but not between them. You could trade a signing bonus for a team offsite, but not turn it into a permanent raise.

That small distinction changed everything.

Instead of “I need more budget,” conversations became “I can shift this part of my budget.” Finance got clarity. Leaders got ownership.

We still had a few special categories, things like hosting costs or recruiting fees, but those were the exceptions. For most teams, two buckets were enough.

What I love about this system is that it strikes the right balance between control and flexibility. It protects the company plan while giving leaders room to operate. And it turns the budgeting process from something people dread into something that actually builds trust.

Budgets are one of those things that seem like a finance exercise. But really, they’re a management tool. They tell people: here’s the space you can play in. Here’s where you have freedom. Here’s how you stay aligned.

There’s been a lot of chatter lately about how hard it is to raise money if you’re not an AI company. Harry Stebbings posted recently that the old benchmark for great SaaS growth, the classic triple triple double double, doesn’t cut it anymore. You have to be growing even faster than that to get funded.

And he’s not wrong. I’ve been hearing the same thing from investors. Unless you’re an AI business or showing extreme acceleration, it’s a tough market to raise in right now.

It sounds a little crazy, but it’s also a reflection of how AI is reshaping the way investors think about SaaS multiples and company value. Dollars chase growth, and right now the biggest growth stories are all AI-shaped.

For years, SaaS was the gold standard of recurring revenue. Predictable, compounding, capital-efficient. Investors loved it. But the transformational nature of AI, along with the insane examples of growth and the ability of these companies to scale with such low headcount, just makes them absolutely pop off the charts.

So it makes sense that the market is pouring capital into that shift.

That doesn’t mean you can’t still build a massive business if you’re growing at a normal triple triple double double pace. Of course you can. If I were an investor, I’d still want to back those companies. But it does mean the trade-offs are real. Dollars get allocated where the narrative feels explosive, where there’s transformation, not just execution.

What will be really interesting is when the first true AI native company goes public. Because when that happens, it will reset the benchmarks. Public investors will start comparing those businesses to traditional SaaS companies, and money will move accordingly. You’re already seeing that in the private markets, with AI companies pulling capital and attention away from traditional software.

So I’ve been thinking a lot about how to categorize what’s actually happening. If you’re running a SaaS company today, it’s worth asking where you fit in this new hierarchy.

At the top are AI native companies. The OpenAIs, Anthropics, and a wave of startups like DualEntry, Rillet, and others that are built entirely AI first. We don’t know how they’re really trading yet since most are private, but all signs point to eye-popping valuations. It’s understandable, given their growth and the frontier ahead.

Next are companies transforming into AI businesses. These are traditional software companies that are rebuilding their products and positioning around AI. Intercom is a good example. They’ve essentially reimagined the company to be an AI first platform for customer engagement. And it’s honestly hard to think of another example of a company that’s truly done this successfully… that’s transformed their product, positioning, and revenue model into something AI first in such a short amount of time. The valuations they’ll be able to justify will depend on how truly and deeply they’re able to make those transformations.

Then come the AI advantaged companies. They may not be AI first, but AI meaningfully strengthens their core business. Think of companies like HubSpot, which has vast proprietary CRM data that can power better automation, or Zendesk, which can use its support conversations to train models and improve routing and response times. For them, AI isn’t existential, it’s additive.

And finally, the AI susceptible companies. These are the ones investors are nervous about, where AI might plausibly recreate the core value proposition. Think of marketing automation platforms like Constant Contact or Outreach, where so much of the value comes from templated campaigns and automated workflows. It’s easy to imagine AI stepping in and generating personalized campaigns on the fly, eliminating entire layers of that stack.

One of the big bets most investors are making (I think!) is that if you’re not in the first (AI native) category, there’s an opportunity to completely rebuild and rethink your market from an AI native perspective. Which means if you’re in one of the last two categories, and you’re not actively transforming into an AI business, there’s probably a startup being founded right now that’s going to do it for you. An AI native company that will rip apart your product, your go-to-market, and your entire category.

I’m trying something new this week. Instead of a single deep dive, here’s a set of four things I found interesting, each shaping the way we think about finance, metrics, and systems.

1. Founders are using creative accounting to boost lofty ‘ARR’ — Fortune

In SaaS, ARR had a clear definition for years. With AI, new pricing models (tokens, credits, usage based tiers) have made it much harder to define. Layer in the pressure to show huge ARR fast and the rewards that follow big numbers and you get founders stretching the definition, knowingly or not.

Quote: “AI’s new usage based pricing models have created gray zones where startups can claim recurring revenue that isn’t truly recurring.”

Takeaway: This is exactly why having a strong finance leader matters. VCs are skeptical, they are digging deeper, and clarity is more valuable than hype. When too much money is flying around with too many expectations, that is when definitions bend and integrity gets tested.

2. AI startup DualEntry raises $90 million to deepen ERP market push — Reuters

Capital is pouring into the financial backbone. DualEntry’s pitch is to migrate off legacy ERP in days instead of months, automate the majority of manual tasks, and rebuild the general ledger for an AI first world.

Quote: “DualEntry says it can migrate a company’s financials in under 24 hours, a process that typically takes months.”

Takeaway: There is clearly a thesis in venture right now that ERP is going to be rebuilt from the ground up. DualEntry is just the latest. Rillet and Quanta are two others in the mix. The open question is whether any of them can really land inside the enterprise and into publicly traded companies. If they can, that is when things get scary for incumbents like NetSuite.

3. The AI Application Spending Report: Where Startup Dollars Really Go — a16z

Follow the money. This report shows where startups are actually spending AI budgets. Not just models, but infrastructure, vertical apps, and workflow automation. For finance leaders, it is a clear signal — the back office is no longer in the shadows. It is where AI spend is concentrating.

Quote: “The majority of startup AI spend is going toward vertical applications that reshape existing workflows.”

Takeaway: Where capital concentrates, ecosystems mature fastest. ERP and finance stacks are now squarely in the flow.

4. Scaling AI with Vision — Glenn Hopper (Substack)

Glenn makes a pragmatic case that AI in finance is not about replacement, it is about scale. Faster closes, cleaner audit trails, and real time reporting are what let a lean team operate like a large one. This is not hype, it is the operator’s version of AI — turning small finance teams into outsized leverage.

Quote: “AI will not replace the finance team — it will scale them.”

Takeaway: AI is the multiplier, not the silver bullet. The lever that lets small teams punch far above their weight.

What ties all four together is the same theme: finance is being re platformed. Metrics are under scrutiny, ERP is up for grabs, and AI is moving into operating reality.

I was on a call the other day with a founder, and he told me point blank:
“Don’t hire another SDR. Hire a GTM engineer.”

That stuck with me.

GTM engineering is a term I have been hearing more and more lately. Clay talks about it constantly. And the idea is basically this. Instead of just hiring more SDRs, you build the systems those SDRs can run on. You create workflows, automations, and integrations that allow one SDR to do the work of many.

Think of it as RevOps with engineering horsepower. These are people who can write scripts, pull from APIs, stitch together tools, and design workflows that scale outreach and demand generation.

What does a GTM engineer actually do?

Here are some of the things I have seen GTM engineers own:

• Automate prospecting. Pull enrichment data from multiple sources, clean it, and route it into the right SDR queue.

• Trigger outreach. When a prospect hits your site or engages with content, an SDR gets prompted automatically to follow up.

• Build internal tools. Dashboards, workflows, and utilities that take manual list building or data entry off the SDR’s plate.

• Experiment and scale. Pilot a new outbound workflow, measure results, and roll it out across the team if it works.

• Keep the system reliable. Monitor for failures, ensure deliverability, and maintain observability so nothing silently breaks.

The promise is clear. When you do this well, SDRs get to spend their time on the part that actually matters: being human. They can personalize outreach, build connection, and add emotional resonance. The machine handles the busywork. The humans do what humans do best.

Who do you hire for this role?

I asked that exact question. What is the background of a GTM engineer?

The answer: usually a technically minded ops person. Someone who has worked in RevOps, marketing ops, or sales ops. They understand systems, enjoy tinkering with workflows, and can pick up APIs or no code tools without fear.

They are not full blown software engineers. But they are closer to engineering than most ops hires. They live at the intersection of systems, process, and experimentation.

The GTM engineer’s toolbox

A few years ago, ops meant Salesforce, Outreach, ZoomInfo, and maybe Zapier. Today there is a new wave of tools designed specifically for automation, enrichment, and AI-driven workflows.

Some of the most common ones:

• n8n — Visual workflow engine, popular because you can add AI “decision nodes” and build more complex flows than Zapier.

• Clay — Beyond enrichment, Clay now has Claygent, an AI agent that can research prospects, pull in public data, and enrich profiles before they ever hit an SDR’s queue.

• Bardeen — A browser based automation assistant that turns repetitive tasks into playbooks.

• Persana — AI pipeline automation platform that handles prospecting, personalization, and signal-based triggers.

• StackAI — AI native workflow builder where decision making and copy generation are built in from the start.

• Traditional iPaaS (Zapier, Make, Workato, Tray) — Still widely used, now with AI extensions, but increasingly complemented by the above.

Why this matters

This is why people are so excited about GTM engineering. It is leverage. If you crack it, your SDRs become dramatically more productive. They can focus on actual connection, while the system handles the grunt work.

At Equals, we are going to try it. Because right now, GTM engineering is still an advantage.

Where I am cautious

I do have two caveats.

First, once everyone starts doing this, the pendulum may swing back. If every company has automated SDR workflows and AI written outreach, then the thing that will really stand out is human touch. Genuine personalization. A real human reaching out to another human.

Second, in the earliest stages of company building, there is no shortcut. Finding your first customers, learning what resonates, figuring out how to sell—those are human problems. Founders and early team members still have to do that work themselves.

Closing thought

So yes, GTM engineering is real and powerful. It is a movement worth paying attention to. It is something we are going to test ourselves.

Maybe my caveats make me old school. But I would love to hear what others think.

At Equals, we just launched a Google Analytics connector. Sounds simple enough. But what it really got me thinking about is why it’s so damn hard for SaaS companies to build a clear view of their funnel.

A funnel should be a pretty basic thing to set up: visitors at the top, paying customers at the bottom, a few steps in between. But most companies mess this up. Here’s why.

Step one: your funnel needs to be made of steps

A funnel isn’t a collection of random activities. It’s a sequence of discrete, irreversible moments.

• You start a trial. You can cancel later, but you can’t un-start it.

• And to become a paid customer, you had to have started a trial. You can churn, but you can’t go back to “never paid.”

Each step has to be mutually exclusive and sequential. You can’t skip ahead. This is what makes conversion rates meaningful.

Too many companies blur these lines. They’ll count things like “invited a teammate” as funnel stages. That’s not a step, that’s an action. If your funnel isn’t a clear staircase, it’ll become a maze to untangle what’s going on.

Step two: everything must tie together

The biggest mistake I see is companies not having a common identifier across their funnel.

• They know someone signed up for a trial.

• Separately, they know someone became a paying customer.

• But they can’t prove it was the same person.

Why? Because the identifiers don’t connect. The cookie in Google Analytics never got tied to the trial account, which never got tied to the billing record.

And once you lose that chain of custody, your reporting system falls apart. You can’t answer the most basic questions about acquisition, retention, or payback.

It also means you’ll never be able to do a proper LTV to CAC analysis. If you can’t tie a customer’s cookie from a paid ad all the way through to their lifetime value, you’ll never know whether that marketing channel is truly profitable.

Step three: make it operational

A funnel isn’t a dashboard. It’s a system. It should run like a product… with an owner and a feedback loop.

Most companies build a giant dashboard, glance at it once, and then move on. That doesn’t work. I recommend companies treat their funnel as a daily pulse. The data is structured in a way that’s simple, digestible, and consistent. Everyone knows when and how often to look at it.

• You review the same funnel stages, in the same sequence, every day or every week.

• You track volume, conversion, and growth… metrics that are simple enough for everyone to understand and rally around.

• You make it part of the team’s operating rhythm.

This consistency does two things.

First, it plays defense: you catch problems quickly, before they become existential. If trials from paid ads suddenly drop, you’ll know right away.

Second, it turns into offense: by building the habit, you spot opportunities others miss. Maybe a bump in conversion shows up early, or a new channel is quietly outperforming the rest. With a daily pulse, those signals don’t get lost.

When you operationalize your funnel this way, it stops being a static report and becomes the heartbeat of your growth engine.

When you step into the first finance or ops role at a startup, there’s no playbook.

If you’re like me, you’re usually asked to touch anything with a number… from fundraising models, to board meetings, to pedantic (but important!) ARR definitions, to funnels, cash, headcount, and product metrics. It’s equal parts analyst, operator, and owner.

I was recently on a podcast with some friends and former Intercomrades building a cool company called Format (we’re happy customers at Equals) and we chatted through many of these. I thought it would be fun to codify them in writing.

One. Act like an owner, not a scorekeeper.
It’s easy to get stuck just reporting numbers. Close the books, share dashboards, show people the numbers! After all, that’s your job, right? The real value comes when you treat the data as an input, and YOU act as if you owned the company. E.g. if I were CEO, what would I do with this information? What decision does this analysis unlock? What would I recommend we do next. You’re not just putting the numbers together and presenting them, you’re thinking through how to impact the business. Your stakeholders will deeply appreciate that.

Two. Momentum is fuel. Build it with a daily pulse.
Startups live off energy. And one of the fastest ways to create energy is to look at the numbers every single day. Not once a month. Every day. Push a few simple metrics to the team right where they work… Slack, email, whatever. Don’t make people hunt in a dashboard. When the numbers show up daily, they spark conversations and action. If you don’t think you have something that you can look at every day (e.g. you’re mega enterprise) then find something higher up the funnel that you can (e.g. sales activity, traffic, etc.). That all builds momentum. And once you have momentum, everything feels easier.

Three. Keep it obvious.
Complex metrics look smart but don’t travel. People nod along to Net Revenue Retention, but especially as you start to discuss with a broad set of folks across the organization, they often times don’t really understand what it means or how it comes together. It’s a composite metric (e.g. it’s made up of a bunch of different metrics) and therefore can be quite complex to pull apart or interpret. My preference, when really trying to deal with scaled metrics is to focus on obvious numbers: customer counts, simple rates. If someone can explain it in a sentence, they can act on it. Make it really easy for people to understand what they’re being measured against and how they can influence it.

Four. Pick your “use case.”
When you’re the first finance or ops hire, you’ll get pulled in a million directions. It’s kind of like building your own startup within a startup. You have to pick the things that you’re going to be great at, and who they serve. Because everyone wants your help and if you try to do that, well you know what happens. SO, I think a lot about what can you do to focus. For me, I found that the more I focused on the question of “how does revenue come to be” the more that led me down analysis and work that was of tremendous service to the company - and therefore built me a lot of credibility. Find the thing you’re hired to help with (I’d surmise it’s the same as my question “how does revenue come to be” … or alternatively it’s “how do we not run out of money” but be careful with that one"). Follow that and people will trust you with more.

Five. Sweat the details.
In this role, trust is everything. If your numbers don’t tie out or your model doesn’t line up with actuals, credibility disappears instantly. This is why AI really hasn’t yet solved for the job of the CFO, it gets us 95% of the way there but the great CFOs are made or broken in that last 5%. I remember back in my IBM days, one of the most important things that I learned was to triple check that every single number in any deck, email, or sheet I shared tied back to our source of truth. That was the best habit that I could’ve built. When your work is clean, crisp, and precise, people notice. Over time, that precision compounds into trust. And trust is what gives you influence across the company.

Six. Don’t get stuck in analysis.
It’s tempting to keep digging. More cuts, more models, more data. But analysis only matters if it leads to action. The fastest way to learn is to make a change and see what happens. Create tight loops: act, measure, adjust. Your job isn’t to make things more complicated—it’s to make decisions easier. I say this with the appropriate caveat that it is ALSO your job to turn over every stone in an analysis. Make sure you’re looking at the things that you need to - but not all of that needs to be shared with your stakeholders. Move quickly. Look at all that you can, reasonably. Share what matters.

Seven. Remember there are no rules.
Every founder and board will expect something different from their first finance or ops hire. Some want a great planner, others want someone who’s an expert fundraiser, others need a great partner to an enterprise sales team, others need someone who can scale analytics, or billing, etc. There’s no ONE way to do it. The role is what you make it. Focus on what creates clarity and momentum for the business—and design the rest around that.

Ok that’s all. Hope you enjoyed. ⚡️

When people talk about great operators, they usually rattle off technical skills or functional achievements. A finance leader who can model twelve scenarios in a spreadsheet. A growth leader who knows attribution models inside and out. A product leader who can ship fast.

But what really sets exceptional operators apart is something subtler. They build a flywheel of influence by asking better questions… e.g. built on curiosity. Those questions lead to context, context leads to sharper judgment, sharper judgment leads to more influence. And the cycle keeps spinning.

Curiosity is a practice, not a trait

We’re quick to describe curiosity as innate. Someone is “naturally curious” or “just not that curious.” I used to buy into that. But over time, I’ve realized curiosity isn’t something you’re born with or not.

It’s a practice.

Like going to the gym, it ebbs and flows. It gets stronger the more you work it. It compounds when you surround yourself with feedback loops. And it can just as easily atrophy in the wrong environment.

The best operators I’ve worked with—whether in finance, product, or growth—are the ones who built curiosity into their daily rhythm.

The Overheard List

When I was an analyst, I kept what I called an “overheard list.”

Every time I caught a comment in a meeting—“customers are churning because of price,” “our enterprise deals always slip at the last minute,” “marketing leads aren’t converting”—I’d jot it down.

Not to prove or disprove everything. The act of writing was the practice. It tuned my ear toward unresolved assumptions. It gave me a backlog of potential investigations. It sparked connections I wouldn’t have found otherwise.

Sometimes the questions went nowhere. Sometimes they changed the trajectory of a decision. Either way, the practice built momentum.

Eminem read the dictionary for inspiration. Operators can read their overheard list.

The curiosity flywheel

The more you lean into curiosity, the more it compounds:

Curiosity → Better questions → Deeper understanding → More influence → More context → Even more curiosity.

It’s a virtuous cycle. And it doesn’t just apply to analysts.

A curious finance leader notices the odd trend in sales efficiency before it becomes a board question.
A curious PM spots the unexpected usage pattern that becomes a growth lever.
A curious CRO asks why one AE consistently outperforms and rewrites the playbook.

What kills curiosity

Curiosity is fragile. Organizations crush it without even noticing.

Bad data. Overly prescriptive stakeholders. Too many meetings. Unclear priorities. A culture of “just execute.” All of these suffocate curiosity before it can grow.

Great leaders act like great parents: fanning the flame when they see it, softening the blow when it runs into dead-ends, creating space for exploration.

Building your own practice

The specifics don’t matter as much as the intent. You could:

• Keep an overheard list.

• Write three questions after every important meeting.

• Block an hour a week to chase curiosities guilt-free.

• Treat every unexplained metric as a mystery worth investigating.

The point is to practice. To make curiosity a muscle.

Because curiosity isn’t what makes you practice. It’s the practice that makes you curious.

Over the years I have come to believe that in the early days of a startup, finance and analytics are better together.

I learned this lesson first hand at Intercom. My background before that was purely finance. FP&A at IBM, then FP&A again at a 150 person startup. When I joined Intercom, I was asked to take on analytics as well. At the time I made the joke that numbers are numbers, so why not.

But it was not an accident. I had already seen how often the work of finance and analytics overlapped. Both teams pulling the same data. Both trying to build models that explained what was happening in the business. Both wasting time duplicating efforts.

That overlap becomes obvious once you strip finance back to its core in the early days. Early finance is not payroll. It is not accounts receivable. It is not even planning and forecasting.

Early finance is organizing and defining metrics. It is cleaning up messy data. It is building scrappy first models for things like marketing attribution or customer lifetime value. It is creating a culture of using numbers to understand what is going on.

That description should sound familiar. Because it is exactly what analytics does in the early days.

The connection goes further. Forecasting and planning, which are among the most important jobs of finance, are only as useful as the analysis underneath them. You cannot build a real plan until you have taken the business apart and understood how it works. Flip the statement around and it is just as true. If you deeply understand how the business works, the plan often writes itself.

This is why I think of great finance teams as always being built on a foundation of great analysis.

On the flip side, many of the more sophisticated things people think of as analytics are not useful early on. Unless your product is built around data science, you do not need machine learning models in year one. You do not need perfect marketing attribution. You do not need the most elaborate lifetime value model. In fact, trying to build these things too early often slows you down. At that stage you want speed and direction. Correlation over causation. Scrappy over sophisticated.

The real job of finance and analytics in the early days is the same. Create insights. Build understanding. Help the company grow by giving everyone a clearer picture of what is actually happening.

This is why I believe that when you bring finance and analytics together in the beginning, you get something more powerful than either alone.

A framework for the first operator

If you are the first finance or analytics hire, here is what this looks like in practice:

1. Define the core metrics
Start with ARR, churn, retention, payback period, CAC. Write down clear definitions. Socialize them until everyone uses the same language.

2. Clean and centralize data
Do not aim for a perfect warehouse. Do aim for a reliable set of source tables that you can trust. Early credibility is built on trust in the numbers.

3. Build scrappy first models
Use spreadsheets and simple SQL. Do not worry about causality or statistical purity. A good directional LTV model or rough marketing attribution beats nothing every time.

4. Establish a cadence of sharing insights
Weekly or monthly reports. A lightweight dashboard. Even a daily email. The key is consistency. Build the muscle across the company that decisions are grounded in data.

5. Layer in planning once the basics are in place
With metrics, data, and models in place, you can move into forecasting and scenario planning. At this point you will be building on solid ground rather than guessing.

Looking back, I am glad Intercom forced me to wear both hats. At the time it felt like a stretch. Finance was my comfort zone. Analytics felt like a different discipline. But the truth is that the two were always connected.

That experience changed the way I think about the first operator role. You are not just keeping score. You are not just reporting numbers. You are creating the lens through which the company sees itself. And that lens is built by combining finance and analytics into one view of the business.

So if you find yourself as the first operator at a startup, do not get hung up on whether your title says finance or analytics. The job is both. The real mandate is to help the company understand itself so it can make better decisions and grow.

I wish I’d heard this sooner in my Intercom days.

Years back I sat down with Bill Losch, who helped take Okta from a couple hundred people through IPO. I asked him what most finance leaders get wrong when they start building their team.

His answer was not quite what I expected or what I’d considered before. Most CFOs overweight corporate work and underweight embedded work. They race to hire people who can crank out board decks, models, and KPI slides before they hire people who sit with the teams and help them understand how the business actually works.

Bill had made that mistake earlier in his career. At Okta, he flipped the playbook and went embedded first. It made all the difference.

Corporate pulls you in. Embedded moves you forward.

It’s easy to see why corporate FP&A gets all the attention. Boards ask for it. Investors expect it. Once you’re public, Wall Street grades it. The temptation is to live inside the master model and push numbers down.

But that only gets you so far.

Embedded finance is what really drives the business. It’s the finance partner sitting next to the Head of Sales or the Growth PM, asking the questions that actually change decisions this week. What incentives should we create across the sales team to drive growth? How can we best understand the channels that are most profitably driving MQLs? Where in our funnel are users dropping off and what product can we build to mitigate that?

Without these foundational understandings of how the business works, the corporate model is really just built on a house of cards. The embedded partnerships lead to a far richer understanding of the business. It doesn’t work in reverse.

Bill’s blueprint at Okta

When Bill joined Okta around 140 people, he didn’t build a centralized FP&A team first. He hired an FP&A lead who knew both worlds… someone who could hold the corporate model together but also knew how to partner with operators. Together they seeded embedded partners across the org.

There was a bias tho that he had to unlearn, and conciously work against as he built the team. And that was to more heavily staff embedded FP&A folks than on the corporate, strategic side. That changes later as IPO prep got real, but never did corporate outweigh the embedded partnership side of things.

My First Operator take

This is exactly how I think about the “first operator” role in finance. Early on, your job isn’t to sit at the highest level of the company and crank board slides. It’s to be in the trenches with the teams, helping them understand their corner of the business and make better decisions.

That’s the whole point. Not just reporting on stuff. Not just forecasting. Actually helping people test hypotheses, spot constraints, and see clearly how their work ties to results.

Finance has this unfair advantage: we get a view across the whole company. But that vantage point only becomes valuable if we’re close to the work.

A quick framework to apply right now

Think in three lanes:

Lane 1. Partner deeply.
Put finance owners inside your highest-leverage functions. Sales, Marketing, Growth, Product. Give them one concrete weekly objective.

Lane 2. Wire the loop.
Replace static budget check-ins with a weekly operating review. Let your embedded partners own the story of one meaningful change each week.

Lane 3. Keep corporate tight but thin.
Maintain one master model. Keep it simple enough to edit in a meeting. Connect it directly to what the embedded folks are learning.

A word to finance people

Here’s the part I really want finance folks to hear.

Everybody wants to be in corporate. I know I did. It feels like the sexy stuff. You get to sit close to the CEO, work on board decks, roll up the numbers. I get it.

But if all you’ve done is corporate FP&A, you’re honestly not that interesting a finance person to me.

The people I find most impressive are the ones who’ve been embedded… who have sat with a sales team and really understood how pipeline works, or dug into marketing spend and figured out what actually converts, or partnered with an R&D lead on resourcing tradeoffs. That experience makes you ten times more valuable when you do move into corporate finance, because you actually understand the guts of the business.

So if you’re early in your career, bias toward the front lines. Go deep with operators. Learn the mechanics. That’s how you become the kind of finance leader who can later run both sides of the house

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When people think about analytics, they picture the aha moment.

The killer insight.

The thing that changes the trajectory.

But in my experience, 95% of analytics isn’t about breakthroughs. It’s about defense.

I first learned this from Ray Ko back when he was advising us at Intercom. Ray came out of Facebook’s growth team, and he had a philosophy that he drilled into us: growth isn’t about offense. It’s about playing great defense.

The slowdown that taught the lesson

Ray once told me about a morning at Facebook when new user growth suddenly cratered. Active users flatlined, and nobody knew why.

So they went into full investigation mode. Was it a specific country? A channel that had dried up? A change in the funnel? They chased down every angle.

Eventually they discovered something almost embarrassingly simple: the confirmation emails weren’t being delivered. A few major providers had started throttling them, so tens of thousands of people who had signed up couldn’t confirm their accounts.

Fixing the deliverability issue got growth back on track. But the real lesson was bigger. That slowdown forced the team to realize just how critical email confirmation was to Facebook’s growth.

The most important part: nobody would have thought of this as a growth lever. No PM was pounding the table saying, “The future of growth is email deliverability!” They were dreaming about sexy new features, viral loops, all the glamorous stuff. Meanwhile, email deliverability turned out to be one of the biggest levers they had.

That’s what made it so powerful. A slowdown turned defense into offense.

What it looked like at Intercom

At Intercom, we had dashboards for everything. The problem was, no one looked at them. So six months in, I built something simple we called The Daily Pulse.

Every day at 5 pm, an email went out to the entire company with a handful of charts—installs, trials, cancellations, page views. Nothing fancy.

The magic wasn’t in the charts. It was in what happened next. The CEO or a function head would reply-all if something looked off. Suddenly, we were all investigating. Why did trials dip today? Was it because of the Product Hunt launch? Did we break an onboarding flow? Did an ad campaign stop running?

That rhythm forced us to dig in every single day. And just like at Facebook, those investigations often turned slowdowns into opportunities. We learned which campaigns actually worked. We caught instrumentation errors before they derailed a whole week. We spotted when small product changes had outsized impact.

The Daily Pulse wasn’t just defense, it became offense. It helped us make faster decisions and gave us conviction about where to lean in.

A framework for playing defense

Here’s the simple playbook Ray instilled in me and what I carried into Intercom:

1. Push a daily pulse. Pick a handful of metrics that define the heartbeat of your business. Send them to everyone. Every day. No dashboards, no logins… push it to their inbox.

2. Set up real alerting. When something falls outside the normal band, trigger an alert. Don’t wait until the end of the month to realize something broke.

3. Run a fast Friday. End each week with a cross-functional review of the data. Talk about what changed, what broke, and what needs fixing.

4. Build an on-call muscle. Even at small scale, rotate responsibility for investigating anomalies. It builds distributed knowledge and accountability.

5. Treat slowdowns as classrooms. Don’t patch over them with a shiny new idea. Diagnose, fix, and then ask: how can this become offense?

The quiet work that wins

The things that really changed the trajectory at Intercom weren’t the fireworks. They were the boring-sounding rituals—daily pulses, quick alerts, fast Friday meetings.

That’s what Ray taught me: great analytics isn’t about chasing insights. It’s about defense. And if you get the defense right, the offense will take care of itself.

Planning season is one of the most dangerous times for a SaaS company.

When you miss plan, it’s brutal. Morale drops, credibility with the board takes a hit, trust across the team erodes. On the flip side, when you come out of the gate hitting plan, it feels incredible. Momentum builds. Confidence compounds.

That’s why how you build the plan matters so much.

The Goldilocks framework

Jeff Epstein (former Oracle CFO, now at Bessemer) has written about what he calls the “Goldilocks” budget. Not too hot, not too cold, just right.

He points out that every boardroom is filled with competing perspectives:

• The CEO often wants aspirational targets.

• The board chair often wants predictability.

• The CFO often wants realism.

Left unresolved, these differences lead to “denial games.” We miss the budget, then retroactively declare it wasn’t really the budget after all.

The Goldilocks approach offers a middle path: set targets with explicit probabilities.

Revenue with a 50% chance of hitting.
Profit with a 70% chance.
Public guidance (for later-stage companies) at 90%.
Sales quotas at 30%.

The point isn’t the specific numbers. It’s that you’re choosing the risk profile of the plan deliberately, up front, and aligning everyone around it.

Why I like the 50–70% plan

In my own experience, the sweet spot is a plan with a 50–70% chance of hitting.

Too safe, and you don’t push yourself to get creative about how to achieve greater outcomes.

Too aggressive, and you risk repeated misses, which are hard for the company.

A 50–70% plan sets you up to achieve most of the time while still stretching the team beyond the obvious. It forces people to come up with ideas they wouldn’t otherwise. It keeps the company pushing without demoralizing them.

The case against multi-scenario planning

A lot of finance leaders love building multiple plans — Base, Upside, Downside. I think that’s a mistake, especially for early-stage SaaS companies.

Here’s why:

1. Complexity. You’re suddenly maintaining three models, with three different sets of assumptions. For a lean finance team, it’s a nightmare.

2. Confusion. Different stakeholders latch onto different scenarios. Sales points at Upside. Finance clings to Base. Engineering ignores both. It’s chaos.

3. Avoidance of alignment. Multiple scenarios sidestep the real conversation — how much risk are we actually taking on as a business?

One plan forces clarity. It forces alignment. It puts everyone on the same page.

The board conversation

This probabilistic framing is especially powerful with your board.

Instead of: “Here’s the plan, we think we can hit it.”

You can say: “We’re building a plan with a ~60% chance of hitting revenue, ~70% chance of hitting profit.”

That sparks the real conversation: how much risk do we want to take on? Do we want to lean into growth with shorter runway and higher potential valuation? Or preserve runway and take fewer swings?

Once that conversation is had, you’ve created alignment. And alignment is everything.

Cascading the plan

Once the company-level plan is set, the next step is translating it into department-level budgets.

I’ve written before about why I like to frame department budgets in terms of “pools” of spend. But those pools can be transferred, either within a department or outside of a department. That structure empowers leaders to make their own tradeoffs within boundaries. It shifts the conversation from “I need more budget” to “Here’s how I’ll reallocate within my budget.”

The takeaway

Planning isn’t about predicting the future with certainty. It’s about alignment.

A Goldilocks budget gives you a framework to talk about risk. Probabilistic thinking makes the conversation honest. And one plan, not many, keeps the company moving in the same direction.

Finance people have a natural bias that can hurt us more than it helps.

I learned this the hard way.

Back when I was running planning cycles, I’d get the usual headcount asks. Sales would come in with a request for a hundred new hires. Marketing wanted millions in discretionary spend. Product wanted to double their team.

All of that would stress me out. My instinct was to prove a point: if I could run finance lean, maybe everyone else would follow suit.

So I’d slash my own requests. I’d say, “I’ll cut these roles, we can make do without.” I thought I was doing the company a favor — showing discipline, leading by example.

But here’s the thing. That bias hurt me. It hurt the team.

Because finance isn’t just another back office function. When you starve the finance team of resources, you’re starving the company of the infrastructure that makes real growth possible.

We were stretched thin. Projects stalled. Analysts burned out. And I was stuck carrying the weight of an under-resourced function, convincing myself it was “fiscal responsibility.”

It wasn’t. It was a false economy.

Why finance falls into this trap

This mindset is almost baked into our DNA as finance leaders.

• We are wired to save money.

• We see efficiency as a badge of honor.

• We believe “headcount is a vanity metric.”

• And frankly, we know our request for another accountant or analyst will never win out against another sales rep or engineer.

So we grit our teeth and make do. We automate a little more. We take on just one more project. We tell ourselves someone will notice how much we’re doing with so little.

They won’t. And even if they did, it’s not the kind of praise that helps your team succeed.

What I should have done differently

Looking back, there are a few things I wish I had been deliberate about:

1. Plan finance’s needs first

Before you dive into company-wide planning, get clear on what *you* need. What is the org design that lets finance actually support the business? Write it down. Otherwise, you’ll get lost in everyone else’s asks.

2. Identify single points of failure

One payroll person. One billing specialist. One analyst doing all the ARR reporting. That’s not efficiency, that’s risk. If you see those single points of failure, make them a priority.

3. Ask your stakeholders

Don’t plan headcount in isolation. Go to sales, product, marketing and ask: what would a better partnership with finance look like? Where do you need more support? Then pin your asks directly to that feedback. It’s harder for anyone to argue against.

4. Check your own bias

The bias to be lean will always be there. So if your request makes you feel *uncomfortably big*, that probably means you’re in the right zone. Hypergrowth demands that you invest ahead of the growth.

The uncomfortable truth

Finance folks need to get comfortable asking for more.

That doesn’t mean bloat. It doesn’t mean unlimited spend. But it does mean putting aside the reflex to prove how scrappy you can be.

The most responsible thing you can do is to build a finance team that actually scales with the business. That gives everyone else the clarity, the systems, and the guardrails they need to grow.

Running lean might feel noble in the moment. But in the long run, it’s a trap.

Someone reached out recently asking if I had a job description I typically use for hiring the first finance person. They also wanted me to review theirs.

So I thought I’d write up a version that encapsulates my philosophy.

This isn’t for every company. It’s for high-velocity SaaS and AI businesses, where the job of the first finance hire is not bookkeeping or accounting. Those are to be outsourced, in my opinion.

The job of the first finance hire is to help the company see itself clearly.

The first finance hire defines the lens through which the business is viewed. They create the metrics, the frameworks, the reporting… the foundation of how the company understands whether it’s actually working.

That makes it one of the most interesting jobs in a startup. You’re not just reporting the numbers. You’re shaping the company’s identity. You’re working side by side with the CEO and leadership team, setting the context for decisions about where to invest, how fast to grow, what’s working, and what’s not.

The best people for this job are curious and analytical. They care about the business, not just the spreadsheet. They ask the right questions of the data, and cut through the long list of things that can be ‘analyzed’ to get to the most important details - so that the rest of the company can move quickly.

With that in mind, here’s the job description I’d use for a venture-backed, high-velocity SaaS/AI company:

First Finance Hire

Company: High-velocity SaaS/AI startup, venture-backed

About the Role

We’re looking for our first finance hire. Not to keep the books. Not to run payroll. Not to build decks.

We’re looking for someone who wants to be the first operator in the company. Someone who will help us see the business clearly — define the metrics that matter, build the frameworks that guide decisions, and create the lens through which our board and team set priorities.

At a high-velocity SaaS/AI company, finance isn’t a back office function. It’s the heartbeat of how we measure whether this thing is actually working.

You’ll be embedded in the business, close to product, sales, and growth. You’ll help us understand what’s really driving revenue, where to invest next, and how fast we can responsibly scale.

What You’ll Do

• Track everything, focus on what matters. Keep tabs on every metric across the company. Bubble up the ones that matter most to leadership and help us focus on those.

• Build the first models. Own the operating model, revenue plan, and headcount plan. Set them up so they aren’t just spreadsheets but tools for surfacing real tradeoffs and the backdrop for meaningful management discussions.

• Set the data foundation. Develop a true source of truth for company metrics. Define them clearly, build the systems that make them accessible, and own keeping them clean and trustworthy.

• Be a thought partner across the company. Embed with different teams and leaders. Understand what matters to them, ideate together, and proactively bring analysis and ideas that help them do their best work.

• Be up for anything. Fundraising, international expansion, finding an office, tackling legal and tax issues, running HR, building out sales ops. When something new comes up — big or small — we’ll call on you.

What We’re Looking For

• A background in finance, strategy, or operations (experience in SaaS/AI companies a plus).

• Strong analytical chops — you’re as comfortable in SQL as you are in Excel/Equals.

• Experience working with leadership teams to shape decisions, not just report on them.

• Bias toward action. You know that perfect models don’t matter if they don’t drive the business forward.

• Hunger to be early. You want to join a high-growth company before things are figured out, and help build the machine.

Why This Role Matters

Every hire we make right now is company-defining. This role, in particular, shapes how we see ourselves. Get it right, and every decision we make — every dollar we raise, every headcount we add, every market we enter — will be made with clarity and conviction.

Yet finance teams tend to think a whole lot less about engagement metrics. At least I did. And that’s what I quickly want to get into today.

David Sacks wrote a great post on this a while back.

His point was simple: engagement is a leading indicator for retention. If people aren’t coming back to your product, your revenue models will eventually collapse.

The problem is the metrics we borrowed from consumer products don’t always fit SaaS neatly. DAU, WAU, MAU, DAU/MAU. They’re fine, but they’re messy. Weekends drag down the averages. Freemium users create noise. And engagement looks completely different across accounts.

Sacks’ advice was to adjust the lens. Focus on weekdays. Strip out free users. Look at the top accounts. That’s where you really see what “good” looks like.

Over the years I’ve put my own twist on this. It started back at Intercom. Paul Adams, our VP of Product who had just come from Facebook, used to ask me to measure how many users were active 5 out of 7 days a week. His thinking was: if people are in the product basically every workday, then we’re building a habit. That’s a sign of deep product-market fit.

That framing stuck with me. These days my favorite engagement metric is what I call A3x7. How many users are active at least 3 out of 7 days a week.

It’s not perfect. I adjusted it down slightly from A5x7 because I felt like every workday felt a bit too much of a high bar for an enterprise tool. But it’s a much higher bar than MAU or WAU. It shows you who really lives in the product. Who can’t do their job without it. It’s close to approximating what Sacks’ says is good - 60% DAU / WAU (assuming that they’re active really 3 out of 5 eligible work days in a week).

Why this matters: engagement is one of the very best early indicators of whether a company is going to work.

If you gave me the choice between:

• a company with industry-best engagement but no GTM figured out yet, or

• a company with a strong GTM engine but weak engagement,

I’ll take the first one every time.

Because GTM you can figure out. Engagement you can’t fake. Without it, everything else falls apart.

And practically speaking, engagement data gives the operator a lot to work with.

• In a board deck, it helps you tell the story: yes, revenue is still early, but look at how often people are using the product.

• In a GTM conversation, it helps prioritize: which customer profiles are most engaged, and how do we sell to more of them?

• In product, it helps drive focus: what’s working for power users, and how do we replicate it across all users?

That’s why I think engagement belongs right next to ARR and NDR on your dashboard. It’s the bridge between product and finance. It tells you whether the revenue model you’re building actually has a foundation.

I was on a podcast the other day. The host had clearly done her homework. She had gone into my writing and some of the little frameworks and ideas I have put out there over the years. One of the things she brought up was this piece I wrote about how CFOs are really Chief Frameworks Officers.

Then she asked me “So what’s the most important framework…?”

Honestly, I kind of liked my answer, and so I thought I’d share it here.

It’s the one framework I would put above all the others. And it’s not only the most important, it is also the hardest one to get right.

It is the relationship between burn, growth, and valuation.

You are constantly playing this game of triangulation between those three points. It’s the holy grail of running a venture backed SaaS company. Some just get lucky when it comes to this, many get it wrong, and others do it super skillfully.

The basic idea is that you are trying to figure out what we might be able to invest in order to grow at a certain rate, and if we grow at that rate, what that might mean for our valuation when we go raise again. If that is the valuation, is that a raise we feel good about? Is that the ownership we want to preserve? And will that raise get us to the next level. This is the game you sign up for when you take venture funding.

And it is the thing you are always working on as a CFO whether you realize it or not.

The thing I really struggled with early on at Intercom was the pressure I put on myself to have the answer.

Sometimes I would feel like I needed to figure it out myself, run the model, make the calls. Other times it would be me and the CEO locking ourselves in a room for a couple of days, going through all the inputs and scenarios, and then coming back to the rest of the management team saying, “We have got it. Here is the plan. Here are your resources. Here is what we are going to do.”

In some ways that was easier. It is much easier to make those decisions with one other person than it is to do it across a whole group of leaders with different perspectives and priorities.

But here is the big realization I wish I had reached earlier. My job was not to decide the answer. My job was to set up the conversation that would allow us, as a team, to find the answer together.

When I say “set up the conversation,” I mean a few specific things.

It meant building a model that could hold all of the different ways our teams operated. Something that could take the sales team’s inputs — if we hire X number of reps, we can close Y amount of revenue. Something that could take the marketing team’s inputs — if we run these campaigns with this amount of budget, we can generate this many MQLs. And the same for product, engineering, support, operations, talent, all the different functions.

Because every leader has deep expertise in their own area. What they do not necessarily have is the broader context… the bigger picture of what all those inputs mean for the company’s overall burn, and ultimately for the valuation we might be able to raise at in the future.

That is where finance, and specifically the CFO, comes in. You are the one who can stitch it all together. You are the one who can help the team see that bigger picture.

Before I understood that, it was a mess. Sales would come to me and say, “We need 100 headcount to hit our number next year.” I would come back with, “We can only afford 80. Figure it out.” And suddenly I am the bad guy.

They had no idea why the answer was 80. They did not feel like they had any input into that decision. Then they would find out marketing had 50 headcount and a 50 million dollar discretionary budget, and they would be wondering, “Why do they get that?”

It all felt siloed. Territorial. People fighting for resources instead of working toward the same goal.

Usually that is a symptom of the CFO not setting the conversation up the right way.

When we started opening up the whole model, the conversation shifted. We could say, “Here is the burn we can afford next year if we want to raise in a year at a valuation we all feel great about.” We could say, “Here is roughly how that pie might get divided.”

If someone wanted more, the question became, “How do we grow faster so we can raise sooner and at a higher valuation?”

If someone did not like the way the pie was split, the question became, “How do we reshuffle this as a team without changing the size of the pie?”

It did not make the work easier. In fact it meant a lot of thrashing, a lot of debates, a lot of re-running the numbers. It meant the finance team had to act as shepherds of the conversation, keeping everyone on track.

But what we got in return was buy-in. Alignment. Teams that were actually playing together instead of against each other.

That was the biggest lesson for me. The burn, growth, and valuation triangle is not just the most important framework for a CFO to understand. It is the most important conversation for a CFO to facilitate in an early-stage or scaling, venture-backed startup.

I keep hearing the same question lately.

From operators. From investors with portfolio companies. From private companies rolling out AI features.

They’ve either launched a credit-based pricing model or are thinking about it.
And they’re stuck on the same problem:

How do you roll credits into your revenue reporting without overcomplicating it?

The temptation I see isn’t to design a perfect system up front, it’s to over-engineer the solution… to think through every possible edge case, every way it could go wrong, and then build something more complex than it needs to be.

I got on a call with someone last week who was considering building a system designed around rolling averages to smooth credit usage.

If a customer has history, you average their trailing six months of usage and roll that into ARR. If they don’t, you use whatever average you can find. Sounds reasonable. In fact my first reaction was, that makes total sense.

As we got deeper into the conversation tho, it gets pretty complex. When credit purchases are lumpy, those averages add complexity without adding much clarity. In fact, imagine a customer buys a bunch of credits in month one and never buys credits again… you’ll be explaining contraction for the next 6 months for one event. And that’s just across one customer. Across dozens or hundreds, it feels like it’d get messy, your expansion and contraction would require looking across 6 months of history…

My bias is toward simplicity.

It’s far easier to add complexity later than to remove it once it’s embedded. A simpler system is faster to set up, easier to explain, and far easier to inspect when something doesn’t look right.

That conversation ended us with a simpler recommendation which admittedly works really only for high-volume, self-serve businesses selling one-off credits:

Track credits separately from your subscription ARR. You can give them their own column or line item in your ARR calculation so they remain distinct from the recurring component of your subscription model. Then, count those credits as expansion or contraction in ARR as they happen.

Yes, that means more volatility at the individual customer level. That’s intentional. This is a portfolio strategy, you’re accepting that when you peel back the curtain on any single customer, it’s going to look spiky. That’s okay.

The bet is that over time, the ups and downs of individual customers will smooth out at the portfolio level, giving you a consistent and predictable baseline. It’s the same principle as investing: picking individual stocks is volatile, but an index fund delivers steadier returns.

Two other important points:

1. Be upfront – Whether you’re reporting to your board, execs, or management team, be clear about the methodology, why you chose simplicity, and what its limitations are. Commit to evolving it as you learn more about customer behavior.

2. Set expectations – Volatility will happen at the customer level. If a large expansion or contraction hits in a quarter, it’s not a surprise, you’ve already discussed that it’s possible and agreed on how to handle it.

If you’re sales-led with only a handful of large customers, this approach won’t work… a single customer’s swing could shift the entire portfolio. But for high-volume, self-serve companies, starting simple and managing at the portfolio level will save you from unnecessary complexity.

Show up with a 10-slide deck, 10 different charts, and 10 separate blocks of commentary, when one table and one page would’ve done the job better.

I used to sit in a ton of these meetings. Monthly business reviews. Forecast reviews. Internal syncs.

And I saw this happen all the time.

Each slide would tell a small part of the story: Visits. Then leads. Then trials. Then ARPU. Then churn. Then ARR.

By the end of it, the room had no idea what actually happened.

You’d walk through 10 metrics, one by one, and everyone would leave wondering what the hell we were supposed to take away…

I get it. A 10-slide deck feels like you’ve done more work. It gives you something to talk through. It looks comprehensive.

But more often than not, it’s just noise.

It fills the time, but not with anything useful.

You confuse people instead of clarifying things.

You spark inane debates about sub-metrics instead of focusing on the one or two things that matter.

You end up talking at the business when you should be talking about the business…

Here’s a better way.

Drop the deck. Show me something like this instead:

One table.

Five takeaways.

Everything that matters, all in one place.

Look at what this view makes immediately clear:

1. Traffic is growing steadily. But lead volume is flat—meaning your new traffic isn’t converting.

2. As a result, your visit-to-paid conversion is falling.

3. ARPU is increasing, which is helping soften the blow.

4. Churn is improving.

5. And ARR is still growing strong—over 20% month-over-month.

That’s the story. And now we can actually have a conversation about what to do next.

To be clear: this isn’t about cutting corners.

You still need to do the work.
You still need to understand every part of the funnel.
You still need to be ready to dig in if someone asks a question.

But instead of throwing all that analysis onto 10 different slides, you say:

“I’ve looked at everything. Here’s what matters.”

And that takes real confidence.

It’s scary to walk into a meeting with one slide and an opinion.

Because now there’s no filler. No crutch. You can’t hide behind the deck.

But it’s also the highest-leverage thing you can do.

You’re creating space for discussion.

You’re helping the team focus.

You’re making the business smarter.

And here’s the broader lesson—for analysts, operators, founders, anyone:

Most of the time, there aren’t ten things to fix.

There aren’t ten priorities.

There aren’t ten metrics that deserve equal attention.

There’s one. Maybe two.

And the faster you can surface those and get aligned around them, the better.

So next time you’re putting together a business review or an exec update...

Don’t ask “what else should I include?”

Ask: “Could this all just be one table?”

Because if the answer is yes, that’s probably your best slide.

And everything else is just you talking to fill the time.

I saw this post the other day summarizing some advice Marc Andreessen gave to college students recently.

It deeply resonated with my own experience, so I thought I’d simply write a simple version of my story in the hopes that it inspires anyone else out there similarly.

My twist, that the first finance hire is actually the best position to jump into a company. But of course I’m biased!

Catching the founder bug

I was at Stanford and took a class called Engineering Entrepreneurship. It was taught by Steve Blank, who wrote The Four Steps to the Epiphany. The class was all about starting a real company. You had to go talk to customers, find a real problem, build something, and then pitch it to actual VCs. Ten weeks later, you’d be standing in front of them explaining what you built and whether anyone cared.

I remember thinking it was one of the most fun things I’d ever done.

Naturally, the next thing I did was join IBM.

I figured I’d try the big company thing. Maybe I’d like the stability. But it didn’t take long to realize that wasn’t what I wanted. I didn’t feel ownership. I didn’t feel responsible for anything meaningful. I felt like a cog in the wheel.

So I started moving toward earlier stage companies. First a 150-person startup. Then, a couple years later, Intercom, which at the time was just 20 people.

I didn’t have some grand plan. I just had a general pull. The smaller the company, the more I got to do. The more I got to do, the more I cared. The more I cared, the more fun I had.

And I always kind of knew that I wanted to start something eventually. I just didn’t have the right idea yet. I didn’t feel like I understood something deeply enough to build a product around it. And I also didn’t have the confidence in myself to figure it out from zero.

But I knew I could get closer.

How I Ended Up at Intercom

I wasn’t looking when the opportunity came up. A friend who I really trusted who was deep into the startup scene reached out and told me about the company. Which got me interested…

I met Eoghan, the CEO, and remember just feeling his energy. He had huge ambition. He did the classic ‘map out the vision’ on the whiteboard thing and I walked out thinking yea this thing could be big.

Then I started digging into their blog. Des, one of the co-founders, was writing a lot at the time. And I was really impressed. The writing was thoughtful, sharp, clear. I remember meeting Des in person and telling him how much I loved what he was writing. It gave me so much confidence that the team was smart and knew what they were doing.

Then I met Mamoon Hamid, the series A lead investor from Social Capital. I spent probably 45 minutes with him and left super impressed. Mostly I saw it as a chance to start building relationships in the investor world which I really wanted and thought would come in handy later. Both true.

The final part of my process was the least scientific but probably the most impactful. It was going on Twitter and searching for “Intercom.” The praise was really stellar. I’d not seen anything else like it, frankly. People saying they couldn’t live without the product. Screenshots. Love letters. I was currently in a company that was not product-first, where growth was mostly driven by sales and marketing. It worked for a while, but when CAC went up or LTV dropped, we had no product leverage. And we didn’t know how to build it.

Intercom was the opposite. It was product-first. The market clearly loved it. All those things got me excited and I took the jump, not knowing what it’d be come, or really expecting much. I really just thought; this’ll be a cool learning experience, I’m going to try my hardest to make it a success, and if it doesn’t I’ll figure out the next thing from there. One day at a time. But I was excited about the finance position, I thought it had a really interesting vantage point in the company.

What the First Finance Hire Actually Does

When I came in, Intercom was 20 people. I was the first finance hire.

At the beginning, I was focused on understanding how the business actually worked. I wanted to break it all down. Where did traffic come from? How did it convert? What were the steps in the funnel? What turned into ARR? What were the components of ARR? How did engagement play into it?

I wanted to be able to tell the full story. Once we had that, we could start making better decisions. We could see what was working, what wasn’t, and make real predictions.

From there, my role just kept expanding. I ran our weekly exec meeting. I ran our forecast review. I pulled together our board decks. I got pulled into board meetings. I was part of every fundraise… starting with our Series B just a few months after I joined, and every round after that.

I didn’t care about accounting. I didn’t care about getting the financials perfect. I wasn’t obsessed with whether payroll ran on time or if AP was up to date. I outsourced or automated as much of that as I could. Those things are really the trappings of the first finance position. My view was outsource them all and get to the good stuff. What’s the good stuff? Understanding the drivers of the business and giving teams clarity. I wanted to help the growth team see which experiments were working. I wanted to help product see if what they shipped was actually moving the needle. When we brought on sales and marketing, I wanted to give them visibility into their funnel, their numbers, and where they could improve.

Metrics became the thing that pulled the company together. They sat right at the intersection of product, marketing, sales, support, and strategy. They mattered to everyone, the board, the CEO, the exec team. And because I was the one building those metrics, I got pulled into everything. Which was kind of what I expected, wanted, and I didn’t really know at the time… but would really help set up life for me as a founder.

How It Set Me Up to Be a Founder

That role, being the first finance hire, is what eventually gave me the idea and the confidence to start my own company.

I spent years at Intercom building a deep understanding of how SaaS companies grow. I saw first hand what it was like to run a business. The plethora of tradeoffs and decisions that need to get made on a daily basis. The meetings. The monotony. The late nights. The mistakes. That’s again why I love the finance seat; you get to see it all! And be a part of it all. And by doing something so deeply and intensely for so long, I got to see the parts of the job that were fundamentally broken- and that’s where the spark for Equals came from.

Lastly, the role gave gave me access. I had built relationships with investors. When I went out to raise money for Equals, it was the Intercom founders who introduced me to folks I didn’t know. And I had a long list of folks I could reach out to who already knew my work.

So If You Want to Start a Company One Day…

There’s no singular path. Mine is quite different from many other founders. But if that’s what you want to do, this one worked for me;

Find a great early-stage company. Join early. Do great work. Build trust with the founders. Build trust with the investors. Learn how the business works. Become a partner to every function. Keep a list of every problem you run into. Pay attention to what bugs you and what’s missing. Reminds me of this post by Marc Andreessen:

You’ll either come up with the idea, or meet the people, or build the skills, or get the confidence, to go out and start something.

That’s what happened for me.

And it all started with the best job in startups that isn’t founder, that first finance role.

Lately I’ve been thinking about the difference between metrics that help you raise money… and metrics that help you build something that lasts.

Those two things aren’t always the same.

It’s easy to get caught up in the numbers that look great in a pitch deck: explosive ARR growth, logo count, burn multiples, all of that. Especially right now, in this moment we’re in with AI, where companies are hitting $100M in ARR in under a year. That kind of growth is staggering. And honestly, it’s worth admiring… those are teams moving fast, executing hard, and capturing a huge amount of attention.

But here’s the thing: we’ve also seen how quickly some of those stories unravel. Growth spikes, but it doesn’t stick. Customers churn. Use cases don’t hold up. Value isn’t there. The business deflates.

So if I were advising a founder, someone trying to build something enduring, I’d be thinking about a different set of metrics.

If I were an investor, I’d take deep engagement over fast revenue growth any day.

One of the investors I admire most is Mamoon Hamid at Kleiner Perkins. He was an early investor in Intercom, and one of his favorite ways to evaluate a product is using a super simple huristic: a stacked bar chart showing the number of days users are active in the product.

What he’s looking for is a bell curve with a fat tail. He wants to see people who are using the product every single day. That, to him, is the clearest signal that a product is deeply embedded in someone’s workflow or life.

And I agree. If people are logging in every day… if they’re in it, living in it… you’ve built something valuable. And from there, you can figure out the rest. You can get smarter about pricing. You can layer in new acquisition strategies. You can run experiments. But it all starts from product obsession.

The inverse, when you have a business that’s growing fast, but no one actually uses the product, is much harder to fix. That’s a business built on shaky ground.

From there, I look at retention. It’s the next layer up. Are customers sticking around? Are they getting value commensurate with what they’re paying?

Because you can have engagement, but if the pricing is wrong or the problem isn’t urgent enough, people won’t stick. And once you have a retention problem, it becomes nearly impossible to build something that compounds. You’re just pouring new users into a leaky bucket.

I tend to think of retention as the intersection of engagement and pricing. It’s where product meets the commercial side of the business. And it’s one of the strongest signals that you’re on solid ground.

Then I start looking at acquisition. And not just top-line growth or lead volume, I want to understand how those leads are coming in. Can you break it down by source, by channel, by campaign? Do you know what’s repeatable? Is there any causality, or is it just stuff “working” because of some big spike, like a launch or a wave of press?

Too often, especially in the early days, growth is built on these one-time events. And they matter! But they don’t scale. What you want to see is some version of: “We did X, and it led to Y.” That’s the foundation of a go-to-market motion that can be optimized and expanded.

Last thing I look at is CAC and LTV. Not because I think they’re great operational metrics, but because they tell you something important about long-term viability.

In my experience, CAC only goes up. You start with your best channels. The cheapest wins. The audiences that are most primed to say yes. But as you grow, those get tapped out. You have to move up the funnel. You have to go broader. You have to start paying more to acquire the next marginal customer.

That’s fine, it’s expected. But it means your LTV better be going up too. That the product is getting better, stickier, more valuable. So what I’m really looking for is: how much room do you have? How much headroom is there in your CAC:LTV ratio for things to get harder, more expensive, and still work?

Anyway. These are just some things I’d be thinking about in building something I wanted to last, and things we think deeply about in building Equals.