The Odd Little Book All Founders Should Read On Selling Their Company

I recently read The Magic Box Paradigm by Ezra Roizen. It’s self-published, was first released in 2016 [1] , and you won’t find it on most startup reading lists. The writing is uneven and inconsistent. The metaphors are weird. There are too many TLAs (three-letter acronyms). Nevertheless, I think all founders should read it — early and often. Early, meaning years before you contemplate selling your company; often, because if you read it early, you’ll need a periodic refresh.

Everyone in M&A has heard the expression “great companies are bought, not sold.” It gets knowing nods in board meetings, but is then promptly ignored in practice. The reason it’s hard to internalize isn’t that the idea is obscure — it’s that acting on it requires you to behave in ways that feel completely wrong. It requires you to slow down, stay deliberately vague, and resist the urge to pitch. For a founder who has spent years getting good at pitching, that turns out to be genuinely difficult to do. Knowing something and behaving consistently with it are two different things. [2]

The Magic Box

Roizen’s central metaphor is the “magic box.” Some things are popsicles — they have known, comparable value, and you can auction them with reasonable confidence in the outcome. Startups are not popsicles. They’re magic boxes. A startup’s value isn’t fixed or objectively discoverable; it depends almost entirely on who’s opening the box and what they plan to build once they have it. The same company can be worth $50M to one acquirer and $500M to another — not because of negotiating leverage, but because of genuine strategic fit. Which means the job isn’t to run a wide process and let the market discover your price. It’s to find the buyer for whom your value is highest and help them see it — ideally before you ever hire a banker.

The Trail of Tears

The best part of the book is the start of Chapter 6, describing what Roizen calls the sad path. I call it the Trail of Tears, because founders walk it constantly — and the thing that makes it a tragicomedy is that every single step feels reasonable at the time.

These two pages are worth the price of the book alone (highlighting mine).

Tragicomedy.

For those who can’t read the images, it goes like this. One of startup Alpha’s investors happens to meet GiantCo’s head of corporate development at a conference. Corpdev thinks Alpha might be worth a look. The investor, delighted to add value, makes an introduction. Value added! [3]

The next day Alpha’s CEO gets an email from Corpdev asking for a deck he can socialize with the relevant product teams. The CEO panics slightly — what do I send? — and settles on his most recent investor presentation. It worked great for raising a big round. It’s got product detail, market sizing, competitive positioning, go-to-market strategy. Should do just fine.

Corpdev reviews the deck. He sees some potential but no clarity on where Alpha’s products might fit into GiantCo’s portfolio. He forwards it to a few product leads and a general manager. The deck is salesy. It was designed to solicit investment in Alpha as a standalone company. The deck’s salesy quality is read by GiantCo as a sign that Alpha is trying to sell itself.

A presentation is scheduled. The relevant GM — probably the best potential internal champion — can’t make it. The demo goes well. GiantCo’s attendees are engaged. The meeting ends with enthusiasm and a commitment to follow up.

Corpdev follows up by sending Alpha’s CEO a list of boilerplate diligence questions: financials, cap table, customer concentration, licensing. The kind of get-to-know-ya questions that corporate development types like. Roizen’s line here is worth remembering: Corpdev is using an X-ray when a telescope is what’s needed. Alpha’s CEO, under pressure from his investor for updates, has his finance team pull together a packet in response. Everything is proceeding mechnically at this point.

Corpdev now takes a critical look. Revenue is concentrated. Burn is high. Valuation expectations are probably rich given the cap table. Alpha is too early and GiantCo is too busy. The eventual reply: we really like what you’re doing, but it doesn’t map to any current priorities. Let’s stay in touch and try to connect again at next year’s conference. In short, you’re a nice guy/gal, but let’s be friends.

The investor wants an update. The CEO has to explain that nothing happened.

The wrong deck. The wrong follow-up. The right GM missing from the meeting. An X-ray instead of a telescope. No chance for the idea to form inside GiantCo. And now the bad news needs to be broken to an investor who was just trying to help.

It’s a sad path indeed. And the reason it works so well as a teaching device is that the CEO didn’t do anything stupid. They made reasonable calls at every step. That’s the point.

This literally happens every day. It wastes time. It’s demotivates founders, raising and then dashing expectations. Worse yet, its leaves the company with a residual “those guys are for sale” taint — a mark that’s hard to see and even harder to erase. [4]

The Partner Big Idea

What should have happened instead? Roizen’s answer is what he calls the Partner Big Idea (PBI). The mechanics of building a PBI are more involved than I’ll go into here — read the book — but the core principle is this: the deal has to become their idea, not yours.

The investor presentation was the original sin. It accidentally signaled that Alpha was for sale, which put GiantCo in evaluation mode rather than strategy-building mode. What Alpha needed wasn’t a buyer to evaluate it. It needed a champion within GiantCo — ideally that GM who missed the meeting — to develop a strategic vision that Alpha was necessary to execute. Not “Alpha is an interesting acquisition target” but “here’s a thing that we need that we can’t build without Alpha.”

Building that requires a totally different set of behaviors. It means getting to the right person quickly — the GM or product leader whose roadmap would actually change — and not spending lots of time with Corpdev. It means asking more questions than you answer. It means leaving the story incomplete enough that the other side has room to build it with you. Incompleteness, in this context, is a feature. It gives the champion something to build and own. [5]

Corpdev is not that person. Corpdev manages process and filters opportunities. They can help once a deal is real, but they rarely create the reason for the deal to happen. If your primary relationships are with Corpdev, you’re operating inside a system designed to evaluate, not to originate. And it’s a system that, left to its own devices, will evaluate your company on a financial, not a strategic, basis.

An Investment Banker Weighs In

I asked an investment banker friend, who works regularly with top strategic buyers, about the book and its relevance today. He had three key observations.

First, the importance of partnerships as a precursor to M&A has only grown since the book was written. Companies partner, integrate products, share customers. Over time, the relationship gets embedded in each side’s roadmap. At that point the “big idea” isn’t hypothetical — the buyer doesn’t just believe in the opportunity, they depend on it. The magic box becomes a dependency.

Second, geography matters to some more than the book acknowledges. Snowflake, for example, drew a reasonably hard line for a long time: a deal couldn’t happen unless the technical team relocated to one of their engineering hubs. With return-to-office (RTO) continuing to gain momementum, I think this will continue to increase in importance.

Third, don’t underestimate the importance of team buy-in. Strategic acquirers aren’t just buying code, they’re buying the team that builds it and they can tell the difference between teams are cashing out (and who will work until exactly the day their handcuffs disappear) and teams who are genuinely excited about a combined future. As a reflection of this, buyers are increasingly splitting the payment, sending more money to the retention pool and less money to the cap table. This creates a tension between investors and employees, but it all gets negotiated in the process.

But What About Banker-led Processes?

At this point, you might reasonably ask: how does all this square with the standard advice to hire a banker and run a process? Aren’t these two ideas in tension?

Not really. They operate on different timelines.

The work Roizen is describing is long-term and strategic. It’s about shaping how a potential acquirer sees your company years before any process begins — helping the right person inside the right company build a strategy that depends on you. You’re not selling the company. You’re teaching someone else why they might need to buy it. The Magic Box Paradigm is about getting bought.

A banker-led process is something else entirely. It’s about getting sold. It’s a short-term mechanism to create urgency, surface alternatives, and establish price. It can accelerate a deal. It can’t create the underlying reason for one to exist.

If the strategic groundwork has been laid — if there are multiple potential acquirers who already “get it” — then a process can work extremely well. It forces those buyers to act, on a timeline, in competition with both PE sponsors and one another.

If the ground hasn’t been laid, then the process tends to default to financial evaluation. You get Corpdev questions, lukewarm interest, and a lot of “not a priority right now.” In other words, a scaled-up and more formal version of the sad path.

One nuance here, having lived it: the hardest part is aligning timelines.

A PE-led auction runs on a clock. You set dates, people prepare bids, and the banker’s job is to keep everyone moving in a tight, predictable cadence. That’s how you create urgency and price tension.

Strategics don’t work that way. They need time — to line up a champion, to socialize the idea internally, to get product, finance, and executive buy-in. Occasionally they can turn on a dime, but that’s the exception, not the rule.

The tension is obvious. Run the process too fast and you lose the strategics. Run it too slow and you lose the auction dynamics. This is why you need to have relationships in place with strategics well before your banker process begins. Otherwise they simply cannot keep up.

The banker’s real job, in this context, is to try to align those timelines. Because the worst outcome is hearing what I once heard: “We’re very interested, but we can’t possibly execute on that timeline, so we’re going to drop out.”

And once that happens, you’ve lost exactly the buyer who might have valued you the most. Utter process failure. Think: You had one job!

So the two ideas aren’t in conflict. They’re parallel. Do the strategic work early — years before you’d contemplate selling. Then, if and when the time comes, use a banker to run a disciplined process on top of it.

Read it Early

This is not a book about how to run an M&A process. It’s a book about how deals actually form — which is a different and more important topic. The sad path exists because most founders don’t think about this until they’re already in it, at which point it’s very hard to correct.

Read it at least four years before you think you need it. Let it shape how you build relationships with potential acquirers. Help the right person inside the right company build a strategy they can’t execute without you — and make sure they realize it before you ever hire a banker.

If you do that, you may not need a process at all. And if you don’t, you can’t count on a process to save you.

# # #

Notes

[1] With a second edition published in 2023

[2] This is actually a broader problem in business. The list of things people nod at in board meetings and then promptly ignore would fill several books.

[3] Roizen’s deadpan “Value added!” is one of the funnier lines in the book.

[4] To be clear, the taint is that they’re always for sale and nobody wants to buy them. Imagine the house on a street with a perennial for-sale sign in front of it.

[5] This is counterintuitive enough that it’s worth sitting with. The instinct is to show up with a complete, polished narrative — that’s what pitching trains you to do. But a complete narrative leaves nothing for the other side to build. Their investment in the idea comes from the act of building it.

Be the CMO Everyone Wants to Work With

I’m a big fan of Dave Gerhardt and the Exit Five marketing community he has built. While I have always liked the idea of peer-networking communities, I think they change from vitamin to painkiller in times of rapid change. Why? Because the playbooks haven’t been written yet.

Take SaaS, for example. Today, you can find scores of blogs — including mine — that talk about how to run SaaS businesses, how to plan SaaS businesses, and how to produce and interpret SaaS metrics. Twenty years ago virtually none of that existed. You needed to figure it out. And one of the best ways to figure it out was to spend time with other people who were doing the same figuring.

There’s a time for timeless wisdom and there’s a time for talking to other people who are doing the same thing as you are, right now. I think AI and the massive disruption it creates in marketing — from content to workflow to performance marketing to analytics — means it’s an awesome time to join a marketing peer-networking community.

I tell this to literally every CMO I work with:

  • Come to me for timeless wisdom (if not fleeting opinions).
  • Join Exit Five (or equivalent) to talk to peers who face the same challenges you do, every day.

That’s not to say that following thought leaders isn’t a great tactic, too. I keep an eye on Emily Kramer, Elena Verna, Carilu Dietrich, Alice de Courcy, and Jon Miller. And I can’t wait for Rand Fishkin’s rumored new book on Zero-Click Marketing. If there are other marketing thought leaders you think I should be following, please let me know.

All this is why I was thrilled when Dave Gerhardt invited me to speak at Exit Five’s Marketing Leadership Retreat on March 19-20 in Phoenix. Given the above, I knew I wasn’t going to be the person delivering fresh-from-the-trenches information on AI tools and methods. The audience is 100x more qualified than I am to do that.

So what did I want to offer up instead? Some timeless wisdom. Specifically, timeless wisdom not just on how to successfully do the CMO job, but on how to be the CMO everyone wants to work with.

Why that topic? And bear in mind it takes a lot for me to pick a title that ends with a preposition. Because I thought it captured the key to success in an important way.

  • It’s not just about doing the job. Yes, that’s quite hard already but if you only focus on that you ironically increase your odds of becoming a statistic.
  • It’s not just about keeping the job. And yes, there’s an art to that, a big part of that is simply remembering to market marketing.
  • It’s about helping the boss do their job which not only increases your strategic value, but makes you more “sticky” in your role.
  • It’s about doing all that while being the CMO that everyone wants to work with (EW2WW)

Why does that last point matter?

  • Marketing is inherently a service organization, so a strong internal customer service orientation never hurts.
  • Being the CMO EW2WW helps you keep your job. With a median tenure of 18-24 months, this should never be too far from a CMO’s mind. Belt and suspenders.
  • If everyone wants to work with you, it helps you find your next job. Board members and recruiters — your top two job sources after peers — will seek you out when the time comes.
  • It might well help you get promoted to COO or CEO. If everyone wants to work with you, they might well give you a shot at the next level.
  • It frames things as a positioning problem. And we know a lot about solving positioning problems, working backwards from a desired result.

Ultimately, I’m saying that CMOs should want to position themselves as the CMO EW2WW.

Put differently: “Marketer, position thyself.

(Adapted without permission from Luke 4:23.)

With that as background, here are the slides from the presentation, embedded below, and downloadable here. I had a fairly miserable time in Gamma building them so apologies for the upside-down funnel, some of the formatting, graphics, and mechanics (e.g., the absence of copyright notice and slide numbers). There is only so much time I’m willing to spend explaining to a chatbot what expression to put on the reflected image of a face in a mirror. And once you drop into PowerPoint to make changes, there seems to be no going back.

Thanks again to Dave for having me and to Allison Saxon for working with me to make it happen.

Why I’m Not Worried About Running Out of Work in the Age of AI

When the auto industry was being decimated and jobs offshored to Japan, many of us instinctively reached for our college economics textbook and started thinking about fish and coconuts — the example Paul Samuelson used to explain David Ricardo’s theory of comparative advantage in international trade.

Well, if Japan is better at making cars than we are, then they should make cars and we should do other things. It’ll be rough for the displaced auto workers but it’s merely a transition period and, like Ricardo says, it’ll be a win/win for both countries and we’ll all be better off in the end.

If you put aside the many concerns with basing policy solely on comparative advantage (e.g., national security, supply-chain resilience, support for fledgling industries, externalities), you could at least get a good night’s sleep.

After all, as I pointed out in my 2026 predictions post, while it’s painful in the short term, we typically don’t miss the jobs displaced by technology.

I don’t think anyone misses elevator operators, a job that peaked in the 1950s and is all but extinct today. The same is true of switchboard operators, pin setters, lamplighters, linotype setters, icemen, typists, shorthand secretaries, and slubber doffers. Yet all these jobs were automated away. While I’m sure the transition was rough for any given elevator operator, as a society we don’t miss them. Most coal miners work hard so their children won’t have to be coal miners.

It sucks for them — the displaced workers — of course, but collectively it’s good for us. Or, in econ-speak, trade creates net national gains, but the costs are often geographically concentrated and painful (e.g., the Rust Belt). If you want to know what it feels like to be on the other end of this displacement, listen to some Bruce Springsteen: Youngstown, My Hometown, Independence Day, and my favorite, The River. That’s what it feels like in a dying town. That’s the darkness on the edge of town.

The pain is real and while occupational transfers are a solution, some are easier than others. An autoworker could transition to another manufacturing job (though often at lower wages) but would be unlikely to become a mechanical engineer. For the economy in general, jobs transition from one sector to another. For any given person, one displacement could be the end of the line.

Another excerpt from the predictions post:

Let’s remember where the word Luddite comes from. Today, we use the word to describe people opposed to new technology. But the Luddites were part of a labor movement; they didn’t destroy machines because they were technophobes, they destroyed them to protect jobs. So the transitions can be rough — and come with social unrest — when jobs are automated away. But we still don’t miss the jobs they fought for.

So, with our safe emotional distance from a 1970s auto worker, what would we have wanted to tell them at the start of the disruption? I’d say:

  • This isn’t a blip, it’s a trend. This is not a problem you wait out.
  • You could try to fight your way through this, but the odds will not be forever in your favor.
  • If you’re nearing retirement, ride this one to the beach and go retire.
  • If you’re early or mid-career, you need to switch horses.
  • Switching early is better than switching late. The goal is to get onto the next horse before everyone else realizes the race has moved.

The popular horse-switching fantasy answer is retraining. “Go back to school and become an engineer.” In theory, yes. In practice, rarely. The jump from an assembly-line worker to an engineer requires years of schooling and a different educational foundation.

The real move for the autoworker was sideways, not upward: industrial maintenance, tool and die work, welding, industrial electrical work, construction trades, trucking, or logistics.

None of these are glamorous answers, but they share an important property: they’re adjacent to the skill set the person already has. They’re realistic.

And now, without the emotional distance we have from a 1970s auto worker, let’s try applying the same thinking to ourselves. One way to do that is to imagine what someone from the year 2070 might write back to us.

They might start the same way.

This isn’t a blip, it’s a trend. Don’t assume this wave of AI capability will conveniently stall just below your job description. You can try to fight your way through it, but the odds will not be forever in your favor. The job you’re defending may still exist, but the number of people needed to do it may fall dramatically.

They might add something else we don’t particularly like hearing: if you’re late in your career, you may be able to ride this out. Large organizations change slowly and adoption curves usually take time. But I think less so in this case — it might prove to be a short ride. If you’re early or mid-career, you should be thinking about switching horses.

But here the analogy gets interesting.

The auto worker in 1975 often had a high-school education and a skill set tightly tied to the factory floor. The realistic moves were sideways into adjacent trades.

The knowledge worker today, by contrast, is already highly trained. Many have college or graduate degrees and have spent years building transferable skills: writing, analysis, design, coding, planning, advising.

In other words, knowledge workers should be much easier to redeploy than auto workers ever were.

So yes, this all hits different because this time it’s us. That’s where we lose the emotional distance, and why the people trying to scare us sound so convincing.

Citrini’s recent thought experiment imagines a negative feedback loop with no natural brake. As they describe it:

AI capabilities improved, companies needed fewer workers, white-collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved …

Repeat.

In that scenario, the problem isn’t sudden unemployment so much as a slow erosion of the income base that supports the modern consumer economy. Highly paid white-collar workers lose their jobs, move into lower-paying work, compress wages across the service sector, and reduce spending at the same time.

The one thing I find most missing from the AI future analyses I’ve read is a simple realization: in my experience, there is always, always more work yet to do.

This relates to how I’ve always thought about the total available market (TAM) for business intelligence (BI). Over the decades I’ve worked in and around BI, I’ve been asked countless times whether the market was saturated or saturating.

My answer was always the same.

One day I’ll go to a cocktail party and ask everyone in the room: “Does everybody have all the information they need to make data-informed management decisions?” If everyone says yes, then on that day the BI market will be saturated.

But that day strikes me as a long way off. It did 40 years ago and it still does today. We have accomplished so, so much in BI and analytics. But there is still so much more to do.

Since no AI post would be complete without a reference to Jevons Paradox, let’s invoke it here: When technology makes something cheaper or more efficient, we tend to use more of it, not less. When steam engines became more efficient, coal consumption didn’t fall — it exploded because steam power became economical in far more applications. The same dynamic has repeated itself with computing, storage, and bandwidth. Every time technology dramatically increases our ability to do something, we don’t run out of work. We discover many more things worth doing.

The vast majority of management and strategic consulting today is about focus. And the reason focus matters so much is simple: execution bandwidth is limited. Organizations can only get so many things right.

So managers spend an enormous amount of time cutting, cutting, and cutting. Prioritizing, prioritizing, and prioritizing. Literally speaking, if we had to list the one key to business success, it’s focus. Why? Because there is so much work that we could do. So many strategies that we can imagine. So many things that we could do to help our customers.

It’s not that there’s only a little more work to do. There is an unfathomable amount of work yet to be done. The job of management is pruning that list down to something the organization can actually execute.

Which is why the idea that we’re somehow going to run out of work strikes me as absurd. It feels like a theory written by people who haven’t actually spent much time doing the work in the first place — serving customers, building products, and running businesses. There is always more that could be done.

There’s also an irony here. Increasingly, the people who say SaaS will stand for “service as a software” are the same people saying AI will massively disrupt employment. But when you sell service-as-a-software you are definitionally in the solutions business, solving problems, not merely delivering data and algorithms. And when you look at your customers through the lens of “problems yet to be solved,” you quickly discover that the list is very, very long.

So no, I’m not particularly worried about us running out of work to do.

I do think the transition could be difficult, especially if you’re not ready for it. So, given everything we’ve said thus far, here’s the advice I’d give to a knowledge worker heading into this transition.

You want to be driving the tools, not driven by them. Aggressively learn AI. Be the person who knows the most about solving problems using AI tools — integrating them, automating workflows. Not just generating content.

You want to understand the business outcome of the work you do. For example, if you’re in public relations, your end goal is awareness. Learn how to use these tools not just to generate articles but to drive awareness through whatever channels and mechanisms will make that possible.

You want to know a little about how the tools work, but not too much. People like me are naturally drawn to understanding the internals, but given the complexity of modern AI systems the more practical skill will be knowing how to use them.

One profession today that looks closest to the autoworker analogy is copywriting. If you’re a copywriter, you should already be learning multimedia and learning how to use AI tools to generate and manage all forms of content. Five years from now you won’t be manually writing and editing. You might be checking work, guiding it, generating variations, and certainly personalizing it at scale.

I know it’s a cliché, but I’ll end with a famous line from Wayne Gretzky, who attributed his success “not to skating where the puck is, but to where the puck is going.”

The puck is going toward heavy AI automation. That will create a new class of jobs around using AI to automate work — and another layer above that focused on managing, directing, and orchestrating those systems to actually run and grow businesses.

Skate there.

The Silicon Valley Canon, Circa 1998: A Stroll Down Software Memory Lane

Something fun happened today. A reader reached out who had been digging through my early-2000s and 2010s posts trying to understand the history of the software industry. That immediately got my attention because I love people who study history. It’s the best way to understand the present. And a great way to avoid repeating the mistakes of those who preceded you.

So I’m always happy when someone wants to talk about software history.

His specific request was interesting: he was looking for case studies or books that were popular at the time — something that would help him understand how people in the industry were thinking back then.

I decided to do him one better. In my view, the real canon of books that shaped enterprise software thinking was largely written before 2000. So I assembled the following reading list: a set of 1990s-era books on software, strategy, marketing, and the industry itself that many of us were reading while the enterprise software industry was taking shape.

Think of it as a reading-list stroll down software, and Silicon Valley, memory lane.

1990s Era Tech Marketing and Strategy Books

High-Tech Marketing

Positioning / Marketing Foundations

Technology Strategy / Innovation

Product Marketing Culture

Enterprise Sales / Go-to-Market

Economics of Software / Networks

Enterprise Technology Industry Case Studies

Software Engineering

Classical Strategy

Why The Rule of 40 is Becoming the Rule of 60 — and What You Can Do About It.

[This article was previously published in the Topline newsletter; see notes.]

The idea was always that, once at scale, software companies could print money.

With SaaS, revenue recurred. If you could buy a dollar of annual recurring revenue (ARR) for one, or even two, dollars, then why not buy a lot of them? You’d break even on customer acquisition costs in year one or two — and everything after that was gravy. Raise VC, invest in sales and marketing, and grow, grow, grow. 

All the better if your market was greenfield and switching costs were high. When the music stopped, the company with the most market share would win. And for a long time, the music wasn’t stopping.  So, for market share: grab, grab, grab. 

Throw in cheap money — courtesy of low interest rates — and you get the Growth at All Costs (GAAC) era of SaaS. During GAAC, a (growth, profit) profile of (100%, -100%) was more attractive than (60%, -40%), which in turn beat (40%, 0%). Growth dominated everything.

Then the wind shifted.

Investors asked, “Why wait forever to print money?” Even if 40%+ mature margins weren’t required, why not produce some profit now?

Private equity (PE) became the most common exit path. And PE wants fixer-uppers, not teardowns. Improving margins in a profitable business is far easier than turning around an unprofitable one.

Moreover, PE enhances returns with leverage. Borrowing 1–2X ARR to finance a deal generates interest expense equal to 10–20% of revenue. If you’re not producing 20%+ margins, you won’t have the cash to service the debt. Losing money becomes a non-starter.

This spirit of balance crystallized in the Rule of 40: growth rate plus profit margin should equal at least 40. Grow as fast as you want — just don’t lose too much money (110%, -70%). Or grow more slowly and produce enough profit to compensate (20%, 20%).

The Rule of R40 (R40) didn’t replace GAAC overnight. It existed during the GAAC era as a disciplining metric — but it became more binding when capital tightened. 

In the post-GAAC era, R40 “worked.” Companies that complied with R40 generally traded at higher revenue multiples than those that didn’t. Statistically, it outperformed growth or margin alone as a predictor of valuation. More nuanced metrics were proposed, like the Rule of X, to remind us that a point of growth carries more weight than a point of profit. Even so, R40 remained the headline metric.

Then the wind shifted again. 

Investors began looking past NRR to GRR, exposing soft SaaS underbellies where expansion masked churn. Cohort analysis replaced year-over-year snapshots, and — because of lot of expansion often happens in year one — they generally showed a less pretty picture. Interest rates rose. Leverage became more expensive. Multiples compressed.

On top of all this, AI fears went viral, amplifying uncertainty across markets.

In short:

Ladies and gentlemen, we interrupt coverage of the SaaSacre to bring you live footage of the SaaSpocalypse.  

PE is a demanding overlord, largely unsympathetic to the daily pressures of customers and markets. While VC sees itself as “partnering with founders” to build a business, PE sees itself as “underwriting a model” — that they fully expect to achieve.

When the prevailing price of SaaS companies falls from 30X to 15X EBITDA, the model doesn’t bend. It breaks. 

Indulge me in an example and some arithmetic to make this concrete. Assume PE bought a company in 2023 for 30X EBITDA — $180M total — financing half with debt. That’s $90M of equity targeting a 3X return over four to six years. Under R40, the equity grows nicely in Years One and Two to 1.4X and 1.9X. Then multiples are cut in half, and the equity collapses to 0.7X.

What saves the deal? The Rule of 60. Maintain 20% growth while doubling EBITDA margins to 40%. Instead of 0.7X, the equity rebounds to 2.5X. The path to 3X+ reappears.

For the PE partner, switching to the Rule of 60 (R60) turns what would have been a 1.1X infield single into a 3.1X stand-up double. The trick, of course, is that management needs to figure out how expand EBITDA margins to 40% while preserving 20% growth. And here, unfortunately, “management” means you.

To summarize:

  • Why the change to R60? Because the model fails without it.
  • How do you double EBITDA while holding growth at 20%? That’s the hard part.

Nor is this some passing obsession. R60 isn’t PLG. It isn’t ABM. It isn’t a fad the board will tire of next quarter. It’s the financial model. That thing is out there. You can’t bargain with it. You can’t reason with it. It doesn’t feel pity or remorse or fear. And it absolutely will not stop. Ever. Until you are —

Okay, I know financial models aren’t Cyberdyne Series T-800 Terminators, though they sure can feel like them sometimes. 

But enough warnings about the inevitability of this change. Let’s switch to what you can do about it.

Here are 12 ideas to help you drive increased productivity and remain sane while doing it:

1. Ignore macro whiplash.

Don’t get wound up by techno-optimists or doomers. Watching the narrative swing day to day is as unproductive as tracking your stock price tick by tick — it will drive you insane. You have a job to do. Focus on it.

2. Reframe your job around efficiency.

Don’t measure success by team size or budget — if you ever did. Your job is to hit the ARR target while increasing ARR per seller and ARR per S&M dollar. Growing faster than your costs is the mandate now — and the skill your next employer will pay for.

3. Allocate the efficiency burden intelligently.

Work with your CEO to distribute the margin expansion burden intelligently across R&D, G&A, COGS, and S&M. Pro rata allocation is easy but rarely optimal. Don’t default to cutting S&M simply because it benchmarks high — or because the product-oriented founder won’t touch R&D and the CF-No refuses to trim G&A. Get in a room and have a hard conversation.

4. Operate as one revenue team.

Sales, Marketing, and Success must build the plan together and share accountability. Align on pipeline quality, win rate, churn, and NRR. While most teams think they’re aligned, few actually are. If you’re not answering each other’s calls on the first ring — or reallocating budget across departmental lines when needed — it’s not tight enough.

5. Increase street prices.

Raise list prices or discount less. If your category is PE-backed, your competitors face the same margin pressure and are likely doing the same. No one should be racing to the bottom right now. Show pricing discipline — and expand margins in the process.

6. Try “heretical” moves in your sales model.

Let reps run their own demos. Charge for POCs. Push SDRs into real discovery — or eliminate inbound SDRs altogether. Disqualify aggressively and walk away from bad-fit segments. If PLG applies, feed sales only PQLs. Go back to the ideas you once dismissed as crazy in brainstorming meetings and reconsider them. Let new constraints force new behavior.

Growing faster than your costs is the mandate now — and the skill your next employer will pay for.

7. Build a partner channel.

Start with partners as a lead source, then develop real channel leverage. Hire channel managers with meaningful quotas — effectively running partners as a leveraged sales force. If you need to improve GTM productivity, the channel isn’t “extra.” It’s structural leverage.

8. Improve deal mechanics.

Go back to basics with the sales velocity formula: opportunities × ASP × win rate ÷ sales cycle. Improve any variable and revenue per day increases. The most overlooked levers are win rate — start with rigorous win/loss analysis — and sales cycle length. Identify where deals stall and systematically accelerate them from discovery to demo to POC to legal. Time kills all deals.

9. Lean into AI for real work.

Move beyond experiments. Embed AI into workflows — content, analytics, segmentation, attribution, automation. It may take longer at first, but production use is the goal. Charter your Ops leader to know the leading AI tools in the GTM stack, educate the GTM leadership team on them, and develop a clear adoption roadmap.

10. Automate — but protect trust while you’re doing it.

Many companies will successfully automate with AI but will quietly erode customer trust in the process. Keep humans accessible in your workflows; escalation out of a chatbot should be effortless. Automate content generation, but don’t flood customers with slop. Never forget: There may be a human on the other side of your AI — even if sometimes it’s just another agent.

11. Engage with peer groups.

The fastest way to learn what’s working is by talking to operators doing the same job elsewhere. Shared intelligence compounds, which is exactly why communities like Pavilion and Exit 5 matter. Sometimes you want timeless wisdom; sometimes you need to talk to someone doing the exact same job as you at another company. Do both.

12. Protect your job by evolving it.

AI will eliminate some roles and create others. Be on the right side of that shift. Redesign your workflows, raise the productivity bar, and position yourself as the person who knows how to get leverage from the new tools. Then bring your team with you.

Adjusting to the New Reality

In this article, we’ve traced the path from GAAC to the Rule of 40 — and why capital markets are now pointing us toward the Rule of 60. Unless multiples suddenly double, hope is not a strategy, and margin pressure isn’t temporary but structural.

The 12 ideas above are about regaining control. Tune out the noise, redefine the mission around productivity, distribute the burden intelligently, and try the things you once thought were off-limits.

Use the new constraints to change behavior. And behavior change is where real performance improvement begins.

You can wait for multiples to bail you out or you can build a business that works at today’s multiples. The companies that figure this out won’t just endure this cycle, they’ll outperform in it. And the market will reward them — and the people who built them — accordingly.

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Notes

This article was originally published in the Topline newsletter, a spin-off from the Pavilion go-to-market community. Since then, I’ve applied a few edits and style changes, but it’s largely the same content. Because Topline is a GTM newsletter, I wrote actions for the GTM executive, notably omitting AI product strategy. Because I often write for founders, not just about GTM but the whole company, this omission generated some confusion.