We're in the middle of an AI boom. Capital is abundant, expectations are high, and nearly every company is trying to position itself as a winner in the coming transformation. Amid all the excitement, a basic challenge remains: separating what exists today from what might exist tomorrow. Customers, employees, journalists, investors, and boards are all trying to answer the same question: what's real, what's aspirational, and what's simply marketing?

That makes now an unusually good time to read Katie Prescott's The Curious Case of Mike Lynch: The Improbable Life and Death of a Tech Billionaire.

While the technologies are different, the book explores a timeless question: what happens when exceptionally smart people become committed not merely to success itself, but also to sustaining the aura of success?

Published in 2025, the 464-page book tells the remarkable story of Mike Lynch, founder of Autonomy, once Britain's most successful software entrepreneur. Prescott, technology business editor at The Times, became fascinated by the Autonomy saga while covering it as a journalist and eventually decided to write the book. The result is a highly readable biography that succeeds in making both Lynch and the surrounding drama accessible to readers who may know little about either.

Before diving in, I should disclose that I've never been a fan of either Autonomy or Lynch. During my time as CEO of MarkLogic, we competed against them in some deals. I hired several Autonomy alumni and knew people from prior jobs who ended up in relatively senior positions at the company. So, my perspective comes from having observed Autonomy as a competitor and from following the story through friends and colleagues for many years.

I met Lynch once during a discussion about a potential strategic partnership. Nothing came of it. What surprised me most was how little interest he seemed to have in our technology. MarkLogic's technology was pretty remarkable at the time, yet Lynch seemed far more interested in the business. Perhaps that was because our technology wasn't Bayesian. Perhaps it was because he had already become more interested in building companies than building technology. Or perhaps, lacking our founder in the meeting, he simply concluded that we weren't smart enough to explain it properly. Whatever the reason, I left with the impression that I had spoken less to the brilliant computer scientist I'd expected and more to yet another businessman.

For readers unfamiliar with the story, Lynch founded Autonomy in Cambridge in 1996 and built it into one of Europe's most successful software companies. In 2011, HP acquired Autonomy for roughly $11 billion. A year later, HP wrote down most of the acquisition's value and alleged it had been misled about Autonomy's financial performance.

What followed was a saga that lasted more than a decade: civil litigation, criminal prosecutions, extradition battles, accounting controversies, witness cooperation agreements, jury trials, and endless public debate. Lynch was ultimately acquitted by a US jury in 2024, only to die weeks later when his superyacht, the Bayesian, sank off the coast of Sicily.

Prescott excels at biography. By the end of the book, readers know Lynch well: the brilliant Cambridge academic, the obsessive technologist, the eccentric billionaire, the relentless competitor, the difficult boss, the loyal friend, the dog lover, the collector of unusual properties, and the larger-than-life personality who inspired extraordinary devotion among colleagues and employees. Whatever one thinks of Lynch, Prescott succeeds in making him vividly human.

The book is equally strong in explaining why Lynch mattered. Many Americans have never heard of him. For a generation of British entrepreneurs, however, he represented something important: proof that a globally significant software company could be built outside Silicon Valley. Lynch wasn't merely a successful founder. He was a symbol. One senses throughout the book that the stakes eventually became larger than Autonomy itself.

Prescott also deserves credit for making a highly technical story readable. Revenue recognition, reseller transactions, accounting standards, extradition proceedings, and civil litigation do not naturally lend themselves to compelling narrative. Yet the book remains engaging throughout.

The biggest thing I took away from the book, however, had little to do with accounting. It had to do with success — or, more precisely, the aura of success.

I've long believed that, at least in Silicon Valley-style companies, corporate scandals are rarely about money in the narrow sense. Sure, there is plenty of money involved. But the money isn't really the point. The point is an all-consuming desire to be seen as winning: to build and sustain an aura of success around a company that becomes self-reinforcing.

Why'd you buy from XYZco? Because they're the leader. How do you know they're the leader? Because they're growing fastest. Because they've raised the most money. Because they did that publicity stunt. Because their CEO is on all the conference stages. Because they've got the best strategy. How do you know it's the best strategy? Because they're growing the fastest. Because they're the market leader.

Eventually the reasoning becomes circular.

Success attracts customers, employees, investors, journalists, partners, and more success. Customers buy because other customers are buying. Employees join because other employees want to join. Investors invest because other investors are investing. The company starts selling two products: its actual product and the story of its success.

The danger comes when preserving the aura becomes more important than the underlying reality. That observation leads me to two reservations about the book.

The first concerns loyalty.

Throughout the narrative, people are frequently described in terms of loyalty, betrayal, friendship, and allegiance. Witnesses "turn." Former colleagues "stand by" Lynch. Others remain loyal despite enormous pressure.

These are understandable ways to describe events, but I found myself repeatedly wanting the book to step outside Lynch's point of view and ask a different question: not who remained loyal, but who was right.

One passage describes Stouffer Egan's cooperation with prosecutors as an act of betrayal. But betrayal is a curious word. If Egan was lying to save himself, it would fit perfectly. If he was telling the truth, it would become much harder to justify.

This distinction matters because loyalty and truth are not the same thing. If someone testifies truthfully about misconduct, is that disloyalty or integrity? Conversely, if someone refuses to provide damaging evidence, is that loyalty or something else entirely?

The answer depends on what actually happened and whose point of view you adopt. Yet when the media discuss corporate scandals they routinely adopt the language of the accused. The discussion quietly shifts from the pursuit of truth to the perspective of the accused.

My second reservation is more significant and concerns what struck me as an under-explored inconsistency at the heart of the story.

Prescott repeatedly portrays Lynch as brilliant, intensely competitive, deeply involved, extraordinarily persuasive, detail-oriented, and at times controlling. Former colleagues describe being mesmerized by him. The book paints a picture of a founder who exerted enormous influence over both the company and the people around him. More than once, witnesses describe people being "under his spell." Even some who later testified against him continued to speak admiringly of his intellect and force of personality.

At the same time, the book describes allegations involving backdated contracts, reseller transactions, side agreements, misleading emails, unusual hardware transactions, and other efforts allegedly designed to sustain reported growth and hit quarterly targets.

What surprised me was not that these allegations existed. It was how little time the book spends reconciling them with the portrait it paints of Lynch.

The book seems to ask readers to accept two propositions simultaneously. First, that Lynch was brilliant, intensely involved in the business, obsessed with performance, frustrated by Wall Street's expectations, and capable of drilling into remarkable levels of detail. Second, that practices allegedly occurring repeatedly over multiple years somehow escaped the attention of a man portrayed as obsessively focused on the very metrics they affected.

Perhaps both propositions are true. But the more convincing the book becomes on the first point, the harder it becomes not to question the second. Reading the book, I was reminded of the famous courtroom scene in A Few Good Men (edited for brevity.

Kaffee: A moment ago, you said that you ordered Lt. Kendrick to tell his men that Santiago wasn't to be touched.

Jessup: That's right.

Kaffee: And Lt. Kendrick was clear on what you wanted?

Jessup: Crystal.

Kaffee: Any chance Lt. Kendrick ignored the order?

Jessup: Ignored the order?

Kaffee: Any chance he forgot about it?

Jessup: No.

Kaffee: When Lt. Kendrick spoke to the platoon and ordered them not to touch Santiago, any chance they ignored him?

Jessup: We follow orders, son. We follow orders or people die. It's that simple. Are we clear?

Kaffee: Crystal. Colonel, I just have one more question ... if you gave an order that Santiago wasn't to be touched, and your orders are always followed, then why would Santiago be in danger? Why would it be necessary to transfer him off the base?

I found myself asking a similar question while reading about Autonomy.

If Lynch was as brilliant, detail-oriented, involved, and controlling as the book convincingly portrays him to be, how exactly are readers meant to reconcile that with the degree of ignorance his defense appears to require?

To be fair, this issue was explored at trial. Witnesses were pressed on what Lynch knew, when he knew it, and whether they could place him in specific discussions or transactions. The defense argued that Lynch focused on technology, strategy, and the business while delegating finance and accounting matters to others, particularly CFO Sushovan Hussain. That debate was clearly part of the case. What I found under-explored, however, was the broader tension between the portrait of Lynch presented throughout the book — brilliant, deeply involved, intensely competitive, obsessed with results, and capable of exerting enormous influence over the organization — and the degree of ignorance that defense appears to require. That's not primarily a legal question. But I finished the book wishing Prescott had spent more time wrestling with it.

And even if one resolves that inconsistency in Lynch's favor, a second question remains: where did the pressure come from? One of the recurring themes in the testimony is pressure — pressure to make the quarter, pressure to hit the numbers, pressure to sustain growth, pressure to avoid disappointment. Witnesses repeatedly described an organization focused on maintaining the narrative of relentless success. If Lynch was the dominant force inside Autonomy — as Prescott persuasively argues throughout the book — then surely it is worth asking what role he played in creating that environment.

I've seen versions of this story before. Different companies, different industries, different outcomes. But the pattern is familiar. Smart people become trapped by the need to sustain the appearance of success. Eventually the signal can become more important than the reality. When that happens, customers buy from the wrong vendors, employees join the wrong companies, investors allocate capital to the wrong opportunities, and competitors lose deals they should have won. Fraud becomes rust on the flywheel of the tech ecosystem — not just because of the money lost, but because of the trust destroyed.

Despite my criticisms, I strongly recommend the book. Prescott has written a compelling account of one of the most consequential and controversial figures in European software history. She succeeds brilliantly in helping us understand why so many people admired Mike Lynch.

I only wish she had spent a bit more time wrestling with the questions that still linger once the admiration fades.

I spend a lot of time thinking about go-to-market efficiency — how companies decide where to invest their marketing dollars, how they measure what's working, and how they improve over time.

For years, much of marketing measurement has revolved around attribution — trying to determine which programs, campaigns, channels, or touches deserve credit for an opportunity or sale. While attribution works well when dealing with short, linear funnels (e.g., selling toothbrushes online), it gets exponentially harder in B2B environments, where companies sell complex products, engage scores of contacts, and generate hundreds of touchpoints over long sales cycles. This is known as multi-touch attribution (MTA).

It's easy to get cynical about attribution. I've periodically recommended that CMOs drink their coffee from this mug during quarterly business reviews (QBRs).

But if we did indeed "make it up," the question is why. The answer is simple: because "the business" — the CEO, CFO, and board — won't stop asking fundamental questions. Which marketing programs work? Which don't? What should we do more of? What should we do less?

Ever since John Wanamaker famously said that he knew half his marketing budget was wasted but didn't know which half, businesses have been trying to answer those questions. And as marketers, we have to answer them.

We can't say that it's too hard to measure, that tracking has gotten more difficult, or that part of the process happens offline and isn't trackable at all. The CEO, CFO, and board don't care. Saying "it's complicated" instead of answering will get you an express ticket to the unemployment line.

So attribution ends up a bit like the way Churchill described democracy: the worst system, except for all the others that have been tried.

The mistake is expecting attribution to answer every question. Like a hiker lost in the woods, we should triangulate to determine our position. No single bearing is sufficient. Instead, we should combine multiple techniques, including attribution, incrementality testing, marketing mix modeling, journey analytics, and intent signals. No single approach provides the answer, but together they can help us make better decisions.

But the opportunity doesn't stop at measurement.

Reporting and analytics are table stakes – important ones with career-limiting consequences if you get them wrong. But once you've built the data foundation required to understand customer journeys, attribution, engagement, and pipeline, you can use it for much more than reporting.

Historically, marketing systems have been divided into two camps: systems of insight and systems of execution. One set of tools tells you what happened. Another helps you do something about it. There is an opportunity to bring those worlds closer together. The same data used to understand buyer behavior can also be used to influence it.

Suppose your analysis reveals a set of accounts demonstrating strong buying signals. Or a segment of prospects who consistently engage with a particular type of content. Or opportunities that share characteristics with your most successful deals. Why should those insights remain trapped inside reports and dashboards? They can be transformed directly into audiences for advertising, targets for outbound campaigns, priorities for sales development teams, and signals for customer success organizations.

This is where marketing measurement evolves into demand activation. Instead of explaining performance after the fact, the data can be used to improve future outcomes. Marketing teams can build more relevant audiences. Campaigns can be targeted more precisely. Sales teams can focus on the accounts most likely to engage. Marketing and sales can operate from a common view of buyer activity and intent.

The goal is no longer just to understand demand. It's to help create it. The same data foundation that powers analytics can improve execution.

And this is where things get even more interesting. That data foundation can also become the platform on which AI agents operate. As we're rapidly learning in the age of AI, success is often less about having the best model than having the best data. Agents need context. They need memory. They need a rich understanding of accounts, contacts, buying groups, campaigns, engagement, pipeline, and revenue.

Put it together and you get this. Marketing analytics are table stakes. Critical, but table stakes nevertheless. Demand activation is where things get interesting because we're no longer just watching the game — we're playing it. Marketing agents are where things get really interesting because we're putting more players on the field. Dreamdata is well positioned across all three.

That's what excites me about the company. I get the chance to work in a domain I care a lot about. I get to work with a product that is well positioned for the industry's evolution. I get to be at the forefront of marketing agents. And I get to work with Nick, Lars, Steffen, and the rest of the team.

I'm looking forward to the journey. Thanks to Roberto for introducing me to it.

We’ve decided, after 122 episodes, to put The Metrics Brothers podcast on hiatus. Let me explain how I think about that decision.

On October 20, 1974, the Grateful Dead played what was then billed as their final show before an indefinite hiatus. The band was burned out. The touring operation had grown so large that they were increasingly touring to support the machinery of touring itself. It was time for a break. To rethink and regroup.

That’s roughly where Ray and I are with The Metrics Brothers today. While we’re not operating at quite the same scale (and our listeners wear far less tie-dye), the show does have an audience that could fill Madison Square Garden.

We’d rather pause at the top. Our numbers have never been better. We built a sizable audience, sustained strong listening and engagement rates, and regularly charted in the Top 25–50 in the Apple Podcasts Management category. More importantly, we successfully pivoted the show from SaaS metrics into discussions about AI and the changing nature of the software business.

But that pivot also raised the bar. The newer AI-focused episodes demanded more prep, more research, and more spirited brotherly disagreement. At one point, I’m pretty sure Ray left a horse head on my pillow over an argument about agentic workflows. Well, at least, I hope that was Ray.

Podcasts are a treadmill. The weekly cadence is relentless, particularly when you’re trying to produce something thoughtful. What started as a fun hobby slowly acquired deadlines, obligations, production schedules, and all the other characteristics of work.

On my end, the biggest casualty was Kellblog: my posting rate fell by roughly half in 2025 as a direct consequence of the podcast.

So What’s Next?

Ray is writing his own post on what he’ll do going forward. Though when he talks about spending time in “the big house,” I hope he’s referring to a quaint B&B somewhere in Vermont. How’d that tax audit end up, anyway?

As for me, I’ll largely be doing more of the same: working with the Balderton Capital portfolio, serving on my four boards, and helping my advisory clients navigate CXO, strategy, positioning, and go-to-market challenges.

I’ll also be putting more time back into writing — particularly now that I’ve finally completed the migration off WordPress. Writing posts is fun again, thanks to Ghost.

We’d like to offer a huge thanks to everyone who spent time with us across 122 episodes of enterprise software nerdery. Hopefully we provided some useful perspective — and maybe a little laughter — about SaaS, metrics, AI, and the strange evolution of the software industry.

Our most underappreciated content was our 50th-episode Car Talk-style credits. Our Italian growth marketing specialist was Anita Mopipa, our sales productivity expert was Carrie Akwota, our M&A roll-up strategist was Tucker Inn, and our PE financing analyst was Lee Verup. Where was the love? We were operating at NPR-adjacent levels of sophistication here.

Let me conclude by discussing our greatest failure: despite Ray’s many lavish, expense-paid trips to Schenectady, we were never able to land Katie O’Byrnes Irish Pub as our primary sponsor. Though, to be fair, a suspicious amount of Guinness was reported missing during the negotiations.

The hiatus announcement episode is here.

Thanks again to everyone. If you have thoughts on The Metrics Brothers or ideas about what we should do next, please leave them in the comments.

The first time I read Obviously Awesome by April Dunford, I breathed a sigh of relief. Someone had finally written the positioning book that desperately needed to exist — and one that I had long suspected I might someday need to write myself. My reaction came not from the material — which I love — but from seeing it materialized as an actual book. Once I read it, I immediately knew I wouldn’t have enjoyed writing it [1].

That kind of work needs to be tactical, prescriptive, and structured in ways that don’t come naturally to me. I prefer essays, arguments, conceptual frames, maybe with a few nuggets thrown in. But the book needed to exist, and she wrote it brilliantly. I didn’t have to worry anymore. I could stand on the shoulders of giants.

That experience repeated itself recently when I bumped into David Politis at the Work-Bench offices in New York. A veteran founder/CEO (including BetterCloud) and blogger (Not Another CEO), I was excited to discover that he’d written Startup Founder Survival Guide: 50+ Rules to Live By, Learned the Hard Way. I jumped on it shortly thereafter.

Unlike Dunford’s book, it’s less polished, less professionally packaged, and certainly less well known. Nevertheless, the content inside is a gem. Or more precisely, fifty-plus gems. They’re more diamonds in the rough than Tiffany-cut stones, but genuine diamonds nevertheless.

From my selfish perspective, it was another book I didn’t have to write. David captured so many pithy truths — so many things that I also firmly believe and similarly learned the hard way — that I could once again stand on the shoulders of giants.

What The Guide Is and How to Read It

David calls it a guide, and he’s not wrong. Today, it’s effectively a 64-page PDF organized into nine topic areas with roughly a half-dozen lessons per section:

• Finding and hiring the best people (which I keep misreading as “firing and hiring” [2])
• Managing and motivating your team
• Raising money
• Getting the most out of your advisors and investors
• Product strategy and development
• Engaging and delighting your customers
• GTM motion and focus
• Operations
• Growing, evolving, and staying sane as a CEO

Each lesson follows the same three-part template:

• How did I learn this?
• Why does it matter?
• How do you implement it?

It’s a sensible template. It’s also what makes the guide read less like a book and more like a reference work. Most people don’t sit down and read encyclopedias. You look things up. That’s how I’d recommend using this guide: read the table of contents first, dive into the lessons relevant to your current challenges, then skim the rest so you remember what’s there when you eventually need it.

Observations and Critique

The guide is built around a repeated lesson template, and I am generally opposed to template-first writing because it flattens the copy and drains the soul from it [3].

What could have been a narrative-linked series of vivid stories instead becomes fifty-plus one- or two-page lessons. Because the template requires an origin story, you still get glimpses of experience, but as a reader I occasionally felt cheated by the distillation process.

When it comes to writing, David’s not going to get confused with Geoffrey Moore any time soon [4]. The prose can be awkward — see, for example, the title — but he writes with the directness of a native New Yorker.

Thus, the guide reads like a business memo from your blunt New York boss who pronounces coffee “caw-fee.” Clear and direct, if not particularly pretty.

The deeper issue is that many of the lessons sound cliché:

• “Embrace overcommunication.”
• “Grit is the most important attribute for your first hires.”
• “Don’t run out of money.”
• “Trust your gut.”

You’ve heard these before. We all have.

But clichés become clichés for a reason. There’s truth inside them. Politis is effectively trying to rehabilitate lessons that experience has validated but familiarity has dulled.

Take hiring and firing. I learned the rule as:

“You can never fire someone too early.”

David phrases it as:

“When you think it’s time to replace someone, it’s probably too late.”

Same lesson.

I first heard it from a VC in the late 1990s and always considered it a classic VC cliché. But here’s the rub: it’s true. Founders know it intellectually, yet routinely fail to internalize it.

David argues from team fit and credibility. My argument centers more on damage and opportunity cost:

• If you can already see some damage, they’ve probably already done much more.
• Imagine how much better things would be with a star in the role.

Neither argument is wrong. But compressed lessons sometimes lack the depth required to truly change someone’s mind.

Before leaving the critique, I should mention that the guide needs a more polished presentation and layout. Books do get judged by their covers and this guide deserves a better one [5].

Lessons I Might Have Written Myself

Share the good, the bad, and the ugly with candidates

I’m a huge believer in this one.

The instinct in recruiting is to sell — to make the role sound as attractive as possible and worry about the hard parts later. That often backfires.

Candidates who self-select out after hearing the ugly parts save everyone time. Candidates who lean in after hearing them are usually the people you want.

David learned this on SDRs and support reps. I learned it on EAs. But we arrived at the same conclusion.

There is a maximum number of execs you can bring on at one time

Absolutely true, and chronically underappreciated.

Every executive hire is a context switch, a new relationship, and a cascade of organizational changes. Two at once is manageable. Three gets hard.

And remember: they don’t simply need to learn how to do their jobs. They need to learn how to work with each other. David even cites the same group dynamics framework I use [6].

This lesson matters because when you have multiple executive problems simultaneously, you must prioritize them. You need some people stay longer than you’d ideally like simply because you can’t absorb too much organizational change at once.

Recruiting order matters too. If you need both a CRO and a CMO, hire the CRO first.

Embrace overcommunication

Here David and I agree on the soundbite but support it differently.

His experience led him to create a detailed monthly company-wide performance email. I’d never do that for several reasons:

1. In modern companies, you should write every all-hands email as if it’s getting published in TechCrunch and mailed directly to your competitors.
2. Performance discussions are often better handled interactively in town halls.
3. After decades of cock-ups, I’ve grown deeply skeptical of long emails in almost all circumstances [7].

To me, overcommunication is primarily about repetition.

You pick a set of messages and repeat them over and over, well beyond the point of personal boredom. Think of a politician delivering the same stump speech in town after town after town.

It’s not glamorous. But that’s leadership.

Don’t run out of money

Don Valentine, founder of Sequoia Capital, said it best:

“All startups go out of business for the same reason: they run out of money.”

David includes this as a key lesson, and rightly so.

The dangerous thing is that there are long periods when fundraising feels easy, causing founders to forget that windows can slam shut — often faster than you can reposition yourself to raise.

My rule is simple:

• Raise money when you can.
• Raise slightly more than you think you need.

Dilution stings, but it won’t kill you. Running out of oxygen will.

Luck is a big part of success

I’m glad David included this because it’s true and surprisingly hard to discuss honestly.

People often hear discussions of luck as either sour grapes or avoidance of responsibility. But most founders I know actually over-attribute outcomes to themselves, not under-attribute them.

Both when it works and when it doesn't.

Lessons Where I'd Push Back

Create succession plans

Politis recommends succession planning, but I’m pretty firmly against formal succession planning in $0–100M startups.

If you’re lucky enough to hold an executive role in a high-growth startup, your succession plan is simple:

• Keep your job.
• Do it well.
• Continuously learn what’s required to keep doing it well.

I absolutely believe in career and skill development. But formal succession planning often becomes bureaucratic theater. Everyone gets a plan whether they need one or not, and suddenly people start wondering what their “next role” should be instead of focusing on the one they already have.

When you’re worried about an executive’s performance, make a replacement plan. Don’t create a succession-planning movement inside the company [8].

Board meetings should be strategic

Politis argues board meetings should be strategic, and I understand the impulse. Too many are off-Broadway operational theater.

But “make them strategic” oversimplifies the problem.

Board meetings should be whatever the board and management team intentionally decide they should be:

• Some proposal review
• Some operational inspection
• Some discussion
• Some updates

Personally, I like good operational reviews. Many board members don’t. Fine. The key is deliberate design rather than accidental drift.

Partnerships with big companies are usually a waste of time

This one is too absolute.

Yes, large-company partnerships can become time sinks. GSIs can absolutely eat your calendar alive.

But the right partnerships — at the right stage, with the right structure, and with appropriate firewalls around the distraction — can be transformational (e.g., Adaptive Insights and NetSuite, Qualified and Salesforce).

I usually advise startups to begin with regional systems integrators because the motion is more manageable. But avoiding partnerships entirely can become equally dangerous because your competitors will do them and eventually establish table stakes you lack.

While he admits there are exceptions, this lesson nevertheless needs more balance.

Advisors should only be brought on for explicit projects

This one, to me, reflects a common misunderstanding of advisors.

Some founders think advisors exist for network access. That usually disappoints.

Others treat advisors like lightweight consultants assigned to projects. That often disappoints too.

Most advisors don’t actually want homework. That’s part of why they became advisors.

To me, most startups need at most three advisors:

• One for CEO issues
• One who complements the founder’s weakness (e.g., a GTM expert for a product-oriented founder)
• One who can intellectually spar with the founder inside the company’s domain

Sometimes one person covers multiple roles.

Don’t go chasing competitors’ features

This is where my product marketing and competitive background creates the biggest disagreement.

David’s core point is right:

Don’t let competitors dictate your roadmap.

But the argument doesn’t go far enough.

Features matter not just for usage but for winning deals. A feature nobody uses but that demos beautifully and neutralizes objections can still be strategically valuable.

Always remember: if you don’t win the deal, customer delight never enters the equation.

Too many companies romanticize “running their own race” while effectively ignoring the competitive market around them.

Have your own vision, absolutely. But know who you’re selling against and make sure your product can win.

Please God, don’t run your own race.

The Verdict

Politis set out to write the guide he wished had existed when he was starting out. He succeeded.

Startup Founder Survival Guide is packaged, hard-won common sense.

It is not a great cover-to-cover read. It is not Geoffrey Moore. The repeated template occasionally flattens the prose, the stories sometimes feel overly compressed, and the presentation could use some spit and polish.

But none of that is the point.

The point is that the lessons are real. They are the kinds of things experienced operators say to each other — the kinds of things that sound cliché until you’re living through the exact situation they describe.

Politis is trying to rehabilitate common wisdom before founders pay full tuition at the school of hard knocks. That’s a worthwhile project, and he’s done it well enough that thousands of founders have already found it useful.

The guide is packaged common sense. And never forget that common sense, particularly in business, can be surprisingly uncommon.

Notes

[1] Which, perhaps subconsciously, is maybe why I didn’t.

[2] Not sure if there’s a subliminal message there.

[3] I have written before about my distaste for template-first thinking and its cost. See: Dear Marketing: Stop Putting the Template Over the Story. This problem just won't go away.

[4] Moore has a PhD in English literature, and while his books are about marketing and strategy, you can nevertheless tell. His prose is a pleasure. Moore sets a high bar that few business writers surpass. (And yes, I know I’m not one of them.)

[5] And the guide is somewhat strewn across the web in different forms: a Google doc linked from Politis Projects, a PDF in UCSD’s startup founder toolkit, an embedded document in a LinkedIn post, and the Substack version, which the PDF now identifies as the canonical copy. Which is, frankly, a helluva long Substack post.

[6] Tuckman’s stages: forming, storming, norming, performing, and adjourning.

[7] Kellogg’s rule of long emails: don’t write them, and if you somehow find yourself writing one anyway, don’t send it. And if you absolutely must send it to someone — just to get that send-button dopamine hit — send it to yourself.

[8] I understand why succession planning becomes necessary in larger companies and why boards often push it downward. The NACD views succession planning as a core board responsibility. The question is whether startups should.

Saying you want a strategic discussion and walking into a board meeting with a 45-slide operations review deck is like saying you want to eat healthy and driving to Baskin Robbins. The intention and the action are in direct contradiction. And — hint — the action wins every time. If you go to Baskin Robbins, you're having banana splits.

This mismatch is the most common, self-inflicted problem I see in board meetings. The board deck is not a passive document. It is the living, de facto agenda of the meeting — regardless of what's in the cover email or on the agenda slide. What's in the deck will get discussed. What's not in the deck will mostly not. Like a raft in a river, the board meeting will go where the slides take it.

In short: ill-conceived board slides lead to unproductive board meetings.

You may think your board is wonderful or you may think it's not. Either way, your slides can prove you right. A thoughtfully constructed deck can lead to a great meeting. Mindlessly pumping new numbers into the same old template will usually lead to a bad one.

The key: ensure the deck matches the style of desired conversation — segment by segment.

There Are at Least Seven Types of Board Meeting Segment

Not all board meeting segments are the same, and the type should vary with the topic. Before the deck exists, there is a more fundamental question that most CEOs skip: what kind of meeting is this — segment by segment?

In practice, segments fall into a relatively small number of distinct types:

Ops review. The board inspects the troops. Slides should be dense, complete, and accurate — history, targets, plan percent, growth rates. The board wants to understand the health of the business and the extent to which management is on top of the details.

Discussion. You want engagement. Slides should be light — just enough context to orient everyone — followed by a small number of well-framed questions. The board should be doing most of the talking. Your role is to facilitate, not present. A good rule of thumb: 3-5 slides to baseline the audience, then three slides with one question per title and nothing in the body. That blank space is intentional. (For a deeper dive on running these well, see How to Lead a Strategic Board Discussion.)

Presentation. Presenter-led. A product roadmap, a demo, a win/loss readout. The board is mostly listening, with a few questions along the way. It is not a discussion and should not pretend to be one.

Proposal. A presentation with a vote at the end — budget approval, for example. Run it like a presentation, allow time for interactive questions, but leave room at the end for a real discussion before the vote.

Update. A topical update on progress since the last meeting. Effectively a short presentation, often one slide.

Working session. A smaller, deeper dive on a single topic. Minimal slides, if any. More whiteboard than deck.

The discipline is not exotic. Don't think of the meeting as a single homogeneous block. Decompose it into segments. For each one, decide which type it is. Then enforce the rules for that type. That's the magic — and it's surprisingly simple. Few CEOs take the time to do it. Adding this modest degree of rigor will improve your board meetings dramatically.

Ask Your Board What They Want 1-1 — Then Agree as a Group

You cannot design the right meeting if you don't know what the board actually wants from it. And yet most CEOs don't ask — not what board members want more or less of, not who runs the best board meetings they've seen and why. Instead, they open last quarter's deck and start editing. Same structure, same sections, same flow — updated numbers and the strategic topics du jour. And then they're surprised or disappointed when the meeting goes roughly the same way as last quarter. They shouldn't be.

Different board members will have different preferences. Some want a rigorous operational review. Others find that tedious and want strategy. Neither is wrong, but you can't design a meeting that works for the group without understanding the mix.

Go one-on-one first. You'll get far more candid answers than you ever will in a group setting. Then bring that input back to the full board and align explicitly. That conversation, done once and revisited periodically, is what allows you to design a format that actually works. Once you have agreement, hold to it. If the board has agreed to split time between ops and strategy, protect both segments. Don't let one expand simply because it's easier to run.

One more thing on the math: once you've agreed on structure, the arithmetic becomes unforgiving fast. An hour, three topics, five board members speaking once for two minutes each — that's thirty minutes before you've framed a single topic or allowed any real back-and-forth. Slide count is time allocation, and time allocation is the de facto meeting agenda.

Building the Right Slides for Each Segment

For an ops review, lean into it — and commit to making it better over time. The right metrics aren't always obvious. Consider setting up a working group to define them, or simply ask for feedback after each meeting and iterate slowly. Either way, the goal is continuous improvement, not repetition. Just don't have one by default because you think you're supposed to, leaving everyone — including the board — wishing they were somewhere else. Personally, I happen to like them. Many board members do not. Know your audience — and the easiest way to do that is to just ask.

For a discussion, the slides serve a different purpose entirely. They are not there to present conclusions — they are there to structure a conversation. A short amount of context, then a small number of well-framed questions: one per slide, question in the title, little or nothing in the body. That blank space is not a lack of preparation. It's a deliberate signal that the value lies in the discussion, not the presentation.

For proposals, structure them clearly: situation, recommendation, ask. If you've done the one-on-one work in advance, the decision itself should not come as a surprise.

Consistency Is the Whole Game

When board meetings feel unproductive, the instinct is often to manage the board more tightly — more pre-meeting calls, more alignment, more expectation-setting. Those can help. But they're treating the symptom.

Look at your slides first. Eight or nine times out of ten, that's actually the issue. The slides took the meeting somewhere you didn't intend. That gap — between the nominal agenda and the de facto agenda as revealed in the slides — is where the problem lives. And it usually comes from one of two places.

First, a lack of segment-type discipline: nobody explicitly decided what kind of segment this was, so it defaulted to presentation when maybe the board wanted a discussion. Or the board wanted a proposal and got a presentation.

Second, a mismatch between the agreed segment type and the slides actually built for it: everyone aligned that this was a discussion, but somehow you ended up with 45 slides. I guess it's going to be a presentation, then.

Either way, the deck is the tell. Look at it honestly — segment by segment, type by type — and you will almost always find the answer. And once you find the answer, you can start to fix it. Map your topics by segment-type and then build slides accordingly. Or accept that the deck you built is the meeting you're going to have.

I think we are in the brute-force era of AI.

By that I mean that progress is coming less from fundamentally new ideas and more from applying massive amounts of data and compute to existing architectures. Larger pretraining corpora, longer context windows, more GPUs — more of everything.

And to be clear, this approach works. Today’s frontier models are materially better than their predecessors. They reason more coherently, generalize more broadly, and fail less often. But when you ask why they are better, the answer is usually some version of “we scaled it,” not “we discovered something fundamentally new.”

In other words, we are getting more, not different.

The Bitter Lesson

This pattern maps cleanly to Richard Sutton’s Bitter Lesson, which argues that the biggest long-run gains in AI come not from clever, human-designed features, but from methods that scale with compute.

That lesson has proven remarkably durable. It explains the transition from symbolic AI to machine learning, from feature engineering to deep learning, and from narrow models to large language models. And today, we are applying it aggressively, perhaps even reflexively. When in doubt, scale it.

Bittersweet Models

But the latest generation of systems complicates the story in an interesting way. Models such as DeepSeek V4 are simultaneously enormous and increasingly efficient.

On paper, these models are huge. They operate at trillion-parameter scale, with massive context windows and extensive training runs. But at inference time, they do not behave like monolithic, fully activated networks. Instead, they rely on a set of techniques designed to reduce the amount of computation required per query.

Mixture-of-experts architectures activate only a subset of parameters for any given token. Sparse connectivity reduces unnecessary computation. Quantization lowers precision in ways that preserve accuracy while dramatically improving efficiency. Compression techniques optimize how model weights and intermediate representations are stored and accessed.

The result is a system that is large in principle but relatively efficient in practice. Training remains brute force. Inference increasingly does not.

Constraint as a Driver of Efficiency

It is not coincidental that much of this efficiency work is emerging from environments where compute is constrained. When you cannot simply add more GPUs, you are forced to extract more value from the ones you have.

That dynamic has a long history in engineering. Systems built under constraint tend to be more efficient, more elegant, and more disciplined. Anyone who has written low-level code on constrained hardware has seen this firsthand. When you only have kilobytes of memory, you learn to think differently about how software is structured.

When capital and compute are abundant, that discipline is often deferred. It is faster, and often more effective in the short term, to follow the Bitter Lesson and scale.

A Two-Phase Pattern

All of this suggests a refinement to the original thesis. We are not simply in a brute-force era. We are in the first phase of a two-phase cycle.

In the first phase, brute force dominates because it is the fastest way to discover what works. In the second phase, efficiency becomes the priority because it is the only way to make those discoveries economically viable at scale.

We have seen this pattern repeatedly in other domains. Early cloud infrastructure was heavily overprovisioned before cost optimization became a discipline. Early web applications prioritized functionality over performance before latency and efficiency became central concerns. Early software systems accumulated features before being refactored into more coherent architectures.

AI appears to be following the same trajectory.

The Role of Capital and Competition

The current emphasis on scale is also a function of the competitive and financial environment. When capital is available, it is rational to prioritize speed over efficiency. Scaling works, it produces visible results, and it is easier to fund.

That is why the leading labs — OpenAI, Anthropic, Google, Meta, and Microsoft — continue to push the frontier outward. Larger models win benchmarks, attract attention, and help establish early leadership.

At the same time, the competitive landscape is shifting quickly, and it is not yet clear how durable any of these advantages will be. Unlike earlier platform battles, switching costs in AI may prove lower, particularly at the model layer. If that is true, then efficiency, cost structure, and system design could become more important sources of differentiation over time.

The Bitterest Lesson

The original Bitter Lesson argues that scaling wins. A more uncomfortable extension of that idea is that scaling alone is not sufficient.

The reason is not that scaling stops working. It is that scaling does not solve for efficiency. Training costs remain high, inference costs matter at scale, and latency and energy consumption are real constraints in production systems.

At some point, those factors become first-order concerns. When they do, architecture, optimization, and system design reassert themselves.

That leads to what might be called the bitterest lesson: the Bitter Lesson itself is not infinitely scalable because it is not inherently efficient.

Which raises a natural question. Have we become too “Bitter Lesson–pilled”? Have we overlearned the idea that scaling is the answer to every problem, and underinvested in the kinds of engineering discipline that make systems practical and economical?

Likely Direction

None of this implies that scaling is over. Frontier models will continue to improve, and there will be meaningful advances along that path. But it does suggest that the long-term shape of the market will not be defined by brute force alone.

More likely, we will see a combination of approaches: models that are large at training time, increasingly efficient at inference, and embedded in systems that rely on retrieval, tools, and composition to deliver results.

The brute-force era does not end so much as it evolves. What begins as pure scaling gradually incorporates efficiency, specialization, and better system design.

And historically, that second phase is where a great deal of the economic value is created.

A few years ago I wrote a post called The Decomposition of Marketing. The argument was that startup CEOs were increasingly breaking marketing apart — moving product marketing under product and moving demandgen under sales — in order to get strong leadership in both product marketing and demandgen without having to find the rare CMO who's genuinely great at both.

I think that trend has accelerated. And it's created a problem that nobody seems to be talking about.

Mind The Gap

Here's what happens when you decompose marketing in a multi-product organization.

In the fully decomposed organization, there is no traditional CMO. What you have instead is three distinct leaders: an SVP of Demandgen who reports to the CRO and is laser-focused on pipeline; one or more VPs of Product Marketing who report into the CPO (or product GMs) and own their individual products; and an SVP of Corporate Marketing — sometimes carrying the CMO title, sometimes not — who owns brand and communications.

Each product team has its own product marketer (or team leader), focused on their product's positioning, messaging, and launch. The CPO loves it because they no longer have to work (i.e., "waste time ") with marketing to build their story. The product marketers love it because they're not spread thin. The demandgen leader even likes it because they know who to work with on product-specific campaigns.

The problem is that you now have three product marketers, each telling the story of their individual product. Each one is perfectly capable of answering "why buy mine?" for their specific product. None of them is positioned — organizationally or politically — to answer a different question: why buy this product line, and where is it going?

That question falls through the org chart. And it matters enormously.

Because buyers — especially enterprise buyers — don't just buy products. They buy into a vision. They're making a bet on a vendor. They want to know: does this company have a coherent strategy, or did they just bolt three products together and call it a platform? Is this a company I can grow with, or am I buying the first of many painful migrations?

Nobody in the decomposed org is answering that question well. The product marketers are heads-down on their products. The SVP of Demandgen is focused on pipeline. The SVP of Corporate Marketing is telling the brand story — which is emotional, values-driven, and important — but it isn't a product story. Brand is about feeling. Product line marketing is about logic: why this set of products, why together, why now, and why us.

In a small startup, the CEO fills this gap. That's fine. The CEO should be the chief storyteller when the company is young and the product line is simple. But as the company grows, scales, and adds products, the CEO can no longer own this story in any operational sense. It becomes nobody's job. Which means it either doesn't get done or gets done by someone pinch hitting in the role (e.g., the VP of brand doing their best, one of the product marketing VPs moonlighting).

Given that this integrated story is typically more important to buyers than each of the product-specific ones, this is a problem. The thing that matters most to buyers is literally no one's day job.

This Isn't Theoretical

I've seen this gap in practice many times. In one case, a growing multi-product SaaS business with strong product marketers, great demand generation, and a real brand — but nobody whose job it is to walk a prospective customer through the logic of the product line: why these products together, how they reinforce each other, what the roadmap implies about where the company is going, and why that matters for a buyer making a three-year bet.

I also have a friend at a large enterprise software company who was recently given a role that, officially, sits somewhere in marketing strategy or marketing operations. But when you listen to what they actually do, it's product line marketing. They own the integrated product story. They work across business units to ensure the message hangs together. They work on cross-product expansion playbooks and sales analytics. They help the company answer the questions that no individual product team can answer alone.

I think they call it product ops or marketing ops or such. But it's product line marketing. Call it what you will, but the job needs to be done.

What Product Line Marketing Actually Is

Let me be precise, because the naming here matters.

Product line marketing is holistic, but it's deeper than brand. Brand is emotional and important, but it's not about product-oriented arguments. And while it certainly involves communications, product line marketing is not a pure comms role, like public relations. Nor is it product marketing in the traditional sense, because that's specific to a given product.

Product line marketing is the discipline of telling the story of a company's product portfolio: why these products belong together, what the vision is for the combined offering, how a customer should think about buying one versus two versus all of them, and why the holistic roadmap is going where it's going.

It lives between brand and product. It's more specific than corporate messaging and more strategic than individual product messaging. It's the connective tissue that most multi-product companies are missing.

Beyond the story itself, product line marketing should own some things that are inherently cross-product in nature.

The standard sales deck and demo. Someone has to own the pitch that walks a prospect through the whole portfolio story. If three product teams each own a piece of it, you don't have a deck — you have a Frankendeck. Owning the sales deck is obvious. What's less obvious is that product line marketing should also own the standard demo — because the demo is just the same story told with the product as the visual instead of slides. If the deck and the demo tell different stories, you have a problem. They should be the same narrative, expressed in two different media.

Analyst relations. Because product to analyst mapping is rarely 1-1 and because analysts themselves want to understand the integrated story, analyst (and influencer) relations should be in the product line marketing team. This also enables you to hire a top-class analyst relations leader and leverage them across products. It also centralizes basic but important things like contract negotiation, inquiry management, briefing and strategy day scheduling, and the general responsibility for squeezing every last drop of value out of the money you've invested. (Too many vendors write the big check and then fail to get value.)

Social media. Maintaining a consistent social presence is too important to leave to chance — and too expensive to duplicate across product teams. Individual PMMs can and should contribute to social conversations in their area, but owning and maintaining the social presence needs to be someone's actual job. Centralizing it in product line marketing delivers both economies of scale and consistency of message. The alternative — having each product team manage their own presence, or delegating it to a PR/comms person whose instincts are corporate rather than product — produces the kind of fragmented, low-signal output that gets tolerated but rarely drives results.

Why This Is Coming Back

Product line marketing isn't a new idea. At Business Objects back in the day, when we had a portfolio of BI products, someone had to own the story of how they fit together. Big enterprise software companies — IBM, SAP, Oracle — have always had versions of this role, even if they called it something else.

What's new is that the org structures that made product line marketing unnecessary — single-product companies, integrated marketing teams, CMOs who owned both message and demand — are now widely being replaced by decomposed structures. More companies are going multi-product faster. More are moving product marketing into the product org. And as they do, the gap grows.

The solution isn't to reverse the decomposition. Putting product marketing back under marketing doesn't necessarily produce better product marketing. The decomposition, in many cases, is the right call.

The solution is to recognize that decomposition creates a new need. When you break marketing apart, you need someone to hold the whole story together. That's product line marketing. Call it what you want. But make sure someone's doing it.

If you're running a decomposed marketing organization — or if you've recently moved in that direction — ask yourself who owns the following: the integrated product line story, the standard sales deck and demo, analyst relations, and social media. If the answer is "nobody" or "everyone sort of" or "we cover that patchwork by the product units," that's your answer. You have a gap. And the fix is to create a product line marketing function, give it a real mandate, and staff it with someone who can speak credibly to the whole portfolio.

The thing that matters most to your buyers shouldn't be nobody's job.

Have you dealt with this problem? I'd love to hear how you've solved it — or not. Leave a comment or drop me a note.

Just a quick post to highlight that Exit Five has published the audio from my presentation at their recent leadership retreat.

The presentation was entitled How To Be the CMO Everyone Wants To Work With, and the audio is about an hour long. Exit Five founder Dave Gerhardt published it as Episode 342 of The Dave Gerhardt Show.

Thanks to those who attended and thanks to Dave for inviting me to speak at the event.

There's a question I've been mulling for a while now, and I think it's time to write it down: when is it okay to use generative AI in a given business context, and when does it cross a line? I'll focus on two specific areas I know well — board work and strategic analysis — but I think the principles generalize.

Let me start with what I think is the easy part. Using AI to draft a meeting agenda? Fine. Using it to generate a board deck? Also fine, though you'll probably go to manual edits after the first or second draft. Using it to produce a document summary? Fine [1]. These are tasks where AI is essentially doing the grunt work of organizing information you already possess, and where the human judgment — yours — is the thing that actually matters.

Using AI to produce final documents?  That's dicier today -- ask anyone in legal -- but I think there's a simple rule that applies to all of these examples.

That rule?  Use AI to do whatever you want, but you own the output. Not the AI. You. If it's wrong, that's on you. If it misses something important, that's on you. The moment you present something to a meeting, a customer, or a board, you are vouching for it. Saying "well, AI generated that part" is not a defense. It's an abdication of duty.

The "Ad Hominem" Problem

Here's something that bothers me about the discourse around AI-generated content: people hear, or even suspect [2], that AI wrote something and it's immediately dismissed — not because of anything wrong with the content, but because of how it was produced. That's a logical fallacy. Specifically, it's a variant of ad hominem: attacking the source rather than the argument [3].

I frequently need to remind people of this. Judge what was said, not who — or what — said it [4] [5].  If the analysis is sound, the framing is useful, and the questions raised are the right ones, then the mechanism of production is largely beside the point. The quality of the thinking is what matters and what should be challenged.

That said — and this is important — the inverse is also true. Producing AI-generated content and presenting it as your own thinking is not okay. The problem isn't that AI helped. The problem is the pretense that you did the thinking when you didn't. Ownership means you've read it, challenged it, corrected it where it was wrong, and can defend it. If you can't do that, you haven't done your job.

AI as Calculator

I've always thought the right analogy for AI is the calculator. A wildly more powerful calculator, obviously, but a calculator nonetheless.

When calculators became ubiquitous, people lamented the loss of slide rule proficiency. And yes, something was lost. But the point of mathematics was never arithmetic. It was reasoning. If the calculator handled the arithmetic error-free, you could spend more time on the part that actually matters. The same logic applies here: there's a lot more to argument and strategy than copywriting or slide formatting. If AI can handle the scaffolding, you should be able to spend more time on the substance.

The complication — and it's a real one — is that AI can start to approximate thinking in ways a calculator never could. A calculator doesn't write your memo. It doesn't suggest your strategy. It doesn't synthesize twenty pages of board material into five crisp questions. AI does all of that. And that creates a temptation toward laziness that calculators simply didn't. The laziness is the problem, not the temptation toward it.

There's also research starting to emerge suggesting that relying too heavily on AI can actually impair your own reasoning. You offload the synthesis, and you stop synthesizing. You offload the framing, and you stop framing. The cognitive muscle atrophies.

I was not surprised when I read reports that people with long streaks in Duolingo couldn't speak well in practice. In my view, as a half-decent French speaker: if it doesn't feel like work, you're probably not learning [6].  Corrolary:  if it doesn't feel like work, you're definitely not working.

How I Use AI in Board Work

Here's what I often do with AI today. I sit on several boards, and I'll sometimes load a board deck into a generative AI tool before the meeting. I ask for a summary. Then I'll ask how it thinks the company is doing. I'll then ask for the top 5 questions to ask in the meeting. Then, I'll go read the deck with an eye toward what's been extracted [7].

And then I'll go back and challenge the AI. I think issue three is more important than issue one. I think it missed issue seven totally. I think issue two isn't an issue; the company's fixed it already. Often, I'll bring competition into the picture because (in my humble opinion) most boards don't spend enough time thinking about competition [8].

And here's the question I've been wrestling with: should I be transparent about using AI to help generate those issues (or questions) when I bring it to a board meeting?

My instinct is yes. If I want to send the CEO a list of top five issues facing the company before the meeting, I have two choices:

• I can pretend I wrote it myself, unassisted.  Complete with typos and hyphens instead of em-dashes.
• I can say, "here's what I generated with Claude after iterating on your board deck" and copy/paste the final transcript.

Now, I know what management can think: "Well, we could have asked Claude, too" [9]. And I'm okay with that. My response would be: "Well, then, why didn't you?" I just want the best topics list.

To me, the question isn't where the list came from.  The question is whether it's the right list.  That's the only question that matters.  Boards have very limited time together.  We should think hard and use all available tools to ensure that we're spending that time on the right issues.

The point isn't the slides or the questions list or the agenda or the summary.  The point is the conversation.  To maximize value, we need to be having the right conversation.  No talking about things easy to talk about.  Not going through the motions.  Not death marches through templates, much as I love both templates and death marches.

This takes me back to calculators.  I can check the math on your board slides using pencil and paper.  Or I can use a calculator.  Or I can upload your table and ask Claude to check the math.  We can take a test with our calculators secretly on our laps or with them in plain sight on our desks.

I vote for the second option. Use all available tools. Don't use them clandestinely. Use them out in the open. But don't abdicate to them. Own the output. This isn't Claude's list of our top five challenges. It's my list, built using Claude [10].  Better yet, it's my list, period.  (But I'm not going to hide that I used Claude to help build it much as I wouldn't hide that I used a computer and a keyboard.)

That's where I am. I'm curious where you are. Is there a line you've drawn in your own work? Do you think transparency about AI assistance is a norm we should be enforcing, or are we creating a two-tiered standard we'd never apply to other tools? Let me know in the comments.

# # #

Notes

[1] Just as long as you also read it or are prepared to say, "I didn't read it, I only read a summary."  FWIW, I find it useful to generate a summary, read it, and then read the document.  And sometimes, then go back to the summary.  The summary ends up serving as a reading guide.

[2] Thanks to tells like the dreaded em-dash.

[3] I had to air quote ad hominem because -- thanks to my high school Latin teacher, Mr. Maddaloni -- I know that ad hominem means literally "toward the man." There is thus not only gendered language (heck, it was nearly 3,000 years ago) but considerable irony in speaking of ad hominem attacks on a machine. Ad machina, anyone?

[4] By the way, this is the exact opposite of most social media behavior.

[5] This isn't just good intellectual hygiene — it's a reliable way to reduce or eliminate bias. When you evaluate an argument on its merits rather than its source, you sidestep a whole class of distortions: the tendency to over-credit ideas from high-status people, to dismiss ideas from unexpected sources, or to reject a perfectly sound analysis because you don't like the messenger. It's a discipline worth practicing whether the source is a junior analyst, a competitor, or a language model. The argument either holds up or it doesn't. That's the only test that matters.

[6] This is a critique on gamification, but also is highly related to the topic of customer value metrics, about which I've written with my Balderton EIR colleague Dan Teodosiu.

[7] By the way, the wordier the board deck, the more this process helps.

[8] This itself could be a long discussion but remember three things: my first job in marketing was competitive analyst; I believe strategy is either "the plan to win" (Burgelman) or "the way to overcome our biggest challenge" (Rumelt), and ergo it cannot be done without looking at the market. North Stars are great, but they don't tell you about the army you're going to face when you hit latitude 55 degrees 45 minutes.

[9] And this is probably the kindest thought.  Others might include:

• Perfect — now the monkeys have flamethrowers
• Fantastic — it's like giving toddlers espresso and a whiteboard
• Great — now the VCs can skip even faster to the wrong conclusion
• Terrific — now it's gut feel with citations
• Right — so now we're pattern matching with turbo-autocomplete

(And those are manual em-dashes.)

[10] I assume that we are not all going to have the same AI conversation or all use the same tools. The way I push Claude is going to be different from the way another person does.

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.

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.

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.

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

• Marketing High Technology — William Davidow (1986)
• Crossing the Chasm — Geoffrey Moore (1991)
• The Gorilla Game — Geoffrey Moore, Paul Johnson & Tom Kippola (1998)
• The Regis Touch — Regis McKenna (1985)

Positioning / Marketing Foundations

• Positioning: The Battle for Your Mind — Al Ries & Jack Trout (1981)
• The 22 Immutable Laws of Marketing — Al Ries & Jack Trout (1993)
• Ogilvy on Advertising — David Ogilvy (1983)

Technology Strategy / Innovation

• The Innovator’s Dilemma — Clayton Christensen (1997)
• Competing for the Future — Gary Hamel & C. K. Prahalad (1994)
• Only the Paranoid Survive — Andrew Grove (1996)
• The Discipline of Market Leaders — Michael Treacy & Fred Wiersema (1995)

Product Marketing Culture

• The Macintosh Way — Guy Kawasaki (1990)

Enterprise Sales / Go-to-Market

• SPIN Selling — Neil Rackham (1988)
• Solution Selling — Michael Bosworth (1995)

Economics of Software / Networks

• Information Rules — Carl Shapiro & Hal Varian (1998)

Enterprise Technology Industry Case Studies

• Softwar: An Intimate Portrait of Larry Ellison and Oracle — Matthew Symonds (2003)
• The Ingres Papers: Anatomy of a Relational Database System — Michael Stonebraker et al (1985).  Technical background on Ingres that provides useful context prior to reading Softwar.
• The New New Thing — Michael Lewis (1999)
• Hard Drive: Bill Gates and the Making of the Microsoft Empire — James Wallace & Jim Erickson (1992)
• Accidental Empires — Robert X. Cringely (1992)

Software Engineering

• The Mythical Man-Month — Frederick P. Brooks Jr. (1975)

Classical Strategy

• The Art of War — Sun Tzu

[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.

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.

I did this analysis last year and it became a popular post, so I figured I'd do the same retrospective today. Following are the ten most-read Kellblog posts in 2025, regardless of the year in which they were written -- and it includes some golden oldies.

1. What it really means to be a manager, director, VP (2015). Now at ten years old, this post is a perennial favorite. I wrote it because I got tired of answering the question and something about my answer clearly struck a note with a lot of people. (Hint: the answer's not in your job leveling system.)
2. How to navigate the pipeline crisis (2025). In this post I wrote about what I saw as a general pipeline crisis in the industry, shared some interesting posts on it, and then tried to put myself back in the CMO chair and answer: what would I do about it?
3. The one key to dealing with senior executives: answer the question! (2012). If the manager vs. director post (above) gets the most traffic, this post gets the most in-person mentions. Think: "Dave, I forwarded your ATFQ post about a dozen times this year." This issue bothered me 13 years ago when I wrote the post and evidently non-answered questions are still bothering people today. If someone, particularly a customer or an executive, asks you a question: answer it.
4. Kellblog predictions for 2025 (2025). I scored these an 8 out of 10. Go here to read my predictions for 2026, the 12th annual post in this series. These posts are more industry commentary and analysis than simply a list of things I think are going to happen. And they require Herculean effort. This year's post was 7,644 words with 166 links and took 65 hours to write.
5. Your ICP starts as an aspiration and ends as a regression (2025). I love the pithy title of this one. This post discusses the evolution of your ideal customer profile (ICP) which starts out as a wink in the founder's eye and should, over time, end up the result of a regression analysis. That is, you start out by deciding who you want to focus on and then, over time and as a function of your definition of "success," the data should tell you.
6. De-mystifying the growth-adjusted enterprise value to revenue multiple: introducing the ERG ratio (2024). I first heard of the PEG ratio in Peter Lynch's classic, One Up on Wall Street. This post takes the same idea -- growth adjusting -- and applies it to price/sales as opposed to price/earnings. Much as I love the metric, I was frankly surprised to see this one up here.
7. The SaaS Rule of 40 (2017). Another classic, from eight years back. See this year's predictions to understand why I believe the Rule of 40 might well become the Rule of 60 in 2026.
8. A CEO's high-level guide to GTM troubleshooting (2025). An integration and repackaging of a lot of my advice specifically written for the CEO and to help them troubleshoot their go-to-market (GTM) issues. I was happy to see this one up here.
9. The pipeline progression chart: why I like it better than tracking rolling-four-quarter pipeline (2022). Give the CRO rolling-four-quarter sales targets and I'll be in favor of tracking rolling-four-quarter pipeline. Meantime, we need to track it by quarter and this chart shows you how. Don't even get me started on people who want to track annual pipeline.
10. Six tips on presenting to the board of directors (2025). A post I wrote to help executive staff make a good impression on the board by losing any prior board PTSD, making a deck from scratch (not recycling slides), cutting to the chase, taking certain things offline, and of course ATFQ.

Technically, my Best of Kellogg post also made the list, so if you've not checked that out lately, perhaps you should. I've recently revised it as I do about once a year.

I was happy to see that five of the ten top posts were from 2025, which I think hits the right balance of healthy re-use of the classics along with some endorsement of my new material. Thanks for reading.