Off Kilter 218: The Product Marketing Anti-Advantage.

tl;dr: Product marketing needs to be replaced with something better.

Right now, AI firms are posting identical job descriptions: “Senior Product Marketer. 5+ years SaaS experience, Google/Meta/LinkedIn certified, proven demand gen track record.”

Here’s the irony. They’re trying to build the future atop a growth playbook from the past that’s now obsolete. They’re raising hundreds of millions to discover what works in AI markets that are still forming while hiring marketers credentialed in fully decayed SaaS playbooks that do the opposite, having long ago excised the discovery capabilities market formation requires under the guise of “efficiency.”

It’s a fundamental mismatch: diminished-return optimization applied to market discovery.

The Product Marketing Decay Cycle

Between 2012 and 2021, modern-day tech product marketing (PMMs, growth marketers, demand gen, PLG specialists, etc.) emerged, creating a genuine competitive advantage. As SaaS matured and digital platforms became essential infrastructure, product marketing rapidly professionalized. It translated complex products into compelling narratives, ran sophisticated growth plays across fragmenting digital channels, and enabled sales with positioning that resonated. Tech companies with strong product marketing capabilities outperformed those without.

Yet the resulting commoditization and diminished returns toward obsolescence were as systematic as they were predictable. Pioneering playbooks became industry standards. Platform certifications became universal requirements. Everything became optimized for financial efficiency rather than strategic advantage.

Research reveals the depth of what happened: almost complete overlap in frameworks across product marketing methodologies, with roughly 75% of job postings requiring identical platform credentials as baseline qualifications.

Rita McGrath’s transient advantage framework, introduced in Off Kilter 217, explains the cycle: over time, advantages launch, ramp up, get exploited, and then decay. Product marketing followed this arc perfectly. Success drove professionalization, which in turn drove commoditization, which then led to obsolescence.

Platforms like Google, Meta, and LinkedIn deliberately accelerated this pattern for their own advantage by using certification programs to encode platform dependency as professional competence.

For a significant period, ZIRP-era economics masked an accelerating decay. Abundant capital meant metrics looked fine while differentiation eroded. When cheap money disappeared in 2021, the diminished returns became impossible to ignore. But the diagnosis of the problem was wrong. Companies assumed execution failure requiring optimization, not competitive decay requiring disengagement. Finance governance intensified: optimize harder, measure more, automate with AI. But applying efficiency discipline to an already-decayed advantage doesn’t make you win; it just accelerates failure.

The result is straightforward. The modern SaaS organization is squeezing a growth lemon that no longer has any juice.

Look at the numbers if you don’t believe me. The share of SaaS companies stuck in low or flat growth has surged as median and even top‑quartile growth rates have compressed materially since 2021. Growth rates have slowed sharply across all revenue bands. Customer acquisition costs have increased, while payback periods stretch to and beyond 3 years for less efficient companies.

Why would any AI company want to replicate this?

What Decay Looks Like

The decayed state of product marketing manifests in three recognizable patterns I consistently see in the tech clients I work with:

The Platform Tax Collector. Because of the certification trap laid by platforms and its subsequent encoding into the professional psyche, product marketers have become experts at feeding platform algorithms, optimizing for click-through rates and marketing-qualified leads, while these same platforms extract more and deliver less. Unintentionally, they’ve become platform tax collectors rather than growth drivers for the companies they work for.

The Product Delusion. Because product marketing is directly adjacent to product, it’s structurally incentivized to obsess over feature differentiation and battlecards. This is what the LinkedIn B2B Institute calls “The Product Delusion.” The engineering-led belief that the best specs always win. (Plot spoiler: they don’t) Meanwhile, buyers satisfice, setting “good enough” thresholds for functionality before deciding based on other factors such as brand familiarity, trust, support, innovation philosophy, roadmap alignment, and who they deem most likely to be a long-term “partner” rather than a short-term “vendor.”

Research consistently shows that 70-80% of the B2B buying journey occurs before any vendor contact. Yet product marketing exquisitely optimizes for the final 20%, crowded into the bottom of the funnel alongside sales, where it’s measured by metrics such as MQLs, sales enablement effectiveness, and revenue generation. This is akin to every player on a kindergarten soccer team chasing the ball instead of maintaining positional discipline. As we speak, product marketing teams across entire categories are fighting feature wars for a tiny fraction of in-market customers while ignoring the vast majority who need a compelling brand narrative before they’ll even pay attention.

Fragmentation at Scale. Since each product gets its own marketing team that handles its positioning, messaging, and customer acquisition motions, while paying lip service to the brand, it results in corporations marketing products atomistically (each trying to out-feature competing products, no matter how minutely) instead of marketing products in a way that reinforces the brand cohesively (where the brand becomes a halo that creates permission across all products). As a result, customers experience fragmented noise. Multiple voices, confusing value propositions, copycat claims, and featuritis that blur into incoherence.

In large organizations with genuine scale advantages, a proliferation of product marketing often leads to competing as if they’re a loose federation of small businesses rather than a market-leading, scaled player. Paradoxically, this benefits no one but smaller competitors, which can quickly replicate product features and product marketing playbooks, but lack the benefits of scale. The result of such weakness is rarely to elevate the brand, but rather to perfectly optimize each individual product “tree,” while the market ignores the confusion of the ensuing forest.

Combine these challenges, then multiply them by every competitor running identical playbooks in identical ways, using identical tactics down to the pixel level, and the outcome becomes clear: everything same, instantly forgettable brands, commoditized products, weak funnels, slower growth, poorer leads, and increasingly extreme pressure on sales to overperform in winning the leads they do get. Typically, while wondering why a competitor with an “inferior product” is winning. (Hint: not everyone is set up to act like a kindergarten soccer team all chasing after the ball at the same time.)

The Strategic Mirage

Of course, product marketing isn’t stupid. It knows something is wrong. It just isn’t acknowledging that it’s structural. As a result, its members are scrambling upstream, claiming ownership of revenue, win rates, and strategy. Some are even being relabeled with senior titles and leadership access.

But this is largely a mirage. True strategic authority requires portfolio lifecycle management, decision rights over disengagement, and capital reallocation power. Meanwhile, even strategic product marketers are limited to a product-level scope, with optimization mandates that are subordinate to sales metrics. Research shows that 44% are 1-2-person teams managing 5+ products with little or no budget. More responsibilities under a fancier title isn’t strategic empowerment, it’s tactical overload masquerading as elevation.

AI companies don’t have to follow this path. They have a different option: deliberately replace a SaaS-era product marketing function with a new infrastructure designed for AI-era conditions.

Experimentation is Non-Negotiable.

AI companies face two external realities that reshape how they should think about marketing.

The markets you will serve are still forming. Preferences haven’t yet crystallized. No decisive selection factors have been identified. No one knows which marketing activities create a real advantage yet. You can’t simply slap the old model onto a new paradigm and expect it to work. Instead, you must discover what does work through systematic experimentation.

This creates an uncomfortable truth: discovery requires inefficiency. Most experiments will fail. Many tactics you try won’t work. Significant capital will be spent learning what doesn’t create advantage before finding what does. This isn’t optional, it’s the necessary cost of navigating uncertainty. You can’t afford to view it as a failure of execution, instead it’s a necessary investment in learning.

As SaaS-era playbooks calcified, product marketing systematically eliminated its discovery capacity in the name of efficiency. Every tactic measured against backward-looking ROI. Every channel justified by the proven performance of the past. Every playbook certified only after it reduced waste. As a result, it’s a function structurally incapable of tolerating discovery-phase inefficiency, which is exactly what new market formation demands.

Advantages will be copied instantly. When SaaS-era growth playbooks were forming, the world was relatively stable. Since then, marketing’s advantage lifecycles have been permanently compressed. Today, the copying of specific tactics is happening at AI speed, and platform certification programs are commoditizing new approaches within months.

This means your primary advantage source can’t be inherent to any single set of tactics or channels. Instead, it must be inherent in your marketing organization’s capacity to continuously discover, leverage, and disengage from tactics that offer an advantage before competitors saturate and commoditize them. All while maintaining customer-focused coherence. Unfocused experiments without an organizing logic create chaos. What matters is how fast you cycle your learning while maintaining meaningful coherence of the customer experience.

This requires fundamentally different capabilities.

What Replaces Product Marketing

An experimentation infrastructure requires a new organizing principle and four core capabilities, all designed to discover and exploit advantage rather than execute playbooks.

The organizing principle: brand-as-worldview.

Not a distinctive logo and identity (though this certainly helps), but a coherent philosophy of what the company stands for and how it creates value. This is what centers the business. It determines which experiments run, which advantages fit, and which transitions make sense. Every product decision, channel experiment, and tactical pivot filtered through questions like: Does this reinforce what we stand for? Does it create customer value consistent with our story? Is it creating preference before engagement?

Brand-as-worldview represents the coherence layer that provides stability, allowing you to then operate with extreme agility. As such, it’s the capstone above the portfolio of transient marketing advantages that sit below. Its coherence enables rapid experimentation without decline into fragmented chaos.

Four new capabilities:

Strategic Intelligence. Not just traditional thin-data (surveys, platform statistics, reported preferences) but thick-data (observed behavior, emotional context, unarticulated needs). A deep understanding of how customers decide, especially in the currently invisible 70% of the buyer journey, where preferences form. What drives decisions? What creates consideration before comparison? What are their fears, aspirations, and barriers?

This intelligence must become infrastructure. Every decision and experiment informed by the same customer reality, not platform vanity or sales pressure.

Experimental Rigor. Hypothesis testing must be scientifically rigorous and measured against the creation of competitive advantage. Are we creating preference before comparison begins? Are we discovering advantages that competitors haven’t yet saturated? Not guided by platform efficiency or sales support metrics. Instead, guided by the curiosity and discipline necessary to test systematically and learn how to rapidly shift investment when necessary.

Portfolio Discipline. You must build organizational capacity to recognize when optimization reaches natural limits and provide the authority to disengage from diminishing returns before they become a drain on resources. This means killing what might be fully optimized and working, but is in rapid decay, while competitors still think it’s effective. Here’s the hard part. Your next sources of advantage must already be in development, ready for the moment of capital reallocation. Without this, “disengage” becomes “switch off and panic.” This is why the inefficiency of discovery is so important. It’s not a cost, it’s the process through which future advantage is created.

Worldview Coherence. This means treating the brand as an organizing principle that prevents product-centric fragmentation, while guiding real choices. “We stand for X, therefore we measure Y, invest in Z, organize around A, and say no to B.” This means developing the capability to require that every product, test, channel, tactic, playbook, and message be filtered through the brand: Does it reinforce what we stand for? Does it cohere with everything else? Does it mean what we want it to mean to customers? This prevents the chaos of random tactics while enabling the necessary velocity to discover advantages before competitors do.

AI will enable this infrastructure. Not by automating commodity tactics, but by helping find advantages, exploit them, and then flag when they’re approaching decay before competitors saturate.

The CEO Mandate

None of this will work without CEO level commitment. It’s non-negotiable. This isn’t twiddling around the edges. It’s the underlying hypothesis for a new model of marketing designed specifically to discover your way to success under the constraints of uncertainty:

1. First, establish Worldview Clarity. The CEO/CMO defines the worldview. What the brand stands for, your organizing philosophy, and your theory of customer value creation grant permission for everything else. This is the strategic layer above all tactical execution. This must be stable.

2. Subordinate marketing to worldview. All marketing activities operate under this layer of brand coherence rather than being chaotically and independently optimized product by product.

3. Build the experimental capabilities necessary to execute rapidly. Invest in a strategic customer intelligence infrastructure, establish experimental rigor measured against advantage creation, develop portfolio discipline with lifecycle authority, and rigorously utilize worldview coherence as the organizing filter.

This means accepting two things. First, products no longer center marketing activities; the brand, as an organizing principle for the customer, does. Second, in an AI era where preferences are still forming and advantages decay quickly, experimental inefficiency is a strategic investment in discovery and the creation of future advantage, not operational waste.

The AI Choice

Nobody knows what the AI future holds, although plenty are positioning themselves for profit by pretending they do. That’s exactly my point. What I’m suggesting here isn’t a playbook or a framework. I have no crystal ball. It’s a hypothesis for how AI firms can experiment their way to success through uncertainty.

The specifics, which worldview to choose, which experiments to run, which channels to rely on, which tactics to double down on, must be discovered.

What works in cybersecurity AI won’t work in creative AI. What works now won’t work tomorrow. What works within one corporate culture won’t work in all. What works for one audience won’t work for others.

AI companies have a unique opportunity. You’re raising massive capital without pre-hardened systems or orthodoxy. You have a window before these factors harden.

And you have a choice.

You could install a commoditized SaaS-era growth infrastructure and compress a decade of past decay into the next year or two.

Or you could build a new model for a new era. An experimentation infrastructure under worldview coherence, governed by competitive advantage discovery, that maintains the discipline to disengage from decaying advantages before competitors realize they’re dead, and that always has new advantages lined up to exploit.

The old world is dead. Time to build a new one.

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