There’s a meeting happening everywhere right now. The founders / buying committee have done its homework. The product demo went well. The pricing is within budget. Stakeholders are aligned in principle. And then someone asks the question that reliably derails the whole thing: “Shouldn’t we wait to see how this category shakes out before we commit?”

Nobody pushes back hard. The decision: hold.

This is the freeze. It is one of the defining dynamics in B2B software buying right now, and most GTM teams aren’t designed to address it. If my experience is any indication, it’s happening in B2C too. What products are worth using today? Are they worth using tomorrow?

Stack Overflow's 2025 Developer Survey (65,000 respondents) described the market in two words that have stuck with me: "willing but reluctant." That's a market-trust problem, and most GTM teams aren't built to solve it.

The conventional wisdom on buyer hesitation focuses on friction: too expensive, wrong feature set, integration complexity. Those objections have solutions. Lower the price. Build the integration. Shorten the trial period. The freeze resists all of those interventions because the objection isn’t about the product as it exists today. It’s a forecast problem. Buyers are being asked to make a bet on the future state of a market that is genuinely hard to forecast. And the rational response to genuine uncertainty, for many buyers, is to not bet at all.

The buyer question has shifted. It used to be: “Is this the best tool for this job?” Now it’s: “Is this the right time to buy any tool for this job?” That question has no process. We rationalize it because it keeps humans productive doing what we’ve always done. But it produces delay.

Why This Is Structurally Different

Software buying hesitation isn't new. What's new is the underlying mechanism: temporal uncertainty. It doesn't care how good your product is today. It's a question about your product's position in a market six months from now.

A legal team evaluating Harvey right now faces exactly this. Anthropic just announced native legal tooling built into Claude. Does Harvey's moat hold? Does the category collapse into the foundation model layer? Harvey is better positioned than most to capture durable value in legal AI. But it's natural to hesitate. Committing to a vendor layer above a platform feels premature when the platform keeps moving down the stack. That hesitation is rational. It's also the freeze.

Three structural forces are driving it simultaneously.

1. The pace of AI capability acceleration has decoupled product development from buyer decision-making. A category leader from 2023 can find itself in legacy status by 2025 because a newer entrant rebuilt core functionality with an AI-native architecture. X is full of this: the "another AI wrapper" critique became a legitimate procurement instinct, not just a dismissive meme. Buyers who made reasonable purchasing decisions two years ago are watching some of those bets expire: Two-thirds of software buyers now experience purchase regret or implementation disruptions (Gartner, 2025). That number changes how every subsequent decision gets made.

2. Vendor mortality risk has become real for the first time in a generation. Buyers have watched vendors get acquihired, pivoted into something unrecognizable, or quietly shut down mid-contract. It now factors into buying logic within procurement, within teams, and in your own personal decision-making. That's a market where sellers can't close and buyers can't commit.

3. The proliferation of new entrants has collapsed the signal that used to help buyers orient. When a category has three serious competitors, you can do a real evaluation. When it has forty startup vendors, most of which launched in the past 9 to 18 months and are largely indistinguishable from the outside, the rational move is to wait for a winner. The problem is that no winner is emerging on a timeline that matches a buyer’s patience. Employees are increasingly being tested on their ability to scale themselves with AI tools. Meanwhile, 90% of workers say they’re overwhelmed by the number of software tools they already use, and 59% say it’s harder than ever to be productive as a result. More options, less clarity, higher cost of being wrong.

Put these together and you get a buyer type that didn’t exist five years ago: the well-qualified, budget-approved, legitimately interested prospect who won’t commit until the market settles. And the market isn’t settling.

This is me, by the way. These days I vacillate between trying five products that are 95% similar and the urge to wait for Claude to build the functionality instead. Every day there’s new product from Anthropic and a hundred new startups launching on X only to face this cognitive dissonance: do I bet on the great AI-native SaaS product that leverages a model in a genuinely usable UX? Or the LLM that does 80% of that for free through a chat interface? The “I just use ChatGPT for that now” pattern is real. I’ve said it. You’ve said it. Software vendors are losing deals to it.

How The Freeze Shows Up

Sales cycles are elongating, and founders are increasingly reporting being ghosted at the finish line by promising deals that unexpectedly go quiet without a clear rejection. It’s not price, consensus, or economic pressure. The buyer is waiting for a catalyst the normal deal motion doesn’t provide. No discount addresses temporal uncertainty. The average B2B buying cycle stretched to ten months in 2025. The buying journey itself has shifted to 60% software selection, 40% validation. Buyers are spending significantly more time pressure-testing whether a category will hold, not just whether a product is good.

Free tier retention is climbing while paid conversion flatlines. Seventy percent of enterprises haven’t moved beyond basic integration of the AI tools they already have. The free tier hasn’t failed them. It’s become the rational default. Commitment feels premature. Free is the right answer for a buyer who has decided to wait without deciding to leave.

Churn concentrates at renewal with customers citing “evaluating alternatives.” A significant portion of this isn’t product-driven. It’s buyers who were never fully committed treating the renewal window as a re-evaluation checkpoint. They came in willing to try. They’re not willing to recommit without more confidence that the category itself is stable.

Each symptom has a conventional intervention. None of them work because they’re addressing the wrong diagnosis.

What Software Builders Should Do

You can’t fully solve temporal uncertainty through sales tactics. Some buyers are going to wait regardless. The goal is to separate freeze-induced hesitation from hesitation that’s actually about your product. Its also about building growth-oriented GTM motions for buyers who don’t yet trust the market. Many well-advised founders understand the value of PLG in this environment where so many are paralyzed by choice.

Compete on compounding value, not roadmap promises. If a buyer’s concern is that your product may be obsolete in eighteen months, showing a roadmap isn’t the answer. Roadmaps are promises, and buyers worried about the future don’t find promises reassuring. The better answer is genuine institutional depth: the data your product accumulates about their business, the workflow integrations that take months to rebuild, the context a new tool starts without. That’s a more convincing response to temporal uncertainty than any feature preview. It fosters a growing commitment from your customers.

Make low-commitment onboarding a deliberate strategy. A recent HBR study found that AI tools don’t reduce work, they intensify it. Employees worked faster, took on broader scope, extended work into more hours. That’s the real onboarding burden buyers are weighing before they sign. The right response isn’t to discount harder toward an annual contract. It’s to design a PLG path where short-term commitment leads naturally to long-term commitment because the customer hit visible value at thirty, ninety, and one-eighty days. A customer who comes in monthly, converts at month four, and stays for three years is more valuable than an annual contract extracted before they were ready. Rejecting the long term contract is probably the only part of “SaaS is dead” that likely has real legs.

Make the case for your durability explicitly. Profitable businesses, well-capitalized companies, and clear category leaders all have something to say to a buyer worried about vendor mortality. That case belongs in your sales narrative. Say it directly: how long you’re capitalized, what your retention looks like, why you’ll be around. Buyers won’t infer it from a funding announcement.

What Software Buyers Should Do

The freeze is rational behavior. But rational isn’t the same as optimal.

Thirty percent of organizations are already wasting money on redundant AI software they’re barely using. The answer isn’t to buy more, but to buy smarter and move faster on the ones that clear the bar. The tools buyers are deferring decisions on are being actively used by their competitors right now. A team that spent six months building with an AI-powered operations tool while another team debated whether to wait for the next model release didn’t just save time. They built workflows, institutional habits, and organizational knowledge that takes months to replicate. The cost of not deciding accumulates quietly.

A workable framework: if switching costs are genuinely low and the technology is in flux, optimize for reversibility. Start with PLG-oriented self-serve products where you can experience real activation value before committing to anything. Buy short-term, stay mobile, don’t build deep integrations into tools you might replace in a year. If the job-to-be-done is stable even when the tool market isn’t, optimize for current fit. You can always switch when something better emerges, and the cost of switching a good tool for a better one is almost always lower than six more months on a worse workflow.

The consolidation signal is already visible. VCs are publicly predicting that enterprises will spend more on AI in 2026 but through fewer vendors. The market is telling you a winner-take-most dynamic is coming in most categories. The time to act on that signal is before the winner is obvious.

Certainty Always Arrives Late

Software markets do consolidate. Winners emerge. Categories that look chaotic from inside the disruption often look obvious in retrospect.

The buyers who wait for certainty will find themselves in a familiar position: the signal becomes clear at exactly the moment early adopters have already built the institutional knowledge, the workflows, and the data depth that latecomers start without. Certainty is a lagging indicator. It arrives after the window closes.

For software builders, the implication is direct. Your GTM motion needs to be designed for buyers who don’t trust the market yet, not just buyers who haven’t been convinced about your product. That’s a different problem. The discount doesn’t solve it. The feature doesn’t solve it. Low-friction entry, compounding value, and the confidence to commit do.

It’s a weird time. On every level. There’s a fear that pure software is uninvestable right now. The moats are disappearing and founders may not even know what’s worth building going forward.

But we all need software. For every instance, we’ll decide when we are best served by AI-native SaaS, personalized software, or the LLMs we use. Most are happy to pay for the right value exchange, so the key is to encourage product exploration and earn the commitment, and institutional investment, of your customers as the ground continues to shift under our feet.

Before we get started, a quick note. I owe a small apology for the long silence between posts. When I launched this newsletter, I’d hoped to publish something every couple of weeks, but life had other plans. Between family, advising early-stage startups, and thinking through my own next chapter, I’ve been busier than expected.

That said, I’ve continued to collect ideas and draft stories I’m excited to share. This post is one I originally queued up a few months ago. But in that short time, AI’s trajectory and the conversation around it has shifted dramatically. Coming back to it, I realized the original framing no longer fit today’s agentic landscape. So I’ve revisited the piece, expanding it with new thinking about AI’s role in either reinforcing or potentially replacing marketplaces as we know them. I focus in particular on a long-standing challenge for these platforms: circumvention.

Thanks for reading and I look forward to your notes and comments.

Picture a guest and a host on Airbnb. They’re on the brink of finalizing a booking, then one party shares a phone number that slips through the content scrubber. Communications on the platform go silent, and poof, the deal is completed offline. This single act lowers the platform’s revenue, damages trust, and makes future transactions harder to control. Many marketplace founders I advise share frustrations about this pattern, often termed “disintermediation.” It’s a problem as old as online marketplaces themselves.

The key questions to understand are:

• how much value does your platform need to create to justify keeping transactions within the marketplace?

• In which cases is each party incentivized to transact off-platform?

• Is disintermediation always nefarious, or are there innocent reasons for it?

One thing is certain: disintermediation will never be zero, no matter what you do. It’s a constant game of whack-a-mole that requires a mix of “carrots and sticks” to reinforce the desired behavior. Marketplaces that succeed in minimizing it often combine pricing strategies, monetization experiments (such as subscriptions), a forced transition to a buyer-commit model, and value-added benefits like payment guarantees or insurance coverage. But these traditional tactics only go so far.

That’s where AI presents an entirely new frontier.

Why Disintermediation Happens

Most marketplaces rely on commissions or take rates for revenue. When users spot a chance to dodge fees, they may do so, especially for high-dollar, long-duration, or repetitive exchanges. Airbnb and Upwork serve as prime examples:

• Airbnb charges a 14–16% guest fee and a 3% host fee. A study published by UC Irvine and University of Virginia business graduate programs revealed that 18% of guest-host interactions led to offline bookings. High-value rentals are most at risk.

• Upwork scans chat messages for contact information or payment terms, warning or suspending violators. Even then, a conservative estimate of 7% of freelancers that bypass the customer may have cost the company as much as $84M in lost commissions based on 2023 FY extrapolations.

Both cases highlight a core issue: if a platform doesn’t provide enough value, users will find ways around it. Airbnb Plus, the on-platform certification program the company introduced to verify its highest quality listings, reduced offline bookings by about 6% more than strict contact-blocking alone. This highlights the impact of positively reinforcing trust signals over punitive restrictions. That’s not a simple solution to ship, of course.

So why aren’t there more tried-and-true product features, or even SaaS solutions, that meaningfully curb this behavior? The simple answer: marketplaces are HARD. Each one is different, and what works for Platform A might not apply to Platform B. Marketplace teams have gone on countless crusades to tackle disintermediation, but the challenge is compounded by opportunity cost. Given the choice, most leadership teams would rather focus on growth experiments with clear ROI than chase the nebulous impact of reducing funnel leakage from circumvention. As a result, disintermediation roadmaps often take a backseat, much to the frustration of Trust & Safety teams (I may be speaking from experience on this one).

Should we ever expect to see truly game-changing solutions? Maybe this isn’t a battle every marketplace needs to fight alone. AI advancements are opening the door to scalable, automated solutions.

AI Tactics for Reducing Disintermediation

1. Smarter Detection with Large Language Models (LLM)

For years, marketplaces relied on rule-based filters to catch messages containing personal details or hints at offline transactions. Airbnb famously developed regex-based scrubbing tools to block phone numbers and email addresses from being shared before a transaction was finalized. The problem? Users always found creative ways to work around these filters.

Most people already know this part: Enter LLMs. Instead of rigid keyword-matching, LLMs use Natural Language Processing (NLP) to interpret context, making them much harder to trick. They can scan peer-to-peer messaging, detect circumvention attempts, and flag high-risk transactions with far greater accuracy. By training AI models on user data and unstructured data sources, marketplaces can pinpoint areas where disintermediation is most likely to occur without frustrating legitimate users. That’s a major first step in this process.

2. Automated Decision-Making and Enforcement

Once you flag risk, what happens next? Historically, high-growth marketplaces had two options:

1. Hire more Trust & Safety agents to review flagged messages as content outstrips the team’s capacity.

2. Engage a BPO (Business Process Outsourcing), a costly and training-intensive choice feasible only for the largest platforms.

Processes with decision trees are made and evolved to take action on circumvention. But without a smart risk-ranked algorithm and consistently applied moderation of user content, these manual reviews are time consuming and largely ineffective for marketplace Ops teams.

Now, Agentic AI changes the game. AI agents, with human-in-the-loop oversight, can analyze each case and apply an appropriate response. This can range from a gentle warning to an account suspension. Over time, this feedback loop refines the system’s risk model, reducing false positives and improving enforcement accuracy. Trust & Safety teams can now scale their coverage without ballooning headcount costs. Some startups are building these tools natively with the help of low-code / no-code admin app builders like Superblocks.

Today’s agentic AI systems offer dramatic gains in scaling trust & safety workflows. But these capabilities still require deliberate human-in-the-loop oversight, especially for edge or high-stakes cases which come with the territory if your marketplace connects two unknown parties to transact. Real-time review remains essential to limit unintended actions and maintain governance standards.

Sidebar: In practice, marketplace teams should treat agentic tools as augmenters, not replacements: human reviewers focus on exceptions and strategic decisions, while AI handles consistent, high-volume enforcement. This hybrid model helps maintain user trust without reverting to full manual triage. That is the audacious scaler at this time and its ok that its not fully autonomous.

One company leading the way here is SafetyKit. Instead of legacy keywords or partial ML screening, SafetyKit’s LLM-based agents process 100% of Upwork job posts, matching against platform-specific policies and workflows. The result? A 95% accuracy rate that’s well above the ~80% human benchmark. This leads to $1M/year in savings and faster policy iteration cycles. This kind of AI-native T&S tooling removes a messy, manual bottleneck and creates a runway for real-time disintermediation enforcement to become scalable and continuous.

3. AI Intermediaries for Negotiations

Many transactions require back-and-forth communication before both parties commit. This is the norm in most double-commit marketplaces (where both supply and demand must agree before a transaction happens).

Here’s where AI-powered negotiators step in.

Instead of exposing personal details, an AI agent can facilitate pricing discussions, add-on selections, and deal structuring within the marketplace itself. Take Peerspace as an example:

• A host might set a minimum and maximum pricing threshold, as well as permissible add-ons.

• A guest might have a strict budget but need specific services (e.g., catering included for under $1,500).

• An AI intermediary can automatically align incentives, suggest trade-offs (e.g., reducing the guest list in exchange for a lower price), and present an optimized offer to both parties.

• In the ideal scenario, the AI agent has done the heavy lifting and reached consensus. The transaction is secured out of convenience (big-time value driver).

Considering how to implement this “negotiator” agent? I found this interesting: new research shows that how AI negotiators “sound” matters. In a March 2025 study where LLM agents engaged in 120,000 negotiation rounds, “warmth” correlated with more frequent deals and enhanced ongoing relationships—but at the cost of lower individual-value claims. Conversely, more dominant agents extracted higher immediate value. This opens a design choice for a marketplace: should a platform’s agents prioritize volume and retention (warm tone), or revenue per deal (assertive tone)? Implementing dual-tier agent personas (e.g. one with conciliatory language for first-time or trust-building offers, another more assertive for upsells) may help balance revenue and relational goals. But this still feels like a rabbit hole only the most forward thinking startups and “superplatforms” are ready to go down at this point.

Zooming back out: Now, imagine each side having their own AI agent, negotiating in real-time to reach a fair deal. This approach removes friction, eliminates unnecessary contact information exchanges, and keeps transactions within the marketplace. Feels pretty utopian, doesn’t it?

The Future of AI-Driven Marketplace Integrity

We are at the forefront of AI-powered solutions like these, yet Agentic AI workflows are not yet commonplace. That will change in the back half of this year and early 2026. Early-stage marketplace founders I advise are already testing AI-driven approaches to disintermediation, and as adoption grows, the benefits will be too large to ignore:

• LLM-driven scanning offers smarter detection.

• Agentic AI enables automated enforcement, making marketplace Ops teams far more productive.

• AI negotiators streamline deals, keeping discussions (and transactions) on-platform.

These capabilities will help today’s marketplaces win, but they’re just the beginning. The real disruption may come when AI agents evolve from enforcement tools to autonomous actors in the market. Let’s look ahead at how that might reshape the disintermediation problem entirely.

Envisioning a Parallel Future

Picture this scenario: AI agents not just fighting off circumvention, but acting as competitive gatekeepers, steering users away from marketplaces entirely. Perhaps first-party LLM tools (ChatGPT, Claude, Perplexity) truly become the meta-aggregators for everything?

In the emerging “agentic economy,” assistant agents (on behalf of users) and service agents (on behalf of businesses) may transact directly, bypassing centralized platforms. Disintermediation becomes a potential extinction event for today’s marketplaces. Seem viable?

Hard to say for certain, but marketplaces still hold structural advantages:

• Liquidity & Network Effects: AI agents need scale—both supply and demand—to match offers and provide the variety that drives efficient growth across markets.

• Search cost reduction: Marketplaces aggregate signal, trust, and discovery better than ad-hoc one-off agent-to-agent matches.

• Trust infrastructure: Platforms offer identity verification, dispute resolution, insurance, refund guarantees, and customer support. These are non-negotiables for users transacting real goods & services who are also wary of anonymized bots.

• Marketing & reach: Marketplaces amplify visibility and ratings in ways fragmented agent networks cannot easily replicate.

Still, we should sequence this strategically: assuming everyone will inevitably leverage an agent to take undesirable tasks off their plate, marketplace operators should begin prototyping agent-friendly integrations. Exposing structured APIs (e.g. agent-to-marketplace negotiation protocols), enabling agent discovery widgets, or offering agent-certified deals ensures that AI agents enhance rather than fragment the ecosystem. It goes beyond defending turf. Make the platform undesirable for agents to bypass without losing those foundational benefits. The “carrot” approach might just work for robots too!

As we shift toward an agentic economy, where AI assistants negotiate and transact autonomously, marketplaces face a strategic inflection point. Build agent-aware systems from the ground up or risk becoming obsolete. Platforms that sequence LLM-driven negotiation, enforcement, and certification systems alongside agent-friendly APIs will not just defend revenue, they’ll become integral infrastructure in the next wave of digital commerce.

Turning the Tide And Staying Ahead

Disintermediation will always be a challenge, but with the right AI strategies, I believe it can be reduced to a manageable level. First, marketplaces should rejoice in the early wins that today’s AI tools can deliver. Second, they should start planning for what happens as users begin working with surrogate agents. The marketplace winners of the next decade will be those that embrace these innovations, not just to protect their revenue, but to create seamless, frictionless, repeatable, and reliable user experiences. AI won’t eliminate disintermediation, but it may redefine who owns the transaction entirely. The marketplaces that stay ahead will be those that evolve from gatekeepers into orchestration layers that serve both humans and agents, without losing what made the platform valuable in the first place.

It’s time to stop playing whack-a-mole and start leveraging AI to build marketplaces that users (and soon, their autonomous stand-ins) want to stay in. Now the question is which marketplaces are going to lead the way there. Maybe fighting disintermediation and product led growth initiatives can coexist on the roadmap?

Let’s continue the conversation in the comments below.

Product Marketing Managers (PMMs) had quietly reclaimed their rightful place at the center of marketing leadership, owning GTM strategy.1

I wanted to share my reflections of the evolution of marketing leadership from my own experience, market observations, and conversations with founders who are entering scale-up mode.

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The Branding Era: Captivating Audiences through Storytelling

In the mid-2000s, marketing leadership was synonymous with storytelling. When I graduated from UC Berkeley, my brain was packed full of iconic marketing campaigns like Apple’s rebellious ‘Think Different’ and Nike’s edgy Charles Barkley ‘I am not a role model’ ads. At the time, I genuinely believed that great marketing meant making people feel something so strongly they’d tattoo your logo somewhere. We were taught to strive for deep emotional connections and the growth would flow from that foundation.

At Electronic Arts, early in my career, product marketing was king. PMMs owned everything: brand strategy, voice of the customer, positioning frameworks, forecasts, and comprehensive go-to-market plans. We crafted sharp, impactful positioning statements known internally as "The X," a concise, powerful expression of exactly what our products were, how they differed, and why anyone should care. It was rigorous, nuanced, and fundamentally brand-driven.

This isn’t to say that the “Product” aspect on PMM was light. On the contrary, we also spent hours each day in the development “pits” chasing down designers, producers, engineers—anyone who would listen— to provide our gathering of customer insights that might inform the game we were building. That was marketing leadership: strategic, cross-functional, GM-like.

But the digital wave was coming, and with it, a profound shift.

The Digital Shift: Data Takes Center Stage

In the early 2010s, the marketing landscape underwent a seismic shift. The rise of digital platforms transformed the Chief Marketing Officer's (CMO) role from a brand storyteller to a data-driven strategist. The 2011 Spencer Stuart report highlighted this evolution, noting that "a host of factors, largely technology-enabled, are making the life of the CMO increasingly complex."

This complexity arose from the transition to highly targeted customer outreach, the emergence of online aggregators, and the growing importance of direct distribution channels. CMOs were now expected to mine and analyze data to drive pricing and revenue management, significantly impacting operational performance. The marketing function's influence expanded beyond traditional boundaries, demanding a new set of skills and a shift in mindset.

During this period, Product Marketing Managers (PMMs), who sat somewhere between the brand-performance continuum, were often sidelined. Almost overnight, we watched our roles get sliced up and handed out to specialists in SEO, performance marketing, growth hacking, brand, and content creation. It was like a bunch of role-player athletes showing up to gameday without a head coach. Suddenly, we were left wondering, ‘What exactly is my role here now?’ In that hyper-specialized moment, PMMs spent a lot of time trying to figure out where their seat at the table had gone; or if there even was a table anymore.

Their roles, traditionally associated with creative branding and messaging, were perceived as less critical in an environment that prioritized measurable results and rapid growth. The emphasis was on performance marketing, with a focus on user acquisition, engagement, and conversion. PMMs, seen as the custodians of brand and positioning, were viewed as non-essential in the data-centric marketing paradigm.

This shift led to a reevaluation of marketing leadership. The modern CMO was expected to have a strong background in analytics and performance marketing, often at the expense of traditional product marketing expertise. The PMM role, once central to marketing strategy, became a "nice to have" rather than a necessity. This realignment reflected the industry's focus on quantifiable outcomes and the growing importance of digital channels.

In retrospect, I get why these assumptions were made and it made all the sense in the world to prioritize marketing leaders that could seek out and pull those customers in a quantifiable manner. The generalist positioning around product marketing didn’t align with the transformation happening in digital spaces.

Confusion Around the PMM Role

Let’s discuss my understanding of what it means to be a product marketing leader, standing independent of both brand and performance marketing. Your views may differ from mine; perhaps product marketing feels more like a B2B construct vs. B2C. In organizational design, deciding whether product marketing is necessary is a classic case of “what problem are we solving for?” Ultimately, someone needs to be accountable for distilling the customer’s needs and communicating those to any team that plans to distribute something of value back to the market. That’s where it starts.

But despite the comeback, ‘Product Marketing’ remains hilariously misunderstood. It’s right up there with ‘Growth Marketer’ in the running for ‘Most Ambiguous Job Title Ever.’ I’ve seen founders look for PMMs and end up wanting someone who can simultaneously launch a content strategy, set pricing, and debug code in the same day. As a founder myself, I deeply feel that importance. But I’m here to tell you that approach isn’t very sustainable.

PMMs frequently find themselves tasked with jumping around a broad marketing playbook. Some organizations mistakenly expect their PMMs to function simultaneously as technical product managers, sales managers, or multi-channel marketing experts. Unsurprisingly, this rarely works out well, as it dilutes their core strengths and effectiveness. They should have a broad skillset, but their work requires prioritization and cross-functional coordination vs. ownership of every specialization.

Another frequent challenge is the heavy reliance on market research inherent in the PMM role. Companies appreciate the insights PMMs bring but rarely have the patience required for comprehensive research processes. To address this, companies increasingly prioritize candidates with relevant vertical and product experience to expedite the learning curve and accelerate outcomes.

That means that PMM leaders need to master the strategic know-how of many marketing disciplines while being able to distill efficient insights about a product’s fit in the market. It takes years to build these different skills into strong capabilities. Those who can are incredibly powerful chess pieces in an organization.

The Return of Product Marketing Leadership

The good news here is that companies are realizing that generalist, cross-functional team leadership is needed in this world of specialized GTM channels. There's been a resurgence in the value placed on Product Marketing Managers. Companies have recognized that PMMs possess a unique blend of skills crucial for navigating today's complex marketing landscape and it shows in the talent markets. According to the 2025 Betts Compensation Guide, product marketers are earning anywhere from 15-60% premium on total comp when compared to other marketing specialties.

It certainly feels like the pendulum in marketing leadership has swung back towards product marketing in this era. So what’s changed? A few things:

1. Commoditized performance marketing: playbooks are optimized across most mature digital channels. Marketers will find a ceiling on efficient growth here faster than the emergence of new media platforms / channels.

2. Customer optionality: In a hyper-competitive environment, alignment across marketing and product is super important. These two functions must collaborate closely to understand and build for customer needs. They also should agree on how their product and positioning are differentiated from other options in the market.

3. Focus on full funnel growth: Along with the rise of the Growth function, the requirements for marketing leadership increasingly demand a full funnel, revenue accountable view of the business. A strategic leader needs to understand and prioritize different parts of the funnel, from awareness through monetization and retention. It’s simply no longer good enough for marketing to close their laptop after demand gen teams reach their MQL targets.

4. Cross-functional alignment: PMMs have traditionally been the “glue” across many functions. Their market knowledge and deep understanding of target customers uniquely positions them to direct go-to-market strategy. Coordinating cross-functional teams across an organization dismantles organizational silos to create customer-centricity, effectively driving the business. In some orgs, this part of product marketing has been syphoned to operations roles like BizOps, program management, or marketing ops.

tl;dr: PMMs aren’t just the ‘positioning folks’ anymore. They’re strategic Swiss Army knives, decoding customer journeys, rallying internal teams around shared goals, and making sure every customer interaction actually makes sense. In other words, they’re finally getting the credit they deserve.

Looking back, I wasn’t expecting this to be the primary takeaway from the CMO event I attended. I knew there would be a heavy emphasis on the role of marketing alongside the cresting wave of AI. But every topic seemed to come back to the strategic importance of collaboration with other functions, agile adaptability in uncertain times, and bringing creativity back to the forefront of marketing. These are the core competencies of a great product marketer.

In essence, PMMs are not just marketers; they are strategic leaders who understand product intricacies, market dynamics, and customer expectations. They leverage data-driven insights to build conviction and bring the company together. Their holistic approach to GTM strategies makes invaluable and well-suited for GM, CMO, CRO, and CEO roles.

I'd love your thoughts: How do you view the role of product marketing in shaping GTM strategies in your organization? What are the competencies you are looking for the most in your marketing leaders?

Let’s continue the conversation in the comments below.

Hi, I’m glad you’re here.

I’m a dad, a founder, and a dreamer. Over the past 20 years, my career has taken me through more than a few sharp turns: from Fortune 500 companies to raw, pre-product startups. I’ve helped launch blockbuster video games at Electronic Arts, built go-to-market strategies for massive consumer brands, and more recently, co-founded a marketplace business (Peerspace) that grew from a rough idea into a platform enabling millions of people to create meaningful moments together around the world.

I’ve led teams, built functions from scratch, and scaled operations across marketing, growth, sales, and customer experience. I’ve worked with consumer audiences, SMBs, and enterprise buyers. And somewhere in all of that, I’ve made plenty of mistakes and had a few wins worth celebrating.

So what is all this?

I stepped back from the day-to-day at Peerspace late last year, and for the first time in a long time, I took a breath. I reconnected with friends and former colleagues. I advised startup founders. I somehow hit the 20-year mark in my working life. And I started reflecting, not just on the lessons learned, but on how often those lessons didn’t come when or how I expected them.

I kept a list of things I wanted to write about: stories, observations, frameworks, themes I wish I had heard earlier in my own career. Some are tactical. Some are messy. Most revolve around a simple truth: very little in work or life follows a straight line.

Your startup will zig when it should’ve zagged. Your role will outgrow you, or you’ll outgrow it. Life will throw in a few curveballs too. And through all of it, there’s a need to make meaning, find leverage, and keep moving forward.

That’s what No Fixed Path is about.

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Who is this for?

If you’re a founder, operator, builder, or just someone trying to figure out how to grow through change, this is for you.

I’ll share thoughts on marketplaces, SMBs, AI, growth strategy, and the evolving state of marketing. But I’ll also touch on more personal terrain: career transitions, team leadership, decision-making, and the quieter parts of professional life we don’t always talk about.

I hope you’ll find a few insights that stick and maybe even share some of your own. I’d love to hear what resonates (or doesn’t). What you’d like to read more of. And how we can challenge each other to get better.

What kind of commitment is this?

I’ll aim to post every couple of weeks. Some essays will be longer and reflective. Others might be quick hits, frameworks, or notes from conversations I’m having. If I fall behind, thanks in advance for bearing with me—this is a passion project, not a job.

Right now, everything will be free. If there’s ever enough momentum or interest to do more, like deeper dives, group chats, or private Q&As, I may explore a paid tier. But no pressure. If you’re here, that already means a lot.

This all sounds great.

I’m excited to kick this off. The first real post is coming soon.

In the meantime, thanks for reading! Welcome to No Fixed Path.