The Guardian describes WPP as "beleaguered." An agency besieged by the tides of the market, surrounded by problems on all its sides.

WPP Restructures, becomes a single company- no longer a Holdco.

WPP Restructures, becomes a single company- no longer a Holdco.

The Guardian describes WPP as “beleaguered.” An agency besieged by the tides of the market, surrounded by problems on all its sides.

This comes after the announcement by CEO Cindy Rose that WPP would be consolidated under a single company; no longer will the organization be a holding company, but rather 4 different operating units working under the same umbrella. With one single P&L.

The implications of this move are far and wide. First, agencies will report into WPP Creative, which they hope will streamline communications and create stable workflows. Second, they will employ agentic AI to scale global workflows.

The focus here is to create an investment-grade balance sheet. One of the main motives behind this restructure is this.

This is what she had to say:

“Today, we are unveiling a bold plan for a simpler, more integrated WPP. Our intention is to stabilise the business…”

But there is a key line in the report, which implies that there are job cuts to be expected. While this may not come as a surprise, it is a stark reality that must be addressed. Here’s what she says,

“Our recent underperformance has been driven by excessive organizational complexity, a lack of an integrated operating model, and inconsistent strategic execution. While disappointing, I see huge potential as these issues are all within our power to fix and we’re already making great progress.”

The Future of the Creative

The future of the creative seems to be complex because, yes, attributing revenue to creativity isn’t easy. Even ads that can now be managed down to the tee cannot be 100% attributed.

And with AI, it seems like the creative has lost its purpose- if thinking is outsourced, where does the value lie?

Time will tell what the answer to this question is. One that is part existential and part financial.

Retail Media Networks

A Guide to Retail Media Networks: The Market Has Moved Past Basics; Why Haven’t You?

A Guide to Retail Media Networks: The Market Has Moved Past Basics; Why Haven’t You?

Searches for ‘what is retail media network’ dropped. While searches for ‘top retail media networks’ jumped. It’s a signal. And it’s time marketers take it seriously.

A year ago, marketers were Googling ‘what is a retail media network.’ That query has dropped 20%. Meanwhile, ‘top retail media networks’ is up 5% and climbing.

That’s not a small data point. That’s a market growing up. something we’ve already seen unfold in broader retail media advertising and adtech companies’ analysis.

The education phase is done. Brands aren’t debating whether retail media networks belong in the plan anymore. They’re asking which ones are worth the budget and which ones are quietly burning it.

That shift in search intent reflects something real- the questions have gotten harder. And most strategies haven’t kept pace.

That gap is exactly what this piece addresses.

What is a Retail Media Network- and Why the Definition Keeps Getting Stretched

A retail media network is the advertising infrastructure a retailer builds on top of its owned digital properties- website, app, in-store screens- and monetizes by letting brands buy access to shoppers inside that ecosystem.

The ads run at the point of purchase. The targeting runs on the retailer’s first-party shopper data. which closely mirrors the mechanics behind targeted display advertising models.

That’s the clean version.

The messier version is that the retail media network gets stretched constantly. Commerce media gets used interchangeably with it. And while the logic overlaps, these terms don’t mean the same thing.

Retail media is defined by its ecosystem- it lives inside a retailer’s owned environment, uses their data, and reaches shoppers mid-purchase intent. Commerce media applies purchase-intent signals to advertising outside of retail environments. Financial platforms, travel booking sites, food delivery apps.

They aren’t retailers, but they carry behaviorally rich transaction data.

Retail media has a clear boundary. Commerce media doesn’t. Worth knowing which one you’re actually buying before you sign off on the brief.

The Three Layers of Retail Media Network Most Brands Never Look At

Most retail media strategy conversations skip straight to ad formats and budgets. The architecture underneath is where the actual value- and the actual risk- lives.

1. Data infrastructure is the first layer.

It consists of the retailer’s first-party data: purchase history, browsing behavior, loyalty signals, and identity graphs built over years of customer interaction. This is the asset that makes the network valuable.

Strip it out, and an RMN is just a publisher with a cart on the same domain.

2. The second layer is ad serving and targeting technology.

The platform that ingests that data, segments audiences, matches bids to inventory, and delivers the right ad to the right shopper at the right moment. the same ad tech platforms that power much of today’s digital advertising infrastructure. Amazon and Walmart have built proprietary stacks.

3. The third layer is measurement and attribution.

The part that closes the loop between ad exposure and purchase. And the part that’s least standardized across networks- which is where most of the enterprise frustration actually lives.

How Do Retail Media Networks Work?

A brand running a campaign through a retail media network looks roughly like this.

The brand identifies a product and an objective- usually visibility at high-intent moments, or driving trial with lapsed buyers. Then the RMN matches that objective to available inventory- sponsored search, display, or off-site extensions. often leveraging programmatic advertising platforms for scale and precision. It segments the audience using first-party data.

Finally, the campaign runs. The shopper comes across the ad while browsing or searching on the retailer’s platform. A purchase may or may not happen. The RMN reports back on what it saw.

But that last step is where it gets complicated.

The RMN is both the seller of the inventory and the provider of the measurement. And there’s no universal framework forcing them to report it consistently. Amazon reports ROAS one way. Walmart reports it again. But Criteo‘s methodology differs from both.

Apples aren’t comparable with oranges. And yet, most brands are doing exactly that every quarter. a recurring issue in programmatic vs display network advertising debates.

Why Retail Media Networks Are Booming in 2026- and What That Growth Is Really Built On

US advertisers spent $60.32 billion on retail media in 2025. That number could climb to $71.09 billion in 2026.

The scale is real. But the reason behind it matters more than the number.

Retail media networks showed up with something the rest of the channel mix couldn’t deliver: first-party data tied directly to purchase behavior, closed-loop attribution, and a provable connection to conversion.

After years of brand safety concerns on open web programmatic, platform volatility, and attribution gaps from cookie deprecation and dark social, risks of paid advertising that brands are increasingly wary of, that’s not a selling point. That’s a survival line.

For a marketer in a room with a CFO asking hard questions about incrementality, retail media is the one-line item they can point to with some confidence. That’s why budgets are moving.

What’s Driving the Budget Shift Beyond the ‘First-Party Data’ Pitch

Buyer confidence has taken a beating.

Inflation, geopolitical friction, erratic shifts in consumer behavior- marketers are adjusting strategies at the blink of an eye. Retail media has stayed relatively stable in all of that chaos, especially compared to the volatility shaping performance-driven platforms like Performance Max. A single point of data-backed certainty in a very uncertain channel mix.

But there’s a more structural driver involved here.

The rise of e-commerce has handed retailers something they never had before- granular, real-time transaction data at scale. That data has become an asset class. And retail media networks are how retailers monetize it.

Advertising revenue carries margins that product sales can’t touch. Every major retailer with enough transaction volume has either launched a network or is actively building one.

The supply side of retail media is growing faster than most brand teams realize.

What It Means for How Brands Should Show Up

Retailers built these networks for their own flywheel. More brand ad dollars fund better customer experiences, better data infrastructure, and more inventory for brands to purchase. The loop is self-reinforcing.

But here’s what that means for you: retailers aren’t passive publishers waiting for your creative. They’re becoming platforms.

And the brands being treated like strategic partners aren’t always the biggest spenders. They’re the ones bringing something to the table beyond a media budget. Category intelligence. Co-investment proposals. Actual collaboration.

Most brands still approach retail media the way they approach a billboard placement- at arm’s length, rather than integrating it into a cohesive display advertising strategy. That’s the gap between average retail media performance and genuinely differentiated results.

Top Retail Media Networks in 2026- and the Real Question Behind That Trending Search

The spike in ‘top retail media networks’ as a search query is a direct reflection of a marketer’s pain point.

With dozens of networks now available and budgets under pressure, teams must make defensible choices about where they operate. Spreading thin across every available network is a measurement nightmare before the campaign even launches. particularly without a structured lead tracking framework in place.

  • Amazon Advertising has the dominant networks- the largest share of global retail media spend and the most mature self-serve infrastructure.
  • Walmart Connect is a close second. It entails strong reach across its in-store and digital ecosystem, particularly for CPG brands.
  • Target’s Roundel sits third, and is differentiated by its premium demographics and the first-party loyalty data flowing from Target Circle’s 100+ million members.

Beyond the top three?

Kroger Precision Marketing is the most granular grocery-specific network. Instacart Ads reaches shoppers at peak online grocery intent.

CVS Media Exchange and Walgreens Advertising Group cover pharmacy and value demographics that the platform giants don’t own. Dollar General’s DG Media Network is newer but growing fast in value-oriented categories.

An Example of a Retail Media Network in Practice

Roundel is a useful case because it illustrates what meaningful differentiation in retail media networks actually looks like.

Target built Roundel on behavioral data from over 100 million Target Circle members.

Brands running campaigns through Roundel can segment by actual purchase history, not demographic proxies, and reach that audience both on Target’s own properties and through off-site programmatic extensions. Attribution runs entirely through Target’s transaction data. That means the closed-loop measurements aren’t dependent on self-reported platform metrics.

That’s a different category of offering from a network that’s essentially reselling standard programmatic with a retail logo on top. And that distinction is exactly what the surge in ‘top retail media networks’ searches is trying to sort out.

Marketers aren’t just mapping the landscape any longer. They’re trying to identify which networks have genuinely differentiated data assets and which ones are selling CPMs that could’ve come from anywhere.

Key Challenges of Retail Media Networks for Enterprises

The single biggest structural problem at enterprise scale is measurement fragmentation. Most brands run 3-5 RMNs simultaneously. Every network reports on its own terms. There’s no standardized framework forcing consistency.

The IAB and MRC have been working on retail media measurement standards for years. Progress has been slow. Retailers have no commercial incentive to adopt metrics that make their performance directly comparable to competitors.

So, standardization stays aspirational while fragmentation is operational.

And without clean, comparable data across networks, every budget allocation decision is guessworkthe same challenge enterprise teams face when calculating B2B SaaS marketing ROI.

Sound familiar?

What Enterprises Should Actually Fix About Their Retail Media Network Strategy in 2026

Most retail media ‘strategies’ you see are just media plans with a fancier label, lacking the rigor of true programmatic advertising strategies. A spreadsheet of placements, some budget splits, and a kick-off call with the account team.

That’s not a strategy. And in 2026, the cost of that confusion is compounding.

The Architecture Gap Underneath the Measurement Problem

Underneath the measurement fragmentation is a data architecture problem.

Most RMNs operate as closed systems. Their first-party data doesn’t flow out cleanly. Their reporting doesn’t integrate natively with the brand’s own data infrastructure. Their audience definitions don’t map to the segments the brand uses across other components.

The result is a fragmented patchwork of siloed campaign data across networks, channels, and markets. a problem increasingly addressed through AI and decision-making systems shaping leadership strategy. Without an omnichannel identity framework- which most enterprises don’t have in place- you can’t stitch those signals together without significant manual work.

That’s the real reason AI-led measurement tools are gaining traction right now. Synthesizing cross-network performance signals without a six-week analytics project. Modeling incrementality in real time. Simulating budget allocation scenarios before the money is committed. These capabilities exist. Most teams aren’t using them yet. despite the rapid rise of AI SaaS trends reshaping marketing infrastructure in 2026.

The brands that figure this out first will have a defensible argument for why retail media deserves a bigger slice of the budget.

Where Enterprise RMN Strategy Actually Needs to Go

Retail media networks are sitting on a data asset that most of them are only monetizing in one direction: ad inventory. That’s about to change. A trend that’s gaining momentum? Retailers selling predictive intelligence, not just audiences.

Imagine what a retailer like Kroger or Target actually knows. Their data isn’t limited to who bought what last week. They can see demand shifting in real time- category search velocity, basket composition changes, and reorder gaps.

That signal, processed through an AI layer, becomes something far more valuable than a retargeting audience. It becomes a demand forecast. A prediction of where purchase intent is heading before it arrives.

And then there’s the agentic layer. AI agents are already running paid search campaigns autonomously.

The logical next move is agents that brief, launch, and optimize retail media campaigns end-to-end, similar to how AI-driven lead generation with AI agents is already evolving. pulling from retailer demand signals, adjusting budgets mid-flight, and surfacing anomalies without a human in the loop. The marketer’s role doesn’t disappear. It moves upstream- to strategy definition and retailer relationship management, not campaign execution.

The Next Three Years for Retail Media Networks

That shift has a keen implication for how brands should be building retailer relationships right now.

If the RMN of 2028 is an intelligence platform that also sells ad inventory, the brands locked into purely transactional media buys will be on the outside of that value exchange. The ones with deep data-sharing agreements and joint business planning frameworks will have early access to the signal layer.

That’s the actual moat.

The brands treating retail media networks as ad vendors today are building the wrong kind of relationship for where this is heading, instead of aligning retail media within a broader digital advertising and ad tech ecosystem strategy.

After the Apple-Gemini Tie-Up, Samsung Follows Suit with Perplexity Partnership

After the Apple-Gemini Tie-Up, Samsung Follows Suit with Perplexity Partnership

After the Apple-Gemini Tie-Up, Samsung Follows Suit with Perplexity Partnership

Perplexity made an explosive comeback, just as the market decided it’s dead. Only this time it isn’t another model upgrade, but the dawn of a multi-AI ecosystem, powered by Samsung.

Within the last 24 hours, Samsung’s Galaxy S26 has become the talk of the market because of its privacy display. It’s a historic feat.

The new display hardware shields on-screen visibility for specific apps or the overall phone. The choice is the users to curtail their privacy. But what really added to the fire was its partnership with Perplexity.

The AI development company announced that it partnered with Samsung for its new “AI-first” S26 series. That technically signifies that the hardware will ship with Perplexity AI built in at the system level.

Users can toggle it through just one phrase: “Hey, Plex.”

Why does it matter, you may ask?

It’s the first time in Samsung’s tenure that it has offered OS-level access to a software that isn’t from them or Google. Users aren’t restricted to just one AI assistant; they can choose from multiple ones.

So, what role does Perplexity AI play?

Samsung’s Bixby leverages Perplexity’s API for different forms of complex queries across over 800 million devices as of now. Whether it’s web-based or generative. The assistant will handle all on-device actions, and for research and tasks, it’ll route them to Perplexity, which is running in the background.

What does it mean for S26 users?

Perplexity entails read/write access to all Samsung apps at an OS level. And it empowers Bixby’s search backends through its Sonar APIs. That means- even if users never end up touching Perplexity on Samsung, all of their queries will still flow through its cloud architecture.

That sounds like progress. But it might not be.

Samsung’s strategy mimics more of multi-party data harvesting. And with system-level permissions? The questions about privacy are more imperative than ever. Especially access that doesn’t come with an opt-in feature could turn out to be a red flag.

For now, while users are lost in the wave of this innovative piece of product, the Perplexity-Samsung deal isn’t hitting a dead end here. In Part 2 of the alliance, Samsung Internet is involved. Samsung users Perplexity’s API for browser control and offers the AI browser, Comet, as the default search engine. But one that’s optional.

AI that you choose, not one you’re stuck with.

Samsung is in league with Apple. But is it truly winning the software war? The balance between efficiency and true effectiveness will decide the winner.

Cindy Rose is Reinventing WPP, Once Again: Where Will the Creative Land This Time?

Cindy Rose is Reinventing WPP, Once Again: Where Will the Creative Land This Time?

Cindy Rose is Reinventing WPP, Once Again: Where Will the Creative Land This Time?

Is the end of the prolonged agency-wide transformation for WPP? Cindy Rose thinks so.

WPP recorded disappointing results- with £13.55 billion in revenue, which is an 8.1% YoY decline. To address the root cause of this decline, it was essential to acknowledge a decade-long transformation that the agency never seemed to think it would survive.

Multiple agency consolidations, including two McKinsey reviews. And 3 leadership changes.

Looks like Rose is still holding on to the drowning boat. While the market is skeptical, she is hopeful, especially as she unveils WPP’s “Elevate28” strategy.

Because the root cause of this chaos is being relevant. Cindy Rose realizes that what worked in the past won’t work for WPP today. It’s an awakening that several marketers are gradually coming to. She isn’t alone in this predicament.

The plan starts with an official announcement of WPP Creative- its umbrella unit that will house all of WPP’s creative networks (Ogilvy, VML, and AKQA).

Rose’s strategic vision with this move is to move the agency from being a traditional holding company. As of now, the plan is to pivot into being a single operating company with 4 foundational blocks: Media, Creative, Production, and Enterprise Solutions.

But make no mistake. It isn’t another brand consolidation tactic. WPP’s CEO is doubling down on that. WPP is more like an operating system that’ll help these agency brands gauge WPP’s capabilities while operating as singular agencies and dealing with clients as such. No mergers. No consolidation. Only a structural revamp to present integrated offers.

The move is structural. But that’s an understatement.

What WPP historically saw was massive competition between these brands. And WPP Creative is the modus operandi to eliminate that. One that erases internal silos along with duplicated global + regional layers. Even back-office functions will come together.

As Rose cites a structural cost savings of up to £500 million by 2028, she asserts that £400 million of it is for the restructuring job. The other two priorities are realigning their investments in high-growth areas and talent.

In this vamped framework, Rose doesn’t address WPP as an advertising company. But introduces the plan under a renewed vision: “Our mission is now to be a trusted growth partner for our clients in the era of AI.”

The promise is to “put an end to the job” within the next 18 months. As the workforce (or maybe even clients) takes the brunt of this transformation fatigue, will Cindy Rose’s bold promise come through?

SaaS Market Trends

SaaS Market Trends: The SaaSPocalypse or how to avoid it

SaaS Market Trends: The SaaSPocalypse or how to avoid it

Why the companies that survive 2026 won’t be the ones with the best AI. They’ll be the ones with the best instincts.

Let’s start with what happened.

Between February 3 and February 5, 2026, approximately $285 to $300 billion in software market value evaporated. Not slowly. Not with a warning. In 48 hours. The iShares Expanded Tech-Software Sector ETF, IGV, entered a technical bear market, falling more than 20% from its late-2025 peaks. Salesforce lost nearly 40% of its value over twelve months. ServiceNow dropped 28% year to date. Workday announced layoffs attributed directly to AI efficiency gains. SAP missed cloud backlog targets.

Wall Street traders coined the term. “SaaSpocalypse.” The analysts called it. The executives disputed it. Jensen Huang called the entire narrative “the most illogical thing in the world.”

He might be right. He might be wrong. But neither answer tells you what you actually need to know.

Which side of this crash are you on?

The Numbers Tell One Story. The Real Story Is Somewhere Else.

Here is what the consensus view gets right: public SaaS growth rates have declined every single quarter since the 2021 peak. Not because AI broke something this year. The deceleration started years ago. AI gave the market permission to finally reprice what the numbers had been communicating since 2022. Price-to-sales ratios compressed from 9x to 6x, levels not seen since the mid-2010s. Morgan Stanley flagged that nearly 50% of the $235 billion software loan market is rated B- or lower.

Here is what the consensus view gets wrong: it frames this as a technology problem.

It isn’t.

It’s a substance problem.

Gartner predicts that by 2030, 35% of point-product SaaS tools will be replaced by AI agents or absorbed into larger ecosystems. Notice what that number means. 65% survive. That’s not apocalypse math. That’s a sorting exercise. The question isn’t whether SaaS dies. The question is: what decides who gets sorted into which pile?

And the answer is sitting right there in the earnings reports, in the trading desk chatter, in the CIO surveys, and almost nobody is naming it directly.

The Seat Count Model Was Always a Symptom

The argument Wall Street is having right now centers on seats. If 10 AI agents can do the work of 100 sales reps, you don’t need 100 Salesforce licenses anymore. You need 10. Revenue collapses. Model breaks. The per-seat subscription business, engineered for human headcount, encounters a world where headcount shrinks. forcing companies to rethink their SaaS metrics and revenue architecture.

That’s true. That analysis is correct. But it diagnoses the mechanism, not the disease.

The disease is this: for a decade, SaaS companies built products that were useful but forgettable. Sticky because of switching costs, not because of love. Many brands scaled revenue without strengthening positioning fundamentals outlined in our guide to sustainable SaaS growth. The content marketing that surrounded them was worse. Take any SaaS company and look at its YouTube channel. It’s webinars and stock footage. Their blogs are constructed for keyword ranking, not for thinking. Same topics. Same structure. Same tone. An 85% AI score on a piece that could have been written by anyone, for anyone, about anything.

This eroded something that doesn’t show up in quarterly earnings until it’s too late.

It eroded the relationship between the product and the people it was supposed to serve.

And AI didn’t create that erosion. AI just made it impossible to ignore.

“AI Is Eating the Software Budget.” Yes. But Whose Software?

The FinancialContent analysis from February 2026 put it plainly: the industry has shifted from “software is eating the world” to “AI is eating the software budget.” Hyperscalers alone plan to spend $660 to $690 billion on AI infrastructure in 2026, nearly double 2025 levels. That money comes from somewhere. And a significant portion is coming from enterprise software budgets.

But here is the part of the rotation that deserves more attention: IT budget growth is decelerating to 3.4% in 2026. aligning with broader SaaS marketing budget shifts in 2026. Not collapsing. Decelerating. The money isn’t disappearing. It’s getting more deliberate.

CIOs are in a rationalization phase, reevaluating vendors through ROI scrutiny and measurable B2B SaaS marketing performance. After two years of frantic AI experimentation, they are now asking harder questions. Which of these tools actually changed anything? Which vendors actually know us? Which companies have been giving us something we couldn’t get elsewhere?

That last question is the real sorting mechanism.

Bain’s analysis found something that should make every SaaS marketer stop cold: customers say they would prefer to buy AI-enabled solutions from their incumbent vendors. They trust them. They believe they’re secure. They believe they’ll be around.

But most incumbents have yet to deliver compelling offerings or prove they can win this new spending.

Read that again. The trust exists. The relationship exists. And it’s not being leveraged because the incumbents have been so focused on seat count, on retention metrics, on growth rates, that they forgot to keep being interesting. They forgot to keep being distinctive. They optimized so hard for predictable revenue that they lost the one thing that makes revenue predictable long-term.

A reason to matter.

What the Survivors Have in Common

Look at the companies not getting sorted into the “at risk” pile.

Palantir is trading at a staggering 229x P/E ratio. Not because the market has lost its mind. Because Palantir has spent years being aggressively, almost uncomfortably clear about what it is, what it stands for, what kind of organizations it works with, and why. Love them or hate them, nobody confuses Palantir for someone else.

Datadog is being watched as a likely survivor because it provides what the analysts are calling “digital plumbing.” The infrastructure layer that monitors the AI systems is disrupting everything above it. That’s a product argument. But it’s also a clarity argument. Datadog knows what it does. It does it better than anyone. And it has spent years building the authority that makes that claim credible.

Palantir and Datadog are not the same company. They serve different markets. They have different cultures. But they share something: a distinct point of view on the problem they solve and the world they’re operating in. Their marketing is not interchangeable with their competitors. Their voice is their own.

Now look at the worst performers. Intuit is down more than 34% year to date. Salesforce is down roughly 40% over twelve months. ServiceNow, down 28%. These are good companies with genuinely useful products. But somewhere in the race to scale, they became interchangeable in the minds of buyers in a way that left them exposed the moment a credible alternative appeared.

The story is not about technology. It’s about perception. And perception is built by something that doesn’t appear in any product roadmap.

The Creativity Problem That Everyone Is Pretending Is a Technology Problem

SaaS marketing is in a particularly strange place right now. The teams that replaced their writers with AI systems to save money are now discovering that their content is indistinguishable a mistake many brands are now correcting through structured thought leadership in SaaS marketing. from every other company’s content. Which was already indistinguishable. Which means they have now achieved perfect invisibility at scale.

The buyers noticed before the companies did.

B2B buyers complete 80% of their buying journey before engaging a vendor. which makes lead generation services critical to capturing attention early in the SaaS buyer funnel, which is why optimizing the B2B SaaS funnel is no longer optional. They have a preferred vendor list already. They know their requirements. The job of marketing is not to catch them mid-journey. The job is to be the company they thought of when the journey started.

That is a mindshare problem. And mindshare is not built through volume. It is built through distinctiveness. Through having something to say that nobody else is saying in the same way. Through a perspective on the problem that feels like yours alone.

Alex James, one of LinkedIn’s more clear-eyed voices on B2B, puts it simply: your perspective is your product.

This is not a thought-leadership platitude. It is a structural description of what differentiates the companies that get called from the companies that don’t make the shortlist. Paul Graham says it differently in his essay on great work: find the gaps in the knowledge you’re interested in. Most SaaS companies are not doing this. They are covering the same ground the same way, hoping volume and optimization compensate for sameness.

They don’t. They never did. AI just made the sameness more obvious.

The Practical Reality of 2026 and What It Demands

Here is where this lands in practical terms.

The market is repricing software. That repricing will continue. Companies whose value came primarily from switching costs and seat lock-in are genuinely exposed. Companies whose value comes from something harder to replicate are not.

What’s harder to replicate? Relationships. Authority. A track record of actually solving problems. A content ecosystem that has been answering real questions from real buyers in your niche, supported by strong SEO for SaaS, is what creates defensible authority. for long enough that you own that space in their minds. A brand that feels like it was made by people with opinions, not optimized by a committee.

Jason Lemkin, SaaStr’s founder, makes the bear case cleanly: the 2026 crash is not AI killing SaaS next quarter. It’s the market finally pricing in the deceleration that started in 2021. The growth re-acceleration that investors were betting on never arrived. exposing weak B2B SaaS growth marketing strategy foundations. The companies that don’t adapt will be starved of budget, growth, and eventually relevance.

But adapt to what, exactly? The answer is not “adopt AI faster.” Every company is adopting AI. That’s not the adaptation.

The adaptation is this: in a world where the functional gap between software products compresses, mastering modern SaaS product marketing becomes the real moat. And relationships are built on trust. And trust is built on consistent, authentic, genuinely useful engagement over time.

Not webinars. Not stock footage. Not 85% AI-scored blogs that define B2B marketing in seven steps.

Something people would actually read if they had a choice.

The Buyers Are Running the Math Too

Here’s the thing about the CIO survey showing budget rationalization: those CIOs are not just asking “which tools are redundant?” They are asking a harder question.

Which vendors have actually been worth it?

Worth it is not purely functional. People don’t purely function. Edelman’s 2025 Trust Barometer found that only 44% of global respondents are comfortable with businesses using AI. In B2B, where buying committees average 11 to 13 members with competing agendas, trust becomes the primary sorting mechanism for everything else. The CTO wants security assurance. The CFO wants ROI proof. The CMO wants a vendor who understands the problem well enough to teach them something new.

A vendor who has been teaching them something new, consistently, for two years, supported by consistent SaaS marketing case studies, does not get cut in a rationalization cycle. A vendor whose entire content operation could have been run by a chatbot? Different story.

This is not an argument against AI in marketing. It is an argument for what AI should be doing. Your AI should be doing the parts of the work that don’t require a point of view. Research, formatting, distribution mechanics, segmentation, A/B testing at scale. The parts that require a point of view, the argument, the perspective, the genuine take on what your buyer is dealing with right now, and what it means, that part cannot be outsourced. The moment you outsource it, you become background noise.

And background noise gets cut first.

A Final, Uncomfortable Thought

Goldman Sachs strategist Ben Snider drew a comparison to newspapers. Share prices declined by an average of 95% between 2002 and 2009. The multi-year decline ended only as earnings estimates bottomed. By the time profits recovered, most of the equity value was gone.

That is the bear case. It is worth taking seriously.

But here is what the newspaper analogy misses: the newspapers that survived, the ones that are still here and still profitable, are not the ones that automated fastest. They are the ones who figured out what only they could say. The New York Times. The Financial Times. The Atlantic. They survived because they developed something irreplaceable: a perspective that readers sought out by name.

The software companies that survive the SaaSpocalypse will not be the ones with the most features or the fastest AI adoption. They will be the ones their buyers think about first when the problem appears. The ones whose content their buyers actually read. The ones whose brand feels like it was made by humans who understood the work, not assembled by a process optimized for impressions.

That is what the market is searching for right now.

The companies that understand this will make it through.

The ones still asking how to automate their way out of the problem will not.

NVIDIA Beats Wall Street Expectations, Again

NVIDIA Beats Wall Street Expectations, Again

NVIDIA Beats Wall Street Expectations, Again

NVIDIA reported a record $68 billion quarter, showing its grip on AI demand. But even stellar results don’t erase questions about the sustainability of the AI boom.

This week’s earnings from NVIDIA Corporation were supposed to be the moment of truth on the AI boom. And the numbers delivered.

Revenue jumped past $68 billion, beating Wall Street’s hopes and proving, for now, that demand for AI compute isn’t cooling. The company’s data centre business covered a bulk of that growth. That says a lot, especially how entrenched NVIDIA has become at the centre of modern AI infrastructure.

If you squint at the headlines? That looks like a victory lap, but context matters.

NVIDIA is not just outpacing expectations this quarter. It’s doing so even as scepticism about the wider AI investment wave hangs over markets. After months of talk about an “AI bubble,” it’s tempting to read these results as definitive proof that the boom was real all along. But the nuance here is important.

The strength in NVIDIA’s reports comes from raw demand- big cloud providers, hyperscalers, and enterprise customers are still buying chips to train and run AI systems. That’s not speculative, that’s capital actually spent.

Yet investors didn’t jump up and down after the numbers. Stock moves were modest. That tells you expectations are already sky-high, and any hint of future slowing or margin pressure gets amplified.

There’s also a bigger question few CEOs can answer in a quarterly call: what happens when this build-out phase ends?

NVIDIA’s boss has leaned into the idea that AI compute isn’t just a fad- it’s the backbone of a broader productivity shift. But long-term use cases that generate reliable revenue beyond selling chips remain a bet.

So yes, this quarter looked strong.

Yet the measured reaction suggests the market is telling a simple truth: strong earnings don’t erase deeper debates about how durable the AI economy really is. That’s the real story behind NVIDIA’s numbers.