Why NVIDIA's New Chip Matters More Than You Think

Why NVIDIA’s New Chip Matters More Than You Think

Why NVIDIA’s New Chip Matters More Than You Think

NVIDIA’s upcoming inference chip is more than a speed upgrade. It exposes a growing pressure point in AI economics and signals where the next real competition will unfold.

NVIDIA’s latest chip plans are easy to slot into the usual narrative. Faster hardware. Bigger benchmarks. Another GTC headline.

But this one hits differently.

The focus this time is inference. That’s the part of AI most people actually interact with. Every prompt answered. Every generated line of code. Every AI-powered search result. Training may win headlines, but inference carries the daily load.

And that load is getting heavy.

As models grow more capable, they also grow more demanding. Tasks like reasoning through complex instructions or generating structured software are not light lifts. Companies building on top of large models have quietly run into friction. Latency creeps in. Costs balloon. Infrastructure teams start having uncomfortable conversations.

That is where this chip fits.

It isn’t about chasing bragging rights. It is about tightening the gap between model capability and usable product performance. When responses slow down or compute bills spike, it doesn’t matter how advanced the model is. Users notice the lag. CFOs notice the spend.

There is another layer here. Reports suggest NVIDIA is drawing from newer architectural approaches, including technology tied to Groq. That signals something important. The era of relying on GPU upgrades alone may be fading. Workloads are getting too specific. Too demanding. Too nuanced.

Hardware is starting to specialize.

For tech leaders, this is less about silicon and more about leverage. Inference efficiency shapes margins. It shapes user experience. It shapes how ambitious you can be with your product roadmap.

AI doesn’t only scale with model size. It scales on how efficiently you can serve it. And right now, serving is where the real pressure sits.

OpenAI

OpenAI Shakes Hands with the Trump Administration; Offers its AI for Intricate US Military Networks

OpenAI Shakes Hands with the Trump Administration; Offers its AI for Intricate US Military Networks

There are two specks to the OpenAI-Pentagon narrative. One overly political and one highly ill-judged- product-centric.

There are two easy readings of the OpenAI–Pentagon story.

One turns it into pure politics. The other reduces it to market expansion and enterprise revenue.

Both are incomplete.

It’s about military integration. And military integration is about security.

When a frontier model enters defense workflows, it does not sit there answering casual prompts. It becomes the crux of intelligence analysis, logistics modeling, cybersecurity simulations, and even decision-support systems.

Even if it is not for operating weapons, AI will impact workflows that affect real-world operations.

That raises serious technical questions.

How are models sandboxed in classified environments?

What happens when sensitive data flows into training feedback loops?

Can adversarial actors manipulate outputs through prompt injection or poisoned inputs?

Where does human oversight actually sit in the chain of command?

These are not abstract concerns. Military systems are prime targets for cyber intrusion. Generative models introduce new attack surfaces. One can easily exploit retrieval systems. Fine-tuned instances can drift from baseline behavior. If a model is used to summarize intelligence or simulate threat scenarios, small reasoning errors compound quickly.

At the same time, defense environments are often more disciplined than commercial ones. They demand audit logs. They demand access controls. They demand strict validation layers. In theory, that pressure should improve robustness.

But theory is not assurance.

For tech leaders, the real issue is this: when AI becomes embedded in national security infrastructure, the tolerance for ambiguity drops to zero. Safety documentation cannot be marketing copy. Guardrails cannot be symbolic.

The OpenAI–Pentagon agreement forces the industry to confront a harder truth. Frontier AI is no longer just productivity software. It is infrastructure. And infrastructure demands security standards that match the stakes.

That’s the real story.

The Future of Retail Media And Its Impact On Advertising

The Future of Retail Media And Its Impact On Advertising

The Future of Retail Media And Its Impact On Advertising

Why the most consequential shift in advertising right now has nothing to do with where you are placing ads, and everything to do with whether you understood your buyer before you bought the inventory.

Here is a number that should reframe how you think about B2B advertising in 2026.

US advertisers will spend $69.33 billion on retail media this year. Up from $58.79 billion in 2025. A channel that barely registered as a budget line five years ago now accounts for nearly 18% of all US digital ad spending. Retail media is not a niche. It is not a test-and-learn experiment. It is the third pillar of digital advertising, sitting alongside search and social, and it is growing faster than either of them, a shift already explored in depth in our breakdown of retail media advertising and adtech companies.

Retail media will outpace social media networks entirely by 2028. That is the current trajectory.

And most B2B advertisers are watching it happen from the outside.

Not because they have evaluated the channel and found it irrelevant. Because they have not evaluated it at all. The assumption, running quietly beneath most B2B marketing plans, is that retail media is a CPG problem. A Procter and Gamble problem. A sponsored product on Amazon has a problem.

That assumption is now expensive.

What Retail Media Actually Is, Before We Talk About What It Means

The definition has stretched. A lot.

Retail media started as advertising inventory owned by retailers, placed on their own platforms, targeted using their own purchase data. Amazon sells sponsored product placements to brands that want to appear when someone searches for a category. That was the original model.

It worked because it had something other digital channels did not: first-party transactional data. Not behavioral signals. Not modeled audiences. Actual purchase history. A company knows that this specific account, at this specific organization, bought a particular category of product three times in the past year. That is a fundamentally different quality of signal than what Google or Meta ever had.

But the definition has since expanded into territory that directly concerns B2B advertisers. Retail media now includes off-site advertising, where retailers take their first-party audience data and use it to place ads across third-party publisher websites and programmatic exchanges. a structure closely aligned with modern programmatic advertising strategies. It includes connected television integrations. It includes in-store digital signage with closed-loop attribution. It includes the entire concept of a retail media network, or RMN, which is any company that monetizes its audience data and owned inventory to serve advertisers.

There are now 277 retail media networks operating globally as of late 2025. That number is expected to grow. Payment platforms are turning transaction data into advertising infrastructure. Airlines, hotels, and financial services companies are all building media networks on the back of their purchase and behavioral data.

That last part is the B2B entry point.

When a platform that knows the procurement behavior of hundreds of thousands of businesses starts offering advertising inventory targeted to those buyers, that is not a CPG problem anymore. That is your problem. That is your opportunity, or your competitor’s.

The Fragmentation Problem, Which Is Also the Opportunity

Retail media has a fragmentation issue. It is severe enough that analysts talk about it constantly, and it is worth understanding technically before drawing the strategic conclusions.

The average organization is currently working with six retail media networks. a level of ecosystem complexity similar to what brands face when navigating multiple programmatic advertising platforms. That number is projected to reach eleven by 2026. Each network runs its own data infrastructure, its own identity system, its own reporting logic, its own measurement standards. Brands managing campaigns across even five or six of these networks are, in practice, operating in five or six different systems with five or six different versions of what ROI means, a challenge that mirrors broader B2B SaaS marketing ROI measurement issues.

Amazon and Walmart alone will capture 89% of incremental retail media spend in 2026. The remaining 11% is distributed across 275 other networks. Which means the ecosystem is simultaneously dominated by two giants and violently fragmented everywhere else.

The measurement problem is compounding this. Thirty-six percent of marketers cite difficulty proving investment incrementality as a reason they might reduce retail media spend. Another 32% point to lower ROI compared to other channels. These are not complaints about a channel that does not work. There are complaints about a channel where the infrastructure for proving that it works is still being built. Unified measurement across networks, where you can see exposed versus unexposed lift across all your activations in a single view, is only now becoming available at a small number of networks.

Here is the strategic point buried inside all of this technical complexity: the brands that figure out measurement and cross-network attribution now, while most competitors are still confused, will have a data advantage that compounds. In retail media, clarity about what is actually driving results is a competitive asset.

In B2B advertising, that is doubly true. Because the buyer journey you are trying to influence is not a single transaction, it reflects the complexity of modern B2B lead generation strategies. It is a nine-to-twelve-month committee process. The measurement problem in retail media is difficult. The measurement problem in B2B is harder. Any advertiser who gets serious about closed-loop attribution in retail media will have developed capabilities that transfer directly.

The B2B Buyer’s Attention Is the Most Fragmented Thing in Advertising

US B2B digital ad spending reached just over $20 billion in 2025. That number sounds large. Divided across the actual problem it is trying to solve, it looks different.

The average B2B purchase now involves around ten people. Seventy-two percent of B2B purchases involve high-complexity buying groups spanning multiple functions, including IT, operations, finance, and end users. These people are not in the same room. They are not on the same channel. They are not reading the same content or consuming the same media. They move at different speeds and have different concerns. And they are, individually, completing most of their research independently before anyone on the selling side gets involved.

Eighty-three percent of buyers have fully or mostly defined their purchase requirements before speaking with sales. Ninety-four percent are using large language models at some point in their buying process. Seventy-two percent encountered Google’s AI Overviews during their research.

The buying committee’s attention is not fragmented in the way a consumer’s attention is fragmented. A consumer flips between TikTok, YouTube, and email. A B2B buying committee member is doing something more specific and harder to reach: they are conducting private research, consulting peers outside their organization, running their own evaluations, and arriving at conclusions that vendors never get to see or influence. Formal buying committees are giving way to what analysts are now calling fluid networks of influence, where internal stakeholders, external peers, AI agents, and digital communities all shape decisions long before sellers are contacted.

You cannot interrupt your way into that process.

That sentence is the entire strategic case for moving from reactive to proactive B2B advertising.

Reactive Advertising Is Chasing the Buyer After the Decision Has Been Made

Most B2B advertising is reactive, relying heavily on intent triggers and traditional demand generation vs lead generation models. It responds to signals. Someone searches for a keyword: serve them an ad. Someone visits the pricing page: trigger a retargeting sequence. Someone engages with a competitor’s content: launch an ABM campaign targeting their account.

These tactics are not wrong. They work in a narrow sense. They capture demand that already exists. They remind a buyer who already has your brand in their consideration set that you are still there.

But they do nothing about the buyer who has never heard of you. They do nothing about the eight other members of the buying committee who did not trigger the signal. They do nothing about the vendor preference that was formed six months before anyone searched for a keyword.

The Demand Works analysis of B2B buyer intent trends for 2026 frames it clearly: the buyer journey is a series of fragmented micro-moments, and success requires orchestrating immediate, context-aware responses to those moments before they happen. Not after. The strategy that wins is the one that anticipates need rather than reacts to expressed intent.

This is where retail media’s architecture offers a model that B2B advertisers should study, even if the direct application takes a different form.

Retail media works because it places a brand in front of a buyer at the moment of highest commercial intent, using data that describes the buyer’s actual behavior rather than their modeled profile. The sponsored product appears when someone is actively looking for the category. The off-site ad follows a shopper who has demonstrated purchase intent within a specific category across the open web. The in-store digital screen reaches the buyer at the physical point of decision.

The B2B equivalent of this is not a sponsored listing on Amazon. It is being present, with a message that actually fits the buyer’s context, at every point in the long pre-purchase research process that the buying committee is conducting without you.

Seventy-five percent of buyers turn to peers when creating a vendor shortlist. Reinforcing the power of thought leadership in SaaS marketing. Top-of-mind awareness determines whether a vendor is considered at all. Being on the shortlist is the outcome of months of low-level, ambient presence that most B2B advertising plans never account for.

Partnerships Are the Infrastructure of Proactive Advertising

There is a lesson in how the best retail media networks actually work that B2B advertisers miss because they read the surface feature and not the underlying structure.

A retail media network’s value is not the inventory. Amazon has plenty of inventory. The value is the data that makes the inventory addressable. First-party purchase history. Behavioral signals from actual commercial activity. The ability to identify, with precision, which accounts are in an active buying cycle and what they are buying.

No B2B advertiser can build that infrastructure alone. The data is distributed. The buyer’s research happens across dozens of platforms and channels. Their intent signals are generated in places the advertiser has no access to.

This is why partnerships are not a nice-to-have in proactive B2B advertising. They are the mechanism.

The first category of partnership is data. Intent data providers aggregate behavioral signals from across the web, including content consumption, research activity, and engagement patterns at the account level, forming the backbone of behavioral targeting for high-quality leads. They build the dynamic account-level intent profiles that modern B2B buying research describes, consolidating signals from multiple stakeholders within an organization into a picture of the buying committee’s collective needs. This is the retail media first-party data advantage, reconstructed for the B2B context.

The second category is channel. Most B2B buying committees include Millennial and Gen Z members who are digitally native and carry consumer-grade expectations into business purchasing decisions. They are not waiting to be reached by a cold email sequence. They are on LinkedIn, which took 9% of all B2B digital ad spend to $4.59 billion in revenue in 2025, alongside broader social media lead generation strategies. They are watching YouTube. They are in Slack communities, private Discords, and specialized forums. They are consuming creator-led content from individual voices they trust, which is why 73% of leaders in the Edelman study said thought leadership was a more trustworthy basis for assessing vendor capabilities than company marketing.

A proactive advertising strategy is one that maps where each member of the target buying committee spends their attention, aligning closely with structured B2B SaaS growth marketing strategy frameworks. before they are in an active buying cycle, and builds presence at each of those points. Not with generic brand advertising. With content and messages that are specifically calibrated to that member’s role, concern, and stage of awareness.

The third category is the one most B2B advertisers skip entirely: partnerships with the organizations and voices that already have credibility with your target buyers. Retail media’s influencer integration problem, where creator and influencer campaigns remain loosely connected to the broader ecosystem without standardized booking or attribution, maps directly onto B2B’s creator and thought leadership gap. The brands that have figured out how to make individual voices synonymous with the category they sell into do not struggle for attention. Their buyers come to them already believing the value proposition, because they heard it from someone they trusted.

Understanding the Buyer Is Not a Research Project. It Is an Ongoing Practice.

Here is where most B2B advertising plans break down.

They are built on research that was conducted once, at the start of the year, by a team that read a few industry reports and spoke to three existing customers. The ICP was defined. The personas were documented. The messaging framework was finalized. And then the year proceeded, with every campaign running against that static picture of the buyer.

Meanwhile, the actual buyers changed their problem statements an average of 3.2 times during complex purchases in 2025. Fifty-four percent of buying groups are actively evolving their decision-making models. The point of first contact in the sales process moved forward by six to seven weeks in a single year, as economic pressure pushed buyers to engage vendors earlier while still arriving with fully formed requirements.

The retail media model has something useful to say about this, too. The reason RMNs with closed-loop measurement outperform other channels is not that they have better targeting. It is that they have real feedback. They know, from actual transaction data, whether an ad exposure translated into a purchase. That feedback shapes the next campaign. The loop closes, and the system learns.

B2B advertisers who want to move from reactive to proactive need the same loop. Not annual persona refreshes. A continuous feedback mechanism that pulls in what the sales team is hearing, what the support team is seeing, and what the content that is performing is telling you about what buyers are actually worried about right now.

Elsa Dithmer of Auvik puts the content side of this cleanly: high-value content should provide actionable insights that empower buyers. When it is well-optimized, highly relevant, and consistently delivers value, it establishes authority, nurtures prospects, and ultimately accelerates pipeline velocity. The operative word is consistently. A proactive advertising strategy is not a campaign. It is a practice.

The Practical Shape of What This Looks Like

Let’s be concrete.

A proactive B2B advertising program has three layers running simultaneously, each addressing a different part of the buying committee at a different stage of awareness.

The first layer is the ambient authority building. This is the content, the creator partnerships, the thought leadership that runs continuously in the channels where your target buying committee members spend time, whether or not they are in an active buying cycle. It is not optimized for clicks or form submissions. It is optimized for mindshare, for becoming the name that comes up when someone in the committee mentions the category in a peer conversation. This layer is the one that puts you on the shortlist before the search starts.

The second layer is intent-activated precision. This is where the data partnerships matter. When account-level intent signals indicate that a target account is entering an active research phase, the advertising activates at a higher level of specificity. Messages tailored to the committee member’s role. Content that matches the problem framing that purchase research suggests is the primary concern. Coordinated across channels so that the CFO, the CTO, and the operations leader are all seeing relevant material at the same time, even if the material is different for each of them.

The third layer is conversion-stage proof. This is where the reactive advertising that most B2B teams are already doing actually belongs. Retargeting, case studies, competitor comparisons, and ROI calculators. The mechanisms for closing the deal with a buyer who is already on the shortlist. This layer is no less important than the first two. It is simply downstream from them. The mistake most teams make is running only this layer and wondering why the shortlist was already set before they showed up.

Retail media’s closed-loop measurement infrastructure is the model for what holds all three layers together. You need to know which exposures in the first layer are showing up as account-level intent signals in the second, which requires a robust lead tracking system. You need to know which intent-activated campaigns are translating into shortlist inclusions. You need to know, at the account level, whether the investment is working. This requires data partnerships, clean room arrangements with publishers, and the discipline to build attribution models that account for a nine-month buying cycle rather than a seven-day click window.

It is more complex than reactive advertising. It is also the only advertising that reaches buyers before their minds are made up.

The Underlying Shift That Makes All of This Urgent

Gartner’s projection is that by 2027, 95% of B2B buying journeys will begin in a language model. Eighty-nine percent of buyers already name generative AI as one of their most important sources across every phase of purchase.

This changes the reactive advertising equation further. If the buyer is beginning their research in a language model, keyword-triggered advertising does not reach them at the start of the process. It reaches them later, if at all. The language model does not serve banner ads. It synthesizes information from sources it considers authoritative. Which means the B2B advertiser’s job is now, in part, to be the authoritative source that the language model cites, a challenge deeply connected to SEO for SaaS.

That is an SEO and content authority problem as much as an advertising problem. And it is one more reason why the first layer of the proactive strategy, the ambient authority building, is not optional. It is the infrastructure that makes everything else work in a world where the buyer’s first twenty queries about your category go to a chatbot.

The brands that have spent two years consistently producing genuinely useful material about the problems their buyers face, in their own voice, with their own perspective, will be cited. reflecting the long-term discipline outlined in the SaaS content marketing playbook. The brands that produced 85% AI-scored content optimized for keyword clusters will not.

A Thought to Leave With

Retail media reached $165 billion globally in 2026 because it solved a real problem. Advertisers needed to reach buyers with high purchase intent, using data that actually described commercial behavior, in a way that connected ad exposure to sales outcomes.

The B2B advertising problem is structurally identical. Reach the right members of the buying committee. Use data that describes actual commercial behavior and intent. Connect the advertising investment to revenue outcomes in a way the CFO will believe.

The tools exist. The data partnerships are available. The channel mix, from LinkedIn to creator networks to intent-activated programmatic to the emerging retail media networks that are building B2B-relevant inventory, is more sophisticated than it has ever been.

What does not exist, in most B2B organizations, is the will to build the proactive infrastructure before the reactive one fails. Most teams will wait until the pipeline number forces the conversation. Until the marketing-attributed opportunities dry up. Until the sales team starts complaining that they are showing up to deals where the decision has already been made.

By then, the shortlist will have been set for six months.

The buyers who will call you in the second half of 2026 are forming their opinions right now. They are reading content that either includes your voice or does not. They are in conversations with peers who either mention your name or do not. They are doing research in language models that either cite your authority or return someone else’s.

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

The chain of partnerships keeps on increasing. Is this a collaboration or a consolidation of power?

The surface reading of the Google-Meta chip deal is straightforward. Meta has agreed to rent Google’s tensor processing units through Google Cloud to train and run its next-generation large language models, in a multi-year agreement worth billions of dollars. Google gets a major enterprise customer. Meta gets another compute supplier. Clean transaction. Except nothing about this is clean.

Meta’s AI infrastructure spending is projected to reach $135 billion in 2026. The company has 30 data centres planned, 26 of them in the United States. That is not a company with a compute problem. That is a company executing a deliberate strategy to ensure no single supplier can hold it hostage. This week alone, Meta has deals running simultaneously with Nvidia, AMD, and now Google. Morningstar analysts are calling it a multipronged silicon strategy. We would call it something blunter: leverage, bought in advance and at scale.

The timing is not incidental. Meta has been running into serious problems with the AI chips it is designing internally, scrapping its most advanced in-house training chip last week. When your own silicon program hits a wall, you move fast, and you move wide. The Google deal is partly a diversification. It is also insurance.

For Google, the stakes are different but equally structural. Google first developed TPUs more than a decade ago for internal workloads. The Meta deal represents a major expansion of its TPU commercialisation strategy, which previously kept the chips largely inside Google’s own infrastructure. Google is now forming a joint venture to lease TPUs to other AI customers, with some Cloud executives estimating the expansion could capture as much as 10 percent of Nvidia’s annual revenue. In October, Anthropic signed a deal for access to up to one million TPUs. Meta follows. The pattern is becoming a market.

So is this a consolidation of power, an expansion of it, or something that resembles creative collaboration? We think the honest answer is: it is none of those things individually. It is two dominant players using each other to reduce their dependence on a third dominant player. NVIDIA sits at the centre of the AI race in a way that makes every other company in the ecosystem uncomfortable, and the deals being signed this week are the market’s response to that discomfort.

The partnership economy does not always produce partners. Sometimes it produces mutual hedges dressed in press release language. This is one of those.

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.